Parameters
Basic Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
| prompt | string | "" | Text prompt describing what to generate |
| quality | selection | ”low” | Image quality level, options: “low”, “medium”, “high” |
| size | selection | ”auto” | Output image size, options: “auto”, “1024x1024”, “1024x1536”, “1536x1024” |
Image Editing Parameters
| Parameter | Type | Description |
|---|---|---|
| image | image | Input image for editing, supports multiple images |
| mask | mask | Optional mask to specify areas to modify (single image only) |
Optional Parameters
| Parameter | Type | Description |
|---|---|---|
| background | selection | Background options: “opaque” or “transparent” |
| seed | integer | Random seed (not yet implemented in backend) |
| n | integer | Number of images to generate, range 1-8 |
Output
| Output | Type | Description |
|---|---|---|
| IMAGE | image | Generated image result |
How It Works
The OpenAI GPT Image 1 node combines GPT-4’s language understanding with image generation. It analyzes the text prompt to understand its meaning and intent, then generates matching images. In image editing mode, the node can modify existing images. Using a mask allows precise control over which areas to change. Note that mask input only works with single image input. Users can control the output by adjusting parameters like quality level, size, background handling, and number of generations.Source Code
[Node source code (Updated on 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,)