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Cross Attention Control is an unofficial Stable Diffusion implementation of Prompt-to-Prompt image editing with cross-attention control. It modifies diffusion-model attention maps during inference so prompt changes can produce more controlled edits. The method is designed to avoid manual masks while requiring no additional training or fine-tuning. The notebooks include examples for editing images generated from the same seed. The project also adds image inversion using a modified inverse DDIM process to recover a latent representation from an existing image. Additional techniques help preserve compatibility with other schedulers and improve inversion at higher classifier-free guidance values.
Features
- Prompt-to-Prompt image editing
- Cross-attention map manipulation
- Mask-free image modifications
- No additional model training required
- Existing image inversion
- Modified inverse DDIM processing
Project Samples
Categories
Deep Learning FrameworksLicense
MIT LicenseFollow Cross Attention Control
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