Text-to-Image Generation

Text-to-image generation turns natural-language prompts into novel images using diffusion or autoregressive models.

  Model Families

  Generation Pipeline

1. Tokenize text prompt.
2. Encode into text embeddings (CLIP).
3. Run diffusion denoising steps guided by embeddings.
4. Decode latent into RGB image.
5. (Optional) Upscale with ESRGAN.

  Design Trade-offs

• Higher guidance scale improves prompt fidelity but can overshoot colors.
• More diffusion steps increase quality but raise latency.
• Safety filters (NSFW) may falsely block abstract art.

  Current Trends (2025)

• Sparse axial attention speeds SDXL inference 1.8×¹.
• Multi-modal editing pipelines enable text edits on existing images.

  Implementation Tips

1. Clamp guidance scale between 5–9 for balanced outputs.
2. Use 50 steps for drafts, 150 for finals.
3. Cache precomputed CLIP embeddings for popular prompts.

  References

1. Stability AI Research Blog, Faster Latent Diffusion with Sparse Axial Attention, 2025. ↩