FluxTrym

Dead simple web UI for training FLUX LoRA with LOW VRAM (12GB/16GB/20GB) support.

• Frontend: The WebUI forked from FluxGym (Gradio UI created by https://github.com/cocktailpeanut/fluxgym/) but i customized it with my ideas
• Backend: The Training script powered by Kohya Scripts

2. Install Manually

First clone Fluxgym and kohya-ss/sd-scripts:

git clone https://github.com/laichaoyi/FluxTrym.git
cd FluxTrym
git clone -b sd3 https://github.com/kohya-ss/sd-scripts

Now activate a venv from the root FluxTrym folder:

If you're on Windows:

python -m venv env
env\Scripts\activate

If your're on Linux:

python -m venv env
source env/bin/activate

This will create an env folder right below the FluxTrym folder:

/FluxTrym
  app.py
  requirements.txt
  /sd-scripts
  /env

Now go to the sd-scripts folder and install dependencies to the activated environment:

cd sd-scripts
pip install -r requirements.txt

Now come back to the root folder and install the app dependencies:

cd ..
pip install -r requirements.txt

Finally, install pytorch Nightly:

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121

Now let's download the model checkpoints.

First, download the following models under the models/clip foder:

• https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/clip_l.safetensors?download=true
• https://huggingface.co/comfyanonymous/flux_text_encoders/resolve/main/t5xxl_fp16.safetensors?download=true

Second, download the following model under the models/vae folder:

• https://huggingface.co/cocktailpeanut/xulf-dev/resolve/main/ae.sft?download=true

Finally, donwload the following model under the models/unet folder:

• https://huggingface.co/cocktailpeanut/xulf-dev/resolve/main/flux1-dev.sft?download=true

The result file structure will be something like:

/models
  /clip
    clip_l.safetensors
    t5xxl_fp16.safetensors
  /unet
    flux1-dev.sft
  /vae
    ae.sft
/sd-scripts
/outputs
/env
app.py
requirements.txt
...

Start

Go back to the root fluxgym folder, with the venv activated, run:

python app.py

Make sure to have the venv activated before running python app.py.

Windows: env/Scripts/activate Linux: source env/bin/activate

Usage

The usage is pretty straightforward:

1. Enter the lora info
2. Upload images and caption them (using the trigger word)
3. Click "start".

That's all!