Instructions to use Qwen/Qwen-Image-Edit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Qwen/Qwen-Image-Edit 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", torch_dtype=torch.bfloat16, device_map="cuda") 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
Is it possible to use qwen_vl_2.5_32b as clip? Will the performance improve.
#14
by codexq - opened
As the text_encoder folder provide qwen_vl_2.5_7b, will qwen_vl_2.5_32b do a better work?
Since the pre-learning was originally done with "qwen_vl_2.5_7b", there is no reason to change it.
did you find an answer? I'm curious as well but it doesn't seem to work when I just swap the 7b clip for 32b