Instructions to use R1000/flux-RealismLora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use R1000/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("R1000/flux-RealismLora") prompt = "photoreal" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
This repository provides a checkpoint with trained LoRA photorealism for FLUX.1-dev model by Black Forest Labs
ComfyUI
See our github for comfy ui workflows.

Training details
XLabs AI team is happy to publish fine-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.
Inference
python3 demo_lora_inference.py \
--checkpoint lora.safetensors \
--prompt " handsome girl in a suit covered with bold tattoos and holding a pistol. Animatrix illustration style, fantasy style, natural photo cinematic"
License
lora.safetensors falls under the FLUX.1 [dev] Non-Commercial License
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Model tree for R1000/flux-RealismLora
Base model
black-forest-labs/FLUX.1-dev