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
deleted
#24
by deleted - opened
deleted
I don't get that? If you first have to load the text encoder, then get vectors for your prompts and conditioning, you can unload the text encoders and the load the diffusion model.
So, the smaller the diffusion model, the more chance it has of fitting into ram?
deleted changed discussion title from Whats the point of these if you still have to use the full-sized text_encoder? to deleted