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Merge upstream/master, keep local README.md

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GitHub Actions 2025-12-18 00:34:21 +00:00
commit 75065d03cc
9 changed files with 196 additions and 103 deletions
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15
comfy/k_diffusion/sampling.py
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@ -1618,6 +1618,17 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
x = x + sde_noise * sigmas[i + 1] * s_noise
return x
@torch.no_grad()
def sample_exp_heun_2_x0(model, x, sigmas, extra_args=None, callback=None, disable=None, solver_type="phi_2"):
"""Deterministic exponential Heun second order method in data prediction (x0) and logSNR time."""
return sample_seeds_2(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=0.0, s_noise=0.0, noise_sampler=None, r=1.0, solver_type=solver_type)
@torch.no_grad()
def sample_exp_heun_2_x0_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type="phi_2"):
"""Stochastic exponential Heun second order method in data prediction (x0) and logSNR time."""
return sample_seeds_2(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=1.0, solver_type=solver_type)
@torch.no_grad()
def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3):
@ -1765,7 +1776,7 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
# Predictor
if sigmas[i + 1] == 0:
# Denoising step
x = denoised
x_pred = denoised
else:
tau_t = tau_func(sigmas[i + 1])
curr_lambdas = lambdas[i - predictor_order_used + 1:i + 1]
@ -1786,7 +1797,7 @@ def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=F
if tau_t > 0 and s_noise > 0:
noise = noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * tau_t ** 2 * h).expm1().neg().sqrt() * s_noise
x_pred = x_pred + noise
return x
return x_pred
@torch.no_grad()

2
comfy/samplers.py
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@ -720,7 +720,7 @@ class Sampler:
sigma = float(sigmas[0])
return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2", "exp_heun_2_x0", "exp_heun_2_x0_sde", "dpm_2", "dpm_2_ancestral",
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
"dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_2m_sde_heun", "dpmpp_2m_sde_heun_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
"ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp",

4
comfy_api/latest/_io.py
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@ -1556,12 +1556,12 @@ class _ComfyNodeBaseInternal(_ComfyNodeInternal):
@final
@classmethod
def PREPARE_CLASS_CLONE(cls, v3_data: V3Data) -> type[ComfyNode]:
def PREPARE_CLASS_CLONE(cls, v3_data: V3Data | None) -> type[ComfyNode]:
"""Creates clone of real node class to prevent monkey-patching."""
c_type: type[ComfyNode] = cls if is_class(cls) else type(cls)
type_clone: type[ComfyNode] = shallow_clone_class(c_type)
# set hidden
type_clone.hidden = HiddenHolder.from_dict(v3_data["hidden_inputs"])
type_clone.hidden = HiddenHolder.from_dict(v3_data["hidden_inputs"] if v3_data else None)
return type_clone
@final

52
comfy_api_nodes/apis/openai_api.py Normal file
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@ -0,0 +1,52 @@
from pydantic import BaseModel, Field
class Datum2(BaseModel):
b64_json: str | None = Field(None, description="Base64 encoded image data")
revised_prompt: str | None = Field(None, description="Revised prompt")
url: str | None = Field(None, description="URL of the image")
class InputTokensDetails(BaseModel):
image_tokens: int | None = None
text_tokens: int | None = None
class Usage(BaseModel):
input_tokens: int | None = None
input_tokens_details: InputTokensDetails | None = None
output_tokens: int | None = None
total_tokens: int | None = None
class OpenAIImageGenerationResponse(BaseModel):
data: list[Datum2] | None = None
usage: Usage | None = None
class OpenAIImageEditRequest(BaseModel):
background: str | None = Field(None, description="Background transparency")
model: str = Field(...)
moderation: str | None = Field(None)
n: int | None = Field(None, description="The number of images to generate")
output_compression: int | None = Field(None, description="Compression level for JPEG or WebP (0-100)")
output_format: str | None = Field(None)
prompt: str = Field(...)
quality: str | None = Field(None, description="Size of the image (e.g., 1024x1024, 1536x1024, auto)")
size: str | None = Field(None, description="Size of the output image")
class OpenAIImageGenerationRequest(BaseModel):
background: str | None = Field(None, description="Background transparency")
model: str | None = Field(None)
moderation: str | None = Field(None)
n: int | None = Field(
None,
description="The number of images to generate.",
)
output_compression: int | None = Field(None, description="Compression level for JPEG or WebP (0-100)")
output_format: str | None = Field(None)
prompt: str = Field(...)
quality: str | None = Field(None, description="The quality of the generated image")
size: str | None = Field(None, description="Size of the image (e.g., 1024x1024, 1536x1024, auto)")
style: str | None = Field(None, description="Style of the image (only for dall-e-3)")

209
comfy_api_nodes/nodes_openai.py
View File

@ -1,46 +1,45 @@
from io import BytesIO
import base64
import os
from enum import Enum
from inspect import cleandoc
from io import BytesIO
import numpy as np
import torch
from PIL import Image
import folder_paths
import base64
from comfy_api.latest import IO, ComfyExtension
from typing_extensions import override
import folder_paths
from comfy_api.latest import IO, ComfyExtension, Input
from comfy_api_nodes.apis import (
OpenAIImageGenerationRequest,
OpenAIImageEditRequest,
OpenAIImageGenerationResponse,
OpenAICreateResponse,
OpenAIResponse,
CreateModelResponseProperties,
Item,
OutputContent,
InputImageContent,
Detail,
InputTextContent,
InputMessage,
InputMessageContentList,
InputContent,
InputFileContent,
InputImageContent,
InputMessage,
InputMessageContentList,
InputTextContent,
Item,
OpenAICreateResponse,
OpenAIResponse,
OutputContent,
)
from comfy_api_nodes.apis.openai_api import (
OpenAIImageEditRequest,
OpenAIImageGenerationRequest,
OpenAIImageGenerationResponse,
)
from comfy_api_nodes.util import (
downscale_image_tensor,
download_url_to_bytesio,
validate_string,
tensor_to_base64_string,
ApiEndpoint,
sync_op,
download_url_to_bytesio,
downscale_image_tensor,
poll_op,
sync_op,
tensor_to_base64_string,
text_filepath_to_data_uri,
validate_string,
)
RESPONSES_ENDPOINT = "/proxy/openai/v1/responses"
STARTING_POINT_ID_PATTERN = r"<starting_point_id:(.*)>"
@ -98,9 +97,6 @@ async def validate_and_cast_response(response, timeout: int = None) -> torch.Ten
class OpenAIDalle2(IO.ComfyNode):
"""
Generates images synchronously via OpenAI's DALL·E 2 endpoint.
"""
@classmethod
def define_schema(cls):
@ -108,7 +104,7 @@ class OpenAIDalle2(IO.ComfyNode):
node_id="OpenAIDalle2",
display_name="OpenAI DALL·E 2",
category="api node/image/OpenAI",
description=cleandoc(cls.__doc__ or ""),
description="Generates images synchronously via OpenAI's DALL·E 2 endpoint.",
inputs=[
IO.String.Input(
"prompt",
@ -234,9 +230,6 @@ class OpenAIDalle2(IO.ComfyNode):
class OpenAIDalle3(IO.ComfyNode):
"""
Generates images synchronously via OpenAI's DALL·E 3 endpoint.
"""
@classmethod
def define_schema(cls):
@ -244,7 +237,7 @@ class OpenAIDalle3(IO.ComfyNode):
node_id="OpenAIDalle3",
display_name="OpenAI DALL·E 3",
category="api node/image/OpenAI",
description=cleandoc(cls.__doc__ or ""),
description="Generates images synchronously via OpenAI's DALL·E 3 endpoint.",
inputs=[
IO.String.Input(
"prompt",
@ -326,10 +319,16 @@ class OpenAIDalle3(IO.ComfyNode):
return IO.NodeOutput(await validate_and_cast_response(response))
def calculate_tokens_price_image_1(response: OpenAIImageGenerationResponse) -> float | None:
# https://platform.openai.com/docs/pricing
return ((response.usage.input_tokens * 10.0) + (response.usage.output_tokens * 40.0)) / 1_000_000.0
def calculate_tokens_price_image_1_5(response: OpenAIImageGenerationResponse) -> float | None:
return ((response.usage.input_tokens * 8.0) + (response.usage.output_tokens * 32.0)) / 1_000_000.0
class OpenAIGPTImage1(IO.ComfyNode):
"""
Generates images synchronously via OpenAI's GPT Image 1 endpoint.
"""
@classmethod
def define_schema(cls):
@ -337,13 +336,13 @@ class OpenAIGPTImage1(IO.ComfyNode):
node_id="OpenAIGPTImage1",
display_name="OpenAI GPT Image 1",
category="api node/image/OpenAI",
description=cleandoc(cls.__doc__ or ""),
description="Generates images synchronously via OpenAI's GPT Image 1 endpoint.",
inputs=[
IO.String.Input(
"prompt",
default="",
multiline=True,
tooltip="Text prompt for GPT Image 1",
tooltip="Text prompt for GPT Image",
),
IO.Int.Input(
"seed",
@ -365,8 +364,8 @@ class OpenAIGPTImage1(IO.ComfyNode):
),
IO.Combo.Input(
"background",
default="opaque",
options=["opaque", "transparent"],
default="auto",
options=["auto", "opaque", "transparent"],
tooltip="Return image with or without background",
optional=True,
),
@ -397,6 +396,11 @@ class OpenAIGPTImage1(IO.ComfyNode):
tooltip="Optional mask for inpainting (white areas will be replaced)",
optional=True,
),
IO.Combo.Input(
"model",
options=["gpt-image-1", "gpt-image-1.5"],
optional=True,
),
],
outputs=[
IO.Image.Output(),
@ -412,32 +416,34 @@ class OpenAIGPTImage1(IO.ComfyNode):
@classmethod
async def execute(
cls,
prompt,
seed=0,
quality="low",
background="opaque",
image=None,
mask=None,
n=1,
size="1024x1024",
prompt: str,
seed: int = 0,
quality: str = "low",
background: str = "opaque",
image: Input.Image | None = None,
mask: Input.Image | None = None,
n: int = 1,
size: str = "1024x1024",
model: str = "gpt-image-1",
) -> IO.NodeOutput:
validate_string(prompt, strip_whitespace=False)
model = "gpt-image-1"
path = "/proxy/openai/images/generations"
content_type = "application/json"
request_class = OpenAIImageGenerationRequest
files = []
if mask is not None and image is None:
raise ValueError("Cannot use a mask without an input image")
if model == "gpt-image-1":
price_extractor = calculate_tokens_price_image_1
elif model == "gpt-image-1.5":
price_extractor = calculate_tokens_price_image_1_5
else:
raise ValueError(f"Unknown model: {model}")
if image is not None:
path = "/proxy/openai/images/edits"
request_class = OpenAIImageEditRequest
content_type = "multipart/form-data"
files = []
batch_size = image.shape[0]
for i in range(batch_size):
single_image = image[i : i + 1]
scaled_image = downscale_image_tensor(single_image).squeeze()
single_image = image[i: i + 1]
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)
@ -450,44 +456,59 @@ class OpenAIGPTImage1(IO.ComfyNode):
else:
files.append(("image[]", (f"image_{i}.png", img_byte_arr, "image/png")))
if mask is not None:
if image is None:
raise Exception("Cannot use a mask without an input image")
if image.shape[0] != 1:
raise Exception("Cannot use a mask with multiple image")
if mask.shape[1:] != image.shape[1:-1]:
raise Exception("Mask and Image must be the same size")
batch, height, width = mask.shape
rgba_mask = torch.zeros(height, width, 4, device="cpu")
rgba_mask[:, :, 3] = 1 - mask.squeeze().cpu()
if mask is not None:
if image.shape[0] != 1:
raise Exception("Cannot use a mask with multiple image")
if mask.shape[1:] != 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)).squeeze()
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")))
# Build the operation
response = await sync_op(
cls,
ApiEndpoint(path=path, method="POST"),
response_model=OpenAIImageGenerationResponse,
data=request_class(
model=model,
prompt=prompt,
quality=quality,
background=background,
n=n,
seed=seed,
size=size,
),
files=files if files else None,
content_type=content_type,
)
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,
prompt=prompt,
quality=quality,
background=background,
n=n,
seed=seed,
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,
prompt=prompt,
quality=quality,
background=background,
n=n,
seed=seed,
size=size,
moderation="low",
),
price_extractor=price_extractor,
)
return IO.NodeOutput(await validate_and_cast_response(response))

2
comfy_api_nodes/util/conversions.py
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@ -129,7 +129,7 @@ def pil_to_bytesio(img: Image.Image, mime_type: str = "image/png") -> BytesIO:
return img_byte_arr
def downscale_image_tensor(image, total_pixels=1536 * 1024) -> torch.Tensor:
def downscale_image_tensor(image: torch.Tensor, total_pixels: int = 1536 * 1024) -> torch.Tensor:
"""Downscale input image tensor to roughly the specified total pixels."""
samples = image.movedim(-1, 1)
total = int(total_pixels)

11
comfy_extras/nodes_custom_sampler.py
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@ -671,7 +671,16 @@ class SamplerSEEDS2(io.ComfyNode):
io.Float.Input("s_noise", default=1.0, min=0.0, max=100.0, step=0.01, round=False, tooltip="SDE noise multiplier"),
io.Float.Input("r", default=0.5, min=0.01, max=1.0, step=0.01, round=False, tooltip="Relative step size for the intermediate stage (c2 node)"),
],
outputs=[io.Sampler.Output()]
outputs=[io.Sampler.Output()],
description=(
"This sampler node can represent multiple samplers:\n\n"
"seeds_2\n"
"- default setting\n\n"
"exp_heun_2_x0\n"
"- solver_type=phi_2, r=1.0, eta=0.0\n\n"
"exp_heun_2_x0_sde\n"
"- solver_type=phi_2, r=1.0, eta=1.0, s_noise=1.0"
)
)
@classmethod

2
comfyui_version.py
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@ -1,3 +1,3 @@
# This file is automatically generated by the build process when version is
# updated in pyproject.toml.
__version__ = "0.4.0"
__version__ = "0.5.0"

2
pyproject.toml
View File

@ -1,6 +1,6 @@
[project]
name = "ComfyUI"
version = "0.4.0"
version = "0.5.0"
readme = "README.md"
license = { file = "LICENSE" }
requires-python = ">=3.9"