tailwindlabs / tailwindcss
github.com·21h ago
Developer Tools
Replicate Blog·Feb 24·5 min read
How to prompt Seedream 5.0
TL;DR
Image generation models traditionally struggle with precise control and iterative refinement—users cannot easily edit outputs or apply domain-specific knowledge. Seedream 5.0 introduces multi-step reasoning (breaking complex requests into stages), example-based editing (learning from reference images), and deep domain knowledge integration to address these gaps.
✦ Why It Matters
Engineers can now generate and iteratively refine images with precise control using structured prompts and reference examples.
Key Takeaways
- Use multi-step reasoning to break complex image requests into sequential logical stages for better results.
- Provide reference images as examples to guide style, composition, and visual direction in outputs.
- Leverage domain-specific knowledge integration for higher accuracy in specialized or technical image generation tasks.
- Iterate on generated images through example-based editing rather than regenerating from scratch.
Full Summary
Prior image generation systems often produced outputs that were difficult to refine or control precisely, limiting their utility for professional workflows. Seedream 5.0 adds three core capabilities: multi-step reasoning enables the model to decompose complex generation tasks into sequential logical steps rather than attempting everything at once; example-based editing allows users to provide reference images that guide the model's output style and composition; deep domain knowledge integration embeds specialized expertise into the generation process, improving accuracy in technical or niche domains.
The approach combines these techniques to give users finer-grained control over image synthesis. Engineers can now prompt the system with structured, multi-stage requests and reference examples to achieve more predictable and editable results, making the tool more practical for iterative design and professional applications.