Diffuse to Choose
Amazon's Diffuse to Choose is a diffusion-based image-conditioned inpainting model built for "virtual try-on" scenarios where product images must be placed naturally into user scenes.
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Image-conditioned inpainting: Balances fast inference with high-fidelity product identity preservation, a trade-off that pure text-conditioned inpainting struggles with.
Accurate semantic manipulation: Successfully inserts reference products into masked regions while respecting scene lighting, perspective, and interactions with surrounding objects.
Zero-shot strength: Outperforms existing zero-shot diffusion inpainting methods on both automatic metrics and user studies for product-insertion tasks.
Beats few-shot personalization: Surpasses even few-shot personalization methods like DreamPaint without requiring per-product fine-tuning, making it practical at catalog scale.