From 5edbdf4364c6c89c3c6a5c6630807b59cb7652ba Mon Sep 17 00:00:00 2001 From: "Daxiong (Lin)" Date: Thu, 23 Apr 2026 22:51:20 +0800 Subject: [PATCH 01/38] 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 02/38] 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 03/38] 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 04/38] 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 05/38] 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 06/38] 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 07/38] 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 08/38] 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 09/38] 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 10/38] 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 11/38] 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 12/38] 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 13/38] 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 14/38] 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 15/38] 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 16/38] 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 17/38] 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": [ + { + "id": "3b5ed000-6ab3-4458-91f7-8d6d366b0b40", + "version": 1, + "state": { + "lastGroupId": 3, + "lastNodeId": 142, + "lastLinkId": 245, + "lastRerouteId": 0 + }, + "revision": 0, + "config": {}, + "name": "Crop Images 2x2", + "inputNode": { + "id": -10, + "bounding": [ + -10, + 1570, + 120, + 60 + ] + }, + "outputNode": { + "id": -20, + "bounding": [ + 2919.9998608196274, + 1435, + 120, + 140 + ] + }, + "inputs": [ + { + "id": "741854dd-bfb1-4700-ba8c-3b9dea59d021", + "name": "image", + "type": "IMAGE", + "linkIds": [ + 2, + 11, + 13, + 30, + 32 + ], + "localized_name": "image", + "pos": [ + 90, + 1590 + ] + } + ], + "outputs": [ + { + "id": "0eaca6d4-679a-433e-9703-bfa6dceacb18", + "name": "IMAGE", + "type": "IMAGE", + "linkIds": [ + 41 + ], + "localized_name": "IMAGE", + "label": "top_left", + "pos": [ + 2939.9998608196274, + 1455 + ] + }, + { + "id": "fff5a1ad-3a74-4c87-938c-ee0fff55f840", + "name": "IMAGE_1", + "type": "IMAGE", + "linkIds": [ + 42 + ], + "localized_name": "IMAGE_1", + "label": "bottom_left", + "pos": [ + 2939.9998608196274, + 1475 + ] + }, + { + "id": "08f40978-fb25-4d98-b716-b61e43b16043", + "name": "IMAGE_2", + "type": "IMAGE", + "linkIds": [ + 43 + ], + "localized_name": "IMAGE_2", + "label": "top_right", + "pos": [ + 2939.9998608196274, + 1495 + ] + }, + { + "id": "17b9416f-3369-43c1-b62f-3e31fc2a7e32", + "name": "IMAGE_3", + "type": "IMAGE", + "linkIds": [ + 44 + ], + "localized_name": "IMAGE_3", + "label": "bottom_right", + "pos": [ + 2939.9998608196274, + 1515 + ] + }, + { + "id": "430e2f3b-c617-4549-9daf-3ebf5be423a3", + "name": "IMAGE_4", + "type": "IMAGE", + "linkIds": [ + 240 + ], + "label": "images", + "pos": [ + 2939.9998608196274, + 1535 + ] + } + ], + "widgets": [], + "nodes": [ + { + "id": 7, + "type": "ComfyMathExpression", + "pos": [ + 740, + 1390 + ], + "size": [ + 370, + 190 + ], + "flags": {}, + "order": 1, + "mode": 0, + "inputs": [ + { + "label": "a", + "localized_name": "values.a", + "name": "values.a", + "type": 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"#3f789e", + "font_size": 24, + "flags": {} + } + ], + "links": [ + { + "id": 41, + "origin_id": 23, + "origin_slot": 0, + "target_id": 229, + "target_slot": 0, + "type": "CONDITIONING" + }, + { + "id": 101, + "origin_id": 23, + "origin_slot": 0, + "target_id": 46, + "target_slot": 0, + "type": "CONDITIONING" + }, + { + "id": 62, + "origin_id": 34, + "origin_slot": 0, + "target_id": 23, + "target_slot": 0, + "type": "CLIP" + }, + { + "id": 85, + "origin_id": 31, + "origin_slot": 0, + "target_id": 39, + "target_slot": 0, + "type": "MODEL" + }, + { + "id": 86, + "origin_id": 39, + "origin_slot": 0, + "target_id": 3, + "target_slot": 0, + "type": "MODEL" + }, + { + "id": 77, + "origin_id": 38, + "origin_slot": 0, + "target_id": 3, + "target_slot": 1, + "type": "CONDITIONING" + }, + { + "id": 78, + "origin_id": 38, + "origin_slot": 1, + "target_id": 3, + "target_slot": 2, + "type": "CONDITIONING" + }, + { + "id": 88, + "origin_id": 38, + "origin_slot": 2, + "target_id": 3, + "target_slot": 3, + "type": "LATENT" + }, + { + "id": 7, + "origin_id": 3, + "origin_slot": 0, + "target_id": 231, + "target_slot": 0, + "type": "LATENT" + }, + { + "id": 60, + "origin_id": 230, + "origin_slot": 0, + "target_id": 231, + "target_slot": 1, + "type": "VAE" + }, + { + "id": 80, + "origin_id": 229, + "origin_slot": 0, + "target_id": 38, + "target_slot": 0, + "type": "CONDITIONING" + }, + { + "id": 102, + "origin_id": 46, + "origin_slot": 0, + "target_id": 38, + "target_slot": 1, + "type": "CONDITIONING" + }, + { + "id": 82, + "origin_id": 230, + "origin_slot": 0, + "target_id": 38, + "target_slot": 2, + "type": "VAE" + }, + { + "id": 99, + "origin_id": -10, + "origin_slot": 0, + "target_id": 38, + "target_slot": 3, + "type": "IMAGE" + }, + { + "id": 100, + "origin_id": -10, + "origin_slot": 1, + "target_id": 38, + "target_slot": 4, + "type": "MASK" + }, + { + "id": 95, + "origin_id": 231, + "origin_slot": 0, + "target_id": -20, + "target_slot": 0, + "type": "IMAGE" + }, + { + "id": 277, + "origin_id": -10, + "origin_slot": 2, + "target_id": 23, + "target_slot": 1, + "type": "STRING" + }, + { + "id": 282, + "origin_id": -10, + "origin_slot": 3, + "target_id": 3, + "target_slot": 4, + "type": "INT" + }, + { + "id": 283, + "origin_id": -10, + "origin_slot": 4, + "target_id": 31, + "target_slot": 0, + "type": "COMBO" + }, + { + "id": 284, + "origin_id": -10, + "origin_slot": 5, + "target_id": 34, + "target_slot": 0, + "type": "COMBO" + }, + { + "id": 285, + "origin_id": -10, + "origin_slot": 6, + "target_id": 34, + "target_slot": 1, + "type": "COMBO" + }, + { + "id": 286, + "origin_id": -10, + "origin_slot": 7, + "target_id": 230, + "target_slot": 0, + "type": "COMBO" + } + ], + "extra": { + "workflowRendererVersion": "LG" + }, + "category": "Image generation and editing/Inpaint image" + } + ] + }, + "extra": { + "ds": { + "scale": 0.8480949417360862, + "offset": [ + 833.9510730024642, + 210.32152847588895 + ] + }, + "ue_links": [] + } +} \ No newline at end of file diff --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, + 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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", 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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, + 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"type": "IMAGE" + }, + { + "id": 627, + "origin_id": -10, + "origin_slot": 8, + "target_id": 302, + "target_slot": 0, + "type": "INT" + }, + { + "id": 628, + "origin_id": 303, + "origin_slot": 0, + "target_id": 323, + "target_slot": 0, + "type": "INT" + }, + { + "id": 629, + "origin_id": 302, + "origin_slot": 0, + "target_id": 323, + "target_slot": 1, + "type": "INT" + }, + { + "id": 630, + "origin_id": 323, + "origin_slot": 1, + "target_id": 307, + "target_slot": 1, + "type": "INT" + }, + { + "id": 631, + "origin_id": 323, + "origin_slot": 1, + "target_id": 297, + "target_slot": 2, + "type": "INT" + } + ], + "extra": { + "workflowRendererVersion": "Vue-corrected" + }, + "category": "Video generation and editing/Text to video" + } + ] + }, + "extra": { + "ue_links": [] + } +} \ No newline at end of file From 115f418b64e0c62251f81aa181b41ed7e933c339 Mon Sep 17 00:00:00 2001 From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com> Date: Sun, 26 Apr 2026 20:23:57 -0700 Subject: [PATCH 18/38] 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 19/38] [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 20/38] 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 21/38] 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 22/38] 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 23/38] 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 24/38] 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 25/38] 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 26/38] 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 27/38] 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 28/38] 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 29/38] 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 30/38] 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 31/38] 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 32/38] 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 33/38] 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 34/38] 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 35/38] 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 36/38] 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 37/38] 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 38/38] 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")