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:
- ab7878117f19718b2ef33885560099346d7f95a399ccb6b91757a2bed2690c25
- Size of remote file:
- 13.1 GB
- SHA256:
- 5a988c2e28bc3ba7267182a67bab52a0539601144a74d19a9444d41e7e4a7164
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