Research map / Image generation / Editing and style transfer
A4A: Adapter for Adapter Transfer via All-for-All Mapping for Cross-Architecture Models
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Proposes A4A, an all-for-all mapping framework that transfers attention-based IP-Adapters from SD1.5 (U-Net) to SDXL (U-Net) and Pixart-Alpha (transformer) using a coupling space and learnable upgraded-space features.
Problem
Adapters built for a base text-to-image model (e.g., SD1.5) must be retrained for upgraded architectures; existing adapter transfer methods only handle addition-based adapters and fail to transfer attention-based adapters across architectures such as U-Net to transformer.
Approach
Aggregate all attention features of the pretrained adapter, project them into a unified coupling space of common dimension, then use randomly initialized learnable features that attend to these features and are aligned to every cross-attention layer of the upgraded model, enabling all-for-all layer mapping.
Key results
- On SDXL, A4A reaches CLIP-score 0.9154 vs 0.9124 for PTA-UM and 0.7894 for X-Adapter, with IDA 0.5127 vs 0.4531 (PTA-UM) and 0.062 (X-Adapter).
- A4A matches PTA-UM's IDA at about 0.5M samples versus 64M samples for PTA-UM training.
- On Pixart-Alpha (transformer), transferring from SD1.5 with A4A gives higher IDA than training IP-Adapter from scratch over 30k steps, per the IDA curve (Fig. 7).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Transfer IP-Adapter with A4A instead of X-Adapter | IP-Adapter transferred to SDXL · SDXL, IP customization | CLIP-score: 0.7894 -> 0.9154 | Helped |
| Transfer IP-Adapter with A4A instead of X-Adapter | IP-Adapter transferred to SDXL · SDXL, ID customization | IDA: 0.062 -> 0.5127 | Helped |
| Transfer IP-Adapter from SD1.5 with A4A instead of using the SDXL pretrained adapter | IP-Adapter · SDXL, IP customization | CLIP-score: 0.9124 -> 0.9154 | Helped |
| Transfer IP-Adapter from SD1.5 with A4A instead of using the SDXL pretrained adapter | IP-Adapter · SDXL, ID customization | IDA: 0.4531 -> 0.5127 | Helped |
| Fine-tune the pretrained adapter during A4A training (ablation) | A4A · SDXL, ID customization | IDA | Helped |
Surprising findings
- Transferred adapter on SDXL matches or slightly exceeds the officially trained PTA-UM on CLIP-score while using far fewer samples.
Limitations
- Evaluation limited to personalized human (ID) and object (IP) generation with a single adapter type (IP-Adapter).
- Pixart-Alpha comparison uses only a from-scratch IP-Adapter baseline since no official Pixart IP-Adapter exists.
- Validation set is small (100 reference images).
Experimental setup
Datasets: CelebAMask-HQ OpenImages
Benchmarks: CelebAMask-HQ validation (100 images)
Metrics: IDA (identity alignment score) CLIP-score sample count
Baselines: X-Adapter PTA-UM (pretrained IP-Adapter on SDXL) IP-Adapter trained from scratch on Pixart-Alpha
Builds on: IP-Adapter BLIP-2 X-Adapter Ctrl-Adapter
Closest papers by meaning
- FouRA: Fourier Low-Rank Adaptation (NeurIPS 2024 · similarity 0.85)
- UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image Generation (CVPR 2025 · similarity 0.84)
- X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model (CVPR 2024 · similarity 0.84)
- T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models (AAAI 2024 · similarity 0.84)
- Adapters Strike Back (CVPR 2024 · similarity 0.83)
- SimDA: Simple Diffusion Adapter for Efficient Video Generation (CVPR 2024 · similarity 0.83)
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