Instructions to use ovedrive/Qwen-Image-Edit-2511-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ovedrive/Qwen-Image-Edit-2511-4bit 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("ovedrive/Qwen-Image-Edit-2511-4bit", 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] - Notebooks
- Google Colab
- Kaggle
request for generation steps
#5
by kartikvyas1 - opened
hi, this is too cool. could you please tell how this was generated from bf16 mode. would love to learn. thanks in advance.
like how many sample inputs were taken. did you also use the deepcompressor framework shared by svdquant authors. if so how to tweak it for qwen2511.
quantization is not same as training. You can check the docs on huggingface for quantization and diffusers. There is no other model/lora external layers added , its still the same original Qwen.
if you mean qwen-2512, the nf4 is already provided by me in another repo.
ovedrive changed discussion status to closed