File size: 7,510 Bytes
e1a8883 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Install the dependencies\n",
"\n",
"force_reinstall= False\n",
"\n",
"# Set to true only if you want to install the dependencies again.\n",
"\n",
"#--------------------\n",
"with open('/dev/null', 'w') as devnull:import requests, os, time, importlib;open('/workspace/sdxllorarunpod.py', 'wb').write(requests.get('https://huggingface.co/datasets/TheLastBen/RNPD/raw/main/Scripts/sdxllorarunpod.py').content);os.chdir('/workspace');import sdxllorarunpod;importlib.reload(sdxllorarunpod);from sdxllorarunpod import *;restored=False;restoreda=False;Deps(force_reinstall)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Download the model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Run the cell to download the model\n",
"\n",
"#-------------\n",
"MODEL_NAMExl=dls_xlf(\"\", \"\", \"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create/Load a Session"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Session_Name = \"Example-Session\"\n",
"\n",
"# Enter the session name, it if it exists, it will load it, otherwise it'll create an new session.\n",
"\n",
"#-----------------\n",
"[WORKSPACE, Session_Name, INSTANCE_NAME, OUTPUT_DIR, SESSION_DIR, INSTANCE_DIR, CAPTIONS_DIR, MDLPTH, MODEL_NAMExl]=sess_xl(Session_Name, MODEL_NAMExl if 'MODEL_NAMExl' in locals() else \"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Instance Images\n",
"The most important step is to rename the instance pictures to one unique unknown identifier"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Remove_existing_instance_images= True\n",
"\n",
"# Set to False to keep the existing instance images if any.\n",
"\n",
"\n",
"IMAGES_FOLDER_OPTIONAL= \"\"\n",
"\n",
"# If you prefer to specify directly the folder of the pictures instead of uploading, this will add the pictures to the existing (if any) instance images. Leave EMPTY to upload.\n",
"\n",
"\n",
"Smart_crop_images = True\n",
"\n",
"# Automatically crop your input images.\n",
"\n",
"Crop_size = 1024\n",
"\n",
"# 1024 is the native resolution\n",
"\n",
"\n",
"#--------------------------------------------\n",
"\n",
"# Disabled when \"Smart_crop_images\" is set to \"True\"\n",
"\n",
"Resize_to_1024_and_keep_aspect_ratio = False\n",
"\n",
"# Will resize the smallest dimension to 1024 without cropping while keeping the aspect ratio (make sure you have enough VRAM)\n",
"\n",
"\n",
"# Check out this example for naming : https://i.imgur.com/d2lD3rz.jpeg\n",
"\n",
"#-----------------\n",
"uplder(Remove_existing_instance_images, Smart_crop_images, Crop_size, Resize_to_1024_and_keep_aspect_ratio, IMAGES_FOLDER_OPTIONAL, INSTANCE_DIR, CAPTIONS_DIR)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Manual Captioning"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Open a tool to manually caption the instance images.\n",
"\n",
"#-----------------\n",
"caption(CAPTIONS_DIR, INSTANCE_DIR)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train LoRA"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Training Settings\n",
"\n",
"# Epoch = Number of steps/images\n",
"\n",
"\n",
"UNet_Training_Epochs= 120\n",
"\n",
"UNet_Learning_Rate= \"1e-6\"\n",
"\n",
"# Keep the learning rate between 1e-6 and 3e-6\n",
"\n",
"\n",
"Text_Encoder_Training_Epochs= 40\n",
"\n",
"# The training is highly affected by this value, a total of 300 steps (not epochs) is enough, set to 0 if enhancing existing concepts\n",
"\n",
"Text_Encoder_Learning_Rate= \"1e-6\"\n",
"\n",
"# Keep the learning rate at 1e-6 or lower\n",
"\n",
"\n",
"External_Captions= False\n",
"\n",
"# Load the captions from a text file for each instance image\n",
"\n",
"\n",
"LoRA_Dim = 64\n",
"\n",
"# Dimension of the LoRa model, between 64 and 128 is good enough\n",
"\n",
"\n",
"Save_VRAM = False\n",
"\n",
"# Use as low as 10GB VRAM with Dim = 64\n",
"\n",
"\n",
"Intermediary_Save_Epoch = \"[30,60]\"\n",
"\n",
"# [30,60] means it will save intermediary models at epoch 30 and epoch 60, you can add as many as you want like [30,60,80,100]\n",
"\n",
"\n",
"#-----------------\n",
"dbtrainxl(UNet_Training_Epochs, Text_Encoder_Training_Epochs, UNet_Learning_Rate, Text_Encoder_Learning_Rate, LoRA_Dim, False, 1024, MODEL_NAMExl, SESSION_DIR, INSTANCE_DIR, CAPTIONS_DIR, External_Captions, INSTANCE_NAME, Session_Name, OUTPUT_DIR, 0, Save_VRAM, Intermediary_Save_Epoch)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test the Trained Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ComfyUI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Args=\"--listen --port 3000 --preview-method auto\"\n",
"\n",
"\n",
"Huggingface_token_optional= \"\"\n",
"\n",
"# Restore your backed-up Comfy folder by entering your huggingface token, leave it empty to start fresh or continue with the existing sd folder (if any).\n",
"\n",
"#--------------------\n",
"restored=sdcmff(Huggingface_token_optional, MDLPTH, restored)\n",
"!python /workspace/ComfyUI/main.py $Args"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A1111"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"User = \"\"\n",
"\n",
"Password= \"\"\n",
"\n",
"# Add credentials to your Gradio interface (optional).\n",
"\n",
"\n",
"Huggingface_token_optional= \"\"\n",
"\n",
"# Restore your backed-up SD folder by entering your huggingface token, leave it empty to start fresh or continue with the existing sd folder (if any).\n",
"\n",
"#-----------------\n",
"configf, restoreda=test(MDLPTH, User, Password, Huggingface_token_optional, restoreda)\n",
"!python /workspace/sd/stable-diffusion-webui/webui.py $configf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Free up space"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Display a list of sessions from which you can remove any session you don't need anymore\n",
"\n",
"#-------------------------\n",
"clean()"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|