Instructions to use chestnutlzj/Edit-R1-Qwen-Image-Edit-2509 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use chestnutlzj/Edit-R1-Qwen-Image-Edit-2509 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("chestnutlzj/Edit-R1-Qwen-Image-Edit-2509") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
Update model card with Uniworld-V2 paper link and main code repository
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by nielsr HF Staff - opened
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license: apache-2.0
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library_name: diffusers
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pipeline_tag: image-to-image
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<a href="https://github.com/PKU-YuanGroup/
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library_name: diffusers
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license: apache-2.0
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pipeline_tag: image-to-image
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# Edit-R1-Qwen-Image-Edit-2509: A model from UniWorld-V2
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This model is a checkpoint (`Edit-R1-Qwen-Image-Edit-2509`) developed using the **Edit-R1** framework, as presented in the paper [Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback](https://huggingface.co/papers/2510.16888). The **Edit-R1** framework focuses on reinforcing instruction-based image editing through Diffusion Negative-aware Finetuning (DiffusionNFT) and MLLM Implicit Feedback.
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<a href="https://huggingface.co/papers/2510.16888"><b>Paper</b></a> | <a href="https://github.com/PKU-YuanGroup/UniWorld-V2"><b>Code</b></a> | <a href="https://github.com/PKU-YuanGroup/Edit-R1"><b>Dataset</b></a>
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