Instructions to use FallnAI/flux-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FallnAI/flux-quantized with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FallnAI/flux-quantized", 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
- Xet hash:
- a173850dd6efb13af571bfc5f8d1e3d21016527ea2dc8c52767bd2a40f5686cd
- Size of remote file:
- 1.94 GB
- SHA256:
- 7cfe77b44c0cece4419e82b7b45cfa4c0505bb02952528e54364fe981425de0b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.