mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-09-04 06:37:06 +08:00
Set Content-Type header when uploading files (#36)
This commit is contained in:
Christian Byrne
Robin Huang
parent
a55f29662d
commit
36a201cec7
@ -738,22 +738,28 @@ class ApiClient:
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def upload_file(
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upload_url: str,
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file: io.BytesIO | str,
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mime_type: str | None = None,
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):
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"""Upload a file to the API. Make sure the file has a filename equal to what the url expects.
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Args:
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upload_url: The URL to upload to
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file: Either a file path string, BytesIO object, or tuple of (file_path, filename)
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mime_type: The mime type of the file
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mime_type: Optional mime type to set for the upload
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"""
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headers = {}
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if mime_type:
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headers["Content-Type"] = mime_type
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if isinstance(file, io.BytesIO):
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file.seek(0) # Ensure we're at the start of the file
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data = file.read()
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return requests.put(upload_url, data=data)
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return requests.put(upload_url, data=data, headers=headers)
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elif isinstance(file, str):
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with open(file, "rb") as f:
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data = f.read()
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return requests.put(upload_url, data=data)
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return requests.put(upload_url, data=data, headers=headers)
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class ApiEndpoint(Generic[T, R]):
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"""Defines an API endpoint with its request and response types"""
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@ -51,7 +51,16 @@ from comfy_api_nodes.apis.recraft_api import (
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RecraftIO,
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get_v3_substyles,
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)
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from comfy_api_nodes.apis.client import ApiClient, ApiEndpoint, HttpMethod, SynchronousOperation, PollingOperation, EmptyRequest, UploadRequest, UploadResponse
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from comfy_api_nodes.apis.client import (
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ApiClient,
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ApiEndpoint,
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HttpMethod,
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SynchronousOperation,
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PollingOperation,
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EmptyRequest,
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UploadRequest,
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UploadResponse,
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)
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import numpy as np
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from PIL import Image
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@ -65,9 +74,10 @@ import uuid
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import folder_paths
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from io import BytesIO
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def downscale_input(image, total_pixels=1536*1024):
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samples = image.movedim(-1,1)
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#downscaling input images to roughly the same size as the outputs
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def downscale_input(image, total_pixels=1536 * 1024):
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samples = image.movedim(-1, 1)
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# downscaling input images to roughly the same size as the outputs
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total = int(total_pixels)
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scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
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if scale_by >= 1:
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@ -76,9 +86,10 @@ def downscale_input(image, total_pixels=1536*1024):
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height = round(samples.shape[2] * scale_by)
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s = common_upscale(samples, width, height, "lanczos", "disabled")
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s = s.movedim(1,-1)
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s = s.movedim(1, -1)
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return s
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def validate_and_cast_response(response):
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# validate raw JSON response
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data = response.data
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@ -117,29 +128,46 @@ def validate_and_cast_response(response):
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return torch.stack(image_tensors, dim=0)
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def validate_aspect_ratio(aspect_ratio: str, minimum_ratio: float, maximum_ratio: float, minimum_ratio_str: str, maximum_ratio_str: str):
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def validate_aspect_ratio(
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aspect_ratio: str,
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minimum_ratio: float,
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maximum_ratio: float,
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minimum_ratio_str: str,
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maximum_ratio_str: str,
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):
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# get ratio values
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numbers = aspect_ratio.split(':')
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numbers = aspect_ratio.split(":")
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if len(numbers) != 2:
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raise Exception(f"Aspect ratio must be in the format X:Y, such as 16:9, but was {aspect_ratio}.")
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raise Exception(
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f"Aspect ratio must be in the format X:Y, such as 16:9, but was {aspect_ratio}."
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)
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try:
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numerator = int(numbers[0])
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denominator = int(numbers[1])
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except ValueError:
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raise Exception(f"Aspect ratio must contain numbers separated by ':', such as 16:9, but was {aspect_ratio}.")
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calculated_ratio = numerator/denominator
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raise Exception(
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f"Aspect ratio must contain numbers separated by ':', such as 16:9, but was {aspect_ratio}."
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)
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calculated_ratio = numerator / denominator
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# if not close to minimum and maximum, check bounds
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if not math.isclose(calculated_ratio, minimum_ratio) or not math.isclose(calculated_ratio, maximum_ratio):
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if not math.isclose(calculated_ratio, minimum_ratio) or not math.isclose(
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calculated_ratio, maximum_ratio
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):
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if calculated_ratio < minimum_ratio:
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raise Exception(f"Aspect ratio cannot reduce to any less than {minimum_ratio_str} ({minimum_ratio}), but was {aspect_ratio} ({calculated_ratio}).")
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raise Exception(
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f"Aspect ratio cannot reduce to any less than {minimum_ratio_str} ({minimum_ratio}), but was {aspect_ratio} ({calculated_ratio})."
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)
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elif calculated_ratio > maximum_ratio:
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raise Exception(f"Aspect ratio cannot reduce to any greater than {maximum_ratio_str} ({maximum_ratio}), but was {aspect_ratio} ({calculated_ratio}).")
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raise Exception(
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f"Aspect ratio cannot reduce to any greater than {maximum_ratio_str} ({maximum_ratio}), but was {aspect_ratio} ({calculated_ratio})."
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)
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return aspect_ratio
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def mimetype_to_extension(mime_type: str) -> str:
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"""Converts a MIME type to a file extension."""
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return mime_type.split('/')[-1].lower()
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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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@ -153,7 +181,7 @@ def download_url_to_bytesio(url: str, timeout: int = None) -> BytesIO:
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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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response.raise_for_status() # Raises HTTPError for bad responses (4XX or 5XX)
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return BytesIO(response.content)
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@ -178,35 +206,42 @@ def bytesio_to_image_tensor(image_bytesio: BytesIO, mode: str = "RGBA") -> torch
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def process_image_response(response: requests.Response):
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'''Uses content from a Response object and converts it to a torch.Tensor'''
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"""Uses content from a Response object and converts it to a torch.Tensor"""
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return bytesio_to_image_tensor(BytesIO(response.content))
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def _tensor_to_pil(image: torch.Tensor, total_pixels: int = 2048*2048) -> Image.Image:
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def _tensor_to_pil(image: torch.Tensor, total_pixels: int = 2048 * 2048) -> Image.Image:
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"""Converts a single torch.Tensor image [H, W, C] to a PIL Image, optionally downscaling."""
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if len(image.shape) > 3:
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image = image[0]
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# TODO: remove alpha if not allowed and present
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input_tensor = image.cpu()
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input_tensor = downscale_input(input_tensor.unsqueeze(0), total_pixels=total_pixels).squeeze()
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input_tensor = downscale_input(
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input_tensor.unsqueeze(0), total_pixels=total_pixels
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).squeeze()
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image_np = (input_tensor.numpy() * 255).astype(np.uint8)
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img = Image.fromarray(image_np)
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return img
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def _pil_to_bytesio(img: Image.Image, mime_type: str = 'image/png') -> BytesIO:
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def _pil_to_bytesio(img: Image.Image, mime_type: str = "image/png") -> BytesIO:
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"""Converts a PIL Image to a BytesIO object."""
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img_byte_arr = io.BytesIO()
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# Derive PIL format from MIME type (e.g., 'image/png' -> 'PNG')
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pil_format = mime_type.split('/')[-1].upper()
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if pil_format == 'JPG':
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pil_format = 'JPEG'
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pil_format = mime_type.split("/")[-1].upper()
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if pil_format == "JPG":
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pil_format = "JPEG"
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img.save(img_byte_arr, format=pil_format)
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img_byte_arr.seek(0)
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return img_byte_arr
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def tensor_to_bytesio(image: torch.Tensor, name: Optional[str] = None, total_pixels: int = 2048*2048, mime_type: str = 'image/png') -> BytesIO:
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def tensor_to_bytesio(
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image: torch.Tensor,
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name: Optional[str] = None,
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total_pixels: int = 2048 * 2048,
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mime_type: str = "image/png",
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) -> BytesIO:
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"""Converts a torch.Tensor image to a named BytesIO object.
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Args:
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@ -220,11 +255,17 @@ def tensor_to_bytesio(image: torch.Tensor, name: Optional[str] = None, total_pix
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"""
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pil_image = _tensor_to_pil(image, total_pixels=total_pixels)
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img_binary = _pil_to_bytesio(pil_image, mime_type=mime_type)
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img_binary.name = f"{name if name else uuid.uuid4()}.{mimetype_to_extension(mime_type)}"
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img_binary.name = (
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f"{name if name else uuid.uuid4()}.{mimetype_to_extension(mime_type)}"
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)
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return img_binary
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def tensor_to_base64_string(image_tensor: torch.Tensor, total_pixels: int = 2048*2048, mime_type: str = 'image/png') -> str:
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def tensor_to_base64_string(
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image_tensor: torch.Tensor,
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total_pixels: int = 2048 * 2048,
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mime_type: str = "image/png",
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) -> str:
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"""Convert [B, H, W, C] or [H, W, C] tensor to a base64 string.
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Args:
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@ -243,7 +284,11 @@ def tensor_to_base64_string(image_tensor: torch.Tensor, total_pixels: int = 2048
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return base64_encoded_string
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def tensor_to_data_uri(image_tensor: torch.Tensor, total_pixels: int = 2048 * 2048, mime_type: str = 'image/png') -> str:
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def tensor_to_data_uri(
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image_tensor: torch.Tensor,
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total_pixels: int = 2048 * 2048,
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mime_type: str = "image/png",
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) -> str:
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"""Converts a tensor image to a Data URI string.
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Args:
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@ -258,7 +303,9 @@ def tensor_to_data_uri(image_tensor: torch.Tensor, total_pixels: int = 2048 * 20
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return f"data:{mime_type};base64,{base64_string}"
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def upload_images_to_comfyapi(image: torch.Tensor, max_images=8, auth_token=None) -> list[str]:
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def upload_images_to_comfyapi(
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image: torch.Tensor, max_images=8, auth_token=None, mime_type: str = "image/png"
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) -> list[str]:
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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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@ -271,7 +318,7 @@ def upload_images_to_comfyapi(image: torch.Tensor, max_images=8, auth_token=None
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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)
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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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operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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@ -280,13 +327,13 @@ def upload_images_to_comfyapi(image: torch.Tensor, max_images=8, auth_token=None
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request_model=UploadRequest,
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response_model=UploadResponse,
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),
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request=UploadRequest(filename=img_binary.name),
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request=UploadRequest(filename=img_binary.name, mime_type=mime_type),
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auth_token=auth_token,
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)
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response = operation.execute()
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upload_response = ApiClient.upload_file(
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response.upload_url, img_binary
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response.upload_url, img_binary, mime_type=mime_type
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)
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# verify success
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try:
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@ -1174,6 +1221,7 @@ class FluxProUltraImageNode(ComfyNodeABC):
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img.save(img_byte_arr, format="PNG")
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return base64.b64encode(img_byte_arr.getvalue()).decode()
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class LumaReferenceNode(ComfyNodeABC):
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"""
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Holds an image and weight for use with Luma Generate Image node.
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@ -1224,6 +1272,7 @@ class LumaConceptsNode(ComfyNodeABC):
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"""
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Holds one or more Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes.
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"""
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RETURN_TYPES = (LumaIO.LUMA_CONCEPTS,)
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RETURN_NAMES = ("luma_concepts",)
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DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
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@ -1234,24 +1283,35 @@ class LumaConceptsNode(ComfyNodeABC):
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def INPUT_TYPES(s):
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return {
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"required": {
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"concept1": (get_luma_concepts(include_none=True), ),
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"concept2": (get_luma_concepts(include_none=True), ),
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"concept3": (get_luma_concepts(include_none=True), ),
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"concept4": (get_luma_concepts(include_none=True), ),
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"concept1": (get_luma_concepts(include_none=True),),
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"concept2": (get_luma_concepts(include_none=True),),
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"concept3": (get_luma_concepts(include_none=True),),
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"concept4": (get_luma_concepts(include_none=True),),
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},
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"optional": {
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"luma_concepts": (LumaIO.LUMA_CONCEPTS, {
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"tooltip": "Optional Camera Concepts to add to the ones chosen here."
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}),
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}
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"luma_concepts": (
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LumaIO.LUMA_CONCEPTS,
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{
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"tooltip": "Optional Camera Concepts to add to the ones chosen here."
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},
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),
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},
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}
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def create_concepts(self, concept1: str, concept2: str, concept3: str, concept4: str, luma_concepts: LumaConceptChain=None):
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def create_concepts(
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self,
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concept1: str,
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concept2: str,
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concept3: str,
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concept4: str,
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luma_concepts: LumaConceptChain = None,
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):
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chain = LumaConceptChain(str_list=[concept1, concept2, concept3, concept4])
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if luma_concepts is not None:
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chain = luma_concepts.clone_and_merge(chain)
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return (chain,)
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class LumaImageGenerationNode(ComfyNodeABC):
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"""
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Generates images synchronously based on prompt and aspect ratio.
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@ -1421,6 +1481,7 @@ class LumaImageGenerationNode(ComfyNodeABC):
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)
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return self._convert_luma_refs(chain, max_refs=1, auth_token=auth_token)
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class LumaImageModifyNode(ComfyNodeABC):
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"""
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Modifies images synchronously based on prompt and aspect ratio.
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@ -1525,6 +1586,7 @@ class LumaImageModifyNode(ComfyNodeABC):
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img = process_image_response(img_response)
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return (img,)
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class LumaTextToVideoGenerationNode(ComfyNodeABC):
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"""
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Generates videos synchronously based on prompt and output_size.
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@ -1566,29 +1628,49 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
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},
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),
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"duration": ([dur.value for dur in LumaVideoModelOutputDuration],),
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"loop": (IO.BOOLEAN, {
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"default": False,
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}),
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"seed": (IO.INT, {
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"default": 0,
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"min": 0,
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"max": 0xFFFFFFFFFFFFFFFF,
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"control_after_generate": True,
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"tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
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}),
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"loop": (
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IO.BOOLEAN,
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{
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"default": False,
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},
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),
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"seed": (
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IO.INT,
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{
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"default": 0,
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"min": 0,
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"max": 0xFFFFFFFFFFFFFFFF,
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"control_after_generate": True,
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"tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
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},
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),
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},
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"optional": {
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"luma_concepts": (LumaIO.LUMA_CONCEPTS, {
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"tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node."
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}),
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"luma_concepts": (
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LumaIO.LUMA_CONCEPTS,
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{
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"tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node."
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},
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),
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},
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"hidden": {
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"auth_token": "AUTH_TOKEN_COMFY_ORG",
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},
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}
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def api_call(self, prompt: str, model: str, aspect_ratio: str, resolution: str, duration: str, loop: bool, seed,
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luma_concepts: LumaConceptChain=None, auth_token=None, **kwargs):
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def api_call(
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self,
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prompt: str,
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model: str,
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aspect_ratio: str,
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resolution: str,
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duration: str,
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loop: bool,
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seed,
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luma_concepts: LumaConceptChain = None,
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auth_token=None,
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**kwargs,
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):
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operation = SynchronousOperation(
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endpoint=ApiEndpoint(
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path="/proxy/luma/generations",
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@ -1603,7 +1685,7 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
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aspect_ratio=aspect_ratio,
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duration=duration,
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loop=loop,
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concepts=luma_concepts.create_api_model() if luma_concepts else None
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concepts=luma_concepts.create_api_model() if luma_concepts else None,
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),
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auth_token=auth_token,
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)
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@ -1683,24 +1765,37 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
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),
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},
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"optional": {
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"first_image": (IO.IMAGE, {
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"tooltip": "First frame of generated video."
|
||||
}),
|
||||
"last_image": (IO.IMAGE, {
|
||||
"tooltip": "Last frame of generated video."
|
||||
}),
|
||||
"luma_concepts": (LumaIO.LUMA_CONCEPTS, {
|
||||
"tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node."
|
||||
}),
|
||||
"first_image": (
|
||||
IO.IMAGE,
|
||||
{"tooltip": "First frame of generated video."},
|
||||
),
|
||||
"last_image": (IO.IMAGE, {"tooltip": "Last frame of generated video."}),
|
||||
"luma_concepts": (
|
||||
LumaIO.LUMA_CONCEPTS,
|
||||
{
|
||||
"tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node."
|
||||
},
|
||||
),
|
||||
},
|
||||
"hidden": {
|
||||
"auth_token": "AUTH_TOKEN_COMFY_ORG",
|
||||
},
|
||||
}
|
||||
|
||||
def api_call(self, prompt: str, model: str, resolution: str, duration: str, loop: bool, seed,
|
||||
first_image: torch.Tensor=None, last_image: torch.Tensor=None, luma_concepts: LumaConceptChain=None,
|
||||
auth_token=None, **kwargs):
|
||||
def api_call(
|
||||
self,
|
||||
prompt: str,
|
||||
model: str,
|
||||
resolution: str,
|
||||
duration: str,
|
||||
loop: bool,
|
||||
seed,
|
||||
first_image: torch.Tensor = None,
|
||||
last_image: torch.Tensor = None,
|
||||
luma_concepts: LumaConceptChain = None,
|
||||
auth_token=None,
|
||||
**kwargs,
|
||||
):
|
||||
if first_image is None and last_image is None:
|
||||
raise Exception(
|
||||
"At least one of first_image and last_image requires an input."
|
||||
@ -1717,12 +1812,12 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
|
||||
request=LumaGenerationRequest(
|
||||
prompt=prompt,
|
||||
model=model,
|
||||
aspect_ratio=LumaAspectRatio.ratio_16_9, # ignored, but still needed by the API for some reason
|
||||
aspect_ratio=LumaAspectRatio.ratio_16_9, # ignored, but still needed by the API for some reason
|
||||
resolution=resolution,
|
||||
duration=duration,
|
||||
loop=loop,
|
||||
keyframes=keyframes,
|
||||
concepts=luma_concepts.create_api_model() if luma_concepts else None
|
||||
concepts=luma_concepts.create_api_model() if luma_concepts else None,
|
||||
),
|
||||
auth_token=auth_token,
|
||||
)
|
||||
|
||||
@ -193,7 +193,7 @@ class RunwayImageToVideoNode(ComfyNodeABC):
|
||||
prompt_images_tensor = torch.cat(prompt_images_tensors, dim=0)
|
||||
|
||||
download_urls = upload_images_to_comfyapi(
|
||||
prompt_images_tensor, max_images=2, auth_token=auth_token
|
||||
prompt_images_tensor, max_images=2, auth_token=auth_token, mime_type="image/png"
|
||||
)
|
||||
|
||||
# Create a list of detailed image objects
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user