From 8d723d2caa959a8c4b14050c30dd83cf931e3dc1 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Sun, 29 Mar 2026 16:43:24 -0700 Subject: [PATCH 001/140] Fix/tweak pinned memory accounting (#13221) * mm: Lower windows pin threshold Some workflows have more extranous use of shared GPU memory than is accounted for in the 5% pin headroom. Lower this for safety. * mm: Remove pin count clearing threshold. TOTAL_PINNED_MEMORY is shared between the legacy and aimdo pinning systems, however this catch-all assumes only the legacy system exists. Remove the catch-all as the PINNED_MEMORY buffer is coherent already. --- comfy/model_management.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index ce079cf2f..0eebf1ded 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -1326,9 +1326,9 @@ MAX_PINNED_MEMORY = -1 if not args.disable_pinned_memory: if is_nvidia() or is_amd(): if WINDOWS: - MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45 # Windows limit is apparently 50% + MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.40 # Windows limit is apparently 50% else: - MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95 + MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.90 logging.info("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024))) PINNING_ALLOWED_TYPES = set(["Tensor", "Parameter", "QuantizedTensor"]) @@ -1403,8 +1403,6 @@ def unpin_memory(tensor): if torch.cuda.cudart().cudaHostUnregister(ptr) == 0: TOTAL_PINNED_MEMORY -= PINNED_MEMORY.pop(ptr) - if len(PINNED_MEMORY) == 0: - TOTAL_PINNED_MEMORY = 0 return True else: logging.warning("Unpin error.") From 537c10d231cf691ada070fa98978a2ddadb064fc Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 29 Mar 2026 19:07:38 -0700 Subject: [PATCH 002/140] Update README.md with latest AMD Linux pytorch. (#13228) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 62c4f528c..021f88a31 100644 --- a/README.md +++ b/README.md @@ -232,7 +232,7 @@ Put your VAE in: models/vae AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version: -```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.1``` +```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm7.2``` This is the command to install the nightly with ROCm 7.2 which might have some performance improvements: From 55e6478526590572b88272045b5014d9b60fa2a0 Mon Sep 17 00:00:00 2001 From: Christian Byrne Date: Sun, 29 Mar 2026 21:02:44 -0700 Subject: [PATCH 003/140] Rename utils/string nodes with Text prefix and add search aliases (#13227) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Rename all 11 nodes in the utils/string category to include a "Text" prefix for better discoverability and natural sorting. Regex nodes get user-friendly names without "Regex" in the display name. Renames: - Concatenate → Text Concatenate - Substring → Text Substring - Length → Text Length - Case Converter → Text Case Converter - Trim → Text Trim - Replace → Text Replace - Contains → Text Contains - Compare → Text Compare - Regex Match → Text Match - Regex Extract → Text Extract Substring - Regex Replace → Text Replace (Regex) All renamed nodes include their old display name as a search alias so users can still find them by searching the original name. Regex nodes also include "regex" as a search alias. --- comfy_extras/nodes_string.py | 44 ++++++++++++++++++------------------ 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/comfy_extras/nodes_string.py b/comfy_extras/nodes_string.py index b4e5f148a..75a8bb4ee 100644 --- a/comfy_extras/nodes_string.py +++ b/comfy_extras/nodes_string.py @@ -9,9 +9,9 @@ class StringConcatenate(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StringConcatenate", - display_name="Concatenate", + display_name="Text Concatenate", category="utils/string", - search_aliases=["text concat", "join text", "merge text", "combine strings", "concat", "concatenate", "append text", "combine text", "string"], + search_aliases=["Concatenate", "text concat", "join text", "merge text", "combine strings", "concat", "concatenate", "append text", "combine text", "string"], inputs=[ io.String.Input("string_a", multiline=True), io.String.Input("string_b", multiline=True), @@ -32,8 +32,8 @@ class StringSubstring(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StringSubstring", - search_aliases=["extract text", "text portion"], - display_name="Substring", + search_aliases=["Substring", "extract text", "text portion"], + display_name="Text Substring", category="utils/string", inputs=[ io.String.Input("string", multiline=True), @@ -55,8 +55,8 @@ class StringLength(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StringLength", - search_aliases=["character count", "text size"], - display_name="Length", + search_aliases=["character count", "text size", "string length"], + display_name="Text Length", category="utils/string", inputs=[ io.String.Input("string", multiline=True), @@ -76,8 +76,8 @@ class CaseConverter(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="CaseConverter", - search_aliases=["text case", "uppercase", "lowercase", "capitalize"], - display_name="Case Converter", + search_aliases=["Case Converter", "text case", "uppercase", "lowercase", "capitalize"], + display_name="Text Case Converter", category="utils/string", inputs=[ io.String.Input("string", multiline=True), @@ -109,8 +109,8 @@ class StringTrim(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StringTrim", - search_aliases=["clean whitespace", "remove whitespace"], - display_name="Trim", + search_aliases=["Trim", "clean whitespace", "remove whitespace", "strip"], + display_name="Text Trim", category="utils/string", inputs=[ io.String.Input("string", multiline=True), @@ -140,8 +140,8 @@ class StringReplace(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StringReplace", - search_aliases=["find and replace", "substitute", "swap text"], - display_name="Replace", + search_aliases=["Replace", "find and replace", "substitute", "swap text"], + display_name="Text Replace", category="utils/string", inputs=[ io.String.Input("string", multiline=True), @@ -163,8 +163,8 @@ class StringContains(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StringContains", - search_aliases=["text includes", "string includes"], - display_name="Contains", + search_aliases=["Contains", "text includes", "string includes"], + display_name="Text Contains", category="utils/string", inputs=[ io.String.Input("string", multiline=True), @@ -191,8 +191,8 @@ class StringCompare(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="StringCompare", - search_aliases=["text match", "string equals", "starts with", "ends with"], - display_name="Compare", + search_aliases=["Compare", "text match", "string equals", "starts with", "ends with"], + display_name="Text Compare", category="utils/string", inputs=[ io.String.Input("string_a", multiline=True), @@ -227,8 +227,8 @@ class RegexMatch(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="RegexMatch", - search_aliases=["pattern match", "text contains", "string match"], - display_name="Regex Match", + search_aliases=["Regex Match", "regex", "pattern match", "text contains", "string match"], + display_name="Text Match", category="utils/string", inputs=[ io.String.Input("string", multiline=True), @@ -268,8 +268,8 @@ class RegexExtract(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="RegexExtract", - search_aliases=["pattern extract", "text parser", "parse text"], - display_name="Regex Extract", + search_aliases=["Regex Extract", "regex", "pattern extract", "text parser", "parse text"], + display_name="Text Extract Substring", category="utils/string", inputs=[ io.String.Input("string", multiline=True), @@ -343,8 +343,8 @@ class RegexReplace(io.ComfyNode): def define_schema(cls): return io.Schema( node_id="RegexReplace", - search_aliases=["pattern replace", "find and replace", "substitution"], - display_name="Regex Replace", + search_aliases=["Regex Replace", "regex", "pattern replace", "regex replace", "substitution"], + display_name="Text Replace (Regex)", category="utils/string", description="Find and replace text using regex patterns.", inputs=[ From 076639fed99742f43ffbf0b0df34bb8fd105b8e9 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 30 Mar 2026 20:11:02 -0700 Subject: [PATCH 004/140] Update README with note on model support (#13235) Added note about additional supported models in ComfyUI. --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 021f88a31..a47506fc8 100644 --- a/README.md +++ b/README.md @@ -61,6 +61,7 @@ See what ComfyUI can do with the [newer template workflows](https://comfy.org/wo ## Features - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything. +- NOTE: There are many more models supported than the list below, if you want to see what is supported see our templates list inside ComfyUI. - Image Models - SD1.x, SD2.x ([unCLIP](https://comfyanonymous.github.io/ComfyUI_examples/unclip/)) - [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/) From e2ddf28d78f190b27d136668a7dc15c7f0ec75dc Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 31 Mar 2026 14:27:17 -0700 Subject: [PATCH 005/140] Fix some fp8 scaled checkpoints no longer working. (#13239) --- comfy/sd.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/comfy/sd.py b/comfy/sd.py index 7425765a4..5b6b59ea4 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -1745,6 +1745,8 @@ def load_diffusion_model_state_dict(sd, model_options={}, metadata=None, disable temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True) if len(temp_sd) > 0: sd = temp_sd + if custom_operations is None: + sd, metadata = comfy.utils.convert_old_quants(sd, "", metadata=metadata) parameters = comfy.utils.calculate_parameters(sd) weight_dtype = comfy.utils.weight_dtype(sd) From 7d437687c260df7772c603658111148e0e863e59 Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Wed, 1 Apr 2026 11:23:25 +0800 Subject: [PATCH 006/140] chore: update workflow templates to v0.9.41 (#13242) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 6f0659a00..0d88fdcfb 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.8 -comfyui-workflow-templates==0.9.39 +comfyui-workflow-templates==0.9.41 comfyui-embedded-docs==0.4.3 torch torchsde From 0c63b4f6e3ce807a0f85c1826710bcf18ade3e2c Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 1 Apr 2026 17:22:06 -0700 Subject: [PATCH 007/140] Remove dead code. (#13251) --- comfy/ldm/modules/encoders/noise_aug_modules.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/comfy/ldm/modules/encoders/noise_aug_modules.py b/comfy/ldm/modules/encoders/noise_aug_modules.py index a5d866030..c853e4298 100644 --- a/comfy/ldm/modules/encoders/noise_aug_modules.py +++ b/comfy/ldm/modules/encoders/noise_aug_modules.py @@ -3,12 +3,9 @@ from ..diffusionmodules.openaimodel import Timestep import torch class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation): - def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs): + def __init__(self, *args, timestep_dim=256, **kwargs): super().__init__(*args, **kwargs) - if clip_stats_path is None: - clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim) - else: - clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu") + clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim) self.register_buffer("data_mean", clip_mean[None, :], persistent=False) self.register_buffer("data_std", clip_std[None, :], persistent=False) self.time_embed = Timestep(timestep_dim) From 76b75f3ad755ef5ff78b3670abbab549fb080243 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 2 Apr 2026 13:39:34 -0700 Subject: [PATCH 008/140] Fix some issue with insecure browsers. (#13261) If you are on a recent chromium or chrome based browser this doesn't affect you. This is to give time for the lazy firefox devs to implement PNA. --- server.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/server.py b/server.py index 27b14825e..881da8e66 100644 --- a/server.py +++ b/server.py @@ -146,6 +146,10 @@ def is_loopback(host): def create_origin_only_middleware(): @web.middleware async def origin_only_middleware(request: web.Request, handler): + if 'Sec-Fetch-Site' in request.headers: + sec_fetch_site = request.headers['Sec-Fetch-Site'] + if sec_fetch_site == 'cross-site': + return web.Response(status=403) #this code is used to prevent the case where a random website can queue comfy workflows by making a POST to 127.0.0.1 which browsers don't prevent for some dumb reason. #in that case the Host and Origin hostnames won't match #I know the proper fix would be to add a cookie but this should take care of the problem in the meantime From 5de94e70ec116a93cbd110fbfaec266ae1f423c5 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Fri, 3 Apr 2026 09:51:47 +0300 Subject: [PATCH 009/140] feat(api-nodes): new Partner nodes for Wan2.7 (#13264) Signed-off-by: bigcat88 --- comfy_api_nodes/apis/wan.py | 226 ++++++++ comfy_api_nodes/nodes_wan.py | 1011 +++++++++++++++++++++++++++++----- 2 files changed, 1089 insertions(+), 148 deletions(-) create mode 100644 comfy_api_nodes/apis/wan.py diff --git a/comfy_api_nodes/apis/wan.py b/comfy_api_nodes/apis/wan.py new file mode 100644 index 000000000..44b65e4f6 --- /dev/null +++ b/comfy_api_nodes/apis/wan.py @@ -0,0 +1,226 @@ +from pydantic import BaseModel, Field + + +class Text2ImageInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: str | None = Field(None) + + +class Image2ImageInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: str | None = Field(None) + images: list[str] = Field(..., min_length=1, max_length=2) + + +class Text2VideoInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: str | None = Field(None) + audio_url: str | None = Field(None) + + +class Image2VideoInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: str | None = Field(None) + img_url: str = Field(...) + audio_url: str | None = Field(None) + + +class Reference2VideoInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: str | None = Field(None) + reference_video_urls: list[str] = Field(...) + + +class Txt2ImageParametersField(BaseModel): + size: str = Field(...) + n: int = Field(1, description="Number of images to generate.") # we support only value=1 + seed: int = Field(..., ge=0, le=2147483647) + prompt_extend: bool = Field(True) + watermark: bool = Field(False) + + +class Image2ImageParametersField(BaseModel): + size: str | None = Field(None) + n: int = Field(1, description="Number of images to generate.") # we support only value=1 + seed: int = Field(..., ge=0, le=2147483647) + watermark: bool = Field(False) + + +class Text2VideoParametersField(BaseModel): + size: str = Field(...) + seed: int = Field(..., ge=0, le=2147483647) + duration: int = Field(5, ge=5, le=15) + prompt_extend: bool = Field(True) + watermark: bool = Field(False) + audio: bool = Field(False, description="Whether to generate audio automatically.") + shot_type: str = Field("single") + + +class Image2VideoParametersField(BaseModel): + resolution: str = Field(...) + seed: int = Field(..., ge=0, le=2147483647) + duration: int = Field(5, ge=5, le=15) + prompt_extend: bool = Field(True) + watermark: bool = Field(False) + audio: bool = Field(False, description="Whether to generate audio automatically.") + shot_type: str = Field("single") + + +class Reference2VideoParametersField(BaseModel): + size: str = Field(...) + duration: int = Field(5, ge=5, le=15) + shot_type: str = Field("single") + seed: int = Field(..., ge=0, le=2147483647) + watermark: bool = Field(False) + + +class Text2ImageTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Text2ImageInputField = Field(...) + parameters: Txt2ImageParametersField = Field(...) + + +class Image2ImageTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Image2ImageInputField = Field(...) + parameters: Image2ImageParametersField = Field(...) + + +class Text2VideoTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Text2VideoInputField = Field(...) + parameters: Text2VideoParametersField = Field(...) + + +class Image2VideoTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Image2VideoInputField = Field(...) + parameters: Image2VideoParametersField = Field(...) + + +class Reference2VideoTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Reference2VideoInputField = Field(...) + parameters: Reference2VideoParametersField = Field(...) + + +class Wan27MediaItem(BaseModel): + type: str = Field(...) + url: str = Field(...) + + +class Wan27ReferenceVideoInputField(BaseModel): + prompt: str = Field(...) + negative_prompt: str | None = Field(None) + media: list[Wan27MediaItem] = Field(...) + + +class Wan27ReferenceVideoParametersField(BaseModel): + resolution: str = Field(...) + ratio: str | None = Field(None) + duration: int = Field(5, ge=2, le=10) + watermark: bool = Field(False) + seed: int = Field(..., ge=0, le=2147483647) + + +class Wan27ReferenceVideoTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Wan27ReferenceVideoInputField = Field(...) + parameters: Wan27ReferenceVideoParametersField = Field(...) + + +class Wan27ImageToVideoInputField(BaseModel): + prompt: str | None = Field(None) + negative_prompt: str | None = Field(None) + media: list[Wan27MediaItem] = Field(...) + + +class Wan27ImageToVideoParametersField(BaseModel): + resolution: str = Field(...) + duration: int = Field(5, ge=2, le=15) + prompt_extend: bool = Field(True) + watermark: bool = Field(False) + seed: int = Field(..., ge=0, le=2147483647) + + +class Wan27ImageToVideoTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Wan27ImageToVideoInputField = Field(...) + parameters: Wan27ImageToVideoParametersField = Field(...) + + +class Wan27VideoEditInputField(BaseModel): + prompt: str = Field(...) + media: list[Wan27MediaItem] = Field(...) + + +class Wan27VideoEditParametersField(BaseModel): + resolution: str = Field(...) + ratio: str | None = Field(None) + duration: int = Field(0) + audio_setting: str = Field("auto") + watermark: bool = Field(False) + seed: int = Field(..., ge=0, le=2147483647) + + +class Wan27VideoEditTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Wan27VideoEditInputField = Field(...) + parameters: Wan27VideoEditParametersField = Field(...) + + +class Wan27Text2VideoParametersField(BaseModel): + resolution: str = Field(...) + ratio: str | None = Field(None) + duration: int = Field(5, ge=2, le=15) + prompt_extend: bool = Field(True) + watermark: bool = Field(False) + seed: int = Field(..., ge=0, le=2147483647) + + +class Wan27Text2VideoTaskCreationRequest(BaseModel): + model: str = Field(...) + input: Text2VideoInputField = Field(...) + parameters: Wan27Text2VideoParametersField = Field(...) + + +class TaskCreationOutputField(BaseModel): + task_id: str = Field(...) + task_status: str = Field(...) + + +class TaskCreationResponse(BaseModel): + output: TaskCreationOutputField | None = Field(None) + request_id: str = Field(...) + code: str | None = Field(None, description="Error code for the failed request.") + message: str | None = Field(None, description="Details about the failed request.") + + +class TaskResult(BaseModel): + url: str | None = Field(None) + code: str | None = Field(None) + message: str | None = Field(None) + + +class ImageTaskStatusOutputField(TaskCreationOutputField): + task_id: str = Field(...) + task_status: str = Field(...) + results: list[TaskResult] | None = Field(None) + + +class VideoTaskStatusOutputField(TaskCreationOutputField): + task_id: str = Field(...) + task_status: str = Field(...) + video_url: str | None = Field(None) + code: str | None = Field(None) + message: str | None = Field(None) + + +class ImageTaskStatusResponse(BaseModel): + output: ImageTaskStatusOutputField | None = Field(None) + request_id: str = Field(...) + + +class VideoTaskStatusResponse(BaseModel): + output: VideoTaskStatusOutputField | None = Field(None) + request_id: str = Field(...) diff --git a/comfy_api_nodes/nodes_wan.py b/comfy_api_nodes/nodes_wan.py index e2afe7f9c..d1470894a 100644 --- a/comfy_api_nodes/nodes_wan.py +++ b/comfy_api_nodes/nodes_wan.py @@ -1,9 +1,40 @@ import re -from pydantic import BaseModel, Field from typing_extensions import override from comfy_api.latest import IO, ComfyExtension, Input +from comfy_api_nodes.apis.wan import ( + Image2ImageInputField, + Image2ImageParametersField, + Image2ImageTaskCreationRequest, + Image2VideoInputField, + Image2VideoParametersField, + Image2VideoTaskCreationRequest, + ImageTaskStatusResponse, + Reference2VideoInputField, + Reference2VideoParametersField, + Reference2VideoTaskCreationRequest, + TaskCreationResponse, + Text2ImageInputField, + Text2ImageTaskCreationRequest, + Text2VideoInputField, + Text2VideoParametersField, + Text2VideoTaskCreationRequest, + Txt2ImageParametersField, + VideoTaskStatusResponse, + Wan27ImageToVideoInputField, + Wan27ImageToVideoParametersField, + Wan27ImageToVideoTaskCreationRequest, + Wan27MediaItem, + Wan27ReferenceVideoInputField, + Wan27ReferenceVideoParametersField, + Wan27ReferenceVideoTaskCreationRequest, + Wan27Text2VideoParametersField, + Wan27Text2VideoTaskCreationRequest, + Wan27VideoEditInputField, + Wan27VideoEditParametersField, + Wan27VideoEditTaskCreationRequest, +) from comfy_api_nodes.util import ( ApiEndpoint, audio_to_base64_string, @@ -13,157 +44,14 @@ from comfy_api_nodes.util import ( poll_op, sync_op, tensor_to_base64_string, + upload_audio_to_comfyapi, + upload_image_to_comfyapi, upload_video_to_comfyapi, validate_audio_duration, + validate_string, validate_video_duration, ) - -class Text2ImageInputField(BaseModel): - prompt: str = Field(...) - negative_prompt: str | None = Field(None) - - -class Image2ImageInputField(BaseModel): - prompt: str = Field(...) - negative_prompt: str | None = Field(None) - images: list[str] = Field(..., min_length=1, max_length=2) - - -class Text2VideoInputField(BaseModel): - prompt: str = Field(...) - negative_prompt: str | None = Field(None) - audio_url: str | None = Field(None) - - -class Image2VideoInputField(BaseModel): - prompt: str = Field(...) - negative_prompt: str | None = Field(None) - img_url: str = Field(...) - audio_url: str | None = Field(None) - - -class Reference2VideoInputField(BaseModel): - prompt: str = Field(...) - negative_prompt: str | None = Field(None) - reference_video_urls: list[str] = Field(...) - - -class Txt2ImageParametersField(BaseModel): - size: str = Field(...) - n: int = Field(1, description="Number of images to generate.") # we support only value=1 - seed: int = Field(..., ge=0, le=2147483647) - prompt_extend: bool = Field(True) - watermark: bool = Field(False) - - -class Image2ImageParametersField(BaseModel): - size: str | None = Field(None) - n: int = Field(1, description="Number of images to generate.") # we support only value=1 - seed: int = Field(..., ge=0, le=2147483647) - watermark: bool = Field(False) - - -class Text2VideoParametersField(BaseModel): - size: str = Field(...) - seed: int = Field(..., ge=0, le=2147483647) - duration: int = Field(5, ge=5, le=15) - prompt_extend: bool = Field(True) - watermark: bool = Field(False) - audio: bool = Field(False, description="Whether to generate audio automatically.") - shot_type: str = Field("single") - - -class Image2VideoParametersField(BaseModel): - resolution: str = Field(...) - seed: int = Field(..., ge=0, le=2147483647) - duration: int = Field(5, ge=5, le=15) - prompt_extend: bool = Field(True) - watermark: bool = Field(False) - audio: bool = Field(False, description="Whether to generate audio automatically.") - shot_type: str = Field("single") - - -class Reference2VideoParametersField(BaseModel): - size: str = Field(...) - duration: int = Field(5, ge=5, le=15) - shot_type: str = Field("single") - seed: int = Field(..., ge=0, le=2147483647) - watermark: bool = Field(False) - - -class Text2ImageTaskCreationRequest(BaseModel): - model: str = Field(...) - input: Text2ImageInputField = Field(...) - parameters: Txt2ImageParametersField = Field(...) - - -class Image2ImageTaskCreationRequest(BaseModel): - model: str = Field(...) - input: Image2ImageInputField = Field(...) - parameters: Image2ImageParametersField = Field(...) - - -class Text2VideoTaskCreationRequest(BaseModel): - model: str = Field(...) - input: Text2VideoInputField = Field(...) - parameters: Text2VideoParametersField = Field(...) - - -class Image2VideoTaskCreationRequest(BaseModel): - model: str = Field(...) - input: Image2VideoInputField = Field(...) - parameters: Image2VideoParametersField = Field(...) - - -class Reference2VideoTaskCreationRequest(BaseModel): - model: str = Field(...) - input: Reference2VideoInputField = Field(...) - parameters: Reference2VideoParametersField = Field(...) - - -class TaskCreationOutputField(BaseModel): - task_id: str = Field(...) - task_status: str = Field(...) - - -class TaskCreationResponse(BaseModel): - output: TaskCreationOutputField | None = Field(None) - request_id: str = Field(...) - code: str | None = Field(None, description="Error code for the failed request.") - message: str | None = Field(None, description="Details about the failed request.") - - -class TaskResult(BaseModel): - url: str | None = Field(None) - code: str | None = Field(None) - message: str | None = Field(None) - - -class ImageTaskStatusOutputField(TaskCreationOutputField): - task_id: str = Field(...) - task_status: str = Field(...) - results: list[TaskResult] | None = Field(None) - - -class VideoTaskStatusOutputField(TaskCreationOutputField): - task_id: str = Field(...) - task_status: str = Field(...) - video_url: str | None = Field(None) - code: str | None = Field(None) - message: str | None = Field(None) - - -class ImageTaskStatusResponse(BaseModel): - output: ImageTaskStatusOutputField | None = Field(None) - request_id: str = Field(...) - - -class VideoTaskStatusResponse(BaseModel): - output: VideoTaskStatusOutputField | None = Field(None) - request_id: str = Field(...) - - RES_IN_PARENS = re.compile(r"\((\d+)\s*[x×]\s*(\d+)\)") @@ -179,7 +67,6 @@ class WanTextToImageApi(IO.ComfyNode): IO.Combo.Input( "model", options=["wan2.5-t2i-preview"], - default="wan2.5-t2i-preview", tooltip="Model to use.", ), IO.String.Input( @@ -936,6 +823,829 @@ class WanReferenceVideoApi(IO.ComfyNode): return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) +class Wan2TextToVideoApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="Wan2TextToVideoApi", + display_name="Wan 2.7 Text to Video", + category="api node/video/Wan", + description="Generates a video based on a text prompt using the Wan 2.7 model.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "wan2.7-t2v", + [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt describing the elements and visual features. " + "Supports English and Chinese.", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative prompt describing what to avoid.", + ), + IO.Combo.Input( + "resolution", + options=["720P", "1080P"], + ), + IO.Combo.Input( + "ratio", + options=["16:9", "9:16", "1:1", "4:3", "3:4"], + ), + IO.Int.Input( + "duration", + default=5, + min=2, + max=15, + step=1, + display_mode=IO.NumberDisplay.number, + ), + ], + ), + ], + ), + IO.Audio.Input( + "audio", + optional=True, + tooltip="Audio for driving video generation (e.g., lip sync, beat-matched motion). " + "Duration: 3s-30s. If not provided, the model automatically generates matching " + "background music or sound effects.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Boolean.Input( + "prompt_extend", + default=True, + tooltip="Whether to enhance the prompt with AI assistance.", + advanced=True, + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add an AI-generated watermark to the result.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $ppsTable := { "720p": 0.1, "1080p": 0.15 }; + $pps := $lookup($ppsTable, $res); + { "type": "usd", "usd": $pps * $dur } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + seed: int, + prompt_extend: bool, + watermark: bool, + audio: Input.Audio | None = None, + ): + validate_string(model["prompt"], strip_whitespace=False, min_length=1) + audio_url = None + if audio is not None: + validate_audio_duration(audio, 1.5, 60.0) + audio_url = await upload_audio_to_comfyapi( + cls, audio, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg" + ) + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", + method="POST", + ), + response_model=TaskCreationResponse, + data=Wan27Text2VideoTaskCreationRequest( + model=model["model"], + input=Text2VideoInputField( + prompt=model["prompt"], + negative_prompt=model["negative_prompt"] or None, + audio_url=audio_url, + ), + parameters=Wan27Text2VideoParametersField( + resolution=model["resolution"], + ratio=model["ratio"], + duration=model["duration"], + seed=seed, + prompt_extend=prompt_extend, + watermark=watermark, + ), + ), + ) + if not initial_response.output: + raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + poll_interval=7, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + +class Wan2ImageToVideoApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="Wan2ImageToVideoApi", + display_name="Wan 2.7 Image to Video", + category="api node/video/Wan", + description="Generate a video from a first-frame image, with optional last-frame image and audio.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "wan2.7-i2v", + [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt describing the elements and visual features. " + "Supports English and Chinese.", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative prompt describing what to avoid.", + ), + IO.Combo.Input( + "resolution", + options=["720P", "1080P"], + ), + IO.Int.Input( + "duration", + default=5, + min=2, + max=15, + step=1, + display_mode=IO.NumberDisplay.number, + ), + ], + ), + ], + ), + IO.Image.Input( + "first_frame", + tooltip="First frame image. The output aspect ratio is derived from this image.", + ), + IO.Image.Input( + "last_frame", + optional=True, + tooltip="Last frame image. The model generates a video transitioning from first to last frame.", + ), + IO.Audio.Input( + "audio", + optional=True, + tooltip="Audio for driving video generation (e.g., lip sync, beat-matched motion). " + "Duration: 2s-30s. If not provided, the model automatically generates matching " + "background music or sound effects.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Boolean.Input( + "prompt_extend", + default=True, + tooltip="Whether to enhance the prompt with AI assistance.", + advanced=True, + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add an AI-generated watermark to the result.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $ppsTable := { "720p": 0.1, "1080p": 0.15 }; + $pps := $lookup($ppsTable, $res); + { "type": "usd", "usd": $pps * $dur } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + first_frame: Input.Image, + seed: int, + prompt_extend: bool, + watermark: bool, + last_frame: Input.Image | None = None, + audio: Input.Audio | None = None, + ): + media = [ + Wan27MediaItem( + type="first_frame", + url=await upload_image_to_comfyapi(cls, image=first_frame), + ) + ] + if last_frame is not None: + media.append( + Wan27MediaItem( + type="last_frame", + url=await upload_image_to_comfyapi(cls, image=last_frame), + ) + ) + if audio is not None: + validate_audio_duration(audio, 2.0, 30.0) + audio_url = await upload_audio_to_comfyapi( + cls, audio, container_format="mp3", codec_name="libmp3lame", mime_type="audio/mpeg" + ) + media.append(Wan27MediaItem(type="driving_audio", url=audio_url)) + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", + method="POST", + ), + response_model=TaskCreationResponse, + data=Wan27ImageToVideoTaskCreationRequest( + model=model["model"], + input=Wan27ImageToVideoInputField( + prompt=model["prompt"] or None, + negative_prompt=model["negative_prompt"] or None, + media=media, + ), + parameters=Wan27ImageToVideoParametersField( + resolution=model["resolution"], + duration=model["duration"], + seed=seed, + prompt_extend=prompt_extend, + watermark=watermark, + ), + ), + ) + if not initial_response.output: + raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + poll_interval=7, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + +class Wan2VideoContinuationApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="Wan2VideoContinuationApi", + display_name="Wan 2.7 Video Continuation", + category="api node/video/Wan", + description="Continue a video from where it left off, with optional last-frame control.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "wan2.7-i2v", + [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt describing the elements and visual features. Supports English and Chinese.", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative prompt describing what to avoid.", + ), + IO.Combo.Input( + "resolution", + options=["720P", "1080P"], + ), + IO.Int.Input( + "duration", + default=5, + min=2, + max=15, + step=1, + display_mode=IO.NumberDisplay.number, + tooltip="Total output duration in seconds. The model generates continuation " + "to fill the remaining time after the input clip.", + ), + ], + ), + ], + ), + IO.Video.Input( + "first_clip", + tooltip="Input video to continue from. Duration: 2s-10s. " + "The output aspect ratio is derived from this video.", + ), + IO.Image.Input( + "last_frame", + optional=True, + tooltip="Last frame image. The continuation will transition towards this frame.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Boolean.Input( + "prompt_extend", + default=True, + tooltip="Whether to enhance the prompt with AI assistance.", + advanced=True, + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add an AI-generated watermark to the result.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $ppsTable := { "720p": 0.1, "1080p": 0.15 }; + $pps := $lookup($ppsTable, $res); + $outputPrice := $pps * $dur; + { + "type": "range_usd", + "min_usd": 2 * $pps + $outputPrice, + "max_usd": 5 * $pps + $outputPrice + } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + first_clip: Input.Video, + prompt: str = "", + negative_prompt: str = "", + last_frame: Input.Image | None = None, + seed: int = 0, + prompt_extend: bool = True, + watermark: bool = False, + ): + validate_video_duration(first_clip, min_duration=2, max_duration=10) + media = [ + Wan27MediaItem( + type="first_clip", + url=await upload_video_to_comfyapi(cls, first_clip), + ) + ] + if last_frame is not None: + media.append( + Wan27MediaItem( + type="last_frame", + url=await upload_image_to_comfyapi(cls, image=last_frame), + ) + ) + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", + method="POST", + ), + response_model=TaskCreationResponse, + data=Wan27ImageToVideoTaskCreationRequest( + model=model["model"], + input=Wan27ImageToVideoInputField( + prompt=model["prompt"] or None, + negative_prompt=model["negative_prompt"] or None, + media=media, + ), + parameters=Wan27ImageToVideoParametersField( + resolution=model["resolution"], + duration=model["duration"], + seed=seed, + prompt_extend=prompt_extend, + watermark=watermark, + ), + ), + ) + if not initial_response.output: + raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + poll_interval=7, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + +class Wan2VideoEditApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="Wan2VideoEditApi", + display_name="Wan 2.7 Video Edit", + category="api node/video/Wan", + description="Edit a video using text instructions, reference images, or style transfer.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "wan2.7-videoedit", + [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Editing instructions or style transfer requirements.", + ), + IO.Combo.Input( + "resolution", + options=["720P", "1080P"], + ), + IO.Combo.Input( + "ratio", + options=["16:9", "9:16", "1:1", "4:3", "3:4"], + tooltip="Aspect ratio. If not changed, approximates the input video ratio.", + ), + IO.Combo.Input( + "duration", + options=["auto", "2", "3", "4", "5", "6", "7", "8", "9", "10"], + default="auto", + tooltip="Output duration in seconds. 'auto' matches the input video duration. " + "A specific value truncates from the start of the video.", + ), + IO.Autogrow.Input( + "reference_images", + template=IO.Autogrow.TemplateNames( + IO.Image.Input("reference_image"), + names=[ + "image1", + "image2", + "image3", + "image4", + ], + min=0, + ), + ), + ], + ), + ], + ), + IO.Video.Input( + "video", + tooltip="The video to edit.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Combo.Input( + "audio_setting", + options=["auto", "origin"], + default="auto", + tooltip="'auto': model decides whether to regenerate audio based on the prompt. " + "'origin': preserve the original audio from the input video.", + advanced=True, + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add an AI-generated watermark to the result.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $ppsTable := { "720p": 0.1, "1080p": 0.15 }; + $pps := $lookup($ppsTable, $res); + { "type": "usd", "usd": $pps, "format": { "suffix": "/second", "note": "(input + output)" } } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + video: Input.Video, + seed: int, + audio_setting: str, + watermark: bool, + ): + validate_string(model["prompt"], strip_whitespace=False, min_length=1) + validate_video_duration(video, min_duration=2, max_duration=10) + duration = 0 if model["duration"] == "auto" else int(model["duration"]) + media = [Wan27MediaItem(type="video", url=await upload_video_to_comfyapi(cls, video))] + reference_images = model.get("reference_images", {}) + for key in reference_images: + media.append( + Wan27MediaItem( + type="reference_image", url=await upload_image_to_comfyapi(cls, image=reference_images[key]) + ) + ) + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", + method="POST", + ), + response_model=TaskCreationResponse, + data=Wan27VideoEditTaskCreationRequest( + model=model["model"], + input=Wan27VideoEditInputField(prompt=model["prompt"], media=media), + parameters=Wan27VideoEditParametersField( + resolution=model["resolution"], + ratio=model["ratio"], + duration=duration, + audio_setting=audio_setting, + watermark=watermark, + seed=seed, + ), + ), + ) + if not initial_response.output: + raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + poll_interval=7, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + +class Wan2ReferenceVideoApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="Wan2ReferenceVideoApi", + display_name="Wan 2.7 Reference to Video", + category="api node/video/Wan", + description="Generate a video featuring a person or object from reference materials. " + "Supports single-character performances and multi-character interactions.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "wan2.7-r2v", + [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt describing the video. Use identifiers such as 'character1' and " + "'character2' to refer to the reference characters.", + ), + IO.String.Input( + "negative_prompt", + multiline=True, + default="", + tooltip="Negative prompt describing what to avoid.", + ), + IO.Combo.Input( + "resolution", + options=["720P", "1080P"], + ), + IO.Combo.Input( + "ratio", + options=["16:9", "9:16", "1:1", "4:3", "3:4"], + ), + IO.Int.Input( + "duration", + default=5, + min=2, + max=10, + step=1, + display_mode=IO.NumberDisplay.number, + ), + IO.Autogrow.Input( + "reference_videos", + template=IO.Autogrow.TemplateNames( + IO.Video.Input("reference_video"), + names=["video1", "video2", "video3"], + min=0, + ), + ), + IO.Autogrow.Input( + "reference_images", + template=IO.Autogrow.TemplateNames( + IO.Image.Input("reference_image"), + names=["image1", "image2", "image3", "image4", "image5"], + min=0, + ), + ), + ], + ), + ], + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add an AI-generated watermark to the result.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $ppsTable := { "720p": 0.1, "1080p": 0.15 }; + $pps := $lookup($ppsTable, $res); + $outputPrice := $pps * $dur; + { + "type": "range_usd", + "min_usd": $outputPrice, + "max_usd": 5 * $pps + $outputPrice + } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + seed: int, + watermark: bool, + ): + validate_string(model["prompt"], strip_whitespace=False, min_length=1) + media = [] + reference_videos = model.get("reference_videos", {}) + for key in reference_videos: + media.append( + Wan27MediaItem(type="reference_video", url=await upload_video_to_comfyapi(cls, reference_videos[key])) + ) + reference_images = model.get("reference_images", {}) + for key in reference_images: + media.append( + Wan27MediaItem( + type="reference_image", + url=await upload_image_to_comfyapi(cls, image=reference_images[key]), + ) + ) + if not media: + raise ValueError("At least one reference video or reference image must be provided.") + if len(media) > 5: + raise ValueError( + f"Too many references ({len(media)}). The maximum total of reference videos and images is 5." + ) + + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", + method="POST", + ), + response_model=TaskCreationResponse, + data=Wan27ReferenceVideoTaskCreationRequest( + model=model["model"], + input=Wan27ReferenceVideoInputField( + prompt=model["prompt"], + negative_prompt=model["negative_prompt"] or None, + media=media, + ), + parameters=Wan27ReferenceVideoParametersField( + resolution=model["resolution"], + ratio=model["ratio"], + duration=model["duration"], + watermark=watermark, + seed=seed, + ), + ), + ) + if not initial_response.output: + raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + poll_interval=7, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + class WanApiExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: @@ -945,6 +1655,11 @@ class WanApiExtension(ComfyExtension): WanTextToVideoApi, WanImageToVideoApi, WanReferenceVideoApi, + Wan2TextToVideoApi, + Wan2ImageToVideoApi, + Wan2VideoContinuationApi, + Wan2VideoEditApi, + Wan2ReferenceVideoApi, ] From eb0686bbb60c83e44c3a3e4f7defd0f589cfef10 Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Fri, 3 Apr 2026 14:52:10 +0800 Subject: [PATCH 010/140] Update template to 0.9.43 (#13265) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 0d88fdcfb..1031ffa88 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.8 -comfyui-workflow-templates==0.9.41 +comfyui-workflow-templates==0.9.43 comfyui-embedded-docs==0.4.3 torch torchsde From f21f6b22125a80d714be6f2a3a0f3a58850daee5 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 3 Apr 2026 12:29:06 -0700 Subject: [PATCH 011/140] Add portable release for intel XPU. (#13272) --- .github/workflows/release-stable-all.yml | 36 ++++++++++++------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/.github/workflows/release-stable-all.yml b/.github/workflows/release-stable-all.yml index 8f07a7b1c..d7cf69fe2 100644 --- a/.github/workflows/release-stable-all.yml +++ b/.github/workflows/release-stable-all.yml @@ -20,29 +20,12 @@ jobs: git_tag: ${{ inputs.git_tag }} cache_tag: "cu130" python_minor: "13" - python_patch: "11" + python_patch: "12" rel_name: "nvidia" rel_extra_name: "" test_release: true secrets: inherit - release_nvidia_cu128: - permissions: - contents: "write" - packages: "write" - pull-requests: "read" - name: "Release NVIDIA cu128" - uses: ./.github/workflows/stable-release.yml - with: - git_tag: ${{ inputs.git_tag }} - cache_tag: "cu128" - python_minor: "12" - python_patch: "10" - rel_name: "nvidia" - rel_extra_name: "_cu128" - test_release: true - secrets: inherit - release_nvidia_cu126: permissions: contents: "write" @@ -76,3 +59,20 @@ jobs: rel_extra_name: "" test_release: false secrets: inherit + + release_xpu: + permissions: + contents: "write" + packages: "write" + pull-requests: "read" + name: "Release Intel XPU" + uses: ./.github/workflows/stable-release.yml + with: + git_tag: ${{ inputs.git_tag }} + cache_tag: "xpu" + python_minor: "13" + python_patch: "12" + rel_name: "intel" + rel_extra_name: "" + test_release: true + secrets: inherit From 13917b388028aee922efce6b4714db96e8cfea36 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 4 Apr 2026 16:02:47 -0700 Subject: [PATCH 012/140] Nightly Nvidia pytorch is now cu132 (#13288) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a47506fc8..7a7dd5614 100644 --- a/README.md +++ b/README.md @@ -276,7 +276,7 @@ Nvidia users should install stable pytorch using this command: This is the command to install pytorch nightly instead which might have performance improvements. -```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130``` +```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu132``` #### Troubleshooting From 8cbbea8f6a571d8d2a859608bb9434103de769d7 Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Sun, 5 Apr 2026 13:31:11 +0800 Subject: [PATCH 013/140] chore: update workflow templates to v0.9.44 (#13290) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 1031ffa88..1a8e1ea1c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.8 -comfyui-workflow-templates==0.9.43 +comfyui-workflow-templates==0.9.44 comfyui-embedded-docs==0.4.3 torch torchsde From 4b1444fc7a7d1dc542020f509dab2e2b90a4f16a Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 5 Apr 2026 16:37:27 -0700 Subject: [PATCH 014/140] Update README.md with new frontend release cycle. (#13301) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7a7dd5614..1eeb810de 100644 --- a/README.md +++ b/README.md @@ -137,7 +137,7 @@ ComfyUI follows a weekly release cycle targeting Monday but this regularly chang - Builds a new release using the latest stable core version 3. **[ComfyUI Frontend](https://github.com/Comfy-Org/ComfyUI_frontend)** - - Weekly frontend updates are merged into the core repository + - Every 2+ weeks frontend updates are merged into the core repository - Features are frozen for the upcoming core release - Development continues for the next release cycle From 7083484a48b97bdcf6263cd0b64c4994364debc3 Mon Sep 17 00:00:00 2001 From: Terry Jia Date: Mon, 6 Apr 2026 17:54:02 -0400 Subject: [PATCH 015/140] image histogram node (#13153) * image histogram node * update color curve blueprint using image histogram node --------- Co-authored-by: guill --- blueprints/Color Curves.json | 2 +- 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v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\n// GIMP-compatible curve lookup with manual linear interpolation.\n// Matches gimp_curve_map_value_inline() from gimpcurve-map.c:\n// index = value * (n_samples - 1)\n// f = fract(index)\n// result = (1-f) * samples[floor] + f * samples[ceil]\n//\n// Uses texelFetch (NEAREST) to avoid GPU half-texel offset issues\n// that occur with texture() + GL_LINEAR on small 256x1 LUTs.\nfloat applyCurve(sampler2D curve, float value) {\n value = clamp(value, 0.0, 1.0);\n\n float pos = value * 255.0;\n int lo = int(floor(pos));\n int hi = min(lo + 1, 255);\n float f = pos - float(lo);\n\n float a = texelFetch(curve, ivec2(lo, 0), 0).r;\n float b = texelFetch(curve, ivec2(hi, 0), 0).r;\n\n return a + f * (b - a);\n}\n\nvoid main() {\n vec4 color = texture(u_image0, v_texCoord);\n\n // GIMP order: per-channel curves first, then RGB master curve.\n // See gimp_curve_map_pixels() default case in gimpcurve-map.c:\n // dest = colors_curve( 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"links": [38]}], "properties": {"Node name for S&R": "ImageHistogram"}, "widgets_values": []}], "groups": [], "links": [{"id": 29, "origin_id": -10, "origin_slot": 0, "target_id": 8, "target_slot": 0, "type": "IMAGE"}, {"id": 28, "origin_id": 8, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "IMAGE"}, {"id": 30, "origin_id": 4, "origin_slot": 0, "target_id": 8, "target_slot": 2, "type": "CURVE"}, {"id": 31, "origin_id": 5, "origin_slot": 0, "target_id": 8, "target_slot": 3, "type": "CURVE"}, {"id": 32, "origin_id": 6, "origin_slot": 0, "target_id": 8, "target_slot": 4, "type": "CURVE"}, {"id": 33, "origin_id": 7, "origin_slot": 0, "target_id": 8, "target_slot": 5, "type": "CURVE"}, {"id": 34, "origin_id": -10, "origin_slot": 0, "target_id": 9, "target_slot": 0, "type": "IMAGE"}, {"id": 35, "origin_id": 9, "origin_slot": 0, "target_id": 4, "target_slot": 1, "type": "HISTOGRAM"}, {"id": 36, "origin_id": 9, "origin_slot": 2, "target_id": 5, "target_slot": 1, "type": "HISTOGRAM"}, {"id": 37, "origin_id": 9, "origin_slot": 3, "target_id": 6, "target_slot": 1, "type": "HISTOGRAM"}, {"id": 38, "origin_id": 9, "origin_slot": 4, "target_id": 7, "target_slot": 1, "type": "HISTOGRAM"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Image Tools/Color adjust"}]}} \ No newline at end of file diff --git a/comfy_extras/nodes_curve.py b/comfy_extras/nodes_curve.py index 9016a84f9..9803e8034 100644 --- a/comfy_extras/nodes_curve.py +++ b/comfy_extras/nodes_curve.py @@ -1,5 +1,7 @@ from __future__ import annotations +import numpy as np + from comfy_api.latest import ComfyExtension, io from comfy_api.input import CurveInput from typing_extensions import override @@ -32,10 +34,58 @@ class CurveEditor(io.ComfyNode): return io.NodeOutput(result, ui=ui) if ui else io.NodeOutput(result) +class ImageHistogram(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="ImageHistogram", + display_name="Image Histogram", + category="utils", + inputs=[ + io.Image.Input("image"), + ], + outputs=[ + io.Histogram.Output("rgb"), + io.Histogram.Output("luminance"), + io.Histogram.Output("red"), + io.Histogram.Output("green"), + io.Histogram.Output("blue"), + ], + ) + + @classmethod + def execute(cls, image) -> io.NodeOutput: + img = image[0].cpu().numpy() + img_uint8 = np.clip(img * 255, 0, 255).astype(np.uint8) + + def bincount(data): + return np.bincount(data.ravel(), minlength=256)[:256] + + hist_r = bincount(img_uint8[:, :, 0]) + hist_g = bincount(img_uint8[:, :, 1]) + hist_b = bincount(img_uint8[:, :, 2]) + + # Average of R, G, B histograms (same as Photoshop's RGB composite) + rgb = ((hist_r + hist_g + hist_b) // 3).tolist() + + # ITU-R BT.709-6, Item 3.2 (p.6) — Derivation of luminance signal + # https://www.itu.int/rec/R-REC-BT.709-6-201506-I/en + lum = 0.2126 * img[:, :, 0] + 0.7152 * img[:, :, 1] + 0.0722 * img[:, :, 2] + luminance = bincount(np.clip(lum * 255, 0, 255).astype(np.uint8)).tolist() + + return io.NodeOutput( + rgb, + luminance, + hist_r.tolist(), + hist_g.tolist(), + hist_b.tolist(), + ) + + class CurveExtension(ComfyExtension): @override async def get_node_list(self): - return [CurveEditor] + return [CurveEditor, ImageHistogram] async def comfy_entrypoint(): From 61c23874363495c59a598cf1270809f1a6b2aa89 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 6 Apr 2026 18:12:16 -0700 Subject: [PATCH 016/140] Ace step empty latent nodes follow intermediate dtype. (#13313) --- comfy_extras/nodes_ace.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_ace.py b/comfy_extras/nodes_ace.py index 9cf84ab4d..cbfaf913d 100644 --- a/comfy_extras/nodes_ace.py +++ b/comfy_extras/nodes_ace.py @@ -80,7 +80,7 @@ class EmptyAceStepLatentAudio(io.ComfyNode): @classmethod def execute(cls, seconds, batch_size) -> io.NodeOutput: length = int(seconds * 44100 / 512 / 8) - latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device()) + latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) return io.NodeOutput({"samples": latent, "type": "audio"}) @@ -103,7 +103,7 @@ class EmptyAceStep15LatentAudio(io.ComfyNode): @classmethod def execute(cls, seconds, batch_size) -> io.NodeOutput: length = round((seconds * 48000 / 1920)) - latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device()) + latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) return io.NodeOutput({"samples": latent, "type": "audio"}) class ReferenceAudio(io.ComfyNode): From 50076f34396fd2e5a875c60e42574e1756b4917b Mon Sep 17 00:00:00 2001 From: Terry Jia Date: Mon, 6 Apr 2026 23:33:55 -0400 Subject: [PATCH 017/140] format blueprint (#13315) Co-authored-by: guill --- blueprints/Brightness and Contrast.json | 440 +- .../Canny to Image (Z-Image-Turbo).json | 1578 ++++- blueprints/Canny to Video (LTX 2.0).json | 3620 +++++++++++- blueprints/Chromatic Aberration.json | 385 +- blueprints/Color Adjustment.json | 604 +- blueprints/Color Balance.json | 1137 +++- blueprints/Color Curves.json | 616 +- .../Depth to Image (Z-Image-Turbo).json | 2486 +++++++- blueprints/Depth to Video (ltx 2.0).json | 5212 ++++++++++++++++- blueprints/Edge-Preserving Blur.json | 459 +- blueprints/Film Grain.json | 588 +- blueprints/Glow.json | 583 +- blueprints/Hue and Saturation.json | 760 ++- blueprints/Image Blur.json | 382 +- blueprints/Image Captioning (gemini).json | 318 +- blueprints/Image Channels.json | 323 +- blueprints/Image Edit (Flux.2 Klein 4B).json | 1841 +++++- blueprints/Image Edit (Qwen 2511).json | 1493 ++++- blueprints/Image Inpainting (Qwen-image).json | 1927 +++++- blueprints/Image Levels.json | 751 ++- .../Image Outpainting (Qwen-Image).json | 2753 ++++++++- blueprints/Image Upscale(Z-image-Turbo).json | 1315 ++++- blueprints/Image to Depth Map (Lotus).json | 968 ++- .../Image to Layers(Qwen-Image Layered).json | 1416 ++++- .../Image to Model (Hunyuan3d 2.1).json | 785 ++- blueprints/Image to Video (Wan 2.2).json | 2053 ++++++- blueprints/Pose to Image (Z-Image-Turbo).json | 1323 ++++- blueprints/Pose to Video (LTX 2.0).json | 3789 +++++++++++- blueprints/Prompt Enhance.json | 279 +- blueprints/Sharpen.json | 310 +- blueprints/Text to Audio (ACE-Step 1.5).json | 1522 ++++- blueprints/Text to Image (Z-Image-Turbo).json | 1079 +++- blueprints/Text to Video (Wan 2.2).json | 1590 ++++- blueprints/Unsharp Mask.json | 442 +- blueprints/Video Captioning (Gemini).json | 315 +- blueprints/Video Inpaint(Wan2.1 VACE).json | 2388 +++++++- blueprints/Video Stitch.json | 592 +- blueprints/Video Upscale(GAN x4).json | 421 +- 38 files changed, 48805 insertions(+), 38 deletions(-) diff --git a/blueprints/Brightness and Contrast.json b/blueprints/Brightness and Contrast.json index 2c7e60eb1..6a234139d 100644 --- a/blueprints/Brightness and Contrast.json +++ b/blueprints/Brightness and Contrast.json @@ -1 +1,439 @@ -{"revision": 0, "last_node_id": 140, "last_link_id": 0, "nodes": [{"id": 140, "type": "916dff42-6166-4d45-b028-04eaf69fbb35", "pos": [500, 1440], "size": [250, 178], "flags": {}, "order": 2, "mode": 0, "inputs": [{"label": "image", "localized_name": "images.image0", "name": "images.image0", "type": "IMAGE", "link": null}], 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\n fragColor = vec4(red, green, blue, original.a);\n}", + "from_input" + ] + }, + { + "id": 18, + "type": "PrimitiveFloat", + "pos": [ + 3810, + -2430 + ], + "size": [ + 270, + 58 + ], + "flags": {}, + "order": 0, + "mode": 0, + "inputs": [ + { + "label": "amount", + "localized_name": "value", + "name": "value", + "type": "FLOAT", + "widget": { + "name": "value" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": "FLOAT", + "name": "FLOAT", + "type": "FLOAT", + "links": [ + 22 + ] + } + ], + "properties": { + "Node name for S&R": "PrimitiveFloat", + "min": 0, + "max": 100, + "step": 1 + }, + "widgets_values": [ + 30 + ] + }, + { + "id": 17, + "type": "CustomCombo", + "pos": [ + 3520, + -2320 + ], + "size": [ + 270, + 222 + ], + "flags": {}, + "order": 1, + "mode": 0, + "inputs": [ + { + "label": "mode", + "localized_name": "choice", + "name": "choice", + "type": "COMBO", + "widget": { + "name": "choice" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": 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200u),\n toGaussian(grainPixel, 300u)\n );\n } else {\n // Smooth mode: interpolated with quintic curve\n g = smoothNoise(grainUV);\n grainRGB = vec3(\n smoothNoise(grainUV, 100u),\n smoothNoise(grainUV, 200u),\n smoothNoise(grainUV, 300u)\n );\n }\n \n // Luminance weighting (less grain in highlights)\n float lumWeight = mix(1.0, 1.0 - luma, clamp(u_float3, 0.0, 1.0));\n \n // Strength\n float strength = u_float0 * 0.15;\n \n // Color vs monochrome grain\n vec3 grainColor = mix(vec3(g), grainRGB, clamp(u_float2, 0.0, 1.0));\n \n color.rgb += grainColor * strength * lumWeight;\n fragColor0 = vec4(clamp(color.rgb, 0.0, 1.0), color.a);\n}\n", + "from_input" + ] + }, + { + "id": 21, + "type": "CustomCombo", + "pos": [ + 4280, + -780 + ], + "size": [ + 210, + 153.8888931274414 + ], + "flags": {}, + "order": 0, + "mode": 0, + "inputs": [ + { + "label": "grain_mode", + "localized_name": "choice", + "name": "choice", + "type": "COMBO", + "widget": { + "name": "choice" + }, + "link": null + } 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center);\n\n float inner = baseWidth * 0.5;\n float outer = inner + feather;\n\n return 1.0 - smoothstep(inner, outer, d);\n}\n\nfloat getModeWeight(float hue, int mode, float overlap) {\n if (mode == MODE_MASTER || mode == MODE_COLORIZE) return 1.0;\n\n if (mode == MODE_RED) {\n return max(\n getHueWeight(hue, 0.0, overlap),\n getHueWeight(hue, 1.0, overlap)\n );\n }\n\n float center = float(mode - 1) / 6.0;\n return getHueWeight(hue, center, overlap);\n}\n\n//=============================================================================\n// Adjustment Functions\n//=============================================================================\n\nfloat adjustLightness(float l, float amount) {\n return amount > 0.0\n ? l + (1.0 - l) * amount\n : l + l * amount;\n}\n\nfloat adjustBrightness(float b, float amount) {\n return clamp(b + amount, 0.0, 1.0);\n}\n\nfloat adjustSaturation(float s, float amount) {\n return amount > 0.0\n ? s + (1.0 - s) * amount\n : s + s * amount;\n}\n\nvec3 colorize(vec3 rgb, float hue, float sat, float light) {\n float lum = dot(rgb, vec3(0.299, 0.587, 0.114));\n float l = adjustLightness(lum, light);\n\n vec3 hsl = vec3(fract(hue), clamp(sat, 0.0, 1.0), clamp(l, 0.0, 1.0));\n return hsl2rgb(hsl);\n}\n\n//=============================================================================\n// Main\n//=============================================================================\n\nvoid main() {\n vec4 original = texture(u_image0, v_texCoord);\n\n float hueShift = u_float0 / 360.0; // -180..180 -> -0.5..0.5\n float satAmount = u_float1 / 100.0; // -100..100 -> -1..1\n float lightAmount= u_float2 / 100.0; // -100..100 -> -1..1\n float overlap = u_float3 / 100.0; // 0..100 -> 0..1\n\n vec3 result;\n\n if (u_int0 == MODE_COLORIZE) {\n result = colorize(original.rgb, hueShift, satAmount, lightAmount);\n fragColor = vec4(result, original.a);\n return;\n }\n\n vec3 hsx = (u_int1 == COLORSPACE_HSL)\n ? rgb2hsl(original.rgb)\n : 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"fragment_shader" + }, + "link": null + }, + { + "localized_name": "size_mode", + "name": "size_mode", + "type": "COMFY_DYNAMICCOMBO_V3", + "widget": { + "name": "size_mode" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": "IMAGE0", + "name": "IMAGE0", + "type": "IMAGE", + "links": [ + 3 + ] + }, + { + "localized_name": "IMAGE1", + "name": "IMAGE1", + "type": "IMAGE", + "links": [] + }, + { + "localized_name": "IMAGE2", + "name": "IMAGE2", + "type": "IMAGE", + "links": [] + }, + { + "localized_name": "IMAGE3", + "name": "IMAGE3", + "type": "IMAGE", + "links": [] + } + ], + "properties": { + "Node name for S&R": "GLSLShader" + }, + "widgets_values": [ + "#version 300 es\n#pragma passes 2\nprecision highp float;\n\n// Blur type constants\nconst int BLUR_GAUSSIAN = 0;\nconst int BLUR_BOX = 1;\nconst int BLUR_RADIAL = 2;\n\n// Radial blur config\nconst int RADIAL_SAMPLES = 12;\nconst float RADIAL_STRENGTH = 0.0003;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0; // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)\nuniform float u_float0; // Blur radius/amount\nuniform int u_pass; // Pass index (0 = horizontal, 1 = vertical)\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nvoid main() {\n vec2 texelSize = 1.0 / u_resolution;\n float radius = max(u_float0, 0.0);\n\n // Radial (angular) blur - single pass, doesn't use separable\n if (u_int0 == BLUR_RADIAL) {\n // Only execute on first pass\n if (u_pass > 0) {\n fragColor0 = texture(u_image0, v_texCoord);\n return;\n }\n\n vec2 center = vec2(0.5);\n vec2 dir = v_texCoord - center;\n float dist = length(dir);\n\n if (dist < 1e-4) {\n fragColor0 = texture(u_image0, v_texCoord);\n return;\n }\n\n vec4 sum = vec4(0.0);\n float totalWeight = 0.0;\n float angleStep = radius * RADIAL_STRENGTH;\n\n dir /= dist;\n\n float cosStep = cos(angleStep);\n float sinStep = sin(angleStep);\n\n float negAngle = -float(RADIAL_SAMPLES) * angleStep;\n vec2 rotDir = vec2(\n dir.x * cos(negAngle) - dir.y * sin(negAngle),\n dir.x * sin(negAngle) + dir.y * cos(negAngle)\n );\n\n for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {\n vec2 uv = center + rotDir * dist;\n float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);\n sum += texture(u_image0, uv) * w;\n totalWeight += w;\n\n rotDir = vec2(\n rotDir.x * cosStep - rotDir.y * sinStep,\n rotDir.x * sinStep + rotDir.y * cosStep\n );\n }\n\n fragColor0 = sum / max(totalWeight, 0.001);\n return;\n }\n\n // Separable Gaussian / Box blur\n int samples = int(ceil(radius));\n\n if (samples == 0) {\n fragColor0 = texture(u_image0, v_texCoord);\n return;\n }\n\n // Direction: pass 0 = horizontal, pass 1 = vertical\n vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\n\n vec4 color = vec4(0.0);\n float totalWeight = 0.0;\n float sigma = radius / 2.0;\n\n for (int i = -samples; i <= samples; i++) {\n vec2 offset = dir * float(i) * texelSize;\n vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n float weight;\n if (u_int0 == BLUR_GAUSSIAN) {\n weight = gaussian(float(i), sigma);\n } else {\n // BLUR_BOX\n weight = 1.0;\n }\n\n color += sample_color * weight;\n totalWeight += weight;\n }\n\n fragColor0 = color / totalWeight;\n}\n", + "from_input" + ] + } + ], + "groups": [], + "links": [ + { + "id": 10, + "origin_id": 10, + "origin_slot": 0, + "target_id": 1, + "target_slot": 2, + "type": "FLOAT" + }, + { + "id": 11, + "origin_id": 12, + "origin_slot": 1, + "target_id": 1, + "target_slot": 4, + "type": "INT" + }, + { + "id": 9, + "origin_id": -10, + "origin_slot": 0, + "target_id": 1, + "target_slot": 0, + "type": "IMAGE" + }, + { + "id": 3, + "origin_id": 1, + "origin_slot": 0, + "target_id": -20, + "target_slot": 0, + "type": "IMAGE" + } + ], + "extra": { + "workflowRendererVersion": "LG" + }, + "category": "Image Tools/Blur" + } + ] + } +} diff --git a/blueprints/Image Captioning (gemini).json b/blueprints/Image Captioning (gemini).json index 89ebac802..98cfb8999 100644 --- a/blueprints/Image Captioning (gemini).json +++ b/blueprints/Image Captioning (gemini).json @@ -1 +1,317 @@ -{"revision": 0, "last_node_id": 231, "last_link_id": 0, "nodes": [{"id": 231, "type": "e3e78497-720e-45a2-b4fb-c7bfdb80dd11", "pos": [23.13283014087665, 1034.468391137315], "size": [280, 260], "flags": {}, "order": 8, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "type": "IMAGE", "link": null}, {"name": "prompt", "type": "STRING", "widget": {"name": "prompt"}, "link": null}, {"name": "model", "type": "COMBO", "widget": {"name": "model"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": []}], "properties": {"proxyWidgets": [["-1", "prompt"], ["-1", "model"], ["1", "seed"]], "cnr_id": "comfy-core", "ver": "0.13.0"}, "widgets_values": ["Describe this image", "gemini-2.5-pro"], "title": "Image Captioning(Gemini)"}], "links": [], "version": 0.4, "definitions": {"subgraphs": [{"id": "e3e78497-720e-45a2-b4fb-c7bfdb80dd11", "version": 1, "state": {"lastGroupId": 1, "lastNodeId": 16, "lastLinkId": 16, "lastRerouteId": 0}, "revision": 0, "config": {}, "name": "Image Captioning(Gemini)", "inputNode": {"id": -10, "bounding": [-6870, 2530, 120, 100]}, "outputNode": {"id": -20, "bounding": [-6240, 2530, 120, 60]}, "inputs": [{"id": "97cb8fa5-0514-4e05-b206-46fa6d7b5589", "name": "images", "type": "IMAGE", "linkIds": [1], "localized_name": "images", "shape": 7, "pos": [-6770, 2550]}, {"id": "d8cbd7eb-636a-4d7b-8ff6-b22f1755e26c", "name": "prompt", "type": "STRING", "linkIds": [15], "pos": [-6770, 2570]}, {"id": "b034e26a-d114-4604-aec2-32783e86aa6b", "name": "model", "type": "COMBO", "linkIds": [16], "pos": [-6770, 2590]}], "outputs": [{"id": "e12c6e80-5210-4328-a581-bc8924c53070", "name": "STRING", "type": "STRING", "linkIds": [6], "localized_name": "STRING", "pos": [-6220, 2550]}], "widgets": [], "nodes": [{"id": 1, "type": "GeminiNode", "pos": [-6690, 2360], "size": [390, 430], "flags": {}, "order": 0, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "shape": 7, "type": "IMAGE", "link": 1}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": null}, {"localized_name": "video", "name": "video", "shape": 7, "type": "VIDEO", "link": null}, {"localized_name": "files", "name": "files", "shape": 7, "type": "GEMINI_INPUT_FILES", "link": null}, {"localized_name": "prompt", "name": "prompt", "type": "STRING", "widget": {"name": "prompt"}, "link": 15}, {"localized_name": "model", "name": "model", "type": "COMBO", "widget": {"name": "model"}, "link": 16}, {"localized_name": "seed", "name": "seed", "type": "INT", "widget": {"name": "seed"}, "link": null}, {"localized_name": "system_prompt", "name": "system_prompt", "shape": 7, "type": "STRING", "widget": {"name": "system_prompt"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": [6]}], "properties": {"cnr_id": "comfy-core", "ver": "0.5.1", "Node name for S&R": "GeminiNode"}, "widgets_values": ["Describe this image", "gemini-2.5-pro", 511865409297955, "randomize", "- Role: AI Image Analysis and Description Specialist\n- Background: The user requires a prompt that enables AI to analyze images and generate detailed descriptions which can be used as drawing prompts to create similar images. This is essential for tasks like content creation, design inspiration, and artistic exploration.\n- Profile: As an AI Image Analysis and Description Specialist, you possess extensive knowledge in computer vision, image processing, and natural language generation. You are adept at interpreting visual data and translating it into descriptive text that can guide the creation of new images.\n- Skills: Proficiency in image recognition, feature extraction, descriptive language generation, and understanding of artistic elements such as composition, color, and texture.\n- Goals: To analyze the provided image, generate a comprehensive and detailed description that captures the key visual elements, and ensure this description can effectively serve as a drawing prompt for creating similar images.\n- Constrains: The description must be clear, concise, and specific enough to guide the creation of a similar image. It should avoid ambiguity and focus on the most salient features of the image. The output should only contain the drawing prompt.\n- OutputFormat: A detailed text description of the image, highlighting key visual elements such as objects, colors, composition, and any unique features.\n- Workflow:\n 1. Analyze the image to identify key visual elements including objects, colors, and composition.\n 2. Generate a detailed description that captures the essence of the image, ensuring it is specific and actionable.\n 3. Refine the description to ensure clarity and conciseness, making it suitable for use as a drawing prompt."], "color": "#432", "bgcolor": "#653"}], "groups": [], "links": [{"id": 1, "origin_id": -10, "origin_slot": 0, "target_id": 1, "target_slot": 0, "type": "IMAGE"}, {"id": 6, "origin_id": 1, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "*"}, {"id": 15, "origin_id": -10, "origin_slot": 1, "target_id": 1, "target_slot": 4, "type": "STRING"}, {"id": 16, "origin_id": -10, "origin_slot": 2, "target_id": 1, "target_slot": 5, "type": "COMBO"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Text generation/Image Captioning"}]}} +{ + "revision": 0, + "last_node_id": 231, + "last_link_id": 0, + "nodes": [ + { + "id": 231, + "type": "e3e78497-720e-45a2-b4fb-c7bfdb80dd11", + "pos": [ + 23.13283014087665, + 1034.468391137315 + ], + "size": [ + 280, + 260 + ], + "flags": {}, + "order": 8, + "mode": 0, + "inputs": [ + { + "localized_name": "images", + "name": "images", + "type": "IMAGE", + "link": null + }, + { + "name": "prompt", + "type": "STRING", + "widget": { + "name": "prompt" + }, + "link": null + }, + { + "name": "model", + "type": "COMBO", + "widget": { + "name": "model" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": "STRING", + "name": "STRING", + "type": "STRING", + "links": [] + } + ], + "properties": { + "proxyWidgets": [ + [ + "-1", + "prompt" + ], + [ + "-1", + "model" + ], + [ + "1", + "seed" + ] + ], + "cnr_id": "comfy-core", + "ver": "0.13.0" + }, + "widgets_values": [ + "Describe this image", + "gemini-2.5-pro" + ], + "title": "Image Captioning(Gemini)" + } + ], + "links": [], + "version": 0.4, + "definitions": { + "subgraphs": [ + { + "id": "e3e78497-720e-45a2-b4fb-c7bfdb80dd11", + "version": 1, + "state": { + "lastGroupId": 1, + "lastNodeId": 16, + "lastLinkId": 16, + "lastRerouteId": 0 + }, + "revision": 0, + "config": {}, + "name": "Image Captioning(Gemini)", + "inputNode": { + "id": -10, + "bounding": [ + -6870, + 2530, + 120, + 100 + ] + }, + "outputNode": { + "id": -20, + "bounding": [ + -6240, + 2530, + 120, + 60 + ] + }, + "inputs": [ + { + "id": "97cb8fa5-0514-4e05-b206-46fa6d7b5589", + "name": "images", + "type": "IMAGE", + "linkIds": [ + 1 + ], + "localized_name": "images", + "shape": 7, + "pos": [ + -6770, + 2550 + ] + }, + { + "id": "d8cbd7eb-636a-4d7b-8ff6-b22f1755e26c", + "name": "prompt", + "type": "STRING", + "linkIds": [ + 15 + ], + "pos": [ + -6770, + 2570 + ] + }, + { + "id": "b034e26a-d114-4604-aec2-32783e86aa6b", + "name": "model", + "type": "COMBO", + "linkIds": [ + 16 + ], + "pos": [ + -6770, + 2590 + ] + } + ], + "outputs": [ + { + "id": "e12c6e80-5210-4328-a581-bc8924c53070", + "name": "STRING", + "type": "STRING", + "linkIds": [ + 6 + ], + "localized_name": "STRING", + "pos": [ + -6220, + 2550 + ] + } + ], + "widgets": [], + "nodes": [ + { + "id": 1, + "type": "GeminiNode", + "pos": [ + -6690, + 2360 + ], + "size": [ + 390, + 430 + ], + "flags": {}, + "order": 0, + "mode": 0, + "inputs": [ + { + "localized_name": "images", + "name": "images", + "shape": 7, + "type": "IMAGE", + "link": 1 + }, + { + "localized_name": "audio", + "name": "audio", + "shape": 7, + "type": "AUDIO", + "link": null + }, + { + "localized_name": "video", + "name": "video", + "shape": 7, + "type": "VIDEO", + "link": null + }, + { + "localized_name": "files", + "name": "files", + "shape": 7, + "type": "GEMINI_INPUT_FILES", + "link": null + }, + { + "localized_name": "prompt", + "name": "prompt", + "type": "STRING", + "widget": { + "name": "prompt" + }, + "link": 15 + }, + { + "localized_name": "model", + "name": "model", + "type": "COMBO", + "widget": { + "name": "model" + }, + "link": 16 + }, + { + "localized_name": "seed", + "name": "seed", + "type": "INT", + "widget": { + "name": "seed" + }, + "link": null + }, + { + "localized_name": "system_prompt", + "name": "system_prompt", + "shape": 7, + "type": "STRING", + "widget": { + "name": "system_prompt" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": "STRING", + "name": "STRING", + "type": "STRING", + "links": [ + 6 + ] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.5.1", + "Node name for S&R": "GeminiNode" + }, + "widgets_values": [ + "Describe this image", + "gemini-2.5-pro", + 511865409297955, + "randomize", + "- Role: AI Image Analysis and Description Specialist\n- Background: The user requires a prompt that enables AI to analyze images and generate detailed descriptions which can be used as drawing prompts to create similar images. This is essential for tasks like content creation, design inspiration, and artistic exploration.\n- Profile: As an AI Image Analysis and Description Specialist, you possess extensive knowledge in computer vision, image processing, and natural language generation. You are adept at interpreting visual data and translating it into descriptive text that can guide the creation of new images.\n- Skills: Proficiency in image recognition, feature extraction, descriptive language generation, and understanding of artistic elements such as composition, color, and texture.\n- Goals: To analyze the provided image, generate a comprehensive and detailed description that captures the key visual elements, and ensure this description can effectively serve as a drawing prompt for creating similar images.\n- Constrains: The description must be clear, concise, and specific enough to guide the creation of a similar image. It should avoid ambiguity and focus on the most salient features of the image. The output should only contain the drawing prompt.\n- OutputFormat: A detailed text description of the image, highlighting key visual elements such as objects, colors, composition, and any unique features.\n- Workflow:\n 1. Analyze the image to identify key visual elements including objects, colors, and composition.\n 2. Generate a detailed description that captures the essence of the image, ensuring it is specific and actionable.\n 3. Refine the description to ensure clarity and conciseness, making it suitable for use as a drawing prompt." + ], + "color": "#432", + "bgcolor": "#653" + } + ], + "groups": [], + "links": [ + { + "id": 1, + "origin_id": -10, + "origin_slot": 0, + "target_id": 1, + "target_slot": 0, + "type": "IMAGE" + }, + { + "id": 6, + "origin_id": 1, + "origin_slot": 0, + "target_id": -20, + "target_slot": 0, + "type": "*" + }, + { + "id": 15, + "origin_id": -10, + "origin_slot": 1, + "target_id": 1, + "target_slot": 4, + "type": "STRING" + }, + { + "id": 16, + "origin_id": -10, + "origin_slot": 2, + "target_id": 1, + "target_slot": 5, + "type": "COMBO" + } + ], + "extra": { + "workflowRendererVersion": "LG" + }, + "category": "Text generation/Image Captioning" + } + ] + } +} diff --git a/blueprints/Image Channels.json b/blueprints/Image Channels.json index cb3488883..9c7b675b2 100644 --- a/blueprints/Image Channels.json +++ b/blueprints/Image Channels.json @@ -1 +1,322 @@ -{"revision": 0, 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If you have enough VRAM, you can change the size"], "color": "#222", "bgcolor": "#000"}, {"id": 105, "type": "MarkdownNote", "pos": [-469.9999795985529, 279.9998197772136], "size": [480, 170.65104166666669], "flags": {}, "order": 5, "mode": 0, "inputs": [], "outputs": [], "title": "VRAM Usage", "properties": {"ue_properties": {"version": "7.1", "widget_ue_connectable": {}, "input_ue_unconnectable": {}}}, "widgets_values": ["## GPU:RTX4090D 24GB\n\n| Model | Size |VRAM Usage | 1st Generation | 2nd Generation |\n|---------------------|-------|-----------|---------------|-----------------|\n| fp8_scaled |640*640| 84% | ≈ 536s | ≈ 513s |\n| fp8_scaled + 4steps LoRA | 640*640 | 83% | ≈ 97s | ≈ 71s |"], "color": "#222", "bgcolor": "#000"}, {"id": 66, "type": "MarkdownNote", "pos": [-469.9999795985529, -320.00012452364496], "size": [480, 572.1354166666667], "flags": {}, "order": 6, "mode": 0, "inputs": [], "outputs": [], "title": "Model Links", "properties": {"ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": ["[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\n)\n\n**Diffusion Model**\n- [wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors)\n- [wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors)\n\n**LoRA**\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors)\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\n\n**Text Encoder** \n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n\nFile save location\n\n```\nComfyUI/\n├───📂 models/\n│ ├───📂 diffusion_models/\n│ │ ├─── wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors\n│ │ └─── wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors\n│ ├───📂 loras/\n│ │ ├─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\n│ │ └─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\n│ ├───📂 text_encoders/\n│ │ └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \n│ └───📂 vae/\n│ └── wan_2.1_vae.safetensors\n```\n"], "color": "#222", "bgcolor": "#000"}, {"id": 115, "type": "Note", "pos": [29.999978639114225, -470.00010361843204], "size": [360, 88], "flags": {}, "order": 7, "mode": 0, "inputs": [], "outputs": [], "title": "About 4 Steps LoRA", "properties": {"ue_properties": {"widget_ue_connectable": {}, "version": "7.1", "input_ue_unconnectable": {}}}, "widgets_values": ["Using the Wan2.2 Lighting LoRA will result in the loss of video dynamics, but it will reduce the generation time. 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If you have enough VRAM, you can change the size" + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 105, + "type": "MarkdownNote", + "pos": [ + -469.9999795985529, + 279.9998197772136 + ], + "size": [ + 480, + 170.65104166666669 + ], + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "VRAM Usage", + "properties": { + "ue_properties": { + "version": "7.1", + "widget_ue_connectable": {}, + "input_ue_unconnectable": {} + } + }, + "widgets_values": [ + "## GPU:RTX4090D 24GB\n\n| Model | Size |VRAM Usage | 1st Generation | 2nd Generation |\n|---------------------|-------|-----------|---------------|-----------------|\n| fp8_scaled |640*640| 84% | ≈ 536s | ≈ 513s |\n| fp8_scaled + 4steps LoRA | 640*640 | 83% | ≈ 97s | ≈ 71s |" + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 66, + "type": "MarkdownNote", + "pos": [ + -469.9999795985529, + -320.00012452364496 + ], + "size": [ + 480, + 572.1354166666667 + ], + "flags": {}, + "order": 6, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "Model Links", + "properties": { + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.1", + "input_ue_unconnectable": {} + } + }, + "widgets_values": [ + "[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\n)\n\n**Diffusion Model**\n- [wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors)\n- [wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors)\n\n**LoRA**\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors)\n- [wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\n\n**Text Encoder** \n- [umt5_xxl_fp8_e4m3fn_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors)\n\n\nFile save location\n\n```\nComfyUI/\n├───📂 models/\n│ ├───📂 diffusion_models/\n│ │ ├─── wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors\n│ │ └─── wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors\n│ ├───📂 loras/\n│ │ ├─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\n│ │ └─── wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors\n│ ├───📂 text_encoders/\n│ │ └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \n│ └───📂 vae/\n│ └── wan_2.1_vae.safetensors\n```\n" + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 115, + "type": "Note", + "pos": [ + 29.999978639114225, + -470.00010361843204 + ], + "size": [ + 360, + 88 + ], + "flags": {}, + "order": 7, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "About 4 Steps LoRA", + "properties": { + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.1", + "input_ue_unconnectable": {} + } + }, + "widgets_values": [ + "Using the Wan2.2 Lighting LoRA will result in the loss of video dynamics, but it will reduce the generation time. This template provides two workflows, and you can enable one as needed." + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 117, + "type": "CreateVideo", + "pos": [ + 1030, + 650 + ], + "size": [ + 270, + 78 + ], + "flags": {}, + "order": 18, + "mode": 0, + "inputs": [ + { + "localized_name": "images", + "name": "images", + "type": "IMAGE", + "link": 220 + }, + { + "localized_name": "audio", + "name": "audio", + "shape": 7, + "type": "AUDIO", + "link": null + }, + { + "localized_name": "fps", + "name": "fps", + "type": "FLOAT", + "widget": { + "name": "fps" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": "VIDEO", + "name": "VIDEO", + "type": "VIDEO", + "links": [ + 221 + ] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.11.0", + "Node name for S&R": "CreateVideo" + }, + "widgets_values": [ + 16 + ] + }, + { + "id": 87, + "type": "VAEDecode", + "pos": [ + 1020, + 540 + ], + "size": [ + 210, + 46 + ], + "flags": {}, + "order": 15, + "mode": 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loras/\n│ │ ├───wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors\n│ │ └───wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors\n│ ├───📂 text_encoders/\n│ │ └─── umt5_xxl_fp8_e4m3fn_scaled.safetensors \n│ └───📂 vae/\n│ └── wan_2.1_vae.safetensors\n```\n"], "color": "#222", "bgcolor": "#000"}, {"id": 87, "type": "VAEDecode", "pos": [1020.3331497597994, 471.3333837135574], "size": [210, 46], "flags": {"collapsed": false}, "order": 14, "mode": 0, "inputs": [{"localized_name": "samples", "name": "samples", "type": "LATENT", "link": 157}, {"localized_name": "vae", "name": "vae", "type": "VAE", "link": 158}], "outputs": [{"localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "slot_index": 0, "links": [195]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "VAEDecode"}, "widgets_values": []}, {"id": 72, "type": "CLIPTextEncode", "pos": [440.3333139376125, 331.3333305479798], "size": [500, 170], "flags": {}, "order": 6, "mode": 0, "inputs": 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This template provides two workflows, and you can enable one as needed." + ], + "color": "#222", + "bgcolor": "#000" + }, + { + "id": 62, + "type": "MarkdownNote", + "pos": [ + -489.666771800538, + -278.666700527147 + ], + "size": [ + 479.9869791666667, + 542.1354166666667 + ], + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "Model Links", + "properties": {}, + "widgets_values": [ + "[Tutorial](https://docs.comfy.org/tutorials/video/wan/wan2_2\n) | [教程](https://docs.comfy.org/zh-CN/tutorials/video/wan/wan2_2\n)\n\n**Diffusion Model** \n- [wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors)\n- [wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/diffusion_models/wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors)\n\n**LoRA**\n\n- [wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors)\n- [wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/loras/wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors)\n\n**VAE**\n- [wan_2.1_vae.safetensors](https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors)\n\n**Text Encoder** \n- 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"GeminiNode", "pos": [-6690, 2360], "size": [390, 430], "flags": {}, "order": 0, "mode": 0, "inputs": [{"localized_name": "images", "name": "images", "shape": 7, "type": "IMAGE", "link": null}, {"localized_name": "audio", "name": "audio", "shape": 7, "type": "AUDIO", "link": null}, {"localized_name": "video", "name": "video", "shape": 7, "type": "VIDEO", "link": 17}, {"localized_name": "files", "name": "files", "shape": 7, "type": "GEMINI_INPUT_FILES", "link": null}, {"localized_name": "prompt", "name": "prompt", "type": "STRING", "widget": {"name": "prompt"}, "link": 15}, {"localized_name": "model", "name": "model", "type": "COMBO", "widget": {"name": "model"}, "link": 16}, {"localized_name": "seed", "name": "seed", "type": "INT", "widget": {"name": "seed"}, "link": null}, {"localized_name": "system_prompt", "name": "system_prompt", "shape": 7, "type": "STRING", "widget": {"name": "system_prompt"}, "link": null}], "outputs": [{"localized_name": "STRING", "name": "STRING", "type": "STRING", "links": [6]}], "properties": {"cnr_id": "comfy-core", "ver": "0.5.1", "Node name for S&R": "GeminiNode"}, "widgets_values": ["Describe this video", "gemini-2.5-pro", 511865409297955, "randomize", "- Role: AI Video Analysis and Description Specialist\n- Background: The user requires a prompt that enables AI to analyze videos (including frame sequences, dynamic movements, audio-visual elements) and generate detailed, structured descriptions. These descriptions must be directly usable as video generation prompts to create similar videos, serving core tasks such as video content creation, creative inspiration extraction, and artistic style exploration.\n- Profile: As an AI Video Analysis and Description Specialist, you possess expertise in computer vision, video temporal sequence processing, motion analysis, and multi-modal natural language generation. You excel at interpreting dynamic visual data (frame-by-frame features + continuous motion) and translating it into precise descriptive text that fully guides the creation of new videos with matching style, rhythm, and content.\n- Skills: Proficiency in video frame feature extraction, motion trajectory recognition, temporal rhythm analysis, scene/shot segmentation, color grading detection, camera movement identification (pan/tilt/zoom/dolly), audio-visual element correlation analysis, and descriptive language generation that captures both static visual features and dynamic temporal characteristics. Mastery of artistic elements in video: composition (per frame + dynamic framing), color palette (consistent + transitional), texture (surface details + motion blur), pacing (frame rate, shot duration), and sound style (background music, ambient sound cues).\n- Goals: To analyze the provided video comprehensively, generate a detailed, structured description that captures all key video elements (static visual features + dynamic motion/temporal characteristics + audio-visual style), and ensure this description can directly serve as a high-quality prompt for creating similar videos.\n- Constraints: \n 1. The description must be clear, structured, and specific enough to guide end-to-end video creation (including frame rate, shot duration, camera movement, motion speed, color transitions).\n 2. Avoid ambiguity; focus on the most salient static (per-frame) and dynamic (temporal) features of the video.\n 3. Prioritize video-specific elements: motion trajectory, shot types (close-up/wide shot/etc.), camera movement, frame rate, scene transitions, rhythm/pacing, and temporal color changes.\n 4. The output must only contain the video generation prompt (no extra explanations).\n- OutputFormat: A detailed, hierarchical text description of the video, structured as follows:\n 1. Core Content & Narrative: Brief overview of the video's subject and temporal progression\n 2. Visual Style (Static): Per-frame key elements (objects, colors, composition, lighting, texture)\n 3. Dynamic Elements (Temporal): Motion details (speed, trajectory, direction), camera movement (type, speed, direction), shot duration/frame rate, scene transitions\n 4. Audio-Visual Style: Color grading (consistent/transitional), rhythm/pacing, and implied audio style (if discernible)\n- Workflow:\n 1. Analyze the video to segment shots/scenes, identify frame-by-frame static visual elements (objects, colors, composition) and cross-frame dynamic elements (motion, camera movement, temporal changes).\n 2. Extract video-specific technical features: frame rate, shot duration, scene transition types, motion speed/rhythm.\n 3. Generate a structured, detailed description that captures the essence of the video (static + dynamic + temporal characteristics), ensuring specificity and actionability for video generation.\n 4. Refine the description for clarity, conciseness, and alignment with video generation prompt norms (e.g., including frame rate, camera movement terms, motion speed descriptors)."], "color": "#432", "bgcolor": "#653"}], "groups": [], "links": [{"id": 6, "origin_id": 1, "origin_slot": 0, "target_id": -20, "target_slot": 0, "type": "*"}, {"id": 15, "origin_id": -10, "origin_slot": 0, "target_id": 1, "target_slot": 4, "type": "STRING"}, {"id": 16, "origin_id": -10, "origin_slot": 1, "target_id": 1, "target_slot": 5, "type": "COMBO"}, {"id": 17, "origin_id": -10, "origin_slot": 2, "target_id": 1, "target_slot": 2, "type": "VIDEO"}], "extra": {"workflowRendererVersion": "LG"}, "category": "Text generation/Video Captioning"}]}} +{ + "revision": 0, + "last_node_id": 233, + "last_link_id": 0, + "nodes": [ + { + "id": 233, + "type": "dcf32045-0ee4-4efc-9aca-9f26f3a157be", + "pos": [ + 0, + 1140 + ], + "size": [ + 400, + 260 + ], + "flags": {}, + "order": 7, + "mode": 0, + "inputs": [ + { + "name": "prompt", + "type": "STRING", + "widget": { + "name": "prompt" + }, + "link": null + }, + { + "name": "model", + "type": "COMBO", + "widget": { + "name": "model" + }, + "link": null + }, + { + "name": "video", + "type": "VIDEO", + "link": null + } + ], + "outputs": [ + { + "localized_name": "STRING", + "name": "STRING", + "type": "STRING", + "links": [] + } + ], + "title": "Video Captioning(Gemini)", + "properties": { + "proxyWidgets": [ + [ + "-1", + "prompt" + ], + [ + "-1", + "model" + ], + [ + "1", + "seed" + ] + ], + "cnr_id": "comfy-core", + "ver": "0.13.0" + }, + "widgets_values": [ + "Describe this video", + "gemini-2.5-pro" + ] + } + ], + "links": [], + "version": 0.4, + "definitions": { + "subgraphs": [ + { + "id": "dcf32045-0ee4-4efc-9aca-9f26f3a157be", + "version": 1, + "state": { + "lastGroupId": 1, + "lastNodeId": 16, + "lastLinkId": 17, + "lastRerouteId": 0 + }, + "revision": 0, + "config": {}, + "name": "Video Captioning(Gemini)", + "inputNode": { + "id": -10, + "bounding": [ + -6870, + 2530, + 120, + 100 + ] + }, + "outputNode": { + "id": -20, + "bounding": [ + -6240, + 2530, + 120, + 60 + ] + }, + "inputs": [ + { + "id": "d8cbd7eb-636a-4d7b-8ff6-b22f1755e26c", + "name": "prompt", + "type": "STRING", + "linkIds": [ + 15 + ], + "pos": [ + -6770, + 2550 + ] + }, + { + "id": "b034e26a-d114-4604-aec2-32783e86aa6b", + "name": "model", + "type": "COMBO", + "linkIds": [ + 16 + ], + "pos": [ + -6770, + 2570 + ] + }, + { + "id": "f7363f60-a106-4e06-90af-df5f53355b98", + "name": "video", + "type": "VIDEO", + "linkIds": [ + 17 + ], + "pos": [ + -6770, + 2590 + ] + } + ], + "outputs": [ + { + "id": "e12c6e80-5210-4328-a581-bc8924c53070", + "name": "STRING", + "type": "STRING", + "linkIds": [ + 6 + ], + "localized_name": "STRING", + "pos": [ + -6220, + 2550 + ] + } + ], + "widgets": [], + "nodes": [ + { + "id": 1, + "type": "GeminiNode", + "pos": [ + -6690, + 2360 + ], + "size": [ + 390, + 430 + ], + "flags": {}, + "order": 0, + "mode": 0, + "inputs": [ + { + "localized_name": "images", + "name": "images", + "shape": 7, + "type": "IMAGE", + "link": null + }, + { + "localized_name": "audio", + "name": "audio", + "shape": 7, + "type": "AUDIO", + "link": null + }, + { + "localized_name": "video", + "name": "video", + "shape": 7, + "type": "VIDEO", + "link": 17 + }, + { + "localized_name": "files", + "name": "files", + "shape": 7, + "type": "GEMINI_INPUT_FILES", + "link": null + }, + { + "localized_name": "prompt", + "name": "prompt", + "type": "STRING", + "widget": { + "name": "prompt" + }, + "link": 15 + }, + { + "localized_name": "model", + "name": "model", + "type": "COMBO", + "widget": { + "name": "model" + }, + "link": 16 + }, + { + "localized_name": "seed", + "name": "seed", + "type": "INT", + "widget": { + "name": "seed" + }, + "link": null + }, + { + "localized_name": "system_prompt", + "name": "system_prompt", + "shape": 7, + "type": "STRING", + "widget": { + "name": "system_prompt" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": "STRING", + "name": "STRING", + "type": "STRING", + "links": [ + 6 + ] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.5.1", + "Node name for S&R": "GeminiNode" + }, + "widgets_values": [ + "Describe this video", + "gemini-2.5-pro", + 511865409297955, + "randomize", + "- Role: AI Video Analysis and Description Specialist\n- Background: The user requires a prompt that enables AI to analyze videos (including frame sequences, dynamic movements, audio-visual elements) and generate detailed, structured descriptions. These descriptions must be directly usable as video generation prompts to create similar videos, serving core tasks such as video content creation, creative inspiration extraction, and artistic style exploration.\n- Profile: As an AI Video Analysis and Description Specialist, you possess expertise in computer vision, video temporal sequence processing, motion analysis, and multi-modal natural language generation. You excel at interpreting dynamic visual data (frame-by-frame features + continuous motion) and translating it into precise descriptive text that fully guides the creation of new videos with matching style, rhythm, and content.\n- Skills: Proficiency in video frame feature extraction, motion trajectory recognition, temporal rhythm analysis, scene/shot segmentation, color grading detection, camera movement identification (pan/tilt/zoom/dolly), audio-visual element correlation analysis, and descriptive language generation that captures both static visual features and dynamic temporal characteristics. Mastery of artistic elements in video: composition (per frame + dynamic framing), color palette (consistent + transitional), texture (surface details + motion blur), pacing (frame rate, shot duration), and sound style (background music, ambient sound cues).\n- Goals: To analyze the provided video comprehensively, generate a detailed, structured description that captures all key video elements (static visual features + dynamic motion/temporal characteristics + audio-visual style), and ensure this description can directly serve as a high-quality prompt for creating similar videos.\n- Constraints: \n 1. The description must be clear, structured, and specific enough to guide end-to-end video creation (including frame rate, shot duration, camera movement, motion speed, color transitions).\n 2. Avoid ambiguity; focus on the most salient static (per-frame) and dynamic (temporal) features of the video.\n 3. Prioritize video-specific elements: motion trajectory, shot types (close-up/wide shot/etc.), camera movement, frame rate, scene transitions, rhythm/pacing, and temporal color changes.\n 4. The output must only contain the video generation prompt (no extra explanations).\n- OutputFormat: A detailed, hierarchical text description of the video, structured as follows:\n 1. Core Content & Narrative: Brief overview of the video's subject and temporal progression\n 2. Visual Style (Static): Per-frame key elements (objects, colors, composition, lighting, texture)\n 3. Dynamic Elements (Temporal): Motion details (speed, trajectory, direction), camera movement (type, speed, direction), shot duration/frame rate, scene transitions\n 4. Audio-Visual Style: Color grading (consistent/transitional), rhythm/pacing, and implied audio style (if discernible)\n- Workflow:\n 1. Analyze the video to segment shots/scenes, identify frame-by-frame static visual elements (objects, colors, composition) and cross-frame dynamic elements (motion, camera movement, temporal changes).\n 2. Extract video-specific technical features: frame rate, shot duration, scene transition types, motion speed/rhythm.\n 3. Generate a structured, detailed description that captures the essence of the video (static + dynamic + temporal characteristics), ensuring specificity and actionability for video generation.\n 4. Refine the description for clarity, conciseness, and alignment with video generation prompt norms (e.g., including frame rate, camera movement terms, motion speed descriptors)." + ], + "color": "#432", + "bgcolor": "#653" + } + ], + "groups": [], + "links": [ + { + "id": 6, + "origin_id": 1, + "origin_slot": 0, + "target_id": -20, + "target_slot": 0, + "type": "*" + }, + { + "id": 15, + "origin_id": -10, + "origin_slot": 0, + "target_id": 1, + "target_slot": 4, + "type": "STRING" + }, + { + "id": 16, + "origin_id": -10, + "origin_slot": 1, + "target_id": 1, + "target_slot": 5, + "type": "COMBO" + }, + { + "id": 17, + "origin_id": -10, + "origin_slot": 2, + "target_id": 1, + "target_slot": 2, + "type": "VIDEO" + } + ], + "extra": { + "workflowRendererVersion": "LG" + }, + "category": "Text generation/Video Captioning" + } + ] + } +} diff --git a/blueprints/Video Inpaint(Wan2.1 VACE).json b/blueprints/Video Inpaint(Wan2.1 VACE).json index a7c6db003..f404e6773 100644 --- 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40862c07760a33ebd2e54b16b640f9e9f51f8946 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 7 Apr 2026 00:13:47 -0700 Subject: [PATCH 018/140] Support Ace Step 1.5 XL model. (#13317) --- comfy/ldm/ace/ace_step15.py | 18 +++++++++++------- comfy/model_detection.py | 9 +++++++++ 2 files changed, 20 insertions(+), 7 deletions(-) diff --git a/comfy/ldm/ace/ace_step15.py b/comfy/ldm/ace/ace_step15.py index 1d7dc59a8..2ca2d26c4 100644 --- a/comfy/ldm/ace/ace_step15.py +++ b/comfy/ldm/ace/ace_step15.py @@ -611,6 +611,7 @@ class AceStepDiTModel(nn.Module): intermediate_size, patch_size, audio_acoustic_hidden_dim, + condition_dim=None, layer_types=None, sliding_window=128, rms_norm_eps=1e-6, @@ -640,7 +641,7 @@ class AceStepDiTModel(nn.Module): self.time_embed = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations) self.time_embed_r = TimestepEmbedding(256, hidden_size, dtype=dtype, device=device, operations=operations) - self.condition_embedder = Linear(hidden_size, hidden_size, dtype=dtype, device=device) + self.condition_embedder = Linear(condition_dim, hidden_size, dtype=dtype, device=device) if layer_types is None: layer_types = ["full_attention"] * num_layers @@ -1035,6 +1036,9 @@ class AceStepConditionGenerationModel(nn.Module): fsq_dim=2048, fsq_levels=[8, 8, 8, 5, 5, 5], fsq_input_num_quantizers=1, + encoder_hidden_size=2048, + encoder_intermediate_size=6144, + encoder_num_heads=16, audio_model=None, dtype=None, device=None, @@ -1054,24 +1058,24 @@ class AceStepConditionGenerationModel(nn.Module): self.decoder = AceStepDiTModel( in_channels, hidden_size, num_dit_layers, num_heads, num_kv_heads, head_dim, - intermediate_size, patch_size, audio_acoustic_hidden_dim, + intermediate_size, patch_size, audio_acoustic_hidden_dim, condition_dim=encoder_hidden_size, layer_types=layer_types, sliding_window=sliding_window, rms_norm_eps=rms_norm_eps, dtype=dtype, device=device, operations=operations ) self.encoder = AceStepConditionEncoder( - text_hidden_dim, timbre_hidden_dim, hidden_size, num_lyric_layers, num_timbre_layers, - num_heads, num_kv_heads, head_dim, intermediate_size, rms_norm_eps, + text_hidden_dim, timbre_hidden_dim, encoder_hidden_size, num_lyric_layers, num_timbre_layers, + encoder_num_heads, num_kv_heads, head_dim, encoder_intermediate_size, rms_norm_eps, dtype=dtype, device=device, operations=operations ) self.tokenizer = AceStepAudioTokenizer( - audio_acoustic_hidden_dim, hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps, + audio_acoustic_hidden_dim, encoder_hidden_size, pool_window_size, fsq_dim=fsq_dim, fsq_levels=fsq_levels, fsq_input_num_quantizers=fsq_input_num_quantizers, num_layers=num_tokenizer_layers, head_dim=head_dim, rms_norm_eps=rms_norm_eps, dtype=dtype, device=device, operations=operations ) self.detokenizer = AudioTokenDetokenizer( - hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim, + encoder_hidden_size, pool_window_size, audio_acoustic_hidden_dim, num_layers=2, head_dim=head_dim, dtype=dtype, device=device, operations=operations ) - self.null_condition_emb = nn.Parameter(torch.empty(1, 1, hidden_size, dtype=dtype, device=device)) + self.null_condition_emb = nn.Parameter(torch.empty(1, 1, encoder_hidden_size, dtype=dtype, device=device)) def prepare_condition( self, diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 1c8ae2325..8bed6828d 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -696,6 +696,15 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): if '{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix) in state_dict_keys: dit_config = {} dit_config["audio_model"] = "ace1.5" + head_dim = 128 + dit_config["hidden_size"] = state_dict['{}decoder.layers.0.self_attn_norm.weight'.format(key_prefix)].shape[0] + dit_config["intermediate_size"] = state_dict['{}decoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0] + dit_config["num_heads"] = state_dict['{}decoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim + + dit_config["encoder_hidden_size"] = state_dict['{}encoder.lyric_encoder.layers.0.input_layernorm.weight'.format(key_prefix)].shape[0] + dit_config["encoder_num_heads"] = state_dict['{}encoder.lyric_encoder.layers.0.self_attn.q_proj.weight'.format(key_prefix)].shape[0] // head_dim + dit_config["encoder_intermediate_size"] = state_dict['{}encoder.lyric_encoder.layers.0.mlp.gate_proj.weight'.format(key_prefix)].shape[0] + dit_config["num_dit_layers"] = count_blocks(state_dict_keys, '{}decoder.layers.'.format(key_prefix) + '{}.') return dit_config if '{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix) in state_dict_keys: # RT-DETR_v4 From b615af1c65b674d6e4433b986792c69b5efda676 Mon Sep 17 00:00:00 2001 From: huemin <100716027+huemin-art@users.noreply.github.com> Date: Tue, 7 Apr 2026 00:44:18 -0700 Subject: [PATCH 019/140] Add support for small flux.2 decoder (#13314) --- comfy/ldm/models/autoencoder.py | 3 ++- comfy/sd.py | 11 +++++++++-- 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py index 4f50810dc..582b44e69 100644 --- a/comfy/ldm/models/autoencoder.py +++ b/comfy/ldm/models/autoencoder.py @@ -155,6 +155,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine): def __init__(self, embed_dim: int, **kwargs): self.max_batch_size = kwargs.pop("max_batch_size", None) ddconfig = kwargs.pop("ddconfig") + decoder_ddconfig = kwargs.pop("decoder_ddconfig", ddconfig) super().__init__( encoder_config={ "target": "comfy.ldm.modules.diffusionmodules.model.Encoder", @@ -162,7 +163,7 @@ class AutoencodingEngineLegacy(AutoencodingEngine): }, decoder_config={ "target": "comfy.ldm.modules.diffusionmodules.model.Decoder", - "params": ddconfig, + "params": decoder_ddconfig, }, **kwargs, ) diff --git a/comfy/sd.py b/comfy/sd.py index 5b6b59ea4..f331feefb 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -556,12 +556,19 @@ class VAE: old_memory_used_decode = self.memory_used_decode self.memory_used_decode = lambda shape, dtype: old_memory_used_decode(shape, dtype) * 4.0 + decoder_ch = sd['decoder.conv_in.weight'].shape[0] // ddconfig['ch_mult'][-1] + if decoder_ch != ddconfig['ch']: + decoder_ddconfig = ddconfig.copy() + decoder_ddconfig['ch'] = decoder_ch + else: + decoder_ddconfig = None + if 'post_quant_conv.weight' in sd: - self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1]) + self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1], **({"decoder_ddconfig": decoder_ddconfig} if decoder_ddconfig is not None else {})) else: self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig}, - decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig}) + decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': decoder_ddconfig if decoder_ddconfig is not None else ddconfig}) elif "decoder.layers.1.layers.0.beta" in sd: config = {} param_key = None From 2d861fb1462b7007e208aaa34363c7dcdbc3ed3b Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 8 Apr 2026 18:39:29 -0700 Subject: [PATCH 020/140] Basic intel standalone package .bat (#13333) --- .ci/windows_intel_base_files/run_intel_gpu.bat | 2 ++ 1 file changed, 2 insertions(+) create mode 100755 .ci/windows_intel_base_files/run_intel_gpu.bat diff --git a/.ci/windows_intel_base_files/run_intel_gpu.bat b/.ci/windows_intel_base_files/run_intel_gpu.bat new file mode 100755 index 000000000..274d7c948 --- /dev/null +++ b/.ci/windows_intel_base_files/run_intel_gpu.bat @@ -0,0 +1,2 @@ +.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build +pause From 3d4aca8084b49cb37a3dc87c6ecc11d2488b566e Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 9 Apr 2026 18:56:49 -0700 Subject: [PATCH 021/140] Bump comfyui-frontend-package version to 1.42.10 (#13346) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 1a8e1ea1c..1731422a8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.42.8 +comfyui-frontend-package==1.42.10 comfyui-workflow-templates==0.9.44 comfyui-embedded-docs==0.4.3 torch From e6be419a30f666f5428eb0f7ae0918b6ea9bdac4 Mon Sep 17 00:00:00 2001 From: Terry Jia Date: Thu, 9 Apr 2026 21:58:05 -0400 Subject: [PATCH 022/140] should use 0 as defalut for brightness (#13345) --- blueprints/Brightness and Contrast.json | 2 +- blueprints/Glow.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/blueprints/Brightness and Contrast.json b/blueprints/Brightness and Contrast.json index 6a234139d..90bfe999d 100644 --- a/blueprints/Brightness and Contrast.json +++ b/blueprints/Brightness and Contrast.json @@ -182,7 +182,7 @@ ] }, "widgets_values": [ - 50 + 0 ] }, { diff --git a/blueprints/Glow.json b/blueprints/Glow.json index 42cf63e8a..8c690fc68 100644 --- a/blueprints/Glow.json +++ b/blueprints/Glow.json @@ -316,7 +316,7 @@ "step": 1 }, "widgets_values": [ - 30 + 0 ] }, { From 5410ed34f52097b296341d116d3f432604930c21 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Fri, 10 Apr 2026 21:01:15 +0300 Subject: [PATCH 023/140] fix(api-nodes): fix GrokVideoReferenceNode price badge (#13354) --- comfy_api_nodes/nodes_grok.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_api_nodes/nodes_grok.py b/comfy_api_nodes/nodes_grok.py index dabc899d6..f42d84616 100644 --- a/comfy_api_nodes/nodes_grok.py +++ b/comfy_api_nodes/nodes_grok.py @@ -558,7 +558,7 @@ class GrokVideoReferenceNode(IO.ComfyNode): ( $res := $lookup(widgets, "model.resolution"); $dur := $lookup(widgets, "model.duration"); - $refs := inputGroups["model.reference_images"]; + $refs := $lookup(inputGroups, "model.reference_images"); $rate := $res = "720p" ? 0.07 : 0.05; $price := ($rate * $dur + 0.002 * $refs) * 1.43; {"type":"usd","usd": $price} From b920bdd77defb6bdff2fec5b6bb10153493db9e3 Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Sat, 11 Apr 2026 03:50:40 +0800 Subject: [PATCH 024/140] chore: update workflow templates to v0.9.45 (#13353) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 1731422a8..cfb4d4fb8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.10 -comfyui-workflow-templates==0.9.44 +comfyui-workflow-templates==0.9.45 comfyui-embedded-docs==0.4.3 torch torchsde From a1344238901efc5ea199d8094cb16fca36ceb28b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Sat, 11 Apr 2026 00:26:55 +0300 Subject: [PATCH 025/140] SDPose: resize input always (#13349) --- comfy/ldm/modules/sdpose.py | 2 +- comfy_extras/nodes_rtdetr.py | 8 +++-- comfy_extras/nodes_sdpose.py | 68 +++++++++++++++++++----------------- 3 files changed, 42 insertions(+), 36 deletions(-) diff --git a/comfy/ldm/modules/sdpose.py b/comfy/ldm/modules/sdpose.py index d67b60b76..1a9585fc2 100644 --- a/comfy/ldm/modules/sdpose.py +++ b/comfy/ldm/modules/sdpose.py @@ -90,7 +90,7 @@ class HeatmapHead(torch.nn.Module): origin_max = np.max(hm[k]) dr = np.zeros((H + 2 * border, W + 2 * border), dtype=np.float32) dr[border:-border, border:-border] = hm[k].copy() - dr = gaussian_filter(dr, sigma=2.0) + dr = gaussian_filter(dr, sigma=2.0, truncate=2.5) hm[k] = dr[border:-border, border:-border].copy() cur_max = np.max(hm[k]) if cur_max > 0: diff --git a/comfy_extras/nodes_rtdetr.py b/comfy_extras/nodes_rtdetr.py index 61307e268..7feaf3ab3 100644 --- a/comfy_extras/nodes_rtdetr.py +++ b/comfy_extras/nodes_rtdetr.py @@ -32,10 +32,12 @@ class RTDETR_detect(io.ComfyNode): def execute(cls, model, image, threshold, class_name, max_detections) -> io.NodeOutput: B, H, W, C = image.shape - image_in = comfy.utils.common_upscale(image.movedim(-1, 1), 640, 640, "bilinear", crop="disabled") - comfy.model_management.load_model_gpu(model) - results = model.model.diffusion_model(image_in, (W, H)) # list of B dicts + results = [] + for i in range(0, B, 32): + batch = image[i:i + 32] + image_in = comfy.utils.common_upscale(batch.movedim(-1, 1), 640, 640, "bilinear", crop="disabled") + results.extend(model.model.diffusion_model(image_in, (W, H))) all_bbox_dicts = [] diff --git a/comfy_extras/nodes_sdpose.py b/comfy_extras/nodes_sdpose.py index 46b5fb226..7d54967d5 100644 --- a/comfy_extras/nodes_sdpose.py +++ b/comfy_extras/nodes_sdpose.py @@ -1,5 +1,6 @@ import torch import comfy.utils +import comfy.model_management import numpy as np import math import colorsys @@ -410,7 +411,9 @@ class SDPoseDrawKeypoints(io.ComfyNode): pose_outputs.append(canvas) pose_outputs_np = np.stack(pose_outputs) if len(pose_outputs) > 1 else np.expand_dims(pose_outputs[0], 0) - final_pose_output = torch.from_numpy(pose_outputs_np).float() / 255.0 + final_pose_output = torch.from_numpy(pose_outputs_np).to( + device=comfy.model_management.intermediate_device(), + dtype=comfy.model_management.intermediate_dtype()) / 255.0 return io.NodeOutput(final_pose_output) class SDPoseKeypointExtractor(io.ComfyNode): @@ -459,6 +462,27 @@ class SDPoseKeypointExtractor(io.ComfyNode): model_h = int(head.heatmap_size[0]) * 4 # e.g. 192 * 4 = 768 model_w = int(head.heatmap_size[1]) * 4 # e.g. 256 * 4 = 1024 + def _resize_to_model(imgs): + """Aspect-preserving resize + zero-pad BHWC images to (model_h, model_w). Returns (resized_bhwc, scale, pad_top, pad_left).""" + h, w = imgs.shape[-3], imgs.shape[-2] + scale = min(model_h / h, model_w / w) + sh, sw = int(round(h * scale)), int(round(w * scale)) + pt, pl = (model_h - sh) // 2, (model_w - sw) // 2 + chw = imgs.permute(0, 3, 1, 2).float() + scaled = comfy.utils.common_upscale(chw, sw, sh, upscale_method="bilinear", crop="disabled") + padded = torch.zeros(scaled.shape[0], scaled.shape[1], model_h, model_w, dtype=scaled.dtype, device=scaled.device) + padded[:, :, pt:pt + sh, pl:pl + sw] = scaled + return padded.permute(0, 2, 3, 1), scale, pt, pl + + def _remap_keypoints(kp, scale, pad_top, pad_left, offset_x=0, offset_y=0): + """Remap keypoints from model space back to original image space.""" + kp = kp.copy() if isinstance(kp, np.ndarray) else np.array(kp, dtype=np.float32) + invalid = kp[..., 0] < 0 + kp[..., 0] = (kp[..., 0] - pad_left) / scale + offset_x + kp[..., 1] = (kp[..., 1] - pad_top) / scale + offset_y + kp[invalid] = -1 + return kp + def _run_on_latent(latent_batch): """Run one forward pass and return (keypoints_list, scores_list) for the batch.""" nonlocal captured_feat @@ -504,36 +528,19 @@ class SDPoseKeypointExtractor(io.ComfyNode): if x2 <= x1 or y2 <= y1: continue - crop_h_px, crop_w_px = y2 - y1, x2 - x1 crop = img[:, y1:y2, x1:x2, :] # (1, crop_h, crop_w, C) - - # scale to fit inside (model_h, model_w) while preserving aspect ratio, then pad to exact model size. - scale = min(model_h / crop_h_px, model_w / crop_w_px) - scaled_h, scaled_w = int(round(crop_h_px * scale)), int(round(crop_w_px * scale)) - pad_top, pad_left = (model_h - scaled_h) // 2, (model_w - scaled_w) // 2 - - crop_chw = crop.permute(0, 3, 1, 2).float() # BHWC → BCHW - scaled = comfy.utils.common_upscale(crop_chw, scaled_w, scaled_h, upscale_method="bilinear", crop="disabled") - padded = torch.zeros(1, scaled.shape[1], model_h, model_w, dtype=scaled.dtype, device=scaled.device) - padded[:, :, pad_top:pad_top + scaled_h, pad_left:pad_left + scaled_w] = scaled - crop_resized = padded.permute(0, 2, 3, 1) # BCHW → BHWC + crop_resized, scale, pad_top, pad_left = _resize_to_model(crop) latent_crop = vae.encode(crop_resized) kp_batch, sc_batch = _run_on_latent(latent_crop) - kp, sc = kp_batch[0], sc_batch[0] # (K, 2), coords in model pixel space - - # remove padding offset, undo scale, offset to full-image coordinates. - kp = kp.copy() if isinstance(kp, np.ndarray) else np.array(kp, dtype=np.float32) - kp[..., 0] = (kp[..., 0] - pad_left) / scale + x1 - kp[..., 1] = (kp[..., 1] - pad_top) / scale + y1 - + kp = _remap_keypoints(kp_batch[0], scale, pad_top, pad_left, x1, y1) img_keypoints.append(kp) - img_scores.append(sc) + img_scores.append(sc_batch[0]) else: - # No bboxes for this image – run on the full image - latent_img = vae.encode(img) + img_resized, scale, pad_top, pad_left = _resize_to_model(img) + latent_img = vae.encode(img_resized) kp_batch, sc_batch = _run_on_latent(latent_img) - img_keypoints.append(kp_batch[0]) + img_keypoints.append(_remap_keypoints(kp_batch[0], scale, pad_top, pad_left)) img_scores.append(sc_batch[0]) all_keypoints.append(img_keypoints) @@ -541,19 +548,16 @@ class SDPoseKeypointExtractor(io.ComfyNode): pbar.update(1) else: # full-image mode, batched - tqdm_pbar = tqdm(total=total_images, desc="Extracting keypoints") - for batch_start in range(0, total_images, batch_size): - batch_end = min(batch_start + batch_size, total_images) - latent_batch = vae.encode(image[batch_start:batch_end]) - + for batch_start in tqdm(range(0, total_images, batch_size), desc="Extracting keypoints"): + batch_resized, scale, pad_top, pad_left = _resize_to_model(image[batch_start:batch_start + batch_size]) + latent_batch = vae.encode(batch_resized) kp_batch, sc_batch = _run_on_latent(latent_batch) for kp, sc in zip(kp_batch, sc_batch): - all_keypoints.append([kp]) + all_keypoints.append([_remap_keypoints(kp, scale, pad_top, pad_left)]) all_scores.append([sc]) - tqdm_pbar.update(1) - pbar.update(batch_end - batch_start) + pbar.update(len(kp_batch)) openpose_frames = _to_openpose_frames(all_keypoints, all_scores, height, width) return io.NodeOutput(openpose_frames) From a2840e75520b7dc40958866b3c4da1345d5cfa9c Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 10 Apr 2026 18:48:26 -0700 Subject: [PATCH 026/140] Make ImageUpscaleWithModel node work with intermediate device and dtype. (#13357) --- comfy_extras/nodes_upscale_model.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py index db4f9d231..d3ee3f1c1 100644 --- a/comfy_extras/nodes_upscale_model.py +++ b/comfy_extras/nodes_upscale_model.py @@ -6,6 +6,7 @@ import comfy.utils import folder_paths from typing_extensions import override from comfy_api.latest import ComfyExtension, io +import comfy.model_management try: from spandrel_extra_arches import EXTRA_REGISTRY @@ -78,13 +79,15 @@ class ImageUpscaleWithModel(io.ComfyNode): tile = 512 overlap = 32 + output_device = comfy.model_management.intermediate_device() + oom = True try: while oom: try: steps = in_img.shape[0] * comfy.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap) pbar = comfy.utils.ProgressBar(steps) - s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar) + s = comfy.utils.tiled_scale(in_img, lambda a: upscale_model(a.float()), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar, output_device=output_device) oom = False except Exception as e: model_management.raise_non_oom(e) @@ -94,7 +97,7 @@ class ImageUpscaleWithModel(io.ComfyNode): finally: upscale_model.to("cpu") - s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0) + s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0).to(comfy.model_management.intermediate_dtype()) return io.NodeOutput(s) upscale = execute # TODO: remove From 55ebd287ee72f637615496d5de916b2645577f0c Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 11 Apr 2026 18:06:36 -0700 Subject: [PATCH 027/140] Add a supports_fp64 function. (#13368) --- comfy/ldm/flux/math.py | 2 +- comfy/model_management.py | 15 +++++++++++++++ 2 files changed, 16 insertions(+), 1 deletion(-) diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py index 824daf5e6..6d0aed827 100644 --- a/comfy/ldm/flux/math.py +++ b/comfy/ldm/flux/math.py @@ -16,7 +16,7 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transforme def rope(pos: Tensor, dim: int, theta: int) -> Tensor: assert dim % 2 == 0 - if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled(): + if not comfy.model_management.supports_fp64(pos.device): device = torch.device("cpu") else: device = pos.device diff --git a/comfy/model_management.py b/comfy/model_management.py index 0eebf1ded..bcf1399c4 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -1732,6 +1732,21 @@ def supports_mxfp8_compute(device=None): return True +def supports_fp64(device=None): + if is_device_mps(device): + return False + + if is_intel_xpu(): + return False + + if is_directml_enabled(): + return False + + if is_ixuca(): + return False + + return True + def extended_fp16_support(): # TODO: check why some models work with fp16 on newer torch versions but not on older if torch_version_numeric < (2, 7): From 31283d2892f54caf9bfdf6edb9c98cbfa88c5f0c Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 11 Apr 2026 19:29:31 -0700 Subject: [PATCH 028/140] Implement Ernie Image model. (#13369) --- comfy/ldm/ernie/model.py | 303 +++++++++++++++++++++++++++++++++++ comfy/model_base.py | 12 ++ comfy/model_detection.py | 5 + comfy/sd.py | 8 + comfy/supported_models.py | 34 +++- comfy/text_encoders/ernie.py | 38 +++++ comfy/text_encoders/flux.py | 4 +- comfy/text_encoders/llama.py | 32 ++++ 8 files changed, 433 insertions(+), 3 deletions(-) create mode 100644 comfy/ldm/ernie/model.py create mode 100644 comfy/text_encoders/ernie.py diff --git a/comfy/ldm/ernie/model.py b/comfy/ldm/ernie/model.py new file mode 100644 index 000000000..1f8f08376 --- /dev/null +++ b/comfy/ldm/ernie/model.py @@ -0,0 +1,303 @@ +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +from comfy.ldm.modules.attention import optimized_attention +import comfy.model_management + +def rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor: + assert dim % 2 == 0 + if not comfy.model_management.supports_fp64(pos.device): + device = torch.device("cpu") + else: + device = pos.device + + scale = torch.arange(0, dim, 2, dtype=torch.float64, device=device) / dim + omega = 1.0 / (theta**scale) + out = torch.einsum("...n,d->...nd", pos, omega) + out = torch.stack([torch.cos(out), torch.sin(out)], dim=0) + return out.to(dtype=torch.float32, device=pos.device) + +def apply_rotary_emb(x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor: + rot_dim = freqs_cis.shape[-1] + x, x_pass = x_in[..., :rot_dim], x_in[..., rot_dim:] + cos_ = freqs_cis[0] + sin_ = freqs_cis[1] + x1, x2 = x.chunk(2, dim=-1) + x_rotated = torch.cat((-x2, x1), dim=-1) + return torch.cat((x * cos_ + x_rotated * sin_, x_pass), dim=-1) + +class ErnieImageEmbedND3(nn.Module): + def __init__(self, dim: int, theta: int, axes_dim: tuple): + super().__init__() + self.dim = dim + self.theta = theta + self.axes_dim = list(axes_dim) + + def forward(self, ids: torch.Tensor) -> torch.Tensor: + emb = torch.cat([rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(3)], dim=-1) + emb = emb.unsqueeze(3) # [2, B, S, 1, head_dim//2] + return torch.stack([emb, emb], dim=-1).reshape(*emb.shape[:-1], -1) # [B, S, 1, head_dim] + +class ErnieImagePatchEmbedDynamic(nn.Module): + def __init__(self, in_channels: int, embed_dim: int, patch_size: int, operations, device=None, dtype=None): + super().__init__() + self.patch_size = patch_size + self.proj = operations.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size, bias=True, device=device, dtype=dtype) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.proj(x) + batch_size, dim, height, width = x.shape + return x.reshape(batch_size, dim, height * width).transpose(1, 2).contiguous() + +class Timesteps(nn.Module): + def __init__(self, num_channels: int, flip_sin_to_cos: bool = False): + super().__init__() + self.num_channels = num_channels + self.flip_sin_to_cos = flip_sin_to_cos + + def forward(self, timesteps: torch.Tensor) -> torch.Tensor: + half_dim = self.num_channels // 2 + exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device) / half_dim + emb = torch.exp(exponent) + emb = timesteps[:, None].float() * emb[None, :] + if self.flip_sin_to_cos: + emb = torch.cat([torch.cos(emb), torch.sin(emb)], dim=-1) + else: + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) + return emb + +class TimestepEmbedding(nn.Module): + def __init__(self, in_channels: int, time_embed_dim: int, operations, device=None, dtype=None): + super().__init__() + Linear = operations.Linear + self.linear_1 = Linear(in_channels, time_embed_dim, bias=True, device=device, dtype=dtype) + self.act = nn.SiLU() + self.linear_2 = Linear(time_embed_dim, time_embed_dim, bias=True, device=device, dtype=dtype) + + def forward(self, sample: torch.Tensor) -> torch.Tensor: + sample = self.linear_1(sample) + sample = self.act(sample) + sample = self.linear_2(sample) + return sample + +class ErnieImageAttention(nn.Module): + def __init__(self, query_dim: int, heads: int, dim_head: int, eps: float = 1e-6, operations=None, device=None, dtype=None): + super().__init__() + self.heads = heads + self.head_dim = dim_head + self.inner_dim = heads * dim_head + + Linear = operations.Linear + RMSNorm = operations.RMSNorm + + self.to_q = Linear(query_dim, self.inner_dim, bias=False, device=device, dtype=dtype) + self.to_k = Linear(query_dim, self.inner_dim, bias=False, device=device, dtype=dtype) + self.to_v = Linear(query_dim, self.inner_dim, bias=False, device=device, dtype=dtype) + + self.norm_q = RMSNorm(dim_head, eps=eps, elementwise_affine=True, device=device, dtype=dtype) + self.norm_k = RMSNorm(dim_head, eps=eps, elementwise_affine=True, device=device, dtype=dtype) + + self.to_out = nn.ModuleList([Linear(self.inner_dim, query_dim, bias=False, device=device, dtype=dtype)]) + + def forward(self, x: torch.Tensor, attention_mask: torch.Tensor = None, image_rotary_emb: torch.Tensor = None) -> torch.Tensor: + B, S, _ = x.shape + + q_flat = self.to_q(x) + k_flat = self.to_k(x) + v_flat = self.to_v(x) + + query = q_flat.view(B, S, self.heads, self.head_dim) + key = k_flat.view(B, S, self.heads, self.head_dim) + + query = self.norm_q(query) + key = self.norm_k(key) + + if image_rotary_emb is not None: + query = apply_rotary_emb(query, image_rotary_emb) + key = apply_rotary_emb(key, image_rotary_emb) + + query, key = query.to(x.dtype), key.to(x.dtype) + + q_flat = query.reshape(B, S, -1) + k_flat = key.reshape(B, S, -1) + + hidden_states = optimized_attention(q_flat, k_flat, v_flat, self.heads, mask=attention_mask) + + return self.to_out[0](hidden_states) + +class ErnieImageFeedForward(nn.Module): + def __init__(self, hidden_size: int, ffn_hidden_size: int, operations, device=None, dtype=None): + super().__init__() + Linear = operations.Linear + self.gate_proj = Linear(hidden_size, ffn_hidden_size, bias=False, device=device, dtype=dtype) + self.up_proj = Linear(hidden_size, ffn_hidden_size, bias=False, device=device, dtype=dtype) + self.linear_fc2 = Linear(ffn_hidden_size, hidden_size, bias=False, device=device, dtype=dtype) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.linear_fc2(self.up_proj(x) * F.gelu(self.gate_proj(x))) + +class ErnieImageSharedAdaLNBlock(nn.Module): + def __init__(self, hidden_size: int, num_heads: int, ffn_hidden_size: int, eps: float = 1e-6, operations=None, device=None, dtype=None): + super().__init__() + RMSNorm = operations.RMSNorm + + self.adaLN_sa_ln = RMSNorm(hidden_size, eps=eps, device=device, dtype=dtype) + self.self_attention = ErnieImageAttention( + query_dim=hidden_size, + dim_head=hidden_size // num_heads, + heads=num_heads, + eps=eps, + operations=operations, + device=device, + dtype=dtype + ) + self.adaLN_mlp_ln = RMSNorm(hidden_size, eps=eps, device=device, dtype=dtype) + self.mlp = ErnieImageFeedForward(hidden_size, ffn_hidden_size, operations=operations, device=device, dtype=dtype) + + def forward(self, x, rotary_pos_emb, temb, attention_mask=None): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = temb + + residual = x + x_norm = self.adaLN_sa_ln(x) + x_norm = (x_norm.float() * (1 + scale_msa.float()) + shift_msa.float()).to(x.dtype) + + attn_out = self.self_attention(x_norm, attention_mask=attention_mask, image_rotary_emb=rotary_pos_emb) + x = residual + (gate_msa.float() * attn_out.float()).to(x.dtype) + + residual = x + x_norm = self.adaLN_mlp_ln(x) + x_norm = (x_norm.float() * (1 + scale_mlp.float()) + shift_mlp.float()).to(x.dtype) + + return residual + (gate_mlp.float() * self.mlp(x_norm).float()).to(x.dtype) + +class ErnieImageAdaLNContinuous(nn.Module): + def __init__(self, hidden_size: int, eps: float = 1e-6, operations=None, device=None, dtype=None): + super().__init__() + LayerNorm = operations.LayerNorm + Linear = operations.Linear + self.norm = LayerNorm(hidden_size, elementwise_affine=False, eps=eps, device=device, dtype=dtype) + self.linear = Linear(hidden_size, hidden_size * 2, device=device, dtype=dtype) + + def forward(self, x: torch.Tensor, conditioning: torch.Tensor) -> torch.Tensor: + scale, shift = self.linear(conditioning).chunk(2, dim=-1) + x = self.norm(x) + x = x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + return x + +class ErnieImageModel(nn.Module): + def __init__( + self, + hidden_size: int = 4096, + num_attention_heads: int = 32, + num_layers: int = 36, + ffn_hidden_size: int = 12288, + in_channels: int = 128, + out_channels: int = 128, + patch_size: int = 1, + text_in_dim: int = 3072, + rope_theta: int = 256, + rope_axes_dim: tuple = (32, 48, 48), + eps: float = 1e-6, + qk_layernorm: bool = True, + device=None, + dtype=None, + operations=None, + **kwargs + ): + super().__init__() + self.dtype = dtype + self.hidden_size = hidden_size + self.num_heads = num_attention_heads + self.head_dim = hidden_size // num_attention_heads + self.patch_size = patch_size + self.out_channels = out_channels + + Linear = operations.Linear + + self.x_embedder = ErnieImagePatchEmbedDynamic(in_channels, hidden_size, patch_size, operations, device, dtype) + self.text_proj = Linear(text_in_dim, hidden_size, bias=False, device=device, dtype=dtype) if text_in_dim != hidden_size else None + + self.time_proj = Timesteps(hidden_size, flip_sin_to_cos=False) + self.time_embedding = TimestepEmbedding(hidden_size, hidden_size, operations, device, dtype) + + self.pos_embed = ErnieImageEmbedND3(dim=self.head_dim, theta=rope_theta, axes_dim=rope_axes_dim) + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + Linear(hidden_size, 6 * hidden_size, device=device, dtype=dtype) + ) + + self.layers = nn.ModuleList([ + ErnieImageSharedAdaLNBlock(hidden_size, num_attention_heads, ffn_hidden_size, eps, operations, device, dtype) + for _ in range(num_layers) + ]) + + self.final_norm = ErnieImageAdaLNContinuous(hidden_size, eps, operations, device, dtype) + self.final_linear = Linear(hidden_size, patch_size * patch_size * out_channels, device=device, dtype=dtype) + + def forward(self, x, timesteps, context, **kwargs): + device, dtype = x.device, x.dtype + B, C, H, W = x.shape + p, Hp, Wp = self.patch_size, H // self.patch_size, W // self.patch_size + N_img = Hp * Wp + + img_bsh = self.x_embedder(x) + + text_bth = context + if self.text_proj is not None and text_bth.numel() > 0: + text_bth = self.text_proj(text_bth) + Tmax = text_bth.shape[1] + + hidden_states = torch.cat([img_bsh, text_bth], dim=1) + + text_ids = torch.zeros((B, Tmax, 3), device=device, dtype=torch.float32) + text_ids[:, :, 0] = torch.linspace(0, Tmax - 1, steps=Tmax, device=x.device, dtype=torch.float32) + index = float(Tmax) + + transformer_options = kwargs.get("transformer_options", {}) + rope_options = transformer_options.get("rope_options", None) + + h_len, w_len = float(Hp), float(Wp) + h_offset, w_offset = 0.0, 0.0 + + if rope_options is not None: + h_len = (h_len - 1.0) * rope_options.get("scale_y", 1.0) + 1.0 + w_len = (w_len - 1.0) * rope_options.get("scale_x", 1.0) + 1.0 + index += rope_options.get("shift_t", 0.0) + h_offset += rope_options.get("shift_y", 0.0) + w_offset += rope_options.get("shift_x", 0.0) + + image_ids = torch.zeros((Hp, Wp, 3), device=device, dtype=torch.float32) + image_ids[:, :, 0] = image_ids[:, :, 1] + index + image_ids[:, :, 1] = image_ids[:, :, 1] + torch.linspace(h_offset, h_len - 1 + h_offset, steps=Hp, device=device, dtype=torch.float32).unsqueeze(1) + image_ids[:, :, 2] = image_ids[:, :, 2] + torch.linspace(w_offset, w_len - 1 + w_offset, steps=Wp, device=device, dtype=torch.float32).unsqueeze(0) + + image_ids = image_ids.view(1, N_img, 3).expand(B, -1, -1) + + rotary_pos_emb = self.pos_embed(torch.cat([image_ids, text_ids], dim=1)).to(x.dtype) + del image_ids, text_ids + + sample = self.time_proj(timesteps.to(dtype)).to(self.time_embedding.linear_1.weight.dtype) + c = self.time_embedding(sample) + + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = [ + t.unsqueeze(1).contiguous() for t in self.adaLN_modulation(c).chunk(6, dim=-1) + ] + + temb = [shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp] + for layer in self.layers: + hidden_states = layer(hidden_states, rotary_pos_emb, temb) + + hidden_states = self.final_norm(hidden_states, c).type_as(hidden_states) + + patches = self.final_linear(hidden_states)[:, :N_img, :] + output = ( + patches.view(B, Hp, Wp, p, p, self.out_channels) + .permute(0, 5, 1, 3, 2, 4) + .contiguous() + .view(B, self.out_channels, H, W) + ) + + return output diff --git a/comfy/model_base.py b/comfy/model_base.py index c2ae646aa..5c2668ba9 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -53,6 +53,7 @@ import comfy.ldm.kandinsky5.model import comfy.ldm.anima.model import comfy.ldm.ace.ace_step15 import comfy.ldm.rt_detr.rtdetr_v4 +import comfy.ldm.ernie.model import comfy.model_management import comfy.patcher_extension @@ -1962,3 +1963,14 @@ class Kandinsky5Image(Kandinsky5): class RT_DETR_v4(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.rt_detr.rtdetr_v4.RTv4) + +class ErnieImage(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ernie.model.ErnieImageModel) + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) + return out diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 8bed6828d..ca06cdd1e 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -713,6 +713,11 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["enc_h"] = state_dict['{}encoder.pan_blocks.1.cv4.conv.weight'.format(key_prefix)].shape[0] return dit_config + if '{}layers.0.mlp.linear_fc2.weight'.format(key_prefix) in state_dict_keys: # Ernie Image + dit_config = {} + dit_config["image_model"] = "ernie" + return dit_config + if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys: return None diff --git a/comfy/sd.py b/comfy/sd.py index f331feefb..e573804a5 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -62,6 +62,7 @@ import comfy.text_encoders.anima import comfy.text_encoders.ace15 import comfy.text_encoders.longcat_image import comfy.text_encoders.qwen35 +import comfy.text_encoders.ernie import comfy.model_patcher import comfy.lora @@ -1235,6 +1236,7 @@ class TEModel(Enum): QWEN35_4B = 25 QWEN35_9B = 26 QWEN35_27B = 27 + MINISTRAL_3_3B = 28 def detect_te_model(sd): @@ -1301,6 +1303,8 @@ def detect_te_model(sd): return TEModel.MISTRAL3_24B else: return TEModel.MISTRAL3_24B_PRUNED_FLUX2 + if weight.shape[0] == 3072: + return TEModel.MINISTRAL_3_3B return TEModel.LLAMA3_8 return None @@ -1458,6 +1462,10 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip elif te_model == TEModel.QWEN3_06B: clip_target.clip = comfy.text_encoders.anima.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.anima.AnimaTokenizer + elif te_model == TEModel.MINISTRAL_3_3B: + clip_target.clip = comfy.text_encoders.ernie.te(**llama_detect(clip_data)) + clip_target.tokenizer = comfy.text_encoders.ernie.ErnieTokenizer + tokenizer_data["tekken_model"] = clip_data[0].get("tekken_model", None) else: # clip_l if clip_type == CLIPType.SD3: diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 9a5612716..58d4ce731 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -26,6 +26,7 @@ import comfy.text_encoders.z_image import comfy.text_encoders.anima import comfy.text_encoders.ace15 import comfy.text_encoders.longcat_image +import comfy.text_encoders.ernie from . import supported_models_base from . import latent_formats @@ -1749,6 +1750,37 @@ class RT_DETR_v4(supported_models_base.BASE): def clip_target(self, state_dict={}): return None -models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima, RT_DETR_v4] + +class ErnieImage(supported_models_base.BASE): + unet_config = { + "image_model": "ernie", + } + + sampling_settings = { + "multiplier": 1000.0, + "shift": 3.0, + } + + memory_usage_factor = 10.0 + + unet_extra_config = {} + latent_format = latent_formats.Flux2 + + supported_inference_dtypes = [torch.bfloat16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.ErnieImage(self, device=device) + return out + + def clip_target(self, state_dict={}): + pref = self.text_encoder_key_prefix[0] + hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}ministral3_3b.transformer.".format(pref)) + return supported_models_base.ClipTarget(comfy.text_encoders.ernie.ErnieTokenizer, comfy.text_encoders.ernie.te(**hunyuan_detect)) + + +models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima, RT_DETR_v4, ErnieImage] models += [SVD_img2vid] diff --git a/comfy/text_encoders/ernie.py b/comfy/text_encoders/ernie.py new file mode 100644 index 000000000..8c56c1c11 --- /dev/null +++ b/comfy/text_encoders/ernie.py @@ -0,0 +1,38 @@ +from .flux import Mistral3Tokenizer +from comfy import sd1_clip +import comfy.text_encoders.llama + +class Ministral3_3BTokenizer(Mistral3Tokenizer): + def __init__(self, embedding_directory=None, embedding_size=5120, embedding_key='mistral3_24b', tokenizer_data={}): + return super().__init__(embedding_directory=embedding_directory, embedding_size=embedding_size, embedding_key=embedding_key, tokenizer_data=tokenizer_data) + +class ErnieTokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="ministral3_3b", tokenizer=Mistral3Tokenizer) + + def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, **kwargs): + tokens = super().tokenize_with_weights(text, return_word_ids=return_word_ids, disable_weights=True, **kwargs) + return tokens + + +class Ministral3_3BModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}): + textmodel_json_config = {} + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 1, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Ministral3_3B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + + +class ErnieTEModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, name="ministral3_3b", clip_model=Ministral3_3BModel): + super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options) + + +def te(dtype_llama=None, llama_quantization_metadata=None): + class ErnieTEModel_(ErnieTEModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if dtype_llama is not None: + dtype = dtype_llama + if llama_quantization_metadata is not None: + model_options = model_options.copy() + model_options["quantization_metadata"] = llama_quantization_metadata + super().__init__(device=device, dtype=dtype, model_options=model_options) + return ErnieTEModel diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py index 1ae398789..d5eb91dcb 100644 --- a/comfy/text_encoders/flux.py +++ b/comfy/text_encoders/flux.py @@ -116,9 +116,9 @@ class MistralTokenizerClass: return LlamaTokenizerFast(**kwargs) class Mistral3Tokenizer(sd1_clip.SDTokenizer): - def __init__(self, embedding_directory=None, tokenizer_data={}): + def __init__(self, embedding_directory=None, embedding_size=5120, embedding_key='mistral3_24b', tokenizer_data={}): self.tekken_data = tokenizer_data.get("tekken_model", None) - super().__init__("", pad_with_end=False, embedding_directory=embedding_directory, embedding_size=5120, embedding_key='mistral3_24b', tokenizer_class=MistralTokenizerClass, has_end_token=False, pad_to_max_length=False, pad_token=11, start_token=1, max_length=99999999, min_length=1, pad_left=True, tokenizer_args=load_mistral_tokenizer(self.tekken_data), tokenizer_data=tokenizer_data) + super().__init__("", pad_with_end=False, embedding_directory=embedding_directory, embedding_size=embedding_size, embedding_key=embedding_key, tokenizer_class=MistralTokenizerClass, has_end_token=False, pad_to_max_length=False, pad_token=11, start_token=1, max_length=99999999, min_length=1, pad_left=True, disable_weights=True, tokenizer_args=load_mistral_tokenizer(self.tekken_data), tokenizer_data=tokenizer_data) def state_dict(self): return {"tekken_model": self.tekken_data} diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py index 06f2fbf74..6cdc47757 100644 --- a/comfy/text_encoders/llama.py +++ b/comfy/text_encoders/llama.py @@ -60,6 +60,29 @@ class Mistral3Small24BConfig: final_norm: bool = True lm_head: bool = False +@dataclass +class Ministral3_3BConfig: + vocab_size: int = 131072 + hidden_size: int = 3072 + intermediate_size: int = 9216 + num_hidden_layers: int = 26 + num_attention_heads: int = 32 + num_key_value_heads: int = 8 + max_position_embeddings: int = 262144 + rms_norm_eps: float = 1e-5 + rope_theta: float = 1000000.0 + transformer_type: str = "llama" + head_dim = 128 + rms_norm_add = False + mlp_activation = "silu" + qkv_bias = False + rope_dims = None + q_norm = None + k_norm = None + rope_scale = None + final_norm: bool = True + lm_head: bool = False + @dataclass class Qwen25_3BConfig: vocab_size: int = 151936 @@ -946,6 +969,15 @@ class Mistral3Small24B(BaseLlama, torch.nn.Module): self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype +class Ministral3_3B(BaseLlama, torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + config = Ministral3_3BConfig(**config_dict) + self.num_layers = config.num_hidden_layers + + self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) + self.dtype = dtype + class Qwen25_3B(BaseLlama, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() From 971932346ac6e6e02c1e1e8cfe34df2f0e1cea3e Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 12 Apr 2026 20:27:38 -0700 Subject: [PATCH 029/140] Update quant doc so it's not completely wrong. (#13381) There is still more that needs to be fixed. --- QUANTIZATION.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/QUANTIZATION.md b/QUANTIZATION.md index 1693e13f3..300822029 100644 --- a/QUANTIZATION.md +++ b/QUANTIZATION.md @@ -139,9 +139,9 @@ Example: "_quantization_metadata": { "format_version": "1.0", "layers": { - "model.layers.0.mlp.up_proj": "float8_e4m3fn", - "model.layers.0.mlp.down_proj": "float8_e4m3fn", - "model.layers.1.mlp.up_proj": "float8_e4m3fn" + "model.layers.0.mlp.up_proj": {"format": "float8_e4m3fn"}, + "model.layers.0.mlp.down_proj": {"format": "float8_e4m3fn"}, + "model.layers.1.mlp.up_proj": {"format": "float8_e4m3fn"} } } } @@ -165,4 +165,4 @@ Activation quantization (e.g., for FP8 Tensor Core operations) requires `input_s 3. **Compute scales**: Derive `input_scale` from collected statistics 4. **Store in checkpoint**: Save `input_scale` parameters alongside weights -The calibration dataset should be representative of your target use case. For diffusion models, this typically means a diverse set of prompts and generation parameters. \ No newline at end of file +The calibration dataset should be representative of your target use case. For diffusion models, this typically means a diverse set of prompts and generation parameters. From c2657d5fb9ccfae150c8e5d0e1b39780a0cc33e9 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 12 Apr 2026 20:37:13 -0700 Subject: [PATCH 030/140] Fix typo. (#13382) --- comfy/text_encoders/ernie.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/text_encoders/ernie.py b/comfy/text_encoders/ernie.py index 8c56c1c11..2c7df78fe 100644 --- a/comfy/text_encoders/ernie.py +++ b/comfy/text_encoders/ernie.py @@ -3,7 +3,7 @@ from comfy import sd1_clip import comfy.text_encoders.llama class Ministral3_3BTokenizer(Mistral3Tokenizer): - def __init__(self, embedding_directory=None, embedding_size=5120, embedding_key='mistral3_24b', tokenizer_data={}): + def __init__(self, embedding_directory=None, embedding_size=5120, embedding_key='ministral3_3b', tokenizer_data={}): return super().__init__(embedding_directory=embedding_directory, embedding_size=embedding_size, embedding_key=embedding_key, tokenizer_data=tokenizer_data) class ErnieTokenizer(sd1_clip.SD1Tokenizer): From ee2db7488df047b0a6f3de98f21bd7902c85a707 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Mon, 13 Apr 2026 08:26:19 +0300 Subject: [PATCH 031/140] feat(api-nodes): add SeeDance 2.0 nodes (#13364) Signed-off-by: bigcat88 --- comfy_api_nodes/apis/bytedance.py | 52 +++ comfy_api_nodes/nodes_bytedance.py | 611 +++++++++++++++++++++++++++-- 2 files changed, 634 insertions(+), 29 deletions(-) diff --git a/comfy_api_nodes/apis/bytedance.py b/comfy_api_nodes/apis/bytedance.py index 18455396d..3755323ac 100644 --- a/comfy_api_nodes/apis/bytedance.py +++ b/comfy_api_nodes/apis/bytedance.py @@ -52,6 +52,26 @@ class TaskImageContent(BaseModel): role: Literal["first_frame", "last_frame", "reference_image"] | None = Field(None) +class TaskVideoContentUrl(BaseModel): + url: str = Field(...) + + +class TaskVideoContent(BaseModel): + type: str = Field("video_url") + video_url: TaskVideoContentUrl = Field(...) + role: str = Field("reference_video") + + +class TaskAudioContentUrl(BaseModel): + url: str = Field(...) + + +class TaskAudioContent(BaseModel): + type: str = Field("audio_url") + audio_url: TaskAudioContentUrl = Field(...) + role: str = Field("reference_audio") + + class Text2VideoTaskCreationRequest(BaseModel): model: str = Field(...) content: list[TaskTextContent] = Field(..., min_length=1) @@ -64,6 +84,17 @@ class Image2VideoTaskCreationRequest(BaseModel): generate_audio: bool | None = Field(...) +class Seedance2TaskCreationRequest(BaseModel): + model: str = Field(...) + content: list[TaskTextContent | TaskImageContent | TaskVideoContent | TaskAudioContent] = Field(..., min_length=1) + generate_audio: bool | None = Field(None) + resolution: str | None = Field(None) + ratio: str | None = Field(None) + duration: int | None = Field(None, ge=4, le=15) + seed: int | None = Field(None, ge=0, le=2147483647) + watermark: bool | None = Field(None) + + class TaskCreationResponse(BaseModel): id: str = Field(...) @@ -77,12 +108,27 @@ class TaskStatusResult(BaseModel): video_url: str = Field(...) +class TaskStatusUsage(BaseModel): + completion_tokens: int = Field(0) + total_tokens: int = Field(0) + + class TaskStatusResponse(BaseModel): id: str = Field(...) model: str = Field(...) status: Literal["queued", "running", "cancelled", "succeeded", "failed"] = Field(...) error: TaskStatusError | None = Field(None) content: TaskStatusResult | None = Field(None) + usage: TaskStatusUsage | None = Field(None) + + +# Dollars per 1K tokens, keyed by (model_id, has_video_input). +SEEDANCE2_PRICE_PER_1K_TOKENS = { + ("dreamina-seedance-2-0-260128", False): 0.007, + ("dreamina-seedance-2-0-260128", True): 0.0043, + ("dreamina-seedance-2-0-fast-260128", False): 0.0056, + ("dreamina-seedance-2-0-fast-260128", True): 0.0033, +} RECOMMENDED_PRESETS = [ @@ -112,6 +158,12 @@ RECOMMENDED_PRESETS_SEEDREAM_4 = [ ("Custom", None, None), ] +# Seedance 2.0 reference video pixel count limits per model. +SEEDANCE2_REF_VIDEO_PIXEL_LIMITS = { + "dreamina-seedance-2-0-260128": {"min": 409_600, "max": 927_408}, + "dreamina-seedance-2-0-fast-260128": {"min": 409_600, "max": 927_408}, +} + # The time in this dictionary are given for 10 seconds duration. VIDEO_TASKS_EXECUTION_TIME = { "seedance-1-0-lite-t2v-250428": { diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py index de0c22e70..1cca72f6e 100644 --- a/comfy_api_nodes/nodes_bytedance.py +++ b/comfy_api_nodes/nodes_bytedance.py @@ -8,16 +8,23 @@ from comfy_api.latest import IO, ComfyExtension, Input from comfy_api_nodes.apis.bytedance import ( RECOMMENDED_PRESETS, RECOMMENDED_PRESETS_SEEDREAM_4, + SEEDANCE2_PRICE_PER_1K_TOKENS, + SEEDANCE2_REF_VIDEO_PIXEL_LIMITS, VIDEO_TASKS_EXECUTION_TIME, Image2VideoTaskCreationRequest, ImageTaskCreationResponse, + Seedance2TaskCreationRequest, Seedream4Options, Seedream4TaskCreationRequest, + TaskAudioContent, + TaskAudioContentUrl, TaskCreationResponse, TaskImageContent, TaskImageContentUrl, TaskStatusResponse, TaskTextContent, + TaskVideoContent, + TaskVideoContentUrl, Text2ImageTaskCreationRequest, Text2VideoTaskCreationRequest, ) @@ -29,7 +36,10 @@ from comfy_api_nodes.util import ( image_tensor_pair_to_batch, poll_op, sync_op, + upload_audio_to_comfyapi, + upload_image_to_comfyapi, upload_images_to_comfyapi, + upload_video_to_comfyapi, validate_image_aspect_ratio, validate_image_dimensions, validate_string, @@ -46,12 +56,56 @@ SEEDREAM_MODELS = { # Long-running tasks endpoints(e.g., video) BYTEPLUS_TASK_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks" BYTEPLUS_TASK_STATUS_ENDPOINT = "/proxy/byteplus/api/v3/contents/generations/tasks" # + /{task_id} +BYTEPLUS_SEEDANCE2_TASK_STATUS_ENDPOINT = "/proxy/byteplus-seedance2/api/v3/contents/generations/tasks" # + /{task_id} + +SEEDANCE_MODELS = { + "Seedance 2.0": "dreamina-seedance-2-0-260128", + "Seedance 2.0 Fast": "dreamina-seedance-2-0-fast-260128", +} DEPRECATED_MODELS = {"seedance-1-0-lite-t2v-250428", "seedance-1-0-lite-i2v-250428"} + logger = logging.getLogger(__name__) +def _validate_ref_video_pixels(video: Input.Video, model_id: str, index: int) -> None: + """Validate reference video pixel count against Seedance 2.0 model limits.""" + limits = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id) + if not limits: + return + try: + w, h = video.get_dimensions() + except Exception: + return + pixels = w * h + min_px = limits.get("min") + max_px = limits.get("max") + if min_px and pixels < min_px: + raise ValueError( + f"Reference video {index} is too small: {w}x{h} = {pixels:,}px. " f"Minimum is {min_px:,}px for this model." + ) + if max_px and pixels > max_px: + raise ValueError( + f"Reference video {index} is too large: {w}x{h} = {pixels:,}px. " + f"Maximum is {max_px:,}px for this model. Try downscaling the video." + ) + + +def _seedance2_price_extractor(model_id: str, has_video_input: bool): + """Returns a price_extractor closure for Seedance 2.0 poll_op.""" + rate = SEEDANCE2_PRICE_PER_1K_TOKENS.get((model_id, has_video_input)) + if rate is None: + return None + + def extractor(response: TaskStatusResponse) -> float | None: + if response.usage is None: + return None + return response.usage.total_tokens * 1.43 * rate / 1_000.0 + + return extractor + + def get_image_url_from_response(response: ImageTaskCreationResponse) -> str: if response.error: error_msg = f"ByteDance request failed. Code: {response.error['code']}, message: {response.error['message']}" @@ -335,8 +389,7 @@ class ByteDanceSeedreamNode(IO.ComfyNode): mp_provided = out_num_pixels / 1_000_000.0 if ("seedream-4-5" in model or "seedream-5-0" in model) and out_num_pixels < 3686400: raise ValueError( - f"Minimum image resolution for the selected model is 3.68MP, " - f"but {mp_provided:.2f}MP provided." + f"Minimum image resolution for the selected model is 3.68MP, " f"but {mp_provided:.2f}MP provided." ) if "seedream-4-0" in model and out_num_pixels < 921600: raise ValueError( @@ -952,33 +1005,6 @@ class ByteDanceImageReferenceNode(IO.ComfyNode): ) -async def process_video_task( - cls: type[IO.ComfyNode], - payload: Text2VideoTaskCreationRequest | Image2VideoTaskCreationRequest, - estimated_duration: int | None, -) -> IO.NodeOutput: - if payload.model in DEPRECATED_MODELS: - logger.warning( - "Model '%s' is deprecated and will be deactivated on May 13, 2026. " - "Please switch to a newer model. Recommended: seedance-1-0-pro-fast-251015.", - payload.model, - ) - initial_response = await sync_op( - cls, - ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"), - data=payload, - response_model=TaskCreationResponse, - ) - response = await poll_op( - cls, - ApiEndpoint(path=f"{BYTEPLUS_TASK_STATUS_ENDPOINT}/{initial_response.id}"), - status_extractor=lambda r: r.status, - estimated_duration=estimated_duration, - response_model=TaskStatusResponse, - ) - return IO.NodeOutput(await download_url_to_video_output(response.content.video_url)) - - def raise_if_text_params(prompt: str, text_params: list[str]) -> None: for i in text_params: if f"--{i} " in prompt: @@ -1040,6 +1066,530 @@ PRICE_BADGE_VIDEO = IO.PriceBadge( ) +def _seedance2_text_inputs(): + return [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Text prompt for video generation.", + ), + IO.Combo.Input( + "resolution", + options=["480p", "720p"], + tooltip="Resolution of the output video.", + ), + IO.Combo.Input( + "ratio", + options=["16:9", "4:3", "1:1", "3:4", "9:16", "21:9", "adaptive"], + tooltip="Aspect ratio of the output video.", + ), + IO.Int.Input( + "duration", + default=7, + min=4, + max=15, + step=1, + tooltip="Duration of the output video in seconds (4-15).", + display_mode=IO.NumberDisplay.slider, + ), + IO.Boolean.Input( + "generate_audio", + default=True, + tooltip="Enable audio generation for the output video.", + ), + ] + + +class ByteDance2TextToVideoNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ByteDance2TextToVideoNode", + display_name="ByteDance Seedance 2.0 Text to Video", + category="api node/video/ByteDance", + description="Generate video using Seedance 2.0 models based on a text prompt.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option("Seedance 2.0", _seedance2_text_inputs()), + IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_text_inputs()), + ], + tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed controls whether the node should re-run; " + "results are non-deterministic regardless of seed.", + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add a watermark to the video.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $rate480 := 10044; + $rate720 := 21600; + $m := widgets.model; + $pricePer1K := $contains($m, "fast") ? 0.008008 : 0.01001; + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $rate := $res = "720p" ? $rate720 : $rate480; + $cost := $dur * $rate * $pricePer1K / 1000; + {"type": "usd", "usd": $cost, "format": {"approximate": true}} + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + seed: int, + watermark: bool, + ) -> IO.NodeOutput: + validate_string(model["prompt"], strip_whitespace=True, min_length=1) + model_id = SEEDANCE_MODELS[model["model"]] + initial_response = await sync_op( + cls, + ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"), + data=Seedance2TaskCreationRequest( + model=model_id, + content=[TaskTextContent(text=model["prompt"])], + generate_audio=model["generate_audio"], + resolution=model["resolution"], + ratio=model["ratio"], + duration=model["duration"], + seed=seed, + watermark=watermark, + ), + response_model=TaskCreationResponse, + ) + response = await poll_op( + cls, + ApiEndpoint(path=f"{BYTEPLUS_SEEDANCE2_TASK_STATUS_ENDPOINT}/{initial_response.id}"), + response_model=TaskStatusResponse, + status_extractor=lambda r: r.status, + price_extractor=_seedance2_price_extractor(model_id, has_video_input=False), + poll_interval=9, + ) + return IO.NodeOutput(await download_url_to_video_output(response.content.video_url)) + + +class ByteDance2FirstLastFrameNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ByteDance2FirstLastFrameNode", + display_name="ByteDance Seedance 2.0 First-Last-Frame to Video", + category="api node/video/ByteDance", + description="Generate video using Seedance 2.0 from a first frame image and optional last frame image.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option("Seedance 2.0", _seedance2_text_inputs()), + IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_text_inputs()), + ], + tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.", + ), + IO.Image.Input( + "first_frame", + tooltip="First frame image for the video.", + ), + IO.Image.Input( + "last_frame", + tooltip="Last frame image for the video.", + optional=True, + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed controls whether the node should re-run; " + "results are non-deterministic regardless of seed.", + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add a watermark to the video.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $rate480 := 10044; + $rate720 := 21600; + $m := widgets.model; + $pricePer1K := $contains($m, "fast") ? 0.008008 : 0.01001; + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $rate := $res = "720p" ? $rate720 : $rate480; + $cost := $dur * $rate * $pricePer1K / 1000; + {"type": "usd", "usd": $cost, "format": {"approximate": true}} + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + first_frame: Input.Image, + seed: int, + watermark: bool, + last_frame: Input.Image | None = None, + ) -> IO.NodeOutput: + validate_string(model["prompt"], strip_whitespace=True, min_length=1) + model_id = SEEDANCE_MODELS[model["model"]] + + content: list[TaskTextContent | TaskImageContent] = [ + TaskTextContent(text=model["prompt"]), + TaskImageContent( + image_url=TaskImageContentUrl( + url=await upload_image_to_comfyapi(cls, first_frame, wait_label="Uploading first frame.") + ), + role="first_frame", + ), + ] + if last_frame is not None: + content.append( + TaskImageContent( + image_url=TaskImageContentUrl( + url=await upload_image_to_comfyapi(cls, last_frame, wait_label="Uploading last frame.") + ), + role="last_frame", + ), + ) + + initial_response = await sync_op( + cls, + ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"), + data=Seedance2TaskCreationRequest( + model=model_id, + content=content, + generate_audio=model["generate_audio"], + resolution=model["resolution"], + ratio=model["ratio"], + duration=model["duration"], + seed=seed, + watermark=watermark, + ), + response_model=TaskCreationResponse, + ) + response = await poll_op( + cls, + ApiEndpoint(path=f"{BYTEPLUS_SEEDANCE2_TASK_STATUS_ENDPOINT}/{initial_response.id}"), + response_model=TaskStatusResponse, + status_extractor=lambda r: r.status, + price_extractor=_seedance2_price_extractor(model_id, has_video_input=False), + poll_interval=9, + ) + return IO.NodeOutput(await download_url_to_video_output(response.content.video_url)) + + +def _seedance2_reference_inputs(): + return [ + *_seedance2_text_inputs(), + IO.Autogrow.Input( + "reference_images", + template=IO.Autogrow.TemplateNames( + IO.Image.Input("reference_image"), + names=[ + "image_1", + "image_2", + "image_3", + "image_4", + "image_5", + "image_6", + "image_7", + "image_8", + "image_9", + ], + min=0, + ), + ), + IO.Autogrow.Input( + "reference_videos", + template=IO.Autogrow.TemplateNames( + IO.Video.Input("reference_video"), + names=["video_1", "video_2", "video_3"], + min=0, + ), + ), + IO.Autogrow.Input( + "reference_audios", + template=IO.Autogrow.TemplateNames( + IO.Audio.Input("reference_audio"), + names=["audio_1", "audio_2", "audio_3"], + min=0, + ), + ), + ] + + +class ByteDance2ReferenceNode(IO.ComfyNode): + + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="ByteDance2ReferenceNode", + display_name="ByteDance Seedance 2.0 Reference to Video", + category="api node/video/ByteDance", + description="Generate, edit, or extend video using Seedance 2.0 with reference images, " + "videos, and audio. Supports multimodal reference, video editing, and video extension.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option("Seedance 2.0", _seedance2_reference_inputs()), + IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_reference_inputs()), + ], + tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed controls whether the node should re-run; " + "results are non-deterministic regardless of seed.", + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add a watermark to the video.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends( + widgets=["model", "model.resolution", "model.duration"], + input_groups=["model.reference_videos"], + ), + expr=""" + ( + $rate480 := 10044; + $rate720 := 21600; + $m := widgets.model; + $hasVideo := $lookup(inputGroups, "model.reference_videos") > 0; + $noVideoPricePer1K := $contains($m, "fast") ? 0.008008 : 0.01001; + $videoPricePer1K := $contains($m, "fast") ? 0.004719 : 0.006149; + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $rate := $res = "720p" ? $rate720 : $rate480; + $noVideoCost := $dur * $rate * $noVideoPricePer1K / 1000; + $minVideoFactor := $ceil($dur * 5 / 3); + $minVideoCost := $minVideoFactor * $rate * $videoPricePer1K / 1000; + $maxVideoCost := (15 + $dur) * $rate * $videoPricePer1K / 1000; + $hasVideo + ? { + "type": "range_usd", + "min_usd": $minVideoCost, + "max_usd": $maxVideoCost, + "format": {"approximate": true} + } + : { + "type": "usd", + "usd": $noVideoCost, + "format": {"approximate": true} + } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + seed: int, + watermark: bool, + ) -> IO.NodeOutput: + validate_string(model["prompt"], strip_whitespace=True, min_length=1) + + reference_images = model.get("reference_images", {}) + reference_videos = model.get("reference_videos", {}) + reference_audios = model.get("reference_audios", {}) + + if not reference_images and not reference_videos: + raise ValueError("At least one reference image or video is required.") + + model_id = SEEDANCE_MODELS[model["model"]] + has_video_input = len(reference_videos) > 0 + total_video_duration = 0.0 + for i, key in enumerate(reference_videos, 1): + video = reference_videos[key] + _validate_ref_video_pixels(video, model_id, i) + try: + dur = video.get_duration() + if dur < 1.8: + raise ValueError(f"Reference video {i} is too short: {dur:.1f}s. Minimum duration is 1.8 seconds.") + total_video_duration += dur + except ValueError: + raise + except Exception: + pass + if total_video_duration > 15.1: + raise ValueError(f"Total reference video duration is {total_video_duration:.1f}s. Maximum is 15.1 seconds.") + + total_audio_duration = 0.0 + for i, key in enumerate(reference_audios, 1): + audio = reference_audios[key] + dur = int(audio["waveform"].shape[-1]) / int(audio["sample_rate"]) + if dur < 1.8: + raise ValueError(f"Reference audio {i} is too short: {dur:.1f}s. Minimum duration is 1.8 seconds.") + total_audio_duration += dur + if total_audio_duration > 15.1: + raise ValueError(f"Total reference audio duration is {total_audio_duration:.1f}s. Maximum is 15.1 seconds.") + + content: list[TaskTextContent | TaskImageContent | TaskVideoContent | TaskAudioContent] = [ + TaskTextContent(text=model["prompt"]), + ] + for i, key in enumerate(reference_images, 1): + content.append( + TaskImageContent( + image_url=TaskImageContentUrl( + url=await upload_image_to_comfyapi( + cls, + image=reference_images[key], + wait_label=f"Uploading image {i}", + ), + ), + role="reference_image", + ), + ) + for i, key in enumerate(reference_videos, 1): + content.append( + TaskVideoContent( + video_url=TaskVideoContentUrl( + url=await upload_video_to_comfyapi( + cls, + reference_videos[key], + wait_label=f"Uploading video {i}", + ), + ), + ), + ) + for key in reference_audios: + content.append( + TaskAudioContent( + audio_url=TaskAudioContentUrl( + url=await upload_audio_to_comfyapi( + cls, + reference_audios[key], + container_format="mp3", + codec_name="libmp3lame", + mime_type="audio/mpeg", + ), + ), + ), + ) + initial_response = await sync_op( + cls, + ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"), + data=Seedance2TaskCreationRequest( + model=model_id, + content=content, + generate_audio=model["generate_audio"], + resolution=model["resolution"], + ratio=model["ratio"], + duration=model["duration"], + seed=seed, + watermark=watermark, + ), + response_model=TaskCreationResponse, + ) + response = await poll_op( + cls, + ApiEndpoint(path=f"{BYTEPLUS_SEEDANCE2_TASK_STATUS_ENDPOINT}/{initial_response.id}"), + response_model=TaskStatusResponse, + status_extractor=lambda r: r.status, + price_extractor=_seedance2_price_extractor(model_id, has_video_input=has_video_input), + poll_interval=9, + ) + return IO.NodeOutput(await download_url_to_video_output(response.content.video_url)) + + +async def process_video_task( + cls: type[IO.ComfyNode], + payload: Text2VideoTaskCreationRequest | Image2VideoTaskCreationRequest, + estimated_duration: int | None, +) -> IO.NodeOutput: + if payload.model in DEPRECATED_MODELS: + logger.warning( + "Model '%s' is deprecated and will be deactivated on May 13, 2026. " + "Please switch to a newer model. Recommended: seedance-1-0-pro-fast-251015.", + payload.model, + ) + initial_response = await sync_op( + cls, + ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"), + data=payload, + response_model=TaskCreationResponse, + ) + response = await poll_op( + cls, + ApiEndpoint(path=f"{BYTEPLUS_TASK_STATUS_ENDPOINT}/{initial_response.id}"), + status_extractor=lambda r: r.status, + estimated_duration=estimated_duration, + response_model=TaskStatusResponse, + ) + return IO.NodeOutput(await download_url_to_video_output(response.content.video_url)) + + class ByteDanceExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: @@ -1050,6 +1600,9 @@ class ByteDanceExtension(ComfyExtension): ByteDanceImageToVideoNode, ByteDanceFirstLastFrameNode, ByteDanceImageReferenceNode, + ByteDance2TextToVideoNode, + ByteDance2FirstLastFrameNode, + ByteDance2ReferenceNode, ] From 559501e4b88595b3ddf0cff98c14443f4f307c78 Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Mon, 13 Apr 2026 14:19:09 +0800 Subject: [PATCH 032/140] chore: update workflow templates to v0.9.47 (#13385) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index cfb4d4fb8..393c1e1ff 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.10 -comfyui-workflow-templates==0.9.45 +comfyui-workflow-templates==0.9.47 comfyui-embedded-docs==0.4.3 torch torchsde From acd718598eca0b944a1a7a82072a9dec40d3d4f7 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 13 Apr 2026 03:02:36 -0400 Subject: [PATCH 033/140] ComfyUI v0.19.0 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 61d7672ca..0da11d5fa 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.18.1" +__version__ = "0.19.0" diff --git a/pyproject.toml b/pyproject.toml index 1fc9402a1..e8d4a9742 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.18.1" +version = "0.19.0" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.10" From 402ff1cdb7a266eae58c696dfbce4a62d21070a3 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 13 Apr 2026 13:38:42 -0700 Subject: [PATCH 034/140] Fix issue with ernie image. (#13393) --- comfy/ldm/ernie/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/ldm/ernie/model.py b/comfy/ldm/ernie/model.py index 1f8f08376..3dbab8dc0 100644 --- a/comfy/ldm/ernie/model.py +++ b/comfy/ldm/ernie/model.py @@ -279,7 +279,7 @@ class ErnieImageModel(nn.Module): rotary_pos_emb = self.pos_embed(torch.cat([image_ids, text_ids], dim=1)).to(x.dtype) del image_ids, text_ids - sample = self.time_proj(timesteps.to(dtype)).to(self.time_embedding.linear_1.weight.dtype) + sample = self.time_proj(timesteps).to(dtype) c = self.time_embedding(sample) shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = [ From 722bc733196bbeef5b7ccdc95bb0e7e4156bb591 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 13 Apr 2026 17:43:57 -0700 Subject: [PATCH 035/140] Make text generation work with ministral model. (#13395) Needs template before it works properly. --- comfy/text_encoders/llama.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py index 6cdc47757..6ea8e36b1 100644 --- a/comfy/text_encoders/llama.py +++ b/comfy/text_encoders/llama.py @@ -82,6 +82,7 @@ class Ministral3_3BConfig: rope_scale = None final_norm: bool = True lm_head: bool = False + stop_tokens = [2] @dataclass class Qwen25_3BConfig: @@ -969,7 +970,7 @@ class Mistral3Small24B(BaseLlama, torch.nn.Module): self.model = Llama2_(config, device=device, dtype=dtype, ops=operations) self.dtype = dtype -class Ministral3_3B(BaseLlama, torch.nn.Module): +class Ministral3_3B(BaseLlama, BaseQwen3, BaseGenerate, torch.nn.Module): def __init__(self, config_dict, dtype, device, operations): super().__init__() config = Ministral3_3BConfig(**config_dict) From 35dfcbbb28a8b8b2833d598403f138ed480de58d Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Tue, 14 Apr 2026 08:21:01 +0300 Subject: [PATCH 036/140] [Partner Nodes] add Sonilo Audio nodes (#13391) * feat(api-nodes): add Sonilo nodes Signed-off-by: bigcat88 * fix: do not spam frontend with each chunk arrival Signed-off-by: bigcat88 * updated pricing badge Signed-off-by: bigcat88 --------- Signed-off-by: bigcat88 --- comfy_api_nodes/nodes_sonilo.py | 287 ++++++++++++++++++++++++++++++++ 1 file changed, 287 insertions(+) create mode 100644 comfy_api_nodes/nodes_sonilo.py diff --git a/comfy_api_nodes/nodes_sonilo.py b/comfy_api_nodes/nodes_sonilo.py new file mode 100644 index 000000000..5518f5902 --- /dev/null +++ b/comfy_api_nodes/nodes_sonilo.py @@ -0,0 +1,287 @@ +import base64 +import json +import logging +import time +from urllib.parse import urljoin + +import aiohttp +from typing_extensions import override + +from comfy_api.latest import IO, ComfyExtension, Input +from comfy_api_nodes.util import ( + ApiEndpoint, + audio_bytes_to_audio_input, + upload_video_to_comfyapi, + validate_string, +) +from comfy_api_nodes.util._helpers import ( + default_base_url, + get_auth_header, + get_node_id, + is_processing_interrupted, +) +from comfy_api_nodes.util.common_exceptions import ProcessingInterrupted +from server import PromptServer + +logger = logging.getLogger(__name__) + + +class SoniloVideoToMusic(IO.ComfyNode): + """Generate music from video using Sonilo's AI model.""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="SoniloVideoToMusic", + display_name="Sonilo Video to Music", + category="api node/audio/Sonilo", + description="Generate music from video content using Sonilo's AI model. " + "Analyzes the video and creates matching music.", + inputs=[ + IO.Video.Input( + "video", + tooltip="Input video to generate music from. Maximum duration: 6 minutes.", + ), + IO.String.Input( + "prompt", + default="", + multiline=True, + tooltip="Optional text prompt to guide music generation. " + "Leave empty for best quality - the model will fully analyze the video content.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="Seed for reproducibility. Currently ignored by the Sonilo " + "service but kept for graph consistency.", + ), + ], + outputs=[IO.Audio.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + expr='{"type":"usd","usd":0.009,"format":{"suffix":"/second"}}', + ), + ) + + @classmethod + async def execute( + cls, + video: Input.Video, + prompt: str = "", + seed: int = 0, + ) -> IO.NodeOutput: + video_url = await upload_video_to_comfyapi(cls, video, max_duration=360) + form = aiohttp.FormData() + form.add_field("video_url", video_url) + if prompt.strip(): + form.add_field("prompt", prompt.strip()) + audio_bytes = await _stream_sonilo_music( + cls, + ApiEndpoint(path="/proxy/sonilo/v2m/generate", method="POST"), + form, + ) + return IO.NodeOutput(audio_bytes_to_audio_input(audio_bytes)) + + +class SoniloTextToMusic(IO.ComfyNode): + """Generate music from a text prompt using Sonilo's AI model.""" + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="SoniloTextToMusic", + display_name="Sonilo Text to Music", + category="api node/audio/Sonilo", + description="Generate music from a text prompt using Sonilo's AI model. " + "Leave duration at 0 to let the model infer it from the prompt.", + inputs=[ + IO.String.Input( + "prompt", + default="", + multiline=True, + tooltip="Text prompt describing the music to generate.", + ), + IO.Int.Input( + "duration", + default=0, + min=0, + max=360, + tooltip="Target duration in seconds. Set to 0 to let the model " + "infer the duration from the prompt. Maximum: 6 minutes.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=0xFFFFFFFFFFFFFFFF, + control_after_generate=True, + tooltip="Seed for reproducibility. Currently ignored by the Sonilo " + "service but kept for graph consistency.", + ), + ], + outputs=[IO.Audio.Output()], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["duration"]), + expr=""" + ( + widgets.duration > 0 + ? {"type":"usd","usd": 0.005 * widgets.duration} + : {"type":"usd","usd": 0.005, "format":{"suffix":"/second"}} + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + prompt: str, + duration: int = 0, + seed: int = 0, + ) -> IO.NodeOutput: + validate_string(prompt, strip_whitespace=True, min_length=1) + form = aiohttp.FormData() + form.add_field("prompt", prompt) + if duration > 0: + form.add_field("duration", str(duration)) + audio_bytes = await _stream_sonilo_music( + cls, + ApiEndpoint(path="/proxy/sonilo/t2m/generate", method="POST"), + form, + ) + return IO.NodeOutput(audio_bytes_to_audio_input(audio_bytes)) + + +async def _stream_sonilo_music( + cls: type[IO.ComfyNode], + endpoint: ApiEndpoint, + form: aiohttp.FormData, +) -> bytes: + """POST ``form`` to Sonilo, read the NDJSON stream, and return the first stream's audio bytes.""" + url = urljoin(default_base_url().rstrip("/") + "/", endpoint.path.lstrip("/")) + + headers: dict[str, str] = {} + headers.update(get_auth_header(cls)) + headers.update(endpoint.headers) + + node_id = get_node_id(cls) + start_ts = time.monotonic() + last_chunk_status_ts = 0.0 + audio_streams: dict[int, list[bytes]] = {} + title: str | None = None + + timeout = aiohttp.ClientTimeout(total=1200.0, sock_read=300.0) + async with aiohttp.ClientSession(timeout=timeout) as session: + PromptServer.instance.send_progress_text("Status: Queued", node_id) + async with session.post(url, data=form, headers=headers) as resp: + if resp.status >= 400: + msg = await _extract_error_message(resp) + raise Exception(f"Sonilo API error ({resp.status}): {msg}") + + while True: + if is_processing_interrupted(): + raise ProcessingInterrupted("Task cancelled") + + raw_line = await resp.content.readline() + if not raw_line: + break + + line = raw_line.decode("utf-8").strip() + if not line: + continue + + try: + evt = json.loads(line) + except json.JSONDecodeError: + logger.warning("Sonilo: skipping malformed NDJSON line") + continue + + evt_type = evt.get("type") + if evt_type == "error": + code = evt.get("code", "UNKNOWN") + message = evt.get("message", "Unknown error") + raise Exception(f"Sonilo generation error ({code}): {message}") + if evt_type == "duration": + duration_sec = evt.get("duration_sec") + if duration_sec is not None: + PromptServer.instance.send_progress_text( + f"Status: Generating\nVideo duration: {duration_sec:.1f}s", + node_id, + ) + elif evt_type in ("titles", "title"): + # v2m sends a "titles" list, t2m sends a scalar "title" + if evt_type == "titles": + titles = evt.get("titles", []) + if titles: + title = titles[0] + else: + title = evt.get("title") or title + if title: + PromptServer.instance.send_progress_text( + f"Status: Generating\nTitle: {title}", + node_id, + ) + elif evt_type == "audio_chunk": + stream_idx = evt.get("stream_index", 0) + chunk_data = base64.b64decode(evt["data"]) + + if stream_idx not in audio_streams: + audio_streams[stream_idx] = [] + audio_streams[stream_idx].append(chunk_data) + + now = time.monotonic() + if now - last_chunk_status_ts >= 1.0: + total_chunks = sum(len(chunks) for chunks in audio_streams.values()) + elapsed = int(now - start_ts) + status_lines = ["Status: Receiving audio"] + if title: + status_lines.append(f"Title: {title}") + status_lines.append(f"Chunks received: {total_chunks}") + status_lines.append(f"Time elapsed: {elapsed}s") + PromptServer.instance.send_progress_text("\n".join(status_lines), node_id) + last_chunk_status_ts = now + elif evt_type == "complete": + break + + if not audio_streams: + raise Exception("Sonilo API returned no audio data.") + + PromptServer.instance.send_progress_text("Status: Completed", node_id) + selected_stream = 0 if 0 in audio_streams else min(audio_streams) + return b"".join(audio_streams[selected_stream]) + + +async def _extract_error_message(resp: aiohttp.ClientResponse) -> str: + """Extract a human-readable error message from an HTTP error response.""" + try: + error_body = await resp.json() + detail = error_body.get("detail", {}) + if isinstance(detail, dict): + return detail.get("message", str(detail)) + return str(detail) + except Exception: + return await resp.text() + + +class SoniloExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[IO.ComfyNode]]: + return [SoniloVideoToMusic, SoniloTextToMusic] + + +async def comfy_entrypoint() -> SoniloExtension: + return SoniloExtension() From fed4ac031a4a34ea5c76e41e80dddd9f048b22b6 Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Tue, 14 Apr 2026 14:24:37 +0800 Subject: [PATCH 037/140] chore: update workflow templates to v0.9.50 (#13399) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 393c1e1ff..7e8dac182 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.10 -comfyui-workflow-templates==0.9.47 +comfyui-workflow-templates==0.9.50 comfyui-embedded-docs==0.4.3 torch torchsde From c16db7fd6978eee76fca65626c02e61eaedb5e72 Mon Sep 17 00:00:00 2001 From: Comfy Org PR Bot Date: Wed, 15 Apr 2026 03:13:35 +0900 Subject: [PATCH 038/140] Bump comfyui-frontend-package to 1.42.11 (#13398) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 7e8dac182..7f065e0d4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.42.10 +comfyui-frontend-package==1.42.11 comfyui-workflow-templates==0.9.50 comfyui-embedded-docs==0.4.3 torch From c5569e862794c419094ee5c9d5ad224634b9ddd6 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 14 Apr 2026 11:42:23 -0700 Subject: [PATCH 039/140] Add string output to preview text node. (#13406) --- comfy_extras/nodes_preview_any.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_preview_any.py b/comfy_extras/nodes_preview_any.py index b0a6f279d..0a1558f2b 100644 --- a/comfy_extras/nodes_preview_any.py +++ b/comfy_extras/nodes_preview_any.py @@ -11,7 +11,7 @@ class PreviewAny(): "required": {"source": (IO.ANY, {})}, } - RETURN_TYPES = () + RETURN_TYPES = (IO.STRING,) FUNCTION = "main" OUTPUT_NODE = True @@ -33,7 +33,7 @@ class PreviewAny(): except Exception: value = 'source exists, but could not be serialized.' - return {"ui": {"text": (value,)}} + return {"ui": {"text": (value,)}, "result": (value,)} NODE_CLASS_MAPPINGS = { "PreviewAny": PreviewAny, From 7ce3f64c784430e15731d344affffb48c55a0eaa Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Wed, 15 Apr 2026 08:35:27 +0800 Subject: [PATCH 040/140] Update workflow templates to v0.9.54 (#13412) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 7f065e0d4..e45a20aaf 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.11 -comfyui-workflow-templates==0.9.50 +comfyui-workflow-templates==0.9.54 comfyui-embedded-docs==0.4.3 torch torchsde From cb0bbde402cfb72559cc8b00f679d7735dff5c40 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 14 Apr 2026 19:54:47 -0700 Subject: [PATCH 041/140] Fix ernie on devices that don't support fp64. (#13414) --- comfy/ldm/ernie/model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/ldm/ernie/model.py b/comfy/ldm/ernie/model.py index 3dbab8dc0..f7cdb51e6 100644 --- a/comfy/ldm/ernie/model.py +++ b/comfy/ldm/ernie/model.py @@ -15,7 +15,7 @@ def rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor: scale = torch.arange(0, dim, 2, dtype=torch.float64, device=device) / dim omega = 1.0 / (theta**scale) - out = torch.einsum("...n,d->...nd", pos, omega) + out = torch.einsum("...n,d->...nd", pos.to(device), omega) out = torch.stack([torch.cos(out), torch.sin(out)], dim=0) return out.to(dtype=torch.float32, device=pos.device) From 8f374716ee98d378d403ebc61250e091ecd3a25c Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Tue, 14 Apr 2026 22:56:13 -0400 Subject: [PATCH 042/140] ComfyUI v0.19.1 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 0da11d5fa..3c6dac3d9 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.19.0" +__version__ = "0.19.1" diff --git a/pyproject.toml b/pyproject.toml index e8d4a9742..006ed9985 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.19.0" +version = "0.19.1" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.10" From 1de83f91c34a3396c667d764224054ba87027e82 Mon Sep 17 00:00:00 2001 From: Jun Yamog Date: Wed, 15 Apr 2026 21:10:36 +1200 Subject: [PATCH 043/140] Fix OOM regression in _apply() for quantized models during inference (#13372) Skip unnecessary clone of inference-mode tensors when already inside torch.inference_mode(), matching the existing guard in set_attr_param. The unconditional clone introduced in 20561aa9 caused transient VRAM doubling during model movement for FP8/quantized models. --- comfy/ops.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/ops.py b/comfy/ops.py index b5cd1d47e..7a9b4b84c 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -1151,7 +1151,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec if param is None: continue p = fn(param) - if p.is_inference(): + if (not torch.is_inference_mode_enabled()) and p.is_inference(): p = p.clone() self.register_parameter(key, torch.nn.Parameter(p, requires_grad=False)) for key, buf in self._buffers.items(): From e9a2d1e4cc34ade8a655b386a3919a6d05aa290a Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 15 Apr 2026 19:59:08 -0700 Subject: [PATCH 044/140] Add a way to disable default template in text gen node. (#13424) --- comfy_extras/nodes_textgen.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_textgen.py b/comfy_extras/nodes_textgen.py index f1aeb63fa..eed26c582 100644 --- a/comfy_extras/nodes_textgen.py +++ b/comfy_extras/nodes_textgen.py @@ -35,6 +35,7 @@ class TextGenerate(io.ComfyNode): io.Int.Input("max_length", default=256, min=1, max=2048), io.DynamicCombo.Input("sampling_mode", options=sampling_options, display_name="Sampling Mode"), io.Boolean.Input("thinking", optional=True, default=False, tooltip="Operate in thinking mode if the model supports it."), + io.Boolean.Input("use_default_template", optional=True, default=True, tooltip="Use the built in system prompt/template if the model has one.", advanced=True), ], outputs=[ io.String.Output(display_name="generated_text"), @@ -42,9 +43,9 @@ class TextGenerate(io.ComfyNode): ) @classmethod - def execute(cls, clip, prompt, max_length, sampling_mode, image=None, thinking=False) -> io.NodeOutput: + def execute(cls, clip, prompt, max_length, sampling_mode, image=None, thinking=False, use_default_template=True) -> io.NodeOutput: - tokens = clip.tokenize(prompt, image=image, skip_template=False, min_length=1, thinking=thinking) + tokens = clip.tokenize(prompt, image=image, skip_template=not use_default_template, min_length=1, thinking=thinking) # Get sampling parameters from dynamic combo do_sample = sampling_mode.get("sampling_mode") == "on" From b41ab53b6f289b4d7688ab96e0a06248ec1fd86b Mon Sep 17 00:00:00 2001 From: Bedovyy <137917911+bedovyy@users.noreply.github.com> Date: Thu, 16 Apr 2026 23:11:58 +0900 Subject: [PATCH 045/140] Use `ErnieTEModel_` not `ErnieTEModel`. (#13431) --- comfy/text_encoders/ernie.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/text_encoders/ernie.py b/comfy/text_encoders/ernie.py index 2c7df78fe..46d24d222 100644 --- a/comfy/text_encoders/ernie.py +++ b/comfy/text_encoders/ernie.py @@ -35,4 +35,4 @@ def te(dtype_llama=None, llama_quantization_metadata=None): model_options = model_options.copy() model_options["quantization_metadata"] = llama_quantization_metadata super().__init__(device=device, dtype=dtype, model_options=model_options) - return ErnieTEModel + return ErnieTEModel_ From d0c53c50c2a1edf11aa63967d09aa3efbfd43cfe Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Fri, 17 Apr 2026 04:32:04 +0300 Subject: [PATCH 046/140] feat(api-nodes): add 1080p resolution for SeeDance 2.0 model (#13437) Signed-off-by: bigcat88 --- comfy_api_nodes/nodes_bytedance.py | 38 ++++++++++++++++++++---------- 1 file changed, 25 insertions(+), 13 deletions(-) diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py index 1cca72f6e..429c32444 100644 --- a/comfy_api_nodes/nodes_bytedance.py +++ b/comfy_api_nodes/nodes_bytedance.py @@ -1066,7 +1066,7 @@ PRICE_BADGE_VIDEO = IO.PriceBadge( ) -def _seedance2_text_inputs(): +def _seedance2_text_inputs(resolutions: list[str]): return [ IO.String.Input( "prompt", @@ -1076,7 +1076,7 @@ def _seedance2_text_inputs(): ), IO.Combo.Input( "resolution", - options=["480p", "720p"], + options=resolutions, tooltip="Resolution of the output video.", ), IO.Combo.Input( @@ -1114,8 +1114,8 @@ class ByteDance2TextToVideoNode(IO.ComfyNode): IO.DynamicCombo.Input( "model", options=[ - IO.DynamicCombo.Option("Seedance 2.0", _seedance2_text_inputs()), - IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_text_inputs()), + IO.DynamicCombo.Option("Seedance 2.0", _seedance2_text_inputs(["480p", "720p", "1080p"])), + IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_text_inputs(["480p", "720p"])), ], tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.", ), @@ -1152,11 +1152,14 @@ class ByteDance2TextToVideoNode(IO.ComfyNode): ( $rate480 := 10044; $rate720 := 21600; + $rate1080 := 48800; $m := widgets.model; $pricePer1K := $contains($m, "fast") ? 0.008008 : 0.01001; $res := $lookup(widgets, "model.resolution"); $dur := $lookup(widgets, "model.duration"); - $rate := $res = "720p" ? $rate720 : $rate480; + $rate := $res = "1080p" ? $rate1080 : + $res = "720p" ? $rate720 : + $rate480; $cost := $dur * $rate * $pricePer1K / 1000; {"type": "usd", "usd": $cost, "format": {"approximate": true}} ) @@ -1195,6 +1198,7 @@ class ByteDance2TextToVideoNode(IO.ComfyNode): status_extractor=lambda r: r.status, price_extractor=_seedance2_price_extractor(model_id, has_video_input=False), poll_interval=9, + max_poll_attempts=180, ) return IO.NodeOutput(await download_url_to_video_output(response.content.video_url)) @@ -1212,8 +1216,8 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode): IO.DynamicCombo.Input( "model", options=[ - IO.DynamicCombo.Option("Seedance 2.0", _seedance2_text_inputs()), - IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_text_inputs()), + IO.DynamicCombo.Option("Seedance 2.0", _seedance2_text_inputs(["480p", "720p", "1080p"])), + IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_text_inputs(["480p", "720p"])), ], tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.", ), @@ -1259,11 +1263,14 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode): ( $rate480 := 10044; $rate720 := 21600; + $rate1080 := 48800; $m := widgets.model; $pricePer1K := $contains($m, "fast") ? 0.008008 : 0.01001; $res := $lookup(widgets, "model.resolution"); $dur := $lookup(widgets, "model.duration"); - $rate := $res = "720p" ? $rate720 : $rate480; + $rate := $res = "1080p" ? $rate1080 : + $res = "720p" ? $rate720 : + $rate480; $cost := $dur * $rate * $pricePer1K / 1000; {"type": "usd", "usd": $cost, "format": {"approximate": true}} ) @@ -1324,13 +1331,14 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode): status_extractor=lambda r: r.status, price_extractor=_seedance2_price_extractor(model_id, has_video_input=False), poll_interval=9, + max_poll_attempts=180, ) return IO.NodeOutput(await download_url_to_video_output(response.content.video_url)) -def _seedance2_reference_inputs(): +def _seedance2_reference_inputs(resolutions: list[str]): return [ - *_seedance2_text_inputs(), + *_seedance2_text_inputs(resolutions), IO.Autogrow.Input( "reference_images", template=IO.Autogrow.TemplateNames( @@ -1382,8 +1390,8 @@ class ByteDance2ReferenceNode(IO.ComfyNode): IO.DynamicCombo.Input( "model", options=[ - IO.DynamicCombo.Option("Seedance 2.0", _seedance2_reference_inputs()), - IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_reference_inputs()), + IO.DynamicCombo.Option("Seedance 2.0", _seedance2_reference_inputs(["480p", "720p", "1080p"])), + IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_reference_inputs(["480p", "720p"])), ], tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.", ), @@ -1423,13 +1431,16 @@ class ByteDance2ReferenceNode(IO.ComfyNode): ( $rate480 := 10044; $rate720 := 21600; + $rate1080 := 48800; $m := widgets.model; $hasVideo := $lookup(inputGroups, "model.reference_videos") > 0; $noVideoPricePer1K := $contains($m, "fast") ? 0.008008 : 0.01001; $videoPricePer1K := $contains($m, "fast") ? 0.004719 : 0.006149; $res := $lookup(widgets, "model.resolution"); $dur := $lookup(widgets, "model.duration"); - $rate := $res = "720p" ? $rate720 : $rate480; + $rate := $res = "1080p" ? $rate1080 : + $res = "720p" ? $rate720 : + $rate480; $noVideoCost := $dur * $rate * $noVideoPricePer1K / 1000; $minVideoFactor := $ceil($dur * 5 / 3); $minVideoCost := $minVideoFactor * $rate * $videoPricePer1K / 1000; @@ -1559,6 +1570,7 @@ class ByteDance2ReferenceNode(IO.ComfyNode): status_extractor=lambda r: r.status, price_extractor=_seedance2_price_extractor(model_id, has_video_input=has_video_input), poll_interval=9, + max_poll_attempts=180, ) return IO.NodeOutput(await download_url_to_video_output(response.content.video_url)) From 1391579c33db4921a5d40c7e0e71a938b28eb047 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 16 Apr 2026 21:20:16 -0700 Subject: [PATCH 047/140] Add JsonExtractString node. (#13435) --- comfy_extras/nodes_string.py | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) diff --git a/comfy_extras/nodes_string.py b/comfy_extras/nodes_string.py index 75a8bb4ee..604076c4e 100644 --- a/comfy_extras/nodes_string.py +++ b/comfy_extras/nodes_string.py @@ -1,4 +1,5 @@ import re +import json from typing_extensions import override from comfy_api.latest import ComfyExtension, io @@ -375,6 +376,39 @@ class RegexReplace(io.ComfyNode): return io.NodeOutput(result) +class JsonExtractString(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="JsonExtractString", + display_name="Extract String from JSON", + category="utils/string", + search_aliases=["json", "extract json", "parse json", "json value", "read json"], + inputs=[ + io.String.Input("json_string", multiline=True), + io.String.Input("key", multiline=False), + ], + outputs=[ + io.String.Output(), + ] + ) + + @classmethod + def execute(cls, json_string, key): + try: + data = json.loads(json_string) + if isinstance(data, dict) and key in data: + value = data[key] + if value is None: + return io.NodeOutput("") + + return io.NodeOutput(str(value)) + + return io.NodeOutput("") + + except (json.JSONDecodeError, TypeError): + return io.NodeOutput("") + class StringExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[io.ComfyNode]]: @@ -390,6 +424,7 @@ class StringExtension(ComfyExtension): RegexMatch, RegexExtract, RegexReplace, + JsonExtractString, ] async def comfy_entrypoint() -> StringExtension: From c033bbf516ad8fcd079b45c318e73ee8b5e22962 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 17 Apr 2026 00:26:35 -0400 Subject: [PATCH 048/140] ComfyUI v0.19.2 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 3c6dac3d9..98b8337b4 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.19.1" +__version__ = "0.19.2" diff --git a/pyproject.toml b/pyproject.toml index 006ed9985..c4b006486 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.19.1" +version = "0.19.2" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.10" From 05f75311489c94e905d958c2bc4b22db5be78699 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Sat, 18 Apr 2026 02:20:09 +1000 Subject: [PATCH 049/140] nodes_textgen: Implement use_default_template for LTX (#13451) --- comfy_extras/nodes_textgen.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy_extras/nodes_textgen.py b/comfy_extras/nodes_textgen.py index eed26c582..1f46d820f 100644 --- a/comfy_extras/nodes_textgen.py +++ b/comfy_extras/nodes_textgen.py @@ -161,12 +161,12 @@ class TextGenerateLTX2Prompt(TextGenerate): ) @classmethod - def execute(cls, clip, prompt, max_length, sampling_mode, image=None, thinking=False) -> io.NodeOutput: + def execute(cls, clip, prompt, max_length, sampling_mode, image=None, thinking=False, use_default_template=True) -> io.NodeOutput: if image is None: formatted_prompt = f"system\n{LTX2_T2V_SYSTEM_PROMPT.strip()}\nuser\nUser Raw Input Prompt: {prompt}.\nmodel\n" else: formatted_prompt = f"system\n{LTX2_I2V_SYSTEM_PROMPT.strip()}\nuser\n\n\n\nUser Raw Input Prompt: {prompt}.\nmodel\n" - return super().execute(clip, formatted_prompt, max_length, sampling_mode, image, thinking) + return super().execute(clip, formatted_prompt, max_length, sampling_mode, image, thinking, use_default_template) class TextgenExtension(ComfyExtension): From 541fd10bbe5ac5e963619bb9594c4993f977e9e1 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Fri, 17 Apr 2026 19:44:08 +0300 Subject: [PATCH 050/140] fix(api-nodes): corrected StabilityAI price badges (#13454) Signed-off-by: bigcat88 --- comfy_api_nodes/nodes_stability.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/comfy_api_nodes/nodes_stability.py b/comfy_api_nodes/nodes_stability.py index 9ef13c83b..906d8ff35 100644 --- a/comfy_api_nodes/nodes_stability.py +++ b/comfy_api_nodes/nodes_stability.py @@ -401,7 +401,7 @@ class StabilityUpscaleConservativeNode(IO.ComfyNode): ], is_api_node=True, price_badge=IO.PriceBadge( - expr="""{"type":"usd","usd":0.25}""", + expr="""{"type":"usd","usd":0.4}""", ), ) @@ -510,7 +510,7 @@ class StabilityUpscaleCreativeNode(IO.ComfyNode): ], is_api_node=True, price_badge=IO.PriceBadge( - expr="""{"type":"usd","usd":0.25}""", + expr="""{"type":"usd","usd":0.6}""", ), ) @@ -593,7 +593,7 @@ class StabilityUpscaleFastNode(IO.ComfyNode): ], is_api_node=True, price_badge=IO.PriceBadge( - expr="""{"type":"usd","usd":0.01}""", + expr="""{"type":"usd","usd":0.02}""", ), ) From 4f48be41388f67022d58f4f07f2f785adb8bfeea Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Fri, 17 Apr 2026 20:02:06 +0300 Subject: [PATCH 051/140] feat(api-nodes): add new "arrow-1.1" and "arrow-1.1-max" SVG models (#13447) Signed-off-by: bigcat88 --- comfy_api_nodes/nodes_quiver.py | 135 +++++++++++++++----------------- 1 file changed, 65 insertions(+), 70 deletions(-) diff --git a/comfy_api_nodes/nodes_quiver.py b/comfy_api_nodes/nodes_quiver.py index 61533263f..28862e368 100644 --- a/comfy_api_nodes/nodes_quiver.py +++ b/comfy_api_nodes/nodes_quiver.py @@ -17,6 +17,44 @@ from comfy_api_nodes.util import ( ) from comfy_extras.nodes_images import SVG +_ARROW_MODELS = ["arrow-1.1", "arrow-1.1-max", "arrow-preview"] + + +def _arrow_sampling_inputs(): + """Shared sampling inputs for all Arrow model variants.""" + return [ + IO.Float.Input( + "temperature", + default=1.0, + min=0.0, + max=2.0, + step=0.1, + display_mode=IO.NumberDisplay.slider, + tooltip="Randomness control. Higher values increase randomness.", + advanced=True, + ), + IO.Float.Input( + "top_p", + default=1.0, + min=0.05, + max=1.0, + step=0.05, + display_mode=IO.NumberDisplay.slider, + tooltip="Nucleus sampling parameter.", + advanced=True, + ), + IO.Float.Input( + "presence_penalty", + default=0.0, + min=-2.0, + max=2.0, + step=0.1, + display_mode=IO.NumberDisplay.slider, + tooltip="Token presence penalty.", + advanced=True, + ), + ] + class QuiverTextToSVGNode(IO.ComfyNode): @classmethod @@ -39,6 +77,7 @@ class QuiverTextToSVGNode(IO.ComfyNode): default="", tooltip="Additional style or formatting guidance.", optional=True, + advanced=True, ), IO.Autogrow.Input( "reference_images", @@ -53,43 +92,7 @@ class QuiverTextToSVGNode(IO.ComfyNode): ), IO.DynamicCombo.Input( "model", - options=[ - IO.DynamicCombo.Option( - "arrow-preview", - [ - IO.Float.Input( - "temperature", - default=1.0, - min=0.0, - max=2.0, - step=0.1, - display_mode=IO.NumberDisplay.slider, - tooltip="Randomness control. Higher values increase randomness.", - advanced=True, - ), - IO.Float.Input( - "top_p", - default=1.0, - min=0.05, - max=1.0, - step=0.05, - display_mode=IO.NumberDisplay.slider, - tooltip="Nucleus sampling parameter.", - advanced=True, - ), - IO.Float.Input( - "presence_penalty", - default=0.0, - min=-2.0, - max=2.0, - step=0.1, - display_mode=IO.NumberDisplay.slider, - tooltip="Token presence penalty.", - advanced=True, - ), - ], - ), - ], + options=[IO.DynamicCombo.Option(m, _arrow_sampling_inputs()) for m in _ARROW_MODELS], tooltip="Model to use for SVG generation.", ), IO.Int.Input( @@ -112,7 +115,16 @@ class QuiverTextToSVGNode(IO.ComfyNode): ], is_api_node=True, price_badge=IO.PriceBadge( - expr="""{"type":"usd","usd":0.429}""", + depends_on=IO.PriceBadgeDepends(widgets=["model"]), + expr=""" + ( + $contains(widgets.model, "max") + ? {"type":"usd","usd":0.3575} + : $contains(widgets.model, "preview") + ? {"type":"usd","usd":0.429} + : {"type":"usd","usd":0.286} + ) + """, ), ) @@ -176,12 +188,13 @@ class QuiverImageToSVGNode(IO.ComfyNode): "auto_crop", default=False, tooltip="Automatically crop to the dominant subject.", + advanced=True, ), IO.DynamicCombo.Input( "model", options=[ IO.DynamicCombo.Option( - "arrow-preview", + m, [ IO.Int.Input( "target_size", @@ -189,39 +202,12 @@ class QuiverImageToSVGNode(IO.ComfyNode): min=128, max=4096, tooltip="Square resize target in pixels.", - ), - IO.Float.Input( - "temperature", - default=1.0, - min=0.0, - max=2.0, - step=0.1, - display_mode=IO.NumberDisplay.slider, - tooltip="Randomness control. Higher values increase randomness.", - advanced=True, - ), - IO.Float.Input( - "top_p", - default=1.0, - min=0.05, - max=1.0, - step=0.05, - display_mode=IO.NumberDisplay.slider, - tooltip="Nucleus sampling parameter.", - advanced=True, - ), - IO.Float.Input( - "presence_penalty", - default=0.0, - min=-2.0, - max=2.0, - step=0.1, - display_mode=IO.NumberDisplay.slider, - tooltip="Token presence penalty.", advanced=True, ), + *_arrow_sampling_inputs(), ], - ), + ) + for m in _ARROW_MODELS ], tooltip="Model to use for SVG vectorization.", ), @@ -245,7 +231,16 @@ class QuiverImageToSVGNode(IO.ComfyNode): ], is_api_node=True, price_badge=IO.PriceBadge( - expr="""{"type":"usd","usd":0.429}""", + depends_on=IO.PriceBadgeDepends(widgets=["model"]), + expr=""" + ( + $contains(widgets.model, "max") + ? {"type":"usd","usd":0.3575} + : $contains(widgets.model, "preview") + ? {"type":"usd","usd":0.429} + : {"type":"usd","usd":0.286} + ) + """, ), ) From f8d92cf3138092050955fabf9172b3defcd89484 Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Sat, 18 Apr 2026 01:16:39 +0800 Subject: [PATCH 052/140] chore: update workflow templates to v0.9.57 (#13455) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index e45a20aaf..3de845f48 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.11 -comfyui-workflow-templates==0.9.54 +comfyui-workflow-templates==0.9.57 comfyui-embedded-docs==0.4.3 torch torchsde From 9635c2ec9b92f8fa1113660ace7660c1fea67e0e Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Fri, 17 Apr 2026 20:31:37 +0300 Subject: [PATCH 053/140] fix(api-nodes): make "obj" output optional in Hunyuan3D Text and Image to 3D (#13449) Signed-off-by: bigcat88 Co-authored-by: Jedrzej Kosinski --- comfy_api_nodes/nodes_hunyuan3d.py | 34 ++++++++++++++++++++++-------- 1 file changed, 25 insertions(+), 9 deletions(-) diff --git a/comfy_api_nodes/nodes_hunyuan3d.py b/comfy_api_nodes/nodes_hunyuan3d.py index 44c94a98e..5fc31bccd 100644 --- a/comfy_api_nodes/nodes_hunyuan3d.py +++ b/comfy_api_nodes/nodes_hunyuan3d.py @@ -221,14 +221,17 @@ class TencentTextToModelNode(IO.ComfyNode): response_model=To3DProTaskResultResponse, status_extractor=lambda r: r.Status, ) - obj_result = await download_and_extract_obj_zip(get_file_from_response(result.ResultFile3Ds, "obj").Url) + obj_file_response = get_file_from_response(result.ResultFile3Ds, "obj", raise_if_not_found=False) + obj_result = None + if obj_file_response: + obj_result = await download_and_extract_obj_zip(obj_file_response.Url) return IO.NodeOutput( f"{task_id}.glb", await download_url_to_file_3d( get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id ), - obj_result.obj, - obj_result.texture, + obj_result.obj if obj_result else None, + obj_result.texture if obj_result else None, ) @@ -378,17 +381,30 @@ class TencentImageToModelNode(IO.ComfyNode): response_model=To3DProTaskResultResponse, status_extractor=lambda r: r.Status, ) - obj_result = await download_and_extract_obj_zip(get_file_from_response(result.ResultFile3Ds, "obj").Url) + obj_file_response = get_file_from_response(result.ResultFile3Ds, "obj", raise_if_not_found=False) + if obj_file_response: + obj_result = await download_and_extract_obj_zip(obj_file_response.Url) + return IO.NodeOutput( + f"{task_id}.glb", + await download_url_to_file_3d( + get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id + ), + obj_result.obj, + obj_result.texture, + obj_result.metallic if obj_result.metallic is not None else torch.zeros(1, 1, 1, 3), + obj_result.normal if obj_result.normal is not None else torch.zeros(1, 1, 1, 3), + obj_result.roughness if obj_result.roughness is not None else torch.zeros(1, 1, 1, 3), + ) return IO.NodeOutput( f"{task_id}.glb", await download_url_to_file_3d( get_file_from_response(result.ResultFile3Ds, "glb").Url, "glb", task_id=task_id ), - obj_result.obj, - obj_result.texture, - obj_result.metallic if obj_result.metallic is not None else torch.zeros(1, 1, 1, 3), - obj_result.normal if obj_result.normal is not None else torch.zeros(1, 1, 1, 3), - obj_result.roughness if obj_result.roughness is not None else torch.zeros(1, 1, 1, 3), + None, + None, + None, + None, + None, ) From 3086026401180c9216bcb6ace442a4e3587d2c66 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Fri, 17 Apr 2026 13:35:01 -0400 Subject: [PATCH 054/140] ComfyUI v0.19.3 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 98b8337b4..2a1eb9905 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.19.2" +__version__ = "0.19.3" diff --git a/pyproject.toml b/pyproject.toml index c4b006486..8fa92ecbe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.19.2" +version = "0.19.3" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.10" From b9dedea57d9f8be9861811aef3ced3e221eb8068 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Sun, 19 Apr 2026 06:02:01 +0300 Subject: [PATCH 055/140] feat: SUPIR model support (CORE-17) (#13250) --- .../modules/diffusionmodules/openaimodel.py | 30 ++- comfy/ldm/supir/__init__.py | 0 comfy/ldm/supir/supir_modules.py | 226 ++++++++++++++++++ comfy/ldm/supir/supir_patch.py | 103 ++++++++ comfy/model_patcher.py | 4 + comfy_extras/nodes_model_patch.py | 104 ++++++++ comfy_extras/nodes_post_processing.py | 224 +++++++++++++++++ 7 files changed, 680 insertions(+), 11 deletions(-) create mode 100644 comfy/ldm/supir/__init__.py create mode 100644 comfy/ldm/supir/supir_modules.py create mode 100644 comfy/ldm/supir/supir_patch.py diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py index 295310df6..4b92c44cf 100644 --- a/comfy/ldm/modules/diffusionmodules/openaimodel.py +++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py @@ -34,6 +34,16 @@ class TimestepBlock(nn.Module): #This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index" def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None): for layer in ts: + if "patches" in transformer_options and "forward_timestep_embed_patch" in transformer_options["patches"]: + found_patched = False + for class_type, handler in transformer_options["patches"]["forward_timestep_embed_patch"]: + if isinstance(layer, class_type): + x = handler(layer, x, emb, context, transformer_options, output_shape, time_context, num_video_frames, image_only_indicator) + found_patched = True + break + if found_patched: + continue + if isinstance(layer, VideoResBlock): x = layer(x, emb, num_video_frames, image_only_indicator) elif isinstance(layer, TimestepBlock): @@ -49,15 +59,6 @@ def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, out elif isinstance(layer, Upsample): x = layer(x, output_shape=output_shape) else: - if "patches" in transformer_options and "forward_timestep_embed_patch" in transformer_options["patches"]: - found_patched = False - for class_type, handler in transformer_options["patches"]["forward_timestep_embed_patch"]: - if isinstance(layer, class_type): - x = handler(layer, x, emb, context, transformer_options, output_shape, time_context, num_video_frames, image_only_indicator) - found_patched = True - break - if found_patched: - continue x = layer(x) return x @@ -894,6 +895,12 @@ class UNetModel(nn.Module): h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) h = apply_control(h, control, 'middle') + if "middle_block_after_patch" in transformer_patches: + patch = transformer_patches["middle_block_after_patch"] + for p in patch: + out = p({"h": h, "x": x, "emb": emb, "context": context, "y": y, + "timesteps": timesteps, "transformer_options": transformer_options}) + h = out["h"] for id, module in enumerate(self.output_blocks): transformer_options["block"] = ("output", id) @@ -905,8 +912,9 @@ class UNetModel(nn.Module): for p in patch: h, hsp = p(h, hsp, transformer_options) - h = th.cat([h, hsp], dim=1) - del hsp + if hsp is not None: + h = th.cat([h, hsp], dim=1) + del hsp if len(hs) > 0: output_shape = hs[-1].shape else: diff --git a/comfy/ldm/supir/__init__.py b/comfy/ldm/supir/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/comfy/ldm/supir/supir_modules.py b/comfy/ldm/supir/supir_modules.py new file mode 100644 index 000000000..7389b01d2 --- /dev/null +++ b/comfy/ldm/supir/supir_modules.py @@ -0,0 +1,226 @@ +import torch +import torch.nn as nn + +from comfy.ldm.modules.diffusionmodules.util import timestep_embedding +from comfy.ldm.modules.diffusionmodules.openaimodel import Downsample, TimestepEmbedSequential, ResBlock, SpatialTransformer +from comfy.ldm.modules.attention import optimized_attention + + +class ZeroSFT(nn.Module): + def __init__(self, label_nc, norm_nc, concat_channels=0, dtype=None, device=None, operations=None): + super().__init__() + + ks = 3 + pw = ks // 2 + + self.param_free_norm = operations.GroupNorm(32, norm_nc + concat_channels, dtype=dtype, device=device) + + nhidden = 128 + + self.mlp_shared = nn.Sequential( + operations.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw, dtype=dtype, device=device), + nn.SiLU() + ) + self.zero_mul = operations.Conv2d(nhidden, norm_nc + concat_channels, kernel_size=ks, padding=pw, dtype=dtype, device=device) + self.zero_add = operations.Conv2d(nhidden, norm_nc + concat_channels, kernel_size=ks, padding=pw, dtype=dtype, device=device) + + self.zero_conv = operations.Conv2d(label_nc, norm_nc, 1, 1, 0, dtype=dtype, device=device) + self.pre_concat = bool(concat_channels != 0) + + def forward(self, c, h, h_ori=None, control_scale=1): + if h_ori is not None and self.pre_concat: + h_raw = torch.cat([h_ori, h], dim=1) + else: + h_raw = h + + h = h + self.zero_conv(c) + if h_ori is not None and self.pre_concat: + h = torch.cat([h_ori, h], dim=1) + actv = self.mlp_shared(c) + gamma = self.zero_mul(actv) + beta = self.zero_add(actv) + h = self.param_free_norm(h) + h = torch.addcmul(h + beta, h, gamma) + if h_ori is not None and not self.pre_concat: + h = torch.cat([h_ori, h], dim=1) + return torch.lerp(h_raw, h, control_scale) + + +class _CrossAttnInner(nn.Module): + """Inner cross-attention module matching the state_dict layout of the original CrossAttention.""" + def __init__(self, query_dim, context_dim, heads, dim_head, dtype=None, device=None, operations=None): + super().__init__() + inner_dim = dim_head * heads + self.heads = heads + self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_out = nn.Sequential( + operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), + ) + + def forward(self, x, context): + q = self.to_q(x) + k = self.to_k(context) + v = self.to_v(context) + return self.to_out(optimized_attention(q, k, v, self.heads)) + + +class ZeroCrossAttn(nn.Module): + def __init__(self, context_dim, query_dim, dtype=None, device=None, operations=None): + super().__init__() + heads = query_dim // 64 + dim_head = 64 + self.attn = _CrossAttnInner(query_dim, context_dim, heads, dim_head, dtype=dtype, device=device, operations=operations) + self.norm1 = operations.GroupNorm(32, query_dim, dtype=dtype, device=device) + self.norm2 = operations.GroupNorm(32, context_dim, dtype=dtype, device=device) + + def forward(self, context, x, control_scale=1): + b, c, h, w = x.shape + x_in = x + + x = self.attn( + self.norm1(x).flatten(2).transpose(1, 2), + self.norm2(context).flatten(2).transpose(1, 2), + ).transpose(1, 2).unflatten(2, (h, w)) + + return x_in + x * control_scale + + +class GLVControl(nn.Module): + """SUPIR's Guided Latent Vector control encoder. Truncated UNet (input + middle blocks only).""" + def __init__( + self, + in_channels=4, + model_channels=320, + num_res_blocks=2, + attention_resolutions=(4, 2), + channel_mult=(1, 2, 4), + num_head_channels=64, + transformer_depth=(1, 2, 10), + context_dim=2048, + adm_in_channels=2816, + use_linear_in_transformer=True, + use_checkpoint=False, + dtype=None, + device=None, + operations=None, + **kwargs, + ): + super().__init__() + self.model_channels = model_channels + time_embed_dim = model_channels * 4 + + self.time_embed = nn.Sequential( + operations.Linear(model_channels, time_embed_dim, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device), + ) + + self.label_emb = nn.Sequential( + nn.Sequential( + operations.Linear(adm_in_channels, time_embed_dim, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=dtype, device=device), + ) + ) + + self.input_blocks = nn.ModuleList([ + TimestepEmbedSequential( + operations.Conv2d(in_channels, model_channels, 3, padding=1, dtype=dtype, device=device) + ) + ]) + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for nr in range(num_res_blocks): + layers = [ + ResBlock(ch, time_embed_dim, 0, out_channels=mult * model_channels, + dtype=dtype, device=device, operations=operations) + ] + ch = mult * model_channels + if ds in attention_resolutions: + num_heads = ch // num_head_channels + layers.append( + SpatialTransformer(ch, num_heads, num_head_channels, + depth=transformer_depth[level], context_dim=context_dim, + use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, + dtype=dtype, device=device, operations=operations) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + if level != len(channel_mult) - 1: + self.input_blocks.append( + TimestepEmbedSequential( + Downsample(ch, True, out_channels=ch, dtype=dtype, device=device, operations=operations) + ) + ) + ds *= 2 + + num_heads = ch // num_head_channels + self.middle_block = TimestepEmbedSequential( + ResBlock(ch, time_embed_dim, 0, dtype=dtype, device=device, operations=operations), + SpatialTransformer(ch, num_heads, num_head_channels, + depth=transformer_depth[-1], context_dim=context_dim, + use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, + dtype=dtype, device=device, operations=operations), + ResBlock(ch, time_embed_dim, 0, dtype=dtype, device=device, operations=operations), + ) + + self.input_hint_block = TimestepEmbedSequential( + operations.Conv2d(in_channels, model_channels, 3, padding=1, dtype=dtype, device=device) + ) + + def forward(self, x, timesteps, xt, context=None, y=None, **kwargs): + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) + emb = self.time_embed(t_emb) + self.label_emb(y) + + guided_hint = self.input_hint_block(x, emb, context) + + hs = [] + h = xt + for module in self.input_blocks: + if guided_hint is not None: + h = module(h, emb, context) + h += guided_hint + guided_hint = None + else: + h = module(h, emb, context) + hs.append(h) + h = self.middle_block(h, emb, context) + hs.append(h) + return hs + + +class SUPIR(nn.Module): + """ + SUPIR model containing GLVControl (control encoder) and project_modules (adapters). + State dict keys match the original SUPIR checkpoint layout: + control_model.* -> GLVControl + project_modules.* -> nn.ModuleList of ZeroSFT/ZeroCrossAttn + """ + def __init__(self, device=None, dtype=None, operations=None): + super().__init__() + + self.control_model = GLVControl(dtype=dtype, device=device, operations=operations) + + project_channel_scale = 2 + cond_output_channels = [320] * 4 + [640] * 3 + [1280] * 3 + project_channels = [int(c * project_channel_scale) for c in [160] * 4 + [320] * 3 + [640] * 3] + concat_channels = [320] * 2 + [640] * 3 + [1280] * 4 + [0] + cross_attn_insert_idx = [6, 3] + + self.project_modules = nn.ModuleList() + for i in range(len(cond_output_channels)): + self.project_modules.append(ZeroSFT( + project_channels[i], cond_output_channels[i], + concat_channels=concat_channels[i], + dtype=dtype, device=device, operations=operations, + )) + + for i in cross_attn_insert_idx: + self.project_modules.insert(i, ZeroCrossAttn( + cond_output_channels[i], concat_channels[i], + dtype=dtype, device=device, operations=operations, + )) diff --git a/comfy/ldm/supir/supir_patch.py b/comfy/ldm/supir/supir_patch.py new file mode 100644 index 000000000..b67ab4cd8 --- /dev/null +++ b/comfy/ldm/supir/supir_patch.py @@ -0,0 +1,103 @@ +import torch +from comfy.ldm.modules.diffusionmodules.openaimodel import Upsample + + +class SUPIRPatch: + """ + Holds GLVControl (control encoder) + project_modules (ZeroSFT/ZeroCrossAttn adapters). + Runs GLVControl lazily on first patch invocation per step, applies adapters through + middle_block_after_patch, output_block_merge_patch, and forward_timestep_embed_patch. + """ + SIGMA_MAX = 14.6146 + + def __init__(self, model_patch, project_modules, hint_latent, strength_start, strength_end): + self.model_patch = model_patch # CoreModelPatcher wrapping GLVControl + self.project_modules = project_modules # nn.ModuleList of ZeroSFT/ZeroCrossAttn + self.hint_latent = hint_latent # encoded LQ image latent + self.strength_start = strength_start + self.strength_end = strength_end + self.cached_features = None + self.adapter_idx = 0 + self.control_idx = 0 + self.current_control_idx = 0 + self.active = True + + def _ensure_features(self, kwargs): + """Run GLVControl on first call per step, cache results.""" + if self.cached_features is not None: + return + x = kwargs["x"] + b = x.shape[0] + hint = self.hint_latent.to(device=x.device, dtype=x.dtype) + if hint.shape[0] != b: + hint = hint.expand(b, -1, -1, -1) if hint.shape[0] == 1 else hint.repeat((b + hint.shape[0] - 1) // hint.shape[0], 1, 1, 1)[:b] + self.cached_features = self.model_patch.model.control_model( + hint, kwargs["timesteps"], x, + kwargs["context"], kwargs["y"] + ) + self.adapter_idx = len(self.project_modules) - 1 + self.control_idx = len(self.cached_features) - 1 + + def _get_control_scale(self, kwargs): + if self.strength_start == self.strength_end: + return self.strength_end + sigma = kwargs["transformer_options"].get("sigmas") + if sigma is None: + return self.strength_end + s = sigma[0].item() if sigma.dim() > 0 else sigma.item() + t = min(s / self.SIGMA_MAX, 1.0) + return t * (self.strength_start - self.strength_end) + self.strength_end + + def middle_after(self, kwargs): + """middle_block_after_patch: run GLVControl lazily, apply last adapter after middle block.""" + self.cached_features = None # reset from previous step + self.current_scale = self._get_control_scale(kwargs) + self.active = self.current_scale > 0 + if not self.active: + return {"h": kwargs["h"]} + self._ensure_features(kwargs) + h = kwargs["h"] + h = self.project_modules[self.adapter_idx]( + self.cached_features[self.control_idx], h, control_scale=self.current_scale + ) + self.adapter_idx -= 1 + self.control_idx -= 1 + return {"h": h} + + def output_block(self, h, hsp, transformer_options): + """output_block_patch: ZeroSFT adapter fusion replaces cat([h, hsp]). Returns (h, None) to skip cat.""" + if not self.active: + return h, hsp + self.current_control_idx = self.control_idx + h = self.project_modules[self.adapter_idx]( + self.cached_features[self.control_idx], hsp, h, control_scale=self.current_scale + ) + self.adapter_idx -= 1 + self.control_idx -= 1 + return h, None + + def pre_upsample(self, layer, x, emb, context, transformer_options, output_shape, *args, **kw): + """forward_timestep_embed_patch for Upsample: extra cross-attn adapter before upsample.""" + block_type, _ = transformer_options["block"] + if block_type == "output" and self.active and self.cached_features is not None: + x = self.project_modules[self.adapter_idx]( + self.cached_features[self.current_control_idx], x, control_scale=self.current_scale + ) + self.adapter_idx -= 1 + return layer(x, output_shape=output_shape) + + def to(self, device_or_dtype): + if isinstance(device_or_dtype, torch.device): + self.cached_features = None + if self.hint_latent is not None: + self.hint_latent = self.hint_latent.to(device_or_dtype) + return self + + def models(self): + return [self.model_patch] + + def register(self, model_patcher): + """Register all patches on a cloned model patcher.""" + model_patcher.set_model_patch(self.middle_after, "middle_block_after_patch") + model_patcher.set_model_output_block_patch(self.output_block) + model_patcher.set_model_patch((Upsample, self.pre_upsample), "forward_timestep_embed_patch") diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 6deb71e12..93d19d6fe 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -506,6 +506,10 @@ class ModelPatcher: def set_model_noise_refiner_patch(self, patch): self.set_model_patch(patch, "noise_refiner") + def set_model_middle_block_after_patch(self, patch): + self.set_model_patch(patch, "middle_block_after_patch") + + def set_model_rope_options(self, scale_x, shift_x, scale_y, shift_y, scale_t, shift_t, **kwargs): rope_options = self.model_options["transformer_options"].get("rope_options", {}) rope_options["scale_x"] = scale_x diff --git a/comfy_extras/nodes_model_patch.py b/comfy_extras/nodes_model_patch.py index 176e6bc2f..748559a6b 100644 --- a/comfy_extras/nodes_model_patch.py +++ b/comfy_extras/nodes_model_patch.py @@ -7,7 +7,10 @@ import comfy.model_management import comfy.ldm.common_dit import comfy.latent_formats import comfy.ldm.lumina.controlnet +import comfy.ldm.supir.supir_modules from comfy.ldm.wan.model_multitalk import WanMultiTalkAttentionBlock, MultiTalkAudioProjModel +from comfy_api.latest import io +from comfy.ldm.supir.supir_patch import SUPIRPatch class BlockWiseControlBlock(torch.nn.Module): @@ -266,6 +269,27 @@ class ModelPatchLoader: out_dim=sd["audio_proj.norm.weight"].shape[0], device=comfy.model_management.unet_offload_device(), operations=comfy.ops.manual_cast) + elif 'model.control_model.input_hint_block.0.weight' in sd or 'control_model.input_hint_block.0.weight' in sd: + prefix_replace = {} + if 'model.control_model.input_hint_block.0.weight' in sd: + prefix_replace["model.control_model."] = "control_model." + prefix_replace["model.diffusion_model.project_modules."] = "project_modules." + else: + prefix_replace["control_model."] = "control_model." + prefix_replace["project_modules."] = "project_modules." + + # Extract denoise_encoder weights before filter_keys discards them + de_prefix = "first_stage_model.denoise_encoder." + denoise_encoder_sd = {} + for k in list(sd.keys()): + if k.startswith(de_prefix): + denoise_encoder_sd[k[len(de_prefix):]] = sd.pop(k) + + sd = comfy.utils.state_dict_prefix_replace(sd, prefix_replace, filter_keys=True) + sd.pop("control_model.mask_LQ", None) + model = comfy.ldm.supir.supir_modules.SUPIR(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast) + if denoise_encoder_sd: + model.denoise_encoder_sd = denoise_encoder_sd model_patcher = comfy.model_patcher.CoreModelPatcher(model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device()) model.load_state_dict(sd, assign=model_patcher.is_dynamic()) @@ -565,9 +589,89 @@ class MultiTalkModelPatch(torch.nn.Module): ) +class SUPIRApply(io.ComfyNode): + @classmethod + def define_schema(cls) -> io.Schema: + return io.Schema( + node_id="SUPIRApply", + category="model_patches/supir", + is_experimental=True, + inputs=[ + io.Model.Input("model"), + io.ModelPatch.Input("model_patch"), + io.Vae.Input("vae"), + io.Image.Input("image"), + io.Float.Input("strength_start", default=1.0, min=0.0, max=10.0, step=0.01, + tooltip="Control strength at the start of sampling (high sigma)."), + io.Float.Input("strength_end", default=1.0, min=0.0, max=10.0, step=0.01, + tooltip="Control strength at the end of sampling (low sigma). Linearly interpolated from start."), + io.Float.Input("restore_cfg", default=4.0, min=0.0, max=20.0, step=0.1, advanced=True, + tooltip="Pulls denoised output toward the input latent. Higher = stronger fidelity to input. 0 to disable."), + io.Float.Input("restore_cfg_s_tmin", default=0.05, min=0.0, max=1.0, step=0.01, advanced=True, + tooltip="Sigma threshold below which restore_cfg is disabled."), + ], + outputs=[io.Model.Output()], + ) + + @classmethod + def _encode_with_denoise_encoder(cls, vae, model_patch, image): + """Encode using denoise_encoder weights from SUPIR checkpoint if available.""" + denoise_sd = getattr(model_patch.model, 'denoise_encoder_sd', None) + if not denoise_sd: + return vae.encode(image) + + # Clone VAE patcher, apply denoise_encoder weights to clone, encode + orig_patcher = vae.patcher + vae.patcher = orig_patcher.clone() + patches = {f"encoder.{k}": (v,) for k, v in denoise_sd.items()} + vae.patcher.add_patches(patches, strength_patch=1.0, strength_model=0.0) + try: + return vae.encode(image) + finally: + vae.patcher = orig_patcher + + @classmethod + def execute(cls, *, model: io.Model.Type, model_patch: io.ModelPatch.Type, vae: io.Vae.Type, image: io.Image.Type, + strength_start: float, strength_end: float, restore_cfg: float, restore_cfg_s_tmin: float) -> io.NodeOutput: + model_patched = model.clone() + hint_latent = model.get_model_object("latent_format").process_in( + cls._encode_with_denoise_encoder(vae, model_patch, image[:, :, :, :3])) + patch = SUPIRPatch(model_patch, model_patch.model.project_modules, hint_latent, strength_start, strength_end) + patch.register(model_patched) + + if restore_cfg > 0.0: + # Round-trip to match original pipeline: decode hint, re-encode with regular VAE + latent_format = model.get_model_object("latent_format") + decoded = vae.decode(latent_format.process_out(hint_latent)) + x_center = latent_format.process_in(vae.encode(decoded[:, :, :, :3])) + sigma_max = 14.6146 + + def restore_cfg_function(args): + denoised = args["denoised"] + sigma = args["sigma"] + if sigma.dim() > 0: + s = sigma[0].item() + else: + s = sigma.item() + if s > restore_cfg_s_tmin: + ref = x_center.to(device=denoised.device, dtype=denoised.dtype) + b = denoised.shape[0] + if ref.shape[0] != b: + ref = ref.expand(b, -1, -1, -1) if ref.shape[0] == 1 else ref.repeat((b + ref.shape[0] - 1) // ref.shape[0], 1, 1, 1)[:b] + sigma_val = sigma.view(-1, 1, 1, 1) if sigma.dim() > 0 else sigma + d_center = denoised - ref + denoised = denoised - d_center * ((sigma_val / sigma_max) ** restore_cfg) + return denoised + + model_patched.set_model_sampler_post_cfg_function(restore_cfg_function) + + return io.NodeOutput(model_patched) + + NODE_CLASS_MAPPINGS = { "ModelPatchLoader": ModelPatchLoader, "QwenImageDiffsynthControlnet": QwenImageDiffsynthControlnet, "ZImageFunControlnet": ZImageFunControlnet, "USOStyleReference": USOStyleReference, + "SUPIRApply": SUPIRApply, } diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py index 9037c3d20..c932b747a 100644 --- a/comfy_extras/nodes_post_processing.py +++ b/comfy_extras/nodes_post_processing.py @@ -6,6 +6,7 @@ from PIL import Image import math from enum import Enum from typing import TypedDict, Literal +import kornia import comfy.utils import comfy.model_management @@ -660,6 +661,228 @@ class BatchImagesMasksLatentsNode(io.ComfyNode): return io.NodeOutput(batched) +class ColorTransfer(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="ColorTransfer", + category="image/postprocessing", + description="Match the colors of one image to another using various algorithms.", + search_aliases=["color match", "color grading", "color correction", "match colors", "color transform", "mkl", "reinhard", "histogram"], + inputs=[ + io.Image.Input("image_target", tooltip="Image(s) to apply the color transform to."), + io.Image.Input("image_ref", optional=True, tooltip="Reference image(s) to match colors to. If not provided, processing is skipped"), + io.Combo.Input("method", options=['reinhard_lab', 'mkl_lab', 'histogram'],), + io.DynamicCombo.Input("source_stats", + tooltip="per_frame: each frame matched to image_ref individually. uniform: pool stats across all source frames as baseline, match to image_ref. target_frame: use one chosen frame as the baseline for the transform to image_ref, applied uniformly to all frames (preserves relative differences)", + options=[ + io.DynamicCombo.Option("per_frame", []), + io.DynamicCombo.Option("uniform", []), + io.DynamicCombo.Option("target_frame", [ + io.Int.Input("target_index", default=0, min=0, max=10000, + tooltip="Frame index used as the source baseline for computing the transform to image_ref"), + ]), + ]), + io.Float.Input("strength", default=1.0, min=0.0, max=10.0, step=0.01), + ], + outputs=[ + io.Image.Output(display_name="image"), + ], + ) + + @staticmethod + def _to_lab(images, i, device): + return kornia.color.rgb_to_lab( + images[i:i+1].to(device, dtype=torch.float32).permute(0, 3, 1, 2)) + + @staticmethod + def _pool_stats(images, device, is_reinhard, eps): + """Two-pass pooled mean + std/cov across all frames.""" + N, C = images.shape[0], images.shape[3] + HW = images.shape[1] * images.shape[2] + mean = torch.zeros(C, 1, device=device, dtype=torch.float32) + for i in range(N): + mean += ColorTransfer._to_lab(images, i, device).view(C, -1).mean(dim=-1, keepdim=True) + mean /= N + acc = torch.zeros(C, 1 if is_reinhard else C, device=device, dtype=torch.float32) + for i in range(N): + centered = ColorTransfer._to_lab(images, i, device).view(C, -1) - mean + if is_reinhard: + acc += (centered * centered).mean(dim=-1, keepdim=True) + else: + acc += centered @ centered.T / HW + if is_reinhard: + return mean, torch.sqrt(acc / N).clamp_min_(eps) + return mean, acc / N + + @staticmethod + def _frame_stats(lab_flat, hw, is_reinhard, eps): + """Per-frame mean + std/cov.""" + mean = lab_flat.mean(dim=-1, keepdim=True) + if is_reinhard: + return mean, lab_flat.std(dim=-1, keepdim=True, unbiased=False).clamp_min_(eps) + centered = lab_flat - mean + return mean, centered @ centered.T / hw + + @staticmethod + def _mkl_matrix(cov_s, cov_r, eps): + """Compute MKL 3x3 transform matrix from source and ref covariances.""" + eig_val_s, eig_vec_s = torch.linalg.eigh(cov_s) + sqrt_val_s = torch.sqrt(eig_val_s.clamp_min(0)).clamp_min_(eps) + + scaled_V = eig_vec_s * sqrt_val_s.unsqueeze(0) + mid = scaled_V.T @ cov_r @ scaled_V + eig_val_m, eig_vec_m = torch.linalg.eigh(mid) + sqrt_m = torch.sqrt(eig_val_m.clamp_min(0)) + + inv_sqrt_s = 1.0 / sqrt_val_s + inv_scaled_V = eig_vec_s * inv_sqrt_s.unsqueeze(0) + M_half = (eig_vec_m * sqrt_m.unsqueeze(0)) @ eig_vec_m.T + return inv_scaled_V @ M_half @ inv_scaled_V.T + + @staticmethod + def _histogram_lut(src, ref, bins=256): + """Build per-channel LUT from source and ref histograms. src/ref: (C, HW) in [0,1].""" + s_bins = (src * (bins - 1)).long().clamp(0, bins - 1) + r_bins = (ref * (bins - 1)).long().clamp(0, bins - 1) + s_hist = torch.zeros(src.shape[0], bins, device=src.device, dtype=src.dtype) + r_hist = torch.zeros(src.shape[0], bins, device=src.device, dtype=src.dtype) + ones_s = torch.ones_like(src) + ones_r = torch.ones_like(ref) + s_hist.scatter_add_(1, s_bins, ones_s) + r_hist.scatter_add_(1, r_bins, ones_r) + s_cdf = s_hist.cumsum(1) + s_cdf = s_cdf / s_cdf[:, -1:] + r_cdf = r_hist.cumsum(1) + r_cdf = r_cdf / r_cdf[:, -1:] + return torch.searchsorted(r_cdf, s_cdf).clamp_max_(bins - 1).float() / (bins - 1) + + @classmethod + def _pooled_cdf(cls, images, device, num_bins=256): + """Build pooled CDF across all frames, one frame at a time.""" + C = images.shape[3] + hist = torch.zeros(C, num_bins, device=device, dtype=torch.float32) + for i in range(images.shape[0]): + frame = images[i].to(device, dtype=torch.float32).permute(2, 0, 1).reshape(C, -1) + bins = (frame * (num_bins - 1)).long().clamp(0, num_bins - 1) + hist.scatter_add_(1, bins, torch.ones_like(frame)) + cdf = hist.cumsum(1) + return cdf / cdf[:, -1:] + + @classmethod + def _build_histogram_transform(cls, image_target, image_ref, device, stats_mode, target_index, B): + """Build per-frame or uniform LUT transform for histogram mode.""" + if stats_mode == 'per_frame': + return None # LUT computed per-frame in the apply loop + + r_cdf = cls._pooled_cdf(image_ref, device) + if stats_mode == 'target_frame': + ti = min(target_index, B - 1) + s_cdf = cls._pooled_cdf(image_target[ti:ti+1], device) + else: + s_cdf = cls._pooled_cdf(image_target, device) + return torch.searchsorted(r_cdf, s_cdf).clamp_max_(255).float() / 255.0 + + @classmethod + def _build_lab_transform(cls, image_target, image_ref, device, stats_mode, target_index, is_reinhard): + """Build transform parameters for Lab-based methods. Returns a transform function.""" + eps = 1e-6 + B, H, W, C = image_target.shape + B_ref = image_ref.shape[0] + single_ref = B_ref == 1 + HW = H * W + HW_ref = image_ref.shape[1] * image_ref.shape[2] + + # Precompute ref stats + if single_ref or stats_mode in ('uniform', 'target_frame'): + ref_mean, ref_sc = cls._pool_stats(image_ref, device, is_reinhard, eps) + + # Uniform/target_frame: precompute single affine transform + if stats_mode in ('uniform', 'target_frame'): + if stats_mode == 'target_frame': + ti = min(target_index, B - 1) + s_lab = cls._to_lab(image_target, ti, device).view(C, -1) + s_mean, s_sc = cls._frame_stats(s_lab, HW, is_reinhard, eps) + else: + s_mean, s_sc = cls._pool_stats(image_target, device, is_reinhard, eps) + + if is_reinhard: + scale = ref_sc / s_sc + offset = ref_mean - scale * s_mean + return lambda src_flat, **_: src_flat * scale + offset + T = cls._mkl_matrix(s_sc, ref_sc, eps) + offset = ref_mean - T @ s_mean + return lambda src_flat, **_: T @ src_flat + offset + + # per_frame + def per_frame_transform(src_flat, frame_idx): + s_mean, s_sc = cls._frame_stats(src_flat, HW, is_reinhard, eps) + + if single_ref: + r_mean, r_sc = ref_mean, ref_sc + else: + ri = min(frame_idx, B_ref - 1) + r_mean, r_sc = cls._frame_stats(cls._to_lab(image_ref, ri, device).view(C, -1), HW_ref, is_reinhard, eps) + + centered = src_flat - s_mean + if is_reinhard: + return centered * (r_sc / s_sc) + r_mean + T = cls._mkl_matrix(centered @ centered.T / HW, r_sc, eps) + return T @ centered + r_mean + + return per_frame_transform + + @classmethod + def execute(cls, image_target, image_ref, method, source_stats, strength=1.0) -> io.NodeOutput: + stats_mode = source_stats["source_stats"] + target_index = source_stats.get("target_index", 0) + + if strength == 0 or image_ref is None: + return io.NodeOutput(image_target) + + device = comfy.model_management.get_torch_device() + intermediate_device = comfy.model_management.intermediate_device() + intermediate_dtype = comfy.model_management.intermediate_dtype() + + B, H, W, C = image_target.shape + B_ref = image_ref.shape[0] + pbar = comfy.utils.ProgressBar(B) + out = torch.empty(B, H, W, C, device=intermediate_device, dtype=intermediate_dtype) + + if method == 'histogram': + uniform_lut = cls._build_histogram_transform( + image_target, image_ref, device, stats_mode, target_index, B) + + for i in range(B): + src = image_target[i].to(device, dtype=torch.float32).permute(2, 0, 1) + src_flat = src.reshape(C, -1) + if uniform_lut is not None: + lut = uniform_lut + else: + ri = min(i, B_ref - 1) + ref = image_ref[ri].to(device, dtype=torch.float32).permute(2, 0, 1).reshape(C, -1) + lut = cls._histogram_lut(src_flat, ref) + bin_idx = (src_flat * 255).long().clamp(0, 255) + matched = lut.gather(1, bin_idx).view(C, H, W) + result = matched if strength == 1.0 else torch.lerp(src, matched, strength) + out[i] = result.permute(1, 2, 0).clamp_(0, 1).to(device=intermediate_device, dtype=intermediate_dtype) + pbar.update(1) + else: + transform = cls._build_lab_transform(image_target, image_ref, device, stats_mode, target_index, is_reinhard=method == "reinhard_lab") + + for i in range(B): + src_frame = cls._to_lab(image_target, i, device) + corrected = transform(src_frame.view(C, -1), frame_idx=i) + if strength == 1.0: + result = kornia.color.lab_to_rgb(corrected.view(1, C, H, W)) + else: + result = kornia.color.lab_to_rgb(torch.lerp(src_frame, corrected.view(1, C, H, W), strength)) + out[i] = result.squeeze(0).permute(1, 2, 0).clamp_(0, 1).to(device=intermediate_device, dtype=intermediate_dtype) + pbar.update(1) + + return io.NodeOutput(out) + + class PostProcessingExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[io.ComfyNode]]: @@ -673,6 +896,7 @@ class PostProcessingExtension(ComfyExtension): BatchImagesNode, BatchMasksNode, BatchLatentsNode, + ColorTransfer, # BatchImagesMasksLatentsNode, ] From 3d816db07f9721525c8326bc8d525cd81f00a7fa Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 18 Apr 2026 20:02:29 -0700 Subject: [PATCH 056/140] Some optimizations to make Ernie inference a bit faster. (#13472) --- comfy/ldm/ernie/model.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/comfy/ldm/ernie/model.py b/comfy/ldm/ernie/model.py index f7cdb51e6..eba661aec 100644 --- a/comfy/ldm/ernie/model.py +++ b/comfy/ldm/ernie/model.py @@ -118,8 +118,6 @@ class ErnieImageAttention(nn.Module): query = apply_rotary_emb(query, image_rotary_emb) key = apply_rotary_emb(key, image_rotary_emb) - query, key = query.to(x.dtype), key.to(x.dtype) - q_flat = query.reshape(B, S, -1) k_flat = key.reshape(B, S, -1) @@ -161,16 +159,16 @@ class ErnieImageSharedAdaLNBlock(nn.Module): residual = x x_norm = self.adaLN_sa_ln(x) - x_norm = (x_norm.float() * (1 + scale_msa.float()) + shift_msa.float()).to(x.dtype) + x_norm = x_norm * (1 + scale_msa) + shift_msa attn_out = self.self_attention(x_norm, attention_mask=attention_mask, image_rotary_emb=rotary_pos_emb) - x = residual + (gate_msa.float() * attn_out.float()).to(x.dtype) + x = residual + gate_msa * attn_out residual = x x_norm = self.adaLN_mlp_ln(x) - x_norm = (x_norm.float() * (1 + scale_mlp.float()) + shift_mlp.float()).to(x.dtype) + x_norm = x_norm * (1 + scale_mlp) + shift_mlp - return residual + (gate_mlp.float() * self.mlp(x_norm).float()).to(x.dtype) + return residual + gate_mlp * self.mlp(x_norm) class ErnieImageAdaLNContinuous(nn.Module): def __init__(self, hidden_size: int, eps: float = 1e-6, operations=None, device=None, dtype=None): @@ -183,7 +181,7 @@ class ErnieImageAdaLNContinuous(nn.Module): def forward(self, x: torch.Tensor, conditioning: torch.Tensor) -> torch.Tensor: scale, shift = self.linear(conditioning).chunk(2, dim=-1) x = self.norm(x) - x = x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + x = torch.addcmul(shift.unsqueeze(1), x, 1 + scale.unsqueeze(1)) return x class ErnieImageModel(nn.Module): From 138571da955d85935baa09371ac2b67ea8b7a8ca Mon Sep 17 00:00:00 2001 From: Abdul Rehman <76230556+Abdulrehman-PIAIC80387@users.noreply.github.com> Date: Sun, 19 Apr 2026 08:21:22 +0500 Subject: [PATCH 057/140] fix: append directory type annotation to internal files endpoint response (#13078) (#13305) --- api_server/routes/internal/internal_routes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api_server/routes/internal/internal_routes.py b/api_server/routes/internal/internal_routes.py index b224306da..1477afa01 100644 --- a/api_server/routes/internal/internal_routes.py +++ b/api_server/routes/internal/internal_routes.py @@ -67,7 +67,7 @@ class InternalRoutes: (entry for entry in os.scandir(directory) if is_visible_file(entry)), key=lambda entry: -entry.stat().st_mtime ) - return web.json_response([entry.name for entry in sorted_files], status=200) + return web.json_response([f"{entry.name} [{directory_type}]" for entry in sorted_files], status=200) def get_app(self): From fc5f4a996b9c752a716badbbc11bacc396281466 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 19 Apr 2026 17:26:12 -0700 Subject: [PATCH 058/140] Add link to Intel portable to Readme. (#13477) --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 1eeb810de..f05311421 100644 --- a/README.md +++ b/README.md @@ -195,7 +195,9 @@ The portable above currently comes with python 3.13 and pytorch cuda 13.0. Updat #### Alternative Downloads: -[Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z) +[Portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z) + +[Experimental portable for Intel GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_intel.7z) [Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs). From 543e9fba64e968426aa562baca1c0f4c5054b61c Mon Sep 17 00:00:00 2001 From: Octopus Date: Tue, 21 Apr 2026 06:30:23 +0800 Subject: [PATCH 059/140] fix: pin SQLAlchemy>=2.0 in requirements.txt (fixes #13036) (#13316) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 3de845f48..63d7c41cf 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,7 +19,7 @@ scipy tqdm psutil alembic -SQLAlchemy +SQLAlchemy>=2.0 filelock av>=14.2.0 comfy-kitchen>=0.2.8 From c514890325b150c0a2a22732e3ae571afea189cb Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 20 Apr 2026 18:59:26 -0700 Subject: [PATCH 060/140] Refactor io to IO in nodes_ace.py (#13485) --- comfy_extras/nodes_ace.py | 104 +++++++++++++++++++------------------- 1 file changed, 52 insertions(+), 52 deletions(-) diff --git a/comfy_extras/nodes_ace.py b/comfy_extras/nodes_ace.py index cbfaf913d..1602add84 100644 --- a/comfy_extras/nodes_ace.py +++ b/comfy_extras/nodes_ace.py @@ -3,136 +3,136 @@ from typing_extensions import override import comfy.model_management import node_helpers -from comfy_api.latest import ComfyExtension, io +from comfy_api.latest import ComfyExtension, IO -class TextEncodeAceStepAudio(io.ComfyNode): +class TextEncodeAceStepAudio(IO.ComfyNode): @classmethod def define_schema(cls): - return io.Schema( + return IO.Schema( node_id="TextEncodeAceStepAudio", category="conditioning", inputs=[ - io.Clip.Input("clip"), - io.String.Input("tags", multiline=True, dynamic_prompts=True), - io.String.Input("lyrics", multiline=True, dynamic_prompts=True), - io.Float.Input("lyrics_strength", default=1.0, min=0.0, max=10.0, step=0.01), + IO.Clip.Input("clip"), + IO.String.Input("tags", multiline=True, dynamic_prompts=True), + IO.String.Input("lyrics", multiline=True, dynamic_prompts=True), + IO.Float.Input("lyrics_strength", default=1.0, min=0.0, max=10.0, step=0.01), ], - outputs=[io.Conditioning.Output()], + outputs=[IO.Conditioning.Output()], ) @classmethod - def execute(cls, clip, tags, lyrics, lyrics_strength) -> io.NodeOutput: + def execute(cls, clip, tags, lyrics, lyrics_strength) -> IO.NodeOutput: tokens = clip.tokenize(tags, lyrics=lyrics) conditioning = clip.encode_from_tokens_scheduled(tokens) conditioning = node_helpers.conditioning_set_values(conditioning, {"lyrics_strength": lyrics_strength}) - return io.NodeOutput(conditioning) + return IO.NodeOutput(conditioning) -class TextEncodeAceStepAudio15(io.ComfyNode): +class TextEncodeAceStepAudio15(IO.ComfyNode): @classmethod def define_schema(cls): - return io.Schema( + return IO.Schema( node_id="TextEncodeAceStepAudio1.5", category="conditioning", inputs=[ - io.Clip.Input("clip"), - io.String.Input("tags", multiline=True, dynamic_prompts=True), - io.String.Input("lyrics", multiline=True, dynamic_prompts=True), - io.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True), - io.Int.Input("bpm", default=120, min=10, max=300), - io.Float.Input("duration", default=120.0, min=0.0, max=2000.0, step=0.1), - io.Combo.Input("timesignature", options=['2', '3', '4', '6']), - io.Combo.Input("language", options=["en", "ja", "zh", "es", "de", "fr", "pt", "ru", "it", "nl", "pl", "tr", "vi", "cs", "fa", "id", "ko", "uk", "hu", "ar", "sv", "ro", "el"]), - io.Combo.Input("keyscale", options=[f"{root} {quality}" for quality in ["major", "minor"] for root in ["C", "C#", "Db", "D", "D#", "Eb", "E", "F", "F#", "Gb", "G", "G#", "Ab", "A", "A#", "Bb", "B"]]), - io.Boolean.Input("generate_audio_codes", default=True, tooltip="Enable the LLM that generates audio codes. This can be slow but will increase the quality of the generated audio. Turn this off if you are giving the model an audio reference.", advanced=True), - io.Float.Input("cfg_scale", default=2.0, min=0.0, max=100.0, step=0.1, advanced=True), - io.Float.Input("temperature", default=0.85, min=0.0, max=2.0, step=0.01, advanced=True), - io.Float.Input("top_p", default=0.9, min=0.0, max=2000.0, step=0.01, advanced=True), - io.Int.Input("top_k", default=0, min=0, max=100, advanced=True), - io.Float.Input("min_p", default=0.000, min=0.0, max=1.0, step=0.001, advanced=True), + IO.Clip.Input("clip"), + IO.String.Input("tags", multiline=True, dynamic_prompts=True), + IO.String.Input("lyrics", multiline=True, dynamic_prompts=True), + IO.Int.Input("seed", default=0, min=0, max=0xffffffffffffffff, control_after_generate=True), + IO.Int.Input("bpm", default=120, min=10, max=300), + IO.Float.Input("duration", default=120.0, min=0.0, max=2000.0, step=0.1), + IO.Combo.Input("timesignature", options=['2', '3', '4', '6']), + IO.Combo.Input("language", options=["en", "ja", "zh", "es", "de", "fr", "pt", "ru", "it", "nl", "pl", "tr", "vi", "cs", "fa", "id", "ko", "uk", "hu", "ar", "sv", "ro", "el"]), + IO.Combo.Input("keyscale", options=[f"{root} {quality}" for quality in ["major", "minor"] for root in ["C", "C#", "Db", "D", "D#", "Eb", "E", "F", "F#", "Gb", "G", "G#", "Ab", "A", "A#", "Bb", "B"]]), + IO.Boolean.Input("generate_audio_codes", default=True, tooltip="Enable the LLM that generates audio codes. This can be slow but will increase the quality of the generated audio. Turn this off if you are giving the model an audio reference.", advanced=True), + IO.Float.Input("cfg_scale", default=2.0, min=0.0, max=100.0, step=0.1, advanced=True), + IO.Float.Input("temperature", default=0.85, min=0.0, max=2.0, step=0.01, advanced=True), + IO.Float.Input("top_p", default=0.9, min=0.0, max=2000.0, step=0.01, advanced=True), + IO.Int.Input("top_k", default=0, min=0, max=100, advanced=True), + IO.Float.Input("min_p", default=0.000, min=0.0, max=1.0, step=0.001, advanced=True), ], - outputs=[io.Conditioning.Output()], + outputs=[IO.Conditioning.Output()], ) @classmethod - def execute(cls, clip, tags, lyrics, seed, bpm, duration, timesignature, language, keyscale, generate_audio_codes, cfg_scale, temperature, top_p, top_k, min_p) -> io.NodeOutput: + def execute(cls, clip, tags, lyrics, seed, bpm, duration, timesignature, language, keyscale, generate_audio_codes, cfg_scale, temperature, top_p, top_k, min_p) -> IO.NodeOutput: tokens = clip.tokenize(tags, lyrics=lyrics, bpm=bpm, duration=duration, timesignature=int(timesignature), language=language, keyscale=keyscale, seed=seed, generate_audio_codes=generate_audio_codes, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, min_p=min_p) conditioning = clip.encode_from_tokens_scheduled(tokens) - return io.NodeOutput(conditioning) + return IO.NodeOutput(conditioning) -class EmptyAceStepLatentAudio(io.ComfyNode): +class EmptyAceStepLatentAudio(IO.ComfyNode): @classmethod def define_schema(cls): - return io.Schema( + return IO.Schema( node_id="EmptyAceStepLatentAudio", display_name="Empty Ace Step 1.0 Latent Audio", category="latent/audio", inputs=[ - io.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1), - io.Int.Input( + IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.1), + IO.Int.Input( "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch." ), ], - outputs=[io.Latent.Output()], + outputs=[IO.Latent.Output()], ) @classmethod - def execute(cls, seconds, batch_size) -> io.NodeOutput: + def execute(cls, seconds, batch_size) -> IO.NodeOutput: length = int(seconds * 44100 / 512 / 8) latent = torch.zeros([batch_size, 8, 16, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) - return io.NodeOutput({"samples": latent, "type": "audio"}) + return IO.NodeOutput({"samples": latent, "type": "audio"}) -class EmptyAceStep15LatentAudio(io.ComfyNode): +class EmptyAceStep15LatentAudio(IO.ComfyNode): @classmethod def define_schema(cls): - return io.Schema( + return IO.Schema( node_id="EmptyAceStep1.5LatentAudio", display_name="Empty Ace Step 1.5 Latent Audio", category="latent/audio", inputs=[ - io.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.01), - io.Int.Input( + IO.Float.Input("seconds", default=120.0, min=1.0, max=1000.0, step=0.01), + IO.Int.Input( "batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch." ), ], - outputs=[io.Latent.Output()], + outputs=[IO.Latent.Output()], ) @classmethod - def execute(cls, seconds, batch_size) -> io.NodeOutput: + def execute(cls, seconds, batch_size) -> IO.NodeOutput: length = round((seconds * 48000 / 1920)) latent = torch.zeros([batch_size, 64, length], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) - return io.NodeOutput({"samples": latent, "type": "audio"}) + return IO.NodeOutput({"samples": latent, "type": "audio"}) -class ReferenceAudio(io.ComfyNode): +class ReferenceAudio(IO.ComfyNode): @classmethod def define_schema(cls): - return io.Schema( + return IO.Schema( node_id="ReferenceTimbreAudio", display_name="Reference Audio", category="advanced/conditioning/audio", is_experimental=True, description="This node sets the reference audio for ace step 1.5", inputs=[ - io.Conditioning.Input("conditioning"), - io.Latent.Input("latent", optional=True), + IO.Conditioning.Input("conditioning"), + IO.Latent.Input("latent", optional=True), ], outputs=[ - io.Conditioning.Output(), + IO.Conditioning.Output(), ] ) @classmethod - def execute(cls, conditioning, latent=None) -> io.NodeOutput: + def execute(cls, conditioning, latent=None) -> IO.NodeOutput: if latent is not None: conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_audio_timbre_latents": [latent["samples"]]}, append=True) - return io.NodeOutput(conditioning) + return IO.NodeOutput(conditioning) class AceExtension(ComfyExtension): @override - async def get_node_list(self) -> list[type[io.ComfyNode]]: + async def get_node_list(self) -> list[type[IO.ComfyNode]]: return [ TextEncodeAceStepAudio, EmptyAceStepLatentAudio, From e75f775ae8e9b1a1fd2b78806c86338fd830bcd7 Mon Sep 17 00:00:00 2001 From: Comfy Org PR Bot Date: Tue, 21 Apr 2026 16:43:11 +0900 Subject: [PATCH 061/140] Bump comfyui-frontend-package to 1.42.12 (#13489) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 63d7c41cf..671bd5693 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.42.11 +comfyui-frontend-package==1.42.12 comfyui-workflow-templates==0.9.57 comfyui-embedded-docs==0.4.3 torch From ad94d472216ba52ab2660536af44faa92cf4b5d0 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 21 Apr 2026 08:02:42 -0700 Subject: [PATCH 062/140] Make the ltx audio vae more native. (#13486) --- comfy/ldm/lightricks/vae/audio_vae.py | 55 +++------------------------ comfy/sd.py | 18 +++++++++ comfy_extras/nodes_audio.py | 2 +- comfy_extras/nodes_lt_audio.py | 36 ++++++++---------- 4 files changed, 41 insertions(+), 70 deletions(-) diff --git a/comfy/ldm/lightricks/vae/audio_vae.py b/comfy/ldm/lightricks/vae/audio_vae.py index fa0a00748..dd5320c8f 100644 --- a/comfy/ldm/lightricks/vae/audio_vae.py +++ b/comfy/ldm/lightricks/vae/audio_vae.py @@ -4,9 +4,6 @@ import math import torch import torchaudio -import comfy.model_management -import comfy.model_patcher -import comfy.utils as utils from comfy.ldm.mmaudio.vae.distributions import DiagonalGaussianDistribution from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier from comfy.ldm.lightricks.vae.causal_audio_autoencoder import ( @@ -43,30 +40,6 @@ class AudioVAEComponentConfig: return cls(autoencoder=audio_config, vocoder=vocoder_config) - -class ModelDeviceManager: - """Manages device placement and GPU residency for the composed model.""" - - def __init__(self, module: torch.nn.Module): - load_device = comfy.model_management.get_torch_device() - offload_device = comfy.model_management.vae_offload_device() - self.patcher = comfy.model_patcher.ModelPatcher(module, load_device, offload_device) - - def ensure_model_loaded(self) -> None: - comfy.model_management.free_memory( - self.patcher.model_size(), - self.patcher.load_device, - ) - comfy.model_management.load_model_gpu(self.patcher) - - def move_to_load_device(self, tensor: torch.Tensor) -> torch.Tensor: - return tensor.to(self.patcher.load_device) - - @property - def load_device(self): - return self.patcher.load_device - - class AudioLatentNormalizer: """Applies per-channel statistics in patch space and restores original layout.""" @@ -132,23 +105,17 @@ class AudioPreprocessor: class AudioVAE(torch.nn.Module): """High-level Audio VAE wrapper exposing encode and decode entry points.""" - def __init__(self, state_dict: dict, metadata: dict): + def __init__(self, metadata: dict): super().__init__() component_config = AudioVAEComponentConfig.from_metadata(metadata) - vae_sd = utils.state_dict_prefix_replace(state_dict, {"audio_vae.": ""}, filter_keys=True) - vocoder_sd = utils.state_dict_prefix_replace(state_dict, {"vocoder.": ""}, filter_keys=True) - self.autoencoder = CausalAudioAutoencoder(config=component_config.autoencoder) if "bwe" in component_config.vocoder: self.vocoder = VocoderWithBWE(config=component_config.vocoder) else: self.vocoder = Vocoder(config=component_config.vocoder) - self.autoencoder.load_state_dict(vae_sd, strict=False) - self.vocoder.load_state_dict(vocoder_sd, strict=False) - autoencoder_config = self.autoencoder.get_config() self.normalizer = AudioLatentNormalizer( AudioPatchifier( @@ -168,18 +135,12 @@ class AudioVAE(torch.nn.Module): n_fft=autoencoder_config["n_fft"], ) - self.device_manager = ModelDeviceManager(self) - - def encode(self, audio: dict) -> torch.Tensor: + def encode(self, audio, sample_rate=44100) -> torch.Tensor: """Encode a waveform dictionary into normalized latent tensors.""" - waveform = audio["waveform"] - waveform_sample_rate = audio["sample_rate"] + waveform = audio + waveform_sample_rate = sample_rate input_device = waveform.device - # Ensure that Audio VAE is loaded on the correct device. - self.device_manager.ensure_model_loaded() - - waveform = self.device_manager.move_to_load_device(waveform) expected_channels = self.autoencoder.encoder.in_channels if waveform.shape[1] != expected_channels: if waveform.shape[1] == 1: @@ -190,7 +151,7 @@ class AudioVAE(torch.nn.Module): ) mel_spec = self.preprocessor.waveform_to_mel( - waveform, waveform_sample_rate, device=self.device_manager.load_device + waveform, waveform_sample_rate, device=waveform.device ) latents = self.autoencoder.encode(mel_spec) @@ -204,17 +165,13 @@ class AudioVAE(torch.nn.Module): """Decode normalized latent tensors into an audio waveform.""" original_shape = latents.shape - # Ensure that Audio VAE is loaded on the correct device. - self.device_manager.ensure_model_loaded() - - latents = self.device_manager.move_to_load_device(latents) latents = self.normalizer.denormalize(latents) target_shape = self.target_shape_from_latents(original_shape) mel_spec = self.autoencoder.decode(latents, target_shape=target_shape) waveform = self.run_vocoder(mel_spec) - return self.device_manager.move_to_load_device(waveform) + return waveform def target_shape_from_latents(self, latents_shape): batch, _, time, _ = latents_shape diff --git a/comfy/sd.py b/comfy/sd.py index e573804a5..a4d3ee269 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -12,6 +12,7 @@ from .ldm.cascade.stage_c_coder import StageC_coder from .ldm.audio.autoencoder import AudioOobleckVAE import comfy.ldm.genmo.vae.model import comfy.ldm.lightricks.vae.causal_video_autoencoder +import comfy.ldm.lightricks.vae.audio_vae import comfy.ldm.cosmos.vae import comfy.ldm.wan.vae import comfy.ldm.wan.vae2_2 @@ -805,6 +806,23 @@ class VAE: self.downscale_index_formula = (4, 8, 8) self.memory_used_encode = lambda shape, dtype: (700 * (max(1, (shape[-3] ** 0.66 * 0.11)) * shape[-2] * shape[-1]) * model_management.dtype_size(dtype)) self.memory_used_decode = lambda shape, dtype: (50 * (max(1, (shape[-3] ** 0.65 * 0.26)) * shape[-2] * shape[-1] * 32 * 32) * model_management.dtype_size(dtype)) + elif "vocoder.resblocks.0.convs1.0.weight" in sd or "vocoder.vocoder.resblocks.0.convs1.0.weight" in sd: # LTX Audio + self.first_stage_model = comfy.ldm.lightricks.vae.audio_vae.AudioVAE(metadata=metadata) + self.memory_used_encode = lambda shape, dtype: (shape[2] * 330) * model_management.dtype_size(dtype) + self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 87000) * model_management.dtype_size(dtype) + self.latent_channels = self.first_stage_model.latent_channels + self.audio_sample_rate_output = self.first_stage_model.output_sample_rate + self.autoencoder = self.first_stage_model.autoencoder # TODO: remove hack for ltxv custom nodes + self.output_channels = 2 + self.pad_channel_value = "replicate" + self.upscale_ratio = 4096 + self.downscale_ratio = 4096 + self.latent_dim = 2 + self.process_output = lambda audio: audio + self.process_input = lambda audio: audio + self.working_dtypes = [torch.float32] + self.disable_offload = True + self.extra_1d_channel = 16 else: logging.warning("WARNING: No VAE weights detected, VAE not initalized.") self.first_stage_model = None diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py index a395392d8..5f514716f 100644 --- a/comfy_extras/nodes_audio.py +++ b/comfy_extras/nodes_audio.py @@ -104,7 +104,7 @@ def vae_decode_audio(vae, samples, tile=None, overlap=None): std = torch.std(audio, dim=[1, 2], keepdim=True) * 5.0 std[std < 1.0] = 1.0 audio /= std - vae_sample_rate = getattr(vae, "audio_sample_rate", 44100) + vae_sample_rate = getattr(vae, "audio_sample_rate_output", getattr(vae, "audio_sample_rate", 44100)) return {"waveform": audio, "sample_rate": vae_sample_rate if "sample_rate" not in samples else samples["sample_rate"]} diff --git a/comfy_extras/nodes_lt_audio.py b/comfy_extras/nodes_lt_audio.py index 3e4222264..3ec635c75 100644 --- a/comfy_extras/nodes_lt_audio.py +++ b/comfy_extras/nodes_lt_audio.py @@ -3,9 +3,8 @@ import comfy.utils import comfy.model_management import torch -from comfy.ldm.lightricks.vae.audio_vae import AudioVAE from comfy_api.latest import ComfyExtension, io - +from comfy_extras.nodes_audio import VAEEncodeAudio class LTXVAudioVAELoader(io.ComfyNode): @classmethod @@ -28,10 +27,14 @@ class LTXVAudioVAELoader(io.ComfyNode): def execute(cls, ckpt_name: str) -> io.NodeOutput: ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name) sd, metadata = comfy.utils.load_torch_file(ckpt_path, return_metadata=True) - return io.NodeOutput(AudioVAE(sd, metadata)) + sd = comfy.utils.state_dict_prefix_replace(sd, {"audio_vae.": "autoencoder.", "vocoder.": "vocoder."}, filter_keys=True) + vae = comfy.sd.VAE(sd=sd, metadata=metadata) + vae.throw_exception_if_invalid() + + return io.NodeOutput(vae) -class LTXVAudioVAEEncode(io.ComfyNode): +class LTXVAudioVAEEncode(VAEEncodeAudio): @classmethod def define_schema(cls) -> io.Schema: return io.Schema( @@ -50,15 +53,8 @@ class LTXVAudioVAEEncode(io.ComfyNode): ) @classmethod - def execute(cls, audio, audio_vae: AudioVAE) -> io.NodeOutput: - audio_latents = audio_vae.encode(audio) - return io.NodeOutput( - { - "samples": audio_latents, - "sample_rate": int(audio_vae.sample_rate), - "type": "audio", - } - ) + def execute(cls, audio, audio_vae) -> io.NodeOutput: + return super().execute(audio_vae, audio) class LTXVAudioVAEDecode(io.ComfyNode): @@ -80,12 +76,12 @@ class LTXVAudioVAEDecode(io.ComfyNode): ) @classmethod - def execute(cls, samples, audio_vae: AudioVAE) -> io.NodeOutput: + def execute(cls, samples, audio_vae) -> io.NodeOutput: audio_latent = samples["samples"] if audio_latent.is_nested: audio_latent = audio_latent.unbind()[-1] - audio = audio_vae.decode(audio_latent).to(audio_latent.device) - output_audio_sample_rate = audio_vae.output_sample_rate + audio = audio_vae.decode(audio_latent).movedim(-1, 1).to(audio_latent.device) + output_audio_sample_rate = audio_vae.first_stage_model.output_sample_rate return io.NodeOutput( { "waveform": audio, @@ -143,17 +139,17 @@ class LTXVEmptyLatentAudio(io.ComfyNode): frames_number: int, frame_rate: int, batch_size: int, - audio_vae: AudioVAE, + audio_vae, ) -> io.NodeOutput: """Generate empty audio latents matching the reference pipeline structure.""" assert audio_vae is not None, "Audio VAE model is required" z_channels = audio_vae.latent_channels - audio_freq = audio_vae.latent_frequency_bins - sampling_rate = int(audio_vae.sample_rate) + audio_freq = audio_vae.first_stage_model.latent_frequency_bins + sampling_rate = int(audio_vae.first_stage_model.sample_rate) - num_audio_latents = audio_vae.num_of_latents_from_frames(frames_number, frame_rate) + num_audio_latents = audio_vae.first_stage_model.num_of_latents_from_frames(frames_number, frame_rate) audio_latents = torch.zeros( (batch_size, z_channels, num_audio_latents, audio_freq), From b38dd0ff23037cd4f03f12274a5c7e04d224febd Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Tue, 21 Apr 2026 20:45:10 +0300 Subject: [PATCH 063/140] feat(api-nodes): add automatic downscaling of videos for ByteDance 2 nodes (#13465) --- comfy_api_nodes/apis/bytedance.py | 13 +++- comfy_api_nodes/nodes_bytedance.py | 33 +++++++-- comfy_api_nodes/util/__init__.py | 2 + comfy_api_nodes/util/conversions.py | 104 +++++++++++++++++++++++++--- 4 files changed, 134 insertions(+), 18 deletions(-) diff --git a/comfy_api_nodes/apis/bytedance.py b/comfy_api_nodes/apis/bytedance.py index 3755323ac..dc3bc3213 100644 --- a/comfy_api_nodes/apis/bytedance.py +++ b/comfy_api_nodes/apis/bytedance.py @@ -158,10 +158,17 @@ RECOMMENDED_PRESETS_SEEDREAM_4 = [ ("Custom", None, None), ] -# Seedance 2.0 reference video pixel count limits per model. +# Seedance 2.0 reference video pixel count limits per model and output resolution. SEEDANCE2_REF_VIDEO_PIXEL_LIMITS = { - "dreamina-seedance-2-0-260128": {"min": 409_600, "max": 927_408}, - "dreamina-seedance-2-0-fast-260128": {"min": 409_600, "max": 927_408}, + "dreamina-seedance-2-0-260128": { + "480p": {"min": 409_600, "max": 927_408}, + "720p": {"min": 409_600, "max": 927_408}, + "1080p": {"min": 409_600, "max": 2_073_600}, + }, + "dreamina-seedance-2-0-fast-260128": { + "480p": {"min": 409_600, "max": 927_408}, + "720p": {"min": 409_600, "max": 927_408}, + }, } # The time in this dictionary are given for 10 seconds duration. diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py index 429c32444..bc564782d 100644 --- a/comfy_api_nodes/nodes_bytedance.py +++ b/comfy_api_nodes/nodes_bytedance.py @@ -35,6 +35,7 @@ from comfy_api_nodes.util import ( get_number_of_images, image_tensor_pair_to_batch, poll_op, + resize_video_to_pixel_budget, sync_op, upload_audio_to_comfyapi, upload_image_to_comfyapi, @@ -69,9 +70,12 @@ DEPRECATED_MODELS = {"seedance-1-0-lite-t2v-250428", "seedance-1-0-lite-i2v-2504 logger = logging.getLogger(__name__) -def _validate_ref_video_pixels(video: Input.Video, model_id: str, index: int) -> None: - """Validate reference video pixel count against Seedance 2.0 model limits.""" - limits = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id) +def _validate_ref_video_pixels(video: Input.Video, model_id: str, resolution: str, index: int) -> None: + """Validate reference video pixel count against Seedance 2.0 model limits for the selected resolution.""" + model_limits = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id) + if not model_limits: + return + limits = model_limits.get(resolution) if not limits: return try: @@ -1373,6 +1377,14 @@ def _seedance2_reference_inputs(resolutions: list[str]): min=0, ), ), + IO.Boolean.Input( + "auto_downscale", + default=False, + advanced=True, + optional=True, + tooltip="Automatically downscale reference videos that exceed the model's pixel budget " + "for the selected resolution. Aspect ratio is preserved; videos already within limits are untouched.", + ), ] @@ -1480,10 +1492,23 @@ class ByteDance2ReferenceNode(IO.ComfyNode): model_id = SEEDANCE_MODELS[model["model"]] has_video_input = len(reference_videos) > 0 + + if model.get("auto_downscale") and reference_videos: + max_px = ( + SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}) + .get(model["resolution"], {}) + .get("max") + ) + if max_px: + for key in reference_videos: + reference_videos[key] = resize_video_to_pixel_budget( + reference_videos[key], max_px + ) + total_video_duration = 0.0 for i, key in enumerate(reference_videos, 1): video = reference_videos[key] - _validate_ref_video_pixels(video, model_id, i) + _validate_ref_video_pixels(video, model_id, model["resolution"], i) try: dur = video.get_duration() if dur < 1.8: diff --git a/comfy_api_nodes/util/__init__.py b/comfy_api_nodes/util/__init__.py index 0cb9a47c7..f3584aba9 100644 --- a/comfy_api_nodes/util/__init__.py +++ b/comfy_api_nodes/util/__init__.py @@ -19,6 +19,7 @@ from .conversions import ( image_tensor_pair_to_batch, pil_to_bytesio, resize_mask_to_image, + resize_video_to_pixel_budget, tensor_to_base64_string, tensor_to_bytesio, tensor_to_pil, @@ -90,6 +91,7 @@ __all__ = [ "image_tensor_pair_to_batch", "pil_to_bytesio", "resize_mask_to_image", + "resize_video_to_pixel_budget", "tensor_to_base64_string", "tensor_to_bytesio", "tensor_to_pil", diff --git a/comfy_api_nodes/util/conversions.py b/comfy_api_nodes/util/conversions.py index 82b6d22a5..be5d5719b 100644 --- a/comfy_api_nodes/util/conversions.py +++ b/comfy_api_nodes/util/conversions.py @@ -129,22 +129,38 @@ def pil_to_bytesio(img: Image.Image, mime_type: str = "image/png") -> BytesIO: return img_byte_arr +def _compute_downscale_dims(src_w: int, src_h: int, total_pixels: int) -> tuple[int, int] | None: + """Return downscaled (w, h) with even dims fitting ``total_pixels``, or None if already fits. + + Source aspect ratio is preserved; output may drift by a fraction of a percent because both dimensions + are rounded down to even values (many codecs require divisible-by-2). + """ + pixels = src_w * src_h + if pixels <= total_pixels: + return None + scale = math.sqrt(total_pixels / pixels) + new_w = max(2, int(src_w * scale)) + new_h = max(2, int(src_h * scale)) + new_w -= new_w % 2 + new_h -= new_h % 2 + return new_w, new_h + + def downscale_image_tensor(image: torch.Tensor, total_pixels: int = 1536 * 1024) -> torch.Tensor: - """Downscale input image tensor to roughly the specified total pixels.""" + """Downscale input image tensor to roughly the specified total pixels. + + Output dimensions are rounded down to even values so that the result is guaranteed to fit within ``total_pixels`` + and is compatible with codecs that require even dimensions (e.g. yuv420p). + """ samples = image.movedim(-1, 1) - total = int(total_pixels) - scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) - if scale_by >= 1: + dims = _compute_downscale_dims(samples.shape[3], samples.shape[2], int(total_pixels)) + if dims is None: return image - width = round(samples.shape[3] * scale_by) - height = round(samples.shape[2] * scale_by) - - s = common_upscale(samples, width, height, "lanczos", "disabled") - s = s.movedim(1, -1) - return s + new_w, new_h = dims + return common_upscale(samples, new_w, new_h, "lanczos", "disabled").movedim(1, -1) -def downscale_image_tensor_by_max_side(image: torch.Tensor, *, max_side: int) -> torch.Tensor: +def downscale_image_tensor_by_max_side(image: torch.Tensor, *, max_side: int) -> torch.Tensor: """Downscale input image tensor so the largest dimension is at most max_side pixels.""" samples = image.movedim(-1, 1) height, width = samples.shape[2], samples.shape[3] @@ -399,6 +415,72 @@ def trim_video(video: Input.Video, duration_sec: float) -> Input.Video: raise RuntimeError(f"Failed to trim video: {str(e)}") from e +def resize_video_to_pixel_budget(video: Input.Video, total_pixels: int) -> Input.Video: + """Downscale a video to fit within ``total_pixels`` (w * h), preserving aspect ratio. + + Returns the original video object untouched when it already fits. Preserves frame rate, duration, and audio. + Aspect ratio is preserved up to a fraction of a percent (even-dim rounding). + """ + src_w, src_h = video.get_dimensions() + scale_dims = _compute_downscale_dims(src_w, src_h, total_pixels) + if scale_dims is None: + return video + return _apply_video_scale(video, scale_dims) + + +def _apply_video_scale(video: Input.Video, scale_dims: tuple[int, int]) -> Input.Video: + """Re-encode ``video`` scaled to ``scale_dims`` with a single decode/encode pass.""" + out_w, out_h = scale_dims + output_buffer = BytesIO() + input_container = None + output_container = None + + try: + input_source = video.get_stream_source() + input_container = av.open(input_source, mode="r") + output_container = av.open(output_buffer, mode="w", format="mp4") + + video_stream = output_container.add_stream("h264", rate=video.get_frame_rate()) + video_stream.width = out_w + video_stream.height = out_h + video_stream.pix_fmt = "yuv420p" + + audio_stream = None + for stream in input_container.streams: + if isinstance(stream, av.AudioStream): + audio_stream = output_container.add_stream("aac", rate=stream.sample_rate) + audio_stream.sample_rate = stream.sample_rate + audio_stream.layout = stream.layout + break + + for frame in input_container.decode(video=0): + frame = frame.reformat(width=out_w, height=out_h, format="yuv420p") + for packet in video_stream.encode(frame): + output_container.mux(packet) + for packet in video_stream.encode(): + output_container.mux(packet) + + if audio_stream is not None: + input_container.seek(0) + for audio_frame in input_container.decode(audio=0): + for packet in audio_stream.encode(audio_frame): + output_container.mux(packet) + for packet in audio_stream.encode(): + output_container.mux(packet) + + output_container.close() + input_container.close() + output_buffer.seek(0) + return InputImpl.VideoFromFile(output_buffer) + + except Exception as e: + if input_container is not None: + input_container.close() + if output_container is not None: + output_container.close() + raise RuntimeError(f"Failed to resize video: {str(e)}") from e + + def _f32_pcm(wav: torch.Tensor) -> torch.Tensor: """Convert audio to float 32 bits PCM format. Copy-paste from nodes_audio.py file.""" if wav.dtype.is_floating_point: From eb2222538739c4ebd396cd0a40cb6d80befd04fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Tue, 21 Apr 2026 20:46:37 +0300 Subject: [PATCH 064/140] Support standalone LTXV audio VAEs (#13499) --- comfy/sd.py | 1 + 1 file changed, 1 insertion(+) diff --git a/comfy/sd.py b/comfy/sd.py index a4d3ee269..736fe35de 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -807,6 +807,7 @@ class VAE: self.memory_used_encode = lambda shape, dtype: (700 * (max(1, (shape[-3] ** 0.66 * 0.11)) * shape[-2] * shape[-1]) * model_management.dtype_size(dtype)) self.memory_used_decode = lambda shape, dtype: (50 * (max(1, (shape[-3] ** 0.65 * 0.26)) * shape[-2] * shape[-1] * 32 * 32) * model_management.dtype_size(dtype)) elif "vocoder.resblocks.0.convs1.0.weight" in sd or "vocoder.vocoder.resblocks.0.convs1.0.weight" in sd: # LTX Audio + sd = comfy.utils.state_dict_prefix_replace(sd, {"audio_vae.": "autoencoder."}) self.first_stage_model = comfy.ldm.lightricks.vae.audio_vae.AudioVAE(metadata=metadata) self.memory_used_encode = lambda shape, dtype: (shape[2] * 330) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (shape[2] * shape[3] * 87000) * model_management.dtype_size(dtype) From 1e1d4f12548ceb296e61af1dff217bb8e4414345 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Tue, 21 Apr 2026 21:27:35 +0300 Subject: [PATCH 065/140] [Partner Nodes] added 4K resolution for Veo models; added Veo 3 Lite model (#13330) * feat(api nodes): added 4K resolution for Veo models; added Veo 3 Lite model Signed-off-by: bigcat88 * increase poll_interval from 5 to 9 --------- Signed-off-by: bigcat88 Co-authored-by: Jedrzej Kosinski --- comfy_api_nodes/nodes_veo2.py | 171 +++++++++++++++++++++++++--------- 1 file changed, 128 insertions(+), 43 deletions(-) diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py index 13fc1cc36..084b086a8 100644 --- a/comfy_api_nodes/nodes_veo2.py +++ b/comfy_api_nodes/nodes_veo2.py @@ -24,8 +24,9 @@ from comfy_api_nodes.util import ( AVERAGE_DURATION_VIDEO_GEN = 32 MODELS_MAP = { "veo-2.0-generate-001": "veo-2.0-generate-001", - "veo-3.1-generate": "veo-3.1-generate-preview", - "veo-3.1-fast-generate": "veo-3.1-fast-generate-preview", + "veo-3.1-generate": "veo-3.1-generate-001", + "veo-3.1-fast-generate": "veo-3.1-fast-generate-001", + "veo-3.1-lite": "veo-3.1-lite-generate-001", "veo-3.0-generate-001": "veo-3.0-generate-001", "veo-3.0-fast-generate-001": "veo-3.0-fast-generate-001", } @@ -247,17 +248,8 @@ class VeoVideoGenerationNode(IO.ComfyNode): raise Exception("Video generation completed but no video was returned") -class Veo3VideoGenerationNode(VeoVideoGenerationNode): - """ - Generates videos from text prompts using Google's Veo 3 API. - - Supported models: - - veo-3.0-generate-001 - - veo-3.0-fast-generate-001 - - This node extends the base Veo node with Veo 3 specific features including - audio generation and fixed 8-second duration. - """ +class Veo3VideoGenerationNode(IO.ComfyNode): + """Generates videos from text prompts using Google's Veo 3 API.""" @classmethod def define_schema(cls): @@ -279,6 +271,13 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode): default="16:9", tooltip="Aspect ratio of the output video", ), + IO.Combo.Input( + "resolution", + options=["720p", "1080p", "4k"], + default="720p", + tooltip="Output video resolution. 4K is not available for veo-3.1-lite and veo-3.0 models.", + optional=True, + ), IO.String.Input( "negative_prompt", multiline=True, @@ -289,11 +288,11 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode): IO.Int.Input( "duration_seconds", default=8, - min=8, + min=4, max=8, - step=1, + step=2, display_mode=IO.NumberDisplay.number, - tooltip="Duration of the output video in seconds (Veo 3 only supports 8 seconds)", + tooltip="Duration of the output video in seconds", optional=True, ), IO.Boolean.Input( @@ -332,10 +331,10 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode): options=[ "veo-3.1-generate", "veo-3.1-fast-generate", + "veo-3.1-lite", "veo-3.0-generate-001", "veo-3.0-fast-generate-001", ], - default="veo-3.0-generate-001", tooltip="Veo 3 model to use for video generation", optional=True, ), @@ -356,21 +355,111 @@ class Veo3VideoGenerationNode(VeoVideoGenerationNode): ], is_api_node=True, price_badge=IO.PriceBadge( - depends_on=IO.PriceBadgeDepends(widgets=["model", "generate_audio"]), + depends_on=IO.PriceBadgeDepends(widgets=["model", "generate_audio", "resolution", "duration_seconds"]), expr=""" ( $m := widgets.model; + $r := widgets.resolution; $a := widgets.generate_audio; - ($contains($m,"veo-3.0-fast-generate-001") or $contains($m,"veo-3.1-fast-generate")) - ? {"type":"usd","usd": ($a ? 1.2 : 0.8)} - : ($contains($m,"veo-3.0-generate-001") or $contains($m,"veo-3.1-generate")) - ? {"type":"usd","usd": ($a ? 3.2 : 1.6)} - : {"type":"range_usd","min_usd":0.8,"max_usd":3.2} + $seconds := widgets.duration_seconds; + $pps := + $contains($m, "lite") + ? ($r = "1080p" ? ($a ? 0.08 : 0.05) : ($a ? 0.05 : 0.03)) + : $contains($m, "3.1-fast") + ? ($r = "4k" ? ($a ? 0.30 : 0.25) : $r = "1080p" ? ($a ? 0.12 : 0.10) : ($a ? 0.10 : 0.08)) + : $contains($m, "3.1-generate") + ? ($r = "4k" ? ($a ? 0.60 : 0.40) : ($a ? 0.40 : 0.20)) + : $contains($m, "3.0-fast") + ? ($a ? 0.15 : 0.10) + : ($a ? 0.40 : 0.20); + {"type":"usd","usd": $pps * $seconds} ) """, ), ) + @classmethod + async def execute( + cls, + prompt, + aspect_ratio="16:9", + resolution="720p", + negative_prompt="", + duration_seconds=8, + enhance_prompt=True, + person_generation="ALLOW", + seed=0, + image=None, + model="veo-3.0-generate-001", + generate_audio=False, + ): + if "lite" in model and resolution == "4k": + raise Exception("4K resolution is not supported by the veo-3.1-lite model.") + + model = MODELS_MAP[model] + + instances = [{"prompt": prompt}] + if image is not None: + image_base64 = tensor_to_base64_string(image) + if image_base64: + instances[0]["image"] = {"bytesBase64Encoded": image_base64, "mimeType": "image/png"} + + parameters = { + "aspectRatio": aspect_ratio, + "personGeneration": person_generation, + "durationSeconds": duration_seconds, + "enhancePrompt": True, + "generateAudio": generate_audio, + } + if negative_prompt: + parameters["negativePrompt"] = negative_prompt + if seed > 0: + parameters["seed"] = seed + if "veo-3.1" in model: + parameters["resolution"] = resolution + + initial_response = await sync_op( + cls, + ApiEndpoint(path=f"/proxy/veo/{model}/generate", method="POST"), + response_model=VeoGenVidResponse, + data=VeoGenVidRequest( + instances=instances, + parameters=parameters, + ), + ) + + poll_response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/veo/{model}/poll", method="POST"), + response_model=VeoGenVidPollResponse, + status_extractor=lambda r: "completed" if r.done else "pending", + data=VeoGenVidPollRequest(operationName=initial_response.name), + poll_interval=9.0, + estimated_duration=AVERAGE_DURATION_VIDEO_GEN, + ) + + if poll_response.error: + raise Exception(f"Veo API error: {poll_response.error.message} (code: {poll_response.error.code})") + + response = poll_response.response + filtered_count = response.raiMediaFilteredCount + if filtered_count: + reasons = response.raiMediaFilteredReasons or [] + reason_part = f": {reasons[0]}" if reasons else "" + raise Exception( + f"Content blocked by Google's Responsible AI filters{reason_part} " + f"({filtered_count} video{'s' if filtered_count != 1 else ''} filtered)." + ) + + if response.videos: + video = response.videos[0] + if video.bytesBase64Encoded: + return IO.NodeOutput(InputImpl.VideoFromFile(BytesIO(base64.b64decode(video.bytesBase64Encoded)))) + if video.gcsUri: + return IO.NodeOutput(await download_url_to_video_output(video.gcsUri)) + raise Exception("Video returned but no data or URL was provided") + raise Exception("Video generation completed but no video was returned") + class Veo3FirstLastFrameNode(IO.ComfyNode): @@ -394,7 +483,7 @@ class Veo3FirstLastFrameNode(IO.ComfyNode): default="", tooltip="Negative text prompt to guide what to avoid in the video", ), - IO.Combo.Input("resolution", options=["720p", "1080p"]), + IO.Combo.Input("resolution", options=["720p", "1080p", "4k"]), IO.Combo.Input( "aspect_ratio", options=["16:9", "9:16"], @@ -424,8 +513,7 @@ class Veo3FirstLastFrameNode(IO.ComfyNode): IO.Image.Input("last_frame", tooltip="End frame"), IO.Combo.Input( "model", - options=["veo-3.1-generate", "veo-3.1-fast-generate"], - default="veo-3.1-fast-generate", + options=["veo-3.1-generate", "veo-3.1-fast-generate", "veo-3.1-lite"], ), IO.Boolean.Input( "generate_audio", @@ -443,26 +531,20 @@ class Veo3FirstLastFrameNode(IO.ComfyNode): ], is_api_node=True, price_badge=IO.PriceBadge( - depends_on=IO.PriceBadgeDepends(widgets=["model", "generate_audio", "duration"]), + depends_on=IO.PriceBadgeDepends(widgets=["model", "generate_audio", "duration", "resolution"]), expr=""" ( - $prices := { - "veo-3.1-fast-generate": { "audio": 0.15, "no_audio": 0.10 }, - "veo-3.1-generate": { "audio": 0.40, "no_audio": 0.20 } - }; $m := widgets.model; - $ga := (widgets.generate_audio = "true"); + $r := widgets.resolution; + $ga := widgets.generate_audio; $seconds := widgets.duration; - $modelKey := - $contains($m, "veo-3.1-fast-generate") ? "veo-3.1-fast-generate" : - $contains($m, "veo-3.1-generate") ? "veo-3.1-generate" : - ""; - $audioKey := $ga ? "audio" : "no_audio"; - $modelPrices := $lookup($prices, $modelKey); - $pps := $lookup($modelPrices, $audioKey); - ($pps != null) - ? {"type":"usd","usd": $pps * $seconds} - : {"type":"range_usd","min_usd": 0.4, "max_usd": 3.2} + $pps := + $contains($m, "lite") + ? ($r = "1080p" ? ($ga ? 0.08 : 0.05) : ($ga ? 0.05 : 0.03)) + : $contains($m, "fast") + ? ($r = "4k" ? ($ga ? 0.30 : 0.25) : $r = "1080p" ? ($ga ? 0.12 : 0.10) : ($ga ? 0.10 : 0.08)) + : ($r = "4k" ? ($ga ? 0.60 : 0.40) : ($ga ? 0.40 : 0.20)); + {"type":"usd","usd": $pps * $seconds} ) """, ), @@ -482,6 +564,9 @@ class Veo3FirstLastFrameNode(IO.ComfyNode): model: str, generate_audio: bool, ): + if "lite" in model and resolution == "4k": + raise Exception("4K resolution is not supported by the veo-3.1-lite model.") + model = MODELS_MAP[model] initial_response = await sync_op( cls, @@ -519,7 +604,7 @@ class Veo3FirstLastFrameNode(IO.ComfyNode): data=VeoGenVidPollRequest( operationName=initial_response.name, ), - poll_interval=5.0, + poll_interval=9.0, estimated_duration=AVERAGE_DURATION_VIDEO_GEN, ) From 102773cd2c13bdbe8729fdc897031dfb61dea346 Mon Sep 17 00:00:00 2001 From: Comfy Org PR Bot Date: Wed, 22 Apr 2026 03:35:45 +0900 Subject: [PATCH 066/140] Bump comfyui-frontend-package to 1.42.14 (#13493) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 671bd5693..ccdd47674 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.42.12 +comfyui-frontend-package==1.42.14 comfyui-workflow-templates==0.9.57 comfyui-embedded-docs==0.4.3 torch From 43a1263b609b923b2f69a0510bcf7ac95097e41b Mon Sep 17 00:00:00 2001 From: AustinMroz Date: Tue, 21 Apr 2026 17:58:59 -0700 Subject: [PATCH 067/140] Add gpt-image-2 as version option (#13501) --- comfy_api_nodes/nodes_openai.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py index 4ee896fa8..90a29c2f2 100644 --- a/comfy_api_nodes/nodes_openai.py +++ b/comfy_api_nodes/nodes_openai.py @@ -363,7 +363,7 @@ class OpenAIGPTImage1(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="OpenAIGPTImage1", - display_name="OpenAI GPT Image 1.5", + display_name="OpenAI GPT Image 2", category="api node/image/OpenAI", description="Generates images synchronously via OpenAI's GPT Image endpoint.", inputs=[ @@ -427,8 +427,8 @@ class OpenAIGPTImage1(IO.ComfyNode): ), IO.Combo.Input( "model", - options=["gpt-image-1", "gpt-image-1.5"], - default="gpt-image-1.5", + options=["gpt-image-1", "gpt-image-1.5", 'gpt-image-2'], + default="gpt-image-2", optional=True, ), ], @@ -487,6 +487,8 @@ class OpenAIGPTImage1(IO.ComfyNode): price_extractor = calculate_tokens_price_image_1 elif model == "gpt-image-1.5": price_extractor = calculate_tokens_price_image_1_5 + elif model == "gpt-image-2": + price_extractor = calculate_tokens_price_image_1_5 else: raise ValueError(f"Unknown model: {model}") From 529c80255f3f2370c39780c62a9454d95344014d Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 21 Apr 2026 19:59:31 -0700 Subject: [PATCH 068/140] Allow logging in comfy app files. (#13505) --- main.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/main.py b/main.py index 12b04719d..dbaf2745c 100644 --- a/main.py +++ b/main.py @@ -9,6 +9,8 @@ import folder_paths import time from comfy.cli_args import args, enables_dynamic_vram from app.logger import setup_logger +setup_logger(log_level=args.verbose, use_stdout=args.log_stdout) + from app.assets.seeder import asset_seeder from app.assets.services import register_output_files import itertools @@ -27,8 +29,6 @@ if __name__ == "__main__": os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1' os.environ['DO_NOT_TRACK'] = '1' -setup_logger(log_level=args.verbose, use_stdout=args.log_stdout) - faulthandler.enable(file=sys.stderr, all_threads=False) import comfy_aimdo.control From 6045c11d8b32d5f761c555d6ca026e4d731ac8d5 Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Wed, 22 Apr 2026 11:45:25 +0800 Subject: [PATCH 069/140] chore: update workflow templates to v0.9.59 (#13507) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index ccdd47674..a25bc0667 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.14 -comfyui-workflow-templates==0.9.57 +comfyui-workflow-templates==0.9.59 comfyui-embedded-docs==0.4.3 torch torchsde From 91e1f45d80fba14d992269b0b98de7a4a14c81b9 Mon Sep 17 00:00:00 2001 From: Matt Miller Date: Tue, 21 Apr 2026 22:31:36 -0700 Subject: [PATCH 070/140] fix(veo): reject 4K resolution for veo-3.0 models in Veo3VideoGenerationNode (#13504) The tooltip on the resolution input states that 4K is not available for veo-3.1-lite or veo-3.0 models, but the execute guard only rejected the lite combination. Selecting 4K with veo-3.0-generate-001 or veo-3.0-fast-generate-001 would fall through and hit the upstream API with an invalid request. Broaden the guard to match the documented behavior and update the error message accordingly. Co-authored-by: Jedrzej Kosinski --- comfy_api_nodes/nodes_veo2.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy_api_nodes/nodes_veo2.py b/comfy_api_nodes/nodes_veo2.py index 084b086a8..2ff75d9b2 100644 --- a/comfy_api_nodes/nodes_veo2.py +++ b/comfy_api_nodes/nodes_veo2.py @@ -393,8 +393,8 @@ class Veo3VideoGenerationNode(IO.ComfyNode): model="veo-3.0-generate-001", generate_audio=False, ): - if "lite" in model and resolution == "4k": - raise Exception("4K resolution is not supported by the veo-3.1-lite model.") + if resolution == "4k" and ("lite" in model or "3.0" in model): + raise Exception("4K resolution is not supported by the veo-3.1-lite or veo-3.0 models.") model = MODELS_MAP[model] From db85cf03ff33f5be09d02f2a52334971209d25d5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Wed, 22 Apr 2026 14:16:02 +0300 Subject: [PATCH 071/140] feat: RIFE and FILM frame interpolation model support (CORE-29) (#13258) * initial RIFE support * Also support FILM * Better RAM usage, reduce FILM VRAM peak * Add model folder placeholder * Fix oom fallback frame loss * Remove torch.compile for now * Rename model input * Shorter input type name --------- --- .../frame_interpolation_models/film_net.py | 258 ++++++++++++++++++ .../frame_interpolation_models/ifnet.py | 128 +++++++++ comfy_extras/nodes_frame_interpolation.py | 211 ++++++++++++++ folder_paths.py | 2 + .../put_frame_interpolation_models_here | 0 nodes.py | 3 +- 6 files changed, 601 insertions(+), 1 deletion(-) create mode 100644 comfy_extras/frame_interpolation_models/film_net.py create mode 100644 comfy_extras/frame_interpolation_models/ifnet.py create mode 100644 comfy_extras/nodes_frame_interpolation.py create mode 100644 models/frame_interpolation/put_frame_interpolation_models_here diff --git a/comfy_extras/frame_interpolation_models/film_net.py b/comfy_extras/frame_interpolation_models/film_net.py new file mode 100644 index 000000000..cf4f6e1e1 --- /dev/null +++ b/comfy_extras/frame_interpolation_models/film_net.py @@ -0,0 +1,258 @@ +"""FILM: Frame Interpolation for Large Motion (ECCV 2022).""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import comfy.ops + +ops = comfy.ops.disable_weight_init + + +class FilmConv2d(nn.Module): + """Conv2d with optional LeakyReLU and FILM-style padding.""" + + def __init__(self, in_channels, out_channels, size, activation=True, device=None, dtype=None, operations=ops): + super().__init__() + self.even_pad = not size % 2 + self.conv = operations.Conv2d(in_channels, out_channels, kernel_size=size, padding=size // 2 if size % 2 else 0, device=device, dtype=dtype) + self.activation = nn.LeakyReLU(0.2) if activation else None + + def forward(self, x): + if self.even_pad: + x = F.pad(x, (0, 1, 0, 1)) + x = self.conv(x) + if self.activation is not None: + x = self.activation(x) + return x + + +def _warp_core(image, flow, grid_x, grid_y): + dtype = image.dtype + H, W = flow.shape[2], flow.shape[3] + dx = flow[:, 0].float() / (W * 0.5) + dy = flow[:, 1].float() / (H * 0.5) + grid = torch.stack([grid_x[None, None, :] + dx, grid_y[None, :, None] + dy], dim=3) + return F.grid_sample(image.float(), grid, mode="bilinear", padding_mode="border", align_corners=False).to(dtype) + + +def build_image_pyramid(image, pyramid_levels): + pyramid = [image] + for _ in range(1, pyramid_levels): + image = F.avg_pool2d(image, 2, 2) + pyramid.append(image) + return pyramid + + +def flow_pyramid_synthesis(residual_pyramid): + flow = residual_pyramid[-1] + flow_pyramid = [flow] + for residual_flow in residual_pyramid[:-1][::-1]: + flow = F.interpolate(flow, size=residual_flow.shape[2:4], mode="bilinear", scale_factor=None).mul_(2).add_(residual_flow) + flow_pyramid.append(flow) + flow_pyramid.reverse() + return flow_pyramid + + +def multiply_pyramid(pyramid, scalar): + return [image * scalar[:, None, None, None] for image in pyramid] + + +def pyramid_warp(feature_pyramid, flow_pyramid, warp_fn): + return [warp_fn(features, flow) for features, flow in zip(feature_pyramid, flow_pyramid)] + + +def concatenate_pyramids(pyramid1, pyramid2): + return [torch.cat([f1, f2], dim=1) for f1, f2 in zip(pyramid1, pyramid2)] + + +class SubTreeExtractor(nn.Module): + def __init__(self, in_channels=3, channels=64, n_layers=4, device=None, dtype=None, operations=ops): + super().__init__() + convs = [] + for i in range(n_layers): + out_ch = channels << i + convs.append(nn.Sequential( + FilmConv2d(in_channels, out_ch, 3, device=device, dtype=dtype, operations=operations), + FilmConv2d(out_ch, out_ch, 3, device=device, dtype=dtype, operations=operations))) + in_channels = out_ch + self.convs = nn.ModuleList(convs) + + def forward(self, image, n): + head = image + pyramid = [] + for i, layer in enumerate(self.convs): + head = layer(head) + pyramid.append(head) + if i < n - 1: + head = F.avg_pool2d(head, 2, 2) + return pyramid + + +class FeatureExtractor(nn.Module): + def __init__(self, in_channels=3, channels=64, sub_levels=4, device=None, dtype=None, operations=ops): + super().__init__() + self.extract_sublevels = SubTreeExtractor(in_channels, channels, sub_levels, device=device, dtype=dtype, operations=operations) + self.sub_levels = sub_levels + + def forward(self, image_pyramid): + sub_pyramids = [self.extract_sublevels(image_pyramid[i], min(len(image_pyramid) - i, self.sub_levels)) + for i in range(len(image_pyramid))] + feature_pyramid = [] + for i in range(len(image_pyramid)): + features = sub_pyramids[i][0] + for j in range(1, self.sub_levels): + if j <= i: + features = torch.cat([features, sub_pyramids[i - j][j]], dim=1) + feature_pyramid.append(features) + # Free sub-pyramids no longer needed by future levels + if i >= self.sub_levels - 1: + sub_pyramids[i - self.sub_levels + 1] = None + return feature_pyramid + + +class FlowEstimator(nn.Module): + def __init__(self, in_channels, num_convs, num_filters, device=None, dtype=None, operations=ops): + super().__init__() + self._convs = nn.ModuleList() + for _ in range(num_convs): + self._convs.append(FilmConv2d(in_channels, num_filters, 3, device=device, dtype=dtype, operations=operations)) + in_channels = num_filters + self._convs.append(FilmConv2d(in_channels, num_filters // 2, 1, device=device, dtype=dtype, operations=operations)) + self._convs.append(FilmConv2d(num_filters // 2, 2, 1, activation=False, device=device, dtype=dtype, operations=operations)) + + def forward(self, features_a, features_b): + net = torch.cat([features_a, features_b], dim=1) + for conv in self._convs: + net = conv(net) + return net + + +class PyramidFlowEstimator(nn.Module): + def __init__(self, filters=64, flow_convs=(3, 3, 3, 3), flow_filters=(32, 64, 128, 256), device=None, dtype=None, operations=ops): + super().__init__() + in_channels = filters << 1 + predictors = [] + for i in range(len(flow_convs)): + predictors.append(FlowEstimator(in_channels, flow_convs[i], flow_filters[i], device=device, dtype=dtype, operations=operations)) + in_channels += filters << (i + 2) + self._predictor = predictors[-1] + self._predictors = nn.ModuleList(predictors[:-1][::-1]) + + def forward(self, feature_pyramid_a, feature_pyramid_b, warp_fn): + levels = len(feature_pyramid_a) + v = self._predictor(feature_pyramid_a[-1], feature_pyramid_b[-1]) + residuals = [v] + # Coarse-to-fine: shared predictor for deep levels, then specialized predictors for fine levels + steps = [(i, self._predictor) for i in range(levels - 2, len(self._predictors) - 1, -1)] + steps += [(len(self._predictors) - 1 - k, p) for k, p in enumerate(self._predictors)] + for i, predictor in steps: + v = F.interpolate(v, size=feature_pyramid_a[i].shape[2:4], mode="bilinear").mul_(2) + v_residual = predictor(feature_pyramid_a[i], warp_fn(feature_pyramid_b[i], v)) + residuals.append(v_residual) + v = v.add_(v_residual) + residuals.reverse() + return residuals + + +def _get_fusion_channels(level, filters): + # Per direction: multi-scale features + RGB image (3ch) + flow (2ch), doubled for both directions + return (sum(filters << i for i in range(level)) + 3 + 2) * 2 + + +class Fusion(nn.Module): + def __init__(self, n_layers=4, specialized_layers=3, filters=64, device=None, dtype=None, operations=ops): + super().__init__() + self.output_conv = operations.Conv2d(filters, 3, kernel_size=1, device=device, dtype=dtype) + self.convs = nn.ModuleList() + in_channels = _get_fusion_channels(n_layers, filters) + increase = 0 + for i in range(n_layers)[::-1]: + num_filters = (filters << i) if i < specialized_layers else (filters << specialized_layers) + self.convs.append(nn.ModuleList([ + FilmConv2d(in_channels, num_filters, 2, activation=False, device=device, dtype=dtype, operations=operations), + FilmConv2d(in_channels + (increase or num_filters), num_filters, 3, device=device, dtype=dtype, operations=operations), + FilmConv2d(num_filters, num_filters, 3, device=device, dtype=dtype, operations=operations)])) + in_channels = num_filters + increase = _get_fusion_channels(i, filters) - num_filters // 2 + + def forward(self, pyramid): + net = pyramid[-1] + for k, layers in enumerate(self.convs): + i = len(self.convs) - 1 - k + net = layers[0](F.interpolate(net, size=pyramid[i].shape[2:4], mode="nearest")) + net = layers[2](layers[1](torch.cat([pyramid[i], net], dim=1))) + return self.output_conv(net) + + +class FILMNet(nn.Module): + def __init__(self, pyramid_levels=7, fusion_pyramid_levels=5, specialized_levels=3, sub_levels=4, + filters=64, flow_convs=(3, 3, 3, 3), flow_filters=(32, 64, 128, 256), device=None, dtype=None, operations=ops): + super().__init__() + self.pyramid_levels = pyramid_levels + self.fusion_pyramid_levels = fusion_pyramid_levels + self.extract = FeatureExtractor(3, filters, sub_levels, device=device, dtype=dtype, operations=operations) + self.predict_flow = PyramidFlowEstimator(filters, flow_convs, flow_filters, device=device, dtype=dtype, operations=operations) + self.fuse = Fusion(sub_levels, specialized_levels, filters, device=device, dtype=dtype, operations=operations) + self._warp_grids = {} + + def get_dtype(self): + return self.extract.extract_sublevels.convs[0][0].conv.weight.dtype + + def _build_warp_grids(self, H, W, device): + """Pre-compute warp grids for all pyramid levels.""" + if (H, W) in self._warp_grids: + return + self._warp_grids = {} # clear old resolution grids to prevent memory leaks + for _ in range(self.pyramid_levels): + self._warp_grids[(H, W)] = ( + torch.linspace(-(1 - 1 / W), 1 - 1 / W, W, dtype=torch.float32, device=device), + torch.linspace(-(1 - 1 / H), 1 - 1 / H, H, dtype=torch.float32, device=device), + ) + H, W = H // 2, W // 2 + + def warp(self, image, flow): + grid_x, grid_y = self._warp_grids[(flow.shape[2], flow.shape[3])] + return _warp_core(image, flow, grid_x, grid_y) + + def extract_features(self, img): + """Extract image and feature pyramids for a single frame. Can be cached across pairs.""" + image_pyramid = build_image_pyramid(img, self.pyramid_levels) + feature_pyramid = self.extract(image_pyramid) + return image_pyramid, feature_pyramid + + def forward(self, img0, img1, timestep=0.5, cache=None): + # FILM uses a scalar timestep per batch element (spatially-varying timesteps not supported) + t = timestep.mean(dim=(1, 2, 3)).item() if isinstance(timestep, torch.Tensor) else timestep + return self.forward_multi_timestep(img0, img1, [t], cache=cache) + + def forward_multi_timestep(self, img0, img1, timesteps, cache=None): + """Compute flow once, synthesize at multiple timesteps. Expects batch=1 inputs.""" + self._build_warp_grids(img0.shape[2], img0.shape[3], img0.device) + + image_pyr0, feat_pyr0 = cache["img0"] if cache and "img0" in cache else self.extract_features(img0) + image_pyr1, feat_pyr1 = cache["img1"] if cache and "img1" in cache else self.extract_features(img1) + + fwd_flow = flow_pyramid_synthesis(self.predict_flow(feat_pyr0, feat_pyr1, self.warp))[:self.fusion_pyramid_levels] + bwd_flow = flow_pyramid_synthesis(self.predict_flow(feat_pyr1, feat_pyr0, self.warp))[:self.fusion_pyramid_levels] + + # Build warp targets and free full pyramids (only first fpl levels needed from here) + fpl = self.fusion_pyramid_levels + p2w = [concatenate_pyramids(image_pyr0[:fpl], feat_pyr0[:fpl]), + concatenate_pyramids(image_pyr1[:fpl], feat_pyr1[:fpl])] + del image_pyr0, image_pyr1, feat_pyr0, feat_pyr1 + + results = [] + dt_tensors = torch.tensor(timesteps, device=img0.device, dtype=img0.dtype) + for idx in range(len(timesteps)): + batch_dt = dt_tensors[idx:idx + 1] + bwd_scaled = multiply_pyramid(bwd_flow, batch_dt) + fwd_scaled = multiply_pyramid(fwd_flow, 1 - batch_dt) + fwd_warped = pyramid_warp(p2w[0], bwd_scaled, self.warp) + bwd_warped = pyramid_warp(p2w[1], fwd_scaled, self.warp) + aligned = [torch.cat([fw, bw, bf, ff], dim=1) + for fw, bw, bf, ff in zip(fwd_warped, bwd_warped, bwd_scaled, fwd_scaled)] + del fwd_warped, bwd_warped, bwd_scaled, fwd_scaled + results.append(self.fuse(aligned)) + del aligned + return torch.cat(results, dim=0) diff --git a/comfy_extras/frame_interpolation_models/ifnet.py b/comfy_extras/frame_interpolation_models/ifnet.py new file mode 100644 index 000000000..03cb34c50 --- /dev/null +++ b/comfy_extras/frame_interpolation_models/ifnet.py @@ -0,0 +1,128 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import comfy.ops + +ops = comfy.ops.disable_weight_init + + +def _warp(img, flow, warp_grids): + B, _, H, W = img.shape + base_grid, flow_div = warp_grids[(H, W)] + flow_norm = torch.cat([flow[:, 0:1] / flow_div[0], flow[:, 1:2] / flow_div[1]], 1).float() + grid = (base_grid.expand(B, -1, -1, -1) + flow_norm).permute(0, 2, 3, 1) + return F.grid_sample(img.float(), grid, mode="bilinear", padding_mode="border", align_corners=True).to(img.dtype) + + +class Head(nn.Module): + def __init__(self, out_ch=4, device=None, dtype=None, operations=ops): + super().__init__() + self.cnn0 = operations.Conv2d(3, 16, 3, 2, 1, device=device, dtype=dtype) + self.cnn1 = operations.Conv2d(16, 16, 3, 1, 1, device=device, dtype=dtype) + self.cnn2 = operations.Conv2d(16, 16, 3, 1, 1, device=device, dtype=dtype) + self.cnn3 = operations.ConvTranspose2d(16, out_ch, 4, 2, 1, device=device, dtype=dtype) + self.relu = nn.LeakyReLU(0.2, True) + + def forward(self, x): + x = self.relu(self.cnn0(x)) + x = self.relu(self.cnn1(x)) + x = self.relu(self.cnn2(x)) + return self.cnn3(x) + + +class ResConv(nn.Module): + def __init__(self, c, device=None, dtype=None, operations=ops): + super().__init__() + self.conv = operations.Conv2d(c, c, 3, 1, 1, device=device, dtype=dtype) + self.beta = nn.Parameter(torch.ones((1, c, 1, 1), device=device, dtype=dtype)) + self.relu = nn.LeakyReLU(0.2, True) + + def forward(self, x): + return self.relu(torch.addcmul(x, self.conv(x), self.beta)) + + +class IFBlock(nn.Module): + def __init__(self, in_planes, c=64, device=None, dtype=None, operations=ops): + super().__init__() + self.conv0 = nn.Sequential( + nn.Sequential(operations.Conv2d(in_planes, c // 2, 3, 2, 1, device=device, dtype=dtype), nn.LeakyReLU(0.2, True)), + nn.Sequential(operations.Conv2d(c // 2, c, 3, 2, 1, device=device, dtype=dtype), nn.LeakyReLU(0.2, True))) + self.convblock = nn.Sequential(*(ResConv(c, device=device, dtype=dtype, operations=operations) for _ in range(8))) + self.lastconv = nn.Sequential(operations.ConvTranspose2d(c, 4 * 13, 4, 2, 1, device=device, dtype=dtype), nn.PixelShuffle(2)) + + def forward(self, x, flow=None, scale=1): + x = F.interpolate(x, scale_factor=1.0 / scale, mode="bilinear") + if flow is not None: + flow = F.interpolate(flow, scale_factor=1.0 / scale, mode="bilinear").div_(scale) + x = torch.cat((x, flow), 1) + feat = self.convblock(self.conv0(x)) + tmp = F.interpolate(self.lastconv(feat), scale_factor=scale, mode="bilinear") + return tmp[:, :4] * scale, tmp[:, 4:5], tmp[:, 5:] + + +class IFNet(nn.Module): + def __init__(self, head_ch=4, channels=(192, 128, 96, 64, 32), device=None, dtype=None, operations=ops): + super().__init__() + self.encode = Head(out_ch=head_ch, device=device, dtype=dtype, operations=operations) + block_in = [7 + 2 * head_ch] + [8 + 4 + 8 + 2 * head_ch] * 4 + self.blocks = nn.ModuleList([IFBlock(block_in[i], channels[i], device=device, dtype=dtype, operations=operations) for i in range(5)]) + self.scale_list = [16, 8, 4, 2, 1] + self.pad_align = 64 + self._warp_grids = {} + + def get_dtype(self): + return self.encode.cnn0.weight.dtype + + def _build_warp_grids(self, H, W, device): + if (H, W) in self._warp_grids: + return + self._warp_grids = {} # clear old resolution grids to prevent memory leaks + grid_y, grid_x = torch.meshgrid( + torch.linspace(-1.0, 1.0, H, device=device, dtype=torch.float32), + torch.linspace(-1.0, 1.0, W, device=device, dtype=torch.float32), indexing="ij") + self._warp_grids[(H, W)] = ( + torch.stack((grid_x, grid_y), dim=0).unsqueeze(0), + torch.tensor([(W - 1.0) / 2.0, (H - 1.0) / 2.0], dtype=torch.float32, device=device)) + + def warp(self, img, flow): + return _warp(img, flow, self._warp_grids) + + def extract_features(self, img): + """Extract head features for a single frame. Can be cached across pairs.""" + return self.encode(img) + + def forward(self, img0, img1, timestep=0.5, cache=None): + if not isinstance(timestep, torch.Tensor): + timestep = torch.full((img0.shape[0], 1, img0.shape[2], img0.shape[3]), timestep, device=img0.device, dtype=img0.dtype) + + self._build_warp_grids(img0.shape[2], img0.shape[3], img0.device) + + B = img0.shape[0] + f0 = cache["img0"].expand(B, -1, -1, -1) if cache and "img0" in cache else self.encode(img0) + f1 = cache["img1"].expand(B, -1, -1, -1) if cache and "img1" in cache else self.encode(img1) + flow = mask = feat = None + warped_img0, warped_img1 = img0, img1 + for i, block in enumerate(self.blocks): + if flow is None: + flow, mask, feat = block(torch.cat((img0, img1, f0, f1, timestep), 1), None, scale=self.scale_list[i]) + else: + fd, mask, feat = block( + torch.cat((warped_img0, warped_img1, self.warp(f0, flow[:, :2]), self.warp(f1, flow[:, 2:4]), timestep, mask, feat), 1), + flow, scale=self.scale_list[i]) + flow = flow.add_(fd) + warped_img0 = self.warp(img0, flow[:, :2]) + warped_img1 = self.warp(img1, flow[:, 2:4]) + return torch.lerp(warped_img1, warped_img0, torch.sigmoid(mask)) + + +def detect_rife_config(state_dict): + head_ch = state_dict["encode.cnn3.weight"].shape[1] # ConvTranspose2d: (in_ch, out_ch, kH, kW) + channels = [] + for i in range(5): + key = f"blocks.{i}.conv0.1.0.weight" + if key in state_dict: + channels.append(state_dict[key].shape[0]) + if len(channels) != 5: + raise ValueError(f"Unsupported RIFE model: expected 5 blocks, found {len(channels)}") + return head_ch, channels diff --git a/comfy_extras/nodes_frame_interpolation.py b/comfy_extras/nodes_frame_interpolation.py new file mode 100644 index 000000000..a3b00d36e --- /dev/null +++ b/comfy_extras/nodes_frame_interpolation.py @@ -0,0 +1,211 @@ +import torch +from tqdm import tqdm +from typing_extensions import override + +import comfy.model_patcher +import comfy.utils +import folder_paths +from comfy import model_management +from comfy_extras.frame_interpolation_models.ifnet import IFNet, detect_rife_config +from comfy_extras.frame_interpolation_models.film_net import FILMNet +from comfy_api.latest import ComfyExtension, io + +FrameInterpolationModel = io.Custom("INTERP_MODEL") + + +class FrameInterpolationModelLoader(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="FrameInterpolationModelLoader", + display_name="Load Frame Interpolation Model", + category="loaders", + inputs=[ + io.Combo.Input("model_name", options=folder_paths.get_filename_list("frame_interpolation"), + tooltip="Select a frame interpolation model to load. Models must be placed in the 'frame_interpolation' folder."), + ], + outputs=[ + FrameInterpolationModel.Output(), + ], + ) + + @classmethod + def execute(cls, model_name) -> io.NodeOutput: + model_path = folder_paths.get_full_path_or_raise("frame_interpolation", model_name) + sd = comfy.utils.load_torch_file(model_path, safe_load=True) + + model = cls._detect_and_load(sd) + dtype = torch.float16 if model_management.should_use_fp16(model_management.get_torch_device()) else torch.float32 + model.eval().to(dtype) + patcher = comfy.model_patcher.ModelPatcher( + model, + load_device=model_management.get_torch_device(), + offload_device=model_management.unet_offload_device(), + ) + return io.NodeOutput(patcher) + + @classmethod + def _detect_and_load(cls, sd): + # Try FILM + if "extract.extract_sublevels.convs.0.0.conv.weight" in sd: + model = FILMNet() + model.load_state_dict(sd) + return model + + # Try RIFE (needs key remapping for raw checkpoints) + sd = comfy.utils.state_dict_prefix_replace(sd, {"module.": "", "flownet.": ""}) + key_map = {} + for k in sd: + for i in range(5): + if k.startswith(f"block{i}."): + key_map[k] = f"blocks.{i}.{k[len(f'block{i}.'):]}" + if key_map: + sd = {key_map.get(k, k): v for k, v in sd.items()} + sd = {k: v for k, v in sd.items() if not k.startswith(("teacher.", "caltime."))} + + try: + head_ch, channels = detect_rife_config(sd) + except (KeyError, ValueError): + raise ValueError("Unrecognized frame interpolation model format") + model = IFNet(head_ch=head_ch, channels=channels) + model.load_state_dict(sd) + return model + + +class FrameInterpolate(io.ComfyNode): + @classmethod + def define_schema(cls): + return io.Schema( + node_id="FrameInterpolate", + display_name="Frame Interpolate", + category="image/video", + search_aliases=["rife", "film", "frame interpolation", "slow motion", "interpolate frames", "vfi"], + inputs=[ + FrameInterpolationModel.Input("interp_model"), + io.Image.Input("images"), + io.Int.Input("multiplier", default=2, min=2, max=16), + ], + outputs=[ + io.Image.Output(), + ], + ) + + @classmethod + def execute(cls, interp_model, images, multiplier) -> io.NodeOutput: + offload_device = model_management.intermediate_device() + + num_frames = images.shape[0] + if num_frames < 2 or multiplier < 2: + return io.NodeOutput(images) + + model_management.load_model_gpu(interp_model) + device = interp_model.load_device + dtype = interp_model.model_dtype() + inference_model = interp_model.model + + # Free VRAM for inference activations (model weights + ~20x a single frame's worth) + H, W = images.shape[1], images.shape[2] + activation_mem = H * W * 3 * images.element_size() * 20 + model_management.free_memory(activation_mem, device) + align = getattr(inference_model, "pad_align", 1) + + # Prepare a single padded frame on device for determining output dimensions + def prepare_frame(idx): + frame = images[idx:idx + 1].movedim(-1, 1).to(dtype=dtype, device=device) + if align > 1: + from comfy.ldm.common_dit import pad_to_patch_size + frame = pad_to_patch_size(frame, (align, align), padding_mode="reflect") + return frame + + # Count total interpolation passes for progress bar + total_pairs = num_frames - 1 + num_interp = multiplier - 1 + total_steps = total_pairs * num_interp + pbar = comfy.utils.ProgressBar(total_steps) + tqdm_bar = tqdm(total=total_steps, desc="Frame interpolation") + + batch = num_interp # reduced on OOM and persists across pairs (same resolution = same limit) + t_values = [t / multiplier for t in range(1, multiplier)] + + out_dtype = model_management.intermediate_dtype() + total_out_frames = total_pairs * multiplier + 1 + result = torch.empty((total_out_frames, 3, H, W), dtype=out_dtype, device=offload_device) + result[0] = images[0].movedim(-1, 0).to(out_dtype) + out_idx = 1 + + # Pre-compute timestep tensor on device (padded dimensions needed) + sample = prepare_frame(0) + pH, pW = sample.shape[2], sample.shape[3] + ts_full = torch.tensor(t_values, device=device, dtype=dtype).reshape(num_interp, 1, 1, 1) + ts_full = ts_full.expand(-1, 1, pH, pW) + del sample + + multi_fn = getattr(inference_model, "forward_multi_timestep", None) + feat_cache = {} + prev_frame = None + + try: + for i in range(total_pairs): + img0_single = prev_frame if prev_frame is not None else prepare_frame(i) + img1_single = prepare_frame(i + 1) + prev_frame = img1_single + + # Cache features: img1 of pair N becomes img0 of pair N+1 + feat_cache["img0"] = feat_cache.pop("next") if "next" in feat_cache else inference_model.extract_features(img0_single) + feat_cache["img1"] = inference_model.extract_features(img1_single) + feat_cache["next"] = feat_cache["img1"] + + used_multi = False + if multi_fn is not None: + # Models with timestep-independent flow can compute it once for all timesteps + try: + mids = multi_fn(img0_single, img1_single, t_values, cache=feat_cache) + result[out_idx:out_idx + num_interp] = mids[:, :, :H, :W].to(out_dtype) + out_idx += num_interp + pbar.update(num_interp) + tqdm_bar.update(num_interp) + used_multi = True + except model_management.OOM_EXCEPTION: + model_management.soft_empty_cache() + multi_fn = None # fall through to single-timestep path + + if not used_multi: + j = 0 + while j < num_interp: + b = min(batch, num_interp - j) + try: + img0 = img0_single.expand(b, -1, -1, -1) + img1 = img1_single.expand(b, -1, -1, -1) + mids = inference_model(img0, img1, timestep=ts_full[j:j + b], cache=feat_cache) + result[out_idx:out_idx + b] = mids[:, :, :H, :W].to(out_dtype) + out_idx += b + pbar.update(b) + tqdm_bar.update(b) + j += b + except model_management.OOM_EXCEPTION: + if batch <= 1: + raise + batch = max(1, batch // 2) + model_management.soft_empty_cache() + + result[out_idx] = images[i + 1].movedim(-1, 0).to(out_dtype) + out_idx += 1 + finally: + tqdm_bar.close() + + # BCHW -> BHWC + result = result.movedim(1, -1).clamp_(0.0, 1.0) + return io.NodeOutput(result) + + +class FrameInterpolationExtension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + FrameInterpolationModelLoader, + FrameInterpolate, + ] + + +async def comfy_entrypoint() -> FrameInterpolationExtension: + return FrameInterpolationExtension() diff --git a/folder_paths.py b/folder_paths.py index 9c96540e3..80f4b291a 100644 --- a/folder_paths.py +++ b/folder_paths.py @@ -52,6 +52,8 @@ folder_names_and_paths["model_patches"] = ([os.path.join(models_dir, "model_patc folder_names_and_paths["audio_encoders"] = ([os.path.join(models_dir, "audio_encoders")], supported_pt_extensions) +folder_names_and_paths["frame_interpolation"] = ([os.path.join(models_dir, "frame_interpolation")], supported_pt_extensions) + output_directory = os.path.join(base_path, "output") temp_directory = os.path.join(base_path, "temp") input_directory = os.path.join(base_path, "input") diff --git a/models/frame_interpolation/put_frame_interpolation_models_here b/models/frame_interpolation/put_frame_interpolation_models_here new file mode 100644 index 000000000..e69de29bb diff --git a/nodes.py b/nodes.py index 299b3d758..bb38e07b8 100644 --- a/nodes.py +++ b/nodes.py @@ -2457,7 +2457,8 @@ async def init_builtin_extra_nodes(): "nodes_number_convert.py", "nodes_painter.py", "nodes_curve.py", - "nodes_rtdetr.py" + "nodes_rtdetr.py", + "nodes_frame_interpolation.py", ] import_failed = [] From cc6f9500a1b972e9dca14e769f4b70a8927ffa43 Mon Sep 17 00:00:00 2001 From: Octopus Date: Thu, 23 Apr 2026 06:05:43 +0800 Subject: [PATCH 072/140] fix: use Parameter assignment for Stable_Zero123 cc_projection weights (fixes #13492) (#13518) On Windows with aimdo enabled, disable_weight_init.Linear uses lazy initialization that sets weight and bias to None to avoid unnecessary memory allocation. This caused a crash when copy_() was called on the None weight attribute in Stable_Zero123.__init__. Replace copy_() with direct torch.nn.Parameter assignment, which works correctly on both Windows (aimdo enabled) and other platforms. --- comfy/model_base.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy/model_base.py b/comfy/model_base.py index 5c2668ba9..1c7695761 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -578,8 +578,8 @@ class Stable_Zero123(BaseModel): def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None): super().__init__(model_config, model_type, device=device) self.cc_projection = comfy.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device) - self.cc_projection.weight.copy_(cc_projection_weight) - self.cc_projection.bias.copy_(cc_projection_bias) + self.cc_projection.weight = torch.nn.Parameter(cc_projection_weight.clone()) + self.cc_projection.bias = torch.nn.Parameter(cc_projection_bias.clone()) def extra_conds(self, **kwargs): out = {} From 9949c19c632eb6cad50024e02816df86e7d41b27 Mon Sep 17 00:00:00 2001 From: blepping <157360029+blepping@users.noreply.github.com> Date: Wed, 22 Apr 2026 16:08:19 -0600 Subject: [PATCH 073/140] Derive InterruptProcessingException from BaseException (#13523) --- comfy/model_management.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index bcf1399c4..3b39d6080 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -1801,7 +1801,7 @@ def debug_memory_summary(): return torch.cuda.memory.memory_summary() return "" -class InterruptProcessingException(Exception): +class InterruptProcessingException(BaseException): pass interrupt_processing_mutex = threading.RLock() From cb388e2912f9d3adf50e3510ed1c470ad5c9bc79 Mon Sep 17 00:00:00 2001 From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com> Date: Thu, 23 Apr 2026 07:12:06 +0900 Subject: [PATCH 074/140] bump manager version to 4.2.1 (#13516) --- manager_requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manager_requirements.txt b/manager_requirements.txt index f770ec933..a079d3492 100644 --- a/manager_requirements.txt +++ b/manager_requirements.txt @@ -1 +1 @@ -comfyui_manager==4.1 +comfyui_manager==4.2.1 From ec4b1659ab751b7da07bfff8fa28660c7e82c00b Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Thu, 23 Apr 2026 08:13:38 +1000 Subject: [PATCH 075/140] ModelPatcherDynamic: force cast stray weights on comfy layers (#13487) the mixed_precision ops can have input_scale parameters that are used in tensor math but arent a weight or bias so dont get proper VRAM management. Treat these as force-castable parameters like the non comfy weight, random params are buffers already are. --- comfy/model_patcher.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index 93d19d6fe..ee56f8523 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -685,9 +685,9 @@ class ModelPatcher: sd.pop(k) return sd - def patch_weight_to_device(self, key, device_to=None, inplace_update=False, return_weight=False): + def patch_weight_to_device(self, key, device_to=None, inplace_update=False, return_weight=False, force_cast=False): weight, set_func, convert_func = get_key_weight(self.model, key) - if key not in self.patches: + if key not in self.patches and not force_cast: return weight inplace_update = self.weight_inplace_update or inplace_update @@ -695,7 +695,7 @@ class ModelPatcher: if key not in self.backup and not return_weight: self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update) - temp_dtype = comfy.model_management.lora_compute_dtype(device_to) + temp_dtype = comfy.model_management.lora_compute_dtype(device_to) if key in self.patches else None if device_to is not None: temp_weight = comfy.model_management.cast_to_device(weight, device_to, temp_dtype, copy=True) else: @@ -703,9 +703,10 @@ class ModelPatcher: if convert_func is not None: temp_weight = convert_func(temp_weight, inplace=True) - out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key) + out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key) if key in self.patches else temp_weight if set_func is None: - out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=comfy.utils.string_to_seed(key)) + if key in self.patches: + out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=comfy.utils.string_to_seed(key)) if return_weight: return out_weight elif inplace_update: @@ -1584,7 +1585,7 @@ class ModelPatcherDynamic(ModelPatcher): key = key_param_name_to_key(n, param_key) if key in self.backup: comfy.utils.set_attr_param(self.model, key, self.backup[key].weight) - self.patch_weight_to_device(key, device_to=device_to) + self.patch_weight_to_device(key, device_to=device_to, force_cast=True) weight, _, _ = get_key_weight(self.model, key) if weight is not None: self.model.model_loaded_weight_memory += weight.numel() * weight.element_size() @@ -1609,6 +1610,10 @@ class ModelPatcherDynamic(ModelPatcher): m._v = vbar.alloc(v_weight_size) allocated_size += v_weight_size + for param in params: + if param not in ("weight", "bias"): + force_load_param(self, param, device_to) + else: for param in params: key = key_param_name_to_key(n, param) From 0be87b082a68bca19ea25a9208120ba5090bea8d Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 22 Apr 2026 17:21:43 -0700 Subject: [PATCH 076/140] Update logging level for invalid version format (#13526) --- utils/install_util.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils/install_util.py b/utils/install_util.py index 34489aec5..fdba23a8f 100644 --- a/utils/install_util.py +++ b/utils/install_util.py @@ -39,7 +39,7 @@ def get_required_packages_versions(): if len(s) == 2: version_str = s[-1] if not is_valid_version(version_str): - logging.error(f"Invalid version format in requirements.txt: {version_str}") + logging.debug(f"Invalid version format for {s[0]} in requirements.txt: {version_str}") continue out[s[0]] = version_str return out.copy() From e988df72f8828085c1671d49f96ec50382f11c80 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Thu, 23 Apr 2026 03:59:55 +0300 Subject: [PATCH 077/140] [Partner Nodes] add SD2 real human support (#13509) * feat(api-nodes): add SD2 real human support Signed-off-by: bigcat88 * fix: add validation before uploading Assets Signed-off-by: bigcat88 * Add asset_id and group_id displaying on the node Signed-off-by: bigcat88 * extend poll_op to use instead of custom async cycle Signed-off-by: bigcat88 * added the polling for the "Active" status after asset creation Signed-off-by: bigcat88 * updated tooltip for group_id * allow usage of real human in the ByteDance2FirstLastFrame node * add reference count limits * corrected price in status when input assets contain video Signed-off-by: bigcat88 --------- Signed-off-by: bigcat88 --- comfy_api_nodes/apis/bytedance.py | 35 +++ comfy_api_nodes/nodes_bytedance.py | 468 +++++++++++++++++++++++++++-- comfy_api_nodes/util/client.py | 9 +- 3 files changed, 494 insertions(+), 18 deletions(-) diff --git a/comfy_api_nodes/apis/bytedance.py b/comfy_api_nodes/apis/bytedance.py index dc3bc3213..eafabbefe 100644 --- a/comfy_api_nodes/apis/bytedance.py +++ b/comfy_api_nodes/apis/bytedance.py @@ -122,6 +122,41 @@ class TaskStatusResponse(BaseModel): usage: TaskStatusUsage | None = Field(None) +class GetAssetResponse(BaseModel): + id: str = Field(...) + name: str | None = Field(None) + url: str | None = Field(None) + asset_type: str = Field(...) + group_id: str = Field(...) + status: str = Field(...) + error: TaskStatusError | None = Field(None) + + +class SeedanceCreateVisualValidateSessionResponse(BaseModel): + session_id: str = Field(...) + h5_link: str = Field(...) + + +class SeedanceGetVisualValidateSessionResponse(BaseModel): + session_id: str = Field(...) + status: str = Field(...) + group_id: str | None = Field(None) + error_code: str | None = Field(None) + error_message: str | None = Field(None) + + +class SeedanceCreateAssetRequest(BaseModel): + group_id: str = Field(...) + url: str = Field(...) + asset_type: str = Field(...) + name: str | None = Field(None, max_length=64) + project_name: str | None = Field(None) + + +class SeedanceCreateAssetResponse(BaseModel): + asset_id: str = Field(...) + + # Dollars per 1K tokens, keyed by (model_id, has_video_input). SEEDANCE2_PRICE_PER_1K_TOKENS = { ("dreamina-seedance-2-0-260128", False): 0.007, diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py index bc564782d..de192c5ac 100644 --- a/comfy_api_nodes/nodes_bytedance.py +++ b/comfy_api_nodes/nodes_bytedance.py @@ -1,5 +1,6 @@ import logging import math +import re import torch from typing_extensions import override @@ -11,9 +12,14 @@ from comfy_api_nodes.apis.bytedance import ( SEEDANCE2_PRICE_PER_1K_TOKENS, SEEDANCE2_REF_VIDEO_PIXEL_LIMITS, VIDEO_TASKS_EXECUTION_TIME, + GetAssetResponse, Image2VideoTaskCreationRequest, ImageTaskCreationResponse, Seedance2TaskCreationRequest, + SeedanceCreateAssetRequest, + SeedanceCreateAssetResponse, + SeedanceCreateVisualValidateSessionResponse, + SeedanceGetVisualValidateSessionResponse, Seedream4Options, Seedream4TaskCreationRequest, TaskAudioContent, @@ -44,10 +50,16 @@ from comfy_api_nodes.util import ( validate_image_aspect_ratio, validate_image_dimensions, validate_string, + validate_video_dimensions, + validate_video_duration, ) +from server import PromptServer BYTEPLUS_IMAGE_ENDPOINT = "/proxy/byteplus/api/v3/images/generations" +_VERIFICATION_POLL_TIMEOUT_SEC = 120 +_VERIFICATION_POLL_INTERVAL_SEC = 3 + SEEDREAM_MODELS = { "seedream 5.0 lite": "seedream-5-0-260128", "seedream-4-5-251128": "seedream-4-5-251128", @@ -96,6 +108,169 @@ def _validate_ref_video_pixels(video: Input.Video, model_id: str, resolution: st ) +async def _resolve_reference_assets( + cls: type[IO.ComfyNode], + asset_ids: list[str], +) -> tuple[dict[str, str], dict[str, str], dict[str, str]]: + """Look up each asset, validate Active status, group by asset_type. + + Returns (image_assets, video_assets, audio_assets), each mapping asset_id -> "asset://". + """ + image_assets: dict[str, str] = {} + video_assets: dict[str, str] = {} + audio_assets: dict[str, str] = {} + for i, raw_id in enumerate(asset_ids, 1): + asset_id = (raw_id or "").strip() + if not asset_id: + continue + result = await sync_op( + cls, + ApiEndpoint(path=f"/proxy/seedance/assets/{asset_id}"), + response_model=GetAssetResponse, + ) + if result.status != "Active": + extra = f" {result.error.code}: {result.error.message}" if result.error else "" + raise ValueError(f"Reference asset {i} (Id={asset_id}) is not Active (Status={result.status}).{extra}") + asset_uri = f"asset://{asset_id}" + if result.asset_type == "Image": + image_assets[asset_id] = asset_uri + elif result.asset_type == "Video": + video_assets[asset_id] = asset_uri + elif result.asset_type == "Audio": + audio_assets[asset_id] = asset_uri + return image_assets, video_assets, audio_assets + + +_ASSET_REF_RE = re.compile(r"\basset ?(\d{1,2})\b", re.IGNORECASE) + + +def _build_asset_labels( + reference_assets: dict[str, str], + image_asset_uris: dict[str, str], + video_asset_uris: dict[str, str], + audio_asset_uris: dict[str, str], + n_reference_images: int, + n_reference_videos: int, + n_reference_audios: int, +) -> dict[int, str]: + """Map asset slot number (from 'asset_N' keys) to its positional label. + + Asset entries are appended to `content` after the reference_images/videos/audios, + so their 1-indexed labels continue from the count of existing same-type refs: + one reference_images entry + one Image-type asset -> asset labelled "Image 2". + """ + image_n = n_reference_images + video_n = n_reference_videos + audio_n = n_reference_audios + labels: dict[int, str] = {} + for slot_key, raw_id in reference_assets.items(): + asset_id = (raw_id or "").strip() + if not asset_id: + continue + try: + slot_num = int(slot_key.rsplit("_", 1)[-1]) + except ValueError: + continue + if asset_id in image_asset_uris: + image_n += 1 + labels[slot_num] = f"Image {image_n}" + elif asset_id in video_asset_uris: + video_n += 1 + labels[slot_num] = f"Video {video_n}" + elif asset_id in audio_asset_uris: + audio_n += 1 + labels[slot_num] = f"Audio {audio_n}" + return labels + + +def _rewrite_asset_refs(prompt: str, labels: dict[int, str]) -> str: + """Case-insensitively replace 'assetNN' (1-2 digit) tokens with their labels.""" + if not labels: + return prompt + + def _sub(m: "re.Match[str]") -> str: + return labels.get(int(m.group(1)), m.group(0)) + + return _ASSET_REF_RE.sub(_sub, prompt) + + +async def _obtain_group_id_via_h5_auth(cls: type[IO.ComfyNode]) -> str: + session = await sync_op( + cls, + ApiEndpoint(path="/proxy/seedance/visual-validate/sessions", method="POST"), + response_model=SeedanceCreateVisualValidateSessionResponse, + ) + logger.warning("Seedance authentication required. Open link: %s", session.h5_link) + + h5_text = f"Open this link in your browser and complete face verification:\n\n{session.h5_link}" + + result = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/seedance/visual-validate/sessions/{session.session_id}"), + response_model=SeedanceGetVisualValidateSessionResponse, + status_extractor=lambda r: r.status, + completed_statuses=["completed"], + failed_statuses=["failed"], + poll_interval=_VERIFICATION_POLL_INTERVAL_SEC, + max_poll_attempts=(_VERIFICATION_POLL_TIMEOUT_SEC // _VERIFICATION_POLL_INTERVAL_SEC) - 1, + estimated_duration=_VERIFICATION_POLL_TIMEOUT_SEC - 1, + extra_text=h5_text, + ) + + if not result.group_id: + raise RuntimeError(f"Seedance session {session.session_id} completed without a group_id") + + logger.warning("Seedance authentication complete. New GroupId: %s", result.group_id) + PromptServer.instance.send_progress_text( + f"Authentication complete. New GroupId: {result.group_id}", cls.hidden.unique_id + ) + return result.group_id + + +async def _resolve_group_id(cls: type[IO.ComfyNode], group_id: str) -> str: + if group_id and group_id.strip(): + return group_id.strip() + return await _obtain_group_id_via_h5_auth(cls) + + +async def _create_seedance_asset( + cls: type[IO.ComfyNode], + *, + group_id: str, + url: str, + name: str, + asset_type: str, +) -> str: + req = SeedanceCreateAssetRequest( + group_id=group_id, + url=url, + asset_type=asset_type, + name=name or None, + ) + result = await sync_op( + cls, + ApiEndpoint(path="/proxy/seedance/assets", method="POST"), + response_model=SeedanceCreateAssetResponse, + data=req, + ) + return result.asset_id + + +async def _wait_for_asset_active(cls: type[IO.ComfyNode], asset_id: str, group_id: str) -> GetAssetResponse: + """Poll the newly created asset until its status becomes Active.""" + return await poll_op( + cls, + ApiEndpoint(path=f"/proxy/seedance/assets/{asset_id}"), + response_model=GetAssetResponse, + status_extractor=lambda r: r.status, + completed_statuses=["Active"], + failed_statuses=["Failed"], + poll_interval=5, + max_poll_attempts=1200, + extra_text=f"Waiting for asset pre-processing...\n\nasset_id: {asset_id}\n\ngroup_id: {group_id}", + ) + + def _seedance2_price_extractor(model_id: str, has_video_input: bool): """Returns a price_extractor closure for Seedance 2.0 poll_op.""" rate = SEEDANCE2_PRICE_PER_1K_TOKENS.get((model_id, has_video_input)) @@ -1228,12 +1403,27 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode): IO.Image.Input( "first_frame", tooltip="First frame image for the video.", + optional=True, ), IO.Image.Input( "last_frame", tooltip="Last frame image for the video.", optional=True, ), + IO.String.Input( + "first_frame_asset_id", + default="", + tooltip="Seedance asset_id to use as the first frame. " + "Mutually exclusive with the first_frame image input.", + optional=True, + ), + IO.String.Input( + "last_frame_asset_id", + default="", + tooltip="Seedance asset_id to use as the last frame. " + "Mutually exclusive with the last_frame image input.", + optional=True, + ), IO.Int.Input( "seed", default=0, @@ -1286,24 +1476,54 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode): async def execute( cls, model: dict, - first_frame: Input.Image, seed: int, watermark: bool, + first_frame: Input.Image | None = None, last_frame: Input.Image | None = None, + first_frame_asset_id: str = "", + last_frame_asset_id: str = "", ) -> IO.NodeOutput: validate_string(model["prompt"], strip_whitespace=True, min_length=1) model_id = SEEDANCE_MODELS[model["model"]] + first_frame_asset_id = first_frame_asset_id.strip() + last_frame_asset_id = last_frame_asset_id.strip() + + if first_frame is not None and first_frame_asset_id: + raise ValueError("Provide only one of first_frame or first_frame_asset_id, not both.") + if first_frame is None and not first_frame_asset_id: + raise ValueError("Either first_frame or first_frame_asset_id is required.") + if last_frame is not None and last_frame_asset_id: + raise ValueError("Provide only one of last_frame or last_frame_asset_id, not both.") + + asset_ids_to_resolve = [a for a in (first_frame_asset_id, last_frame_asset_id) if a] + image_assets: dict[str, str] = {} + if asset_ids_to_resolve: + image_assets, _, _ = await _resolve_reference_assets(cls, asset_ids_to_resolve) + for aid in asset_ids_to_resolve: + if aid not in image_assets: + raise ValueError(f"Asset {aid} is not an Image asset.") + + if first_frame_asset_id: + first_frame_url = image_assets[first_frame_asset_id] + else: + first_frame_url = await upload_image_to_comfyapi(cls, first_frame, wait_label="Uploading first frame.") + content: list[TaskTextContent | TaskImageContent] = [ TaskTextContent(text=model["prompt"]), TaskImageContent( - image_url=TaskImageContentUrl( - url=await upload_image_to_comfyapi(cls, first_frame, wait_label="Uploading first frame.") - ), + image_url=TaskImageContentUrl(url=first_frame_url), role="first_frame", ), ] - if last_frame is not None: + if last_frame_asset_id: + content.append( + TaskImageContent( + image_url=TaskImageContentUrl(url=image_assets[last_frame_asset_id]), + role="last_frame", + ), + ) + elif last_frame is not None: content.append( TaskImageContent( image_url=TaskImageContentUrl( @@ -1385,6 +1605,24 @@ def _seedance2_reference_inputs(resolutions: list[str]): tooltip="Automatically downscale reference videos that exceed the model's pixel budget " "for the selected resolution. Aspect ratio is preserved; videos already within limits are untouched.", ), + IO.Autogrow.Input( + "reference_assets", + template=IO.Autogrow.TemplateNames( + IO.String.Input("reference_asset"), + names=[ + "asset_1", + "asset_2", + "asset_3", + "asset_4", + "asset_5", + "asset_6", + "asset_7", + "asset_8", + "asset_9", + ], + min=0, + ), + ), ] @@ -1486,24 +1724,42 @@ class ByteDance2ReferenceNode(IO.ComfyNode): reference_images = model.get("reference_images", {}) reference_videos = model.get("reference_videos", {}) reference_audios = model.get("reference_audios", {}) + reference_assets = model.get("reference_assets", {}) - if not reference_images and not reference_videos: - raise ValueError("At least one reference image or video is required.") + reference_image_assets, reference_video_assets, reference_audio_assets = await _resolve_reference_assets( + cls, list(reference_assets.values()) + ) + + if not reference_images and not reference_videos and not reference_image_assets and not reference_video_assets: + raise ValueError("At least one reference image or video or asset is required.") + + total_images = len(reference_images) + len(reference_image_assets) + if total_images > 9: + raise ValueError( + f"Too many reference images: {total_images} " + f"(images={len(reference_images)}, image assets={len(reference_image_assets)}). Maximum is 9." + ) + total_videos = len(reference_videos) + len(reference_video_assets) + if total_videos > 3: + raise ValueError( + f"Too many reference videos: {total_videos} " + f"(videos={len(reference_videos)}, video assets={len(reference_video_assets)}). Maximum is 3." + ) + total_audios = len(reference_audios) + len(reference_audio_assets) + if total_audios > 3: + raise ValueError( + f"Too many reference audios: {total_audios} " + f"(audios={len(reference_audios)}, audio assets={len(reference_audio_assets)}). Maximum is 3." + ) model_id = SEEDANCE_MODELS[model["model"]] - has_video_input = len(reference_videos) > 0 + has_video_input = total_videos > 0 if model.get("auto_downscale") and reference_videos: - max_px = ( - SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}) - .get(model["resolution"], {}) - .get("max") - ) + max_px = SEEDANCE2_REF_VIDEO_PIXEL_LIMITS.get(model_id, {}).get(model["resolution"], {}).get("max") if max_px: for key in reference_videos: - reference_videos[key] = resize_video_to_pixel_budget( - reference_videos[key], max_px - ) + reference_videos[key] = resize_video_to_pixel_budget(reference_videos[key], max_px) total_video_duration = 0.0 for i, key in enumerate(reference_videos, 1): @@ -1531,8 +1787,19 @@ class ByteDance2ReferenceNode(IO.ComfyNode): if total_audio_duration > 15.1: raise ValueError(f"Total reference audio duration is {total_audio_duration:.1f}s. Maximum is 15.1 seconds.") + asset_labels = _build_asset_labels( + reference_assets, + reference_image_assets, + reference_video_assets, + reference_audio_assets, + len(reference_images), + len(reference_videos), + len(reference_audios), + ) + prompt_text = _rewrite_asset_refs(model["prompt"], asset_labels) + content: list[TaskTextContent | TaskImageContent | TaskVideoContent | TaskAudioContent] = [ - TaskTextContent(text=model["prompt"]), + TaskTextContent(text=prompt_text), ] for i, key in enumerate(reference_images, 1): content.append( @@ -1573,6 +1840,21 @@ class ByteDance2ReferenceNode(IO.ComfyNode): ), ), ) + for url in reference_image_assets.values(): + content.append( + TaskImageContent( + image_url=TaskImageContentUrl(url=url), + role="reference_image", + ), + ) + for url in reference_video_assets.values(): + content.append( + TaskVideoContent(video_url=TaskVideoContentUrl(url=url)), + ) + for url in reference_audio_assets.values(): + content.append( + TaskAudioContent(audio_url=TaskAudioContentUrl(url=url)), + ) initial_response = await sync_op( cls, ApiEndpoint(path=BYTEPLUS_TASK_ENDPOINT, method="POST"), @@ -1627,6 +1909,156 @@ async def process_video_task( return IO.NodeOutput(await download_url_to_video_output(response.content.video_url)) +class ByteDanceCreateImageAsset(IO.ComfyNode): + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="ByteDanceCreateImageAsset", + display_name="ByteDance Create Image Asset", + category="api node/image/ByteDance", + description=( + "Create a Seedance 2.0 personal image asset. Uploads the input image and " + "registers it in the given asset group. If group_id is empty, runs a real-person " + "H5 authentication flow to create a new group before adding the asset." + ), + inputs=[ + IO.Image.Input("image", tooltip="Image to register as a personal asset."), + IO.String.Input( + "group_id", + default="", + tooltip="Reuse an existing Seedance asset group ID to skip repeated human verification for the " + "same person. Leave empty to run real-person authentication in the browser and create a new group.", + ), + # IO.String.Input( + # "name", + # default="", + # tooltip="Asset name (up to 64 characters).", + # ), + ], + outputs=[ + IO.String.Output(display_name="asset_id"), + IO.String.Output(display_name="group_id"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + # is_api_node=True, + ) + + @classmethod + async def execute( + cls, + image: Input.Image, + group_id: str = "", + # name: str = "", + ) -> IO.NodeOutput: + # if len(name) > 64: + # raise ValueError("Name of asset can not be greater then 64 symbols") + validate_image_dimensions(image, min_width=300, max_width=6000, min_height=300, max_height=6000) + validate_image_aspect_ratio(image, min_ratio=(0.4, 1), max_ratio=(2.5, 1)) + resolved_group = await _resolve_group_id(cls, group_id) + asset_id = await _create_seedance_asset( + cls, + group_id=resolved_group, + url=await upload_image_to_comfyapi(cls, image), + name="", + asset_type="Image", + ) + await _wait_for_asset_active(cls, asset_id, resolved_group) + PromptServer.instance.send_progress_text( + f"Please save the asset_id and group_id for reuse.\n\nasset_id: {asset_id}\n\n" + f"group_id: {resolved_group}", + cls.hidden.unique_id, + ) + return IO.NodeOutput(asset_id, resolved_group) + + +class ByteDanceCreateVideoAsset(IO.ComfyNode): + + @classmethod + def define_schema(cls) -> IO.Schema: + return IO.Schema( + node_id="ByteDanceCreateVideoAsset", + display_name="ByteDance Create Video Asset", + category="api node/video/ByteDance", + description=( + "Create a Seedance 2.0 personal video asset. Uploads the input video and " + "registers it in the given asset group. If group_id is empty, runs a real-person " + "H5 authentication flow to create a new group before adding the asset." + ), + inputs=[ + IO.Video.Input("video", tooltip="Video to register as a personal asset."), + IO.String.Input( + "group_id", + default="", + tooltip="Reuse an existing Seedance asset group ID to skip repeated human verification for the " + "same person. Leave empty to run real-person authentication in the browser and create a new group.", + ), + # IO.String.Input( + # "name", + # default="", + # tooltip="Asset name (up to 64 characters).", + # ), + ], + outputs=[ + IO.String.Output(display_name="asset_id"), + IO.String.Output(display_name="group_id"), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + # is_api_node=True, + ) + + @classmethod + async def execute( + cls, + video: Input.Video, + group_id: str = "", + # name: str = "", + ) -> IO.NodeOutput: + # if len(name) > 64: + # raise ValueError("Name of asset can not be greater then 64 symbols") + validate_video_duration(video, min_duration=2, max_duration=15) + validate_video_dimensions(video, min_width=300, max_width=6000, min_height=300, max_height=6000) + + w, h = video.get_dimensions() + if h > 0: + ratio = w / h + if not (0.4 <= ratio <= 2.5): + raise ValueError(f"Asset video aspect ratio (W/H) must be in [0.4, 2.5], got {ratio:.3f} ({w}x{h}).") + pixels = w * h + if not (409_600 <= pixels <= 927_408): + raise ValueError( + f"Asset video total pixels (W×H) must be in [409600, 927408], " f"got {pixels:,} ({w}x{h})." + ) + + fps = float(video.get_frame_rate()) + if not (24 <= fps <= 60): + raise ValueError(f"Asset video FPS must be in [24, 60], got {fps:.2f}.") + + resolved_group = await _resolve_group_id(cls, group_id) + asset_id = await _create_seedance_asset( + cls, + group_id=resolved_group, + url=await upload_video_to_comfyapi(cls, video), + name="", + asset_type="Video", + ) + await _wait_for_asset_active(cls, asset_id, resolved_group) + PromptServer.instance.send_progress_text( + f"Please save the asset_id and group_id for reuse.\n\nasset_id: {asset_id}\n\n" + f"group_id: {resolved_group}", + cls.hidden.unique_id, + ) + return IO.NodeOutput(asset_id, resolved_group) + + class ByteDanceExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: @@ -1640,6 +2072,8 @@ class ByteDanceExtension(ComfyExtension): ByteDance2TextToVideoNode, ByteDance2FirstLastFrameNode, ByteDance2ReferenceNode, + ByteDanceCreateImageAsset, + ByteDanceCreateVideoAsset, ] diff --git a/comfy_api_nodes/util/client.py b/comfy_api_nodes/util/client.py index 9d730b81a..b0cf97ae4 100644 --- a/comfy_api_nodes/util/client.py +++ b/comfy_api_nodes/util/client.py @@ -156,6 +156,7 @@ async def poll_op( estimated_duration: int | None = None, cancel_endpoint: ApiEndpoint | None = None, cancel_timeout: float = 10.0, + extra_text: str | None = None, ) -> M: raw = await poll_op_raw( cls, @@ -176,6 +177,7 @@ async def poll_op( estimated_duration=estimated_duration, cancel_endpoint=cancel_endpoint, cancel_timeout=cancel_timeout, + extra_text=extra_text, ) if not isinstance(raw, dict): raise Exception("Expected JSON response to validate into a Pydantic model, got non-JSON (binary or text).") @@ -260,6 +262,7 @@ async def poll_op_raw( estimated_duration: int | None = None, cancel_endpoint: ApiEndpoint | None = None, cancel_timeout: float = 10.0, + extra_text: str | None = None, ) -> dict[str, Any]: """ Polls an endpoint until the task reaches a terminal state. Displays time while queued/processing, @@ -299,6 +302,7 @@ async def poll_op_raw( price=state.price, is_queued=state.is_queued, processing_elapsed_seconds=int(proc_elapsed), + extra_text=extra_text, ) await asyncio.sleep(1.0) except Exception as exc: @@ -389,6 +393,7 @@ async def poll_op_raw( price=state.price, is_queued=False, processing_elapsed_seconds=int(state.base_processing_elapsed), + extra_text=extra_text, ) return resp_json @@ -462,6 +467,7 @@ def _display_time_progress( price: float | None = None, is_queued: bool | None = None, processing_elapsed_seconds: int | None = None, + extra_text: str | None = None, ) -> None: if estimated_total is not None and estimated_total > 0 and is_queued is False: pe = processing_elapsed_seconds if processing_elapsed_seconds is not None else elapsed_seconds @@ -469,7 +475,8 @@ def _display_time_progress( time_line = f"Time elapsed: {int(elapsed_seconds)}s (~{remaining}s remaining)" else: time_line = f"Time elapsed: {int(elapsed_seconds)}s" - _display_text(node_cls, time_line, status=status, price=price) + text = f"{time_line}\n\n{extra_text}" if extra_text else time_line + _display_text(node_cls, text, status=status, price=price) async def _diagnose_connectivity() -> dict[str, bool]: From 749d5b4e8d4308c67fee6faa4ef4dfbde23087f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Thu, 23 Apr 2026 07:07:43 +0300 Subject: [PATCH 078/140] feat: SAM (segment anything) 3.1 support (CORE-34) (#13408) --- comfy/ldm/sam3/detector.py | 596 ++++++++++ comfy/ldm/sam3/sam.py | 425 +++++++ comfy/ldm/sam3/tracker.py | 1785 ++++++++++++++++++++++++++++++ comfy/model_base.py | 5 + comfy/model_detection.py | 12 + comfy/supported_models.py | 53 +- comfy/text_encoders/sam3_clip.py | 97 ++ comfy_extras/nodes_sam3.py | 529 +++++++++ nodes.py | 1 + 9 files changed, 3502 insertions(+), 1 deletion(-) create mode 100644 comfy/ldm/sam3/detector.py create mode 100644 comfy/ldm/sam3/sam.py create mode 100644 comfy/ldm/sam3/tracker.py create mode 100644 comfy/text_encoders/sam3_clip.py create mode 100644 comfy_extras/nodes_sam3.py diff --git a/comfy/ldm/sam3/detector.py b/comfy/ldm/sam3/detector.py new file mode 100644 index 000000000..6ae919a79 --- /dev/null +++ b/comfy/ldm/sam3/detector.py @@ -0,0 +1,596 @@ +# SAM3 detector: transformer encoder-decoder, segmentation head, geometry encoder, scoring. + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision.ops import roi_align + +from comfy.ldm.modules.attention import optimized_attention +from comfy.ldm.sam3.tracker import SAM3Tracker, SAM31Tracker +from comfy.ldm.sam3.sam import SAM3VisionBackbone # noqa: used in __init__ +from comfy.ldm.sam3.sam import MLP, PositionEmbeddingSine + +TRACKER_CLASSES = {"SAM3": SAM3Tracker, "SAM31": SAM31Tracker} +from comfy.ops import cast_to_input + + +def box_cxcywh_to_xyxy(x): + cx, cy, w, h = x.unbind(-1) + return torch.stack([cx - 0.5 * w, cy - 0.5 * h, cx + 0.5 * w, cy + 0.5 * h], dim=-1) + + +def gen_sineembed_for_position(pos_tensor, num_feats=256): + """Per-coordinate sinusoidal embedding: (..., N) -> (..., N * num_feats).""" + assert num_feats % 2 == 0 + hdim = num_feats // 2 + freqs = 10000.0 ** (2 * (torch.arange(hdim, dtype=torch.float32, device=pos_tensor.device) // 2) / hdim) + embeds = [] + for c in range(pos_tensor.shape[-1]): + raw = (pos_tensor[..., c].float() * 2 * math.pi).unsqueeze(-1) / freqs + embeds.append(torch.stack([raw[..., 0::2].sin(), raw[..., 1::2].cos()], dim=-1).flatten(-2)) + return torch.cat(embeds, dim=-1).to(pos_tensor.dtype) + + +class SplitMHA(nn.Module): + """Multi-head attention with separate Q/K/V projections (split from fused in_proj_weight).""" + def __init__(self, d_model, num_heads=8, device=None, dtype=None, operations=None): + super().__init__() + self.num_heads = num_heads + self.q_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.k_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.v_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.out_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + + def forward(self, q_input, k_input=None, v_input=None, mask=None): + q = self.q_proj(q_input) + if k_input is None: + k = self.k_proj(q_input) + v = self.v_proj(q_input) + else: + k = self.k_proj(k_input) + v = self.v_proj(v_input if v_input is not None else k_input) + if mask is not None and mask.ndim == 2: + mask = mask[:, None, None, :] # [B, T] -> [B, 1, 1, T] for SDPA broadcast + dtype = q.dtype # manual_cast may produce mixed dtypes + out = optimized_attention(q, k.to(dtype), v.to(dtype), self.num_heads, mask=mask) + return self.out_proj(out) + + +class MLPWithNorm(nn.Module): + """MLP with residual connection and output LayerNorm.""" + def __init__(self, input_dim, hidden_dim, output_dim, num_layers, residual=True, device=None, dtype=None, operations=None): + super().__init__() + dims = [input_dim] + [hidden_dim] * (num_layers - 1) + [output_dim] + self.layers = nn.ModuleList([ + operations.Linear(dims[i], dims[i + 1], device=device, dtype=dtype) + for i in range(num_layers) + ]) + self.out_norm = operations.LayerNorm(output_dim, device=device, dtype=dtype) + self.residual = residual and (input_dim == output_dim) + + def forward(self, x): + orig = x + for i, layer in enumerate(self.layers): + x = layer(x) + if i < len(self.layers) - 1: + x = F.relu(x) + if self.residual: + x = x + orig + return self.out_norm(x) + + +class EncoderLayer(nn.Module): + def __init__(self, d_model=256, num_heads=8, dim_ff=2048, device=None, dtype=None, operations=None): + super().__init__() + self.self_attn = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations) + self.cross_attn_image = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations) + self.linear1 = operations.Linear(d_model, dim_ff, device=device, dtype=dtype) + self.linear2 = operations.Linear(dim_ff, d_model, device=device, dtype=dtype) + self.norm1 = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.norm2 = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.norm3 = operations.LayerNorm(d_model, device=device, dtype=dtype) + + def forward(self, x, pos, text_memory=None, text_mask=None): + normed = self.norm1(x) + q_k = normed + pos + x = x + self.self_attn(q_k, q_k, normed) + if text_memory is not None: + normed = self.norm2(x) + x = x + self.cross_attn_image(normed, text_memory, text_memory, mask=text_mask) + normed = self.norm3(x) + x = x + self.linear2(F.relu(self.linear1(normed))) + return x + + +class TransformerEncoder(nn.Module): + """Checkpoint: transformer.encoder.layers.N.*""" + def __init__(self, d_model=256, num_heads=8, dim_ff=2048, num_layers=6, device=None, dtype=None, operations=None): + super().__init__() + self.layers = nn.ModuleList([ + EncoderLayer(d_model, num_heads, dim_ff, device=device, dtype=dtype, operations=operations) + for _ in range(num_layers) + ]) + + def forward(self, x, pos, text_memory=None, text_mask=None): + for layer in self.layers: + x = layer(x, pos, text_memory, text_mask) + return x + + +class DecoderLayer(nn.Module): + def __init__(self, d_model=256, num_heads=8, dim_ff=2048, device=None, dtype=None, operations=None): + super().__init__() + self.self_attn = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations) + self.cross_attn = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations) + self.ca_text = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations) + self.norm1 = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.norm2 = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.norm3 = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.catext_norm = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.linear1 = operations.Linear(d_model, dim_ff, device=device, dtype=dtype) + self.linear2 = operations.Linear(dim_ff, d_model, device=device, dtype=dtype) + + def forward(self, x, memory, x_pos, memory_pos, text_memory=None, text_mask=None, cross_attn_bias=None): + q_k = x + x_pos + x = self.norm2(x + self.self_attn(q_k, q_k, x)) + if text_memory is not None: + x = self.catext_norm(x + self.ca_text(x + x_pos, text_memory, text_memory, mask=text_mask)) + x = self.norm1(x + self.cross_attn(x + x_pos, memory + memory_pos, memory, mask=cross_attn_bias)) + x = self.norm3(x + self.linear2(F.relu(self.linear1(x)))) + return x + + +class TransformerDecoder(nn.Module): + def __init__(self, d_model=256, num_heads=8, dim_ff=2048, num_layers=6, + num_queries=200, device=None, dtype=None, operations=None): + super().__init__() + self.d_model = d_model + self.num_queries = num_queries + + self.layers = nn.ModuleList([ + DecoderLayer(d_model, num_heads, dim_ff, device=device, dtype=dtype, operations=operations) + for _ in range(num_layers) + ]) + self.norm = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.query_embed = operations.Embedding(num_queries, d_model, device=device, dtype=dtype) + self.reference_points = operations.Embedding(num_queries, 4, device=device, dtype=dtype) # Reference points: Embedding(num_queries, 4) — learned anchor boxes + self.ref_point_head = MLP(d_model * 2, d_model, d_model, 2, device=device, dtype=dtype, operations=operations) # ref_point_head input: 512 (4 coords * 128 sine features each) + self.bbox_embed = MLP(d_model, d_model, 4, 3, device=device, dtype=dtype, operations=operations) + + self.boxRPB_embed_x = MLP(2, d_model, num_heads, 2, device=device, dtype=dtype, operations=operations) + self.boxRPB_embed_y = MLP(2, d_model, num_heads, 2, device=device, dtype=dtype, operations=operations) + + self.presence_token = operations.Embedding(1, d_model, device=device, dtype=dtype) + self.presence_token_head = MLP(d_model, d_model, 1, 3, device=device, dtype=dtype, operations=operations) + self.presence_token_out_norm = operations.LayerNorm(d_model, device=device, dtype=dtype) + + @staticmethod + def _inverse_sigmoid(x): + return torch.log(x / (1 - x + 1e-6) + 1e-6) + + def _compute_box_rpb(self, ref_points, H, W): + """Box rotary position bias: (B, Q, 4) cxcywh -> (B, n_heads, Q+1, H*W) bias.""" + boxes_xyxy = box_cxcywh_to_xyxy(ref_points) + B, Q, _ = boxes_xyxy.shape + coords_h = torch.arange(H, device=ref_points.device, dtype=torch.float32) / H + coords_w = torch.arange(W, device=ref_points.device, dtype=torch.float32) / W + deltas_x = coords_w.view(1, 1, -1, 1) - boxes_xyxy[:, :, None, 0:3:2] + deltas_y = coords_h.view(1, 1, -1, 1) - boxes_xyxy[:, :, None, 1:4:2] + + log2_8 = float(math.log2(8)) + def log_scale(d): + return torch.sign(d * 8) * torch.log2(torch.abs(d * 8) + 1.0) / log2_8 + + rpb_x = self.boxRPB_embed_x(log_scale(deltas_x).to(ref_points.dtype)) + rpb_y = self.boxRPB_embed_y(log_scale(deltas_y).to(ref_points.dtype)) + + bias = (rpb_y.unsqueeze(3) + rpb_x.unsqueeze(2)).flatten(2, 3).permute(0, 3, 1, 2) + pres_bias = torch.zeros(B, bias.shape[1], 1, bias.shape[3], device=bias.device, dtype=bias.dtype) + return torch.cat([pres_bias, bias], dim=2) + + def forward(self, memory, memory_pos, text_memory=None, text_mask=None, H=72, W=72): + B = memory.shape[0] + tgt = cast_to_input(self.query_embed.weight, memory).unsqueeze(0).expand(B, -1, -1) + presence_out = cast_to_input(self.presence_token.weight, memory)[None].expand(B, -1, -1) + ref_points = cast_to_input(self.reference_points.weight, memory).unsqueeze(0).expand(B, -1, -1).sigmoid() + + for layer_idx, layer in enumerate(self.layers): + query_pos = self.ref_point_head(gen_sineembed_for_position(ref_points, self.d_model)) + tgt_with_pres = torch.cat([presence_out, tgt], dim=1) + pos_with_pres = torch.cat([torch.zeros_like(presence_out), query_pos], dim=1) + tgt_with_pres = layer(tgt_with_pres, memory, pos_with_pres, memory_pos, + text_memory, text_mask, self._compute_box_rpb(ref_points, H, W)) + presence_out, tgt = tgt_with_pres[:, :1], tgt_with_pres[:, 1:] + if layer_idx < len(self.layers) - 1: + ref_inv = self._inverse_sigmoid(ref_points) + ref_points = (ref_inv + self.bbox_embed(self.norm(tgt))).sigmoid().detach() + + query_out = self.norm(tgt) + ref_inv = self._inverse_sigmoid(ref_points) + boxes = (ref_inv + self.bbox_embed(query_out)).sigmoid() + presence = self.presence_token_head(self.presence_token_out_norm(presence_out)).squeeze(-1) + return {"decoder_output": query_out, "pred_boxes": boxes, "presence": presence} + + +class Transformer(nn.Module): + def __init__(self, d_model=256, num_heads=8, dim_ff=2048, enc_layers=6, dec_layers=6, + num_queries=200, device=None, dtype=None, operations=None): + super().__init__() + self.encoder = TransformerEncoder(d_model, num_heads, dim_ff, enc_layers, device=device, dtype=dtype, operations=operations) + self.decoder = TransformerDecoder(d_model, num_heads, dim_ff, dec_layers, num_queries, device=device, dtype=dtype, operations=operations) + + +class GeometryEncoder(nn.Module): + def __init__(self, d_model=256, num_heads=8, num_layers=3, roi_size=7, device=None, dtype=None, operations=None): + super().__init__() + self.d_model = d_model + self.roi_size = roi_size + self.pos_enc = PositionEmbeddingSine(num_pos_feats=d_model, normalize=True) + self.points_direct_project = operations.Linear(2, d_model, device=device, dtype=dtype) + self.points_pool_project = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.points_pos_enc_project = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.boxes_direct_project = operations.Linear(4, d_model, device=device, dtype=dtype) + self.boxes_pool_project = operations.Conv2d(d_model, d_model, kernel_size=roi_size, device=device, dtype=dtype) + self.boxes_pos_enc_project = operations.Linear(d_model + 2, d_model, device=device, dtype=dtype) + self.label_embed = operations.Embedding(2, d_model, device=device, dtype=dtype) + self.cls_embed = operations.Embedding(1, d_model, device=device, dtype=dtype) + self.norm = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.img_pre_norm = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.encode = nn.ModuleList([ + EncoderLayer(d_model, num_heads, 2048, device=device, dtype=dtype, operations=operations) + for _ in range(num_layers) + ]) + self.encode_norm = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.final_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + + def _encode_points(self, coords, labels, img_feat_2d): + """Encode point prompts: direct + pool + pos_enc + label. coords: [B, N, 2] normalized.""" + B, N, _ = coords.shape + embed = self.points_direct_project(coords) + # Pool features from backbone at point locations via grid_sample + grid = (coords * 2 - 1).unsqueeze(2) # [B, N, 1, 2] in [-1, 1] + sampled = F.grid_sample(img_feat_2d, grid, align_corners=False) # [B, C, N, 1] + embed = embed + self.points_pool_project(sampled.squeeze(-1).permute(0, 2, 1)) # [B, N, C] + # Positional encoding of coordinates + x, y = coords[:, :, 0], coords[:, :, 1] # [B, N] + pos_x, pos_y = self.pos_enc._encode_xy(x.flatten(), y.flatten()) + enc = torch.cat([pos_x, pos_y], dim=-1).view(B, N, -1) + embed = embed + self.points_pos_enc_project(cast_to_input(enc, embed)) + embed = embed + cast_to_input(self.label_embed(labels.long()), embed) + return embed + + def _encode_boxes(self, boxes, labels, img_feat_2d): + """Encode box prompts: direct + pool + pos_enc + label. boxes: [B, N, 4] normalized cxcywh.""" + B, N, _ = boxes.shape + embed = self.boxes_direct_project(boxes) + # ROI align from backbone at box regions + H, W = img_feat_2d.shape[-2:] + boxes_xyxy = box_cxcywh_to_xyxy(boxes) + scale = torch.tensor([W, H, W, H], dtype=boxes_xyxy.dtype, device=boxes_xyxy.device) + boxes_scaled = boxes_xyxy * scale + sampled = roi_align(img_feat_2d, boxes_scaled.view(-1, 4).split(N), self.roi_size) + proj = self.boxes_pool_project(sampled).view(B, N, -1) # Conv2d(roi_size) -> [B*N, C, 1, 1] -> [B, N, C] + embed = embed + proj + # Positional encoding of box center + size + cx, cy, w, h = boxes[:, :, 0], boxes[:, :, 1], boxes[:, :, 2], boxes[:, :, 3] + enc = self.pos_enc.encode_boxes(cx.flatten(), cy.flatten(), w.flatten(), h.flatten()) + enc = enc.view(B, N, -1) + embed = embed + self.boxes_pos_enc_project(cast_to_input(enc, embed)) + embed = embed + cast_to_input(self.label_embed(labels.long()), embed) + return embed + + def forward(self, points=None, boxes=None, image_features=None): + """Encode geometry prompts. image_features: [B, HW, C] flattened backbone features.""" + # Prepare 2D image features for pooling + img_feat_2d = None + if image_features is not None: + B = image_features.shape[0] + HW, C = image_features.shape[1], image_features.shape[2] + hw = int(math.sqrt(HW)) + img_normed = self.img_pre_norm(image_features) + img_feat_2d = img_normed.permute(0, 2, 1).view(B, C, hw, hw) + + embeddings = [] + if points is not None: + coords, labels = points + embeddings.append(self._encode_points(coords, labels, img_feat_2d)) + if boxes is not None: + B = boxes.shape[0] + box_labels = torch.ones(B, boxes.shape[1], dtype=torch.long, device=boxes.device) + embeddings.append(self._encode_boxes(boxes, box_labels, img_feat_2d)) + if not embeddings: + return None + geo = torch.cat(embeddings, dim=1) + geo = self.norm(geo) + if image_features is not None: + for layer in self.encode: + geo = layer(geo, torch.zeros_like(geo), image_features) + geo = self.encode_norm(geo) + return self.final_proj(geo) + + +class PixelDecoder(nn.Module): + """Top-down FPN pixel decoder with GroupNorm + ReLU + nearest interpolation.""" + def __init__(self, d_model=256, num_stages=3, device=None, dtype=None, operations=None): + super().__init__() + self.conv_layers = nn.ModuleList([operations.Conv2d(d_model, d_model, kernel_size=3, padding=1, device=device, dtype=dtype) for _ in range(num_stages)]) + self.norms = nn.ModuleList([operations.GroupNorm(8, d_model, device=device, dtype=dtype) for _ in range(num_stages)]) + + def forward(self, backbone_features): + prev = backbone_features[-1] + for i, feat in enumerate(backbone_features[:-1][::-1]): + prev = F.relu(self.norms[i](self.conv_layers[i](feat + F.interpolate(prev, size=feat.shape[-2:], mode="nearest")))) + return prev + + +class MaskPredictor(nn.Module): + def __init__(self, d_model=256, device=None, dtype=None, operations=None): + super().__init__() + self.mask_embed = MLP(d_model, d_model, d_model, 3, device=device, dtype=dtype, operations=operations) + + def forward(self, query_embeddings, pixel_features): + mask_embed = self.mask_embed(query_embeddings) + return torch.einsum("bqc,bchw->bqhw", mask_embed, pixel_features) + + +class SegmentationHead(nn.Module): + def __init__(self, d_model=256, num_heads=8, device=None, dtype=None, operations=None): + super().__init__() + self.d_model = d_model + self.pixel_decoder = PixelDecoder(d_model, 3, device=device, dtype=dtype, operations=operations) + self.mask_predictor = MaskPredictor(d_model, device=device, dtype=dtype, operations=operations) + self.cross_attend_prompt = SplitMHA(d_model, num_heads, device=device, dtype=dtype, operations=operations) + self.cross_attn_norm = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.instance_seg_head = operations.Conv2d(d_model, d_model, kernel_size=1, device=device, dtype=dtype) + self.semantic_seg_head = operations.Conv2d(d_model, 1, kernel_size=1, device=device, dtype=dtype) + + def forward(self, query_embeddings, backbone_features, encoder_hidden_states=None, prompt=None, prompt_mask=None): + if encoder_hidden_states is not None and prompt is not None: + enc_normed = self.cross_attn_norm(encoder_hidden_states) + enc_cross = self.cross_attend_prompt(enc_normed, prompt, prompt, mask=prompt_mask) + encoder_hidden_states = enc_cross + encoder_hidden_states + + if encoder_hidden_states is not None: + B, H, W = encoder_hidden_states.shape[0], backbone_features[-1].shape[-2], backbone_features[-1].shape[-1] + encoder_visual = encoder_hidden_states[:, :H * W].permute(0, 2, 1).view(B, self.d_model, H, W) + backbone_features = list(backbone_features) + backbone_features[-1] = encoder_visual + + pixel_features = self.pixel_decoder(backbone_features) + instance_features = self.instance_seg_head(pixel_features) + masks = self.mask_predictor(query_embeddings, instance_features) + return masks + + +class DotProductScoring(nn.Module): + def __init__(self, d_model=256, device=None, dtype=None, operations=None): + super().__init__() + self.hs_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.prompt_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.prompt_mlp = MLPWithNorm(d_model, 2048, d_model, 2, device=device, dtype=dtype, operations=operations) + self.scale = 1.0 / (d_model ** 0.5) + + def forward(self, query_embeddings, prompt_embeddings, prompt_mask=None): + prompt = self.prompt_mlp(prompt_embeddings) + if prompt_mask is not None: + weight = prompt_mask.unsqueeze(-1).to(dtype=prompt.dtype) + pooled = (prompt * weight).sum(dim=1) / weight.sum(dim=1).clamp(min=1) + else: + pooled = prompt.mean(dim=1) + hs = self.hs_proj(query_embeddings) + pp = self.prompt_proj(pooled).unsqueeze(-1).to(hs.dtype) + scores = torch.matmul(hs, pp) + return (scores * self.scale).clamp(-12.0, 12.0).squeeze(-1) + + +class SAM3Detector(nn.Module): + def __init__(self, d_model=256, embed_dim=1024, num_queries=200, device=None, dtype=None, operations=None, **kwargs): + super().__init__() + image_model = kwargs.pop("image_model", "SAM3") + for k in ("num_heads", "num_head_channels"): + kwargs.pop(k, None) + multiplex = image_model == "SAM31" + # SAM3: 4 FPN levels, drop last (scalp=1); SAM3.1: 3 levels, use all (scalp=0) + self.scalp = 0 if multiplex else 1 + self.backbone = nn.ModuleDict({ + "vision_backbone": SAM3VisionBackbone(embed_dim=embed_dim, d_model=d_model, multiplex=multiplex, device=device, dtype=dtype, operations=operations, **kwargs), + "language_backbone": nn.ModuleDict({"resizer": operations.Linear(embed_dim, d_model, device=device, dtype=dtype)}), + }) + self.transformer = Transformer(d_model=d_model, num_queries=num_queries, device=device, dtype=dtype, operations=operations) + self.segmentation_head = SegmentationHead(d_model=d_model, device=device, dtype=dtype, operations=operations) + self.geometry_encoder = GeometryEncoder(d_model=d_model, device=device, dtype=dtype, operations=operations) + self.dot_prod_scoring = DotProductScoring(d_model=d_model, device=device, dtype=dtype, operations=operations) + + def _get_backbone_features(self, images): + """Run backbone and return (detector_features, detector_positions, tracker_features, tracker_positions).""" + bb = self.backbone["vision_backbone"] + if bb.multiplex: + all_f, all_p, tf, tp = bb(images, tracker_mode="propagation") + else: + all_f, all_p, tf, tp = bb(images, need_tracker=True) + return all_f, all_p, tf, tp + + @staticmethod + def _run_geo_layer(layer, x, memory, memory_pos): + x = x + layer.self_attn(layer.norm1(x)) + x = x + layer.cross_attn_image(layer.norm2(x), memory + memory_pos, memory) + x = x + layer.linear2(F.relu(layer.linear1(layer.norm3(x)))) + return x + + def _detect(self, features, positions, text_embeddings=None, text_mask=None, + points=None, boxes=None): + """Shared detection: geometry encoding, transformer, scoring, segmentation.""" + B = features[0].shape[0] + # Scalp for encoder (use top-level feature), but keep all levels for segmentation head + seg_features = features + if self.scalp > 0: + features = features[:-self.scalp] + positions = positions[:-self.scalp] + enc_feat, enc_pos = features[-1], positions[-1] + _, _, H, W = enc_feat.shape + img_flat = enc_feat.flatten(2).permute(0, 2, 1) + pos_flat = enc_pos.flatten(2).permute(0, 2, 1) + + has_prompts = text_embeddings is not None or points is not None or boxes is not None + if has_prompts: + geo_enc = self.geometry_encoder + geo_prompts = geo_enc(points=points, boxes=boxes, image_features=img_flat) + geo_cls = geo_enc.norm(geo_enc.final_proj(cast_to_input(geo_enc.cls_embed.weight, img_flat).view(1, 1, -1).expand(B, -1, -1))) + for layer in geo_enc.encode: + geo_cls = self._run_geo_layer(layer, geo_cls, img_flat, pos_flat) + geo_cls = geo_enc.encode_norm(geo_cls) + if text_embeddings is not None and text_embeddings.shape[0] != B: + text_embeddings = text_embeddings.expand(B, -1, -1) + if text_mask is not None and text_mask.shape[0] != B: + text_mask = text_mask.expand(B, -1) + parts = [t for t in [text_embeddings, geo_prompts, geo_cls] if t is not None] + text_embeddings = torch.cat(parts, dim=1) + n_new = text_embeddings.shape[1] - (text_mask.shape[1] if text_mask is not None else 0) + if text_mask is not None: + text_mask = torch.cat([text_mask, torch.ones(B, n_new, dtype=torch.bool, device=text_mask.device)], dim=1) + else: + text_mask = torch.ones(B, text_embeddings.shape[1], dtype=torch.bool, device=text_embeddings.device) + + memory = self.transformer.encoder(img_flat, pos_flat, text_embeddings, text_mask) + dec_out = self.transformer.decoder(memory, pos_flat, text_embeddings, text_mask, H, W) + query_out, pred_boxes = dec_out["decoder_output"], dec_out["pred_boxes"] + + if text_embeddings is not None: + scores = self.dot_prod_scoring(query_out, text_embeddings, text_mask) + else: + scores = torch.zeros(B, query_out.shape[1], device=query_out.device) + + masks = self.segmentation_head(query_out, seg_features, encoder_hidden_states=memory, prompt=text_embeddings, prompt_mask=text_mask) + return box_cxcywh_to_xyxy(pred_boxes), scores, masks, dec_out + + def forward(self, images, text_embeddings=None, text_mask=None, points=None, boxes=None, threshold=0.3, orig_size=None): + features, positions, _, _ = self._get_backbone_features(images) + + if text_embeddings is not None: + text_embeddings = self.backbone["language_backbone"]["resizer"](text_embeddings) + if text_mask is not None: + text_mask = text_mask.bool() + + boxes_xyxy, scores, masks, dec_out = self._detect( + features, positions, text_embeddings, text_mask, points, boxes) + + if orig_size is not None: + oh, ow = orig_size + boxes_xyxy = boxes_xyxy * torch.tensor([ow, oh, ow, oh], device=boxes_xyxy.device, dtype=boxes_xyxy.dtype) + masks = F.interpolate(masks, size=orig_size, mode="bilinear", align_corners=False) + + return { + "boxes": boxes_xyxy, + "scores": scores, + "masks": masks, + "presence": dec_out.get("presence"), + } + + def forward_from_trunk(self, trunk_out, text_embeddings, text_mask): + """Run detection using a pre-computed ViTDet trunk output. + + text_embeddings must already be resized through language_backbone.resizer. + Returns dict with boxes (normalized xyxy), scores, masks at detector resolution. + """ + bb = self.backbone["vision_backbone"] + features = [conv(trunk_out) for conv in bb.convs] + positions = [cast_to_input(bb.position_encoding(f), f) for f in features] + + if text_mask is not None: + text_mask = text_mask.bool() + + boxes_xyxy, scores, masks, _ = self._detect(features, positions, text_embeddings, text_mask) + return {"boxes": boxes_xyxy, "scores": scores, "masks": masks} + + +class SAM3Model(nn.Module): + def __init__(self, device=None, dtype=None, operations=None, **kwargs): + super().__init__() + self.dtype = dtype + image_model = kwargs.get("image_model", "SAM3") + tracker_cls = TRACKER_CLASSES[image_model] + self.detector = SAM3Detector(device=device, dtype=dtype, operations=operations, **kwargs) + self.tracker = tracker_cls(device=device, dtype=dtype, operations=operations, **kwargs) + + def forward(self, images, **kwargs): + return self.detector(images, **kwargs) + + def forward_segment(self, images, point_inputs=None, box_inputs=None, mask_inputs=None): + """Interactive segmentation using SAM decoder with point/box/mask prompts. + + Args: + images: [B, 3, 1008, 1008] preprocessed images + point_inputs: {"point_coords": [B, N, 2], "point_labels": [B, N]} in 1008x1008 pixel space + box_inputs: [B, 2, 2] box corners (top-left, bottom-right) in 1008x1008 pixel space + mask_inputs: [B, 1, H, W] coarse mask logits to refine + Returns: + [B, 1, image_size, image_size] high-res mask logits + """ + bb = self.detector.backbone["vision_backbone"] + if bb.multiplex: + _, _, tracker_features, tracker_positions = bb(images, tracker_mode="interactive") + else: + _, _, tracker_features, tracker_positions = bb(images, need_tracker=True) + if self.detector.scalp > 0: + tracker_features = tracker_features[:-self.detector.scalp] + tracker_positions = tracker_positions[:-self.detector.scalp] + + high_res = list(tracker_features[:-1]) + backbone_feat = tracker_features[-1] + B, C, H, W = backbone_feat.shape + # Add no-memory embedding (init frame path) + no_mem = getattr(self.tracker, 'interactivity_no_mem_embed', None) + if no_mem is None: + no_mem = getattr(self.tracker, 'no_mem_embed', None) + if no_mem is not None: + feat_flat = backbone_feat.flatten(2).permute(0, 2, 1) + feat_flat = feat_flat + cast_to_input(no_mem, feat_flat) + backbone_feat = feat_flat.view(B, H, W, C).permute(0, 3, 1, 2) + + num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1) + _, high_res_masks, _, _ = self.tracker._forward_sam_heads( + backbone_features=backbone_feat, + point_inputs=point_inputs, + mask_inputs=mask_inputs, + box_inputs=box_inputs, + high_res_features=high_res, + multimask_output=(0 < num_pts <= 1), + ) + return high_res_masks + + def forward_video(self, images, initial_masks, pbar=None, text_prompts=None, + new_det_thresh=0.5, max_objects=0, detect_interval=1): + """Track video with optional per-frame text-prompted detection.""" + bb = self.detector.backbone["vision_backbone"] + + def backbone_fn(frame, frame_idx=None): + trunk_out = bb.trunk(frame) + if bb.multiplex: + _, _, tf, tp = bb(frame, tracker_mode="propagation", cached_trunk=trunk_out, tracker_only=True) + else: + _, _, tf, tp = bb(frame, need_tracker=True, cached_trunk=trunk_out, tracker_only=True) + return tf, tp, trunk_out + + detect_fn = None + if text_prompts: + resizer = self.detector.backbone["language_backbone"]["resizer"] + resized = [(resizer(emb), m.bool() if m is not None else None) for emb, m in text_prompts] + def detect_fn(trunk_out): + all_scores, all_masks = [], [] + for emb, mask in resized: + det = self.detector.forward_from_trunk(trunk_out, emb, mask) + all_scores.append(det["scores"]) + all_masks.append(det["masks"]) + return {"scores": torch.cat(all_scores, dim=1), "masks": torch.cat(all_masks, dim=1)} + + if hasattr(self.tracker, 'track_video_with_detection'): + return self.tracker.track_video_with_detection( + backbone_fn, images, initial_masks, detect_fn, + new_det_thresh=new_det_thresh, max_objects=max_objects, + detect_interval=detect_interval, backbone_obj=bb, pbar=pbar) + # SAM3 (non-multiplex) — no detection support, requires initial masks + if initial_masks is None: + raise ValueError("SAM3 (non-multiplex) requires initial_mask for video tracking") + return self.tracker.track_video(backbone_fn, images, initial_masks, pbar=pbar, backbone_obj=bb) diff --git a/comfy/ldm/sam3/sam.py b/comfy/ldm/sam3/sam.py new file mode 100644 index 000000000..272781d45 --- /dev/null +++ b/comfy/ldm/sam3/sam.py @@ -0,0 +1,425 @@ +# SAM3 shared components: primitives, ViTDet backbone, FPN neck, position encodings. + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from comfy.ldm.modules.attention import optimized_attention +from comfy.ldm.flux.math import apply_rope +from comfy.ldm.flux.layers import EmbedND +from comfy.ops import cast_to_input + + +class MLP(nn.Module): + def __init__(self, input_dim, hidden_dim, output_dim, num_layers, sigmoid_output=False, device=None, dtype=None, operations=None): + super().__init__() + dims = [input_dim] + [hidden_dim] * (num_layers - 1) + [output_dim] + self.layers = nn.ModuleList([operations.Linear(dims[i], dims[i + 1], device=device, dtype=dtype) for i in range(num_layers)]) + self.sigmoid_output = sigmoid_output + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = F.relu(layer(x)) if i < len(self.layers) - 1 else layer(x) + return torch.sigmoid(x) if self.sigmoid_output else x + + +class SAMAttention(nn.Module): + def __init__(self, embedding_dim, num_heads, downsample_rate=1, kv_in_dim=None, device=None, dtype=None, operations=None): + super().__init__() + self.num_heads = num_heads + internal_dim = embedding_dim // downsample_rate + kv_dim = kv_in_dim if kv_in_dim is not None else embedding_dim + self.q_proj = operations.Linear(embedding_dim, internal_dim, device=device, dtype=dtype) + self.k_proj = operations.Linear(kv_dim, internal_dim, device=device, dtype=dtype) + self.v_proj = operations.Linear(kv_dim, internal_dim, device=device, dtype=dtype) + self.out_proj = operations.Linear(internal_dim, embedding_dim, device=device, dtype=dtype) + + def forward(self, q, k, v): + q = self.q_proj(q) + k = self.k_proj(k) + v = self.v_proj(v) + return self.out_proj(optimized_attention(q, k, v, self.num_heads)) + + +class TwoWayAttentionBlock(nn.Module): + def __init__(self, embedding_dim, num_heads, mlp_dim=2048, attention_downsample_rate=2, skip_first_layer_pe=False, device=None, dtype=None, operations=None): + super().__init__() + self.skip_first_layer_pe = skip_first_layer_pe + self.self_attn = SAMAttention(embedding_dim, num_heads, device=device, dtype=dtype, operations=operations) + self.cross_attn_token_to_image = SAMAttention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate, device=device, dtype=dtype, operations=operations) + self.cross_attn_image_to_token = SAMAttention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate, device=device, dtype=dtype, operations=operations) + self.mlp = nn.Sequential(operations.Linear(embedding_dim, mlp_dim, device=device, dtype=dtype), nn.ReLU(), operations.Linear(mlp_dim, embedding_dim, device=device, dtype=dtype)) + self.norm1 = operations.LayerNorm(embedding_dim, device=device, dtype=dtype) + self.norm2 = operations.LayerNorm(embedding_dim, device=device, dtype=dtype) + self.norm3 = operations.LayerNorm(embedding_dim, device=device, dtype=dtype) + self.norm4 = operations.LayerNorm(embedding_dim, device=device, dtype=dtype) + + def forward(self, queries, keys, query_pe, key_pe): + if self.skip_first_layer_pe: + queries = self.norm1(self.self_attn(queries, queries, queries)) + else: + q = queries + query_pe + queries = self.norm1(queries + self.self_attn(q, q, queries)) + q, k = queries + query_pe, keys + key_pe + queries = self.norm2(queries + self.cross_attn_token_to_image(q, k, keys)) + queries = self.norm3(queries + self.mlp(queries)) + q, k = queries + query_pe, keys + key_pe + keys = self.norm4(keys + self.cross_attn_image_to_token(k, q, queries)) + return queries, keys + + +class TwoWayTransformer(nn.Module): + def __init__(self, depth=2, embedding_dim=256, num_heads=8, mlp_dim=2048, attention_downsample_rate=2, device=None, dtype=None, operations=None): + super().__init__() + self.layers = nn.ModuleList([ + TwoWayAttentionBlock(embedding_dim, num_heads, mlp_dim, attention_downsample_rate, + skip_first_layer_pe=(i == 0), device=device, dtype=dtype, operations=operations) + for i in range(depth) + ]) + self.final_attn_token_to_image = SAMAttention(embedding_dim, num_heads, downsample_rate=attention_downsample_rate, device=device, dtype=dtype, operations=operations) + self.norm_final = operations.LayerNorm(embedding_dim, device=device, dtype=dtype) + + def forward(self, image_embedding, image_pe, point_embedding): + queries, keys = point_embedding, image_embedding + for layer in self.layers: + queries, keys = layer(queries, keys, point_embedding, image_pe) + q, k = queries + point_embedding, keys + image_pe + queries = self.norm_final(queries + self.final_attn_token_to_image(q, k, keys)) + return queries, keys + + +class PositionEmbeddingRandom(nn.Module): + """Fourier feature positional encoding with random gaussian projection.""" + def __init__(self, num_pos_feats=64, scale=None): + super().__init__() + self.register_buffer("positional_encoding_gaussian_matrix", (scale or 1.0) * torch.randn(2, num_pos_feats)) + + def _encode(self, normalized_coords): + """Map normalized [0,1] coordinates to fourier features via random projection. Computes in fp32.""" + orig_dtype = normalized_coords.dtype + proj_matrix = self.positional_encoding_gaussian_matrix.to(device=normalized_coords.device, dtype=torch.float32) + projected = 2 * math.pi * (2 * normalized_coords.float() - 1) @ proj_matrix + return torch.cat([projected.sin(), projected.cos()], dim=-1).to(orig_dtype) + + def forward(self, size, device=None): + h, w = size + dev = device if device is not None else self.positional_encoding_gaussian_matrix.device + ones = torch.ones((h, w), device=dev, dtype=torch.float32) + norm_xy = torch.stack([(ones.cumsum(1) - 0.5) / w, (ones.cumsum(0) - 0.5) / h], dim=-1) + return self._encode(norm_xy).permute(2, 0, 1).unsqueeze(0) + + def forward_with_coords(self, pixel_coords, image_size): + norm = pixel_coords.clone() + norm[:, :, 0] /= image_size[1] + norm[:, :, 1] /= image_size[0] + return self._encode(norm) + + +# ViTDet backbone + FPN neck + +def window_partition(x: torch.Tensor, window_size: int): + B, H, W, C = x.shape + pad_h = (window_size - H % window_size) % window_size + pad_w = (window_size - W % window_size) % window_size + if pad_h > 0 or pad_w > 0: + x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) + Hp, Wp = H + pad_h, W + pad_w + x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows, (Hp, Wp) + + +def window_unpartition(windows: torch.Tensor, window_size: int, pad_hw, hw): + Hp, Wp = pad_hw + H, W = hw + B = windows.shape[0] // (Hp * Wp // window_size // window_size) + x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) + if Hp > H or Wp > W: + x = x[:, :H, :W, :].contiguous() + return x + + +def rope_2d(end_x: int, end_y: int, dim: int, theta: float = 10000.0, scale_pos: float = 1.0): + """Generate 2D axial RoPE using flux EmbedND. Returns [1, 1, HW, dim//2, 2, 2].""" + t = torch.arange(end_x * end_y, dtype=torch.float32) + ids = torch.stack([(t % end_x) * scale_pos, + torch.div(t, end_x, rounding_mode="floor") * scale_pos], dim=-1) + return EmbedND(dim=dim, theta=theta, axes_dim=[dim // 2, dim // 2])(ids.unsqueeze(0)) + + +class _ViTMLP(nn.Module): + def __init__(self, dim, mlp_ratio=4.0, device=None, dtype=None, operations=None): + super().__init__() + hidden = int(dim * mlp_ratio) + self.fc1 = operations.Linear(dim, hidden, device=device, dtype=dtype) + self.act = nn.GELU() + self.fc2 = operations.Linear(hidden, dim, device=device, dtype=dtype) + + def forward(self, x): + return self.fc2(self.act(self.fc1(x))) + + +class Attention(nn.Module): + """ViTDet multi-head attention with fused QKV projection.""" + + def __init__(self, dim, num_heads=8, qkv_bias=True, use_rope=False, device=None, dtype=None, operations=None): + super().__init__() + self.num_heads = num_heads + self.head_dim = dim // num_heads + self.use_rope = use_rope + self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, device=device, dtype=dtype) + self.proj = operations.Linear(dim, dim, device=device, dtype=dtype) + + def forward(self, x, freqs_cis=None): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim) + q, k, v = qkv.permute(2, 0, 3, 1, 4).unbind(dim=0) + if self.use_rope and freqs_cis is not None: + q, k = apply_rope(q, k, freqs_cis) + return self.proj(optimized_attention(q, k, v, self.num_heads, skip_reshape=True)) + + +class Block(nn.Module): + def __init__(self, dim, num_heads, mlp_ratio=4.0, qkv_bias=True, window_size=0, use_rope=False, device=None, dtype=None, operations=None): + super().__init__() + self.window_size = window_size + self.norm1 = operations.LayerNorm(dim, device=device, dtype=dtype) + self.attn = Attention(dim, num_heads, qkv_bias, use_rope, device=device, dtype=dtype, operations=operations) + self.norm2 = operations.LayerNorm(dim, device=device, dtype=dtype) + self.mlp = _ViTMLP(dim, mlp_ratio, device=device, dtype=dtype, operations=operations) + + def forward(self, x, freqs_cis=None): + shortcut = x + x = self.norm1(x) + if self.window_size > 0: + H, W = x.shape[1], x.shape[2] + x, pad_hw = window_partition(x, self.window_size) + x = x.view(x.shape[0], self.window_size * self.window_size, -1) + x = self.attn(x, freqs_cis=freqs_cis) + x = x.view(-1, self.window_size, self.window_size, x.shape[-1]) + x = window_unpartition(x, self.window_size, pad_hw, (H, W)) + else: + B, H, W, C = x.shape + x = x.view(B, H * W, C) + x = self.attn(x, freqs_cis=freqs_cis) + x = x.view(B, H, W, C) + x = shortcut + x + x = x + self.mlp(self.norm2(x)) + return x + + +class PatchEmbed(nn.Module): + def __init__(self, patch_size=14, in_chans=3, embed_dim=1024, device=None, dtype=None, operations=None): + super().__init__() + self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=False, device=device, dtype=dtype) + + def forward(self, x): + return self.proj(x) + + +class ViTDet(nn.Module): + def __init__(self, img_size=1008, patch_size=14, embed_dim=1024, depth=32, num_heads=16, mlp_ratio=4.625, qkv_bias=True, window_size=24, + global_att_blocks=(7, 15, 23, 31), use_rope=True, pretrain_img_size=336, device=None, dtype=None, operations=None, **kwargs): + super().__init__() + self.img_size = img_size + self.patch_size = patch_size + self.embed_dim = embed_dim + self.num_heads = num_heads + self.global_att_blocks = set(global_att_blocks) + + self.patch_embed = PatchEmbed(patch_size, 3, embed_dim, device=device, dtype=dtype, operations=operations) + + num_patches = (pretrain_img_size // patch_size) ** 2 + 1 # +1 for cls token + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim, device=device, dtype=dtype)) + + self.ln_pre = operations.LayerNorm(embed_dim, device=device, dtype=dtype) + + grid_size = img_size // patch_size + pretrain_grid = pretrain_img_size // patch_size + + self.blocks = nn.ModuleList() + for i in range(depth): + is_global = i in self.global_att_blocks + self.blocks.append(Block( + embed_dim, num_heads, mlp_ratio, qkv_bias, + window_size=0 if is_global else window_size, + use_rope=use_rope, + device=device, dtype=dtype, operations=operations, + )) + + if use_rope: + rope_scale = pretrain_grid / grid_size + self.register_buffer("freqs_cis", rope_2d(grid_size, grid_size, embed_dim // num_heads, scale_pos=rope_scale), persistent=False) + self.register_buffer("freqs_cis_window", rope_2d(window_size, window_size, embed_dim // num_heads), persistent=False) + else: + self.freqs_cis = None + self.freqs_cis_window = None + + def _get_pos_embed(self, num_tokens): + pos = self.pos_embed + if pos.shape[1] == num_tokens: + return pos + cls_pos = pos[:, :1] + spatial_pos = pos[:, 1:] + old_size = int(math.sqrt(spatial_pos.shape[1])) + new_size = int(math.sqrt(num_tokens - 1)) if num_tokens > 1 else old_size + spatial_2d = spatial_pos.reshape(1, old_size, old_size, -1).permute(0, 3, 1, 2) + tiles_h = new_size // old_size + 1 + tiles_w = new_size // old_size + 1 + tiled = spatial_2d.tile([1, 1, tiles_h, tiles_w])[:, :, :new_size, :new_size] + tiled = tiled.permute(0, 2, 3, 1).reshape(1, new_size * new_size, -1) + return torch.cat([cls_pos, tiled], dim=1) + + def forward(self, x): + x = self.patch_embed(x) + B, C, Hp, Wp = x.shape + x = x.permute(0, 2, 3, 1).reshape(B, Hp * Wp, C) + + pos = cast_to_input(self._get_pos_embed(Hp * Wp + 1), x) + x = x + pos[:, 1:Hp * Wp + 1] + + x = x.view(B, Hp, Wp, C) + x = self.ln_pre(x) + + freqs_cis_global = self.freqs_cis + freqs_cis_win = self.freqs_cis_window + if freqs_cis_global is not None: + freqs_cis_global = cast_to_input(freqs_cis_global, x) + if freqs_cis_win is not None: + freqs_cis_win = cast_to_input(freqs_cis_win, x) + + for block in self.blocks: + fc = freqs_cis_win if block.window_size > 0 else freqs_cis_global + x = block(x, freqs_cis=fc) + + return x.permute(0, 3, 1, 2) + + +class FPNScaleConv(nn.Module): + def __init__(self, in_dim, out_dim, scale, device=None, dtype=None, operations=None): + super().__init__() + if scale == 4.0: + self.dconv_2x2_0 = operations.ConvTranspose2d(in_dim, in_dim // 2, kernel_size=2, stride=2, device=device, dtype=dtype) + self.dconv_2x2_1 = operations.ConvTranspose2d(in_dim // 2, in_dim // 4, kernel_size=2, stride=2, device=device, dtype=dtype) + proj_in = in_dim // 4 + elif scale == 2.0: + self.dconv_2x2 = operations.ConvTranspose2d(in_dim, in_dim // 2, kernel_size=2, stride=2, device=device, dtype=dtype) + proj_in = in_dim // 2 + elif scale == 1.0: + proj_in = in_dim + elif scale == 0.5: + self.pool = nn.MaxPool2d(kernel_size=2, stride=2) + proj_in = in_dim + self.scale = scale + self.conv_1x1 = operations.Conv2d(proj_in, out_dim, kernel_size=1, device=device, dtype=dtype) + self.conv_3x3 = operations.Conv2d(out_dim, out_dim, kernel_size=3, padding=1, device=device, dtype=dtype) + + def forward(self, x): + if self.scale == 4.0: + x = F.gelu(self.dconv_2x2_0(x)) + x = self.dconv_2x2_1(x) + elif self.scale == 2.0: + x = self.dconv_2x2(x) + elif self.scale == 0.5: + x = self.pool(x) + x = self.conv_1x1(x) + x = self.conv_3x3(x) + return x + + +class PositionEmbeddingSine(nn.Module): + """2D sinusoidal position encoding (DETR-style) with result caching.""" + def __init__(self, num_pos_feats=256, temperature=10000.0, normalize=True, scale=None): + super().__init__() + assert num_pos_feats % 2 == 0 + self.half_dim = num_pos_feats // 2 + self.temperature = temperature + self.normalize = normalize + self.scale = scale if scale is not None else 2 * math.pi + self._cache = {} + + def _sincos(self, vals): + """Encode 1D values to interleaved sin/cos features.""" + freqs = self.temperature ** (2 * (torch.arange(self.half_dim, dtype=torch.float32, device=vals.device) // 2) / self.half_dim) + raw = vals[..., None] * self.scale / freqs + return torch.stack((raw[..., 0::2].sin(), raw[..., 1::2].cos()), dim=-1).flatten(-2) + + def _encode_xy(self, x, y): + """Encode normalized x, y coordinates to sinusoidal features. Returns (pos_x, pos_y) each [N, half_dim].""" + dim_t = self.temperature ** (2 * (torch.arange(self.half_dim, dtype=torch.float32, device=x.device) // 2) / self.half_dim) + pos_x = x[:, None] * self.scale / dim_t + pos_y = y[:, None] * self.scale / dim_t + pos_x = torch.stack((pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2).flatten(1) + pos_y = torch.stack((pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2).flatten(1) + return pos_x, pos_y + + def encode_boxes(self, cx, cy, w, h): + """Encode box center + size to [N, d_model+2] features.""" + pos_x, pos_y = self._encode_xy(cx, cy) + return torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1) + + def forward(self, x): + B, C, H, W = x.shape + key = (H, W, x.device) + if key not in self._cache: + gy = torch.arange(H, dtype=torch.float32, device=x.device) + gx = torch.arange(W, dtype=torch.float32, device=x.device) + if self.normalize: + gy, gx = gy / (H - 1 + 1e-6), gx / (W - 1 + 1e-6) + yy, xx = torch.meshgrid(gy, gx, indexing="ij") + self._cache[key] = torch.cat((self._sincos(yy), self._sincos(xx)), dim=-1).permute(2, 0, 1).unsqueeze(0) + return self._cache[key].expand(B, -1, -1, -1) + + +class SAM3VisionBackbone(nn.Module): + def __init__(self, embed_dim=1024, d_model=256, multiplex=False, device=None, dtype=None, operations=None, **kwargs): + super().__init__() + self.trunk = ViTDet(embed_dim=embed_dim, device=device, dtype=dtype, operations=operations, **kwargs) + self.position_encoding = PositionEmbeddingSine(num_pos_feats=d_model, normalize=True) + self.multiplex = multiplex + + fpn_args = dict(device=device, dtype=dtype, operations=operations) + if multiplex: + scales = [4.0, 2.0, 1.0] + self.convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales]) + self.propagation_convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales]) + self.interactive_convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales]) + else: + scales = [4.0, 2.0, 1.0, 0.5] + self.convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales]) + self.sam2_convs = nn.ModuleList([FPNScaleConv(embed_dim, d_model, s, **fpn_args) for s in scales]) + + def forward(self, images, need_tracker=False, tracker_mode=None, cached_trunk=None, tracker_only=False): + backbone_out = cached_trunk if cached_trunk is not None else self.trunk(images) + + if tracker_only: + # Skip detector FPN when only tracker features are needed (video tracking) + if self.multiplex: + tracker_convs = self.propagation_convs if tracker_mode == "propagation" else self.interactive_convs + else: + tracker_convs = self.sam2_convs + tracker_features = [conv(backbone_out) for conv in tracker_convs] + tracker_positions = [cast_to_input(self.position_encoding(f), f) for f in tracker_features] + return None, None, tracker_features, tracker_positions + + features = [conv(backbone_out) for conv in self.convs] + positions = [cast_to_input(self.position_encoding(f), f) for f in features] + + if self.multiplex: + if tracker_mode == "propagation": + tracker_convs = self.propagation_convs + elif tracker_mode == "interactive": + tracker_convs = self.interactive_convs + else: + return features, positions, None, None + elif need_tracker: + tracker_convs = self.sam2_convs + else: + return features, positions, None, None + + tracker_features = [conv(backbone_out) for conv in tracker_convs] + tracker_positions = [cast_to_input(self.position_encoding(f), f) for f in tracker_features] + return features, positions, tracker_features, tracker_positions diff --git a/comfy/ldm/sam3/tracker.py b/comfy/ldm/sam3/tracker.py new file mode 100644 index 000000000..6ff6369d1 --- /dev/null +++ b/comfy/ldm/sam3/tracker.py @@ -0,0 +1,1785 @@ +# SAM3 video tracker: memory encoder, memory attention, SAM mask decoder/prompt encoder. + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from tqdm import tqdm + +try: + import cv2 + _HAS_CV2 = True +except ImportError: + from scipy import ndimage + _HAS_CV2 = False + +import comfy.model_management +from comfy.ldm.modules.attention import optimized_attention +from comfy.ldm.sam3.sam import rope_2d, PositionEmbeddingSine +from comfy.ops import cast_to_input +from comfy.ldm.flux.math import apply_rope1 +from comfy.ldm.cascade.common import LayerNorm2d_op +from comfy.ldm.sam3.sam import MLP, PositionEmbeddingRandom +from comfy.ldm.sam3.sam import TwoWayTransformer as SAMTwoWayTransformer + +NO_OBJ_SCORE = -1024.0 + + +def to_spatial(x, H, W): + """Reshape (B, H*W, C) → (B, C, H, W).""" + return x.view(x.shape[0], H, W, -1).permute(0, 3, 1, 2) + +class MultiplexState: + """Tracks object-to-slot assignments for multiplex tracking. Provides mux/demux operations.""" + + def __init__(self, num_objects, multiplex_count, device, dtype): + self.multiplex_count = multiplex_count + self.device = device + self.dtype = dtype + self._build(num_objects) + + def mux(self, x): + """[N_obj, ...] -> [num_buckets, multiplex_count, ...]""" + out_shape = (self.num_buckets, self.multiplex_count) + x.shape[1:] + return (self.mux_matrix.to(device=x.device, dtype=x.dtype) @ x.reshape(self.total_valid_entries, -1)).view(out_shape) + + def demux(self, x): + """[num_buckets, multiplex_count, ...] -> [N_obj, ...]""" + out_shape = (self.total_valid_entries,) + x.shape[2:] + flat = x.reshape(self.num_buckets * self.multiplex_count, -1) + return (self.demux_matrix.to(device=x.device, dtype=x.dtype) @ flat).view(out_shape) + + def get_valid_object_mask(self): + """[num_buckets, multiplex_count] bool tensor, True for valid slots.""" + return (self.mux_matrix.sum(dim=1) > 0).reshape(self.num_buckets, self.multiplex_count) + + def _build(self, num_objects): + M = self.multiplex_count + self.num_buckets = (num_objects + M - 1) // M + self.total_valid_entries = num_objects + total_slots = self.num_buckets * M + self.mux_matrix = torch.zeros(total_slots, num_objects, device=self.device, dtype=self.dtype) + self.demux_matrix = torch.zeros(num_objects, total_slots, device=self.device, dtype=self.dtype) + oids = torch.arange(num_objects, device=self.device) + slots = (oids // M) * M + (oids % M) + self.mux_matrix[slots, oids] = 1.0 + self.demux_matrix[oids, slots] = 1.0 + + def add_objects(self, n_new): + """Grow multiplex state for n_new additional objects.""" + self._build(self.total_valid_entries + n_new) + +def _compute_mask_overlap(masks_a, masks_b): + """Max of IoU and IoM (intersection over minimum area). More robust to size differences.""" + a_flat = (masks_a > 0).float().flatten(1) + b_flat = (masks_b > 0).float().flatten(1) + intersection = a_flat @ b_flat.T + area_a = a_flat.sum(1, keepdim=True) + area_b = b_flat.sum(1, keepdim=True).T + iou = intersection / (area_a + area_b - intersection).clamp(min=1) + iom = intersection / torch.min(area_a.expand_as(iou), area_b.expand_as(iou)).clamp(min=1) + return torch.max(iou, iom) + + +def _nms_masks(masks, scores, thresh=0.5): + """Mask-based NMS using IoU+IoM overlap. Returns (filtered_masks, filtered_scores).""" + order = scores.argsort(descending=True) + masks, scores = masks[order], scores[order] + keep = [] + for i in range(masks.shape[0]): + if keep: + if _compute_mask_overlap(masks[i:i+1], masks[torch.tensor(keep, device=masks.device)]).max() >= thresh: + continue + keep.append(i) + return masks[keep], scores[keep] + + +def _get_connected_components(mask_bin): + """Get connected component labels and areas. mask_bin: [B, 1, H, W] uint8.""" + labels_list, areas_list = [], [] + for i in range(mask_bin.shape[0]): + m = mask_bin[i, 0].cpu().numpy() + if _HAS_CV2: + _, labeled, stats, _ = cv2.connectedComponentsWithStats(m, connectivity=8) + areas = stats[labeled, cv2.CC_STAT_AREA].astype('int32') + else: + labeled, num_features = ndimage.label(m) + areas = np.zeros_like(m, dtype=np.int32) + for c in range(1, num_features + 1): + component = labeled == c + areas[component] = component.sum() + labels_list.append(torch.from_numpy(labeled).to(mask_bin.device)) + areas_list.append(torch.from_numpy(areas).to(device=mask_bin.device, dtype=torch.int32)) + return torch.stack(labels_list).unsqueeze(1), torch.stack(areas_list).unsqueeze(1) + + +def fill_holes_in_mask_scores(mask, max_area=0): + """Remove small foreground sprinkles and fill small background holes using connected components.""" + if max_area <= 0: + return mask + + # Fill holes: small connected components in background → foreground + mask_bg = (mask <= 0).to(torch.uint8) + _, areas_bg = _get_connected_components(mask_bg) + small_bg = mask_bg.bool() & (areas_bg <= max_area) + mask = torch.where(small_bg, 0.1, mask) + + # Remove sprinkles: small connected components in foreground → background + # Only remove if area < min(max_area, half of total foreground area) + mask_fg = (mask > 0).to(torch.uint8) + fg_area_thresh = mask_fg.sum(dim=(2, 3), keepdim=True, dtype=torch.int32) + fg_area_thresh.floor_divide_(2).clamp_(max=max_area) + _, areas_fg = _get_connected_components(mask_fg) + small_fg = mask_fg.bool() & (areas_fg <= fg_area_thresh) + mask = torch.where(small_fg, -0.1, mask) + + return mask + + +def apply_rope_memory(q, k, freqs, num_heads, num_k_exclude_rope=0): + """Apply 2D axial RoPE to memory attention using flux rope format. + + Args: + q: [B, Nq, C] projected queries (current frame features) + k: [B, Nk, C] projected keys (memory tokens) + freqs: [1, Nq, dim//2, 2, 2] flux-format rotation matrices for one frame + num_heads: number of attention heads + num_k_exclude_rope: number of trailing k tokens to skip RoPE (object pointers) + """ + B, Nq, C = q.shape + head_dim = C // num_heads + + # freqs shape: [1, 1, Nq, dim//2, 2, 2] (heads broadcast dim already included) + q_h = q.view(B, Nq, num_heads, head_dim).transpose(1, 2) + q_h = apply_rope1(q_h, freqs) + q = q_h.transpose(1, 2).reshape(B, Nq, C) + + # Apply RoPE to k (excluding last num_k_exclude_rope tokens) + Nk = k.shape[1] + num_k_rope = Nk - num_k_exclude_rope + if num_k_rope > 0: + # Repeat freqs for multiple frames of spatial memory + Nf = freqs.shape[2] # spatial positions in one frame + if num_k_rope > Nf: + r = (num_k_rope + Nf - 1) // Nf + pe_k = freqs.repeat(1, 1, r, 1, 1, 1)[:, :, :num_k_rope] + else: + pe_k = freqs[:, :, :num_k_rope] + + k_h = k[:, :num_k_rope].view(B, num_k_rope, num_heads, head_dim).transpose(1, 2) + k_h = apply_rope1(k_h, pe_k) + k = k.clone() + k[:, :num_k_rope] = k_h.transpose(1, 2).reshape(B, num_k_rope, C) + + return q, k + + +def get_1d_sine_pe(pos_inds, dim, temperature=10000): + """1D sinusoidal positional encoding for temporal positions.""" + pe_dim = dim // 2 + dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device) + dim_t = temperature ** (2 * (dim_t // 2) / pe_dim) + pos_embed = pos_inds.unsqueeze(-1) / dim_t + return torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1) + + +def _pad_to_buckets(tensor, target_buckets): + """Pad a [num_buckets, ...] tensor to target_buckets along dim 0 if needed.""" + if tensor.shape[0] >= target_buckets: + return tensor + pad_shape = (target_buckets - tensor.shape[0],) + tensor.shape[1:] + return torch.cat([tensor, torch.zeros(pad_shape, device=tensor.device, dtype=tensor.dtype)], dim=0) + + +def pack_masks(masks): + """Pack binary masks [*, H, W] to bit-packed [*, H, W//8] uint8. W must be divisible by 8.""" + binary = masks > 0 + shifts = torch.arange(8, device=masks.device) + return (binary.view(*masks.shape[:-1], -1, 8) * (1 << shifts)).sum(-1).byte() + + +def unpack_masks(packed): + """Unpack bit-packed [*, H, W//8] uint8 to bool [*, H, W*8].""" + shifts = torch.arange(8, device=packed.device) + return ((packed.unsqueeze(-1) >> shifts) & 1).view(*packed.shape[:-1], -1).bool() + + +def _compute_backbone(backbone_fn, frame, frame_idx=None): + """Compute backbone features for a single frame. Returns (vision_feats, vision_pos, feat_sizes, features, trunk_out).""" + features, positions, trunk_out = backbone_fn(frame, frame_idx=frame_idx) + feat_sizes = [(x.shape[-2], x.shape[-1]) for x in features] + vision_feats = [x.flatten(2).permute(0, 2, 1) for x in features] + vision_pos = [x.flatten(2).permute(0, 2, 1) for x in positions] + return vision_feats, vision_pos, feat_sizes, features, trunk_out + + +def collect_memory_tokens(output_dict, frame_idx, num_maskmem, maskmem_tpos_enc, device, + collect_image_feats=False, tpos_v2=False, num_buckets=None): + """Collect spatial memory, position encodings, and optionally image features from past frames.""" + to_cat_memory, to_cat_memory_pos = [], [] + to_cat_image_feat, to_cat_image_pos = [], [] + + def _append(out, tpos_idx): + feats = out["maskmem_features"].to(device) + if num_buckets is not None: + feats = _pad_to_buckets(feats, num_buckets) + to_cat_memory.append(feats.flatten(2).permute(0, 2, 1)) + enc = out["maskmem_pos_enc"][-1].to(device).flatten(2).permute(0, 2, 1) + if num_buckets is not None: + enc = _pad_to_buckets(enc, num_buckets) + tpos = cast_to_input(maskmem_tpos_enc[tpos_idx], enc) + to_cat_memory_pos.append(enc + tpos) + if collect_image_feats and "image_features" in out: + to_cat_image_feat.append(out["image_features"].to(device)) + to_cat_image_pos.append(out["image_pos_enc"].to(device) + tpos) + + cond_outputs = output_dict["cond_frame_outputs"] + for t, out in cond_outputs.items(): + if tpos_v2: + t_pos = frame_idx - t + tpos_idx = num_maskmem - t_pos - 1 if 0 < t_pos < num_maskmem else num_maskmem - 1 + else: + tpos_idx = num_maskmem - 1 + _append(out, tpos_idx) + + for t_pos in range(1, num_maskmem): + out = output_dict["non_cond_frame_outputs"].get(frame_idx - (num_maskmem - t_pos), None) + if out is None or out.get("maskmem_features") is None: + continue + _append(out, num_maskmem - t_pos - 1) + + return to_cat_memory, to_cat_memory_pos, to_cat_image_feat, to_cat_image_pos, cond_outputs + + +def compute_tpos_enc(rel_pos_list, device, d_model, proj_layer, dtype=None, max_abs_pos=None): + """Temporal position encoding for object pointers.""" + pos_enc = torch.tensor(rel_pos_list, dtype=torch.float32, device=device) / max((max_abs_pos or 2) - 1, 1) + pos_enc = get_1d_sine_pe(pos_enc, dim=d_model) + if dtype is not None: + pos_enc = pos_enc.to(dtype) + return proj_layer(pos_enc) + + +def forward_sam_heads(backbone_features, prompt_encoder, mask_decoder, obj_ptr_proj, no_obj_fn, + image_size, point_inputs=None, mask_inputs=None, box_inputs=None, + high_res_features=None, multimask_output=False): + """Shared SAM prompt encoder + mask decoder forward for both SAM3 and SAM3.1 trackers.""" + device = backbone_features.device + # Batch size from inputs (mask_inputs may have N_obj > 1 while backbone is batch 1) + if mask_inputs is not None: + B = mask_inputs.shape[0] + elif box_inputs is not None: + B = box_inputs.shape[0] + elif point_inputs is not None: + B = point_inputs["point_coords"].shape[0] + else: + B = backbone_features.shape[0] + + if point_inputs is not None: + sam_point_coords = point_inputs["point_coords"] + sam_point_labels = point_inputs["point_labels"] + else: + sam_point_coords = torch.zeros(B, 1, 2, device=device) + sam_point_labels = -torch.ones(B, 1, dtype=torch.int32, device=device) + + if mask_inputs is not None: + prompt_size = (prompt_encoder.image_embedding_size[0] * 4, prompt_encoder.image_embedding_size[1] * 4) + if mask_inputs.shape[-2:] != prompt_size: + sam_mask_prompt = F.interpolate(mask_inputs, size=prompt_size, mode="bilinear", align_corners=False, antialias=True) + else: + sam_mask_prompt = mask_inputs + else: + sam_mask_prompt = None + + sparse, dense = prompt_encoder(points=(sam_point_coords, sam_point_labels), boxes=box_inputs, masks=sam_mask_prompt) + sparse = cast_to_input(sparse, backbone_features) + dense = cast_to_input(dense, backbone_features) + image_pe = cast_to_input(prompt_encoder.get_dense_pe(), backbone_features) + + low_res_multimasks, ious, sam_output_tokens, object_score_logits = mask_decoder( + image_embeddings=backbone_features, image_pe=image_pe, + sparse_prompt_embeddings=sparse, dense_prompt_embeddings=dense, + high_res_features=high_res_features, multimask_output=multimask_output, return_all=True, + ) + + is_obj_appearing = object_score_logits > 0 + low_res_multimasks = torch.where(is_obj_appearing[:, None, None], low_res_multimasks, + torch.tensor(NO_OBJ_SCORE, device=device, dtype=low_res_multimasks.dtype)) + high_res_multimasks = F.interpolate(low_res_multimasks, size=(image_size, image_size), mode="bilinear", align_corners=False) + + sam_output_token = sam_output_tokens[:, 0] + if multimask_output: + best_iou_inds = torch.argmax(ious, dim=-1) + batch_inds = torch.arange(B, device=device) + low_res_masks = low_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1) + high_res_masks = high_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1) + if sam_output_tokens.size(1) > 1: + sam_output_token = sam_output_tokens[batch_inds, best_iou_inds] + else: + low_res_masks, high_res_masks = low_res_multimasks, high_res_multimasks + + obj_ptr = obj_ptr_proj(sam_output_token) + obj_ptr = no_obj_fn(obj_ptr, is_obj_appearing) + + return low_res_masks, high_res_masks, obj_ptr, object_score_logits + + +def use_mask_as_output(backbone_features, high_res_features, mask_inputs, mask_downsample, + prompt_encoder, mask_decoder, obj_ptr_proj, no_obj_fn, image_size, backbone_stride): + """Shared mask-as-output for both SAM3 and SAM3.1 trackers.""" + out_scale, out_bias = 20.0, -10.0 + mask_inputs_float = cast_to_input(mask_inputs, backbone_features) + high_res_masks = mask_inputs_float * out_scale + out_bias + low_res_masks = F.interpolate(high_res_masks, size=(image_size // backbone_stride * 4,) * 2, + mode="bilinear", align_corners=False, antialias=True) + _, _, obj_ptr, _ = forward_sam_heads( + backbone_features, prompt_encoder, mask_decoder, obj_ptr_proj, no_obj_fn, + image_size, mask_inputs=mask_downsample(mask_inputs_float), high_res_features=high_res_features, + ) + is_obj_appearing = torch.any(mask_inputs.flatten(1) > 0.0, dim=1)[..., None] + alpha = is_obj_appearing.to(obj_ptr.dtype) + object_score_logits = out_scale * alpha + out_bias + return low_res_masks, high_res_masks, obj_ptr, object_score_logits + + +# Split attention with configurable input dims (for asymmetric cross-attention) +class SplitAttn(nn.Module): + def __init__(self, embed_dim, num_heads=1, kv_dim=None, internal_dim=None, device=None, dtype=None, operations=None): + super().__init__() + self.num_heads = num_heads + kv_dim = kv_dim or embed_dim + internal_dim = internal_dim or embed_dim + self.q_proj = operations.Linear(embed_dim, internal_dim, device=device, dtype=dtype) + self.k_proj = operations.Linear(kv_dim, internal_dim, device=device, dtype=dtype) + self.v_proj = operations.Linear(kv_dim, internal_dim, device=device, dtype=dtype) + self.out_proj = operations.Linear(internal_dim, embed_dim, device=device, dtype=dtype) + + def forward(self, q, k=None, v=None, rope=None, num_k_exclude_rope=0): + if k is None: + k = q + if v is None: + v = k + q = self.q_proj(q) + k = self.k_proj(k) + v = self.v_proj(v) + if rope is not None: + q, k = apply_rope_memory(q, k, rope, self.num_heads, num_k_exclude_rope) + out = optimized_attention(q, k, v, self.num_heads) + return self.out_proj(out) + + +class MemoryAttnLayer(nn.Module): + def __init__(self, d_model=256, num_heads=1, kv_dim=64, dim_ff=2048, device=None, dtype=None, operations=None): + super().__init__() + self.num_heads = num_heads + self.self_attn = SplitAttn(d_model, num_heads, device=device, dtype=dtype, operations=operations) + self.cross_attn_image = SplitAttn(d_model, num_heads, kv_dim=kv_dim, device=device, dtype=dtype, operations=operations) + self.linear1 = operations.Linear(d_model, dim_ff, device=device, dtype=dtype) + self.linear2 = operations.Linear(dim_ff, d_model, device=device, dtype=dtype) + self.norm1 = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.norm2 = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.norm3 = operations.LayerNorm(d_model, device=device, dtype=dtype) + + def forward(self, x, memory, memory_pos=None, rope=None, num_k_exclude_rope=0): + x = x + self.self_attn(self.norm1(x), rope=rope) + mem_k = memory + memory_pos if memory_pos is not None else memory + x = x + self.cross_attn_image(self.norm2(x), mem_k, memory, rope=rope, num_k_exclude_rope=num_k_exclude_rope) + normed = self.norm3(x) + x = x + self.linear2(F.relu(self.linear1(normed))) + return x + + +class MemoryAttnEncoder(nn.Module): + def __init__(self, d_model=256, num_heads=1, kv_dim=64, dim_ff=2048, num_layers=4, image_size=1008, patch_size=14, + device=None, dtype=None, operations=None): + super().__init__() + self.layers = nn.ModuleList([ + MemoryAttnLayer(d_model, num_heads, kv_dim, dim_ff, device=device, dtype=dtype, operations=operations) + for _ in range(num_layers) + ]) + self.norm = operations.LayerNorm(d_model, device=device, dtype=dtype) + hw = image_size // patch_size + self.register_buffer("_rope", rope_2d(hw, hw, d_model // num_heads), persistent=False) + + def forward(self, x, memory, src_pos=None, memory_pos=None, num_k_exclude_rope=0): + if src_pos is not None: + x = x + 0.1 * src_pos + + rope = self._rope.to(device=x.device) + for layer in self.layers: + x = layer(x, memory, memory_pos=memory_pos, rope=rope, num_k_exclude_rope=num_k_exclude_rope) + return self.norm(x) + + +class MemoryTransformer(nn.Module): + def __init__(self, d_model=256, num_heads=1, kv_dim=64, dim_ff=2048, num_layers=4, device=None, dtype=None, operations=None): + super().__init__() + self.encoder = MemoryAttnEncoder(d_model, num_heads, kv_dim, dim_ff, num_layers, device=device, dtype=dtype, operations=operations) + + +def _upscale_masks(output_upscaling, conv_s0, conv_s1, src_out, high_res_features): + """Shared upscaling for SAM mask decoders: deconv + high-res feature integration.""" + dc1, ln1, act1, dc2, act2 = output_upscaling + if high_res_features is not None: + upscaled = act1(ln1(dc1(src_out) + conv_s1(high_res_features[1]))) + upscaled = act2(dc2(upscaled) + conv_s0(high_res_features[0])) + else: + upscaled = act2(dc2(act1(ln1(dc1(src_out))))) + return upscaled + + +class SAMMaskDecoder(nn.Module): + def __init__(self, d_model=256, num_multimask_outputs=3, device=None, dtype=None, operations=None): + super().__init__() + self.num_mask_tokens = num_multimask_outputs + 1 + + self.transformer = SAMTwoWayTransformer(depth=2, embedding_dim=d_model, num_heads=8, mlp_dim=2048, device=device, dtype=dtype, operations=operations) + + self.iou_token = operations.Embedding(1, d_model, device=device, dtype=dtype) + self.mask_tokens = operations.Embedding(self.num_mask_tokens, d_model, device=device, dtype=dtype) + self.obj_score_token = operations.Embedding(1, d_model, device=device, dtype=dtype) + + # Output upscaling: d_model -> d_model//4 -> d_model//8 at 4x resolution + LN2d = LayerNorm2d_op(operations) + self.output_upscaling = nn.Sequential( + operations.ConvTranspose2d(d_model, d_model // 4, kernel_size=2, stride=2, device=device, dtype=dtype), LN2d(d_model // 4, device=device, dtype=dtype), nn.GELU(), + operations.ConvTranspose2d(d_model // 4, d_model // 8, kernel_size=2, stride=2, device=device, dtype=dtype), nn.GELU(), + ) + + # High-res feature integration + self.conv_s0 = operations.Conv2d(d_model, d_model // 8, kernel_size=1, device=device, dtype=dtype) + self.conv_s1 = operations.Conv2d(d_model, d_model // 4, kernel_size=1, device=device, dtype=dtype) + + # Per-mask hypernetwork MLPs + self.output_hypernetworks_mlps = nn.ModuleList([ + MLP(d_model, d_model, d_model // 8, 3, device=device, dtype=dtype, operations=operations) + for _ in range(self.num_mask_tokens) + ]) + + self.iou_prediction_head = MLP(d_model, d_model, self.num_mask_tokens, 3, device=device, dtype=dtype, operations=operations) + self.pred_obj_score_head = MLP(d_model, d_model, 1, 3, device=device, dtype=dtype, operations=operations) + + def forward(self, image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, + high_res_features=None, multimask_output=False, return_all=False): + B = sparse_prompt_embeddings.shape[0] + ref = sparse_prompt_embeddings + # Token order: [obj_score(1), iou(1), mask(num_mask_tokens)] + tokens = torch.cat([cast_to_input(self.obj_score_token.weight, ref), + cast_to_input(self.iou_token.weight, ref), + cast_to_input(self.mask_tokens.weight, ref)], dim=0) + tokens = torch.cat([tokens.unsqueeze(0).expand(B, -1, -1), sparse_prompt_embeddings], dim=1) + + src = image_embeddings + if src.shape[0] != B: + src = src.expand(B, -1, -1, -1) + src = src + dense_prompt_embeddings + pos_src = image_pe.expand(B, -1, -1, -1) + + b, c, h, w = src.shape + src_flat = src.flatten(2).permute(0, 2, 1) + pos_flat = pos_src.flatten(2).permute(0, 2, 1) + + hs, src_out = self.transformer(src_flat, pos_flat, tokens) + + obj_score_token_out = hs[:, 0, :] + iou_token_out = hs[:, 1, :] + mask_tokens_out = hs[:, 2:2 + self.num_mask_tokens, :] + + src_out = src_out.permute(0, 2, 1).view(b, c, h, w) + upscaled = _upscale_masks(self.output_upscaling, self.conv_s0, self.conv_s1, src_out, high_res_features) + + hyper_in = torch.stack([ + mlp(mask_tokens_out[:, i, :]) for i, mlp in enumerate(self.output_hypernetworks_mlps) + ], dim=1) + + masks = (hyper_in @ upscaled.flatten(2)).view(B, self.num_mask_tokens, upscaled.shape[2], upscaled.shape[3]) + iou_pred = self.iou_prediction_head(iou_token_out) + object_score_logits = self.pred_obj_score_head(obj_score_token_out) + + if multimask_output: + out_masks = masks[:, 1:] + out_iou = iou_pred[:, 1:] + out_tokens = mask_tokens_out[:, 1:] + else: + out_masks = masks[:, 0:1] + out_iou = iou_pred[:, 0:1] + out_tokens = mask_tokens_out[:, 0:1] + + if return_all: + return out_masks, out_iou, out_tokens, object_score_logits + return out_masks, out_iou + + +class SAMPromptEncoder(nn.Module): + def __init__(self, d_model=256, image_embedding_size=(72, 72), input_image_size=(1008, 1008), device=None, dtype=None, operations=None): + super().__init__() + self.embed_dim = d_model + self.image_embedding_size = image_embedding_size + self.input_image_size = input_image_size + + self.pe_layer = PositionEmbeddingRandom(d_model // 2) + self.point_embeddings = nn.ModuleList([ + operations.Embedding(1, d_model, device=device, dtype=dtype) for _ in range(4) + ]) + self.not_a_point_embed = operations.Embedding(1, d_model, device=device, dtype=dtype) + + LN2d = LayerNorm2d_op(operations) + self.mask_downscaling = nn.Sequential( + operations.Conv2d(1, 4, kernel_size=2, stride=2, device=device, dtype=dtype), + LN2d(4, device=device, dtype=dtype), nn.GELU(), + operations.Conv2d(4, 16, kernel_size=2, stride=2, device=device, dtype=dtype), + LN2d(16, device=device, dtype=dtype), nn.GELU(), + operations.Conv2d(16, d_model, kernel_size=1, device=device, dtype=dtype), + ) + self.no_mask_embed = operations.Embedding(1, d_model, device=device, dtype=dtype) + + def get_dense_pe(self): + return self.pe_layer(self.image_embedding_size) + + def forward(self, points=None, boxes=None, masks=None): + ref = points[0] if points is not None else boxes if boxes is not None else masks + B = 1 + sparse = torch.empty((B, 0, self.embed_dim), device=ref.device, dtype=ref.dtype) + + if points is not None: + coords, labels = points + B = coords.shape[0] + # Pad with an extra point (label=-1) when no boxes are provided (matching reference) + if boxes is None: + coords = torch.cat([coords, torch.zeros(B, 1, 2, device=coords.device, dtype=coords.dtype)], dim=1) + labels = torch.cat([labels, -torch.ones(B, 1, device=labels.device, dtype=labels.dtype)], dim=1) + pe = self.pe_layer.forward_with_coords(coords + 0.5, self.input_image_size) + for i in range(4): + pe[labels == i] += cast_to_input(self.point_embeddings[i].weight, ref) + invalid = (labels == -1) + pe[invalid] = 0.0 + pe[invalid] += cast_to_input(self.not_a_point_embed.weight, ref) + sparse = torch.cat([sparse.expand(B, -1, -1), pe], dim=1) + + if boxes is not None: + B = boxes.shape[0] + corners = self.pe_layer.forward_with_coords((boxes.reshape(-1, 2, 2) + 0.5), self.input_image_size) + corners[:, 0] += cast_to_input(self.point_embeddings[2].weight, ref) + corners[:, 1] += cast_to_input(self.point_embeddings[3].weight, ref) + sparse = torch.cat([sparse.expand(B, -1, -1), corners], dim=1) + + if masks is not None: + dense = self.mask_downscaling(masks) + else: + dense = cast_to_input(self.no_mask_embed.weight, ref).reshape(1, -1, 1, 1).expand( + B, -1, self.image_embedding_size[0], self.image_embedding_size[1]) + + return sparse, dense + + +class CXBlock(nn.Module): + def __init__(self, dim=256, kernel_size=7, device=None, dtype=None, operations=None): + super().__init__() + self.dwconv = operations.Conv2d(dim, dim, kernel_size=kernel_size, padding=kernel_size // 2, groups=dim, device=device, dtype=dtype) + self.norm = operations.LayerNorm(dim, device=device, dtype=dtype) + self.pwconv1 = operations.Linear(dim, 4 * dim, device=device, dtype=dtype) + self.pwconv2 = operations.Linear(4 * dim, dim, device=device, dtype=dtype) + self.gamma = nn.Parameter(torch.ones(dim, device=device, dtype=dtype)) + + def forward(self, x): + residual = x + x = self.dwconv(x).permute(0, 2, 3, 1) + x = self.pwconv2(F.gelu(self.pwconv1(self.norm(x)))) + x.mul_(cast_to_input(self.gamma, x)) + return residual + x.permute(0, 3, 1, 2) + + +class MaskDownSampler(nn.Module): + def __init__(self, out_dim=256, in_chans=1, channels=None, interpol_size=(1152, 1152), device=None, dtype=None, operations=None): + super().__init__() + self.interpol_size = list(interpol_size) if interpol_size else None + if channels is None: + channels = [4, 16, 64, out_dim] # SAM3 default + LN2d = LayerNorm2d_op(operations) + layers = [] + prev = in_chans + for ch in channels: + layers += [operations.Conv2d(prev, ch, kernel_size=3, stride=2, padding=1, device=device, dtype=dtype), + LN2d(ch, device=device, dtype=dtype), nn.GELU()] + prev = ch + layers.append(operations.Conv2d(prev, out_dim, kernel_size=1, device=device, dtype=dtype)) + self.encoder = nn.Sequential(*layers) + + def forward(self, x): + if self.interpol_size is not None and list(x.shape[-2:]) != self.interpol_size: + x = F.interpolate(x, size=self.interpol_size, mode="bilinear", align_corners=False, antialias=True) + return self.encoder(x) + + +class Fuser(nn.Module): + def __init__(self, dim=256, num_layers=2, device=None, dtype=None, operations=None): + super().__init__() + self.layers = nn.Sequential(*[CXBlock(dim, device=device, dtype=dtype, operations=operations) for _ in range(num_layers)]) + + def forward(self, x): + return self.layers(x) + + +# --- SAM3.1 Multiplex components --- + +class DecoupledMemoryAttnLayer(nn.Module): + """Decoupled cross-attention layer for SAM3.1: fuses image and memory projections.""" + + def __init__(self, d_model=256, num_heads=1, dim_ff=2048, device=None, dtype=None, operations=None): + super().__init__() + self.num_heads = num_heads + # Self-attention projections (flat, not nested) + self.self_attn_q_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.self_attn_k_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.self_attn_v_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.self_attn_out_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + # Cross-attention projections + self.cross_attn_q_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.cross_attn_k_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.cross_attn_v_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.cross_attn_out_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + # Image cross-attention (q/k only, fused with cross_attn) + self.image_cross_attn_q_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.image_cross_attn_k_proj = operations.Linear(d_model, d_model, device=device, dtype=dtype) + # FFN + self.linear1 = operations.Linear(d_model, dim_ff, device=device, dtype=dtype) + self.linear2 = operations.Linear(dim_ff, d_model, device=device, dtype=dtype) + self.norm1 = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.norm2 = operations.LayerNorm(d_model, device=device, dtype=dtype) + self.norm3 = operations.LayerNorm(d_model, device=device, dtype=dtype) + + def forward(self, image, x, memory_image, memory, memory_image_pos=None, + rope=None, num_k_exclude_rope=0): + # Self-attention with RoPE + normed = self.norm1(x) + q = self.self_attn_q_proj(normed) + k = self.self_attn_k_proj(normed) + v = self.self_attn_v_proj(normed) + if rope is not None: + q, k = apply_rope_memory(q, k, rope, self.num_heads, 0) + x = x + self.self_attn_out_proj(optimized_attention(q, k, v, self.num_heads)) + + # Decoupled cross-attention: fuse image and memory projections + normed = self.norm2(x) + q = self.image_cross_attn_q_proj(image) + self.cross_attn_q_proj(normed) + k = self.image_cross_attn_k_proj(memory_image) + self.cross_attn_k_proj(memory) + if memory_image_pos is not None: + k = k + memory_image_pos + v = self.cross_attn_v_proj(memory) + if rope is not None: + q, k = apply_rope_memory(q, k, rope, self.num_heads, num_k_exclude_rope) + x = x + self.cross_attn_out_proj(optimized_attention(q, k, v, self.num_heads)) + + # FFN + x = x + self.linear2(F.gelu(self.linear1(self.norm3(x)))) + return image, x + + +class DecoupledMemoryEncoder(nn.Module): + """Memory attention encoder for SAM3.1 with decoupled cross-attention.""" + + def __init__(self, d_model=256, num_heads=1, dim_ff=2048, num_layers=4, image_size=1008, patch_size=14, + device=None, dtype=None, operations=None): + super().__init__() + self.layers = nn.ModuleList([ + DecoupledMemoryAttnLayer(d_model, num_heads, dim_ff, device=device, dtype=dtype, operations=operations) + for _ in range(num_layers) + ]) + self.norm = operations.LayerNorm(d_model, device=device, dtype=dtype) + hw = image_size // patch_size + self.register_buffer("_rope", rope_2d(hw, hw, d_model // num_heads), persistent=False) + + def forward(self, x, memory, memory_pos=None, src_pos=None, num_k_exclude_rope=0, + memory_image=None, memory_image_pos=None): + image = x # constant residual for decoupled cross-attention + output = x + if src_pos is not None: + output = output + 0.1 * src_pos + + B, _, C = x.shape + rope = self._rope.to(device=x.device) + + # memory_image: raw backbone features from past frames for decoupled cross-attention + if memory_image is None: + # Fallback: use spatial portion of memory (without obj pointers) + num_spatial = memory.shape[1] - num_k_exclude_rope + memory_image = memory[:, :num_spatial] + memory_image_pos = memory_pos[:, :num_spatial] if memory_pos is not None else None + # Pad memory_image to match memory length (zeros for obj pointer tokens) + if memory_image.shape[1] < memory.shape[1]: + pad_len = memory.shape[1] - memory_image.shape[1] + pad = torch.zeros(B, pad_len, C, device=memory.device, dtype=memory.dtype) + memory_image = torch.cat([memory_image, pad], dim=1) + if memory_image_pos is not None: + ptr_pos = memory_pos[:, -pad_len:] if memory_pos is not None else torch.zeros_like(pad) + memory_image_pos = torch.cat([memory_image_pos, ptr_pos], dim=1) + + for layer in self.layers: + image, output = layer(image, output, memory_image, memory, + memory_image_pos=memory_image_pos, rope=rope, + num_k_exclude_rope=num_k_exclude_rope) + + return self.norm(output) + + +class DecoupledMemoryTransformer(nn.Module): + def __init__(self, d_model=256, num_heads=1, dim_ff=2048, num_layers=4, device=None, dtype=None, operations=None): + super().__init__() + self.encoder = DecoupledMemoryEncoder(d_model, num_heads, dim_ff, num_layers, + device=device, dtype=dtype, operations=operations) + + +class MemoryBackbone(nn.Module): + """Memory encoder: downsamples mask, fuses with pixel features, optionally compresses.""" + + def __init__(self, d_model=256, out_dim=None, in_chans=1, channels=None, device=None, dtype=None, operations=None): + super().__init__() + self.mask_downsampler = MaskDownSampler(d_model, in_chans=in_chans, channels=channels, device=device, dtype=dtype, operations=operations) + self.pix_feat_proj = operations.Conv2d(d_model, d_model, kernel_size=1, device=device, dtype=dtype) + self.fuser = Fuser(d_model, num_layers=2, device=device, dtype=dtype, operations=operations) + self.has_out_proj = out_dim is not None and out_dim != d_model + if self.has_out_proj: + self.out_proj = operations.Conv2d(d_model, out_dim, kernel_size=1, device=device, dtype=dtype) + feat_dim = out_dim + else: + feat_dim = d_model + self.position_encoding = PositionEmbeddingSine(num_pos_feats=feat_dim, normalize=True) + + def forward(self, image_features, mask_for_mem, skip_mask_sigmoid=False): + if not skip_mask_sigmoid: + mask_for_mem = mask_for_mem.sigmoid() + mask_features = self.mask_downsampler(cast_to_input(mask_for_mem, image_features)) + if mask_features.shape[-2:] != image_features.shape[-2:]: + mask_features = F.interpolate(mask_features, size=image_features.shape[-2:], mode="bilinear", align_corners=False) + features = self.pix_feat_proj(image_features) + mask_features + features = self.fuser(features) + if self.has_out_proj: + features = self.out_proj(features) + pos = cast_to_input(self.position_encoding(features), features) + return {"vision_features": features, "vision_pos_enc": [pos]} + + +class MultiplexMaskDecoder(nn.Module): + """SAM mask decoder for SAM3.1 multiplex: predicts masks for num_multiplex objects simultaneously. + + Uses multimask_outputs_only=True: num_mask_output_per_object = num_multimask_outputs (no +1). + Hypernetwork MLPs are shared across multiplex objects. + Token order: [obj_score_token(M), iou_token(M), mask_tokens(M*T)]. + """ + + def __init__(self, d_model=256, num_multiplex=16, num_multimask_outputs=3, device=None, dtype=None, operations=None): + super().__init__() + self.num_multiplex = num_multiplex + self.num_mask_output_per_object = num_multimask_outputs # 3 (multimask_outputs_only) + total_mask_tokens = num_multiplex * self.num_mask_output_per_object # 48 + + self.transformer = SAMTwoWayTransformer(depth=2, embedding_dim=d_model, num_heads=8, mlp_dim=2048, device=device, dtype=dtype, operations=operations) + + self.obj_score_token = operations.Embedding(num_multiplex, d_model, device=device, dtype=dtype) + self.iou_token = operations.Embedding(num_multiplex, d_model, device=device, dtype=dtype) + self.mask_tokens = operations.Embedding(total_mask_tokens, d_model, device=device, dtype=dtype) + + LN2d = LayerNorm2d_op(operations) + self.output_upscaling = nn.Sequential( + operations.ConvTranspose2d(d_model, d_model // 4, kernel_size=2, stride=2, device=device, dtype=dtype), + LN2d(d_model // 4, device=device, dtype=dtype), nn.GELU(), + operations.ConvTranspose2d(d_model // 4, d_model // 8, kernel_size=2, stride=2, device=device, dtype=dtype), nn.GELU(), + ) + self.conv_s0 = operations.Conv2d(d_model, d_model // 8, kernel_size=1, device=device, dtype=dtype) + self.conv_s1 = operations.Conv2d(d_model, d_model // 4, kernel_size=1, device=device, dtype=dtype) + + # Shared across all multiplex objects (one per mask output) + self.output_hypernetworks_mlps = nn.ModuleList([ + MLP(d_model, d_model, d_model // 8, 3, device=device, dtype=dtype, operations=operations) + for _ in range(self.num_mask_output_per_object) + ]) + self.iou_prediction_head = MLP(d_model, d_model, self.num_mask_output_per_object, 3, device=device, dtype=dtype, operations=operations) + self.pred_obj_score_head = MLP(d_model, d_model, 1, 3, device=device, dtype=dtype, operations=operations) + + def forward(self, image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings, + high_res_features=None, multimask_output=False, return_all=False, extra_per_object_embeddings=None): + B = sparse_prompt_embeddings.shape[0] + M = self.num_multiplex + T = self.num_mask_output_per_object + + # Token order: [obj_score(M), iou(M), mask(M*T)] + ref = sparse_prompt_embeddings + mask_tokens = cast_to_input(self.mask_tokens.weight, ref) + if extra_per_object_embeddings is not None: + mask_tokens = mask_tokens.view(1, M, T, -1).expand(B, -1, -1, -1) + extra_per_object_embeddings.unsqueeze(2) + mask_tokens = mask_tokens.flatten(1, 2) # [B, M*T, C] + other_tokens = torch.cat([cast_to_input(self.obj_score_token.weight, ref), + cast_to_input(self.iou_token.weight, ref)], dim=0).unsqueeze(0).expand(B, -1, -1) + tokens = torch.cat([other_tokens, mask_tokens, sparse_prompt_embeddings], dim=1) + else: + tokens = torch.cat([cast_to_input(self.obj_score_token.weight, ref), + cast_to_input(self.iou_token.weight, ref), mask_tokens], dim=0) + tokens = torch.cat([tokens.unsqueeze(0).expand(B, -1, -1), sparse_prompt_embeddings], dim=1) + + src = image_embeddings + if src.shape[0] != B: + src = src.expand(B, -1, -1, -1) + src = src + dense_prompt_embeddings + pos_src = image_pe.expand(B, -1, -1, -1) + + b, c, h, w = src.shape + hs, src_out = self.transformer(src.flatten(2).permute(0, 2, 1), pos_src.flatten(2).permute(0, 2, 1), tokens) + + # Parse output tokens + obj_score_token_out = hs[:, :M] + iou_token_out = hs[:, M:2 * M] + mask_tokens_out = hs[:, 2 * M:2 * M + M * T] + + src_out = src_out.permute(0, 2, 1).view(b, c, h, w) + upscaled = _upscale_masks(self.output_upscaling, self.conv_s0, self.conv_s1, src_out, high_res_features) + + # Reshape mask tokens to [B, M, T, C] and apply shared hypernetwork MLPs per mask output index + mask_tokens_2d = mask_tokens_out.view(B, M, T, -1) + hyper_in = torch.stack([ + self.output_hypernetworks_mlps[i](mask_tokens_2d[:, :, i, :]) # [B, M, C//8] + for i in range(T) + ], dim=2) # [B, M, T, C//8] + + # Generate masks: [B, M*T, H*W] -> [B, M, T, H, W] + masks = torch.bmm(hyper_in.flatten(1, 2), upscaled.flatten(2)).view(b, M, T, upscaled.shape[2], upscaled.shape[3]) + + # IoU and object scores + iou_pred = self.iou_prediction_head(iou_token_out).view(b, M, T) + object_score_logits = self.pred_obj_score_head(obj_score_token_out) # [B, M, 1] + + # multimask_outputs_only: always output all T masks (no singlemask token) + sam_tokens_out = mask_tokens_2d[:, :, 0:1] # [B, M, 1, C] + + if return_all: + return masks, iou_pred, sam_tokens_out, object_score_logits + return masks, iou_pred + + +class SAM3Tracker(nn.Module): + def __init__(self, d_model=256, mem_dim=64, num_maskmem=7, device=None, dtype=None, operations=None, **kwargs): + super().__init__() + + # Memory attention transformer + self.transformer = MemoryTransformer(d_model, num_heads=1, kv_dim=mem_dim, dim_ff=2048, num_layers=4, + device=device, dtype=dtype, operations=operations) + # SAM components + self.sam_mask_decoder = SAMMaskDecoder(d_model, device=device, dtype=dtype, operations=operations) + self.sam_prompt_encoder = SAMPromptEncoder(d_model, device=device, dtype=dtype, operations=operations) + + # Memory backbone + self.maskmem_backbone = MemoryBackbone(d_model, out_dim=mem_dim, device=device, dtype=dtype, operations=operations) + + # Standalone parameters + self.maskmem_tpos_enc = nn.Parameter(torch.zeros(num_maskmem, 1, 1, mem_dim, device=device, dtype=dtype)) + self.no_mem_embed = nn.Parameter(torch.zeros(1, 1, d_model, device=device, dtype=dtype)) + self.register_buffer("no_mem_pos_enc", torch.zeros(1, 1, d_model, device=device, dtype=dtype)) # checkpoint key, unused in forward + self.no_obj_embed_spatial = nn.Parameter(torch.zeros(1, mem_dim, device=device, dtype=dtype)) + self.no_obj_ptr = nn.Parameter(torch.zeros(1, d_model, device=device, dtype=dtype)) + + # Object pointer projection + self.obj_ptr_proj = MLP(d_model, d_model, d_model, 3, device=device, dtype=dtype, operations=operations) + self.obj_ptr_tpos_proj = operations.Linear(d_model, mem_dim, device=device, dtype=dtype) + + # Mask downsample: Conv2d stride 4 to reduce GT mask to SAM logit scale + self.mask_downsample = operations.Conv2d(1, 1, kernel_size=4, stride=4, device=device, dtype=dtype) + + # Config + self.d_model = d_model + self.mem_dim = mem_dim + self.num_maskmem = num_maskmem + self.image_size = 1008 + self.backbone_stride = 14 + self.max_obj_ptrs_in_encoder = 16 + self.sigmoid_scale_for_mem_enc = 20.0 + self.sigmoid_bias_for_mem_enc = -10.0 + + def _no_obj_blend(self, obj_ptr, is_obj): + alpha = is_obj.to(obj_ptr.dtype) + return torch.lerp(cast_to_input(self.no_obj_ptr, obj_ptr), obj_ptr, alpha) + + def _forward_sam_heads(self, backbone_features, point_inputs=None, mask_inputs=None, box_inputs=None, + high_res_features=None, multimask_output=False): + return forward_sam_heads(backbone_features, self.sam_prompt_encoder, self.sam_mask_decoder, + self.obj_ptr_proj, self._no_obj_blend, self.image_size, + point_inputs, mask_inputs, box_inputs, high_res_features, multimask_output) + + def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs): + return use_mask_as_output(backbone_features, high_res_features, mask_inputs, + self.mask_downsample, self.sam_prompt_encoder, self.sam_mask_decoder, + self.obj_ptr_proj, self._no_obj_blend, self.image_size, self.backbone_stride) + + def _prepare_memory_conditioned_features(self, frame_idx, is_init_cond_frame, current_vision_feats, current_vision_pos_embeds, feat_sizes, output_dict, num_frames): + """Fuse current frame features with memory from previous frames.""" + B = current_vision_feats[-1].shape[0] + C = self.d_model + H, W = feat_sizes[-1] + device = current_vision_feats[-1].device + + if self.num_maskmem == 0: + return current_vision_feats[-1].permute(0, 2, 1).view(B, C, H, W) + + if is_init_cond_frame: + # First conditioning frame: no memory yet, add no_mem_embed + pix_feat = current_vision_feats[-1] + cast_to_input(self.no_mem_embed, current_vision_feats[-1]) + return to_spatial(pix_feat, H, W) + + to_cat_memory, to_cat_memory_pos, _, _, cond_outputs = collect_memory_tokens( + output_dict, frame_idx, self.num_maskmem, self.maskmem_tpos_enc, device) + + max_obj_ptrs = min(num_frames, self.max_obj_ptrs_in_encoder) + pos_and_ptrs = [] + for t, out in cond_outputs.items(): + if t <= frame_idx: + pos_and_ptrs.append(((frame_idx - t), out["obj_ptr"].to(device))) + for t_diff in range(1, max_obj_ptrs): + t = frame_idx - t_diff + if t < 0: + break + out = output_dict["non_cond_frame_outputs"].get(t, None) + if out is not None: + pos_and_ptrs.append((t_diff, out["obj_ptr"].to(device))) + + num_obj_ptr_tokens = 0 + if len(pos_and_ptrs) > 0: + pos_list, ptrs_list = zip(*pos_and_ptrs) + obj_ptrs = torch.stack(ptrs_list, dim=1) # [B, N, C=256] + + # Temporal position encoding for pointers + obj_pos = compute_tpos_enc( + list(pos_list), device, self.d_model, self.obj_ptr_tpos_proj, + max_abs_pos=max_obj_ptrs, dtype=current_vision_feats[-1].dtype + ) # [N, mem_dim=64] + obj_pos = obj_pos.unsqueeze(0).expand(B, -1, -1) # [B, N, 64] + + # Split each 256-dim pointer into 4 x 64-dim tokens + if self.mem_dim < C: + N = obj_ptrs.shape[1] + obj_ptrs = obj_ptrs.view(B, N, C // self.mem_dim, self.mem_dim) # [B, N, 4, 64] + obj_ptrs = obj_ptrs.reshape(B, N * (C // self.mem_dim), self.mem_dim) # [B, N*4, 64] + obj_pos = obj_pos.unsqueeze(2).expand(-1, -1, C // self.mem_dim, -1) + obj_pos = obj_pos.reshape(B, N * (C // self.mem_dim), self.mem_dim) # [B, N*4, 64] + + to_cat_memory.append(obj_ptrs) + to_cat_memory_pos.append(obj_pos) + num_obj_ptr_tokens = obj_ptrs.shape[1] + + if len(to_cat_memory) == 0: + # No memory available yet, add no_mem_embed + pix_feat = current_vision_feats[-1] + cast_to_input(self.no_mem_embed, current_vision_feats[-1]) + return to_spatial(pix_feat, H, W) + + # Concatenate all memory and position encodings [B, total_mem, mem_dim=64] + memory = torch.cat(to_cat_memory, dim=1) + memory_pos = torch.cat(to_cat_memory_pos, dim=1) + + # Run memory attention encoder + pix_feat = current_vision_feats[-1] # [B, HW, C] + src_pos = current_vision_pos_embeds[-1] # [B, HW, C] + + pix_feat_with_mem = self.transformer.encoder( + x=pix_feat, + memory=memory, + src_pos=src_pos, + memory_pos=memory_pos, + num_k_exclude_rope=num_obj_ptr_tokens, + ) + return to_spatial(pix_feat_with_mem, H, W) + + def _encode_new_memory(self, pix_feat, pred_masks_high_res, object_score_logits, is_mask_from_pts=False): + """Encode predicted mask into memory features.""" + if is_mask_from_pts: + mask_for_mem = (pred_masks_high_res > 0).to(pix_feat.dtype) + else: + mask_for_mem = torch.sigmoid(pred_masks_high_res) + + mask_for_mem.mul_(self.sigmoid_scale_for_mem_enc).add_(self.sigmoid_bias_for_mem_enc) + + maskmem_out = self.maskmem_backbone(pix_feat, mask_for_mem, skip_mask_sigmoid=True) + maskmem_features = maskmem_out["vision_features"] + maskmem_pos_enc = maskmem_out["vision_pos_enc"] + + # Add no_obj_embed for occluded objects + alpha = (object_score_logits > 0).to(maskmem_features.dtype)[..., None, None] + no_obj = cast_to_input(self.no_obj_embed_spatial, maskmem_features)[..., None, None].expand_as(maskmem_features) + return maskmem_features + (1 - alpha) * no_obj, maskmem_pos_enc + + def track_step(self, frame_idx, is_init_cond_frame, current_vision_feats, current_vision_pos_embeds, feat_sizes, mask_inputs, output_dict, + num_frames, point_inputs=None): + """Track one frame: fuse with memory, predict mask, encode memory.""" + current_out = {} + + # High-res features for SAM head [stride-8, stride-4] + if len(current_vision_feats) > 1: + high_res_features = [ + x.view(x.shape[0], feat_sizes[i][0], feat_sizes[i][1], -1).permute(0, 3, 1, 2) + for i, x in enumerate(current_vision_feats[:-1]) + ] + else: + high_res_features = None + + # Top-level feature for memory + H, W = feat_sizes[-1] + + if mask_inputs is not None: + # Conditioning frame: use mask directly + pix_feat = to_spatial(current_vision_feats[-1], H, W) + sam_outputs = self._use_mask_as_output(pix_feat, high_res_features, mask_inputs) + else: + # Track frame: fuse with memory, then SAM decoder + pix_feat_with_mem = self._prepare_memory_conditioned_features( + frame_idx=frame_idx, + is_init_cond_frame=is_init_cond_frame, + current_vision_feats=current_vision_feats, + current_vision_pos_embeds=current_vision_pos_embeds, + feat_sizes=feat_sizes, + output_dict=output_dict, + num_frames=num_frames, + ) + # Use multimask for point prompts on init frames (picks best of 3 candidates) + num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1) + multimask_output = is_init_cond_frame and 0 < num_pts <= 1 + sam_outputs = self._forward_sam_heads( + backbone_features=pix_feat_with_mem, + point_inputs=point_inputs, + high_res_features=high_res_features, + multimask_output=multimask_output, + ) + + (low_res_masks, high_res_masks, obj_ptr, object_score_logits) = sam_outputs + + # Clean low-res masks: remove sprinkles and fill holes + low_res_masks = fill_holes_in_mask_scores(low_res_masks, max_area=200) + high_res_masks = F.interpolate(low_res_masks, size=(self.image_size, self.image_size), mode="bilinear", align_corners=False) + + current_out["pred_masks"] = low_res_masks + current_out["pred_masks_high_res"] = high_res_masks + current_out["obj_ptr"] = obj_ptr + current_out["object_score_logits"] = object_score_logits + + # Encode memory + if self.num_maskmem > 0: + pix_feat = to_spatial(current_vision_feats[-1], H, W) + maskmem_features, maskmem_pos_enc = self._encode_new_memory( + pix_feat=pix_feat, + pred_masks_high_res=high_res_masks, + object_score_logits=object_score_logits, + is_mask_from_pts=(point_inputs is not None), + ) + current_out["maskmem_features"] = maskmem_features + current_out["maskmem_pos_enc"] = maskmem_pos_enc + else: + current_out["maskmem_features"] = None + current_out["maskmem_pos_enc"] = None + + return current_out + + def _compute_backbone_frame(self, backbone_fn, frame, frame_idx=None): + vision_feats, vision_pos, feat_sizes, _, _ = _compute_backbone(backbone_fn, frame, frame_idx) + # SAM3: drop last FPN level + return vision_feats[:-1], vision_pos[:-1], feat_sizes[:-1] + + def _track_single_object(self, backbone_fn, images, initial_mask, pbar=None): + """Track one object, computing backbone per frame to save VRAM.""" + N = images.shape[0] + device, dt = images.device, images.dtype + output_dict = {"cond_frame_outputs": {}, "non_cond_frame_outputs": {}} + all_masks = [] + + for frame_idx in tqdm(range(N), desc="tracking"): + vision_feats, vision_pos, feat_sizes = self._compute_backbone_frame( + backbone_fn, images[frame_idx:frame_idx + 1], frame_idx=frame_idx) + mask_input = None + if frame_idx == 0: + mask_input = F.interpolate(initial_mask.to(device=device, dtype=dt), + size=(self.image_size, self.image_size), mode="bilinear", align_corners=False) + mask_input = (mask_input > 0.5).to(dt) + + current_out = self.track_step( + frame_idx=frame_idx, is_init_cond_frame=(frame_idx == 0), + current_vision_feats=vision_feats, current_vision_pos_embeds=vision_pos, + feat_sizes=feat_sizes, mask_inputs=mask_input, output_dict=output_dict, num_frames=N) + + if frame_idx == 0: + output_dict["cond_frame_outputs"][frame_idx] = current_out + else: + output_dict["non_cond_frame_outputs"][frame_idx] = current_out + lookback = max(self.num_maskmem, self.max_obj_ptrs_in_encoder) + for old_idx in list(output_dict["non_cond_frame_outputs"]): + if old_idx < frame_idx - lookback: + del output_dict["non_cond_frame_outputs"][old_idx] + # Move masks to CPU immediately to free VRAM + all_masks.append(current_out["pred_masks_high_res"].to(comfy.model_management.intermediate_device())) + if pbar is not None: + pbar.update(1) + + return torch.cat(all_masks, dim=0) # [N, 1, H, W] + + def track_video(self, backbone_fn, images, initial_masks, pbar=None, **kwargs): + """Track one or more objects across video frames. + + Args: + backbone_fn: callable that returns (sam2_features, sam2_positions, trunk_out) for a frame + images: [N, 3, 1008, 1008] video frames + initial_masks: [N_obj, 1, H, W] binary masks for first frame (one per object) + pbar: optional progress bar + + Returns: + [N, N_obj, image_size, image_size] predicted mask logits per frame per object + """ + N_obj = initial_masks.shape[0] + per_object = [] + for obj_idx in range(N_obj): + obj_masks = self._track_single_object( + backbone_fn, images, initial_masks[obj_idx:obj_idx + 1], pbar=pbar) + per_object.append(obj_masks) + + return torch.cat(per_object, dim=1) # [N, N_obj, H, W] + + +class SAM31Tracker(nn.Module): + """SAM3.1 multiplex tracker: decoupled memory attention, dual decoder, 16-object multiplex.""" + + def __init__(self, d_model=256, mem_dim=256, num_maskmem=7, num_multiplex=16, device=None, dtype=None, operations=None, **kwargs): + super().__init__() + self.d_model = d_model + self.mem_dim = mem_dim + self.num_maskmem = num_maskmem + self.num_multiplex = num_multiplex + self.image_size = 1008 + self.backbone_stride = 14 + self.max_obj_ptrs_in_encoder = 16 + self.sigmoid_scale_for_mem_enc = 2.0 + self.sigmoid_bias_for_mem_enc = -1.0 + + # Memory attention (decoupled cross-attention, 8 heads matching reference) + self.transformer = DecoupledMemoryTransformer(d_model, num_heads=8, dim_ff=2048, num_layers=4, + device=device, dtype=dtype, operations=operations) + + # Propagation decoder (multiplex: 16 objects, multimask_outputs_only) + self.sam_mask_decoder = MultiplexMaskDecoder(d_model, num_multiplex, num_multimask_outputs=3, + device=device, dtype=dtype, operations=operations) + # Interactive decoder (single object, same as SAM3) + self.interactive_sam_mask_decoder = SAMMaskDecoder(d_model, num_multimask_outputs=3, + device=device, dtype=dtype, operations=operations) + self.interactive_sam_prompt_encoder = SAMPromptEncoder(d_model, device=device, dtype=dtype, operations=operations) + + # Memory backbone (mem_dim=256, no out_proj compression) + self.maskmem_backbone = MemoryBackbone(d_model, in_chans=num_multiplex * 2, channels=[16, 64, 256, 1024], + device=device, dtype=dtype, operations=operations) + + # Standalone parameters + self.maskmem_tpos_enc = nn.Parameter(torch.zeros(num_maskmem, 1, 1, mem_dim, device=device, dtype=dtype)) + self.no_obj_embed_spatial = nn.Parameter(torch.zeros(num_multiplex, mem_dim, device=device, dtype=dtype)) + self.interactivity_no_mem_embed = nn.Parameter(torch.zeros(1, 1, d_model, device=device, dtype=dtype)) + + # Object pointer projection + self.obj_ptr_proj = MLP(d_model, d_model, d_model, 3, device=device, dtype=dtype, operations=operations) + self.obj_ptr_tpos_proj = operations.Linear(d_model, mem_dim, device=device, dtype=dtype) + self.no_obj_ptr_linear = operations.Linear(d_model, d_model, device=device, dtype=dtype) + self.interactive_obj_ptr_proj = MLP(d_model, d_model, d_model, 3, device=device, dtype=dtype, operations=operations) + + # Interactive mask downsample + self.interactive_mask_downsample = operations.Conv2d(1, 1, kernel_size=4, stride=4, device=device, dtype=dtype) + + # Multiplex validity embeddings + self.output_valid_embed = nn.Parameter(torch.zeros(num_multiplex, d_model, device=device, dtype=dtype)) + self.output_invalid_embed = nn.Parameter(torch.zeros(num_multiplex, d_model, device=device, dtype=dtype)) + + # Position encoding for image (used by multiplex decoder) + self.image_pe_layer = PositionEmbeddingRandom(d_model // 2) + + def _no_obj_blend(self, obj_ptr, is_obj): + alpha = is_obj.to(obj_ptr.dtype) + return torch.lerp(self.no_obj_ptr_linear(obj_ptr), obj_ptr, alpha) + + def _forward_sam_heads(self, backbone_features, point_inputs=None, mask_inputs=None, box_inputs=None, + high_res_features=None, multimask_output=False): + return forward_sam_heads(backbone_features, self.interactive_sam_prompt_encoder, self.interactive_sam_mask_decoder, + self.interactive_obj_ptr_proj, self._no_obj_blend, self.image_size, + point_inputs, mask_inputs, box_inputs, high_res_features, multimask_output) + + def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs): + return use_mask_as_output(backbone_features, high_res_features, mask_inputs, + self.interactive_mask_downsample, self.interactive_sam_prompt_encoder, + self.interactive_sam_mask_decoder, self.interactive_obj_ptr_proj, + self._no_obj_blend, self.image_size, self.backbone_stride) + + def _prepare_memory_conditioned_features(self, frame_idx, is_init_cond_frame, current_vision_feats, + current_vision_pos_embeds, feat_sizes, output_dict, num_frames, + multiplex_state=None): + B = current_vision_feats[-1].shape[0] + C = self.d_model + H, W = feat_sizes[-1] + device = current_vision_feats[-1].device + num_buc = multiplex_state.num_buckets if multiplex_state is not None else None + + if self.num_maskmem == 0: + return current_vision_feats[-1].permute(0, 2, 1).view(B, C, H, W) + + if is_init_cond_frame: + pix_feat = current_vision_feats[-1] + cast_to_input(self.interactivity_no_mem_embed, current_vision_feats[-1]) + return to_spatial(pix_feat, H, W) + + to_cat_memory, to_cat_memory_pos, to_cat_image_feat, to_cat_image_pos, cond_outputs = collect_memory_tokens( + output_dict, frame_idx, self.num_maskmem, self.maskmem_tpos_enc, device, + collect_image_feats=True, tpos_v2=True, num_buckets=num_buc) + + max_obj_ptrs = min(num_frames, self.max_obj_ptrs_in_encoder) + pos_and_ptrs = [] + for t, out in cond_outputs.items(): + if t <= frame_idx and "obj_ptr" in out: + ptr = out["obj_ptr"].to(device) + if num_buc is not None: + ptr = _pad_to_buckets(ptr, num_buc) + pos_and_ptrs.append(((frame_idx - t), ptr)) + for t_diff in range(1, max_obj_ptrs): + t = frame_idx - t_diff + if t < 0: + break + out = output_dict["non_cond_frame_outputs"].get(t, None) + if out is not None and "obj_ptr" in out: + ptr = out["obj_ptr"].to(device) + if num_buc is not None: + ptr = _pad_to_buckets(ptr, num_buc) + pos_and_ptrs.append((t_diff, ptr)) + + num_obj_ptr_tokens = 0 + if len(pos_and_ptrs) > 0: + pos_list, ptrs_list = zip(*pos_and_ptrs) + obj_ptrs = torch.stack(ptrs_list, dim=1) # [num_buckets, N, M, C] + B_ptr = obj_ptrs.shape[0] + N_ptrs = obj_ptrs.shape[1] + M = obj_ptrs.shape[2] + obj_ptrs = obj_ptrs.reshape(B_ptr, N_ptrs * M, -1) + obj_pos = compute_tpos_enc(list(pos_list), device, self.d_model, self.obj_ptr_tpos_proj, + max_abs_pos=max_obj_ptrs, dtype=current_vision_feats[-1].dtype) + obj_pos = obj_pos.unsqueeze(0).expand(B_ptr, -1, -1) + obj_pos = obj_pos.unsqueeze(2).expand(-1, -1, M, -1).reshape(B_ptr, N_ptrs * M, -1) + to_cat_memory.append(obj_ptrs) + to_cat_memory_pos.append(obj_pos) + num_obj_ptr_tokens = obj_ptrs.shape[1] + + if len(to_cat_memory) == 0: + pix_feat = current_vision_feats[-1] + cast_to_input(self.interactivity_no_mem_embed, current_vision_feats[-1]) + return to_spatial(pix_feat, H, W) + + memory = torch.cat(to_cat_memory, dim=1) + memory_pos = torch.cat(to_cat_memory_pos, dim=1) + + # Expand vision features to num_buckets if memory has more buckets than B + mem_B = memory.shape[0] + x = current_vision_feats[-1] + x_pos = current_vision_pos_embeds[-1] + if x.shape[0] < mem_B: + x = x.expand(mem_B, -1, -1) + x_pos = x_pos.expand(mem_B, -1, -1) + + if len(to_cat_image_feat) > 0: + # Decoupled cross-attention: separate image features from memory + memory_image = cast_to_input(torch.cat(to_cat_image_feat, dim=1), x) + memory_image_pos = cast_to_input(torch.cat(to_cat_image_pos, dim=1), x) + if memory_image.shape[0] < mem_B: + memory_image = memory_image.expand(mem_B, -1, -1) + memory_image_pos = memory_image_pos.expand(mem_B, -1, -1) + pix_feat_with_mem = self.transformer.encoder( + x=x, + memory=cast_to_input(memory, x), + memory_pos=cast_to_input(memory_pos, x), + src_pos=cast_to_input(x_pos, x), + num_k_exclude_rope=num_obj_ptr_tokens, + memory_image=memory_image, + memory_image_pos=memory_image_pos, + ) + else: + pix_feat_with_mem = self.transformer.encoder( + x=x, + memory=memory, + memory_pos=memory_pos, + src_pos=x_pos, + num_k_exclude_rope=num_obj_ptr_tokens, + ) + return to_spatial(pix_feat_with_mem, H, W) + + def _encode_new_memory(self, pix_feat, pred_masks_high_res, object_score_logits, is_mask_from_pts=False, + multiplex_state=None, is_conditioning=False, cond_obj_mask=None): + if is_mask_from_pts: + mask_for_mem = (pred_masks_high_res > 0).to(pix_feat.dtype) + else: + mask_for_mem = torch.sigmoid(pred_masks_high_res) + mask_for_mem.mul_(self.sigmoid_scale_for_mem_enc).add_(self.sigmoid_bias_for_mem_enc) + + # Mux masks: [N_obj, 1, H, W] -> [num_buckets, M, H, W] + mux_masks = multiplex_state.mux(mask_for_mem[:, 0]) + + # Conditioning channel: 1.0 = clean mask (trust it), 0.0 = propagation (noisy) + N_obj = mask_for_mem.shape[0] + cond_values = torch.full((N_obj,), 0.0, device=mask_for_mem.device, dtype=mask_for_mem.dtype) + if is_conditioning: + cond_values[:] = 1.0 + elif cond_obj_mask is not None: + cond_values[cond_obj_mask] = 1.0 + cond_spatial = cond_values.view(-1, 1, 1, 1).expand_as(mask_for_mem[:, 0:1, :, :]).squeeze(1) + mux_cond = multiplex_state.mux(cond_spatial) # [num_buckets, M, H, W] + mux_input = torch.cat([mux_masks, mux_cond], dim=1) # [num_buckets, 2*M, H, W] + + maskmem_out = self.maskmem_backbone(pix_feat, mux_input, skip_mask_sigmoid=True) + maskmem_features = maskmem_out["vision_features"] + maskmem_pos_enc = maskmem_out["vision_pos_enc"] + + # Add no_obj_embed_spatial for occluded objects + is_obj = (object_score_logits > 0).float() # [N_obj, 1] + mux_is_obj = multiplex_state.mux(is_obj) # [num_buckets, M, 1] + no_obj_embed = cast_to_input(self.no_obj_embed_spatial, maskmem_features) # [M, C] + no_obj_spatial = no_obj_embed.unsqueeze(0)[..., None, None] # [1, M, C, 1, 1] + # Expand and sum across multiplex slots weighted by (1 - is_obj) + alpha = mux_is_obj[..., None, None] # [num_buckets, M, 1, 1, 1] + per_slot_no_obj = ((1 - alpha) * no_obj_spatial).sum(dim=1) # [num_buckets, C, 1, 1] + maskmem_features = maskmem_features + per_slot_no_obj.expand_as(maskmem_features) + + return maskmem_features, maskmem_pos_enc + + def _forward_propagation(self, backbone_features, high_res_features=None, multiplex_state=None): + """Propagation path using the multiplex SAM decoder (no prompts).""" + B = backbone_features.shape[0] + device = backbone_features.device + + # Suppression embeddings from valid object mask + valid_mask = cast_to_input(multiplex_state.get_valid_object_mask().unsqueeze(-1).float(), backbone_features) + output_valid = cast_to_input(self.output_valid_embed, backbone_features).unsqueeze(0) + output_invalid = cast_to_input(self.output_invalid_embed, backbone_features).unsqueeze(0) + extra_embed = valid_mask * output_valid + (1 - valid_mask) * output_invalid + + image_pe = self.image_pe_layer((backbone_features.shape[-2], backbone_features.shape[-1]), device=backbone_features.device) + image_pe = cast_to_input(image_pe, backbone_features) + + masks, iou_pred, sam_tokens_out, object_score_logits = self.sam_mask_decoder( + image_embeddings=backbone_features, image_pe=image_pe, + sparse_prompt_embeddings=torch.empty(B, 0, self.d_model, device=device, dtype=backbone_features.dtype), + dense_prompt_embeddings=torch.zeros(B, self.d_model, *backbone_features.shape[-2:], device=device, dtype=backbone_features.dtype), + high_res_features=high_res_features, multimask_output=True, return_all=True, + extra_per_object_embeddings=extra_embed.expand(B, -1, -1), + ) + # masks: [B=num_buckets, M, T, H, W] + # Demux to per-object: [N_obj, T, H, W] + masks_obj = multiplex_state.demux(masks) + iou_obj = multiplex_state.demux(iou_pred) + score_obj = multiplex_state.demux(object_score_logits) + tokens_obj = multiplex_state.demux(sam_tokens_out) + + # Select best mask by IoU for each object + best_idx = torch.argmax(iou_obj, dim=-1) # [N_obj] + N_obj = masks_obj.shape[0] + obj_range = torch.arange(N_obj, device=device) + low_res_masks = masks_obj[obj_range, best_idx].unsqueeze(1) # [N_obj, 1, H, W] + # Suppress masks for objects with low confidence + is_obj = score_obj > 0 + low_res_masks = torch.where(is_obj[:, :, None, None], low_res_masks, + torch.tensor(NO_OBJ_SCORE, device=device, dtype=low_res_masks.dtype)) + high_res_masks = F.interpolate(low_res_masks.float(), size=(self.image_size, self.image_size), mode="bilinear", align_corners=False) + + # Object pointer: compute per-object, mux for storage as [num_buckets, M, C] + sam_token = tokens_obj[:, 0] # [N_obj, C] + obj_ptr = self.obj_ptr_proj(sam_token) + is_obj = (score_obj > 0).float() + no_obj = self.no_obj_ptr_linear(obj_ptr) + obj_ptr = is_obj * obj_ptr + (1 - is_obj) * no_obj + obj_ptr_muxed = multiplex_state.mux(obj_ptr) # [num_buckets, M, C] + + return low_res_masks, high_res_masks, obj_ptr_muxed, score_obj + + def track_step(self, frame_idx, is_init_cond_frame, current_vision_feats, current_vision_pos_embeds, + feat_sizes, mask_inputs, output_dict, num_frames, point_inputs=None, + interactive_high_res=None, interactive_backbone=None, propagation_high_res=None, + multiplex_state=None, run_mem_encoder=True): + current_out = {} + H, W = feat_sizes[-1] + + if mask_inputs is not None: + # Conditioning frame: use interactive features if available, else propagation + if interactive_backbone is not None: + pix_feat = interactive_backbone + # Add no_mem_embed for interactive path + pix_flat = pix_feat.flatten(2) + bf = pix_flat.permute(0, 2, 1) + cast_to_input(self.interactivity_no_mem_embed, pix_flat) + pix_feat = to_spatial(bf, H, W) + hi_res = interactive_high_res + else: + # Fallback: interactive backbone not available (e.g. called outside track_video). + # Propagation features work but may produce lower-quality conditioning. + pix_feat = to_spatial(current_vision_feats[-1], H, W) + hi_res = propagation_high_res + sam_outputs = self._use_mask_as_output(pix_feat, hi_res, mask_inputs) + elif point_inputs is not None: + # Interactive path: use interactive SAM decoder + pix_feat_with_mem = self._prepare_memory_conditioned_features( + frame_idx=frame_idx, is_init_cond_frame=is_init_cond_frame, + current_vision_feats=current_vision_feats, current_vision_pos_embeds=current_vision_pos_embeds, + feat_sizes=feat_sizes, output_dict=output_dict, num_frames=num_frames, + multiplex_state=multiplex_state, + ) + hi_res = interactive_high_res if interactive_high_res is not None else propagation_high_res + num_pts = point_inputs["point_labels"].size(1) + multimask_output = is_init_cond_frame and 0 < num_pts <= 1 + sam_outputs = self._forward_sam_heads( + backbone_features=pix_feat_with_mem, point_inputs=point_inputs, + high_res_features=hi_res, multimask_output=multimask_output, + ) + else: + # Propagation path: use multiplex SAM decoder with propagation features + pix_feat_with_mem = self._prepare_memory_conditioned_features( + frame_idx=frame_idx, is_init_cond_frame=is_init_cond_frame, + current_vision_feats=current_vision_feats, current_vision_pos_embeds=current_vision_pos_embeds, + feat_sizes=feat_sizes, output_dict=output_dict, num_frames=num_frames, + multiplex_state=multiplex_state, + ) + sam_outputs = self._forward_propagation(pix_feat_with_mem, propagation_high_res, + multiplex_state=multiplex_state) + + (low_res_masks, high_res_masks, obj_ptr, object_score_logits) = sam_outputs + + # Mux obj_ptr if it came from interactive path (shape [B, C]) vs propagation ([num_buckets, M, C]) + if multiplex_state is not None and obj_ptr.dim() == 2: + obj_ptr = multiplex_state.mux(obj_ptr) # [N_obj, C] -> [num_buckets, M, C] + + # Encode memory (can be deferred with run_mem_encoder=False) + if run_mem_encoder and self.num_maskmem > 0: + pix_feat = to_spatial(current_vision_feats[-1], H, W) + maskmem_features, maskmem_pos_enc = self._encode_new_memory( + pix_feat=pix_feat, pred_masks_high_res=high_res_masks, + object_score_logits=object_score_logits, + is_mask_from_pts=(point_inputs is not None), + multiplex_state=multiplex_state, + is_conditioning=(mask_inputs is not None), + ) + current_out["maskmem_features"] = maskmem_features + current_out["maskmem_pos_enc"] = maskmem_pos_enc + else: + current_out["maskmem_features"] = None + current_out["maskmem_pos_enc"] = None + + # Store propagation image features for decoupled memory attention + current_out["image_features"] = current_vision_feats[-1] # [B, HW, C] + current_out["image_pos_enc"] = current_vision_pos_embeds[-1] # [B, HW, C] + + current_out["pred_masks"] = low_res_masks + current_out["pred_masks_high_res"] = high_res_masks + current_out["obj_ptr"] = obj_ptr + current_out["object_score_logits"] = object_score_logits + + return current_out + + def _compute_backbone_frame(self, backbone_fn, frame, frame_idx=None): + vision_feats, vision_pos, feat_sizes, features, trunk_out = _compute_backbone(backbone_fn, frame, frame_idx) + return vision_feats, vision_pos, feat_sizes, list(features[:-1]), trunk_out + + @staticmethod + def _suppress_recently_occluded(low_res_masks, last_occluded, frame_idx, threshold=0.3): + """Suppress overlapping masks for objects that were most recently occluded. + Prevents corrupted masks from occluded objects from contaminating other objects.""" + N_obj = low_res_masks.shape[0] + if N_obj <= 1: + return low_res_masks + binary = low_res_masks[:, 0] > 0 # [N_obj, H, W] + iou = _compute_mask_overlap(low_res_masks[:, 0], low_res_masks[:, 0]) + overlapping = torch.triu(iou >= threshold, diagonal=1) # [N, N] upper triangle + last_occ_i = last_occluded.unsqueeze(1) # [N, 1] + last_occ_j = last_occluded.unsqueeze(0) # [1, N] + # Suppress the more recently occluded object in each overlapping pair + suppress_i = overlapping & (last_occ_i > last_occ_j) & (last_occ_j > -1) + suppress_j = overlapping & (last_occ_j > last_occ_i) & (last_occ_i > -1) + to_suppress = suppress_i.any(dim=1) | suppress_j.any(dim=0) + # Update last_occluded for occluded/suppressed objects + is_empty = ~binary.any(dim=(-1, -2)) + newly_occluded = is_empty | to_suppress + last_occluded[newly_occluded] = frame_idx + # Suppress masks + low_res_masks[to_suppress] = -10.0 + return low_res_masks + + def _deferred_memory_encode(self, current_out, N_obj, vision_feats, feat_sizes, mux_state, device, + cond_obj_mask=None): + """Deferred memory encoding for propagation frames. cond_obj_mask: per-object bool for conditioning.""" + low_res_masks = current_out["pred_masks"] # [N_obj, 1, H_low, W_low] + + if N_obj > 1: + lr = low_res_masks.squeeze(1) # [N_obj, H, W] + max_obj = torch.argmax(lr, dim=0, keepdim=True) + batch_inds = torch.arange(N_obj, device=device)[:, None, None] + pixel_nol = torch.where(max_obj == batch_inds, lr, torch.clamp(lr, max=-10.0)) + area_before = (lr > 0).sum(dim=(-1, -2)).float().clamp(min=1) + area_after = (pixel_nol > 0).sum(dim=(-1, -2)).float() + shrink_ok = (area_after / area_before) >= 0.3 + low_res_masks = torch.where( + shrink_ok[:, None, None, None].expand_as(low_res_masks), + low_res_masks, torch.clamp(low_res_masks, max=-10.0)) + + interpol_size = self.maskmem_backbone.mask_downsampler.interpol_size + mem_masks = F.interpolate(low_res_masks, size=interpol_size, + mode="bilinear", align_corners=False) + + obj_scores = torch.where( + (mem_masks > 0).any(dim=(-1, -2)), 10.0, -10.0) + + pix_feat = to_spatial(vision_feats[-1], feat_sizes[-1][0], feat_sizes[-1][1]) + maskmem_features, maskmem_pos_enc = self._encode_new_memory( + pix_feat=pix_feat, pred_masks_high_res=mem_masks, + object_score_logits=obj_scores, + multiplex_state=mux_state, cond_obj_mask=cond_obj_mask) + current_out["maskmem_features"] = maskmem_features + current_out["maskmem_pos_enc"] = maskmem_pos_enc + + def _add_detected_objects(self, new_masks, mux_state, vision_feats, feat_sizes, current_out): + """Grow MultiplexState with new detections, merge masks, re-encode memory. Modifies current_out.""" + n_old = mux_state.total_valid_entries + mux_state.add_objects(new_masks.shape[0]) + N_obj = mux_state.total_valid_entries + # Stored memory with old bucket counts is padded at read time by _pad_to_buckets + for k in ("pred_masks", "pred_masks_high_res"): + det = F.interpolate(new_masks.unsqueeze(1), size=current_out[k].shape[-2:], + mode="bilinear", align_corners=False) + current_out[k] = torch.cat([current_out[k], det], dim=0) + if self.num_maskmem > 0: + # Mark new objects as conditioning (clean detection masks) so model trusts them + cond_mask = torch.zeros(N_obj, dtype=torch.bool, device=new_masks.device) + cond_mask[n_old:] = True + self._deferred_memory_encode(current_out, N_obj, vision_feats, feat_sizes, + mux_state, new_masks.device, cond_obj_mask=cond_mask) + + def _condition_with_masks(self, masks, frame_idx, vision_feats, vision_pos, feat_sizes, + high_res_prop, output_dict, N, mux_state, backbone_obj, frame, + trunk_out, threshold=0.5): + """Condition tracker with masks on a frame.""" + mask_input = F.interpolate(masks if masks.dim() == 4 else masks.unsqueeze(1), + size=(self.image_size, self.image_size), mode="bilinear", align_corners=False) + mask_input = (mask_input > threshold).to(masks.dtype) + hi_res = lo_feat = None + if backbone_obj is not None and backbone_obj.multiplex: + _, _, itf, _ = backbone_obj(frame, tracker_mode="interactive", cached_trunk=trunk_out, tracker_only=True) + hi_res, lo_feat = itf[:-1], itf[-1] + current_out = self.track_step( + frame_idx=frame_idx, is_init_cond_frame=True, current_vision_feats=vision_feats, + current_vision_pos_embeds=vision_pos, feat_sizes=feat_sizes, mask_inputs=mask_input, + output_dict=output_dict, num_frames=N, interactive_high_res=hi_res, + interactive_backbone=lo_feat, propagation_high_res=high_res_prop, + multiplex_state=mux_state, run_mem_encoder=True) + output_dict["cond_frame_outputs"][frame_idx] = current_out + return current_out + + def _match_and_add_detections(self, det_masks, det_scores, current_out, mux_state, + vision_feats, feat_sizes, device, max_objects=0, + keep_alive=None): + """Match detections against tracked masks, add new objects, recondition degraded tracks. + Updates keep_alive counters: +1 for matched tracks, -1 for unmatched.""" + N_obj = mux_state.total_valid_entries + if det_masks.shape[0] == 0: + if keep_alive is not None: + for i in range(N_obj): + keep_alive[i] = max(-4, keep_alive.get(i, 0) - 1) + return [] + + # Match at low-res (like reference) + trk_masks = current_out["pred_masks"][:, 0] # [N_obj, H_low, W_low] + det_resized = F.interpolate(det_masks.unsqueeze(1), size=trk_masks.shape[-2:], + mode="bilinear", align_corners=False)[:, 0] + overlap = _compute_mask_overlap(det_resized, trk_masks) + + # Update keep_alive and find matched tracks + matched = set() + if overlap.shape[1] > 0: + matched = set((overlap >= 0.5).any(dim=0).nonzero(as_tuple=True)[0].tolist()) + if keep_alive is not None: + for i in range(N_obj): + if i in matched: + keep_alive[i] = min(8, keep_alive.get(i, 0) + 1) + else: + keep_alive[i] = max(-4, keep_alive.get(i, 0) - 1) + + # Recondition: high-confidence detections (>=0.8) with high overlap refresh tracked masks + reconditioned = False + if det_scores is not None and overlap.shape[1] > 0: + HIGH_CONF = 0.8 + for det_idx in range(overlap.shape[0]): + if det_scores[det_idx] < HIGH_CONF: + continue + best_trk = overlap[det_idx].argmax().item() + if overlap[det_idx, best_trk] >= 0.5: + # Replace tracked mask with fresh detection mask + current_out["pred_masks"][best_trk] = det_resized[det_idx].unsqueeze(0) + det_hr = F.interpolate(det_masks[det_idx:det_idx+1].unsqueeze(1), + size=current_out["pred_masks_high_res"].shape[-2:], + mode="bilinear", align_corners=False) + current_out["pred_masks_high_res"][best_trk] = det_hr[0] + reconditioned = True + + # Re-encode memory if any tracks were reconditioned + if reconditioned and self.num_maskmem > 0: + self._deferred_memory_encode(current_out, N_obj, vision_feats, feat_sizes, mux_state, device) + + # Add new detections (not matching any track) + if max_objects > 0 and N_obj >= max_objects: + return [] + max_overlap = overlap.max(dim=1)[0] if overlap.shape[1] > 0 else torch.zeros(overlap.shape[0], device=device) + new_dets = max_overlap < 0.5 + if new_dets.any(): + if max_objects > 0: + slots = max_objects - N_obj + new_dets = new_dets & (torch.cumsum(new_dets.int(), 0) <= slots) + self._add_detected_objects(det_masks[new_dets], mux_state, + vision_feats, feat_sizes, current_out) + if keep_alive is not None: + for i in range(N_obj, mux_state.total_valid_entries): + keep_alive[i] = 1 + return det_scores[new_dets].tolist() if det_scores is not None else [0.0] * new_dets.sum().item() + return [] + + def track_video_with_detection(self, backbone_fn, images, initial_masks, detect_fn=None, + new_det_thresh=0.5, max_objects=0, detect_interval=1, + backbone_obj=None, pbar=None): + """Track with optional per-frame detection. Returns [N, max_N_obj, H, W] mask logits.""" + N, device, dt = images.shape[0], images.device, images.dtype + output_dict = {"cond_frame_outputs": {}, "non_cond_frame_outputs": {}} + all_masks = [] + idev = comfy.model_management.intermediate_device() + mux_state = None + if initial_masks is not None: + mux_state = MultiplexState(initial_masks.shape[0], self.num_multiplex, device, dt) + obj_scores = [] # per-object detection score (1.0 for initial masks) + keep_alive = {} if detect_fn is not None else None + last_occluded = torch.empty(0, device=device, dtype=torch.long) # per-object last occluded frame + + # Prefetch next frame's backbone on a separate CUDA stream + prefetch = False + backbone_stream = None + if comfy.model_management.is_device_cuda(device): + try: + backbone_stream = torch.cuda.Stream(device=device) + prefetch = True + except RuntimeError: + pass + cur_bb = self._compute_backbone_frame(backbone_fn, images[0:1], frame_idx=0) + + for frame_idx in tqdm(range(N), desc="tracking"): + vision_feats, vision_pos, feat_sizes, high_res_prop, trunk_out = cur_bb + + # Start next frame's backbone on separate stream (overlaps with current frame's work) + if prefetch and frame_idx + 1 < N: + backbone_stream.wait_stream(torch.cuda.current_stream(device)) + with torch.cuda.stream(backbone_stream): + next_bb = self._compute_backbone_frame( + backbone_fn, images[frame_idx + 1:frame_idx + 2], frame_idx=frame_idx + 1) + + # Per-frame detection with NMS (skip if no detect_fn, or interval/max not met) + det_masks = torch.empty(0, device=device) + det_scores = None + run_det = (detect_fn is not None + and frame_idx % max(detect_interval, 1) == 0 + and not (max_objects > 0 and mux_state is not None + and mux_state.total_valid_entries >= max_objects)) + if run_det: + det_out = detect_fn(trunk_out) + scores = det_out["scores"][0].sigmoid() + keep = scores > new_det_thresh + det_masks, det_scores = det_out["masks"][0][keep], scores[keep] + if det_masks.shape[0] > 1: + det_masks, det_scores = _nms_masks(det_masks, det_scores) + + if frame_idx == 0 and initial_masks is not None: + current_out = self._condition_with_masks( + initial_masks.to(device=device, dtype=dt), frame_idx, vision_feats, vision_pos, + feat_sizes, high_res_prop, output_dict, N, mux_state, backbone_obj, + images[frame_idx:frame_idx + 1], trunk_out) + last_occluded = torch.full((mux_state.total_valid_entries,), -1, device=device, dtype=torch.long) + obj_scores = [1.0] * mux_state.total_valid_entries + if keep_alive is not None: + for i in range(mux_state.total_valid_entries): + keep_alive[i] = 8 + elif mux_state is None or mux_state.total_valid_entries == 0: + if det_masks.shape[0] > 0: + if max_objects > 0: + det_scores = det_scores[:max_objects] + det_masks = det_masks[:max_objects] + mux_state = MultiplexState(det_masks.shape[0], self.num_multiplex, device, dt) + current_out = self._condition_with_masks( + det_masks, frame_idx, vision_feats, vision_pos, feat_sizes, high_res_prop, + output_dict, N, mux_state, backbone_obj, + images[frame_idx:frame_idx + 1], trunk_out, threshold=0.0) + last_occluded = torch.full((mux_state.total_valid_entries,), -1, device=device, dtype=torch.long) + obj_scores = det_scores[:mux_state.total_valid_entries].tolist() + if keep_alive is not None: + for i in range(mux_state.total_valid_entries): + keep_alive[i] = 1 + else: + all_masks.append(None) + if pbar is not None: + pbar.update(1) + # Skip to backbone advance at end of loop + if frame_idx + 1 < N: + if prefetch: + torch.cuda.current_stream(device).wait_stream(backbone_stream) + cur_bb = next_bb + else: + cur_bb = self._compute_backbone_frame(backbone_fn, images[frame_idx + 1:frame_idx + 2], frame_idx=frame_idx + 1) + continue + else: + N_obj = mux_state.total_valid_entries + current_out = self.track_step( + frame_idx=frame_idx, is_init_cond_frame=False, current_vision_feats=vision_feats, + current_vision_pos_embeds=vision_pos, feat_sizes=feat_sizes, mask_inputs=None, + output_dict=output_dict, num_frames=N, propagation_high_res=high_res_prop, + multiplex_state=mux_state, run_mem_encoder=False) + current_out["pred_masks"] = fill_holes_in_mask_scores( + current_out["pred_masks"], max_area=16) + if last_occluded.shape[0] == N_obj and N_obj > 1: + self._suppress_recently_occluded( + current_out["pred_masks"], last_occluded, frame_idx) + if self.num_maskmem > 0: + self._deferred_memory_encode(current_out, N_obj, vision_feats, feat_sizes, mux_state, device) + output_dict["non_cond_frame_outputs"][frame_idx] = current_out + lookback = max(self.num_maskmem, self.max_obj_ptrs_in_encoder) + for old_idx in list(output_dict["non_cond_frame_outputs"]): + if old_idx < frame_idx - lookback: + del output_dict["non_cond_frame_outputs"][old_idx] + n_before = mux_state.total_valid_entries + new_obj_scores = self._match_and_add_detections(det_masks, det_scores, current_out, mux_state, + vision_feats, feat_sizes, device, max_objects, + keep_alive if run_det else None) + n_added = mux_state.total_valid_entries - n_before + if n_added > 0: + last_occluded = torch.cat([last_occluded, + torch.full((n_added,), -1, device=device, dtype=torch.long)]) + obj_scores.extend(new_obj_scores) + + masks_out = current_out["pred_masks_high_res"][:, 0] + if keep_alive is not None: + for i in range(masks_out.shape[0]): + if keep_alive.get(i, 0) <= 0: + masks_out[i] = NO_OBJ_SCORE + N_obj_now = mux_state.total_valid_entries if mux_state is not None else 0 + if N_obj_now > 0: + all_masks.append(pack_masks(masks_out).to(idev)) + else: + all_masks.append(None) + if pbar is not None: + pbar.update(1) + + # Next frame's backbone + if frame_idx + 1 < N: + if prefetch: + torch.cuda.current_stream(device).wait_stream(backbone_stream) + cur_bb = next_bb + else: + cur_bb = self._compute_backbone_frame(backbone_fn, images[frame_idx + 1:frame_idx + 2], frame_idx=frame_idx + 1) + + if not all_masks or all(m is None for m in all_masks): + return {"packed_masks": None, "n_frames": N, "scores": []} + + max_obj = max(m.shape[0] for m in all_masks if m is not None) + sample = next(m for m in all_masks if m is not None) + empty_packed = torch.zeros(max_obj, *sample.shape[1:], dtype=torch.uint8, device=sample.device) + for i, m in enumerate(all_masks): + if m is None: + all_masks[i] = empty_packed + elif m.shape[0] < max_obj: + pad = torch.zeros(max_obj - m.shape[0], *m.shape[1:], dtype=torch.uint8, device=m.device) + all_masks[i] = torch.cat([m, pad], dim=0) + return {"packed_masks": torch.stack(all_masks, dim=0), "n_frames": N, "scores": obj_scores} diff --git a/comfy/model_base.py b/comfy/model_base.py index 1c7695761..787ea1145 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -54,6 +54,7 @@ import comfy.ldm.anima.model import comfy.ldm.ace.ace_step15 import comfy.ldm.rt_detr.rtdetr_v4 import comfy.ldm.ernie.model +import comfy.ldm.sam3.detector import comfy.model_management import comfy.patcher_extension @@ -1974,3 +1975,7 @@ class ErnieImage(BaseModel): if cross_attn is not None: out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn) return out + +class SAM3(BaseModel): + def __init__(self, model_config, model_type=ModelType.FLOW, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.sam3.detector.SAM3Model) diff --git a/comfy/model_detection.py b/comfy/model_detection.py index ca06cdd1e..724a241bf 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -718,6 +718,14 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): dit_config["image_model"] = "ernie" return dit_config + if 'detector.backbone.vision_backbone.trunk.blocks.0.attn.qkv.weight' in state_dict_keys: # SAM3 / SAM3.1 + if 'detector.transformer.decoder.query_embed.weight' in state_dict_keys: + dit_config = {} + dit_config["image_model"] = "SAM3" + if 'detector.backbone.vision_backbone.propagation_convs.0.conv_1x1.weight' in state_dict_keys: + dit_config["image_model"] = "SAM31" + return dit_config + if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys: return None @@ -873,6 +881,10 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal return model_config def unet_prefix_from_state_dict(state_dict): + # SAM3: detector.* and tracker.* at top level, no common prefix + if any(k.startswith("detector.") for k in state_dict) and any(k.startswith("tracker.") for k in state_dict): + return "" + candidates = ["model.diffusion_model.", #ldm/sgm models "model.model.", #audio models "net.", #cosmos diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 58d4ce731..8886f32d5 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -1781,6 +1781,57 @@ class ErnieImage(supported_models_base.BASE): return supported_models_base.ClipTarget(comfy.text_encoders.ernie.ErnieTokenizer, comfy.text_encoders.ernie.te(**hunyuan_detect)) -models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima, RT_DETR_v4, ErnieImage] +class SAM3(supported_models_base.BASE): + unet_config = {"image_model": "SAM3"} + supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32] + text_encoder_key_prefix = ["detector.backbone.language_backbone."] + unet_extra_prefix = "" + + def process_clip_state_dict(self, state_dict): + clip_keys = getattr(self, "_clip_stash", {}) + clip_keys = utils.state_dict_prefix_replace(clip_keys, {"detector.backbone.language_backbone.": "", "backbone.language_backbone.": ""}, filter_keys=True) + clip_keys = utils.clip_text_transformers_convert(clip_keys, "encoder.", "sam3_clip.transformer.") + return {k: v for k, v in clip_keys.items() if not k.startswith("resizer.")} + + def process_unet_state_dict(self, state_dict): + self._clip_stash = {k: state_dict.pop(k) for k in list(state_dict.keys()) if "language_backbone" in k and "resizer" not in k} + # SAM3.1: remap tracker.model.* -> tracker.* + for k in list(state_dict.keys()): + if k.startswith("tracker.model."): + state_dict["tracker." + k[len("tracker.model."):]] = state_dict.pop(k) + # SAM3.1: remove per-block freqs_cis buffers (computed dynamically) + for k in [k for k in list(state_dict.keys()) if ".attn.freqs_cis" in k]: + state_dict.pop(k) + # Split fused QKV projections + for k in [k for k in list(state_dict.keys()) if k.endswith((".in_proj_weight", ".in_proj_bias"))]: + t = state_dict.pop(k) + base, suffix = k.rsplit(".in_proj_", 1) + s = ".weight" if suffix == "weight" else ".bias" + d = t.shape[0] // 3 + state_dict[base + ".q_proj" + s] = t[:d] + state_dict[base + ".k_proj" + s] = t[d:2*d] + state_dict[base + ".v_proj" + s] = t[2*d:] + # Remap tracker SAM decoder transformer key names to match sam.py TwoWayTransformer + for k in list(state_dict.keys()): + if "sam_mask_decoder.transformer." not in k: + continue + new_k = k.replace(".mlp.lin1.", ".mlp.0.").replace(".mlp.lin2.", ".mlp.2.").replace(".norm_final_attn.", ".norm_final.") + if new_k != k: + state_dict[new_k] = state_dict.pop(k) + return state_dict + + def get_model(self, state_dict, prefix="", device=None): + return model_base.SAM3(self, device=device) + + def clip_target(self, state_dict={}): + import comfy.text_encoders.sam3_clip + return supported_models_base.ClipTarget(comfy.text_encoders.sam3_clip.SAM3TokenizerWrapper, comfy.text_encoders.sam3_clip.SAM3ClipModelWrapper) + + +class SAM31(SAM3): + unet_config = {"image_model": "SAM31"} + + +models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima, RT_DETR_v4, ErnieImage, SAM3, SAM31] models += [SVD_img2vid] diff --git a/comfy/text_encoders/sam3_clip.py b/comfy/text_encoders/sam3_clip.py new file mode 100644 index 000000000..11cb7d9db --- /dev/null +++ b/comfy/text_encoders/sam3_clip.py @@ -0,0 +1,97 @@ +import re +from comfy import sd1_clip + +SAM3_CLIP_CONFIG = { + "architectures": ["CLIPTextModel"], + "hidden_act": "quick_gelu", + "hidden_size": 1024, + "intermediate_size": 4096, + "num_attention_heads": 16, + "num_hidden_layers": 24, + "max_position_embeddings": 32, + "projection_dim": 512, + "vocab_size": 49408, + "layer_norm_eps": 1e-5, + "eos_token_id": 49407, +} + + +class SAM3ClipModel(sd1_clip.SDClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + super().__init__(device=device, dtype=dtype, max_length=32, layer="last", textmodel_json_config=SAM3_CLIP_CONFIG, special_tokens={"start": 49406, "end": 49407, "pad": 0}, return_projected_pooled=False, return_attention_masks=True, enable_attention_masks=True, model_options=model_options) + + +class SAM3Tokenizer(sd1_clip.SDTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(max_length=32, pad_with_end=False, pad_token=0, embedding_directory=embedding_directory, embedding_size=1024, embedding_key="sam3_clip", tokenizer_data=tokenizer_data) + self.disable_weights = True + + +def _parse_prompts(text): + """Split comma-separated prompts with optional :N max detections per category""" + text = text.replace("(", "").replace(")", "") + parts = [p.strip() for p in text.split(",") if p.strip()] + result = [] + for part in parts: + m = re.match(r'^(.+?)\s*:\s*([\d.]+)\s*$', part) + if m: + text_part = m.group(1).strip() + val = m.group(2) + max_det = max(1, round(float(val))) + result.append((text_part, max_det)) + else: + result.append((part, 1)) + return result + + +class SAM3TokenizerWrapper(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="l", tokenizer=SAM3Tokenizer, name="sam3_clip") + + def tokenize_with_weights(self, text: str, return_word_ids=False, **kwargs): + parsed = _parse_prompts(text) + if len(parsed) <= 1 and (not parsed or parsed[0][1] == 1): + return super().tokenize_with_weights(text, return_word_ids, **kwargs) + # Tokenize each prompt part separately, store per-part batches and metadata + inner = getattr(self, self.clip) + per_prompt = [] + for prompt_text, max_det in parsed: + batches = inner.tokenize_with_weights(prompt_text, return_word_ids, **kwargs) + per_prompt.append((batches, max_det)) + # Main output uses first prompt's tokens (for compatibility) + out = {self.clip_name: per_prompt[0][0], "sam3_per_prompt": per_prompt} + return out + + +class SAM3ClipModelWrapper(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs): + super().__init__(device=device, dtype=dtype, model_options=model_options, clip_name="l", clip_model=SAM3ClipModel, name="sam3_clip") + + def encode_token_weights(self, token_weight_pairs): + per_prompt = token_weight_pairs.pop("sam3_per_prompt", None) + if per_prompt is None: + return super().encode_token_weights(token_weight_pairs) + + # Encode each prompt separately, pack into extra dict + inner = getattr(self, self.clip) + multi_cond = [] + first_pooled = None + for batches, max_det in per_prompt: + out = inner.encode_token_weights(batches) + cond, pooled = out[0], out[1] + extra = out[2] if len(out) > 2 else {} + if first_pooled is None: + first_pooled = pooled + multi_cond.append({ + "cond": cond, + "attention_mask": extra.get("attention_mask"), + "max_detections": max_det, + }) + + # Return first prompt as main (for non-SAM3 consumers), all prompts in metadata + main = multi_cond[0] + main_extra = {} + if main["attention_mask"] is not None: + main_extra["attention_mask"] = main["attention_mask"] + main_extra["sam3_multi_cond"] = multi_cond + return (main["cond"], first_pooled, main_extra) diff --git a/comfy_extras/nodes_sam3.py b/comfy_extras/nodes_sam3.py new file mode 100644 index 000000000..5cf92ccb3 --- /dev/null +++ b/comfy_extras/nodes_sam3.py @@ -0,0 +1,529 @@ +""" +SAM3 (Segment Anything 3) nodes for detection, segmentation, and video tracking. +""" + +from typing_extensions import override + +import json +import os +import torch +import torch.nn.functional as F +import comfy.model_management +import comfy.utils +import folder_paths +from comfy_api.latest import ComfyExtension, io, ui +import av +from fractions import Fraction + + +def _extract_text_prompts(conditioning, device, dtype): + """Extract list of (text_embeddings, text_mask) from conditioning.""" + cond_meta = conditioning[0][1] + multi = cond_meta.get("sam3_multi_cond") + prompts = [] + if multi is not None: + for entry in multi: + emb = entry["cond"].to(device=device, dtype=dtype) + mask = entry["attention_mask"].to(device) if entry["attention_mask"] is not None else None + if mask is None: + mask = torch.ones(emb.shape[0], emb.shape[1], dtype=torch.int64, device=device) + prompts.append((emb, mask, entry.get("max_detections", 1))) + else: + emb = conditioning[0][0].to(device=device, dtype=dtype) + mask = cond_meta.get("attention_mask") + if mask is not None: + mask = mask.to(device) + else: + mask = torch.ones(emb.shape[0], emb.shape[1], dtype=torch.int64, device=device) + prompts.append((emb, mask, 1)) + return prompts + + +def _refine_mask(sam3_model, orig_image_hwc, coarse_mask, box_xyxy, H, W, device, dtype, iterations): + """Refine a coarse detector mask via SAM decoder, cropping to the detection box. + + Returns: [1, H, W] binary mask + """ + def _coarse_fallback(): + return (F.interpolate(coarse_mask.unsqueeze(0).unsqueeze(0), size=(H, W), + mode="bilinear", align_corners=False)[0] > 0).float() + + if iterations <= 0: + return _coarse_fallback() + + pad_frac = 0.1 + x1, y1, x2, y2 = box_xyxy.tolist() + bw, bh = x2 - x1, y2 - y1 + cx1 = max(0, int(x1 - bw * pad_frac)) + cy1 = max(0, int(y1 - bh * pad_frac)) + cx2 = min(W, int(x2 + bw * pad_frac)) + cy2 = min(H, int(y2 + bh * pad_frac)) + if cx2 <= cx1 or cy2 <= cy1: + return _coarse_fallback() + + crop = orig_image_hwc[cy1:cy2, cx1:cx2, :3] + crop_1008 = comfy.utils.common_upscale(crop.unsqueeze(0).movedim(-1, 1), 1008, 1008, "bilinear", crop="disabled") + crop_frame = crop_1008.to(device=device, dtype=dtype) + crop_h, crop_w = cy2 - cy1, cx2 - cx1 + + # Crop coarse mask and refine via SAM on the cropped image + mask_h, mask_w = coarse_mask.shape[-2:] + mx1, my1 = int(cx1 / W * mask_w), int(cy1 / H * mask_h) + mx2, my2 = int(cx2 / W * mask_w), int(cy2 / H * mask_h) + if mx2 <= mx1 or my2 <= my1: + return _coarse_fallback() + mask_logit = coarse_mask[..., my1:my2, mx1:mx2].unsqueeze(0).unsqueeze(0) + for _ in range(iterations): + coarse_input = F.interpolate(mask_logit, size=(1008, 1008), mode="bilinear", align_corners=False) + mask_logit = sam3_model.forward_segment(crop_frame, mask_inputs=coarse_input) + + refined_crop = F.interpolate(mask_logit, size=(crop_h, crop_w), mode="bilinear", align_corners=False) + full_mask = torch.zeros(1, 1, H, W, device=device, dtype=dtype) + full_mask[:, :, cy1:cy2, cx1:cx2] = refined_crop + coarse_full = F.interpolate(coarse_mask.unsqueeze(0).unsqueeze(0), size=(H, W), mode="bilinear", align_corners=False) + return ((full_mask[0] > 0) | (coarse_full[0] > 0)).float() + + + +class SAM3_Detect(io.ComfyNode): + """Open-vocabulary detection and segmentation using text, box, or point prompts.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="SAM3_Detect", + display_name="SAM3 Detect", + category="detection/", + search_aliases=["sam3", "segment anything", "open vocabulary", "text detection", "segment"], + inputs=[ + io.Model.Input("model", display_name="model"), + io.Image.Input("image", display_name="image"), + io.Conditioning.Input("conditioning", display_name="conditioning", optional=True, tooltip="Text conditioning from CLIPTextEncode"), + io.BoundingBox.Input("bboxes", display_name="bboxes", force_input=True, optional=True, tooltip="Bounding boxes to segment within"), + io.String.Input("positive_coords", display_name="positive_coords", force_input=True, optional=True, tooltip="Positive point prompts as JSON [{\"x\": int, \"y\": int}, ...] (pixel coords)"), + io.String.Input("negative_coords", display_name="negative_coords", force_input=True, optional=True, tooltip="Negative point prompts as JSON [{\"x\": int, \"y\": int}, ...] (pixel coords)"), + io.Float.Input("threshold", display_name="threshold", default=0.5, min=0.0, max=1.0, step=0.01), + io.Int.Input("refine_iterations", display_name="refine_iterations", default=2, min=0, max=5, tooltip="SAM decoder refinement passes (0=use raw detector masks)"), + io.Boolean.Input("individual_masks", display_name="individual_masks", default=False, tooltip="Output per-object masks instead of union"), + ], + outputs=[ + io.Mask.Output("masks"), + io.BoundingBox.Output("bboxes"), + ], + ) + + @classmethod + def execute(cls, model, image, conditioning=None, bboxes=None, positive_coords=None, negative_coords=None, threshold=0.5, refine_iterations=2, individual_masks=False) -> io.NodeOutput: + B, H, W, C = image.shape + image_in = comfy.utils.common_upscale(image[..., :3].movedim(-1, 1), 1008, 1008, "bilinear", crop="disabled") + + # Convert bboxes to normalized cxcywh format, per-frame list of [1, N, 4] tensors. + # Supports: single dict (all frames), list[dict] (all frames), list[list[dict]] (per-frame). + def _boxes_to_tensor(box_list): + coords = [] + for d in box_list: + cx = (d["x"] + d["width"] / 2) / W + cy = (d["y"] + d["height"] / 2) / H + coords.append([cx, cy, d["width"] / W, d["height"] / H]) + return torch.tensor([coords], dtype=torch.float32) # [1, N, 4] + + per_frame_boxes = None + if bboxes is not None: + if isinstance(bboxes, dict): + # Single box → same for all frames + shared = _boxes_to_tensor([bboxes]) + per_frame_boxes = [shared] * B + elif isinstance(bboxes, list) and len(bboxes) > 0 and isinstance(bboxes[0], list): + # list[list[dict]] → per-frame boxes + per_frame_boxes = [_boxes_to_tensor(frame_boxes) if frame_boxes else None for frame_boxes in bboxes] + # Pad to B if fewer frames provided + while len(per_frame_boxes) < B: + per_frame_boxes.append(per_frame_boxes[-1] if per_frame_boxes else None) + elif isinstance(bboxes, list) and len(bboxes) > 0: + # list[dict] → same boxes for all frames + shared = _boxes_to_tensor(bboxes) + per_frame_boxes = [shared] * B + + # Parse point prompts from JSON (KJNodes PointsEditor format: [{"x": int, "y": int}, ...]) + pos_pts = json.loads(positive_coords) if positive_coords else [] + neg_pts = json.loads(negative_coords) if negative_coords else [] + has_points = len(pos_pts) > 0 or len(neg_pts) > 0 + + comfy.model_management.load_model_gpu(model) + device = comfy.model_management.get_torch_device() + dtype = model.model.get_dtype() + sam3_model = model.model.diffusion_model + + # Build point inputs for tracker SAM decoder path + point_inputs = None + if has_points: + all_coords = [[p["x"] / W * 1008, p["y"] / H * 1008] for p in pos_pts] + \ + [[p["x"] / W * 1008, p["y"] / H * 1008] for p in neg_pts] + all_labels = [1] * len(pos_pts) + [0] * len(neg_pts) + point_inputs = { + "point_coords": torch.tensor([all_coords], dtype=dtype, device=device), + "point_labels": torch.tensor([all_labels], dtype=torch.int32, device=device), + } + + cond_list = _extract_text_prompts(conditioning, device, dtype) if conditioning is not None and len(conditioning) > 0 else [] + has_text = len(cond_list) > 0 + + # Run per-image through detector (text/boxes) and/or tracker (points) + all_bbox_dicts = [] + all_masks = [] + pbar = comfy.utils.ProgressBar(B) + + for b in range(B): + frame = image_in[b:b+1].to(device=device, dtype=dtype) + b_boxes = None + if per_frame_boxes is not None and per_frame_boxes[b] is not None: + b_boxes = per_frame_boxes[b].to(device=device, dtype=dtype) + + frame_bbox_dicts = [] + frame_masks = [] + + # Point prompts: tracker SAM decoder path with iterative refinement + if point_inputs is not None: + mask_logit = sam3_model.forward_segment(frame, point_inputs=point_inputs) + for _ in range(max(0, refine_iterations - 1)): + mask_logit = sam3_model.forward_segment(frame, mask_inputs=mask_logit) + mask = F.interpolate(mask_logit, size=(H, W), mode="bilinear", align_corners=False) + frame_masks.append((mask[0] > 0).float()) + + # Box prompts: SAM decoder path (segment inside each box) + if b_boxes is not None and not has_text: + for box_cxcywh in b_boxes[0]: + cx, cy, bw, bh = box_cxcywh.tolist() + # Convert cxcywh normalized → xyxy in 1008 space → [1, 2, 2] corners + sam_box = torch.tensor([[[(cx - bw/2) * 1008, (cy - bh/2) * 1008], + [(cx + bw/2) * 1008, (cy + bh/2) * 1008]]], + device=device, dtype=dtype) + mask_logit = sam3_model.forward_segment(frame, box_inputs=sam_box) + for _ in range(max(0, refine_iterations - 1)): + mask_logit = sam3_model.forward_segment(frame, mask_inputs=mask_logit) + mask = F.interpolate(mask_logit, size=(H, W), mode="bilinear", align_corners=False) + frame_masks.append((mask[0] > 0).float()) + + # Text prompts: run detector per text prompt (each detects one category) + for text_embeddings, text_mask, max_det in cond_list: + results = sam3_model( + frame, text_embeddings=text_embeddings, text_mask=text_mask, + boxes=b_boxes, threshold=threshold, orig_size=(H, W)) + + pred_boxes = results["boxes"][0] + scores = results["scores"][0] + masks = results["masks"][0] + + probs = scores.sigmoid() + keep = probs > threshold + kept_boxes = pred_boxes[keep].cpu() + kept_scores = probs[keep].cpu() + kept_masks = masks[keep] + + order = kept_scores.argsort(descending=True)[:max_det] + kept_boxes = kept_boxes[order] + kept_scores = kept_scores[order] + kept_masks = kept_masks[order] + + for box, score in zip(kept_boxes, kept_scores): + frame_bbox_dicts.append({ + "x": float(box[0]), "y": float(box[1]), + "width": float(box[2] - box[0]), "height": float(box[3] - box[1]), + "score": float(score), + }) + for m, box in zip(kept_masks, kept_boxes): + frame_masks.append(_refine_mask( + sam3_model, image[b], m, box, H, W, device, dtype, refine_iterations)) + + all_bbox_dicts.append(frame_bbox_dicts) + if len(frame_masks) > 0: + combined = torch.cat(frame_masks, dim=0) # [N_obj, H, W] + if individual_masks: + all_masks.append(combined) + else: + all_masks.append((combined > 0).any(dim=0).float()) + else: + if individual_masks: + all_masks.append(torch.zeros(0, H, W, device=comfy.model_management.intermediate_device())) + else: + all_masks.append(torch.zeros(H, W, device=comfy.model_management.intermediate_device())) + pbar.update(1) + + idev = comfy.model_management.intermediate_device() + all_masks = [m.to(idev) for m in all_masks] + mask_out = torch.cat(all_masks, dim=0) if individual_masks else torch.stack(all_masks) + return io.NodeOutput(mask_out, all_bbox_dicts) + + +SAM3TrackData = io.Custom("SAM3_TRACK_DATA") + +class SAM3_VideoTrack(io.ComfyNode): + """Track objects across video frames using SAM3's memory-based tracker.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="SAM3_VideoTrack", + display_name="SAM3 Video Track", + category="detection/", + search_aliases=["sam3", "video", "track", "propagate"], + inputs=[ + io.Image.Input("images", display_name="images", tooltip="Video frames as batched images"), + io.Model.Input("model", display_name="model"), + io.Mask.Input("initial_mask", display_name="initial_mask", optional=True, tooltip="Mask(s) for the first frame to track (one per object)"), + io.Conditioning.Input("conditioning", display_name="conditioning", optional=True, tooltip="Text conditioning for detecting new objects during tracking"), + io.Float.Input("detection_threshold", display_name="detection_threshold", default=0.5, min=0.0, max=1.0, step=0.01, tooltip="Score threshold for text-prompted detection"), + io.Int.Input("max_objects", display_name="max_objects", default=0, min=0, tooltip="Max tracked objects (0=unlimited). Initial masks count toward this limit."), + io.Int.Input("detect_interval", display_name="detect_interval", default=1, min=1, tooltip="Run detection every N frames (1=every frame). Higher values save compute."), + ], + outputs=[ + SAM3TrackData.Output("track_data", display_name="track_data"), + ], + ) + + @classmethod + def execute(cls, images, model, initial_mask=None, conditioning=None, detection_threshold=0.5, max_objects=0, detect_interval=1) -> io.NodeOutput: + N, H, W, C = images.shape + + comfy.model_management.load_model_gpu(model) + device = comfy.model_management.get_torch_device() + dtype = model.model.get_dtype() + sam3_model = model.model.diffusion_model + + frames = images[..., :3].movedim(-1, 1) + frames_in = comfy.utils.common_upscale(frames, 1008, 1008, "bilinear", crop="disabled").to(device=device, dtype=dtype) + + init_masks = None + if initial_mask is not None: + init_masks = initial_mask.unsqueeze(1).to(device=device, dtype=dtype) + + pbar = comfy.utils.ProgressBar(N) + + text_prompts = None + if conditioning is not None and len(conditioning) > 0: + text_prompts = [(emb, mask) for emb, mask, _ in _extract_text_prompts(conditioning, device, dtype)] + elif initial_mask is None: + raise ValueError("Either initial_mask or conditioning must be provided") + + result = sam3_model.forward_video( + images=frames_in, initial_masks=init_masks, pbar=pbar, text_prompts=text_prompts, + new_det_thresh=detection_threshold, max_objects=max_objects, + detect_interval=detect_interval) + result["orig_size"] = (H, W) + return io.NodeOutput(result) + + +class SAM3_TrackPreview(io.ComfyNode): + """Visualize tracked objects with distinct colors as a video preview. No tensor output — saves to temp video.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="SAM3_TrackPreview", + display_name="SAM3 Track Preview", + category="detection/", + inputs=[ + SAM3TrackData.Input("track_data", display_name="track_data"), + io.Image.Input("images", display_name="images", optional=True), + io.Float.Input("opacity", display_name="opacity", default=0.5, min=0.0, max=1.0, step=0.05), + io.Float.Input("fps", display_name="fps", default=24.0, min=1.0, max=120.0, step=1.0), + ], + is_output_node=True, + ) + + COLORS = [ + (0.12, 0.47, 0.71), (1.0, 0.5, 0.05), (0.17, 0.63, 0.17), (0.84, 0.15, 0.16), + (0.58, 0.4, 0.74), (0.55, 0.34, 0.29), (0.89, 0.47, 0.76), (0.5, 0.5, 0.5), + (0.74, 0.74, 0.13), (0.09, 0.75, 0.81), (0.94, 0.76, 0.06), (0.42, 0.68, 0.84), + ] + + # 5x3 bitmap font atlas for digits 0-9 [10, 5, 3] + _glyph_cache = {} # (device, scale) -> (glyphs, outlines, gh, gw, oh, ow) + + @staticmethod + def _get_glyphs(device, scale=3): + key = (device, scale) + if key in SAM3_TrackPreview._glyph_cache: + return SAM3_TrackPreview._glyph_cache[key] + atlas = torch.tensor([ + [[1,1,1],[1,0,1],[1,0,1],[1,0,1],[1,1,1]], + [[0,1,0],[1,1,0],[0,1,0],[0,1,0],[1,1,1]], + [[1,1,1],[0,0,1],[1,1,1],[1,0,0],[1,1,1]], + [[1,1,1],[0,0,1],[1,1,1],[0,0,1],[1,1,1]], + [[1,0,1],[1,0,1],[1,1,1],[0,0,1],[0,0,1]], + [[1,1,1],[1,0,0],[1,1,1],[0,0,1],[1,1,1]], + [[1,1,1],[1,0,0],[1,1,1],[1,0,1],[1,1,1]], + [[1,1,1],[0,0,1],[0,0,1],[0,0,1],[0,0,1]], + [[1,1,1],[1,0,1],[1,1,1],[1,0,1],[1,1,1]], + [[1,1,1],[1,0,1],[1,1,1],[0,0,1],[1,1,1]], + ], dtype=torch.bool) + glyphs, outlines = [], [] + for d in range(10): + g = atlas[d].repeat_interleave(scale, 0).repeat_interleave(scale, 1) + padded = F.pad(g.float().unsqueeze(0).unsqueeze(0), (1,1,1,1)) + o = (F.max_pool2d(padded, 3, stride=1, padding=1)[0, 0] > 0) + glyphs.append(g.to(device)) + outlines.append(o.to(device)) + gh, gw = glyphs[0].shape + oh, ow = outlines[0].shape + SAM3_TrackPreview._glyph_cache[key] = (glyphs, outlines, gh, gw, oh, ow) + return SAM3_TrackPreview._glyph_cache[key] + + @staticmethod + def _draw_number_gpu(frame, number, cx, cy, color, scale=3): + """Draw a number on a GPU tensor [H, W, 3] float 0-1 at (cx, cy) with outline.""" + H, W = frame.shape[:2] + device = frame.device + glyphs, outlines, gh, gw, oh, ow = SAM3_TrackPreview._get_glyphs(device, scale) + color_t = torch.tensor(color, device=device, dtype=frame.dtype) + digs = [int(d) for d in str(number)] + total_w = len(digs) * (gw + scale) - scale + x0 = cx - total_w // 2 + y0 = cy - gh // 2 + for i, d in enumerate(digs): + dx = x0 + i * (gw + scale) + # Black outline + oy0, ox0 = y0 - 1, dx - 1 + osy1, osx1 = max(0, -oy0), max(0, -ox0) + osy2, osx2 = min(oh, H - oy0), min(ow, W - ox0) + if osy2 > osy1 and osx2 > osx1: + fy1, fx1 = oy0 + osy1, ox0 + osx1 + frame[fy1:fy1+(osy2-osy1), fx1:fx1+(osx2-osx1)][outlines[d][osy1:osy2, osx1:osx2]] = 0 + # Colored fill + sy1, sx1 = max(0, -y0), max(0, -dx) + sy2, sx2 = min(gh, H - y0), min(gw, W - dx) + if sy2 > sy1 and sx2 > sx1: + fy1, fx1 = y0 + sy1, dx + sx1 + frame[fy1:fy1+(sy2-sy1), fx1:fx1+(sx2-sx1)][glyphs[d][sy1:sy2, sx1:sx2]] = color_t + + @classmethod + def execute(cls, track_data, images=None, opacity=0.5, fps=24.0) -> io.NodeOutput: + + from comfy.ldm.sam3.tracker import unpack_masks + packed = track_data["packed_masks"] + H, W = track_data["orig_size"] + if images is not None: + H, W = images.shape[1], images.shape[2] + if packed is None: + N, N_obj = track_data["n_frames"], 0 + else: + N, N_obj = packed.shape[0], packed.shape[1] + + import uuid + gpu = comfy.model_management.get_torch_device() + temp_dir = folder_paths.get_temp_directory() + filename = f"sam3_track_preview_{uuid.uuid4().hex[:8]}.mp4" + filepath = os.path.join(temp_dir, filename) + with av.open(filepath, mode='w') as output: + stream = output.add_stream('h264', rate=Fraction(round(fps * 1000), 1000)) + stream.width = W + stream.height = H + stream.pix_fmt = 'yuv420p' + + frame_cpu = torch.empty(H, W, 3, dtype=torch.uint8) + frame_np = frame_cpu.numpy() + if N_obj > 0: + colors_t = torch.tensor([cls.COLORS[i % len(cls.COLORS)] for i in range(N_obj)], + device=gpu, dtype=torch.float32) + grid_y = torch.arange(H, device=gpu).view(1, H, 1) + grid_x = torch.arange(W, device=gpu).view(1, 1, W) + for t in range(N): + if images is not None and t < images.shape[0]: + frame = images[t].clone() + else: + frame = torch.zeros(H, W, 3) + + if N_obj > 0: + frame_binary = unpack_masks(packed[t:t+1].to(gpu)) # [1, N_obj, H, W] bool + frame_masks = F.interpolate(frame_binary.float(), size=(H, W), mode="nearest")[0] + frame_gpu = frame.to(gpu) + bool_masks = frame_masks > 0.5 + any_mask = bool_masks.any(dim=0) + if any_mask.any(): + obj_idx_map = bool_masks.to(torch.uint8).argmax(dim=0) + color_overlay = colors_t[obj_idx_map] + mask_3d = any_mask.unsqueeze(-1) + frame_gpu = torch.where(mask_3d, frame_gpu * (1 - opacity) + color_overlay * opacity, frame_gpu) + area = bool_masks.sum(dim=(-1, -2)).clamp_(min=1) + cy = (bool_masks * grid_y).sum(dim=(-1, -2)) // area + cx = (bool_masks * grid_x).sum(dim=(-1, -2)) // area + has = area > 1 + scores = track_data.get("scores", []) + for obj_idx in range(N_obj): + if has[obj_idx]: + _cx, _cy = int(cx[obj_idx]), int(cy[obj_idx]) + color = cls.COLORS[obj_idx % len(cls.COLORS)] + SAM3_TrackPreview._draw_number_gpu(frame_gpu, obj_idx, _cx, _cy, color) + if obj_idx < len(scores) and scores[obj_idx] < 1.0: + SAM3_TrackPreview._draw_number_gpu(frame_gpu, int(scores[obj_idx] * 100), + _cx, _cy + 5 * 3 + 3, color, scale=2) + frame_cpu.copy_(frame_gpu.clamp_(0, 1).mul_(255).byte()) + else: + frame_cpu.copy_(frame.clamp_(0, 1).mul_(255).byte()) + + vframe = av.VideoFrame.from_ndarray(frame_np, format='rgb24') + output.mux(stream.encode(vframe.reformat(format='yuv420p'))) + output.mux(stream.encode(None)) + return io.NodeOutput(ui=ui.PreviewVideo([ui.SavedResult(filename, "", io.FolderType.temp)])) + + +class SAM3_TrackToMask(io.ComfyNode): + """Select tracked objects by index and output as mask.""" + + @classmethod + def define_schema(cls): + return io.Schema( + node_id="SAM3_TrackToMask", + display_name="SAM3 Track to Mask", + category="detection/", + inputs=[ + SAM3TrackData.Input("track_data", display_name="track_data"), + io.String.Input("object_indices", display_name="object_indices", default="", + tooltip="Comma-separated object indices to include (e.g. '0,2,3'). Empty = all objects."), + ], + outputs=[ + io.Mask.Output("masks", display_name="masks"), + ], + ) + + @classmethod + def execute(cls, track_data, object_indices="") -> io.NodeOutput: + from comfy.ldm.sam3.tracker import unpack_masks + packed = track_data["packed_masks"] + H, W = track_data["orig_size"] + + if packed is None: + N = track_data["n_frames"] + return io.NodeOutput(torch.zeros(N, H, W, device=comfy.model_management.intermediate_device())) + + N, N_obj = packed.shape[0], packed.shape[1] + + if object_indices.strip(): + indices = [int(i.strip()) for i in object_indices.split(",") if i.strip().isdigit()] + indices = [i for i in indices if 0 <= i < N_obj] + else: + indices = list(range(N_obj)) + + if not indices: + return io.NodeOutput(torch.zeros(N, H, W, device=comfy.model_management.intermediate_device())) + + selected = packed[:, indices] + binary = unpack_masks(selected) # [N, len(indices), Hm, Wm] bool + union = binary.any(dim=1, keepdim=True).float() + mask_out = F.interpolate(union, size=(H, W), mode="bilinear", align_corners=False)[:, 0] + return io.NodeOutput(mask_out) + + +class SAM3Extension(ComfyExtension): + @override + async def get_node_list(self) -> list[type[io.ComfyNode]]: + return [ + SAM3_Detect, + SAM3_VideoTrack, + SAM3_TrackPreview, + SAM3_TrackToMask, + ] + + +async def comfy_entrypoint() -> SAM3Extension: + return SAM3Extension() diff --git a/nodes.py b/nodes.py index bb38e07b8..fb83da896 100644 --- a/nodes.py +++ b/nodes.py @@ -2459,6 +2459,7 @@ async def init_builtin_extra_nodes(): "nodes_curve.py", "nodes_rtdetr.py", "nodes_frame_interpolation.py", + "nodes_sam3.py" ] import_failed = [] From 3cdc0d523f080deb22fee24bfb0080180cde4f6e Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Thu, 23 Apr 2026 08:47:33 +0300 Subject: [PATCH 079/140] [Partner Nodes] GPTImage: fix price badges, add new resolutions (#13519) * fix(api-nodes): fixed price badges, add new resolutions Signed-off-by: bigcat88 * proper calculate the total run cost when "n > 1" Signed-off-by: bigcat88 --------- Signed-off-by: bigcat88 --- comfy_api_nodes/nodes_openai.py | 59 +++++++++++++++++++++++++-------- 1 file changed, 46 insertions(+), 13 deletions(-) diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py index 90a29c2f2..bbb758068 100644 --- a/comfy_api_nodes/nodes_openai.py +++ b/comfy_api_nodes/nodes_openai.py @@ -357,6 +357,10 @@ def calculate_tokens_price_image_1_5(response: OpenAIImageGenerationResponse) -> return ((response.usage.input_tokens * 8.0) + (response.usage.output_tokens * 32.0)) / 1_000_000.0 +def calculate_tokens_price_image_2_0(response: OpenAIImageGenerationResponse) -> float | None: + return ((response.usage.input_tokens * 8.0) + (response.usage.output_tokens * 30.0)) / 1_000_000.0 + + class OpenAIGPTImage1(IO.ComfyNode): @classmethod @@ -401,7 +405,17 @@ class OpenAIGPTImage1(IO.ComfyNode): IO.Combo.Input( "size", default="auto", - options=["auto", "1024x1024", "1024x1536", "1536x1024"], + options=[ + "auto", + "1024x1024", + "1024x1536", + "1536x1024", + "2048x2048", + "2048x1152", + "1152x2048", + "3840x2160", + "2160x3840", + ], tooltip="Image size", optional=True, ), @@ -427,7 +441,7 @@ class OpenAIGPTImage1(IO.ComfyNode): ), IO.Combo.Input( "model", - options=["gpt-image-1", "gpt-image-1.5", 'gpt-image-2'], + options=["gpt-image-1", "gpt-image-1.5", "gpt-image-2"], default="gpt-image-2", optional=True, ), @@ -442,23 +456,36 @@ class OpenAIGPTImage1(IO.ComfyNode): ], is_api_node=True, price_badge=IO.PriceBadge( - depends_on=IO.PriceBadgeDepends(widgets=["quality", "n"]), + depends_on=IO.PriceBadgeDepends(widgets=["quality", "n", "model"]), expr=""" ( $ranges := { - "low": [0.011, 0.02], - "medium": [0.046, 0.07], - "high": [0.167, 0.3] + "gpt-image-1": { + "low": [0.011, 0.02], + "medium": [0.042, 0.07], + "high": [0.167, 0.25] + }, + "gpt-image-1.5": { + "low": [0.009, 0.02], + "medium": [0.034, 0.062], + "high": [0.133, 0.22] + }, + "gpt-image-2": { + "low": [0.0048, 0.012], + "medium": [0.041, 0.112], + "high": [0.165, 0.43] + } }; - $range := $lookup($ranges, widgets.quality); - $n := widgets.n; + $range := $lookup($lookup($ranges, widgets.model), widgets.quality); + $nRaw := widgets.n; + $n := ($nRaw != null and $nRaw != 0) ? $nRaw : 1; ($n = 1) - ? {"type":"range_usd","min_usd": $range[0], "max_usd": $range[1]} + ? {"type":"range_usd","min_usd": $range[0], "max_usd": $range[1], "format": {"approximate": true}} : { "type":"range_usd", - "min_usd": $range[0], - "max_usd": $range[1], - "format": { "suffix": " x " & $string($n) & "/Run" } + "min_usd": $range[0] * $n, + "max_usd": $range[1] * $n, + "format": { "suffix": "/Run", "approximate": true } } ) """, @@ -483,12 +510,18 @@ class OpenAIGPTImage1(IO.ComfyNode): if mask is not None and image is None: raise ValueError("Cannot use a mask without an input image") + if model in ("gpt-image-1", "gpt-image-1.5"): + if size not in ("auto", "1024x1024", "1024x1536", "1536x1024"): + raise ValueError(f"Resolution {size} is only supported by GPT Image 2 model") + if model == "gpt-image-1": price_extractor = calculate_tokens_price_image_1 elif model == "gpt-image-1.5": price_extractor = calculate_tokens_price_image_1_5 elif model == "gpt-image-2": - price_extractor = calculate_tokens_price_image_1_5 + price_extractor = calculate_tokens_price_image_2_0 + if background == "transparent": + raise ValueError("Transparent background is not supported for GPT Image 2 model") else: raise ValueError(f"Unknown model: {model}") From 5edbdf4364c6c89c3c6a5c6630807b59cb7652ba Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Thu, 23 Apr 2026 22:51:20 +0800 Subject: [PATCH 080/140] chore: update workflow templates to v0.9.61 (#13533) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index a25bc0667..8a6ecf6d8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.14 -comfyui-workflow-templates==0.9.59 +comfyui-workflow-templates==0.9.61 comfyui-embedded-docs==0.4.3 torch torchsde From 2a14e1e96afdb8ca744663e3f3f5970c5d023f5b Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Thu, 23 Apr 2026 23:15:47 +0800 Subject: [PATCH 081/140] chore: update embedded docs to v0.4.4 (#13535) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 8a6ecf6d8..419124f48 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ comfyui-frontend-package==1.42.14 comfyui-workflow-templates==0.9.61 -comfyui-embedded-docs==0.4.3 +comfyui-embedded-docs==0.4.4 torch torchsde torchvision From abf3d56f27948b122dbcba35847b59e5ff299030 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Thu, 23 Apr 2026 18:49:54 +0300 Subject: [PATCH 082/140] add 4K resolution to Kling nodes (#13536) Signed-off-by: bigcat88 --- comfy_api_nodes/nodes_kling.py | 91 ++++++++++++++++++++++++++-------- 1 file changed, 70 insertions(+), 21 deletions(-) diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py index 9a37ccc53..709b3726c 100644 --- a/comfy_api_nodes/nodes_kling.py +++ b/comfy_api_nodes/nodes_kling.py @@ -276,6 +276,7 @@ async def finish_omni_video_task(cls: type[IO.ComfyNode], response: TaskStatusRe cls, ApiEndpoint(path=f"/proxy/kling/v1/videos/omni-video/{response.data.task_id}"), response_model=TaskStatusResponse, + max_poll_attempts=280, status_extractor=lambda r: (r.data.task_status if r.data else None), ) return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url)) @@ -862,7 +863,7 @@ class OmniProTextToVideoNode(IO.ComfyNode): ), IO.Combo.Input("aspect_ratio", options=["16:9", "9:16", "1:1"]), IO.Int.Input("duration", default=5, min=3, max=15, display_mode=IO.NumberDisplay.slider), - IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True), + IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p", optional=True), IO.DynamicCombo.Input( "storyboards", options=[ @@ -904,12 +905,13 @@ class OmniProTextToVideoNode(IO.ComfyNode): depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution", "model_name", "generate_audio"]), expr=""" ( - $mode := (widgets.resolution = "720p") ? "std" : "pro"; + $res := widgets.resolution; + $mode := $res = "4k" ? "4k" : ($res = "720p" ? "std" : "pro"); $isV3 := $contains(widgets.model_name, "v3"); $audio := $isV3 and widgets.generate_audio; $rates := $audio - ? {"std": 0.112, "pro": 0.14} - : {"std": 0.084, "pro": 0.112}; + ? {"std": 0.112, "pro": 0.14, "4k": 0.42} + : {"std": 0.084, "pro": 0.112, "4k": 0.42}; {"type":"usd","usd": $lookup($rates, $mode) * widgets.duration} ) """, @@ -934,6 +936,8 @@ class OmniProTextToVideoNode(IO.ComfyNode): raise ValueError("kling-video-o1 only supports durations of 5 or 10 seconds.") if generate_audio: raise ValueError("kling-video-o1 does not support audio generation.") + if resolution == "4k": + raise ValueError("kling-video-o1 does not support 4k resolution.") stories_enabled = storyboards is not None and storyboards["storyboards"] != "disabled" if stories_enabled and model_name == "kling-video-o1": raise ValueError("kling-video-o1 does not support storyboards.") @@ -963,6 +967,12 @@ class OmniProTextToVideoNode(IO.ComfyNode): f"must equal the global duration ({duration}s)." ) + if resolution == "4k": + mode = "4k" + elif resolution == "1080p": + mode = "pro" + else: + mode = "std" response = await sync_op( cls, ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"), @@ -972,7 +982,7 @@ class OmniProTextToVideoNode(IO.ComfyNode): prompt=prompt, aspect_ratio=aspect_ratio, duration=str(duration), - mode="pro" if resolution == "1080p" else "std", + mode=mode, multi_shot=multi_shot, multi_prompt=multi_prompt_list, shot_type="customize" if multi_shot else None, @@ -1014,7 +1024,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode): optional=True, tooltip="Up to 6 additional reference images.", ), - IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True), + IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p", optional=True), IO.DynamicCombo.Input( "storyboards", options=[ @@ -1061,12 +1071,13 @@ class OmniProFirstLastFrameNode(IO.ComfyNode): depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution", "model_name", "generate_audio"]), expr=""" ( - $mode := (widgets.resolution = "720p") ? "std" : "pro"; + $res := widgets.resolution; + $mode := $res = "4k" ? "4k" : ($res = "720p" ? "std" : "pro"); $isV3 := $contains(widgets.model_name, "v3"); $audio := $isV3 and widgets.generate_audio; $rates := $audio - ? {"std": 0.112, "pro": 0.14} - : {"std": 0.084, "pro": 0.112}; + ? {"std": 0.112, "pro": 0.14, "4k": 0.42} + : {"std": 0.084, "pro": 0.112, "4k": 0.42}; {"type":"usd","usd": $lookup($rates, $mode) * widgets.duration} ) """, @@ -1093,6 +1104,8 @@ class OmniProFirstLastFrameNode(IO.ComfyNode): raise ValueError("kling-video-o1 does not support durations greater than 10 seconds.") if generate_audio: raise ValueError("kling-video-o1 does not support audio generation.") + if resolution == "4k": + raise ValueError("kling-video-o1 does not support 4k resolution.") stories_enabled = storyboards is not None and storyboards["storyboards"] != "disabled" if stories_enabled and model_name == "kling-video-o1": raise ValueError("kling-video-o1 does not support storyboards.") @@ -1161,6 +1174,12 @@ class OmniProFirstLastFrameNode(IO.ComfyNode): validate_image_aspect_ratio(i, (1, 2.5), (2.5, 1)) for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference frame(s)"): image_list.append(OmniParamImage(image_url=i)) + if resolution == "4k": + mode = "4k" + elif resolution == "1080p": + mode = "pro" + else: + mode = "std" response = await sync_op( cls, ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"), @@ -1170,7 +1189,7 @@ class OmniProFirstLastFrameNode(IO.ComfyNode): prompt=prompt, duration=str(duration), image_list=image_list, - mode="pro" if resolution == "1080p" else "std", + mode=mode, sound="on" if generate_audio else "off", multi_shot=multi_shot, multi_prompt=multi_prompt_list, @@ -1204,7 +1223,7 @@ class OmniProImageToVideoNode(IO.ComfyNode): "reference_images", tooltip="Up to 7 reference images.", ), - IO.Combo.Input("resolution", options=["1080p", "720p"], optional=True), + IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p", optional=True), IO.DynamicCombo.Input( "storyboards", options=[ @@ -1251,12 +1270,13 @@ class OmniProImageToVideoNode(IO.ComfyNode): depends_on=IO.PriceBadgeDepends(widgets=["duration", "resolution", "model_name", "generate_audio"]), expr=""" ( - $mode := (widgets.resolution = "720p") ? "std" : "pro"; + $res := widgets.resolution; + $mode := $res = "4k" ? "4k" : ($res = "720p" ? "std" : "pro"); $isV3 := $contains(widgets.model_name, "v3"); $audio := $isV3 and widgets.generate_audio; $rates := $audio - ? {"std": 0.112, "pro": 0.14} - : {"std": 0.084, "pro": 0.112}; + ? {"std": 0.112, "pro": 0.14, "4k": 0.42} + : {"std": 0.084, "pro": 0.112, "4k": 0.42}; {"type":"usd","usd": $lookup($rates, $mode) * widgets.duration} ) """, @@ -1282,6 +1302,8 @@ class OmniProImageToVideoNode(IO.ComfyNode): raise ValueError("kling-video-o1 does not support durations greater than 10 seconds.") if generate_audio: raise ValueError("kling-video-o1 does not support audio generation.") + if resolution == "4k": + raise ValueError("kling-video-o1 does not support 4k resolution.") stories_enabled = storyboards is not None and storyboards["storyboards"] != "disabled" if stories_enabled and model_name == "kling-video-o1": raise ValueError("kling-video-o1 does not support storyboards.") @@ -1320,6 +1342,12 @@ class OmniProImageToVideoNode(IO.ComfyNode): image_list: list[OmniParamImage] = [] for i in await upload_images_to_comfyapi(cls, reference_images, wait_label="Uploading reference image"): image_list.append(OmniParamImage(image_url=i)) + if resolution == "4k": + mode = "4k" + elif resolution == "1080p": + mode = "pro" + else: + mode = "std" response = await sync_op( cls, ApiEndpoint(path="/proxy/kling/v1/videos/omni-video", method="POST"), @@ -1330,7 +1358,7 @@ class OmniProImageToVideoNode(IO.ComfyNode): aspect_ratio=aspect_ratio, duration=str(duration), image_list=image_list, - mode="pro" if resolution == "1080p" else "std", + mode=mode, sound="on" if generate_audio else "off", multi_shot=multi_shot, multi_prompt=multi_prompt_list, @@ -2860,7 +2888,7 @@ class KlingVideoNode(IO.ComfyNode): IO.DynamicCombo.Option( "kling-v3", [ - IO.Combo.Input("resolution", options=["1080p", "720p"]), + IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p"), IO.Combo.Input( "aspect_ratio", options=["16:9", "9:16", "1:1"], @@ -2913,7 +2941,11 @@ class KlingVideoNode(IO.ComfyNode): ), expr=""" ( - $rates := {"1080p": {"off": 0.112, "on": 0.168}, "720p": {"off": 0.084, "on": 0.126}}; + $rates := { + "4k": {"off": 0.42, "on": 0.42}, + "1080p": {"off": 0.112, "on": 0.168}, + "720p": {"off": 0.084, "on": 0.126} + }; $res := $lookup(widgets, "model.resolution"); $audio := widgets.generate_audio ? "on" : "off"; $rate := $lookup($lookup($rates, $res), $audio); @@ -2943,7 +2975,12 @@ class KlingVideoNode(IO.ComfyNode): start_frame: Input.Image | None = None, ) -> IO.NodeOutput: _ = seed - mode = "pro" if model["resolution"] == "1080p" else "std" + if model["resolution"] == "4k": + mode = "4k" + elif model["resolution"] == "1080p": + mode = "pro" + else: + mode = "std" custom_multi_shot = False if multi_shot["multi_shot"] == "disabled": shot_type = None @@ -3025,6 +3062,7 @@ class KlingVideoNode(IO.ComfyNode): cls, ApiEndpoint(path=poll_path), response_model=TaskStatusResponse, + max_poll_attempts=280, status_extractor=lambda r: (r.data.task_status if r.data else None), ) return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url)) @@ -3057,7 +3095,7 @@ class KlingFirstLastFrameNode(IO.ComfyNode): IO.DynamicCombo.Option( "kling-v3", [ - IO.Combo.Input("resolution", options=["1080p", "720p"]), + IO.Combo.Input("resolution", options=["4k", "1080p", "720p"], default="1080p"), ], ), ], @@ -3089,7 +3127,11 @@ class KlingFirstLastFrameNode(IO.ComfyNode): ), expr=""" ( - $rates := {"1080p": {"off": 0.112, "on": 0.168}, "720p": {"off": 0.084, "on": 0.126}}; + $rates := { + "4k": {"off": 0.42, "on": 0.42}, + "1080p": {"off": 0.112, "on": 0.168}, + "720p": {"off": 0.084, "on": 0.126} + }; $res := $lookup(widgets, "model.resolution"); $audio := widgets.generate_audio ? "on" : "off"; $rate := $lookup($lookup($rates, $res), $audio); @@ -3118,6 +3160,12 @@ class KlingFirstLastFrameNode(IO.ComfyNode): validate_image_aspect_ratio(end_frame, (1, 2.5), (2.5, 1)) image_url = await upload_image_to_comfyapi(cls, first_frame, wait_label="Uploading first frame") image_tail_url = await upload_image_to_comfyapi(cls, end_frame, wait_label="Uploading end frame") + if model["resolution"] == "4k": + mode = "4k" + elif model["resolution"] == "1080p": + mode = "pro" + else: + mode = "std" response = await sync_op( cls, ApiEndpoint(path="/proxy/kling/v1/videos/image2video", method="POST"), @@ -3127,7 +3175,7 @@ class KlingFirstLastFrameNode(IO.ComfyNode): image=image_url, image_tail=image_tail_url, prompt=prompt, - mode="pro" if model["resolution"] == "1080p" else "std", + mode=mode, duration=str(duration), sound="on" if generate_audio else "off", ), @@ -3140,6 +3188,7 @@ class KlingFirstLastFrameNode(IO.ComfyNode): cls, ApiEndpoint(path=f"/proxy/kling/v1/videos/image2video/{response.data.task_id}"), response_model=TaskStatusResponse, + max_poll_attempts=280, status_extractor=lambda r: (r.data.task_status if r.data else None), ) return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url)) From 6fbb6b6f49ccd1d7d336368540b71248e3701dde Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Thu, 23 Apr 2026 21:13:17 +0300 Subject: [PATCH 083/140] Fix LTXV Reference Audio node (#13531) --- comfy_extras/nodes_lt.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py index d7c2e8744..19d8a387f 100644 --- a/comfy_extras/nodes_lt.py +++ b/comfy_extras/nodes_lt.py @@ -1,6 +1,7 @@ import nodes import node_helpers import torch +import torchaudio import comfy.model_management import comfy.model_sampling import comfy.samplers @@ -711,7 +712,14 @@ class LTXVReferenceAudio(io.ComfyNode): @classmethod def execute(cls, model, positive, negative, reference_audio, audio_vae, identity_guidance_scale, start_percent, end_percent) -> io.NodeOutput: # Encode reference audio to latents and patchify - audio_latents = audio_vae.encode(reference_audio) + sample_rate = reference_audio["sample_rate"] + vae_sample_rate = getattr(audio_vae, "audio_sample_rate", 44100) + if vae_sample_rate != sample_rate: + waveform = torchaudio.functional.resample(reference_audio["waveform"], sample_rate, vae_sample_rate) + else: + waveform = reference_audio["waveform"] + + audio_latents = audio_vae.encode(waveform.movedim(1, -1)) b, c, t, f = audio_latents.shape ref_tokens = audio_latents.permute(0, 2, 1, 3).reshape(b, t, c * f) ref_audio = {"tokens": ref_tokens} From ef8f3cbcdc214b3b1647d3ad845aae99a3bf95d1 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Fri, 24 Apr 2026 04:14:13 +1000 Subject: [PATCH 084/140] comfy-aimdo 0.2.14: Hotfix async allocator estimations (#13534) This was doing an over-estimate of VRAM used by the async allocator when lots of little small tensors were in play. Also change the versioning scheme to == so we can roll forward aimdo without worrying about stable regressions downstream in comfyUI core. --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 419124f48..7a2e4e0a2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,7 +23,7 @@ SQLAlchemy>=2.0 filelock av>=14.2.0 comfy-kitchen>=0.2.8 -comfy-aimdo>=0.2.12 +comfy-aimdo==0.2.14 requests simpleeval>=1.0.0 blake3 From 084e08c6e2d1c2c450fb74ec4f2ac39c31ea69bc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Thu, 23 Apr 2026 21:14:42 +0300 Subject: [PATCH 085/140] Disable sageattention for SAM3 (#13529) Causes Nans --- comfy/ldm/sam3/detector.py | 2 +- comfy/ldm/sam3/sam.py | 4 ++-- comfy/ldm/sam3/tracker.py | 6 +++--- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/comfy/ldm/sam3/detector.py b/comfy/ldm/sam3/detector.py index 6ae919a79..12d3a01ab 100644 --- a/comfy/ldm/sam3/detector.py +++ b/comfy/ldm/sam3/detector.py @@ -54,7 +54,7 @@ class SplitMHA(nn.Module): if mask is not None and mask.ndim == 2: mask = mask[:, None, None, :] # [B, T] -> [B, 1, 1, T] for SDPA broadcast dtype = q.dtype # manual_cast may produce mixed dtypes - out = optimized_attention(q, k.to(dtype), v.to(dtype), self.num_heads, mask=mask) + out = optimized_attention(q, k.to(dtype), v.to(dtype), self.num_heads, mask=mask, low_precision_attention=False) return self.out_proj(out) diff --git a/comfy/ldm/sam3/sam.py b/comfy/ldm/sam3/sam.py index 272781d45..75cb457cf 100644 --- a/comfy/ldm/sam3/sam.py +++ b/comfy/ldm/sam3/sam.py @@ -40,7 +40,7 @@ class SAMAttention(nn.Module): q = self.q_proj(q) k = self.k_proj(k) v = self.v_proj(v) - return self.out_proj(optimized_attention(q, k, v, self.num_heads)) + return self.out_proj(optimized_attention(q, k, v, self.num_heads, low_precision_attention=False)) class TwoWayAttentionBlock(nn.Module): @@ -179,7 +179,7 @@ class Attention(nn.Module): q, k, v = qkv.permute(2, 0, 3, 1, 4).unbind(dim=0) if self.use_rope and freqs_cis is not None: q, k = apply_rope(q, k, freqs_cis) - return self.proj(optimized_attention(q, k, v, self.num_heads, skip_reshape=True)) + return self.proj(optimized_attention(q, k, v, self.num_heads, skip_reshape=True, low_precision_attention=False)) class Block(nn.Module): diff --git a/comfy/ldm/sam3/tracker.py b/comfy/ldm/sam3/tracker.py index 6ff6369d1..8f7481003 100644 --- a/comfy/ldm/sam3/tracker.py +++ b/comfy/ldm/sam3/tracker.py @@ -364,7 +364,7 @@ class SplitAttn(nn.Module): v = self.v_proj(v) if rope is not None: q, k = apply_rope_memory(q, k, rope, self.num_heads, num_k_exclude_rope) - out = optimized_attention(q, k, v, self.num_heads) + out = optimized_attention(q, k, v, self.num_heads, low_precision_attention=False) return self.out_proj(out) @@ -657,7 +657,7 @@ class DecoupledMemoryAttnLayer(nn.Module): v = self.self_attn_v_proj(normed) if rope is not None: q, k = apply_rope_memory(q, k, rope, self.num_heads, 0) - x = x + self.self_attn_out_proj(optimized_attention(q, k, v, self.num_heads)) + x = x + self.self_attn_out_proj(optimized_attention(q, k, v, self.num_heads, low_precision_attention=False)) # Decoupled cross-attention: fuse image and memory projections normed = self.norm2(x) @@ -668,7 +668,7 @@ class DecoupledMemoryAttnLayer(nn.Module): v = self.cross_attn_v_proj(memory) if rope is not None: q, k = apply_rope_memory(q, k, rope, self.num_heads, num_k_exclude_rope) - x = x + self.cross_attn_out_proj(optimized_attention(q, k, v, self.num_heads)) + x = x + self.cross_attn_out_proj(optimized_attention(q, k, v, self.num_heads, low_precision_attention=False)) # FFN x = x + self.linear2(F.gelu(self.linear1(self.norm3(x)))) From 2327fa1c908602076318e5ffca02a45d4a7e6af8 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Fri, 24 Apr 2026 08:20:24 +1000 Subject: [PATCH 086/140] execution: Add anti-cycle validation (#13169) Currently if the graph contains a cycle, the just inifitiate recursions, hits a catch all then throws a generic error against the output node that seeded the validation. Instead, fail the offending cycling mode chain and handlng it as an error in its own right. Co-authored-by: guill --- execution.py | 38 ++++++++++++++++++++++++++++++++------ 1 file changed, 32 insertions(+), 6 deletions(-) diff --git a/execution.py b/execution.py index 5e02dffb2..e15eb4bda 100644 --- a/execution.py +++ b/execution.py @@ -811,11 +811,30 @@ class PromptExecutor: self._notify_prompt_lifecycle("end", prompt_id) -async def validate_inputs(prompt_id, prompt, item, validated): +async def validate_inputs(prompt_id, prompt, item, validated, visiting=None): + if visiting is None: + visiting = [] + unique_id = item if unique_id in validated: return validated[unique_id] + if unique_id in visiting: + cycle_path_nodes = visiting[visiting.index(unique_id):] + [unique_id] + cycle_nodes = list(dict.fromkeys(cycle_path_nodes)) + cycle_path = " -> ".join(f"{node_id} ({prompt[node_id]['class_type']})" for node_id in cycle_path_nodes) + for node_id in cycle_nodes: + validated[node_id] = (False, [{ + "type": "dependency_cycle", + "message": "Dependency cycle detected", + "details": cycle_path, + "extra_info": { + "node_id": node_id, + "cycle_nodes": cycle_nodes, + } + }], node_id) + return validated[unique_id] + inputs = prompt[unique_id]['inputs'] class_type = prompt[unique_id]['class_type'] obj_class = nodes.NODE_CLASS_MAPPINGS[class_type] @@ -899,7 +918,11 @@ async def validate_inputs(prompt_id, prompt, item, validated): errors.append(error) continue try: - r = await validate_inputs(prompt_id, prompt, o_id, validated) + visiting.append(unique_id) + try: + r = await validate_inputs(prompt_id, prompt, o_id, validated, visiting) + finally: + visiting.pop() if r[0] is False: # `r` will be set in `validated[o_id]` already valid = False @@ -1048,10 +1071,13 @@ async def validate_inputs(prompt_id, prompt, item, validated): errors.append(error) continue - if len(errors) > 0 or valid is not True: - ret = (False, errors, unique_id) - else: - ret = (True, [], unique_id) + ret = validated.get(unique_id, (True, [], unique_id)) + # Recursive cycle detection may have already populated an error on us. Join it. + ret = ( + ret[0] and valid is True and not errors, + ret[1] + [error for error in errors if error not in ret[1]], + unique_id, + ) validated[unique_id] = ret return ret From 47ccecaee009cce148e8c2a5bdc2ecb302cc52ee Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Fri, 24 Apr 2026 07:56:13 +0800 Subject: [PATCH 087/140] chore: update workflow templates to v0.9.62 (#13539) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 7a2e4e0a2..346ce4b76 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.14 -comfyui-workflow-templates==0.9.61 +comfyui-workflow-templates==0.9.62 comfyui-embedded-docs==0.4.4 torch torchsde From c5d9edacd0d92cf2b6d9f82e6b60d6250c269e9e Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 23 Apr 2026 19:19:00 -0700 Subject: [PATCH 088/140] Print more tensor values in the preview any node. (#13544) --- comfy_extras/nodes_preview_any.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/comfy_extras/nodes_preview_any.py b/comfy_extras/nodes_preview_any.py index 0a1558f2b..17e25d514 100644 --- a/comfy_extras/nodes_preview_any.py +++ b/comfy_extras/nodes_preview_any.py @@ -1,5 +1,6 @@ import json from comfy.comfy_types.node_typing import IO +import torch # Preview Any - original implement from # https://github.com/rgthree/rgthree-comfy/blob/main/py/display_any.py @@ -19,6 +20,7 @@ class PreviewAny(): SEARCH_ALIASES = ["show output", "inspect", "debug", "print value", "show text"] def main(self, source=None): + torch.set_printoptions(edgeitems=6) value = 'None' if isinstance(source, str): value = source @@ -33,6 +35,7 @@ class PreviewAny(): except Exception: value = 'source exists, but could not be serialized.' + torch.set_printoptions() return {"ui": {"text": (value,)}, "result": (value,)} NODE_CLASS_MAPPINGS = { From 00d2f4047db3de6c14f965f6f34354d5ed5d0ccc Mon Sep 17 00:00:00 2001 From: Terry Jia Date: Thu, 23 Apr 2026 23:42:22 -0400 Subject: [PATCH 089/140] fix: use textureSize instead of u_resolution for texel size in blur/sharpen shaders (#13347) * fix: use textureSize instead of u_resolution for texel size in blur/sharpen shaders * fix: remove unused u_resolution uniform and fix Glow shader texelSize --------- Co-authored-by: guill --- blueprints/.glsl/Glow_30.frag | 3 +-- blueprints/.glsl/Image_Blur_1.frag | 3 +-- blueprints/.glsl/Sharpen_23.frag | 3 +-- blueprints/.glsl/Unsharp_Mask_26.frag | 3 +-- blueprints/Glow.json | 2 +- blueprints/Image Blur.json | 2 +- blueprints/Sharpen.json | 2 +- blueprints/Unsharp Mask.json | 2 +- 8 files changed, 8 insertions(+), 12 deletions(-) diff --git a/blueprints/.glsl/Glow_30.frag b/blueprints/.glsl/Glow_30.frag index 0ee152628..f3c85a212 100644 --- a/blueprints/.glsl/Glow_30.frag +++ b/blueprints/.glsl/Glow_30.frag @@ -2,7 +2,6 @@ precision mediump float; uniform sampler2D u_image0; -uniform vec2 u_resolution; uniform int u_int0; // Blend mode uniform int u_int1; // Color tint uniform float u_float0; // Intensity @@ -75,7 +74,7 @@ void main() { float t0 = threshold - 0.15; float t1 = threshold + 0.15; - vec2 texelSize = 1.0 / u_resolution; + vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0)); float radius2 = radius * radius; float sampleScale = clamp(radius * 0.75, 0.35, 1.0); diff --git a/blueprints/.glsl/Image_Blur_1.frag b/blueprints/.glsl/Image_Blur_1.frag index 83238111d..1819e1695 100644 --- a/blueprints/.glsl/Image_Blur_1.frag +++ b/blueprints/.glsl/Image_Blur_1.frag @@ -12,7 +12,6 @@ const int RADIAL_SAMPLES = 12; const float RADIAL_STRENGTH = 0.0003; uniform sampler2D u_image0; -uniform vec2 u_resolution; uniform int u_int0; // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL) uniform float u_float0; // Blur radius/amount uniform int u_pass; // Pass index (0 = horizontal, 1 = vertical) @@ -25,7 +24,7 @@ float gaussian(float x, float sigma) { } void main() { - vec2 texelSize = 1.0 / u_resolution; + vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0)); float radius = max(u_float0, 0.0); // Radial (angular) blur - single pass, doesn't use separable diff --git a/blueprints/.glsl/Sharpen_23.frag b/blueprints/.glsl/Sharpen_23.frag index c03f94b66..e7463a329 100644 --- a/blueprints/.glsl/Sharpen_23.frag +++ b/blueprints/.glsl/Sharpen_23.frag @@ -2,14 +2,13 @@ precision highp float; uniform sampler2D u_image0; -uniform vec2 u_resolution; uniform float u_float0; // strength [0.0 – 2.0] typical: 0.3–1.0 in vec2 v_texCoord; layout(location = 0) out vec4 fragColor0; void main() { - vec2 texel = 1.0 / u_resolution; + vec2 texel = 1.0 / vec2(textureSize(u_image0, 0)); // Sample center and neighbors vec4 center = texture(u_image0, v_texCoord); diff --git a/blueprints/.glsl/Unsharp_Mask_26.frag b/blueprints/.glsl/Unsharp_Mask_26.frag index f5990cb4a..d968c9c03 100644 --- a/blueprints/.glsl/Unsharp_Mask_26.frag +++ b/blueprints/.glsl/Unsharp_Mask_26.frag @@ -2,7 +2,6 @@ precision highp float; uniform sampler2D u_image0; -uniform vec2 u_resolution; uniform float u_float0; // amount [0.0 - 3.0] typical: 0.5-1.5 uniform float u_float1; // radius [0.5 - 10.0] blur radius in pixels uniform float u_float2; // threshold [0.0 - 0.1] min difference to sharpen @@ -19,7 +18,7 @@ float getLuminance(vec3 color) { } void main() { - vec2 texel = 1.0 / u_resolution; + vec2 texel = 1.0 / vec2(textureSize(u_image0, 0)); float radius = max(u_float1, 0.5); float amount = u_float0; float threshold = u_float2; diff --git a/blueprints/Glow.json b/blueprints/Glow.json index 8c690fc68..1dafb2d35 100644 --- a/blueprints/Glow.json +++ b/blueprints/Glow.json @@ -268,7 +268,7 @@ "Node name for S&R": "GLSLShader" }, "widgets_values": [ - "#version 300 es\nprecision mediump float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0; // Blend mode\nuniform int u_int1; // Color tint\nuniform float u_float0; // Intensity\nuniform float u_float1; // Radius\nuniform float u_float2; // Threshold\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst int BLEND_ADD = 0;\nconst int BLEND_SCREEN = 1;\nconst int BLEND_SOFT = 2;\nconst int BLEND_OVERLAY = 3;\nconst int BLEND_LIGHTEN = 4;\n\nconst float GOLDEN_ANGLE = 2.39996323;\nconst int MAX_SAMPLES = 48;\nconst vec3 LUMA = vec3(0.299, 0.587, 0.114);\n\nfloat hash(vec2 p) {\n p = fract(p * vec2(123.34, 456.21));\n p += dot(p, p + 45.32);\n return fract(p.x * p.y);\n}\n\nvec3 hexToRgb(int h) {\n return vec3(\n float((h >> 16) & 255),\n float((h >> 8) & 255),\n float(h & 255)\n ) * (1.0 / 255.0);\n}\n\nvec3 blend(vec3 base, vec3 glow, int mode) {\n if (mode == BLEND_SCREEN) {\n return 1.0 - (1.0 - base) * (1.0 - glow);\n }\n if (mode == BLEND_SOFT) {\n return mix(\n base - (1.0 - 2.0 * glow) * base * (1.0 - base),\n base + (2.0 * glow - 1.0) * (sqrt(base) - base),\n step(0.5, glow)\n );\n }\n if (mode == BLEND_OVERLAY) {\n return mix(\n 2.0 * base * glow,\n 1.0 - 2.0 * (1.0 - base) * (1.0 - glow),\n step(0.5, base)\n );\n }\n if (mode == BLEND_LIGHTEN) {\n return max(base, glow);\n }\n return base + glow;\n}\n\nvoid main() {\n vec4 original = texture(u_image0, v_texCoord);\n \n float intensity = u_float0 * 0.05;\n float radius = u_float1 * u_float1 * 0.012;\n \n if (intensity < 0.001 || radius < 0.1) {\n fragColor = original;\n return;\n }\n \n float threshold = 1.0 - u_float2 * 0.01;\n float t0 = threshold - 0.15;\n float t1 = threshold + 0.15;\n \n vec2 texelSize = 1.0 / u_resolution;\n float radius2 = radius * radius;\n \n float sampleScale = clamp(radius * 0.75, 0.35, 1.0);\n int samples = int(float(MAX_SAMPLES) * sampleScale);\n \n float noise = hash(gl_FragCoord.xy);\n float angleOffset = noise * GOLDEN_ANGLE;\n float radiusJitter = 0.85 + noise * 0.3;\n \n float ca = cos(GOLDEN_ANGLE);\n float sa = sin(GOLDEN_ANGLE);\n vec2 dir = vec2(cos(angleOffset), sin(angleOffset));\n \n vec3 glow = vec3(0.0);\n float totalWeight = 0.0;\n \n // Center tap\n float centerMask = smoothstep(t0, t1, dot(original.rgb, LUMA));\n glow += original.rgb * centerMask * 2.0;\n totalWeight += 2.0;\n \n for (int i = 1; i < MAX_SAMPLES; i++) {\n if (i >= samples) break;\n \n float fi = float(i);\n float dist = sqrt(fi / float(samples)) * radius * radiusJitter;\n \n vec2 offset = dir * dist * texelSize;\n vec3 c = texture(u_image0, v_texCoord + offset).rgb;\n float mask = smoothstep(t0, t1, dot(c, LUMA));\n \n float w = 1.0 - (dist * dist) / (radius2 * 1.5);\n w = max(w, 0.0);\n w *= w;\n \n glow += c * mask * w;\n totalWeight += w;\n \n dir = vec2(\n dir.x * ca - dir.y * sa,\n dir.x * sa + dir.y * ca\n );\n }\n \n glow *= intensity / max(totalWeight, 0.001);\n \n if (u_int1 > 0) {\n glow *= hexToRgb(u_int1);\n }\n \n vec3 result = blend(original.rgb, glow, u_int0);\n result += (noise - 0.5) * (1.0 / 255.0);\n \n fragColor = vec4(clamp(result, 0.0, 1.0), original.a);\n}", + "#version 300 es\nprecision mediump float;\n\nuniform sampler2D u_image0;\nuniform int u_int0; // Blend mode\nuniform int u_int1; // Color tint\nuniform float u_float0; // Intensity\nuniform float u_float1; // Radius\nuniform float u_float2; // Threshold\n\nin vec2 v_texCoord;\nout vec4 fragColor;\n\nconst int BLEND_ADD = 0;\nconst int BLEND_SCREEN = 1;\nconst int BLEND_SOFT = 2;\nconst int BLEND_OVERLAY = 3;\nconst int BLEND_LIGHTEN = 4;\n\nconst float GOLDEN_ANGLE = 2.39996323;\nconst int MAX_SAMPLES = 48;\nconst vec3 LUMA = vec3(0.299, 0.587, 0.114);\n\nfloat hash(vec2 p) {\n p = fract(p * vec2(123.34, 456.21));\n p += dot(p, p + 45.32);\n return fract(p.x * p.y);\n}\n\nvec3 hexToRgb(int h) {\n return vec3(\n float((h >> 16) & 255),\n float((h >> 8) & 255),\n float(h & 255)\n ) * (1.0 / 255.0);\n}\n\nvec3 blend(vec3 base, vec3 glow, int mode) {\n if (mode == BLEND_SCREEN) {\n return 1.0 - (1.0 - base) * (1.0 - glow);\n }\n if (mode == BLEND_SOFT) {\n return mix(\n base - (1.0 - 2.0 * glow) * base * (1.0 - base),\n base + (2.0 * glow - 1.0) * (sqrt(base) - base),\n step(0.5, glow)\n );\n }\n if (mode == BLEND_OVERLAY) {\n return mix(\n 2.0 * base * glow,\n 1.0 - 2.0 * (1.0 - base) * (1.0 - glow),\n step(0.5, base)\n );\n }\n if (mode == BLEND_LIGHTEN) {\n return max(base, glow);\n }\n return base + glow;\n}\n\nvoid main() {\n vec4 original = texture(u_image0, v_texCoord);\n \n float intensity = u_float0 * 0.05;\n float radius = u_float1 * u_float1 * 0.012;\n \n if (intensity < 0.001 || radius < 0.1) {\n fragColor = original;\n return;\n }\n \n float threshold = 1.0 - u_float2 * 0.01;\n float t0 = threshold - 0.15;\n float t1 = threshold + 0.15;\n \n vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));\n float radius2 = radius * radius;\n \n float sampleScale = clamp(radius * 0.75, 0.35, 1.0);\n int samples = int(float(MAX_SAMPLES) * sampleScale);\n \n float noise = hash(gl_FragCoord.xy);\n float angleOffset = noise * GOLDEN_ANGLE;\n float radiusJitter = 0.85 + noise * 0.3;\n \n float ca = cos(GOLDEN_ANGLE);\n float sa = sin(GOLDEN_ANGLE);\n vec2 dir = vec2(cos(angleOffset), sin(angleOffset));\n \n vec3 glow = vec3(0.0);\n float totalWeight = 0.0;\n \n // Center tap\n float centerMask = smoothstep(t0, t1, dot(original.rgb, LUMA));\n glow += original.rgb * centerMask * 2.0;\n totalWeight += 2.0;\n \n for (int i = 1; i < MAX_SAMPLES; i++) {\n if (i >= samples) break;\n \n float fi = float(i);\n float dist = sqrt(fi / float(samples)) * radius * radiusJitter;\n \n vec2 offset = dir * dist * texelSize;\n vec3 c = texture(u_image0, v_texCoord + offset).rgb;\n float mask = smoothstep(t0, t1, dot(c, LUMA));\n \n float w = 1.0 - (dist * dist) / (radius2 * 1.5);\n w = max(w, 0.0);\n w *= w;\n \n glow += c * mask * w;\n totalWeight += w;\n \n dir = vec2(\n dir.x * ca - dir.y * sa,\n dir.x * sa + dir.y * ca\n );\n }\n \n glow *= intensity / max(totalWeight, 0.001);\n \n if (u_int1 > 0) {\n glow *= hexToRgb(u_int1);\n }\n \n vec3 result = blend(original.rgb, glow, u_int0);\n result += (noise - 0.5) * (1.0 / 255.0);\n \n fragColor = vec4(clamp(result, 0.0, 1.0), original.a);\n}", "from_input" ] }, diff --git a/blueprints/Image Blur.json b/blueprints/Image Blur.json index b1d449e32..3c7a784b0 100644 --- a/blueprints/Image Blur.json +++ b/blueprints/Image Blur.json @@ -331,7 +331,7 @@ "Node name for S&R": "GLSLShader" }, "widgets_values": [ - "#version 300 es\n#pragma passes 2\nprecision highp float;\n\n// Blur type constants\nconst int BLUR_GAUSSIAN = 0;\nconst int BLUR_BOX = 1;\nconst int BLUR_RADIAL = 2;\n\n// Radial blur config\nconst int RADIAL_SAMPLES = 12;\nconst float RADIAL_STRENGTH = 0.0003;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform int u_int0; // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)\nuniform float u_float0; // Blur radius/amount\nuniform int u_pass; // Pass index (0 = horizontal, 1 = vertical)\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nvoid main() {\n vec2 texelSize = 1.0 / u_resolution;\n float radius = max(u_float0, 0.0);\n\n // Radial (angular) blur - single pass, doesn't use separable\n if (u_int0 == BLUR_RADIAL) {\n // Only execute on first pass\n if (u_pass > 0) {\n fragColor0 = texture(u_image0, v_texCoord);\n return;\n }\n\n vec2 center = vec2(0.5);\n vec2 dir = v_texCoord - center;\n float dist = length(dir);\n\n if (dist < 1e-4) {\n fragColor0 = texture(u_image0, v_texCoord);\n return;\n }\n\n vec4 sum = vec4(0.0);\n float totalWeight = 0.0;\n float angleStep = radius * RADIAL_STRENGTH;\n\n dir /= dist;\n\n float cosStep = cos(angleStep);\n float sinStep = sin(angleStep);\n\n float negAngle = -float(RADIAL_SAMPLES) * angleStep;\n vec2 rotDir = vec2(\n dir.x * cos(negAngle) - dir.y * sin(negAngle),\n dir.x * sin(negAngle) + dir.y * cos(negAngle)\n );\n\n for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {\n vec2 uv = center + rotDir * dist;\n float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);\n sum += texture(u_image0, uv) * w;\n totalWeight += w;\n\n rotDir = vec2(\n rotDir.x * cosStep - rotDir.y * sinStep,\n rotDir.x * sinStep + rotDir.y * cosStep\n );\n }\n\n fragColor0 = sum / max(totalWeight, 0.001);\n return;\n }\n\n // Separable Gaussian / Box blur\n int samples = int(ceil(radius));\n\n if (samples == 0) {\n fragColor0 = texture(u_image0, v_texCoord);\n return;\n }\n\n // Direction: pass 0 = horizontal, pass 1 = vertical\n vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\n\n vec4 color = vec4(0.0);\n float totalWeight = 0.0;\n float sigma = radius / 2.0;\n\n for (int i = -samples; i <= samples; i++) {\n vec2 offset = dir * float(i) * texelSize;\n vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n float weight;\n if (u_int0 == BLUR_GAUSSIAN) {\n weight = gaussian(float(i), sigma);\n } else {\n // BLUR_BOX\n weight = 1.0;\n }\n\n color += sample_color * weight;\n totalWeight += weight;\n }\n\n fragColor0 = color / totalWeight;\n}\n", + "#version 300 es\n#pragma passes 2\nprecision highp float;\n\n// Blur type constants\nconst int BLUR_GAUSSIAN = 0;\nconst int BLUR_BOX = 1;\nconst int BLUR_RADIAL = 2;\n\n// Radial blur config\nconst int RADIAL_SAMPLES = 12;\nconst float RADIAL_STRENGTH = 0.0003;\n\nuniform sampler2D u_image0;\nuniform int u_int0; // Blur type (BLUR_GAUSSIAN, BLUR_BOX, BLUR_RADIAL)\nuniform float u_float0; // Blur radius/amount\nuniform int u_pass; // Pass index (0 = horizontal, 1 = vertical)\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nvoid main() {\n vec2 texelSize = 1.0 / vec2(textureSize(u_image0, 0));\n float radius = max(u_float0, 0.0);\n\n // Radial (angular) blur - single pass, doesn't use separable\n if (u_int0 == BLUR_RADIAL) {\n // Only execute on first pass\n if (u_pass > 0) {\n fragColor0 = texture(u_image0, v_texCoord);\n return;\n }\n\n vec2 center = vec2(0.5);\n vec2 dir = v_texCoord - center;\n float dist = length(dir);\n\n if (dist < 1e-4) {\n fragColor0 = texture(u_image0, v_texCoord);\n return;\n }\n\n vec4 sum = vec4(0.0);\n float totalWeight = 0.0;\n float angleStep = radius * RADIAL_STRENGTH;\n\n dir /= dist;\n\n float cosStep = cos(angleStep);\n float sinStep = sin(angleStep);\n\n float negAngle = -float(RADIAL_SAMPLES) * angleStep;\n vec2 rotDir = vec2(\n dir.x * cos(negAngle) - dir.y * sin(negAngle),\n dir.x * sin(negAngle) + dir.y * cos(negAngle)\n );\n\n for (int i = -RADIAL_SAMPLES; i <= RADIAL_SAMPLES; i++) {\n vec2 uv = center + rotDir * dist;\n float w = 1.0 - abs(float(i)) / float(RADIAL_SAMPLES);\n sum += texture(u_image0, uv) * w;\n totalWeight += w;\n\n rotDir = vec2(\n rotDir.x * cosStep - rotDir.y * sinStep,\n rotDir.x * sinStep + rotDir.y * cosStep\n );\n }\n\n fragColor0 = sum / max(totalWeight, 0.001);\n return;\n }\n\n // Separable Gaussian / Box blur\n int samples = int(ceil(radius));\n\n if (samples == 0) {\n fragColor0 = texture(u_image0, v_texCoord);\n return;\n }\n\n // Direction: pass 0 = horizontal, pass 1 = vertical\n vec2 dir = (u_pass == 0) ? vec2(1.0, 0.0) : vec2(0.0, 1.0);\n\n vec4 color = vec4(0.0);\n float totalWeight = 0.0;\n float sigma = radius / 2.0;\n\n for (int i = -samples; i <= samples; i++) {\n vec2 offset = dir * float(i) * texelSize;\n vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n float weight;\n if (u_int0 == BLUR_GAUSSIAN) {\n weight = gaussian(float(i), sigma);\n } else {\n // BLUR_BOX\n weight = 1.0;\n }\n\n color += sample_color * weight;\n totalWeight += weight;\n }\n\n fragColor0 = color / totalWeight;\n}\n", "from_input" ] } diff --git a/blueprints/Sharpen.json b/blueprints/Sharpen.json index bb79f61fc..f332400fd 100644 --- a/blueprints/Sharpen.json +++ b/blueprints/Sharpen.json @@ -267,7 +267,7 @@ "Node name for S&R": "GLSLShader" }, "widgets_values": [ - "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0; // strength [0.0 – 2.0] typical: 0.3–1.0\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nvoid main() {\n vec2 texel = 1.0 / u_resolution;\n \n // Sample center and neighbors\n vec4 center = texture(u_image0, v_texCoord);\n vec4 top = texture(u_image0, v_texCoord + vec2( 0.0, -texel.y));\n vec4 bottom = texture(u_image0, v_texCoord + vec2( 0.0, texel.y));\n vec4 left = texture(u_image0, v_texCoord + vec2(-texel.x, 0.0));\n vec4 right = texture(u_image0, v_texCoord + vec2( texel.x, 0.0));\n \n // Edge enhancement (Laplacian)\n vec4 edges = center * 4.0 - top - bottom - left - right;\n \n // Add edges back scaled by strength\n vec4 sharpened = center + edges * u_float0;\n \n fragColor0 = vec4(clamp(sharpened.rgb, 0.0, 1.0), center.a);\n}", + "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // strength [0.0 – 2.0] typical: 0.3–1.0\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nvoid main() {\n vec2 texel = 1.0 / vec2(textureSize(u_image0, 0));\n \n // Sample center and neighbors\n vec4 center = texture(u_image0, v_texCoord);\n vec4 top = texture(u_image0, v_texCoord + vec2( 0.0, -texel.y));\n vec4 bottom = texture(u_image0, v_texCoord + vec2( 0.0, texel.y));\n vec4 left = texture(u_image0, v_texCoord + vec2(-texel.x, 0.0));\n vec4 right = texture(u_image0, v_texCoord + vec2( texel.x, 0.0));\n \n // Edge enhancement (Laplacian)\n vec4 edges = center * 4.0 - top - bottom - left - right;\n \n // Add edges back scaled by strength\n vec4 sharpened = center + edges * u_float0;\n \n fragColor0 = vec4(clamp(sharpened.rgb, 0.0, 1.0), center.a);\n}", "from_input" ] } diff --git a/blueprints/Unsharp Mask.json b/blueprints/Unsharp Mask.json index b673eb703..137acaa43 100644 --- a/blueprints/Unsharp Mask.json +++ b/blueprints/Unsharp Mask.json @@ -383,7 +383,7 @@ "Node name for S&R": "GLSLShader" }, "widgets_values": [ - "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform vec2 u_resolution;\nuniform float u_float0; // amount [0.0 - 3.0] typical: 0.5-1.5\nuniform float u_float1; // radius [0.5 - 10.0] blur radius in pixels\nuniform float u_float2; // threshold [0.0 - 0.1] min difference to sharpen\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nfloat getLuminance(vec3 color) {\n return dot(color, vec3(0.2126, 0.7152, 0.0722));\n}\n\nvoid main() {\n vec2 texel = 1.0 / u_resolution;\n float radius = max(u_float1, 0.5);\n float amount = u_float0;\n float threshold = u_float2;\n\n vec4 original = texture(u_image0, v_texCoord);\n\n // Gaussian blur for the \"unsharp\" mask\n int samples = int(ceil(radius));\n float sigma = radius / 2.0;\n\n vec4 blurred = vec4(0.0);\n float totalWeight = 0.0;\n\n for (int x = -samples; x <= samples; x++) {\n for (int y = -samples; y <= samples; y++) {\n vec2 offset = vec2(float(x), float(y)) * texel;\n vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n float dist = length(vec2(float(x), float(y)));\n float weight = gaussian(dist, sigma);\n blurred += sample_color * weight;\n totalWeight += weight;\n }\n }\n blurred /= totalWeight;\n\n // Unsharp mask = original - blurred\n vec3 mask = original.rgb - blurred.rgb;\n\n // Luminance-based threshold with smooth falloff\n float lumaDelta = abs(getLuminance(original.rgb) - getLuminance(blurred.rgb));\n float thresholdScale = smoothstep(0.0, threshold, lumaDelta);\n mask *= thresholdScale;\n\n // Sharpen: original + mask * amount\n vec3 sharpened = original.rgb + mask * amount;\n\n fragColor0 = vec4(clamp(sharpened, 0.0, 1.0), original.a);\n}\n", + "#version 300 es\nprecision highp float;\n\nuniform sampler2D u_image0;\nuniform float u_float0; // amount [0.0 - 3.0] typical: 0.5-1.5\nuniform float u_float1; // radius [0.5 - 10.0] blur radius in pixels\nuniform float u_float2; // threshold [0.0 - 0.1] min difference to sharpen\n\nin vec2 v_texCoord;\nlayout(location = 0) out vec4 fragColor0;\n\nfloat gaussian(float x, float sigma) {\n return exp(-(x * x) / (2.0 * sigma * sigma));\n}\n\nfloat getLuminance(vec3 color) {\n return dot(color, vec3(0.2126, 0.7152, 0.0722));\n}\n\nvoid main() {\n vec2 texel = 1.0 / vec2(textureSize(u_image0, 0));\n float radius = max(u_float1, 0.5);\n float amount = u_float0;\n float threshold = u_float2;\n\n vec4 original = texture(u_image0, v_texCoord);\n\n // Gaussian blur for the \"unsharp\" mask\n int samples = int(ceil(radius));\n float sigma = radius / 2.0;\n\n vec4 blurred = vec4(0.0);\n float totalWeight = 0.0;\n\n for (int x = -samples; x <= samples; x++) {\n for (int y = -samples; y <= samples; y++) {\n vec2 offset = vec2(float(x), float(y)) * texel;\n vec4 sample_color = texture(u_image0, v_texCoord + offset);\n\n float dist = length(vec2(float(x), float(y)));\n float weight = gaussian(dist, sigma);\n blurred += sample_color * weight;\n totalWeight += weight;\n }\n }\n blurred /= totalWeight;\n\n // Unsharp mask = original - blurred\n vec3 mask = original.rgb - blurred.rgb;\n\n // Luminance-based threshold with smooth falloff\n float lumaDelta = abs(getLuminance(original.rgb) - getLuminance(blurred.rgb));\n float thresholdScale = smoothstep(0.0, threshold, lumaDelta);\n mask *= thresholdScale;\n\n // Sharpen: original + mask * amount\n vec3 sharpened = original.rgb + mask * amount;\n\n fragColor0 = vec4(clamp(sharpened, 0.0, 1.0), original.a);\n}\n", "from_input" ] } From 2e0503780d8cd4285d2b883ba5ba1ea152eb194e Mon Sep 17 00:00:00 2001 From: Terry Jia Date: Thu, 23 Apr 2026 23:51:34 -0400 Subject: [PATCH 090/140] range type (#13322) Co-authored-by: guill --- comfy_api/input/__init__.py | 2 + comfy_api/latest/_input/__init__.py | 2 + comfy_api/latest/_input/range_types.py | 70 ++++++++++++++++++++++++++ comfy_api/latest/_io.py | 38 ++++++++++++++ 4 files changed, 112 insertions(+) create mode 100644 comfy_api/latest/_input/range_types.py diff --git a/comfy_api/input/__init__.py b/comfy_api/input/__init__.py index 16d4acfd1..dc33533cc 100644 --- a/comfy_api/input/__init__.py +++ b/comfy_api/input/__init__.py @@ -9,6 +9,7 @@ from comfy_api.latest._input import ( CurveInput, MonotoneCubicCurve, LinearCurve, + RangeInput, ) __all__ = [ @@ -21,4 +22,5 @@ __all__ = [ "CurveInput", "MonotoneCubicCurve", "LinearCurve", + "RangeInput", ] diff --git a/comfy_api/latest/_input/__init__.py b/comfy_api/latest/_input/__init__.py index 05cd3d40a..f0229717e 100644 --- a/comfy_api/latest/_input/__init__.py +++ b/comfy_api/latest/_input/__init__.py @@ -1,5 +1,6 @@ from .basic_types import ImageInput, AudioInput, MaskInput, LatentInput from .curve_types import CurvePoint, CurveInput, MonotoneCubicCurve, LinearCurve +from .range_types import RangeInput from .video_types import VideoInput __all__ = [ @@ -12,4 +13,5 @@ __all__ = [ "CurveInput", "MonotoneCubicCurve", "LinearCurve", + "RangeInput", ] diff --git a/comfy_api/latest/_input/range_types.py b/comfy_api/latest/_input/range_types.py new file mode 100644 index 000000000..f4c5cb290 --- /dev/null +++ b/comfy_api/latest/_input/range_types.py @@ -0,0 +1,70 @@ +from __future__ import annotations + +import logging +import math +import numpy as np + +logger = logging.getLogger(__name__) + + +class RangeInput: + """Represents a levels/range adjustment: input range [min, max] with + optional midpoint (gamma control). + + Generates a 1D LUT identical to GIMP's levels mapping: + 1. Normalize input to [0, 1] using [min, max] + 2. Apply gamma correction: pow(value, 1/gamma) + 3. Clamp to [0, 1] + + The midpoint field is a position in [0, 1] representing where the + midtone falls within [min, max]. It maps to gamma via: + gamma = -log2(midpoint) + So midpoint=0.5 → gamma=1.0 (linear). + """ + + def __init__(self, min_val: float, max_val: float, midpoint: float | None = None): + self.min_val = min_val + self.max_val = max_val + self.midpoint = midpoint + + @staticmethod + def from_raw(data) -> RangeInput: + if isinstance(data, RangeInput): + return data + if isinstance(data, dict): + return RangeInput( + min_val=float(data.get("min", 0.0)), + max_val=float(data.get("max", 1.0)), + midpoint=float(data["midpoint"]) if data.get("midpoint") is not None else None, + ) + raise TypeError(f"Cannot convert {type(data)} to RangeInput") + + def to_lut(self, size: int = 256) -> np.ndarray: + """Generate a float64 lookup table mapping [0, 1] input through this + levels adjustment. + + The LUT maps normalized input values (0..1) to output values (0..1), + matching the GIMP levels formula. + """ + xs = np.linspace(0.0, 1.0, size, dtype=np.float64) + + in_range = self.max_val - self.min_val + if abs(in_range) < 1e-10: + return np.where(xs >= self.min_val, 1.0, 0.0).astype(np.float64) + + # Normalize: map [min, max] → [0, 1] + result = (xs - self.min_val) / in_range + result = np.clip(result, 0.0, 1.0) + + # Gamma correction from midpoint + if self.midpoint is not None and self.midpoint > 0 and self.midpoint != 0.5: + gamma = max(-math.log2(self.midpoint), 0.001) + inv_gamma = 1.0 / gamma + mask = result > 0 + result[mask] = np.power(result[mask], inv_gamma) + + return result + + def __repr__(self) -> str: + mid = f", midpoint={self.midpoint}" if self.midpoint is not None else "" + return f"RangeInput(min={self.min_val}, max={self.max_val}{mid})" diff --git a/comfy_api/latest/_io.py b/comfy_api/latest/_io.py index fdeffea2d..4942ed46c 100644 --- a/comfy_api/latest/_io.py +++ b/comfy_api/latest/_io.py @@ -1266,6 +1266,43 @@ class Histogram(ComfyTypeIO): Type = list[int] +@comfytype(io_type="RANGE") +class Range(ComfyTypeIO): + from comfy_api.input import RangeInput + if TYPE_CHECKING: + Type = RangeInput + + class Input(WidgetInput): + def __init__(self, id: str, display_name: str=None, optional=False, tooltip: str=None, + socketless: bool=True, default: dict=None, + display: str=None, + gradient_stops: list=None, + show_midpoint: bool=None, + midpoint_scale: str=None, + value_min: float=None, + value_max: float=None, + advanced: bool=None): + super().__init__(id, display_name, optional, tooltip, None, default, socketless, None, None, None, None, advanced) + if default is None: + self.default = {"min": 0.0, "max": 1.0} + self.display = display + self.gradient_stops = gradient_stops + self.show_midpoint = show_midpoint + self.midpoint_scale = midpoint_scale + self.value_min = value_min + self.value_max = value_max + + def as_dict(self): + return super().as_dict() | prune_dict({ + "display": self.display, + "gradient_stops": self.gradient_stops, + "show_midpoint": self.show_midpoint, + "midpoint_scale": self.midpoint_scale, + "value_min": self.value_min, + "value_max": self.value_max, + }) + + DYNAMIC_INPUT_LOOKUP: dict[str, Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]] = {} def register_dynamic_input_func(io_type: str, func: Callable[[dict[str, Any], dict[str, Any], tuple[str, dict[str, Any]], str, list[str] | None], None]): DYNAMIC_INPUT_LOOKUP[io_type] = func @@ -2276,5 +2313,6 @@ __all__ = [ "BoundingBox", "Curve", "Histogram", + "Range", "NodeReplace", ] From 443074eee92fb0f41b38b83404010069fdb25860 Mon Sep 17 00:00:00 2001 From: Matt Miller Date: Thu, 23 Apr 2026 21:00:25 -0700 Subject: [PATCH 091/140] Add OpenAPI 3.1 specification for ComfyUI API (#13397) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add OpenAPI 3.1 specification for ComfyUI API Adds a comprehensive OpenAPI 3.1 spec documenting all HTTP endpoints exposed by ComfyUI's server, including prompt execution, queue management, file uploads, userdata, settings, system stats, object info, assets, and internal routes. The spec was validated against the source code with adversarial review from multiple models, and passes Spectral linting with zero errors. Also removes openapi.yaml from .gitignore so the spec is tracked. * Mark /api/history endpoints as deprecated Address Jacob's review feedback on PR #13397 by explicitly marking the three /api/history operations as deprecated in the OpenAPI spec: * GET /api/history -> superseded by GET /api/jobs * POST /api/history -> superseded by /api/jobs management * GET /api/history/{prompt_id} -> superseded by GET /api/jobs/{job_id} Each operation gains deprecated: true plus a description that names the replacement. A formal sunset timeline (RFC 8594 Deprecation and RFC 8553 Sunset headers, minimum-runway policy) is being defined separately and will be applied as a follow-up. * Address Spectral lint findings in openapi.yaml - Add operation descriptions to 52 endpoints (prompt, queue, upload, view, models, userdata, settings, assets, internal, etc.) - Add schema descriptions to 22 component schemas - Add parameter descriptions to 8 path parameters that were missing them - Remove 6 unused component schemas: TaskOutput, EmbeddingsResponse, ExtensionsResponse, LogRawResponse, UserInfo, UserDataFullInfo No wire/shape changes. Reduces Spectral findings from 92 to 4. The remaining 4 are real issues (WebSocket 101 on /ws, loose error schema, and two snake_case warnings on real wire field names) and are worth addressing separately. * fix(openapi): address jtreminio oneOf review on /api/userdata Restructure the UserData response schemas to address the review feedback on the `oneOf` without a discriminator, and fix two accuracy bugs found while doing it. Changes - GET /api/userdata response: extract the inline `oneOf` to a named schema (`ListUserdataResponse`) and add the missing third variant returned when `split=true` and `full_info=false` (array of `[relative_path, ...path_components]`). Previously only two of the three actual server response shapes were described. - UserDataResponse (POST endpoints): correct the description — this schema is a single item, not a list — and point at the canonical `GetUserDataResponseFullFile` schema instead of the duplicate `UserDataResponseFull`. Also removes the malformed blank line in `UserDataResponseShort`. - Delete the now-unused `UserDataResponseFull` and `UserDataResponseShort` schemas (replaced by reuse of `GetUserDataResponseFullFile` and an inline string variant). - Add an `x-variant-selector` vendor extension to both `oneOf` sites documenting which query-parameter combination selects which branch, since a true OpenAPI `discriminator` is not applicable (the variants are type-disjoint and the selector lives in the request, not the response body). This keeps the shapes the server actually emits (no wire-breaking change) while making the selection rule explicit for SDK generators and readers. --------- Co-authored-by: guill --- .gitignore | 1 - openapi.yaml | 3231 ++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 3231 insertions(+), 1 deletion(-) create mode 100644 openapi.yaml diff --git a/.gitignore b/.gitignore index 2700ad5c2..0ab4ba75e 100644 --- a/.gitignore +++ b/.gitignore @@ -21,6 +21,5 @@ venv*/ *.log web_custom_versions/ .DS_Store -openapi.yaml filtered-openapi.yaml uv.lock diff --git a/openapi.yaml b/openapi.yaml new file mode 100644 index 000000000..77d0e2318 --- /dev/null +++ b/openapi.yaml @@ -0,0 +1,3231 @@ +openapi: 3.1.0 +info: + title: ComfyUI API + description: | + API for ComfyUI - A powerful and modular stable diffusion GUI and backend. + + This API allows you to interact with ComfyUI programmatically, including: + - Submitting and managing workflow executions + - Querying node/object information + - Uploading and viewing files + - Managing user settings and data + - Asset management (feature-gated) + + ## Dual-path routing + Every route registered via `self.routes` in the ComfyUI server is available at + both its bare path (e.g. `/prompt`) and an `/api`-prefixed path (e.g. `/api/prompt`). + This spec uses the `/api`-prefixed versions as canonical. + + ## Multi-user mode + When ComfyUI is started with `--multi-user`, the `Comfy-User` header identifies + the active user for settings, userdata, and history isolation. This is **not** a + security mechanism — it is an organisational convenience with no authentication + or authorisation behind it. + version: 1.0.0 + license: + name: GNU General Public License v3.0 + url: https://github.com/comfyanonymous/ComfyUI/blob/master/LICENSE + +servers: + - url: / + description: Default ComfyUI server (typically http://127.0.0.1:8188) + +tags: + - name: prompt + description: Workflow submission and prompt info + - name: queue + description: Queue inspection and management + - name: history + description: Execution history + - name: upload + description: File upload endpoints + - name: view + description: File viewing / download + - name: system + description: System stats and feature flags + - name: node + description: Node / object_info definitions + - name: model + description: Model folder and file listing + - name: user + description: User management (multi-user mode) + - name: userdata + description: Per-user file storage + - name: settings + description: Per-user settings + - name: extensions + description: Frontend extension JS files + - name: subgraph + description: Global subgraph blueprints + - name: internal + description: Internal / debug endpoints + - name: assets + description: Asset management (feature-gated behind enable-assets) + +paths: + # --------------------------------------------------------------------------- + # WebSocket + # --------------------------------------------------------------------------- + /ws: + get: + operationId: connectWebSocket + tags: [system] + summary: WebSocket connection for real-time updates + description: | + Upgrades to a WebSocket connection that streams execution progress, + node status, and output messages. The server sends an initial `status` + message with the session ID (SID) on connect. + + ## Message types (server → client) + The server sends JSON messages with a `type` field. See the + `x-websocket-messages` list below for the schema of each message type. + parameters: + - name: clientId + in: query + required: false + schema: + type: string + description: Client identifier. If omitted the server assigns one. + responses: + "101": + description: WebSocket upgrade successful + x-websocket-messages: + - type: status + schema: + $ref: "#/components/schemas/StatusWsMessage" + - type: progress + schema: + $ref: "#/components/schemas/ProgressWsMessage" + - type: progress_text + schema: + $ref: "#/components/schemas/ProgressTextWsMessage" + - type: progress_state + schema: + $ref: "#/components/schemas/ProgressStateWsMessage" + - type: executing + schema: + $ref: "#/components/schemas/ExecutingWsMessage" + - type: executed + schema: + $ref: "#/components/schemas/ExecutedWsMessage" + - type: execution_start + schema: + $ref: "#/components/schemas/ExecutionStartWsMessage" + - type: execution_success + schema: + $ref: "#/components/schemas/ExecutionSuccessWsMessage" + - type: execution_cached + schema: + $ref: "#/components/schemas/ExecutionCachedWsMessage" + - type: execution_interrupted + schema: + $ref: "#/components/schemas/ExecutionInterruptedWsMessage" + - type: execution_error + schema: + $ref: "#/components/schemas/ExecutionErrorWsMessage" + - type: logs + schema: + $ref: "#/components/schemas/LogsWsMessage" + - type: notification + schema: + $ref: "#/components/schemas/NotificationWsMessage" + - type: feature_flags + schema: + $ref: "#/components/schemas/FeatureFlagsWsMessage" + - type: asset_download + schema: + $ref: "#/components/schemas/AssetDownloadWsMessage" + - type: asset_export + schema: + $ref: "#/components/schemas/AssetExportWsMessage" + + # --------------------------------------------------------------------------- + # Prompt + # --------------------------------------------------------------------------- + /api/prompt: + get: + operationId: getPromptInfo + tags: [prompt] + summary: Get queue status + description: Returns how many items remain in the execution queue. + responses: + "200": + description: Queue info + content: + application/json: + schema: + $ref: "#/components/schemas/PromptInfo" + post: + operationId: executePrompt + tags: [prompt] + summary: Submit a workflow for execution + description: Submits a workflow for execution. The server validates the graph, assigns a `prompt_id`, and enqueues it. Clients listen on `/ws` for execution progress and output messages. + requestBody: + required: true + content: + application/json: + schema: + $ref: "#/components/schemas/PromptRequest" + responses: + "200": + description: Prompt accepted + content: + application/json: + schema: + $ref: "#/components/schemas/PromptResponse" + "400": + description: Validation or node errors + content: + application/json: + schema: + $ref: "#/components/schemas/PromptErrorResponse" + + # --------------------------------------------------------------------------- + # Queue + # --------------------------------------------------------------------------- + /api/queue: + get: + operationId: getQueue + tags: [queue] + summary: Get running and pending queue items + description: Returns the server's current execution queue, split into the currently-running prompt and the list of pending prompts. + responses: + "200": + description: Queue contents + content: + application/json: + schema: + $ref: "#/components/schemas/QueueInfo" + post: + operationId: manageQueue + tags: [queue] + summary: Clear or delete items from the queue + description: Mutates the execution queue. Supports clearing all queued prompts or deleting individual prompts by ID. + requestBody: + required: true + content: + application/json: + schema: + $ref: "#/components/schemas/QueueManageRequest" + responses: + "200": + description: Queue updated + + /api/interrupt: + post: + operationId: interruptExecution + tags: [queue] + summary: Interrupt current execution + description: Interrupts the prompt that is currently executing. The next queued prompt (if any) will start immediately after. + requestBody: + required: false + content: + application/json: + schema: + type: object + properties: + prompt_id: + type: string + format: uuid + description: "If provided, only interrupts this specific running prompt. Otherwise interrupts all." + responses: + "200": + description: Interrupt signal sent + + /api/free: + post: + operationId: freeMemory + tags: [queue] + summary: Free GPU memory and/or unload models + description: Frees GPU memory by unloading models and/or freeing the resident model cache, controlled by the request flags. + requestBody: + required: false + content: + application/json: + schema: + type: object + properties: + unload_models: + type: boolean + description: Unload all models from VRAM/RAM + free_memory: + type: boolean + description: Run garbage collection and free cached memory + responses: + "200": + description: Memory freed + + # --------------------------------------------------------------------------- + # Jobs + # --------------------------------------------------------------------------- + /api/jobs: + get: + operationId: listJobs + tags: [queue] + summary: List jobs with filtering and pagination + description: Returns a paginated list of completed prompt executions, newest first. + parameters: + - name: status + in: query + schema: + type: string + description: Filter by job status + - name: workflow_id + in: query + schema: + type: string + description: Filter by workflow ID + - name: sort_by + in: query + schema: + type: string + description: Field to sort by + - name: sort_order + in: query + schema: + type: string + enum: [asc, desc] + description: Sort direction + - name: limit + in: query + schema: + type: integer + description: Maximum number of results (default is unlimited/None) + - name: offset + in: query + schema: + type: integer + default: 0 + description: Pagination offset + responses: + "200": + description: Jobs list + content: + application/json: + schema: + type: object + properties: + jobs: + type: array + items: + $ref: "#/components/schemas/JobEntry" + pagination: + $ref: "#/components/schemas/PaginationInfo" + + /api/jobs/{job_id}: + get: + operationId: getJob + tags: [queue] + summary: Get a single job by ID + description: Returns the full record for a single completed prompt execution, including its outputs, status, and metadata. + parameters: + - name: job_id + in: path + description: The job (prompt) ID to fetch. + required: true + schema: + type: string + format: uuid + responses: + "200": + description: Job detail + content: + application/json: + schema: + $ref: "#/components/schemas/JobDetailResponse" + "404": + description: Job not found + + # --------------------------------------------------------------------------- + # History + # --------------------------------------------------------------------------- + /api/history: + get: + operationId: getHistory + tags: [history] + summary: Get execution history + deprecated: true + description: | + **Deprecated.** Superseded by `GET /api/jobs`, which returns the same + execution records in a paginated, filterable format. Planned for removal + no earlier than a future major release; sunset timeline TBD. + + Returns a dictionary keyed by prompt_id. Each value is a HistoryEntry + containing prompt metadata, outputs, status, and node meta. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + - name: max_items + in: query + schema: + type: integer + description: Maximum number of history entries to return + - name: offset + in: query + schema: + type: integer + description: Pagination offset (number of entries to skip) + responses: + "200": + description: History dictionary keyed by prompt_id + content: + application/json: + schema: + type: object + additionalProperties: + $ref: "#/components/schemas/HistoryEntry" + post: + operationId: manageHistory + tags: [history] + summary: Clear or delete history entries + deprecated: true + description: | + **Deprecated.** Superseded by the forthcoming job-management endpoints + under `/api/jobs`. Planned for removal no earlier than a future major + release; sunset timeline TBD. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + requestBody: + required: true + content: + application/json: + schema: + $ref: "#/components/schemas/HistoryManageRequest" + responses: + "200": + description: History updated + + /api/history/{prompt_id}: + get: + operationId: getHistoryByPromptId + tags: [history] + summary: Get history for a specific prompt + deprecated: true + description: | + **Deprecated.** Superseded by `GET /api/jobs/{job_id}`, which returns + the same execution record. Planned for removal no earlier than a future + major release; sunset timeline TBD. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + - name: prompt_id + in: path + description: The prompt ID to fetch history for. + required: true + schema: + type: string + format: uuid + responses: + "200": + description: Single-entry history dictionary. Returns an empty object `{}` if the prompt_id is not found. + content: + application/json: + schema: + type: object + additionalProperties: + $ref: "#/components/schemas/HistoryEntry" + + # --------------------------------------------------------------------------- + # Upload + # --------------------------------------------------------------------------- + /api/upload/image: + post: + operationId: uploadImage + tags: [upload] + summary: Upload an image file + description: Uploads an image file into one of the input/output/temp directories so it can be referenced by workflow nodes. + requestBody: + required: true + content: + multipart/form-data: + schema: + type: object + required: + - image + properties: + image: + type: string + format: binary + description: Image file to upload + type: + type: string + enum: [input, temp, output] + default: input + description: Target directory type + overwrite: + type: string + description: 'Set to "true" to overwrite existing files' + subfolder: + type: string + description: Subfolder within the target directory + responses: + "200": + description: Upload result + content: + application/json: + schema: + $ref: "#/components/schemas/UploadResult" + "400": + description: No file provided or invalid request + + /api/upload/mask: + post: + operationId: uploadMask + tags: [upload] + summary: Upload a mask image + description: Uploads a mask image associated with a previously-uploaded reference image. + requestBody: + required: true + content: + multipart/form-data: + schema: + type: object + required: + - image + - original_ref + properties: + image: + type: string + format: binary + description: Mask image (alpha channel is used) + original_ref: + type: object + description: Reference to the original image file + required: + - filename + properties: + filename: + type: string + description: Filename of the original image + additionalProperties: true + type: + type: string + enum: [input, temp, output] + default: input + description: Target directory type + overwrite: + type: string + description: 'Set to "true" to overwrite existing files' + subfolder: + type: string + description: Subfolder within the target directory + responses: + "200": + description: Upload result + content: + application/json: + schema: + $ref: "#/components/schemas/UploadResult" + "400": + description: No file provided or invalid request + + # --------------------------------------------------------------------------- + # View + # --------------------------------------------------------------------------- + /api/view: + get: + operationId: viewFile + tags: [view] + summary: View or download a file + description: Serves a file (image, audio, or video) from the input/output/temp directory identified by the query parameters. + parameters: + - name: filename + in: query + required: true + schema: + type: string + description: Name of the file to view + - name: type + in: query + schema: + type: string + enum: [input, output, temp] + default: output + description: Directory type + - name: subfolder + in: query + schema: + type: string + description: Subfolder within the directory + - name: preview + in: query + schema: + type: string + description: Preview format hint (e.g. "webp;90") + - name: channel + in: query + schema: + type: string + enum: [rgba, rgb, a] + description: Channel extraction mode + responses: + "200": + description: File content + content: + image/*: + schema: + type: string + format: binary + video/*: + schema: + type: string + format: binary + audio/*: + schema: + type: string + format: binary + application/octet-stream: + schema: + type: string + format: binary + "404": + description: File not found + + /api/view_metadata/{folder_name}: + get: + operationId: viewMetadata + tags: [view] + summary: Get metadata for a file (e.g. safetensors header) + description: Returns embedded metadata parsed from a file in the given folder — for example, the header of a safetensors model. + parameters: + - name: folder_name + in: path + required: true + schema: + type: string + description: Folder type (output, input, temp, etc.) + - name: filename + in: query + required: true + schema: + type: string + description: Filename to read metadata from + responses: + "200": + description: File metadata + content: + application/json: + schema: + type: object + additionalProperties: true + "404": + description: File or metadata not found + + # --------------------------------------------------------------------------- + # System + # --------------------------------------------------------------------------- + /api/system_stats: + get: + operationId: getSystemStats + tags: [system] + summary: Get system statistics + description: Returns hardware, Python, VRAM, and runtime statistics for the running ComfyUI process. + responses: + "200": + description: System stats + content: + application/json: + schema: + $ref: "#/components/schemas/SystemStatsResponse" + + /api/features: + get: + operationId: getFeatures + tags: [system] + summary: Get enabled feature flags + description: Returns a dictionary of feature flag names to their enabled state. + responses: + "200": + description: Feature flags + content: + application/json: + schema: + type: object + additionalProperties: + type: boolean + + # --------------------------------------------------------------------------- + # Node / Object Info + # --------------------------------------------------------------------------- + /api/object_info: + get: + operationId: getObjectInfo + tags: [node] + summary: Get all node definitions + description: | + Returns a dictionary of every registered node class, keyed by class name. + Each value is a NodeInfo object describing inputs, outputs, category, etc. + responses: + "200": + description: All node definitions + content: + application/json: + schema: + type: object + additionalProperties: + $ref: "#/components/schemas/NodeInfo" + + /api/object_info/{node_class}: + get: + operationId: getObjectInfoByClass + tags: [node] + summary: Get a single node definition + description: Returns the `NodeInfo` definition for a single registered node class. + parameters: + - name: node_class + in: path + required: true + schema: + type: string + description: Node class name (e.g. "KSampler") + responses: + "200": + description: Single node definition + content: + application/json: + schema: + type: object + additionalProperties: + $ref: "#/components/schemas/NodeInfo" + "404": + description: Node class not found + + /api/embeddings: + get: + operationId: getEmbeddings + tags: [node] + summary: List available embedding names + description: Returns the list of text-encoder embeddings available on disk. + responses: + "200": + description: Embedding names + content: + application/json: + schema: + type: array + items: + type: string + + # --------------------------------------------------------------------------- + # Models + # --------------------------------------------------------------------------- + /api/models: + get: + operationId: getModelTypes + tags: [model] + summary: List model folder type names + description: Returns an array of model type names (e.g. checkpoints, loras, vae). + responses: + "200": + description: Model type names + content: + application/json: + schema: + type: array + items: + type: string + + /api/models/{folder}: + get: + operationId: getModelsByFolder + tags: [model] + summary: List model filenames in a folder + description: Returns the names of model files in the given folder. This endpoint predates `/api/experiment/models/{folder}` and returns names only — prefer the experiment endpoint for new integrations. + parameters: + - name: folder + in: path + required: true + schema: + type: string + description: Model folder type name + responses: + "200": + description: Model filenames + content: + application/json: + schema: + type: array + items: + type: string + "404": + description: Unknown folder type + + /api/experiment/models: + get: + operationId: getExperimentModels + tags: [model] + summary: List model folders with paths + description: Returns an array of model folder objects with name and folder paths. + responses: + "200": + description: Model folders + content: + application/json: + schema: + type: array + items: + $ref: "#/components/schemas/ModelFolder" + + /api/experiment/models/{folder}: + get: + operationId: getExperimentModelsByFolder + tags: [model] + summary: List model files with metadata + description: Returns the model files in the given folder with richer metadata (path index, mtime, size) than the legacy `/api/models/{folder}` endpoint. + parameters: + - name: folder + in: path + required: true + schema: + type: string + description: Model folder type name + responses: + "200": + description: Model files with metadata + content: + application/json: + schema: + type: array + items: + $ref: "#/components/schemas/ModelFile" + "404": + description: Unknown folder type + + /api/experiment/models/preview/{folder}/{path_index}/{filename}: + get: + operationId: getModelPreview + tags: [model] + summary: Get model preview image + description: Returns the preview image associated with a model file, if one exists alongside the model on disk. + parameters: + - name: folder + in: path + required: true + schema: + type: string + description: Model folder type name + - name: path_index + in: path + required: true + schema: + type: integer + description: Path index within the folder + - name: filename + in: path + required: true + schema: + type: string + description: Model filename + responses: + "200": + description: Preview image (WebP) + content: + image/webp: + schema: + type: string + format: binary + "404": + description: Preview not found + + # --------------------------------------------------------------------------- + # Users + # --------------------------------------------------------------------------- + /api/users: + get: + operationId: getUsers + tags: [user] + summary: Get user storage info + description: | + Returns user storage configuration. In single-user mode returns + `{"storage": "server", "migrated": true/false}`. In multi-user mode + returns `{"storage": "server", "users": {"user_id": "user_dir", ...}}`. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + responses: + "200": + description: User info + content: + application/json: + schema: + type: object + properties: + storage: + type: string + description: Storage backend type (always "server") + migrated: + type: boolean + description: Whether migration from browser storage is complete (single-user) + users: + type: object + additionalProperties: + type: string + description: Map of user_id to directory name (multi-user) + post: + operationId: createUser + tags: [user] + summary: Create a new user (multi-user mode) + description: Creates a new user entry. Only meaningful when ComfyUI is running in multi-user mode. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + requestBody: + required: true + content: + application/json: + schema: + type: object + required: + - username + properties: + username: + type: string + description: Username for the new user + responses: + "200": + description: Created user ID + content: + application/json: + schema: + type: string + description: The generated user_id + "400": + description: Username already exists or invalid + + # --------------------------------------------------------------------------- + # Userdata + # --------------------------------------------------------------------------- + /api/userdata: + get: + operationId: listUserdata + tags: [userdata] + summary: List files in a userdata directory + description: Lists files in the authenticated user's data directory. Returns either filename strings or full objects depending on the `full_info` query parameter. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + - name: dir + in: query + required: true + schema: + type: string + description: Directory path relative to the user's data folder + - name: recurse + in: query + schema: + type: boolean + description: Recurse into subdirectories + - name: full_info + in: query + schema: + type: boolean + description: Return full file info objects instead of just names + - name: split + in: query + schema: + type: boolean + description: Split paths into directory components + responses: + "200": + description: File listing + content: + application/json: + schema: + $ref: "#/components/schemas/ListUserdataResponse" + "404": + description: Directory not found + + /api/v2/userdata: + get: + operationId: listUserdataV2 + tags: [userdata] + summary: List files in userdata (v2 format) + description: Lists files in the authenticated user's data directory using the v2 response shape, which always returns full objects. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + - name: path + in: query + schema: + type: string + description: Directory path relative to user data root + responses: + "200": + description: File listing with metadata + content: + application/json: + schema: + type: array + items: + type: object + properties: + name: + type: string + path: + type: string + type: + type: string + enum: [file, directory] + size: + type: integer + modified: + type: number + description: Unix timestamp + + /api/userdata/{file}: + get: + operationId: getUserdataFile + tags: [userdata] + summary: Read a userdata file + description: Reads the contents of a file from the authenticated user's data directory. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + - name: file + in: path + required: true + schema: + type: string + description: File path relative to user data directory + responses: + "200": + description: File content + content: + application/octet-stream: + schema: + type: string + format: binary + "404": + description: File not found + post: + operationId: writeUserdataFile + tags: [userdata] + summary: Write or create a userdata file + description: Writes (creates or replaces) a file in the authenticated user's data directory. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + - name: file + in: path + required: true + schema: + type: string + description: File path relative to user data directory + - name: overwrite + in: query + schema: + type: boolean + description: Allow overwriting existing files + - name: full_info + in: query + schema: + type: boolean + description: Return full file info in response + requestBody: + required: true + content: + application/octet-stream: + schema: + type: string + format: binary + application/json: + schema: {} + responses: + "200": + description: File written + content: + application/json: + schema: + $ref: "#/components/schemas/UserDataResponse" + "409": + description: File exists and overwrite not set + delete: + operationId: deleteUserdataFile + tags: [userdata] + summary: Delete a userdata file + description: Deletes a file from the authenticated user's data directory. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + - name: file + in: path + required: true + schema: + type: string + description: File path relative to user data directory + responses: + "204": + description: File deleted + "404": + description: File not found + + /api/userdata/{file}/move/{dest}: + post: + operationId: moveUserdataFile + tags: [userdata] + summary: Move or rename a userdata file + description: Renames or moves a file within the authenticated user's data directory. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + - name: file + in: path + required: true + schema: + type: string + description: Source file path + - name: dest + in: path + required: true + schema: + type: string + description: Destination file path + - name: overwrite + in: query + schema: + type: boolean + description: Allow overwriting at destination + - name: full_info + in: query + schema: + type: boolean + description: Return full file info in response + responses: + "200": + description: File moved + content: + application/json: + schema: + $ref: "#/components/schemas/UserDataResponse" + "404": + description: Source file not found + "409": + description: Destination exists and overwrite not set + + # --------------------------------------------------------------------------- + # Settings + # --------------------------------------------------------------------------- + /api/settings: + get: + operationId: getSettings + tags: [settings] + summary: Get all user settings + description: Returns all settings for the authenticated user. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + responses: + "200": + description: Settings object + content: + application/json: + schema: + type: object + additionalProperties: true + post: + operationId: updateSettings + tags: [settings] + summary: Update user settings (partial merge) + description: Replaces the authenticated user's settings with the provided object. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + requestBody: + required: true + content: + application/json: + schema: + type: object + additionalProperties: true + description: Partial settings to merge + responses: + "200": + description: Settings updated + + /api/settings/{id}: + get: + operationId: getSetting + tags: [settings] + summary: Get a single setting by key + description: Returns the value of a single setting, identified by key. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + - name: id + in: path + required: true + schema: + type: string + description: Setting key + responses: + "200": + description: Setting value (null if the setting does not exist) + content: + application/json: + schema: + nullable: true + description: The setting value (any JSON type), or null if not set + post: + operationId: updateSetting + tags: [settings] + summary: Set a single setting value + description: Sets the value of a single setting, identified by key. + parameters: + - $ref: "#/components/parameters/ComfyUserHeader" + - name: id + in: path + required: true + schema: + type: string + description: Setting key + requestBody: + required: true + content: + application/json: + schema: + description: The setting value (any JSON type) + responses: + "200": + description: Setting updated + + # --------------------------------------------------------------------------- + # Extensions / Templates / i18n + # --------------------------------------------------------------------------- + /api/extensions: + get: + operationId: getExtensions + tags: [extensions] + summary: List frontend extension JS file paths + description: Returns the list of frontend extension JS URLs registered by custom nodes, to be loaded by the frontend on startup. + responses: + "200": + description: Array of JS file paths + content: + application/json: + schema: + type: array + items: + type: string + description: Relative path to extension JS file + + /api/workflow_templates: + get: + operationId: getWorkflowTemplates + tags: [extensions] + summary: Get workflow template mappings + description: Returns a map of custom node names to their provided workflow template names. + responses: + "200": + description: Template mappings + content: + application/json: + schema: + type: object + additionalProperties: + type: array + items: + type: string + description: Map of node pack name to array of template names + + /api/i18n: + get: + operationId: getI18n + tags: [extensions] + summary: Get internationalisation translation strings + description: Returns the URLs of translation files contributed by custom nodes, keyed by locale. + responses: + "200": + description: Translation map + content: + application/json: + schema: + type: object + additionalProperties: true + description: Nested map of locale to translation key-value pairs + + # --------------------------------------------------------------------------- + # Subgraphs + # --------------------------------------------------------------------------- + /api/global_subgraphs: + get: + operationId: getGlobalSubgraphs + tags: [subgraph] + summary: List global subgraph blueprints + description: Returns a dictionary of subgraph IDs to their metadata. + responses: + "200": + description: Subgraph metadata dictionary + content: + application/json: + schema: + type: object + additionalProperties: + $ref: "#/components/schemas/GlobalSubgraphInfo" + + /api/global_subgraphs/{id}: + get: + operationId: getGlobalSubgraph + tags: [subgraph] + summary: Get a global subgraph with full data + description: Returns the blueprint for a globally-registered subgraph, used by the frontend to materialize the subgraph node. + parameters: + - name: id + in: path + required: true + schema: + type: string + description: Subgraph identifier + responses: + "200": + description: Full subgraph data + content: + application/json: + schema: + $ref: "#/components/schemas/GlobalSubgraphData" + "404": + description: Subgraph not found + + # --------------------------------------------------------------------------- + # Node Replacements + # --------------------------------------------------------------------------- + /api/node_replacements: + get: + operationId: getNodeReplacements + tags: [node] + summary: Get node replacement mappings + description: | + Returns a dictionary mapping deprecated or replaced node class names + to their replacement node information. + responses: + "200": + description: Replacement mappings + content: + application/json: + schema: + type: object + additionalProperties: true + + # --------------------------------------------------------------------------- + # Internal (x-internal: true) + # --------------------------------------------------------------------------- + /internal/logs: + get: + operationId: getInternalLogs + tags: [internal] + summary: Get server logs as text + description: Returns structured ComfyUI log entries from the in-memory log buffer. + x-internal: true + responses: + "200": + description: Log text + content: + text/plain: + schema: + type: string + + /internal/logs/raw: + get: + operationId: getInternalLogsRaw + tags: [internal] + summary: Get raw structured log entries + description: Returns the raw ComfyUI log buffer as text, together with metadata about the current size limit. + x-internal: true + responses: + "200": + description: Structured log data + content: + application/json: + schema: + type: object + properties: + entries: + type: array + items: + type: object + properties: + t: + type: number + description: Timestamp + m: + type: string + description: Message + size: + type: object + properties: + cols: + type: integer + rows: + type: integer + + /internal/logs/subscribe: + patch: + operationId: subscribeToLogs + tags: [internal] + summary: Subscribe or unsubscribe a WebSocket client to log streaming + description: Subscribes or unsubscribes the current client from live log streaming over the WebSocket. + x-internal: true + requestBody: + required: true + content: + application/json: + schema: + type: object + required: + - clientId + - enabled + properties: + clientId: + type: string + description: WebSocket client ID + enabled: + type: boolean + description: Enable or disable log streaming for this client + responses: + "200": + description: Subscription updated + + /internal/folder_paths: + get: + operationId: getInternalFolderPaths + tags: [internal] + summary: Get configured folder paths + description: Returns the filesystem paths ComfyUI is configured to load models and other assets from, keyed by folder type. + x-internal: true + responses: + "200": + description: Dictionary of folder type to paths + content: + application/json: + schema: + type: object + additionalProperties: + type: array + items: + type: array + items: + type: string + description: Map of folder type name to list of [path, ...] entries + + /internal/files/{directory_type}: + get: + operationId: getInternalFiles + tags: [internal] + summary: List files in a directory type + description: Lists the files present in one of ComfyUI's known directories (input, output, or temp). + x-internal: true + parameters: + - name: directory_type + in: path + required: true + schema: + type: string + description: Directory type (e.g. output, input, temp) + responses: + "200": + description: Array of filenames + content: + application/json: + schema: + type: array + items: + type: string + + # --------------------------------------------------------------------------- + # Assets (x-feature-gate: enable-assets) + # --------------------------------------------------------------------------- + /api/assets/hash/{hash}: + head: + operationId: checkAssetByHash + tags: [assets] + summary: Check if an asset with the given hash exists + description: Returns 204 if an asset with the given content hash already exists, 404 otherwise. Used by clients to deduplicate uploads before transferring bytes. + x-feature-gate: enable-assets + parameters: + - name: hash + in: path + required: true + schema: + type: string + description: "Blake3 hash of the asset (e.g. blake3:abc123...)" + responses: + "200": + description: Asset exists + "404": + description: No asset with this hash + + /api/assets: + get: + operationId: listAssets + tags: [assets] + summary: List assets with filtering and pagination + description: Returns a paginated list of assets, optionally filtered by tags, name, or other query parameters. + x-feature-gate: enable-assets + parameters: + - name: limit + in: query + schema: + type: integer + default: 50 + - name: offset + in: query + schema: + type: integer + default: 0 + - name: include_tags + in: query + schema: + type: array + items: + type: string + style: form + explode: true + description: Tags that assets must have (AND logic) + - name: exclude_tags + in: query + schema: + type: array + items: + type: string + style: form + explode: true + description: Tags that assets must not have + - name: name_contains + in: query + schema: + type: string + description: Filter assets whose name contains this substring + - name: metadata_filter + in: query + schema: + type: string + description: JSON-encoded metadata key/value filter + - name: sort + in: query + schema: + type: string + description: Field to sort by + - name: order + in: query + schema: + type: string + enum: [asc, desc] + description: Sort direction + responses: + "200": + description: Asset list + content: + application/json: + schema: + $ref: "#/components/schemas/ListAssetsResponse" + post: + operationId: createAsset + tags: [assets] + summary: Upload a new asset + description: Uploads a new asset (binary content plus metadata) and registers it in the asset database. + x-feature-gate: enable-assets + requestBody: + required: true + content: + multipart/form-data: + schema: + type: object + required: + - file + properties: + file: + type: string + format: binary + description: Asset file to upload + name: + type: string + description: Display name for the asset + tags: + type: string + description: Comma-separated tags + user_metadata: + type: string + description: JSON-encoded user metadata + hash: + type: string + description: "Blake3 hash of the file content (e.g. blake3:abc123...)" + mime_type: + type: string + description: MIME type of the file (overrides auto-detected type) + preview_id: + type: string + format: uuid + description: ID of an existing asset to use as the preview image + responses: + "201": + description: Asset created + content: + application/json: + schema: + $ref: "#/components/schemas/AssetCreated" + + /api/assets/from-hash: + post: + operationId: createAssetFromHash + tags: [assets] + summary: Create an asset reference from an existing hash + description: Registers a new asset that references existing content by hash, without re-uploading the bytes. + x-feature-gate: enable-assets + requestBody: + required: true + content: + application/json: + schema: + type: object + required: + - hash + - name + properties: + hash: + type: string + description: Blake3 hash of existing content + name: + type: string + description: Display name + tags: + type: array + items: + type: string + user_metadata: + type: object + additionalProperties: true + responses: + "201": + description: Asset created from hash + content: + application/json: + schema: + $ref: "#/components/schemas/AssetCreated" + + /api/assets/{id}: + get: + operationId: getAsset + tags: [assets] + summary: Get asset metadata + description: Returns the metadata for a single asset. + x-feature-gate: enable-assets + parameters: + - name: id + in: path + description: The asset ID. + required: true + schema: + type: string + format: uuid + responses: + "200": + description: Asset metadata + content: + application/json: + schema: + $ref: "#/components/schemas/Asset" + "404": + description: Asset not found + put: + operationId: updateAsset + tags: [assets] + summary: Update asset metadata + description: Updates the mutable metadata of an asset (name, tags, etc.). Binary content is immutable. + x-feature-gate: enable-assets + parameters: + - name: id + in: path + description: The asset ID. + required: true + schema: + type: string + format: uuid + requestBody: + required: true + content: + application/json: + schema: + type: object + properties: + name: + type: string + description: New display name for the asset + user_metadata: + type: object + additionalProperties: true + description: Custom user metadata to set + preview_id: + type: string + format: uuid + description: ID of the asset to use as the preview + responses: + "200": + description: Asset updated + content: + application/json: + schema: + $ref: "#/components/schemas/AssetUpdated" + delete: + operationId: deleteAsset + tags: [assets] + summary: Delete an asset + description: Removes an asset entry. Depending on the server configuration, the underlying content may also be deleted. + x-feature-gate: enable-assets + parameters: + - name: id + in: path + description: The asset ID. + required: true + schema: + type: string + format: uuid + - name: delete_content + in: query + schema: + type: boolean + description: Also delete the underlying content file + responses: + "204": + description: Asset deleted + + /api/assets/{id}/content: + get: + operationId: getAssetContent + tags: [assets] + summary: Download asset file content + description: Returns the binary content of an asset. Supports range requests. + x-feature-gate: enable-assets + parameters: + - name: id + in: path + description: The asset ID. + required: true + schema: + type: string + format: uuid + responses: + "200": + description: Asset file content + content: + application/octet-stream: + schema: + type: string + format: binary + "404": + description: Asset not found + + /api/assets/{id}/tags: + post: + operationId: addAssetTags + tags: [assets] + summary: Add tags to an asset + description: Adds one or more tags to an asset. + x-feature-gate: enable-assets + parameters: + - name: id + in: path + description: The asset ID. + required: true + schema: + type: string + format: uuid + requestBody: + required: true + content: + application/json: + schema: + type: object + required: + - tags + properties: + tags: + type: array + items: + type: string + responses: + "200": + description: Tags added + content: + application/json: + schema: + $ref: "#/components/schemas/TagsModificationResponse" + delete: + operationId: removeAssetTags + tags: [assets] + summary: Remove tags from an asset + description: Removes one or more tags from an asset. + x-feature-gate: enable-assets + parameters: + - name: id + in: path + description: The asset ID. + required: true + schema: + type: string + format: uuid + requestBody: + required: true + content: + application/json: + schema: + type: object + required: + - tags + properties: + tags: + type: array + items: + type: string + responses: + "200": + description: Tags removed + content: + application/json: + schema: + $ref: "#/components/schemas/TagsModificationResponse" + + /api/tags: + get: + operationId: listTags + tags: [assets] + summary: List all known tags with counts + description: Returns the list of all tags known to the asset database, with counts. + x-feature-gate: enable-assets + parameters: + - name: limit + in: query + schema: + type: integer + - name: offset + in: query + schema: + type: integer + - name: search + in: query + schema: + type: string + description: Search term for tag name + responses: + "200": + description: Tag list + content: + application/json: + schema: + $ref: "#/components/schemas/ListTagsResponse" + + /api/assets/tags/refine: + get: + operationId: refineAssetTags + tags: [assets] + summary: Get tag counts for assets matching current filters + description: Returns suggested additional tags that would refine a filtered asset query, together with the count of assets each tag would select. + x-feature-gate: enable-assets + parameters: + - name: include_tags + in: query + schema: + type: array + items: + type: string + style: form + explode: true + description: Tags that assets must have (AND logic) + - name: exclude_tags + in: query + schema: + type: array + items: + type: string + style: form + explode: true + description: Tags that assets must not have + - name: name_contains + in: query + schema: + type: string + description: Filter assets whose name contains this substring + - name: metadata_filter + in: query + schema: + type: string + description: JSON-encoded metadata key/value filter + - name: limit + in: query + schema: + type: integer + - name: offset + in: query + schema: + type: integer + - name: sort + in: query + schema: + type: string + description: Field to sort by + - name: order + in: query + schema: + type: string + enum: [asc, desc] + description: Sort direction + responses: + "200": + description: Tag histogram + content: + application/json: + schema: + $ref: "#/components/schemas/AssetTagHistogramResponse" + + /api/assets/seed: + post: + operationId: seedAssets + tags: [assets] + summary: Trigger asset scan/seed from filesystem + description: Starts a background job that scans the configured directories and registers any assets not yet present in the asset database. + x-feature-gate: enable-assets + requestBody: + required: false + content: + application/json: + schema: + type: object + properties: + roots: + type: array + items: + type: string + description: Root folder paths to scan (if omitted, scans all) + responses: + "200": + description: Seed started + content: + application/json: + schema: + type: object + properties: + status: + type: string + + /api/assets/seed/status: + get: + operationId: getAssetSeedStatus + tags: [assets] + summary: Get asset scan progress + description: Returns the progress and status of the most recently-started asset seed job. + x-feature-gate: enable-assets + responses: + "200": + description: Scan progress + content: + application/json: + schema: + type: object + additionalProperties: true + description: Scan progress details (files scanned, total, status, etc.) + + /api/assets/seed/cancel: + post: + operationId: cancelAssetSeed + tags: [assets] + summary: Cancel an in-progress asset scan + description: Requests cancellation of the currently-running asset seed job. + x-feature-gate: enable-assets + responses: + "200": + description: Scan cancelled + content: + application/json: + schema: + type: object + properties: + status: + type: string + + /api/assets/prune: + post: + operationId: pruneAssets + tags: [assets] + summary: Mark assets whose backing files no longer exist on disk + description: Starts a background job that removes asset entries whose underlying content no longer exists on disk. + x-feature-gate: enable-assets + responses: + "200": + description: Prune result + content: + application/json: + schema: + type: object + properties: + status: + type: string + marked: + type: integer + description: Number of assets marked as missing + +components: + parameters: + ComfyUserHeader: + name: Comfy-User + in: header + required: false + schema: + type: string + description: | + Identifies the active user in multi-user mode. Used for settings, + userdata, and history isolation. This is not a security mechanism — + it is an organisational convenience with no authentication behind it. + + schemas: + # ------------------------------------------------------------------- + # Prompt + # ------------------------------------------------------------------- + PromptRequest: + type: object + description: A workflow submission. Wraps the prompt graph plus optional client identifier and extra per-request data. + required: + - prompt + properties: + prompt: + type: object + description: | + The workflow graph to execute. Keys are node IDs (strings); + values are objects with class_type and inputs. + additionalProperties: true + number: + type: number + description: Priority number for the queue (lower numbers have higher priority) + front: + type: boolean + description: If true, adds the prompt to the front of the queue + extra_data: + type: object + description: Extra data associated with the prompt (e.g. extra_pnginfo) + additionalProperties: true + client_id: + type: string + description: WebSocket client ID to receive progress updates + prompt_id: + type: string + format: uuid + description: "Client-supplied prompt ID. Server generates a UUID if omitted." + partial_execution_targets: + type: array + items: + type: string + description: List of node IDs to execute (partial graph execution) + + PromptResponse: + type: object + description: Server acknowledgement of a workflow submission. Includes the assigned `prompt_id` and current queue position. + properties: + prompt_id: + type: string + format: uuid + description: Unique identifier for the prompt execution + number: + type: number + description: Priority number in the queue + node_errors: + type: object + description: Validation errors keyed by node ID + additionalProperties: + $ref: "#/components/schemas/NodeError" + error: + description: Top-level prompt error (string message or structured error) + oneOf: + - type: string + - $ref: "#/components/schemas/PromptError" + + PromptErrorResponse: + type: object + description: Error response when prompt validation fails + additionalProperties: true + + PromptError: + type: object + description: Structured prompt validation error + properties: + type: + type: string + message: + type: string + details: + type: string + + Error: + type: object + description: Detailed node-level error + properties: + type: + type: string + message: + type: string + details: + type: string + extra_info: + type: object + properties: + input_name: + type: string + additionalProperties: true + + NodeError: + type: object + description: Error details for a single node + properties: + errors: + type: array + items: + $ref: "#/components/schemas/Error" + class_type: + type: string + description: The node's class type + dependent_outputs: + type: array + items: {} + + PromptInfo: + type: object + description: Summary of a queued or recently-executed prompt, as returned by the queue and history endpoints. + properties: + exec_info: + type: object + properties: + queue_remaining: + type: integer + description: Number of items remaining in the queue + + # ------------------------------------------------------------------- + # Queue + # ------------------------------------------------------------------- + QueueInfo: + type: object + description: Queue information with pending and running items + properties: + queue_running: + type: array + description: Currently running queue items + items: + type: array + description: | + Queue item tuple: [number, prompt_id, prompt, extra_data, outputs_to_execute, sensitive] + items: {} + prefixItems: + - type: number + description: Priority number + - type: string + format: uuid + description: prompt_id + - type: object + description: prompt graph + additionalProperties: true + - type: object + description: extra_data + additionalProperties: true + - type: array + description: outputs_to_execute (list of output node IDs) + items: + type: string + - type: object + description: sensitive data (may be omitted) + additionalProperties: true + queue_pending: + type: array + description: Pending queue items (oldest first) + items: + type: array + description: | + Queue item tuple: [number, prompt_id, prompt, extra_data, outputs_to_execute, sensitive] + items: {} + prefixItems: + - type: number + description: Priority number + - type: string + format: uuid + description: prompt_id + - type: object + description: prompt graph + additionalProperties: true + - type: object + description: extra_data + additionalProperties: true + - type: array + description: outputs_to_execute (list of output node IDs) + items: + type: string + - type: object + description: sensitive data (may be omitted) + additionalProperties: true + + QueueManageRequest: + type: object + description: Request to clear or delete from queue + properties: + clear: + type: boolean + description: If true, clear all pending items + delete: + type: array + items: + type: string + description: Array of prompt IDs to delete from queue + + # ------------------------------------------------------------------- + # History + # ------------------------------------------------------------------- + HistoryEntry: + type: object + description: A single execution history entry + properties: + prompt: + type: array + description: | + Prompt tuple: [number, prompt_id, prompt_graph, extra_data, output_node_ids] + items: {} + outputs: + type: object + description: Output data from execution keyed by node ID + additionalProperties: true + status: + type: object + description: Execution status (status_str, completed, messages, etc.) + additionalProperties: true + meta: + type: object + description: Metadata about the execution and nodes + additionalProperties: true + + HistoryManageRequest: + type: object + description: Request to clear or delete history entries + properties: + clear: + type: boolean + description: If true, clear all history + delete: + type: array + items: + type: string + description: Array of prompt IDs to delete from history + + # ------------------------------------------------------------------- + # Jobs + # ------------------------------------------------------------------- + JobEntry: + type: object + description: Lightweight job data for list views + required: + - id + - status + properties: + id: + type: string + format: uuid + description: Unique job identifier (same as prompt_id) + status: + type: string + description: Current job status + create_time: + type: number + description: Job creation timestamp + execution_start_time: + type: number + description: Workflow execution start timestamp + execution_end_time: + type: number + description: Workflow execution end timestamp + preview_output: + type: object + additionalProperties: true + description: Primary preview output + outputs_count: + type: integer + description: Total number of output files + + JobDetailResponse: + type: object + description: Full job details including workflow and outputs + required: + - id + - status + properties: + id: + type: string + format: uuid + status: + type: string + workflow: + type: object + additionalProperties: true + description: Full ComfyUI workflow + outputs: + type: object + additionalProperties: true + description: Full outputs object from execution + execution_error: + $ref: "#/components/schemas/ExecutionError" + create_time: + type: number + update_time: + type: number + execution_start_time: + type: number + execution_end_time: + type: number + preview_output: + type: object + additionalProperties: true + outputs_count: + type: integer + execution_status: + type: object + additionalProperties: true + execution_meta: + type: object + additionalProperties: true + + ExecutionError: + type: object + description: Detailed execution error from ComfyUI + properties: + node_id: + type: string + description: ID of the node that failed + node_type: + type: string + description: Type name of the node + exception_message: + type: string + description: Human-readable error message + exception_type: + type: string + description: Python exception type + traceback: + type: array + items: + type: string + description: Traceback lines + current_inputs: + type: object + additionalProperties: true + current_outputs: + type: object + additionalProperties: true + + PaginationInfo: + type: object + description: Pagination metadata returned alongside list responses. + properties: + offset: + type: integer + limit: + type: integer + total: + type: integer + has_more: + type: boolean + + # ------------------------------------------------------------------- + # Upload / View + # ------------------------------------------------------------------- + UploadResult: + type: object + description: Response body returned by the image/mask upload endpoints, describing where the uploaded file now lives. + properties: + name: + type: string + description: Saved filename (may be renamed to avoid collisions) + subfolder: + type: string + description: Subfolder the file was saved to + type: + type: string + description: Directory type (input, temp) + + # ------------------------------------------------------------------- + # System + # ------------------------------------------------------------------- + DeviceStats: + type: object + description: GPU/compute device statistics + required: + - name + - type + - index + properties: + name: + type: string + description: Device name + type: + type: string + description: Device type (cuda, mps, cpu, etc.) + index: + type: number + description: Device index + vram_total: + type: number + description: Total VRAM in bytes + vram_free: + type: number + description: Free VRAM in bytes + torch_vram_total: + type: number + description: Total PyTorch-managed VRAM in bytes + torch_vram_free: + type: number + description: Free PyTorch-managed VRAM in bytes + + SystemStatsResponse: + type: object + description: Hardware, VRAM, Python, and ComfyUI version information for the running process. + required: + - system + - devices + properties: + system: + type: object + required: + - os + - python_version + - embedded_python + - comfyui_version + - pytorch_version + - argv + - ram_total + - ram_free + properties: + os: + type: string + description: Operating system + python_version: + type: string + description: Python version + embedded_python: + type: boolean + description: Whether using embedded Python + comfyui_version: + type: string + description: ComfyUI version string + pytorch_version: + type: string + description: PyTorch version + required_frontend_version: + type: string + description: Required frontend version + argv: + type: array + items: + type: string + description: Command line arguments + ram_total: + type: number + description: Total RAM in bytes + ram_free: + type: number + description: Free RAM in bytes + installed_templates_version: + type: string + nullable: true + description: Version of the currently installed workflow templates + required_templates_version: + type: string + nullable: true + description: Minimum required workflow templates version for this ComfyUI build + devices: + type: array + items: + $ref: "#/components/schemas/DeviceStats" + + # ------------------------------------------------------------------- + # Node / Object Info + # ------------------------------------------------------------------- + NodeInfo: + type: object + description: 'Definition of a registered node class: its inputs, outputs, category, and display metadata.' + properties: + input: + type: object + description: Input specifications (required and optional groups) + additionalProperties: true + input_order: + type: object + description: Ordered input names per group + additionalProperties: + type: array + items: + type: string + output: + type: array + items: + type: string + description: Output type names + output_is_list: + type: array + items: + type: boolean + description: Whether each output is a list + output_name: + type: array + items: + type: string + description: Display names of outputs + name: + type: string + description: Internal class name + display_name: + type: string + description: Human-readable display name + description: + type: string + description: Node description + python_module: + type: string + description: Python module implementing the node + category: + type: string + description: Node category path + output_node: + type: boolean + description: Whether this is an output node + output_tooltips: + type: array + items: + type: string + description: Tooltips for each output + deprecated: + type: boolean + description: Whether the node is deprecated + experimental: + type: boolean + description: Whether the node is experimental + api_node: + type: boolean + description: Whether this is an API node + is_input_list: + type: boolean + description: Whether the node accepts list inputs + dev_only: + type: boolean + description: Whether the node is developer-only (hidden in production UI) + has_intermediate_output: + type: boolean + description: Whether the node emits intermediate output during execution + search_aliases: + type: array + items: + type: string + description: Alternative search terms for finding this node + essentials_category: + type: string + description: Category override used by the essentials pack + + # ------------------------------------------------------------------- + # Models + # ------------------------------------------------------------------- + ModelFolder: + type: object + description: A configured model folder and the list of disk paths it resolves to. + required: + - name + - folders + properties: + name: + type: string + description: Model folder type name (e.g. "checkpoints") + folders: + type: array + items: + type: string + description: Filesystem paths for this model type + + ModelFile: + type: object + description: A single model file in a folder, with filesystem metadata. + required: + - name + - pathIndex + properties: + name: + type: string + description: Model filename + pathIndex: + type: integer + description: Index into the folder's paths array + modified: + type: number + description: File modification timestamp + created: + type: number + description: File creation timestamp + size: + type: integer + format: int64 + description: File size in bytes + + # ------------------------------------------------------------------- + # Subgraphs + # ------------------------------------------------------------------- + GlobalSubgraphInfo: + type: object + description: Metadata for a global subgraph blueprint (without full data) + required: + - source + - name + - info + properties: + source: + type: string + description: Source type ("templates" or "custom_node") + name: + type: string + description: Display name of the subgraph blueprint + info: + type: object + description: Additional information about the subgraph + required: + - node_pack + properties: + node_pack: + type: string + description: The node pack/module providing this subgraph + data: + type: string + description: The full subgraph JSON data (may be empty in list view) + + GlobalSubgraphData: + type: object + description: Full data for a global subgraph blueprint + required: + - source + - name + - info + - data + properties: + source: + type: string + description: Source type ("templates" or "custom_node") + name: + type: string + description: Display name of the subgraph blueprint + info: + type: object + description: Additional information about the subgraph + required: + - node_pack + properties: + node_pack: + type: string + description: The node pack/module providing this subgraph + data: + type: string + description: The full subgraph JSON data as a string + + # ------------------------------------------------------------------- + # Userdata + # ------------------------------------------------------------------- + UserDataResponse: + description: | + Response body for the POST endpoints `/api/userdata/{file}` and + `/api/userdata/{file}/move/{dest}`. Returns a single item whose + shape depends on the `full_info` query parameter. + x-variant-selector: + full_info=true: file-info object (`GetUserDataResponseFullFile`) + default: relative path string + oneOf: + - $ref: "#/components/schemas/GetUserDataResponseFullFile" + - type: string + description: Relative path of the written or moved file. Returned when `full_info` is absent or false. + + ListUserdataResponse: + description: | + Response body for `GET /api/userdata`. The array item shape is + determined by the `full_info` and `split` query parameters. + x-variant-selector: + full_info=true: array of file-info objects (`GetUserDataResponseFullFile`) + split=true: array of `[relative_path, ...path_components]` arrays + default: array of relative path strings + oneOf: + - type: array + items: + $ref: "#/components/schemas/GetUserDataResponseFullFile" + description: Returned when `full_info=true`. + - type: array + items: + type: array + items: + type: string + minItems: 2 + description: | + Returned when `split=true` and `full_info=false`. Each inner + array is `[relative_path, ...path_components]`. + - type: array + items: + type: string + description: Default shape — array of file paths relative to the user data root. + + GetUserDataResponseFullFile: + type: object + description: A single entry in a full-info user data listing. + properties: + path: + type: string + description: File name or path relative to the user directory + created: + type: number + description: Unix timestamp of file creation + size: + type: integer + description: File size in bytes + modified: + type: integer + format: int64 + description: Unix timestamp of last modification in milliseconds + + # ------------------------------------------------------------------- + # Assets + # ------------------------------------------------------------------- + Asset: + type: object + description: A registered asset — an input/output file tracked in the asset database with content hash and metadata. + required: + - id + - name + - size + - created_at + - updated_at + properties: + id: + type: string + format: uuid + description: Unique identifier for the asset + name: + type: string + description: Name of the asset file + asset_hash: + type: string + description: Blake3 hash of the asset content + pattern: "^blake3:[a-f0-9]{64}$" + size: + type: integer + format: int64 + description: Size of the asset in bytes + mime_type: + type: string + description: MIME type of the asset + tags: + type: array + items: + type: string + description: Tags associated with the asset + user_metadata: + type: object + description: Custom user metadata + additionalProperties: true + metadata: + type: object + description: System-managed metadata (read-only) + additionalProperties: true + readOnly: true + preview_url: + type: string + format: uri + description: URL for asset preview/thumbnail + preview_id: + type: string + format: uuid + description: ID of the preview asset if available + prompt_id: + type: string + format: uuid + description: ID of the prompt that created this asset + created_at: + type: string + format: date-time + updated_at: + type: string + format: date-time + last_access_time: + type: string + format: date-time + is_immutable: + type: boolean + description: Whether this asset is immutable + + AssetCreated: + description: Response body returned after successfully registering a new asset. + allOf: + - $ref: "#/components/schemas/Asset" + - type: object + required: + - created_new + properties: + created_new: + type: boolean + description: Whether this was a new creation (true) or returned existing (false) + + AssetUpdated: + type: object + description: Response body returned after updating an asset's metadata. + required: + - id + - updated_at + properties: + id: + type: string + format: uuid + name: + type: string + asset_hash: + type: string + pattern: "^blake3:[a-f0-9]{64}$" + tags: + type: array + items: + type: string + mime_type: + type: string + user_metadata: + type: object + additionalProperties: true + updated_at: + type: string + format: date-time + + ListAssetsResponse: + type: object + description: Paginated list of assets. + required: + - assets + - total + - has_more + properties: + assets: + type: array + items: + $ref: "#/components/schemas/Asset" + total: + type: integer + has_more: + type: boolean + + TagInfo: + type: object + description: A tag known to the asset database, with the number of assets bearing it. + required: + - name + - count + properties: + name: + type: string + count: + type: integer + + ListTagsResponse: + type: object + description: Flat list of all tags, with counts. + required: + - tags + - total + - has_more + properties: + tags: + type: array + items: + $ref: "#/components/schemas/TagInfo" + total: + type: integer + has_more: + type: boolean + + AssetTagHistogramResponse: + type: object + description: Tags that would refine a filtered asset query, with the count of assets each tag would additionally select. + required: + - tag_counts + properties: + tag_counts: + type: object + additionalProperties: + type: integer + description: Map of tag names to occurrence counts + + TagsModificationResponse: + type: object + description: Response body returned after adding or removing tags on an asset. + required: + - total_tags + properties: + added: + type: array + items: + type: string + description: Tags successfully added + removed: + type: array + items: + type: string + description: Tags successfully removed + already_present: + type: array + items: + type: string + description: Tags already present (for add) + not_present: + type: array + items: + type: string + description: Tags not present (for remove) + total_tags: + type: array + items: + type: string + description: All tags on the asset after the operation + + # ------------------------------------------------------------------- + # Result / Output types + # ------------------------------------------------------------------- + ResultItem: + type: object + description: A single output file reference + properties: + filename: + type: string + subfolder: + type: string + type: + type: string + enum: [input, output, temp] + display_name: + type: string + + NodeOutputs: + type: object + description: | + Outputs from a single node execution. Known keys are listed below, + but custom nodes may add arbitrary keys (additionalProperties). + properties: + images: + type: array + items: + $ref: "#/components/schemas/ResultItem" + audio: + type: array + items: + $ref: "#/components/schemas/ResultItem" + video: + type: array + items: + $ref: "#/components/schemas/ResultItem" + animated: + type: array + items: + type: boolean + text: + oneOf: + - type: string + - type: array + items: + type: string + additionalProperties: true + + TerminalSize: + type: object + description: Terminal dimensions + properties: + cols: + type: number + row: + type: number + + LogEntry: + type: object + description: A single log entry + properties: + t: + type: string + description: Timestamp + m: + type: string + description: Log message + + StatusWsMessageStatus: + type: object + description: Inner payload of a `status` WebSocket message, describing the execution queue state. + properties: + exec_info: + type: object + required: + - queue_remaining + properties: + queue_remaining: + type: integer + + StatusWsMessage: + type: object + description: Initial status message sent on connect + queue status updates + properties: + status: + $ref: "#/components/schemas/StatusWsMessageStatus" + sid: + type: string + description: Session ID assigned by the server + + ProgressWsMessage: + type: object + description: Node execution progress (step N of M) + required: + - value + - max + - prompt_id + - node + properties: + value: + type: integer + description: Current step + max: + type: integer + description: Total steps + prompt_id: + type: string + node: + type: string + description: Node ID currently executing + + ProgressTextWsMessage: + type: object + description: Text-based progress update from a node + properties: + nodeId: + type: string + text: + type: string + prompt_id: + type: string + + NodeProgressState: + type: object + description: Progress state for a single node + properties: + value: + type: number + max: + type: number + state: + type: string + enum: [pending, running, finished, error] + node_id: + type: string + prompt_id: + type: string + display_node_id: + type: string + parent_node_id: + type: string + real_node_id: + type: string + + ProgressStateWsMessage: + type: object + description: Bulk progress state for all nodes in a prompt + required: + - prompt_id + - nodes + properties: + prompt_id: + type: string + nodes: + type: object + description: Map of node ID to progress state + additionalProperties: + $ref: "#/components/schemas/NodeProgressState" + + ExecutingWsMessage: + type: object + description: Fired when a node begins execution + required: + - node + - display_node + - prompt_id + properties: + node: + type: string + description: Node ID + display_node: + type: string + description: Display node ID (may differ for subgraphs) + prompt_id: + type: string + + ExecutedWsMessage: + type: object + description: Fired when a node completes execution with output + required: + - node + - display_node + - prompt_id + - output + properties: + node: + type: string + display_node: + type: string + prompt_id: + type: string + output: + $ref: "#/components/schemas/NodeOutputs" + merge: + type: boolean + description: Whether to merge with existing output + + ExecutionWsMessageBase: + type: object + description: Base fields for execution lifecycle messages + required: + - prompt_id + - timestamp + properties: + prompt_id: + type: string + timestamp: + type: integer + description: Unix timestamp in milliseconds + + ExecutionStartWsMessage: + allOf: + - $ref: "#/components/schemas/ExecutionWsMessageBase" + description: Fired when prompt execution begins + + ExecutionSuccessWsMessage: + allOf: + - $ref: "#/components/schemas/ExecutionWsMessageBase" + description: Fired when prompt execution completes successfully + + ExecutionCachedWsMessage: + allOf: + - $ref: "#/components/schemas/ExecutionWsMessageBase" + - type: object + properties: + nodes: + type: array + items: + type: string + description: List of node IDs that were cached + description: Fired when nodes are served from cache + + ExecutionInterruptedWsMessage: + allOf: + - $ref: "#/components/schemas/ExecutionWsMessageBase" + - type: object + properties: + node_id: + type: string + node_type: + type: string + executed: + type: array + items: + type: string + description: Node IDs that completed before interruption + description: Fired when execution is interrupted by user + + ExecutionErrorWsMessage: + allOf: + - $ref: "#/components/schemas/ExecutionWsMessageBase" + - type: object + properties: + node_id: + type: string + node_type: + type: string + executed: + type: array + items: + type: string + exception_message: + type: string + exception_type: + type: string + traceback: + type: array + items: + type: string + current_inputs: {} + current_outputs: {} + description: Fired when a node throws an exception during execution + + LogsWsMessage: + type: object + description: Streaming log entries from the server + properties: + size: + $ref: "#/components/schemas/TerminalSize" + entries: + type: array + items: + $ref: "#/components/schemas/LogEntry" + + NotificationWsMessage: + type: object + description: Server notification (e.g. model download complete) + properties: + value: + type: string + id: + type: string + + FeatureFlagsWsMessage: + type: object + description: Feature flags sent on connect + additionalProperties: true + + AssetDownloadWsMessage: + type: object + description: Asset download progress + required: + - task_id + - asset_name + - bytes_total + - bytes_downloaded + - progress + - status + properties: + task_id: + type: string + asset_name: + type: string + bytes_total: + type: number + bytes_downloaded: + type: number + progress: + type: number + description: 0.0 to 1.0 + status: + type: string + enum: [created, running, completed, failed] + asset_id: + type: string + error: + type: string + + AssetExportWsMessage: + type: object + description: Bulk asset export progress + required: + - task_id + - assets_total + - assets_attempted + - assets_failed + - bytes_total + - bytes_processed + - progress + - status + properties: + task_id: + type: string + export_name: + type: string + assets_total: + type: number + assets_attempted: + type: number + assets_failed: + type: number + bytes_total: + type: number + bytes_processed: + type: number + progress: + type: number + description: 0.0 to 1.0 + status: + type: string + enum: [created, running, completed, failed] + error: + type: string From 7636599389a6798e813d6036fb0dcf08295e7971 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Fri, 24 Apr 2026 16:54:10 +0300 Subject: [PATCH 092/140] chore(api-nodes): add upcoming-deprecation notice to Sora nodes (#13549) --- comfy_api_nodes/nodes_sora.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/comfy_api_nodes/nodes_sora.py b/comfy_api_nodes/nodes_sora.py index afc18bb25..4d9075dcf 100644 --- a/comfy_api_nodes/nodes_sora.py +++ b/comfy_api_nodes/nodes_sora.py @@ -33,9 +33,13 @@ class OpenAIVideoSora2(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="OpenAIVideoSora2", - display_name="OpenAI Sora - Video", + display_name="OpenAI Sora - Video (Deprecated)", category="api node/video/Sora", - description="OpenAI video and audio generation.", + description=( + "OpenAI video and audio generation.\n\n" + "DEPRECATION NOTICE: OpenAI will stop serving the Sora v2 API in September 2026. " + "This node will be removed from ComfyUI at that time." + ), inputs=[ IO.Combo.Input( "model", From 4304c15e9b4acb45fa9241e8e1723f8ce6397550 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 24 Apr 2026 13:46:10 -0700 Subject: [PATCH 093/140] Properly load higher bit depth videos. (#13542) --- comfy_api/latest/_input_impl/video_types.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py index 1b4993aa7..bd8090635 100644 --- a/comfy_api/latest/_input_impl/video_types.py +++ b/comfy_api/latest/_input_impl/video_types.py @@ -248,8 +248,8 @@ class VideoFromFile(VideoInput): continue if self.__duration and frame.pts >= end_pts: break - img = frame.to_ndarray(format='rgb24') # shape: (H, W, 3) - img = torch.from_numpy(img) / 255.0 # shape: (H, W, 3) + img = frame.to_ndarray(format='gbrpf32le') # shape: (H, W, 3) + img = torch.from_numpy(img) frames.append(img) images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 3, 0, 0) From 5e3f15a830ff27d3563ef4b43e9f6a0321ea36cd Mon Sep 17 00:00:00 2001 From: Comfy Org PR Bot Date: Sat, 25 Apr 2026 09:21:39 +0900 Subject: [PATCH 094/140] Bump comfyui-frontend-package to 1.42.15 (#13556) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 346ce4b76..6c7457e03 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,4 @@ -comfyui-frontend-package==1.42.14 +comfyui-frontend-package==1.42.15 comfyui-workflow-templates==0.9.62 comfyui-embedded-docs==0.4.4 torch From df22bcd5e192ce0b1ae09eaf2e423d0a12cf6638 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 25 Apr 2026 18:02:58 -0700 Subject: [PATCH 095/140] Support loading the alpha channel of videos. (#13564) Not exposed in nodes yet. --- comfy_api/latest/_input_impl/video_types.py | 25 ++++++++++++++++----- comfy_api/latest/_util/video_types.py | 5 ++--- 2 files changed, 22 insertions(+), 8 deletions(-) diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py index bd8090635..eb4d3701d 100644 --- a/comfy_api/latest/_input_impl/video_types.py +++ b/comfy_api/latest/_input_impl/video_types.py @@ -240,19 +240,34 @@ class VideoFromFile(VideoInput): start_time = self.__start_time # Get video frames frames = [] + alphas = None start_pts = int(start_time / video_stream.time_base) end_pts = int((start_time + self.__duration) / video_stream.time_base) container.seek(start_pts, stream=video_stream) + image_format = 'gbrpf32le' for frame in container.decode(video_stream): + if alphas is None: + for comp in frame.format.components: + if comp.is_alpha: + alphas = [] + image_format = 'gbrapf32le' + break + if frame.pts < start_pts: continue if self.__duration and frame.pts >= end_pts: break - img = frame.to_ndarray(format='gbrpf32le') # shape: (H, W, 3) - img = torch.from_numpy(img) - frames.append(img) - images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 3, 0, 0) + img = frame.to_ndarray(format=image_format) # shape: (H, W, 4) + if alphas is None: + frames.append(torch.from_numpy(img)) + else: + frames.append(torch.from_numpy(img[..., :-1])) + alphas.append(torch.from_numpy(img[..., -1:])) + + images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 0, 0, 3) + if alphas is not None: + alphas = torch.stack(alphas) if len(alphas) > 0 else torch.zeros(0, 0, 0, 1) # Get frame rate frame_rate = Fraction(video_stream.average_rate) if video_stream.average_rate else Fraction(1) @@ -295,7 +310,7 @@ class VideoFromFile(VideoInput): }) metadata = container.metadata - return VideoComponents(images=images, audio=audio, frame_rate=frame_rate, metadata=metadata) + return VideoComponents(images=images, alpha=alphas, audio=audio, frame_rate=frame_rate, metadata=metadata) def get_components(self) -> VideoComponents: if isinstance(self.__file, io.BytesIO): diff --git a/comfy_api/latest/_util/video_types.py b/comfy_api/latest/_util/video_types.py index fd3b5a510..c92477f08 100644 --- a/comfy_api/latest/_util/video_types.py +++ b/comfy_api/latest/_util/video_types.py @@ -3,7 +3,7 @@ from dataclasses import dataclass from enum import Enum from fractions import Fraction from typing import Optional -from .._input import ImageInput, AudioInput +from .._input import ImageInput, AudioInput, MaskInput class VideoCodec(str, Enum): AUTO = "auto" @@ -48,5 +48,4 @@ class VideoComponents: frame_rate: Fraction audio: Optional[AudioInput] = None metadata: Optional[dict] = None - - + alpha: Optional[MaskInput] = None From 7385eb28004a45c723345b132cd4dcd327aabf9a Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Sun, 26 Apr 2026 22:59:16 +0800 Subject: [PATCH 096/140] Add new ComfyUI blueprints and fix subgraph naming (#13371) * Remove local tag from subgraph name * New Subgraph blueprints * Remove duplicate blueprint * Update Subgraph size * Update subgraph * Update Blueprint * Remove local tag from subgraph name * New Subgraph blueprints * Remove duplicate blueprint * Update Subgraph size * Update subgraph * Update Blueprint * Update LTX 2.0 Pose to Video * Fix crop blueprint split coverage Made-with: Cursor * Clean up image edit blueprint metadata Made-with: Cursor * Update subgraph blueprints --------- Co-authored-by: Jedrzej Kosinski --- blueprints/Crop Images 2x2.json | 1620 +++++++ blueprints/Crop Images 3x3.json | 2957 ++++++++++++ .../Depth to Image (Z-Image-Turbo).json | 4 +- blueprints/Depth to Video (ltx 2.0).json | 4 +- .../First-Last-Frame to Video (LTX-2.3).json | 3360 +++++++++++++ .../Image Edit (FireRed Image Edit 1.1).json | 2148 +++++++++ blueprints/Image Edit (Flux.2 Klein 4B).json | 4 +- .../Image Edit (LongCat Image Edit).json | 1427 ++++++ .../Image Inpainting (Flux.1 Fill Dev).json | 1205 +++++ blueprints/Image Inpainting (Qwen-image).json | 4 +- .../Image Outpainting (Qwen-Image).json | 4 +- ... Image to Layers(Qwen-Image-Layered).json} | 418 +- blueprints/Image to Video (LTX-2.3).json | 4233 ++++++++++++++++ blueprints/Pose to Video (LTX 2.0).json | 2004 ++++---- blueprints/Text to Image (Flux.1 Dev).json | 1046 ++++ .../Text to Image (Flux.1 Krea Dev).json | 1040 ++++ .../Text to Image (NetaYume Lumina).json | 1468 ++++++ .../Text to Image (Qwen-Image 2512).json | 1951 ++++++++ blueprints/Text to Image (Qwen-Image).json | 1881 ++++++++ blueprints/Text to Video (LTX-2.3).json | 4296 +++++++++++++++++ 20 files changed, 29993 insertions(+), 1081 deletions(-) create mode 100644 blueprints/Crop Images 2x2.json create mode 100644 blueprints/Crop Images 3x3.json create mode 100644 blueprints/First-Last-Frame to Video (LTX-2.3).json create mode 100644 blueprints/Image Edit (FireRed Image Edit 1.1).json create mode 100644 blueprints/Image Edit (LongCat Image Edit).json create mode 100644 blueprints/Image Inpainting (Flux.1 Fill Dev).json rename blueprints/{Image to Layers(Qwen-Image Layered).json => Image to Layers(Qwen-Image-Layered).json} (83%) create mode 100644 blueprints/Image to Video (LTX-2.3).json create mode 100644 blueprints/Text to Image (Flux.1 Dev).json create mode 100644 blueprints/Text to Image (Flux.1 Krea Dev).json create mode 100644 blueprints/Text to Image (NetaYume Lumina).json create mode 100644 blueprints/Text to Image (Qwen-Image 2512).json create mode 100644 blueprints/Text to Image (Qwen-Image).json create mode 100644 blueprints/Text to Video (LTX-2.3).json diff --git a/blueprints/Crop Images 2x2.json b/blueprints/Crop Images 2x2.json new file mode 100644 index 000000000..2aa42cfc3 --- /dev/null +++ b/blueprints/Crop Images 2x2.json @@ -0,0 +1,1620 @@ +{ + "revision": 0, + "last_node_id": 139, + "last_link_id": 0, + "nodes": [ + { + "id": 135, + "type": "3b5ed000-6ab3-4458-91f7-8d6d366b0b40", + "pos": [ + -2479.9999801712506, + 2019.9999372732784 + ], + "size": [ + 230, + 170 + ], + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [ + { + "localized_name": "image", + "name": "image", + "type": "IMAGE", + "link": null + } + ], + "outputs": [ + { + "label": "top_left", + "localized_name": "IMAGE", + "name": "IMAGE", + "type": "IMAGE", + "links": [] + }, + { + "label": "bottom_left", + "localized_name": "IMAGE_1", + "name": "IMAGE_1", + "type": "IMAGE", + "links": [] + }, + { + "label": "top_right", + "localized_name": "IMAGE_2", + "name": "IMAGE_2", + "type": "IMAGE", + "links": [] + }, + { + "label": "bottom_right", + "localized_name": "IMAGE_3", + "name": "IMAGE_3", + "type": "IMAGE", + "links": [] + }, + { + "label": "images", + "name": "IMAGE_4", + "type": "IMAGE", + "links": [] + } + ], + "properties": { + "proxyWidgets": [], + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "cnr_id": "comfy-core", + "ver": "0.18.1" + }, + "widgets_values": [], + "title": "Crop Images 2x2" + } + ], + "links": [], + "version": 0.4, + "definitions": { + "subgraphs": [ + { + 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--git a/blueprints/Image Inpainting (Qwen-image).json b/blueprints/Image Inpainting (Qwen-image).json index d06f31dd2..95b2909fa 100644 --- a/blueprints/Image Inpainting (Qwen-image).json +++ b/blueprints/Image Inpainting (Qwen-image).json @@ -124,7 +124,7 @@ }, "revision": 0, "config": {}, - "name": "local-Image Inpainting (Qwen-image)", + "name": "Image Inpainting (Qwen-image)", "inputNode": { "id": -10, "bounding": [ @@ -1923,4 +1923,4 @@ "workflowRendererVersion": "LG" }, "version": 0.4 -} +} \ No newline at end of file diff --git a/blueprints/Image Outpainting (Qwen-Image).json b/blueprints/Image Outpainting (Qwen-Image).json index bf2c4241a..218fdc775 100644 --- a/blueprints/Image Outpainting (Qwen-Image).json +++ b/blueprints/Image Outpainting (Qwen-Image).json @@ -204,7 +204,7 @@ }, "revision": 0, "config": {}, - "name": "local-Image Outpainting (Qwen-Image)", + "name": "Image Outpainting (Qwen-Image)", "inputNode": { "id": -10, "bounding": [ @@ -2749,4 +2749,4 @@ } }, "version": 0.4 -} +} \ No newline at end of file diff --git a/blueprints/Image to Layers(Qwen-Image Layered).json b/blueprints/Image to Layers(Qwen-Image-Layered).json similarity index 83% rename from blueprints/Image to Layers(Qwen-Image Layered).json rename to blueprints/Image to Layers(Qwen-Image-Layered).json index 164ffbd8d..8a525e7a5 100644 --- a/blueprints/Image to Layers(Qwen-Image Layered).json +++ b/blueprints/Image to Layers(Qwen-Image-Layered).json @@ -1,15 +1,14 @@ { - "id": "1a761372-7c82-4016-b9bf-fa285967e1e9", "revision": 0, - "last_node_id": 83, + "last_node_id": 176, "last_link_id": 0, "nodes": [ { - "id": 83, - "type": "f754a936-daaf-4b6e-9658-41fdc54d301d", + "id": 176, + "type": "2d2e3c8e-53b3-4618-be52-6d1d99382f0e", "pos": [ - 61.999827823554256, - 153.3332507624185 + -1150, + 200 ], "size": [ 400, @@ -56,6 +55,38 @@ "name": "layers" }, "link": null + }, + { + "name": "seed", + "type": "INT", + "widget": { + "name": "seed" + }, + "link": null + }, + { + "name": "unet_name", + "type": "COMBO", + "widget": { + "name": "unet_name" + }, + "link": null + }, + { + "name": "clip_name", + "type": "COMBO", + "widget": { + "name": "clip_name" + }, + "link": null + }, + { + "name": "vae_name", + "type": "COMBO", + "widget": { + "name": "vae_name" + }, + "link": null } ], "outputs": [ @@ -66,28 +97,41 @@ "links": [] } ], + "title": "Image to Layers (Qwen-Image-Layered)", "properties": { "proxyWidgets": [ [ - "-1", + "6", "text" ], [ - "-1", + "3", "steps" ], [ - "-1", + "3", "cfg" ], [ - "-1", + "83", "layers" ], [ "3", "seed" ], + [ + "37", + "unet_name" + ], + [ + "38", + "clip_name" + ], + [ + "39", + "vae_name" + ], [ "3", "control_after_generate" @@ -95,6 +139,11 @@ ], "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -103,25 +152,20 @@ "secondTabOffset": 80, "secondTabWidth": 65 }, - "widgets_values": [ - "", - 20, - 2.5, - 2 - ] + "widgets_values": [] } ], "links": [], - "groups": [], + "version": 0.4, "definitions": { "subgraphs": [ { - "id": "f754a936-daaf-4b6e-9658-41fdc54d301d", + "id": "2d2e3c8e-53b3-4618-be52-6d1d99382f0e", "version": 1, "state": { - "lastGroupId": 3, - "lastNodeId": 83, - "lastLinkId": 159, + "lastGroupId": 8, + "lastNodeId": 176, + "lastLinkId": 380, "lastRerouteId": 0 }, "revision": 0, @@ -130,10 +174,10 @@ "inputNode": { "id": -10, "bounding": [ - -510, - 523, + -720, + 720, 120, - 140 + 220 ] }, "outputNode": { @@ -156,8 +200,8 @@ ], "localized_name": "image", "pos": [ - -410, - 543 + -620, + 740 ] }, { @@ -168,8 +212,8 @@ 150 ], "pos": [ - -410, - 563 + -620, + 760 ] }, { @@ -180,8 +224,8 @@ 153 ], "pos": [ - -410, - 583 + -620, + 780 ] }, { @@ -192,8 +236,8 @@ 154 ], "pos": [ - -410, - 603 + -620, + 800 ] }, { @@ -204,8 +248,56 @@ 159 ], "pos": [ - -410, - 623 + -620, + 820 + ] + }, + { + "id": "9f76338b-f4ca-4bb3-b61a-57b3f233061e", + "name": "seed", + "type": "INT", + "linkIds": [ + 377 + ], + "pos": [ + -620, + 840 + ] + }, + { + "id": "8d0422d5-5eee-4f7e-9817-dc613cc62eca", + "name": "unet_name", + "type": "COMBO", + "linkIds": [ + 378 + ], + "pos": [ + -620, + 860 + ] + }, + { + "id": "552eece2-a735-4d00-ae78-ded454622bc1", + "name": "clip_name", + "type": "COMBO", + "linkIds": [ + 379 + ], + "pos": [ + -620, + 880 + ] + }, + { + "id": "1e6d141c-d0f9-4a2b-895c-b6780e57cfa0", + "name": "vae_name", + "type": "COMBO", + "linkIds": [ + 380 + ], + "pos": [ + -620, + 900 ] } ], @@ -231,14 +323,14 @@ "type": "CLIPLoader", "pos": [ -320, - 310 + 360 ], "size": [ - 346.7470703125, - 106 + 350, + 150 ], "flags": {}, - "order": 0, + "order": 5, "mode": 0, "inputs": [ { @@ -248,7 +340,7 @@ "widget": { "name": "clip_name" }, - "link": null + "link": 379 }, { "localized_name": "type", @@ -283,9 +375,14 @@ } ], "properties": { - "Node name for S&R": "CLIPLoader", "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "Node name for S&R": "CLIPLoader", "models": [ { "name": "qwen_2.5_vl_7b_fp8_scaled.safetensors", @@ -312,14 +409,14 @@ "type": "VAELoader", "pos": [ -320, - 460 + 580 ], "size": [ - 346.7470703125, - 58 + 350, + 110 ], "flags": {}, - "order": 1, + "order": 6, "mode": 0, "inputs": [ { @@ -329,7 +426,7 @@ "widget": { "name": "vae_name" }, - "link": null + "link": 380 } ], "outputs": [ @@ -345,9 +442,14 @@ } ], "properties": { - "Node name for S&R": "VAELoader", "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "Node name for S&R": "VAELoader", "models": [ { "name": "qwen_image_layered_vae.safetensors", @@ -375,11 +477,11 @@ 420 ], "size": [ - 425.27801513671875, - 180.6060791015625 + 430, + 190 ], "flags": {}, - "order": 3, + "order": 2, "mode": 0, "inputs": [ { @@ -411,9 +513,14 @@ ], "title": "CLIP Text Encode (Negative Prompt)", "properties": { - "Node name for S&R": "CLIPTextEncode", "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "Node name for S&R": "CLIPTextEncode", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -432,12 +539,12 @@ "id": 70, "type": "ReferenceLatent", "pos": [ - 330, - 670 + 140, + 700 ], "size": [ - 204.1666717529297, - 46 + 210, + 50 ], "flags": { "collapsed": true @@ -470,9 +577,14 @@ } ], "properties": { - "Node name for S&R": "ReferenceLatent", "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "Node name for S&R": "ReferenceLatent", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -480,19 +592,18 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [] + } }, { "id": 69, "type": "ReferenceLatent", "pos": [ - 330, - 710 + 160, + 820 ], "size": [ - 204.1666717529297, - 46 + 210, + 50 ], "flags": { "collapsed": true @@ -525,9 +636,14 @@ } ], "properties": { - "Node name for S&R": "ReferenceLatent", "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "Node name for S&R": "ReferenceLatent", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -535,8 +651,7 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [] + } }, { "id": 66, @@ -547,10 +662,10 @@ ], "size": [ 270, - 58 + 110 ], "flags": {}, - "order": 4, + "order": 7, "mode": 0, "inputs": [ { @@ -580,9 +695,14 @@ } ], "properties": { - "Node name for S&R": "ModelSamplingAuraFlow", "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "Node name for S&R": "ModelSamplingAuraFlow", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -600,11 +720,11 @@ "type": "LatentCutToBatch", "pos": [ 830, - 160 + 140 ], "size": [ 270, - 82 + 140 ], "flags": {}, "order": 11, @@ -646,9 +766,14 @@ } ], "properties": { - "Node name for S&R": "LatentCutToBatch", "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "Node name for S&R": "LatentCutToBatch", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -666,12 +791,12 @@ "id": 71, "type": "VAEEncode", "pos": [ - 100, - 690 + -280, + 780 ], "size": [ - 140, - 46 + 230, + 100 ], "flags": { "collapsed": false @@ -704,9 +829,14 @@ } ], "properties": { - "Node name for S&R": "VAEEncode", "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "Node name for S&R": "VAEEncode", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -714,24 +844,23 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [] + } }, { "id": 8, "type": "VAEDecode", "pos": [ 850, - 310 + 370 ], "size": [ 210, - 46 + 50 ], "flags": { "collapsed": true }, - "order": 7, + "order": 3, "mode": 0, "inputs": [ { @@ -759,9 +888,14 @@ } ], "properties": { - "Node name for S&R": "VAEDecode", "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "Node name for S&R": "VAEDecode", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -769,8 +903,7 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [] + } }, { "id": 6, @@ -780,11 +913,11 @@ 180 ], "size": [ - 422.84503173828125, - 164.31304931640625 + 430, + 170 ], "flags": {}, - "order": 6, + "order": 1, "mode": 0, "inputs": [ { @@ -816,9 +949,14 @@ ], "title": "CLIP Text Encode (Positive Prompt)", "properties": { - "Node name for S&R": "CLIPTextEncode", "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "Node name for S&R": "CLIPTextEncode", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -838,14 +976,14 @@ "type": "KSampler", "pos": [ 530, - 280 + 340 ], "size": [ 270, 400 ], "flags": {}, - "order": 5, + "order": 0, "mode": 0, "inputs": [ { @@ -879,7 +1017,7 @@ "widget": { "name": "seed" }, - "link": null + "link": 377 }, { "localized_name": "steps", @@ -939,9 +1077,14 @@ } ], "properties": { - "Node name for S&R": "KSampler", "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "Node name for S&R": "KSampler", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -964,12 +1107,12 @@ "id": 78, "type": "GetImageSize", "pos": [ - 80, - 790 + -280, + 930 ], "size": [ - 210, - 136 + 230, + 140 ], "flags": {}, "order": 12, @@ -1007,9 +1150,14 @@ } ], "properties": { - "Node name for S&R": "GetImageSize", "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "Node name for S&R": "GetImageSize", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -1017,23 +1165,23 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [] + } }, { "id": 83, "type": "EmptyQwenImageLayeredLatentImage", "pos": [ - 320, - 790 + -280, + 1120 ], "size": [ - 330.9341796875, - 130 + 340, + 200 ], "flags": {}, "order": 13, "mode": 0, + "showAdvanced": true, "inputs": [ { "localized_name": "width", @@ -1083,9 +1231,14 @@ } ], "properties": { - "Node name for S&R": "EmptyQwenImageLayeredLatentImage", "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "Node name for S&R": "EmptyQwenImageLayeredLatentImage", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -1109,11 +1262,11 @@ 180 ], "size": [ - 346.7470703125, - 82 + 350, + 110 ], "flags": {}, - "order": 2, + "order": 4, "mode": 0, "inputs": [ { @@ -1123,7 +1276,7 @@ "widget": { "name": "unet_name" }, - "link": null + "link": 378 }, { "localized_name": "weight_dtype", @@ -1147,9 +1300,14 @@ } ], "properties": { - "Node name for S&R": "UNETLoader", "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "input_ue_unconnectable": {}, + "version": "7.7" + }, + "Node name for S&R": "UNETLoader", "models": [ { "name": "qwen_image_layered_bf16.safetensors", @@ -1191,8 +1349,8 @@ "bounding": [ -330, 110, - 366.7470703125, - 421.6 + 370, + 610 ], "color": "#3f789e", "font_size": 24, @@ -1391,6 +1549,38 @@ "target_id": 83, "target_slot": 2, "type": "INT" + }, + { + "id": 377, + "origin_id": -10, + "origin_slot": 5, + "target_id": 3, + "target_slot": 4, + "type": "INT" + }, + { + "id": 378, + "origin_id": -10, + "origin_slot": 6, + "target_id": 37, + "target_slot": 0, + "type": "COMBO" + }, + { + "id": 379, + "origin_id": -10, + "origin_slot": 7, + "target_id": 38, + "target_slot": 0, + "type": "COMBO" + }, + { + "id": 380, + "origin_id": -10, + "origin_slot": 8, + "target_id": 39, + "target_slot": 0, + "type": "COMBO" } ], "extra": { @@ -1400,7 +1590,6 @@ } ] }, - "config": {}, "extra": { "ds": { "scale": 1.14, @@ -1409,7 +1598,6 @@ 6.855893974423647 ] }, - "workflowRendererVersion": "LG" - }, - "version": 0.4 -} + "ue_links": [] + } +} \ No newline at end of file diff --git a/blueprints/Image to Video (LTX-2.3).json b/blueprints/Image to Video (LTX-2.3).json new file mode 100644 index 000000000..86a601130 --- /dev/null +++ b/blueprints/Image to Video (LTX-2.3).json @@ -0,0 +1,4233 @@ +{ + "revision": 0, + "last_node_id": 320, + "last_link_id": 0, + "nodes": [ + { + "id": 320, + "type": 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"name": "strength_1" + }, + "link": null + }, + { + "name": "noise_seed", + "type": "INT", + "widget": { + "name": "noise_seed" + }, "link": null }, { @@ -69,6 +66,7 @@ "link": null }, { + "label": "control_lora", "name": "lora_name", "type": "COMBO", "widget": { @@ -77,7 +75,15 @@ "link": null }, { - "label": "distll_lora", + "name": "text_encoder", + "type": "COMBO", + "widget": { + "name": "text_encoder" + }, + "link": null + }, + { + "label": "distill_lora", "name": "lora_name_1", "type": "COMBO", "widget": { @@ -93,30 +99,6 @@ "name": "model_name" }, "link": null - }, - { - "name": "resize_type.width", - "type": "INT", - "widget": { - "name": "resize_type.width" - }, - "link": null - }, - { - "name": "resize_type.height", - "type": "INT", - "widget": { - "name": "resize_type.height" - }, - "link": null - }, - { - "name": "length", - "type": "INT", - "widget": { - "name": "length" - }, - "link": null } ], "outputs": [ @@ -130,56 +112,49 @@ "properties": { "proxyWidgets": [ [ - 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"type": "KSamplerSelect", "pos": [ - -697.721823660531, - 3841.1107362825187 + -690, + 3940 ], "size": [ - 269.97395833333337, - 58 + 270, + 110 ], "flags": {}, "order": 0, @@ -521,6 +463,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.56", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "KSamplerSelect", "enableTabs": false, "tabWidth": 65, @@ -538,12 +485,12 @@ "id": 99, "type": "ManualSigmas", "pos": [ - 410.27824286284044, - 3851.110970278795 + 450, + 3910 ], "size": [ - 269.97395833333337, - 58 + 270, + 110 ], "flags": {}, "order": 1, @@ -572,6 +519,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "ManualSigmas", "enableTabs": false, "tabWidth": 65, @@ -589,15 +541,15 @@ "id": 100, "type": "LatentUpscaleModelLoader", "pos": [ - -69.72208571196083, - 3701.1104657166875 + -70, + 3790 ], "size": [ - 389.97395833333337, - 58 + 390, + 110 ], "flags": {}, - "order": 2, + "order": 11, "mode": 0, "inputs": [ { @@ -607,7 +559,7 @@ "widget": { "name": "model_name" }, - "link": 407 + "link": 1774 } ], "outputs": [ @@ -623,21 +575,26 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.7.0", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LatentUpscaleModelLoader", - "models": [ - { - "name": "ltx-2-spatial-upscaler-x2-1.0.safetensors", - "url": "https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-spatial-upscaler-x2-1.0.safetensors", - "directory": "latent_upscale_models" - } - ], "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, "hasSecondTab": false, "secondTabText": "Send Back", "secondTabOffset": 80, - "secondTabWidth": 65 + "secondTabWidth": 65, + "models": [ + { + "name": "ltx-2-spatial-upscaler-x2-1.0.safetensors", + "url": "https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-spatial-upscaler-x2-1.0.safetensors", + "directory": "latent_upscale_models" + } + ] }, "widgets_values": [ "ltx-2-spatial-upscaler-x2-1.0.safetensors" @@ -647,15 +604,15 @@ "id": 101, "type": "LTXVConcatAVLatent", "pos": [ - 410.27824286284044, - 4101.110949206838 + 450, + 4220 ], "size": [ - 269.97395833333337, - 46 + 270, + 120 ], "flags": {}, - "order": 18, + "order": 12, "mode": 0, "inputs": [ { @@ -684,6 +641,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LTXVConcatAVLatent", "enableTabs": false, "tabWidth": 65, @@ -692,22 +654,21 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [] + } }, { "id": 108, "type": "CFGGuider", "pos": [ - 410.27824286284044, - 3701.1104657166875 + 450, + 3720 ], "size": [ - 269.97395833333337, - 98 + 270, + 160 ], "flags": {}, - "order": 22, + "order": 18, "mode": 0, "inputs": [ { @@ -751,6 +712,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.71", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "CFGGuider", "enableTabs": false, "tabWidth": 65, @@ -764,19 +730,101 @@ 1 ] }, + { + "id": 111, + "type": "LTXVEmptyLatentAudio", + "pos": [ + -1100, + 4940 + ], + "size": [ + 270, + 170 + ], + "flags": {}, + "order": 20, + "mode": 0, + "inputs": [ + { + "localized_name": "audio_vae", + "name": "audio_vae", + "type": "VAE", + "link": 285 + }, + { + "localized_name": "frames_number", + "name": "frames_number", + "type": "INT", + "widget": { + "name": "frames_number" + }, + "link": 329 + }, + { + "localized_name": "frame_rate", + "name": "frame_rate", + "type": "INT", + "widget": { + "name": "frame_rate" + }, + "link": 354 + }, + { + "localized_name": "batch_size", + "name": "batch_size", + "type": "INT", + "widget": { + "name": "batch_size" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": "Latent", + "name": "Latent", + "type": "LATENT", + "links": [ + 300 + ] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.3.68", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, + "Node name for S&R": "LTXVEmptyLatentAudio", + "enableTabs": false, + "tabWidth": 65, + "tabXOffset": 10, + "hasSecondTab": false, + "secondTabText": "Send Back", + "secondTabOffset": 80, + "secondTabWidth": 65 + }, + "widgets_values": [ + 97, + 25, + 1 + ] + }, { "id": 123, "type": "SamplerCustomAdvanced", "pos": [ - -387.72197839215096, - 3521.1103425011374 + -380, + 3530 ], "size": [ - 213.09895833333334, - 106 + 230, + 170 ], "flags": {}, - "order": 31, + "order": 29, "mode": 0, "inputs": [ { @@ -829,6 +877,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.60", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "SamplerCustomAdvanced", "enableTabs": false, "tabWidth": 65, @@ -837,22 +890,21 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [] + } }, { "id": 114, "type": "LTXVConditioning", "pos": [ - -1133.7215420073496, - 4141.110347554622 + -1130, + 4140 ], "size": [ - 269.97395833333337, - 78 + 270, + 130 ], "flags": {}, - "order": 27, + "order": 23, "mode": 0, "inputs": [ { @@ -898,6 +950,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.56", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LTXVConditioning", "enableTabs": false, "tabWidth": 65, @@ -915,15 +972,15 @@ "id": 119, "type": "CLIPTextEncode", "pos": [ - -1163.7218246405453, - 3881.1109034489627 + -1160, + 3880 ], "size": [ 400, - 88 + 200 ], "flags": {}, - "order": 12, + "order": 27, "mode": 0, "inputs": [ { @@ -955,6 +1012,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.56", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "CLIPTextEncode", "enableTabs": false, "tabWidth": 65, @@ -974,15 +1036,15 @@ "id": 116, "type": "LTXVConcatAVLatent", "pos": [ - -519.7217122979332, - 4701.110031965835 + -520, + 4830 ], "size": [ - 187.5, - 46 + 230, + 100 ], "flags": {}, - "order": 29, + "order": 25, "mode": 0, "inputs": [ { @@ -1012,6 +1074,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.7.0", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LTXVConcatAVLatent", "enableTabs": false, "tabWidth": 65, @@ -1020,22 +1087,21 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [] + } }, { "id": 122, "type": "LTXVSeparateAVLatent", "pos": [ - -393.72183921949465, - 3801.1107787938904 + -380, + 3810 ], "size": [ - 239.97395833333334, - 46 + 240, + 100 ], "flags": {}, - "order": 30, + "order": 28, "mode": 0, "inputs": [ { @@ -1066,6 +1132,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LTXVSeparateAVLatent", "enableTabs": false, "tabWidth": 65, @@ -1074,22 +1145,21 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [] + } }, { "id": 124, "type": "CLIPTextEncode", "pos": [ - -1174.7214530029996, - 3515.1112854387566 + -1170, + 3510 ], "size": [ - 409.97395833333337, - 88 + 410, + 320 ], "flags": {}, - "order": 32, + "order": 30, "mode": 0, "inputs": [ { @@ -1121,6 +1191,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.56", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "CLIPTextEncode", "enableTabs": false, "tabWidth": 65, @@ -1140,15 +1215,15 @@ "id": 98, "type": "KSamplerSelect", "pos": [ - 410.27824286284044, - 3981.1101681370833 + 450, + 4070 ], "size": [ - 269.97395833333337, - 58 + 270, + 110 ], "flags": {}, - "order": 3, + "order": 2, "mode": 0, "inputs": [ { @@ -1174,6 +1249,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.75", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "KSamplerSelect", "enableTabs": false, "tabWidth": 65, @@ -1191,12 +1271,12 @@ "id": 105, "type": "LoraLoaderModelOnly", "pos": [ - -69.72208571196083, - 3571.110499039739 + -70, + 3570 ], "size": [ - 389.97395833333337, - 82 + 390, + 140 ], "flags": {}, "order": 15, @@ -1215,7 +1295,7 @@ "widget": { "name": "lora_name" }, - "link": 406 + "link": 1773 }, { "localized_name": "strength_model", @@ -1240,21 +1320,26 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.75", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LoraLoaderModelOnly", - "models": [ - { - "name": "ltx-2-19b-distilled-lora-384.safetensors", - "url": "https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-19b-distilled-lora-384.safetensors", - "directory": "loras" - } - ], "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, "hasSecondTab": false, "secondTabText": "Send Back", "secondTabOffset": 80, - "secondTabWidth": 65 + "secondTabWidth": 65, + "models": [ + { + "name": "ltx-2-19b-distilled-lora-384.safetensors", + "url": "https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-19b-distilled-lora-384.safetensors", + "directory": "loras" + } + ] }, "widgets_values": [ "ltx-2-19b-distilled-lora-384.safetensors", @@ -1265,15 +1350,15 @@ "id": 95, "type": "LTXVScheduler", "pos": [ - -699.7218704597861, - 3981.1101681370833 + -690, + 4130 ], "size": [ - 269.97395833333337, - 154 + 270, + 170 ], "flags": {}, - "order": 17, + "order": 8, "mode": 0, "inputs": [ { @@ -1342,6 +1427,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.56", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LTXVScheduler", "enableTabs": false, "tabWidth": 65, @@ -1363,15 +1453,15 @@ "id": 126, "type": "RandomNoise", "pos": [ - -697.721823660531, - 3521.1103425011374 + -690, + 3520 ], "size": [ - 269.97395833333337, - 82 + 270, + 110 ], "flags": {}, - "order": 4, + "order": 31, "mode": 0, "inputs": [ { @@ -1381,7 +1471,7 @@ "widget": { "name": "noise_seed" }, - "link": null + "link": 1767 } ], "outputs": [ @@ -1397,6 +1487,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.56", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "RandomNoise", "enableTabs": false, "tabWidth": 65, @@ -1408,22 +1503,22 @@ }, "widgets_values": [ 0, - "randomize" + "fixed" ] }, { "id": 107, "type": "SamplerCustomAdvanced", "pos": [ - 710.2782734905775, - 3571.110499039739 + 730, + 3570 ], "size": [ - 212.36979166666669, - 106 + 230, + 170 ], "flags": {}, - "order": 21, + "order": 17, "mode": 0, "inputs": [ { @@ -1476,6 +1571,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.75", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "SamplerCustomAdvanced", "enableTabs": false, "tabWidth": 65, @@ -1484,22 +1584,21 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [] + } }, { - "id": 143, + "id": 187, "type": "RandomNoise", "pos": [ - 410.27824286284044, - 3571.110499039739 + 450, + 3570 ], "size": [ - 269.97395833333337, - 82 + 270, + 110 ], "flags": {}, - "order": 5, + "order": 3, "mode": 0, "inputs": [ { @@ -1525,6 +1624,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.56", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "RandomNoise", "enableTabs": false, "tabWidth": 65, @@ -1543,12 +1647,12 @@ "id": 139, "type": "LTXVAudioVAEDecode", "pos": [ - 1130.2783163694094, - 3841.1107362825187 + 1130, + 3840 ], "size": [ - 239.97395833333334, - 46 + 240, + 100 ], "flags": {}, "order": 35, @@ -1565,7 +1669,7 @@ "localized_name": "audio_vae", "name": "audio_vae", "type": "VAE", - "link": 383 + "link": 340 } ], "outputs": [ @@ -1581,6 +1685,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.7.0", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LTXVAudioVAEDecode", "enableTabs": false, "tabWidth": 65, @@ -1589,22 +1698,21 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [] + } }, { "id": 106, "type": "CreateVideo", "pos": [ - 1420.2783925712918, - 3761.1104019496292 + 1420, + 3760 ], "size": [ - 269.97395833333337, - 78 + 270, + 130 ], "flags": {}, - "order": 20, + "order": 16, "mode": 0, "inputs": [ { @@ -1643,6 +1751,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "CreateVideo", "enableTabs": false, "tabWidth": 65, @@ -1660,15 +1773,15 @@ "id": 134, "type": "LoraLoaderModelOnly", "pos": [ - -1649.721454901846, - 3761.1104019496292 + -1650, + 3750 ], "size": [ - 419.97395833333337, - 82 + 420, + 140 ], "flags": {}, - "order": 13, + "order": 33, "mode": 0, "inputs": [ { @@ -1684,7 +1797,7 @@ "widget": { "name": "lora_name" }, - "link": 404 + "link": 1769 }, { "localized_name": "strength_model", @@ -1710,21 +1823,26 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.7.0", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LoraLoaderModelOnly", - "models": [ - { - "name": "ltx-2-19b-ic-lora-pose-control.safetensors", - "url": "https://huggingface.co/Lightricks/LTX-2-19b-IC-LoRA-Pose-Control/resolve/main/ltx-2-19b-ic-lora-pose-control.safetensors", - "directory": "loras" - } - ], "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, "hasSecondTab": false, "secondTabText": "Send Back", "secondTabOffset": 80, - "secondTabWidth": 65 + "secondTabWidth": 65, + "models": [ + { + "name": "ltx-2-19b-ic-lora-pose-control.safetensors", + "url": "https://huggingface.co/Lightricks/LTX-2-19b-IC-LoRA-Pose-Control/resolve/main/ltx-2-19b-ic-lora-pose-control.safetensors", + "directory": "loras" + } + ] }, "widgets_values": [ "ltx-2-19b-ic-lora-pose-control.safetensors", @@ -1737,12 +1855,12 @@ "id": 138, "type": "LTXVSeparateAVLatent", "pos": [ - 730.2784619127078, - 3731.1109580277 + 740, + 3810 ], "size": [ - 193.2916015625, - 46 + 230, + 100 ], "flags": {}, "order": 34, @@ -1777,6 +1895,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LTXVSeparateAVLatent", "enableTabs": false, "tabWidth": 65, @@ -1785,22 +1908,21 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [] + } }, { - "id": 144, + "id": 188, "type": "VAEDecodeTiled", "pos": [ - 1120.2783619435547, - 3641.110599376351 + 1120, + 3640 ], "size": [ - 269.97395833333337, + 270, 150 ], "flags": {}, - "order": 36, + "order": 38, "mode": 0, "inputs": [ { @@ -1865,6 +1987,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.7.0", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "VAEDecodeTiled", "enableTabs": false, "tabWidth": 65, @@ -1885,15 +2012,15 @@ "id": 113, "type": "VAEDecode", "pos": [ - 1130.2783163694094, - 3531.1113453160738 + 1130, + 3530 ], "size": [ - 239.97395833333334, - 46 + 240, + 100 ], "flags": {}, - "order": 26, + "order": 22, "mode": 0, "inputs": [ { @@ -1920,6 +2047,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.75", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "VAEDecode", "enableTabs": false, "tabWidth": 65, @@ -1928,22 +2060,21 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [] + } }, { "id": 145, "type": "PrimitiveInt", "pos": [ - -1600, - 4940 + -1610, + 4800 ], "size": [ - 269.97395833333337, - 82 + 270, + 110 ], "flags": {}, - "order": 6, + "order": 4, "mode": 0, "inputs": [ { @@ -1969,6 +2100,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.7.0", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "PrimitiveInt", "enableTabs": false, "tabWidth": 65, @@ -1987,15 +2123,15 @@ "id": 148, "type": "PrimitiveFloat", "pos": [ - -1600, - 5070 + -1610, + 4930 ], "size": [ - 269.97395833333337, - 58 + 270, + 110 ], "flags": {}, - "order": 7, + "order": 5, "mode": 0, "inputs": [ { @@ -2022,6 +2158,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.7.0", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "PrimitiveFloat", "enableTabs": false, "tabWidth": 65, @@ -2035,19 +2176,105 @@ 24 ] }, + { + "id": 115, + "type": "EmptyLTXVLatentVideo", + "pos": [ + -1100, + 4740 + ], + "size": [ + 270, + 200 + ], + "flags": {}, + "order": 24, + "mode": 0, + "inputs": [ + { + "localized_name": "width", + "name": "width", + "type": "INT", + "widget": { + "name": "width" + }, + "link": 296 + }, + { + "localized_name": "height", + "name": "height", + "type": "INT", + "widget": { + "name": "height" + }, + "link": 297 + }, + { + "localized_name": "length", + "name": "length", + "type": "INT", + "widget": { + "name": "length" + }, + "link": 330 + }, + { + "localized_name": "batch_size", + "name": "batch_size", + "type": "INT", + "widget": { + "name": "batch_size" + }, + "link": null + } + ], + "outputs": [ + { + "localized_name": "LATENT", + "name": "LATENT", + "type": "LATENT", + "links": [ + 360 + ] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.3.60", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, + "Node name for S&R": "EmptyLTXVLatentVideo", + "enableTabs": false, + "tabWidth": 65, + "tabXOffset": 10, + "hasSecondTab": false, + "secondTabText": "Send Back", + "secondTabOffset": 80, + "secondTabWidth": 65 + }, + "widgets_values": [ + 768, + 512, + 97, + 1 + ] + }, { "id": 118, "type": "Reroute", "pos": [ - -229.7217758812614, - 4211.111007032079 + -350, + 3980 ], "size": [ - 75, - 26 + 230, + 40 ], "flags": {}, - "order": 14, + "order": 26, "mode": 0, "inputs": [ { @@ -2069,22 +2296,29 @@ ], "properties": { "showOutputText": false, - "horizontal": false + "horizontal": false, + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + } } }, { - "id": 151, + "id": 189, "type": "LTXVImgToVideoInplace", "pos": [ - -19.72161465663438, - 4071.1107364662485 + 180, + 4040 ], "size": [ - 269.97395833333337, - 122 + 260, + 190 ], - "flags": {}, - "order": 38, + "flags": { + "collapsed": false + }, + "order": 39, "mode": 0, "inputs": [ { @@ -2097,7 +2331,7 @@ "localized_name": "image", "name": "image", "type": "IMAGE", - "link": 398 + "link": 379 }, { "localized_name": "latent", @@ -2112,7 +2346,7 @@ "widget": { "name": "strength" }, - "link": 371 + "link": 1759 }, { "localized_name": "bypass", @@ -2137,6 +2371,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.7.0", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LTXVImgToVideoInplace", "enableTabs": false, "tabWidth": 65, @@ -2155,15 +2394,15 @@ "id": 104, "type": "LTXVCropGuides", "pos": [ - -9.721939801202097, - 3841.1107362825187 + -90, + 4210 ], "size": [ - 239.97395833333334, - 66 + 240, + 120 ], "flags": {}, - "order": 19, + "order": 14, "mode": 0, "inputs": [ { @@ -2215,6 +2454,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.68", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LTXVCropGuides", "enableTabs": false, "tabWidth": 65, @@ -2223,22 +2467,21 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [] + } }, { "id": 112, "type": "LTXVLatentUpsampler", "pos": [ - -9.721939801202097, - 3961.111517352274 + -90, + 4030 ], "size": [ - 259.97395833333337, - 66 + 260, + 120 ], "flags": {}, - "order": 25, + "order": 21, "mode": 0, "inputs": [ { @@ -2274,6 +2517,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.7.0", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LTXVLatentUpsampler", "enableTabs": false, "tabWidth": 65, @@ -2282,22 +2530,117 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 + } + }, + { + "id": 154, + "type": "MarkdownNote", + "pos": [ + -1640, + 5050 + ], + "size": [ + 350, + 170 + ], + "flags": { + "collapsed": false }, - "widgets_values": [] + "order": 6, + "mode": 0, + "inputs": [], + "outputs": [], + "title": "Frame Rate Note", + "properties": { + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + } + }, + "widgets_values": [ + "Please make sure the frame rate value is the same in both boxes" + ], + "color": "#432", + "bgcolor": "#653" + }, + { + "id": 96, + "type": "LTXVAudioVAELoader", + "pos": [ + -1650, + 3970 + ], + "size": [ + 420, + 110 + ], + "flags": {}, + "order": 9, + "mode": 0, + "inputs": [ + { + "localized_name": "ckpt_name", + "name": "ckpt_name", + "type": "COMBO", + "widget": { + "name": "ckpt_name" + }, + "link": 1770 + } + ], + "outputs": [ + { + "localized_name": "Audio VAE", + "name": "Audio VAE", + "type": "VAE", + "links": [ + 285, + 340 + ] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.3.68", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, + "Node name for S&R": "LTXVAudioVAELoader", + "enableTabs": false, + "tabWidth": 65, + "tabXOffset": 10, + "hasSecondTab": false, + "secondTabText": "Send Back", + "secondTabOffset": 80, + "secondTabWidth": 65, + "models": [ + { + "name": "ltx-2-19b-dev-fp8.safetensors", + "url": "https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-19b-dev-fp8.safetensors", + "directory": "checkpoints" + } + ] + }, + "widgets_values": [ + "ltx-2-19b-dev-fp8.safetensors" + ] }, { "id": 97, "type": "LTXAVTextEncoderLoader", "pos": [ - -1649.721454901846, - 4041.1110828665023 + -1650, + 4160 ], "size": [ - 419.97395833333337, - 106 + 420, + 150 ], "flags": {}, - "order": 8, + "order": 10, "mode": 0, "inputs": [ { @@ -2307,7 +2650,7 @@ "widget": { "name": "text_encoder" }, - "link": 405 + "link": 1772 }, { "localized_name": "ckpt_name", @@ -2316,7 +2659,7 @@ "widget": { "name": "ckpt_name" }, - "link": 403 + "link": 1771 }, { "localized_name": "device", @@ -2342,7 +2685,19 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.7.0", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LTXAVTextEncoderLoader", + "enableTabs": false, + "tabWidth": 65, + "tabXOffset": 10, + "hasSecondTab": false, + "secondTabText": "Send Back", + "secondTabOffset": 80, + "secondTabWidth": 65, "models": [ { "name": "ltx-2-19b-dev-fp8.safetensors", @@ -2354,17 +2709,10 @@ "url": "https://huggingface.co/Comfy-Org/ltx-2/resolve/main/split_files/text_encoders/gemma_3_12B_it_fp4_mixed.safetensors", "directory": "text_encoders" } - ], - "enableTabs": false, - "tabWidth": 65, - "tabXOffset": 10, - "hasSecondTab": false, - "secondTabText": "Send Back", - "secondTabOffset": 80, - "secondTabWidth": 65 + ] }, "widgets_values": [ - "ltx-2-19b-ic-lora-pose-control.safetensors", + "gemma_3_12B_it_fp4_mixed.safetensors", "ltx-2-19b-dev-fp8.safetensors", "default" ] @@ -2373,15 +2721,15 @@ "id": 103, "type": "CheckpointLoaderSimple", "pos": [ - -1649.721454901846, - 3591.1104777840524 + -1650, + 3520 ], "size": [ - 419.97395833333337, - 98 + 420, + 160 ], "flags": {}, - "order": 9, + "order": 13, "mode": 0, "inputs": [ { @@ -2391,7 +2739,7 @@ "widget": { "name": "ckpt_name" }, - "link": 401 + "link": 1768 } ], "outputs": [ @@ -2424,137 +2772,89 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.56", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "CheckpointLoaderSimple", + "enableTabs": false, + "tabWidth": 65, + "tabXOffset": 10, + "hasSecondTab": false, + "secondTabText": "Send Back", + "secondTabOffset": 80, + "secondTabWidth": 65, "models": [ { "name": "ltx-2-19b-dev-fp8.safetensors", "url": "https://huggingface.co/Lightricks/LTX-2/resolve/main/ltx-2-19b-dev-fp8.safetensors", "directory": "checkpoints" } - ], - "enableTabs": false, - "tabWidth": 65, - "tabXOffset": 10, - "hasSecondTab": false, - "secondTabText": "Send Back", - "secondTabOffset": 80, - "secondTabWidth": 65 + ] }, "widgets_values": [ "ltx-2-19b-dev-fp8.safetensors" ] }, { - "id": 156, - "type": "LTXVAudioVAELoader", + "id": 110, + "type": "GetImageSize", "pos": [ - -1636.9543279290153, - 3911.095334870057 + -1610, + 4630 ], "size": [ - 399.0494791666667, - 58 + 260, + 120 ], "flags": {}, - "order": 10, + "order": 19, "mode": 0, "inputs": [ - { - "localized_name": "ckpt_name", - "name": "ckpt_name", - "type": "COMBO", - "widget": { - "name": "ckpt_name" - }, - "link": 402 - } - ], - "outputs": [ - { - "localized_name": "Audio VAE", - "name": "Audio VAE", - "type": "VAE", - "links": [ - 382, - 383 - ] - } - ], - "properties": { - "cnr_id": "comfy-core", - "ver": "0.11.0", - "Node name for S&R": "LTXVAudioVAELoader" - }, - "widgets_values": [ - "ltx-2-19b-dev-fp8.safetensors" - ] - }, - { - "id": 149, - "type": "LTXVImgToVideoInplace", - "pos": [ - -1089.7215608128167, - 4401.110560478942 - ], - "size": [ - 269.97395833333337, - 122 - ], - "flags": {}, - "order": 37, - "mode": 0, - "inputs": [ - { - "localized_name": "vae", - "name": "vae", - "type": "VAE", - "link": 359 - }, { "localized_name": "image", "name": "image", "type": "IMAGE", - "link": 399 - }, - { - "localized_name": "latent", - "name": "latent", - "type": "LATENT", - "link": 360 - }, - { - "localized_name": "strength", - "name": "strength", - "type": "FLOAT", - "widget": { - "name": "strength" - }, - "link": 370 - }, - { - "localized_name": "bypass", - "name": "bypass", - "type": "BOOLEAN", - "widget": { - "name": "bypass" - }, - "link": 363 + "link": 381 } ], "outputs": [ { - "localized_name": "latent", - "name": "latent", - "type": "LATENT", + "localized_name": "width", + "name": "width", + "type": "INT", "links": [ - 357 + 296 + ] + }, + { + "localized_name": "height", + "name": "height", + "type": "INT", + "links": [ + 297 + ] + }, + { + "localized_name": "batch_size", + "name": "batch_size", + "type": "INT", + "links": [ + 329, + 330 ] } ], "properties": { "cnr_id": "comfy-core", "ver": "0.7.0", - "Node name for S&R": "LTXVImgToVideoInplace", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, + "Node name for S&R": "GetImageSize", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -2562,25 +2862,21 @@ "secondTabText": "Send Back", "secondTabOffset": 80, "secondTabWidth": 65 - }, - "widgets_values": [ - 1, - false - ] + } }, { "id": 132, "type": "LTXVAddGuide", "pos": [ - -599.7217670603999, - 4421.110609115862 + -600, + 4550 ], "size": [ - 269.97395833333337, - 162 + 270, + 240 ], "flags": {}, - "order": 33, + "order": 32, "mode": 0, "inputs": [ { @@ -2611,7 +2907,7 @@ "localized_name": "image", "name": "image", "type": "IMAGE", - "link": 395 + "link": 348 }, { "localized_name": "frame_idx", @@ -2663,6 +2959,11 @@ "properties": { "cnr_id": "comfy-core", "ver": "0.3.75", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, "Node name for S&R": "LTXVAddGuide", "enableTabs": false, "tabWidth": 65, @@ -2678,114 +2979,76 @@ ] }, { - "id": 154, - "type": "MarkdownNote", + "id": 149, + "type": "LTXVImgToVideoInplace", "pos": [ - -1630, - 5190 + -1090, + 4530 ], "size": [ - 350, - 88 - ], - "flags": { - "collapsed": false - }, - "order": 11, - "mode": 0, - "inputs": [], - "outputs": [], - "title": "Frame Rate Note", - "properties": {}, - "widgets_values": [ - "Please make sure the frame rate value is the same in both boxes" - ], - "color": "#432", - "bgcolor": "#653" - }, - { - "id": 159, - "type": "ResizeImageMaskNode", - "pos": [ - -1610, - 4580 - ], - "size": [ - 284.375, - 154 + 270, + 180 ], "flags": {}, - "order": 39, + "order": 36, "mode": 0, "inputs": [ { - "localized_name": "input", - "name": "input", - "type": "IMAGE,MASK", - "link": 400 + "localized_name": "vae", + "name": "vae", + "type": "VAE", + "link": 359 }, { - "localized_name": "resize_type", - "name": "resize_type", - "type": "COMFY_DYNAMICCOMBO_V3", + "localized_name": "image", + "name": "image", + "type": "IMAGE", + "link": 364 + }, + { + "localized_name": "latent", + "name": "latent", + "type": "LATENT", + "link": 360 + }, + { + "localized_name": "strength", + "name": "strength", + "type": "FLOAT", "widget": { - "name": "resize_type" + "name": "strength" }, - "link": null + "link": 1758 }, { - "localized_name": "width", - "name": "resize_type.width", - "type": "INT", + "localized_name": "bypass", + "name": "bypass", + "type": "BOOLEAN", "widget": { - "name": "resize_type.width" - }, - "link": 408 - }, - { - "localized_name": "height", - "name": "resize_type.height", - "type": "INT", - "widget": { - "name": "resize_type.height" - }, - "link": 409 - }, - { - "localized_name": "crop", - "name": "resize_type.crop", - "type": "COMBO", - "widget": { - "name": "resize_type.crop" - }, - "link": null - }, - { - "localized_name": "scale_method", - "name": "scale_method", - "type": "COMBO", - "widget": { - "name": "scale_method" + "name": "bypass" }, "link": null } ], "outputs": [ { - "localized_name": "resized", - "name": "resized", - "type": "IMAGE,MASK", + "localized_name": "latent", + "name": "latent", + "type": "LATENT", "links": [ - 391, - 392, - 395 + 357 ] } ], "properties": { "cnr_id": "comfy-core", "ver": "0.7.0", - "Node name for S&R": "ResizeImageMaskNode", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, + "Node name for S&R": "LTXVImgToVideoInplace", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -2795,139 +3058,69 @@ "secondTabWidth": 65 }, "widgets_values": [ - "scale dimensions", - 1280, - 720, - "center", - "lanczos" + 1, + false ] }, { - "id": 110, - "type": "GetImageSize", + "id": 155, + "type": "ImageScaleBy", "pos": [ - -1600, - 4780 + -1620, + 4440 ], "size": [ - 259.97395833333337, - 66 + 280, + 140 ], "flags": {}, - "order": 23, + "order": 37, "mode": 0, "inputs": [ { "localized_name": "image", "name": "image", "type": "IMAGE", - "link": 391 - } - ], - "outputs": [ - { - "localized_name": "width", - "name": "width", - "type": "INT", - "links": [ - 296 - ] + "link": 380 }, { - "localized_name": "height", - "name": "height", - "type": "INT", - "links": [ - 297 - ] - }, - { - "localized_name": "batch_size", - "name": "batch_size", - "type": "INT", - "links": [] - } - ], - "properties": { - "cnr_id": "comfy-core", - "ver": "0.7.0", - "Node name for S&R": "GetImageSize", - "enableTabs": false, - "tabWidth": 65, - "tabXOffset": 10, - "hasSecondTab": false, - "secondTabText": "Send Back", - "secondTabOffset": 80, - "secondTabWidth": 65 - }, - "widgets_values": [] - }, - { - "id": 115, - "type": "EmptyLTXVLatentVideo", - "pos": [ - -1099.721794809093, - 4611.11072170357 - ], - "size": [ - 269.97395833333337, - 130 - ], - "flags": {}, - "order": 28, - "mode": 0, - "inputs": [ - { - "localized_name": "width", - "name": "width", - "type": "INT", + "localized_name": "upscale_method", + "name": "upscale_method", + "type": "COMBO", "widget": { - "name": "width" + "name": "upscale_method" }, - "link": 296 + "link": null }, { - "localized_name": "height", - "name": "height", - "type": "INT", + "localized_name": "scale_by", + "name": "scale_by", + "type": "FLOAT", "widget": { - "name": "height" - }, - "link": 297 - }, - { - "localized_name": "length", - "name": "length", - "type": "INT", - "widget": { - "name": "length" - }, - "link": 410 - }, - { - "localized_name": "batch_size", - "name": "batch_size", - "type": "INT", - "widget": { - "name": "batch_size" + "name": "scale_by" }, "link": null } ], "outputs": [ { - "localized_name": "LATENT", - "name": "LATENT", - "type": "LATENT", + "localized_name": "IMAGE", + "name": "IMAGE", + "type": "IMAGE", "links": [ - 360 + 381 ] } ], "properties": { "cnr_id": "comfy-core", - "ver": "0.3.60", - "Node name for S&R": "EmptyLTXVLatentVideo", + "ver": "0.5.1", + "ue_properties": { + "widget_ue_connectable": {}, + "version": "7.7", + "input_ue_unconnectable": {} + }, + "Node name for S&R": "ImageScaleBy", "enableTabs": false, "tabWidth": 65, "tabXOffset": 10, @@ -2937,87 +3130,8 @@ "secondTabWidth": 65 }, "widgets_values": [ - 768, - 512, - 97, - 1 - ] - }, - { - "id": 111, - "type": "LTXVEmptyLatentAudio", - "pos": [ - -1099.721794809093, - 4811.110229576288 - ], - "size": [ - 269.97395833333337, - 106 - ], - "flags": {}, - "order": 24, - "mode": 0, - "inputs": [ - { - "localized_name": "audio_vae", - "name": "audio_vae", - "type": "VAE", - "link": 382 - }, - { - "localized_name": "frames_number", - "name": "frames_number", - "type": "INT", - "widget": { - "name": "frames_number" - }, - "link": null - }, - { - "localized_name": "frame_rate", - "name": "frame_rate", - "type": "INT", - "widget": { - "name": "frame_rate" - }, - "link": 354 - }, - { - "localized_name": "batch_size", - "name": "batch_size", - "type": "INT", - "widget": { - "name": "batch_size" - }, - "link": null - } - ], - "outputs": [ - { - "localized_name": "Latent", - "name": "Latent", - "type": "LATENT", - "links": [ - 300 - ] - } - ], - "properties": { - "cnr_id": "comfy-core", - "ver": "0.3.68", - "Node name for S&R": "LTXVEmptyLatentAudio", - "enableTabs": false, - "tabWidth": 65, - "tabXOffset": 10, - "hasSecondTab": false, - "secondTabText": "Send Back", - "secondTabOffset": 80, - "secondTabWidth": 65 - }, - "widgets_values": [ - 97, - 25, - 1 + "lanczos", + 0.5 ] } ], @@ -3028,8 +3142,8 @@ "bounding": [ -1660, 3440, - 440, - 820 + 450, + 940 ], "color": "#3f789e", "font_size": 24, @@ -3041,8 +3155,8 @@ "bounding": [ -700, 3440, - 570, - 820 + 580, + 940 ], "color": "#3f789e", "font_size": 24, @@ -3054,8 +3168,8 @@ "bounding": [ -1180, 3440, - 440, - 820 + 450, + 940 ], "color": "#3f789e", "font_size": 24, @@ -3066,7 +3180,7 @@ "title": "Latent", "bounding": [ -1180, - 4290, + 4420, 1050, 680 ], @@ -3080,8 +3194,8 @@ "bounding": [ -100, 3440, - 1090, - 820 + 1110, + 940 ], "color": "#3f789e", "font_size": 24, @@ -3091,10 +3205,10 @@ "id": 6, "title": "Sampler", "bounding": [ - 350, + 410, 3480, - 620, - 750 + 590, + 880 ], "color": "#3f789e", "font_size": 24, @@ -3106,8 +3220,8 @@ "bounding": [ -90, 3480, - 430, - 310 + 450, + 480 ], "color": "#3f789e", "font_size": 24, @@ -3117,8 +3231,8 @@ "id": 11, "title": "Frame rate", "bounding": [ - -1610, - 4860, + -1620, + 4730, 290, 271.6 ], @@ -3184,6 +3298,22 @@ "target_slot": 2, "type": "CONDITIONING" }, + { + "id": 285, + "origin_id": 96, + "origin_slot": 0, + "target_id": 111, + "target_slot": 0, + "type": "VAE" + }, + { + "id": 329, + "origin_id": 110, + "origin_slot": 2, + "target_id": 111, + "target_slot": 1, + "type": "INT" + }, { "id": 260, "origin_id": 126, @@ -3240,6 +3370,14 @@ "target_slot": 1, "type": "INT" }, + { + "id": 330, + "origin_id": 110, + "origin_slot": 2, + "target_id": 115, + "target_slot": 2, + "type": "INT" + }, { "id": 325, "origin_id": 103, @@ -3360,6 +3498,14 @@ "target_slot": 0, "type": "LATENT" }, + { + "id": 340, + "origin_id": 96, + "origin_slot": 0, + "target_id": 139, + "target_slot": 1, + "type": "VAE" + }, { "id": 337, "origin_id": 138, @@ -3490,23 +3636,31 @@ }, { "id": 347, - "origin_id": 143, + "origin_id": 187, "origin_slot": 0, "target_id": 107, "target_slot": 0, "type": "NOISE" }, + { + "id": 348, + "origin_id": -10, + "origin_slot": 1, + "target_id": 132, + "target_slot": 4, + "type": "IMAGE" + }, { "id": 351, "origin_id": 138, "origin_slot": 0, - "target_id": 144, + "target_id": 188, "target_slot": 0, "type": "LATENT" }, { "id": 352, - "origin_id": 144, + "origin_id": 188, "origin_slot": 0, "target_id": 106, "target_slot": 0, @@ -3516,7 +3670,7 @@ "id": 353, "origin_id": 103, "origin_slot": 2, - "target_id": 144, + "target_id": 188, "target_slot": 1, "type": "VAE" }, @@ -3569,16 +3723,16 @@ "type": "LATENT" }, { - "id": 363, + "id": 364, "origin_id": -10, "origin_slot": 2, "target_id": 149, - "target_slot": 4, - "type": "BOOLEAN" + "target_slot": 1, + "type": "IMAGE" }, { "id": 365, - "origin_id": 151, + "origin_id": 189, "origin_slot": 0, "target_id": 101, "target_slot": 0, @@ -3588,7 +3742,7 @@ "id": 366, "origin_id": 112, "origin_slot": 0, - "target_id": 151, + "target_id": 189, "target_slot": 2, "type": "LATENT" }, @@ -3596,92 +3750,68 @@ "id": 367, "origin_id": 118, "origin_slot": 0, - "target_id": 151, + "target_id": 189, "target_slot": 0, "type": "VAE" }, { "id": 368, "origin_id": -10, - "origin_slot": 2, - "target_id": 151, + "origin_slot": 4, + "target_id": 189, "target_slot": 4, "type": "BOOLEAN" }, { - "id": 370, + "id": 379, "origin_id": -10, - "origin_slot": 1, - "target_id": 149, - "target_slot": 3, - "type": "FLOAT" - }, - { - "id": 371, - "origin_id": -10, - "origin_slot": 1, - "target_id": 151, - "target_slot": 3, - "type": "FLOAT" - }, - { - "id": 382, - "origin_id": 156, - "origin_slot": 0, - "target_id": 111, - "target_slot": 0, - "type": "VAE" - }, - { - "id": 383, - "origin_id": 156, - "origin_slot": 0, - "target_id": 139, + "origin_slot": 2, + "target_id": 189, "target_slot": 1, - "type": "VAE" + "type": "IMAGE" }, { - "id": 391, - "origin_id": 159, + "id": 380, + "origin_id": -10, + "origin_slot": 1, + "target_id": 155, + "target_slot": 0, + "type": "IMAGE" + }, + { + "id": 381, + "origin_id": 155, "origin_slot": 0, "target_id": 110, "target_slot": 0, "type": "IMAGE" }, { - "id": 395, - "origin_id": 159, - "origin_slot": 0, - "target_id": 132, - "target_slot": 4, - "type": "IMAGE" - }, - { - "id": 398, - "origin_id": -10, - "origin_slot": 3, - "target_id": 151, - "target_slot": 1, - "type": "IMAGE" - }, - { - "id": 399, + "id": 1758, "origin_id": -10, "origin_slot": 3, "target_id": 149, - "target_slot": 1, - "type": "IMAGE" + "target_slot": 3, + "type": "FLOAT" }, { - "id": 400, + "id": 1759, + "origin_id": -10, + "origin_slot": 3, + "target_id": 189, + "target_slot": 3, + "type": "FLOAT" + }, + { + "id": 1767, "origin_id": -10, "origin_slot": 4, - "target_id": 159, + "target_id": 126, "target_slot": 0, - "type": "IMAGE,MASK" + "type": "INT" }, { - "id": 401, + "id": 1768, "origin_id": -10, "origin_slot": 5, "target_id": 103, @@ -3689,23 +3819,7 @@ "type": "COMBO" }, { - "id": 402, - "origin_id": -10, - "origin_slot": 5, - "target_id": 156, - "target_slot": 0, - "type": "COMBO" - }, - { - "id": 403, - "origin_id": -10, - "origin_slot": 5, - "target_id": 97, - "target_slot": 1, - "type": "COMBO" - }, - { - "id": 404, + "id": 1769, "origin_id": -10, "origin_slot": 6, "target_id": 134, @@ -3713,52 +3827,44 @@ "type": "COMBO" }, { - "id": 405, + "id": 1770, "origin_id": -10, - "origin_slot": 6, + "origin_slot": 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Subject: [PATCH 097/140] Make EmptySD3LatentImage node use intermediate dtype. (#13577) --- comfy_extras/nodes_sd3.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py index c43844a1a..6655c1ba7 100644 --- a/comfy_extras/nodes_sd3.py +++ b/comfy_extras/nodes_sd3.py @@ -54,7 +54,7 @@ class EmptySD3LatentImage(io.ComfyNode): @classmethod def execute(cls, width, height, batch_size=1) -> io.NodeOutput: - latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=comfy.model_management.intermediate_device()) + latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()) return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 8}) generate = execute # TODO: remove From 6968a70e603a0d2c80387aa139b06276635b36e3 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Mon, 27 Apr 2026 19:53:08 +0300 Subject: [PATCH 098/140] [Partner Nodes] HappyHorse model (#13582) * feat(api-nodes): add nodes for HappyHorse model Signed-off-by: bigcat88 * fix price badges Signed-off-by: bigcat88 * fix: allow durations up to 15 s Signed-off-by: bigcat88 --------- Signed-off-by: bigcat88 --- comfy_api_nodes/apis/wan.py | 4 +- comfy_api_nodes/nodes_wan.py | 555 +++++++++++++++++++++++++++++++++++ 2 files changed, 557 insertions(+), 2 deletions(-) diff --git a/comfy_api_nodes/apis/wan.py b/comfy_api_nodes/apis/wan.py index 44b65e4f6..c64acae97 100644 --- a/comfy_api_nodes/apis/wan.py +++ b/comfy_api_nodes/apis/wan.py @@ -118,7 +118,7 @@ class Wan27ReferenceVideoInputField(BaseModel): class Wan27ReferenceVideoParametersField(BaseModel): resolution: str = Field(...) ratio: str | None = Field(None) - duration: int = Field(5, ge=2, le=10) + duration: int = Field(5, ge=2, le=15) watermark: bool = Field(False) seed: int = Field(..., ge=0, le=2147483647) @@ -157,7 +157,7 @@ class Wan27VideoEditInputField(BaseModel): class Wan27VideoEditParametersField(BaseModel): resolution: str = Field(...) ratio: str | None = Field(None) - duration: int = Field(0) + duration: int | None = Field(0) audio_setting: str = Field("auto") watermark: bool = Field(False) seed: int = Field(..., ge=0, le=2147483647) diff --git a/comfy_api_nodes/nodes_wan.py b/comfy_api_nodes/nodes_wan.py index d1470894a..7d7466fb6 100644 --- a/comfy_api_nodes/nodes_wan.py +++ b/comfy_api_nodes/nodes_wan.py @@ -1646,6 +1646,557 @@ class Wan2ReferenceVideoApi(IO.ComfyNode): return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) +class HappyHorseTextToVideoApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="HappyHorseTextToVideoApi", + display_name="HappyHorse Text to Video", + category="api node/video/Wan", + description="Generates a video based on a text prompt using the HappyHorse model.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "happyhorse-1.0-t2v", + [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt describing the elements and visual features. " + "Supports English and Chinese.", + ), + IO.Combo.Input( + "resolution", + options=["720P", "1080P"], + ), + IO.Combo.Input( + "ratio", + options=["16:9", "9:16", "1:1", "4:3", "3:4"], + ), + IO.Int.Input( + "duration", + default=5, + min=3, + max=15, + step=1, + display_mode=IO.NumberDisplay.number, + ), + ], + ), + ], + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add an AI-generated watermark to the result.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $ppsTable := { "720p": 0.14, "1080p": 0.24 }; + $pps := $lookup($ppsTable, $res); + { "type": "usd", "usd": $pps * $dur } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + seed: int, + watermark: bool, + ): + validate_string(model["prompt"], strip_whitespace=False, min_length=1) + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", + method="POST", + ), + response_model=TaskCreationResponse, + data=Wan27Text2VideoTaskCreationRequest( + model=model["model"], + input=Text2VideoInputField( + prompt=model["prompt"], + negative_prompt=None, + ), + parameters=Wan27Text2VideoParametersField( + resolution=model["resolution"], + ratio=model["ratio"], + duration=model["duration"], + seed=seed, + watermark=watermark, + ), + ), + ) + if not initial_response.output: + raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + poll_interval=7, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + +class HappyHorseImageToVideoApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="HappyHorseImageToVideoApi", + display_name="HappyHorse Image to Video", + category="api node/video/Wan", + description="Generate a video from a first-frame image using the HappyHorse model.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "happyhorse-1.0-i2v", + [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt describing the elements and visual features. " + "Supports English and Chinese.", + ), + IO.Combo.Input( + "resolution", + options=["720P", "1080P"], + ), + IO.Int.Input( + "duration", + default=5, + min=3, + max=15, + step=1, + display_mode=IO.NumberDisplay.number, + ), + ], + ), + ], + ), + IO.Image.Input( + "first_frame", + tooltip="First frame image. The output aspect ratio is derived from this image.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add an AI-generated watermark to the result.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $ppsTable := { "720p": 0.14, "1080p": 0.24 }; + $pps := $lookup($ppsTable, $res); + { "type": "usd", "usd": $pps * $dur } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + first_frame: Input.Image, + seed: int, + watermark: bool, + ): + media = [ + Wan27MediaItem( + type="first_frame", + url=await upload_image_to_comfyapi(cls, image=first_frame), + ) + ] + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", + method="POST", + ), + response_model=TaskCreationResponse, + data=Wan27ImageToVideoTaskCreationRequest( + model=model["model"], + input=Wan27ImageToVideoInputField( + prompt=model["prompt"] or None, + negative_prompt=None, + media=media, + ), + parameters=Wan27ImageToVideoParametersField( + resolution=model["resolution"], + duration=model["duration"], + seed=seed, + watermark=watermark, + ), + ), + ) + if not initial_response.output: + raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + poll_interval=7, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + +class HappyHorseVideoEditApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="HappyHorseVideoEditApi", + display_name="HappyHorse Video Edit", + category="api node/video/Wan", + description="Edit a video using text instructions or reference images with the HappyHorse model. " + "Output duration is 3-15s and matches the input video; inputs longer than 15s are truncated.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "happyhorse-1.0-video-edit", + [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Editing instructions or style transfer requirements.", + ), + IO.Combo.Input( + "resolution", + options=["720P", "1080P"], + ), + IO.Combo.Input( + "ratio", + options=["16:9", "9:16", "1:1", "4:3", "3:4"], + tooltip="Aspect ratio. If not changed, approximates the input video ratio.", + ), + IO.Autogrow.Input( + "reference_images", + template=IO.Autogrow.TemplateNames( + IO.Image.Input("reference_image"), + names=[ + "image1", + "image2", + "image3", + "image4", + "image5", + ], + min=0, + ), + ), + ], + ), + ], + ), + IO.Video.Input( + "video", + tooltip="The video to edit.", + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add an AI-generated watermark to the result.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution"]), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $ppsTable := { "720p": 0.14, "1080p": 0.24 }; + $pps := $lookup($ppsTable, $res); + { "type": "usd", "usd": $pps, "format": { "suffix": "/second" } } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + video: Input.Video, + seed: int, + watermark: bool, + ): + validate_string(model["prompt"], strip_whitespace=False, min_length=1) + validate_video_duration(video, min_duration=3, max_duration=60) + media = [Wan27MediaItem(type="video", url=await upload_video_to_comfyapi(cls, video))] + reference_images = model.get("reference_images", {}) + for key in reference_images: + media.append( + Wan27MediaItem( + type="reference_image", url=await upload_image_to_comfyapi(cls, image=reference_images[key]) + ) + ) + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", + method="POST", + ), + response_model=TaskCreationResponse, + data=Wan27VideoEditTaskCreationRequest( + model=model["model"], + input=Wan27VideoEditInputField(prompt=model["prompt"], media=media), + parameters=Wan27VideoEditParametersField( + resolution=model["resolution"], + ratio=model["ratio"], + duration=None, + watermark=watermark, + seed=seed, + ), + ), + ) + if not initial_response.output: + raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + poll_interval=7, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + +class HappyHorseReferenceVideoApi(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="HappyHorseReferenceVideoApi", + display_name="HappyHorse Reference to Video", + category="api node/video/Wan", + description="Generate a video featuring a person or object from reference materials with the HappyHorse " + "model. Supports single-character performances and multi-character interactions.", + inputs=[ + IO.DynamicCombo.Input( + "model", + options=[ + IO.DynamicCombo.Option( + "happyhorse-1.0-r2v", + [ + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Prompt describing the video. Use identifiers such as 'character1' and " + "'character2' to refer to the reference characters.", + ), + IO.Combo.Input( + "resolution", + options=["720P", "1080P"], + ), + IO.Combo.Input( + "ratio", + options=["16:9", "9:16", "1:1", "4:3", "3:4"], + ), + IO.Int.Input( + "duration", + default=5, + min=3, + max=15, + step=1, + display_mode=IO.NumberDisplay.number, + ), + IO.Autogrow.Input( + "reference_images", + template=IO.Autogrow.TemplateNames( + IO.Image.Input("reference_image"), + names=[ + "image1", + "image2", + "image3", + "image4", + "image5", + "image6", + "image7", + "image8", + "image9", + ], + min=1, + ), + ), + ], + ), + ], + ), + IO.Int.Input( + "seed", + default=0, + min=0, + max=2147483647, + step=1, + display_mode=IO.NumberDisplay.number, + control_after_generate=True, + tooltip="Seed to use for generation.", + ), + IO.Boolean.Input( + "watermark", + default=False, + tooltip="Whether to add an AI-generated watermark to the result.", + advanced=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution", "model.duration"]), + expr=""" + ( + $res := $lookup(widgets, "model.resolution"); + $dur := $lookup(widgets, "model.duration"); + $ppsTable := { "720p": 0.14, "1080p": 0.24 }; + $pps := $lookup($ppsTable, $res); + { "type": "usd", "usd": $pps * $dur } + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + model: dict, + seed: int, + watermark: bool, + ): + validate_string(model["prompt"], strip_whitespace=False, min_length=1) + media = [] + reference_images = model.get("reference_images", {}) + for key in reference_images: + media.append( + Wan27MediaItem( + type="reference_image", + url=await upload_image_to_comfyapi(cls, image=reference_images[key]), + ) + ) + if not media: + raise ValueError("At least one reference reference image must be provided.") + + initial_response = await sync_op( + cls, + ApiEndpoint( + path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", + method="POST", + ), + response_model=TaskCreationResponse, + data=Wan27ReferenceVideoTaskCreationRequest( + model=model["model"], + input=Wan27ReferenceVideoInputField( + prompt=model["prompt"], + negative_prompt=None, + media=media, + ), + parameters=Wan27ReferenceVideoParametersField( + resolution=model["resolution"], + ratio=model["ratio"], + duration=model["duration"], + watermark=watermark, + seed=seed, + ), + ), + ) + if not initial_response.output: + raise Exception(f"An unknown error occurred: {initial_response.code} - {initial_response.message}") + response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}"), + response_model=VideoTaskStatusResponse, + status_extractor=lambda x: x.output.task_status, + poll_interval=7, + ) + return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) + + class WanApiExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: @@ -1660,6 +2211,10 @@ class WanApiExtension(ComfyExtension): Wan2VideoContinuationApi, Wan2VideoEditApi, Wan2ReferenceVideoApi, + HappyHorseTextToVideoApi, + HappyHorseImageToVideoApi, + HappyHorseVideoEditApi, + HappyHorseReferenceVideoApi, ] From 1233f077b1b96ec1f8c7c39e83bbe1a734b36424 Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Tue, 28 Apr 2026 01:06:03 +0800 Subject: [PATCH 099/140] chore: update workflow templates to v0.9.63 (#13586) Co-authored-by: Jedrzej Kosinski --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 6c7457e03..66a130a9b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.15 -comfyui-workflow-templates==0.9.62 +comfyui-workflow-templates==0.9.63 comfyui-embedded-docs==0.4.4 torch torchsde From 75143eeb06b14bc93db71de207945f6f888be4e0 Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 27 Apr 2026 13:24:36 -0400 Subject: [PATCH 100/140] ComfyUI v0.20.0 --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 2a1eb9905..9c547a228 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.19.3" +__version__ = "0.20.0" diff --git a/pyproject.toml b/pyproject.toml index 8fa92ecbe..785837c09 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.19.3" +version = "0.20.0" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.10" From 64b8457f55cd7fb54ca7a956d9c73b505e903e0c Mon Sep 17 00:00:00 2001 From: comfyanonymous Date: Mon, 27 Apr 2026 16:10:14 -0400 Subject: [PATCH 101/140] ComfyUI v0.20.1 because github is broken again and messed up my release. --- comfyui_version.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/comfyui_version.py b/comfyui_version.py index 9c547a228..53e7156e3 100644 --- a/comfyui_version.py +++ b/comfyui_version.py @@ -1,3 +1,3 @@ # This file is automatically generated by the build process when version is # updated in pyproject.toml. -__version__ = "0.20.0" +__version__ = "0.20.1" diff --git a/pyproject.toml b/pyproject.toml index 785837c09..633dac517 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "ComfyUI" -version = "0.20.0" +version = "0.20.1" readme = "README.md" license = { file = "LICENSE" } requires-python = ">=3.10" From 3cbf015578ac04c30b10078887a774a4b4e45fe4 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Mon, 27 Apr 2026 16:44:12 -0700 Subject: [PATCH 102/140] Read audio and video at the same time in video loader node. (#13591) --- comfy_api/latest/_input_impl/video_types.py | 135 ++++++++++++-------- 1 file changed, 82 insertions(+), 53 deletions(-) diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py index eb4d3701d..812b3eb30 100644 --- a/comfy_api/latest/_input_impl/video_types.py +++ b/comfy_api/latest/_input_impl/video_types.py @@ -12,6 +12,7 @@ import numpy as np import math import torch from .._util import VideoContainer, VideoCodec, VideoComponents +import logging def container_to_output_format(container_format: str | None) -> str | None: @@ -238,32 +239,86 @@ class VideoFromFile(VideoInput): start_time = max(self._get_raw_duration() + self.__start_time, 0) else: start_time = self.__start_time + # Get video frames frames = [] + audio_frames = [] alphas = None start_pts = int(start_time / video_stream.time_base) end_pts = int((start_time + self.__duration) / video_stream.time_base) - container.seek(start_pts, stream=video_stream) - image_format = 'gbrpf32le' - for frame in container.decode(video_stream): - if alphas is None: - for comp in frame.format.components: - if comp.is_alpha: - alphas = [] - image_format = 'gbrapf32le' - break - if frame.pts < start_pts: - continue - if self.__duration and frame.pts >= end_pts: + if start_pts != 0: + container.seek(start_pts, stream=video_stream) + + image_format = 'gbrpf32le' + audio = None + + streams = [video_stream] + has_first_audio_frame = False + checked_alpha = False + + # Default to False so we decode until EOF if duration is 0 + video_done = False + audio_done = True + + if len(container.streams.audio): + audio_stream = container.streams.audio[-1] + streams += [audio_stream] + resampler = av.audio.resampler.AudioResampler(format='fltp') + audio_done = False + + for packet in container.demux(*streams): + if video_done and audio_done: break - img = frame.to_ndarray(format=image_format) # shape: (H, W, 4) - if alphas is None: - frames.append(torch.from_numpy(img)) - else: - frames.append(torch.from_numpy(img[..., :-1])) - alphas.append(torch.from_numpy(img[..., -1:])) + if packet.stream.type == "video": + if video_done: + continue + try: + for frame in packet.decode(): + if frame.pts < start_pts: + continue + if self.__duration and frame.pts >= end_pts: + video_done = True + break + + if not checked_alpha: + for comp in frame.format.components: + if comp.is_alpha: + alphas = [] + image_format = 'gbrapf32le' + break + checked_alpha = True + + img = frame.to_ndarray(format=image_format) # shape: (H, W, 4) + if alphas is None: + frames.append(torch.from_numpy(img)) + else: + frames.append(torch.from_numpy(img[..., :-1])) + alphas.append(torch.from_numpy(img[..., -1:])) + except av.error.InvalidDataError: + logging.info("pyav decode error") + + elif packet.stream.type == "audio": + if audio_done: + continue + + aframes = itertools.chain.from_iterable( + map(resampler.resample, packet.decode()) + ) + for frame in aframes: + if self.__duration and frame.time > start_time + self.__duration: + audio_done = True + break + + if not has_first_audio_frame: + offset_seconds = start_time - frame.pts * audio_stream.time_base + to_skip = max(0, int(offset_seconds * audio_stream.sample_rate)) + if to_skip < frame.samples: + has_first_audio_frame = True + audio_frames.append(frame.to_ndarray()[..., to_skip:]) + else: + audio_frames.append(frame.to_ndarray()) images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 0, 0, 3) if alphas is not None: @@ -272,42 +327,16 @@ class VideoFromFile(VideoInput): # Get frame rate frame_rate = Fraction(video_stream.average_rate) if video_stream.average_rate else Fraction(1) - # Get audio if available - audio = None - container.seek(start_pts, stream=video_stream) - # Use last stream for consistency - if len(container.streams.audio): - audio_stream = container.streams.audio[-1] - audio_frames = [] - resample = av.audio.resampler.AudioResampler(format='fltp').resample - frames = itertools.chain.from_iterable( - map(resample, container.decode(audio_stream)) - ) + if len(audio_frames) > 0: + audio_data = np.concatenate(audio_frames, axis=1) # shape: (channels, total_samples) + if self.__duration: + audio_data = audio_data[..., :int(self.__duration * audio_stream.sample_rate)] - has_first_frame = False - for frame in frames: - offset_seconds = start_time - frame.pts * audio_stream.time_base - to_skip = max(0, int(offset_seconds * audio_stream.sample_rate)) - if to_skip < frame.samples: - has_first_frame = True - break - if has_first_frame: - audio_frames.append(frame.to_ndarray()[..., to_skip:]) - - for frame in frames: - if self.__duration and frame.time > start_time + self.__duration: - break - audio_frames.append(frame.to_ndarray()) # shape: (channels, samples) - if len(audio_frames) > 0: - audio_data = np.concatenate(audio_frames, axis=1) # shape: (channels, total_samples) - if self.__duration: - audio_data = audio_data[..., :int(self.__duration * audio_stream.sample_rate)] - - audio_tensor = torch.from_numpy(audio_data).unsqueeze(0) # shape: (1, channels, total_samples) - audio = AudioInput({ - "waveform": audio_tensor, - "sample_rate": int(audio_stream.sample_rate) if audio_stream.sample_rate else 1, - }) + audio_tensor = torch.from_numpy(audio_data).unsqueeze(0) # shape: (1, channels, total_samples) + audio = AudioInput({ + "waveform": audio_tensor, + "sample_rate": int(audio_stream.sample_rate) if audio_stream.sample_rate else 1, + }) metadata = container.metadata return VideoComponents(images=images, alpha=alphas, audio=audio, frame_rate=frame_rate, metadata=metadata) From b47f15f25a2a96b5e9fd7efb4ffa5d988038d6ff Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Tue, 28 Apr 2026 12:22:31 +1000 Subject: [PATCH 103/140] fix: Handle un-inited meta-tensors in models (fixes a CPU TE crash) (CORE-67) (#13578) --- comfy/model_patcher.py | 5 ++++- comfy/ops.py | 16 +++++++++++++++- 2 files changed, 19 insertions(+), 2 deletions(-) diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index ee56f8523..e259aed63 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -31,6 +31,7 @@ import comfy.float import comfy.hooks import comfy.lora import comfy.model_management +import comfy.ops import comfy.patcher_extension import comfy.utils from comfy.comfy_types import UnetWrapperFunction @@ -856,7 +857,9 @@ class ModelPatcher: if m.comfy_patched_weights == True: continue - for param in params: + for param, param_value in params.items(): + if hasattr(m, "comfy_cast_weights") and getattr(param_value, "is_meta", False): + comfy.ops.disable_weight_init._zero_init_parameter(m, param) key = key_param_name_to_key(n, param) self.unpin_weight(key) self.patch_weight_to_device(key, device_to=device_to) diff --git a/comfy/ops.py b/comfy/ops.py index 7a9b4b84c..050f7cda0 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -79,14 +79,21 @@ def cast_to_input(weight, input, non_blocking=False, copy=True): return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy) -def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant): +def materialize_meta_param(s, param_keys): + for param_key in param_keys: + param = getattr(s, param_key, None) + if param is not None and getattr(param, "is_meta", False): + setattr(s, param_key, torch.nn.Parameter(torch.zeros(param.shape, dtype=param.dtype), requires_grad=param.requires_grad)) + +def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant): #vbar doesn't support CPU weights, but some custom nodes have weird paths #that might switch the layer to the CPU and expect it to work. We have to take #a clone conservatively as we are mmapped and some SFT files are packed misaligned #If you are a custom node author reading this, please move your layer to the GPU #or declare your ModelPatcher as CPU in the first place. if comfy.model_management.is_device_cpu(device): + materialize_meta_param(s, ["weight", "bias"]) weight = s.weight.to(dtype=dtype, copy=True) if isinstance(weight, QuantizedTensor): weight = weight.dequantize() @@ -108,6 +115,7 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu xfer_dest = comfy_aimdo.torch.aimdo_to_tensor(s._v, device) if not resident: + materialize_meta_param(s, ["weight", "bias"]) cast_geometry = comfy.memory_management.tensors_to_geometries([ s.weight, s.bias ]) cast_dest = None @@ -306,6 +314,12 @@ class CastWeightBiasOp: bias_function = [] class disable_weight_init: + @staticmethod + def _zero_init_parameter(module, name): + param = getattr(module, name) + device = None if getattr(param, "is_meta", False) else param.device + setattr(module, name, torch.nn.Parameter(torch.zeros(param.shape, device=device, dtype=param.dtype), requires_grad=False)) + @staticmethod def _lazy_load_from_state_dict(module, state_dict, prefix, local_metadata, missing_keys, unexpected_keys, weight_shape, From ed201fff08fbbd3dbcc500b252a9f41e8051c256 Mon Sep 17 00:00:00 2001 From: Matt Miller Date: Mon, 27 Apr 2026 19:51:33 -0700 Subject: [PATCH 104/140] ci: dispatch tag push to Comfy-Org/cloud (#13541) Fires on v* tag push (earlier than release.published, which can lag) and triggers a repository_dispatch on Comfy-Org/cloud with event_type comfyui_tag_pushed. Legacy desktop dispatch in release-webhook.yml is left untouched. --- .github/workflows/tag-dispatch-cloud.yml | 45 ++++++++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 .github/workflows/tag-dispatch-cloud.yml diff --git a/.github/workflows/tag-dispatch-cloud.yml b/.github/workflows/tag-dispatch-cloud.yml new file mode 100644 index 000000000..53a0e91d6 --- /dev/null +++ b/.github/workflows/tag-dispatch-cloud.yml @@ -0,0 +1,45 @@ +name: Tag Dispatch to Cloud + +on: + push: + tags: + - 'v*' + +jobs: + dispatch-cloud: + runs-on: ubuntu-latest + steps: + - name: Send repository dispatch to cloud + env: + DISPATCH_TOKEN: ${{ secrets.CLOUD_REPO_DISPATCH_TOKEN }} + RELEASE_TAG: ${{ github.ref_name }} + run: | + set -euo pipefail + + if [ -z "${DISPATCH_TOKEN:-}" ]; then + echo "::error::CLOUD_REPO_DISPATCH_TOKEN is required but not set." + exit 1 + fi + + RELEASE_URL="https://github.com/${{ github.repository }}/releases/tag/${RELEASE_TAG}" + + PAYLOAD="$(jq -n \ + --arg release_tag "$RELEASE_TAG" \ + --arg release_url "$RELEASE_URL" \ + '{ + event_type: "comfyui_tag_pushed", + client_payload: { + release_tag: $release_tag, + release_url: $release_url + } + }')" + + curl -fsSL \ + -X POST \ + -H "Accept: application/vnd.github+json" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer ${DISPATCH_TOKEN}" \ + https://api.github.com/repos/Comfy-Org/cloud/dispatches \ + -d "$PAYLOAD" + + echo "✅ Dispatched ComfyUI tag ${RELEASE_TAG} to Comfy-Org/cloud" From c0d77a5d53828b8027a4f333e41473253150b614 Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Tue, 28 Apr 2026 15:59:59 +0800 Subject: [PATCH 105/140] Change the `save 3d model` node's filename prefix to `3d/ComfyUI` (CORE-106) (#12826) * Change save 3d model's filename prefix to 3d/ComfyUI As this node has already changed from `Save GLB` to `Save 3D Model`, using the filename prefix `3d` will be better than `mesh` * use lowercase --------- --- comfy_extras/nodes_hunyuan3d.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_extras/nodes_hunyuan3d.py b/comfy_extras/nodes_hunyuan3d.py index df0c3e4b1..fa55ead59 100644 --- a/comfy_extras/nodes_hunyuan3d.py +++ b/comfy_extras/nodes_hunyuan3d.py @@ -637,7 +637,7 @@ class SaveGLB(IO.ComfyNode): ], tooltip="Mesh or 3D file to save", ), - IO.String.Input("filename_prefix", default="mesh/ComfyUI"), + IO.String.Input("filename_prefix", default="3d/ComfyUI"), ], hidden=[IO.Hidden.prompt, IO.Hidden.extra_pnginfo] ) From 24de8dc01bc6c857be12f25ba24fb753a48cb0c2 Mon Sep 17 00:00:00 2001 From: Gilad Schreiber Date: Tue, 28 Apr 2026 11:18:19 +0300 Subject: [PATCH 106/140] Fix SolidMask and MaskComposite device mismatch with --gpu-only (#13296) SolidMask had a hardcoded device="cpu" while other nodes (e.g. EmptyImage) follow intermediate_device(). This causes a RuntimeError when MaskComposite combines masks from different device sources under --gpu-only. - SolidMask: use intermediate_device() instead of hardcoded "cpu" - MaskComposite: align source device to destination before operating Co-authored-by: Alexis Rolland Co-authored-by: Jedrzej Kosinski --- comfy_extras/nodes_mask.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py index c44602597..8ca947718 100644 --- a/comfy_extras/nodes_mask.py +++ b/comfy_extras/nodes_mask.py @@ -2,6 +2,7 @@ import numpy as np import scipy.ndimage import torch import comfy.utils +import comfy.model_management import node_helpers from typing_extensions import override from comfy_api.latest import ComfyExtension, IO, UI @@ -188,7 +189,7 @@ class SolidMask(IO.ComfyNode): @classmethod def execute(cls, value, width, height) -> IO.NodeOutput: - out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu") + out = torch.full((1, height, width), value, dtype=torch.float32, device=comfy.model_management.intermediate_device()) return IO.NodeOutput(out) solid = execute # TODO: remove @@ -262,6 +263,7 @@ class MaskComposite(IO.ComfyNode): def execute(cls, destination, source, x, y, operation) -> IO.NodeOutput: output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone() source = source.reshape((-1, source.shape[-2], source.shape[-1])) + source = source.to(output.device) left, top = (x, y,) right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2])) From 13519934ba4220bba47e51c185a63fc837c3d6e2 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 28 Apr 2026 13:27:42 -0700 Subject: [PATCH 107/140] Handle metadata rotation in pyav code. (#13605) --- comfy_api/latest/_input_impl/video_types.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py index 812b3eb30..b2daa3d7d 100644 --- a/comfy_api/latest/_input_impl/video_types.py +++ b/comfy_api/latest/_input_impl/video_types.py @@ -291,6 +291,9 @@ class VideoFromFile(VideoInput): checked_alpha = True img = frame.to_ndarray(format=image_format) # shape: (H, W, 4) + if frame.rotation != 0: + k = int(round(frame.rotation // 90)) + img = np.rot90(img, k=k, axes=(0, 1)).copy() if alphas is None: frames.append(torch.from_numpy(img)) else: From e514119e1e3b73d5f4190295f3847f07ba228ea8 Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Wed, 29 Apr 2026 06:34:37 +1000 Subject: [PATCH 108/140] comfy-aimdo v0.3.0 (#13604) Comfy-aimdo 0.3.0 contains several major new features. multi-GPU support ARM support AMD support Refactorings include: Linkless architecture - linkage is now performed purely at runtime to stop host library lookups completely and only interact with the torch-loaded Nvidia stack. Elimination of cudart integration on linux. Its no consistent with windows. Misc bugfixes and minor features. --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 66a130a9b..12c5ff7a9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,7 +23,7 @@ SQLAlchemy>=2.0 filelock av>=14.2.0 comfy-kitchen>=0.2.8 -comfy-aimdo==0.2.14 +comfy-aimdo==0.3.0 requests simpleeval>=1.0.0 blake3 From c7a517c2f9d182ea777c7e625ef532865dcff8b6 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 28 Apr 2026 14:59:55 -0700 Subject: [PATCH 109/140] Make pyav loading code handle tRNS PNG. (#13607) --- comfy_api/latest/_input_impl/video_types.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py index b2daa3d7d..6ed41bba8 100644 --- a/comfy_api/latest/_input_impl/video_types.py +++ b/comfy_api/latest/_input_impl/video_types.py @@ -284,7 +284,7 @@ class VideoFromFile(VideoInput): if not checked_alpha: for comp in frame.format.components: - if comp.is_alpha: + if comp.is_alpha or frame.format.name == "pal8": alphas = [] image_format = 'gbrapf32le' break From dae3d3475179fd796e2901e7d1f9e00aeb515a2f Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Tue, 28 Apr 2026 15:15:06 -0700 Subject: [PATCH 110/140] Use pyav to load images instead of pillow. (#13594) On failure (ex: animated webp files) fallback to old pillow code. This should fix the extra precision in high bit depth images (like 16 bit PNG) being discarded when loaded by Pillow and potentially add support for more image formats. --- nodes.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/nodes.py b/nodes.py index fb83da896..e73a0712e 100644 --- a/nodes.py +++ b/nodes.py @@ -32,7 +32,7 @@ import comfy.controlnet from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict, FileLocator from comfy_api.internal import register_versions, ComfyAPIWithVersion from comfy_api.version_list import supported_versions -from comfy_api.latest import io, ComfyExtension +from comfy_api.latest import io, ComfyExtension, InputImpl import comfy.clip_vision @@ -1716,6 +1716,10 @@ class LoadImage: def load_image(self, image): image_path = folder_paths.get_annotated_filepath(image) + components = InputImpl.VideoFromFile(image_path).get_components() + if components.images.shape[0] > 0: + return (components.images, 1.0 - components.alpha[..., -1] if components.alpha is not None else torch.zeros((components.images.shape[0], 64, 64), dtype=torch.float32, device="cpu")) + img = node_helpers.pillow(Image.open, image_path) output_images = [] From fce0398470fe3ecdb7ab4c5c69555ad0fcbdc09e Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Wed, 29 Apr 2026 09:15:02 +1000 Subject: [PATCH 111/140] dynamicVRAM + --cache-ram 2 (CORE-117) (#13603) * pinned_memory: remove JIT RAM pressure release This doesn't work, as freeing intermediates for pins needs to be higher-priority than freeing pins-for-pins if and when you are going to do that. So this is too late as pins-for-pins is model load time and we dont have JIT pins-for-pins. * cacheing: Add a filter to only free intermediates from inactive wfs This is to get priorities in amongst pins straight. * mm: free inactive-ram from RAM cache first Stuff from inactive workflows should be freed before anything else. * caching: purge old ModelPatchers first Dont try and score them, just dump them at the first sign of trouble if they arent part of the workflow. --- comfy/model_management.py | 1 + comfy/pinned_memory.py | 6 ------ comfy_execution/caching.py | 8 +++++++- execution.py | 2 +- 4 files changed, 9 insertions(+), 8 deletions(-) diff --git a/comfy/model_management.py b/comfy/model_management.py index 3b39d6080..95af40012 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -663,6 +663,7 @@ def minimum_inference_memory(): def free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins_required=0, ram_required=0): cleanup_models_gc() + comfy.memory_management.extra_ram_release(max(pins_required, ram_required)) unloaded_model = [] can_unload = [] unloaded_models = [] diff --git a/comfy/pinned_memory.py b/comfy/pinned_memory.py index 6f142282d..6d3ba367a 100644 --- a/comfy/pinned_memory.py +++ b/comfy/pinned_memory.py @@ -2,7 +2,6 @@ import comfy.model_management import comfy.memory_management import comfy_aimdo.host_buffer import comfy_aimdo.torch -import psutil from comfy.cli_args import args @@ -12,11 +11,6 @@ def get_pin(module): def pin_memory(module): if module.pin_failed or args.disable_pinned_memory or get_pin(module) is not None: return - #FIXME: This is a RAM cache trigger event - ram_headroom = comfy.memory_management.RAM_CACHE_HEADROOM - #we split the difference and assume half the RAM cache headroom is for us - if ram_headroom > 0 and psutil.virtual_memory().available < (ram_headroom * 0.5): - comfy.memory_management.extra_ram_release(ram_headroom) size = comfy.memory_management.vram_aligned_size([ module.weight, module.bias ]) diff --git a/comfy_execution/caching.py b/comfy_execution/caching.py index f9c913bdb..ba1e8bc84 100644 --- a/comfy_execution/caching.py +++ b/comfy_execution/caching.py @@ -5,6 +5,7 @@ import psutil import time import torch from typing import Sequence, Mapping, Dict +from comfy.model_patcher import ModelPatcher from comfy_execution.graph import DynamicPrompt from abc import ABC, abstractmethod @@ -523,13 +524,15 @@ class RAMPressureCache(LRUCache): self.timestamps[self.cache_key_set.get_data_key(node_id)] = time.time() super().set_local(node_id, value) - def ram_release(self, target): + def ram_release(self, target, free_active=False): if psutil.virtual_memory().available >= target: return clean_list = [] for key, cache_entry in self.cache.items(): + if not free_active and self.used_generation[key] == self.generation: + continue oom_score = RAM_CACHE_OLD_WORKFLOW_OOM_MULTIPLIER ** (self.generation - self.used_generation[key]) ram_usage = RAM_CACHE_DEFAULT_RAM_USAGE @@ -542,6 +545,9 @@ class RAMPressureCache(LRUCache): scan_list_for_ram_usage(output) elif isinstance(output, torch.Tensor) and output.device.type == 'cpu': ram_usage += output.numel() * output.element_size() + elif isinstance(output, ModelPatcher) and self.used_generation[key] != self.generation: + #old ModelPatchers are the first to go + ram_usage = 1e30 scan_list_for_ram_usage(cache_entry.outputs) oom_score *= ram_usage diff --git a/execution.py b/execution.py index e15eb4bda..5a6d3404c 100644 --- a/execution.py +++ b/execution.py @@ -779,7 +779,7 @@ class PromptExecutor: if self.cache_type == CacheType.RAM_PRESSURE: comfy.model_management.free_memory(0, None, pins_required=ram_headroom, ram_required=ram_headroom) - comfy.memory_management.extra_ram_release(ram_headroom) + ram_release_callback(ram_headroom, free_active=True) else: # Only execute when the while-loop ends without break # Send cached UI for intermediate output nodes that weren't executed From 0e25a6936ef41a56af87a4af174fa519da73b37c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Wed, 29 Apr 2026 22:15:10 +0300 Subject: [PATCH 112/140] Reduce video tiny VAE peak VRAM and decode time (CORE-127) (#13617) * Update taehv.py * Simplify * Simplify pixel_unshuffle dispatch --- comfy/taesd/taehv.py | 37 ++++++++++++++++++++++--------------- 1 file changed, 22 insertions(+), 15 deletions(-) diff --git a/comfy/taesd/taehv.py b/comfy/taesd/taehv.py index 6c06ce19d..696013200 100644 --- a/comfy/taesd/taehv.py +++ b/comfy/taesd/taehv.py @@ -7,6 +7,7 @@ from tqdm.auto import tqdm from collections import namedtuple, deque import comfy.ops +import comfy.model_management operations=comfy.ops.disable_weight_init DecoderResult = namedtuple("DecoderResult", ("frame", "memory")) @@ -47,11 +48,14 @@ class TGrow(nn.Module): x = self.conv(x) return x.reshape(-1, C, H, W) -def apply_model_with_memblocks(model, x, parallel, show_progress_bar): +def apply_model_with_memblocks(model, x, parallel, show_progress_bar, output_device=None, + patch_size=1, decode=False): B, T, C, H, W = x.shape if parallel: x = x.reshape(B*T, C, H, W) + if not decode and patch_size > 1: + x = F.pixel_unshuffle(x, patch_size) # parallel over input timesteps, iterate over blocks for b in tqdm(model, disable=not show_progress_bar): if isinstance(b, MemBlock): @@ -62,20 +66,27 @@ def apply_model_with_memblocks(model, x, parallel, show_progress_bar): x = b(x, mem) else: x = b(x) - BT, C, H, W = x.shape - T = BT // B - x = x.view(B, T, C, H, W) + if decode and patch_size > 1: + x = F.pixel_shuffle(x, patch_size) + x = x.view(B, x.shape[0] // B, *x.shape[1:]) + x = x.to(output_device) else: out = [] - work_queue = deque([TWorkItem(xt, 0) for t, xt in enumerate(x.reshape(B, T * C, H, W).chunk(T, dim=1))]) + # Chunk along the time dim directly (chunks are [B,1,C,H,W] views, squeeze to [B,C,H,W] views). + # Avoids forcing a contiguous copy when x is non-contiguous (e.g. after movedim in encode/decode). + work_queue = deque([TWorkItem(xt.squeeze(1), 0) for xt in x.chunk(T, dim=1)]) progress_bar = tqdm(range(T), disable=not show_progress_bar) mem = [None] * len(model) while work_queue: xt, i = work_queue.popleft() if i == 0: progress_bar.update(1) + if not decode and patch_size > 1: + xt = F.pixel_unshuffle(xt, patch_size) if i == len(model): - out.append(xt) + if decode and patch_size > 1: + xt = F.pixel_shuffle(xt, patch_size) + out.append(xt.to(output_device)) del xt else: b = model[i] @@ -165,24 +176,20 @@ class TAEHV(nn.Module): def encode(self, x, **kwargs): x = x.movedim(2, 1) # [B, C, T, H, W] -> [B, T, C, H, W] - if self.patch_size > 1: - B, T, C, H, W = x.shape - x = x.reshape(B * T, C, H, W) - x = F.pixel_unshuffle(x, self.patch_size) - x = x.reshape(B, T, C * self.patch_size ** 2, H // self.patch_size, W // self.patch_size) if x.shape[1] % self.t_downscale != 0: # pad at end to multiple of t_downscale n_pad = self.t_downscale - x.shape[1] % self.t_downscale padding = x[:, -1:].repeat_interleave(n_pad, dim=1) x = torch.cat([x, padding], 1) - x = apply_model_with_memblocks(self.encoder, x, self.parallel, self.show_progress_bar).movedim(2, 1) + x = apply_model_with_memblocks(self.encoder, x, self.parallel, self.show_progress_bar, + patch_size=self.patch_size).movedim(2, 1) return self.process_out(x) def decode(self, x, **kwargs): x = x.unsqueeze(0) if x.ndim == 4 else x # [T, C, H, W] -> [1, T, C, H, W] x = x.movedim(1, 2) if x.shape[1] != self.latent_channels else x # [B, T, C, H, W] or [B, C, T, H, W] x = self.process_in(x).movedim(2, 1) # [B, C, T, H, W] -> [B, T, C, H, W] - x = apply_model_with_memblocks(self.decoder, x, self.parallel, self.show_progress_bar) - if self.patch_size > 1: - x = F.pixel_shuffle(x, self.patch_size) + x = apply_model_with_memblocks(self.decoder, x, self.parallel, self.show_progress_bar, + output_device=comfy.model_management.intermediate_device(), + patch_size=self.patch_size, decode=True) return x[:, self.frames_to_trim:].movedim(2, 1) From 5eeae3f1d823e3f072896d6c72185e3c84373739 Mon Sep 17 00:00:00 2001 From: Talmaj Date: Thu, 30 Apr 2026 01:30:08 +0200 Subject: [PATCH 113/140] Cogvideox (#13402) --------- Co-authored-by: kijai <40791699+kijai@users.noreply.github.com> Co-authored-by: Talmaj Marinc --- comfy/latent_formats.py | 7 + comfy/ldm/cogvideo/__init__.py | 0 comfy/ldm/cogvideo/model.py | 573 ++++++++++++++++++++++++++++++++ comfy/ldm/cogvideo/vae.py | 566 +++++++++++++++++++++++++++++++ comfy/model_base.py | 60 ++++ comfy/model_detection.py | 48 +++ comfy/model_sampling.py | 24 ++ comfy/sd.py | 12 + comfy/supported_models.py | 49 ++- comfy/text_encoders/cogvideo.py | 6 + nodes.py | 2 +- 11 files changed, 1345 insertions(+), 2 deletions(-) create mode 100644 comfy/ldm/cogvideo/__init__.py create mode 100644 comfy/ldm/cogvideo/model.py create mode 100644 comfy/ldm/cogvideo/vae.py create mode 100644 comfy/text_encoders/cogvideo.py diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 6a57bca1c..0f4059ebe 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -783,3 +783,10 @@ class ZImagePixelSpace(ChromaRadiance): No VAE encoding/decoding — the model operates directly on RGB pixels. """ pass + +class CogVideoX(LatentFormat): + latent_channels = 16 + latent_dimensions = 3 + + def __init__(self): + self.scale_factor = 1.15258426 diff --git a/comfy/ldm/cogvideo/__init__.py b/comfy/ldm/cogvideo/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/comfy/ldm/cogvideo/model.py b/comfy/ldm/cogvideo/model.py new file mode 100644 index 000000000..fb475ed53 --- /dev/null +++ b/comfy/ldm/cogvideo/model.py @@ -0,0 +1,573 @@ +# CogVideoX 3D Transformer - ported to ComfyUI native ops +# Architecture reference: diffusers CogVideoXTransformer3DModel +# Style reference: comfy/ldm/wan/model.py + +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +from comfy.ldm.modules.attention import optimized_attention +import comfy.patcher_extension +import comfy.ldm.common_dit + + +def _get_1d_rotary_pos_embed(dim, pos, theta=10000.0): + """Returns (cos, sin) each with shape [seq_len, dim]. + + Frequencies are computed at dim//2 resolution then repeat_interleaved + to full dim, matching CogVideoX's interleaved (real, imag) pair format. + """ + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float32, device=pos.device) / dim)) + angles = torch.outer(pos.float(), freqs.float()) + cos = angles.cos().repeat_interleave(2, dim=-1).float() + sin = angles.sin().repeat_interleave(2, dim=-1).float() + return (cos, sin) + + +def apply_rotary_emb(x, freqs_cos_sin): + """Apply CogVideoX rotary embedding to query or key tensor. + + x: [B, heads, seq_len, head_dim] + freqs_cos_sin: (cos, sin) each [seq_len, head_dim//2] + + Uses interleaved pair rotation (same as diffusers CogVideoX/Flux). + head_dim is reshaped to (-1, 2) pairs, rotated, then flattened back. + """ + cos, sin = freqs_cos_sin + cos = cos[None, None, :, :].to(x.device) + sin = sin[None, None, :, :].to(x.device) + + # Interleaved pairs: [B, H, S, D] -> [B, H, S, D//2, 2] -> (real, imag) + x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) + x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) + + return (x.float() * cos + x_rotated.float() * sin).to(x.dtype) + + +def get_timestep_embedding(timesteps, dim, flip_sin_to_cos=True, downscale_freq_shift=0, scale=1, max_period=10000): + half = dim // 2 + freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half) + args = timesteps[:, None].float() * freqs[None] * scale + embedding = torch.cat([torch.sin(args), torch.cos(args)], dim=-1) + if flip_sin_to_cos: + embedding = torch.cat([embedding[:, half:], embedding[:, :half]], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + +def get_3d_sincos_pos_embed(embed_dim, spatial_size, temporal_size, spatial_interpolation_scale=1.0, temporal_interpolation_scale=1.0, device=None): + if isinstance(spatial_size, int): + spatial_size = (spatial_size, spatial_size) + + grid_w = torch.arange(spatial_size[0], dtype=torch.float32, device=device) / spatial_interpolation_scale + grid_h = torch.arange(spatial_size[1], dtype=torch.float32, device=device) / spatial_interpolation_scale + grid_t = torch.arange(temporal_size, dtype=torch.float32, device=device) / temporal_interpolation_scale + + grid_t, grid_h, grid_w = torch.meshgrid(grid_t, grid_h, grid_w, indexing="ij") + + embed_dim_spatial = 2 * (embed_dim // 3) + embed_dim_temporal = embed_dim // 3 + + pos_embed_spatial = _get_2d_sincos_pos_embed(embed_dim_spatial, grid_h, grid_w, device=device) + pos_embed_temporal = _get_1d_sincos_pos_embed(embed_dim_temporal, grid_t[:, 0, 0], device=device) + + T, H, W = grid_t.shape + pos_embed_temporal = pos_embed_temporal.unsqueeze(1).unsqueeze(1).expand(-1, H, W, -1) + pos_embed = torch.cat([pos_embed_temporal, pos_embed_spatial], dim=-1) + + return pos_embed + + +def _get_2d_sincos_pos_embed(embed_dim, grid_h, grid_w, device=None): + T, H, W = grid_h.shape + half_dim = embed_dim // 2 + pos_h = _get_1d_sincos_pos_embed(half_dim, grid_h.reshape(-1), device=device).reshape(T, H, W, half_dim) + pos_w = _get_1d_sincos_pos_embed(half_dim, grid_w.reshape(-1), device=device).reshape(T, H, W, half_dim) + return torch.cat([pos_h, pos_w], dim=-1) + + +def _get_1d_sincos_pos_embed(embed_dim, pos, device=None): + half = embed_dim // 2 + freqs = torch.exp(-math.log(10000.0) * torch.arange(start=0, end=half, dtype=torch.float32, device=device) / half) + args = pos.float().reshape(-1)[:, None] * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if embed_dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + + +class CogVideoXPatchEmbed(nn.Module): + def __init__(self, patch_size=2, patch_size_t=None, in_channels=16, dim=1920, + text_dim=4096, bias=True, sample_width=90, sample_height=60, + sample_frames=49, temporal_compression_ratio=4, + max_text_seq_length=226, spatial_interpolation_scale=1.875, + temporal_interpolation_scale=1.0, use_positional_embeddings=True, + use_learned_positional_embeddings=True, + device=None, dtype=None, operations=None): + super().__init__() + self.patch_size = patch_size + self.patch_size_t = patch_size_t + self.dim = dim + self.sample_height = sample_height + self.sample_width = sample_width + self.sample_frames = sample_frames + self.temporal_compression_ratio = temporal_compression_ratio + self.max_text_seq_length = max_text_seq_length + self.spatial_interpolation_scale = spatial_interpolation_scale + self.temporal_interpolation_scale = temporal_interpolation_scale + self.use_positional_embeddings = use_positional_embeddings + self.use_learned_positional_embeddings = use_learned_positional_embeddings + + if patch_size_t is None: + self.proj = operations.Conv2d(in_channels, dim, kernel_size=patch_size, stride=patch_size, bias=bias, device=device, dtype=dtype) + else: + self.proj = operations.Linear(in_channels * patch_size * patch_size * patch_size_t, dim, device=device, dtype=dtype) + + self.text_proj = operations.Linear(text_dim, dim, device=device, dtype=dtype) + + if use_positional_embeddings or use_learned_positional_embeddings: + persistent = use_learned_positional_embeddings + pos_embedding = self._get_positional_embeddings(sample_height, sample_width, sample_frames) + self.register_buffer("pos_embedding", pos_embedding, persistent=persistent) + + def _get_positional_embeddings(self, sample_height, sample_width, sample_frames, device=None): + post_patch_height = sample_height // self.patch_size + post_patch_width = sample_width // self.patch_size + post_time_compression_frames = (sample_frames - 1) // self.temporal_compression_ratio + 1 + if self.patch_size_t is not None: + post_time_compression_frames = post_time_compression_frames // self.patch_size_t + num_patches = post_patch_height * post_patch_width * post_time_compression_frames + + pos_embedding = get_3d_sincos_pos_embed( + self.dim, + (post_patch_width, post_patch_height), + post_time_compression_frames, + self.spatial_interpolation_scale, + self.temporal_interpolation_scale, + device=device, + ) + pos_embedding = pos_embedding.reshape(-1, self.dim) + joint_pos_embedding = pos_embedding.new_zeros( + 1, self.max_text_seq_length + num_patches, self.dim, requires_grad=False + ) + joint_pos_embedding.data[:, self.max_text_seq_length:].copy_(pos_embedding) + return joint_pos_embedding + + def forward(self, text_embeds, image_embeds): + input_dtype = text_embeds.dtype + text_embeds = self.text_proj(text_embeds.to(self.text_proj.weight.dtype)).to(input_dtype) + batch_size, num_frames, channels, height, width = image_embeds.shape + + proj_dtype = self.proj.weight.dtype + if self.patch_size_t is None: + image_embeds = image_embeds.reshape(-1, channels, height, width) + image_embeds = self.proj(image_embeds.to(proj_dtype)).to(input_dtype) + image_embeds = image_embeds.view(batch_size, num_frames, *image_embeds.shape[1:]) + image_embeds = image_embeds.flatten(3).transpose(2, 3) + image_embeds = image_embeds.flatten(1, 2) + else: + p = self.patch_size + p_t = self.patch_size_t + image_embeds = image_embeds.permute(0, 1, 3, 4, 2) + image_embeds = image_embeds.reshape( + batch_size, num_frames // p_t, p_t, height // p, p, width // p, p, channels + ) + image_embeds = image_embeds.permute(0, 1, 3, 5, 7, 2, 4, 6).flatten(4, 7).flatten(1, 3) + image_embeds = self.proj(image_embeds.to(proj_dtype)).to(input_dtype) + + embeds = torch.cat([text_embeds, image_embeds], dim=1).contiguous() + + if self.use_positional_embeddings or self.use_learned_positional_embeddings: + text_seq_length = text_embeds.shape[1] + num_image_patches = image_embeds.shape[1] + + if self.use_learned_positional_embeddings: + image_pos = self.pos_embedding[ + :, self.max_text_seq_length:self.max_text_seq_length + num_image_patches + ].to(device=embeds.device, dtype=embeds.dtype) + else: + image_pos = get_3d_sincos_pos_embed( + self.dim, + (width // self.patch_size, height // self.patch_size), + num_image_patches // ((height // self.patch_size) * (width // self.patch_size)), + self.spatial_interpolation_scale, + self.temporal_interpolation_scale, + device=embeds.device, + ).reshape(1, num_image_patches, self.dim).to(dtype=embeds.dtype) + + # Build joint: zeros for text + sincos for image + joint_pos = torch.zeros(1, text_seq_length + num_image_patches, self.dim, device=embeds.device, dtype=embeds.dtype) + joint_pos[:, text_seq_length:] = image_pos + embeds = embeds + joint_pos + + return embeds + + +class CogVideoXLayerNormZero(nn.Module): + def __init__(self, time_dim, dim, elementwise_affine=True, eps=1e-5, bias=True, + device=None, dtype=None, operations=None): + super().__init__() + self.silu = nn.SiLU() + self.linear = operations.Linear(time_dim, 6 * dim, bias=bias, device=device, dtype=dtype) + self.norm = operations.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype) + + def forward(self, hidden_states, encoder_hidden_states, temb): + shift, scale, gate, enc_shift, enc_scale, enc_gate = self.linear(self.silu(temb)).chunk(6, dim=1) + hidden_states = self.norm(hidden_states) * (1 + scale)[:, None, :] + shift[:, None, :] + encoder_hidden_states = self.norm(encoder_hidden_states) * (1 + enc_scale)[:, None, :] + enc_shift[:, None, :] + return hidden_states, encoder_hidden_states, gate[:, None, :], enc_gate[:, None, :] + + +class CogVideoXAdaLayerNorm(nn.Module): + def __init__(self, time_dim, dim, elementwise_affine=True, eps=1e-5, + device=None, dtype=None, operations=None): + super().__init__() + self.silu = nn.SiLU() + self.linear = operations.Linear(time_dim, 2 * dim, device=device, dtype=dtype) + self.norm = operations.LayerNorm(dim, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype) + + def forward(self, x, temb): + temb = self.linear(self.silu(temb)) + shift, scale = temb.chunk(2, dim=1) + x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :] + return x + + +class CogVideoXBlock(nn.Module): + def __init__(self, dim, num_heads, head_dim, time_dim, + eps=1e-5, ff_inner_dim=None, ff_bias=True, + device=None, dtype=None, operations=None): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.head_dim = head_dim + + self.norm1 = CogVideoXLayerNormZero(time_dim, dim, eps=eps, device=device, dtype=dtype, operations=operations) + + # Self-attention (joint text + latent) + self.q = operations.Linear(dim, dim, bias=True, device=device, dtype=dtype) + self.k = operations.Linear(dim, dim, bias=True, device=device, dtype=dtype) + self.v = operations.Linear(dim, dim, bias=True, device=device, dtype=dtype) + self.norm_q = operations.LayerNorm(head_dim, eps=1e-6, elementwise_affine=True, device=device, dtype=dtype) + self.norm_k = operations.LayerNorm(head_dim, eps=1e-6, elementwise_affine=True, device=device, dtype=dtype) + self.attn_out = operations.Linear(dim, dim, bias=True, device=device, dtype=dtype) + + self.norm2 = CogVideoXLayerNormZero(time_dim, dim, eps=eps, device=device, dtype=dtype, operations=operations) + + # Feed-forward (GELU approximate) + inner_dim = ff_inner_dim or dim * 4 + self.ff_proj = operations.Linear(dim, inner_dim, bias=ff_bias, device=device, dtype=dtype) + self.ff_out = operations.Linear(inner_dim, dim, bias=ff_bias, device=device, dtype=dtype) + + def forward(self, hidden_states, encoder_hidden_states, temb, image_rotary_emb=None, transformer_options=None): + if transformer_options is None: + transformer_options = {} + text_seq_length = encoder_hidden_states.size(1) + + # Norm & modulate + norm_hidden, norm_encoder, gate_msa, enc_gate_msa = self.norm1(hidden_states, encoder_hidden_states, temb) + + # Joint self-attention + qkv_input = torch.cat([norm_encoder, norm_hidden], dim=1) + b, s, _ = qkv_input.shape + n, d = self.num_heads, self.head_dim + + q = self.q(qkv_input).view(b, s, n, d) + k = self.k(qkv_input).view(b, s, n, d) + v = self.v(qkv_input) + + q = self.norm_q(q).view(b, s, n, d) + k = self.norm_k(k).view(b, s, n, d) + + # Apply rotary embeddings to image tokens only (diffusers format: [B, heads, seq, head_dim]) + if image_rotary_emb is not None: + q_img = q[:, text_seq_length:].transpose(1, 2) # [B, heads, img_seq, head_dim] + k_img = k[:, text_seq_length:].transpose(1, 2) + q_img = apply_rotary_emb(q_img, image_rotary_emb) + k_img = apply_rotary_emb(k_img, image_rotary_emb) + q = torch.cat([q[:, :text_seq_length], q_img.transpose(1, 2)], dim=1) + k = torch.cat([k[:, :text_seq_length], k_img.transpose(1, 2)], dim=1) + + attn_out = optimized_attention( + q.reshape(b, s, n * d), + k.reshape(b, s, n * d), + v, + heads=self.num_heads, + transformer_options=transformer_options, + ) + + attn_out = self.attn_out(attn_out) + + attn_encoder, attn_hidden = attn_out.split([text_seq_length, s - text_seq_length], dim=1) + + hidden_states = hidden_states + gate_msa * attn_hidden + encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder + + # Norm & modulate for FF + norm_hidden, norm_encoder, gate_ff, enc_gate_ff = self.norm2(hidden_states, encoder_hidden_states, temb) + + # Feed-forward (GELU on concatenated text + latent) + ff_input = torch.cat([norm_encoder, norm_hidden], dim=1) + ff_output = self.ff_out(F.gelu(self.ff_proj(ff_input), approximate="tanh")) + + hidden_states = hidden_states + gate_ff * ff_output[:, text_seq_length:] + encoder_hidden_states = encoder_hidden_states + enc_gate_ff * ff_output[:, :text_seq_length] + + return hidden_states, encoder_hidden_states + + +class CogVideoXTransformer3DModel(nn.Module): + def __init__(self, + num_attention_heads=30, + attention_head_dim=64, + in_channels=16, + out_channels=16, + flip_sin_to_cos=True, + freq_shift=0, + time_embed_dim=512, + ofs_embed_dim=None, + text_embed_dim=4096, + num_layers=30, + dropout=0.0, + attention_bias=True, + sample_width=90, + sample_height=60, + sample_frames=49, + patch_size=2, + patch_size_t=None, + temporal_compression_ratio=4, + max_text_seq_length=226, + spatial_interpolation_scale=1.875, + temporal_interpolation_scale=1.0, + use_rotary_positional_embeddings=False, + use_learned_positional_embeddings=False, + patch_bias=True, + image_model=None, + device=None, + dtype=None, + operations=None, + ): + super().__init__() + self.dtype = dtype + dim = num_attention_heads * attention_head_dim + self.dim = dim + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + self.in_channels = in_channels + self.out_channels = out_channels + self.patch_size = patch_size + self.patch_size_t = patch_size_t + self.max_text_seq_length = max_text_seq_length + self.use_rotary_positional_embeddings = use_rotary_positional_embeddings + + # 1. Patch embedding + self.patch_embed = CogVideoXPatchEmbed( + patch_size=patch_size, + patch_size_t=patch_size_t, + in_channels=in_channels, + dim=dim, + text_dim=text_embed_dim, + bias=patch_bias, + sample_width=sample_width, + sample_height=sample_height, + sample_frames=sample_frames, + temporal_compression_ratio=temporal_compression_ratio, + max_text_seq_length=max_text_seq_length, + spatial_interpolation_scale=spatial_interpolation_scale, + temporal_interpolation_scale=temporal_interpolation_scale, + use_positional_embeddings=not use_rotary_positional_embeddings, + use_learned_positional_embeddings=use_learned_positional_embeddings, + device=device, dtype=torch.float32, operations=operations, + ) + + # 2. Time embedding + self.time_proj_dim = dim + self.time_proj_flip = flip_sin_to_cos + self.time_proj_shift = freq_shift + self.time_embedding_linear_1 = operations.Linear(dim, time_embed_dim, device=device, dtype=dtype) + self.time_embedding_act = nn.SiLU() + self.time_embedding_linear_2 = operations.Linear(time_embed_dim, time_embed_dim, device=device, dtype=dtype) + + # Optional OFS embedding (CogVideoX 1.5 I2V) + self.ofs_proj_dim = ofs_embed_dim + if ofs_embed_dim: + self.ofs_embedding_linear_1 = operations.Linear(ofs_embed_dim, ofs_embed_dim, device=device, dtype=dtype) + self.ofs_embedding_act = nn.SiLU() + self.ofs_embedding_linear_2 = operations.Linear(ofs_embed_dim, ofs_embed_dim, device=device, dtype=dtype) + else: + self.ofs_embedding_linear_1 = None + + # 3. Transformer blocks + self.blocks = nn.ModuleList([ + CogVideoXBlock( + dim=dim, + num_heads=num_attention_heads, + head_dim=attention_head_dim, + time_dim=time_embed_dim, + eps=1e-5, + device=device, dtype=dtype, operations=operations, + ) + for _ in range(num_layers) + ]) + + self.norm_final = operations.LayerNorm(dim, eps=1e-5, elementwise_affine=True, device=device, dtype=dtype) + + # 4. Output + self.norm_out = CogVideoXAdaLayerNorm( + time_dim=time_embed_dim, dim=dim, eps=1e-5, + device=device, dtype=dtype, operations=operations, + ) + + if patch_size_t is None: + output_dim = patch_size * patch_size * out_channels + else: + output_dim = patch_size * patch_size * patch_size_t * out_channels + + self.proj_out = operations.Linear(dim, output_dim, device=device, dtype=dtype) + + self.spatial_interpolation_scale = spatial_interpolation_scale + self.temporal_interpolation_scale = temporal_interpolation_scale + self.temporal_compression_ratio = temporal_compression_ratio + + def forward(self, x, timestep, context, ofs=None, transformer_options=None, **kwargs): + if transformer_options is None: + transformer_options = {} + return comfy.patcher_extension.WrapperExecutor.new_class_executor( + self._forward, + self, + comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options) + ).execute(x, timestep, context, ofs, transformer_options, **kwargs) + + def _forward(self, x, timestep, context, ofs=None, transformer_options=None, **kwargs): + if transformer_options is None: + transformer_options = {} + # ComfyUI passes [B, C, T, H, W] + batch_size, channels, t, h, w = x.shape + + # Pad to patch size (temporal + spatial), same pattern as WAN + p_t = self.patch_size_t if self.patch_size_t is not None else 1 + x = comfy.ldm.common_dit.pad_to_patch_size(x, (p_t, self.patch_size, self.patch_size)) + + # CogVideoX expects [B, T, C, H, W] + x = x.permute(0, 2, 1, 3, 4) + batch_size, num_frames, channels, height, width = x.shape + + # Time embedding + t_emb = get_timestep_embedding(timestep, self.time_proj_dim, self.time_proj_flip, self.time_proj_shift) + t_emb = t_emb.to(dtype=x.dtype) + emb = self.time_embedding_linear_2(self.time_embedding_act(self.time_embedding_linear_1(t_emb))) + + if self.ofs_embedding_linear_1 is not None and ofs is not None: + ofs_emb = get_timestep_embedding(ofs, self.ofs_proj_dim, self.time_proj_flip, self.time_proj_shift) + ofs_emb = ofs_emb.to(dtype=x.dtype) + ofs_emb = self.ofs_embedding_linear_2(self.ofs_embedding_act(self.ofs_embedding_linear_1(ofs_emb))) + emb = emb + ofs_emb + + # Patch embedding + hidden_states = self.patch_embed(context, x) + + text_seq_length = context.shape[1] + encoder_hidden_states = hidden_states[:, :text_seq_length] + hidden_states = hidden_states[:, text_seq_length:] + + # Rotary embeddings (if used) + image_rotary_emb = None + if self.use_rotary_positional_embeddings: + post_patch_height = height // self.patch_size + post_patch_width = width // self.patch_size + if self.patch_size_t is None: + post_time = num_frames + else: + post_time = num_frames // self.patch_size_t + image_rotary_emb = self._get_rotary_emb(post_patch_height, post_patch_width, post_time, device=x.device) + + # Transformer blocks + for i, block in enumerate(self.blocks): + hidden_states, encoder_hidden_states = block( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + temb=emb, + image_rotary_emb=image_rotary_emb, + transformer_options=transformer_options, + ) + + hidden_states = self.norm_final(hidden_states) + + # Output projection + hidden_states = self.norm_out(hidden_states, temb=emb) + hidden_states = self.proj_out(hidden_states) + + # Unpatchify + p = self.patch_size + p_t = self.patch_size_t + + if p_t is None: + output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p) + output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4) + else: + output = hidden_states.reshape( + batch_size, (num_frames + p_t - 1) // p_t, height // p, width // p, -1, p_t, p, p + ) + output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2) + + # Back to ComfyUI format [B, C, T, H, W] and crop padding + output = output.permute(0, 2, 1, 3, 4)[:, :, :t, :h, :w] + return output + + def _get_rotary_emb(self, h, w, t, device): + """Compute CogVideoX 3D rotary positional embeddings. + + For CogVideoX 1.5 (patch_size_t != None): uses "slice" mode — grid positions + are integer arange computed at max_size, then sliced to actual size. + For CogVideoX 1.0 (patch_size_t == None): uses "linspace" mode with crop coords + scaled by spatial_interpolation_scale. + """ + d = self.attention_head_dim + dim_t = d // 4 + dim_h = d // 8 * 3 + dim_w = d // 8 * 3 + + if self.patch_size_t is not None: + # CogVideoX 1.5: "slice" mode — positions are simple integer indices + # Compute at max(sample_size, actual_size) then slice to actual + base_h = self.patch_embed.sample_height // self.patch_size + base_w = self.patch_embed.sample_width // self.patch_size + max_h = max(base_h, h) + max_w = max(base_w, w) + + grid_h = torch.arange(max_h, device=device, dtype=torch.float32) + grid_w = torch.arange(max_w, device=device, dtype=torch.float32) + grid_t = torch.arange(t, device=device, dtype=torch.float32) + else: + # CogVideoX 1.0: "linspace" mode with interpolation scale + grid_h = torch.linspace(0, h - 1, h, device=device, dtype=torch.float32) * self.spatial_interpolation_scale + grid_w = torch.linspace(0, w - 1, w, device=device, dtype=torch.float32) * self.spatial_interpolation_scale + grid_t = torch.arange(t, device=device, dtype=torch.float32) + + freqs_t = _get_1d_rotary_pos_embed(dim_t, grid_t) + freqs_h = _get_1d_rotary_pos_embed(dim_h, grid_h) + freqs_w = _get_1d_rotary_pos_embed(dim_w, grid_w) + + t_cos, t_sin = freqs_t + h_cos, h_sin = freqs_h + w_cos, w_sin = freqs_w + + # Slice to actual size (for "slice" mode where grids may be larger) + t_cos, t_sin = t_cos[:t], t_sin[:t] + h_cos, h_sin = h_cos[:h], h_sin[:h] + w_cos, w_sin = w_cos[:w], w_sin[:w] + + # Broadcast and concatenate into [T*H*W, head_dim] + t_cos = t_cos[:, None, None, :].expand(-1, h, w, -1) + t_sin = t_sin[:, None, None, :].expand(-1, h, w, -1) + h_cos = h_cos[None, :, None, :].expand(t, -1, w, -1) + h_sin = h_sin[None, :, None, :].expand(t, -1, w, -1) + w_cos = w_cos[None, None, :, :].expand(t, h, -1, -1) + w_sin = w_sin[None, None, :, :].expand(t, h, -1, -1) + + cos = torch.cat([t_cos, h_cos, w_cos], dim=-1).reshape(t * h * w, -1) + sin = torch.cat([t_sin, h_sin, w_sin], dim=-1).reshape(t * h * w, -1) + return (cos, sin) diff --git a/comfy/ldm/cogvideo/vae.py b/comfy/ldm/cogvideo/vae.py new file mode 100644 index 000000000..d4e6f321e --- /dev/null +++ b/comfy/ldm/cogvideo/vae.py @@ -0,0 +1,566 @@ +# CogVideoX VAE - ported to ComfyUI native ops +# Architecture reference: diffusers AutoencoderKLCogVideoX +# Style reference: comfy/ldm/wan/vae.py + +import numpy as np + +import torch +import torch.nn as nn +import torch.nn.functional as F + +import comfy.ops +ops = comfy.ops.disable_weight_init + + +class CausalConv3d(nn.Module): + """Causal 3D convolution with temporal padding. + + Uses comfy.ops.Conv3d with autopad='causal_zero' fast path: when input has + a single temporal frame and no cache, the 3D conv weight is sliced to act + as a 2D conv, avoiding computation on zero-padded temporal dimensions. + """ + def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, pad_mode="constant"): + super().__init__() + if isinstance(kernel_size, int): + kernel_size = (kernel_size,) * 3 + + time_kernel, height_kernel, width_kernel = kernel_size + self.time_kernel_size = time_kernel + self.pad_mode = pad_mode + + height_pad = (height_kernel - 1) // 2 + width_pad = (width_kernel - 1) // 2 + self.time_causal_padding = (width_pad, width_pad, height_pad, height_pad, time_kernel - 1, 0) + + stride = stride if isinstance(stride, tuple) else (stride, 1, 1) + dilation = (dilation, 1, 1) + self.conv = ops.Conv3d( + in_channels, out_channels, kernel_size, + stride=stride, dilation=dilation, + padding=(0, height_pad, width_pad), + ) + + def forward(self, x, conv_cache=None): + if self.pad_mode == "replicate": + x = F.pad(x, self.time_causal_padding, mode="replicate") + conv_cache = None + else: + kernel_t = self.time_kernel_size + if kernel_t > 1: + if conv_cache is None and x.shape[2] == 1: + # Fast path: single frame, no cache. All temporal padding + # frames are copies of the input (replicate-style), so the + # 3D conv reduces to a 2D conv with summed temporal kernel. + w = comfy.ops.cast_to_input(self.conv.weight, x) + b = comfy.ops.cast_to_input(self.conv.bias, x) if self.conv.bias is not None else None + w2d = w.sum(dim=2, keepdim=True) + out = F.conv3d(x, w2d, b, + self.conv.stride, self.conv.padding, + self.conv.dilation, self.conv.groups) + return out, None + cached = [conv_cache] if conv_cache is not None else [x[:, :, :1]] * (kernel_t - 1) + x = torch.cat(cached + [x], dim=2) + conv_cache = x[:, :, -self.time_kernel_size + 1:].clone() if self.time_kernel_size > 1 else None + + out = self.conv(x) + return out, conv_cache + + +def _interpolate_zq(zq, target_size): + """Interpolate latent z to target (T, H, W), matching CogVideoX's first-frame-special handling.""" + t = target_size[0] + if t > 1 and t % 2 == 1: + z_first = F.interpolate(zq[:, :, :1], size=(1, target_size[1], target_size[2])) + z_rest = F.interpolate(zq[:, :, 1:], size=(t - 1, target_size[1], target_size[2])) + return torch.cat([z_first, z_rest], dim=2) + return F.interpolate(zq, size=target_size) + + +class SpatialNorm3D(nn.Module): + """Spatially conditioned normalization.""" + def __init__(self, f_channels, zq_channels, groups=32): + super().__init__() + self.norm_layer = ops.GroupNorm(num_channels=f_channels, num_groups=groups, eps=1e-6, affine=True) + self.conv_y = CausalConv3d(zq_channels, f_channels, kernel_size=1, stride=1) + self.conv_b = CausalConv3d(zq_channels, f_channels, kernel_size=1, stride=1) + + def forward(self, f, zq, conv_cache=None): + new_cache = {} + conv_cache = conv_cache or {} + + if zq.shape[-3:] != f.shape[-3:]: + zq = _interpolate_zq(zq, f.shape[-3:]) + + conv_y, new_cache["conv_y"] = self.conv_y(zq, conv_cache=conv_cache.get("conv_y")) + conv_b, new_cache["conv_b"] = self.conv_b(zq, conv_cache=conv_cache.get("conv_b")) + + return self.norm_layer(f) * conv_y + conv_b, new_cache + + +class ResnetBlock3D(nn.Module): + """3D ResNet block with optional spatial norm.""" + def __init__(self, in_channels, out_channels=None, temb_channels=512, groups=32, + eps=1e-6, act_fn="silu", spatial_norm_dim=None, pad_mode="first"): + super().__init__() + out_channels = out_channels or in_channels + self.in_channels = in_channels + self.out_channels = out_channels + self.spatial_norm_dim = spatial_norm_dim + + if act_fn == "silu": + self.nonlinearity = nn.SiLU() + elif act_fn == "swish": + self.nonlinearity = nn.SiLU() + else: + self.nonlinearity = nn.SiLU() + + if spatial_norm_dim is None: + self.norm1 = ops.GroupNorm(num_channels=in_channels, num_groups=groups, eps=eps) + self.norm2 = ops.GroupNorm(num_channels=out_channels, num_groups=groups, eps=eps) + else: + self.norm1 = SpatialNorm3D(in_channels, spatial_norm_dim, groups=groups) + self.norm2 = SpatialNorm3D(out_channels, spatial_norm_dim, groups=groups) + + self.conv1 = CausalConv3d(in_channels, out_channels, kernel_size=3, pad_mode=pad_mode) + + if temb_channels > 0: + self.temb_proj = ops.Linear(temb_channels, out_channels) + + self.conv2 = CausalConv3d(out_channels, out_channels, kernel_size=3, pad_mode=pad_mode) + + if in_channels != out_channels: + self.conv_shortcut = ops.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + else: + self.conv_shortcut = None + + def forward(self, x, temb=None, zq=None, conv_cache=None): + new_cache = {} + conv_cache = conv_cache or {} + residual = x + + if zq is not None: + x, new_cache["norm1"] = self.norm1(x, zq, conv_cache=conv_cache.get("norm1")) + else: + x = self.norm1(x) + + x = self.nonlinearity(x) + x, new_cache["conv1"] = self.conv1(x, conv_cache=conv_cache.get("conv1")) + + if temb is not None and hasattr(self, "temb_proj"): + x = x + self.temb_proj(self.nonlinearity(temb))[:, :, None, None, None] + + if zq is not None: + x, new_cache["norm2"] = self.norm2(x, zq, conv_cache=conv_cache.get("norm2")) + else: + x = self.norm2(x) + + x = self.nonlinearity(x) + x, new_cache["conv2"] = self.conv2(x, conv_cache=conv_cache.get("conv2")) + + if self.conv_shortcut is not None: + residual = self.conv_shortcut(residual) + + return x + residual, new_cache + + +class Downsample3D(nn.Module): + """3D downsampling with optional temporal compression.""" + def __init__(self, in_channels, out_channels, kernel_size=3, stride=2, padding=0, compress_time=False): + super().__init__() + self.conv = ops.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding) + self.compress_time = compress_time + + def forward(self, x): + if self.compress_time: + b, c, t, h, w = x.shape + x = x.permute(0, 3, 4, 1, 2).reshape(b * h * w, c, t) + if t % 2 == 1: + x_first, x_rest = x[..., 0], x[..., 1:] + if x_rest.shape[-1] > 0: + x_rest = F.avg_pool1d(x_rest, kernel_size=2, stride=2) + x = torch.cat([x_first[..., None], x_rest], dim=-1) + x = x.reshape(b, h, w, c, x.shape[-1]).permute(0, 3, 4, 1, 2) + else: + x = F.avg_pool1d(x, kernel_size=2, stride=2) + x = x.reshape(b, h, w, c, x.shape[-1]).permute(0, 3, 4, 1, 2) + + pad = (0, 1, 0, 1) + x = F.pad(x, pad, mode="constant", value=0) + b, c, t, h, w = x.shape + x = x.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w) + x = self.conv(x) + x = x.reshape(b, t, x.shape[1], x.shape[2], x.shape[3]).permute(0, 2, 1, 3, 4) + return x + + +class Upsample3D(nn.Module): + """3D upsampling with optional temporal decompression.""" + def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, compress_time=False): + super().__init__() + self.conv = ops.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding) + self.compress_time = compress_time + + def forward(self, x): + if self.compress_time: + if x.shape[2] > 1 and x.shape[2] % 2 == 1: + x_first, x_rest = x[:, :, 0], x[:, :, 1:] + x_first = F.interpolate(x_first, scale_factor=2.0) + x_rest = F.interpolate(x_rest, scale_factor=2.0) + x = torch.cat([x_first[:, :, None, :, :], x_rest], dim=2) + elif x.shape[2] > 1: + x = F.interpolate(x, scale_factor=2.0) + else: + x = x.squeeze(2) + x = F.interpolate(x, scale_factor=2.0) + x = x[:, :, None, :, :] + else: + b, c, t, h, w = x.shape + x = x.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w) + x = F.interpolate(x, scale_factor=2.0) + x = x.reshape(b, t, c, *x.shape[2:]).permute(0, 2, 1, 3, 4) + + b, c, t, h, w = x.shape + x = x.permute(0, 2, 1, 3, 4).reshape(b * t, c, h, w) + x = self.conv(x) + x = x.reshape(b, t, *x.shape[1:]).permute(0, 2, 1, 3, 4) + return x + + +class DownBlock3D(nn.Module): + def __init__(self, in_channels, out_channels, temb_channels=0, num_layers=1, + eps=1e-6, act_fn="silu", groups=32, add_downsample=True, + compress_time=False, pad_mode="first"): + super().__init__() + self.resnets = nn.ModuleList([ + ResnetBlock3D( + in_channels=in_channels if i == 0 else out_channels, + out_channels=out_channels, + temb_channels=temb_channels, + groups=groups, eps=eps, act_fn=act_fn, pad_mode=pad_mode, + ) + for i in range(num_layers) + ]) + self.downsamplers = nn.ModuleList([Downsample3D(out_channels, out_channels, compress_time=compress_time)]) if add_downsample else None + + def forward(self, x, temb=None, zq=None, conv_cache=None): + new_cache = {} + conv_cache = conv_cache or {} + for i, resnet in enumerate(self.resnets): + x, new_cache[f"resnet_{i}"] = resnet(x, temb, zq, conv_cache=conv_cache.get(f"resnet_{i}")) + if self.downsamplers is not None: + for ds in self.downsamplers: + x = ds(x) + return x, new_cache + + +class MidBlock3D(nn.Module): + def __init__(self, in_channels, temb_channels=0, num_layers=1, + eps=1e-6, act_fn="silu", groups=32, spatial_norm_dim=None, pad_mode="first"): + super().__init__() + self.resnets = nn.ModuleList([ + ResnetBlock3D( + in_channels=in_channels, out_channels=in_channels, + temb_channels=temb_channels, groups=groups, eps=eps, + act_fn=act_fn, spatial_norm_dim=spatial_norm_dim, pad_mode=pad_mode, + ) + for _ in range(num_layers) + ]) + + def forward(self, x, temb=None, zq=None, conv_cache=None): + new_cache = {} + conv_cache = conv_cache or {} + for i, resnet in enumerate(self.resnets): + x, new_cache[f"resnet_{i}"] = resnet(x, temb, zq, conv_cache=conv_cache.get(f"resnet_{i}")) + return x, new_cache + + +class UpBlock3D(nn.Module): + def __init__(self, in_channels, out_channels, temb_channels=0, num_layers=1, + eps=1e-6, act_fn="silu", groups=32, spatial_norm_dim=16, + add_upsample=True, compress_time=False, pad_mode="first"): + super().__init__() + self.resnets = nn.ModuleList([ + ResnetBlock3D( + in_channels=in_channels if i == 0 else out_channels, + out_channels=out_channels, + temb_channels=temb_channels, groups=groups, eps=eps, + act_fn=act_fn, spatial_norm_dim=spatial_norm_dim, pad_mode=pad_mode, + ) + for i in range(num_layers) + ]) + self.upsamplers = nn.ModuleList([Upsample3D(out_channels, out_channels, compress_time=compress_time)]) if add_upsample else None + + def forward(self, x, temb=None, zq=None, conv_cache=None): + new_cache = {} + conv_cache = conv_cache or {} + for i, resnet in enumerate(self.resnets): + x, new_cache[f"resnet_{i}"] = resnet(x, temb, zq, conv_cache=conv_cache.get(f"resnet_{i}")) + if self.upsamplers is not None: + for us in self.upsamplers: + x = us(x) + return x, new_cache + + +class Encoder3D(nn.Module): + def __init__(self, in_channels=3, out_channels=16, + block_out_channels=(128, 256, 256, 512), + layers_per_block=3, act_fn="silu", + eps=1e-6, groups=32, pad_mode="first", + temporal_compression_ratio=4): + super().__init__() + temporal_compress_level = int(np.log2(temporal_compression_ratio)) + + self.conv_in = CausalConv3d(in_channels, block_out_channels[0], kernel_size=3, pad_mode=pad_mode) + + self.down_blocks = nn.ModuleList() + output_channel = block_out_channels[0] + for i in range(len(block_out_channels)): + input_channel = output_channel + output_channel = block_out_channels[i] + is_final = i == len(block_out_channels) - 1 + compress_time = i < temporal_compress_level + + self.down_blocks.append(DownBlock3D( + in_channels=input_channel, out_channels=output_channel, + temb_channels=0, num_layers=layers_per_block, + eps=eps, act_fn=act_fn, groups=groups, + add_downsample=not is_final, compress_time=compress_time, + )) + + self.mid_block = MidBlock3D( + in_channels=block_out_channels[-1], temb_channels=0, + num_layers=2, eps=eps, act_fn=act_fn, groups=groups, pad_mode=pad_mode, + ) + + self.norm_out = ops.GroupNorm(groups, block_out_channels[-1], eps=1e-6) + self.conv_act = nn.SiLU() + self.conv_out = CausalConv3d(block_out_channels[-1], 2 * out_channels, kernel_size=3, pad_mode=pad_mode) + + def forward(self, x, conv_cache=None): + new_cache = {} + conv_cache = conv_cache or {} + + x, new_cache["conv_in"] = self.conv_in(x, conv_cache=conv_cache.get("conv_in")) + + for i, block in enumerate(self.down_blocks): + key = f"down_block_{i}" + x, new_cache[key] = block(x, None, None, conv_cache.get(key)) + + x, new_cache["mid_block"] = self.mid_block(x, None, None, conv_cache=conv_cache.get("mid_block")) + + x = self.norm_out(x) + x = self.conv_act(x) + x, new_cache["conv_out"] = self.conv_out(x, conv_cache=conv_cache.get("conv_out")) + + return x, new_cache + + +class Decoder3D(nn.Module): + def __init__(self, in_channels=16, out_channels=3, + block_out_channels=(128, 256, 256, 512), + layers_per_block=3, act_fn="silu", + eps=1e-6, groups=32, pad_mode="first", + temporal_compression_ratio=4): + super().__init__() + reversed_channels = list(reversed(block_out_channels)) + temporal_compress_level = int(np.log2(temporal_compression_ratio)) + + self.conv_in = CausalConv3d(in_channels, reversed_channels[0], kernel_size=3, pad_mode=pad_mode) + + self.mid_block = MidBlock3D( + in_channels=reversed_channels[0], temb_channels=0, + num_layers=2, eps=eps, act_fn=act_fn, groups=groups, + spatial_norm_dim=in_channels, pad_mode=pad_mode, + ) + + self.up_blocks = nn.ModuleList() + output_channel = reversed_channels[0] + for i in range(len(block_out_channels)): + prev_channel = output_channel + output_channel = reversed_channels[i] + is_final = i == len(block_out_channels) - 1 + compress_time = i < temporal_compress_level + + self.up_blocks.append(UpBlock3D( + in_channels=prev_channel, out_channels=output_channel, + temb_channels=0, num_layers=layers_per_block + 1, + eps=eps, act_fn=act_fn, groups=groups, + spatial_norm_dim=in_channels, + add_upsample=not is_final, compress_time=compress_time, + )) + + self.norm_out = SpatialNorm3D(reversed_channels[-1], in_channels, groups=groups) + self.conv_act = nn.SiLU() + self.conv_out = CausalConv3d(reversed_channels[-1], out_channels, kernel_size=3, pad_mode=pad_mode) + + def forward(self, sample, conv_cache=None): + new_cache = {} + conv_cache = conv_cache or {} + + x, new_cache["conv_in"] = self.conv_in(sample, conv_cache=conv_cache.get("conv_in")) + + x, new_cache["mid_block"] = self.mid_block(x, None, sample, conv_cache=conv_cache.get("mid_block")) + + for i, block in enumerate(self.up_blocks): + key = f"up_block_{i}" + x, new_cache[key] = block(x, None, sample, conv_cache=conv_cache.get(key)) + + x, new_cache["norm_out"] = self.norm_out(x, sample, conv_cache=conv_cache.get("norm_out")) + x = self.conv_act(x) + x, new_cache["conv_out"] = self.conv_out(x, conv_cache=conv_cache.get("conv_out")) + + return x, new_cache + + + +class AutoencoderKLCogVideoX(nn.Module): + """CogVideoX VAE. Spatial tiling/slicing handled by ComfyUI's VAE wrapper. + + Uses rolling temporal decode: conv_in + mid_block + temporal up_blocks run + on the full (low-res) tensor, then the expensive spatial-only up_blocks + + norm_out + conv_out are processed in small temporal chunks with conv_cache + carrying causal state between chunks. This keeps peak VRAM proportional to + chunk_size rather than total frame count. + """ + + def __init__(self, + in_channels=3, out_channels=3, + block_out_channels=(128, 256, 256, 512), + latent_channels=16, layers_per_block=3, + act_fn="silu", eps=1e-6, groups=32, + temporal_compression_ratio=4, + ): + super().__init__() + self.latent_channels = latent_channels + self.temporal_compression_ratio = temporal_compression_ratio + + self.encoder = Encoder3D( + in_channels=in_channels, out_channels=latent_channels, + block_out_channels=block_out_channels, layers_per_block=layers_per_block, + act_fn=act_fn, eps=eps, groups=groups, + temporal_compression_ratio=temporal_compression_ratio, + ) + self.decoder = Decoder3D( + in_channels=latent_channels, out_channels=out_channels, + block_out_channels=block_out_channels, layers_per_block=layers_per_block, + act_fn=act_fn, eps=eps, groups=groups, + temporal_compression_ratio=temporal_compression_ratio, + ) + + self.num_latent_frames_batch_size = 2 + self.num_sample_frames_batch_size = 8 + + def encode(self, x): + t = x.shape[2] + frame_batch = self.num_sample_frames_batch_size + remainder = t % frame_batch + conv_cache = None + enc = [] + + # Process remainder frames first so only the first chunk can have an + # odd temporal dimension — where Downsample3D's first-frame-special + # handling in temporal compression is actually correct. + if remainder > 0: + chunk, conv_cache = self.encoder(x[:, :, :remainder], conv_cache=conv_cache) + enc.append(chunk.to(x.device)) + + for start in range(remainder, t, frame_batch): + chunk, conv_cache = self.encoder(x[:, :, start:start + frame_batch], conv_cache=conv_cache) + enc.append(chunk.to(x.device)) + + enc = torch.cat(enc, dim=2) + mean, _ = enc.chunk(2, dim=1) + return mean + + def decode(self, z): + return self._decode_rolling(z) + + def _decode_batched(self, z): + """Original batched decode - processes 2 latent frames through full decoder.""" + t = z.shape[2] + frame_batch = self.num_latent_frames_batch_size + num_batches = max(t // frame_batch, 1) + conv_cache = None + dec = [] + for i in range(num_batches): + remaining = t % frame_batch + start = frame_batch * i + (0 if i == 0 else remaining) + end = frame_batch * (i + 1) + remaining + chunk, conv_cache = self.decoder(z[:, :, start:end], conv_cache=conv_cache) + dec.append(chunk.cpu()) + return torch.cat(dec, dim=2).to(z.device) + + def _decode_rolling(self, z): + """Rolling decode - processes low-res layers on full tensor, then rolls + through expensive high-res layers in temporal chunks.""" + decoder = self.decoder + device = z.device + + # Determine which up_blocks have temporal upsample vs spatial-only. + # Temporal up_blocks are cheap (low res), spatial-only are expensive. + temporal_compress_level = int(np.log2(self.temporal_compression_ratio)) + split_at = temporal_compress_level # first N up_blocks do temporal upsample + + # Phase 1: conv_in + mid_block + temporal up_blocks on full tensor (low/medium res) + x, _ = decoder.conv_in(z) + x, _ = decoder.mid_block(x, None, z) + + for i in range(split_at): + x, _ = decoder.up_blocks[i](x, None, z) + + # Phase 2: remaining spatial-only up_blocks + norm_out + conv_out in temporal chunks + remaining_blocks = list(range(split_at, len(decoder.up_blocks))) + chunk_size = 4 # pixel frames per chunk through high-res layers + t_expanded = x.shape[2] + + if t_expanded <= chunk_size or len(remaining_blocks) == 0: + # Small enough to process in one go + for i in remaining_blocks: + x, _ = decoder.up_blocks[i](x, None, z) + x, _ = decoder.norm_out(x, z) + x = decoder.conv_act(x) + x, _ = decoder.conv_out(x) + return x + + # Expand z temporally once to match Phase 2's time dimension. + # z stays at latent spatial resolution so this is small (~16 MB vs ~1.3 GB + # for the old approach of pre-interpolating to every pixel resolution). + z_time_expanded = _interpolate_zq(z, (t_expanded, z.shape[3], z.shape[4])) + + # Process in temporal chunks, interpolating spatially per-chunk to avoid + # allocating full [B, C, t_expanded, H, W] tensors at each resolution. + dec_out = [] + conv_caches = {} + + for chunk_start in range(0, t_expanded, chunk_size): + chunk_end = min(chunk_start + chunk_size, t_expanded) + x_chunk = x[:, :, chunk_start:chunk_end] + z_t_chunk = z_time_expanded[:, :, chunk_start:chunk_end] + z_spatial_cache = {} + + for i in remaining_blocks: + block = decoder.up_blocks[i] + cache_key = f"up_block_{i}" + hw_key = (x_chunk.shape[3], x_chunk.shape[4]) + if hw_key not in z_spatial_cache: + if z_t_chunk.shape[3] == hw_key[0] and z_t_chunk.shape[4] == hw_key[1]: + z_spatial_cache[hw_key] = z_t_chunk + else: + z_spatial_cache[hw_key] = F.interpolate(z_t_chunk, size=(z_t_chunk.shape[2], hw_key[0], hw_key[1])) + x_chunk, new_cache = block(x_chunk, None, z_spatial_cache[hw_key], conv_cache=conv_caches.get(cache_key)) + conv_caches[cache_key] = new_cache + + hw_key = (x_chunk.shape[3], x_chunk.shape[4]) + if hw_key not in z_spatial_cache: + z_spatial_cache[hw_key] = F.interpolate(z_t_chunk, size=(z_t_chunk.shape[2], hw_key[0], hw_key[1])) + x_chunk, new_cache = decoder.norm_out(x_chunk, z_spatial_cache[hw_key], conv_cache=conv_caches.get("norm_out")) + conv_caches["norm_out"] = new_cache + x_chunk = decoder.conv_act(x_chunk) + x_chunk, new_cache = decoder.conv_out(x_chunk, conv_cache=conv_caches.get("conv_out")) + conv_caches["conv_out"] = new_cache + + dec_out.append(x_chunk.cpu()) + del z_spatial_cache + + del x, z_time_expanded + return torch.cat(dec_out, dim=2).to(device) diff --git a/comfy/model_base.py b/comfy/model_base.py index 787ea1145..50dab5782 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -52,6 +52,7 @@ import comfy.ldm.qwen_image.model import comfy.ldm.kandinsky5.model import comfy.ldm.anima.model import comfy.ldm.ace.ace_step15 +import comfy.ldm.cogvideo.model import comfy.ldm.rt_detr.rtdetr_v4 import comfy.ldm.ernie.model import comfy.ldm.sam3.detector @@ -81,6 +82,7 @@ class ModelType(Enum): IMG_TO_IMG = 9 FLOW_COSMOS = 10 IMG_TO_IMG_FLOW = 11 + V_PREDICTION_DDPM = 12 def model_sampling(model_config, model_type): @@ -115,6 +117,8 @@ def model_sampling(model_config, model_type): s = comfy.model_sampling.ModelSamplingCosmosRFlow elif model_type == ModelType.IMG_TO_IMG_FLOW: c = comfy.model_sampling.IMG_TO_IMG_FLOW + elif model_type == ModelType.V_PREDICTION_DDPM: + c = comfy.model_sampling.V_PREDICTION_DDPM class ModelSampling(s, c): pass @@ -1979,3 +1983,59 @@ class ErnieImage(BaseModel): class SAM3(BaseModel): def __init__(self, model_config, model_type=ModelType.FLOW, device=None): super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.sam3.detector.SAM3Model) + +class CogVideoX(BaseModel): + def __init__(self, model_config, model_type=ModelType.V_PREDICTION_DDPM, image_to_video=False, device=None): + super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cogvideo.model.CogVideoXTransformer3DModel) + self.image_to_video = image_to_video + + def concat_cond(self, **kwargs): + noise = kwargs.get("noise", None) + # Detect extra channels needed (e.g. 32 - 16 = 16 for ref latent) + extra_channels = self.diffusion_model.in_channels - noise.shape[1] + if extra_channels == 0: + return None + + image = kwargs.get("concat_latent_image", None) + device = kwargs["device"] + + if image is None: + shape = list(noise.shape) + shape[1] = extra_channels + return torch.zeros(shape, dtype=noise.dtype, layout=noise.layout, device=noise.device) + + latent_dim = self.latent_format.latent_channels + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + + if noise.ndim == 5 and image.ndim == 5: + if image.shape[-3] < noise.shape[-3]: + image = torch.nn.functional.pad(image, (0, 0, 0, 0, 0, noise.shape[-3] - image.shape[-3]), "constant", 0) + elif image.shape[-3] > noise.shape[-3]: + image = image[:, :, :noise.shape[-3]] + + for i in range(0, image.shape[1], latent_dim): + image[:, i:i + latent_dim] = self.process_latent_in(image[:, i:i + latent_dim]) + image = utils.resize_to_batch_size(image, noise.shape[0]) + + if image.shape[1] > extra_channels: + image = image[:, :extra_channels] + elif image.shape[1] < extra_channels: + repeats = extra_channels // image.shape[1] + remainder = extra_channels % image.shape[1] + parts = [image] * repeats + if remainder > 0: + parts.append(image[:, :remainder]) + image = torch.cat(parts, dim=1) + + return image + + def extra_conds(self, **kwargs): + out = super().extra_conds(**kwargs) + # OFS embedding (CogVideoX 1.5 I2V), default 2.0 as used by SparkVSR + if self.diffusion_model.ofs_proj_dim is not None: + ofs = kwargs.get("ofs", None) + if ofs is None: + noise = kwargs.get("noise", None) + ofs = torch.full((noise.shape[0],), 2.0, device=noise.device, dtype=noise.dtype) + out['ofs'] = comfy.conds.CONDRegular(ofs) + return out diff --git a/comfy/model_detection.py b/comfy/model_detection.py index 724a241bf..d9b67dcdf 100644 --- a/comfy/model_detection.py +++ b/comfy/model_detection.py @@ -490,6 +490,54 @@ def detect_unet_config(state_dict, key_prefix, metadata=None): return dit_config + if '{}blocks.0.norm1.linear.weight'.format(key_prefix) in state_dict_keys: # CogVideoX + dit_config = {} + dit_config["image_model"] = "cogvideox" + + # Extract config from weight shapes + norm1_weight = state_dict['{}blocks.0.norm1.linear.weight'.format(key_prefix)] + time_embed_dim = norm1_weight.shape[1] + dim = norm1_weight.shape[0] // 6 + + dit_config["num_attention_heads"] = dim // 64 + dit_config["attention_head_dim"] = 64 + dit_config["time_embed_dim"] = time_embed_dim + dit_config["num_layers"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.') + + # Detect in_channels from patch_embed + patch_proj_key = '{}patch_embed.proj.weight'.format(key_prefix) + if patch_proj_key in state_dict_keys: + w = state_dict[patch_proj_key] + if w.ndim == 4: + # Conv2d: [out, in, kh, kw] — CogVideoX 1.0 + dit_config["in_channels"] = w.shape[1] + dit_config["patch_size"] = w.shape[2] + elif w.ndim == 2: + # Linear: [out, in_channels * patch_size * patch_size * patch_size_t] — CogVideoX 1.5 + dit_config["patch_size"] = 2 + dit_config["patch_size_t"] = 2 + dit_config["in_channels"] = w.shape[1] // (2 * 2 * 2) # 256 // 8 = 32 + + text_proj_key = '{}patch_embed.text_proj.weight'.format(key_prefix) + if text_proj_key in state_dict_keys: + dit_config["text_embed_dim"] = state_dict[text_proj_key].shape[1] + + # Detect OFS embedding + ofs_key = '{}ofs_embedding_linear_1.weight'.format(key_prefix) + if ofs_key in state_dict_keys: + dit_config["ofs_embed_dim"] = state_dict[ofs_key].shape[1] + + # Detect positional embedding type + pos_key = '{}patch_embed.pos_embedding'.format(key_prefix) + if pos_key in state_dict_keys: + dit_config["use_learned_positional_embeddings"] = True + dit_config["use_rotary_positional_embeddings"] = False + else: + dit_config["use_learned_positional_embeddings"] = False + dit_config["use_rotary_positional_embeddings"] = True + + return dit_config + if '{}head.modulation'.format(key_prefix) in state_dict_keys: # Wan 2.1 dit_config = {} dit_config["image_model"] = "wan2.1" diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py index 13860e6a2..cf2b5db5f 100644 --- a/comfy/model_sampling.py +++ b/comfy/model_sampling.py @@ -54,6 +54,30 @@ class V_PREDICTION(EPS): sigma = reshape_sigma(sigma, model_output.ndim) return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 +class V_PREDICTION_DDPM: + """CogVideoX v-prediction: model receives raw x_t (unscaled), predicts velocity v. + x_0 = sqrt(alpha) * x_t - sqrt(1-alpha) * v + = x_t / sqrt(sigma^2 + 1) - v * sigma / sqrt(sigma^2 + 1) + """ + def calculate_input(self, sigma, noise): + return noise + + def calculate_denoised(self, sigma, model_output, model_input): + sigma = reshape_sigma(sigma, model_output.ndim) + return model_input / (sigma ** 2 + 1.0) ** 0.5 - model_output * sigma / (sigma ** 2 + 1.0) ** 0.5 + + def noise_scaling(self, sigma, noise, latent_image, max_denoise=False): + sigma = reshape_sigma(sigma, noise.ndim) + if max_denoise: + noise = noise * torch.sqrt(1.0 + sigma ** 2.0) + else: + noise = noise * sigma + noise += latent_image + return noise + + def inverse_noise_scaling(self, sigma, latent): + return latent + class EDM(V_PREDICTION): def calculate_denoised(self, sigma, model_output, model_input): sigma = reshape_sigma(sigma, model_output.ndim) diff --git a/comfy/sd.py b/comfy/sd.py index 736fe35de..9158317f1 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -18,6 +18,7 @@ import comfy.ldm.wan.vae import comfy.ldm.wan.vae2_2 import comfy.ldm.hunyuan3d.vae import comfy.ldm.ace.vae.music_dcae_pipeline +import comfy.ldm.cogvideo.vae import comfy.ldm.hunyuan_video.vae import comfy.ldm.mmaudio.vae.autoencoder import comfy.pixel_space_convert @@ -652,6 +653,17 @@ class VAE: self.memory_used_encode = lambda shape, dtype: (1400 * 9 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype) self.memory_used_decode = lambda shape, dtype: (3600 * 4 * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype) + elif "decoder.conv_in.conv.weight" in sd and "decoder.mid_block.resnets.0.norm1.norm_layer.weight" in sd: # CogVideoX VAE + self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8) + self.upscale_index_formula = (4, 8, 8) + self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8) + self.downscale_index_formula = (4, 8, 8) + self.latent_dim = 3 + self.latent_channels = sd["encoder.conv_out.conv.weight"].shape[0] // 2 + self.first_stage_model = comfy.ldm.cogvideo.vae.AutoencoderKLCogVideoX(latent_channels=self.latent_channels) + self.memory_used_decode = lambda shape, dtype: (2800 * max(2, ((shape[2] - 1) * 4) + 1) * shape[3] * shape[4] * (8 * 8)) * model_management.dtype_size(dtype) + self.memory_used_encode = lambda shape, dtype: (1400 * max(1, shape[2]) * shape[3] * shape[4]) * model_management.dtype_size(dtype) + self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32] elif "decoder.conv_in.conv.weight" in sd: ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} ddconfig["conv3d"] = True diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 8886f32d5..92d0305c5 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -27,6 +27,7 @@ import comfy.text_encoders.anima import comfy.text_encoders.ace15 import comfy.text_encoders.longcat_image import comfy.text_encoders.ernie +import comfy.text_encoders.cogvideo from . import supported_models_base from . import latent_formats @@ -1832,6 +1833,52 @@ class SAM31(SAM3): unet_config = {"image_model": "SAM31"} -models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima, RT_DETR_v4, ErnieImage, SAM3, SAM31] +class CogVideoX_T2V(supported_models_base.BASE): + unet_config = { + "image_model": "cogvideox", + } + + sampling_settings = { + "linear_start": 0.00085, + "linear_end": 0.012, + "beta_schedule": "linear", + "zsnr": True, + } + + unet_extra_config = {} + latent_format = latent_formats.CogVideoX + + supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] + + vae_key_prefix = ["vae."] + text_encoder_key_prefix = ["text_encoders."] + + def get_model(self, state_dict, prefix="", device=None): + # CogVideoX 1.5 (patch_size_t=2) has different training base dimensions for RoPE + if self.unet_config.get("patch_size_t") is not None: + self.unet_config.setdefault("sample_height", 96) + self.unet_config.setdefault("sample_width", 170) + self.unet_config.setdefault("sample_frames", 81) + out = model_base.CogVideoX(self, device=device) + return out + + def clip_target(self, state_dict={}): + return supported_models_base.ClipTarget(comfy.text_encoders.cogvideo.CogVideoXT5Tokenizer, comfy.text_encoders.sd3_clip.T5XXLModel) + +class CogVideoX_I2V(CogVideoX_T2V): + unet_config = { + "image_model": "cogvideox", + "in_channels": 32, + } + + def get_model(self, state_dict, prefix="", device=None): + if self.unet_config.get("patch_size_t") is not None: + self.unet_config.setdefault("sample_height", 96) + self.unet_config.setdefault("sample_width", 170) + self.unet_config.setdefault("sample_frames", 81) + out = model_base.CogVideoX(self, image_to_video=True, device=device) + return out + +models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima, RT_DETR_v4, ErnieImage, SAM3, SAM31, CogVideoX_I2V, CogVideoX_T2V] models += [SVD_img2vid] diff --git a/comfy/text_encoders/cogvideo.py b/comfy/text_encoders/cogvideo.py new file mode 100644 index 000000000..f1e8e3f5d --- /dev/null +++ b/comfy/text_encoders/cogvideo.py @@ -0,0 +1,6 @@ +import comfy.text_encoders.sd3_clip + + +class CogVideoXT5Tokenizer(comfy.text_encoders.sd3_clip.T5XXLTokenizer): + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, min_length=226) diff --git a/nodes.py b/nodes.py index e73a0712e..7aeb05b32 100644 --- a/nodes.py +++ b/nodes.py @@ -2463,7 +2463,7 @@ async def init_builtin_extra_nodes(): "nodes_curve.py", "nodes_rtdetr.py", "nodes_frame_interpolation.py", - "nodes_sam3.py" + "nodes_sam3.py", ] import_failed = [] From a164c82913d3e04d92d0f6630fc4c850ec184ef3 Mon Sep 17 00:00:00 2001 From: blepping <157360029+blepping@users.noreply.github.com> Date: Wed, 29 Apr 2026 17:37:30 -0600 Subject: [PATCH 114/140] Add high quality preview support for Flux2 latents (#13496) --- comfy/latent_formats.py | 1 + comfy/sd.py | 5 +- comfy/taesd/taesd.py | 102 ++++++++++++++++++++++++++++++---------- nodes.py | 53 +++++++-------------- 4 files changed, 100 insertions(+), 61 deletions(-) diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py index 0f4059ebe..3dac5be18 100644 --- a/comfy/latent_formats.py +++ b/comfy/latent_formats.py @@ -224,6 +224,7 @@ class Flux2(LatentFormat): self.latent_rgb_factors_bias = [-0.0329, -0.0718, -0.0851] self.latent_rgb_factors_reshape = lambda t: t.reshape(t.shape[0], 32, 2, 2, t.shape[-2], t.shape[-1]).permute(0, 1, 4, 2, 5, 3).reshape(t.shape[0], 32, t.shape[-2] * 2, t.shape[-1] * 2) + self.taesd_decoder_name = "taef2_decoder" def process_in(self, latent): return latent diff --git a/comfy/sd.py b/comfy/sd.py index 9158317f1..ee66490f5 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -479,7 +479,10 @@ class VAE: encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) elif "taesd_decoder.1.weight" in sd: - self.latent_channels = sd["taesd_decoder.1.weight"].shape[1] + if isinstance(metadata, dict) and "tae_latent_channels" in metadata: + self.latent_channels = metadata["tae_latent_channels"] + else: + self.latent_channels = sd["taesd_decoder.1.weight"].shape[1] self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels) elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade self.first_stage_model = StageA() diff --git a/comfy/taesd/taesd.py b/comfy/taesd/taesd.py index ce36f1a84..05d370209 100644 --- a/comfy/taesd/taesd.py +++ b/comfy/taesd/taesd.py @@ -17,32 +17,79 @@ class Clamp(nn.Module): return torch.tanh(x / 3) * 3 class Block(nn.Module): - def __init__(self, n_in, n_out): + def __init__(self, n_in: int, n_out: int, use_midblock_gn: bool = False): super().__init__() self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out)) self.skip = comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() self.fuse = nn.ReLU() - def forward(self, x): + if not use_midblock_gn: + self.pool = None + return + n_gn = n_in * 4 + self.pool = nn.Sequential( + comfy.ops.disable_weight_init.Conv2d(n_in, n_gn, 1, bias=False), + comfy.ops.disable_weight_init.GroupNorm(4, n_gn), + nn.ReLU(inplace=True), + comfy.ops.disable_weight_init.Conv2d(n_gn, n_in, 1, bias=False), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if self.pool is not None: + x = x + self.pool(x) return self.fuse(self.conv(x) + self.skip(x)) -def Encoder(latent_channels=4): - return nn.Sequential( - conv(3, 64), Block(64, 64), - conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), - conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), - conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), - conv(64, latent_channels), - ) +class Encoder(nn.Sequential): + def __init__(self, latent_channels: int = 4, use_gn: bool = False): + super().__init__( + conv(3, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64, use_gn), Block(64, 64, use_gn), Block(64, 64, use_gn), + conv(64, latent_channels), + ) +class Decoder(nn.Sequential): + def __init__(self, latent_channels: int = 4, use_gn: bool = False): + super().__init__( + Clamp(), conv(latent_channels, 64), nn.ReLU(), + Block(64, 64, use_gn), Block(64, 64, use_gn), Block(64, 64, use_gn), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), conv(64, 3), + ) + +class DecoderFlux2(Decoder): + def __init__(self, latent_channels: int = 128, use_gn: bool = True): + if latent_channels != 128 or not use_gn: + raise ValueError("Unexpected parameters for Flux2 TAE module") + super().__init__(latent_channels=32, use_gn=True) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + B, C, H, W = x.shape + x = ( + x + .reshape(B, 32, 2, 2, H, W) + .permute(0, 1, 4, 2, 5, 3) + .reshape(B, 32, H * 2, W * 2) + ) + return super().forward(x) + +class EncoderFlux2(Encoder): + def __init__(self, latent_channels: int = 128, use_gn: bool = True): + if latent_channels != 128 or not use_gn: + raise ValueError("Unexpected parameters for Flux2 TAE module") + super().__init__(latent_channels=32, use_gn=True) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + result = super().forward(x) + B, C, H, W = result.shape + return ( + result + .reshape(B, C, H // 2, 2, W // 2, 2) + .permute(0, 1, 3, 5, 2, 4) + .reshape(B, 128, H // 2, W // 2) + ) -def Decoder(latent_channels=4): - return nn.Sequential( - Clamp(), conv(latent_channels, 64), nn.ReLU(), - Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), - Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), - Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), - Block(64, 64), conv(64, 3), - ) class TAESD(nn.Module): latent_magnitude = 3 @@ -51,8 +98,15 @@ class TAESD(nn.Module): def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4): """Initialize pretrained TAESD on the given device from the given checkpoints.""" super().__init__() - self.taesd_encoder = Encoder(latent_channels=latent_channels) - self.taesd_decoder = Decoder(latent_channels=latent_channels) + if latent_channels == 128: + encoder_class = EncoderFlux2 + decoder_class = DecoderFlux2 + else: + encoder_class = Encoder + decoder_class = Decoder + self.taesd_encoder = encoder_class(latent_channels=latent_channels) + self.taesd_decoder = decoder_class(latent_channels=latent_channels) + self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) self.vae_shift = torch.nn.Parameter(torch.tensor(0.0)) if encoder_path is not None: @@ -61,19 +115,19 @@ class TAESD(nn.Module): self.taesd_decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True)) @staticmethod - def scale_latents(x): + def scale_latents(x: torch.Tensor) -> torch.Tensor: """raw latents -> [0, 1]""" return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) @staticmethod - def unscale_latents(x): + def unscale_latents(x: torch.Tensor) -> torch.Tensor: """[0, 1] -> raw latents""" return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) - def decode(self, x): + def decode(self, x: torch.Tensor) -> torch.Tensor: x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale) x_sample = x_sample.sub(0.5).mul(2) return x_sample - def encode(self, x): + def encode(self, x: torch.Tensor) -> torch.Tensor: return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift diff --git a/nodes.py b/nodes.py index 7aeb05b32..99dc07227 100644 --- a/nodes.py +++ b/nodes.py @@ -728,50 +728,26 @@ class LoraLoaderModelOnly(LoraLoader): class VAELoader: video_taes = ["taehv", "lighttaew2_2", "lighttaew2_1", "lighttaehy1_5", "taeltx_2"] - image_taes = ["taesd", "taesdxl", "taesd3", "taef1"] + image_taes = ["taesd", "taesdxl", "taesd3", "taef1", "taef2"] + @staticmethod def vae_list(s): vaes = folder_paths.get_filename_list("vae") approx_vaes = folder_paths.get_filename_list("vae_approx") - sdxl_taesd_enc = False - sdxl_taesd_dec = False - sd1_taesd_enc = False - sd1_taesd_dec = False - sd3_taesd_enc = False - sd3_taesd_dec = False - f1_taesd_enc = False - f1_taesd_dec = False - + have_img_encoder, have_img_decoder = set(), set() for v in approx_vaes: - if v.startswith("taesd_decoder."): - sd1_taesd_dec = True - elif v.startswith("taesd_encoder."): - sd1_taesd_enc = True - elif v.startswith("taesdxl_decoder."): - sdxl_taesd_dec = True - elif v.startswith("taesdxl_encoder."): - sdxl_taesd_enc = True - elif v.startswith("taesd3_decoder."): - sd3_taesd_dec = True - elif v.startswith("taesd3_encoder."): - sd3_taesd_enc = True - elif v.startswith("taef1_encoder."): - f1_taesd_dec = True - elif v.startswith("taef1_decoder."): - f1_taesd_enc = True - else: + parts = v.split("_", 1) + if len(parts) != 2 or parts[0] not in s.image_taes: for tae in s.video_taes: if v.startswith(tae): vaes.append(v) - - if sd1_taesd_dec and sd1_taesd_enc: - vaes.append("taesd") - if sdxl_taesd_dec and sdxl_taesd_enc: - vaes.append("taesdxl") - if sd3_taesd_dec and sd3_taesd_enc: - vaes.append("taesd3") - if f1_taesd_dec and f1_taesd_enc: - vaes.append("taef1") + break + continue + if parts[1].startswith("encoder."): + have_img_encoder.add(parts[0]) + elif parts[1].startswith("decoder."): + have_img_decoder.add(parts[0]) + vaes += [k for k in have_img_decoder if k in have_img_encoder] vaes.append("pixel_space") return vaes @@ -827,6 +803,11 @@ class VAELoader: else: vae_path = folder_paths.get_full_path_or_raise("vae", vae_name) sd, metadata = comfy.utils.load_torch_file(vae_path, return_metadata=True) + if vae_name == "taef2": + if metadata is None: + metadata = {"tae_latent_channels": 128} + else: + metadata["tae_latent_channels"] = 128 vae = comfy.sd.VAE(sd=sd, metadata=metadata) vae.throw_exception_if_invalid() return (vae,) From d10fc2d6524043d2322968b518168910b1e9b530 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Wed, 29 Apr 2026 20:05:31 -0700 Subject: [PATCH 115/140] Lower peak mem usage for 8 bit formats with pyav. (#13626) --- comfy_api/latest/_input_impl/video_types.py | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py index 6ed41bba8..9a107fb76 100644 --- a/comfy_api/latest/_input_impl/video_types.py +++ b/comfy_api/latest/_input_impl/video_types.py @@ -251,6 +251,7 @@ class VideoFromFile(VideoInput): container.seek(start_pts, stream=video_stream) image_format = 'gbrpf32le' + process_image_format = lambda a: a audio = None streams = [video_stream] @@ -283,11 +284,25 @@ class VideoFromFile(VideoInput): break if not checked_alpha: + alpha_channel = False for comp in frame.format.components: if comp.is_alpha or frame.format.name == "pal8": alphas = [] - image_format = 'gbrapf32le' + alpha_channel = True break + if frame.format.name in ("yuvj420p", "rgb24", "rgba", "pal8"): + process_image_format = lambda a: a.float() / 255.0 + if alpha_channel: + image_format = 'rgba' + else: + image_format = 'rgb24' + else: + process_image_format = lambda a: a + if alpha_channel: + image_format = 'gbrapf32le' + else: + image_format = 'gbrpf32le' + checked_alpha = True img = frame.to_ndarray(format=image_format) # shape: (H, W, 4) @@ -323,9 +338,9 @@ class VideoFromFile(VideoInput): else: audio_frames.append(frame.to_ndarray()) - images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 0, 0, 3) + images = process_image_format(torch.stack(frames)) if len(frames) > 0 else torch.zeros(0, 0, 0, 3) if alphas is not None: - alphas = torch.stack(alphas) if len(alphas) > 0 else torch.zeros(0, 0, 0, 1) + alphas = process_image_format(torch.stack(alphas)) if len(alphas) > 0 else torch.zeros(0, 0, 0, 1) # Get frame rate frame_rate = Fraction(video_stream.average_rate) if video_stream.average_rate else Fraction(1) From a7d82baa06e6b2e3d19c38c244118909fe270d49 Mon Sep 17 00:00:00 2001 From: Jedrzej Kosinski Date: Wed, 29 Apr 2026 20:30:01 -0700 Subject: [PATCH 116/140] Fix SQLAlchemy version format in requirements.txt (#13547) Change SQLAlchemy>=2.0 to SQLAlchemy>=2.0.0 to satisfy the X.Y.Z version format expected by install_util.is_valid_version(). --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 12c5ff7a9..c3d51e2fa 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,7 +19,7 @@ scipy tqdm psutil alembic -SQLAlchemy>=2.0 +SQLAlchemy>=2.0.0 filelock av>=14.2.0 comfy-kitchen>=0.2.8 From 38ecad8f8af30965eb1017b0eb6a552c751b84a4 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Thu, 30 Apr 2026 11:09:33 +0300 Subject: [PATCH 117/140] feat(api-nodes): allow custom resolutions for GPTImage2 node (#13631) Signed-off-by: bigcat88 --- comfy_api_nodes/nodes_openai.py | 51 +++++++++++++++++++++++++++++---- 1 file changed, 46 insertions(+), 5 deletions(-) diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py index bbb758068..843681817 100644 --- a/comfy_api_nodes/nodes_openai.py +++ b/comfy_api_nodes/nodes_openai.py @@ -415,8 +415,9 @@ class OpenAIGPTImage1(IO.ComfyNode): "1152x2048", "3840x2160", "2160x3840", + "Custom", ], - tooltip="Image size", + tooltip="Image size. Select 'Custom' to use the custom width and height (GPT Image 2 only).", optional=True, ), IO.Int.Input( @@ -445,6 +446,26 @@ class OpenAIGPTImage1(IO.ComfyNode): default="gpt-image-2", optional=True, ), + IO.Int.Input( + "custom_width", + default=1024, + min=1024, + max=3840, + step=16, + tooltip="Used only when `size` is 'Custom'. Must be a multiple of 16 (GPT Image 2 only).", + optional=True, + advanced=True, + ), + IO.Int.Input( + "custom_height", + default=1024, + min=1024, + max=3840, + step=16, + tooltip="Used only when `size` is 'Custom'. Must be a multiple of 16 (GPT Image 2 only).", + optional=True, + advanced=True, + ), ], outputs=[ IO.Image.Output(), @@ -471,9 +492,9 @@ class OpenAIGPTImage1(IO.ComfyNode): "high": [0.133, 0.22] }, "gpt-image-2": { - "low": [0.0048, 0.012], - "medium": [0.041, 0.112], - "high": [0.165, 0.43] + "low": [0.0048, 0.019], + "medium": [0.041, 0.168], + "high": [0.165, 0.67] } }; $range := $lookup($lookup($ranges, widgets.model), widgets.quality); @@ -503,6 +524,8 @@ class OpenAIGPTImage1(IO.ComfyNode): mask: Input.Image | None = None, n: int = 1, size: str = "1024x1024", + custom_width: int = 1024, + custom_height: int = 1024, model: str = "gpt-image-1", ) -> IO.NodeOutput: validate_string(prompt, strip_whitespace=False) @@ -510,7 +533,25 @@ class OpenAIGPTImage1(IO.ComfyNode): if mask is not None and image is None: raise ValueError("Cannot use a mask without an input image") - if model in ("gpt-image-1", "gpt-image-1.5"): + if size == "Custom": + if model != "gpt-image-2": + raise ValueError("Custom resolution is only supported by GPT Image 2 model") + if custom_width % 16 != 0 or custom_height % 16 != 0: + raise ValueError(f"Custom width and height must be multiples of 16, got {custom_width}x{custom_height}") + if max(custom_width, custom_height) > 3840: + raise ValueError(f"Custom resolution max edge must be <= 3840, got {custom_width}x{custom_height}") + ratio = max(custom_width, custom_height) / min(custom_width, custom_height) + if ratio > 3: + raise ValueError( + f"Custom resolution aspect ratio must not exceed 3:1, got {custom_width}x{custom_height}" + ) + total_pixels = custom_width * custom_height + if not 655_360 <= total_pixels <= 8_294_400: + raise ValueError( + f"Custom resolution total pixels must be between 655,360 and 8,294,400, got {total_pixels}" + ) + size = f"{custom_width}x{custom_height}" + elif model in ("gpt-image-1", "gpt-image-1.5"): if size not in ("auto", "1024x1024", "1024x1536", "1536x1024"): raise ValueError(f"Resolution {size} is only supported by GPT Image 2 model") From b633244635e577e199944cd4f027df79afa16dbf Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Thu, 30 Apr 2026 21:49:08 +0300 Subject: [PATCH 118/140] [Partner Nodes] ByteDance: virtual portrait library for regular images (#13638) * feat(api-nodes-bytedance): use the virtual portrait library for regular images Signed-off-by: bigcat88 * fix: include shape in image dedup hash Signed-off-by: bigcat88 --------- Signed-off-by: bigcat88 --- comfy_api_nodes/apis/bytedance.py | 5 ++++ comfy_api_nodes/nodes_bytedance.py | 38 ++++++++++++++++++++++++++---- 2 files changed, 39 insertions(+), 4 deletions(-) diff --git a/comfy_api_nodes/apis/bytedance.py b/comfy_api_nodes/apis/bytedance.py index eafabbefe..c05bd6893 100644 --- a/comfy_api_nodes/apis/bytedance.py +++ b/comfy_api_nodes/apis/bytedance.py @@ -157,6 +157,11 @@ class SeedanceCreateAssetResponse(BaseModel): asset_id: str = Field(...) +class SeedanceVirtualLibraryCreateAssetRequest(BaseModel): + url: str = Field(..., description="Publicly accessible URL of the image asset to upload.") + hash: str = Field(..., description="Dedup key. Re-submitting the same hash returns the existing asset id.") + + # Dollars per 1K tokens, keyed by (model_id, has_video_input). SEEDANCE2_PRICE_PER_1K_TOKENS = { ("dreamina-seedance-2-0-260128", False): 0.007, diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py index de192c5ac..fee0ab888 100644 --- a/comfy_api_nodes/nodes_bytedance.py +++ b/comfy_api_nodes/nodes_bytedance.py @@ -1,3 +1,4 @@ +import hashlib import logging import math import re @@ -20,6 +21,7 @@ from comfy_api_nodes.apis.bytedance import ( SeedanceCreateAssetResponse, SeedanceCreateVisualValidateSessionResponse, SeedanceGetVisualValidateSessionResponse, + SeedanceVirtualLibraryCreateAssetRequest, Seedream4Options, Seedream4TaskCreationRequest, TaskAudioContent, @@ -271,6 +273,30 @@ async def _wait_for_asset_active(cls: type[IO.ComfyNode], asset_id: str, group_i ) +async def _seedance_virtual_library_upload_image_asset( + cls: type[IO.ComfyNode], + image: torch.Tensor, + *, + wait_label: str = "Uploading image", +) -> str: + """Upload an image into the caller's per-customer Seedance virtual library.""" + public_url = await upload_image_to_comfyapi(cls, image, wait_label=wait_label) + normalized = image.detach().cpu().contiguous().to(torch.float32) + digest = hashlib.sha256() + digest.update(str(tuple(normalized.shape)).encode("utf-8")) + digest.update(b"\0") + digest.update(normalized.numpy().tobytes()) + image_hash = digest.hexdigest() + create_resp = await sync_op( + cls, + ApiEndpoint(path="/proxy/seedance/virtual-library/assets", method="POST"), + response_model=SeedanceCreateAssetResponse, + data=SeedanceVirtualLibraryCreateAssetRequest(url=public_url, hash=image_hash), + ) + await _wait_for_asset_active(cls, create_resp.asset_id, group_id="virtual-library") + return f"asset://{create_resp.asset_id}" + + def _seedance2_price_extractor(model_id: str, has_video_input: bool): """Returns a price_extractor closure for Seedance 2.0 poll_op.""" rate = SEEDANCE2_PRICE_PER_1K_TOKENS.get((model_id, has_video_input)) @@ -1507,7 +1533,9 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode): if first_frame_asset_id: first_frame_url = image_assets[first_frame_asset_id] else: - first_frame_url = await upload_image_to_comfyapi(cls, first_frame, wait_label="Uploading first frame.") + first_frame_url = await _seedance_virtual_library_upload_image_asset( + cls, first_frame, wait_label="Uploading first frame." + ) content: list[TaskTextContent | TaskImageContent] = [ TaskTextContent(text=model["prompt"]), @@ -1527,7 +1555,9 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode): content.append( TaskImageContent( image_url=TaskImageContentUrl( - url=await upload_image_to_comfyapi(cls, last_frame, wait_label="Uploading last frame.") + url=await _seedance_virtual_library_upload_image_asset( + cls, last_frame, wait_label="Uploading last frame." + ) ), role="last_frame", ), @@ -1805,9 +1835,9 @@ class ByteDance2ReferenceNode(IO.ComfyNode): content.append( TaskImageContent( image_url=TaskImageContentUrl( - url=await upload_image_to_comfyapi( + url=await _seedance_virtual_library_upload_image_asset( cls, - image=reference_images[key], + reference_images[key], wait_label=f"Uploading image {i}", ), ), From e6e0936128858608c5cc45585be3583176d748b2 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Thu, 30 Apr 2026 16:33:09 -0700 Subject: [PATCH 119/140] Load other jpeg formats without taking so much memory. (#13642) --- comfy_api/latest/_input_impl/video_types.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/comfy_api/latest/_input_impl/video_types.py b/comfy_api/latest/_input_impl/video_types.py index 9a107fb76..942278d88 100644 --- a/comfy_api/latest/_input_impl/video_types.py +++ b/comfy_api/latest/_input_impl/video_types.py @@ -290,7 +290,7 @@ class VideoFromFile(VideoInput): alphas = [] alpha_channel = True break - if frame.format.name in ("yuvj420p", "rgb24", "rgba", "pal8"): + if frame.format.name in ("yuvj420p", "yuvj422p", "yuvj444p", "rgb24", "rgba", "pal8"): process_image_format = lambda a: a.float() / 255.0 if alpha_channel: image_format = 'rgba' From e9c311b2458a327585a7e387558377c8190eebb0 Mon Sep 17 00:00:00 2001 From: Rainer Date: Fri, 1 May 2026 02:33:41 +0300 Subject: [PATCH 120/140] OneTainer ERNIE LoRA support (#13640) --- comfy/lora.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/comfy/lora.py b/comfy/lora.py index 63ee85323..e4337c729 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -342,6 +342,12 @@ def model_lora_keys_unet(model, key_map={}): key_map["base_model.model.{}".format(key_lora)] = k # Official base model loras key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k # LyCORIS/LoKR format + if isinstance(model, comfy.model_base.ErnieImage): + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")] + key_map["transformer.{}".format(key_lora)] = k + return key_map From e8e8fee22476a926090df9f719acd0a553ff8165 Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Fri, 1 May 2026 09:14:28 +0800 Subject: [PATCH 121/140] chore: update workflow templates to v0.9.65 (#13644) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index c3d51e2fa..cb85d970b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.15 -comfyui-workflow-templates==0.9.63 +comfyui-workflow-templates==0.9.65 comfyui-embedded-docs==0.4.4 torch torchsde From 97f58baaaf89e2232b735fab2a3f2d4e24d134c3 Mon Sep 17 00:00:00 2001 From: Jedrzej Kosinski Date: Thu, 30 Apr 2026 18:49:31 -0700 Subject: [PATCH 122/140] Add alexisrolland and rattus128 as code owners (#13648) --- CODEOWNERS | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CODEOWNERS b/CODEOWNERS index 4d5448636..e693955a0 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -1,2 +1,2 @@ # Admins -* @comfyanonymous @kosinkadink @guill +* @comfyanonymous @kosinkadink @guill @alexisrolland @rattus128 From 96f1cee9f5304c1f4e3a176ed02a44cf0a0116ad Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Fri, 1 May 2026 09:15:11 +0300 Subject: [PATCH 123/140] chore(api-nodes): always display the custom width and height in GPTImage2 node (#13651) Signed-off-by: bigcat88 --- comfy_api_nodes/nodes_openai.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/comfy_api_nodes/nodes_openai.py b/comfy_api_nodes/nodes_openai.py index 843681817..21fe470ce 100644 --- a/comfy_api_nodes/nodes_openai.py +++ b/comfy_api_nodes/nodes_openai.py @@ -454,7 +454,6 @@ class OpenAIGPTImage1(IO.ComfyNode): step=16, tooltip="Used only when `size` is 'Custom'. Must be a multiple of 16 (GPT Image 2 only).", optional=True, - advanced=True, ), IO.Int.Input( "custom_height", @@ -464,7 +463,6 @@ class OpenAIGPTImage1(IO.ComfyNode): step=16, tooltip="Used only when `size` is 'Custom'. Must be a multiple of 16 (GPT Image 2 only).", optional=True, - advanced=True, ), ], outputs=[ From cf9cbec5960e38368393137419637f6b9ca7691b Mon Sep 17 00:00:00 2001 From: Talmaj Date: Fri, 1 May 2026 11:20:11 +0200 Subject: [PATCH 124/140] Reformat models variable into multiline array CORE-59 (#13513) Co-authored-by: Talmaj Marinc --- comfy/supported_models.py | 84 ++++++++++++++++++++++++++++++++++++++- 1 file changed, 82 insertions(+), 2 deletions(-) diff --git a/comfy/supported_models.py b/comfy/supported_models.py index 92d0305c5..e6c17fb98 100644 --- a/comfy/supported_models.py +++ b/comfy/supported_models.py @@ -1879,6 +1879,86 @@ class CogVideoX_I2V(CogVideoX_T2V): out = model_base.CogVideoX(self, image_to_video=True, device=device) return out -models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, LongCatImage, FluxSchnell, GenmoMochi, LTXV, LTXAV, HunyuanVideo15_SR_Distilled, HunyuanVideo15, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, ZImagePixelSpace, ZImage, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, WAN21_FlowRVS, WAN21_SCAIL, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, ACEStep15, Omnigen2, QwenImage, Flux2, Kandinsky5Image, Kandinsky5, Anima, RT_DETR_v4, ErnieImage, SAM3, SAM31, CogVideoX_I2V, CogVideoX_T2V] -models += [SVD_img2vid] +models = [ + LotusD, + Stable_Zero123, + SD15_instructpix2pix, + SD15, + SD20, + SD21UnclipL, + SD21UnclipH, + SDXL_instructpix2pix, + SDXLRefiner, + SDXL, + SSD1B, + KOALA_700M, + KOALA_1B, + Segmind_Vega, + SD_X4Upscaler, + Stable_Cascade_C, + Stable_Cascade_B, + SV3D_u, + SV3D_p, + SD3, + StableAudio, + AuraFlow, + PixArtAlpha, + PixArtSigma, + HunyuanDiT, + HunyuanDiT1, + FluxInpaint, + Flux, + LongCatImage, + FluxSchnell, + GenmoMochi, + LTXV, + LTXAV, + HunyuanVideo15_SR_Distilled, + HunyuanVideo15, + HunyuanImage21Refiner, + HunyuanImage21, + HunyuanVideoSkyreelsI2V, + HunyuanVideoI2V, + HunyuanVideo, + CosmosT2V, + CosmosI2V, + CosmosT2IPredict2, + CosmosI2VPredict2, + ZImagePixelSpace, + ZImage, + Lumina2, + WAN22_T2V, + WAN21_T2V, + WAN21_I2V, + WAN21_FunControl2V, + WAN21_Vace, + WAN21_Camera, + WAN22_Camera, + WAN22_S2V, + WAN21_HuMo, + WAN22_Animate, + WAN21_FlowRVS, + WAN21_SCAIL, + Hunyuan3Dv2mini, + Hunyuan3Dv2, + Hunyuan3Dv2_1, + HiDream, + Chroma, + ChromaRadiance, + ACEStep, + ACEStep15, + Omnigen2, + QwenImage, + Flux2, + Kandinsky5Image, + Kandinsky5, + Anima, + RT_DETR_v4, + ErnieImage, + SAM3, + SAM31, + CogVideoX_I2V, + CogVideoX_T2V, + SVD_img2vid, +] From fa7553138e3c75befe6aaf988048d4a0a95c1a32 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Fri, 1 May 2026 21:09:25 +0300 Subject: [PATCH 125/140] chore(api-nodes): remove Moonvalley API nodes (#13659) Signed-off-by: bigcat88 --- comfy_api_nodes/apis/moonvalley.py | 152 -------- comfy_api_nodes/nodes_moonvalley.py | 534 ---------------------------- 2 files changed, 686 deletions(-) delete mode 100644 comfy_api_nodes/apis/moonvalley.py delete mode 100644 comfy_api_nodes/nodes_moonvalley.py diff --git a/comfy_api_nodes/apis/moonvalley.py b/comfy_api_nodes/apis/moonvalley.py deleted file mode 100644 index 7ec7a4ade..000000000 --- a/comfy_api_nodes/apis/moonvalley.py +++ /dev/null @@ -1,152 +0,0 @@ -from enum import Enum -from typing import Optional, Dict, Any - -from pydantic import BaseModel, Field, StrictBytes - - -class MoonvalleyPromptResponse(BaseModel): - error: Optional[Dict[str, Any]] = None - frame_conditioning: Optional[Dict[str, Any]] = None - id: Optional[str] = None - inference_params: Optional[Dict[str, Any]] = None - meta: Optional[Dict[str, Any]] = None - model_params: Optional[Dict[str, Any]] = None - output_url: Optional[str] = None - prompt_text: Optional[str] = None - status: Optional[str] = None - - -class MoonvalleyTextToVideoInferenceParams(BaseModel): - add_quality_guidance: Optional[bool] = Field( - True, description='Whether to add quality guidance' - ) - caching_coefficient: Optional[float] = Field( - 0.3, description='Caching coefficient for optimization' - ) - caching_cooldown: Optional[int] = Field( - 3, description='Number of caching cooldown steps' - ) - caching_warmup: Optional[int] = Field( - 3, description='Number of caching warmup steps' - ) - clip_value: Optional[float] = Field( - 3, description='CLIP value for generation control' - ) - conditioning_frame_index: Optional[int] = Field( - 0, description='Index of the conditioning frame' - ) - cooldown_steps: Optional[int] = Field( - 75, description='Number of cooldown steps (calculated based on num_frames)' - ) - fps: Optional[int] = Field( - 24, description='Frames per second of the generated video' - ) - guidance_scale: Optional[float] = Field( - 10, description='Guidance scale for generation control' - ) - height: Optional[int] = Field( - 1080, description='Height of the generated video in pixels' - ) - negative_prompt: Optional[str] = Field(None, description='Negative prompt text') - num_frames: Optional[int] = Field(64, description='Number of frames to generate') - seed: Optional[int] = Field( - None, description='Random seed for generation (default: random)' - ) - shift_value: Optional[float] = Field( - 3, description='Shift value for generation control' - ) - steps: Optional[int] = Field(80, description='Number of denoising steps') - use_guidance_schedule: Optional[bool] = Field( - True, description='Whether to use guidance scheduling' - ) - use_negative_prompts: Optional[bool] = Field( - False, description='Whether to use negative prompts' - ) - use_timestep_transform: Optional[bool] = Field( - True, description='Whether to use timestep transformation' - ) - warmup_steps: Optional[int] = Field( - 0, description='Number of warmup steps (calculated based on num_frames)' - ) - width: Optional[int] = Field( - 1920, description='Width of the generated video in pixels' - ) - - -class MoonvalleyTextToVideoRequest(BaseModel): - image_url: Optional[str] = None - inference_params: Optional[MoonvalleyTextToVideoInferenceParams] = None - prompt_text: Optional[str] = None - webhook_url: Optional[str] = None - - -class MoonvalleyUploadFileRequest(BaseModel): - file: Optional[StrictBytes] = None - - -class MoonvalleyUploadFileResponse(BaseModel): - access_url: Optional[str] = None - - -class MoonvalleyVideoToVideoInferenceParams(BaseModel): - add_quality_guidance: Optional[bool] = Field( - True, description='Whether to add quality guidance' - ) - caching_coefficient: Optional[float] = Field( - 0.3, description='Caching coefficient for optimization' - ) - caching_cooldown: Optional[int] = Field( - 3, description='Number of caching cooldown steps' - ) - caching_warmup: Optional[int] = Field( - 3, description='Number of caching warmup steps' - ) - clip_value: Optional[float] = Field( - 3, description='CLIP value for generation control' - ) - conditioning_frame_index: Optional[int] = Field( - 0, description='Index of the conditioning frame' - ) - cooldown_steps: Optional[int] = Field( - 36, description='Number of cooldown steps (calculated based on num_frames)' - ) - guidance_scale: Optional[float] = Field( - 15, description='Guidance scale for generation control' - ) - negative_prompt: Optional[str] = Field(None, description='Negative prompt text') - seed: Optional[int] = Field( - None, description='Random seed for generation (default: random)' - ) - shift_value: Optional[float] = Field( - 3, description='Shift value for generation control' - ) - steps: Optional[int] = Field(80, description='Number of denoising steps') - use_guidance_schedule: Optional[bool] = Field( - True, description='Whether to use guidance scheduling' - ) - use_negative_prompts: Optional[bool] = Field( - False, description='Whether to use negative prompts' - ) - use_timestep_transform: Optional[bool] = Field( - True, description='Whether to use timestep transformation' - ) - warmup_steps: Optional[int] = Field( - 24, description='Number of warmup steps (calculated based on num_frames)' - ) - - -class ControlType(str, Enum): - motion_control = 'motion_control' - pose_control = 'pose_control' - - -class MoonvalleyVideoToVideoRequest(BaseModel): - control_type: ControlType = Field( - ..., description='Supported types for video control' - ) - inference_params: Optional[MoonvalleyVideoToVideoInferenceParams] = None - prompt_text: str = Field(..., description='Describes the video to generate') - video_url: str = Field(..., description='Url to control video') - webhook_url: Optional[str] = Field( - None, description='Optional webhook URL for notifications' - ) diff --git a/comfy_api_nodes/nodes_moonvalley.py b/comfy_api_nodes/nodes_moonvalley.py deleted file mode 100644 index 78a230529..000000000 --- a/comfy_api_nodes/nodes_moonvalley.py +++ /dev/null @@ -1,534 +0,0 @@ -import logging - -from typing_extensions import override - -from comfy_api.latest import IO, ComfyExtension, Input -from comfy_api_nodes.apis.moonvalley import ( - MoonvalleyPromptResponse, - MoonvalleyTextToVideoInferenceParams, - MoonvalleyTextToVideoRequest, - MoonvalleyVideoToVideoInferenceParams, - MoonvalleyVideoToVideoRequest, -) -from comfy_api_nodes.util import ( - ApiEndpoint, - download_url_to_video_output, - poll_op, - sync_op, - trim_video, - upload_images_to_comfyapi, - upload_video_to_comfyapi, - validate_container_format_is_mp4, - validate_image_dimensions, - validate_string, -) - -API_UPLOADS_ENDPOINT = "/proxy/moonvalley/uploads" -API_PROMPTS_ENDPOINT = "/proxy/moonvalley/prompts" -API_VIDEO2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/video-to-video" -API_TXT2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/text-to-video" -API_IMG2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/image-to-video" - -MIN_WIDTH = 300 -MIN_HEIGHT = 300 - -MAX_WIDTH = 10000 -MAX_HEIGHT = 10000 - -MIN_VID_WIDTH = 300 -MIN_VID_HEIGHT = 300 - -MAX_VID_WIDTH = 10000 -MAX_VID_HEIGHT = 10000 - -MAX_VIDEO_SIZE = 1024 * 1024 * 1024 # 1 GB max for in-memory video processing - -MOONVALLEY_MAREY_MAX_PROMPT_LENGTH = 5000 - - -def is_valid_task_creation_response(response: MoonvalleyPromptResponse) -> bool: - """Verifies that the initial response contains a task ID.""" - return bool(response.id) - - -def validate_task_creation_response(response) -> None: - if not is_valid_task_creation_response(response): - error_msg = f"Moonvalley Marey API: Initial request failed. Code: {response.code}, Message: {response.message}, Data: {response}" - logging.error(error_msg) - raise RuntimeError(error_msg) - - -def validate_video_to_video_input(video: Input.Video) -> Input.Video: - """ - Validates and processes video input for Moonvalley Video-to-Video generation. - - Args: - video: Input video to validate - - Returns: - Validated and potentially trimmed video - - Raises: - ValueError: If video doesn't meet requirements - MoonvalleyApiError: If video duration is too short - """ - width, height = _get_video_dimensions(video) - _validate_video_dimensions(width, height) - validate_container_format_is_mp4(video) - - return _validate_and_trim_duration(video) - - -def _get_video_dimensions(video: Input.Video) -> tuple[int, int]: - """Extracts video dimensions with error handling.""" - try: - return video.get_dimensions() - except Exception as e: - logging.error("Error getting dimensions of video: %s", e) - raise ValueError(f"Cannot get video dimensions: {e}") from e - - -def _validate_video_dimensions(width: int, height: int) -> None: - """Validates video dimensions meet Moonvalley V2V requirements.""" - supported_resolutions = { - (1920, 1080), - (1080, 1920), - (1152, 1152), - (1536, 1152), - (1152, 1536), - } - - if (width, height) not in supported_resolutions: - supported_list = ", ".join([f"{w}x{h}" for w, h in sorted(supported_resolutions)]) - raise ValueError(f"Resolution {width}x{height} not supported. Supported: {supported_list}") - - -def _validate_and_trim_duration(video: Input.Video) -> Input.Video: - """Validates video duration and trims to 5 seconds if needed.""" - duration = video.get_duration() - _validate_minimum_duration(duration) - return _trim_if_too_long(video, duration) - - -def _validate_minimum_duration(duration: float) -> None: - """Ensures video is at least 5 seconds long.""" - if duration < 5: - raise ValueError("Input video must be at least 5 seconds long.") - - -def _trim_if_too_long(video: Input.Video, duration: float) -> Input.Video: - """Trims video to 5 seconds if longer.""" - if duration > 5: - return trim_video(video, 5) - return video - - -def parse_width_height_from_res(resolution: str): - # Accepts a string like "16:9 (1920 x 1080)" and returns width, height as a dict - res_map = { - "16:9 (1920 x 1080)": {"width": 1920, "height": 1080}, - "9:16 (1080 x 1920)": {"width": 1080, "height": 1920}, - "1:1 (1152 x 1152)": {"width": 1152, "height": 1152}, - "4:3 (1536 x 1152)": {"width": 1536, "height": 1152}, - "3:4 (1152 x 1536)": {"width": 1152, "height": 1536}, - # "21:9 (2560 x 1080)": {"width": 2560, "height": 1080}, - } - return res_map.get(resolution, {"width": 1920, "height": 1080}) - - -def parse_control_parameter(value): - control_map = { - "Motion Transfer": "motion_control", - "Canny": "canny_control", - "Pose Transfer": "pose_control", - "Depth": "depth_control", - } - return control_map.get(value, control_map["Motion Transfer"]) - - -async def get_response(cls: type[IO.ComfyNode], task_id: str) -> MoonvalleyPromptResponse: - return await poll_op( - cls, - ApiEndpoint(path=f"{API_PROMPTS_ENDPOINT}/{task_id}"), - response_model=MoonvalleyPromptResponse, - status_extractor=lambda r: (r.status if r and r.status else None), - poll_interval=16.0, - max_poll_attempts=240, - ) - - -class MoonvalleyImg2VideoNode(IO.ComfyNode): - - @classmethod - def define_schema(cls) -> IO.Schema: - return IO.Schema( - node_id="MoonvalleyImg2VideoNode", - display_name="Moonvalley Marey Image to Video", - category="api node/video/Moonvalley Marey", - description="Moonvalley Marey Image to Video Node", - inputs=[ - IO.Image.Input( - "image", - tooltip="The reference image used to generate the video", - ), - IO.String.Input( - "prompt", - multiline=True, - ), - IO.String.Input( - "negative_prompt", - multiline=True, - default=" gopro, bright, contrast, static, overexposed, vignette, " - "artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, " - "flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, " - "cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, " - "blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, " - "wobbly, weird, low quality, plastic, stock footage, video camera, boring", - tooltip="Negative prompt text", - ), - IO.Combo.Input( - "resolution", - options=[ - "16:9 (1920 x 1080)", - "9:16 (1080 x 1920)", - "1:1 (1152 x 1152)", - "4:3 (1536 x 1152)", - "3:4 (1152 x 1536)", - # "21:9 (2560 x 1080)", - ], - default="16:9 (1920 x 1080)", - tooltip="Resolution of the output video", - ), - IO.Float.Input( - "prompt_adherence", - default=4.5, - min=1.0, - max=20.0, - step=1.0, - tooltip="Guidance scale for generation control", - ), - IO.Int.Input( - "seed", - default=9, - min=0, - max=4294967295, - step=1, - display_mode=IO.NumberDisplay.number, - tooltip="Random seed value", - control_after_generate=True, - ), - IO.Int.Input( - "steps", - default=80, - min=75, # steps should be greater or equal to cooldown_steps(75) + warmup_steps(0) - max=100, - step=1, - tooltip="Number of denoising steps", - ), - ], - outputs=[IO.Video.Output()], - hidden=[ - IO.Hidden.auth_token_comfy_org, - IO.Hidden.api_key_comfy_org, - IO.Hidden.unique_id, - ], - is_api_node=True, - price_badge=IO.PriceBadge( - depends_on=IO.PriceBadgeDepends(), - expr="""{"type":"usd","usd": 1.5}""", - ), - ) - - @classmethod - async def execute( - cls, - image: Input.Image, - prompt: str, - negative_prompt: str, - resolution: str, - prompt_adherence: float, - seed: int, - steps: int, - ) -> IO.NodeOutput: - validate_image_dimensions(image, min_width=300, min_height=300, max_height=MAX_HEIGHT, max_width=MAX_WIDTH) - validate_string(prompt, min_length=1, max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) - validate_string(negative_prompt, field_name="negative_prompt", max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) - width_height = parse_width_height_from_res(resolution) - - inference_params = MoonvalleyTextToVideoInferenceParams( - negative_prompt=negative_prompt, - steps=steps, - seed=seed, - guidance_scale=prompt_adherence, - width=width_height["width"], - height=width_height["height"], - use_negative_prompts=True, - ) - - # Get MIME type from tensor - assuming PNG format for image tensors - mime_type = "image/png" - image_url = (await upload_images_to_comfyapi(cls, image, max_images=1, mime_type=mime_type))[0] - task_creation_response = await sync_op( - cls, - endpoint=ApiEndpoint(path=API_IMG2VIDEO_ENDPOINT, method="POST"), - response_model=MoonvalleyPromptResponse, - data=MoonvalleyTextToVideoRequest( - image_url=image_url, prompt_text=prompt, inference_params=inference_params - ), - ) - validate_task_creation_response(task_creation_response) - final_response = await get_response(cls, task_creation_response.id) - video = await download_url_to_video_output(final_response.output_url) - return IO.NodeOutput(video) - - -class MoonvalleyVideo2VideoNode(IO.ComfyNode): - - @classmethod - def define_schema(cls) -> IO.Schema: - return IO.Schema( - node_id="MoonvalleyVideo2VideoNode", - display_name="Moonvalley Marey Video to Video", - category="api node/video/Moonvalley Marey", - description="", - inputs=[ - IO.String.Input( - "prompt", - multiline=True, - tooltip="Describes the video to generate", - ), - IO.String.Input( - "negative_prompt", - multiline=True, - default=" gopro, bright, contrast, static, overexposed, vignette, " - "artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, " - "flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, " - "cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, " - "blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, " - "wobbly, weird, low quality, plastic, stock footage, video camera, boring", - tooltip="Negative prompt text", - ), - IO.Int.Input( - "seed", - default=9, - min=0, - max=4294967295, - step=1, - display_mode=IO.NumberDisplay.number, - tooltip="Random seed value", - control_after_generate=False, - ), - IO.Video.Input( - "video", - tooltip="The reference video used to generate the output video. Must be at least 5 seconds long. " - "Videos longer than 5s will be automatically trimmed. Only MP4 format supported.", - ), - IO.Combo.Input( - "control_type", - options=["Motion Transfer", "Pose Transfer"], - default="Motion Transfer", - optional=True, - ), - IO.Int.Input( - "motion_intensity", - default=100, - min=0, - max=100, - step=1, - tooltip="Only used if control_type is 'Motion Transfer'", - optional=True, - ), - IO.Int.Input( - "steps", - default=60, - min=60, # steps should be greater or equal to cooldown_steps(36) + warmup_steps(24) - max=100, - step=1, - display_mode=IO.NumberDisplay.number, - tooltip="Number of inference steps", - ), - ], - outputs=[IO.Video.Output()], - hidden=[ - IO.Hidden.auth_token_comfy_org, - IO.Hidden.api_key_comfy_org, - IO.Hidden.unique_id, - ], - is_api_node=True, - price_badge=IO.PriceBadge( - depends_on=IO.PriceBadgeDepends(), - expr="""{"type":"usd","usd": 2.25}""", - ), - ) - - @classmethod - async def execute( - cls, - prompt: str, - negative_prompt: str, - seed: int, - video: Input.Video | None = None, - control_type: str = "Motion Transfer", - motion_intensity: int | None = 100, - steps=60, - prompt_adherence=4.5, - ) -> IO.NodeOutput: - validated_video = validate_video_to_video_input(video) - video_url = await upload_video_to_comfyapi(cls, validated_video) - validate_string(prompt, min_length=1, max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) - validate_string(negative_prompt, field_name="negative_prompt", max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) - - # Only include motion_intensity for Motion Transfer - control_params = {} - if control_type == "Motion Transfer" and motion_intensity is not None: - control_params["motion_intensity"] = motion_intensity - - inference_params = MoonvalleyVideoToVideoInferenceParams( - negative_prompt=negative_prompt, - seed=seed, - control_params=control_params, - steps=steps, - guidance_scale=prompt_adherence, - ) - - task_creation_response = await sync_op( - cls, - endpoint=ApiEndpoint(path=API_VIDEO2VIDEO_ENDPOINT, method="POST"), - response_model=MoonvalleyPromptResponse, - data=MoonvalleyVideoToVideoRequest( - control_type=parse_control_parameter(control_type), - video_url=video_url, - prompt_text=prompt, - inference_params=inference_params, - ), - ) - validate_task_creation_response(task_creation_response) - final_response = await get_response(cls, task_creation_response.id) - return IO.NodeOutput(await download_url_to_video_output(final_response.output_url)) - - -class MoonvalleyTxt2VideoNode(IO.ComfyNode): - - @classmethod - def define_schema(cls) -> IO.Schema: - return IO.Schema( - node_id="MoonvalleyTxt2VideoNode", - display_name="Moonvalley Marey Text to Video", - category="api node/video/Moonvalley Marey", - description="", - inputs=[ - IO.String.Input( - "prompt", - multiline=True, - ), - IO.String.Input( - "negative_prompt", - multiline=True, - default=" gopro, bright, contrast, static, overexposed, vignette, " - "artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, " - "flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, " - "cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, " - "blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, " - "wobbly, weird, low quality, plastic, stock footage, video camera, boring", - tooltip="Negative prompt text", - ), - IO.Combo.Input( - "resolution", - options=[ - "16:9 (1920 x 1080)", - "9:16 (1080 x 1920)", - "1:1 (1152 x 1152)", - "4:3 (1536 x 1152)", - "3:4 (1152 x 1536)", - "21:9 (2560 x 1080)", - ], - default="16:9 (1920 x 1080)", - tooltip="Resolution of the output video", - ), - IO.Float.Input( - "prompt_adherence", - default=4.0, - min=1.0, - max=20.0, - step=1.0, - tooltip="Guidance scale for generation control", - ), - IO.Int.Input( - "seed", - default=9, - min=0, - max=4294967295, - step=1, - display_mode=IO.NumberDisplay.number, - control_after_generate=True, - tooltip="Random seed value", - ), - IO.Int.Input( - "steps", - default=80, - min=75, # steps should be greater or equal to cooldown_steps(75) + warmup_steps(0) - max=100, - step=1, - tooltip="Inference steps", - ), - ], - outputs=[IO.Video.Output()], - hidden=[ - IO.Hidden.auth_token_comfy_org, - IO.Hidden.api_key_comfy_org, - IO.Hidden.unique_id, - ], - is_api_node=True, - price_badge=IO.PriceBadge( - depends_on=IO.PriceBadgeDepends(), - expr="""{"type":"usd","usd": 1.5}""", - ), - ) - - @classmethod - async def execute( - cls, - prompt: str, - negative_prompt: str, - resolution: str, - prompt_adherence: float, - seed: int, - steps: int, - ) -> IO.NodeOutput: - validate_string(prompt, min_length=1, max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) - validate_string(negative_prompt, field_name="negative_prompt", max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH) - width_height = parse_width_height_from_res(resolution) - - inference_params = MoonvalleyTextToVideoInferenceParams( - negative_prompt=negative_prompt, - steps=steps, - seed=seed, - guidance_scale=prompt_adherence, - num_frames=128, - width=width_height["width"], - height=width_height["height"], - ) - - task_creation_response = await sync_op( - cls, - endpoint=ApiEndpoint(path=API_TXT2VIDEO_ENDPOINT, method="POST"), - response_model=MoonvalleyPromptResponse, - data=MoonvalleyTextToVideoRequest(prompt_text=prompt, inference_params=inference_params), - ) - validate_task_creation_response(task_creation_response) - final_response = await get_response(cls, task_creation_response.id) - return IO.NodeOutput(await download_url_to_video_output(final_response.output_url)) - - -class MoonvalleyExtension(ComfyExtension): - @override - async def get_node_list(self) -> list[type[IO.ComfyNode]]: - return [ - MoonvalleyImg2VideoNode, - MoonvalleyTxt2VideoNode, - MoonvalleyVideo2VideoNode, - ] - - -async def comfy_entrypoint() -> MoonvalleyExtension: - return MoonvalleyExtension() From 10b45a71cdac2898693bb42aa0a21e2cb23a2daa Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Sat, 2 May 2026 03:11:30 +0800 Subject: [PATCH 126/140] chore: update workflow templates to v0.9.66 (#13662) Co-authored-by: Jedrzej Kosinski --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index cb85d970b..932034076 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.15 -comfyui-workflow-templates==0.9.65 +comfyui-workflow-templates==0.9.66 comfyui-embedded-docs==0.4.4 torch torchsde From cf758bd2566a04a156496fa77ec2c7fa76ff8873 Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Fri, 1 May 2026 22:48:41 +0300 Subject: [PATCH 127/140] chore(api-nodes): increase default timeout for partner API node tasks (#13663) Signed-off-by: bigcat88 Co-authored-by: Jedrzej Kosinski --- comfy_api_nodes/nodes_bytedance.py | 3 --- comfy_api_nodes/nodes_hitpaw.py | 2 -- comfy_api_nodes/nodes_kling.py | 3 --- comfy_api_nodes/nodes_magnific.py | 5 ----- comfy_api_nodes/nodes_topaz.py | 1 - comfy_api_nodes/nodes_vidu.py | 3 +-- comfy_api_nodes/nodes_wan.py | 1 - comfy_api_nodes/nodes_wavespeed.py | 2 -- comfy_api_nodes/util/client.py | 4 ++-- 9 files changed, 3 insertions(+), 21 deletions(-) diff --git a/comfy_api_nodes/nodes_bytedance.py b/comfy_api_nodes/nodes_bytedance.py index fee0ab888..2f241a775 100644 --- a/comfy_api_nodes/nodes_bytedance.py +++ b/comfy_api_nodes/nodes_bytedance.py @@ -1403,7 +1403,6 @@ class ByteDance2TextToVideoNode(IO.ComfyNode): status_extractor=lambda r: r.status, price_extractor=_seedance2_price_extractor(model_id, has_video_input=False), poll_interval=9, - max_poll_attempts=180, ) return IO.NodeOutput(await download_url_to_video_output(response.content.video_url)) @@ -1585,7 +1584,6 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode): status_extractor=lambda r: r.status, price_extractor=_seedance2_price_extractor(model_id, has_video_input=False), poll_interval=9, - max_poll_attempts=180, ) return IO.NodeOutput(await download_url_to_video_output(response.content.video_url)) @@ -1907,7 +1905,6 @@ class ByteDance2ReferenceNode(IO.ComfyNode): status_extractor=lambda r: r.status, price_extractor=_seedance2_price_extractor(model_id, has_video_input=has_video_input), poll_interval=9, - max_poll_attempts=180, ) return IO.NodeOutput(await download_url_to_video_output(response.content.video_url)) diff --git a/comfy_api_nodes/nodes_hitpaw.py b/comfy_api_nodes/nodes_hitpaw.py index 488080a74..bca5170e4 100644 --- a/comfy_api_nodes/nodes_hitpaw.py +++ b/comfy_api_nodes/nodes_hitpaw.py @@ -178,7 +178,6 @@ class HitPawGeneralImageEnhance(IO.ComfyNode): status_extractor=lambda x: x.data.status, price_extractor=lambda x: request_price, poll_interval=10.0, - max_poll_attempts=480, ) return IO.NodeOutput(await download_url_to_image_tensor(final_response.data.res_url)) @@ -324,7 +323,6 @@ class HitPawVideoEnhance(IO.ComfyNode): status_extractor=lambda x: x.data.status, price_extractor=lambda x: request_price, poll_interval=10.0, - max_poll_attempts=320, ) return IO.NodeOutput(await download_url_to_video_output(final_response.data.res_url)) diff --git a/comfy_api_nodes/nodes_kling.py b/comfy_api_nodes/nodes_kling.py index 709b3726c..efd58fac3 100644 --- a/comfy_api_nodes/nodes_kling.py +++ b/comfy_api_nodes/nodes_kling.py @@ -276,7 +276,6 @@ async def finish_omni_video_task(cls: type[IO.ComfyNode], response: TaskStatusRe cls, ApiEndpoint(path=f"/proxy/kling/v1/videos/omni-video/{response.data.task_id}"), response_model=TaskStatusResponse, - max_poll_attempts=280, status_extractor=lambda r: (r.data.task_status if r.data else None), ) return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url)) @@ -3062,7 +3061,6 @@ class KlingVideoNode(IO.ComfyNode): cls, ApiEndpoint(path=poll_path), response_model=TaskStatusResponse, - max_poll_attempts=280, status_extractor=lambda r: (r.data.task_status if r.data else None), ) return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url)) @@ -3188,7 +3186,6 @@ class KlingFirstLastFrameNode(IO.ComfyNode): cls, ApiEndpoint(path=f"/proxy/kling/v1/videos/image2video/{response.data.task_id}"), response_model=TaskStatusResponse, - max_poll_attempts=280, status_extractor=lambda r: (r.data.task_status if r.data else None), ) return IO.NodeOutput(await download_url_to_video_output(final_response.data.task_result.videos[0].url)) diff --git a/comfy_api_nodes/nodes_magnific.py b/comfy_api_nodes/nodes_magnific.py index 0f53208d4..38b881fea 100644 --- a/comfy_api_nodes/nodes_magnific.py +++ b/comfy_api_nodes/nodes_magnific.py @@ -230,7 +230,6 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode): status_extractor=lambda x: x.status, price_extractor=lambda _: price_usd, poll_interval=10.0, - max_poll_attempts=480, ) return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0])) @@ -391,7 +390,6 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode): status_extractor=lambda x: x.status, price_extractor=lambda _: price_usd, poll_interval=10.0, - max_poll_attempts=480, ) return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0])) @@ -541,7 +539,6 @@ class MagnificImageStyleTransferNode(IO.ComfyNode): response_model=TaskResponse, status_extractor=lambda x: x.status, poll_interval=10.0, - max_poll_attempts=480, ) return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0])) @@ -782,7 +779,6 @@ class MagnificImageRelightNode(IO.ComfyNode): response_model=TaskResponse, status_extractor=lambda x: x.status, poll_interval=10.0, - max_poll_attempts=480, ) return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0])) @@ -924,7 +920,6 @@ class MagnificImageSkinEnhancerNode(IO.ComfyNode): response_model=TaskResponse, status_extractor=lambda x: x.status, poll_interval=10.0, - max_poll_attempts=480, ) return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0])) diff --git a/comfy_api_nodes/nodes_topaz.py b/comfy_api_nodes/nodes_topaz.py index b18b31af1..fe3666ec9 100644 --- a/comfy_api_nodes/nodes_topaz.py +++ b/comfy_api_nodes/nodes_topaz.py @@ -453,7 +453,6 @@ class TopazVideoEnhance(IO.ComfyNode): progress_extractor=lambda x: getattr(x, "progress", 0), price_extractor=lambda x: (x.estimates.cost[0] * 0.08 if x.estimates and x.estimates.cost[0] else None), poll_interval=10.0, - max_poll_attempts=320, ) return IO.NodeOutput(await download_url_to_video_output(final_response.download.url)) diff --git a/comfy_api_nodes/nodes_vidu.py b/comfy_api_nodes/nodes_vidu.py index f04407eb5..8d90cefeb 100644 --- a/comfy_api_nodes/nodes_vidu.py +++ b/comfy_api_nodes/nodes_vidu.py @@ -38,7 +38,7 @@ async def execute_task( cls: type[IO.ComfyNode], vidu_endpoint: str, payload: TaskCreationRequest | TaskExtendCreationRequest | TaskMultiFrameCreationRequest, - max_poll_attempts: int = 320, + max_poll_attempts: int = 480, ) -> list[TaskResult]: task_creation_response = await sync_op( cls, @@ -1097,7 +1097,6 @@ class ViduExtendVideoNode(IO.ComfyNode): video_url=await upload_video_to_comfyapi(cls, video, wait_label="Uploading video"), images=[image_url] if image_url else None, ), - max_poll_attempts=480, ) return IO.NodeOutput(await download_url_to_video_output(results[0].url)) diff --git a/comfy_api_nodes/nodes_wan.py b/comfy_api_nodes/nodes_wan.py index 7d7466fb6..68061bb5c 100644 --- a/comfy_api_nodes/nodes_wan.py +++ b/comfy_api_nodes/nodes_wan.py @@ -818,7 +818,6 @@ class WanReferenceVideoApi(IO.ComfyNode): response_model=VideoTaskStatusResponse, status_extractor=lambda x: x.output.task_status, poll_interval=6, - max_poll_attempts=280, ) return IO.NodeOutput(await download_url_to_video_output(response.output.video_url)) diff --git a/comfy_api_nodes/nodes_wavespeed.py b/comfy_api_nodes/nodes_wavespeed.py index c59fafd3b..65e45f60a 100644 --- a/comfy_api_nodes/nodes_wavespeed.py +++ b/comfy_api_nodes/nodes_wavespeed.py @@ -84,7 +84,6 @@ class WavespeedFlashVSRNode(IO.ComfyNode): response_model=TaskResultResponse, status_extractor=lambda x: "failed" if x.data is None else x.data.status, poll_interval=10.0, - max_poll_attempts=480, ) if final_response.code != 200: raise ValueError( @@ -156,7 +155,6 @@ class WavespeedImageUpscaleNode(IO.ComfyNode): response_model=TaskResultResponse, status_extractor=lambda x: "failed" if x.data is None else x.data.status, poll_interval=10.0, - max_poll_attempts=480, ) if final_response.code != 200: raise ValueError( diff --git a/comfy_api_nodes/util/client.py b/comfy_api_nodes/util/client.py index b0cf97ae4..a0b8d35e1 100644 --- a/comfy_api_nodes/util/client.py +++ b/comfy_api_nodes/util/client.py @@ -148,7 +148,7 @@ async def poll_op( queued_statuses: list[str | int] | None = None, data: BaseModel | None = None, poll_interval: float = 5.0, - max_poll_attempts: int = 160, + max_poll_attempts: int = 480, timeout_per_poll: float = 120.0, max_retries_per_poll: int = 10, retry_delay_per_poll: float = 1.0, @@ -254,7 +254,7 @@ async def poll_op_raw( queued_statuses: list[str | int] | None = None, data: dict[str, Any] | BaseModel | None = None, poll_interval: float = 5.0, - max_poll_attempts: int = 160, + max_poll_attempts: int = 480, timeout_per_poll: float = 120.0, max_retries_per_poll: int = 10, retry_delay_per_poll: float = 1.0, From 63103d519ec960701438e8617452ef64b02609c7 Mon Sep 17 00:00:00 2001 From: Simon Lui <502929+simonlui@users.noreply.github.com> Date: Fri, 1 May 2026 14:16:41 -0700 Subject: [PATCH 128/140] Remove IPEX and clean up checks and add missing synchronize during empty cache. (#13653) --- comfy/cli_args.py | 1 - comfy/model_management.py | 18 +++--------------- 2 files changed, 3 insertions(+), 16 deletions(-) diff --git a/comfy/cli_args.py b/comfy/cli_args.py index dbaadf723..cef1a5e6b 100644 --- a/comfy/cli_args.py +++ b/comfy/cli_args.py @@ -90,7 +90,6 @@ parser.add_argument("--force-channels-last", action="store_true", help="Force ch parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.") parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.") -parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.") parser.add_argument("--supports-fp8-compute", action="store_true", help="ComfyUI will act like if the device supports fp8 compute.") class LatentPreviewMethod(enum.Enum): diff --git a/comfy/model_management.py b/comfy/model_management.py index 95af40012..f86e2a4aa 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -112,10 +112,6 @@ if args.directml is not None: # torch_directml.disable_tiled_resources(True) lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default. -try: - import intel_extension_for_pytorch as ipex # noqa: F401 -except: - pass try: _ = torch.xpu.device_count() @@ -583,9 +579,6 @@ class LoadedModel: real_model = self.model.model - if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and real_model is not None: - with torch.no_grad(): - real_model = ipex.optimize(real_model.eval(), inplace=True, graph_mode=True, concat_linear=True) self.real_model = weakref.ref(real_model) self.model_finalizer = weakref.finalize(real_model, cleanup_models) @@ -1581,10 +1574,7 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma return False if is_intel_xpu(): - if torch_version_numeric < (2, 3): - return True - else: - return torch.xpu.get_device_properties(device).has_fp16 + return torch.xpu.get_device_properties(device).has_fp16 if is_ascend_npu(): return True @@ -1650,10 +1640,7 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma return False if is_intel_xpu(): - if torch_version_numeric < (2, 3): - return True - else: - return torch.xpu.is_bf16_supported() + return torch.xpu.is_bf16_supported() if is_ascend_npu(): return True @@ -1784,6 +1771,7 @@ def soft_empty_cache(force=False): if cpu_state == CPUState.MPS: torch.mps.empty_cache() elif is_intel_xpu(): + torch.xpu.synchronize() torch.xpu.empty_cache() elif is_ascend_npu(): torch.npu.empty_cache() From b5921c8ac2d3cd1171bb33245f4343b1471224ff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Sat, 2 May 2026 00:17:25 +0300 Subject: [PATCH 129/140] SDPose: resize fix (#13656) --- comfy_extras/nodes_sdpose.py | 38 ++++++++++++++++-------------------- 1 file changed, 17 insertions(+), 21 deletions(-) diff --git a/comfy_extras/nodes_sdpose.py b/comfy_extras/nodes_sdpose.py index 7d54967d5..96b6821bd 100644 --- a/comfy_extras/nodes_sdpose.py +++ b/comfy_extras/nodes_sdpose.py @@ -459,27 +459,23 @@ class SDPoseKeypointExtractor(io.ComfyNode): total_images = image.shape[0] captured_feat = None - model_h = int(head.heatmap_size[0]) * 4 # e.g. 192 * 4 = 768 - model_w = int(head.heatmap_size[1]) * 4 # e.g. 256 * 4 = 1024 + model_w = int(head.heatmap_size[0]) * 4 # 192 * 4 = 768 + model_h = int(head.heatmap_size[1]) * 4 # 256 * 4 = 1024 def _resize_to_model(imgs): - """Aspect-preserving resize + zero-pad BHWC images to (model_h, model_w). Returns (resized_bhwc, scale, pad_top, pad_left).""" + """Stretch BHWC images to (model_h, model_w), model expects no aspect preservation.""" h, w = imgs.shape[-3], imgs.shape[-2] - scale = min(model_h / h, model_w / w) - sh, sw = int(round(h * scale)), int(round(w * scale)) - pt, pl = (model_h - sh) // 2, (model_w - sw) // 2 + method = "area" if (model_h <= h and model_w <= w) else "bilinear" chw = imgs.permute(0, 3, 1, 2).float() - scaled = comfy.utils.common_upscale(chw, sw, sh, upscale_method="bilinear", crop="disabled") - padded = torch.zeros(scaled.shape[0], scaled.shape[1], model_h, model_w, dtype=scaled.dtype, device=scaled.device) - padded[:, :, pt:pt + sh, pl:pl + sw] = scaled - return padded.permute(0, 2, 3, 1), scale, pt, pl + scaled = comfy.utils.common_upscale(chw, model_w, model_h, upscale_method=method, crop="disabled") + return scaled.permute(0, 2, 3, 1), model_w / w, model_h / h - def _remap_keypoints(kp, scale, pad_top, pad_left, offset_x=0, offset_y=0): + def _remap_keypoints(kp, scale_x, scale_y, offset_x=0, offset_y=0): """Remap keypoints from model space back to original image space.""" kp = kp.copy() if isinstance(kp, np.ndarray) else np.array(kp, dtype=np.float32) invalid = kp[..., 0] < 0 - kp[..., 0] = (kp[..., 0] - pad_left) / scale + offset_x - kp[..., 1] = (kp[..., 1] - pad_top) / scale + offset_y + kp[..., 0] = kp[..., 0] / scale_x + offset_x + kp[..., 1] = kp[..., 1] / scale_y + offset_y kp[invalid] = -1 return kp @@ -529,18 +525,18 @@ class SDPoseKeypointExtractor(io.ComfyNode): continue crop = img[:, y1:y2, x1:x2, :] # (1, crop_h, crop_w, C) - crop_resized, scale, pad_top, pad_left = _resize_to_model(crop) + crop_resized, sx, sy = _resize_to_model(crop) latent_crop = vae.encode(crop_resized) kp_batch, sc_batch = _run_on_latent(latent_crop) - kp = _remap_keypoints(kp_batch[0], scale, pad_top, pad_left, x1, y1) + kp = _remap_keypoints(kp_batch[0], sx, sy, x1, y1) img_keypoints.append(kp) img_scores.append(sc_batch[0]) else: - img_resized, scale, pad_top, pad_left = _resize_to_model(img) + img_resized, sx, sy = _resize_to_model(img) latent_img = vae.encode(img_resized) kp_batch, sc_batch = _run_on_latent(latent_img) - img_keypoints.append(_remap_keypoints(kp_batch[0], scale, pad_top, pad_left)) + img_keypoints.append(_remap_keypoints(kp_batch[0], sx, sy)) img_scores.append(sc_batch[0]) all_keypoints.append(img_keypoints) @@ -549,12 +545,12 @@ class SDPoseKeypointExtractor(io.ComfyNode): else: # full-image mode, batched for batch_start in tqdm(range(0, total_images, batch_size), desc="Extracting keypoints"): - batch_resized, scale, pad_top, pad_left = _resize_to_model(image[batch_start:batch_start + batch_size]) + batch_resized, sx, sy = _resize_to_model(image[batch_start:batch_start + batch_size]) latent_batch = vae.encode(batch_resized) kp_batch, sc_batch = _run_on_latent(latent_batch) for kp, sc in zip(kp_batch, sc_batch): - all_keypoints.append([_remap_keypoints(kp, scale, pad_top, pad_left)]) + all_keypoints.append([_remap_keypoints(kp, sx, sy)]) all_scores.append([sc]) pbar.update(len(kp_batch)) @@ -727,13 +723,13 @@ class CropByBBoxes(io.ComfyNode): scale = min(output_width / crop_w, output_height / crop_h) scaled_w = int(round(crop_w * scale)) scaled_h = int(round(crop_h * scale)) - scaled = comfy.utils.common_upscale(crop_chw, scaled_w, scaled_h, upscale_method="bilinear", crop="disabled") + scaled = comfy.utils.common_upscale(crop_chw, scaled_w, scaled_h, upscale_method="area", crop="disabled") pad_left = (output_width - scaled_w) // 2 pad_top = (output_height - scaled_h) // 2 resized = torch.zeros(1, num_ch, output_height, output_width, dtype=image.dtype, device=image.device) resized[:, :, pad_top:pad_top + scaled_h, pad_left:pad_left + scaled_w] = scaled else: # "stretch" - resized = comfy.utils.common_upscale(crop_chw, output_width, output_height, upscale_method="bilinear", crop="disabled") + resized = comfy.utils.common_upscale(crop_chw, output_width, output_height, upscale_method="area", crop="disabled") crops.append(resized) if not crops: From 0230e0e7cc389979e509cd6237a7b9244798e69c Mon Sep 17 00:00:00 2001 From: Alexis Rolland Date: Sat, 2 May 2026 06:37:18 +0800 Subject: [PATCH 130/140] Adding kijai (#13664) Co-authored-by: Jedrzej Kosinski --- CODEOWNERS | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CODEOWNERS b/CODEOWNERS index e693955a0..946dbf946 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -1,2 +1,2 @@ # Admins -* @comfyanonymous @kosinkadink @guill @alexisrolland @rattus128 +* @comfyanonymous @kosinkadink @guill @alexisrolland @rattus128 @kijai From 67f6cb35273d00278d2b1ef2a8c3efe21238f22d Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 1 May 2026 17:19:32 -0700 Subject: [PATCH 131/140] List all the portable downloads in the README section. (#13666) --- README.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index f05311421..3b5114633 100644 --- a/README.md +++ b/README.md @@ -193,13 +193,15 @@ If you have trouble extracting it, right click the file -> properties -> unblock The portable above currently comes with python 3.13 and pytorch cuda 13.0. Update your Nvidia drivers if it doesn't start. -#### Alternative Downloads: +#### All Official Portable Downloads: [Portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z) -[Experimental portable for Intel GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_intel.7z) +[Portable for Intel GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_intel.7z) -[Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs). +[Portable for Nvidia GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z) (supports 20 series and above). + +[Portable for Nvidia GPUs with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs). #### How do I share models between another UI and ComfyUI? From 3e3ed8cc2aaa142711e89e1e799956e1e57af62f Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Fri, 1 May 2026 17:19:46 -0700 Subject: [PATCH 132/140] Add script in AMD portable to launch with dynamic vram. (#13667) --- ...ble_smart_memory.bat => run_amd_gpu_enable_dynamic_vram.bat} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename .ci/windows_amd_base_files/{run_amd_gpu_disable_smart_memory.bat => run_amd_gpu_enable_dynamic_vram.bat} (66%) diff --git a/.ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat b/.ci/windows_amd_base_files/run_amd_gpu_enable_dynamic_vram.bat similarity index 66% rename from .ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat rename to .ci/windows_amd_base_files/run_amd_gpu_enable_dynamic_vram.bat index cece0aeb2..94ad31942 100755 --- a/.ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat +++ b/.ci/windows_amd_base_files/run_amd_gpu_enable_dynamic_vram.bat @@ -1,2 +1,2 @@ -.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --disable-smart-memory +.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --enable-dynamic-vram pause From 783782d5d742a7bc38dd0b661e030813bc50839a Mon Sep 17 00:00:00 2001 From: rattus <46076784+rattus128@users.noreply.github.com> Date: Sun, 3 May 2026 09:23:24 +1000 Subject: [PATCH 133/140] Implement block prefetch + Lora Async load + and adopt in LTX (Speedup!) (CORE-111) (#13618) * mm: Use Aimdo raw allocator for cast buffers pytorch manages allocation of growing buffers on streams poorly. Pyt has no windows support for the expandable segments allocator (which is the right tool for this job), while also segmenting the memory by stream such that it can be generally re-used. So kick the problem to aimdo which can just grow a virtual region thats freed per stream. * plan * ops: move cpu handler up to the caller * ops: split up prefetch from weight prep block prefetching API Split up the casting and weight formating/lora stuff in prep for arbitrary prefetch support. * ops: implement block prefetching API allow a model to construct a prefetch list and operate it for increased async offload. * ltxv2: Implement block prefetching * Implement lora async offload Implement async offload of loras. --- comfy/ldm/lightricks/av_model.py | 5 + comfy/lora.py | 15 +++ comfy/model_base.py | 5 + comfy/model_management.py | 22 +++- comfy/model_patcher.py | 13 ++- comfy/model_prefetch.py | 65 +++++++++++ comfy/ops.py | 181 ++++++++++++++++++++++--------- execution.py | 2 + 8 files changed, 251 insertions(+), 57 deletions(-) create mode 100644 comfy/model_prefetch.py diff --git a/comfy/ldm/lightricks/av_model.py b/comfy/ldm/lightricks/av_model.py index 6f2ba41ef..3fb87b4a3 100644 --- a/comfy/ldm/lightricks/av_model.py +++ b/comfy/ldm/lightricks/av_model.py @@ -16,6 +16,7 @@ from comfy.ldm.lightricks.model import ( from comfy.ldm.lightricks.symmetric_patchifier import AudioPatchifier from comfy.ldm.lightricks.embeddings_connector import Embeddings1DConnector import comfy.ldm.common_dit +import comfy.model_prefetch class CompressedTimestep: """Store video timestep embeddings in compressed form using per-frame indexing.""" @@ -907,9 +908,11 @@ class LTXAVModel(LTXVModel): """Process transformer blocks for LTXAV.""" patches_replace = transformer_options.get("patches_replace", {}) blocks_replace = patches_replace.get("dit", {}) + prefetch_queue = comfy.model_prefetch.make_prefetch_queue(list(self.transformer_blocks), vx.device, transformer_options) # Process transformer blocks for i, block in enumerate(self.transformer_blocks): + comfy.model_prefetch.prefetch_queue_pop(prefetch_queue, vx.device, block) if ("double_block", i) in blocks_replace: def block_wrap(args): @@ -982,6 +985,8 @@ class LTXAVModel(LTXVModel): a_prompt_timestep=a_prompt_timestep, ) + comfy.model_prefetch.prefetch_queue_pop(prefetch_queue, vx.device, None) + return [vx, ax] def _process_output(self, x, embedded_timestep, keyframe_idxs, **kwargs): diff --git a/comfy/lora.py b/comfy/lora.py index e4337c729..db8f16bcb 100644 --- a/comfy/lora.py +++ b/comfy/lora.py @@ -17,6 +17,7 @@ """ from __future__ import annotations +import comfy.memory_management import comfy.utils import comfy.model_management import comfy.model_base @@ -473,3 +474,17 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32, ori weight = old_weight return weight + +def prefetch_prepared_value(value, allocate_buffer, stream): + if isinstance(value, torch.Tensor): + dest = allocate_buffer(comfy.memory_management.vram_aligned_size(value)) + comfy.model_management.cast_to_gathered([value], dest, non_blocking=True, stream=stream) + return comfy.memory_management.interpret_gathered_like([value], dest)[0] + elif isinstance(value, weight_adapter.WeightAdapterBase): + return type(value)(value.loaded_keys, prefetch_prepared_value(value.weights, allocate_buffer, stream)) + elif isinstance(value, tuple): + return tuple(prefetch_prepared_value(item, allocate_buffer, stream) for item in value) + elif isinstance(value, list): + return [prefetch_prepared_value(item, allocate_buffer, stream) for item in value] + + return value diff --git a/comfy/model_base.py b/comfy/model_base.py index 50dab5782..b61a2aa09 100644 --- a/comfy/model_base.py +++ b/comfy/model_base.py @@ -214,6 +214,11 @@ class BaseModel(torch.nn.Module): if "latent_shapes" in extra_conds: xc = utils.unpack_latents(xc, extra_conds.pop("latent_shapes")) + transformer_options = transformer_options.copy() + transformer_options["prefetch_dynamic_vbars"] = ( + self.current_patcher is not None and self.current_patcher.is_dynamic() + ) + model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds) if len(model_output) > 1 and not torch.is_tensor(model_output): model_output, _ = utils.pack_latents(model_output) diff --git a/comfy/model_management.py b/comfy/model_management.py index f86e2a4aa..02ad66656 100644 --- a/comfy/model_management.py +++ b/comfy/model_management.py @@ -31,6 +31,7 @@ from contextlib import nullcontext import comfy.memory_management import comfy.utils import comfy.quant_ops +import comfy_aimdo.vram_buffer class VRAMState(Enum): DISABLED = 0 #No vram present: no need to move models to vram @@ -1175,6 +1176,10 @@ stream_counters = {} STREAM_CAST_BUFFERS = {} LARGEST_CASTED_WEIGHT = (None, 0) +STREAM_AIMDO_CAST_BUFFERS = {} +LARGEST_AIMDO_CASTED_WEIGHT = (None, 0) + +DEFAULT_AIMDO_CAST_BUFFER_RESERVATION_SIZE = 16 * 1024 ** 3 def get_cast_buffer(offload_stream, device, size, ref): global LARGEST_CASTED_WEIGHT @@ -1208,13 +1213,26 @@ def get_cast_buffer(offload_stream, device, size, ref): return cast_buffer +def get_aimdo_cast_buffer(offload_stream, device): + cast_buffer = STREAM_AIMDO_CAST_BUFFERS.get(offload_stream, None) + if cast_buffer is None: + cast_buffer = comfy_aimdo.vram_buffer.VRAMBuffer(DEFAULT_AIMDO_CAST_BUFFER_RESERVATION_SIZE, device.index) + STREAM_AIMDO_CAST_BUFFERS[offload_stream] = cast_buffer + + return cast_buffer def reset_cast_buffers(): global LARGEST_CASTED_WEIGHT + global LARGEST_AIMDO_CASTED_WEIGHT + LARGEST_CASTED_WEIGHT = (None, 0) - for offload_stream in STREAM_CAST_BUFFERS: - offload_stream.synchronize() + LARGEST_AIMDO_CASTED_WEIGHT = (None, 0) + for offload_stream in set(STREAM_CAST_BUFFERS) | set(STREAM_AIMDO_CAST_BUFFERS): + if offload_stream is not None: + offload_stream.synchronize() synchronize() + STREAM_CAST_BUFFERS.clear() + STREAM_AIMDO_CAST_BUFFERS.clear() soft_empty_cache() def get_offload_stream(device): diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py index e259aed63..7d2d6883f 100644 --- a/comfy/model_patcher.py +++ b/comfy/model_patcher.py @@ -121,9 +121,20 @@ class LowVramPatch: self.patches = patches self.convert_func = convert_func # TODO: remove self.set_func = set_func + self.prepared_patches = None + + def prepare(self, allocate_buffer, stream): + self.prepared_patches = [ + (patch[0], comfy.lora.prefetch_prepared_value(patch[1], allocate_buffer, stream), patch[2], patch[3], patch[4]) + for patch in self.patches[self.key] + ] + + def clear_prepared(self): + self.prepared_patches = None def __call__(self, weight): - return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype) + patches = self.prepared_patches if self.prepared_patches is not None else self.patches[self.key] + return comfy.lora.calculate_weight(patches, weight, self.key, intermediate_dtype=weight.dtype) LOWVRAM_PATCH_ESTIMATE_MATH_FACTOR = 2 diff --git a/comfy/model_prefetch.py b/comfy/model_prefetch.py new file mode 100644 index 000000000..0ad35deb5 --- /dev/null +++ b/comfy/model_prefetch.py @@ -0,0 +1,65 @@ +import comfy_aimdo.model_vbar +import comfy.model_management +import comfy.ops + +PREFETCH_QUEUES = [] + +def cleanup_prefetched_modules(comfy_modules): + for s in comfy_modules: + prefetch = getattr(s, "_prefetch", None) + if prefetch is None: + continue + for param_key in ("weight", "bias"): + lowvram_fn = getattr(s, param_key + "_lowvram_function", None) + if lowvram_fn is not None: + lowvram_fn.clear_prepared() + if prefetch["signature"] is not None: + comfy_aimdo.model_vbar.vbar_unpin(s._v) + delattr(s, "_prefetch") + +def cleanup_prefetch_queues(): + global PREFETCH_QUEUES + + for queue in PREFETCH_QUEUES: + for entry in queue: + if entry is None or not isinstance(entry, tuple): + continue + _, prefetch_state = entry + comfy_modules = prefetch_state[1] + if comfy_modules is not None: + cleanup_prefetched_modules(comfy_modules) + PREFETCH_QUEUES = [] + +def prefetch_queue_pop(queue, device, module): + if queue is None: + return + + consumed = queue.pop(0) + if consumed is not None: + offload_stream, prefetch_state = consumed + offload_stream.wait_stream(comfy.model_management.current_stream(device)) + _, comfy_modules = prefetch_state + if comfy_modules is not None: + cleanup_prefetched_modules(comfy_modules) + + prefetch = queue[0] + if prefetch is not None: + comfy_modules = [] + for s in prefetch.modules(): + if hasattr(s, "_v"): + comfy_modules.append(s) + + offload_stream = comfy.ops.cast_modules_with_vbar(comfy_modules, None, device, None, True) + comfy.model_management.sync_stream(device, offload_stream) + queue[0] = (offload_stream, (prefetch, comfy_modules)) + +def make_prefetch_queue(queue, device, transformer_options): + if (not transformer_options.get("prefetch_dynamic_vbars", False) + or comfy.model_management.NUM_STREAMS == 0 + or comfy.model_management.is_device_cpu(device) + or not comfy.model_management.device_supports_non_blocking(device)): + return None + + queue = [None] + queue + [None] + PREFETCH_QUEUES.append(queue) + return queue diff --git a/comfy/ops.py b/comfy/ops.py index 050f7cda0..96db1411c 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -86,38 +86,61 @@ def materialize_meta_param(s, param_keys): setattr(s, param_key, torch.nn.Parameter(torch.zeros(param.shape, dtype=param.dtype), requires_grad=param.requires_grad)) -def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant): - #vbar doesn't support CPU weights, but some custom nodes have weird paths - #that might switch the layer to the CPU and expect it to work. We have to take - #a clone conservatively as we are mmapped and some SFT files are packed misaligned - #If you are a custom node author reading this, please move your layer to the GPU - #or declare your ModelPatcher as CPU in the first place. - if comfy.model_management.is_device_cpu(device): - materialize_meta_param(s, ["weight", "bias"]) - weight = s.weight.to(dtype=dtype, copy=True) - if isinstance(weight, QuantizedTensor): - weight = weight.dequantize() - bias = None - if s.bias is not None: - bias = s.bias.to(dtype=bias_dtype, copy=True) - return weight, bias, (None, None, None) - +# FIXME: add n=1 cache hit fast path +def cast_modules_with_vbar(comfy_modules, dtype, device, bias_dtype, non_blocking): offload_stream = None - xfer_dest = None + cast_buffer = None + cast_buffer_offset = 0 + + def ensure_offload_stream(module, required_size, check_largest): + nonlocal offload_stream + nonlocal cast_buffer + + if offload_stream is None: + offload_stream = comfy.model_management.get_offload_stream(device) + if offload_stream is None or not check_largest or len(comfy_modules) != 1: + return + + current_size = 0 if cast_buffer is None else cast_buffer.size() + if current_size < required_size and module is comfy.model_management.LARGEST_AIMDO_CASTED_WEIGHT[0]: + offload_stream = comfy.model_management.get_offload_stream(device) + cast_buffer = None + if required_size > comfy.model_management.LARGEST_AIMDO_CASTED_WEIGHT[1]: + comfy.model_management.LARGEST_AIMDO_CASTED_WEIGHT = (module, required_size) + + def get_cast_buffer(buffer_size): + nonlocal offload_stream + nonlocal cast_buffer + nonlocal cast_buffer_offset + + if buffer_size == 0: + return None + + if offload_stream is None: + return torch.empty((buffer_size,), dtype=torch.uint8, device=device) + + cast_buffer = comfy.model_management.get_aimdo_cast_buffer(offload_stream, device) + buffer = comfy_aimdo.torch.aimdo_to_tensor(cast_buffer.get(buffer_size, cast_buffer_offset), device) + cast_buffer_offset += buffer_size + return buffer + + for s in comfy_modules: + signature = comfy_aimdo.model_vbar.vbar_fault(s._v) + resident = comfy_aimdo.model_vbar.vbar_signature_compare(signature, s._v_signature) + prefetch = { + "signature": signature, + "resident": resident, + } - signature = comfy_aimdo.model_vbar.vbar_fault(s._v) - resident = comfy_aimdo.model_vbar.vbar_signature_compare(signature, s._v_signature) - if signature is not None: if resident: - weight = s._v_weight - bias = s._v_bias - else: - xfer_dest = comfy_aimdo.torch.aimdo_to_tensor(s._v, device) + s._prefetch = prefetch + continue - if not resident: materialize_meta_param(s, ["weight", "bias"]) + xfer_dest = comfy_aimdo.torch.aimdo_to_tensor(s._v, device) if signature is not None else None cast_geometry = comfy.memory_management.tensors_to_geometries([ s.weight, s.bias ]) cast_dest = None + needs_cast = False xfer_source = [ s.weight, s.bias ] @@ -129,22 +152,15 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu if data is None: continue if data.dtype != geometry.dtype: + needs_cast = True cast_dest = xfer_dest - if cast_dest is None: - cast_dest = torch.empty((comfy.memory_management.vram_aligned_size(cast_geometry),), dtype=torch.uint8, device=device) xfer_dest = None break dest_size = comfy.memory_management.vram_aligned_size(xfer_source) - offload_stream = comfy.model_management.get_offload_stream(device) - if xfer_dest is None and offload_stream is not None: - xfer_dest = comfy.model_management.get_cast_buffer(offload_stream, device, dest_size, s) - if xfer_dest is None: - offload_stream = comfy.model_management.get_offload_stream(device) - xfer_dest = comfy.model_management.get_cast_buffer(offload_stream, device, dest_size, s) + ensure_offload_stream(s, dest_size if xfer_dest is None else 0, True) if xfer_dest is None: - xfer_dest = torch.empty((dest_size,), dtype=torch.uint8, device=device) - offload_stream = None + xfer_dest = get_cast_buffer(dest_size) if signature is None and pin is None: comfy.pinned_memory.pin_memory(s) @@ -157,27 +173,54 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu xfer_source = [ pin ] #send it over comfy.model_management.cast_to_gathered(xfer_source, xfer_dest, non_blocking=non_blocking, stream=offload_stream) - comfy.model_management.sync_stream(device, offload_stream) - if cast_dest is not None: + for param_key in ("weight", "bias"): + lowvram_fn = getattr(s, param_key + "_lowvram_function", None) + if lowvram_fn is not None: + ensure_offload_stream(s, cast_buffer_offset, False) + lowvram_fn.prepare(lambda size: get_cast_buffer(size), offload_stream) + + prefetch["xfer_dest"] = xfer_dest + prefetch["cast_dest"] = cast_dest + prefetch["cast_geometry"] = cast_geometry + prefetch["needs_cast"] = needs_cast + s._prefetch = prefetch + + return offload_stream + + +def resolve_cast_module_with_vbar(s, dtype, device, bias_dtype, compute_dtype, want_requant): + + prefetch = getattr(s, "_prefetch", None) + + if prefetch["resident"]: + weight = s._v_weight + bias = s._v_bias + else: + xfer_dest = prefetch["xfer_dest"] + if prefetch["needs_cast"]: + cast_dest = prefetch["cast_dest"] if prefetch["cast_dest"] is not None else torch.empty((comfy.memory_management.vram_aligned_size(prefetch["cast_geometry"]),), dtype=torch.uint8, device=device) for pre_cast, post_cast in zip(comfy.memory_management.interpret_gathered_like([s.weight, s.bias ], xfer_dest), - comfy.memory_management.interpret_gathered_like(cast_geometry, cast_dest)): + comfy.memory_management.interpret_gathered_like(prefetch["cast_geometry"], cast_dest)): if post_cast is not None: post_cast.copy_(pre_cast) xfer_dest = cast_dest - params = comfy.memory_management.interpret_gathered_like(cast_geometry, xfer_dest) + params = comfy.memory_management.interpret_gathered_like(prefetch["cast_geometry"], xfer_dest) weight = params[0] bias = params[1] - if signature is not None: + if prefetch["signature"] is not None: s._v_weight = weight s._v_bias = bias - s._v_signature=signature + s._v_signature = prefetch["signature"] def post_cast(s, param_key, x, dtype, resident, update_weight): lowvram_fn = getattr(s, param_key + "_lowvram_function", None) fns = getattr(s, param_key + "_function", []) + if x is None: + return None + orig = x def to_dequant(tensor, dtype): @@ -205,14 +248,12 @@ def cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compu x = f(x) return x - update_weight = signature is not None + update_weight = prefetch["signature"] is not None + weight = post_cast(s, "weight", weight, dtype, prefetch["resident"], update_weight) + if bias is not None: + bias = post_cast(s, "bias", bias, bias_dtype, prefetch["resident"], update_weight) - weight = post_cast(s, "weight", weight, dtype, resident, update_weight) - if s.bias is not None: - bias = post_cast(s, "bias", bias, bias_dtype, resident, update_weight) - - #FIXME: weird offload return protocol - return weight, bias, (offload_stream, device if signature is not None else None, None) + return weight, bias def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, offloadable=False, compute_dtype=None, want_requant=False): @@ -230,10 +271,46 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of if device is None: device = input.device + def format_return(result, offloadable): + weight, bias, offload_stream = result + return (weight, bias, offload_stream) if offloadable else (weight, bias) + non_blocking = comfy.model_management.device_supports_non_blocking(device) if hasattr(s, "_v"): - return cast_bias_weight_with_vbar(s, dtype, device, bias_dtype, non_blocking, compute_dtype, want_requant) + + #vbar doesn't support CPU weights, but some custom nodes have weird paths + #that might switch the layer to the CPU and expect it to work. We have to take + #a clone conservatively as we are mmapped and some SFT files are packed misaligned + #If you are a custom node author reading this, please move your layer to the GPU + #or declare your ModelPatcher as CPU in the first place. + if comfy.model_management.is_device_cpu(device): + materialize_meta_param(s, ["weight", "bias"]) + weight = s.weight.to(dtype=dtype, copy=True) + if isinstance(weight, QuantizedTensor): + weight = weight.dequantize() + bias = s.bias.to(dtype=bias_dtype, copy=True) if s.bias is not None else None + return format_return((weight, bias, (None, None, None)), offloadable) + + prefetched = hasattr(s, "_prefetch") + offload_stream = None + offload_device = None + if not prefetched: + offload_stream = cast_modules_with_vbar([s], dtype, device, bias_dtype, non_blocking) + comfy.model_management.sync_stream(device, offload_stream) + + weight, bias = resolve_cast_module_with_vbar(s, dtype, device, bias_dtype, compute_dtype, want_requant) + + if not prefetched: + if getattr(s, "_prefetch")["signature"] is not None: + offload_device = device + for param_key in ("weight", "bias"): + lowvram_fn = getattr(s, param_key + "_lowvram_function", None) + if lowvram_fn is not None: + lowvram_fn.clear_prepared() + delattr(s, "_prefetch") + return format_return((weight, bias, (offload_stream, offload_device, None)), offloadable) + if offloadable and (device != s.weight.device or (s.bias is not None and device != s.bias.device)): @@ -280,11 +357,7 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of for f in s.weight_function: weight = f(weight) - if offloadable: - return weight, bias, (offload_stream, weight_a, bias_a) - else: - #Legacy function signature - return weight, bias + return format_return((weight, bias, (offload_stream, weight_a, bias_a)), offloadable) def uncast_bias_weight(s, weight, bias, offload_stream): diff --git a/execution.py b/execution.py index 5a6d3404c..654db8426 100644 --- a/execution.py +++ b/execution.py @@ -15,6 +15,7 @@ import torch from comfy.cli_args import args import comfy.memory_management import comfy.model_management +import comfy.model_prefetch import comfy_aimdo.model_vbar from latent_preview import set_preview_method @@ -537,6 +538,7 @@ async def execute(server, dynprompt, caches, current_item, extra_data, executed, if args.verbose == "DEBUG": comfy_aimdo.control.analyze() comfy.model_management.reset_cast_buffers() + comfy.model_prefetch.cleanup_prefetch_queues() comfy_aimdo.model_vbar.vbars_reset_watermark_limits() if has_pending_tasks: From ef6722f6be7bf073d225d21da47354905a6abd2b Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sat, 2 May 2026 17:34:27 -0700 Subject: [PATCH 134/140] Some cleanups to the load image node. (#13677) --- nodes.py | 29 ++++++++++------------------- 1 file changed, 10 insertions(+), 19 deletions(-) diff --git a/nodes.py b/nodes.py index 99dc07227..710cccffe 100644 --- a/nodes.py +++ b/nodes.py @@ -1694,26 +1694,27 @@ class LoadImage: RETURN_TYPES = ("IMAGE", "MASK") FUNCTION = "load_image" + def load_image(self, image): image_path = folder_paths.get_annotated_filepath(image) + dtype = comfy.model_management.intermediate_dtype() + device = comfy.model_management.intermediate_device() + components = InputImpl.VideoFromFile(image_path).get_components() if components.images.shape[0] > 0: - return (components.images, 1.0 - components.alpha[..., -1] if components.alpha is not None else torch.zeros((components.images.shape[0], 64, 64), dtype=torch.float32, device="cpu")) + return (components.images.to(device=device, dtype=dtype), (1.0 - components.alpha[..., -1]).to(device=device, dtype=dtype) if components.alpha is not None else torch.zeros((components.images.shape[0], 64, 64), dtype=dtype, device=device)) + # This code is left here to handle animated webp which pyav does not support loading img = node_helpers.pillow(Image.open, image_path) output_images = [] output_masks = [] w, h = None, None - dtype = comfy.model_management.intermediate_dtype() - for i in ImageSequence.Iterator(img): i = node_helpers.pillow(ImageOps.exif_transpose, i) - if i.mode == 'I': - i = i.point(lambda i: i * (1 / 255)) image = i.convert("RGB") if len(output_images) == 0: @@ -1728,25 +1729,15 @@ class LoadImage: if 'A' in i.getbands(): mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 mask = 1. - torch.from_numpy(mask) - elif i.mode == 'P' and 'transparency' in i.info: - mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0 - mask = 1. - torch.from_numpy(mask) else: - mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") + mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu") output_images.append(image.to(dtype=dtype)) output_masks.append(mask.unsqueeze(0).to(dtype=dtype)) - if img.format == "MPO": - break # ignore all frames except the first one for MPO format + output_image = torch.cat(output_images, dim=0) + output_mask = torch.cat(output_masks, dim=0) - if len(output_images) > 1: - output_image = torch.cat(output_images, dim=0) - output_mask = torch.cat(output_masks, dim=0) - else: - output_image = output_images[0] - output_mask = output_masks[0] - - return (output_image, output_mask) + return (output_image.to(device=device, dtype=dtype), output_mask.to(device=device, dtype=dtype)) @classmethod def IS_CHANGED(s, image): From 1d23a875ed0d4644538265635c1259be08a3370e Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Sun, 3 May 2026 10:06:55 +0800 Subject: [PATCH 135/140] chore: update workflow templates to v0.9.68 (#13678) --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 932034076..32826e25a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ comfyui-frontend-package==1.42.15 -comfyui-workflow-templates==0.9.66 +comfyui-workflow-templates==0.9.68 comfyui-embedded-docs==0.4.4 torch torchsde From f756d801a1e5fbafe81cdfdf8a1c0aadf54c9bea Mon Sep 17 00:00:00 2001 From: Alexander Piskun <13381981+bigcat88@users.noreply.github.com> Date: Sun, 3 May 2026 05:29:00 +0300 Subject: [PATCH 136/140] [Partner Nodes] Topaz Astra 2 model (#13672) * feat(api-nodes): add Topaz Astra 2 model Signed-off-by: bigcat88 * feat(api-nodes): make Astra 2 the default Topaz upscaler model Reorder UPSCALER_MODELS_MAP and the upscaler_model dynamic combo so "Astra 2" appears first, surfacing it as the default selection. --------- Signed-off-by: bigcat88 Co-authored-by: Marwan Mostafa --- comfy_api_nodes/apis/topaz.py | 9 +- comfy_api_nodes/nodes_topaz.py | 361 ++++++++++++++++++++++++++++++++- 2 files changed, 365 insertions(+), 5 deletions(-) diff --git a/comfy_api_nodes/apis/topaz.py b/comfy_api_nodes/apis/topaz.py index a9e6235a7..f91980e3d 100644 --- a/comfy_api_nodes/apis/topaz.py +++ b/comfy_api_nodes/apis/topaz.py @@ -1,4 +1,4 @@ -from typing import Optional, Union +from typing import Optional from pydantic import BaseModel, Field @@ -72,8 +72,11 @@ class VideoEnhancementFilter(BaseModel): grain: Optional[float] = Field(None, description="Grain after AI model processing") grainSize: Optional[float] = Field(None, description="Size of generated grain") recoverOriginalDetailValue: Optional[float] = Field(None, description="Source details into the output video") - creativity: Optional[str] = Field(None, description="Creativity level(high, low) for slc-1 only") + creativity: float | str | None = Field(None, description="slc-1/slp-2.5: enum (low/middle/high). ast-2: decimal 0.0-1.0.") isOptimizedMode: Optional[bool] = Field(None, description="Set to true for Starlight Creative (slc-1) only") + prompt: str | None = Field(None, description="Descriptive scene prompt (ast-2 only)") + sharp: float | None = Field(None, description="ast-2 pre-enhance sharpness") + realism: float | None = Field(None, description="ast-2 realism control") class OutputInformationVideo(BaseModel): @@ -90,7 +93,7 @@ class Overrides(BaseModel): class CreateVideoRequest(BaseModel): source: CreateVideoRequestSource = Field(...) - filters: list[Union[VideoFrameInterpolationFilter, VideoEnhancementFilter]] = Field(...) + filters: list[VideoFrameInterpolationFilter | VideoEnhancementFilter] = Field(...) output: OutputInformationVideo = Field(...) overrides: Overrides = Field(Overrides(isPaidDiffusion=True)) diff --git a/comfy_api_nodes/nodes_topaz.py b/comfy_api_nodes/nodes_topaz.py index fe3666ec9..e79c16d3c 100644 --- a/comfy_api_nodes/nodes_topaz.py +++ b/comfy_api_nodes/nodes_topaz.py @@ -36,11 +36,15 @@ from comfy_api_nodes.util import ( ) UPSCALER_MODELS_MAP = { + "Astra 2": "ast-2", "Starlight (Astra) Fast": "slf-1", "Starlight (Astra) Creative": "slc-1", "Starlight Precise 2.5": "slp-2.5", } +AST2_MAX_FRAMES = 9000 +AST2_MAX_FRAMES_WITH_PROMPT = 450 + class TopazImageEnhance(IO.ComfyNode): @classmethod @@ -230,13 +234,20 @@ class TopazVideoEnhance(IO.ComfyNode): def define_schema(cls): return IO.Schema( node_id="TopazVideoEnhance", - display_name="Topaz Video Enhance", + display_name="Topaz Video Enhance (Legacy)", category="api node/video/Topaz", description="Breathe new life into video with powerful upscaling and recovery technology.", inputs=[ IO.Video.Input("video"), IO.Boolean.Input("upscaler_enabled", default=True), - IO.Combo.Input("upscaler_model", options=list(UPSCALER_MODELS_MAP.keys())), + IO.Combo.Input( + "upscaler_model", + options=[ + "Starlight (Astra) Fast", + "Starlight (Astra) Creative", + "Starlight Precise 2.5", + ], + ), IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"]), IO.Combo.Input( "upscaler_creativity", @@ -304,6 +315,7 @@ class TopazVideoEnhance(IO.ComfyNode): IO.Hidden.unique_id, ], is_api_node=True, + is_deprecated=True, ) @classmethod @@ -457,12 +469,357 @@ class TopazVideoEnhance(IO.ComfyNode): return IO.NodeOutput(await download_url_to_video_output(final_response.download.url)) +class TopazVideoEnhanceV2(IO.ComfyNode): + @classmethod + def define_schema(cls): + return IO.Schema( + node_id="TopazVideoEnhanceV2", + display_name="Topaz Video Enhance", + category="api node/video/Topaz", + description="Breathe new life into video with powerful upscaling and recovery technology.", + inputs=[ + IO.Video.Input("video"), + IO.DynamicCombo.Input( + "upscaler_model", + options=[ + IO.DynamicCombo.Option( + "Astra 2", + [ + IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"]), + IO.Float.Input( + "creativity", + default=0.5, + min=0.0, + max=1.0, + step=0.1, + display_mode=IO.NumberDisplay.slider, + tooltip="Creative strength of the upscale.", + ), + IO.String.Input( + "prompt", + multiline=True, + default="", + tooltip="Optional descriptive (not instructive) scene prompt." + f"Capping input at {AST2_MAX_FRAMES_WITH_PROMPT} frames (~15s @ 30fps) when set.", + ), + IO.Float.Input( + "sharp", + default=0.5, + min=0.0, + max=1.0, + step=0.01, + display_mode=IO.NumberDisplay.slider, + tooltip="Pre-enhance sharpness: " + "0.0=Gaussian blur, 0.5=passthrough (default), 1.0=USM sharpening.", + advanced=True, + ), + IO.Float.Input( + "realism", + default=0.0, + min=0.0, + max=1.0, + step=0.01, + display_mode=IO.NumberDisplay.slider, + tooltip="Pulls output toward photographic realism." + "Leave at 0 for the model default.", + advanced=True, + ), + ], + ), + IO.DynamicCombo.Option( + "Starlight (Astra) Fast", + [IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"]),], + ), + IO.DynamicCombo.Option( + "Starlight (Astra) Creative", + [ + IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"]), + IO.Combo.Input( + "creativity", + options=["low", "middle", "high"], + default="low", + tooltip="Creative strength of the upscale.", + ), + ], + ), + IO.DynamicCombo.Option( + "Starlight Precise 2.5", + [IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"])], + ), + IO.DynamicCombo.Option("Disabled", []), + ], + ), + IO.DynamicCombo.Input( + "interpolation_model", + options=[ + IO.DynamicCombo.Option("Disabled", []), + IO.DynamicCombo.Option( + "apo-8", + [ + IO.Int.Input( + "interpolation_frame_rate", + default=60, + min=15, + max=240, + display_mode=IO.NumberDisplay.number, + tooltip="Output frame rate.", + ), + IO.Int.Input( + "interpolation_slowmo", + default=1, + min=1, + max=16, + display_mode=IO.NumberDisplay.number, + tooltip="Slow-motion factor applied to the input video. " + "For example, 2 makes the output twice as slow and doubles the duration.", + advanced=True, + ), + IO.Boolean.Input( + "interpolation_duplicate", + default=False, + tooltip="Analyze the input for duplicate frames and remove them.", + advanced=True, + ), + IO.Float.Input( + "interpolation_duplicate_threshold", + default=0.01, + min=0.001, + max=0.1, + step=0.001, + display_mode=IO.NumberDisplay.number, + tooltip="Detection sensitivity for duplicate frames.", + advanced=True, + ), + ], + ), + ], + ), + IO.Combo.Input( + "dynamic_compression_level", + options=["Low", "Mid", "High"], + default="Low", + tooltip="CQP level.", + optional=True, + ), + ], + outputs=[ + IO.Video.Output(), + ], + hidden=[ + IO.Hidden.auth_token_comfy_org, + IO.Hidden.api_key_comfy_org, + IO.Hidden.unique_id, + ], + is_api_node=True, + price_badge=IO.PriceBadge( + depends_on=IO.PriceBadgeDepends(widgets=[ + "upscaler_model", + "upscaler_model.upscaler_resolution", + "interpolation_model", + ]), + expr=""" + ( + $model := $lookup(widgets, "upscaler_model"); + $res := $lookup(widgets, "upscaler_model.upscaler_resolution"); + $interp := $lookup(widgets, "interpolation_model"); + $is4k := $contains($res, "4k"); + $hasInterp := $interp != "disabled"; + $rates := { + "starlight (astra) fast": {"hd": 0.43, "uhd": 0.85}, + "starlight precise 2.5": {"hd": 0.70, "uhd": 1.54}, + "astra 2": {"hd": 1.72, "uhd": 2.85}, + "starlight (astra) creative": {"hd": 2.25, "uhd": 3.99} + }; + $surcharge := $is4k ? 0.28 : 0.14; + $entry := $lookup($rates, $model); + $base := $is4k ? $entry.uhd : $entry.hd; + $hi := $base + ($hasInterp ? $surcharge : 0); + $model = "disabled" + ? {"type":"text","text":"Interpolation only"} + : ($hasInterp + ? {"type":"text","text":"~" & $string($base) & "–" & $string($hi) & " credits/src frame"} + : {"type":"text","text":"~" & $string($base) & " credits/src frame"}) + ) + """, + ), + ) + + @classmethod + async def execute( + cls, + video: Input.Video, + upscaler_model: dict, + interpolation_model: dict, + dynamic_compression_level: str = "Low", + ) -> IO.NodeOutput: + upscaler_choice = upscaler_model["upscaler_model"] + interpolation_choice = interpolation_model["interpolation_model"] + if upscaler_choice == "Disabled" and interpolation_choice == "Disabled": + raise ValueError("There is nothing to do: both upscaling and interpolation are disabled.") + validate_container_format_is_mp4(video) + src_width, src_height = video.get_dimensions() + src_frame_rate = int(video.get_frame_rate()) + duration_sec = video.get_duration() + src_video_stream = video.get_stream_source() + target_width = src_width + target_height = src_height + target_frame_rate = src_frame_rate + filters = [] + if upscaler_choice != "Disabled": + if "1080p" in upscaler_model["upscaler_resolution"]: + target_pixel_p = 1080 + max_long_side = 1920 + else: + target_pixel_p = 2160 + max_long_side = 3840 + ar = src_width / src_height + if src_width >= src_height: + # Landscape or Square; Attempt to set height to target (e.g., 2160), calculate width + target_height = target_pixel_p + target_width = int(target_height * ar) + # Check if width exceeds standard bounds (for ultra-wide e.g., 21:9 ARs) + if target_width > max_long_side: + target_width = max_long_side + target_height = int(target_width / ar) + else: + # Portrait; Attempt to set width to target (e.g., 2160), calculate height + target_width = target_pixel_p + target_height = int(target_width / ar) + # Check if height exceeds standard bounds + if target_height > max_long_side: + target_height = max_long_side + target_width = int(target_height * ar) + if target_width % 2 != 0: + target_width += 1 + if target_height % 2 != 0: + target_height += 1 + model_id = UPSCALER_MODELS_MAP[upscaler_choice] + if model_id == "slc-1": + filters.append( + VideoEnhancementFilter( + model=model_id, + creativity=upscaler_model["creativity"], + isOptimizedMode=True, + ) + ) + elif model_id == "ast-2": + n_frames = video.get_frame_count() + ast2_prompt = (upscaler_model["prompt"] or "").strip() + if ast2_prompt and n_frames > AST2_MAX_FRAMES_WITH_PROMPT: + raise ValueError( + f"Astra 2 with a prompt is limited to {AST2_MAX_FRAMES_WITH_PROMPT} input frames " + f"(~15s @ 30fps); video has {n_frames}. Clear the prompt or shorten the clip." + ) + if n_frames > AST2_MAX_FRAMES: + raise ValueError(f"Astra 2 is limited to {AST2_MAX_FRAMES} input frames; video has {n_frames}.") + realism = upscaler_model["realism"] + filters.append( + VideoEnhancementFilter( + model=model_id, + creativity=upscaler_model["creativity"], + prompt=(ast2_prompt or None), + sharp=upscaler_model["sharp"], + realism=(realism if realism > 0 else None), + ) + ) + else: + filters.append(VideoEnhancementFilter(model=model_id)) + if interpolation_choice != "Disabled": + target_frame_rate = interpolation_model["interpolation_frame_rate"] + filters.append( + VideoFrameInterpolationFilter( + model=interpolation_choice, + slowmo=interpolation_model["interpolation_slowmo"], + fps=interpolation_model["interpolation_frame_rate"], + duplicate=interpolation_model["interpolation_duplicate"], + duplicate_threshold=interpolation_model["interpolation_duplicate_threshold"], + ), + ) + initial_res = await sync_op( + cls, + ApiEndpoint(path="/proxy/topaz/video/", method="POST"), + response_model=CreateVideoResponse, + data=CreateVideoRequest( + source=CreateVideoRequestSource( + container="mp4", + size=get_fs_object_size(src_video_stream), + duration=int(duration_sec), + frameCount=video.get_frame_count(), + frameRate=src_frame_rate, + resolution=Resolution(width=src_width, height=src_height), + ), + filters=filters, + output=OutputInformationVideo( + resolution=Resolution(width=target_width, height=target_height), + frameRate=target_frame_rate, + audioCodec="AAC", + audioTransfer="Copy", + dynamicCompressionLevel=dynamic_compression_level, + ), + ), + wait_label="Creating task", + final_label_on_success="Task created", + ) + upload_res = await sync_op( + cls, + ApiEndpoint( + path=f"/proxy/topaz/video/{initial_res.requestId}/accept", + method="PATCH", + ), + response_model=VideoAcceptResponse, + wait_label="Preparing upload", + final_label_on_success="Upload started", + ) + if len(upload_res.urls) > 1: + raise NotImplementedError( + "Large files are not currently supported. Please open an issue in the ComfyUI repository." + ) + async with aiohttp.ClientSession(headers={"Content-Type": "video/mp4"}) as session: + if isinstance(src_video_stream, BytesIO): + src_video_stream.seek(0) + async with session.put(upload_res.urls[0], data=src_video_stream, raise_for_status=True) as res: + upload_etag = res.headers["Etag"] + else: + with builtins.open(src_video_stream, "rb") as video_file: + async with session.put(upload_res.urls[0], data=video_file, raise_for_status=True) as res: + upload_etag = res.headers["Etag"] + await sync_op( + cls, + ApiEndpoint( + path=f"/proxy/topaz/video/{initial_res.requestId}/complete-upload", + method="PATCH", + ), + response_model=VideoCompleteUploadResponse, + data=VideoCompleteUploadRequest( + uploadResults=[ + VideoCompleteUploadRequestPart( + partNum=1, + eTag=upload_etag, + ), + ], + ), + wait_label="Finalizing upload", + final_label_on_success="Upload completed", + ) + final_response = await poll_op( + cls, + ApiEndpoint(path=f"/proxy/topaz/video/{initial_res.requestId}/status"), + response_model=VideoStatusResponse, + status_extractor=lambda x: x.status, + progress_extractor=lambda x: getattr(x, "progress", 0), + price_extractor=lambda x: (x.estimates.cost[0] * 0.08 if x.estimates and x.estimates.cost[0] else None), + poll_interval=10.0, + ) + return IO.NodeOutput(await download_url_to_video_output(final_response.download.url)) + + class TopazExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: return [ TopazImageEnhance, TopazVideoEnhance, + TopazVideoEnhanceV2, ] From be95871adccfac92a91ebdc06e52a85511f7b85c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jukka=20Sepp=C3=A4nen?= <40791699+kijai@users.noreply.github.com> Date: Sun, 3 May 2026 05:46:15 +0300 Subject: [PATCH 137/140] feat: Gemma4 text generation support (CORE-30) (#13376) * initial gemma4 support * parity with reference implementation outputs can 100% match transformers with same sdpa flags, checkpoint this and then optimize * Cleanup, video fixes * cleanup, enable fused rms norm by default * update comment * Cleanup * Update sd.py * Various fixes * Add fp8 scaled embedding support * small fixes * Translate think tokens * Fix image encoder attention mask type So it works with basic attention * Handle thinking tokens different only for Gemma4 * Code cleanup * Update nodes_textgen.py * Use embed scale class instead of buffer Slight difference to HF, but technically more accurate and simpler code * Default to fused rms_norm * Update gemma4.py --- comfy/ldm/modules/attention.py | 24 +- comfy/ops.py | 87 +++ comfy/rmsnorm.py | 1 + comfy/sd.py | 17 + comfy/text_encoders/gemma4.py | 1298 ++++++++++++++++++++++++++++++++ comfy/text_encoders/llama.py | 40 +- comfy/text_encoders/lt.py | 3 +- comfy/text_encoders/lumina2.py | 3 +- comfy/text_encoders/qwen35.py | 2 - comfy/utils.py | 7 - comfy_extras/nodes_textgen.py | 13 +- 11 files changed, 1453 insertions(+), 42 deletions(-) create mode 100644 comfy/text_encoders/gemma4.py diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py index b193fe5e8..a68cb8439 100644 --- a/comfy/ldm/modules/attention.py +++ b/comfy/ldm/modules/attention.py @@ -14,6 +14,8 @@ from .sub_quadratic_attention import efficient_dot_product_attention from comfy import model_management +TORCH_HAS_GQA = model_management.torch_version_numeric >= (2, 5) + if model_management.xformers_enabled(): import xformers import xformers.ops @@ -150,7 +152,12 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape b, _, dim_head = q.shape dim_head //= heads - scale = dim_head ** -0.5 + if kwargs.get("enable_gqa", False) and q.shape[-3] != k.shape[-3]: + n_rep = q.shape[-3] // k.shape[-3] + k = k.repeat_interleave(n_rep, dim=-3) + v = v.repeat_interleave(n_rep, dim=-3) + + scale = kwargs.get("scale", dim_head ** -0.5) h = heads if skip_reshape: @@ -219,6 +226,10 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, b, _, dim_head = query.shape dim_head //= heads + if "scale" in kwargs: + # Pre-scale query to match requested scale (cancels internal 1/sqrt(dim_head)) + query = query * (kwargs["scale"] * dim_head ** 0.5) + if skip_reshape: query = query.reshape(b * heads, -1, dim_head) value = value.reshape(b * heads, -1, dim_head) @@ -290,7 +301,7 @@ def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape b, _, dim_head = q.shape dim_head //= heads - scale = dim_head ** -0.5 + scale = kwargs.get("scale", dim_head ** -0.5) if skip_reshape: q, k, v = map( @@ -500,8 +511,13 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha if mask.ndim == 3: mask = mask.unsqueeze(1) + # Pass through extra SDPA kwargs (scale, enable_gqa) if provided + # enable_gqa requires PyTorch 2.5+; older versions use manual KV expansion above + sdpa_keys = ("scale", "enable_gqa") if TORCH_HAS_GQA else ("scale",) + sdpa_extra = {k: v for k, v in kwargs.items() if k in sdpa_keys} + if SDP_BATCH_LIMIT >= b: - out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) + out = comfy.ops.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False, **sdpa_extra) if not skip_output_reshape: out = ( out.transpose(1, 2).reshape(b, -1, heads * dim_head) @@ -519,7 +535,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha k[i : i + SDP_BATCH_LIMIT], v[i : i + SDP_BATCH_LIMIT], attn_mask=m, - dropout_p=0.0, is_causal=False + dropout_p=0.0, is_causal=False, **sdpa_extra ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head) return out diff --git a/comfy/ops.py b/comfy/ops.py index 96db1411c..4f0338346 100644 --- a/comfy/ops.py +++ b/comfy/ops.py @@ -1246,6 +1246,93 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec self._buffers[key] = fn(buf) return self + class Embedding(manual_cast.Embedding): + def _load_from_state_dict(self, state_dict, prefix, local_metadata, + strict, missing_keys, unexpected_keys, error_msgs): + weight_key = f"{prefix}weight" + layer_conf = state_dict.pop(f"{prefix}comfy_quant", None) + if layer_conf is not None: + layer_conf = json.loads(layer_conf.numpy().tobytes()) + + # Only fp8 makes sense for embeddings (per-row dequant via index select). + # Block-scaled formats (NVFP4, MXFP8) can't do per-row lookup efficiently. + quant_format = layer_conf.get("format", None) if layer_conf is not None else None + if quant_format in ["float8_e4m3fn", "float8_e5m2"] and weight_key in state_dict: + self.quant_format = quant_format + qconfig = QUANT_ALGOS[quant_format] + layout_cls = get_layout_class(qconfig["comfy_tensor_layout"]) + weight = state_dict.pop(weight_key) + manually_loaded_keys = [weight_key] + + scale_key = f"{prefix}weight_scale" + scale = state_dict.pop(scale_key, None) + if scale is not None: + scale = scale.float() + manually_loaded_keys.append(scale_key) + + params = layout_cls.Params( + scale=scale if scale is not None else torch.ones((), dtype=torch.float32), + orig_dtype=MixedPrecisionOps._compute_dtype, + orig_shape=(self.num_embeddings, self.embedding_dim), + ) + self.weight = torch.nn.Parameter( + QuantizedTensor(weight.to(dtype=qconfig["storage_t"]), qconfig["comfy_tensor_layout"], params), + requires_grad=False) + + super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) + for k in manually_loaded_keys: + if k in missing_keys: + missing_keys.remove(k) + else: + if layer_conf is not None: + state_dict[f"{prefix}comfy_quant"] = torch.tensor(list(json.dumps(layer_conf).encode('utf-8')), dtype=torch.uint8) + super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) + + def state_dict(self, *args, destination=None, prefix="", **kwargs): + if destination is not None: + sd = destination + else: + sd = {} + + if not hasattr(self, 'weight') or self.weight is None: + return sd + + if isinstance(self.weight, QuantizedTensor): + sd_out = self.weight.state_dict("{}weight".format(prefix)) + for k in sd_out: + sd[k] = sd_out[k] + + quant_conf = {"format": self.quant_format} + sd["{}comfy_quant".format(prefix)] = torch.tensor(list(json.dumps(quant_conf).encode('utf-8')), dtype=torch.uint8) + else: + sd["{}weight".format(prefix)] = self.weight + return sd + + def forward_comfy_cast_weights(self, input, out_dtype=None): + weight = self.weight + + # Optimized path: lookup in fp8, dequantize only the selected rows. + if isinstance(weight, QuantizedTensor) and len(self.weight_function) == 0: + qdata, _, offload_stream = cast_bias_weight(self, device=input.device, dtype=weight.dtype, offloadable=True) + if isinstance(qdata, QuantizedTensor): + scale = qdata._params.scale + qdata = qdata._qdata + else: + scale = None + + x = torch.nn.functional.embedding( + input, qdata, self.padding_idx, self.max_norm, + self.norm_type, self.scale_grad_by_freq, self.sparse) + uncast_bias_weight(self, qdata, None, offload_stream) + target_dtype = out_dtype if out_dtype is not None else weight._params.orig_dtype + x = x.to(dtype=target_dtype) + if scale is not None and scale != 1.0: + x = x * scale.to(dtype=target_dtype) + return x + + # Fallback for non-quantized or weight_function (LoRA) case + return super().forward_comfy_cast_weights(input, out_dtype=out_dtype) + return MixedPrecisionOps def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, model_config=None): diff --git a/comfy/rmsnorm.py b/comfy/rmsnorm.py index ab7cf14fa..e54be98d6 100644 --- a/comfy/rmsnorm.py +++ b/comfy/rmsnorm.py @@ -3,6 +3,7 @@ import comfy.model_management RMSNorm = torch.nn.RMSNorm +# Note: torch's fused F.rms_norm is faster but produces slightly different output than manual implementations (rsqrt/reduction rounding). def rms_norm(x, weight=None, eps=1e-6): if weight is None: return torch.nn.functional.rms_norm(x, (x.shape[-1],), eps=eps) diff --git a/comfy/sd.py b/comfy/sd.py index ee66490f5..9fce0e7d0 100644 --- a/comfy/sd.py +++ b/comfy/sd.py @@ -65,6 +65,7 @@ import comfy.text_encoders.ace15 import comfy.text_encoders.longcat_image import comfy.text_encoders.qwen35 import comfy.text_encoders.ernie +import comfy.text_encoders.gemma4 import comfy.model_patcher import comfy.lora @@ -1271,6 +1272,9 @@ class TEModel(Enum): QWEN35_9B = 26 QWEN35_27B = 27 MINISTRAL_3_3B = 28 + GEMMA_4_E4B = 29 + GEMMA_4_E2B = 30 + GEMMA_4_31B = 31 def detect_te_model(sd): @@ -1296,6 +1300,12 @@ def detect_te_model(sd): return TEModel.BYT5_SMALL_GLYPH return TEModel.T5_BASE if 'model.layers.0.post_feedforward_layernorm.weight' in sd: + if 'model.layers.59.self_attn.q_norm.weight' in sd: + return TEModel.GEMMA_4_31B + if 'model.layers.41.self_attn.q_norm.weight' in sd and 'model.layers.47.self_attn.q_norm.weight' not in sd: + return TEModel.GEMMA_4_E4B + if 'model.layers.34.self_attn.q_norm.weight' in sd and 'model.layers.41.self_attn.q_norm.weight' not in sd: + return TEModel.GEMMA_4_E2B if 'model.layers.47.self_attn.q_norm.weight' in sd: return TEModel.GEMMA_3_12B if 'model.layers.0.self_attn.q_norm.weight' in sd: @@ -1435,6 +1445,13 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip else: clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer + elif te_model in (TEModel.GEMMA_4_E4B, TEModel.GEMMA_4_E2B, TEModel.GEMMA_4_31B): + variant = {TEModel.GEMMA_4_E4B: comfy.text_encoders.gemma4.Gemma4_E4B, + TEModel.GEMMA_4_E2B: comfy.text_encoders.gemma4.Gemma4_E2B, + TEModel.GEMMA_4_31B: comfy.text_encoders.gemma4.Gemma4_31B}[te_model] + clip_target.clip = comfy.text_encoders.gemma4.gemma4_te(**llama_detect(clip_data), model_class=variant) + clip_target.tokenizer = variant.tokenizer + tokenizer_data["tokenizer_json"] = clip_data[0].get("tokenizer_json", None) elif te_model == TEModel.GEMMA_2_2B: clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data)) clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer diff --git a/comfy/text_encoders/gemma4.py b/comfy/text_encoders/gemma4.py new file mode 100644 index 000000000..f050061ed --- /dev/null +++ b/comfy/text_encoders/gemma4.py @@ -0,0 +1,1298 @@ +import torch +import torch.nn as nn +import numpy as np +from dataclasses import dataclass +import math + +from comfy import sd1_clip +import comfy.model_management +from comfy.ldm.modules.attention import optimized_attention_for_device +from comfy.rmsnorm import rms_norm +from comfy.text_encoders.llama import RMSNorm, MLP, BaseLlama, BaseGenerate, _make_scaled_embedding + + +# Intentional minor divergences from transformers -reference implementation: +# - Embedding sqrt(hidden_size) scale applied as a Python scalar (full precision) instead of dtype-matched buffer tensor. +# - RMSNorm uses torch fused F.rms_norm, very slight numerical differences, but considerably faster +# - Input image and audio resizing/resampling slightly different numerically + + +GEMMA4_VISION_CONFIG = {"hidden_size": 768, "image_size": 896, "intermediate_size": 3072, "num_attention_heads": 12, "num_hidden_layers": 16, "patch_size": 16, "head_dim": 64, "rms_norm_eps": 1e-6, "position_embedding_size": 10240, "pooling_kernel_size": 3} +GEMMA4_VISION_31B_CONFIG = {"hidden_size": 1152, "image_size": 896, "intermediate_size": 4304, "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 16, "head_dim": 72, "rms_norm_eps": 1e-6, "position_embedding_size": 10240, "pooling_kernel_size": 3} +GEMMA4_AUDIO_CONFIG = {"hidden_size": 1024, "num_hidden_layers": 12, "num_attention_heads": 8, "intermediate_size": 4096, "conv_kernel_size": 5, "attention_chunk_size": 12, "attention_context_left": 13, "attention_context_right": 0, "attention_logit_cap": 50.0, "output_proj_dims": 1536, "rms_norm_eps": 1e-6, "residual_weight": 0.5} + +@dataclass +class Gemma4Config: + vocab_size: int = 262144 + hidden_size: int = 2560 + intermediate_size: int = 10240 + num_hidden_layers: int = 42 + num_attention_heads: int = 8 + num_key_value_heads: int = 2 + max_position_embeddings: int = 131072 + rms_norm_eps: float = 1e-6 + rope_theta = [1000000.0, 10000.0] + transformer_type: str = "gemma4" + head_dim = 256 + global_head_dim = 512 + rms_norm_add = False + mlp_activation = "gelu_pytorch_tanh" + qkv_bias = False + rope_dims = None + q_norm = "gemma3" + k_norm = "gemma3" + sliding_attention = [512, 512, 512, 512, 512, False] + rope_scale = None + partial_rotary_factor: float = 0.25 + final_norm: bool = True + lm_head: bool = False + final_logit_softcapping: float = 30.0 + hidden_size_per_layer_input: int = 256 + num_kv_shared_layers: int = 18 + use_double_wide_mlp: bool = False + stop_tokens = [1, 50, 106] + vision_config = GEMMA4_VISION_CONFIG + audio_config = GEMMA4_AUDIO_CONFIG + mm_tokens_per_image = 280 + +@dataclass +class Gemma4_E2B_Config(Gemma4Config): + hidden_size: int = 1536 + intermediate_size: int = 6144 + num_hidden_layers: int = 35 + num_key_value_heads: int = 1 + sliding_attention = [512, 512, 512, 512, False] + num_kv_shared_layers: int = 20 + use_double_wide_mlp: bool = True + +@dataclass +class Gemma4_31B_Config(Gemma4Config): + hidden_size: int = 5376 + intermediate_size: int = 21504 + num_hidden_layers: int = 60 + num_attention_heads: int = 32 + num_key_value_heads: int = 16 + sliding_attention = [1024, 1024, 1024, 1024, 1024, False] + hidden_size_per_layer_input: int = 0 + num_kv_shared_layers: int = 0 + audio_config = None + vision_config = GEMMA4_VISION_31B_CONFIG + + +# unfused RoPE as addcmul_ RoPE diverges from reference code +def _apply_rotary_pos_emb(x, freqs_cis): + cos, sin = freqs_cis[0], freqs_cis[1] + half = x.shape[-1] // 2 + out = x * cos + out[..., :half] -= x[..., half:] * sin[..., :half] + out[..., half:] += x[..., :half] * sin[..., half:] + return out + +class Gemma4Attention(nn.Module): + def __init__(self, config, head_dim, device=None, dtype=None, ops=None): + super().__init__() + self.num_heads = config.num_attention_heads + self.num_kv_heads = config.num_key_value_heads + self.hidden_size = config.hidden_size + self.head_dim = head_dim + self.inner_size = self.num_heads * head_dim + + self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=config.qkv_bias, device=device, dtype=dtype) + self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * head_dim, bias=config.qkv_bias, device=device, dtype=dtype) + self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * head_dim, bias=config.qkv_bias, device=device, dtype=dtype) + self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype) + + self.q_norm = None + self.k_norm = None + if config.q_norm == "gemma3": + self.q_norm = RMSNorm(head_dim, eps=config.rms_norm_eps, device=device, dtype=dtype) + if config.k_norm == "gemma3": + self.k_norm = RMSNorm(head_dim, eps=config.rms_norm_eps, device=device, dtype=dtype) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask=None, + freqs_cis=None, + past_key_value=None, + sliding_window=None, + shared_kv=None, + ): + batch_size, seq_length, _ = hidden_states.shape + + xq = self.q_proj(hidden_states) + xq = xq.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2) + if self.q_norm is not None: + xq = self.q_norm(xq) + + if shared_kv is not None: + xk, xv = shared_kv + # Apply RoPE to Q only (K already has RoPE from source layer) + xq = _apply_rotary_pos_emb(xq, freqs_cis) + present_key_value = None + shareable_kv = None + else: + xk = self.k_proj(hidden_states).view(batch_size, seq_length, self.num_kv_heads, self.head_dim) + xv = self.v_proj(hidden_states).view(batch_size, seq_length, self.num_kv_heads, self.head_dim) + if self.k_norm is not None: + xk = self.k_norm(xk) + xv = rms_norm(xv) + xk = xk.transpose(1, 2) + xv = xv.transpose(1, 2) + xq = _apply_rotary_pos_emb(xq, freqs_cis) + xk = _apply_rotary_pos_emb(xk, freqs_cis) + + present_key_value = None + if past_key_value is not None: + cumulative_len = 0 + if len(past_key_value) > 0: + past_key, past_value, cumulative_len = past_key_value + xk = torch.cat((past_key, xk), dim=2) + xv = torch.cat((past_value, xv), dim=2) + new_cumulative = cumulative_len + seq_length + if sliding_window is not None and xk.shape[2] > sliding_window - 1: + cache_k = xk[:, :, -(sliding_window - 1):] + cache_v = xv[:, :, -(sliding_window - 1):] + else: + cache_k = xk + cache_v = xv + present_key_value = (cache_k, cache_v, new_cumulative) + + # KV for sharing: full xk/xv that SDPA sees (not evicted cache) + shareable_kv = (xk, xv) + + # GQA: pass unexpanded KV with enable_gqa when no sliding mask, + # expand heads when sliding mask is present + # has to be done within SDPA itself to match the reference code, pre-scaling expansion causes numerical differences + expand_kv = (self.num_heads != self.num_kv_heads and + sliding_window is not None and + xk.shape[2] >= sliding_window) + if expand_kv: + xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) + xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1) + gqa_kwargs = {} if expand_kv else ({"enable_gqa": True} if self.num_heads != self.num_kv_heads else {}) + output = optimized_attention_for_device(xq.device, mask=attention_mask is not None, small_input=True)(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True, scale=1.0, **gqa_kwargs) + + return self.o_proj(output), present_key_value, shareable_kv + + +class TransformerBlockGemma4(nn.Module): + def __init__(self, config, index, device=None, dtype=None, ops=None): + super().__init__() + if config.sliding_attention is not None: + self.sliding_attention = config.sliding_attention[index % len(config.sliding_attention)] + else: + self.sliding_attention = False + + head_dim = config.head_dim if self.sliding_attention else config.global_head_dim + + self.self_attn = Gemma4Attention(config, head_dim=head_dim, device=device, dtype=dtype, ops=ops) + + num_kv_shared = config.num_kv_shared_layers + first_kv_shared = config.num_hidden_layers - num_kv_shared + mlp_size = config.intermediate_size * 2 if config.use_double_wide_mlp and index >= first_kv_shared else None + self.mlp = MLP(config, device=device, dtype=dtype, ops=ops, intermediate_size=mlp_size) + + self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype) + self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype) + self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype) + self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype) + + self.hidden_size_per_layer_input = config.hidden_size_per_layer_input + if self.hidden_size_per_layer_input: + self.per_layer_input_gate = ops.Linear(config.hidden_size, self.hidden_size_per_layer_input, bias=False, device=device, dtype=dtype) + self.per_layer_projection = ops.Linear(self.hidden_size_per_layer_input, config.hidden_size, bias=False, device=device, dtype=dtype) + self.post_per_layer_input_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype) + self.register_buffer("layer_scalar", torch.ones(1, device=device, dtype=dtype)) + else: + self.layer_scalar = None + + def forward(self, x, attention_mask=None, freqs_cis=None, past_key_value=None, per_layer_input=None, shared_kv=None): + sliding_window = None + if self.sliding_attention: + sliding_window = self.sliding_attention + # For prefill > sliding window, add sliding window restriction to the causal mask. + if x.shape[1] > self.sliding_attention: + sw_mask = torch.zeros(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device) + sw_mask.masked_fill_(torch.ones_like(sw_mask, dtype=torch.bool).tril_(-self.sliding_attention), torch.finfo(x.dtype).min) + attention_mask = attention_mask + sw_mask if attention_mask is not None else sw_mask + freqs_cis = freqs_cis[1] + else: + freqs_cis = freqs_cis[0] + + residual = x + x = self.input_layernorm(x) + x, present_key_value, shareable_kv = self.self_attn( + hidden_states=x, attention_mask=attention_mask, freqs_cis=freqs_cis, + past_key_value=past_key_value, sliding_window=sliding_window, shared_kv=shared_kv, + ) + x = self.post_attention_layernorm(x) + x = residual + x + + residual = x + x = self.pre_feedforward_layernorm(x) + x = self.mlp(x) + x = self.post_feedforward_layernorm(x) + x = residual + x + + if self.hidden_size_per_layer_input and per_layer_input is not None: + residual = x + x = self.per_layer_input_gate(x) + x = torch.nn.functional.gelu(x, approximate="tanh") + x = x * per_layer_input + x = self.per_layer_projection(x) + x = self.post_per_layer_input_norm(x) + x = residual + x + + if self.layer_scalar is not None: + x = x * self.layer_scalar + + return x, present_key_value, shareable_kv + + +class Gemma4Transformer(nn.Module): + def __init__(self, config, device=None, dtype=None, ops=None): + super().__init__() + self.config = config + + self.embed_tokens = _make_scaled_embedding(ops, config.vocab_size, config.hidden_size, config.hidden_size ** 0.5, device, dtype) + + self.layers = nn.ModuleList([ + TransformerBlockGemma4(config, index=i, device=device, dtype=dtype, ops=ops) + for i in range(config.num_hidden_layers) + ]) + + self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype) if config.final_norm else None + + # Precompute RoPE inv_freq on CPU to match reference code's exact value + rope_angles_global = int(config.partial_rotary_factor * config.global_head_dim // 2) + nope_global = config.global_head_dim // 2 - rope_angles_global + global_inv = 1.0 / (config.rope_theta[0] ** (torch.arange(0, 2 * rope_angles_global, 2).float() / config.global_head_dim)) + if nope_global > 0: + global_inv = torch.cat([global_inv, torch.zeros(nope_global)]) + self.register_buffer("_global_inv_freq", global_inv, persistent=False) + + sliding_inv = 1.0 / (config.rope_theta[1] ** (torch.arange(0, config.head_dim, 2).float() / config.head_dim)) + self.register_buffer("_sliding_inv_freq", sliding_inv, persistent=False) + + # Per-layer input mechanism + self.hidden_size_per_layer_input = config.hidden_size_per_layer_input + if self.hidden_size_per_layer_input: + self.embed_tokens_per_layer = _make_scaled_embedding(ops, config.vocab_size, config.num_hidden_layers * self.hidden_size_per_layer_input, self.hidden_size_per_layer_input ** 0.5, device, dtype) + self.per_layer_model_projection = ops.Linear( + config.hidden_size, config.num_hidden_layers * self.hidden_size_per_layer_input, + bias=False, device=device, dtype=dtype) + self.per_layer_projection_norm = RMSNorm( + self.hidden_size_per_layer_input, eps=config.rms_norm_eps, + device=device, dtype=dtype) + + def get_past_len(self, past_key_values): + for kv in past_key_values: + if len(kv) >= 3: + return kv[2] + return 0 + + def _freqs_from_inv(self, inv_freq, position_ids, device, dtype): + """Compute cos/sin from stored inv_freq""" + inv_exp = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(device) + pos_exp = position_ids[:, None, :].float() + freqs = (inv_exp @ pos_exp).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + return emb.cos().unsqueeze(1).to(dtype), emb.sin().unsqueeze(1).to(dtype) + + def compute_freqs_cis(self, position_ids, device, dtype=None): + global_freqs = self._freqs_from_inv(self._global_inv_freq, position_ids, device, dtype) + sliding_freqs = self._freqs_from_inv(self._sliding_inv_freq, position_ids, device, dtype) + return [global_freqs, sliding_freqs] + + def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, + final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=None, + past_key_values=None, input_ids=None): + if embeds is not None: + x = embeds + else: + x = self.embed_tokens(x, out_dtype=dtype) + + seq_len = x.shape[1] + past_len = 0 + if past_key_values is not None and len(past_key_values) > 0: + past_len = self.get_past_len(past_key_values) + + if position_ids is None: + position_ids = torch.arange(past_len, past_len + seq_len, device=x.device).unsqueeze(0) + + freqs_cis = self.compute_freqs_cis(position_ids, x.device, dtype=x.dtype) + + mask = None + min_val = torch.finfo(x.dtype).min + if attention_mask is not None: + mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, seq_len, attention_mask.shape[-1]) + mask = mask.masked_fill(mask.to(torch.bool), min_val) + + if seq_len > 1: + causal_mask = torch.zeros(past_len + seq_len, past_len + seq_len, dtype=x.dtype, device=x.device) + causal_mask.masked_fill_(torch.ones_like(causal_mask, dtype=torch.bool).triu_(1), min_val) + mask = mask + causal_mask if mask is not None else causal_mask + + # Per-layer inputs + per_layer_inputs = None + if self.hidden_size_per_layer_input: + num_layers = self.config.num_hidden_layers + hpl = self.hidden_size_per_layer_input + per_layer_proj = self.per_layer_model_projection(x) * (1.0 / (self.config.hidden_size ** 0.5)) + per_layer_proj = self.per_layer_projection_norm(per_layer_proj.reshape(*x.shape[:-1], num_layers, hpl)) + if input_ids is not None and input_ids.shape[1] == x.shape[1]: + per_layer_emb = self.embed_tokens_per_layer(input_ids).reshape(*input_ids.shape, num_layers, hpl) + per_layer_inputs = (per_layer_proj + per_layer_emb) * (0.5 ** 0.5) + else: + per_layer_inputs = per_layer_proj + + # KV sharing: later layers reuse KV from the last non-shared sliding/global layer + num_kv_shared = self.config.num_kv_shared_layers + first_kv_shared = self.config.num_hidden_layers - num_kv_shared if num_kv_shared > 0 else self.config.num_hidden_layers + shared_sliding_kv = None # KV from last non-shared sliding layer + shared_global_kv = None # KV from last non-shared global layer + + intermediate = None + next_key_values = [] + for i, layer in enumerate(self.layers): + past_kv = past_key_values[i] if past_key_values is not None and len(past_key_values) > 0 else None + + layer_kwargs = {} + if per_layer_inputs is not None: + layer_kwargs['per_layer_input'] = per_layer_inputs[:, :, i, :] + + is_sliding = hasattr(layer, 'sliding_attention') and layer.sliding_attention + if i >= first_kv_shared and num_kv_shared > 0: + shared = shared_sliding_kv if is_sliding else shared_global_kv + if shared is not None: + layer_kwargs['shared_kv'] = shared + + x, current_kv, shareable_kv = layer(x=x, attention_mask=mask, freqs_cis=freqs_cis, past_key_value=past_kv, **layer_kwargs) + + next_key_values.append(current_kv if current_kv is not None else ()) + + # Only track the last sliding/global before the sharing boundary + if i < first_kv_shared and shareable_kv is not None: + if is_sliding: + shared_sliding_kv = shareable_kv + else: + shared_global_kv = shareable_kv + + if i == intermediate_output: + intermediate = x.clone() + + if self.norm is not None: + x = self.norm(x) + + if len(next_key_values) > 0: + return x, intermediate, next_key_values + return x, intermediate + + +class Gemma4Base(BaseLlama, BaseGenerate, torch.nn.Module): + """Common base for all Gemma4 variants: text model + vision.""" + def _init_model(self, config, dtype, device, operations): + self.num_layers = config.num_hidden_layers + self.model = Gemma4Transformer(config, device=device, dtype=dtype, ops=operations) + self.dtype = dtype + self.multi_modal_projector = Gemma4MultiModalProjector(config, dtype=dtype, device=device, ops=operations) + self.vision_model = Gemma4VisionEncoder(config.vision_config, dtype=dtype, device=device, ops=operations) + + def logits(self, x): + logits = super().logits(x) + cap = self.model.config.final_logit_softcapping + if cap: + logits = cap * torch.tanh(logits / cap) + return logits + + def init_kv_cache(self, batch, max_cache_len, device, execution_dtype): + past_key_values = [] + for _ in range(self.model.config.num_hidden_layers): + past_key_values.append(()) + return past_key_values + + def preprocess_embed(self, embed, device): + if embed["type"] == "image": + image = embed.pop("data").movedim(-1, 1) # [B, H, W, C] -> [B, C, H, W] + max_soft_tokens = embed.get("max_soft_tokens", None) + vision_out = self.vision_model(image.to(device, dtype=torch.float32), max_soft_tokens=max_soft_tokens) + return self.multi_modal_projector(vision_out), None + return None, None + + +class Gemma4AudioMixin: + """Adds audio support to a Gemma4 model.""" + def _init_audio(self, config, dtype, device, operations): + self.audio_model = Gemma4AudioEncoder(config.audio_config, dtype=dtype, device=device, ops=operations) + self.audio_projector = Gemma4AudioProjector({"audio_output_proj_dims": config.audio_config["output_proj_dims"], "text_hidden_size": config.hidden_size, "rms_norm_eps": config.rms_norm_eps}, dtype=dtype, device=device, ops=operations) + + def preprocess_embed(self, embed, device): + result, extra = super().preprocess_embed(embed, device) + if result is not None: + return result, extra + if embed["type"] == "audio": + audio = embed.pop("data").to(device, dtype=torch.float32) + audio_mask = embed.pop("mask", None) + if audio_mask is not None: + audio_mask = audio_mask.to(device) + audio_out = self.audio_model(audio, audio_mask=audio_mask) + return self.audio_projector(audio_out), None + return None, None + + +# Vision Encoder + +def _compute_vision_2d_rope(head_dim, pixel_position_ids, theta=100.0, device=None): + """Compute 2D RoPE for vision: separate frequencies for x and y dimensions. + + Args: + head_dim: dimension per head (e.g. 64) + pixel_position_ids: [batch, num_patches, 2] with (x, y) coords + theta: RoPE base frequency + Returns: + (cos, sin) each of shape [batch, num_patches, head_dim] + """ + rotary_dim_per_axis = head_dim // 2 + freq_indices = torch.arange(0, rotary_dim_per_axis, 2, device=device).float() + inv_freq = 1.0 / (theta ** (freq_indices / rotary_dim_per_axis)) + + all_cos, all_sin = [], [] + for i in range(2): # x and y + dim_positions = pixel_position_ids[:, :, i].float() # [batch, num_patches] + freqs = torch.einsum('bi,j->bij', dim_positions, inv_freq.to(device)) # [batch, num_patches, rotary_dim/2] + emb = torch.cat([freqs, freqs], dim=-1) # [batch, num_patches, rotary_dim] + all_cos.append(emb.cos()) + all_sin.append(emb.sin()) + + cos = torch.cat(all_cos, dim=-1).to(pixel_position_ids.device) # [batch, num_patches, head_dim] + sin = torch.cat(all_sin, dim=-1).to(pixel_position_ids.device) + return cos, sin + + +def _apply_vision_2d_rope(x, freqs): + """Apply 2D RoPE (multidimensional) to vision query/key states. + + Splits x and cos/sin into ndim=2 parts, applies 1D RoPE to each independently. + + x: [batch, heads, seq, head_dim] + freqs: (cos, sin) each [batch, seq, head_dim] + """ + cos = freqs[0].unsqueeze(1) # [batch, 1, seq, head_dim] + sin = freqs[1].unsqueeze(1) + half = x.shape[-1] // 2 + a = _apply_rotary_pos_emb(x[..., :half], (cos[..., :half], sin[..., :half])) + b = _apply_rotary_pos_emb(x[..., half:], (cos[..., half:], sin[..., half:])) + return torch.cat([a, b], dim=-1) + + +class ClippedLinear(nn.Module): + """Linear layer with activation clipping (from quantization-aware training). + + Stores input_max/min and output_max/min as buffers loaded from checkpoint. + """ + def __init__(self, in_features, out_features, bias=False, device=None, dtype=None, ops=None): + super().__init__() + self.linear = ops.Linear(in_features, out_features, bias=bias, device=device, dtype=dtype) + self.register_buffer('input_max', torch.tensor(float('inf'), device=device, dtype=dtype)) + self.register_buffer('input_min', torch.tensor(float('-inf'), device=device, dtype=dtype)) + self.register_buffer('output_max', torch.tensor(float('inf'), device=device, dtype=dtype)) + self.register_buffer('output_min', torch.tensor(float('-inf'), device=device, dtype=dtype)) + + @property + def weight(self): + return self.linear.weight + + def forward(self, x): + x = x.clamp(min=self.input_min, max=self.input_max) + x = self.linear(x) + return x.clamp_(min=self.output_min, max=self.output_max) + + +class Gemma4VisionMLP(nn.Module): + """SwiGLU MLP matching gate_proj/up_proj/down_proj structure.""" + def __init__(self, config, device=None, dtype=None, ops=None): + super().__init__() + hidden_size = config["hidden_size"] + intermediate_size = config["intermediate_size"] + self.gate_proj = ClippedLinear(hidden_size, intermediate_size, device=device, dtype=dtype, ops=ops) + self.up_proj = ClippedLinear(hidden_size, intermediate_size, device=device, dtype=dtype, ops=ops) + self.down_proj = ClippedLinear(intermediate_size, hidden_size, device=device, dtype=dtype, ops=ops) + + def forward(self, x): + return self.down_proj(torch.nn.functional.gelu(self.gate_proj(x), approximate="tanh") * self.up_proj(x)) + + +class Gemma4VisionAttention(nn.Module): + def __init__(self, config, device=None, dtype=None, ops=None): + super().__init__() + self.hidden_size = config["hidden_size"] + self.num_heads = config["num_attention_heads"] + self.head_dim = config.get("head_dim", self.hidden_size // self.num_heads) + + self.q_proj = ClippedLinear(self.hidden_size, self.num_heads * self.head_dim, device=device, dtype=dtype, ops=ops) + self.k_proj = ClippedLinear(self.hidden_size, self.num_heads * self.head_dim, device=device, dtype=dtype, ops=ops) + self.v_proj = ClippedLinear(self.hidden_size, self.num_heads * self.head_dim, device=device, dtype=dtype, ops=ops) + self.o_proj = ClippedLinear(self.num_heads * self.head_dim, self.hidden_size, device=device, dtype=dtype, ops=ops) + + self.q_norm = RMSNorm(self.head_dim, eps=config["rms_norm_eps"], device=device, dtype=dtype) + self.k_norm = RMSNorm(self.head_dim, eps=config["rms_norm_eps"], device=device, dtype=dtype) + + def forward(self, x, freqs, attention_mask=None): + batch_size, seq_length, _ = x.shape + + xq = self.q_proj(x).view(batch_size, seq_length, self.num_heads, self.head_dim) + xk = self.k_proj(x).view(batch_size, seq_length, self.num_heads, self.head_dim) + xv = self.v_proj(x).view(batch_size, seq_length, self.num_heads, self.head_dim) + + xq = self.q_norm(xq).transpose(1, 2) + xk = self.k_norm(xk).transpose(1, 2) + xv = rms_norm(xv) + + xq = _apply_vision_2d_rope(xq, freqs) + xk = _apply_vision_2d_rope(xk, freqs) + + xv = xv.to(xq.dtype).transpose(1, 2) + + output = optimized_attention_for_device(xq.device, mask=attention_mask is not None, small_input=True)(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True, scale=1.0) + return self.o_proj(output) + + +class Gemma4VisionLayer(nn.Module): + def __init__(self, config, device=None, dtype=None, ops=None): + super().__init__() + self.self_attn = Gemma4VisionAttention(config, device=device, dtype=dtype, ops=ops) + self.mlp = Gemma4VisionMLP(config, device=device, dtype=dtype, ops=ops) + norm_kwargs = dict(eps=config["rms_norm_eps"], device=device, dtype=dtype) + hidden = config["hidden_size"] + self.input_layernorm = RMSNorm(hidden, **norm_kwargs) + self.post_attention_layernorm = RMSNorm(hidden, **norm_kwargs) + self.pre_feedforward_layernorm = RMSNorm(hidden, **norm_kwargs) + self.post_feedforward_layernorm = RMSNorm(hidden, **norm_kwargs) + + def forward(self, x, freqs, attention_mask=None): + residual = x + x = self.input_layernorm(x) + x = self.self_attn(x, freqs, attention_mask=attention_mask) + x = self.post_attention_layernorm(x) + x = residual + x + + residual = x + x = self.pre_feedforward_layernorm(x) + x = self.mlp(x) + x = self.post_feedforward_layernorm(x) + x = residual + x + return x + + +class Gemma4PatchEmbedder(nn.Module): + """Patch embedding with learned 2D position embeddings via one-hot lookup.""" + def __init__(self, config, device=None, dtype=None, ops=None): + super().__init__() + hidden_size = config["hidden_size"] + patch_size = config["patch_size"] + self.patch_size = patch_size + self.position_embedding_size = config.get("position_embedding_size", 10240) + + self.input_proj = ops.Linear(3 * patch_size * patch_size, hidden_size, bias=False, device=device, dtype=dtype) + self.position_embedding_table = nn.Parameter( + torch.empty(2, self.position_embedding_size, hidden_size, device=device, dtype=dtype) + ) + + def forward(self, patches, pixel_position_ids): + """ + patches: [B, num_patches, 3*patch_size²] in [0,1] range (normalized to [-1,1] inside, matching HF) + pixel_position_ids: [B, num_patches, 2] with (x,y) positions, (-1,-1) for padding + """ + hidden_states = self.input_proj((2.0 * (patches - 0.5)).to(self.input_proj.weight.dtype)) + + clamped_positions = pixel_position_ids.clamp(min=0) + pos_table = comfy.model_management.cast_to_device(self.position_embedding_table, hidden_states.device, hidden_states.dtype) + position_embeddings = pos_table[0][clamped_positions[..., 0]] + pos_table[1][clamped_positions[..., 1]] + + # Zero out position embeddings for padding patches (matching HF) + padding_positions = (pixel_position_ids == -1).all(dim=-1) + position_embeddings = torch.where(padding_positions.unsqueeze(-1), 0.0, position_embeddings) + + return hidden_states + position_embeddings + + +class Gemma4VisionEncoderLayers(nn.Module): + """Wrapper to produce state dict keys as encoder.layers.X.*""" + def __init__(self, config, dtype=None, device=None, ops=None): + super().__init__() + self.layers = nn.ModuleList([ + Gemma4VisionLayer(config, device=device, dtype=dtype, ops=ops) + for _ in range(config["num_hidden_layers"]) + ]) + + +class Gemma4VisionEncoder(nn.Module): + def __init__(self, config, dtype=None, device=None, ops=None): + super().__init__() + self.config = config + self.hidden_size = config["hidden_size"] + self.head_dim = config.get("head_dim", config["hidden_size"] // config["num_attention_heads"]) + self.patch_size = config["patch_size"] + self.pooling_kernel_size = config.get("pooling_kernel_size", 3) + self.root_hidden_size = self.hidden_size ** 0.5 + + self.patch_embedder = Gemma4PatchEmbedder(config, device=device, dtype=dtype, ops=ops) + self.encoder = Gemma4VisionEncoderLayers(config, dtype=dtype, device=device, ops=ops) + + def forward(self, pixel_values, max_soft_tokens=None): + """ + pixel_values: [B, C, H, W] in [0,1] range + max_soft_tokens: if provided, pad to max_soft_tokens * k² total patches + """ + batch_size, _, height, width = pixel_values.shape + ps = self.patch_size + k = self.pooling_kernel_size + patches_h, patches_w = height // ps, width // ps + num_patches = patches_h * patches_w + output_length = max_soft_tokens if max_soft_tokens is not None else num_patches // (k * k) + n_padding = output_length * k * k - num_patches + + # Patchify and build position grid + patches = pixel_values.reshape(batch_size, -1, patches_h, ps, patches_w, ps) + patches = patches.permute(0, 2, 4, 3, 5, 1).reshape(batch_size, num_patches, -1) + grid_y, grid_x = torch.meshgrid(torch.arange(patches_h, device=pixel_values.device), torch.arange(patches_w, device=pixel_values.device), indexing='ij') + position_ids = torch.stack([grid_x.flatten(), grid_y.flatten()], dim=-1).unsqueeze(0).expand(batch_size, -1, -1) + + # Append zero-pixel padding with (-1,-1) positions + if n_padding > 0: + patches = torch.cat([patches, patches.new_zeros(batch_size, n_padding, patches.shape[-1])], dim=1) + position_ids = torch.cat([position_ids, position_ids.new_full((batch_size, n_padding, 2), -1)], dim=1) + + padding = (position_ids == -1).all(dim=-1) + + # Embed, encode, pool + x = self.patch_embedder(patches, position_ids) + freqs = _compute_vision_2d_rope(self.head_dim, position_ids, device=pixel_values.device) + freqs = tuple(t.to(x.dtype) for t in freqs) + if n_padding > 0: + mask = padding.unsqueeze(1).unsqueeze(2).expand(-1, 1, position_ids.shape[1], -1) + mask = torch.zeros_like(mask, dtype=x.dtype).masked_fill_(mask, torch.finfo(x.dtype).min) + else: + mask = None + + for layer in self.encoder.layers: + x = layer(x, freqs, attention_mask=mask) + + if n_padding > 0: + x = x.masked_fill(padding.unsqueeze(-1), 0.0) + + # Average pool by spatial position + clamped = position_ids.clamp(min=0) + max_x = clamped[:, :, 0].max(dim=-1, keepdim=True)[0] + 1 + ki = torch.div(clamped, k, rounding_mode="floor") + ki = ki[:, :, 0] + (max_x // k) * ki[:, :, 1] + weights = torch.nn.functional.one_hot(ki.long(), output_length).float() / (k * k) + x = (weights.transpose(1, 2) @ x.float()).to(x.dtype) + + # Strip empty output tokens + valid_out = ~((weights == 0).all(dim=1)) + if valid_out.any() and not valid_out.all(): + x = x[:, valid_out[0]] if batch_size > 1 else x[valid_out].unsqueeze(0) + + return x * self.root_hidden_size + + +class Gemma4RMSNormProjector(nn.Module): + """Shared projector: parameterless RMSNorm → linear. Used for both vision and audio.""" + def __init__(self, in_dim, out_dim, dtype=None, device=None, ops=None): + super().__init__() + self.embedding_projection = ops.Linear(in_dim, out_dim, bias=False, device=device, dtype=dtype) + + def forward(self, x): + return self.embedding_projection(rms_norm(x)) + + +class Gemma4MultiModalProjector(Gemma4RMSNormProjector): + def __init__(self, config, dtype=None, device=None, ops=None): + super().__init__(config.vision_config["hidden_size"], config.hidden_size, dtype=dtype, device=device, ops=ops) + + +# Audio Encoder + +class Gemma4AudioConvSubsampler(nn.Module): + """2D convolution subsampling for audio features""" + def __init__(self, config, device=None, dtype=None, ops=None): + super().__init__() + eps = config["rms_norm_eps"] + self.layer0 = nn.ModuleDict({ + 'conv': ops.Conv2d(1, 128, kernel_size=3, stride=2, padding=1, bias=False, device=device, dtype=dtype), + 'norm': ops.LayerNorm(128, eps=eps, elementwise_affine=True, bias=False, device=device, dtype=dtype), + }) + self.layer1 = nn.ModuleDict({ + 'conv': ops.Conv2d(128, 32, kernel_size=3, stride=2, padding=1, bias=False, device=device, dtype=dtype), + 'norm': ops.LayerNorm(32, eps=eps, elementwise_affine=True, bias=False, device=device, dtype=dtype), + }) + # proj_input_dim = (128 // 4) * 32 = 1024 + self.input_proj_linear = ops.Linear(1024, config["hidden_size"], bias=False, device=device, dtype=dtype) + + def _conv_layer(self, x, layer, mask): + if mask is not None: + x = x * mask[:, None, :, None].to(x.device) + x = layer['conv'](x.to(layer['conv'].weight.dtype)) + x = torch.relu(layer['norm'](x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2).contiguous()) + if mask is not None: + mask = mask[:, ::2] + return x, mask + + def forward(self, x, mask=None): + x = x.unsqueeze(1) + x, mask = self._conv_layer(x, self.layer0, mask) + x, mask = self._conv_layer(x, self.layer1, mask) + batch_size, _, seq_len, _ = x.shape + x = x.permute(0, 2, 3, 1).contiguous().reshape(batch_size, seq_len, -1) + return self.input_proj_linear(x), mask + + +class Gemma4AudioFeedForward(nn.Module): + """Conformer feed-forward with residual scaling.""" + def __init__(self, config, device=None, dtype=None, ops=None): + super().__init__() + hidden_size = config["hidden_size"] + intermediate_size = config.get("intermediate_size", hidden_size * 4) + self.pre_layer_norm = RMSNorm(hidden_size, eps=config["rms_norm_eps"], device=device, dtype=dtype) + self.ffw_layer_1 = ClippedLinear(hidden_size, intermediate_size, device=device, dtype=dtype, ops=ops) + self.ffw_layer_2 = ClippedLinear(intermediate_size, hidden_size, device=device, dtype=dtype, ops=ops) + self.post_layer_norm = RMSNorm(hidden_size, eps=config["rms_norm_eps"], device=device, dtype=dtype) + self.post_layer_scale = config.get("residual_weight", 0.5) + + def forward(self, x): + residual = x + x = self.pre_layer_norm(x) + x = torch.nn.functional.silu(self.ffw_layer_1(x)) + x = self.ffw_layer_2(x) + x = self.post_layer_norm(x) + x = x * self.post_layer_scale + return x + residual + + +class Gemma4AudioRelPositionalEncoding(nn.Module): + """Sinusoidal relative positional encoding for audio attention.""" + def __init__(self, config, device=None, dtype=None): + super().__init__() + hidden_size = config["hidden_size"] + context_left = config.get("attention_context_left", 13) + context_right = config.get("attention_context_right", 0) + self.chunk_size = config.get("attention_chunk_size", 12) + self.context_size = self.chunk_size + context_left - 1 + context_right + + num_timescales = hidden_size // 2 + log_inc = math.log(10000.0) / max(num_timescales - 1, 1) + inv_timescales = torch.exp(torch.arange(num_timescales) * -log_inc).to(dtype=dtype).unsqueeze(0).unsqueeze(0) + self.register_buffer("inv_timescales", inv_timescales, persistent=False) + + def forward(self, hidden_states): + positions = torch.arange(self.chunk_size, -1, -1, device=hidden_states.device).unsqueeze(-1) + scaled = positions * self.inv_timescales.to(device=hidden_states.device) + return torch.cat([torch.sin(scaled), torch.cos(scaled)], dim=-1).to(dtype=hidden_states.dtype) + + +class Gemma4AudioAttention(nn.Module): + """Chunked block attention with relative position bias and softcap.""" + def __init__(self, config, device=None, dtype=None, ops=None): + super().__init__() + self.hidden_size = config["hidden_size"] + self.num_heads = config["num_attention_heads"] + self.head_dim = self.hidden_size // self.num_heads + self.chunk_size = config.get("attention_chunk_size", 12) + self.max_past_horizon = config.get("attention_context_left", 13) - 1 + self.max_future_horizon = config.get("attention_context_right", 0) + self.context_size = self.chunk_size + self.max_past_horizon + self.max_future_horizon + + self.q_scale = (self.head_dim ** -0.5) / math.log(2) + self.k_scale = math.log(1 + math.e) / math.log(2) + self.register_buffer("softcap", torch.tensor(config.get("attention_logit_cap", 50.0), dtype=dtype), persistent=False) + + self.q_proj = ClippedLinear(self.hidden_size, self.hidden_size, device=device, dtype=dtype, ops=ops) + self.k_proj = ClippedLinear(self.hidden_size, self.hidden_size, device=device, dtype=dtype, ops=ops) + self.v_proj = ClippedLinear(self.hidden_size, self.hidden_size, device=device, dtype=dtype, ops=ops) + self.post = ClippedLinear(self.hidden_size, self.hidden_size, device=device, dtype=dtype, ops=ops) + self.per_dim_scale = nn.Parameter(torch.empty(self.head_dim, device=device, dtype=dtype)) + self.relative_k_proj = ops.Linear(self.hidden_size, self.hidden_size, bias=False, device=device, dtype=dtype) + + def _convert_to_block(self, x): + B, S, H, D = x.shape + num_blocks = (S + self.chunk_size - 1) // self.chunk_size + pad = num_blocks * self.chunk_size - S + x = torch.nn.functional.pad(x, (0, 0, 0, 0, 0, pad)) + return x.reshape(B, num_blocks, self.chunk_size, H, D).contiguous() + + def _extract_block_context(self, x): + x = torch.nn.functional.pad(x, (0, 0, 0, 0, self.max_past_horizon, self.max_future_horizon + self.chunk_size - 1)) + x = x.unfold(1, self.context_size, self.chunk_size) + return torch.movedim(x, -1, 2).contiguous() + + def _rel_shift(self, x): + B, H, NB, BS, PL = x.shape + CS = self.context_size + x = torch.nn.functional.pad(x, (0, CS + 1 - PL)) + x = x.view(B, H, NB, BS * (CS + 1)) + x = x[..., :BS * CS] + return x.view(B, H, NB, BS, CS) + + def _build_blocked_mask(self, seq_len, num_blocks, device, audio_mask=None): + """Build 5D boolean blocked attention mask (True=attend, False=mask)""" + q = torch.arange(seq_len, device=device) + dist = q[:, None] - q[None, :] + mask = (dist >= 0) & (dist < self.max_past_horizon) + if self.max_future_horizon > 0: + mask = mask | ((dist < 0) & ((-dist) < self.max_future_horizon)) + if audio_mask is not None: + mask = mask & audio_mask[0, None, :].bool() + m = mask[None, None] + # Reshape to blocked 5D matching reference code + p = num_blocks * self.chunk_size - seq_len + m = torch.nn.functional.pad(m, (0, p, 0, p), value=False) + m = m.reshape(1, 1, num_blocks, self.chunk_size, -1) + m = torch.nn.functional.pad(m, (self.max_past_horizon, self.max_future_horizon), value=False) + idx = (torch.arange(num_blocks, device=device) * self.chunk_size)[:, None] + torch.arange(self.context_size, device=device)[None, :] + return m.gather(-1, idx[None, None, :, None, :].expand(1, 1, -1, self.chunk_size, -1)) + + def forward(self, x, position_embeddings=None, attn_mask=None): + B, S, _ = x.shape + + q = self.q_proj(x).float().view(B, S, self.num_heads, self.head_dim) + k = self.k_proj(x).float().view(B, S, self.num_heads, self.head_dim) + v = self.v_proj(x).float().view(B, S, self.num_heads, self.head_dim) + + q = q * self.q_scale * torch.nn.functional.softplus(self.per_dim_scale) + k = k * self.k_scale + + q_blocks = self._convert_to_block(q) + k_context = self._extract_block_context(k) + v_context = self._extract_block_context(v) + num_blocks = q_blocks.shape[1] + + rel_k = self.relative_k_proj(position_embeddings).view(-1, self.num_heads, self.head_dim).to(q.dtype) + + queries = q_blocks.permute(0, 3, 1, 2, 4) # [B, H, NB, CS, D] + matrix_ac = queries @ k_context.permute(0, 3, 1, 4, 2) + + queries_flat = queries.reshape(B, self.num_heads, -1, self.head_dim) + matrix_bd = queries_flat @ rel_k.permute(1, 2, 0) + matrix_bd = matrix_bd.reshape(B, self.num_heads, num_blocks, self.chunk_size, -1) + matrix_bd = self._rel_shift(matrix_bd) + + attn_weights = matrix_ac + matrix_bd + attn_weights = torch.tanh(attn_weights / self.softcap) * self.softcap + + # Mask out invalid positions in chunk context (matching reference's masked_fill approach) + if attn_mask is None: + attn_mask = self._build_blocked_mask(S, num_blocks, x.device) + attn_weights = attn_weights.masked_fill(attn_mask.logical_not(), -1e9) + + attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(v.dtype) + out = attn_weights @ v_context.permute(0, 3, 1, 2, 4) + out = out.permute(0, 2, 3, 1, 4).reshape(B, num_blocks * self.chunk_size, -1) + out = out[:, :S].contiguous() + return self.post(out.to(self.post.linear.weight.dtype)) + + +class Gemma4AudioLConv1d(nn.Module): + """Lightweight convolution with standard GLU.""" + def __init__(self, config, device=None, dtype=None, ops=None): + super().__init__() + hidden_size = config["hidden_size"] + conv_kernel_size = config.get("conv_kernel_size", 5) + self.pre_layer_norm = RMSNorm(hidden_size, eps=config["rms_norm_eps"], device=device, dtype=dtype) + self.linear_start = ClippedLinear(hidden_size, hidden_size * 2, device=device, dtype=dtype, ops=ops) + # Causal conv: left-pad only + self.depthwise_conv1d = ops.Conv1d(hidden_size, hidden_size, kernel_size=conv_kernel_size, padding=0, groups=hidden_size, bias=False, device=device, dtype=dtype) + self.conv_left_pad = conv_kernel_size - 1 # causal: pad left by kernel-1 + self.conv_norm = RMSNorm(hidden_size, eps=config["rms_norm_eps"], device=device, dtype=dtype) + self.linear_end = ClippedLinear(hidden_size, hidden_size, device=device, dtype=dtype, ops=ops) + + def forward(self, x): + residual = x + x = self.pre_layer_norm(x) + x = self.linear_start(x) + x = torch.nn.functional.glu(x, dim=-1) + x = x.transpose(1, 2) + x = torch.nn.functional.pad(x, (self.conv_left_pad, 0)) + x = self.depthwise_conv1d(x).transpose(1, 2) + x = self.conv_norm(x) + x = torch.nn.functional.silu(x) + x = self.linear_end(x) + return x + residual + + +class Gemma4AudioLayer(nn.Module): + """Conformer block: FFN1 -> Attention -> LConv -> FFN2.""" + def __init__(self, config, device=None, dtype=None, ops=None): + super().__init__() + self.feed_forward1 = Gemma4AudioFeedForward(config, device=device, dtype=dtype, ops=ops) + self.self_attn = Gemma4AudioAttention(config, device=device, dtype=dtype, ops=ops) + norm_kwargs = dict(eps=config["rms_norm_eps"], device=device, dtype=dtype) + hidden_size = config["hidden_size"] + self.norm_pre_attn = RMSNorm(hidden_size, **norm_kwargs) + self.norm_post_attn = RMSNorm(hidden_size, **norm_kwargs) + self.lconv1d = Gemma4AudioLConv1d(config, device=device, dtype=dtype, ops=ops) + self.feed_forward2 = Gemma4AudioFeedForward(config, device=device, dtype=dtype, ops=ops) + self.norm_out = RMSNorm(hidden_size, **norm_kwargs) + + def forward(self, x, position_embeddings=None, attn_mask=None): + x = self.feed_forward1(x) + + residual = x + x = self.norm_pre_attn(x) + x = self.self_attn(x, position_embeddings=position_embeddings, attn_mask=attn_mask) + x = self.norm_post_attn(x) + x = x + residual + + x = self.lconv1d(x) + x = self.feed_forward2(x) + + x = self.norm_out(x) + return x + + +class Gemma4AudioEncoder(nn.Module): + def __init__(self, config, dtype=None, device=None, ops=None): + super().__init__() + self.hidden_size = config["hidden_size"] + self.output_proj_dims = config.get("output_proj_dims", 1536) + + self.subsample_conv_projection = Gemma4AudioConvSubsampler(config, device=device, dtype=dtype, ops=ops) + self.rel_pos_enc = Gemma4AudioRelPositionalEncoding(config, device=device, dtype=dtype) + + self.layers = nn.ModuleList([ + Gemma4AudioLayer(config, device=device, dtype=dtype, ops=ops) + for _ in range(config["num_hidden_layers"]) + ]) + + self.output_proj = ops.Linear(self.hidden_size, self.output_proj_dims, bias=True, device=device, dtype=dtype) + + def forward(self, audio_features, audio_mask=None): + x, audio_mask = self.subsample_conv_projection(audio_features, audio_mask) + position_embeddings = self.rel_pos_enc(x) + + # Build blocked attention mask once for all layers + attn_mask = self.layers[0].self_attn._build_blocked_mask( + x.shape[1], (x.shape[1] + self.layers[0].self_attn.chunk_size - 1) // self.layers[0].self_attn.chunk_size, + x.device, audio_mask=audio_mask) + + for layer in self.layers: + x = layer(x, position_embeddings=position_embeddings, attn_mask=attn_mask) + + x = self.output_proj(x) + return x + + +class Gemma4AudioProjector(Gemma4RMSNormProjector): + def __init__(self, config, dtype=None, device=None, ops=None): + super().__init__(config.get("audio_output_proj_dims", 1536), config.get("text_hidden_size", 2560), dtype=dtype, device=device, ops=ops) + + +# Tokenizer and Wrappers + +class Gemma4_Tokenizer(): + tokenizer_json_data = None + + def state_dict(self): + if self.tokenizer_json_data is not None: + return {"tokenizer_json": self.tokenizer_json_data} + return {} + + def _extract_mel_spectrogram(self, waveform, sample_rate): + """Extract 128-bin log mel spectrogram. + Uses numpy for FFT/matmul/log to produce bit-identical results with reference code. + """ + # Mix to mono first, then resample to 16kHz + if waveform.dim() > 1 and waveform.shape[0] > 1: + waveform = waveform.mean(dim=0, keepdim=True) + if waveform.dim() == 1: + waveform = waveform.unsqueeze(0) + audio = waveform.squeeze(0).float().numpy() + if sample_rate != 16000: + # Use scipy's resample_poly with a high-quality FIR filter to get as close as possible to librosa's resampling (while still not full match) + from scipy.signal import resample_poly, firwin + from math import gcd + g = gcd(sample_rate, 16000) + up, down = 16000 // g, sample_rate // g + L = max(up, down) + h = firwin(160 * L + 1, 0.96 / L, window=('kaiser', 6.5)) + audio = resample_poly(audio, up, down, window=h).astype(np.float32) + n = len(audio) + + # Pad to multiple of 128, build sample-level mask + if n % 128 != 0: + audio = np.pad(audio, (0, 128 - n % 128)) + mask_raw = np.ones(len(audio), dtype=np.float32) + mask_raw[n:] = 0.0 + + # Semicausal padding: 160 zeros prepended + audio = np.pad(audio, (160, 0)) + mask_raw = np.pad(mask_raw, (160, 0)) + + # Extract 321-sample frames via stride tricks, drop last → 320 + nf = (len(audio) - 321) // 160 + 1 + strides = (audio.strides[0] * 160, audio.strides[0]) + frames = np.lib.stride_tricks.as_strided(audio, (nf, 321), strides)[..., :-1].copy() + + # Periodic Hann window, FFT magnitude, mel filterbank, log + window = (0.5 - 0.5 * np.cos(2 * np.pi * np.arange(320) / 320)).astype(np.float32) + magnitude = np.abs(np.fft.rfft(frames * window, n=512, axis=-1)) + mel_fb = self._build_mel_filterbank() + log_mel = np.log(np.matmul(magnitude, mel_fb) + np.float64(0.001)).astype(np.float32) + + # Frame mask: valid when last sample in window is real audio + mask = mask_raw[np.arange(nf) * 160 + 320].astype(bool) + log_mel = log_mel * mask[:, None] + return torch.from_numpy(log_mel), torch.from_numpy(mask) # [T, 128], [T] + + @staticmethod + def _build_mel_filterbank(): + """Build 128-bin HTK mel filterbank [257, 128] for 512-pt FFT at 16kHz.""" + mel_freqs = np.linspace(0.0, 2595.0 * np.log10(1.0 + 8000.0 / 700.0), 130) + filter_freqs = 700.0 * (10.0 ** (mel_freqs / 2595.0) - 1.0) + fft_freqs = np.linspace(0, 16000 // 2, 257) + filter_diff = np.diff(filter_freqs) + slopes = np.expand_dims(filter_freqs, 0) - np.expand_dims(fft_freqs, 1) + down_slopes = -slopes[:, :-2] / filter_diff[:-1] + up_slopes = slopes[:, 2:] / filter_diff[1:] + return np.maximum(np.zeros(1), np.minimum(down_slopes, up_slopes)) + + def tokenize_with_weights(self, text, return_word_ids=False, image=None, audio=None, video=None, llama_template=None, skip_template=True, thinking=False, **kwargs): + + # Process audio + audio_features = [] + if audio is not None: + waveform = audio["waveform"].squeeze(0) if hasattr(audio, "__getitem__") else audio + sample_rate = audio.get("sample_rate", 16000) if hasattr(audio, "get") else 16000 + mel, mel_mask = self._extract_mel_spectrogram(waveform, sample_rate) + audio_features = [(mel.unsqueeze(0), mel_mask.unsqueeze(0))] # ([1, T, 128], [1, T]) + + # Process image/video frames + is_video = video is not None + source = video if is_video else image + images = [] + if source is not None: + samples = source.movedim(-1, 1) # [B, C, H, W] + num_frames = samples.shape[0] + + # Subsample video to 1fps + if is_video: + fps = kwargs.get("fps", 24) + step = max(1, round(fps)) + indices = list(range(0, num_frames, step)) + if len(indices) == 0: + indices = [0] + samples = samples[indices] + num_frames = len(indices) + + h, w = samples.shape[2], samples.shape[3] + patch_size = 16 + pooling_k = 3 + max_soft_tokens = 70 if is_video else 280 # video uses smaller token budget per frame + max_patches = max_soft_tokens * pooling_k * pooling_k + target_px = max_patches * patch_size * patch_size + factor = (target_px / (h * w)) ** 0.5 + side_mult = pooling_k * patch_size + target_h = max(int(factor * h // side_mult) * side_mult, side_mult) + target_w = max(int(factor * w // side_mult) * side_mult, side_mult) + + import torchvision.transforms.functional as TVF + for i in range(num_frames): + # rescaling to match reference code + s = (samples[i].clamp(0, 1) * 255).to(torch.uint8) # [C, H, W] uint8 + if target_h != h or target_w != w: + s = TVF.resize(s, [target_h, target_w], interpolation=TVF.InterpolationMode.BICUBIC, antialias=True) + s = s.float() * (1.0 / 255.0) + images.append({"pixels": s.unsqueeze(0).movedim(1, -1)[:, :, :, :3], "max_soft_tokens": max_soft_tokens}) + + if text.startswith('<|turn>'): + skip_template = True + + if skip_template: + llama_text = text + else: + if llama_template is not None: + llama_text = llama_template.format(text) + else: + # Build template from modalities present + system = "<|turn>system\n<|think|>\n" if thinking else "" + media = "" + if len(images) > 0: + if is_video: + media += "\n\n" + for i in range(len(images)): + ts = f"{int(i // 60):02d}:{int(i % 60):02d}" + sep = "" if i == 0 else " " + media += f"{sep}{ts} <|image><|video|>" + media += "\n\n" + else: + media += "\n\n" + for i in range(len(images)): + if i > 0: + media += "\n\n\n\n" + media += "<|image><|image|>" + media += "\n\n" + if len(audio_features) > 0: + # Compute audio token count (always at 16kHz) + num_samples = int(waveform.shape[-1] * 16000 / sample_rate) if sample_rate != 16000 else waveform.shape[-1] + _fl = 320 # int(round(16000 * 20.0 / 1000.0)) + _hl = 160 # int(round(16000 * 10.0 / 1000.0)) + _nmel = (num_samples + _fl // 2 - (_fl + 1)) // _hl + 1 + _t = _nmel + for _ in range(2): + _t = (_t + 2 - 3) // 2 + 1 + n_audio_tokens = min(_t, 750) + media += "<|audio>" + "<|audio|>" * n_audio_tokens + "" + llama_text = f"{system}<|turn>user\n{media}{text}\n<|turn>model\n" + + text_tokens = super().tokenize_with_weights(llama_text, return_word_ids) + + def _replace_placeholders(token_list, token_id, embeds): + """Replace first placeholder with embed dict, remove remaining consecutive ones.""" + embed_idx = 0 + i = 0 + while i < len(token_list): + if token_list[i][0] == token_id and embed_idx < len(embeds): + token_list[i] = (embeds[embed_idx],) + token_list[i][1:] + embed_idx += 1 + i += 1 + while i < len(token_list) and token_list[i][0] == token_id: + token_list.pop(i) + else: + i += 1 + + if len(images) > 0: + img_token_id = 258884 if is_video else 258880 + img_embeds = [{"type": "image", "data": img["pixels"], "max_soft_tokens": img["max_soft_tokens"]} for img in images] + for r in text_tokens: + _replace_placeholders(r, img_token_id, img_embeds) + + if len(audio_features) > 0: + aud_embeds = [{"type": "audio", "data": mel, "mask": mask} for mel, mask in audio_features] + for r in text_tokens: + _replace_placeholders(r, 258881, aud_embeds) + + return text_tokens + + +class _Gemma4Tokenizer: + """Tokenizer using the tokenizers (Gemma4 doesn't come with sentencepiece model)""" + def __init__(self, tokenizer_json_bytes=None, **kwargs): + from tokenizers import Tokenizer + if isinstance(tokenizer_json_bytes, torch.Tensor): + tokenizer_json_bytes = bytes(tokenizer_json_bytes.tolist()) + self.tokenizer = Tokenizer.from_str(tokenizer_json_bytes.decode("utf-8")) + + @classmethod + def from_pretrained(cls, tokenizer_data, **kwargs): + return cls(tokenizer_json_bytes=tokenizer_data, **kwargs) + + def __call__(self, text): + return {"input_ids": self.tokenizer.encode(text, add_special_tokens=False).ids} + + def get_vocab(self): + return self.tokenizer.get_vocab() + + def convert_tokens_to_ids(self, tokens): + return [self.tokenizer.token_to_id(t) for t in tokens] + + def decode(self, ids, **kwargs): + return self.tokenizer.decode(ids, skip_special_tokens=kwargs.get("skip_special_tokens", False)) + + +# Tokenizer +class Gemma4SDTokenizer(Gemma4_Tokenizer, sd1_clip.SDTokenizer): + embedding_size = 2560 + def __init__(self, embedding_directory=None, tokenizer_data={}): + tokenizer_json = tokenizer_data.get("tokenizer_json", None) + self.tokenizer_json_data = tokenizer_json + super().__init__(tokenizer_json, pad_with_end=False, embedding_size=self.embedding_size, embedding_key='gemma4', tokenizer_class=_Gemma4Tokenizer, has_start_token=True, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_left=True, disable_weights=True, start_token=2, tokenizer_data=tokenizer_data) + + def decode(self, token_ids, **kwargs): + text = super().decode(token_ids, skip_special_tokens=False) + # Translate thinking channel markers to standard / tags + text = text.replace("<|channel>thought\n", "\n") + text = text.replace("", "") + # Strip remaining special tokens + text = text.replace("", "").replace("", "").strip() + return text + + +class Gemma4Tokenizer(sd1_clip.SD1Tokenizer): + tokenizer_class = Gemma4SDTokenizer + def __init__(self, embedding_directory=None, tokenizer_data={}): + super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma4", tokenizer=self.tokenizer_class) + + +# Model wrappers +class Gemma4Model(sd1_clip.SDClipModel): + model_class = None + def __init__(self, device="cpu", layer="all", layer_idx=None, dtype=None, attention_mask=True, model_options={}): + self.dtypes = set() + self.dtypes.add(dtype) + super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=self.model_class, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) + + def process_tokens(self, tokens, device): + embeds, _, _, _ = super().process_tokens(tokens, device) + return embeds + + def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty=0.0): + if isinstance(tokens, dict): + tokens = next(iter(tokens.values())) + tokens_only = [[t[0] for t in b] for b in tokens] + embeds, _, _, embeds_info = sd1_clip.SDClipModel.process_tokens(self, tokens_only, self.execution_device) + seq_len = embeds.shape[1] + ids = [0] * seq_len + expanded_idx = 0 + embed_map = {info["index"]: info["size"] for info in embeds_info} + for t in tokens_only[0]: + if expanded_idx in embed_map: + expanded_idx += embed_map[expanded_idx] + elif isinstance(t, int): + if expanded_idx < seq_len: + ids[expanded_idx] = t + expanded_idx += 1 + else: + expanded_idx += 1 + initial_token_ids = [ids] + input_ids = torch.tensor(initial_token_ids, device=self.execution_device) + return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, initial_tokens=initial_token_ids[0], presence_penalty=presence_penalty, initial_input_ids=input_ids) + + +def gemma4_te(dtype_llama=None, llama_quantization_metadata=None, model_class=None): + clip_model = type('Gemma4Model_', (Gemma4Model,), {'model_class': model_class}) + class Gemma4TEModel_(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, model_options={}): + if llama_quantization_metadata is not None: + model_options = model_options.copy() + model_options["quantization_metadata"] = llama_quantization_metadata + if dtype_llama is not None: + dtype = dtype_llama + super().__init__(device=device, dtype=dtype, name="gemma4", clip_model=clip_model, model_options=model_options) + return Gemma4TEModel_ + + +# Variants + +def _make_variant(config_cls): + audio = config_cls.audio_config is not None + bases = (Gemma4AudioMixin, Gemma4Base) if audio else (Gemma4Base,) + class Variant(*bases): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self._init_model(config_cls(**config_dict), dtype, device, operations) + if audio: + self._init_audio(self.model.config, dtype, device, operations) + embedding_size = config_cls.hidden_size + if embedding_size != Gemma4SDTokenizer.embedding_size: + tok_cls = type('T', (Gemma4SDTokenizer,), {'embedding_size': embedding_size}) + class Tokenizer(Gemma4Tokenizer): + tokenizer_class = tok_cls + Variant.tokenizer = Tokenizer + else: + Variant.tokenizer = Gemma4Tokenizer + return Variant + +Gemma4_E4B = _make_variant(Gemma4Config) +Gemma4_E2B = _make_variant(Gemma4_E2B_Config) +Gemma4_31B = _make_variant(Gemma4_31B_Config) diff --git a/comfy/text_encoders/llama.py b/comfy/text_encoders/llama.py index 6ea8e36b1..a34c41144 100644 --- a/comfy/text_encoders/llama.py +++ b/comfy/text_encoders/llama.py @@ -521,7 +521,7 @@ class Attention(nn.Module): else: present_key_value = (xk, xv, index + num_tokens) - if sliding_window is not None and xk.shape[2] > sliding_window: + if sliding_window is not None and xk.shape[2] > sliding_window and seq_length == 1: xk = xk[:, :, -sliding_window:] xv = xv[:, :, -sliding_window:] attention_mask = attention_mask[..., -sliding_window:] if attention_mask is not None else None @@ -533,12 +533,12 @@ class Attention(nn.Module): return self.o_proj(output), present_key_value class MLP(nn.Module): - def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None): + def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None, intermediate_size=None): super().__init__() - ops = ops or nn - self.gate_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype) - self.up_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype) - self.down_proj = ops.Linear(config.intermediate_size, config.hidden_size, bias=False, device=device, dtype=dtype) + intermediate_size = intermediate_size or config.intermediate_size + self.gate_proj = ops.Linear(config.hidden_size, intermediate_size, bias=False, device=device, dtype=dtype) + self.up_proj = ops.Linear(config.hidden_size, intermediate_size, bias=False, device=device, dtype=dtype) + self.down_proj = ops.Linear(intermediate_size, config.hidden_size, bias=False, device=device, dtype=dtype) if config.mlp_activation == "silu": self.activation = torch.nn.functional.silu elif config.mlp_activation == "gelu_pytorch_tanh": @@ -647,24 +647,25 @@ class TransformerBlockGemma2(nn.Module): return x, present_key_value +def _make_scaled_embedding(ops, vocab_size, hidden_size, scale, device, dtype): + class ScaledEmbedding(ops.Embedding): + def forward(self, input_ids, out_dtype=None): + return super().forward(input_ids, out_dtype=out_dtype) * scale + return ScaledEmbedding(vocab_size, hidden_size, device=device, dtype=dtype) + + class Llama2_(nn.Module): def __init__(self, config, device=None, dtype=None, ops=None): super().__init__() self.config = config self.vocab_size = config.vocab_size - self.embed_tokens = ops.Embedding( - config.vocab_size, - config.hidden_size, - device=device, - dtype=dtype - ) if self.config.transformer_type == "gemma2" or self.config.transformer_type == "gemma3": transformer = TransformerBlockGemma2 - self.normalize_in = True + self.embed_tokens = _make_scaled_embedding(ops, config.vocab_size, config.hidden_size, config.hidden_size ** 0.5, device, dtype) else: transformer = TransformerBlock - self.normalize_in = False + self.embed_tokens = ops.Embedding(config.vocab_size, config.hidden_size, device=device, dtype=dtype) self.layers = nn.ModuleList([ transformer(config, index=i, device=device, dtype=dtype, ops=ops) @@ -690,15 +691,12 @@ class Llama2_(nn.Module): self.config.rope_dims, device=device) - def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None): + def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, position_ids=None, embeds_info=[], past_key_values=None, input_ids=None): if embeds is not None: x = embeds else: x = self.embed_tokens(x, out_dtype=dtype) - if self.normalize_in: - x *= self.config.hidden_size ** 0.5 - seq_len = x.shape[1] past_len = 0 if past_key_values is not None and len(past_key_values) > 0: @@ -850,7 +848,7 @@ class BaseGenerate: torch.empty([batch, model_config.num_key_value_heads, max_cache_len, model_config.head_dim], device=device, dtype=execution_dtype), 0)) return past_key_values - def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0, presence_penalty=0.0): + def generate(self, embeds=None, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.9, min_p=0.0, repetition_penalty=1.0, seed=42, stop_tokens=None, initial_tokens=[], execution_dtype=None, min_tokens=0, presence_penalty=0.0, initial_input_ids=None): device = embeds.device if stop_tokens is None: @@ -875,14 +873,16 @@ class BaseGenerate: pbar = comfy.utils.ProgressBar(max_length) # Generation loop + current_input_ids = initial_input_ids for step in tqdm(range(max_length), desc="Generating tokens"): - x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values) + x, _, past_key_values = self.model.forward(None, embeds=embeds, attention_mask=None, past_key_values=past_key_values, input_ids=current_input_ids) logits = self.logits(x)[:, -1] next_token = self.sample_token(logits, temperature, top_k, top_p, min_p, repetition_penalty, initial_tokens + generated_token_ids, generator, do_sample=do_sample, presence_penalty=presence_penalty) token_id = next_token[0].item() generated_token_ids.append(token_id) embeds = self.model.embed_tokens(next_token).to(execution_dtype) + current_input_ids = next_token if initial_input_ids is not None else None pbar.update(1) if token_id in stop_tokens: diff --git a/comfy/text_encoders/lt.py b/comfy/text_encoders/lt.py index 5aee1f4c0..bc5cbae28 100644 --- a/comfy/text_encoders/lt.py +++ b/comfy/text_encoders/lt.py @@ -93,8 +93,7 @@ class Gemma3_12BModel(sd1_clip.SDClipModel): def generate(self, tokens, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, presence_penalty): tokens_only = [[t[0] for t in b] for b in tokens] - embeds, _, _, embeds_info = self.process_tokens(tokens_only, self.execution_device) - comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5) + embeds, _, _, _ = self.process_tokens(tokens_only, self.execution_device) return self.transformer.generate(embeds, do_sample, max_length, temperature, top_k, top_p, min_p, repetition_penalty, seed, stop_tokens=[106], presence_penalty=presence_penalty) # 106 is class DualLinearProjection(torch.nn.Module): diff --git a/comfy/text_encoders/lumina2.py b/comfy/text_encoders/lumina2.py index 01ebdfabe..b1f1dbb9f 100644 --- a/comfy/text_encoders/lumina2.py +++ b/comfy/text_encoders/lumina2.py @@ -50,8 +50,7 @@ class Gemma3_4B_Vision_Model(sd1_clip.SDClipModel): super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B_Vision, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options) def process_tokens(self, tokens, device): - embeds, _, _, embeds_info = super().process_tokens(tokens, device) - comfy.utils.normalize_image_embeddings(embeds, embeds_info, self.transformer.model.config.hidden_size ** 0.5) + embeds, _, _, _ = super().process_tokens(tokens, device) return embeds class LuminaModel(sd1_clip.SD1ClipModel): diff --git a/comfy/text_encoders/qwen35.py b/comfy/text_encoders/qwen35.py index ce9b07464..d8ed9cd32 100644 --- a/comfy/text_encoders/qwen35.py +++ b/comfy/text_encoders/qwen35.py @@ -408,8 +408,6 @@ class Qwen35Transformer(Llama2_): nn.Module.__init__(self) self.config = config self.vocab_size = config.vocab_size - self.normalize_in = False - self.embed_tokens = ops.Embedding(config.vocab_size, config.hidden_size, device=device, dtype=dtype) self.layers = nn.ModuleList([ Qwen35TransformerBlock(config, index=i, device=device, dtype=dtype, ops=ops) diff --git a/comfy/utils.py b/comfy/utils.py index 78c491b98..7b7faad3a 100644 --- a/comfy/utils.py +++ b/comfy/utils.py @@ -1446,10 +1446,3 @@ def deepcopy_list_dict(obj, memo=None): memo[obj_id] = res return res -def normalize_image_embeddings(embeds, embeds_info, scale_factor): - """Normalize image embeddings to match text embedding scale""" - for info in embeds_info: - if info.get("type") == "image": - start_idx = info["index"] - end_idx = start_idx + info["size"] - embeds[:, start_idx:end_idx, :] /= scale_factor diff --git a/comfy_extras/nodes_textgen.py b/comfy_extras/nodes_textgen.py index 1f46d820f..1661a1011 100644 --- a/comfy_extras/nodes_textgen.py +++ b/comfy_extras/nodes_textgen.py @@ -32,6 +32,8 @@ class TextGenerate(io.ComfyNode): io.Clip.Input("clip"), io.String.Input("prompt", multiline=True, dynamic_prompts=True, default=""), io.Image.Input("image", optional=True), + io.Image.Input("video", optional=True, tooltip="Video frames as image batch. Assumed to be 24 FPS; subsampled to 1 FPS internally."), + io.Audio.Input("audio", optional=True), io.Int.Input("max_length", default=256, min=1, max=2048), io.DynamicCombo.Input("sampling_mode", options=sampling_options, display_name="Sampling Mode"), io.Boolean.Input("thinking", optional=True, default=False, tooltip="Operate in thinking mode if the model supports it."), @@ -43,9 +45,9 @@ class TextGenerate(io.ComfyNode): ) @classmethod - def execute(cls, clip, prompt, max_length, sampling_mode, image=None, thinking=False, use_default_template=True) -> io.NodeOutput: + def execute(cls, clip, prompt, max_length, sampling_mode, image=None, thinking=False, use_default_template=True, video=None, audio=None) -> io.NodeOutput: - tokens = clip.tokenize(prompt, image=image, skip_template=not use_default_template, min_length=1, thinking=thinking) + tokens = clip.tokenize(prompt, image=image, skip_template=not use_default_template, min_length=1, thinking=thinking, video=video, audio=audio) # Get sampling parameters from dynamic combo do_sample = sampling_mode.get("sampling_mode") == "on" @@ -70,7 +72,8 @@ class TextGenerate(io.ComfyNode): seed=seed ) - generated_text = clip.decode(generated_ids, skip_special_tokens=True) + generated_text = clip.decode(generated_ids) + return io.NodeOutput(generated_text) @@ -161,12 +164,12 @@ class TextGenerateLTX2Prompt(TextGenerate): ) @classmethod - def execute(cls, clip, prompt, max_length, sampling_mode, image=None, thinking=False, use_default_template=True) -> io.NodeOutput: + def execute(cls, clip, prompt, max_length, sampling_mode, image=None, thinking=False, use_default_template=True, video=None, audio=None) -> io.NodeOutput: if image is None: formatted_prompt = f"system\n{LTX2_T2V_SYSTEM_PROMPT.strip()}\nuser\nUser Raw Input Prompt: {prompt}.\nmodel\n" else: formatted_prompt = f"system\n{LTX2_I2V_SYSTEM_PROMPT.strip()}\nuser\n\n\n\nUser Raw Input Prompt: {prompt}.\nmodel\n" - return super().execute(clip, formatted_prompt, max_length, sampling_mode, image, thinking, use_default_template) + return super().execute(clip, formatted_prompt, max_length, sampling_mode, image=image, thinking=thinking, use_default_template=use_default_template, video=video, audio=audio) class TextgenExtension(ComfyExtension): From f6d5068ac0163e7f626c9cec2e7c663cf6fa64a8 Mon Sep 17 00:00:00 2001 From: Alexis Rolland Date: Sun, 3 May 2026 12:20:17 +0800 Subject: [PATCH 138/140] Update README (#13679) Updated the README to include a new screenshot, improved description and add Ernie Image to supported models. --- README.md | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 3b5114633..ee68e8bb8 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@
# ComfyUI -**The most powerful and modular visual AI engine and application.** +**The most powerful and modular AI engine for content creation.** [![Website][website-shield]][website-url] @@ -31,10 +31,15 @@ [github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest [github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases -![ComfyUI Screenshot](https://github.com/user-attachments/assets/7ccaf2c1-9b72-41ae-9a89-5688c94b7abe) +ComfyUI Screenshot
-ComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS. +ComfyUI is the AI creation engine for visual professionals who demand control over every model, every parameter, and every output. Its powerful and modular node graph interface empowers creatives to generate images, videos, 3D models, audio, and more... +- ComfyUI natively supports the latest open-source state of the art models. +- API nodes provide access to the best closed source models such as Nano Banana, Seedance, Hunyuan3D, etc. +- It is available on Windows, Linux, and macOS, locally with our desktop application or on our cloud. +- The most sophisticated workflows can be exposed through a simple UI thanks to App Mode. +- It integrates seamlessly into production pipelines with our API endpoints. ## Get Started @@ -77,6 +82,7 @@ See what ComfyUI can do with the [newer template workflows](https://comfy.org/wo - [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/) - [Flux 2](https://comfyanonymous.github.io/ComfyUI_examples/flux2/) - [Z Image](https://comfyanonymous.github.io/ComfyUI_examples/z_image/) + - Ernie Image - Image Editing Models - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/) - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model) From b5bb83c964519b7574ce9229b2314e04c17592c0 Mon Sep 17 00:00:00 2001 From: Alexis Rolland Date: Sun, 3 May 2026 18:17:08 +0800 Subject: [PATCH 139/140] Fix issue blend images with alpha (#13615) Make ImageBlend and ImageCompositeMasked nodes handle images with different channel counts --- node_helpers.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/node_helpers.py b/node_helpers.py index d3d834516..cac4e88dd 100644 --- a/node_helpers.py +++ b/node_helpers.py @@ -86,6 +86,6 @@ def image_alpha_fix(destination, source): if destination.shape[-1] < source.shape[-1]: source = source[...,:destination.shape[-1]] elif destination.shape[-1] > source.shape[-1]: - destination = torch.nn.functional.pad(destination, (0, 1)) - destination[..., -1] = 1.0 + source = torch.nn.functional.pad(source, (0, 1)) + source[..., -1] = 1.0 return destination, source From d0f0b15cf5d1fbff67390c8d90ec8654c2582f7a Mon Sep 17 00:00:00 2001 From: Alexis Rolland Date: Sun, 3 May 2026 18:48:58 +0800 Subject: [PATCH 140/140] Update ComfyUI screenshot in README (#13683) Update ComfyUI screenshot to showcase a more modern workflow --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index ee68e8bb8..a3bd3ba0a 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,8 @@ [github-downloads-latest-shield]: https://img.shields.io/github/downloads/comfyanonymous/ComfyUI/latest/total?style=flat&label=downloads%40latest [github-downloads-link]: https://github.com/comfyanonymous/ComfyUI/releases -ComfyUI Screenshot +ComfyUI Screenshot +
ComfyUI is the AI creation engine for visual professionals who demand control over every model, every parameter, and every output. Its powerful and modular node graph interface empowers creatives to generate images, videos, 3D models, audio, and more...