Instructions to use vftfnn/flux1-schnell-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vftfnn/flux1-schnell-nf4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vftfnn/flux1-schnell-nf4", 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
FLUX.1-schnell with the transformer in bitsandbytes NF4 and T5 in 4-bit (fp16 compute), saved with diffusers save_pretrained. Load with FluxPipeline.from_pretrained directly.
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Model tree for vftfnn/flux1-schnell-nf4
Base model
black-forest-labs/FLUX.1-schnell