SSR-Encoder: Encoding Selective Subject Representation for Subject-Driven Generation
This addresses the challenge of selective subject representation for users in image generation, offering an incremental improvement over existing methods.
The paper tackles the problem of precise subject selection and focus in zero-shot subject-driven image generation by introducing the SSR-Encoder, which captures subject representations from reference images without test-time fine-tuning, achieving versatile and high-quality image generation as demonstrated in experiments.
Recent advancements in subject-driven image generation have led to zero-shot generation, yet precise selection and focus on crucial subject representations remain challenging. Addressing this, we introduce the SSR-Encoder, a novel architecture designed for selectively capturing any subject from single or multiple reference images. It responds to various query modalities including text and masks, without necessitating test-time fine-tuning. The SSR-Encoder combines a Token-to-Patch Aligner that aligns query inputs with image patches and a Detail-Preserving Subject Encoder for extracting and preserving fine features of the subjects, thereby generating subject embeddings. These embeddings, used in conjunction with original text embeddings, condition the generation process. Characterized by its model generalizability and efficiency, the SSR-Encoder adapts to a range of custom models and control modules. Enhanced by the Embedding Consistency Regularization Loss for improved training, our extensive experiments demonstrate its effectiveness in versatile and high-quality image generation, indicating its broad applicability. Project page: https://ssr-encoder.github.io