diff --git a/README.md b/README.md
index c054b142..45c94b57 100644
--- a/README.md
+++ b/README.md
@@ -117,7 +117,7 @@ Please do not open a bug report unless it is a bug in the code. You are welcome
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.
-### Training in RunPod cloud
+## 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.
@@ -142,26 +142,72 @@ 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 and .txt files inside the newly created dataset folder
+- 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)
-- Run ```huggingface-cli login``` and paste your 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```
+- 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
-
+## 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, **be careful and follow the example `/root/ai-toolkit` paths**.
+
+### 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
+
+
---
diff --git a/config/examples/modal/modal_train_lora_flux_24gb.yaml b/config/examples/modal/modal_train_lora_flux_24gb.yaml
new file mode 100644
index 00000000..51873de0
--- /dev/null
+++ b/config/examples/modal/modal_train_lora_flux_24gb.yaml
@@ -0,0 +1,96 @@
+---
+job: extension
+config:
+ # this name will be the folder and filename name
+ name: "my_first_flux_lora_v1"
+ process:
+ - type: 'sd_trainer'
+ # root folder to save training sessions/samples/weights
+ training_folder: "/root/ai-toolkit/modal_output" # must match MOUNT_DIR from run_modal.py
+ # uncomment to see performance stats in the terminal every N steps
+# performance_log_every: 1000
+ device: cuda:0
+ # if a trigger word is specified, it will be added to captions of training data if it does not already exist
+ # alternatively, in your captions you can add [trigger] and it will be replaced with the trigger word
+# trigger_word: "p3r5on"
+ network:
+ type: "lora"
+ linear: 16
+ linear_alpha: 16
+ save:
+ dtype: float16 # precision to save
+ save_every: 250 # save every this many steps
+ max_step_saves_to_keep: 4 # how many intermittent saves to keep
+ datasets:
+ # datasets are a folder of images. captions need to be txt files with the same name as the image
+ # for instance image2.jpg and image2.txt. Only jpg, jpeg, and png are supported currently
+ # images will automatically be resized and bucketed into the resolution specified
+ # on windows, escape back slashes with another backslash so
+ # "C:\\path\\to\\images\\folder"
+ # your dataset must be placed in /ai-toolkit and /root is for modal to find the dir:
+ - folder_path: "/root/ai-toolkit/your-dataset"
+ caption_ext: "txt"
+ caption_dropout_rate: 0.05 # will drop out the caption 5% of time
+ shuffle_tokens: false # shuffle caption order, split by commas
+ cache_latents_to_disk: true # leave this true unless you know what you're doing
+ resolution: [ 512, 768, 1024 ] # flux enjoys multiple resolutions
+ train:
+ batch_size: 1
+ steps: 2000 # total number of steps to train 500 - 4000 is a good range
+ gradient_accumulation_steps: 1
+ train_unet: true
+ train_text_encoder: false # probably won't work with flux
+ gradient_checkpointing: true # need the on unless you have a ton of vram
+ noise_scheduler: "flowmatch" # for training only
+ optimizer: "adamw8bit"
+ lr: 1e-4
+ # uncomment this to skip the pre training sample
+# skip_first_sample: true
+ # uncomment to completely disable sampling
+# disable_sampling: true
+ # uncomment to use new vell curved weighting. Experimental but may produce better results
+# linear_timesteps: true
+
+ # ema will smooth out learning, but could slow it down. Recommended to leave on.
+ ema_config:
+ use_ema: true
+ ema_decay: 0.99
+
+ # will probably need this if gpu supports it for flux, other dtypes may not work correctly
+ dtype: bf16
+ model:
+ # huggingface model name or path
+ # if you get an error, or get stuck while downloading,
+ # check https://github.com/ostris/ai-toolkit/issues/84, download the model locally and
+ # place it like "/root/ai-toolkit/FLUX.1-dev"
+ name_or_path: "black-forest-labs/FLUX.1-dev"
+ is_flux: true
+ quantize: true # run 8bit mixed precision
+# low_vram: true # uncomment this if the GPU is connected to your monitors. It will use less vram to quantize, but is slower.
+ sample:
+ sampler: "flowmatch" # must match train.noise_scheduler
+ sample_every: 250 # sample every this many steps
+ width: 1024
+ height: 1024
+ prompts:
+ # you can add [trigger] to the prompts here and it will be replaced with the trigger word
+# - "[trigger] holding a sign that says 'I LOVE PROMPTS!'"\
+ - "woman with red hair, playing chess at the park, bomb going off in the background"
+ - "a woman holding a coffee cup, in a beanie, sitting at a cafe"
+ - "a horse is a DJ at a night club, fish eye lens, smoke machine, lazer lights, holding a martini"
+ - "a man showing off his cool new t shirt at the beach, a shark is jumping out of the water in the background"
+ - "a bear building a log cabin in the snow covered mountains"
+ - "woman playing the guitar, on stage, singing a song, laser lights, punk rocker"
+ - "hipster man with a beard, building a chair, in a wood shop"
+ - "photo of a man, white background, medium shot, modeling clothing, studio lighting, white backdrop"
+ - "a man holding a sign that says, 'this is a sign'"
+ - "a bulldog, in a post apocalyptic world, with a shotgun, in a leather jacket, in a desert, with a motorcycle"
+ neg: "" # not used on flux
+ seed: 42
+ walk_seed: true
+ guidance_scale: 4
+ sample_steps: 20
+# you can add any additional meta info here. [name] is replaced with config name at top
+meta:
+ name: "[name]"
+ version: '1.0'
diff --git a/config/examples/modal/modal_train_lora_flux_schnell_24gb.yaml b/config/examples/modal/modal_train_lora_flux_schnell_24gb.yaml
new file mode 100644
index 00000000..6d1e964f
--- /dev/null
+++ b/config/examples/modal/modal_train_lora_flux_schnell_24gb.yaml
@@ -0,0 +1,98 @@
+---
+job: extension
+config:
+ # this name will be the folder and filename name
+ name: "my_first_flux_lora_v1"
+ process:
+ - type: 'sd_trainer'
+ # root folder to save training sessions/samples/weights
+ training_folder: "/root/ai-toolkit/modal_output" # must match MOUNT_DIR from run_modal.py
+ # uncomment to see performance stats in the terminal every N steps
+# performance_log_every: 1000
+ device: cuda:0
+ # if a trigger word is specified, it will be added to captions of training data if it does not already exist
+ # alternatively, in your captions you can add [trigger] and it will be replaced with the trigger word
+# trigger_word: "p3r5on"
+ network:
+ type: "lora"
+ linear: 16
+ linear_alpha: 16
+ save:
+ dtype: float16 # precision to save
+ save_every: 250 # save every this many steps
+ max_step_saves_to_keep: 4 # how many intermittent saves to keep
+ datasets:
+ # datasets are a folder of images. captions need to be txt files with the same name as the image
+ # for instance image2.jpg and image2.txt. Only jpg, jpeg, and png are supported currently
+ # images will automatically be resized and bucketed into the resolution specified
+ # on windows, escape back slashes with another backslash so
+ # "C:\\path\\to\\images\\folder"
+ # your dataset must be placed in /ai-toolkit and /root is for modal to find the dir:
+ - folder_path: "/root/ai-toolkit/your-dataset"
+ caption_ext: "txt"
+ caption_dropout_rate: 0.05 # will drop out the caption 5% of time
+ shuffle_tokens: false # shuffle caption order, split by commas
+ cache_latents_to_disk: true # leave this true unless you know what you're doing
+ resolution: [ 512, 768, 1024 ] # flux enjoys multiple resolutions
+ train:
+ batch_size: 1
+ steps: 2000 # total number of steps to train 500 - 4000 is a good range
+ gradient_accumulation_steps: 1
+ train_unet: true
+ train_text_encoder: false # probably won't work with flux
+ gradient_checkpointing: true # need the on unless you have a ton of vram
+ noise_scheduler: "flowmatch" # for training only
+ optimizer: "adamw8bit"
+ lr: 1e-4
+ # uncomment this to skip the pre training sample
+# skip_first_sample: true
+ # uncomment to completely disable sampling
+# disable_sampling: true
+ # uncomment to use new vell curved weighting. Experimental but may produce better results
+# linear_timesteps: true
+
+ # ema will smooth out learning, but could slow it down. Recommended to leave on.
+ ema_config:
+ use_ema: true
+ ema_decay: 0.99
+
+ # will probably need this if gpu supports it for flux, other dtypes may not work correctly
+ dtype: bf16
+ model:
+ # huggingface model name or path
+ # if you get an error, or get stuck while downloading,
+ # check https://github.com/ostris/ai-toolkit/issues/84, download the models locally and
+ # place them like "/root/ai-toolkit/FLUX.1-schnell" and "/root/ai-toolkit/FLUX.1-schnell-training-adapter"
+ name_or_path: "black-forest-labs/FLUX.1-schnell"
+ assistant_lora_path: "ostris/FLUX.1-schnell-training-adapter" # Required for flux schnell training
+ is_flux: true
+ quantize: true # run 8bit mixed precision
+ # low_vram is painfully slow to fuse in the adapter avoid it unless absolutely necessary
+# low_vram: true # uncomment this if the GPU is connected to your monitors. It will use less vram to quantize, but is slower.
+ sample:
+ sampler: "flowmatch" # must match train.noise_scheduler
+ sample_every: 250 # sample every this many steps
+ width: 1024
+ height: 1024
+ prompts:
+ # you can add [trigger] to the prompts here and it will be replaced with the trigger word
+# - "[trigger] holding a sign that says 'I LOVE PROMPTS!'"\
+ - "woman with red hair, playing chess at the park, bomb going off in the background"
+ - "a woman holding a coffee cup, in a beanie, sitting at a cafe"
+ - "a horse is a DJ at a night club, fish eye lens, smoke machine, lazer lights, holding a martini"
+ - "a man showing off his cool new t shirt at the beach, a shark is jumping out of the water in the background"
+ - "a bear building a log cabin in the snow covered mountains"
+ - "woman playing the guitar, on stage, singing a song, laser lights, punk rocker"
+ - "hipster man with a beard, building a chair, in a wood shop"
+ - "photo of a man, white background, medium shot, modeling clothing, studio lighting, white backdrop"
+ - "a man holding a sign that says, 'this is a sign'"
+ - "a bulldog, in a post apocalyptic world, with a shotgun, in a leather jacket, in a desert, with a motorcycle"
+ neg: "" # not used on flux
+ seed: 42
+ walk_seed: true
+ guidance_scale: 1 # schnell does not do guidance
+ sample_steps: 4 # 1 - 4 works well
+# you can add any additional meta info here. [name] is replaced with config name at top
+meta:
+ name: "[name]"
+ version: '1.0'
diff --git a/notebooks/FLUX_1_dev_LoRA_Training.ipynb b/notebooks/FLUX_1_dev_LoRA_Training.ipynb
index d3174813..8cfcd1fe 100644
--- a/notebooks/FLUX_1_dev_LoRA_Training.ipynb
+++ b/notebooks/FLUX_1_dev_LoRA_Training.ipynb
@@ -1,53 +1,45 @@
{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "provenance": [],
- "machine_shape": "hm",
- "gpuType": "A100"
- },
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3"
- },
- "language_info": {
- "name": "python"
- },
- "accelerator": "GPU"
- },
"cells": [
{
"cell_type": "markdown",
- "source": [
- "# AI Toolkit by Ostris\n",
- "## FLUX.1 Training\n"
- ],
"metadata": {
"collapsed": false,
"id": "zl-S0m3pkQC5"
- }
+ },
+ "source": [
+ "# AI Toolkit by Ostris\n",
+ "## FLUX.1-dev Training\n"
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
- "!git clone https://github.com/ostris/ai-toolkit\n",
- "!mkdir -p /content/dataset"
- ],
+ "!nvidia-smi"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
"metadata": {
"id": "BvAG0GKAh59G"
},
- "execution_count": null,
- "outputs": []
+ "outputs": [],
+ "source": [
+ "!git clone https://github.com/ostris/ai-toolkit\n",
+ "!mkdir -p /content/dataset"
+ ]
},
{
"cell_type": "markdown",
- "source": [
- "Put your image dataset in the `/content/dataset` folder"
- ],
"metadata": {
"id": "UFUW4ZMmnp1V"
- }
+ },
+ "source": [
+ "Put your image dataset in the `/content/dataset` folder"
+ ]
},
{
"cell_type": "code",
@@ -62,6 +54,9 @@
},
{
"cell_type": "markdown",
+ "metadata": {
+ "id": "OV0HnOI6o8V6"
+ },
"source": [
"## Model License\n",
"Training currently only works with FLUX.1-dev. 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.\n",
@@ -69,13 +64,15 @@
"Sign into HF and accept the model access here [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev)\n",
"\n",
"[Get a READ key from huggingface](https://huggingface.co/settings/tokens/new?) and place it in the next cell after running it."
- ],
- "metadata": {
- "id": "OV0HnOI6o8V6"
- }
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3yZZdhFRoj2m"
+ },
+ "outputs": [],
"source": [
"import getpass\n",
"import os\n",
@@ -87,15 +84,15 @@
"os.environ['HF_TOKEN'] = hf_token\n",
"\n",
"print(\"HF_TOKEN environment variable has been set.\")"
- ],
- "metadata": {
- "id": "3yZZdhFRoj2m"
- },
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "9gO2EzQ1kQC8"
+ },
+ "outputs": [],
"source": [
"import os\n",
"import sys\n",
@@ -105,26 +102,26 @@
"from PIL import Image\n",
"import os\n",
"os.environ[\"HF_HUB_ENABLE_HF_TRANSFER\"] = \"1\""
- ],
- "metadata": {
- "id": "9gO2EzQ1kQC8"
- },
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {
+ "id": "N8UUFzVRigbC"
+ },
"source": [
"## Setup\n",
"\n",
"This is your config. It is documented pretty well. Normally you would do this as a yaml file, but for colab, this will work. This will run as is without modification, but feel free to edit as you want."
- ],
- "metadata": {
- "id": "N8UUFzVRigbC"
- }
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "_t28QURYjRQO"
+ },
+ "outputs": [],
"source": [
"from collections import OrderedDict\n",
"\n",
@@ -169,7 +166,7 @@
" ]),\n",
" ('train', OrderedDict([\n",
" ('batch_size', 1),\n",
- " ('steps', 4000), # total number of steps to train 500 - 4000 is a good range\n",
+ " ('steps', 2000), # total number of steps to train 500 - 4000 is a good range\n",
" ('gradient_accumulation_steps', 1),\n",
" ('train_unet', True),\n",
" ('train_text_encoder', False), # probably won't work with flux\n",
@@ -177,9 +174,16 @@
" ('gradient_checkpointing', True), # need the on unless you have a ton of vram\n",
" ('noise_scheduler', 'flowmatch'), # for training only\n",
" ('optimizer', 'adamw8bit'),\n",
- " ('lr', 4e-4),\n",
+ " ('lr', 1e-4),\n",
+ "\n",
" # uncomment this to skip the pre training sample\n",
- " #('skip_first_sample', True),\n",
+ " # ('skip_first_sample', True),\n",
+ "\n",
+ " # uncomment to completely disable sampling\n",
+ " # ('disable_sampling', True),\n",
+ "\n",
+ " # uncomment to use new vell curved weighting. Experimental but may produce better results\n",
+ " # ('linear_timesteps', True),\n",
"\n",
" # ema will smooth out learning, but could slow it down. Recommended to leave on.\n",
" ('ema_config', OrderedDict([\n",
@@ -231,45 +235,57 @@
" ('version', '1.0')\n",
" ]))\n",
"])\n"
- ],
- "metadata": {
- "id": "_t28QURYjRQO"
- },
- "execution_count": null,
- "outputs": []
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {
+ "id": "h6F1FlM2Wb3l"
+ },
"source": [
"## Run it\n",
"\n",
"Below does all the magic. Check your folders to the left. Items will be in output/LoRA/your_name_v1 In the samples folder, there are preiodic sampled. This doesnt work great with colab. They will be in /content/output"
- ],
- "metadata": {
- "id": "h6F1FlM2Wb3l"
- }
+ ]
},
{
"cell_type": "code",
- "source": [
- "run_job(job_to_run)\n"
- ],
+ "execution_count": null,
"metadata": {
"id": "HkajwI8gteOh"
},
- "execution_count": null,
- "outputs": []
+ "outputs": [],
+ "source": [
+ "run_job(job_to_run)\n"
+ ]
},
{
"cell_type": "markdown",
+ "metadata": {
+ "id": "Hblgb5uwW5SD"
+ },
"source": [
"## Done\n",
"\n",
"Check your ourput dir and get your slider\n"
- ],
- "metadata": {
- "id": "Hblgb5uwW5SD"
- }
+ ]
}
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "A100",
+ "machine_shape": "hm",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/FLUX_1_schnell_LoRA_Training.ipynb b/notebooks/FLUX_1_schnell_LoRA_Training.ipynb
new file mode 100644
index 00000000..652d8ccc
--- /dev/null
+++ b/notebooks/FLUX_1_schnell_LoRA_Training.ipynb
@@ -0,0 +1,296 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": false,
+ "id": "zl-S0m3pkQC5"
+ },
+ "source": [
+ "# AI Toolkit by Ostris\n",
+ "## FLUX.1-schnell Training\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3cokMT-WC6rG"
+ },
+ "outputs": [],
+ "source": [
+ "!nvidia-smi"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true,
+ "id": "BvAG0GKAh59G"
+ },
+ "outputs": [],
+ "source": [
+ "!git clone https://github.com/ostris/ai-toolkit\n",
+ "!mkdir -p /content/dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UFUW4ZMmnp1V"
+ },
+ "source": [
+ "Put your image dataset in the `/content/dataset` folder"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true,
+ "id": "XGZqVER_aQJW"
+ },
+ "outputs": [],
+ "source": [
+ "!cd ai-toolkit && git submodule update --init --recursive && pip install -r requirements.txt\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OV0HnOI6o8V6"
+ },
+ "source": [
+ "## Model License\n",
+ "Training currently only works with FLUX.1-dev. 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.\n",
+ "\n",
+ "Sign into HF and accept the model access here [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev)\n",
+ "\n",
+ "[Get a READ key from huggingface](https://huggingface.co/settings/tokens/new?) and place it in the next cell after running it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3yZZdhFRoj2m"
+ },
+ "outputs": [],
+ "source": [
+ "import getpass\n",
+ "import os\n",
+ "\n",
+ "# Prompt for the token\n",
+ "hf_token = getpass.getpass('Enter your HF access token and press enter: ')\n",
+ "\n",
+ "# Set the environment variable\n",
+ "os.environ['HF_TOKEN'] = hf_token\n",
+ "\n",
+ "print(\"HF_TOKEN environment variable has been set.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "9gO2EzQ1kQC8"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import sys\n",
+ "sys.path.append('/content/ai-toolkit')\n",
+ "from toolkit.job import run_job\n",
+ "from collections import OrderedDict\n",
+ "from PIL import Image\n",
+ "import os\n",
+ "os.environ[\"HF_HUB_ENABLE_HF_TRANSFER\"] = \"1\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "N8UUFzVRigbC"
+ },
+ "source": [
+ "## Setup\n",
+ "\n",
+ "This is your config. It is documented pretty well. Normally you would do this as a yaml file, but for colab, this will work. This will run as is without modification, but feel free to edit as you want."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "id": "_t28QURYjRQO"
+ },
+ "outputs": [],
+ "source": [
+ "from collections import OrderedDict\n",
+ "\n",
+ "job_to_run = OrderedDict([\n",
+ " ('job', 'extension'),\n",
+ " ('config', OrderedDict([\n",
+ " # this name will be the folder and filename name\n",
+ " ('name', 'my_first_flux_lora_v1'),\n",
+ " ('process', [\n",
+ " OrderedDict([\n",
+ " ('type', 'sd_trainer'),\n",
+ " # root folder to save training sessions/samples/weights\n",
+ " ('training_folder', '/content/output'),\n",
+ " # uncomment to see performance stats in the terminal every N steps\n",
+ " #('performance_log_every', 1000),\n",
+ " ('device', 'cuda:0'),\n",
+ " # if a trigger word is specified, it will be added to captions of training data if it does not already exist\n",
+ " # alternatively, in your captions you can add [trigger] and it will be replaced with the trigger word\n",
+ " # ('trigger_word', 'image'),\n",
+ " ('network', OrderedDict([\n",
+ " ('type', 'lora'),\n",
+ " ('linear', 16),\n",
+ " ('linear_alpha', 16)\n",
+ " ])),\n",
+ " ('save', OrderedDict([\n",
+ " ('dtype', 'float16'), # precision to save\n",
+ " ('save_every', 250), # save every this many steps\n",
+ " ('max_step_saves_to_keep', 4) # how many intermittent saves to keep\n",
+ " ])),\n",
+ " ('datasets', [\n",
+ " # datasets are a folder of images. captions need to be txt files with the same name as the image\n",
+ " # for instance image2.jpg and image2.txt. Only jpg, jpeg, and png are supported currently\n",
+ " # images will automatically be resized and bucketed into the resolution specified\n",
+ " OrderedDict([\n",
+ " ('folder_path', '/content/dataset'),\n",
+ " ('caption_ext', 'txt'),\n",
+ " ('caption_dropout_rate', 0.05), # will drop out the caption 5% of time\n",
+ " ('shuffle_tokens', False), # shuffle caption order, split by commas\n",
+ " ('cache_latents_to_disk', True), # leave this true unless you know what you're doing\n",
+ " ('resolution', [512, 768, 1024]) # flux enjoys multiple resolutions\n",
+ " ])\n",
+ " ]),\n",
+ " ('train', OrderedDict([\n",
+ " ('batch_size', 1),\n",
+ " ('steps', 2000), # total number of steps to train 500 - 4000 is a good range\n",
+ " ('gradient_accumulation_steps', 1),\n",
+ " ('train_unet', True),\n",
+ " ('train_text_encoder', False), # probably won't work with flux\n",
+ " ('gradient_checkpointing', True), # need the on unless you have a ton of vram\n",
+ " ('noise_scheduler', 'flowmatch'), # for training only\n",
+ " ('optimizer', 'adamw8bit'),\n",
+ " ('lr', 1e-4),\n",
+ "\n",
+ " # uncomment this to skip the pre training sample\n",
+ " # ('skip_first_sample', True),\n",
+ "\n",
+ " # uncomment to completely disable sampling\n",
+ " # ('disable_sampling', True),\n",
+ "\n",
+ " # uncomment to use new vell curved weighting. Experimental but may produce better results\n",
+ " # ('linear_timesteps', True),\n",
+ "\n",
+ " # ema will smooth out learning, but could slow it down. Recommended to leave on.\n",
+ " ('ema_config', OrderedDict([\n",
+ " ('use_ema', True),\n",
+ " ('ema_decay', 0.99)\n",
+ " ])),\n",
+ "\n",
+ " # will probably need this if gpu supports it for flux, other dtypes may not work correctly\n",
+ " ('dtype', 'bf16')\n",
+ " ])),\n",
+ " ('model', OrderedDict([\n",
+ " # huggingface model name or path\n",
+ " ('name_or_path', 'black-forest-labs/FLUX.1-schnell'),\n",
+ " ('assistant_lora_path', 'ostris/FLUX.1-schnell-training-adapter'), # Required for flux schnell training\n",
+ " ('is_flux', True),\n",
+ " ('quantize', True), # run 8bit mixed precision\n",
+ " # low_vram is painfully slow to fuse in the adapter avoid it unless absolutely necessary\n",
+ " #('low_vram', True), # uncomment this if the GPU is connected to your monitors. It will use less vram to quantize, but is slower.\n",
+ " ])),\n",
+ " ('sample', OrderedDict([\n",
+ " ('sampler', 'flowmatch'), # must match train.noise_scheduler\n",
+ " ('sample_every', 250), # sample every this many steps\n",
+ " ('width', 1024),\n",
+ " ('height', 1024),\n",
+ " ('prompts', [\n",
+ " # you can add [trigger] to the prompts here and it will be replaced with the trigger word\n",
+ " #'[trigger] holding a sign that says \\'I LOVE PROMPTS!\\'',\n",
+ " 'woman with red hair, playing chess at the park, bomb going off in the background',\n",
+ " 'a woman holding a coffee cup, in a beanie, sitting at a cafe',\n",
+ " 'a horse is a DJ at a night club, fish eye lens, smoke machine, lazer lights, holding a martini',\n",
+ " 'a man showing off his cool new t shirt at the beach, a shark is jumping out of the water in the background',\n",
+ " 'a bear building a log cabin in the snow covered mountains',\n",
+ " 'woman playing the guitar, on stage, singing a song, laser lights, punk rocker',\n",
+ " 'hipster man with a beard, building a chair, in a wood shop',\n",
+ " 'photo of a man, white background, medium shot, modeling clothing, studio lighting, white backdrop',\n",
+ " 'a man holding a sign that says, \\'this is a sign\\'',\n",
+ " 'a bulldog, in a post apocalyptic world, with a shotgun, in a leather jacket, in a desert, with a motorcycle'\n",
+ " ]),\n",
+ " ('neg', ''), # not used on flux\n",
+ " ('seed', 42),\n",
+ " ('walk_seed', True),\n",
+ " ('guidance_scale', 1), # schnell does not do guidance\n",
+ " ('sample_steps', 4) # 1 - 4 works well\n",
+ " ]))\n",
+ " ])\n",
+ " ])\n",
+ " ])),\n",
+ " # you can add any additional meta info here. [name] is replaced with config name at top\n",
+ " ('meta', OrderedDict([\n",
+ " ('name', '[name]'),\n",
+ " ('version', '1.0')\n",
+ " ]))\n",
+ "])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "h6F1FlM2Wb3l"
+ },
+ "source": [
+ "## Run it\n",
+ "\n",
+ "Below does all the magic. Check your folders to the left. Items will be in output/LoRA/your_name_v1 In the samples folder, there are preiodic sampled. This doesnt work great with colab. They will be in /content/output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "HkajwI8gteOh"
+ },
+ "outputs": [],
+ "source": [
+ "run_job(job_to_run)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Hblgb5uwW5SD"
+ },
+ "source": [
+ "## Done\n",
+ "\n",
+ "Check your ourput dir and get your slider\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "A100",
+ "machine_shape": "hm",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/requirements.txt b/requirements.txt
index 119eeee0..1b03df7e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -29,3 +29,4 @@ pytorch_fid
optimum-quanto
sentencepiece
huggingface_hub
+peft
\ No newline at end of file
diff --git a/run_modal.py b/run_modal.py
new file mode 100644
index 00000000..4675c1cb
--- /dev/null
+++ b/run_modal.py
@@ -0,0 +1,175 @@
+'''
+
+ostris/ai-toolkit on https://modal.com
+Run training with the following command:
+modal run run_modal.py --config-file-list-str=/root/ai-toolkit/config/whatever_you_want.yml
+
+'''
+
+import os
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+import sys
+import modal
+from dotenv import load_dotenv
+# Load the .env file if it exists
+load_dotenv()
+
+sys.path.insert(0, "/root/ai-toolkit")
+# must come before ANY torch or fastai imports
+# import toolkit.cuda_malloc
+
+# turn off diffusers telemetry until I can figure out how to make it opt-in
+os.environ['DISABLE_TELEMETRY'] = 'YES'
+
+# define the volume for storing model outputs, using "creating volumes lazily": https://modal.com/docs/guide/volumes
+# you will find your model, samples and optimizer stored in: https://modal.com/storage/your-username/main/flux-lora-models
+model_volume = modal.Volume.from_name("flux-lora-models", create_if_missing=True)
+
+# modal_output, due to "cannot mount volume on non-empty path" requirement
+MOUNT_DIR = "/root/ai-toolkit/modal_output" # modal_output, due to "cannot mount volume on non-empty path" requirement
+
+# define modal app
+image = (
+ modal.Image.debian_slim(python_version="3.11")
+ # install required system and pip packages, more about this modal approach: https://modal.com/docs/examples/dreambooth_app
+ .apt_install("libgl1", "libglib2.0-0")
+ .pip_install(
+ "python-dotenv",
+ "torch",
+ "diffusers[torch]",
+ "transformers",
+ "ftfy",
+ "torchvision",
+ "oyaml",
+ "opencv-python",
+ "albumentations",
+ "safetensors",
+ "lycoris-lora==1.8.3",
+ "flatten_json",
+ "pyyaml",
+ "tensorboard",
+ "kornia",
+ "invisible-watermark",
+ "einops",
+ "accelerate",
+ "toml",
+ "pydantic",
+ "omegaconf",
+ "k-diffusion",
+ "open_clip_torch",
+ "timm",
+ "prodigyopt",
+ "controlnet_aux==0.0.7",
+ "bitsandbytes",
+ "hf_transfer",
+ "lpips",
+ "pytorch_fid",
+ "optimum-quanto",
+ "sentencepiece",
+ "huggingface_hub",
+ "peft"
+ )
+)
+
+# mount for the entire ai-toolkit directory
+# example: "/Users/username/ai-toolkit" is the local directory, "/root/ai-toolkit" is the remote directory
+code_mount = modal.Mount.from_local_dir("/Users/username/ai-toolkit", remote_path="/root/ai-toolkit")
+
+# create the Modal app with the necessary mounts and volumes
+app = modal.App(name="flux-lora-training", image=image, mounts=[code_mount], volumes={MOUNT_DIR: model_volume})
+
+# Check if we have DEBUG_TOOLKIT in env
+if os.environ.get("DEBUG_TOOLKIT", "0") == "1":
+ # Set torch to trace mode
+ import torch
+ torch.autograd.set_detect_anomaly(True)
+
+import argparse
+from toolkit.job import get_job
+
+def print_end_message(jobs_completed, jobs_failed):
+ failure_string = f"{jobs_failed} failure{'' if jobs_failed == 1 else 's'}" if jobs_failed > 0 else ""
+ completed_string = f"{jobs_completed} completed job{'' if jobs_completed == 1 else 's'}"
+
+ print("")
+ print("========================================")
+ print("Result:")
+ if len(completed_string) > 0:
+ print(f" - {completed_string}")
+ if len(failure_string) > 0:
+ print(f" - {failure_string}")
+ print("========================================")
+
+
+@app.function(
+ # request a GPU with at least 24GB VRAM
+ # more about modal GPU's: https://modal.com/docs/guide/gpu
+ gpu="A100", # gpu="H100"
+ # more about modal timeouts: https://modal.com/docs/guide/timeouts
+ timeout=7200 # 2 hours, increase or decrease if needed
+)
+def main(config_file_list_str: str, recover: bool = False, name: str = None):
+ # convert the config file list from a string to a list
+ config_file_list = config_file_list_str.split(",")
+
+ jobs_completed = 0
+ jobs_failed = 0
+
+ print(f"Running {len(config_file_list)} job{'' if len(config_file_list) == 1 else 's'}")
+
+ for config_file in config_file_list:
+ try:
+ job = get_job(config_file, name)
+
+ job.config['process'][0]['training_folder'] = MOUNT_DIR
+ os.makedirs(MOUNT_DIR, exist_ok=True)
+ print(f"Training outputs will be saved to: {MOUNT_DIR}")
+
+ # run the job
+ job.run()
+
+ # commit the volume after training
+ model_volume.commit()
+
+ job.cleanup()
+ jobs_completed += 1
+ except Exception as e:
+ print(f"Error running job: {e}")
+ jobs_failed += 1
+ if not recover:
+ print_end_message(jobs_completed, jobs_failed)
+ raise e
+
+ print_end_message(jobs_completed, jobs_failed)
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+
+ # require at least one config file
+ parser.add_argument(
+ 'config_file_list',
+ nargs='+',
+ type=str,
+ help='Name of config file (eg: person_v1 for config/person_v1.json/yaml), or full path if it is not in config folder, you can pass multiple config files and run them all sequentially'
+ )
+
+ # flag to continue if a job fails
+ parser.add_argument(
+ '-r', '--recover',
+ action='store_true',
+ help='Continue running additional jobs even if a job fails'
+ )
+
+ # optional name replacement for config file
+ parser.add_argument(
+ '-n', '--name',
+ type=str,
+ default=None,
+ help='Name to replace [name] tag in config file, useful for shared config file'
+ )
+ args = parser.parse_args()
+
+ # convert list of config files to a comma-separated string for Modal compatibility
+ config_file_list_str = ",".join(args.config_file_list)
+
+ main.call(config_file_list_str=config_file_list_str, recover=args.recover, name=args.name)
diff --git a/toolkit/scheduler.py b/toolkit/scheduler.py
index 95ae36d8..f6f8f61a 100644
--- a/toolkit/scheduler.py
+++ b/toolkit/scheduler.py
@@ -26,7 +26,7 @@ def get_lr_scheduler(
optimizer, **kwargs
)
elif name == "constant":
- if 'facor' not in kwargs:
+ if 'factor' not in kwargs:
kwargs['factor'] = 1.0
return torch.optim.lr_scheduler.ConstantLR(optimizer, **kwargs)