AnimeAdapter: Fine-grained and Consistent Zero-shot Anime Character Generation
For anime creators and artists, this provides a lightweight, modular adapter for Stable Diffusion that generates consistent characters without per-subject fine-tuning.
AnimeAdapter enables fine-grained and consistent zero-shot anime character generation from a single reference image, outperforming existing methods in character identity preservation across diverse editing scenarios.
We present a lightweight appearance adapter for Stable Diffusion that enables controllable and consistent anime character generation under diverse editing conditions. Instead of relying on large-scale vision-language models or per-subject fine-tuning, our method injects fine-grained visual features from a single reference image into the diffusion process. Based on CLIP emergent local spatialization, we develop semantic-selective local attention. To further disentangle character appearance from spatial layout, we incorporate pose-aware conditioning during adapter training. The resulting pretrained adapter remains compact, modular, and fully compatible with Stable Diffusion community workflows, while requiring no additional fine-tuning at deployment time. Furthermore, we present a high-quality anime character dataset based on curated and restructured Danbooru prompts, and evaluate our method across several practical character editing scenarios. Our code, model weights, and dataset will be publicly released upon acceptance.