wangbo/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2026-05-17 20:47:31 +08:00
Code Issues Actions 7 Packages Projects Releases Wiki Activity

Merge branch 'Comfy-Org:master' into master

Browse Source
This commit is contained in:
azazeal04 2026-05-11 15:28:33 +02:00 committed by GitHub
commit 4fcd10b486
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 313 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

313
comfy_api_nodes/nodes_openai.py
View File

@ -27,6 +27,7 @@ from comfy_api_nodes.util import (
ApiEndpoint, ApiEndpoint,
download_url_to_bytesio, download_url_to_bytesio,
downscale_image_tensor, downscale_image_tensor,
get_number_of_images,
poll_op, poll_op,
sync_op, sync_op,
tensor_to_base64_string, tensor_to_base64_string,
@ -372,6 +373,7 @@ class OpenAIGPTImage1(IO.ComfyNode):
display_name="OpenAI GPT Image 2", display_name="OpenAI GPT Image 2",
category="api node/image/OpenAI", category="api node/image/OpenAI",
description="Generates images synchronously via OpenAI's GPT Image endpoint.", description="Generates images synchronously via OpenAI's GPT Image endpoint.",
is_deprecated=True,
inputs=[ inputs=[
IO.String.Input( IO.String.Input(
"prompt", "prompt",
@ -640,6 +642,316 @@ class OpenAIGPTImage1(IO.ComfyNode):
return IO.NodeOutput(await validate_and_cast_response(response)) return IO.NodeOutput(await validate_and_cast_response(response))
def _gpt_image_shared_inputs():
"""Inputs shared by all GPT Image models (quality + reference images + mask)."""
return [
IO.Combo.Input(
"quality",
default="low",
options=["low", "medium", "high"],
tooltip="Image quality, affects cost and generation time.",
),
IO.Autogrow.Input(
"images",
template=IO.Autogrow.TemplateNames(
IO.Image.Input("image"),
names=[f"image_{i}" for i in range(1, 17)],
min=0,
),
tooltip="Optional reference image(s) for image editing. Up to 16 images.",
),
IO.Mask.Input(
"mask",
optional=True,
tooltip="Optional mask for inpainting (white areas will be replaced). "
"Requires exactly one reference image.",
),
]
def _gpt_image_legacy_model_inputs():
"""Per-model widget set for legacy gpt-image-1 / gpt-image-1.5 (4 base sizes, transparent bg allowed)."""
return [
IO.Combo.Input(
"size",
default="auto",
options=["auto", "1024x1024", "1024x1536", "1536x1024"],
tooltip="Image size.",
),
IO.Combo.Input(
"background",
default="auto",
options=["auto", "opaque", "transparent"],
tooltip="Return image with or without background.",
),
*_gpt_image_shared_inputs(),
]
class OpenAIGPTImageNodeV2(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="OpenAIGPTImageNodeV2",
display_name="OpenAI GPT Image 2",
category="api node/image/OpenAI",
description="Generates images via OpenAI's GPT Image endpoint.",
inputs=[
IO.String.Input(
"prompt",
default="",
multiline=True,
tooltip="Text prompt for GPT Image",
),
IO.DynamicCombo.Input(
"model",
options=[
IO.DynamicCombo.Option(
"gpt-image-2",
[
IO.Combo.Input(
"size",
default="auto",
options=[
"auto",
"1024x1024",
"1024x1536",
"1536x1024",
"2048x2048",
"2048x1152",
"1152x2048",
"3840x2160",
"2160x3840",
"Custom",
],
tooltip="Image size. Select 'Custom' to use the custom width and height.",
),
IO.Int.Input(
"custom_width",
default=1024,
min=1024,
max=3840,
step=16,
tooltip="Used only when `size` is 'Custom'. Must be a multiple of 16.",
),
IO.Int.Input(
"custom_height",
default=1024,
min=1024,
max=3840,
step=16,
tooltip="Used only when `size` is 'Custom'. Must be a multiple of 16.",
),
IO.Combo.Input(
"background",
default="auto",
options=["auto", "opaque"],
tooltip="Return image with or without background.",
),
*_gpt_image_shared_inputs(),
],
),
IO.DynamicCombo.Option("gpt-image-1.5", _gpt_image_legacy_model_inputs()),
IO.DynamicCombo.Option("gpt-image-1", _gpt_image_legacy_model_inputs()),
],
),
IO.Int.Input(
"n",
default=1,
min=1,
max=8,
step=1,
tooltip="How many images to generate",
display_mode=IO.NumberDisplay.number,
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
step=1,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="not implemented yet in backend",
),
],
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,
price_badge=IO.PriceBadge(
depends_on=IO.PriceBadgeDepends(widgets=["model", "model.quality", "n"]),
expr="""
(
$ranges := {
"gpt-image-1": {
"low": [0.011, 0.02],
"medium": [0.042, 0.07],
"high": [0.167, 0.25]
},
"gpt-image-1.5": {
"low": [0.009, 0.02],
"medium": [0.034, 0.062],
"high": [0.133, 0.22]
},
"gpt-image-2": {
"low": [0.0048, 0.019],
"medium": [0.041, 0.168],
"high": [0.165, 0.67]
}
};
$range := $lookup($lookup($ranges, widgets.model), $lookup(widgets, "model.quality"));
$nRaw := widgets.n;
$n := ($nRaw != null and $nRaw != 0) ? $nRaw : 1;
($n = 1)
? {"type":"range_usd","min_usd": $range[0], "max_usd": $range[1], "format": {"approximate": true}}
: {
"type":"range_usd",
"min_usd": $range[0] * $n,
"max_usd": $range[1] * $n,
"format": { "suffix": "/Run", "approximate": true }
}
)
""",
),
)
@classmethod
async def execute(
cls,
prompt: str,
model: dict,
n: int,
seed: int,
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=False)
model_id = model["model"]
size = model["size"]
background = model["background"]
quality = model["quality"]
custom_width = model.get("custom_width", 1024)
custom_height = model.get("custom_height", 1024)
images_dict = model.get("images") or {}
image_tensors: list[Input.Image] = [t for t in images_dict.values() if t is not None]
n_images = sum(get_number_of_images(t) for t in image_tensors)
mask = model.get("mask")
if mask is not None and n_images == 0:
raise ValueError("Cannot use a mask without an input image")
if size == "Custom":
if custom_width % 16 != 0 or custom_height % 16 != 0:
raise ValueError(
f"Custom width and height must be multiples of 16, got {custom_width}x{custom_height}"
)
if max(custom_width, custom_height) > 3840:
raise ValueError(
f"Custom resolution max edge must be <= 3840, got {custom_width}x{custom_height}"
)
ratio = max(custom_width, custom_height) / min(custom_width, custom_height)
if ratio > 3:
raise ValueError(
f"Custom resolution aspect ratio must not exceed 3:1, got {custom_width}x{custom_height}"
)
total_pixels = custom_width * custom_height
if not 655_360 <= total_pixels <= 8_294_400:
raise ValueError(
f"Custom resolution total pixels must be between 655,360 and 8,294,400, got {total_pixels}"
)
size = f"{custom_width}x{custom_height}"
if model_id == "gpt-image-1":
price_extractor = calculate_tokens_price_image_1
elif model_id == "gpt-image-1.5":
price_extractor = calculate_tokens_price_image_1_5
elif model_id == "gpt-image-2":
price_extractor = calculate_tokens_price_image_2_0
else:
raise ValueError(f"Unknown model: {model_id}")
if image_tensors:
flat: list[torch.Tensor] = []
for tensor in image_tensors:
if len(tensor.shape) == 4:
flat.extend(tensor[i : i + 1] for i in range(tensor.shape[0]))
else:
flat.append(tensor.unsqueeze(0))
files = []
for i, single_image in enumerate(flat):
scaled_image = downscale_image_tensor(single_image, total_pixels=2048 * 2048).squeeze()
image_np = (scaled_image.numpy() * 255).astype(np.uint8)
img = Image.fromarray(image_np)
img_byte_arr = BytesIO()
img.save(img_byte_arr, format="PNG")
img_byte_arr.seek(0)
if len(flat) == 1:
files.append(("image", (f"image_{i}.png", img_byte_arr, "image/png")))
else:
files.append(("image[]", (f"image_{i}.png", img_byte_arr, "image/png")))
if mask is not None:
if len(flat) != 1:
raise Exception("Cannot use a mask with multiple image")
ref_image = flat[0]
if mask.shape[1:] != ref_image.shape[1:-1]:
raise Exception("Mask and Image must be the same size")
_, height, width = mask.shape
rgba_mask = torch.zeros(height, width, 4, device="cpu")
rgba_mask[:, :, 3] = 1 - mask.squeeze().cpu()
scaled_mask = downscale_image_tensor(
rgba_mask.unsqueeze(0), total_pixels=2048 * 2048
).squeeze()
mask_np = (scaled_mask.numpy() * 255).astype(np.uint8)
mask_img = Image.fromarray(mask_np)
mask_img_byte_arr = BytesIO()
mask_img.save(mask_img_byte_arr, format="PNG")
mask_img_byte_arr.seek(0)
files.append(("mask", ("mask.png", mask_img_byte_arr, "image/png")))
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/openai/images/edits", method="POST"),
response_model=OpenAIImageGenerationResponse,
data=OpenAIImageEditRequest(
model=model_id,
prompt=prompt,
quality=quality,
background=background,
n=n,
size=size,
moderation="low",
),
content_type="multipart/form-data",
files=files,
price_extractor=price_extractor,
)
else:
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/openai/images/generations", method="POST"),
response_model=OpenAIImageGenerationResponse,
data=OpenAIImageGenerationRequest(
model=model_id,
prompt=prompt,
quality=quality,
background=background,
n=n,
size=size,
moderation="low",
),
price_extractor=price_extractor,
)
return IO.NodeOutput(await validate_and_cast_response(response))
class OpenAIChatNode(IO.ComfyNode): class OpenAIChatNode(IO.ComfyNode):
""" """
Node to generate text responses from an OpenAI model. Node to generate text responses from an OpenAI model.
@ -999,6 +1311,7 @@ class OpenAIExtension(ComfyExtension):
OpenAIDalle2, OpenAIDalle2,
OpenAIDalle3, OpenAIDalle3,
OpenAIGPTImage1, OpenAIGPTImage1,
OpenAIGPTImageNodeV2,
OpenAIChatNode, OpenAIChatNode,
OpenAIInputFiles, OpenAIInputFiles,
OpenAIChatConfig, OpenAIChatConfig,