Text-to-Image
Diffusers
English
image-editing
SVDQuant
Qwen-Image-Edit-2509
Diffusion
Quantization
ICLR2025
Instructions to use nunchaku-ai/nunchaku-qwen-image-edit-2509 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nunchaku-ai/nunchaku-qwen-image-edit-2509 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nunchaku-ai/nunchaku-qwen-image-edit-2509", torch_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:
- fb9afce471b8b64608c6683943e039a0cf15a0bc3c56ccd4d286d3f130ec1b66
- Size of remote file:
- 13.1 GB
- SHA256:
- 3d1e939d26170fed8dd0e0327f8a78ff2e48cbd87d29d7a54c0e887625dc12e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.