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Merge branch 'master' into fix/sqlalchemy-version-format
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This commit is contained in:
Jedrzej Kosinski 2026-04-28 02:43:27 -07:00 committed by GitHub
commit 7d3498287c
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33 changed files with 30714 additions and 1140 deletions
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45
.github/workflows/tag-dispatch-cloud.yml vendored Normal file
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@ -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"

1620
blueprints/Crop Images 2x2.json Normal file
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blueprints/Crop Images 3x3.json Normal file
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4
blueprints/Depth to Image (Z-Image-Turbo).json
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@ -160,7 +160,7 @@
},
"revision": 0,
"config": {},
"name": "local-Depth to Image (Z-Image-Turbo)",
"name": "Depth to Image (Z-Image-Turbo)",
"inputNode": {
"id": -10,
"bounding": [
@ -2482,4 +2482,4 @@
"VHS_KeepIntermediate": true
},
"version": 0.4
}
}

4
blueprints/Depth to Video (ltx 2.0).json
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@ -261,7 +261,7 @@
},
"revision": 0,
"config": {},
"name": "local-Depth to Video (LTX 2.0)",
"name": "Depth to Video (LTX 2.0)",
"inputNode": {
"id": -10,
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@ -5208,4 +5208,4 @@
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3360
blueprints/First-Last-Frame to Video (LTX-2.3).json Normal file
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2148
blueprints/Image Edit (FireRed Image Edit 1.1).json Normal file
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4
blueprints/Image Edit (Flux.2 Klein 4B).json
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@ -128,7 +128,7 @@
},
"revision": 0,
"config": {},
"name": "local-Image Edit (Flux.2 Klein 4B)",
"name": "Image Edit (Flux.2 Klein 4B)",
"inputNode": {
"id": -10,
"bounding": [
@ -1837,4 +1837,4 @@
}
},
"version": 0.4
}
}

1427
blueprints/Image Edit (LongCat Image Edit).json Normal file
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1205
blueprints/Image Inpainting (Flux.1 Fill Dev).json Normal file
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4
blueprints/Image Inpainting (Qwen-image).json
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@ -124,7 +124,7 @@
},
"revision": 0,
"config": {},
"name": "local-Image Inpainting (Qwen-image)",
"name": "Image Inpainting (Qwen-image)",
"inputNode": {
"id": -10,
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@ -1923,4 +1923,4 @@
"workflowRendererVersion": "LG"
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4
blueprints/Image Outpainting (Qwen-Image).json
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@ -204,7 +204,7 @@
},
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"name": "local-Image Outpainting (Qwen-Image)",
"name": "Image Outpainting (Qwen-Image)",
"inputNode": {
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"bounding": [
@ -2749,4 +2749,4 @@
}
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}

418
blueprints/Image to Layers(Qwen-Image Layered).json → blueprints/Image to Layers(Qwen-Image-Layered).json
View File

@ -1,15 +1,14 @@
{
"id": "1a761372-7c82-4016-b9bf-fa285967e1e9",
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},
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},
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},
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},
{
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},
{
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"widget": {
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},
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}
],
"outputs": [
@ -66,28 +97,41 @@
"links": []
}
],
"title": "Image to Layers (Qwen-Image-Layered)",
"properties": {
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[
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],
[
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[
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[
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[
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@ -95,6 +139,11 @@
],
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"ver": "0.5.1",
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"tabXOffset": 10,
@ -103,25 +152,20 @@
"secondTabOffset": 80,
"secondTabWidth": 65
},
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"",
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2.5,
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]
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@ -130,10 +174,10 @@
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@ -156,8 +200,8 @@
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@ -168,8 +212,8 @@
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]
},
{
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],
"pos": [
-620,
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]
},
{
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"linkIds": [
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],
"pos": [
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]
},
{
"id": "1e6d141c-d0f9-4a2b-895c-b6780e57cfa0",
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],
"pos": [
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]
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],
@ -231,14 +323,14 @@
"type": "CLIPLoader",
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],
"size": [
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],
"flags": {},
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"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",
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],
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{
@ -329,7 +426,7 @@
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},
"link": null
"link": 380
}
],
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@ -345,9 +442,14 @@
}
],
"properties": {
"Node name for S&R": "VAELoader",
"cnr_id": "comfy-core",
"ver": "0.5.1",
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"input_ue_unconnectable": {},
"version": "7.7"
},
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@ -375,11 +477,11 @@
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"order": 2,
"mode": 0,
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{
@ -411,9 +513,14 @@
],
"title": "CLIP Text Encode (Negative Prompt)",
"properties": {
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"cnr_id": "comfy-core",
"ver": "0.5.1",
"ue_properties": {
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},
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@ -432,12 +539,12 @@
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@ -470,9 +577,14 @@
}
],
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"cnr_id": "comfy-core",
"ver": "0.5.1",
"ue_properties": {
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},
"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": []
}
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{
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],
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],
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"collapsed": true
@ -525,9 +636,14 @@
}
],
"properties": {
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"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
},
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@ -547,10 +662,10 @@
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@ -580,9 +695,14 @@
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"tabWidth": 65,
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@ -600,11 +720,11 @@
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],
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],
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@ -646,9 +766,14 @@
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],
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"ver": "0.5.1",
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@ -666,12 +791,12 @@
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@ -704,9 +829,14 @@
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@ -714,24 +844,23 @@
"secondTabText": "Send Back",
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"secondTabWidth": 65
},
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},
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@ -759,9 +888,14 @@
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],
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},
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@ -769,8 +903,7 @@
"secondTabText": "Send Back",
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@ -780,11 +913,11 @@
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@ -838,14 +976,14 @@
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@ -879,7 +1017,7 @@
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},
{
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@ -939,9 +1077,14 @@
}
],
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@ -964,12 +1107,12 @@
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@ -1017,23 +1165,23 @@
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@ -1083,9 +1231,14 @@
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@ -1147,9 +1300,14 @@
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@ -1391,6 +1549,38 @@
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blueprints/Image to Video (LTX-2.3).json Normal file
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blueprints/Pose to Video (LTX 2.0).json
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5
comfy/model_patcher.py
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@ -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)

16
comfy/ops.py
View File

@ -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,

136
comfy_api/latest/_input_impl/video_types.py
View File

@ -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:
@ -238,64 +239,107 @@ 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)
for frame in container.decode(video_stream):
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)
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
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:
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)
# 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, 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):

5
comfy_api/latest/_util/video_types.py
View File

@ -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

4
comfy_api_nodes/apis/wan.py
View File

@ -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)

555
comfy_api_nodes/nodes_wan.py
View File

@ -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,
]

2
comfy_extras/nodes_hunyuan3d.py
View File

@ -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]
)

4
comfy_extras/nodes_mask.py
View File

@ -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]))

2
comfy_extras/nodes_sd3.py
View File

@ -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

2
comfyui_version.py
View File

@ -1,3 +1,3 @@
# This file is automatically generated by the build process when version is
# updated in pyproject.toml.
__version__ = "0.19.3"
__version__ = "0.20.1"

2
pyproject.toml
View File

@ -1,6 +1,6 @@
[project]
name = "ComfyUI"
version = "0.19.3"
version = "0.20.1"
readme = "README.md"
license = { file = "LICENSE" }
requires-python = ">=3.10"

2
requirements.txt
View File

@ -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