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--- |
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language: |
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- en |
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- zh |
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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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tags: |
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- lora |
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--- |
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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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<p align="center"> |
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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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</p> |
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# Performance |
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|Benchmark| Qwen-Image-Edit-2509 | **Edit-R1-Qwen-Image-Edit-2509** | |
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| ---- | ---- | ----| |
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| GEdit-Bench | 7.54 | **7.76** | |
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| ImgEdit | 4.35 | **4.48** | |
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# Usage |
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```python |
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import os |
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import torch |
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from PIL import Image |
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from diffusers import QwenImageEditPlusPipeline |
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pipeline = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509", torch_dtype=torch.bfloat16) |
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print("pipeline loaded") |
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pipeline.load_lora_weights( |
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"chestnutlzj/Edit-R1-Qwen-Image-Edit-2509", |
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adapter_name="lora", |
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) |
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pipeline.set_adapters(["lora"], adapter_weights=[1]) |
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pipeline.to('cuda') |
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pipeline.set_progress_bar_config(disable=None) |
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image1 = Image.open("input1.png") |
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image2 = Image.open("input2.png") |
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prompt = "The magician bear is on the left, the alchemist bear is on the right, facing each other in the central park square." |
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inputs = { |
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"image": [image1, image2], |
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"prompt": prompt, |
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"generator": torch.manual_seed(0), |
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"true_cfg_scale": 4.0, |
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"negative_prompt": " ", |
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"num_inference_steps": 40, |
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"guidance_scale": 1.0, |
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"num_images_per_prompt": 1, |
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} |
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with torch.inference_mode(): |
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output = pipeline(**inputs) |
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output_image = output.images[0] |
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output_image.save("output_image_edit_plus.png") |
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print("image saved at", os.path.abspath("output_image_edit_plus.png")) |
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``` |
