Instructions to use XLabs-AI/flux-RealismLora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use XLabs-AI/flux-RealismLora 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-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("XLabs-AI/flux-RealismLora") 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
metadata
license: other
Lora Photorealism for Flux
This repository provides a checkpoint with trained LoRA photorealism for FLUX.1-dev model by Black Forest Labs
Training details
XLabs AI team is happy to publish fune-tuning Flux scripts, including:
- LoRA π₯
- ControlNet π₯
See our github for train script and train configs.
Training Dataset
Dataset has the following format for the training process:
βββ images/
β βββ 1.png
β βββ 1.json
β βββ 2.png
β βββ 2.json
β βββ ...
A .json file contains "caption" field with a text prompt.
License
lora.safetensors falls under the FLUX.1 [dev] Non-Commercial License
lora.bin falls under the FLUX.1 [dev] Non-Commercial License