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
- Draw Things
- DiffusionBee
sub 16GB and sub 8GB versions
sub 16GB and sub 8GB versions would be really nice in the democratic&friendly OSS spirit.
Also a code example with
accelerate and cpu offloading in the model card would be nice
also with different text encoders (when its available)
thx
I'm hoping they make a <=2B version of the model so we can properly compare it with other existing models fairly, run on low end hardware and maybe even on mobile one day with some quantization and optimizations.
And since this is supposed to be truly open source then maybe release various epochs along the way would be nice for those planning on finetuning on top of it. Just my guess but trying to make an anime finetuned version of the model using the last epoch would probably not offer the best results but who knows.
I'm going to try to use my custom code to convert it to 8 bits which would cut the required VRAM in half. I will report back to this thread if I'm successful. π€
One of the potential improvements we make down the road is a smaller model. Stay tuned for updates. In the meantime, good luck with all the quantization work and let us know if you need any help!