Blended Latent Diffusion enables text-driven local editing of images using latent diffusion models (LDM). The approach combines Blended Diffusion with a text-to-image LDM for faster inference while maintaining editing quality. It addresses image reconstruction limitations inherent in LDMs and handles thin mask scenarios for precise local edits. The method was evaluated against baselines both qualitatively and quantitatively.