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ruihangxu/PhyEdit
PhyEdit is a machine learning model from ruihangxu. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for diffusers. The card lists the license as mit.
This repository contains the release LoRA checkpoint for PhyEdit: Towards Real-World Object Manipulation via Physically-Grounded Image Editing.
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From the Hugging Face model README
This repository contains the release LoRA checkpoint for PhyEdit: Towards Real-World Object Manipulation via Physically-Grounded Image Editing.
| File | Description |
|---|---|
phyedit_lora.safetensors | PhyEdit LoRA weights |
training_config.json | Sanitized release training configuration |
SHA256SUMS | Weight checksum |
Download the checkpoint:
hf download ruihangxu/PhyEdit phyedit_lora.safetensors \
--local-dir checkpoints/PhyEdit
Clone the code repository and run ManipEval sampling:
git clone https://github.com/nenhang/PhyEdit.git
cd PhyEdit
CUDA_VISIBLE_DEVICES=0 \
python -m bench.sample \
--config-path configs/train_deepspeed.yaml \
--pretrained-model-path Qwen/Qwen-Image-Edit-2511 \
--checkpoint-path ../checkpoints/PhyEdit/phyedit_lora.safetensors \
--benchmark-metadata data/RealManip-40K/metadata/test.json \
--output-dir outputs/manipeval \
--base-area 589824 \
--batch-size 8 \
--seeds 42 43 44 45 46 47 48 49
The release checkpoint was trained with an aspect-ratio-preserving base area of
589824 (768^2). This corresponds to 1024 x 576 for 16:9 images and
768 x 768 for square images.
This is a LoRA adapter and requires the Qwen-Image-Edit-2511 base model. Image quality and geometric accuracy depend on the source image, masks, depth and camera estimates, movement magnitude, and sampling seed. Review the licenses and usage terms of the base model, dataset, and external geometry models before use.
The PhyEdit LoRA weights are released under the MIT License. Third-party models and datasets retain their own licenses and terms.
@misc{xu2026phyeditrealworldobjectmanipulation,
title={PhyEdit: Towards Real-World Object Manipulation via Physically-Grounded Image Editing},
author={Ruihang Xu and Dewei Zhou and Xiaolong Shen and Fan Ma and Yi Yang},
year={2026},
url={https://arxiv.org/abs/2604.07230},
}