β οΈ Development Notice: Currently in active development - stability not guaranteed. Frequent updates - check changelogs regularly.
T2ITrainer is a diffusers based training script. It aims to provide simple yet implementation for lora training.
- 2025-06-27: Support kontext nf4 training
Rehosted an nf4 version flux kontext on https://huggingface.co/lrzjason/flux-kontext-nf4
Rehosted an nf4 version flux fill on https://huggingface.co/lrzjason/flux-fill-nf4
download nf4 version flux fill and use it for training could significantly decrease lora training VRAM requirement.
Model Type | VRAM Requirements | Status |
---|---|---|
Kolors | 11GB GPU | β Supported |
SD3.5 (FP16 BS1) | 24GB GPU | β Supported |
Flux Fill,Kontext | 24GB GPU | β Supported |
β Mandatory: Install Microsoft Visual C++ Redistributable if encountering DLL errors
Recommended Method
git clone https://github.com/lrzjason/T2ITrainer.git
cd T2ITrainer
setup.bat
- Handles: Virtual Environment β’ Dependency Installation β’ Model Downloads
Clone Repository π
git clone https://github.com/lrzjason/T2ITrainer.git
cd T2ITrainer
Virtual Environment π οΈ
python -m venv venv
call venv\Scripts\activate
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
Model Downloads π₯
# Kolors
huggingface-cli download Kwai-Kolors/Kolors --local-dir kolors_models/
# NF4 Flux Fill for low gpu
huggingface-cli download "lrzjason/flux-fill-nf4" --local-dir flux_models/fill/
# skip if downloaded nf 4 Flux Fill
huggingface-cli download "black-forest-labs/FLUX.1-fill-dev" --local-dir flux_models/fill/
# SD3.5 Models
huggingface-cli download "stabilityai/stable-diffusion-3.5-large" --local-dir "sd3.5L/"
# NF4 Flux kontext
huggingface-cli download "lrzjason/flux-kontext-nf4" --local-dir flux_models/kontext/
Script | Command | Special Notes |
---|---|---|
Flux kontext | python ui_flux_fill.py |
Requires diffusers>=0.32.0, 24GB VRAM Recommended |
Flux Fill | python ui_flux_fill.py |
Requires diffusers>=0.32.0, 24GB VRAM Recommended |
Kolors | python ui.py |
Needs Fixed VAE |
SD3.5 Large | python ui_sd35.py |
24GB VRAM Recommended |
Inpainting Model Setup
huggingface-cli download "lrzjason/flux-kontext-nf4" --local-dir flux_models/kontext/
For more details (example dataset):
- https://github.com/lrzjason/T2ITrainer/blob/main/doc/flux_kontext.md
- https://huggingface.co/datasets/lrzjason/object_removal_alpha_kontext
Inpainting Model Setup
huggingface-cli download "lrzjason/flux-fill-nf4" --local-dir flux_models/fill/
For more details (example dataset):
- https://github.com/lrzjason/T2ITrainer/blob/main/doc/flux_fill.md
- https://huggingface.co/datasets/lrzjason/ObjectRemovalAlpha
Dev Model Installation
huggingface-cli download "black-forest-labs/FLUX.1-dev" --local-dir flux_models/dev/
Category | Settings |
---|---|
Base Configuration | Rank 16, AdamW, Lr 1e-4 |
24GB GPU | 512 resolution, Batch Size 1 |
VRAM Optimization | Use nf4 based training |
Precision | bf16 |
-
Kolors Workflow:
# ComfyUI Plugins git clone https://github.com/kijai/ComfyUI-KwaiKolorsWrapper git clone https://github.com/MinusZoneAI/ComfyUI-Kolors-MZ
-
Configuration Guide: π CivitAI Article
- Kolors Black Image Issue: Ensure you're using FP16 Fixed VAE
- VRAM Limitations: Adjust
blocks_to_swap
parameter (higher values reduce memory usage) - Windows DLL Errors: Verify VC++ Redistributable installation
- 2025-06-27: Support kontxt nf4 training
- Thanks to @sourceful support me making flux fill lora training script.
- π Twitter: @Lrzjason
- π§ Email: lrzjason@gmail.com
- π¬ QQ Group: 866612947
- π¬ WeChat ID: fkdeai
- π¨ CivitAI: xiaozhijason
- Thanks for diffusers and Terminus Research Group
- Thanks to minienglish1 and Freon in EveryDream Discord for the assistance.
- Special thanks to kohya ss for references from the training codebase.
- Thanks to Kblueleaf for coding reference on hunyuandit gradient checkpoint implementation.
- Thanks to Kolors for the open-source checkpoint.
- Thanks to comfyui for the wonderful codebase.
- Thanks to emojiiii for the setup.bat script and other updates.
- Thanks to Rohit Gandikota and related authors of Concept Sliders https://github.com/rohitgandikota/sliders