mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-08-16 04:43:30 +08:00
converted API nodes to async
This commit is contained in:
bigcat88
parent
97b8a2c26a
commit
99363dc394
@ -1,4 +1,5 @@
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from __future__ import annotations
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import aiohttp
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import io
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import logging
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import mimetypes
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@ -30,7 +31,7 @@ from io import BytesIO
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import av
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def download_url_to_video_output(video_url: str, timeout: int = None) -> VideoFromFile:
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async def download_url_to_video_output(video_url: str, timeout: int = None) -> VideoFromFile:
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"""Downloads a video from a URL and returns a `VIDEO` output.
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Args:
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@ -39,7 +40,7 @@ def download_url_to_video_output(video_url: str, timeout: int = None) -> VideoFr
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Returns:
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A Comfy node `VIDEO` output.
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"""
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video_io = download_url_to_bytesio(video_url, timeout)
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video_io = await download_url_to_bytesio(video_url, timeout)
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if video_io is None:
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error_msg = f"Failed to download video from {video_url}"
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logging.error(error_msg)
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@ -62,7 +63,7 @@ def downscale_image_tensor(image, total_pixels=1536 * 1024) -> torch.Tensor:
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return s
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def validate_and_cast_response(
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async def validate_and_cast_response(
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response, timeout: int = None, node_id: Union[str, None] = None
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) -> torch.Tensor:
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"""Validates and casts a response to a torch.Tensor.
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@ -86,35 +87,24 @@ def validate_and_cast_response(
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image_tensors: list[torch.Tensor] = []
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# Process each image in the data array
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for image_data in data:
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image_url = image_data.url
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b64_data = image_data.b64_json
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async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=timeout)) as session:
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for img_data in data:
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img_bytes: bytes
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if img_data.b64_json:
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img_bytes = base64.b64decode(img_data.b64_json)
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elif img_data.url:
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if node_id:
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PromptServer.instance.send_progress_text(f"Result URL: {img_data.url}", node_id)
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async with session.get(img_data.url) as resp:
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if resp.status != 200:
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raise ValueError("Failed to download generated image")
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img_bytes = await resp.read()
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else:
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raise ValueError("Invalid image payload – neither URL nor base64 data present.")
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if not image_url and not b64_data:
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raise ValueError("No image was generated in the response")
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if b64_data:
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img_data = base64.b64decode(b64_data)
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img = Image.open(io.BytesIO(img_data))
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elif image_url:
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if node_id:
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PromptServer.instance.send_progress_text(
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f"Result URL: {image_url}", node_id
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)
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img_response = requests.get(image_url, timeout=timeout)
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if img_response.status_code != 200:
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raise ValueError("Failed to download the image")
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img = Image.open(io.BytesIO(img_response.content))
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img = img.convert("RGBA")
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# Convert to numpy array, normalize to float32 between 0 and 1
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img_array = np.array(img).astype(np.float32) / 255.0
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img_tensor = torch.from_numpy(img_array)
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# Add to list of tensors
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image_tensors.append(img_tensor)
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pil_img = Image.open(BytesIO(img_bytes)).convert("RGBA")
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arr = np.asarray(pil_img).astype(np.float32) / 255.0
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image_tensors.append(torch.from_numpy(arr))
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return torch.stack(image_tensors, dim=0)
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@ -175,7 +165,7 @@ def mimetype_to_extension(mime_type: str) -> str:
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return mime_type.split("/")[-1].lower()
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def download_url_to_bytesio(url: str, timeout: int = None) -> BytesIO:
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async def download_url_to_bytesio(url: str, timeout: int = None) -> BytesIO:
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"""Downloads content from a URL using requests and returns it as BytesIO.
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Args:
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@ -185,9 +175,11 @@ def download_url_to_bytesio(url: str, timeout: int = None) -> BytesIO:
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Returns:
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BytesIO object containing the downloaded content.
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"""
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response = requests.get(url, stream=True, timeout=timeout)
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response.raise_for_status() # Raises HTTPError for bad responses (4XX or 5XX)
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return BytesIO(response.content)
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timeout_cfg = aiohttp.ClientTimeout(total=timeout) if timeout else None
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async with aiohttp.ClientSession(timeout=timeout_cfg) as session:
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async with session.get(url) as resp:
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resp.raise_for_status() # Raises HTTPError for bad responses (4XX or 5XX)
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return BytesIO(await resp.read())
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def bytesio_to_image_tensor(image_bytesio: BytesIO, mode: str = "RGBA") -> torch.Tensor:
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@ -210,9 +202,9 @@ def bytesio_to_image_tensor(image_bytesio: BytesIO, mode: str = "RGBA") -> torch
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return torch.from_numpy(image_array).unsqueeze(0)
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def download_url_to_image_tensor(url: str, timeout: int = None) -> torch.Tensor:
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async def download_url_to_image_tensor(url: str, timeout: int = None) -> torch.Tensor:
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"""Downloads an image from a URL and returns a [B, H, W, C] tensor."""
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image_bytesio = download_url_to_bytesio(url, timeout)
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image_bytesio = await download_url_to_bytesio(url, timeout)
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return bytesio_to_image_tensor(image_bytesio)
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@ -336,10 +328,10 @@ def text_filepath_to_data_uri(filepath: str) -> str:
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return f"data:{mime_type};base64,{base64_string}"
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def upload_file_to_comfyapi(
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async def upload_file_to_comfyapi(
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file_bytes_io: BytesIO,
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filename: str,
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upload_mime_type: str,
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upload_mime_type: Optional[str],
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auth_kwargs: Optional[dict[str, str]] = None,
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) -> str:
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"""
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@ -354,7 +346,10 @@ def upload_file_to_comfyapi(
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Returns:
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The download URL for the uploaded file.
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"""
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request_object = UploadRequest(file_name=filename, content_type=upload_mime_type)
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if upload_mime_type is None:
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request_object = UploadRequest(file_name=filename)
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else:
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request_object = UploadRequest(file_name=filename, content_type=upload_mime_type)
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operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path="/customers/storage",
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@ -366,12 +361,8 @@ def upload_file_to_comfyapi(
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auth_kwargs=auth_kwargs,
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)
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response: UploadResponse = operation.execute()
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upload_response = ApiClient.upload_file(
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response.upload_url, file_bytes_io, content_type=upload_mime_type
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)
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upload_response.raise_for_status()
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response: UploadResponse = await operation.execute()
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await ApiClient.upload_file(response.upload_url, file_bytes_io, content_type=upload_mime_type)
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return response.download_url
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@ -399,7 +390,7 @@ def video_to_base64_string(
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return base64.b64encode(video_bytes_io.getvalue()).decode("utf-8")
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def upload_video_to_comfyapi(
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async def upload_video_to_comfyapi(
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video: VideoInput,
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auth_kwargs: Optional[dict[str, str]] = None,
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container: VideoContainer = VideoContainer.MP4,
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@ -439,9 +430,7 @@ def upload_video_to_comfyapi(
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video.save_to(video_bytes_io, format=container, codec=codec)
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video_bytes_io.seek(0)
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return upload_file_to_comfyapi(
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video_bytes_io, filename, upload_mime_type, auth_kwargs
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)
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return await upload_file_to_comfyapi(video_bytes_io, filename, upload_mime_type, auth_kwargs)
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def audio_tensor_to_contiguous_ndarray(waveform: torch.Tensor) -> np.ndarray:
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@ -501,7 +490,7 @@ def audio_ndarray_to_bytesio(
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return audio_bytes_io
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def upload_audio_to_comfyapi(
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async def upload_audio_to_comfyapi(
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audio: AudioInput,
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auth_kwargs: Optional[dict[str, str]] = None,
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container_format: str = "mp4",
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@ -527,7 +516,7 @@ def upload_audio_to_comfyapi(
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audio_data_np, sample_rate, container_format, codec_name
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)
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return upload_file_to_comfyapi(audio_bytes_io, filename, mime_type, auth_kwargs)
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return await upload_file_to_comfyapi(audio_bytes_io, filename, mime_type, auth_kwargs)
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def audio_to_base64_string(
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@ -544,7 +533,7 @@ def audio_to_base64_string(
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return base64.b64encode(audio_bytes).decode("utf-8")
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def upload_images_to_comfyapi(
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async def upload_images_to_comfyapi(
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image: torch.Tensor,
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max_images=8,
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auth_kwargs: Optional[dict[str, str]] = None,
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@ -561,55 +550,15 @@ def upload_images_to_comfyapi(
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mime_type: Optional MIME type for the image.
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"""
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# if batch, try to upload each file if max_images is greater than 0
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idx_image = 0
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download_urls: list[str] = []
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is_batch = len(image.shape) > 3
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batch_length = 1
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if is_batch:
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batch_length = image.shape[0]
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while True:
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curr_image = image
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if len(image.shape) > 3:
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curr_image = image[idx_image]
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# get BytesIO version of image
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img_binary = tensor_to_bytesio(curr_image, mime_type=mime_type)
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# first, request upload/download urls from comfy API
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if not mime_type:
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request_object = UploadRequest(file_name=img_binary.name)
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else:
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request_object = UploadRequest(
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file_name=img_binary.name, content_type=mime_type
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)
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operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path="/customers/storage",
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method=HttpMethod.POST,
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request_model=UploadRequest,
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response_model=UploadResponse,
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),
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request=request_object,
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auth_kwargs=auth_kwargs,
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)
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response = operation.execute()
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batch_len = image.shape[0] if is_batch else 1
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upload_response = ApiClient.upload_file(
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response.upload_url, img_binary, content_type=mime_type
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)
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# verify success
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try:
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upload_response.raise_for_status()
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except requests.exceptions.HTTPError as e:
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raise ValueError(f"Could not upload one or more images: {e}") from e
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# add download_url to list
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download_urls.append(response.download_url)
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idx_image += 1
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# stop uploading additional files if done
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if is_batch and max_images > 0:
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if idx_image >= max_images:
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break
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if idx_image >= batch_length:
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break
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for idx in range(min(batch_len, max_images)):
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tensor = image[idx] if is_batch else image
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img_io = tensor_to_bytesio(tensor, mime_type=mime_type)
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url = await upload_file_to_comfyapi(img_io, img_io.name, mime_type, auth_kwargs)
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download_urls.append(url)
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return download_urls
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File diff suppressed because it is too large
Load Diff
@ -99,14 +99,14 @@ def validate_input_image(image: torch.Tensor) -> bool:
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return image.shape[2] < 8000 and image.shape[1] < 8000
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def poll_until_finished(
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async def poll_until_finished(
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auth_kwargs: dict[str, str],
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api_endpoint: ApiEndpoint[Any, TaskStatusResponse],
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estimated_duration: Optional[int] = None,
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node_id: Optional[str] = None,
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) -> TaskStatusResponse:
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"""Polls the Runway API endpoint until the task reaches a terminal state, then returns the response."""
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return PollingOperation(
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return await PollingOperation(
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poll_endpoint=api_endpoint,
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completed_statuses=[
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TaskStatus.SUCCEEDED.value,
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@ -115,7 +115,7 @@ def poll_until_finished(
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TaskStatus.FAILED.value,
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TaskStatus.CANCELLED.value,
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],
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status_extractor=lambda response: (response.status.value),
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status_extractor=lambda response: response.status.value,
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auth_kwargs=auth_kwargs,
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result_url_extractor=get_video_url_from_task_status,
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estimated_duration=estimated_duration,
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@ -167,11 +167,11 @@ class RunwayVideoGenNode(ComfyNodeABC):
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)
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return True
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def get_response(
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async def get_response(
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self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
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) -> RunwayImageToVideoResponse:
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"""Poll the task status until it is finished then get the response."""
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return poll_until_finished(
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return await poll_until_finished(
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auth_kwargs,
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ApiEndpoint(
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path=f"{PATH_GET_TASK_STATUS}/{task_id}",
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@ -183,7 +183,7 @@ class RunwayVideoGenNode(ComfyNodeABC):
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node_id=node_id,
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)
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def generate_video(
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async def generate_video(
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self,
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request: RunwayImageToVideoRequest,
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auth_kwargs: dict[str, str],
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@ -200,15 +200,15 @@ class RunwayVideoGenNode(ComfyNodeABC):
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auth_kwargs=auth_kwargs,
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)
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initial_response = initial_operation.execute()
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initial_response = await initial_operation.execute()
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self.validate_task_created(initial_response)
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task_id = initial_response.id
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final_response = self.get_response(task_id, auth_kwargs, node_id)
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final_response = await self.get_response(task_id, auth_kwargs, node_id)
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self.validate_response(final_response)
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video_url = get_video_url_from_task_status(final_response)
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return (download_url_to_video_output(video_url),)
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return (await download_url_to_video_output(video_url),)
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class RunwayImageToVideoNodeGen3a(RunwayVideoGenNode):
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@ -250,7 +250,7 @@ class RunwayImageToVideoNodeGen3a(RunwayVideoGenNode):
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},
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}
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def api_call(
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async def api_call(
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self,
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prompt: str,
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start_frame: torch.Tensor,
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@ -265,7 +265,7 @@ class RunwayImageToVideoNodeGen3a(RunwayVideoGenNode):
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validate_input_image(start_frame)
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# Upload image
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download_urls = upload_images_to_comfyapi(
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download_urls = await upload_images_to_comfyapi(
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start_frame,
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max_images=1,
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mime_type="image/png",
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@ -274,7 +274,7 @@ class RunwayImageToVideoNodeGen3a(RunwayVideoGenNode):
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if len(download_urls) != 1:
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raise RunwayApiError("Failed to upload one or more images to comfy api.")
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return self.generate_video(
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return await self.generate_video(
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RunwayImageToVideoRequest(
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promptText=prompt,
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seed=seed,
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@ -333,7 +333,7 @@ class RunwayImageToVideoNodeGen4(RunwayVideoGenNode):
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},
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}
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def api_call(
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async def api_call(
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self,
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prompt: str,
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start_frame: torch.Tensor,
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@ -348,7 +348,7 @@ class RunwayImageToVideoNodeGen4(RunwayVideoGenNode):
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validate_input_image(start_frame)
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# Upload image
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download_urls = upload_images_to_comfyapi(
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download_urls = await upload_images_to_comfyapi(
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start_frame,
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max_images=1,
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mime_type="image/png",
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@ -357,7 +357,7 @@ class RunwayImageToVideoNodeGen4(RunwayVideoGenNode):
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if len(download_urls) != 1:
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raise RunwayApiError("Failed to upload one or more images to comfy api.")
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return self.generate_video(
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return await self.generate_video(
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RunwayImageToVideoRequest(
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promptText=prompt,
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seed=seed,
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@ -382,10 +382,10 @@ class RunwayFirstLastFrameNode(RunwayVideoGenNode):
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DESCRIPTION = "Upload first and last keyframes, draft a prompt, and generate a video. More complex transitions, such as cases where the Last frame is completely different from the First frame, may benefit from the longer 10s duration. This would give the generation more time to smoothly transition between the two inputs. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3."
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def get_response(
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async def get_response(
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self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
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) -> RunwayImageToVideoResponse:
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return poll_until_finished(
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return await poll_until_finished(
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auth_kwargs,
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ApiEndpoint(
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path=f"{PATH_GET_TASK_STATUS}/{task_id}",
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@ -437,7 +437,7 @@ class RunwayFirstLastFrameNode(RunwayVideoGenNode):
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},
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}
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def api_call(
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async def api_call(
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self,
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prompt: str,
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start_frame: torch.Tensor,
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@ -455,7 +455,7 @@ class RunwayFirstLastFrameNode(RunwayVideoGenNode):
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# Upload images
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stacked_input_images = image_tensor_pair_to_batch(start_frame, end_frame)
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download_urls = upload_images_to_comfyapi(
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download_urls = await upload_images_to_comfyapi(
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stacked_input_images,
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max_images=2,
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mime_type="image/png",
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@ -464,7 +464,7 @@ class RunwayFirstLastFrameNode(RunwayVideoGenNode):
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if len(download_urls) != 2:
|
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raise RunwayApiError("Failed to upload one or more images to comfy api.")
|
||||
|
||||
return self.generate_video(
|
||||
return await self.generate_video(
|
||||
RunwayImageToVideoRequest(
|
||||
promptText=prompt,
|
||||
seed=seed,
|
||||
@ -543,11 +543,11 @@ class RunwayTextToImageNode(ComfyNodeABC):
|
||||
)
|
||||
return True
|
||||
|
||||
def get_response(
|
||||
async def get_response(
|
||||
self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
|
||||
) -> TaskStatusResponse:
|
||||
"""Poll the task status until it is finished then get the response."""
|
||||
return poll_until_finished(
|
||||
return await poll_until_finished(
|
||||
auth_kwargs,
|
||||
ApiEndpoint(
|
||||
path=f"{PATH_GET_TASK_STATUS}/{task_id}",
|
||||
@ -559,7 +559,7 @@ class RunwayTextToImageNode(ComfyNodeABC):
|
||||
node_id=node_id,
|
||||
)
|
||||
|
||||
def api_call(
|
||||
async def api_call(
|
||||
self,
|
||||
prompt: str,
|
||||
ratio: str,
|
||||
@ -574,7 +574,7 @@ class RunwayTextToImageNode(ComfyNodeABC):
|
||||
reference_images = None
|
||||
if reference_image is not None:
|
||||
validate_input_image(reference_image)
|
||||
download_urls = upload_images_to_comfyapi(
|
||||
download_urls = await upload_images_to_comfyapi(
|
||||
reference_image,
|
||||
max_images=1,
|
||||
mime_type="image/png",
|
||||
@ -605,19 +605,19 @@ class RunwayTextToImageNode(ComfyNodeABC):
|
||||
auth_kwargs=kwargs,
|
||||
)
|
||||
|
||||
initial_response = initial_operation.execute()
|
||||
initial_response = await initial_operation.execute()
|
||||
self.validate_task_created(initial_response)
|
||||
task_id = initial_response.id
|
||||
|
||||
# Poll for completion
|
||||
final_response = self.get_response(
|
||||
final_response = await self.get_response(
|
||||
task_id, auth_kwargs=kwargs, node_id=unique_id
|
||||
)
|
||||
self.validate_response(final_response)
|
||||
|
||||
# Download and return image
|
||||
image_url = get_image_url_from_task_status(final_response)
|
||||
return (download_url_to_image_tensor(image_url),)
|
||||
return (await download_url_to_image_tensor(image_url),)
|
||||
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
|
||||
@ -124,7 +124,7 @@ class StabilityStableImageUltraNode:
|
||||
},
|
||||
}
|
||||
|
||||
def api_call(self, prompt: str, aspect_ratio: str, style_preset: str, seed: int,
|
||||
async def api_call(self, prompt: str, aspect_ratio: str, style_preset: str, seed: int,
|
||||
negative_prompt: str=None, image: torch.Tensor = None, image_denoise: float=None,
|
||||
**kwargs):
|
||||
validate_string(prompt, strip_whitespace=False)
|
||||
@ -163,7 +163,7 @@ class StabilityStableImageUltraNode:
|
||||
content_type="multipart/form-data",
|
||||
auth_kwargs=kwargs,
|
||||
)
|
||||
response_api = operation.execute()
|
||||
response_api = await operation.execute()
|
||||
|
||||
if response_api.finish_reason != "SUCCESS":
|
||||
raise Exception(f"Stable Image Ultra generation failed: {response_api.finish_reason}.")
|
||||
@ -257,7 +257,7 @@ class StabilityStableImageSD_3_5Node:
|
||||
},
|
||||
}
|
||||
|
||||
def api_call(self, model: str, prompt: str, aspect_ratio: str, style_preset: str, seed: int, cfg_scale: float,
|
||||
async def api_call(self, model: str, prompt: str, aspect_ratio: str, style_preset: str, seed: int, cfg_scale: float,
|
||||
negative_prompt: str=None, image: torch.Tensor = None, image_denoise: float=None,
|
||||
**kwargs):
|
||||
validate_string(prompt, strip_whitespace=False)
|
||||
@ -302,7 +302,7 @@ class StabilityStableImageSD_3_5Node:
|
||||
content_type="multipart/form-data",
|
||||
auth_kwargs=kwargs,
|
||||
)
|
||||
response_api = operation.execute()
|
||||
response_api = await operation.execute()
|
||||
|
||||
if response_api.finish_reason != "SUCCESS":
|
||||
raise Exception(f"Stable Diffusion 3.5 Image generation failed: {response_api.finish_reason}.")
|
||||
@ -374,7 +374,7 @@ class StabilityUpscaleConservativeNode:
|
||||
},
|
||||
}
|
||||
|
||||
def api_call(self, image: torch.Tensor, prompt: str, creativity: float, seed: int, negative_prompt: str=None,
|
||||
async def api_call(self, image: torch.Tensor, prompt: str, creativity: float, seed: int, negative_prompt: str=None,
|
||||
**kwargs):
|
||||
validate_string(prompt, strip_whitespace=False)
|
||||
image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read()
|
||||
@ -403,7 +403,7 @@ class StabilityUpscaleConservativeNode:
|
||||
content_type="multipart/form-data",
|
||||
auth_kwargs=kwargs,
|
||||
)
|
||||
response_api = operation.execute()
|
||||
response_api = await operation.execute()
|
||||
|
||||
if response_api.finish_reason != "SUCCESS":
|
||||
raise Exception(f"Stability Upscale Conservative generation failed: {response_api.finish_reason}.")
|
||||
@ -480,7 +480,7 @@ class StabilityUpscaleCreativeNode:
|
||||
},
|
||||
}
|
||||
|
||||
def api_call(self, image: torch.Tensor, prompt: str, creativity: float, style_preset: str, seed: int, negative_prompt: str=None,
|
||||
async def api_call(self, image: torch.Tensor, prompt: str, creativity: float, style_preset: str, seed: int, negative_prompt: str=None,
|
||||
**kwargs):
|
||||
validate_string(prompt, strip_whitespace=False)
|
||||
image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read()
|
||||
@ -512,7 +512,7 @@ class StabilityUpscaleCreativeNode:
|
||||
content_type="multipart/form-data",
|
||||
auth_kwargs=kwargs,
|
||||
)
|
||||
response_api = operation.execute()
|
||||
response_api = await operation.execute()
|
||||
|
||||
operation = PollingOperation(
|
||||
poll_endpoint=ApiEndpoint(
|
||||
@ -527,7 +527,7 @@ class StabilityUpscaleCreativeNode:
|
||||
status_extractor=lambda x: get_async_dummy_status(x),
|
||||
auth_kwargs=kwargs,
|
||||
)
|
||||
response_poll: StabilityResultsGetResponse = operation.execute()
|
||||
response_poll: StabilityResultsGetResponse = await operation.execute()
|
||||
|
||||
if response_poll.finish_reason != "SUCCESS":
|
||||
raise Exception(f"Stability Upscale Creative generation failed: {response_poll.finish_reason}.")
|
||||
@ -563,8 +563,7 @@ class StabilityUpscaleFastNode:
|
||||
},
|
||||
}
|
||||
|
||||
def api_call(self, image: torch.Tensor,
|
||||
**kwargs):
|
||||
async def api_call(self, image: torch.Tensor, **kwargs):
|
||||
image_binary = tensor_to_bytesio(image, total_pixels=4096*4096).read()
|
||||
|
||||
files = {
|
||||
@ -583,7 +582,7 @@ class StabilityUpscaleFastNode:
|
||||
content_type="multipart/form-data",
|
||||
auth_kwargs=kwargs,
|
||||
)
|
||||
response_api = operation.execute()
|
||||
response_api = await operation.execute()
|
||||
|
||||
if response_api.finish_reason != "SUCCESS":
|
||||
raise Exception(f"Stability Upscale Fast failed: {response_api.finish_reason}.")
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user