{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyNaEJSE86MIYL6MGO8lUr3+"},"kernelspec":{"name":"python3","display_name":"Python 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d4c2eebf4eb1914d2d98c52243ce":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}}}}},"cells":[{"cell_type":"markdown","source":["# Introduction\n","\n","This is a training script for a diffusion model called MinImagen. A smaller adaptation of original Imagen architecture introduced by Google."],"metadata":{"id":"yQMHUdQADLcT"}},{"cell_type":"markdown","source":["# Setup"],"metadata":{"id":"Qz5vkO4bEh9b"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"LeDWeQuVl_6s","executionInfo":{"status":"ok","timestamp":1690783982884,"user_tz":-330,"elapsed":117837,"user":{"displayName":"Aditya Patkar","userId":"16560201646582853800"}},"outputId":"6fb30b50-90aa-4217-82e9-8fdea0826b9c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting minimagen\n","  Downloading minimagen-0.0.9-py3-none-any.whl (43 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.0/43.0 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting aiohttp==3.8.1 (from minimagen)\n","  Downloading aiohttp-3.8.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.2 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m21.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting aiosignal==1.2.0 (from minimagen)\n","  Downloading aiosignal-1.2.0-py3-none-any.whl (8.2 kB)\n","Requirement already satisfied: async-timeout==4.0.2 in /usr/local/lib/python3.10/dist-packages (from minimagen) (4.0.2)\n","Collecting attrs==21.4.0 (from minimagen)\n","  Downloading attrs-21.4.0-py2.py3-none-any.whl (60 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.6/60.6 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting certifi==2022.6.15 (from minimagen)\n","  Downloading certifi-2022.6.15-py3-none-any.whl (160 kB)\n","\u001b[2K     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einops==0.4.1 (from minimagen)\n","  Downloading einops-0.4.1-py3-none-any.whl (28 kB)\n","Collecting einops-exts==0.0.3 (from minimagen)\n","  Downloading einops_exts-0.0.3-py3-none-any.whl (3.8 kB)\n","Collecting filelock==3.7.1 (from minimagen)\n","  Downloading filelock-3.7.1-py3-none-any.whl (10 kB)\n","Collecting frozenlist==1.3.0 (from minimagen)\n","  Downloading frozenlist-1.3.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (157 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m157.9/157.9 kB\u001b[0m \u001b[31m19.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting fsspec==2022.5.0 (from minimagen)\n","  Downloading fsspec-2022.5.0-py3-none-any.whl (140 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 kB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting future==0.18.2 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multidict-6.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (114 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting multiprocess==0.70.13 (from minimagen)\n","  Downloading multiprocess-0.70.13-py310-none-any.whl (133 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.1/133.1 kB\u001b[0m \u001b[31m16.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting numpy==1.23.1 (from minimagen)\n","  Downloading numpy-1.23.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.0 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.0/17.0 MB\u001b[0m \u001b[31m99.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting packaging==21.3 (from minimagen)\n","  Downloading packaging-21.3-py3-none-any.whl (40 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uninstall: Pillow\n","    Found existing installation: Pillow 9.4.0\n","    Uninstalling Pillow-9.4.0:\n","      Successfully uninstalled Pillow-9.4.0\n","  Attempting uninstall: numpy\n","    Found existing installation: numpy 1.22.4\n","    Uninstalling numpy-1.22.4:\n","      Successfully uninstalled numpy-1.22.4\n","  Attempting uninstall: multidict\n","    Found existing installation: multidict 6.0.4\n","    Uninstalling multidict-6.0.4:\n","      Successfully uninstalled multidict-6.0.4\n","  Attempting uninstall: idna\n","    Found existing installation: idna 3.4\n","    Uninstalling idna-3.4:\n","      Successfully uninstalled idna-3.4\n","  Attempting uninstall: future\n","    Found existing installation: future 0.18.3\n","    Uninstalling future-0.18.3:\n","      Successfully uninstalled future-0.18.3\n","  Attempting uninstall: fsspec\n","    Found existing installation: fsspec 2023.6.0\n","    Uninstalling fsspec-2023.6.0:\n","      Successfully uninstalled fsspec-2023.6.0\n","  Attempting uninstall: frozenlist\n","    Found existing installation: frozenlist 1.4.0\n","    Uninstalling frozenlist-1.4.0:\n","      Successfully uninstalled frozenlist-1.4.0\n","  Attempting uninstall: filelock\n","    Found existing installation: filelock 3.12.2\n","    Uninstalling filelock-3.12.2:\n","      Successfully uninstalled filelock-3.12.2\n","  Attempting uninstall: charset-normalizer\n","    Found existing installation: charset-normalizer 2.0.12\n","    Uninstalling charset-normalizer-2.0.12:\n","      Successfully uninstalled charset-normalizer-2.0.12\n","  Attempting uninstall: certifi\n","    Found existing installation: certifi 2023.7.22\n","    Uninstalling certifi-2023.7.22:\n","      Successfully uninstalled certifi-2023.7.22\n","  Attempting uninstall: attrs\n","    Found existing installation: attrs 23.1.0\n","    Uninstalling attrs-23.1.0:\n","      Successfully uninstalled attrs-23.1.0\n","  Attempting uninstall: yarl\n","    Found existing installation: yarl 1.9.2\n","    Uninstalling yarl-1.9.2:\n","      Successfully uninstalled yarl-1.9.2\n","  Attempting uninstall: torch\n","    Found existing installation: torch 2.0.1+cu118\n","    Uninstalling torch-2.0.1+cu118:\n","      Successfully uninstalled torch-2.0.1+cu118\n","  Attempting uninstall: requests\n","    Found existing installation: requests 2.27.1\n","    Uninstalling requests-2.27.1:\n","      Successfully uninstalled requests-2.27.1\n","  Attempting uninstall: pyarrow\n","    Found existing installation: pyarrow 9.0.0\n","    Uninstalling pyarrow-9.0.0:\n","      Successfully uninstalled pyarrow-9.0.0\n","  Attempting uninstall: pandas\n","    Found existing installation: pandas 1.5.3\n","    Uninstalling pandas-1.5.3:\n","      Successfully uninstalled pandas-1.5.3\n","  Attempting uninstall: packaging\n","    Found existing installation: packaging 23.1\n","    Uninstalling packaging-23.1:\n","      Successfully uninstalled packaging-23.1\n","  Attempting uninstall: aiosignal\n","    Found existing installation: aiosignal 1.3.1\n","    Uninstalling aiosignal-1.3.1:\n","      Successfully uninstalled aiosignal-1.3.1\n","  Attempting uninstall: torchvision\n","    Found existing installation: torchvision 0.15.2+cu118\n","    Uninstalling torchvision-0.15.2+cu118:\n","      Successfully uninstalled torchvision-0.15.2+cu118\n","  Attempting uninstall: aiohttp\n","    Found existing installation: aiohttp 3.8.5\n","    Uninstalling aiohttp-3.8.5:\n","      Successfully uninstalled aiohttp-3.8.5\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","gcsfs 2023.6.0 requires fsspec==2023.6.0, but you have fsspec 2022.5.0 which is incompatible.\n","google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 1.4.3 which is incompatible.\n","google-colab 1.0.0 requires requests==2.27.1, but you have requests 2.28.1 which is incompatible.\n","torchaudio 2.0.2+cu118 requires torch==2.0.1, but you have torch 1.12.0 which is incompatible.\n","torchdata 0.6.1 requires torch==2.0.1, but you have torch 1.12.0 which is incompatible.\n","torchtext 0.15.2 requires torch==2.0.1, but you have torch 1.12.0 which is incompatible.\n","yfinance 0.2.25 requires pytz>=2022.5, but you have pytz 2022.1 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed Pillow-9.2.0 PyYAML-6.0 aiohttp-3.8.1 aiosignal-1.2.0 attrs-21.4.0 certifi-2022.6.15 charset-normalizer-2.1.0 colorama-0.4.5 datasets-2.3.2 dill-0.3.5.1 einops-0.4.1 einops-exts-0.0.3 filelock-3.7.1 frozenlist-1.3.0 fsspec-2022.5.0 future-0.18.2 huggingface-hub-0.8.1 idna-3.3 minimagen-0.0.9 multidict-6.0.2 multiprocess-0.70.13 numpy-1.23.1 packaging-21.3 pandas-1.4.3 pyarrow-8.0.0 pyparsing-3.0.9 pytz-2022.1 regex-2022.7.9 requests-2.28.1 resize-right-0.0.2 responses-0.18.0 sentencepiece-0.1.96 tdqm-0.0.1 tokenizers-0.12.1 torch-1.12.0 torchvision-0.13.0 tqdm-4.64.0 transformers-4.20.1 typing-extensions-4.3.0 urllib3-1.26.10 xxhash-3.0.0 yarl-1.7.2\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["PIL","certifi","numpy","packaging","torch","tqdm"]}}},"metadata":{}}],"source":["#install the minimagen package\n","!pip install minimagen"]},{"cell_type":"code","source":["#utility imports\n","import os\n","from datetime import datetime\n","\n","#pytorch related imports\n","import torch.utils.data as data_utils\n","from torch import optim\n","\n","#minimagen related imports\n","from minimagen.Imagen import Imagen\n","from minimagen.Unet import Unet, Base, Super, BaseTest, SuperTest\n","from minimagen.generate import load_minimagen, load_params\n","from minimagen.t5 import get_encoded_dim\n","from minimagen.training import get_minimagen_parser, ConceptualCaptions, get_minimagen_dl_opts, \\\n","    create_directory, get_model_size, save_training_info, get_default_args, MinimagenTrain, \\\n","    load_testing_parameters"],"metadata":{"id":"b_4eGJywmHR5","executionInfo":{"status":"error","timestamp":1690968676849,"user_tz":-330,"elapsed":5122,"user":{"displayName":"Aditya Patkar","userId":"16560201646582853800"}},"outputId":"154b276a-c7dd-4469-d5b8-8d1fc03b5159","colab":{"base_uri":"https://localhost:8080/","height":381}},"execution_count":null,"outputs":[{"output_type":"error","ename":"ModuleNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-192156f481b5>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0moptim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mminimagen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImagen\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImagen\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mminimagen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUnet\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mUnet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSuper\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseTest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSuperTest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mminimagen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_minimagen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mload_params\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'minimagen'","","\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"],"errorDetails":{"actions":[{"action":"open_url","actionText":"Open Examples","url":"/notebooks/snippets/importing_libraries.ipynb"}]}}]},{"cell_type":"code","source":["# Get device: Connect to GPU runtime for better performance\n","device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n","\n","# Command line argument parser\n","parser = get_minimagen_parser()\n","class args_cls:\n","  a = 0\n","\n","#get an instance of the args_cls\n","args = args_cls()"],"metadata":{"id":"GoNwdipqmH95"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#directory creation for training\n","timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n","dir_path = f\"./training_{timestamp}\"\n","training_dir = create_directory(dir_path)"],"metadata":{"id":"eq8I0I7MmKFz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#A dictionary of hyperparameters\n","hyperparameters = dict(\n","            PARAMETERS=None,\n","            NUM_WORKERS=0,\n","            BATCH_SIZE=20,\n","            MAX_NUM_WORDS=32,\n","            IMG_SIDE_LEN=128,\n","            EPOCHS=10,\n","            T5_NAME='t5_small',\n","            TRAIN_VALID_FRAC=0.5,\n","            TRAINING_DIRECTORY = '/content/training_20230731_061334',\n","            TIMESTEPS=25,\n","            OPTIM_LR=0.0001,\n","            ACCUM_ITER=1,\n","            CHCKPT_NUM=500,\n","            VALID_NUM=None,\n","            RESTART_DIRECTORY=None,\n","            TESTING=False,\n","            timestamp=None,\n","        )\n","# Replace relevant values in arg dict\n","args.__dict__ = {**args.__dict__, **hyperparameters}"],"metadata":{"id":"8hdqSoAXoxqs"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Data"],"metadata":{"id":"0CvubtDKEmHm"}},{"cell_type":"code","source":["# Load subset of Conceptual Captions dataset.\n","train_dataset, valid_dataset = ConceptualCaptions(args, smalldata=False)\n","indices = torch.arange(1000)\n","\n","#create train and validation datasets with given number of samples\n","train_dataset = data_utils.Subset(train_dataset, indices)\n","valid_dataset = data_utils.Subset(valid_dataset, indices)\n","\n","# Create dataloaders\n","dl_opts = {**get_minimagen_dl_opts(device), 'batch_size': args.BATCH_SIZE, 'num_workers': args.NUM_WORKERS}\n","train_dataloader = torch.utils.data.DataLoader(train_dataset, **dl_opts)\n","valid_dataloader = torch.utils.data.DataLoader(valid_dataset, **dl_opts)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":105,"referenced_widgets":["f77bbc2602d846f8bb1c9e06f7b519ef","d2c87c66057f4e33bdbfe078ee47c0b2","fe33fa70d2c540e9831d408ffb5d3af9","61d7b89f706348548ebcaf0ced92a44e","a51b1054233342feaef0b16d2627a658","c4aa2aa530034af487957db71e9c509f","b4957dccdc5c4b83982212497febf4dc","592c8770c24c4366843336989c8cdb5f","f6c8ccfd74844b7ebafb37f181b51130","178935e2880043dc9eca862c09c05c16","aac7d4c2eebf4eb1914d2d98c52243ce"]},"id":"6LVG7NbZmNQq","executionInfo":{"status":"ok","timestamp":1690786766692,"user_tz":-330,"elapsed":9916,"user":{"displayName":"Aditya Patkar","userId":"16560201646582853800"}},"outputId":"7b91d590-fb10-4c7a-e432-a315c656d54a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:datasets.builder:No config specified, defaulting to: conceptual_captions/unlabeled\n","WARNING:datasets.builder:Reusing dataset conceptual_captions (/root/.cache/huggingface/datasets/conceptual_captions/unlabeled/1.0.0/05266784888422e36944016874c44639bccb39069c2227435168ad8b02d600d8)\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f77bbc2602d846f8bb1c9e06f7b519ef"}},"metadata":{}}]},{"cell_type":"markdown","source":["# UNet"],"metadata":{"id":"vmwLUZh2Eqhn"}},{"cell_type":"code","source":["# Instantiate Unet with default parameters and transfer to GPU if available\n","unets_params = [get_default_args(BaseTest), get_default_args(SuperTest)]\n","unets = [Unet(**unet_params).to(device) for unet_params in unets_params]"],"metadata":{"id":"IaKJG4IamPJD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Specify MinImagen parameters\n","imagen_params = dict(\n","    image_sizes=(int(args.IMG_SIDE_LEN / 2), args.IMG_SIDE_LEN),\n","    timesteps=args.TIMESTEPS,\n","    cond_drop_prob=0.15,\n","    text_encoder_name=args.T5_NAME\n",")\n","\n","# Create MinImagen from UNets with specified imagen parameters\n","imagen = Imagen(unets=unets, **imagen_params).to(device)"],"metadata":{"id":"dl-w2Yy6mQ3Z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Fill in unspecified arguments with defaults\n","unets_params = [{**get_default_args(Unet), **i} for i in unets_params]\n","imagen_params = {**get_default_args(Imagen), **imagen_params}\n","\n","# Get the size of the Imagen model in megabytes\n","model_size_MB = get_model_size(imagen)\n","\n","# Save all training info (config files, model size, etc.)\n","save_training_info(args, timestamp, unets_params, imagen_params, model_size_MB, training_dir)"],"metadata":{"id":"tzkJfhuRmSqg"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# Training"],"metadata":{"id":"0XcZq-Q8EthC"}},{"cell_type":"code","source":["# Create optimizer - Adam\n","optimizer = optim.Adam(imagen.parameters(), lr=args.OPTIM_LR)\n","\n","# Train the MinImagen instance\n","MinimagenTrain(timestamp, args, unets, imagen, train_dataloader, valid_dataloader, training_dir, optimizer, timeout=30)"],"metadata":{"id":"I--5Lt18mUf8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1690788612546,"user_tz":-330,"elapsed":1835395,"user":{"displayName":"Aditya Patkar","userId":"16560201646582853800"}},"outputId":"2e19e526-6526-473f-8e77-007e3df8425d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","-------------------- EPOCH 1 --------------------\n","\n","----------Training...----------\n"]},{"output_type":"stream","name":"stderr","text":["\r0it [00:00, ?it/s]"]},{"output_type":"stream","name":"stdout","text":["\n","----------Validation...----------\n"]},{"output_type":"stream","name":"stderr","text":["\n","  0%|          | 0/5 [00:00<?, ?it/s]\u001b[A\n"," 20%|██        | 1/5 [00:05<00:22,  5.54s/it]\u001b[A\n"," 40%|████      | 2/5 [00:31<00:53, 17.84s/it]\u001b[A\n"," 60%|██████    | 3/5 [00:45<00:31, 15.65s/it]\u001b[A\n"," 80%|████████  | 4/5 [00:51<00:11, 11.95s/it]\u001b[A\n","100%|██████████| 5/5 [01:06<00:00, 13.20s/it]\n","1it [01:14, 74.38s/it]"]},{"output_type":"stream","name":"stdout","text":["Unet 0 avg validation loss:  tensor(1.1316, device='cuda:0')\n","Unet 1 avg validation loss:  tensor(1.0483, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["5it [02:27, 29.42s/it]\n"]},{"output_type":"stream","name":"stdout","text":["\n","-------------------- EPOCH 2 --------------------\n","\n","----------Training...----------\n"]},{"output_type":"stream","name":"stderr","text":["\r0it [00:00, ?it/s]"]},{"output_type":"stream","name":"stdout","text":["\n","----------Validation...----------\n"]},{"output_type":"stream","name":"stderr","text":["\n","  0%|          | 0/5 [00:00<?, ?it/s]\u001b[A\n"," 20%|██        | 1/5 [00:11<00:46, 11.63s/it]\u001b[A\n"," 40%|████      | 2/5 [00:26<00:40, 13.34s/it]\u001b[A\n"," 60%|██████    | 3/5 [00:34<00:21, 10.96s/it]\u001b[A\n"," 80%|████████  | 4/5 [00:41<00:09,  9.59s/it]\u001b[A\n","100%|██████████| 5/5 [02:56<00:00, 35.30s/it]\n","1it [03:10, 190.22s/it]"]},{"output_type":"stream","name":"stdout","text":["Unet 0 avg validation loss:  tensor(1.0965, device='cuda:0')\n","Unet 1 avg validation loss:  tensor(1.0373, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["5it [04:12, 50.50s/it]\n"]},{"output_type":"stream","name":"stdout","text":["\n","-------------------- EPOCH 3 --------------------\n","\n","----------Training...----------\n"]},{"output_type":"stream","name":"stderr","text":["\r0it [00:00, ?it/s]"]},{"output_type":"stream","name":"stdout","text":["\n","----------Validation...----------\n"]},{"output_type":"stream","name":"stderr","text":["\n","  0%|          | 0/5 [00:00<?, ?it/s]\u001b[A\n"," 20%|██        | 1/5 [00:07<00:31,  7.80s/it]\u001b[A\n"," 40%|████      | 2/5 [00:13<00:19,  6.65s/it]\u001b[A\n"," 60%|██████    | 3/5 [00:27<00:20, 10.07s/it]\u001b[A\n"," 80%|████████  | 4/5 [00:32<00:07,  7.78s/it]\u001b[A\n","100%|██████████| 5/5 [02:54<00:00, 34.83s/it]\n","1it [03:54, 234.78s/it]"]},{"output_type":"stream","name":"stdout","text":["Unet 0 avg validation loss:  tensor(1.0735, device='cuda:0')\n","Unet 1 avg validation loss:  tensor(1.0289, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["5it [04:21, 52.38s/it]\n"]},{"output_type":"stream","name":"stdout","text":["\n","-------------------- EPOCH 4 --------------------\n","\n","----------Training...----------\n"]},{"output_type":"stream","name":"stderr","text":["\r0it [00:00, ?it/s]"]},{"output_type":"stream","name":"stdout","text":["\n","----------Validation...----------\n"]},{"output_type":"stream","name":"stderr","text":["\n","  0%|          | 0/5 [00:00<?, ?it/s]\u001b[A\n"," 20%|██        | 1/5 [00:15<01:02, 15.63s/it]\u001b[A\n"," 40%|████      | 2/5 [00:23<00:32, 10.99s/it]\u001b[A\n"," 60%|██████    | 3/5 [00:29<00:17,  8.55s/it]\u001b[A\n"," 80%|████████  | 4/5 [02:48<01:00, 60.17s/it]\u001b[A\n","100%|██████████| 5/5 [02:53<00:00, 34.62s/it]\n","1it [02:57, 177.30s/it]"]},{"output_type":"stream","name":"stdout","text":["Unet 0 avg validation loss:  tensor(1.0502, device='cuda:0')\n","Unet 1 avg validation loss:  tensor(1.0210, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["5it [04:07, 49.47s/it]\n"]},{"output_type":"stream","name":"stdout","text":["\n","-------------------- EPOCH 5 --------------------\n","\n","----------Training...----------\n"]},{"output_type":"stream","name":"stderr","text":["\r0it [00:00, ?it/s]"]},{"output_type":"stream","name":"stdout","text":["\n","----------Validation...----------\n"]},{"output_type":"stream","name":"stderr","text":["\n","  0%|          | 0/5 [00:00<?, ?it/s]\u001b[A\n"," 20%|██        | 1/5 [00:07<00:28,  7.14s/it]\u001b[A\n"," 40%|████      | 2/5 [00:31<00:52, 17.42s/it]\u001b[A\n"," 60%|██████    | 3/5 [00:37<00:23, 11.92s/it]\u001b[A\n"," 80%|████████  | 4/5 [00:43<00:09,  9.72s/it]\u001b[A\n","100%|██████████| 5/5 [00:51<00:00, 10.29s/it]\n","1it [01:16, 76.25s/it]"]},{"output_type":"stream","name":"stdout","text":["Unet 0 avg validation loss:  tensor(1.0274, device='cuda:0')\n","Unet 1 avg validation loss:  tensor(1.0135, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["5it [02:32, 30.58s/it]\n"]},{"output_type":"stream","name":"stdout","text":["\n","-------------------- EPOCH 6 --------------------\n","\n","----------Training...----------\n"]},{"output_type":"stream","name":"stderr","text":["\r0it [00:00, ?it/s]"]},{"output_type":"stream","name":"stdout","text":["\n","----------Validation...----------\n"]},{"output_type":"stream","name":"stderr","text":["\n","  0%|          | 0/5 [00:00<?, ?it/s]\u001b[A\n"," 20%|██        | 1/5 [00:34<02:17, 34.45s/it]\u001b[A\n"," 40%|████      | 2/5 [00:45<01:01, 20.50s/it]\u001b[A\n"," 60%|██████    | 3/5 [00:51<00:28, 14.07s/it]\u001b[A\n"," 80%|████████  | 4/5 [00:56<00:10, 10.56s/it]\u001b[A\n","100%|██████████| 5/5 [01:11<00:00, 14.37s/it]\n","1it [01:17, 77.59s/it]"]},{"output_type":"stream","name":"stdout","text":["Unet 0 avg validation loss:  tensor(1.0088, device='cuda:0')\n","Unet 1 avg validation loss:  tensor(1.0083, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["5it [02:25, 29.04s/it]\n"]},{"output_type":"stream","name":"stdout","text":["\n","-------------------- EPOCH 7 --------------------\n","\n","----------Training...----------\n"]},{"output_type":"stream","name":"stderr","text":["\r0it [00:00, ?it/s]"]},{"output_type":"stream","name":"stdout","text":["\n","----------Validation...----------\n"]},{"output_type":"stream","name":"stderr","text":["\n","  0%|          | 0/5 [00:00<?, ?it/s]\u001b[A\n"," 20%|██        | 1/5 [00:05<00:21,  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10.40s/it]\u001b[A\n"," 40%|████      | 2/5 [00:15<00:22,  7.41s/it]\u001b[A\n"," 60%|██████    | 3/5 [00:30<00:21, 10.65s/it]\u001b[A\n"," 80%|████████  | 4/5 [00:41<00:10, 11.00s/it]\u001b[A\n","100%|██████████| 5/5 [00:51<00:00, 10.24s/it]\n","1it [01:04, 64.48s/it]"]},{"output_type":"stream","name":"stdout","text":["Unet 0 avg validation loss:  tensor(0.9715, device='cuda:0')\n","Unet 1 avg validation loss:  tensor(1.0007, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["5it [02:05, 25.04s/it]\n"]},{"output_type":"stream","name":"stdout","text":["\n","-------------------- EPOCH 9 --------------------\n","\n","----------Training...----------\n"]},{"output_type":"stream","name":"stderr","text":["\r0it [00:00, ?it/s]"]},{"output_type":"stream","name":"stdout","text":["\n","----------Validation...----------\n"]},{"output_type":"stream","name":"stderr","text":["\n","  0%|          | 0/5 [00:00<?, ?it/s]\u001b[A\n"," 20%|██        | 1/5 [00:10<00:41, 10.36s/it]\u001b[A\n"," 40%|████      | 2/5 [00:16<00:23,  7.89s/it]\u001b[A\n"," 60%|██████    | 3/5 [00:24<00:15,  7.73s/it]\u001b[A\n"," 80%|████████  | 4/5 [00:35<00:09,  9.07s/it]\u001b[A\n","100%|██████████| 5/5 [02:51<00:00, 34.28s/it]\n","1it [03:30, 210.85s/it]"]},{"output_type":"stream","name":"stdout","text":["Unet 0 avg validation loss:  tensor(0.9587, device='cuda:0')\n","Unet 1 avg validation loss:  tensor(0.9981, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["5it [04:11, 50.39s/it]\n"]},{"output_type":"stream","name":"stdout","text":["\n","-------------------- EPOCH 10 --------------------\n","\n","----------Training...----------\n"]},{"output_type":"stream","name":"stderr","text":["\r0it [00:00, ?it/s]"]},{"output_type":"stream","name":"stdout","text":["\n","----------Validation...----------\n"]},{"output_type":"stream","name":"stderr","text":["\n","  0%|          | 0/5 [00:00<?, ?it/s]\u001b[A\n"," 20%|██        | 1/5 [00:23<01:32, 23.20s/it]\u001b[A\n"," 40%|████      | 2/5 [00:33<00:46, 15.36s/it]\u001b[A\n"," 60%|██████    | 3/5 [00:37<00:20, 10.14s/it]\u001b[A\n"," 80%|████████  | 4/5 [00:43<00:08,  8.75s/it]\u001b[A\n","100%|██████████| 5/5 [00:50<00:00, 10.04s/it]\n","1it [01:37, 97.50s/it]"]},{"output_type":"stream","name":"stdout","text":["Unet 0 avg validation loss:  tensor(0.9483, device='cuda:0')\n","Unet 1 avg validation loss:  tensor(0.9955, device='cuda:0')\n"]},{"output_type":"stream","name":"stderr","text":["5it [02:03, 24.66s/it]\n"]}]},{"cell_type":"markdown","source":["# Inference"],"metadata":{"id":"BEnz_4zPEwgu"}},{"cell_type":"code","source":["from argparse import ArgumentParser\n","from minimagen.generate import load_minimagen, sample_and_save\n"],"metadata":{"id":"PUWUXmYDmdpm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Specify the caption(s) to generate images for\n","captions = ['happy']"],"metadata":{"id":"PPzAqX0qmeKa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["args_cls"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BGpmb7jamimu","executionInfo":{"status":"ok","timestamp":1690784383671,"user_tz":-330,"elapsed":445,"user":{"displayName":"Aditya Patkar","userId":"16560201646582853800"}},"outputId":"7972ae9b-1573-48de-d4be-923370eeb9e4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["__main__.args_cls"]},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["# Use `sample_and_save` to generate and save the iamges\n","sample_and_save(captions, training_directory='/content/training_20230731_065902')"],"metadata":{"id":"fMxM5zdNmf8e","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1690788695591,"user_tz":-330,"elapsed":3817,"user":{"displayName":"Aditya Patkar","userId":"16560201646582853800"}},"outputId":"01a1d7c8-bfcc-420b-e679-2f57525f8d30"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["0it [00:00, ?it/s]\n","sampling loop time step:   0%|          | 0/25 [00:00<?, ?it/s]\u001b[A\n","sampling loop time step:  12%|█▏        | 3/25 [00:00<00:01, 21.00it/s]\u001b[A\n","sampling loop time step:  24%|██▍       | 6/25 [00:00<00:00, 20.08it/s]\u001b[A\n","sampling loop time step:  36%|███▌      | 9/25 [00:00<00:00, 20.17it/s]\u001b[A\n","sampling loop time step:  48%|████▊     | 12/25 [00:00<00:00, 20.03it/s]\u001b[A\n","sampling loop time step:  60%|██████    | 15/25 [00:00<00:00, 20.02it/s]\u001b[A\n","sampling loop time step:  72%|███████▏  | 18/25 [00:00<00:00, 19.98it/s]\u001b[A\n","sampling loop time step:  80%|████████  | 20/25 [00:01<00:00, 19.70it/s]\u001b[A\n","sampling loop time step:  88%|████████▊ | 22/25 [00:01<00:00, 19.56it/s]\u001b[A\n","sampling loop time step: 100%|██████████| 25/25 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