Instructions to use ovedrive/qwen-image-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ovedrive/qwen-image-4bit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ovedrive/qwen-image-4bit", 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
[Request] Qwen-image-edit-4bit
#1
by jspsoli - opened
I tried to use your model for training with 16Gb on Ai-Toolkit - and it was successful with some minor changes in the source code and at low resolution (512) + vae and text_encoder caching.
If you can, please make a conversion for the newly released Edit model as well.
That’s great. I am able to generate at full recommended settings except that you may have to enable CPU offloading. This is because the final stage of vae and outputting the image takes a spike of GPU .
I saw the news on image edit. I will quantize and upload within a few hours but I will not be able to test it this week.