| # Ostris AI Toolkit |
|
|
| AI Toolkit is an easy to use all in one training suite for diffusion models. I try to support all the latest models on consumer grade hardware. Image and video models. It can be run as a GUI or CLI. It is designed to be easy to use but still have every feature imaginable. Free and open source. |
|
|
|
|
|
|
| ## Supported Models |
|
|
| ### Image |
| - [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) (FLUX.1) |
| - [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev) (FLUX.2) |
| - [black-forest-labs/FLUX.2-klein-base-4B](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-4B) (FLUX.2-klein-base-4B) |
| - [black-forest-labs/FLUX.2-klein-base-9B](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-9B) (FLUX.2-klein-base-9B) |
| - [ostris/Flex.1-alpha](https://huggingface.co/ostris/Flex.1-alpha) (Flex.1) |
| - [ostris/Flex.2-preview](https://huggingface.co/ostris/Flex.2-preview) (Flex.2) |
| - [lodestones/Chroma1-Base](https://huggingface.co/lodestones/Chroma1-Base) (Chroma) |
| - [Alpha-VLLM/Lumina-Image-2.0](https://huggingface.co/Alpha-VLLM/Lumina-Image-2.0) (Lumina2) |
| - [Qwen/Qwen-Image](https://huggingface.co/Qwen/Qwen-Image) (Qwen-Image) |
| - [Qwen/Qwen-Image-2512](https://huggingface.co/Qwen/Qwen-Image-2512) (Qwen-Image-2512) |
| - [HiDream-ai/HiDream-I1-Full](https://huggingface.co/HiDream-ai/HiDream-I1-Full) (HiDream I1) |
| - [OmniGen2/OmniGen2](https://huggingface.co/OmniGen2/OmniGen2) (OmniGen2) |
| - [Tongyi-MAI/Z-Image-Turbo](https://huggingface.co/Tongyi-MAI/Z-Image-Turbo) (Z-Image Turbo) |
| - [Tongyi-MAI/Z-Image](https://huggingface.co/Tongyi-MAI/Z-Image) (Z-Image) |
| - [ostris/Z-Image-De-Turbo](https://huggingface.co/ostris/Z-Image-De-Turbo) (Z-Image De-Turbo) |
| - [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) (SDXL) |
| - [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) (SD 1.5) |
| - [baidu/ERNIE-Image](https://huggingface.co/baidu/ERNIE-Image) (ERNIE-Image) |
| - [NucleusAI/Nucleus-Image](https://huggingface.co/NucleusAI/Nucleus-Image) (Nucleus-Image) |
| - [HiDream-ai/HiDream-O1-Image](https://huggingface.co/HiDream-ai/HiDream-O1-Image) (HiDream O1) |
|
|
| ### Instruction / Edit |
| - [black-forest-labs/FLUX.1-Kontext-dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev) (FLUX.1-Kontext-dev) |
| - [Qwen/Qwen-Image-Edit](https://huggingface.co/Qwen/Qwen-Image-Edit) (Qwen-Image-Edit) |
| - [Qwen/Qwen-Image-Edit-2509](https://huggingface.co/Qwen/Qwen-Image-Edit-2509) (Qwen-Image-Edit-2509) |
| - [Qwen/Qwen-Image-Edit-2511](https://huggingface.co/Qwen/Qwen-Image-Edit-2511) (Qwen-Image-Edit-2511) |
| - [HiDream-ai/HiDream-E1-1](https://huggingface.co/HiDream-ai/HiDream-E1-1) (HiDream E1) |
|
|
| ### Video |
| - [Wan-AI/Wan2.1-T2V-1.3B-Diffusers](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers) (Wan 2.1 1.3B) |
| - [Wan-AI/Wan2.1-I2V-14B-480P-Diffusers](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P-Diffusers) (Wan 2.1 I2V 14B-480P) |
| - [Wan-AI/Wan2.1-I2V-14B-720P-Diffusers](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P-Diffusers) (Wan 2.1 I2V 14B-720P) |
| - [Wan-AI/Wan2.1-T2V-14B-Diffusers](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B-Diffusers) (Wan 2.1 14B) |
| - [Wan-AI/Wan2.2-T2V-A14B-Diffusers](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers) (Wan 2.2 14B) |
| - [Wan-AI/Wan2.2-I2V-A14B-Diffusers](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers) (Wan 2.2 I2V 14B) |
| - [Wan-AI/Wan2.2-TI2V-5B-Diffusers](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers) (Wan 2.2 TI2V 5B) |
| - [Lightricks/LTX-2](https://huggingface.co/Lightricks/LTX-2) (LTX-2) |
| - [Lightricks/LTX-2.3](https://huggingface.co/Lightricks/LTX-2.3) (LTX-2.3) |
|
|
| ### Audio |
| - [ACE-Step/Ace-Step1.5](https://huggingface.co/ACE-Step/Ace-Step1.5) (Ace Step 1.5) |
| - [ACE-Step/acestep-v15-xl-base](https://huggingface.co/ACE-Step/acestep-v15-xl-base) (Ace Step 1.5 XL) |
|
|
| ### Experimental |
| - [lodestones/Zeta-Chroma](https://huggingface.co/lodestones/Zeta-Chroma) (Zeta Chroma) |
|
|
| ## Installation |
|
|
| Requirements: |
| - python >=3.10 (3.12 recommended) |
| - Nvidia GPU with enough ram to do what you need |
| - python venv |
| - git |
|
|
|
|
| Linux: |
| ```bash |
| git clone https://github.com/ostris/ai-toolkit.git |
| cd ai-toolkit |
| python3 -m venv venv |
| source venv/bin/activate |
| # install torch first |
| pip3 install --no-cache-dir torch==2.9.1 torchvision==0.24.1 torchaudio==2.9.1 --index-url https://download.pytorch.org/whl/cu128 |
| pip3 install -r requirements.txt |
| ``` |
|
|
| For devices running **DGX OS** (including DGX Spark), follow [these](dgx_instructions.md) instructions. |
|
|
|
|
| Windows: |
|
|
| If you are having issues with Windows. I recommend using the easy install script at [https://github.com/Tavris1/AI-Toolkit-Easy-Install](https://github.com/Tavris1/AI-Toolkit-Easy-Install) |
|
|
| ```bash |
| git clone https://github.com/ostris/ai-toolkit.git |
| cd ai-toolkit |
| python -m venv venv |
| .\venv\Scripts\activate |
| pip install --no-cache-dir torch==2.9.1 torchvision==0.24.1 torchaudio==2.9.1 --index-url https://download.pytorch.org/whl/cu128 |
| pip install -r requirements.txt |
| ``` |
|
|
| MacOS: |
|
|
| Experimental support for Silicon Macs is available. I do not have a Mac with enough RAM to fully test this |
| so please let me know if there are issues. There is a convience script to install and run on MacOS |
| locates at `./run_mac.zsh` that will install the dependencies locally and run the UI. To run this, |
| do the following: |
|
|
| ```bash |
| git clone https://github.com/ostris/ai-toolkit.git |
| cd ai-toolkit |
| chmod +x run_mac.zsh |
| ./run_mac.zsh |
| ``` |
|
|
|
|
| # AI Toolkit UI |
|
|
| <img src="https://ostris.com/wp-content/uploads/2025/02/toolkit-ui.jpg" alt="AI Toolkit UI" width="100%"> |
|
|
| The AI Toolkit UI is a web interface for the AI Toolkit. It allows you to easily start, stop, and monitor jobs. It also allows you to easily train models with a few clicks. It also allows you to set a token for the UI to prevent unauthorized access so it is mostly safe to run on an exposed server. |
|
|
| ## Running the UI |
|
|
| Requirements: |
| - Node.js > 20 |
|
|
| The UI does not need to be kept running for the jobs to run. It is only needed to start/stop/monitor jobs. The commands below |
| will install / update the UI and it's dependencies and start the UI. |
|
|
| ```bash |
| cd ui |
| npm run build_and_start |
| ``` |
|
|
| You can now access the UI at `http://localhost:8675` or `http://<your-ip>:8675` if you are running it on a server. |
|
|
| ## Securing the UI |
|
|
| If you are hosting the UI on a cloud provider or any network that is not secure, I highly recommend securing it with an auth token. |
| You can do this by setting the environment variable `AI_TOOLKIT_AUTH` to super secure password. This token will be required to access |
| the UI. You can set this when starting the UI like so: |
|
|
| ```bash |
| # Linux |
| AI_TOOLKIT_AUTH=super_secure_password npm run build_and_start |
| |
| # Windows |
| set AI_TOOLKIT_AUTH=super_secure_password && npm run build_and_start |
| |
| # Windows Powershell |
| $env:AI_TOOLKIT_AUTH="super_secure_password"; npm run build_and_start |
| ``` |
|
|
| ### Training |
| 1. Copy the example config file located at `config/examples/train_lora_flux_24gb.yaml` (`config/examples/train_lora_flux_schnell_24gb.yaml` for schnell) to the `config` folder and rename it to `whatever_you_want.yml` |
| 2. Edit the file following the comments in the file |
| 3. Run the file like so `python run.py config/whatever_you_want.yml` |
|
|
| A folder with the name and the training folder from the config file will be created when you start. It will have all |
| checkpoints and images in it. You can stop the training at any time using ctrl+c and when you resume, it will pick back up |
| from the last checkpoint. |
|
|
| IMPORTANT. If you press crtl+c while it is saving, it will likely corrupt that checkpoint. So wait until it is done saving |
|
|
| ### Need help? |
|
|
| Please do not open a bug report unless it is a bug in the code. You are welcome to [Join my Discord](https://discord.gg/VXmU2f5WEU) |
| and ask for help there. However, please refrain from PMing me directly with general question or support. Ask in the discord |
| and I will answer when I can. |
|
|
| ## Gradio UI |
|
|
| To get started training locally with a with a custom UI, once you followed the steps above and `ai-toolkit` is installed: |
|
|
| ```bash |
| cd ai-toolkit #in case you are not yet in the ai-toolkit folder |
| huggingface-cli login #provide a `write` token to publish your LoRA at the end |
| python flux_train_ui.py |
| ``` |
|
|
| You will instantiate a UI that will let you upload your images, caption them, train and publish your LoRA |
|  |
|
|
|
|
| ## Training in RunPod |
| If you would like to use Runpod, but have not signed up yet, please consider using [my Runpod affiliate link](https://runpod.io?ref=h0y9jyr2) to help support this project. |
|
|
|
|
| I maintain an official Runpod Pod template here which can be accessed [here](https://console.runpod.io/deploy?template=0fqzfjy6f3&ref=h0y9jyr2). |
|
|
| I have also created a short video showing how to get started using AI Toolkit with Runpod [here](https://youtu.be/HBNeS-F6Zz8). |
|
|
| ## Training in Modal |
|
|
| ### 1. Setup |
| #### ai-toolkit: |
| ``` |
| git clone https://github.com/ostris/ai-toolkit.git |
| cd ai-toolkit |
| git submodule update --init --recursive |
| python -m venv venv |
| source venv/bin/activate |
| pip install torch |
| pip install -r requirements.txt |
| pip install --upgrade accelerate transformers diffusers huggingface_hub #Optional, run it if you run into issues |
| ``` |
| #### Modal: |
| - Run `pip install modal` to install the modal Python package. |
| - Run `modal setup` to authenticate (if this doesn’t work, try `python -m modal setup`). |
|
|
| #### Hugging Face: |
| - Get a READ token from [here](https://huggingface.co/settings/tokens) and request access to Flux.1-dev model from [here](https://huggingface.co/black-forest-labs/FLUX.1-dev). |
| - Run `huggingface-cli login` and paste your token. |
|
|
| ### 2. Upload your dataset |
| - Drag and drop your dataset folder containing the .jpg, .jpeg, or .png images and .txt files in `ai-toolkit`. |
|
|
| ### 3. Configs |
| - Copy an example config file located at ```config/examples/modal``` to the `config` folder and rename it to ```whatever_you_want.yml```. |
| - Edit the config following the comments in the file, **<ins>be careful and follow the example `/root/ai-toolkit` paths</ins>**. |
|
|
| ### 4. Edit run_modal.py |
| - Set your entire local `ai-toolkit` path at `code_mount = modal.Mount.from_local_dir` like: |
| |
| ``` |
| code_mount = modal.Mount.from_local_dir("/Users/username/ai-toolkit", remote_path="/root/ai-toolkit") |
| ``` |
| - Choose a `GPU` and `Timeout` in `@app.function` _(default is A100 40GB and 2 hour timeout)_. |
|
|
| ### 5. Training |
| - Run the config file in your terminal: `modal run run_modal.py --config-file-list-str=/root/ai-toolkit/config/whatever_you_want.yml`. |
| - You can monitor your training in your local terminal, or on [modal.com](https://modal.com/). |
| - Models, samples and optimizer will be stored in `Storage > flux-lora-models`. |
|
|
| ### 6. Saving the model |
| - Check contents of the volume by running `modal volume ls flux-lora-models`. |
| - Download the content by running `modal volume get flux-lora-models your-model-name`. |
| - Example: `modal volume get flux-lora-models my_first_flux_lora_v1`. |
|
|
| ### Screenshot from Modal |
|
|
| <img width="1728" alt="Modal Traning Screenshot" src="https://github.com/user-attachments/assets/7497eb38-0090-49d6-8ad9-9c8ea7b5388b"> |
|
|
| --- |
|
|
| ## Dataset Preparation |
|
|
| Datasets generally need to be a folder containing images and associated text files. Currently, the only supported |
| formats are jpg, jpeg, and png. Webp currently has issues. The text files should be named the same as the images |
| but with a `.txt` extension. For example `image2.jpg` and `image2.txt`. The text file should contain only the caption. |
| You can add the word `[trigger]` in the caption file and if you have `trigger_word` in your config, it will be automatically |
| replaced. |
|
|
| Images are never upscaled but they are downscaled and placed in buckets for batching. **You do not need to crop/resize your images**. |
| The loader will automatically resize them and can handle varying aspect ratios. |
|
|
|
|
| ## Training Specific Layers |
|
|
| To train specific layers with LoRA, you can use the `only_if_contains` network kwargs. For instance, if you want to train only the 2 layers |
| used by The Last Ben, [mentioned in this post](https://x.com/__TheBen/status/1829554120270987740), you can adjust your |
| network kwargs like so: |
|
|
| ```yaml |
| network: |
| type: "lora" |
| linear: 128 |
| linear_alpha: 128 |
| network_kwargs: |
| only_if_contains: |
| - "transformer.single_transformer_blocks.7.proj_out" |
| - "transformer.single_transformer_blocks.20.proj_out" |
| ``` |
|
|
| The naming conventions of the layers are in diffusers format, so checking the state dict of a model will reveal |
| the suffix of the name of the layers you want to train. You can also use this method to only train specific groups of weights. |
| For instance to only train the `single_transformer` for FLUX.1, you can use the following: |
|
|
| ```yaml |
| network: |
| type: "lora" |
| linear: 128 |
| linear_alpha: 128 |
| network_kwargs: |
| only_if_contains: |
| - "transformer.single_transformer_blocks." |
| ``` |
|
|
| You can also exclude layers by their names by using `ignore_if_contains` network kwarg. So to exclude all the single transformer blocks, |
|
|
|
|
| ```yaml |
| network: |
| type: "lora" |
| linear: 128 |
| linear_alpha: 128 |
| network_kwargs: |
| ignore_if_contains: |
| - "transformer.single_transformer_blocks." |
| ``` |
|
|
| `ignore_if_contains` takes priority over `only_if_contains`. So if a weight is covered by both, |
| if will be ignored. |
|
|
| ## LoKr Training |
|
|
| To learn more about LoKr, read more about it at [KohakuBlueleaf/LyCORIS](https://github.com/KohakuBlueleaf/LyCORIS/blob/main/docs/Guidelines.md). To train a LoKr model, you can adjust the network type in the config file like so: |
|
|
| ```yaml |
| network: |
| type: "lokr" |
| lokr_full_rank: true |
| lokr_factor: 8 |
| ``` |
|
|
| Everything else should work the same including layer targeting. |
|
|
|
|
| ## Support My Work |
|
|
| If you enjoy my projects or use them commercially, please consider sponsoring me. Every bit helps! 💖 |
|
|
| <a href="https://ostris.com/sponsors" target="_blank"><img src="https://ostris.com/wp-content/uploads/2025/05/support-banner2.png" alt="Support my work" style="max-width:100%;height:auto;"></a> |
|
|
| ### Current Sponsors |
|
|
| All of these people / organizations are the ones who selflessly make this project possible. Thank you!! |
|
|
| _Last updated: 2026-03-31 18:10 UTC_ |
|
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| </p> |
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| --- |
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