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
IMPORTANT REQUEST: Please add "banana qwen-image-edit" svdquant versions !
Hello, I've been following your work closely. Your optimizations for consumers with limited VRAM capacity are fantastic and unique in this field. I congratulate you on this and hope you continue to do so. I have a request. It would be very helpful if you could also use SVDQ Quant for the Nano-Banana model (https://huggingface.co/eigen-ai-labs/eigen-banana-qwen-image-edit), which has recently made a difference in image rendering quality and consistency. If possible, we, as nunchaku followers, would be very happy if you released a lightweight 4/8-step quantized model like the NF4. I would appreciate your consideration and evaluation.
This is just a lora. Just load it with the experimental lora loader no? as far as i know this also has some issues and isn't a consistent improvement