> ## Documentation Index > Fetch the complete documentation index at: https://docs.comfy.org/llms.txt > Use this file to discover all available pages before exploring further. # OpenAI GPT Image 1 - ComfyUI 原生节点文档 > 使用OpenAI的GPT-4 Vision模型生成图像的节点 ComfyUI 原生OpenAI GPT Image 1 节点 此节点连接到OpenAI的 GPT Image 1 API，让用户能够通过详细的文本提示词生成图像。GPT Image 1与传统的DALL·E模型不同，它利用了GPT-4的语言理解能力，可以处理更复杂和语境丰富的提示词，生成更符合用户意图的图像内容。 ## 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)] ```python theme={null} 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,) ```