Victor Hall
GitHub
commit
7e09b6dc29
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@ -16,35 +16,79 @@
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"id": "blaLMSbkPHhG"
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},
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"source": [
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"# EveryDream2 Colab Edition\n",
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"<p align=\"center\">\n",
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" <img src=\"https://github.com/victorchall/EveryDream2trainer/blob/562c4341137d1d9f5bf525e6c56fb4b1eefa2b57/doc/ed_logo_comp.jpg?raw=true\" width=\"600\" height=\"300\">\n",
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"</p>\n",
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"\n",
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"Check out documentation here: https://github.com/victorchall/EveryDream2trainer#docs\n",
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"<br>\n",
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"\n",
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"And join the discord: https://discord.gg/uheqxU6sXN"
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"---\n",
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"\n",
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"<div align=\"center\">\n",
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" <font size=\"6\" color=\"yellow\">Colab Edition</font>\n",
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"</div>\n",
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"\n",
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"---\n",
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"\n",
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"<br>\n",
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"\n",
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"Check out the **EveryDream2trainer** documentation and runpod/vastai and local setups here: \n",
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"\n",
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"[📚 **Documentation**](https://github.com/victorchall/EveryDream2trainer#docs)\n",
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"\n",
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"And join our vibrant community on Discord:\n",
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"\n",
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"[💬 **Join the Discord**](https://discord.gg/uheqxU6sXN)\n",
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"\n",
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"If you find this tool useful, please consider subscribing to the project on Patreon or making a one-time donation on Ko-fi. Your donations keep this project alive as a free open-source tool with ongoing enhancements.\n",
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"\n",
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"<br>\n",
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"\n",
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"<p align=\"center\">\n",
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" <a href=\"https://www.patreon.com/everydream\">\n",
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" <img src=\"https://github.com/victorchall/EveryDream2trainer/raw/main/.github/patreon-medium-button.png?raw=true\" width=\"200\" height=\"50\">\n",
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" </a>\n",
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"</p>\n",
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"\n",
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"<br>\n",
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"\n",
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"<p align=\"center\">\n",
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" <a href=\"https://ko-fi.com/everydream\">\n",
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" <img src=\"https://github.com/victorchall/EveryDream2trainer/raw/main/.github/kofibutton_sm.png?raw=true\" width=\"75\" height=\"75\">\n",
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" </a>\n",
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"</p>\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "hAuBbtSvGpau",
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"cellView": "form"
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"cellView": "form",
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"id": "hAuBbtSvGpau"
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},
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"outputs": [],
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"source": [
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"#@markdown # Setup and Install Dependencies\n",
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"from IPython.display import clear_output\n",
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"from IPython.display import clear_output, display, HTML\n",
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"import subprocess\n",
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"from tqdm.auto import tqdm\n",
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"import time\n",
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"import os \n",
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"from tqdm.auto import tqdm\n",
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"import PIL\n",
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"\n",
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"# Defining function for colored text\n",
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"def colored(r, g, b, text):\n",
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" return f\"\\033[38;2;{r};{g};{b}m{text} \\033[38;2;255;255;255m\"\n",
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"\n",
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"#@markdown Optional connect Gdrive But strongly recommended\n",
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"#@markdown This will let you put all your training data and checkpoints directly on your drive. Much faster/easier to continue later, less setup time.\n",
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"#@markdown This will let you put all your training data and checkpoints directly on your drive. \n",
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"#@markdown Much faster/easier to continue later, less setup time.\n",
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"\n",
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"#@markdown Creates /content/drive/MyDrive/everydreamlogs/ckpt\n",
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"Mount_to_Gdrive = True #@param{type:\"boolean\"} \n",
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"\n",
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"# Clone the git repository\n",
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"print(colored(0, 255, 0, 'Cloning git repository...'))\n",
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"!git clone https://github.com/victorchall/EveryDream2trainer.git\n",
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"\n",
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"if Mount_to_Gdrive:\n",
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@ -55,35 +99,37 @@
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"\n",
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"%cd /content/EveryDream2trainer\n",
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"\n",
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"# Download Arial font\n",
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"print(colored(0, 255, 0, 'Downloading Arial font...'))\n",
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"!wget -O arial.ttf https://raw.githubusercontent.com/matomo-org/travis-scripts/master/fonts/Arial.ttf\n",
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"\n",
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"!cp /content/arial.ttf /usr/share/fonts/truetype/\n",
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"\n",
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"\n",
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"packages = [\n",
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" 'transformers==4.27.1',\n",
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" 'transformers==4.29.2',\n",
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" 'diffusers[torch]==0.14.0',\n",
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" 'pynvml==11.4.1',\n",
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" 'bitsandbytes==0.37.2',\n",
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" 'ftfy==6.1.1',\n",
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" 'aiohttp==3.8.4',\n",
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" 'compel~=1.1.3',\n",
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" 'protobuf==3.20.3',\n",
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" 'wandb==0.13.6',\n",
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" 'pyre-extensions==0.0.23',\n",
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" '--no-deps xformers==0.0.19',\n",
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" 'pytorch-lightning==1.9.2',\n",
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" 'protobuf==3.20.1',\n",
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" 'wandb==0.15.3',\n",
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" 'pyre-extensions==0.0.29',\n",
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" 'xformers==0.0.20',\n",
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" 'pytorch-lightning==1.6.5',\n",
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" 'OmegaConf==2.2.3',\n",
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" 'tensorboard>=2.11.0',\n",
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" 'tensorrt'\n",
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" 'wandb',\n",
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" 'colorama',\n",
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" 'keyboard',\n",
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" 'lion-pytorch'\n",
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"]\n",
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"\n",
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"for package in tqdm(packages, desc='Installing packages', unit='package'):\n",
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"print(colored(0, 255, 0, 'Installing packages...'))\n",
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"for package in tqdm(packages, desc='Installing packages', unit='package', bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt}'):\n",
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" if isinstance(package, tuple):\n",
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" package_name, extra_index_url = package\n",
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" cmd = f\"pip install -q {package_name} --extra-index-url {extra_index_url}\"\n",
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" cmd = f\"pip install -I -q {package_name} --extra-index-url {extra_index_url}\"\n",
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" else:\n",
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" cmd = f\"pip install -q {package}\"\n",
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" \n",
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@ -92,28 +138,62 @@
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"clear_output()\n",
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"\n",
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"\n",
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"\n",
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"# Execute Python script\n",
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"print(colored(0, 255, 0, 'Executing Python script...'))\n",
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"!python utils/get_yamls.py\n",
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"clear_output()\n",
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"## ty Google for cutting out install time by 50%\n",
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"print(\"DONE! installing dependencies.\")\n",
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"GPU = !nvidia-smi\n",
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"print(\"GPU details:\")\n",
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"for line in GPU:\n",
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" print(line)\n",
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"\n",
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"print(colored(0, 255, 0, \"DONE! installing dependencies.\"))\n",
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"\n",
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"# Import pynvml and get GPU details\n",
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"import pynvml\n",
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"\n",
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"pynvml.nvmlInit()\n",
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"\n",
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"handle = pynvml.nvmlDeviceGetHandleByIndex(0)\n",
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"\n",
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"gpu_name = pynvml.nvmlDeviceGetName(handle)\n",
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"gpu_memory = pynvml.nvmlDeviceGetMemoryInfo(handle).total / 1024**3\n",
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"cuda_version_number = pynvml.nvmlSystemGetCudaDriverVersion_v2()\n",
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"cuda_version_major = cuda_version_number // 1000\n",
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"cuda_version_minor = (cuda_version_number % 1000) // 10\n",
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"cuda_version = f\"{cuda_version_major}.{cuda_version_minor}\"\n",
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"\n",
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"pynvml.nvmlShutdown()\n",
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"\n",
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"Python_version = !python --version\n",
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"print(\"\\nPython version:\")\n",
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"print(Python_version[0])\n",
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"import torch\n",
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"print(\"\\nPyTorch version:\")\n",
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"print(torch.__version__)\n",
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"import torchvision\n",
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"print(\"\\nTorchvision version:\")\n",
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"print(torchvision.__version__)\n",
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"import xformers\n",
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"print(\"\\nXFormers version:\")\n",
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"print(xformers.__version__)\n",
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"time.sleep(2)\n"
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"\n",
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"display(HTML(f\"\"\"\n",
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"<table style=\"background-color:transparent;\">\n",
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" <tr>\n",
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" <td style=\"background-color:transparent;\"><span style=\"color: #FFFF00;\">Python version:</span></td>\n",
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" <td style=\"background-color:transparent;\"><span style=\"color: #FFFF00;\">{Python_version[0]}</span></td>\n",
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" <td style=\"background-color:transparent;\"><span style=\"color: #FFFF00;\">GPU Name:</span></td>\n",
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" <td style=\"background-color:transparent;\"><span style=\"color: #FFFF00;\">{gpu_name}</span></td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td style=\"background-color:transparent;\"><span style=\"color: #FFFF00;\">PyTorch version:</span></td>\n",
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" <td style=\"background-color:transparent;\"><span style=\"color: #FFFF00;\">{torch.__version__}</span></td>\n",
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" <td style=\"background-color:transparent;\"><span style=\"color: #FFFF00;\">GPU Memory (GB):</span></td>\n",
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" <td style=\"background-color:transparent;\"><span style=\"color: #FFFF00;\">{gpu_memory:.2f}</span></td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td style=\"background-color:transparent;\"><span style=\"color: #FFFF00;\">Torchvision version:</span></td>\n",
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" <td style=\"background-color:transparent;\"><span style=\"color: #FFFF00;\">{torchvision.__version__}</span></td>\n",
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" <td style=\"background-color:transparent;\"><span style=\"color: #FFFF00;\">CUDA version:</span></td>\n",
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" <td style=\"background-color:transparent;\"><span style=\"color: #FFFF00;\">{cuda_version}</span></td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td style=\"background-color:transparent;\"><span style=\"color: #FFFF00;\">XFormers version:</span></td>\n",
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" <td style=\"background-color:transparent;\"><span style=\"color: #FFFF00;\">{xformers.__version__}</span></td>\n",
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" </tr>\n",
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"</table>\n",
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"\"\"\"))\n",
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"\n",
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"time.sleep(2)"
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]
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},
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{
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@ -127,43 +207,45 @@
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"source": [
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"#@title Get A Base Model\n",
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"#@markdown Choose SD1.5, Waifu Diffusion 1.3, SD2.1, or 2.1(512) from the dropdown, or paste your own URL in the box\n",
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"#@markdown * Alternately you can link to a HF repo using NAME/MODEL, this does not save to your Gdrive, if you want to save an hf model use the direct url\n",
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"#@markdown * Alternately you can link to an HF repo using NAME/MODEL, this does not save to your Gdrive, if you want to save an HF model, use the direct URL\n",
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"\n",
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"#@markdown * Link to a set of diffusers on your Gdrive\n",
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"\n",
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"#@markdown * Paste a url, atm there is no support for .safetensors\n",
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"#@markdown * Paste a URL, atm there is no support for .safetensors\n",
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"\n",
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"from IPython.display import clear_output\n",
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"!mkdir input\n",
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"%cd /content/EveryDream2trainer\n",
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"MODEL_LOCATION = \"sd_v1-5+vae.ckpt\" #@param [\"sd_v1-5+vae.ckpt\", \"hakurei/waifu-diffusion-v1-3\", \"stabilityai/stable-diffusion-2-1-base\", \"stabilityai/stable-diffusion-2-1\"] {allow-input: true}\n",
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"MODEL_LOCATION = \"panopstor/EveryDream\" #@param [\"sd_v1-5+vae.ckpt\", \"hakurei/waifu-diffusion-v1-3\", \"stabilityai/stable-diffusion-2-1-base\", \"stabilityai/stable-diffusion-2-1\"] {allow-input: true}\n",
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"\n",
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"if MODEL_LOCATION == \"sd_v1-5+vae.ckpt\":\n",
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" MODEL_LOCATION = \"panopstor/EveryDream\"\n",
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"Flag = False\n",
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"\n",
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"If_Ckpt = False\n",
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"import os\n",
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"\n",
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"download_path = \"\"\n",
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"\n",
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"if \".co\" in MODEL_LOCATION or \"https\" in MODEL_LOCATION or \"www\" in MODEL_LOCATION: #maybe just add a radio button to download this should work for now\n",
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" print(\"Downloading \")\n",
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"if \".co\" in MODEL_LOCATION or \"https\" in MODEL_LOCATION or \"www\" in MODEL_LOCATION:\n",
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" MODEL_URL = MODEL_LOCATION\n",
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" print(\"Downloading...\")\n",
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" !wget $MODEL_LOCATION\n",
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" clear_output()\n",
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" print(\"DONE!\")\n",
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" download_path = os.path.join(os.getcwd(), os.path.basename(MODEL_LOCATION))\n",
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"\n",
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" print(\"Download completed!\")\n",
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" download_path = os.path.join(os.getcwd(), os.path.basename(MODEL_URL))\n",
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"else:\n",
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" save_name = MODEL_LOCATION\n",
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" save_name = MODEL_LOCATION\n",
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"\n",
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"%cd /content/EveryDream2trainer\n",
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"#@markdown * If you chose to link to a .ckpt Select the correct model version in the drop down menu for conversion\n",
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"\n",
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"inference_yaml = \" \"\n",
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"\n",
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"# Check if the downloaded or copied model is a .ckpt file\n",
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"#@markdown Is the model 1.5 or 2.1 based?\n",
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"model_type = \"SD1x\" #@param [\"SD1x\", \"SD2_512_base\", \"SD21\"]\n",
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"\n",
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"if download_path.endswith(\".ckpt\") or MODEL_LOCATION.endswith(\".ckpt\"):\n",
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" Flag = True\n",
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" model_type = \"SD1x\" #@param [\"SD1x\", \"SD2_512_base\", \"SD21\"]\n",
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" If_Ckpt = True\n",
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" save_path = download_path\n",
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" if \".ckpt\" in save_name:\n",
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" save_name = save_name.replace(\".ckpt\", \"\")\n",
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@ -171,6 +253,7 @@
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" img_size = 512\n",
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" upscale_attention = False\n",
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" prediction_type = \"epsilon\"\n",
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"\n",
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" if model_type == \"SD1x\":\n",
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" inference_yaml = \"v1-inference.yaml\"\n",
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" elif model_type == \"SD2_512_base\":\n",
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@ -182,7 +265,7 @@
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" inference_yaml = \"v2-inference-v.yaml\"\n",
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" img_size = 768\n",
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"\n",
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" !python utils/convert_original_stable_diffusion_to_diffusers.py --scheduler_type ddim \\\n",
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" !python utils/convert_original_stable_diffusion_to_diffusers.py --scheduler_type ddim \\\n",
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" --original_config_file $inference_yaml \\\n",
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" --image_size $img_size \\\n",
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" --checkpoint_path $MODEL_LOCATION \\\n",
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@ -190,13 +273,14 @@
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" --upcast_attn False \\\n",
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" --dump_path $save_name\n",
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"\n",
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" # Set the save path to the GDrive directory if cache_to_gdrive is True\n",
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"# Set the save path to the GDrive directory if cache_to_gdrive is True\n",
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"if If_Ckpt:\n",
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" save_name = os.path.join(\"/content/drive/MyDrive/everydreamlogs/ckpt\", save_name)\n",
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"\n",
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"if Flag:\n",
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" save_name = os.path.join(\"/content/drive/MyDrive/everydreamlogs/ckpt\", save_name)\n",
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"if inference_yaml != \" \":\n",
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" print(\"Model saved to: \" + save_name + \". The \" + inference_yaml + \" was used!\")\n",
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"print(\"Model \" + save_name + \" will be used!\")\n"
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" print(\"Model saved to: \" + save_name + \". The \" + inference_yaml + \" was used!\")\n",
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"\n",
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"print(\"Model \" + save_name + \" will be used!\")"
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]
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},
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{
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@ -245,20 +329,27 @@
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"#@markdown * Name your project so you can find it in your logs\n",
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"Project_Name = \"My_Project\" #@param{type: 'string'}\n",
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"\n",
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"# Load the JSON file\n",
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"with open('optimizer.json', 'r') as file:\n",
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"\n",
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"\n",
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"if model_type == 'SD2_512_base' or model_type == 'SD21':\n",
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" file_path = \"/content/EveryDream2trainer/optimizerSD21.json\"\n",
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"else:\n",
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" file_path = \"/content/EveryDream2trainer/optimizer.json\"\n",
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"\n",
|
||||
"with open(file_path, 'r') as file:\n",
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" data = json.load(file)\n",
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"\n",
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"\n",
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"#@markdown * The learning rate affects how much \"training\" is done on the model per training step. It is a very careful balance to select a value that will learn your data and not wreck the model. \n",
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"#@markdown Leave this default unless you are very comfortable with training and know what you are doing.\n",
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"Learning_Rate = 1e-6 #@param{type: 'number'}\n",
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"#@markdown * chosing this will allow you to ignore any settings specific to the text encode and will match it with the Unets settings, recommended for beginers.\n",
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"Match_text_to_Unet = False #@param{type:\"boolean\"}\n",
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"Text_lr = 0.5e-6 #@param {type:\"number\"}\n",
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"Text_lr = 5e-7 #@param {type:\"number\"}\n",
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"#@markdown * A learning rate scheduler can change your learning rate as training progresses.\n",
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"#@markdown * I recommend sticking with constant until you are comfortable with general training. \n",
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"Schedule = \"constant\" #@param [\"constant\", \"polynomial\", \"linear\", \"cosine\"] {allow-input: true}\n",
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"Text_lr_scheduler = \"constant\" #@param [\"constant\", \"polynomial\", \"linear\", \"cosine\"] {allow-input: true}\n",
|
||||
"Schedule = \"linear\" #@param [\"constant\", \"polynomial\", \"linear\", \"cosine\"] {allow-input: true}\n",
|
||||
"Text_lr_scheduler = \"linear\" #@param [\"constant\", \"polynomial\", \"linear\", \"cosine\"] {allow-input: true}\n",
|
||||
"#@markdown * warm up steps are useful for validation and cosine lrs\n",
|
||||
"lr_warmup_steps = 0 #@param{type:\"integer\"}\n",
|
||||
"lr_decay_steps = 0 #@param {type:\"number\"} \n",
|
||||
|
|
@ -280,7 +371,7 @@
|
|||
"data['text_encoder_overrides']['lr_decay_steps'] = Text_lr_decay_steps\n",
|
||||
"\n",
|
||||
"# Save the updated JSON data back to the file\n",
|
||||
"with open('optimizer.json', 'w') as file:\n",
|
||||
"with open(file_path, 'w') as file:\n",
|
||||
" json.dump(data, file, indent=4)\n",
|
||||
"\n",
|
||||
"#@markdown * Resolution to train at (recommend 512). Higher resolution will require lower batch size (below).\n",
|
||||
|
|
@ -302,11 +393,12 @@
|
|||
"#@markdown * Location on your Gdrive where your training images are.\n",
|
||||
"Dataset_Location = \"/content/drive/MyDrive/training_samples\" #@param {type:\"string\"}\n",
|
||||
"\n",
|
||||
"model = save_name\n",
|
||||
"if not resume:\n",
|
||||
" model = save_name\n",
|
||||
"\n",
|
||||
"#@markdown * Max Epochs to train for, this defines how many total times all your training data is used. Default of 100 is a good start if you are training ~30-40 images of one subject. If you have 100 images, you can reduce this to 40-50 and so forth.\n",
|
||||
"\n",
|
||||
"Max_Epochs = 100 #@param {type:\"slider\", min:0, max:200, step:5}\n",
|
||||
"Max_Epochs = 100 #@param {type:\"slider\", min:0, max:200, step:1}\n",
|
||||
"\n",
|
||||
"#@markdown * How often to save checkpoints.\n",
|
||||
"Save_every_N_epoch = 20 #@param{type:\"integer\"}\n",
|
||||
|
|
@ -361,7 +453,7 @@
|
|||
"#@markdown use validation with wandb\n",
|
||||
"\n",
|
||||
"validatation = False #@param{type:\"boolean\"}\n",
|
||||
"\n",
|
||||
"Hide_Warnings = False #@param {type:\"boolean\"}\n",
|
||||
"\n",
|
||||
"extensions = ['.zip', '.7z', '.rar', '.tgz']\n",
|
||||
"uncompressed_dir = 'Training_Data'\n",
|
||||
|
|
@ -394,6 +486,11 @@
|
|||
"if validatation:\n",
|
||||
" validate = \"--validation_config validation_default.json\"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"if Hide_Warnings:\n",
|
||||
" import warnings\n",
|
||||
" warnings.filterwarnings(\"ignore\")\n",
|
||||
"\n",
|
||||
"wandb_settings = \"\"\n",
|
||||
"if wandb_token:\n",
|
||||
" !rm /root/.netrc\n",
|
||||
|
|
@ -446,7 +543,7 @@
|
|||
" --zero_frequency_noise_ratio $zero_frequency_noise\n",
|
||||
"\n",
|
||||
"# Finish the training process\n",
|
||||
"clear_output()\n",
|
||||
"# clear_output()\n",
|
||||
"time.sleep(2)\n",
|
||||
"print(\"Training is complete, select a model to start training again\")\n",
|
||||
"time.sleep(2)\n",
|
||||
|
|
@ -456,8 +553,7 @@
|
|||
" time.sleep(40)\n",
|
||||
" runtime.unassign()\n",
|
||||
"\n",
|
||||
"os.kill(os.getpid(), 9)\n",
|
||||
"\n"
|
||||
"os.kill(os.getpid(), 9)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -519,15 +615,24 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## Optional NoteBook Features, read all the documentation in /content/EveryDream2trainer/doc before proceeding."
|
||||
],
|
||||
"metadata": {
|
||||
"id": "fzXLJVC6OCeP"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"## Optional NoteBook Features, read all the documentation in /content/EveryDream2trainer/doc before proceeding."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"colab": {
|
||||
"background_save": true
|
||||
},
|
||||
"id": "BafdWaYymg0O"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title Remove logs for samples when training (optional) run before training\n",
|
||||
"file_path = \"/content/EveryDream2trainer/utils/sample_generator.py\"\n",
|
||||
|
|
@ -550,170 +655,8 @@
|
|||
"with open(file_path, \"w\") as file:\n",
|
||||
" file.write(content)\n",
|
||||
"\n",
|
||||
"print(\"The specified code block has been deleted.\")\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "BafdWaYymg0O",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title train.json Editor { display-mode: \"form\" }\n",
|
||||
"#title json Editor for ED2\n",
|
||||
"\n",
|
||||
"import json\n",
|
||||
"\n",
|
||||
"data = {\n",
|
||||
" \"disable_textenc_training\": False,\n",
|
||||
" \"disable_xformers\": False,\n",
|
||||
" \"disable_amp\": False,\n",
|
||||
" \"save_optimizer\": False,\n",
|
||||
" \"gradient_checkpointing\": True,\n",
|
||||
" \"wandb\": False,\n",
|
||||
" \"write_schedule\": False,\n",
|
||||
" \"rated_dataset\": False,\n",
|
||||
" \"batch_size\": 10,\n",
|
||||
" \"ckpt_every_n_minutes\": None,\n",
|
||||
" \"clip_grad_norm\": None,\n",
|
||||
" \"clip_skip\": 0,\n",
|
||||
" \"cond_dropout\": 0.04,\n",
|
||||
" \"data_root\": \"X:\\\\my_project_data\\\\project_abc\",\n",
|
||||
" \"flip_p\": 0.0,\n",
|
||||
" \"gpuid\": 0,\n",
|
||||
" \"grad_accum\": 1,\n",
|
||||
" \"logdir\": \"logs\",\n",
|
||||
" \"log_step\": 25,\n",
|
||||
" \"lr\": 1.5e-06,\n",
|
||||
" \"lr_decay_steps\": 0,\n",
|
||||
" \"lr_scheduler\": \"constant\",\n",
|
||||
" \"lr_warmup_steps\": None,\n",
|
||||
" \"max_epochs\": 30,\n",
|
||||
" \"optimizer_config\": \"optimizer.json\",\n",
|
||||
" \"project_name\": \"project_abc\",\n",
|
||||
" \"resolution\": 512,\n",
|
||||
" \"resume_ckpt\": \"sd_v1-5_vae\",\n",
|
||||
" \"run_name\": None,\n",
|
||||
" \"sample_prompts\": \"sample_prompts.txt\",\n",
|
||||
" \"sample_steps\": 300,\n",
|
||||
" \"save_ckpt_dir\": None,\n",
|
||||
" \"save_ckpts_from_n_epochs\": 0,\n",
|
||||
" \"save_every_n_epochs\": 20,\n",
|
||||
" \"seed\": 555,\n",
|
||||
" \"shuffle_tags\": False,\n",
|
||||
" \"validation_config\": \"validation_default.json\",\n",
|
||||
" \"rated_dataset_target_dropout_percent\": 50,\n",
|
||||
" \"zero_frequency_noise_ratio\": 0.02\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"%cd /content/EveryDream2trainer\n",
|
||||
"#@markdown JSON Parameters\n",
|
||||
"findlast = \"\" \n",
|
||||
"Resume_Last_Training_session = False #@param {type:\"boolean\"}\n",
|
||||
"findlast == Resume_Last_Training_session\n",
|
||||
"disable_textenc_training = False #@param {type:\"boolean\"}\n",
|
||||
"data[\"disable_textenc_training\"] = disable_textenc_training\n",
|
||||
"disable_xformers = False #@param {type:\"boolean\"}\n",
|
||||
"data[\"disable_xformers\"] = disable_xformers\n",
|
||||
"gradient_checkpointing = True #@param {type:\"boolean\"}\n",
|
||||
"data[\"gradient_checkpointing\"] = gradient_checkpointing\n",
|
||||
"save_optimizer = False #@param {type:\"boolean\"}\n",
|
||||
"data[\"save_optimizer\"] = save_optimizer \n",
|
||||
"scale_lr = False #@param {type:\"boolean\"}\n",
|
||||
"data[\"scale_lr\"] = scale_lr\n",
|
||||
"shuffle_tags = False #@param {type:\"boolean\"}\n",
|
||||
"data[\"shuffle_tags\"] = shuffle_tags\n",
|
||||
"wandb = False #@param {type:\"boolean\"}\n",
|
||||
"data[\"wandb\"] = wandb\n",
|
||||
"write_schedule = False #@param {type:\"boolean\"}\n",
|
||||
"data[\"write_schedule\"] = write_schedule\n",
|
||||
"rated_dataset = False #@param {type:\"boolean\"}\n",
|
||||
"data[\"rated_dataset\"] = rated_dataset \n",
|
||||
"batch_size = 8 #@param {type:\"integer\"}\n",
|
||||
"data[\"batch_size\"] = batch_size\n",
|
||||
"ckpt_every_n_minutes = None #@param {type:\"raw\"}\n",
|
||||
"data[\"ckpt_every_n_minutes\"] = ckpt_every_n_minutes\n",
|
||||
"clip_grad_norm = None #@param {type:\"raw\"}\n",
|
||||
"data[\"clip_grad_norm\"] = clip_grad_norm\n",
|
||||
"clip_skip = 0 #@param {type:\"integer\"}\n",
|
||||
"data[\"clip_skip\"] = clip_skip\n",
|
||||
"cond_dropout = 0.04 #@param {type:\"number\"}\n",
|
||||
"data[\"cond_dropout\"] = cond_dropout\n",
|
||||
"data_root = \"X:\\\\my_project_data\\\\project_abc\" #@param {type:\"string\"}\n",
|
||||
"data[\"data_root\"] = data_root\n",
|
||||
"flip_p = 0.0 #@param {type:\"number\"}\n",
|
||||
"data[\"flip_p\"] = flip_p\n",
|
||||
"grad_accum = 1 #@param {type:\"integer\"}\n",
|
||||
"data[\"grad_accum\"] = grad_accum\n",
|
||||
"logdir = \"logs\" #@param {type:\"string\"}\n",
|
||||
"data[\"logdir\"] = logdir\n",
|
||||
"log_step = 25 #@param {type:\"integer\"}\n",
|
||||
"data[\"log_step\"] = log_step\n",
|
||||
"lr = 1.5e-06 #@param {type:\"number\"}\n",
|
||||
"data[\"lr\"] = lr\n",
|
||||
"lr_decay_steps = 0 #@param {type:\"integer\"}\n",
|
||||
"data[\"lr_decay_steps\"] = lr_decay_steps\n",
|
||||
"lr_scheduler = \"constant\" #@param {type:\"string\"}\n",
|
||||
"data[\"lr_scheduler\"] = lr_scheduler\n",
|
||||
"lr_warmup_steps = None #@param {type:\"raw\"}\n",
|
||||
"data[\"lr_warmup_steps\"] = lr_warmup_steps\n",
|
||||
"max_epochs = 100 #@param {type:\"integer\"}\n",
|
||||
"data[\"max_epochs\"] = max_epochs\n",
|
||||
"optimizer_config = \"optimizer.json\" #@param {type:\"string\"}\n",
|
||||
"data[\"optimizer_config\"] = optimizer_config\n",
|
||||
"project_name = \"project_abc\" #@param {type:\"string\"}\n",
|
||||
"data[\"project_name\"] = project_name\n",
|
||||
"resolution = 512 #@param {type:\"integer\"}\n",
|
||||
"data[\"resolution\"] = resolution\n",
|
||||
"resume_ckpt = \"sd_v1-5_vae\" #@param {type:\"string\"}\n",
|
||||
"if findlast:\n",
|
||||
" resume_ckpt = \"findlast\"\n",
|
||||
"data[\"resume_ckpt\"] = resume_ckpt\n",
|
||||
"run_name = None #@param {type:\"raw\"}\n",
|
||||
"data[\"run_name\"] = run_name\n",
|
||||
"sample_prompts = \"sample_prompts.txt\" #@param [\"sample_prompts.txt\", \"sample_prompts.json\"]\n",
|
||||
"data[\"sample_prompts\"] = sample_prompts\n",
|
||||
"sample_steps = 300 #@param {type:\"integer\"}\n",
|
||||
"data[\"sample_steps\"] = sample_steps\n",
|
||||
"save_ckpt_dir = None #@param {type:\"raw\"}\n",
|
||||
"data[\"save_ckpt_dir\"] = save_ckpt_dir\n",
|
||||
"save_ckpts_from_n_epochs = 0 #@param {type:\"integer\"}\n",
|
||||
"data[\"save_ckpts_from_n_epochs\"] = save_ckpts_from_n_epochs\n",
|
||||
"save_every_n_epochs = 20 #@param {type:\"integer\"}\n",
|
||||
"data[\"save_every_n_epochs\"] = save_every_n_epochs\n",
|
||||
"seed = 555 #@param {type:\"integer\"}\n",
|
||||
"data[\"seed\"] = seed\n",
|
||||
"validation_config = \"validation_default.json\" #@param {type:\"string\"}\n",
|
||||
"data[\"validation_config\"] = validation_config\n",
|
||||
"rated_dataset_target_dropout_percent = 50 #@param {type:\"integer\"}\n",
|
||||
"data[\"rated_dataset_target_dropout_percent\"] = rated_dataset_target_dropout_percent\n",
|
||||
"zero_frequency_noise_ratio = 0.02 #@param {type:\"number\"}\n",
|
||||
"data[\"zero_frequency_noise_ratio\"] = zero_frequency_noise_ratio\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Display the modified JSON data\n",
|
||||
"print(\"Modified JSON data:\")\n",
|
||||
"print(json.dumps(data, indent=2))\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Save the modified JSON data to a file\n",
|
||||
"filename = \"train.json\" #@param {type:\"string\"}\n",
|
||||
"variable_name = \"\" #@param {type:\"string\"}\n",
|
||||
"\n",
|
||||
"with open(filename, 'w') as file:\n",
|
||||
" json.dump(data, file, indent=2)\n",
|
||||
"\n",
|
||||
"print(f\"Modified JSON data saved to '{filename}'.\")"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "d20kz8EtosWM"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
"print(\"The specified code block has been deleted.\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
|
|
@ -725,7 +668,7 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"#@title Alternate startup script\n",
|
||||
"#@markdown * Edit train.json to setup your paramaters\n",
|
||||
"#@markdown * Edit train.json or chain0.json to setup your paramaters\n",
|
||||
"\n",
|
||||
"#@markdown * Edit using a chain length of 0 will use train.json\n",
|
||||
"\n",
|
||||
|
|
@ -733,8 +676,6 @@
|
|||
"\n",
|
||||
"#@markdown * make sure to check each confguration you will need 1 Json per chain length 3 are provided\n",
|
||||
"\n",
|
||||
"#@markdown * make sure your .Json contain the line Notebook: true\n",
|
||||
"\n",
|
||||
"#@markdown * your locations in the .json can be done in this format /content/drive/MyDrive/ - then the sub folder you wish to use\n",
|
||||
"\n",
|
||||
"%cd /content/EveryDream2trainer\n",
|
||||
|
|
@ -748,18 +689,26 @@
|
|||
" l -= 1\n",
|
||||
" I =+ 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"Need some tools to Manage your large datasets check out https://github.com/victorchall/EveryDream for some usefull tools and captioner"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "ls6mX94trxZV"
|
||||
}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"provenance": [],
|
||||
"gpuType": "T4",
|
||||
"include_colab_link": true
|
||||
},
|
||||
"gpuClass": "standard",
|
||||
"kernelspec": {
|
||||
"display_name": "venv",
|
||||
"language": "python",
|
||||
"display_name": "Python 3",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
|
|
|
|||
Loading…
Reference in New Issue