Instructions to use dx8152/Qwen-Image-Edit-2509-Relight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dx8152/Qwen-Image-Edit-2509-Relight 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-2509", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("dx8152/Qwen-Image-Edit-2509-Relight") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
Could you clarify the differences between the following models
#6
by makisekurisu-jp - opened
relighting-kontext-dev-lora-v3.safetensors,
重新打光.safetensors,
and the converted relight-kontext-lora-single-caption_comfy.safetensors?
The filenames don’t make it clear, and I’d like to understand how they differ in terms of training data, usage, or compatibility.
The root directory of the folder contains Qwen-Edit's LoRa dataset, which was trained using the images and prompts from the folder I uploaded.
The folders in the Kontext directory contain datasets collected from the internet; as mentioned in my description, I am not aware of the datasets they used.