mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-05-06 10:17:59 +08:00
Compare commits
3 Commits
release/v0
...
range-type
| Author | SHA1 | Date | |
|---|---|---|---|
| 8ff57addaa | |||
| 4a8ada2d15 | |||
| 8822627a60 |
1
.gitignore
vendored
1
.gitignore
vendored
@ -21,5 +21,6 @@ venv*/
|
||||
*.log
|
||||
web_custom_versions/
|
||||
.DS_Store
|
||||
openapi.yaml
|
||||
filtered-openapi.yaml
|
||||
uv.lock
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
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Load Diff
@ -160,7 +160,7 @@
|
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@ -2482,4 +2482,4 @@
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|
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|
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@ -261,7 +261,7 @@
|
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@ -5208,4 +5208,4 @@
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@ -128,7 +128,7 @@
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@ -1837,4 +1837,4 @@
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@ -124,7 +124,7 @@
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@ -1923,4 +1923,4 @@
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@ -204,7 +204,7 @@
|
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@ -2749,4 +2749,4 @@
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@ -55,38 +56,6 @@
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@ -97,41 +66,28 @@
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@ -139,11 +95,6 @@
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||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@ -695,14 +580,9 @@
|
||||
}
|
||||
],
|
||||
"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,
|
||||
@ -720,11 +600,11 @@
|
||||
"type": "LatentCutToBatch",
|
||||
"pos": [
|
||||
830,
|
||||
140
|
||||
160
|
||||
],
|
||||
"size": [
|
||||
270,
|
||||
140
|
||||
82
|
||||
],
|
||||
"flags": {},
|
||||
"order": 11,
|
||||
@ -766,14 +646,9 @@
|
||||
}
|
||||
],
|
||||
"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,
|
||||
@ -791,12 +666,12 @@
|
||||
"id": 71,
|
||||
"type": "VAEEncode",
|
||||
"pos": [
|
||||
-280,
|
||||
780
|
||||
100,
|
||||
690
|
||||
],
|
||||
"size": [
|
||||
230,
|
||||
100
|
||||
140,
|
||||
46
|
||||
],
|
||||
"flags": {
|
||||
"collapsed": false
|
||||
@ -829,14 +704,9 @@
|
||||
}
|
||||
],
|
||||
"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,
|
||||
@ -844,23 +714,24 @@
|
||||
"secondTabText": "Send Back",
|
||||
"secondTabOffset": 80,
|
||||
"secondTabWidth": 65
|
||||
}
|
||||
},
|
||||
"widgets_values": []
|
||||
},
|
||||
{
|
||||
"id": 8,
|
||||
"type": "VAEDecode",
|
||||
"pos": [
|
||||
850,
|
||||
370
|
||||
310
|
||||
],
|
||||
"size": [
|
||||
210,
|
||||
50
|
||||
46
|
||||
],
|
||||
"flags": {
|
||||
"collapsed": true
|
||||
},
|
||||
"order": 3,
|
||||
"order": 7,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@ -888,14 +759,9 @@
|
||||
}
|
||||
],
|
||||
"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,
|
||||
@ -903,7 +769,8 @@
|
||||
"secondTabText": "Send Back",
|
||||
"secondTabOffset": 80,
|
||||
"secondTabWidth": 65
|
||||
}
|
||||
},
|
||||
"widgets_values": []
|
||||
},
|
||||
{
|
||||
"id": 6,
|
||||
@ -913,11 +780,11 @@
|
||||
180
|
||||
],
|
||||
"size": [
|
||||
430,
|
||||
170
|
||||
422.84503173828125,
|
||||
164.31304931640625
|
||||
],
|
||||
"flags": {},
|
||||
"order": 1,
|
||||
"order": 6,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@ -949,14 +816,9 @@
|
||||
],
|
||||
"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,
|
||||
@ -976,14 +838,14 @@
|
||||
"type": "KSampler",
|
||||
"pos": [
|
||||
530,
|
||||
340
|
||||
280
|
||||
],
|
||||
"size": [
|
||||
270,
|
||||
400
|
||||
],
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
"order": 5,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@ -1017,7 +879,7 @@
|
||||
"widget": {
|
||||
"name": "seed"
|
||||
},
|
||||
"link": 377
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "steps",
|
||||
@ -1077,14 +939,9 @@
|
||||
}
|
||||
],
|
||||
"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,
|
||||
@ -1107,12 +964,12 @@
|
||||
"id": 78,
|
||||
"type": "GetImageSize",
|
||||
"pos": [
|
||||
-280,
|
||||
930
|
||||
80,
|
||||
790
|
||||
],
|
||||
"size": [
|
||||
230,
|
||||
140
|
||||
210,
|
||||
136
|
||||
],
|
||||
"flags": {},
|
||||
"order": 12,
|
||||
@ -1150,14 +1007,9 @@
|
||||
}
|
||||
],
|
||||
"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,
|
||||
@ -1165,23 +1017,23 @@
|
||||
"secondTabText": "Send Back",
|
||||
"secondTabOffset": 80,
|
||||
"secondTabWidth": 65
|
||||
}
|
||||
},
|
||||
"widgets_values": []
|
||||
},
|
||||
{
|
||||
"id": 83,
|
||||
"type": "EmptyQwenImageLayeredLatentImage",
|
||||
"pos": [
|
||||
-280,
|
||||
1120
|
||||
320,
|
||||
790
|
||||
],
|
||||
"size": [
|
||||
340,
|
||||
200
|
||||
330.9341796875,
|
||||
130
|
||||
],
|
||||
"flags": {},
|
||||
"order": 13,
|
||||
"mode": 0,
|
||||
"showAdvanced": true,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "width",
|
||||
@ -1231,14 +1083,9 @@
|
||||
}
|
||||
],
|
||||
"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,
|
||||
@ -1262,11 +1109,11 @@
|
||||
180
|
||||
],
|
||||
"size": [
|
||||
350,
|
||||
110
|
||||
346.7470703125,
|
||||
82
|
||||
],
|
||||
"flags": {},
|
||||
"order": 4,
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
@ -1276,7 +1123,7 @@
|
||||
"widget": {
|
||||
"name": "unet_name"
|
||||
},
|
||||
"link": 378
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "weight_dtype",
|
||||
@ -1300,14 +1147,9 @@
|
||||
}
|
||||
],
|
||||
"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",
|
||||
@ -1349,8 +1191,8 @@
|
||||
"bounding": [
|
||||
-330,
|
||||
110,
|
||||
370,
|
||||
610
|
||||
366.7470703125,
|
||||
421.6
|
||||
],
|
||||
"color": "#3f789e",
|
||||
"font_size": 24,
|
||||
@ -1549,38 +1391,6 @@
|
||||
"target_id": 83,
|
||||
"target_slot": 2,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 377,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 5,
|
||||
"target_id": 3,
|
||||
"target_slot": 4,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 378,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 6,
|
||||
"target_id": 37,
|
||||
"target_slot": 0,
|
||||
"type": "COMBO"
|
||||
},
|
||||
{
|
||||
"id": 379,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 7,
|
||||
"target_id": 38,
|
||||
"target_slot": 0,
|
||||
"type": "COMBO"
|
||||
},
|
||||
{
|
||||
"id": 380,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 8,
|
||||
"target_id": 39,
|
||||
"target_slot": 0,
|
||||
"type": "COMBO"
|
||||
}
|
||||
],
|
||||
"extra": {
|
||||
@ -1590,6 +1400,7 @@
|
||||
}
|
||||
]
|
||||
},
|
||||
"config": {},
|
||||
"extra": {
|
||||
"ds": {
|
||||
"scale": 1.14,
|
||||
@ -1598,6 +1409,7 @@
|
||||
6.855893974423647
|
||||
]
|
||||
},
|
||||
"ue_links": []
|
||||
}
|
||||
}
|
||||
"workflowRendererVersion": "LG"
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -342,12 +342,6 @@ def model_lora_keys_unet(model, key_map={}):
|
||||
key_map["base_model.model.{}".format(key_lora)] = k # Official base model loras
|
||||
key_map["lycoris_{}".format(key_lora.replace(".", "_"))] = k # LyCORIS/LoKR format
|
||||
|
||||
if isinstance(model, comfy.model_base.ErnieImage):
|
||||
for k in sdk:
|
||||
if k.startswith("diffusion_model.") and k.endswith(".weight"):
|
||||
key_lora = k[len("diffusion_model."):-len(".weight")]
|
||||
key_map["transformer.{}".format(key_lora)] = k
|
||||
|
||||
return key_map
|
||||
|
||||
|
||||
|
||||
@ -240,34 +240,19 @@ 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='rgb24') # shape: (H, W, 3)
|
||||
img = torch.from_numpy(img) / 255.0 # shape: (H, W, 3)
|
||||
frames.append(img)
|
||||
|
||||
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)
|
||||
images = torch.stack(frames) if len(frames) > 0 else torch.zeros(0, 3, 0, 0)
|
||||
|
||||
# Get frame rate
|
||||
frame_rate = Fraction(video_stream.average_rate) if video_stream.average_rate else Fraction(1)
|
||||
@ -310,7 +295,7 @@ class VideoFromFile(VideoInput):
|
||||
})
|
||||
|
||||
metadata = container.metadata
|
||||
return VideoComponents(images=images, alpha=alphas, audio=audio, frame_rate=frame_rate, metadata=metadata)
|
||||
return VideoComponents(images=images, audio=audio, frame_rate=frame_rate, metadata=metadata)
|
||||
|
||||
def get_components(self) -> VideoComponents:
|
||||
if isinstance(self.__file, io.BytesIO):
|
||||
|
||||
@ -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, MaskInput
|
||||
from .._input import ImageInput, AudioInput
|
||||
|
||||
class VideoCodec(str, Enum):
|
||||
AUTO = "auto"
|
||||
@ -48,4 +48,5 @@ class VideoComponents:
|
||||
frame_rate: Fraction
|
||||
audio: Optional[AudioInput] = None
|
||||
metadata: Optional[dict] = None
|
||||
alpha: Optional[MaskInput] = None
|
||||
|
||||
|
||||
|
||||
@ -157,11 +157,6 @@ class SeedanceCreateAssetResponse(BaseModel):
|
||||
asset_id: str = Field(...)
|
||||
|
||||
|
||||
class SeedanceVirtualLibraryCreateAssetRequest(BaseModel):
|
||||
url: str = Field(..., description="Publicly accessible URL of the image asset to upload.")
|
||||
hash: str = Field(..., description="Dedup key. Re-submitting the same hash returns the existing asset id.")
|
||||
|
||||
|
||||
# Dollars per 1K tokens, keyed by (model_id, has_video_input).
|
||||
SEEDANCE2_PRICE_PER_1K_TOKENS = {
|
||||
("dreamina-seedance-2-0-260128", False): 0.007,
|
||||
|
||||
152
comfy_api_nodes/apis/moonvalley.py
Normal file
152
comfy_api_nodes/apis/moonvalley.py
Normal file
@ -0,0 +1,152 @@
|
||||
from enum import Enum
|
||||
from typing import Optional, Dict, Any
|
||||
|
||||
from pydantic import BaseModel, Field, StrictBytes
|
||||
|
||||
|
||||
class MoonvalleyPromptResponse(BaseModel):
|
||||
error: Optional[Dict[str, Any]] = None
|
||||
frame_conditioning: Optional[Dict[str, Any]] = None
|
||||
id: Optional[str] = None
|
||||
inference_params: Optional[Dict[str, Any]] = None
|
||||
meta: Optional[Dict[str, Any]] = None
|
||||
model_params: Optional[Dict[str, Any]] = None
|
||||
output_url: Optional[str] = None
|
||||
prompt_text: Optional[str] = None
|
||||
status: Optional[str] = None
|
||||
|
||||
|
||||
class MoonvalleyTextToVideoInferenceParams(BaseModel):
|
||||
add_quality_guidance: Optional[bool] = Field(
|
||||
True, description='Whether to add quality guidance'
|
||||
)
|
||||
caching_coefficient: Optional[float] = Field(
|
||||
0.3, description='Caching coefficient for optimization'
|
||||
)
|
||||
caching_cooldown: Optional[int] = Field(
|
||||
3, description='Number of caching cooldown steps'
|
||||
)
|
||||
caching_warmup: Optional[int] = Field(
|
||||
3, description='Number of caching warmup steps'
|
||||
)
|
||||
clip_value: Optional[float] = Field(
|
||||
3, description='CLIP value for generation control'
|
||||
)
|
||||
conditioning_frame_index: Optional[int] = Field(
|
||||
0, description='Index of the conditioning frame'
|
||||
)
|
||||
cooldown_steps: Optional[int] = Field(
|
||||
75, description='Number of cooldown steps (calculated based on num_frames)'
|
||||
)
|
||||
fps: Optional[int] = Field(
|
||||
24, description='Frames per second of the generated video'
|
||||
)
|
||||
guidance_scale: Optional[float] = Field(
|
||||
10, description='Guidance scale for generation control'
|
||||
)
|
||||
height: Optional[int] = Field(
|
||||
1080, description='Height of the generated video in pixels'
|
||||
)
|
||||
negative_prompt: Optional[str] = Field(None, description='Negative prompt text')
|
||||
num_frames: Optional[int] = Field(64, description='Number of frames to generate')
|
||||
seed: Optional[int] = Field(
|
||||
None, description='Random seed for generation (default: random)'
|
||||
)
|
||||
shift_value: Optional[float] = Field(
|
||||
3, description='Shift value for generation control'
|
||||
)
|
||||
steps: Optional[int] = Field(80, description='Number of denoising steps')
|
||||
use_guidance_schedule: Optional[bool] = Field(
|
||||
True, description='Whether to use guidance scheduling'
|
||||
)
|
||||
use_negative_prompts: Optional[bool] = Field(
|
||||
False, description='Whether to use negative prompts'
|
||||
)
|
||||
use_timestep_transform: Optional[bool] = Field(
|
||||
True, description='Whether to use timestep transformation'
|
||||
)
|
||||
warmup_steps: Optional[int] = Field(
|
||||
0, description='Number of warmup steps (calculated based on num_frames)'
|
||||
)
|
||||
width: Optional[int] = Field(
|
||||
1920, description='Width of the generated video in pixels'
|
||||
)
|
||||
|
||||
|
||||
class MoonvalleyTextToVideoRequest(BaseModel):
|
||||
image_url: Optional[str] = None
|
||||
inference_params: Optional[MoonvalleyTextToVideoInferenceParams] = None
|
||||
prompt_text: Optional[str] = None
|
||||
webhook_url: Optional[str] = None
|
||||
|
||||
|
||||
class MoonvalleyUploadFileRequest(BaseModel):
|
||||
file: Optional[StrictBytes] = None
|
||||
|
||||
|
||||
class MoonvalleyUploadFileResponse(BaseModel):
|
||||
access_url: Optional[str] = None
|
||||
|
||||
|
||||
class MoonvalleyVideoToVideoInferenceParams(BaseModel):
|
||||
add_quality_guidance: Optional[bool] = Field(
|
||||
True, description='Whether to add quality guidance'
|
||||
)
|
||||
caching_coefficient: Optional[float] = Field(
|
||||
0.3, description='Caching coefficient for optimization'
|
||||
)
|
||||
caching_cooldown: Optional[int] = Field(
|
||||
3, description='Number of caching cooldown steps'
|
||||
)
|
||||
caching_warmup: Optional[int] = Field(
|
||||
3, description='Number of caching warmup steps'
|
||||
)
|
||||
clip_value: Optional[float] = Field(
|
||||
3, description='CLIP value for generation control'
|
||||
)
|
||||
conditioning_frame_index: Optional[int] = Field(
|
||||
0, description='Index of the conditioning frame'
|
||||
)
|
||||
cooldown_steps: Optional[int] = Field(
|
||||
36, description='Number of cooldown steps (calculated based on num_frames)'
|
||||
)
|
||||
guidance_scale: Optional[float] = Field(
|
||||
15, description='Guidance scale for generation control'
|
||||
)
|
||||
negative_prompt: Optional[str] = Field(None, description='Negative prompt text')
|
||||
seed: Optional[int] = Field(
|
||||
None, description='Random seed for generation (default: random)'
|
||||
)
|
||||
shift_value: Optional[float] = Field(
|
||||
3, description='Shift value for generation control'
|
||||
)
|
||||
steps: Optional[int] = Field(80, description='Number of denoising steps')
|
||||
use_guidance_schedule: Optional[bool] = Field(
|
||||
True, description='Whether to use guidance scheduling'
|
||||
)
|
||||
use_negative_prompts: Optional[bool] = Field(
|
||||
False, description='Whether to use negative prompts'
|
||||
)
|
||||
use_timestep_transform: Optional[bool] = Field(
|
||||
True, description='Whether to use timestep transformation'
|
||||
)
|
||||
warmup_steps: Optional[int] = Field(
|
||||
24, description='Number of warmup steps (calculated based on num_frames)'
|
||||
)
|
||||
|
||||
|
||||
class ControlType(str, Enum):
|
||||
motion_control = 'motion_control'
|
||||
pose_control = 'pose_control'
|
||||
|
||||
|
||||
class MoonvalleyVideoToVideoRequest(BaseModel):
|
||||
control_type: ControlType = Field(
|
||||
..., description='Supported types for video control'
|
||||
)
|
||||
inference_params: Optional[MoonvalleyVideoToVideoInferenceParams] = None
|
||||
prompt_text: str = Field(..., description='Describes the video to generate')
|
||||
video_url: str = Field(..., description='Url to control video')
|
||||
webhook_url: Optional[str] = Field(
|
||||
None, description='Optional webhook URL for notifications'
|
||||
)
|
||||
@ -1,4 +1,4 @@
|
||||
from typing import Optional
|
||||
from typing import Optional, Union
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
@ -72,11 +72,8 @@ class VideoEnhancementFilter(BaseModel):
|
||||
grain: Optional[float] = Field(None, description="Grain after AI model processing")
|
||||
grainSize: Optional[float] = Field(None, description="Size of generated grain")
|
||||
recoverOriginalDetailValue: Optional[float] = Field(None, description="Source details into the output video")
|
||||
creativity: float | str | None = Field(None, description="slc-1/slp-2.5: enum (low/middle/high). ast-2: decimal 0.0-1.0.")
|
||||
creativity: Optional[str] = Field(None, description="Creativity level(high, low) for slc-1 only")
|
||||
isOptimizedMode: Optional[bool] = Field(None, description="Set to true for Starlight Creative (slc-1) only")
|
||||
prompt: str | None = Field(None, description="Descriptive scene prompt (ast-2 only)")
|
||||
sharp: float | None = Field(None, description="ast-2 pre-enhance sharpness")
|
||||
realism: float | None = Field(None, description="ast-2 realism control")
|
||||
|
||||
|
||||
class OutputInformationVideo(BaseModel):
|
||||
@ -93,7 +90,7 @@ class Overrides(BaseModel):
|
||||
|
||||
class CreateVideoRequest(BaseModel):
|
||||
source: CreateVideoRequestSource = Field(...)
|
||||
filters: list[VideoFrameInterpolationFilter | VideoEnhancementFilter] = Field(...)
|
||||
filters: list[Union[VideoFrameInterpolationFilter, VideoEnhancementFilter]] = Field(...)
|
||||
output: OutputInformationVideo = Field(...)
|
||||
overrides: Overrides = Field(Overrides(isPaidDiffusion=True))
|
||||
|
||||
|
||||
@ -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=15)
|
||||
duration: int = Field(5, ge=2, le=10)
|
||||
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 | None = Field(0)
|
||||
duration: int = Field(0)
|
||||
audio_setting: str = Field("auto")
|
||||
watermark: bool = Field(False)
|
||||
seed: int = Field(..., ge=0, le=2147483647)
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
import hashlib
|
||||
import logging
|
||||
import math
|
||||
import re
|
||||
@ -21,7 +20,6 @@ from comfy_api_nodes.apis.bytedance import (
|
||||
SeedanceCreateAssetResponse,
|
||||
SeedanceCreateVisualValidateSessionResponse,
|
||||
SeedanceGetVisualValidateSessionResponse,
|
||||
SeedanceVirtualLibraryCreateAssetRequest,
|
||||
Seedream4Options,
|
||||
Seedream4TaskCreationRequest,
|
||||
TaskAudioContent,
|
||||
@ -273,30 +271,6 @@ async def _wait_for_asset_active(cls: type[IO.ComfyNode], asset_id: str, group_i
|
||||
)
|
||||
|
||||
|
||||
async def _seedance_virtual_library_upload_image_asset(
|
||||
cls: type[IO.ComfyNode],
|
||||
image: torch.Tensor,
|
||||
*,
|
||||
wait_label: str = "Uploading image",
|
||||
) -> str:
|
||||
"""Upload an image into the caller's per-customer Seedance virtual library."""
|
||||
public_url = await upload_image_to_comfyapi(cls, image, wait_label=wait_label)
|
||||
normalized = image.detach().cpu().contiguous().to(torch.float32)
|
||||
digest = hashlib.sha256()
|
||||
digest.update(str(tuple(normalized.shape)).encode("utf-8"))
|
||||
digest.update(b"\0")
|
||||
digest.update(normalized.numpy().tobytes())
|
||||
image_hash = digest.hexdigest()
|
||||
create_resp = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path="/proxy/seedance/virtual-library/assets", method="POST"),
|
||||
response_model=SeedanceCreateAssetResponse,
|
||||
data=SeedanceVirtualLibraryCreateAssetRequest(url=public_url, hash=image_hash),
|
||||
)
|
||||
await _wait_for_asset_active(cls, create_resp.asset_id, group_id="virtual-library")
|
||||
return f"asset://{create_resp.asset_id}"
|
||||
|
||||
|
||||
def _seedance2_price_extractor(model_id: str, has_video_input: bool):
|
||||
"""Returns a price_extractor closure for Seedance 2.0 poll_op."""
|
||||
rate = SEEDANCE2_PRICE_PER_1K_TOKENS.get((model_id, has_video_input))
|
||||
@ -1403,6 +1377,7 @@ class ByteDance2TextToVideoNode(IO.ComfyNode):
|
||||
status_extractor=lambda r: r.status,
|
||||
price_extractor=_seedance2_price_extractor(model_id, has_video_input=False),
|
||||
poll_interval=9,
|
||||
max_poll_attempts=180,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
|
||||
|
||||
@ -1532,9 +1507,7 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
|
||||
if first_frame_asset_id:
|
||||
first_frame_url = image_assets[first_frame_asset_id]
|
||||
else:
|
||||
first_frame_url = await _seedance_virtual_library_upload_image_asset(
|
||||
cls, first_frame, wait_label="Uploading first frame."
|
||||
)
|
||||
first_frame_url = await upload_image_to_comfyapi(cls, first_frame, wait_label="Uploading first frame.")
|
||||
|
||||
content: list[TaskTextContent | TaskImageContent] = [
|
||||
TaskTextContent(text=model["prompt"]),
|
||||
@ -1554,9 +1527,7 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
|
||||
content.append(
|
||||
TaskImageContent(
|
||||
image_url=TaskImageContentUrl(
|
||||
url=await _seedance_virtual_library_upload_image_asset(
|
||||
cls, last_frame, wait_label="Uploading last frame."
|
||||
)
|
||||
url=await upload_image_to_comfyapi(cls, last_frame, wait_label="Uploading last frame.")
|
||||
),
|
||||
role="last_frame",
|
||||
),
|
||||
@ -1584,6 +1555,7 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
|
||||
status_extractor=lambda r: r.status,
|
||||
price_extractor=_seedance2_price_extractor(model_id, has_video_input=False),
|
||||
poll_interval=9,
|
||||
max_poll_attempts=180,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
|
||||
|
||||
@ -1833,9 +1805,9 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
|
||||
content.append(
|
||||
TaskImageContent(
|
||||
image_url=TaskImageContentUrl(
|
||||
url=await _seedance_virtual_library_upload_image_asset(
|
||||
url=await upload_image_to_comfyapi(
|
||||
cls,
|
||||
reference_images[key],
|
||||
image=reference_images[key],
|
||||
wait_label=f"Uploading image {i}",
|
||||
),
|
||||
),
|
||||
@ -1905,6 +1877,7 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
|
||||
status_extractor=lambda r: r.status,
|
||||
price_extractor=_seedance2_price_extractor(model_id, has_video_input=has_video_input),
|
||||
poll_interval=9,
|
||||
max_poll_attempts=180,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
|
||||
|
||||
|
||||
@ -178,6 +178,7 @@ class HitPawGeneralImageEnhance(IO.ComfyNode):
|
||||
status_extractor=lambda x: x.data.status,
|
||||
price_extractor=lambda x: request_price,
|
||||
poll_interval=10.0,
|
||||
max_poll_attempts=480,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_image_tensor(final_response.data.res_url))
|
||||
|
||||
@ -323,6 +324,7 @@ class HitPawVideoEnhance(IO.ComfyNode):
|
||||
status_extractor=lambda x: x.data.status,
|
||||
price_extractor=lambda x: request_price,
|
||||
poll_interval=10.0,
|
||||
max_poll_attempts=320,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_video_output(final_response.data.res_url))
|
||||
|
||||
|
||||
@ -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))
|
||||
@ -3061,6 +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))
|
||||
@ -3186,6 +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))
|
||||
|
||||
@ -230,6 +230,7 @@ class MagnificImageUpscalerCreativeNode(IO.ComfyNode):
|
||||
status_extractor=lambda x: x.status,
|
||||
price_extractor=lambda _: price_usd,
|
||||
poll_interval=10.0,
|
||||
max_poll_attempts=480,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
|
||||
|
||||
@ -390,6 +391,7 @@ class MagnificImageUpscalerPreciseV2Node(IO.ComfyNode):
|
||||
status_extractor=lambda x: x.status,
|
||||
price_extractor=lambda _: price_usd,
|
||||
poll_interval=10.0,
|
||||
max_poll_attempts=480,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
|
||||
|
||||
@ -539,6 +541,7 @@ class MagnificImageStyleTransferNode(IO.ComfyNode):
|
||||
response_model=TaskResponse,
|
||||
status_extractor=lambda x: x.status,
|
||||
poll_interval=10.0,
|
||||
max_poll_attempts=480,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
|
||||
|
||||
@ -779,6 +782,7 @@ class MagnificImageRelightNode(IO.ComfyNode):
|
||||
response_model=TaskResponse,
|
||||
status_extractor=lambda x: x.status,
|
||||
poll_interval=10.0,
|
||||
max_poll_attempts=480,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
|
||||
|
||||
@ -920,6 +924,7 @@ class MagnificImageSkinEnhancerNode(IO.ComfyNode):
|
||||
response_model=TaskResponse,
|
||||
status_extractor=lambda x: x.status,
|
||||
poll_interval=10.0,
|
||||
max_poll_attempts=480,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_image_tensor(final_response.generated[0]))
|
||||
|
||||
|
||||
534
comfy_api_nodes/nodes_moonvalley.py
Normal file
534
comfy_api_nodes/nodes_moonvalley.py
Normal file
@ -0,0 +1,534 @@
|
||||
import logging
|
||||
|
||||
from typing_extensions import override
|
||||
|
||||
from comfy_api.latest import IO, ComfyExtension, Input
|
||||
from comfy_api_nodes.apis.moonvalley import (
|
||||
MoonvalleyPromptResponse,
|
||||
MoonvalleyTextToVideoInferenceParams,
|
||||
MoonvalleyTextToVideoRequest,
|
||||
MoonvalleyVideoToVideoInferenceParams,
|
||||
MoonvalleyVideoToVideoRequest,
|
||||
)
|
||||
from comfy_api_nodes.util import (
|
||||
ApiEndpoint,
|
||||
download_url_to_video_output,
|
||||
poll_op,
|
||||
sync_op,
|
||||
trim_video,
|
||||
upload_images_to_comfyapi,
|
||||
upload_video_to_comfyapi,
|
||||
validate_container_format_is_mp4,
|
||||
validate_image_dimensions,
|
||||
validate_string,
|
||||
)
|
||||
|
||||
API_UPLOADS_ENDPOINT = "/proxy/moonvalley/uploads"
|
||||
API_PROMPTS_ENDPOINT = "/proxy/moonvalley/prompts"
|
||||
API_VIDEO2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/video-to-video"
|
||||
API_TXT2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/text-to-video"
|
||||
API_IMG2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/image-to-video"
|
||||
|
||||
MIN_WIDTH = 300
|
||||
MIN_HEIGHT = 300
|
||||
|
||||
MAX_WIDTH = 10000
|
||||
MAX_HEIGHT = 10000
|
||||
|
||||
MIN_VID_WIDTH = 300
|
||||
MIN_VID_HEIGHT = 300
|
||||
|
||||
MAX_VID_WIDTH = 10000
|
||||
MAX_VID_HEIGHT = 10000
|
||||
|
||||
MAX_VIDEO_SIZE = 1024 * 1024 * 1024 # 1 GB max for in-memory video processing
|
||||
|
||||
MOONVALLEY_MAREY_MAX_PROMPT_LENGTH = 5000
|
||||
|
||||
|
||||
def is_valid_task_creation_response(response: MoonvalleyPromptResponse) -> bool:
|
||||
"""Verifies that the initial response contains a task ID."""
|
||||
return bool(response.id)
|
||||
|
||||
|
||||
def validate_task_creation_response(response) -> None:
|
||||
if not is_valid_task_creation_response(response):
|
||||
error_msg = f"Moonvalley Marey API: Initial request failed. Code: {response.code}, Message: {response.message}, Data: {response}"
|
||||
logging.error(error_msg)
|
||||
raise RuntimeError(error_msg)
|
||||
|
||||
|
||||
def validate_video_to_video_input(video: Input.Video) -> Input.Video:
|
||||
"""
|
||||
Validates and processes video input for Moonvalley Video-to-Video generation.
|
||||
|
||||
Args:
|
||||
video: Input video to validate
|
||||
|
||||
Returns:
|
||||
Validated and potentially trimmed video
|
||||
|
||||
Raises:
|
||||
ValueError: If video doesn't meet requirements
|
||||
MoonvalleyApiError: If video duration is too short
|
||||
"""
|
||||
width, height = _get_video_dimensions(video)
|
||||
_validate_video_dimensions(width, height)
|
||||
validate_container_format_is_mp4(video)
|
||||
|
||||
return _validate_and_trim_duration(video)
|
||||
|
||||
|
||||
def _get_video_dimensions(video: Input.Video) -> tuple[int, int]:
|
||||
"""Extracts video dimensions with error handling."""
|
||||
try:
|
||||
return video.get_dimensions()
|
||||
except Exception as e:
|
||||
logging.error("Error getting dimensions of video: %s", e)
|
||||
raise ValueError(f"Cannot get video dimensions: {e}") from e
|
||||
|
||||
|
||||
def _validate_video_dimensions(width: int, height: int) -> None:
|
||||
"""Validates video dimensions meet Moonvalley V2V requirements."""
|
||||
supported_resolutions = {
|
||||
(1920, 1080),
|
||||
(1080, 1920),
|
||||
(1152, 1152),
|
||||
(1536, 1152),
|
||||
(1152, 1536),
|
||||
}
|
||||
|
||||
if (width, height) not in supported_resolutions:
|
||||
supported_list = ", ".join([f"{w}x{h}" for w, h in sorted(supported_resolutions)])
|
||||
raise ValueError(f"Resolution {width}x{height} not supported. Supported: {supported_list}")
|
||||
|
||||
|
||||
def _validate_and_trim_duration(video: Input.Video) -> Input.Video:
|
||||
"""Validates video duration and trims to 5 seconds if needed."""
|
||||
duration = video.get_duration()
|
||||
_validate_minimum_duration(duration)
|
||||
return _trim_if_too_long(video, duration)
|
||||
|
||||
|
||||
def _validate_minimum_duration(duration: float) -> None:
|
||||
"""Ensures video is at least 5 seconds long."""
|
||||
if duration < 5:
|
||||
raise ValueError("Input video must be at least 5 seconds long.")
|
||||
|
||||
|
||||
def _trim_if_too_long(video: Input.Video, duration: float) -> Input.Video:
|
||||
"""Trims video to 5 seconds if longer."""
|
||||
if duration > 5:
|
||||
return trim_video(video, 5)
|
||||
return video
|
||||
|
||||
|
||||
def parse_width_height_from_res(resolution: str):
|
||||
# Accepts a string like "16:9 (1920 x 1080)" and returns width, height as a dict
|
||||
res_map = {
|
||||
"16:9 (1920 x 1080)": {"width": 1920, "height": 1080},
|
||||
"9:16 (1080 x 1920)": {"width": 1080, "height": 1920},
|
||||
"1:1 (1152 x 1152)": {"width": 1152, "height": 1152},
|
||||
"4:3 (1536 x 1152)": {"width": 1536, "height": 1152},
|
||||
"3:4 (1152 x 1536)": {"width": 1152, "height": 1536},
|
||||
# "21:9 (2560 x 1080)": {"width": 2560, "height": 1080},
|
||||
}
|
||||
return res_map.get(resolution, {"width": 1920, "height": 1080})
|
||||
|
||||
|
||||
def parse_control_parameter(value):
|
||||
control_map = {
|
||||
"Motion Transfer": "motion_control",
|
||||
"Canny": "canny_control",
|
||||
"Pose Transfer": "pose_control",
|
||||
"Depth": "depth_control",
|
||||
}
|
||||
return control_map.get(value, control_map["Motion Transfer"])
|
||||
|
||||
|
||||
async def get_response(cls: type[IO.ComfyNode], task_id: str) -> MoonvalleyPromptResponse:
|
||||
return await poll_op(
|
||||
cls,
|
||||
ApiEndpoint(path=f"{API_PROMPTS_ENDPOINT}/{task_id}"),
|
||||
response_model=MoonvalleyPromptResponse,
|
||||
status_extractor=lambda r: (r.status if r and r.status else None),
|
||||
poll_interval=16.0,
|
||||
max_poll_attempts=240,
|
||||
)
|
||||
|
||||
|
||||
class MoonvalleyImg2VideoNode(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls) -> IO.Schema:
|
||||
return IO.Schema(
|
||||
node_id="MoonvalleyImg2VideoNode",
|
||||
display_name="Moonvalley Marey Image to Video",
|
||||
category="api node/video/Moonvalley Marey",
|
||||
description="Moonvalley Marey Image to Video Node",
|
||||
inputs=[
|
||||
IO.Image.Input(
|
||||
"image",
|
||||
tooltip="The reference image used to generate the video",
|
||||
),
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
),
|
||||
IO.String.Input(
|
||||
"negative_prompt",
|
||||
multiline=True,
|
||||
default="<synthetic> <scene cut> gopro, bright, contrast, static, overexposed, vignette, "
|
||||
"artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, "
|
||||
"flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, "
|
||||
"cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, "
|
||||
"blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, "
|
||||
"wobbly, weird, low quality, plastic, stock footage, video camera, boring",
|
||||
tooltip="Negative prompt text",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"resolution",
|
||||
options=[
|
||||
"16:9 (1920 x 1080)",
|
||||
"9:16 (1080 x 1920)",
|
||||
"1:1 (1152 x 1152)",
|
||||
"4:3 (1536 x 1152)",
|
||||
"3:4 (1152 x 1536)",
|
||||
# "21:9 (2560 x 1080)",
|
||||
],
|
||||
default="16:9 (1920 x 1080)",
|
||||
tooltip="Resolution of the output video",
|
||||
),
|
||||
IO.Float.Input(
|
||||
"prompt_adherence",
|
||||
default=4.5,
|
||||
min=1.0,
|
||||
max=20.0,
|
||||
step=1.0,
|
||||
tooltip="Guidance scale for generation control",
|
||||
),
|
||||
IO.Int.Input(
|
||||
"seed",
|
||||
default=9,
|
||||
min=0,
|
||||
max=4294967295,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
tooltip="Random seed value",
|
||||
control_after_generate=True,
|
||||
),
|
||||
IO.Int.Input(
|
||||
"steps",
|
||||
default=80,
|
||||
min=75, # steps should be greater or equal to cooldown_steps(75) + warmup_steps(0)
|
||||
max=100,
|
||||
step=1,
|
||||
tooltip="Number of denoising steps",
|
||||
),
|
||||
],
|
||||
outputs=[IO.Video.Output()],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
depends_on=IO.PriceBadgeDepends(),
|
||||
expr="""{"type":"usd","usd": 1.5}""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
image: Input.Image,
|
||||
prompt: str,
|
||||
negative_prompt: str,
|
||||
resolution: str,
|
||||
prompt_adherence: float,
|
||||
seed: int,
|
||||
steps: int,
|
||||
) -> IO.NodeOutput:
|
||||
validate_image_dimensions(image, min_width=300, min_height=300, max_height=MAX_HEIGHT, max_width=MAX_WIDTH)
|
||||
validate_string(prompt, min_length=1, max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
|
||||
validate_string(negative_prompt, field_name="negative_prompt", max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
|
||||
width_height = parse_width_height_from_res(resolution)
|
||||
|
||||
inference_params = MoonvalleyTextToVideoInferenceParams(
|
||||
negative_prompt=negative_prompt,
|
||||
steps=steps,
|
||||
seed=seed,
|
||||
guidance_scale=prompt_adherence,
|
||||
width=width_height["width"],
|
||||
height=width_height["height"],
|
||||
use_negative_prompts=True,
|
||||
)
|
||||
|
||||
# Get MIME type from tensor - assuming PNG format for image tensors
|
||||
mime_type = "image/png"
|
||||
image_url = (await upload_images_to_comfyapi(cls, image, max_images=1, mime_type=mime_type))[0]
|
||||
task_creation_response = await sync_op(
|
||||
cls,
|
||||
endpoint=ApiEndpoint(path=API_IMG2VIDEO_ENDPOINT, method="POST"),
|
||||
response_model=MoonvalleyPromptResponse,
|
||||
data=MoonvalleyTextToVideoRequest(
|
||||
image_url=image_url, prompt_text=prompt, inference_params=inference_params
|
||||
),
|
||||
)
|
||||
validate_task_creation_response(task_creation_response)
|
||||
final_response = await get_response(cls, task_creation_response.id)
|
||||
video = await download_url_to_video_output(final_response.output_url)
|
||||
return IO.NodeOutput(video)
|
||||
|
||||
|
||||
class MoonvalleyVideo2VideoNode(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls) -> IO.Schema:
|
||||
return IO.Schema(
|
||||
node_id="MoonvalleyVideo2VideoNode",
|
||||
display_name="Moonvalley Marey Video to Video",
|
||||
category="api node/video/Moonvalley Marey",
|
||||
description="",
|
||||
inputs=[
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
tooltip="Describes the video to generate",
|
||||
),
|
||||
IO.String.Input(
|
||||
"negative_prompt",
|
||||
multiline=True,
|
||||
default="<synthetic> <scene cut> gopro, bright, contrast, static, overexposed, vignette, "
|
||||
"artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, "
|
||||
"flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, "
|
||||
"cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, "
|
||||
"blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, "
|
||||
"wobbly, weird, low quality, plastic, stock footage, video camera, boring",
|
||||
tooltip="Negative prompt text",
|
||||
),
|
||||
IO.Int.Input(
|
||||
"seed",
|
||||
default=9,
|
||||
min=0,
|
||||
max=4294967295,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
tooltip="Random seed value",
|
||||
control_after_generate=False,
|
||||
),
|
||||
IO.Video.Input(
|
||||
"video",
|
||||
tooltip="The reference video used to generate the output video. Must be at least 5 seconds long. "
|
||||
"Videos longer than 5s will be automatically trimmed. Only MP4 format supported.",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"control_type",
|
||||
options=["Motion Transfer", "Pose Transfer"],
|
||||
default="Motion Transfer",
|
||||
optional=True,
|
||||
),
|
||||
IO.Int.Input(
|
||||
"motion_intensity",
|
||||
default=100,
|
||||
min=0,
|
||||
max=100,
|
||||
step=1,
|
||||
tooltip="Only used if control_type is 'Motion Transfer'",
|
||||
optional=True,
|
||||
),
|
||||
IO.Int.Input(
|
||||
"steps",
|
||||
default=60,
|
||||
min=60, # steps should be greater or equal to cooldown_steps(36) + warmup_steps(24)
|
||||
max=100,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
tooltip="Number of inference steps",
|
||||
),
|
||||
],
|
||||
outputs=[IO.Video.Output()],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
depends_on=IO.PriceBadgeDepends(),
|
||||
expr="""{"type":"usd","usd": 2.25}""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
prompt: str,
|
||||
negative_prompt: str,
|
||||
seed: int,
|
||||
video: Input.Video | None = None,
|
||||
control_type: str = "Motion Transfer",
|
||||
motion_intensity: int | None = 100,
|
||||
steps=60,
|
||||
prompt_adherence=4.5,
|
||||
) -> IO.NodeOutput:
|
||||
validated_video = validate_video_to_video_input(video)
|
||||
video_url = await upload_video_to_comfyapi(cls, validated_video)
|
||||
validate_string(prompt, min_length=1, max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
|
||||
validate_string(negative_prompt, field_name="negative_prompt", max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
|
||||
|
||||
# Only include motion_intensity for Motion Transfer
|
||||
control_params = {}
|
||||
if control_type == "Motion Transfer" and motion_intensity is not None:
|
||||
control_params["motion_intensity"] = motion_intensity
|
||||
|
||||
inference_params = MoonvalleyVideoToVideoInferenceParams(
|
||||
negative_prompt=negative_prompt,
|
||||
seed=seed,
|
||||
control_params=control_params,
|
||||
steps=steps,
|
||||
guidance_scale=prompt_adherence,
|
||||
)
|
||||
|
||||
task_creation_response = await sync_op(
|
||||
cls,
|
||||
endpoint=ApiEndpoint(path=API_VIDEO2VIDEO_ENDPOINT, method="POST"),
|
||||
response_model=MoonvalleyPromptResponse,
|
||||
data=MoonvalleyVideoToVideoRequest(
|
||||
control_type=parse_control_parameter(control_type),
|
||||
video_url=video_url,
|
||||
prompt_text=prompt,
|
||||
inference_params=inference_params,
|
||||
),
|
||||
)
|
||||
validate_task_creation_response(task_creation_response)
|
||||
final_response = await get_response(cls, task_creation_response.id)
|
||||
return IO.NodeOutput(await download_url_to_video_output(final_response.output_url))
|
||||
|
||||
|
||||
class MoonvalleyTxt2VideoNode(IO.ComfyNode):
|
||||
|
||||
@classmethod
|
||||
def define_schema(cls) -> IO.Schema:
|
||||
return IO.Schema(
|
||||
node_id="MoonvalleyTxt2VideoNode",
|
||||
display_name="Moonvalley Marey Text to Video",
|
||||
category="api node/video/Moonvalley Marey",
|
||||
description="",
|
||||
inputs=[
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
),
|
||||
IO.String.Input(
|
||||
"negative_prompt",
|
||||
multiline=True,
|
||||
default="<synthetic> <scene cut> gopro, bright, contrast, static, overexposed, vignette, "
|
||||
"artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, "
|
||||
"flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, "
|
||||
"cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, "
|
||||
"blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, "
|
||||
"wobbly, weird, low quality, plastic, stock footage, video camera, boring",
|
||||
tooltip="Negative prompt text",
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"resolution",
|
||||
options=[
|
||||
"16:9 (1920 x 1080)",
|
||||
"9:16 (1080 x 1920)",
|
||||
"1:1 (1152 x 1152)",
|
||||
"4:3 (1536 x 1152)",
|
||||
"3:4 (1152 x 1536)",
|
||||
"21:9 (2560 x 1080)",
|
||||
],
|
||||
default="16:9 (1920 x 1080)",
|
||||
tooltip="Resolution of the output video",
|
||||
),
|
||||
IO.Float.Input(
|
||||
"prompt_adherence",
|
||||
default=4.0,
|
||||
min=1.0,
|
||||
max=20.0,
|
||||
step=1.0,
|
||||
tooltip="Guidance scale for generation control",
|
||||
),
|
||||
IO.Int.Input(
|
||||
"seed",
|
||||
default=9,
|
||||
min=0,
|
||||
max=4294967295,
|
||||
step=1,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
control_after_generate=True,
|
||||
tooltip="Random seed value",
|
||||
),
|
||||
IO.Int.Input(
|
||||
"steps",
|
||||
default=80,
|
||||
min=75, # steps should be greater or equal to cooldown_steps(75) + warmup_steps(0)
|
||||
max=100,
|
||||
step=1,
|
||||
tooltip="Inference steps",
|
||||
),
|
||||
],
|
||||
outputs=[IO.Video.Output()],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
depends_on=IO.PriceBadgeDepends(),
|
||||
expr="""{"type":"usd","usd": 1.5}""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
prompt: str,
|
||||
negative_prompt: str,
|
||||
resolution: str,
|
||||
prompt_adherence: float,
|
||||
seed: int,
|
||||
steps: int,
|
||||
) -> IO.NodeOutput:
|
||||
validate_string(prompt, min_length=1, max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
|
||||
validate_string(negative_prompt, field_name="negative_prompt", max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
|
||||
width_height = parse_width_height_from_res(resolution)
|
||||
|
||||
inference_params = MoonvalleyTextToVideoInferenceParams(
|
||||
negative_prompt=negative_prompt,
|
||||
steps=steps,
|
||||
seed=seed,
|
||||
guidance_scale=prompt_adherence,
|
||||
num_frames=128,
|
||||
width=width_height["width"],
|
||||
height=width_height["height"],
|
||||
)
|
||||
|
||||
task_creation_response = await sync_op(
|
||||
cls,
|
||||
endpoint=ApiEndpoint(path=API_TXT2VIDEO_ENDPOINT, method="POST"),
|
||||
response_model=MoonvalleyPromptResponse,
|
||||
data=MoonvalleyTextToVideoRequest(prompt_text=prompt, inference_params=inference_params),
|
||||
)
|
||||
validate_task_creation_response(task_creation_response)
|
||||
final_response = await get_response(cls, task_creation_response.id)
|
||||
return IO.NodeOutput(await download_url_to_video_output(final_response.output_url))
|
||||
|
||||
|
||||
class MoonvalleyExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
|
||||
return [
|
||||
MoonvalleyImg2VideoNode,
|
||||
MoonvalleyTxt2VideoNode,
|
||||
MoonvalleyVideo2VideoNode,
|
||||
]
|
||||
|
||||
|
||||
async def comfy_entrypoint() -> MoonvalleyExtension:
|
||||
return MoonvalleyExtension()
|
||||
@ -415,9 +415,8 @@ class OpenAIGPTImage1(IO.ComfyNode):
|
||||
"1152x2048",
|
||||
"3840x2160",
|
||||
"2160x3840",
|
||||
"Custom",
|
||||
],
|
||||
tooltip="Image size. Select 'Custom' to use the custom width and height (GPT Image 2 only).",
|
||||
tooltip="Image size",
|
||||
optional=True,
|
||||
),
|
||||
IO.Int.Input(
|
||||
@ -446,24 +445,6 @@ 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,
|
||||
),
|
||||
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,
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
IO.Image.Output(),
|
||||
@ -490,9 +471,9 @@ class OpenAIGPTImage1(IO.ComfyNode):
|
||||
"high": [0.133, 0.22]
|
||||
},
|
||||
"gpt-image-2": {
|
||||
"low": [0.0048, 0.019],
|
||||
"medium": [0.041, 0.168],
|
||||
"high": [0.165, 0.67]
|
||||
"low": [0.0048, 0.012],
|
||||
"medium": [0.041, 0.112],
|
||||
"high": [0.165, 0.43]
|
||||
}
|
||||
};
|
||||
$range := $lookup($lookup($ranges, widgets.model), widgets.quality);
|
||||
@ -522,8 +503,6 @@ 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)
|
||||
@ -531,25 +510,7 @@ class OpenAIGPTImage1(IO.ComfyNode):
|
||||
if mask is not None and image is None:
|
||||
raise ValueError("Cannot use a mask without an input image")
|
||||
|
||||
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 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")
|
||||
|
||||
|
||||
@ -33,13 +33,9 @@ class OpenAIVideoSora2(IO.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="OpenAIVideoSora2",
|
||||
display_name="OpenAI Sora - Video (Deprecated)",
|
||||
display_name="OpenAI Sora - Video",
|
||||
category="api node/video/Sora",
|
||||
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."
|
||||
),
|
||||
description="OpenAI video and audio generation.",
|
||||
inputs=[
|
||||
IO.Combo.Input(
|
||||
"model",
|
||||
|
||||
@ -36,15 +36,11 @@ from comfy_api_nodes.util import (
|
||||
)
|
||||
|
||||
UPSCALER_MODELS_MAP = {
|
||||
"Astra 2": "ast-2",
|
||||
"Starlight (Astra) Fast": "slf-1",
|
||||
"Starlight (Astra) Creative": "slc-1",
|
||||
"Starlight Precise 2.5": "slp-2.5",
|
||||
}
|
||||
|
||||
AST2_MAX_FRAMES = 9000
|
||||
AST2_MAX_FRAMES_WITH_PROMPT = 450
|
||||
|
||||
|
||||
class TopazImageEnhance(IO.ComfyNode):
|
||||
@classmethod
|
||||
@ -234,20 +230,13 @@ class TopazVideoEnhance(IO.ComfyNode):
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="TopazVideoEnhance",
|
||||
display_name="Topaz Video Enhance (Legacy)",
|
||||
display_name="Topaz Video Enhance",
|
||||
category="api node/video/Topaz",
|
||||
description="Breathe new life into video with powerful upscaling and recovery technology.",
|
||||
inputs=[
|
||||
IO.Video.Input("video"),
|
||||
IO.Boolean.Input("upscaler_enabled", default=True),
|
||||
IO.Combo.Input(
|
||||
"upscaler_model",
|
||||
options=[
|
||||
"Starlight (Astra) Fast",
|
||||
"Starlight (Astra) Creative",
|
||||
"Starlight Precise 2.5",
|
||||
],
|
||||
),
|
||||
IO.Combo.Input("upscaler_model", options=list(UPSCALER_MODELS_MAP.keys())),
|
||||
IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"]),
|
||||
IO.Combo.Input(
|
||||
"upscaler_creativity",
|
||||
@ -315,7 +304,6 @@ class TopazVideoEnhance(IO.ComfyNode):
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
is_deprecated=True,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
@ -465,350 +453,7 @@ class TopazVideoEnhance(IO.ComfyNode):
|
||||
progress_extractor=lambda x: getattr(x, "progress", 0),
|
||||
price_extractor=lambda x: (x.estimates.cost[0] * 0.08 if x.estimates and x.estimates.cost[0] else None),
|
||||
poll_interval=10.0,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_video_output(final_response.download.url))
|
||||
|
||||
|
||||
class TopazVideoEnhanceV2(IO.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
node_id="TopazVideoEnhanceV2",
|
||||
display_name="Topaz Video Enhance",
|
||||
category="api node/video/Topaz",
|
||||
description="Breathe new life into video with powerful upscaling and recovery technology.",
|
||||
inputs=[
|
||||
IO.Video.Input("video"),
|
||||
IO.DynamicCombo.Input(
|
||||
"upscaler_model",
|
||||
options=[
|
||||
IO.DynamicCombo.Option(
|
||||
"Astra 2",
|
||||
[
|
||||
IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"]),
|
||||
IO.Float.Input(
|
||||
"creativity",
|
||||
default=0.5,
|
||||
min=0.0,
|
||||
max=1.0,
|
||||
step=0.1,
|
||||
display_mode=IO.NumberDisplay.slider,
|
||||
tooltip="Creative strength of the upscale.",
|
||||
),
|
||||
IO.String.Input(
|
||||
"prompt",
|
||||
multiline=True,
|
||||
default="",
|
||||
tooltip="Optional descriptive (not instructive) scene prompt."
|
||||
f"Capping input at {AST2_MAX_FRAMES_WITH_PROMPT} frames (~15s @ 30fps) when set.",
|
||||
),
|
||||
IO.Float.Input(
|
||||
"sharp",
|
||||
default=0.5,
|
||||
min=0.0,
|
||||
max=1.0,
|
||||
step=0.01,
|
||||
display_mode=IO.NumberDisplay.slider,
|
||||
tooltip="Pre-enhance sharpness: "
|
||||
"0.0=Gaussian blur, 0.5=passthrough (default), 1.0=USM sharpening.",
|
||||
advanced=True,
|
||||
),
|
||||
IO.Float.Input(
|
||||
"realism",
|
||||
default=0.0,
|
||||
min=0.0,
|
||||
max=1.0,
|
||||
step=0.01,
|
||||
display_mode=IO.NumberDisplay.slider,
|
||||
tooltip="Pulls output toward photographic realism."
|
||||
"Leave at 0 for the model default.",
|
||||
advanced=True,
|
||||
),
|
||||
],
|
||||
),
|
||||
IO.DynamicCombo.Option(
|
||||
"Starlight (Astra) Fast",
|
||||
[IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"]),],
|
||||
),
|
||||
IO.DynamicCombo.Option(
|
||||
"Starlight (Astra) Creative",
|
||||
[
|
||||
IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"]),
|
||||
IO.Combo.Input(
|
||||
"creativity",
|
||||
options=["low", "middle", "high"],
|
||||
default="low",
|
||||
tooltip="Creative strength of the upscale.",
|
||||
),
|
||||
],
|
||||
),
|
||||
IO.DynamicCombo.Option(
|
||||
"Starlight Precise 2.5",
|
||||
[IO.Combo.Input("upscaler_resolution", options=["FullHD (1080p)", "4K (2160p)"])],
|
||||
),
|
||||
IO.DynamicCombo.Option("Disabled", []),
|
||||
],
|
||||
),
|
||||
IO.DynamicCombo.Input(
|
||||
"interpolation_model",
|
||||
options=[
|
||||
IO.DynamicCombo.Option("Disabled", []),
|
||||
IO.DynamicCombo.Option(
|
||||
"apo-8",
|
||||
[
|
||||
IO.Int.Input(
|
||||
"interpolation_frame_rate",
|
||||
default=60,
|
||||
min=15,
|
||||
max=240,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
tooltip="Output frame rate.",
|
||||
),
|
||||
IO.Int.Input(
|
||||
"interpolation_slowmo",
|
||||
default=1,
|
||||
min=1,
|
||||
max=16,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
tooltip="Slow-motion factor applied to the input video. "
|
||||
"For example, 2 makes the output twice as slow and doubles the duration.",
|
||||
advanced=True,
|
||||
),
|
||||
IO.Boolean.Input(
|
||||
"interpolation_duplicate",
|
||||
default=False,
|
||||
tooltip="Analyze the input for duplicate frames and remove them.",
|
||||
advanced=True,
|
||||
),
|
||||
IO.Float.Input(
|
||||
"interpolation_duplicate_threshold",
|
||||
default=0.01,
|
||||
min=0.001,
|
||||
max=0.1,
|
||||
step=0.001,
|
||||
display_mode=IO.NumberDisplay.number,
|
||||
tooltip="Detection sensitivity for duplicate frames.",
|
||||
advanced=True,
|
||||
),
|
||||
],
|
||||
),
|
||||
],
|
||||
),
|
||||
IO.Combo.Input(
|
||||
"dynamic_compression_level",
|
||||
options=["Low", "Mid", "High"],
|
||||
default="Low",
|
||||
tooltip="CQP level.",
|
||||
optional=True,
|
||||
),
|
||||
],
|
||||
outputs=[
|
||||
IO.Video.Output(),
|
||||
],
|
||||
hidden=[
|
||||
IO.Hidden.auth_token_comfy_org,
|
||||
IO.Hidden.api_key_comfy_org,
|
||||
IO.Hidden.unique_id,
|
||||
],
|
||||
is_api_node=True,
|
||||
price_badge=IO.PriceBadge(
|
||||
depends_on=IO.PriceBadgeDepends(widgets=[
|
||||
"upscaler_model",
|
||||
"upscaler_model.upscaler_resolution",
|
||||
"interpolation_model",
|
||||
]),
|
||||
expr="""
|
||||
(
|
||||
$model := $lookup(widgets, "upscaler_model");
|
||||
$res := $lookup(widgets, "upscaler_model.upscaler_resolution");
|
||||
$interp := $lookup(widgets, "interpolation_model");
|
||||
$is4k := $contains($res, "4k");
|
||||
$hasInterp := $interp != "disabled";
|
||||
$rates := {
|
||||
"starlight (astra) fast": {"hd": 0.43, "uhd": 0.85},
|
||||
"starlight precise 2.5": {"hd": 0.70, "uhd": 1.54},
|
||||
"astra 2": {"hd": 1.72, "uhd": 2.85},
|
||||
"starlight (astra) creative": {"hd": 2.25, "uhd": 3.99}
|
||||
};
|
||||
$surcharge := $is4k ? 0.28 : 0.14;
|
||||
$entry := $lookup($rates, $model);
|
||||
$base := $is4k ? $entry.uhd : $entry.hd;
|
||||
$hi := $base + ($hasInterp ? $surcharge : 0);
|
||||
$model = "disabled"
|
||||
? {"type":"text","text":"Interpolation only"}
|
||||
: ($hasInterp
|
||||
? {"type":"text","text":"~" & $string($base) & "–" & $string($hi) & " credits/src frame"}
|
||||
: {"type":"text","text":"~" & $string($base) & " credits/src frame"})
|
||||
)
|
||||
""",
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
async def execute(
|
||||
cls,
|
||||
video: Input.Video,
|
||||
upscaler_model: dict,
|
||||
interpolation_model: dict,
|
||||
dynamic_compression_level: str = "Low",
|
||||
) -> IO.NodeOutput:
|
||||
upscaler_choice = upscaler_model["upscaler_model"]
|
||||
interpolation_choice = interpolation_model["interpolation_model"]
|
||||
if upscaler_choice == "Disabled" and interpolation_choice == "Disabled":
|
||||
raise ValueError("There is nothing to do: both upscaling and interpolation are disabled.")
|
||||
validate_container_format_is_mp4(video)
|
||||
src_width, src_height = video.get_dimensions()
|
||||
src_frame_rate = int(video.get_frame_rate())
|
||||
duration_sec = video.get_duration()
|
||||
src_video_stream = video.get_stream_source()
|
||||
target_width = src_width
|
||||
target_height = src_height
|
||||
target_frame_rate = src_frame_rate
|
||||
filters = []
|
||||
if upscaler_choice != "Disabled":
|
||||
if "1080p" in upscaler_model["upscaler_resolution"]:
|
||||
target_pixel_p = 1080
|
||||
max_long_side = 1920
|
||||
else:
|
||||
target_pixel_p = 2160
|
||||
max_long_side = 3840
|
||||
ar = src_width / src_height
|
||||
if src_width >= src_height:
|
||||
# Landscape or Square; Attempt to set height to target (e.g., 2160), calculate width
|
||||
target_height = target_pixel_p
|
||||
target_width = int(target_height * ar)
|
||||
# Check if width exceeds standard bounds (for ultra-wide e.g., 21:9 ARs)
|
||||
if target_width > max_long_side:
|
||||
target_width = max_long_side
|
||||
target_height = int(target_width / ar)
|
||||
else:
|
||||
# Portrait; Attempt to set width to target (e.g., 2160), calculate height
|
||||
target_width = target_pixel_p
|
||||
target_height = int(target_width / ar)
|
||||
# Check if height exceeds standard bounds
|
||||
if target_height > max_long_side:
|
||||
target_height = max_long_side
|
||||
target_width = int(target_height * ar)
|
||||
if target_width % 2 != 0:
|
||||
target_width += 1
|
||||
if target_height % 2 != 0:
|
||||
target_height += 1
|
||||
model_id = UPSCALER_MODELS_MAP[upscaler_choice]
|
||||
if model_id == "slc-1":
|
||||
filters.append(
|
||||
VideoEnhancementFilter(
|
||||
model=model_id,
|
||||
creativity=upscaler_model["creativity"],
|
||||
isOptimizedMode=True,
|
||||
)
|
||||
)
|
||||
elif model_id == "ast-2":
|
||||
n_frames = video.get_frame_count()
|
||||
ast2_prompt = (upscaler_model["prompt"] or "").strip()
|
||||
if ast2_prompt and n_frames > AST2_MAX_FRAMES_WITH_PROMPT:
|
||||
raise ValueError(
|
||||
f"Astra 2 with a prompt is limited to {AST2_MAX_FRAMES_WITH_PROMPT} input frames "
|
||||
f"(~15s @ 30fps); video has {n_frames}. Clear the prompt or shorten the clip."
|
||||
)
|
||||
if n_frames > AST2_MAX_FRAMES:
|
||||
raise ValueError(f"Astra 2 is limited to {AST2_MAX_FRAMES} input frames; video has {n_frames}.")
|
||||
realism = upscaler_model["realism"]
|
||||
filters.append(
|
||||
VideoEnhancementFilter(
|
||||
model=model_id,
|
||||
creativity=upscaler_model["creativity"],
|
||||
prompt=(ast2_prompt or None),
|
||||
sharp=upscaler_model["sharp"],
|
||||
realism=(realism if realism > 0 else None),
|
||||
)
|
||||
)
|
||||
else:
|
||||
filters.append(VideoEnhancementFilter(model=model_id))
|
||||
if interpolation_choice != "Disabled":
|
||||
target_frame_rate = interpolation_model["interpolation_frame_rate"]
|
||||
filters.append(
|
||||
VideoFrameInterpolationFilter(
|
||||
model=interpolation_choice,
|
||||
slowmo=interpolation_model["interpolation_slowmo"],
|
||||
fps=interpolation_model["interpolation_frame_rate"],
|
||||
duplicate=interpolation_model["interpolation_duplicate"],
|
||||
duplicate_threshold=interpolation_model["interpolation_duplicate_threshold"],
|
||||
),
|
||||
)
|
||||
initial_res = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(path="/proxy/topaz/video/", method="POST"),
|
||||
response_model=CreateVideoResponse,
|
||||
data=CreateVideoRequest(
|
||||
source=CreateVideoRequestSource(
|
||||
container="mp4",
|
||||
size=get_fs_object_size(src_video_stream),
|
||||
duration=int(duration_sec),
|
||||
frameCount=video.get_frame_count(),
|
||||
frameRate=src_frame_rate,
|
||||
resolution=Resolution(width=src_width, height=src_height),
|
||||
),
|
||||
filters=filters,
|
||||
output=OutputInformationVideo(
|
||||
resolution=Resolution(width=target_width, height=target_height),
|
||||
frameRate=target_frame_rate,
|
||||
audioCodec="AAC",
|
||||
audioTransfer="Copy",
|
||||
dynamicCompressionLevel=dynamic_compression_level,
|
||||
),
|
||||
),
|
||||
wait_label="Creating task",
|
||||
final_label_on_success="Task created",
|
||||
)
|
||||
upload_res = await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(
|
||||
path=f"/proxy/topaz/video/{initial_res.requestId}/accept",
|
||||
method="PATCH",
|
||||
),
|
||||
response_model=VideoAcceptResponse,
|
||||
wait_label="Preparing upload",
|
||||
final_label_on_success="Upload started",
|
||||
)
|
||||
if len(upload_res.urls) > 1:
|
||||
raise NotImplementedError(
|
||||
"Large files are not currently supported. Please open an issue in the ComfyUI repository."
|
||||
)
|
||||
async with aiohttp.ClientSession(headers={"Content-Type": "video/mp4"}) as session:
|
||||
if isinstance(src_video_stream, BytesIO):
|
||||
src_video_stream.seek(0)
|
||||
async with session.put(upload_res.urls[0], data=src_video_stream, raise_for_status=True) as res:
|
||||
upload_etag = res.headers["Etag"]
|
||||
else:
|
||||
with builtins.open(src_video_stream, "rb") as video_file:
|
||||
async with session.put(upload_res.urls[0], data=video_file, raise_for_status=True) as res:
|
||||
upload_etag = res.headers["Etag"]
|
||||
await sync_op(
|
||||
cls,
|
||||
ApiEndpoint(
|
||||
path=f"/proxy/topaz/video/{initial_res.requestId}/complete-upload",
|
||||
method="PATCH",
|
||||
),
|
||||
response_model=VideoCompleteUploadResponse,
|
||||
data=VideoCompleteUploadRequest(
|
||||
uploadResults=[
|
||||
VideoCompleteUploadRequestPart(
|
||||
partNum=1,
|
||||
eTag=upload_etag,
|
||||
),
|
||||
],
|
||||
),
|
||||
wait_label="Finalizing upload",
|
||||
final_label_on_success="Upload completed",
|
||||
)
|
||||
final_response = await poll_op(
|
||||
cls,
|
||||
ApiEndpoint(path=f"/proxy/topaz/video/{initial_res.requestId}/status"),
|
||||
response_model=VideoStatusResponse,
|
||||
status_extractor=lambda x: x.status,
|
||||
progress_extractor=lambda x: getattr(x, "progress", 0),
|
||||
price_extractor=lambda x: (x.estimates.cost[0] * 0.08 if x.estimates and x.estimates.cost[0] else None),
|
||||
poll_interval=10.0,
|
||||
max_poll_attempts=320,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_video_output(final_response.download.url))
|
||||
|
||||
@ -819,7 +464,6 @@ class TopazExtension(ComfyExtension):
|
||||
return [
|
||||
TopazImageEnhance,
|
||||
TopazVideoEnhance,
|
||||
TopazVideoEnhanceV2,
|
||||
]
|
||||
|
||||
|
||||
|
||||
@ -38,7 +38,7 @@ async def execute_task(
|
||||
cls: type[IO.ComfyNode],
|
||||
vidu_endpoint: str,
|
||||
payload: TaskCreationRequest | TaskExtendCreationRequest | TaskMultiFrameCreationRequest,
|
||||
max_poll_attempts: int = 480,
|
||||
max_poll_attempts: int = 320,
|
||||
) -> list[TaskResult]:
|
||||
task_creation_response = await sync_op(
|
||||
cls,
|
||||
@ -1097,6 +1097,7 @@ class ViduExtendVideoNode(IO.ComfyNode):
|
||||
video_url=await upload_video_to_comfyapi(cls, video, wait_label="Uploading video"),
|
||||
images=[image_url] if image_url else None,
|
||||
),
|
||||
max_poll_attempts=480,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_video_output(results[0].url))
|
||||
|
||||
|
||||
@ -818,6 +818,7 @@ class WanReferenceVideoApi(IO.ComfyNode):
|
||||
response_model=VideoTaskStatusResponse,
|
||||
status_extractor=lambda x: x.output.task_status,
|
||||
poll_interval=6,
|
||||
max_poll_attempts=280,
|
||||
)
|
||||
return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
|
||||
|
||||
@ -1645,557 +1646,6 @@ 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]]:
|
||||
@ -2210,10 +1660,6 @@ class WanApiExtension(ComfyExtension):
|
||||
Wan2VideoContinuationApi,
|
||||
Wan2VideoEditApi,
|
||||
Wan2ReferenceVideoApi,
|
||||
HappyHorseTextToVideoApi,
|
||||
HappyHorseImageToVideoApi,
|
||||
HappyHorseVideoEditApi,
|
||||
HappyHorseReferenceVideoApi,
|
||||
]
|
||||
|
||||
|
||||
|
||||
@ -84,6 +84,7 @@ class WavespeedFlashVSRNode(IO.ComfyNode):
|
||||
response_model=TaskResultResponse,
|
||||
status_extractor=lambda x: "failed" if x.data is None else x.data.status,
|
||||
poll_interval=10.0,
|
||||
max_poll_attempts=480,
|
||||
)
|
||||
if final_response.code != 200:
|
||||
raise ValueError(
|
||||
@ -155,6 +156,7 @@ class WavespeedImageUpscaleNode(IO.ComfyNode):
|
||||
response_model=TaskResultResponse,
|
||||
status_extractor=lambda x: "failed" if x.data is None else x.data.status,
|
||||
poll_interval=10.0,
|
||||
max_poll_attempts=480,
|
||||
)
|
||||
if final_response.code != 200:
|
||||
raise ValueError(
|
||||
|
||||
@ -148,7 +148,7 @@ async def poll_op(
|
||||
queued_statuses: list[str | int] | None = None,
|
||||
data: BaseModel | None = None,
|
||||
poll_interval: float = 5.0,
|
||||
max_poll_attempts: int = 480,
|
||||
max_poll_attempts: int = 160,
|
||||
timeout_per_poll: float = 120.0,
|
||||
max_retries_per_poll: int = 10,
|
||||
retry_delay_per_poll: float = 1.0,
|
||||
@ -254,7 +254,7 @@ async def poll_op_raw(
|
||||
queued_statuses: list[str | int] | None = None,
|
||||
data: dict[str, Any] | BaseModel | None = None,
|
||||
poll_interval: float = 5.0,
|
||||
max_poll_attempts: int = 480,
|
||||
max_poll_attempts: int = 160,
|
||||
timeout_per_poll: float = 120.0,
|
||||
max_retries_per_poll: int = 10,
|
||||
retry_delay_per_poll: float = 1.0,
|
||||
|
||||
@ -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(), dtype=comfy.model_management.intermediate_dtype())
|
||||
latent = torch.zeros([batch_size, 16, height // 8, width // 8], device=comfy.model_management.intermediate_device())
|
||||
return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 8})
|
||||
|
||||
generate = execute # TODO: remove
|
||||
|
||||
@ -459,23 +459,27 @@ class SDPoseKeypointExtractor(io.ComfyNode):
|
||||
total_images = image.shape[0]
|
||||
captured_feat = None
|
||||
|
||||
model_w = int(head.heatmap_size[0]) * 4 # 192 * 4 = 768
|
||||
model_h = int(head.heatmap_size[1]) * 4 # 256 * 4 = 1024
|
||||
model_h = int(head.heatmap_size[0]) * 4 # e.g. 192 * 4 = 768
|
||||
model_w = int(head.heatmap_size[1]) * 4 # e.g. 256 * 4 = 1024
|
||||
|
||||
def _resize_to_model(imgs):
|
||||
"""Stretch BHWC images to (model_h, model_w), model expects no aspect preservation."""
|
||||
"""Aspect-preserving resize + zero-pad BHWC images to (model_h, model_w). Returns (resized_bhwc, scale, pad_top, pad_left)."""
|
||||
h, w = imgs.shape[-3], imgs.shape[-2]
|
||||
method = "area" if (model_h <= h and model_w <= w) else "bilinear"
|
||||
scale = min(model_h / h, model_w / w)
|
||||
sh, sw = int(round(h * scale)), int(round(w * scale))
|
||||
pt, pl = (model_h - sh) // 2, (model_w - sw) // 2
|
||||
chw = imgs.permute(0, 3, 1, 2).float()
|
||||
scaled = comfy.utils.common_upscale(chw, model_w, model_h, upscale_method=method, crop="disabled")
|
||||
return scaled.permute(0, 2, 3, 1), model_w / w, model_h / h
|
||||
scaled = comfy.utils.common_upscale(chw, sw, sh, upscale_method="bilinear", crop="disabled")
|
||||
padded = torch.zeros(scaled.shape[0], scaled.shape[1], model_h, model_w, dtype=scaled.dtype, device=scaled.device)
|
||||
padded[:, :, pt:pt + sh, pl:pl + sw] = scaled
|
||||
return padded.permute(0, 2, 3, 1), scale, pt, pl
|
||||
|
||||
def _remap_keypoints(kp, scale_x, scale_y, offset_x=0, offset_y=0):
|
||||
def _remap_keypoints(kp, scale, pad_top, pad_left, offset_x=0, offset_y=0):
|
||||
"""Remap keypoints from model space back to original image space."""
|
||||
kp = kp.copy() if isinstance(kp, np.ndarray) else np.array(kp, dtype=np.float32)
|
||||
invalid = kp[..., 0] < 0
|
||||
kp[..., 0] = kp[..., 0] / scale_x + offset_x
|
||||
kp[..., 1] = kp[..., 1] / scale_y + offset_y
|
||||
kp[..., 0] = (kp[..., 0] - pad_left) / scale + offset_x
|
||||
kp[..., 1] = (kp[..., 1] - pad_top) / scale + offset_y
|
||||
kp[invalid] = -1
|
||||
return kp
|
||||
|
||||
@ -525,18 +529,18 @@ class SDPoseKeypointExtractor(io.ComfyNode):
|
||||
continue
|
||||
|
||||
crop = img[:, y1:y2, x1:x2, :] # (1, crop_h, crop_w, C)
|
||||
crop_resized, sx, sy = _resize_to_model(crop)
|
||||
crop_resized, scale, pad_top, pad_left = _resize_to_model(crop)
|
||||
|
||||
latent_crop = vae.encode(crop_resized)
|
||||
kp_batch, sc_batch = _run_on_latent(latent_crop)
|
||||
kp = _remap_keypoints(kp_batch[0], sx, sy, x1, y1)
|
||||
kp = _remap_keypoints(kp_batch[0], scale, pad_top, pad_left, x1, y1)
|
||||
img_keypoints.append(kp)
|
||||
img_scores.append(sc_batch[0])
|
||||
else:
|
||||
img_resized, sx, sy = _resize_to_model(img)
|
||||
img_resized, scale, pad_top, pad_left = _resize_to_model(img)
|
||||
latent_img = vae.encode(img_resized)
|
||||
kp_batch, sc_batch = _run_on_latent(latent_img)
|
||||
img_keypoints.append(_remap_keypoints(kp_batch[0], sx, sy))
|
||||
img_keypoints.append(_remap_keypoints(kp_batch[0], scale, pad_top, pad_left))
|
||||
img_scores.append(sc_batch[0])
|
||||
|
||||
all_keypoints.append(img_keypoints)
|
||||
@ -545,12 +549,12 @@ class SDPoseKeypointExtractor(io.ComfyNode):
|
||||
|
||||
else: # full-image mode, batched
|
||||
for batch_start in tqdm(range(0, total_images, batch_size), desc="Extracting keypoints"):
|
||||
batch_resized, sx, sy = _resize_to_model(image[batch_start:batch_start + batch_size])
|
||||
batch_resized, scale, pad_top, pad_left = _resize_to_model(image[batch_start:batch_start + batch_size])
|
||||
latent_batch = vae.encode(batch_resized)
|
||||
kp_batch, sc_batch = _run_on_latent(latent_batch)
|
||||
|
||||
for kp, sc in zip(kp_batch, sc_batch):
|
||||
all_keypoints.append([_remap_keypoints(kp, sx, sy)])
|
||||
all_keypoints.append([_remap_keypoints(kp, scale, pad_top, pad_left)])
|
||||
all_scores.append([sc])
|
||||
|
||||
pbar.update(len(kp_batch))
|
||||
@ -723,13 +727,13 @@ class CropByBBoxes(io.ComfyNode):
|
||||
scale = min(output_width / crop_w, output_height / crop_h)
|
||||
scaled_w = int(round(crop_w * scale))
|
||||
scaled_h = int(round(crop_h * scale))
|
||||
scaled = comfy.utils.common_upscale(crop_chw, scaled_w, scaled_h, upscale_method="area", crop="disabled")
|
||||
scaled = comfy.utils.common_upscale(crop_chw, scaled_w, scaled_h, upscale_method="bilinear", crop="disabled")
|
||||
pad_left = (output_width - scaled_w) // 2
|
||||
pad_top = (output_height - scaled_h) // 2
|
||||
resized = torch.zeros(1, num_ch, output_height, output_width, dtype=image.dtype, device=image.device)
|
||||
resized[:, :, pad_top:pad_top + scaled_h, pad_left:pad_left + scaled_w] = scaled
|
||||
else: # "stretch"
|
||||
resized = comfy.utils.common_upscale(crop_chw, output_width, output_height, upscale_method="area", crop="disabled")
|
||||
resized = comfy.utils.common_upscale(crop_chw, output_width, output_height, upscale_method="bilinear", crop="disabled")
|
||||
crops.append(resized)
|
||||
|
||||
if not crops:
|
||||
|
||||
@ -1,3 +1,3 @@
|
||||
# This file is automatically generated by the build process when version is
|
||||
# updated in pyproject.toml.
|
||||
__version__ = "0.20.1"
|
||||
__version__ = "0.19.3"
|
||||
|
||||
3231
openapi.yaml
3231
openapi.yaml
File diff suppressed because it is too large
Load Diff
@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "ComfyUI"
|
||||
version = "0.20.1"
|
||||
version = "0.19.3"
|
||||
readme = "README.md"
|
||||
license = { file = "LICENSE" }
|
||||
requires-python = ">=3.10"
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
comfyui-frontend-package==1.42.15
|
||||
comfyui-workflow-templates==0.9.68
|
||||
comfyui-frontend-package==1.42.14
|
||||
comfyui-workflow-templates==0.9.62
|
||||
comfyui-embedded-docs==0.4.4
|
||||
torch
|
||||
torchsde
|
||||
|
||||
Reference in New Issue
Block a user