参数说明
基本参数
| 参数 | 类型 | 默认值 | 说明 |
|---|---|---|---|
| prompt | 字符串 | "" | 详细描述要生成内容的文本提示词 |
| quality | 选择项 | ”low” | 图像质量级别，可选值：“low”, “medium”, “high” |
| size | 选择项 | ”auto” | 输出图像尺寸，可选值：“auto”, “1024x1024”, “1024x1536”, “1536x1024” |
图像编辑参数
| 参数 | 类型 | 说明 |
|---|---|---|
| image | 图像 | 用于图像编辑的输入图像，支持批量输入多张图像 |
| mask | 掩码 | 指定图像中要修改的区域(可选)，使用掩码时只能输入单张图像 |
可选参数
| 参数 | 类型 | 说明 |
|---|---|---|
| background | 选择项 | 背景处理选项，可选值：“opaque”(不透明), “transparent”(透明) |
| seed | 整数 | 生成的随机种子，当前在后端尚未实现 |
| n | 整数 | 生成的图像数量，范围1-8 |
输出
| 输出 | 类型 | 说明 |
|---|---|---|
| IMAGE | 图像 | 生成的图像结果 |
工作原理
OpenAI GPT Image 1 节点结合了GPT-4的语言理解能力和图像生成技术。它首先分析用户提供的文本提示词，理解其语义内容和意图，然后生成符合描述的图像。 当提供输入图像时，节点可以在图像编辑模式下工作，允许对现有图像进行修改。通过额外提供掩码，用户可以精确控制哪些区域应该被修改，哪些应该保持不变。注意使用掩码时，只能提供单张图像输入。 用户可以通过调整各种参数来控制生成结果，包括质量级别、尺寸、背景处理和生成数量。源码参考
[节点源码 (更新于2025-05-03)]
class OpenAIGPTImage1(ComfyNodeABC):
"""
Generates images synchronously via OpenAI's GPT Image 1 endpoint.
Uses the proxy at /proxy/openai/images/generations. Returned URLs are short‑lived,
so download or cache results if you need to keep them.
"""
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls) -> InputTypeDict:
return {
"required": {
"prompt": (
IO.STRING,
{
"multiline": True,
"default": "",
"tooltip": "Text prompt for GPT Image 1",
},
),
},
"optional": {
"seed": (
IO.INT,
{
"default": 0,
"min": 0,
"max": 2**31 - 1,
"step": 1,
"display": "number",
"control_after_generate": True,
"tooltip": "not implemented yet in backend",
},
),
"quality": (
IO.COMBO,
{
"options": ["low", "medium", "high"],
"default": "low",
"tooltip": "Image quality, affects cost and generation time.",
},
),
"background": (
IO.COMBO,
{
"options": ["opaque", "transparent"],
"default": "opaque",
"tooltip": "Return image with or without background",
},
),
"size": (
IO.COMBO,
{
"options": ["auto", "1024x1024", "1024x1536", "1536x1024"],
"default": "auto",
"tooltip": "Image size",
},
),
"n": (
IO.INT,
{
"default": 1,
"min": 1,
"max": 8,
"step": 1,
"display": "number",
"tooltip": "How many images to generate",
},
),
"image": (
IO.IMAGE,
{
"default": None,
"tooltip": "Optional reference image for image editing.",
},
),
"mask": (
IO.MASK,
{
"default": None,
"tooltip": "Optional mask for inpainting (white areas will be replaced)",
},
),
},
"hidden": {"auth_token": "AUTH_TOKEN_COMFY_ORG"},
}
RETURN_TYPES = (IO.IMAGE,)
FUNCTION = "api_call"
CATEGORY = "api node/image/openai"
DESCRIPTION = cleandoc(__doc__ or "")
API_NODE = True
def api_call(
self,
prompt,
seed=0,
quality="low",
background="opaque",
image=None,
mask=None,
n=1,
size="1024x1024",
auth_token=None,
):
model = "gpt-image-1"
path = "/proxy/openai/images/generations"
content_type="application/json"
request_class = OpenAIImageGenerationRequest
img_binaries = []
mask_binary = None
files = []
if image is not None:
path = "/proxy/openai/images/edits"
request_class = OpenAIImageEditRequest
content_type ="multipart/form-data"
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()
image_np = (scaled_image.numpy() * 255).astype(np.uint8)
img = Image.fromarray(image_np)
img_byte_arr = io.BytesIO()
img.save(img_byte_arr, format="PNG")
img_byte_arr.seek(0)
img_binary = img_byte_arr
img_binary.name = f"image_{i}.png"
img_binaries.append(img_binary)
if batch_size == 1:
files.append(("image", img_binary))
else:
files.append(("image[]", img_binary))
if mask is not None:
if image.shape[0] != 1:
raise Exception("Cannot use a mask with multiple image")
if image is None:
raise Exception("Cannot use a mask without an input 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()
scaled_mask = downscale_image_tensor(rgba_mask.unsqueeze(0)).squeeze()
mask_np = (scaled_mask.numpy() * 255).astype(np.uint8)
mask_img = Image.fromarray(mask_np)
mask_img_byte_arr = io.BytesIO()
mask_img.save(mask_img_byte_arr, format="PNG")
mask_img_byte_arr.seek(0)
mask_binary = mask_img_byte_arr
mask_binary.name = "mask.png"
files.append(("mask", mask_binary))
# Build the operation
operation = SynchronousOperation(
endpoint=ApiEndpoint(
path=path,
method=HttpMethod.POST,
request_model=request_class,
response_model=OpenAIImageGenerationResponse,
),
request=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,
auth_token=auth_token,
)
response = operation.execute()
img_tensor = validate_and_cast_response(response)
return (img_tensor,)