ohamlab-ai-toolkit

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-# AI Toolkit by Ostris
-AI Toolkit is an 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.
-## Support My Work
-If you enjoy my projects or use them commercially, please consider sponsoring me. Every bit helps! 💖
-[Sponsor on GitHub](https://github.com/orgs/ostris) | [Support on Patreon](https://www.patreon.com/ostris) | [Donate on PayPal](https://www.paypal.com/donate/?hosted_button_id=9GEFUKC8T9R9W)
-### Current Sponsors
-All of these people / organizations are the ones who selflessly make this project possible. Thank you!!
-_Last updated: 2025-04-22 16:45 UTC_
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-<img src="https://c10.patreonusercontent.com/4/patreon-media/p/user/138787189/2b5662dcb638466282ac758e3ac651b4/eyJ3IjoyMDB9/1.png?token-time=2145916800&token-hash=IlUAs9JAlVRphfx81V-Jt-nMiSBS8mPewRr9u6pQjaQ%3D" alt="Антон Антонио" width="60" height="60" style="border-radius:8px;margin:5px;">
-</p>
----
-## Installation
-Requirements:
-- python >3.10
-- 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==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/cu126
-pip3 install -r requirements.txt
-```
-Windows:
-```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==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/cu126
-pip install -r requirements.txt
-```
-# 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 > 18
-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
-```
-## FLUX.1 Training
-### Tutorial
-To get started quickly, check out [@araminta_k](https://x.com/araminta_k) tutorial on [Finetuning Flux Dev on a 3090](https://www.youtube.com/watch?v=HzGW_Kyermg) with 24GB VRAM.
-### Requirements
-You currently need a GPU with **at least 24GB of VRAM** to train FLUX.1. If you are using it as your GPU to control
-your monitors, you probably need to set the flag `low_vram: true` in the config file under `model:`. This will quantize
-the model on CPU and should allow it to train with monitors attached. Users have gotten it to work on Windows with WSL,
-but there are some reports of a bug when running on windows natively.
-I have only tested on linux for now. This is still extremely experimental
-and a lot of quantizing and tricks had to happen to get it to fit on 24GB at all.
-### FLUX.1-dev
-FLUX.1-dev has a non-commercial license. Which means anything you train will inherit the
-non-commercial license. It is also a gated model, so you need to accept the license on HF before using it.
-Otherwise, this will fail. Here are the required steps to setup a license.
-1. Sign into HF and accept the model access here [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev)
-2. Make a file named `.env` in the root on this folder
-3. [Get a READ key from huggingface](https://huggingface.co/settings/tokens/new?) and add it to the `.env` file like so `HF_TOKEN=your_key_here`
-### FLUX.1-schnell
-FLUX.1-schnell is Apache 2.0. Anything trained on it can be licensed however you want and it does not require a HF_TOKEN to train.
-However, it does require a special adapter to train with it, [ostris/FLUX.1-schnell-training-adapter](https://huggingface.co/ostris/FLUX.1-schnell-training-adapter).
-It is also highly experimental. For best overall quality, training on FLUX.1-dev is recommended.
-To use it, You just need to add the assistant to the `model` section of your config file like so:
-```yaml
-      model:
-        name_or_path: "black-forest-labs/FLUX.1-schnell"
-        assistant_lora_path: "ostris/FLUX.1-schnell-training-adapter"
-        is_flux: true
-        quantize: true
-```
-You also need to adjust your sample steps since schnell does not require as many
-```yaml
-      sample:
-        guidance_scale: 1  # schnell does not do guidance
-        sample_steps: 4  # 1 - 4 works well
-```
-### 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
-![image](assets/lora_ease_ui.png)
-## Training in RunPod
-Example RunPod template: **runpod/pytorch:2.2.0-py3.10-cuda12.1.1-devel-ubuntu22.04**
-> You need a minimum of 24GB VRAM, pick a GPU by your preference.
-#### Example config ($0.5/hr):
-- 1x A40 (48 GB VRAM)
-- 19 vCPU 100 GB RAM
-#### Custom overrides (you need some storage to clone FLUX.1, store datasets, store trained models and samples):
-- ~120 GB Disk
-- ~120 GB Pod Volume
-- Start Jupyter Notebook
-### 1. Setup
-```
-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
-```
-### 2. Upload your dataset
-- Create a new folder in the root, name it `dataset` or whatever you like.
-- Drag and drop your .jpg, .jpeg, or .png images and .txt files inside the newly created dataset folder.
-### 3. Login into Hugging Face with an Access Token
-- 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.
-### 4. Training
-- Copy an example config file located at ```config/examples``` to the config folder and rename it to ```whatever_you_want.yml```.
-- Edit the config following the comments in the file.
-- Change ```folder_path: "/path/to/images/folder"``` to your dataset path like ```folder_path: "/workspace/ai-toolkit/your-dataset"```.
-- Run the file: ```python run.py config/whatever_you_want.yml```.
-### Screenshot from RunPod
-<img width="1728" alt="RunPod Training Screenshot" src="https://github.com/user-attachments/assets/53a1b8ef-92fa-4481-81a7-bde45a14a7b5">
-## 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.

+title: AI-TOOLKIT-Training
+emoji: 📹⚡️
+colorFrom: pink
+colorTo: gray
+sdk: gradio
+sdk_version: 5.29.0
+app_file: flux_train_ui.py
+pinned: false
+license: apache-2.0
+short_description: fast video generation from images & text