bloc97/CrossAttentionControl
Jupyter notebook implementing cross-attention control for text-guided image editing with Stable Diffusion without requiring masks or model fine-tuning.

This repository provides an unofficial implementation of prompt-to-prompt image editing techniques for Stable Diffusion by modifying the model’s internal cross-attention maps during inference. Users can edit images by editing text prompts rather than drawing masks, achieving fine-grained control over generated content like word replacement and attention re-weighting. The implementation injects code into the diffusers library to manipulate attention layers during the denoising process without additional training.