{ "cells": [ { "cell_type": "markdown", "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": [ "!nvidia-smi" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "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": { "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": null, "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": null, "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", " ('content_or_style', 'balanced'), # content, style, balanced\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-dev'),\n", " ('is_flux', True),\n", " ('quantize', True), # run 8bit mixed precision\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', 4),\n", " ('sample_steps', 20)\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 }