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"metadata": {
"colab": {
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"machine_shape": "hm",
"gpuType": "V100"
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"name": "python3",
"display_name": "Python 3"
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"language_info": {
"name": "python"
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"accelerator": "GPU"
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"cells": [
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"cell_type": "markdown",
"source": [
"# AI Toolkit by Ostris\n",
"## Slider Training\n",
"\n",
"This is a quick colab demo for training sliders like can be found in my CivitAI profile https://civitai.com/user/Ostris/models . I will work on making it more user friendly, but for now, it will get you started."
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"source": [
"!git clone https://github.com/ostris/ai-toolkit"
],
"metadata": {
"id": "BvAG0GKAh59G"
},
"execution_count": null,
"outputs": []
},
{
"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": "code",
"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"
],
"metadata": {
"collapsed": false
},
"outputs": []
},
{
"cell_type": "markdown",
"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"
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{
"cell_type": "code",
"source": [
"from collections import OrderedDict\n",
"\n",
"job_to_run = OrderedDict({\n",
" # This is the config I use on my sliders, It is solid and tested\n",
" 'job': 'train',\n",
" 'config': {\n",
" # the name will be used to create a folder in the output folder\n",
" # it will also replace any [name] token in the rest of this config\n",
" 'name': 'detail_slider_v1',\n",
" # folder will be created with name above in folder below\n",
" # it can be relative to the project root or absolute\n",
" 'training_folder': \"output/LoRA\",\n",
" 'device': 'cuda', # cpu, cuda:0, etc\n",
" # for tensorboard logging, we will make a subfolder for this job\n",
" 'log_dir': \"output/.tensorboard\",\n",
" # you can stack processes for other jobs, It is not tested with sliders though\n",
" # just use one for now\n",
" 'process': [\n",
" {\n",
" 'type': 'slider', # tells runner to run the slider process\n",
" # network is the LoRA network for a slider, I recommend to leave this be\n",
" 'network': {\n",
" 'type': \"lora\",\n",
" # rank / dim of the network. Bigger is not always better. Especially for sliders. 8 is good\n",
" 'linear': 8, # \"rank\" or \"dim\"\n",
" 'linear_alpha': 4, # Do about half of rank \"alpha\"\n",
" # 'conv': 4, # for convolutional layers \"locon\"\n",
" # 'conv_alpha': 4, # Do about half of conv \"alpha\"\n",
" },\n",
" # training config\n",
" 'train': {\n",
" # this is also used in sampling. Stick with ddpm unless you know what you are doing\n",
" 'noise_scheduler': \"ddpm\", # or \"ddpm\", \"lms\", \"euler_a\"\n",
" # how many steps to train. More is not always better. I rarely go over 1000\n",
" 'steps': 100,\n",
" # I have had good results with 4e-4 to 1e-4 at 500 steps\n",
" 'lr': 2e-4,\n",
" # enables gradient checkpoint, saves vram, leave it on\n",
" 'gradient_checkpointing': True,\n",
" # train the unet. I recommend leaving this true\n",
" 'train_unet': True,\n",
" # train the text encoder. I don't recommend this unless you have a special use case\n",
" # for sliders we are adjusting representation of the concept (unet),\n",
" # not the description of it (text encoder)\n",
" 'train_text_encoder': False,\n",
"\n",
" # just leave unless you know what you are doing\n",
" # also supports \"dadaptation\" but set lr to 1 if you use that,\n",
" # but it learns too fast and I don't recommend it\n",
" 'optimizer': \"adamw\",\n",
" # only constant for now\n",
" 'lr_scheduler': \"constant\",\n",
" # we randomly denoise random num of steps form 1 to this number\n",
" # while training. Just leave it\n",
" 'max_denoising_steps': 40,\n",
" # works great at 1. I do 1 even with my 4090.\n",
" # higher may not work right with newer single batch stacking code anyway\n",
" 'batch_size': 1,\n",
" # bf16 works best if your GPU supports it (modern)\n",
" 'dtype': 'bf16', # fp32, bf16, fp16\n",
" # I don't recommend using unless you are trying to make a darker lora. Then do 0.1 MAX\n",
" # although, the way we train sliders is comparative, so it probably won't work anyway\n",
" 'noise_offset': 0.0,\n",
" },\n",
"\n",
" # the model to train the LoRA network on\n",
" 'model': {\n",
" # name_or_path can be a hugging face name, local path or url to model\n",
" # on civit ai with or without modelVersionId. They will be cached in /model folder\n",
" # epicRealisim v5\n",
" 'name_or_path': \"https://civitai.com/models/25694?modelVersionId=134065\",\n",
" 'is_v2': False, # for v2 models\n",
" 'is_v_pred': False, # for v-prediction models (most v2 models)\n",
" # has some issues with the dual text encoder and the way we train sliders\n",
" # it works bit weights need to probably be higher to see it.\n",
" 'is_xl': False, # for SDXL models\n",
" },\n",
"\n",
" # saving config\n",
" 'save': {\n",
" 'dtype': 'float16', # precision to save. I recommend float16\n",
" 'save_every': 50, # save every this many steps\n",
" # this will remove step counts more than this number\n",
" # allows you to save more often in case of a crash without filling up your drive\n",
" 'max_step_saves_to_keep': 2,\n",
" },\n",
"\n",
" # sampling config\n",
" 'sample': {\n",
" # must match train.noise_scheduler, this is not used here\n",
" # but may be in future and in other processes\n",
" 'sampler': \"ddpm\",\n",
" # sample every this many steps\n",
" 'sample_every': 20,\n",
" # image size\n",
" 'width': 512,\n",
" 'height': 512,\n",
" # prompts to use for sampling. Do as many as you want, but it slows down training\n",
" # pick ones that will best represent the concept you are trying to adjust\n",
" # allows some flags after the prompt\n",
" # --m [number] # network multiplier. LoRA weight. -3 for the negative slide, 3 for the positive\n",
" # slide are good tests. will inherit sample.network_multiplier if not set\n",
" # --n [string] # negative prompt, will inherit sample.neg if not set\n",
" # Only 75 tokens allowed currently\n",
" # I like to do a wide positive and negative spread so I can see a good range and stop\n",
" # early if the network is braking down\n",
" 'prompts': [\n",
" \"a woman in a coffee shop, black hat, blonde hair, blue jacket --m -5\",\n",
" \"a woman in a coffee shop, black hat, blonde hair, blue jacket --m -3\",\n",
" \"a woman in a coffee shop, black hat, blonde hair, blue jacket --m 3\",\n",
" \"a woman in a coffee shop, black hat, blonde hair, blue jacket --m 5\",\n",
" \"a golden retriever sitting on a leather couch, --m -5\",\n",
" \"a golden retriever sitting on a leather couch --m -3\",\n",
" \"a golden retriever sitting on a leather couch --m 3\",\n",
" \"a golden retriever sitting on a leather couch --m 5\",\n",
" \"a man with a beard and red flannel shirt, wearing vr goggles, walking into traffic --m -5\",\n",
" \"a man with a beard and red flannel shirt, wearing vr goggles, walking into traffic --m -3\",\n",
" \"a man with a beard and red flannel shirt, wearing vr goggles, walking into traffic --m 3\",\n",
" \"a man with a beard and red flannel shirt, wearing vr goggles, walking into traffic --m 5\",\n",
" ],\n",
" # negative prompt used on all prompts above as default if they don't have one\n",
" 'neg': \"cartoon, fake, drawing, illustration, cgi, animated, anime, monochrome\",\n",
" # seed for sampling. 42 is the answer for everything\n",
" 'seed': 42,\n",
" # walks the seed so s1 is 42, s2 is 43, s3 is 44, etc\n",
" # will start over on next sample_every so s1 is always seed\n",
" # works well if you use same prompt but want different results\n",
" 'walk_seed': False,\n",
" # cfg scale (4 to 10 is good)\n",
" 'guidance_scale': 7,\n",
" # sampler steps (20 to 30 is good)\n",
" 'sample_steps': 20,\n",
" # default network multiplier for all prompts\n",
" # since we are training a slider, I recommend overriding this with --m [number]\n",
" # in the prompts above to get both sides of the slider\n",
" 'network_multiplier': 1.0,\n",
" },\n",
"\n",
" # logging information\n",
" 'logging': {\n",
" 'log_every': 10, # log every this many steps\n",
" 'use_wandb': False, # not supported yet\n",
" 'verbose': False, # probably done need unless you are debugging\n",
" },\n",
"\n",
" # slider training config, best for last\n",
" 'slider': {\n",
" # resolutions to train on. [ width, height ]. This is less important for sliders\n",
" # as we are not teaching the model anything it doesn't already know\n",
" # but must be a size it understands [ 512, 512 ] for sd_v1.5 and [ 768, 768 ] for sd_v2.1\n",
" # and [ 1024, 1024 ] for sd_xl\n",
" # you can do as many as you want here\n",
" 'resolutions': [\n",
" [512, 512],\n",
" # [ 512, 768 ]\n",
" # [ 768, 768 ]\n",
" ],\n",
" # slider training uses 4 combined steps for a single round. This will do it in one gradient\n",
" # step. It is highly optimized and shouldn't take anymore vram than doing without it,\n",
" # since we break down batches for gradient accumulation now. so just leave it on.\n",
" 'batch_full_slide': True,\n",
" # These are the concepts to train on. You can do as many as you want here,\n",
" # but they can conflict outweigh each other. Other than experimenting, I recommend\n",
" # just doing one for good results\n",
" 'targets': [\n",
" # target_class is the base concept we are adjusting the representation of\n",
" # for example, if we are adjusting the representation of a person, we would use \"person\"\n",
" # if we are adjusting the representation of a cat, we would use \"cat\" It is not\n",
" # a keyword necessarily but what the model understands the concept to represent.\n",
" # \"person\" will affect men, women, children, etc but will not affect cats, dogs, etc\n",
" # it is the models base general understanding of the concept and everything it represents\n",
" # you can leave it blank to affect everything. In this example, we are adjusting\n",
" # detail, so we will leave it blank to affect everything\n",
" {\n",
" 'target_class': \"\",\n",
" # positive is the prompt for the positive side of the slider.\n",
" # It is the concept that will be excited and amplified in the model when we slide the slider\n",
" # to the positive side and forgotten / inverted when we slide\n",
" # the slider to the negative side. It is generally best to include the target_class in\n",
" # the prompt. You want it to be the extreme of what you want to train on. For example,\n",
" # if you want to train on fat people, you would use \"an extremely fat, morbidly obese person\"\n",
" # as the prompt. Not just \"fat person\"\n",
" # max 75 tokens for now\n",
" 'positive': \"high detail, 8k, intricate, detailed, high resolution, high res, high quality\",\n",
" # negative is the prompt for the negative side of the slider and works the same as positive\n",
" # it does not necessarily work the same as a negative prompt when generating images\n",
" # these need to be polar opposites.\n",
" # max 76 tokens for now\n",
" 'negative': \"blurry, boring, fuzzy, low detail, low resolution, low res, low quality\",\n",
" # the loss for this target is multiplied by this number.\n",
" # if you are doing more than one target it may be good to set less important ones\n",
" # to a lower number like 0.1 so they don't outweigh the primary target\n",
" 'weight': 1.0,\n",
" },\n",
" ],\n",
" },\n",
" },\n",
" ]\n",
" },\n",
"\n",
" # You can put any information you want here, and it will be saved in the model.\n",
" # The below is an example, but you can put your grocery list in it if you want.\n",
" # It is saved in the model so be aware of that. The software will include this\n",
" # plus some other information for you automatically\n",
" 'meta': {\n",
" # [name] gets replaced with the name above\n",
" 'name': \"[name]\",\n",
" 'version': '1.0',\n",
" # 'creator': {\n",
" # 'name': 'your name',\n",
" # 'email': '[email protected]',\n",
" # 'website': 'https://your.website'\n",
" # }\n",
" }\n",
"})\n"
],
"metadata": {
"id": "_t28QURYjRQO"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"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. Ill update soon."
],
"metadata": {
"id": "h6F1FlM2Wb3l"
}
},
{
"cell_type": "code",
"source": [
"run_job(job_to_run)\n"
],
"metadata": {
"id": "HkajwI8gteOh"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Done\n",
"\n",
"Check your ourput dir and get your slider\n"
],
"metadata": {
"id": "Hblgb5uwW5SD"
}
}
]
}
|