CopyCat: Improving Fine-Grained Subject Consistency in Subject-to-Image Models within Seconds
This work proposes CopyCat, a lightweight model-refinement framework that improves fine-grained subject consistency within only a few seconds by attaching a lightweight Fine-grained Consistency LoRA and optimizing it using a single proxy image, which is used as both the conditioning image and the reconstruction target.