Instructions to use QinmingZhou/OSOR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use QinmingZhou/OSOR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("QinmingZhou/OSOR") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
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## 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.
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## Limitations
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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.
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## 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.
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