Instructions to use black-forest-labs/FLUX.1-Fill-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use black-forest-labs/FLUX.1-Fill-dev 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-Fill-dev", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Diffusion Single File
How to use black-forest-labs/FLUX.1-Fill-dev with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Train FLUX.1-Fill-dev LoRa
Hi, I want to train a LoRa and I found this fork of the diffusers git https://github.com/Sebastian-Zok/FLUX-Fill-LoRa-Training/tree/main?tab=readme-ov-file . Has anyone successfully trained a LoRa with this model? If so, what hardware did you use and what were the settings you were running?
Hi, I want to train a LoRa and I found this fork of the diffusers git https://github.com/Sebastian-Zok/FLUX-Fill-LoRa-Training/tree/main?tab=readme-ov-file . Has anyone successfully trained a LoRa with this model? If so, what hardware did you use and what were the settings you were running?
In one trainer pick the flux lora training and then choose the flux dev fill model.
4080 works just fine with batch size 1
Thank you very much!
does this actually work and give as good results as dev fill normally does for inpanting?
