Workflow:Openai Openai python Image Generation
| Knowledge Sources | |
|---|---|
| Domains | Image_Generation, Computer_Vision, API_Integration |
| Last Updated | 2026-02-15 10:00 GMT |
Overview
End-to-end process for generating and editing images using the OpenAI Images API, with support for standard generation, streaming with partial image previews, image editing, and image variations.
Description
This workflow covers image generation and manipulation through the OpenAI Python SDK. It supports multiple operations: generating images from text prompts using models like gpt-image-1 and dall-e-3, editing existing images with inpainting, creating variations of existing images, and streaming image generation with progressive partial image delivery. Generated images can be returned as URLs or base64-encoded data, and the streaming mode provides intermediate previews during the generation process.
Usage
Execute this workflow when you need to generate images from text descriptions, edit existing images based on prompts, create variations of images, or integrate image generation into applications that benefit from progressive preview rendering. This is appropriate for content creation tools, design assistants, and any application that needs programmatic image generation.
Execution Steps
Step 1: Client Initialization
Create an OpenAI or AsyncOpenAI client. Image generation endpoints are available under client.images with methods for generation, editing, and variation creation. No additional dependencies beyond the core SDK are required.
Key considerations:
- Image generation may have longer response times than text generation
- Configure appropriate timeouts for image generation requests
- Both sync and async clients support all image operations
Step 2: Configure Generation Parameters
Prepare the generation request parameters including the text prompt, model selection (gpt-image-1, dall-e-3, dall-e-2), image size (1024x1024, 1024x1792, 1792x1024), number of images (n), and response format (url or b64_json). For streaming, set stream=True and optionally partial_images to control the number of intermediate previews.
Key considerations:
- gpt-image-1 supports streaming with partial image previews
- Different models support different size options
- b64_json format returns image data directly; url returns temporary URLs
Step 3: Generate Image
Call client.images.generate() with the configured parameters. For standard generation, this returns an ImagesResponse containing a list of Image objects. For streaming, iterate over the returned stream to receive image_generation.partial_image events followed by an image_generation.completed event with the final image.
Key considerations:
- Streaming mode provides progressive rendering feedback
- Partial images are base64-encoded and can be displayed incrementally
- The final completed event contains the full-quality image
Step 4: Edit Image (Optional)
For image editing (inpainting), call client.images.edit() with a source image, a mask indicating the area to modify, a text prompt describing the desired edit, and model/size parameters. The mask should be an RGBA image with transparent areas indicating where to generate new content.
Key considerations:
- The mask must match the source image dimensions
- Transparent areas in the mask indicate where the model should generate
- Image editing supports both file paths and in-memory image data
Step 5: Save or Display Results
Process the generated images by decoding base64 data or downloading from URLs. Save images to files, display them in the application, or pass them to downstream processing. For streaming results, partial images can be displayed as previews and replaced with the final image upon completion.
Key considerations:
- URL-based results are temporary and should be downloaded promptly
- Base64 data can be decoded with standard Python base64.b64decode()
- Streaming partial images enable progressive user interface updates