Instructions to use peteromallet/Qwen-Image-Edit-InSubject with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use peteromallet/Qwen-Image-Edit-InSubject 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("Qwen/Qwen-Image-Edit", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("peteromallet/Qwen-Image-Edit-InSubject") 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
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**QwenEdit InSubject** is a LoRA fine-tune for QwenEdit that significantly improves its ability to preserve subjects while making edits to images. It works effectively with both single subjects and multiple subjects in the same image. While the base model can perform various image edits, it often loses important subject characteristics or distorts the main subjects during the editing process. This LoRA addresses these limitations to provide more accurate subject-preserving image editing.
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**QwenEdit InSubject** is a LoRA fine-tune for QwenEdit that significantly improves its ability to preserve subjects while making edits to images. It works effectively with both single subjects and multiple subjects in the same image. While the base model can perform various image edits, it often loses important subject characteristics or distorts the main subjects during the editing process. This LoRA addresses these limitations to provide more accurate subject-preserving image editing.
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