Instructions to use black-forest-labs/FLUX.1-schnell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use black-forest-labs/FLUX.1-schnell with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", 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
Fine tuning code
when
The implementation is open source on github, it's just a matter of finetuning tool authors adapting it and allowing the model to be unfrozen. OneTrainer devs are already looking at it.
Thank you.
Any comment on the discussion here?: https://github.com/black-forest-labs/flux/issues/9
It appears that since the publicly released models were distilled they are basically untrainable
i have made it trainable: https://github.com/bghira/SimpleTuner/blob/main/documentation/quickstart/FLUX.md
LoRA requires about 40GB GPU. works great. haven't tested for 100k steps yet.
Legend, testing it
Did it work?
I made the same question yesterday in another thread π§΅, but can't find it anymore π
π. Sorry for spamming ππ½
how to fine-tune Flux-Schnell