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import re
from typing import Optional
import torch
from pydantic import BaseModel, Field
from typing_extensions import override
from comfy_api.latest import IO, ComfyExtension, Input
from comfy_api_nodes.util import (
ApiEndpoint,
audio_to_base64_string,
download_url_to_image_tensor,
download_url_to_video_output,
get_number_of_images,
poll_op,
sync_op,
tensor_to_base64_string,
validate_audio_duration,
)
class Text2ImageInputField(BaseModel):
prompt: str = Field(...)
negative_prompt: Optional[str] = Field(None)
class Image2ImageInputField(BaseModel):
prompt: str = Field(...)
negative_prompt: Optional[str] = Field(None)
images: list[str] = Field(..., min_length=1, max_length=2)
class Text2VideoInputField(BaseModel):
prompt: str = Field(...)
negative_prompt: Optional[str] = Field(None)
audio_url: Optional[str] = Field(None)
class Image2VideoInputField(BaseModel):
prompt: str = Field(...)
negative_prompt: Optional[str] = Field(None)
img_url: str = Field(...)
audio_url: Optional[str] = Field(None)
class Txt2ImageParametersField(BaseModel):
size: str = Field(...)
n: int = Field(1, description="Number of images to generate.") # we support only value=1
seed: int = Field(..., ge=0, le=2147483647)
prompt_extend: bool = Field(True)
watermark: bool = Field(True)
class Image2ImageParametersField(BaseModel):
size: Optional[str] = Field(None)
n: int = Field(1, description="Number of images to generate.") # we support only value=1
seed: int = Field(..., ge=0, le=2147483647)
watermark: bool = Field(True)
class Text2VideoParametersField(BaseModel):
size: str = Field(...)
seed: int = Field(..., ge=0, le=2147483647)
duration: int = Field(5, ge=5, le=10)
prompt_extend: bool = Field(True)
watermark: bool = Field(True)
audio: bool = Field(False, description="Should be audio generated automatically")
class Image2VideoParametersField(BaseModel):
resolution: str = Field(...)
seed: int = Field(..., ge=0, le=2147483647)
duration: int = Field(5, ge=5, le=10)
prompt_extend: bool = Field(True)
watermark: bool = Field(True)
audio: bool = Field(False, description="Should be audio generated automatically")
class Text2ImageTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Text2ImageInputField = Field(...)
parameters: Txt2ImageParametersField = Field(...)
class Image2ImageTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Image2ImageInputField = Field(...)
parameters: Image2ImageParametersField = Field(...)
class Text2VideoTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Text2VideoInputField = Field(...)
parameters: Text2VideoParametersField = Field(...)
class Image2VideoTaskCreationRequest(BaseModel):
model: str = Field(...)
input: Image2VideoInputField = Field(...)
parameters: Image2VideoParametersField = Field(...)
class TaskCreationOutputField(BaseModel):
task_id: str = Field(...)
task_status: str = Field(...)
class TaskCreationResponse(BaseModel):
output: Optional[TaskCreationOutputField] = Field(None)
request_id: str = Field(...)
code: Optional[str] = Field(None, description="The error code of the failed request.")
message: Optional[str] = Field(None, description="Details of the failed request.")
class TaskResult(BaseModel):
url: Optional[str] = Field(None)
code: Optional[str] = Field(None)
message: Optional[str] = Field(None)
class ImageTaskStatusOutputField(TaskCreationOutputField):
task_id: str = Field(...)
task_status: str = Field(...)
results: Optional[list[TaskResult]] = Field(None)
class VideoTaskStatusOutputField(TaskCreationOutputField):
task_id: str = Field(...)
task_status: str = Field(...)
video_url: Optional[str] = Field(None)
code: Optional[str] = Field(None)
message: Optional[str] = Field(None)
class ImageTaskStatusResponse(BaseModel):
output: Optional[ImageTaskStatusOutputField] = Field(None)
request_id: str = Field(...)
class VideoTaskStatusResponse(BaseModel):
output: Optional[VideoTaskStatusOutputField] = Field(None)
request_id: str = Field(...)
RES_IN_PARENS = re.compile(r"\((\d+)\s*[x×]\s*(\d+)\)")
class WanTextToImageApi(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="WanTextToImageApi",
display_name="Wan Text to Image",
category="api node/image/Wan",
description="Generates image based on text prompt.",
inputs=[
IO.Combo.Input(
"model",
options=["wan2.5-t2i-preview"],
default="wan2.5-t2i-preview",
tooltip="Model to use.",
),
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt used to describe the elements and visual features, supports English/Chinese.",
),
IO.String.Input(
"negative_prompt",
multiline=True,
default="",
tooltip="Negative text prompt to guide what to avoid.",
optional=True,
),
IO.Int.Input(
"width",
default=1024,
min=768,
max=1440,
step=32,
optional=True,
),
IO.Int.Input(
"height",
default=1024,
min=768,
max=1440,
step=32,
optional=True,
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
step=1,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed to use for generation.",
optional=True,
),
IO.Boolean.Input(
"prompt_extend",
default=True,
tooltip="Whether to enhance the prompt with AI assistance.",
optional=True,
),
IO.Boolean.Input(
"watermark",
default=True,
tooltip='Whether to add an "AI generated" watermark to the result.',
optional=True,
),
],
outputs=[
IO.Image.Output(),
],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
)
@classmethod
async def execute(
cls,
model: str,
prompt: str,
negative_prompt: str = "",
width: int = 1024,
height: int = 1024,
seed: int = 0,
prompt_extend: bool = True,
watermark: bool = True,
):
initial_response = await sync_op(
cls,
ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/text2image/image-synthesis", method="POST"),
response_model=TaskCreationResponse,
data=Text2ImageTaskCreationRequest(
model=model,
input=Text2ImageInputField(prompt=prompt, negative_prompt=negative_prompt),
parameters=Txt2ImageParametersField(
size=f"{width}*{height}",
seed=seed,
prompt_extend=prompt_extend,
watermark=watermark,
),
),
)
if not initial_response.output:
raise Exception(f"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=ImageTaskStatusResponse,
status_extractor=lambda x: x.output.task_status,
estimated_duration=9,
poll_interval=3,
)
return IO.NodeOutput(await download_url_to_image_tensor(str(response.output.results[0].url)))
class WanImageToImageApi(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="WanImageToImageApi",
display_name="Wan Image to Image",
category="api node/image/Wan",
description="Generates an image from one or two input images and a text prompt. "
"The output image is currently fixed at 1.6 MP; its aspect ratio matches the input image(s).",
inputs=[
IO.Combo.Input(
"model",
options=["wan2.5-i2i-preview"],
default="wan2.5-i2i-preview",
tooltip="Model to use.",
),
IO.Image.Input(
"image",
tooltip="Single-image editing or multi-image fusion, maximum 2 images.",
),
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt used to describe the elements and visual features, supports English/Chinese.",
),
IO.String.Input(
"negative_prompt",
multiline=True,
default="",
tooltip="Negative text prompt to guide what to avoid.",
optional=True,
),
# redo this later as an optional combo of recommended resolutions
# IO.Int.Input(
# "width",
# default=1280,
# min=384,
# max=1440,
# step=16,
# optional=True,
# ),
# IO.Int.Input(
# "height",
# default=1280,
# min=384,
# max=1440,
# step=16,
# optional=True,
# ),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
step=1,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed to use for generation.",
optional=True,
),
IO.Boolean.Input(
"watermark",
default=True,
tooltip='Whether to add an "AI generated" watermark to the result.',
optional=True,
),
],
outputs=[
IO.Image.Output(),
],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
)
@classmethod
async def execute(
cls,
model: str,
image: torch.Tensor,
prompt: str,
negative_prompt: str = "",
# width: int = 1024,
# height: int = 1024,
seed: int = 0,
watermark: bool = True,
):
n_images = get_number_of_images(image)
if n_images not in (1, 2):
raise ValueError(f"Expected 1 or 2 input images, got {n_images}.")
images = []
for i in image:
images.append("data:image/png;base64," + tensor_to_base64_string(i, total_pixels=4096 * 4096))
initial_response = await sync_op(
cls,
ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/image2image/image-synthesis", method="POST"),
response_model=TaskCreationResponse,
data=Image2ImageTaskCreationRequest(
model=model,
input=Image2ImageInputField(prompt=prompt, negative_prompt=negative_prompt, images=images),
parameters=Image2ImageParametersField(
# size=f"{width}*{height}",
seed=seed,
watermark=watermark,
),
),
)
if not initial_response.output:
raise Exception(f"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=ImageTaskStatusResponse,
status_extractor=lambda x: x.output.task_status,
estimated_duration=42,
poll_interval=4,
)
return IO.NodeOutput(await download_url_to_image_tensor(str(response.output.results[0].url)))
class WanTextToVideoApi(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="WanTextToVideoApi",
display_name="Wan Text to Video",
category="api node/video/Wan",
description="Generates video based on text prompt.",
inputs=[
IO.Combo.Input(
"model",
options=["wan2.5-t2v-preview"],
default="wan2.5-t2v-preview",
tooltip="Model to use.",
),
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt used to describe the elements and visual features, supports English/Chinese.",
),
IO.String.Input(
"negative_prompt",
multiline=True,
default="",
tooltip="Negative text prompt to guide what to avoid.",
optional=True,
),
IO.Combo.Input(
"size",
options=[
"480p: 1:1 (624x624)",
"480p: 16:9 (832x480)",
"480p: 9:16 (480x832)",
"720p: 1:1 (960x960)",
"720p: 16:9 (1280x720)",
"720p: 9:16 (720x1280)",
"720p: 4:3 (1088x832)",
"720p: 3:4 (832x1088)",
"1080p: 1:1 (1440x1440)",
"1080p: 16:9 (1920x1080)",
"1080p: 9:16 (1080x1920)",
"1080p: 4:3 (1632x1248)",
"1080p: 3:4 (1248x1632)",
],
default="480p: 1:1 (624x624)",
optional=True,
),
IO.Int.Input(
"duration",
default=5,
min=5,
max=10,
step=5,
display_mode=IO.NumberDisplay.number,
tooltip="Available durations: 5 and 10 seconds",
optional=True,
),
IO.Audio.Input(
"audio",
optional=True,
tooltip="Audio must contain a clear, loud voice, without extraneous noise, background music.",
),
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.",
optional=True,
),
IO.Boolean.Input(
"generate_audio",
default=False,
optional=True,
tooltip="If there is no audio input, generate audio automatically.",
),
IO.Boolean.Input(
"prompt_extend",
default=True,
tooltip="Whether to enhance the prompt with AI assistance.",
optional=True,
),
IO.Boolean.Input(
"watermark",
default=True,
tooltip='Whether to add an "AI generated" watermark to the result.',
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,
)
@classmethod
async def execute(
cls,
model: str,
prompt: str,
negative_prompt: str = "",
size: str = "480p: 1:1 (624x624)",
duration: int = 5,
audio: Optional[Input.Audio] = None,
seed: int = 0,
generate_audio: bool = False,
prompt_extend: bool = True,
watermark: bool = True,
):
width, height = RES_IN_PARENS.search(size).groups()
audio_url = None
if audio is not None:
validate_audio_duration(audio, 3.0, 29.0)
audio_url = "data:audio/mp3;base64," + audio_to_base64_string(audio, "mp3", "libmp3lame")
initial_response = await sync_op(
cls,
ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", method="POST"),
response_model=TaskCreationResponse,
data=Text2VideoTaskCreationRequest(
model=model,
input=Text2VideoInputField(prompt=prompt, negative_prompt=negative_prompt, audio_url=audio_url),
parameters=Text2VideoParametersField(
size=f"{width}*{height}",
duration=duration,
seed=seed,
audio=generate_audio,
prompt_extend=prompt_extend,
watermark=watermark,
),
),
)
if not initial_response.output:
raise Exception(f"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,
estimated_duration=120 * int(duration / 5),
poll_interval=6,
)
return IO.NodeOutput(await download_url_to_video_output(response.output.video_url))
class WanImageToVideoApi(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="WanImageToVideoApi",
display_name="Wan Image to Video",
category="api node/video/Wan",
description="Generates video based on the first frame and text prompt.",
inputs=[
IO.Combo.Input(
"model",
options=["wan2.5-i2v-preview"],
default="wan2.5-i2v-preview",
tooltip="Model to use.",
),
IO.Image.Input(
"image",
),
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Prompt used to describe the elements and visual features, supports English/Chinese.",
),
IO.String.Input(
"negative_prompt",
multiline=True,
default="",
tooltip="Negative text prompt to guide what to avoid.",
optional=True,
),
IO.Combo.Input(
"resolution",
options=[
"480P",
"720P",
"1080P",
],
default="480P",
optional=True,
),
IO.Int.Input(
"duration",
default=5,
min=5,
max=10,
step=5,
display_mode=IO.NumberDisplay.number,
tooltip="Available durations: 5 and 10 seconds",
optional=True,
),
IO.Audio.Input(
"audio",
optional=True,
tooltip="Audio must contain a clear, loud voice, without extraneous noise, background music.",
),
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.",
optional=True,
),
IO.Boolean.Input(
"generate_audio",
default=False,
optional=True,
tooltip="If there is no audio input, generate audio automatically.",
),
IO.Boolean.Input(
"prompt_extend",
default=True,
tooltip="Whether to enhance the prompt with AI assistance.",
optional=True,
),
IO.Boolean.Input(
"watermark",
default=True,
tooltip='Whether to add an "AI generated" watermark to the result.',
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,
)
@classmethod
async def execute(
cls,
model: str,
image: torch.Tensor,
prompt: str,
negative_prompt: str = "",
resolution: str = "480P",
duration: int = 5,
audio: Optional[Input.Audio] = None,
seed: int = 0,
generate_audio: bool = False,
prompt_extend: bool = True,
watermark: bool = True,
):
if get_number_of_images(image) != 1:
raise ValueError("Exactly one input image is required.")
image_url = "data:image/png;base64," + tensor_to_base64_string(image, total_pixels=2000 * 2000)
audio_url = None
if audio is not None:
validate_audio_duration(audio, 3.0, 29.0)
audio_url = "data:audio/mp3;base64," + audio_to_base64_string(audio, "mp3", "libmp3lame")
initial_response = await sync_op(
cls,
ApiEndpoint(path="/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis", method="POST"),
response_model=TaskCreationResponse,
data=Image2VideoTaskCreationRequest(
model=model,
input=Image2VideoInputField(
prompt=prompt, negative_prompt=negative_prompt, img_url=image_url, audio_url=audio_url
),
parameters=Image2VideoParametersField(
resolution=resolution,
duration=duration,
seed=seed,
audio=generate_audio,
prompt_extend=prompt_extend,
watermark=watermark,
),
),
)
if not initial_response.output:
raise Exception(f"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,
estimated_duration=120 * int(duration / 5),
poll_interval=6,
)
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]]:
return [
WanTextToImageApi,
WanImageToImageApi,
WanTextToVideoApi,
WanImageToVideoApi,
]
async def comfy_entrypoint() -> WanApiExtension:
return WanApiExtension()
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