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:
- 8c07b1780e7afe2422cb811bd293aea00c24cd5af9b49ef781e26d2025b9baa4
- Size of remote file:
- 11.9 GB
- SHA256:
- d3d3930225f88f6913c708ada0b452afad089110e9bbea27131b4b6d805748b8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.