Instructions to use AmdGoose/FLUX.2-dev-transformer-int8wo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AmdGoose/FLUX.2-dev-transformer-int8wo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AmdGoose/FLUX.2-dev-transformer-int8wo", 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
Commit History
-
Update README documentation
b8eb85a -
Update README + attention-only INT8WO transformer
59bb79a -
Add HF model card metadata
1c4de54 -
Add INT8 weight-only quantized transformer for FLUX.2-dev
Verified signatured98adcb -
initial commit
Verified signaturec1506ae