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Merge branch 'comfyanonymous:master' into feature/custom-node-paths-cli-args

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Sas van Gulik 2025-11-19 13:37:25 +01:00 committed by GitHub
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14 changed files with 341 additions and 53 deletions
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2
.github/PULL_REQUEST_TEMPLATE/api-node.md vendored
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@ -18,4 +18,4 @@ If **Need pricing update**:
- [ ] **QA not required**
### Comms
- [ ] Informed **@Kosinkadink**
- [ ] Informed **Kosinkadink**

2
.github/workflows/api-node-template.yml vendored
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@ -2,7 +2,7 @@ name: Append API Node PR template
on:
pull_request_target:
types: [opened, reopened, synchronize, edited, ready_for_review]
types: [opened, reopened, synchronize, ready_for_review]
paths:
- 'comfy_api_nodes/**' # only run if these files changed

17
.github/workflows/release-stable-all.yml vendored
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@ -43,6 +43,23 @@ jobs:
test_release: true
secrets: inherit
release_nvidia_cu126:
permissions:
contents: "write"
packages: "write"
pull-requests: "read"
name: "Release NVIDIA cu126"
uses: ./.github/workflows/stable-release.yml
with:
git_tag: ${{ inputs.git_tag }}
cache_tag: "cu126"
python_minor: "12"
python_patch: "10"
rel_name: "nvidia"
rel_extra_name: "_cu126"
test_release: true
secrets: inherit
release_amd_rocm:
permissions:
contents: "write"

4
README.md
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@ -183,7 +183,9 @@ Update your Nvidia drivers if it doesn't start.
[Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
[Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z) (Supports Nvidia 10 series and older GPUs).
[Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z).
[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).
#### How do I share models between another UI and ComfyUI?

1
comfy/ldm/flux/math.py
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@ -7,6 +7,7 @@ import comfy.model_management
def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor:
if pe is not None:
q, k = apply_rope(q, k, pe)
heads = q.shape[1]
x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options)

14
comfy/text_encoders/llama.py
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@ -32,6 +32,7 @@ class Llama2Config:
q_norm = None
k_norm = None
rope_scale = None
final_norm: bool = True
@dataclass
class Qwen25_3BConfig:
@ -53,6 +54,7 @@ class Qwen25_3BConfig:
q_norm = None
k_norm = None
rope_scale = None
final_norm: bool = True
@dataclass
class Qwen25_7BVLI_Config:
@ -74,6 +76,7 @@ class Qwen25_7BVLI_Config:
q_norm = None
k_norm = None
rope_scale = None
final_norm: bool = True
@dataclass
class Gemma2_2B_Config:
@ -96,6 +99,7 @@ class Gemma2_2B_Config:
k_norm = None
sliding_attention = None
rope_scale = None
final_norm: bool = True
@dataclass
class Gemma3_4B_Config:
@ -118,6 +122,7 @@ class Gemma3_4B_Config:
k_norm = "gemma3"
sliding_attention = [False, False, False, False, False, 1024]
rope_scale = [1.0, 8.0]
final_norm: bool = True
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None):
@ -366,7 +371,12 @@ class Llama2_(nn.Module):
transformer(config, index=i, device=device, dtype=dtype, ops=ops)
for i in range(config.num_hidden_layers)
])
if config.final_norm:
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
else:
self.norm = None
# self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
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=[]):
@ -421,14 +431,16 @@ class Llama2_(nn.Module):
if i == intermediate_output:
intermediate = x.clone()
if self.norm is not None:
x = self.norm(x)
if all_intermediate is not None:
all_intermediate.append(x.unsqueeze(1).clone())
if all_intermediate is not None:
intermediate = torch.cat(all_intermediate, dim=1)
if intermediate is not None and final_layer_norm_intermediate:
if intermediate is not None and final_layer_norm_intermediate and self.norm is not None:
intermediate = self.norm(intermediate)
return x, intermediate

231
comfy_api_nodes/apis/gemini_api.py
View File

@ -1,22 +1,229 @@
from typing import Optional
from datetime import date
from enum import Enum
from typing import Any
from comfy_api_nodes.apis import GeminiGenerationConfig, GeminiContent, GeminiSafetySetting, GeminiSystemInstructionContent, GeminiTool, GeminiVideoMetadata
from pydantic import BaseModel
from pydantic import BaseModel, Field
class GeminiSafetyCategory(str, Enum):
HARM_CATEGORY_SEXUALLY_EXPLICIT = "HARM_CATEGORY_SEXUALLY_EXPLICIT"
HARM_CATEGORY_HATE_SPEECH = "HARM_CATEGORY_HATE_SPEECH"
HARM_CATEGORY_HARASSMENT = "HARM_CATEGORY_HARASSMENT"
HARM_CATEGORY_DANGEROUS_CONTENT = "HARM_CATEGORY_DANGEROUS_CONTENT"
class GeminiSafetyThreshold(str, Enum):
OFF = "OFF"
BLOCK_NONE = "BLOCK_NONE"
BLOCK_LOW_AND_ABOVE = "BLOCK_LOW_AND_ABOVE"
BLOCK_MEDIUM_AND_ABOVE = "BLOCK_MEDIUM_AND_ABOVE"
BLOCK_ONLY_HIGH = "BLOCK_ONLY_HIGH"
class GeminiSafetySetting(BaseModel):
category: GeminiSafetyCategory
threshold: GeminiSafetyThreshold
class GeminiRole(str, Enum):
user = "user"
model = "model"
class GeminiMimeType(str, Enum):
application_pdf = "application/pdf"
audio_mpeg = "audio/mpeg"
audio_mp3 = "audio/mp3"
audio_wav = "audio/wav"
image_png = "image/png"
image_jpeg = "image/jpeg"
image_webp = "image/webp"
text_plain = "text/plain"
video_mov = "video/mov"
video_mpeg = "video/mpeg"
video_mp4 = "video/mp4"
video_mpg = "video/mpg"
video_avi = "video/avi"
video_wmv = "video/wmv"
video_mpegps = "video/mpegps"
video_flv = "video/flv"
class GeminiInlineData(BaseModel):
data: str | None = Field(
None,
description="The base64 encoding of the image, PDF, or video to include inline in the prompt. "
"When including media inline, you must also specify the media type (mimeType) of the data. Size limit: 20MB",
)
mimeType: GeminiMimeType | None = Field(None)
class GeminiPart(BaseModel):
inlineData: GeminiInlineData | None = Field(None)
text: str | None = Field(None)
class GeminiTextPart(BaseModel):
text: str | None = Field(None)
class GeminiContent(BaseModel):
parts: list[GeminiPart] = Field(...)
role: GeminiRole = Field(..., examples=["user"])
class GeminiSystemInstructionContent(BaseModel):
parts: list[GeminiTextPart] = Field(
...,
description="A list of ordered parts that make up a single message. "
"Different parts may have different IANA MIME types.",
)
role: GeminiRole = Field(
...,
description="The identity of the entity that creates the message. "
"The following values are supported: "
"user: This indicates that the message is sent by a real person, typically a user-generated message. "
"model: This indicates that the message is generated by the model. "
"The model value is used to insert messages from model into the conversation during multi-turn conversations. "
"For non-multi-turn conversations, this field can be left blank or unset.",
)
class GeminiFunctionDeclaration(BaseModel):
description: str | None = Field(None)
name: str = Field(...)
parameters: dict[str, Any] = Field(..., description="JSON schema for the function parameters")
class GeminiTool(BaseModel):
functionDeclarations: list[GeminiFunctionDeclaration] | None = Field(None)
class GeminiOffset(BaseModel):
nanos: int | None = Field(None, ge=0, le=999999999)
seconds: int | None = Field(None, ge=-315576000000, le=315576000000)
class GeminiVideoMetadata(BaseModel):
endOffset: GeminiOffset | None = Field(None)
startOffset: GeminiOffset | None = Field(None)
class GeminiGenerationConfig(BaseModel):
maxOutputTokens: int | None = Field(None, ge=16, le=8192)
seed: int | None = Field(None)
stopSequences: list[str] | None = Field(None)
temperature: float | None = Field(1, ge=0.0, le=2.0)
topK: int | None = Field(40, ge=1)
topP: float | None = Field(0.95, ge=0.0, le=1.0)
class GeminiImageConfig(BaseModel):
aspectRatio: Optional[str] = None
aspectRatio: str | None = Field(None)
resolution: str | None = Field(None)
class GeminiImageGenerationConfig(GeminiGenerationConfig):
responseModalities: Optional[list[str]] = None
imageConfig: Optional[GeminiImageConfig] = None
responseModalities: list[str] | None = Field(None)
imageConfig: GeminiImageConfig | None = Field(None)
class GeminiImageGenerateContentRequest(BaseModel):
contents: list[GeminiContent]
generationConfig: Optional[GeminiImageGenerationConfig] = None
safetySettings: Optional[list[GeminiSafetySetting]] = None
systemInstruction: Optional[GeminiSystemInstructionContent] = None
tools: Optional[list[GeminiTool]] = None
videoMetadata: Optional[GeminiVideoMetadata] = None
contents: list[GeminiContent] = Field(...)
generationConfig: GeminiImageGenerationConfig | None = Field(None)
safetySettings: list[GeminiSafetySetting] | None = Field(None)
systemInstruction: GeminiSystemInstructionContent | None = Field(None)
tools: list[GeminiTool] | None = Field(None)
videoMetadata: GeminiVideoMetadata | None = Field(None)
class GeminiGenerateContentRequest(BaseModel):
contents: list[GeminiContent] = Field(...)
generationConfig: GeminiGenerationConfig | None = Field(None)
safetySettings: list[GeminiSafetySetting] | None = Field(None)
systemInstruction: GeminiSystemInstructionContent | None = Field(None)
tools: list[GeminiTool] | None = Field(None)
videoMetadata: GeminiVideoMetadata | None = Field(None)
class Modality(str, Enum):
MODALITY_UNSPECIFIED = "MODALITY_UNSPECIFIED"
TEXT = "TEXT"
IMAGE = "IMAGE"
VIDEO = "VIDEO"
AUDIO = "AUDIO"
DOCUMENT = "DOCUMENT"
class ModalityTokenCount(BaseModel):
modality: Modality | None = None
tokenCount: int | None = Field(None, description="Number of tokens for the given modality.")
class Probability(str, Enum):
NEGLIGIBLE = "NEGLIGIBLE"
LOW = "LOW"
MEDIUM = "MEDIUM"
HIGH = "HIGH"
UNKNOWN = "UNKNOWN"
class GeminiSafetyRating(BaseModel):
category: GeminiSafetyCategory | None = None
probability: Probability | None = Field(
None,
description="The probability that the content violates the specified safety category",
)
class GeminiCitation(BaseModel):
authors: list[str] | None = None
endIndex: int | None = None
license: str | None = None
publicationDate: date | None = None
startIndex: int | None = None
title: str | None = None
uri: str | None = None
class GeminiCitationMetadata(BaseModel):
citations: list[GeminiCitation] | None = None
class GeminiCandidate(BaseModel):
citationMetadata: GeminiCitationMetadata | None = None
content: GeminiContent | None = None
finishReason: str | None = None
safetyRatings: list[GeminiSafetyRating] | None = None
class GeminiPromptFeedback(BaseModel):
blockReason: str | None = None
blockReasonMessage: str | None = None
safetyRatings: list[GeminiSafetyRating] | None = None
class GeminiUsageMetadata(BaseModel):
cachedContentTokenCount: int | None = Field(
None,
description="Output only. Number of tokens in the cached part in the input (the cached content).",
)
candidatesTokenCount: int | None = Field(None, description="Number of tokens in the response(s).")
candidatesTokensDetails: list[ModalityTokenCount] | None = Field(
None, description="Breakdown of candidate tokens by modality."
)
promptTokenCount: int | None = Field(
None,
description="Number of tokens in the request. When cachedContent is set, this is still the total effective prompt size meaning this includes the number of tokens in the cached content.",
)
promptTokensDetails: list[ModalityTokenCount] | None = Field(
None, description="Breakdown of prompt tokens by modality."
)
thoughtsTokenCount: int | None = Field(None, description="Number of tokens present in thoughts output.")
toolUsePromptTokenCount: int | None = Field(None, description="Number of tokens present in tool-use prompt(s).")
class GeminiGenerateContentResponse(BaseModel):
candidates: list[GeminiCandidate] | None = Field(None)
promptFeedback: GeminiPromptFeedback | None = Field(None)
usageMetadata: GeminiUsageMetadata | None = Field(None)

47
comfy_api_nodes/nodes_gemini.py
View File

@ -3,8 +3,6 @@ API Nodes for Gemini Multimodal LLM Usage via Remote API
See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference
"""
from __future__ import annotations
import base64
import json
import os
@ -12,7 +10,7 @@ import time
import uuid
from enum import Enum
from io import BytesIO
from typing import Literal, Optional
from typing import Literal
import torch
from typing_extensions import override
@ -20,18 +18,17 @@ from typing_extensions import override
import folder_paths
from comfy_api.latest import IO, ComfyExtension, Input
from comfy_api.util import VideoCodec, VideoContainer
from comfy_api_nodes.apis import (
from comfy_api_nodes.apis.gemini_api import (
GeminiContent,
GeminiGenerateContentRequest,
GeminiGenerateContentResponse,
GeminiInlineData,
GeminiMimeType,
GeminiPart,
)
from comfy_api_nodes.apis.gemini_api import (
GeminiImageConfig,
GeminiImageGenerateContentRequest,
GeminiImageGenerationConfig,
GeminiInlineData,
GeminiMimeType,
GeminiPart,
GeminiRole,
)
from comfy_api_nodes.util import (
ApiEndpoint,
@ -57,6 +54,7 @@ class GeminiModel(str, Enum):
gemini_2_5_flash_preview_04_17 = "gemini-2.5-flash-preview-04-17"
gemini_2_5_pro = "gemini-2.5-pro"
gemini_2_5_flash = "gemini-2.5-flash"
gemini_3_0_pro = "gemini-3-pro-preview"
class GeminiImageModel(str, Enum):
@ -103,6 +101,16 @@ def get_parts_by_type(response: GeminiGenerateContentResponse, part_type: Litera
Returns:
List of response parts matching the requested type.
"""
if response.candidates is None:
if response.promptFeedback.blockReason:
feedback = response.promptFeedback
raise ValueError(
f"Gemini API blocked the request. Reason: {feedback.blockReason} ({feedback.blockReasonMessage})"
)
raise NotImplementedError(
"Gemini returned no response candidates. "
"Please report to ComfyUI repository with the example of workflow to reproduce this."
)
parts = []
for part in response.candidates[0].content.parts:
if part_type == "text" and hasattr(part, "text") and part.text:
@ -272,10 +280,10 @@ class GeminiNode(IO.ComfyNode):
prompt: str,
model: str,
seed: int,
images: Optional[torch.Tensor] = None,
audio: Optional[Input.Audio] = None,
video: Optional[Input.Video] = None,
files: Optional[list[GeminiPart]] = None,
images: torch.Tensor | None = None,
audio: Input.Audio | None = None,
video: Input.Video | None = None,
files: list[GeminiPart] | None = None,
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=False)
@ -300,7 +308,7 @@ class GeminiNode(IO.ComfyNode):
data=GeminiGenerateContentRequest(
contents=[
GeminiContent(
role="user",
role=GeminiRole.user,
parts=parts,
)
]
@ -308,7 +316,6 @@ class GeminiNode(IO.ComfyNode):
response_model=GeminiGenerateContentResponse,
)
# Get result output
output_text = get_text_from_response(response)
if output_text:
# Not a true chat history like the OpenAI Chat node. It is emulated so the frontend can show a copy button.
@ -406,7 +413,7 @@ class GeminiInputFiles(IO.ComfyNode):
)
@classmethod
def execute(cls, file: str, GEMINI_INPUT_FILES: Optional[list[GeminiPart]] = None) -> IO.NodeOutput:
def execute(cls, file: str, GEMINI_INPUT_FILES: list[GeminiPart] | None = None) -> IO.NodeOutput:
"""Loads and formats input files for Gemini API."""
if GEMINI_INPUT_FILES is None:
GEMINI_INPUT_FILES = []
@ -421,7 +428,7 @@ class GeminiImage(IO.ComfyNode):
def define_schema(cls):
return IO.Schema(
node_id="GeminiImageNode",
display_name="Google Gemini Image",
display_name="Nano Banana (Google Gemini Image)",
category="api node/image/Gemini",
description="Edit images synchronously via Google API.",
inputs=[
@ -488,8 +495,8 @@ class GeminiImage(IO.ComfyNode):
prompt: str,
model: str,
seed: int,
images: Optional[torch.Tensor] = None,
files: Optional[list[GeminiPart]] = None,
images: torch.Tensor | None = None,
files: list[GeminiPart] | None = None,
aspect_ratio: str = "auto",
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=True, min_length=1)
@ -510,7 +517,7 @@ class GeminiImage(IO.ComfyNode):
endpoint=ApiEndpoint(path=f"{GEMINI_BASE_ENDPOINT}/{model}", method="POST"),
data=GeminiImageGenerateContentRequest(
contents=[
GeminiContent(role="user", parts=parts),
GeminiContent(role=GeminiRole.user, parts=parts),
],
generationConfig=GeminiImageGenerationConfig(
responseModalities=["TEXT", "IMAGE"],

22
comfy_extras/nodes_easycache.py
View File

@ -11,13 +11,13 @@ if TYPE_CHECKING:
def easycache_forward_wrapper(executor, *args, **kwargs):
# get values from args
x: torch.Tensor = args[0]
transformer_options: dict[str] = args[-1]
if not isinstance(transformer_options, dict):
transformer_options = kwargs.get("transformer_options")
if not transformer_options:
transformer_options = args[-2]
easycache: EasyCacheHolder = transformer_options["easycache"]
x: torch.Tensor = args[0][:, :easycache.output_channels]
sigmas = transformer_options["sigmas"]
uuids = transformer_options["uuids"]
if sigmas is not None and easycache.is_past_end_timestep(sigmas):
@ -82,13 +82,13 @@ def easycache_forward_wrapper(executor, *args, **kwargs):
def lazycache_predict_noise_wrapper(executor, *args, **kwargs):
# get values from args
x: torch.Tensor = args[0]
timestep: float = args[1]
model_options: dict[str] = args[2]
easycache: LazyCacheHolder = model_options["transformer_options"]["easycache"]
if easycache.is_past_end_timestep(timestep):
return executor(*args, **kwargs)
# prepare next x_prev
x: torch.Tensor = args[0][:, :easycache.output_channels]
next_x_prev = x
input_change = None
do_easycache = easycache.should_do_easycache(timestep)
@ -173,7 +173,7 @@ def easycache_sample_wrapper(executor, *args, **kwargs):
class EasyCacheHolder:
def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False):
def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False, output_channels: int=None):
self.name = "EasyCache"
self.reuse_threshold = reuse_threshold
self.start_percent = start_percent
@ -202,6 +202,7 @@ class EasyCacheHolder:
self.allow_mismatch = True
self.cut_from_start = True
self.state_metadata = None
self.output_channels = output_channels
def is_past_end_timestep(self, timestep: float) -> bool:
return not (timestep[0] > self.end_t).item()
@ -264,7 +265,7 @@ class EasyCacheHolder:
else:
slicing.append(slice(None))
batch_slice = batch_slice + slicing
x[batch_slice] += self.uuid_cache_diffs[uuid].to(x.device)
x[tuple(batch_slice)] += self.uuid_cache_diffs[uuid].to(x.device)
return x
def update_cache_diff(self, output: torch.Tensor, x: torch.Tensor, uuids: list[UUID]):
@ -283,7 +284,7 @@ class EasyCacheHolder:
else:
slicing.append(slice(None))
skip_dim = False
x = x[slicing]
x = x[tuple(slicing)]
diff = output - x
batch_offset = diff.shape[0] // len(uuids)
for i, uuid in enumerate(uuids):
@ -323,7 +324,7 @@ class EasyCacheHolder:
return self
def clone(self):
return EasyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose)
return EasyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose, output_channels=self.output_channels)
class EasyCacheNode(io.ComfyNode):
@ -350,7 +351,7 @@ class EasyCacheNode(io.ComfyNode):
@classmethod
def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput:
model = model.clone()
model.model_options["transformer_options"]["easycache"] = EasyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose)
model.model_options["transformer_options"]["easycache"] = EasyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose, output_channels=model.model.latent_format.latent_channels)
model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "easycache", easycache_sample_wrapper)
model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, "easycache", easycache_calc_cond_batch_wrapper)
model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "easycache", easycache_forward_wrapper)
@ -358,7 +359,7 @@ class EasyCacheNode(io.ComfyNode):
class LazyCacheHolder:
def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False):
def __init__(self, reuse_threshold: float, start_percent: float, end_percent: float, subsample_factor: int, offload_cache_diff: bool, verbose: bool=False, output_channels: int=None):
self.name = "LazyCache"
self.reuse_threshold = reuse_threshold
self.start_percent = start_percent
@ -382,6 +383,7 @@ class LazyCacheHolder:
self.approx_output_change_rates = []
self.total_steps_skipped = 0
self.state_metadata = None
self.output_channels = output_channels
def has_cache_diff(self) -> bool:
return self.cache_diff is not None
@ -456,7 +458,7 @@ class LazyCacheHolder:
return self
def clone(self):
return LazyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose)
return LazyCacheHolder(self.reuse_threshold, self.start_percent, self.end_percent, self.subsample_factor, self.offload_cache_diff, self.verbose, output_channels=self.output_channels)
class LazyCacheNode(io.ComfyNode):
@classmethod
@ -482,7 +484,7 @@ class LazyCacheNode(io.ComfyNode):
@classmethod
def execute(cls, model: io.Model.Type, reuse_threshold: float, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput:
model = model.clone()
model.model_options["transformer_options"]["easycache"] = LazyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose)
model.model_options["transformer_options"]["easycache"] = LazyCacheHolder(reuse_threshold, start_percent, end_percent, subsample_factor=8, offload_cache_diff=False, verbose=verbose, output_channels=model.model.latent_format.latent_channels)
model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "lazycache", easycache_sample_wrapper)
model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, "lazycache", lazycache_predict_noise_wrapper)
return io.NodeOutput(model)

39
comfy_extras/nodes_nop.py Normal file
View File

@ -0,0 +1,39 @@
from comfy_api.latest import ComfyExtension, io
from typing_extensions import override
# If you write a node that is so useless that it breaks ComfyUI it will be featured in this exclusive list
# "native" block swap nodes are placebo at best and break the ComfyUI memory management system.
# They are also considered harmful because instead of users reporting issues with the built in
# memory management they install these stupid nodes and complain even harder. Now it completely
# breaks with some of the new ComfyUI memory optimizations so I have made the decision to NOP it
# out of all workflows.
class wanBlockSwap(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="wanBlockSwap",
category="",
description="NOP",
inputs=[
io.Model.Input("model"),
],
outputs=[
io.Model.Output(),
],
is_deprecated=True,
)
@classmethod
def execute(cls, model) -> io.NodeOutput:
return io.NodeOutput(model)
class NopExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
wanBlockSwap
]
async def comfy_entrypoint() -> NopExtension:
return NopExtension()

2
comfy_extras/nodes_preview_any.py
View File

@ -39,5 +39,5 @@ NODE_CLASS_MAPPINGS = {
}
NODE_DISPLAY_NAME_MAPPINGS = {
"PreviewAny": "Preview Any",
"PreviewAny": "Preview as Text",
}

2
comfyui_version.py
View File

@ -1,3 +1,3 @@
# This file is automatically generated by the build process when version is
# updated in pyproject.toml.
__version__ = "0.3.68"
__version__ = "0.3.70"

1
nodes.py
View File

@ -2330,6 +2330,7 @@ async def init_builtin_extra_nodes():
"nodes_easycache.py",
"nodes_audio_encoder.py",
"nodes_rope.py",
"nodes_nop.py",
]
import_failed = []

4
pyproject.toml
View File

@ -1,6 +1,6 @@
[project]
name = "ComfyUI"
version = "0.3.68"
version = "0.3.70"
readme = "README.md"
license = { file = "LICENSE" }
requires-python = ">=3.9"
@ -24,7 +24,7 @@ lint.select = [
exclude = ["*.ipynb", "**/generated/*.pyi"]
[tool.pylint]
master.py-version = "3.9"
master.py-version = "3.10"
master.extension-pkg-allow-list = [
"pydantic",
]