Training-Free Multi-Concept Image Editing

Training-free image editing with diffusion models is highlydesirable yet is complex and remains a significant challenge. While recentoptimisation-based methods achieve strong zero-shot edits from text,they still struggle to preserve identity and capture intricate details, suchas facial structure, surface texture, or object-specific geometry, that ex-ist below the level of linguistic abstraction. To address this fundamentalgap, we propose Concept Distillation Sampling (CDS). To the best of ourknowledge, we are the first to introduce a unified, training-free frameworkfor target-less, multi-concept image editing.CDS overcomes this linguistic bottleneck of previous methods by anchor-ing the editing process in the certainty of pretrained LoRA adapters.We integrate a highly stable distillation backbone (featuring orderedtimesteps, regularisation, and negative-prompt guidance) with a noveldynamic weighting mechanism. This approach enables the compositionand control of multiple visual concepts directly within the diffusion pro-cess, utilising spatially-aware priors from pretrained LoRA adapters with-out causing concept clashing. Our method preserves instance conceptidentity without requiring reference samples of the desired edit. Extensivequantitative and qualitative evaluations demonstrate that CDS estab-lishes a new state-of-the-art over existing training-free editing and multi-LoRA composition methods on the InstructPix2Pix and ComposLoRAbenchmarks. Project Page: https://nickyfot.github.io/cds/.