Instructions to use byteshape/Qwen-Image-2512-Humming with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use byteshape/Qwen-Image-2512-Humming with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("byteshape/Qwen-Image-2512-Humming", 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:
- 2d23f1db1ca9f2306beeb8ac25b290bb1ce32852ed25474a466f1c13e52b275b
- Size of remote file:
- 840 kB
- SHA256:
- bb6e5399408fcea23918637e9b7f4cf8bcf404b20e02bd786ffb91aa991c7361
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.