Workflow:Togethercomputer Together python Image Generation
| Knowledge Sources | |
|---|---|
| Domains | Image_Generation, Inference, API_Integration |
| Last Updated | 2026-02-15 16:00 GMT |
Overview
End-to-end process for generating images from text prompts using Together AI's hosted image generation models via the Python SDK.
Description
This workflow covers generating images from textual descriptions using Together AI's image generation API. It supports configuring the model, prompt, image dimensions, number of outputs, negative prompts, and seed for reproducibility. The API returns images as base64-encoded data or URLs depending on the model and configuration. The SDK wraps the REST endpoint with typed request and response models.
Usage
Execute this workflow when you need to generate images from text descriptions using models hosted on Together AI, such as Stable Diffusion XL or other supported image generation models. This applies to content creation, design prototyping, data augmentation with synthetic images, or any application requiring programmatic image generation.
Execution Steps
Step 1: Client Initialization
Create a Together client instance with API key configuration. The same client instance provides access to image generation through the images namespace.
Key considerations:
- API key can be passed explicitly or read from the TOGETHER_API_KEY environment variable
- The client provides access to images.generate() for image creation
Step 2: Prompt Construction
Craft the text prompt describing the desired image. Optionally construct a negative prompt specifying elements to exclude from the generated image.
Key considerations:
- Detailed, descriptive prompts tend to produce better results
- Negative prompts help exclude unwanted elements from the output
- Prompt engineering varies by model (SDXL vs. other architectures)
Step 3: Generation Configuration
Configure generation parameters including the target model, number of images, image dimensions, inference steps, and random seed for reproducibility.
Key considerations:
- Model must be a supported image generation model identifier
- n controls how many images to generate per request (default 1)
- height and width control output dimensions (default 1024x1024)
- steps controls the number of denoising iterations
- seed enables reproducible generation across runs
Step 4: Response Processing
Process the ImageResponse object containing the generated images. Each image in the response data array includes base64-encoded image data that can be decoded and saved to disk or displayed.
Key considerations:
- Response contains a data array with one entry per generated image
- Each entry includes b64_json with the base64-encoded image content
- Decode the base64 data and save to a file with the appropriate extension
- The response includes timing and model metadata