Seedream 4.7 API Coherent Batch Generation

Seedream 4.7 API gives developers access to ByteDance's image generation and editing model family through Atlas Cloud. It includes text to image, sequential generation, image editing, and sequential editing variants.

Use it to generate images from text prompts or modify existing images with targeted instructions. Sequential variants can produce coherent image sets, while editing models are designed to preserve identity, lighting, and local structure.

Create an Atlas Cloud account, obtain an API key, and choose the model variant that matches your workflow. Then follow that model's playground schema to prepare the inputs and submit your request.

Atlas Cloud lists a standard base price of $0.03 per image for every Seedream 4.7 variant. Pricing is pay as you go, so developers can use the models without committing to a subscription.

Choose Text-to-Image for individual prompt based images and Sequential when one request must produce a coherent set. For source image changes, use Edit for individual results or Edit Sequential for coordinated batches.

Each variant has a dedicated playground parameter schema that defines its accepted fields and constraints. Check the schema for your selected model before sending a request because generation and editing workflows use different inputs.

Need several related images from one request? The Sequential model generates a coherent set from a prompt, while Edit Sequential applies coordinated changes across outputs derived from reference inputs.

The Edit variant follows modification instructions while retaining the source image's identity, lighting, and local structure. This makes it suitable when only selected visual elements should change.

For the Text-to-Image variant, ByteDance describes balanced improvements in image quality, aesthetics, and instruction following while retaining the efficiency and cost profile of the 4.0 generation. This comparison does not automatically apply to every editing or sequential workflow.

First, confirm that you selected the correct generation, editing, or sequential variant and that every input follows its dedicated schema. If the request succeeds but the image misses the intent, simplify the instruction and verify that the reference inputs match the desired editing workflow.