Instructions to use fal/AuraFlow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fal/AuraFlow with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fal/AuraFlow", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit History
-
Add fp16 variants to speedup loading and relax memory constraints (#15)
Verified signature- Upload folder using huggingface_hub (a6d1f49d89f51ac60ab172accac6dbaec3b34dc3) Co-authored-by: Sayak Paul <sayakpaul@users.noreply.huggingface.co>f16d500
-
Rename tokenizer.json to tokenizer/tokenizer.json
Verified signature3a70d4c -
Rename special_tokens_map.json to tokenizer/special_tokens_map.json
Verified signature9bde5c8 -
Rename tokenizer.model to tokenizer/tokenizer.model
Verified signature481533f -
Rename tokenizer_config.json to tokenizer/tokenizer_config.json
Verified signature4dcc25b -
Rename added_tokens.json to tokenizer/added_tokens.json
Verified signature14b66a4