Text-to-Image
Diffusers
English
image-editing
SVDQuant
Qwen-Image-Edit
Diffusion
Quantization
ICLR2025
Instructions to use kp-forks/nunchaku-qwen-image-edit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kp-forks/nunchaku-qwen-image-edit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kp-forks/nunchaku-qwen-image-edit", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 49c85ebc9beb8f42693d030e2b0fabde7c06716872791725f6136e70431f4a33
- Size of remote file:
- 13.1 GB
- SHA256:
- 451243ddd3b0726a1604e5cf4131f62f5bb74cd85ac5a919015e6bc2bd98489c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.