Update README.md

README.md

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 ---
 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:
+
+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
+
+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:
+
+```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
+
+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
+
+Inputs:
+
+- `image`: object-present input image.
+- `mask`: binary or soft removal mask.
+
+Outputs:
+
+- `image`: object-removed image.
+- Phase II implementations may also produce or internally use an alpha map for adaptive blending.
+
+## Training Data
+
+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.
+
+## Limitations
+
+OSOR may fail when the target object is extremely large, the mask is severely incorrect, or the background requires ambiguous semantic hallucination. As with other generative editing models, outputs should be reviewed before use in high-stakes contexts.