| --- |
| license: mit |
| library_name: diffusers |
| tags: |
| - object-removal |
| - image-inpainting |
| - diffusion |
| - lora |
| - osor |
| --- |
| |
| # OSOR |
|
|
| OSOR is a one-step diffusion framework for effect-aware object removal. It removes target objects together with associated effects such as shadows, reflections, and residual traces, while requiring only a single denoising step at inference. |
|
|
| Model page: https://huggingface.co/QinmingZhou/OSOR |
|
|
| ## Model Summary |
|
|
| OSOR is trained with a two-phase curriculum: |
|
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| 1. **Phase I** trains one-step removal with hard latent blending and occupancy-guided discriminator supervision. |
| 2. **Phase II** adds alpha prediction and trains with incomplete-mask conditioning, enabling the model to expand the effective removal region beyond conservative user masks. |
|
|
| The code release includes two backbone implementations: |
|
|
| - OSOR-FLUX-Fill |
| - OSOR-SDXL-Inpainting |
|
|
| ## Release Status |
|
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| The model repository contains checkpoints for both released OSOR backbones: |
|
|
| ```text |
| osor-fluxfill/weights/fluxfill_phase1.pth |
| osor-fluxfill/weights/fluxfill_phase2.pth |
| osor-sdxlinpainting/weights/sdxlinpainting_phase1.pth |
| osor-sdxlinpainting/weights/sdxlinpainting_phase2.pth |
| ``` |
|
|
| Download with: |
|
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| ```bash |
| hf download QinmingZhou/OSOR --include "osor-fluxfill/weights/*.pth" --local-dir . |
| hf download QinmingZhou/OSOR --include "osor-sdxlinpainting/weights/*.pth" --local-dir . |
| ``` |
|
|
| ## Intended Use |
|
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| OSOR is intended for research on object removal, image inpainting, and mask-conditioned image editing. Given an object-present image and a user-provided mask, OSOR predicts a clean background with object-associated effects removed. |
|
|
| ## Inputs And Outputs |
|
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| Inputs: |
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| - `image`: object-present input image. |
| - `mask`: binary or soft removal mask. |
|
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| Outputs: |
|
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| - `image`: object-removed image. |
| - Phase II implementations may also produce or internally use an alpha map for adaptive blending. |
|
|
| ## Training Data |
|
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| OSOR is trained on CORNE, a SAVP-verified effect-aware object-removal dataset. Evaluation uses CORNE-Val, AnimeEraseBench, TextEraseBench, and additional paired-background object-removal benchmarks. |
