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Deforum_Stable_Diffusion.ipynb
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "colab_type": "text", | |
| "id": "view-in-github" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/Alzy/e925cff97163a624cf1bde23c62ca46e/deforum_stable_diffusion.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "c442uQJ_gUgy" | |
| }, | |
| "source": [ | |
| "# **Deforum Stable Diffusion v0.5**\n", | |
| "[Stable Diffusion](https://github.com/CompVis/stable-diffusion) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer and the [Stability.ai](https://stability.ai/) Team. [K Diffusion](https://github.com/crowsonkb/k-diffusion) by [Katherine Crowson](https://twitter.com/RiversHaveWings). You need to get the ckpt file and put it on your Google Drive first to use this. It can be downloaded from [HuggingFace](https://huggingface.co/CompVis/stable-diffusion).\n", | |
| "\n", | |
| "Notebook by [deforum](https://discord.gg/upmXXsrwZc)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "LBamKxcmNI7-" | |
| }, | |
| "source": [ | |
| "By using this Notebook, you agree to the following Terms of Use, and license:\n", | |
| "\n", | |
| "**Stablity.AI Model Terms of Use**\n", | |
| "\n", | |
| "This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\n", | |
| "\n", | |
| "The CreativeML OpenRAIL License specifies:\n", | |
| "\n", | |
| "You can't use the model to deliberately produce nor share illegal or harmful outputs or content\n", | |
| "CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n", | |
| "You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\n", | |
| "\n", | |
| "\n", | |
| "Please read the full license here: https://huggingface.co/spaces/CompVis/stable-diffusion-license" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "T4knibRpAQ06" | |
| }, | |
| "source": [ | |
| "# Setup" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "2g-f7cQmf2Nt" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#@markdown **NVIDIA GPU**\n", | |
| "import subprocess\n", | |
| "sub_p_res = subprocess.run(['nvidia-smi', '--query-gpu=name,memory.total,memory.free', '--format=csv,noheader'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n", | |
| "print(sub_p_res)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "c442uQJ_gUgy" | |
| }, | |
| "source": [ | |
| "# **Deforum Stable Diffusion v0.5**\n", | |
| "[Stable Diffusion](https://github.com/CompVis/stable-diffusion) by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer and the [Stability.ai](https://stability.ai/) Team. [K Diffusion](https://github.com/crowsonkb/k-diffusion) by [Katherine Crowson](https://twitter.com/RiversHaveWings). You need to get the ckpt file and put it on your Google Drive first to use this. It can be downloaded from [HuggingFace](https://huggingface.co/CompVis/stable-diffusion).\n", | |
| "\n", | |
| "Notebook by [deforum](https://discord.gg/upmXXsrwZc)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "LBamKxcmNI7-" | |
| }, | |
| "source": [ | |
| "By using this Notebook, you agree to the following Terms of Use, and license:\n", | |
| "\n", | |
| "**Stablity.AI Model Terms of Use**\n", | |
| "\n", | |
| "This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.\n", | |
| "\n", | |
| "The CreativeML OpenRAIL License specifies:\n", | |
| "\n", | |
| "You can't use the model to deliberately produce nor share illegal or harmful outputs or content\n", | |
| "CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license\n", | |
| "You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)\n", | |
| "\n", | |
| "\n", | |
| "Please read the full license here: https://huggingface.co/spaces/CompVis/stable-diffusion-license" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "T4knibRpAQ06" | |
| }, | |
| "source": [ | |
| "# Setup" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "2g-f7cQmf2Nt" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "NVIDIA GeForce RTX 3090, 24576 MiB, 24267 MiB\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#@markdown **NVIDIA GPU**\n", | |
| "import subprocess\n", | |
| "sub_p_res = subprocess.run(['nvidia-smi', '--query-gpu=name,memory.total,memory.free', '--format=csv,noheader'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n", | |
| "print(sub_p_res)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "TxIOPT0G5Lx1" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Local Path Variables:\n", | |
| "\n", | |
| "models_path: /workspace/models\n", | |
| "output_path: /workspace/DSDoutput\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#@markdown **Model and Output Paths**\n", | |
| "# ask for the link\n", | |
| "print(\"Local Path Variables:\\n\")\n", | |
| "\n", | |
| "models_path = \"/workspace/models\" #@param {type:\"string\"}\n", | |
| "output_path = \"/workspace/output\" #@param {type:\"string\"}\n", | |
| "\n", | |
| "#@markdown **Google Drive Path Variables (Optional)**\n", | |
| "mount_google_drive = False #@param {type:\"boolean\"}\n", | |
| "force_remount = False\n", | |
| "\n", | |
| "if mount_google_drive:\n", | |
| " from google.colab import drive # type: ignore\n", | |
| " try:\n", | |
| " drive_path = \"/content/drive\"\n", | |
| " drive.mount(drive_path,force_remount=force_remount)\n", | |
| " models_path_gdrive = \"/content/drive/MyDrive/AI/models\" #@param {type:\"string\"}\n", | |
| " output_path_gdrive = \"/content/drive/MyDrive/AI/StableDiffusion\" #@param {type:\"string\"}\n", | |
| " models_path = models_path_gdrive\n", | |
| " output_path = output_path_gdrive\n", | |
| " except:\n", | |
| " print(\"...error mounting drive or with drive path variables\")\n", | |
| " print(\"...reverting to default path variables\")\n", | |
| "\n", | |
| "import os\n", | |
| "os.makedirs(models_path, exist_ok=True)\n", | |
| "os.makedirs(output_path, exist_ok=True)\n", | |
| "\n", | |
| "print(f\"models_path: {models_path}\")\n", | |
| "print(f\"output_path: {output_path}\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "VRNl2mfepEIe" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Setting up environment...\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Looking in indexes: https://pypi.org/simple, https://download.pytorch.org/whl/cu113\n", | |
| "Collecting torch==1.12.1+cu113\n", | |
| " Downloading https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp37-cp37m-linux_x86_64.whl (1837.8 MB)\n", | |
| "Collecting torchvision==0.13.1+cu113\n", | |
| " Downloading https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp37-cp37m-linux_x86_64.whl (23.4 MB)\n", | |
| "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch==1.12.1+cu113) (4.1.1)\n", | |
| "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.7/site-packages (from torchvision==0.13.1+cu113) (9.0.1)\n", | |
| "Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from torchvision==0.13.1+cu113) (1.21.5)\n", | |
| "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from torchvision==0.13.1+cu113) (2.27.1)\n", | |
| "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision==0.13.1+cu113) (1.26.8)\n", | |
| "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision==0.13.1+cu113) (2022.6.15)\n", | |
| "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision==0.13.1+cu113) (2.0.4)\n", | |
| "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->torchvision==0.13.1+cu113) (3.3)\n", | |
| "Installing collected packages: torch, torchvision\n", | |
| " Attempting uninstall: torch\n", | |
| " Found existing installation: torch 1.12.0\n", | |
| " Uninstalling torch-1.12.0:\n", | |
| " Successfully uninstalled torch-1.12.0\n", | |
| " Attempting uninstall: torchvision\n", | |
| " Found existing installation: torchvision 0.13.0\n", | |
| " Uninstalling torchvision-0.13.0:\n", | |
| " Successfully uninstalled torchvision-0.13.0\n", | |
| "Successfully installed torch-1.12.1+cu113 torchvision-0.13.1+cu113\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Collecting omegaconf==2.2.3\n", | |
| " Downloading omegaconf-2.2.3-py3-none-any.whl (79 kB)\n", | |
| "Collecting einops==0.4.1\n", | |
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| "Collecting torchmetrics==0.9.3\n", | |
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| " Stored in directory: /root/.cache/pip/wheels/8b/8d/53/2af8772d9aec614e3fc65e53d4a993ad73c61daa8bbd85a873\n", | |
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| "Installing collected packages: pyasn1, rsa, pyasn1-modules, oauthlib, multidict, frozenlist, cachetools, yarl, requests-oauthlib, MarkupSafe, google-auth, asynctest, async-timeout, aiosignal, werkzeug, tensorboard-plugin-wit, tensorboard-data-server, protobuf, markdown, grpcio, google-auth-oauthlib, fsspec, aiohttp, absl-py, torchmetrics, tokenizers, tensorboard, regex, pyDeprecate, huggingface-hub, antlr4-python3-runtime, transformers, torchtext, pytorch-lightning, omegaconf, kornia, einops\n", | |
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| " Successfully uninstalled torchtext-0.13.0\n", | |
| "Successfully installed MarkupSafe-2.1.1 absl-py-1.2.0 aiohttp-3.8.3 aiosignal-1.2.0 antlr4-python3-runtime-4.9.3 async-timeout-4.0.2 asynctest-0.13.0 cachetools-5.2.0 einops-0.4.1 frozenlist-1.3.1 fsspec-2022.8.2 google-auth-2.12.0 google-auth-oauthlib-0.4.6 grpcio-1.49.1 huggingface-hub-0.10.0 kornia-0.6.7 markdown-3.4.1 multidict-6.0.2 oauthlib-3.2.1 omegaconf-2.2.3 protobuf-3.19.6 pyDeprecate-0.3.2 pyasn1-0.4.8 pyasn1-modules-0.2.8 pytorch-lightning-1.7.4 regex-2022.9.13 requests-oauthlib-1.3.1 rsa-4.9 tensorboard-2.10.1 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tokenizers-0.12.1 torchmetrics-0.9.3 torchtext-0.13.1 transformers-4.21.2 werkzeug-2.2.2 yarl-1.8.1\n", | |
| "\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "fatal: destination path 'stable-diffusion' already exists and is not an empty directory.\n", | |
| " Running command git fetch -q --tags\n", | |
| " Running command git reset --hard -q 24268930bf1dce879235a7fddd0b2355b84d7ea6\n", | |
| "WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Obtaining taming-transformers from git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers\n", | |
| " Updating ./src/taming-transformers clone (to revision master)\n", | |
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| "Installing collected packages: taming-transformers\n", | |
| " Running setup.py develop for taming-transformers\n", | |
| "Successfully installed taming-transformers-0.0.1\n", | |
| "\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " Running command git fetch -q --tags\n", | |
| " Running command git reset --hard -q d50d76daa670286dd6cacf3bcd80b5e4823fc8e1\n", | |
| "WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n" | |
| ] | |
| }, | |
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| "text": [ | |
| "Obtaining clip from git+https://github.com/openai/CLIP.git@main#egg=clip\n", | |
| " Updating ./src/clip clone (to revision main)\n", | |
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| " Downloading ftfy-6.1.1-py3-none-any.whl (53 kB)\n", | |
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| "Installing collected packages: ftfy, clip\n", | |
| " Running setup.py develop for clip\n", | |
| "Successfully installed clip-1.0 ftfy-6.1.1\n", | |
| "\n", | |
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| " Stored in directory: /root/.cache/pip/wheels/2f/c8/79/83ddc70e0b20f2df3bbac658c2c5d665b76cedd02e67bd61dc\n", | |
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| "Successfully installed accelerate-0.12.0 cycler-0.11.0 fonttools-4.37.4 jsonmerge-1.8.0 kiwisolver-1.4.4 matplotlib-3.5.3 resize-right-0.0.2 timm-0.6.7 torchdiffeq-0.2.3\n", | |
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| "\n", | |
| "Environment set up in 83 seconds\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n", | |
| "fatal: destination path 'AdaBins' already exists and is not an empty directory.\n", | |
| "fatal: destination path 'MiDaS' already exists and is not an empty directory.\n", | |
| "fatal: destination path 'pytorch3d-lite' already exists and is not an empty directory.\n", | |
| "fatal: destination path 'k-diffusion' already exists and is not an empty directory.\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#@markdown **Setup Environment**\n", | |
| "\n", | |
| "setup_environment = True #@param {type:\"boolean\"}\n", | |
| "print_subprocess = True #@param {type:\"boolean\"}\n", | |
| "\n", | |
| "if setup_environment:\n", | |
| " import subprocess, time\n", | |
| " print(\"Setting up environment...\")\n", | |
| " start_time = time.time()\n", | |
| " all_process = [\n", | |
| " ['pip', 'install', 'torch==1.12.1+cu113', 'torchvision==0.13.1+cu113', '--extra-index-url', 'https://download.pytorch.org/whl/cu113'],\n", | |
| " ['pip', 'install', 'omegaconf==2.2.3', 'einops==0.4.1', 'pytorch-lightning==1.7.4', 'torchmetrics==0.9.3', 'torchtext==0.13.1', 'transformers==4.21.2', 'kornia==0.6.7'],\n", | |
| " ['git', 'clone', 'https://github.com/deforum/stable-diffusion'],\n", | |
| " ['pip', 'install', '-e', 'git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers'],\n", | |
| " ['pip', 'install', '-e', 'git+https://github.com/openai/CLIP.git@main#egg=clip'],\n", | |
| " ['pip', 'install', 'accelerate', 'ftfy', 'jsonmerge', 'matplotlib', 'resize-right', 'timm', 'torchdiffeq'],\n", | |
| " ['git', 'clone', 'https://github.com/shariqfarooq123/AdaBins.git'],\n", | |
| " ['git', 'clone', 'https://github.com/isl-org/MiDaS.git'],\n", | |
| " ['git', 'clone', 'https://github.com/MSFTserver/pytorch3d-lite.git'],\n", | |
| " ]\n", | |
| " for process in all_process:\n", | |
| " running = subprocess.run(process,stdout=subprocess.PIPE).stdout.decode('utf-8')\n", | |
| " if print_subprocess:\n", | |
| " print(running)\n", | |
| " \n", | |
| " print(subprocess.run(['git', 'clone', 'https://github.com/deforum/k-diffusion/'], stdout=subprocess.PIPE).stdout.decode('utf-8'))\n", | |
| " with open('k-diffusion/k_diffusion/__init__.py', 'w') as f:\n", | |
| " f.write('')\n", | |
| "\n", | |
| " end_time = time.time()\n", | |
| " print(f\"Environment set up in {end_time-start_time:.0f} seconds\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "81qmVZbrm4uu" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#@markdown **Python Definitions**\n", | |
| "import json\n", | |
| "from IPython import display\n", | |
| "\n", | |
| "import gc, math, os, pathlib, subprocess, sys, time\n", | |
| "import cv2\n", | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "import random\n", | |
| "import requests\n", | |
| "import torch\n", | |
| "import torch.nn as nn\n", | |
| "import torchvision.transforms as T\n", | |
| "import torchvision.transforms.functional as TF\n", | |
| "from contextlib import contextmanager, nullcontext\n", | |
| "from einops import rearrange, repeat\n", | |
| "from omegaconf import OmegaConf\n", | |
| "from PIL import Image\n", | |
| "from pytorch_lightning import seed_everything\n", | |
| "from skimage.exposure import match_histograms\n", | |
| "from torchvision.utils import make_grid\n", | |
| "from tqdm import tqdm, trange\n", | |
| "from types import SimpleNamespace\n", | |
| "from torch import autocast\n", | |
| "import re\n", | |
| "from scipy.ndimage import gaussian_filter\n", | |
| "\n", | |
| "sys.path.extend([\n", | |
| " 'src/taming-transformers',\n", | |
| " 'src/clip',\n", | |
| " 'stable-diffusion/',\n", | |
| " 'k-diffusion',\n", | |
| " 'pytorch3d-lite',\n", | |
| " 'AdaBins',\n", | |
| " 'MiDaS',\n", | |
| "])\n", | |
| "\n", | |
| "import py3d_tools as p3d\n", | |
| "\n", | |
| "from helpers import DepthModel, sampler_fn\n", | |
| "from k_diffusion.external import CompVisDenoiser\n", | |
| "from ldm.util import instantiate_from_config\n", | |
| "from ldm.models.diffusion.ddim import DDIMSampler\n", | |
| "from ldm.models.diffusion.plms import PLMSSampler\n", | |
| "\n", | |
| "def sanitize(prompt):\n", | |
| " whitelist = set('abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ')\n", | |
| " tmp = ''.join(filter(whitelist.__contains__, prompt))\n", | |
| " return tmp.replace(' ', '_')\n", | |
| "\n", | |
| "from functools import reduce\n", | |
| "def construct_RotationMatrixHomogenous(rotation_angles):\n", | |
| " assert(type(rotation_angles)==list and len(rotation_angles)==3)\n", | |
| " RH = np.eye(4,4)\n", | |
| " cv2.Rodrigues(np.array(rotation_angles), RH[0:3, 0:3])\n", | |
| " return RH\n", | |
| "\n", | |
| "# https://en.wikipedia.org/wiki/Rotation_matrix\n", | |
| "def getRotationMatrixManual(rotation_angles):\n", | |
| "\t\n", | |
| " rotation_angles = [np.deg2rad(x) for x in rotation_angles]\n", | |
| " \n", | |
| " phi = rotation_angles[0] # around x\n", | |
| " gamma = rotation_angles[1] # around y\n", | |
| " theta = rotation_angles[2] # around z\n", | |
| " \n", | |
| " # X rotation\n", | |
| " Rphi = np.eye(4,4)\n", | |
| " sp = np.sin(phi)\n", | |
| " cp = np.cos(phi)\n", | |
| " Rphi[1,1] = cp\n", | |
| " Rphi[2,2] = Rphi[1,1]\n", | |
| " Rphi[1,2] = -sp\n", | |
| " Rphi[2,1] = sp\n", | |
| " \n", | |
| " # Y rotation\n", | |
| " Rgamma = np.eye(4,4)\n", | |
| " sg = np.sin(gamma)\n", | |
| " cg = np.cos(gamma)\n", | |
| " Rgamma[0,0] = cg\n", | |
| " Rgamma[2,2] = Rgamma[0,0]\n", | |
| " Rgamma[0,2] = sg\n", | |
| " Rgamma[2,0] = -sg\n", | |
| " \n", | |
| " # Z rotation (in-image-plane)\n", | |
| " Rtheta = np.eye(4,4)\n", | |
| " st = np.sin(theta)\n", | |
| " ct = np.cos(theta)\n", | |
| " Rtheta[0,0] = ct\n", | |
| " Rtheta[1,1] = Rtheta[0,0]\n", | |
| " Rtheta[0,1] = -st\n", | |
| " Rtheta[1,0] = st\n", | |
| " \n", | |
| " R = reduce(lambda x,y : np.matmul(x,y), [Rphi, Rgamma, Rtheta]) \n", | |
| " \n", | |
| " return R\n", | |
| "\n", | |
| "\n", | |
| "def getPoints_for_PerspectiveTranformEstimation(ptsIn, ptsOut, W, H, sidelength):\n", | |
| " \n", | |
| " ptsIn2D = ptsIn[0,:]\n", | |
| " ptsOut2D = ptsOut[0,:]\n", | |
| " ptsOut2Dlist = []\n", | |
| " ptsIn2Dlist = []\n", | |
| " \n", | |
| " for i in range(0,4):\n", | |
| " ptsOut2Dlist.append([ptsOut2D[i,0], ptsOut2D[i,1]])\n", | |
| " ptsIn2Dlist.append([ptsIn2D[i,0], ptsIn2D[i,1]])\n", | |
| " \n", | |
| " pin = np.array(ptsIn2Dlist) + [W/2.,H/2.]\n", | |
| " pout = (np.array(ptsOut2Dlist) + [1.,1.]) * (0.5*sidelength)\n", | |
| " pin = pin.astype(np.float32)\n", | |
| " pout = pout.astype(np.float32)\n", | |
| " \n", | |
| " return pin, pout\n", | |
| "\n", | |
| "def warpMatrix(W, H, theta, phi, gamma, scale, fV):\n", | |
| " \n", | |
| " # M is to be estimated\n", | |
| " M = np.eye(4, 4)\n", | |
| " \n", | |
| " fVhalf = np.deg2rad(fV/2.)\n", | |
| " d = np.sqrt(W*W+H*H)\n", | |
| " sideLength = scale*d/np.cos(fVhalf)\n", | |
| " h = d/(2.0*np.sin(fVhalf))\n", | |
| " n = h-(d/2.0);\n", | |
| " f = h+(d/2.0);\n", | |
| " \n", | |
| " # Translation along Z-axis by -h\n", | |
| " T = np.eye(4,4)\n", | |
| " T[2,3] = -h\n", | |
| " \n", | |
| " # Rotation matrices around x,y,z\n", | |
| " R = getRotationMatrixManual([phi, gamma, theta])\n", | |
| " \n", | |
| " \n", | |
| " # Projection Matrix \n", | |
| " P = np.eye(4,4)\n", | |
| " P[0,0] = 1.0/np.tan(fVhalf)\n", | |
| " P[1,1] = P[0,0]\n", | |
| " P[2,2] = -(f+n)/(f-n)\n", | |
| " P[2,3] = -(2.0*f*n)/(f-n)\n", | |
| " P[3,2] = -1.0\n", | |
| " \n", | |
| " # pythonic matrix multiplication\n", | |
| " F = reduce(lambda x,y : np.matmul(x,y), [P, T, R]) \n", | |
| " \n", | |
| " # shape should be 1,4,3 for ptsIn and ptsOut since perspectiveTransform() expects data in this way. \n", | |
| " # In C++, this can be achieved by Mat ptsIn(1,4,CV_64FC3);\n", | |
| " ptsIn = np.array([[\n", | |
| " [-W/2., H/2., 0.],[ W/2., H/2., 0.],[ W/2.,-H/2., 0.],[-W/2.,-H/2., 0.]\n", | |
| " ]])\n", | |
| " ptsOut = np.array(np.zeros((ptsIn.shape), dtype=ptsIn.dtype))\n", | |
| " ptsOut = cv2.perspectiveTransform(ptsIn, F)\n", | |
| " \n", | |
| " ptsInPt2f, ptsOutPt2f = getPoints_for_PerspectiveTranformEstimation(ptsIn, ptsOut, W, H, sideLength)\n", | |
| " \n", | |
| " # check float32 otherwise OpenCV throws an error\n", | |
| " assert(ptsInPt2f.dtype == np.float32)\n", | |
| " assert(ptsOutPt2f.dtype == np.float32)\n", | |
| " M33 = cv2.getPerspectiveTransform(ptsInPt2f,ptsOutPt2f)\n", | |
| "\n", | |
| " return M33, sideLength\n", | |
| "\n", | |
| "def anim_frame_warp_2d(prev_img_cv2, args, anim_args, keys, frame_idx):\n", | |
| " angle = keys.angle_series[frame_idx]\n", | |
| " zoom = keys.zoom_series[frame_idx]\n", | |
| " translation_x = keys.translation_x_series[frame_idx]\n", | |
| " translation_y = keys.translation_y_series[frame_idx]\n", | |
| "\n", | |
| " center = (args.W // 2, args.H // 2)\n", | |
| " trans_mat = np.float32([[1, 0, translation_x], [0, 1, translation_y]])\n", | |
| " rot_mat = cv2.getRotationMatrix2D(center, angle, zoom)\n", | |
| " trans_mat = np.vstack([trans_mat, [0,0,1]])\n", | |
| " rot_mat = np.vstack([rot_mat, [0,0,1]])\n", | |
| " if anim_args.flip_2d_perspective:\n", | |
| " perspective_flip_theta = keys.perspective_flip_theta_series[frame_idx]\n", | |
| " perspective_flip_phi = keys.perspective_flip_phi_series[frame_idx]\n", | |
| " perspective_flip_gamma = keys.perspective_flip_gamma_series[frame_idx]\n", | |
| " perspective_flip_fv = keys.perspective_flip_fv_series[frame_idx]\n", | |
| " M,sl = warpMatrix(args.W, args.H, perspective_flip_theta, perspective_flip_phi, perspective_flip_gamma, 1., perspective_flip_fv);\n", | |
| " post_trans_mat = np.float32([[1, 0, (args.W-sl)/2], [0, 1, (args.H-sl)/2]])\n", | |
| " post_trans_mat = np.vstack([post_trans_mat, [0,0,1]])\n", | |
| " bM = np.matmul(M, post_trans_mat)\n", | |
| " xform = np.matmul(bM, rot_mat, trans_mat)\n", | |
| " else:\n", | |
| " xform = np.matmul(rot_mat, trans_mat)\n", | |
| "\n", | |
| " return cv2.warpPerspective(\n", | |
| " prev_img_cv2,\n", | |
| " xform,\n", | |
| " (prev_img_cv2.shape[1], prev_img_cv2.shape[0]),\n", | |
| " borderMode=cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE\n", | |
| " )\n", | |
| "\n", | |
| "def anim_frame_warp_3d(prev_img_cv2, depth, anim_args, keys, frame_idx):\n", | |
| " TRANSLATION_SCALE = 1.0/200.0 # matches Disco\n", | |
| " translate_xyz = [\n", | |
| " -keys.translation_x_series[frame_idx] * TRANSLATION_SCALE, \n", | |
| " keys.translation_y_series[frame_idx] * TRANSLATION_SCALE, \n", | |
| " -keys.translation_z_series[frame_idx] * TRANSLATION_SCALE\n", | |
| " ]\n", | |
| " rotate_xyz = [\n", | |
| " math.radians(keys.rotation_3d_x_series[frame_idx]), \n", | |
| " math.radians(keys.rotation_3d_y_series[frame_idx]), \n", | |
| " math.radians(keys.rotation_3d_z_series[frame_idx])\n", | |
| " ]\n", | |
| " rot_mat = p3d.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), \"XYZ\").unsqueeze(0)\n", | |
| " result = transform_image_3d(prev_img_cv2, depth, rot_mat, translate_xyz, anim_args)\n", | |
| " torch.cuda.empty_cache()\n", | |
| " return result\n", | |
| "\n", | |
| "def add_noise(sample: torch.Tensor, noise_amt: float) -> torch.Tensor:\n", | |
| " return sample + torch.randn(sample.shape, device=sample.device) * noise_amt\n", | |
| "\n", | |
| "def get_output_folder(output_path, batch_folder):\n", | |
| " out_path = os.path.join(output_path,time.strftime('%Y-%m'))\n", | |
| " if batch_folder != \"\":\n", | |
| " out_path = os.path.join(out_path, batch_folder)\n", | |
| " os.makedirs(out_path, exist_ok=True)\n", | |
| " return out_path\n", | |
| "\n", | |
| "def load_img(path, shape, use_alpha_as_mask=False):\n", | |
| " # use_alpha_as_mask: Read the alpha channel of the image as the mask image\n", | |
| " if path.startswith('http://') or path.startswith('https://'):\n", | |
| " image = Image.open(requests.get(path, stream=True).raw)\n", | |
| " else:\n", | |
| " image = Image.open(path)\n", | |
| "\n", | |
| " if use_alpha_as_mask:\n", | |
| " image = image.convert('RGBA')\n", | |
| " else:\n", | |
| " image = image.convert('RGB')\n", | |
| "\n", | |
| " image = image.resize(shape, resample=Image.LANCZOS)\n", | |
| "\n", | |
| " mask_image = None\n", | |
| " if use_alpha_as_mask:\n", | |
| " # Split alpha channel into a mask_image\n", | |
| " red, green, blue, alpha = Image.Image.split(image)\n", | |
| " mask_image = alpha.convert('L')\n", | |
| " image = image.convert('RGB')\n", | |
| "\n", | |
| " image = np.array(image).astype(np.float16) / 255.0\n", | |
| " image = image[None].transpose(0, 3, 1, 2)\n", | |
| " image = torch.from_numpy(image)\n", | |
| " image = 2.*image - 1.\n", | |
| "\n", | |
| " return image, mask_image\n", | |
| "\n", | |
| "def load_mask_latent(mask_input, shape):\n", | |
| " # mask_input (str or PIL Image.Image): Path to the mask image or a PIL Image object\n", | |
| " # shape (list-like len(4)): shape of the image to match, usually latent_image.shape\n", | |
| " \n", | |
| " if isinstance(mask_input, str): # mask input is probably a file name\n", | |
| " if mask_input.startswith('http://') or mask_input.startswith('https://'):\n", | |
| " mask_image = Image.open(requests.get(mask_input, stream=True).raw).convert('RGBA')\n", | |
| " else:\n", | |
| " mask_image = Image.open(mask_input).convert('RGBA')\n", | |
| " elif isinstance(mask_input, Image.Image):\n", | |
| " mask_image = mask_input\n", | |
| " else:\n", | |
| " raise Exception(\"mask_input must be a PIL image or a file name\")\n", | |
| "\n", | |
| " mask_w_h = (shape[-1], shape[-2])\n", | |
| " mask = mask_image.resize(mask_w_h, resample=Image.LANCZOS)\n", | |
| " mask = mask.convert(\"L\")\n", | |
| " return mask\n", | |
| "\n", | |
| "def prepare_mask(mask_input, mask_shape, mask_brightness_adjust=1.0, mask_contrast_adjust=1.0):\n", | |
| " # mask_input (str or PIL Image.Image): Path to the mask image or a PIL Image object\n", | |
| " # shape (list-like len(4)): shape of the image to match, usually latent_image.shape\n", | |
| " # mask_brightness_adjust (non-negative float): amount to adjust brightness of the iamge, \n", | |
| " # 0 is black, 1 is no adjustment, >1 is brighter\n", | |
| " # mask_contrast_adjust (non-negative float): amount to adjust contrast of the image, \n", | |
| " # 0 is a flat grey image, 1 is no adjustment, >1 is more contrast\n", | |
| " \n", | |
| " mask = load_mask_latent(mask_input, mask_shape)\n", | |
| "\n", | |
| " # Mask brightness/contrast adjustments\n", | |
| " if mask_brightness_adjust != 1:\n", | |
| " mask = TF.adjust_brightness(mask, mask_brightness_adjust)\n", | |
| " if mask_contrast_adjust != 1:\n", | |
| " mask = TF.adjust_contrast(mask, mask_contrast_adjust)\n", | |
| "\n", | |
| " # Mask image to array\n", | |
| " mask = np.array(mask).astype(np.float32) / 255.0\n", | |
| " mask = np.tile(mask,(4,1,1))\n", | |
| " mask = np.expand_dims(mask,axis=0)\n", | |
| " mask = torch.from_numpy(mask)\n", | |
| "\n", | |
| " if args.invert_mask:\n", | |
| " mask = ( (mask - 0.5) * -1) + 0.5\n", | |
| " \n", | |
| " mask = np.clip(mask,0,1)\n", | |
| " return mask\n", | |
| "\n", | |
| "def maintain_colors(prev_img, color_match_sample, mode):\n", | |
| " if mode == 'Match Frame 0 RGB':\n", | |
| " return match_histograms(prev_img, color_match_sample, multichannel=True)\n", | |
| " elif mode == 'Match Frame 0 HSV':\n", | |
| " prev_img_hsv = cv2.cvtColor(prev_img, cv2.COLOR_RGB2HSV)\n", | |
| " color_match_hsv = cv2.cvtColor(color_match_sample, cv2.COLOR_RGB2HSV)\n", | |
| " matched_hsv = match_histograms(prev_img_hsv, color_match_hsv, multichannel=True)\n", | |
| " return cv2.cvtColor(matched_hsv, cv2.COLOR_HSV2RGB)\n", | |
| " else: # Match Frame 0 LAB\n", | |
| " prev_img_lab = cv2.cvtColor(prev_img, cv2.COLOR_RGB2LAB)\n", | |
| " color_match_lab = cv2.cvtColor(color_match_sample, cv2.COLOR_RGB2LAB)\n", | |
| " matched_lab = match_histograms(prev_img_lab, color_match_lab, multichannel=True)\n", | |
| " return cv2.cvtColor(matched_lab, cv2.COLOR_LAB2RGB)\n", | |
| "\n", | |
| "\n", | |
| "#\n", | |
| "# Callback functions\n", | |
| "#\n", | |
| "class SamplerCallback(object):\n", | |
| " # Creates the callback function to be passed into the samplers for each step\n", | |
| " def __init__(self, args, mask=None, init_latent=None, sigmas=None, sampler=None,\n", | |
| " verbose=False):\n", | |
| " self.sampler_name = args.sampler\n", | |
| " self.dynamic_threshold = args.dynamic_threshold\n", | |
| " self.static_threshold = args.static_threshold\n", | |
| " self.mask = mask\n", | |
| " self.init_latent = init_latent \n", | |
| " self.sigmas = sigmas\n", | |
| " self.sampler = sampler\n", | |
| " self.verbose = verbose\n", | |
| "\n", | |
| " self.batch_size = args.n_samples\n", | |
| " self.save_sample_per_step = args.save_sample_per_step\n", | |
| " self.show_sample_per_step = args.show_sample_per_step\n", | |
| " self.paths_to_image_steps = [os.path.join( args.outdir, f\"{args.timestring}_{index:02}_{args.seed}\") for index in range(args.n_samples) ]\n", | |
| "\n", | |
| " if self.save_sample_per_step:\n", | |
| " for path in self.paths_to_image_steps:\n", | |
| " os.makedirs(path, exist_ok=True)\n", | |
| "\n", | |
| " self.step_index = 0\n", | |
| "\n", | |
| " self.noise = None\n", | |
| " if init_latent is not None:\n", | |
| " self.noise = torch.randn_like(init_latent, device=device)\n", | |
| "\n", | |
| " self.mask_schedule = None\n", | |
| " if sigmas is not None and len(sigmas) > 0:\n", | |
| " self.mask_schedule, _ = torch.sort(sigmas/torch.max(sigmas))\n", | |
| " elif len(sigmas) == 0:\n", | |
| " self.mask = None # no mask needed if no steps (usually happens because strength==1.0)\n", | |
| "\n", | |
| " if self.sampler_name in [\"plms\",\"ddim\"]: \n", | |
| " if mask is not None:\n", | |
| " assert sampler is not None, \"Callback function for stable-diffusion samplers requires sampler variable\"\n", | |
| "\n", | |
| " if self.sampler_name in [\"plms\",\"ddim\"]: \n", | |
| " # Callback function formated for compvis latent diffusion samplers\n", | |
| " self.callback = self.img_callback_\n", | |
| " else: \n", | |
| " # Default callback function uses k-diffusion sampler variables\n", | |
| " self.callback = self.k_callback_\n", | |
| "\n", | |
| " self.verbose_print = print if verbose else lambda *args, **kwargs: None\n", | |
| "\n", | |
| " def view_sample_step(self, latents, path_name_modifier=''):\n", | |
| " if self.save_sample_per_step or self.show_sample_per_step:\n", | |
| " samples = model.decode_first_stage(latents)\n", | |
| " if self.save_sample_per_step:\n", | |
| " fname = f'{path_name_modifier}_{self.step_index:05}.png'\n", | |
| " for i, sample in enumerate(samples):\n", | |
| " sample = sample.double().cpu().add(1).div(2).clamp(0, 1)\n", | |
| " sample = torch.tensor(np.array(sample))\n", | |
| " grid = make_grid(sample, 4).cpu()\n", | |
| " TF.to_pil_image(grid).save(os.path.join(self.paths_to_image_steps[i], fname))\n", | |
| " if self.show_sample_per_step:\n", | |
| " print(path_name_modifier)\n", | |
| " self.display_images(samples)\n", | |
| " return\n", | |
| "\n", | |
| " def display_images(self, images):\n", | |
| " images = images.double().cpu().add(1).div(2).clamp(0, 1)\n", | |
| " images = torch.tensor(np.array(images))\n", | |
| " grid = make_grid(images, 4).cpu()\n", | |
| " display.display(TF.to_pil_image(grid))\n", | |
| " return\n", | |
| "\n", | |
| " # The callback function is applied to the image at each step\n", | |
| " def dynamic_thresholding_(self, img, threshold):\n", | |
| " # Dynamic thresholding from Imagen paper (May 2022)\n", | |
| " s = np.percentile(np.abs(img.cpu()), threshold, axis=tuple(range(1,img.ndim)))\n", | |
| " s = np.max(np.append(s,1.0))\n", | |
| " torch.clamp_(img, -1*s, s)\n", | |
| " torch.FloatTensor.div_(img, s)\n", | |
| "\n", | |
| " # Callback for samplers in the k-diffusion repo, called thus:\n", | |
| " # callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})\n", | |
| " def k_callback_(self, args_dict):\n", | |
| " self.step_index = args_dict['i']\n", | |
| " if self.dynamic_threshold is not None:\n", | |
| " self.dynamic_thresholding_(args_dict['x'], self.dynamic_threshold)\n", | |
| " if self.static_threshold is not None:\n", | |
| " torch.clamp_(args_dict['x'], -1*self.static_threshold, self.static_threshold)\n", | |
| " if self.mask is not None:\n", | |
| " init_noise = self.init_latent + self.noise * args_dict['sigma']\n", | |
| " is_masked = torch.logical_and(self.mask >= self.mask_schedule[args_dict['i']], self.mask != 0 )\n", | |
| " new_img = init_noise * torch.where(is_masked,1,0) + args_dict['x'] * torch.where(is_masked,0,1)\n", | |
| " args_dict['x'].copy_(new_img)\n", | |
| "\n", | |
| " self.view_sample_step(args_dict['denoised'], \"x0_pred\")\n", | |
| "\n", | |
| " # Callback for Compvis samplers\n", | |
| " # Function that is called on the image (img) and step (i) at each step\n", | |
| " def img_callback_(self, img, i):\n", | |
| " self.step_index = i\n", | |
| " # Thresholding functions\n", | |
| " if self.dynamic_threshold is not None:\n", | |
| " self.dynamic_thresholding_(img, self.dynamic_threshold)\n", | |
| " if self.static_threshold is not None:\n", | |
| " torch.clamp_(img, -1*self.static_threshold, self.static_threshold)\n", | |
| " if self.mask is not None:\n", | |
| " i_inv = len(self.sigmas) - i - 1\n", | |
| " init_noise = self.sampler.stochastic_encode(self.init_latent, torch.tensor([i_inv]*self.batch_size).to(device), noise=self.noise)\n", | |
| " is_masked = torch.logical_and(self.mask >= self.mask_schedule[i], self.mask != 0 )\n", | |
| " new_img = init_noise * torch.where(is_masked,1,0) + img * torch.where(is_masked,0,1)\n", | |
| " img.copy_(new_img)\n", | |
| "\n", | |
| " self.view_sample_step(img, \"x\")\n", | |
| "\n", | |
| "def sample_from_cv2(sample: np.ndarray) -> torch.Tensor:\n", | |
| " sample = ((sample.astype(float) / 255.0) * 2) - 1\n", | |
| " sample = sample[None].transpose(0, 3, 1, 2).astype(np.float16)\n", | |
| " sample = torch.from_numpy(sample)\n", | |
| " return sample\n", | |
| "\n", | |
| "def sample_to_cv2(sample: torch.Tensor, type=np.uint8) -> np.ndarray:\n", | |
| " sample_f32 = rearrange(sample.squeeze().cpu().numpy(), \"c h w -> h w c\").astype(np.float32)\n", | |
| " sample_f32 = ((sample_f32 * 0.5) + 0.5).clip(0, 1)\n", | |
| " sample_int8 = (sample_f32 * 255)\n", | |
| " return sample_int8.astype(type)\n", | |
| "\n", | |
| "def transform_image_3d(prev_img_cv2, depth_tensor, rot_mat, translate, anim_args):\n", | |
| " # adapted and optimized version of transform_image_3d from Disco Diffusion https://github.com/alembics/disco-diffusion \n", | |
| " w, h = prev_img_cv2.shape[1], prev_img_cv2.shape[0]\n", | |
| "\n", | |
| " aspect_ratio = float(w)/float(h)\n", | |
| " near, far, fov_deg = anim_args.near_plane, anim_args.far_plane, anim_args.fov\n", | |
| " persp_cam_old = p3d.FoVPerspectiveCameras(near, far, aspect_ratio, fov=fov_deg, degrees=True, device=device)\n", | |
| " persp_cam_new = p3d.FoVPerspectiveCameras(near, far, aspect_ratio, fov=fov_deg, degrees=True, R=rot_mat, T=torch.tensor([translate]), device=device)\n", | |
| "\n", | |
| " # range of [-1,1] is important to torch grid_sample's padding handling\n", | |
| " y,x = torch.meshgrid(torch.linspace(-1.,1.,h,dtype=torch.float32,device=device),torch.linspace(-1.,1.,w,dtype=torch.float32,device=device))\n", | |
| " z = torch.as_tensor(depth_tensor, dtype=torch.float32, device=device)\n", | |
| " xyz_old_world = torch.stack((x.flatten(), y.flatten(), z.flatten()), dim=1)\n", | |
| "\n", | |
| " xyz_old_cam_xy = persp_cam_old.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]\n", | |
| " xyz_new_cam_xy = persp_cam_new.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]\n", | |
| "\n", | |
| " offset_xy = xyz_new_cam_xy - xyz_old_cam_xy\n", | |
| " # affine_grid theta param expects a batch of 2D mats. Each is 2x3 to do rotation+translation.\n", | |
| " identity_2d_batch = torch.tensor([[1.,0.,0.],[0.,1.,0.]], device=device).unsqueeze(0)\n", | |
| " # coords_2d will have shape (N,H,W,2).. which is also what grid_sample needs.\n", | |
| " coords_2d = torch.nn.functional.affine_grid(identity_2d_batch, [1,1,h,w], align_corners=False)\n", | |
| " offset_coords_2d = coords_2d - torch.reshape(offset_xy, (h,w,2)).unsqueeze(0)\n", | |
| "\n", | |
| " image_tensor = rearrange(torch.from_numpy(prev_img_cv2.astype(np.float32)), 'h w c -> c h w').to(device)\n", | |
| " new_image = torch.nn.functional.grid_sample(\n", | |
| " image_tensor.add(1/512 - 0.0001).unsqueeze(0), \n", | |
| " offset_coords_2d, \n", | |
| " mode=anim_args.sampling_mode, \n", | |
| " padding_mode=anim_args.padding_mode, \n", | |
| " align_corners=False\n", | |
| " )\n", | |
| "\n", | |
| " # convert back to cv2 style numpy array\n", | |
| " result = rearrange(\n", | |
| " new_image.squeeze().clamp(0,255), \n", | |
| " 'c h w -> h w c'\n", | |
| " ).cpu().numpy().astype(prev_img_cv2.dtype)\n", | |
| " return result\n", | |
| "\n", | |
| "def check_is_number(value):\n", | |
| " float_pattern = r'^(?=.)([+-]?([0-9]*)(\\.([0-9]+))?)$'\n", | |
| " return re.match(float_pattern, value)\n", | |
| "\n", | |
| "# prompt weighting with colons and number coefficients (like 'bacon:0.75 eggs:0.25')\n", | |
| "# borrowed from https://github.com/kylewlacy/stable-diffusion/blob/0a4397094eb6e875f98f9d71193e350d859c4220/ldm/dream/conditioning.py\n", | |
| "# and https://github.com/raefu/stable-diffusion-automatic/blob/unstablediffusion/modules/processing.py\n", | |
| "def get_uc_and_c(prompts, model, args, frame = 0):\n", | |
| " prompt = prompts[0] # they are the same in a batch anyway\n", | |
| "\n", | |
| " # get weighted sub-prompts\n", | |
| " negative_subprompts, positive_subprompts = split_weighted_subprompts(\n", | |
| " prompt, frame, not args.normalize_prompt_weights\n", | |
| " )\n", | |
| "\n", | |
| " uc = get_learned_conditioning(model, negative_subprompts, \"\", args, -1)\n", | |
| " c = get_learned_conditioning(model, positive_subprompts, prompt, args, 1)\n", | |
| "\n", | |
| " return (uc, c)\n", | |
| "\n", | |
| "def get_learned_conditioning(model, weighted_subprompts, text, args, sign = 1):\n", | |
| " if len(weighted_subprompts) < 1:\n", | |
| " log_tokenization(text, model, args.log_weighted_subprompts, sign)\n", | |
| " c = model.get_learned_conditioning(args.n_samples * [text])\n", | |
| " else:\n", | |
| " c = None\n", | |
| " for subtext, subweight in weighted_subprompts:\n", | |
| " log_tokenization(subtext, model, args.log_weighted_subprompts, sign * subweight)\n", | |
| " if c is None:\n", | |
| " c = model.get_learned_conditioning(args.n_samples * [subtext])\n", | |
| " c *= subweight\n", | |
| " else:\n", | |
| " c.add_(model.get_learned_conditioning(args.n_samples * [subtext]), alpha=subweight)\n", | |
| " \n", | |
| " return c\n", | |
| "\n", | |
| "def parse_weight(match, frame = 0)->float:\n", | |
| " import numexpr\n", | |
| " w_raw = match.group(\"weight\")\n", | |
| " if w_raw == None:\n", | |
| " return 1\n", | |
| " if check_is_number(w_raw):\n", | |
| " return float(w_raw)\n", | |
| " else:\n", | |
| " t = frame\n", | |
| " if len(w_raw) < 3:\n", | |
| " print('the value inside `-characters cannot represent a math function')\n", | |
| " return 1\n", | |
| " return float(numexpr.evaluate(w_raw[1:-1]))\n", | |
| "\n", | |
| "def normalize_prompt_weights(parsed_prompts):\n", | |
| " if len(parsed_prompts) == 0:\n", | |
| " return parsed_prompts\n", | |
| " weight_sum = sum(map(lambda x: x[1], parsed_prompts))\n", | |
| " if weight_sum == 0:\n", | |
| " print(\n", | |
| " \"Warning: Subprompt weights add up to zero. Discarding and using even weights instead.\")\n", | |
| " equal_weight = 1 / max(len(parsed_prompts), 1)\n", | |
| " return [(x[0], equal_weight) for x in parsed_prompts]\n", | |
| " return [(x[0], x[1] / weight_sum) for x in parsed_prompts]\n", | |
| "\n", | |
| "def split_weighted_subprompts(text, frame = 0, skip_normalize=False):\n", | |
| " \"\"\"\n", | |
| " grabs all text up to the first occurrence of ':'\n", | |
| " uses the grabbed text as a sub-prompt, and takes the value following ':' as weight\n", | |
| " if ':' has no value defined, defaults to 1.0\n", | |
| " repeats until no text remaining\n", | |
| " \"\"\"\n", | |
| " prompt_parser = re.compile(\"\"\"\n", | |
| " (?P<prompt> # capture group for 'prompt'\n", | |
| " (?:\\\\\\:|[^:])+ # match one or more non ':' characters or escaped colons '\\:'\n", | |
| " ) # end 'prompt'\n", | |
| " (?: # non-capture group\n", | |
| " :+ # match one or more ':' characters\n", | |
| " (?P<weight>(( # capture group for 'weight'\n", | |
| " -?\\d+(?:\\.\\d+)? # match positive or negative integer or decimal number\n", | |
| " )|( # or\n", | |
| " `[\\S\\s]*?`# a math function\n", | |
| " )))? # end weight capture group, make optional\n", | |
| " \\s* # strip spaces after weight\n", | |
| " | # OR\n", | |
| " $ # else, if no ':' then match end of line\n", | |
| " ) # end non-capture group\n", | |
| " \"\"\", re.VERBOSE)\n", | |
| " negative_prompts = []\n", | |
| " positive_prompts = []\n", | |
| " for match in re.finditer(prompt_parser, text):\n", | |
| " w = parse_weight(match, frame)\n", | |
| " if w < 0:\n", | |
| " # negating the sign as we'll feed this to uc\n", | |
| " negative_prompts.append((match.group(\"prompt\").replace(\"\\\\:\", \":\"), -w))\n", | |
| " elif w > 0:\n", | |
| " positive_prompts.append((match.group(\"prompt\").replace(\"\\\\:\", \":\"), w))\n", | |
| "\n", | |
| " if skip_normalize:\n", | |
| " return (negative_prompts, positive_prompts)\n", | |
| " return (normalize_prompt_weights(negative_prompts), normalize_prompt_weights(positive_prompts))\n", | |
| "\n", | |
| "# shows how the prompt is tokenized\n", | |
| "# usually tokens have '</w>' to indicate end-of-word,\n", | |
| "# but for readability it has been replaced with ' '\n", | |
| "def log_tokenization(text, model, log=False, weight=1):\n", | |
| " if not log:\n", | |
| " return\n", | |
| " tokens = model.cond_stage_model.tokenizer._tokenize(text)\n", | |
| " tokenized = \"\"\n", | |
| " discarded = \"\"\n", | |
| " usedTokens = 0\n", | |
| " totalTokens = len(tokens)\n", | |
| " for i in range(0, totalTokens):\n", | |
| " token = tokens[i].replace('</w>', ' ')\n", | |
| " # alternate color\n", | |
| " s = (usedTokens % 6) + 1\n", | |
| " if i < model.cond_stage_model.max_length:\n", | |
| " tokenized = tokenized + f\"\\x1b[0;3{s};40m{token}\"\n", | |
| " usedTokens += 1\n", | |
| " else: # over max token length\n", | |
| " discarded = discarded + f\"\\x1b[0;3{s};40m{token}\"\n", | |
| " print(f\"\\n>> Tokens ({usedTokens}), Weight ({weight:.2f}):\\n{tokenized}\\x1b[0m\")\n", | |
| " if discarded != \"\":\n", | |
| " print(\n", | |
| " f\">> Tokens Discarded ({totalTokens-usedTokens}):\\n{discarded}\\x1b[0m\"\n", | |
| " )\n", | |
| "\n", | |
| "def generate(args, frame = 0, return_latent=False, return_sample=False, return_c=False):\n", | |
| " seed_everything(args.seed)\n", | |
| " os.makedirs(args.outdir, exist_ok=True)\n", | |
| "\n", | |
| " sampler = PLMSSampler(model) if args.sampler == 'plms' else DDIMSampler(model)\n", | |
| " model_wrap = CompVisDenoiser(model)\n", | |
| " batch_size = args.n_samples\n", | |
| " prompt = args.prompt\n", | |
| " assert prompt is not None\n", | |
| " data = [batch_size * [prompt]]\n", | |
| " precision_scope = autocast if args.precision == \"autocast\" else nullcontext\n", | |
| "\n", | |
| " init_latent = None\n", | |
| " mask_image = None\n", | |
| " init_image = None\n", | |
| " if args.init_latent is not None:\n", | |
| " init_latent = args.init_latent\n", | |
| " elif args.init_sample is not None:\n", | |
| " with precision_scope(\"cuda\"):\n", | |
| " init_latent = model.get_first_stage_encoding(model.encode_first_stage(args.init_sample))\n", | |
| " elif args.use_init and args.init_image != None and args.init_image != '':\n", | |
| " init_image, mask_image = load_img(args.init_image, \n", | |
| " shape=(args.W, args.H), \n", | |
| " use_alpha_as_mask=args.use_alpha_as_mask)\n", | |
| " init_image = init_image.to(device)\n", | |
| " init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)\n", | |
| " with precision_scope(\"cuda\"):\n", | |
| " init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space \n", | |
| "\n", | |
| " if not args.use_init and args.strength > 0 and args.strength_0_no_init:\n", | |
| " print(\"\\nNo init image, but strength > 0. Strength has been auto set to 0, since use_init is False.\")\n", | |
| " print(\"If you want to force strength > 0 with no init, please set strength_0_no_init to False.\\n\")\n", | |
| " args.strength = 0\n", | |
| "\n", | |
| " # Mask functions\n", | |
| " if args.use_mask:\n", | |
| " assert args.mask_file is not None or mask_image is not None, \"use_mask==True: An mask image is required for a mask. Please enter a mask_file or use an init image with an alpha channel\"\n", | |
| " assert args.use_init, \"use_mask==True: use_init is required for a mask\"\n", | |
| " assert init_latent is not None, \"use_mask==True: An latent init image is required for a mask\"\n", | |
| "\n", | |
| "\n", | |
| " mask = prepare_mask(args.mask_file if mask_image is None else mask_image, \n", | |
| " init_latent.shape, \n", | |
| " args.mask_contrast_adjust, \n", | |
| " args.mask_brightness_adjust)\n", | |
| " \n", | |
| " if (torch.all(mask == 0) or torch.all(mask == 1)) and args.use_alpha_as_mask:\n", | |
| " raise Warning(\"use_alpha_as_mask==True: Using the alpha channel from the init image as a mask, but the alpha channel is blank.\")\n", | |
| " \n", | |
| " mask = mask.to(device)\n", | |
| " mask = repeat(mask, '1 ... -> b ...', b=batch_size)\n", | |
| " else:\n", | |
| " mask = None\n", | |
| "\n", | |
| " assert not ( (args.use_mask and args.overlay_mask) and (args.init_sample is None and init_image is None)), \"Need an init image when use_mask == True and overlay_mask == True\"\n", | |
| " \n", | |
| " t_enc = int((1.0-args.strength) * args.steps)\n", | |
| "\n", | |
| " # Noise schedule for the k-diffusion samplers (used for masking)\n", | |
| " k_sigmas = model_wrap.get_sigmas(args.steps)\n", | |
| " k_sigmas = k_sigmas[len(k_sigmas)-t_enc-1:]\n", | |
| "\n", | |
| " if args.sampler in ['plms','ddim']:\n", | |
| " sampler.make_schedule(ddim_num_steps=args.steps, ddim_eta=args.ddim_eta, ddim_discretize='fill', verbose=False)\n", | |
| "\n", | |
| " callback = SamplerCallback(args=args,\n", | |
| " mask=mask, \n", | |
| " init_latent=init_latent,\n", | |
| " sigmas=k_sigmas,\n", | |
| " sampler=sampler,\n", | |
| " verbose=False).callback \n", | |
| "\n", | |
| " results = []\n", | |
| " with torch.no_grad():\n", | |
| " with precision_scope(\"cuda\"):\n", | |
| " with model.ema_scope():\n", | |
| " for prompts in data:\n", | |
| " if isinstance(prompts, tuple):\n", | |
| " prompts = list(prompts)\n", | |
| " if args.prompt_weighting:\n", | |
| " uc, c = get_uc_and_c(prompts, model, args, frame)\n", | |
| " else:\n", | |
| " uc = model.get_learned_conditioning(batch_size * [\"\"])\n", | |
| " c = model.get_learned_conditioning(prompts)\n", | |
| "\n", | |
| "\n", | |
| " if args.scale == 1.0:\n", | |
| " uc = None\n", | |
| " if args.init_c != None:\n", | |
| " c = args.init_c\n", | |
| "\n", | |
| " if args.sampler in [\"klms\",\"dpm2\",\"dpm2_ancestral\",\"heun\",\"euler\",\"euler_ancestral\"]:\n", | |
| " samples = sampler_fn(\n", | |
| " c=c, \n", | |
| " uc=uc, \n", | |
| " args=args, \n", | |
| " model_wrap=model_wrap, \n", | |
| " init_latent=init_latent, \n", | |
| " t_enc=t_enc, \n", | |
| " device=device, \n", | |
| " cb=callback)\n", | |
| " else:\n", | |
| " # args.sampler == 'plms' or args.sampler == 'ddim':\n", | |
| " if init_latent is not None and args.strength > 0:\n", | |
| " z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device))\n", | |
| " else:\n", | |
| " z_enc = torch.randn([args.n_samples, args.C, args.H // args.f, args.W // args.f], device=device)\n", | |
| " if args.sampler == 'ddim':\n", | |
| " samples = sampler.decode(z_enc, \n", | |
| " c, \n", | |
| " t_enc, \n", | |
| " unconditional_guidance_scale=args.scale,\n", | |
| " unconditional_conditioning=uc,\n", | |
| " img_callback=callback)\n", | |
| " elif args.sampler == 'plms': # no \"decode\" function in plms, so use \"sample\"\n", | |
| " shape = [args.C, args.H // args.f, args.W // args.f]\n", | |
| " samples, _ = sampler.sample(S=args.steps,\n", | |
| " conditioning=c,\n", | |
| " batch_size=args.n_samples,\n", | |
| " shape=shape,\n", | |
| " verbose=False,\n", | |
| " unconditional_guidance_scale=args.scale,\n", | |
| " unconditional_conditioning=uc,\n", | |
| " eta=args.ddim_eta,\n", | |
| " x_T=z_enc,\n", | |
| " img_callback=callback)\n", | |
| " else:\n", | |
| " raise Exception(f\"Sampler {args.sampler} not recognised.\")\n", | |
| "\n", | |
| " \n", | |
| " if return_latent:\n", | |
| " results.append(samples.clone())\n", | |
| "\n", | |
| " x_samples = model.decode_first_stage(samples)\n", | |
| "\n", | |
| " if args.use_mask and args.overlay_mask:\n", | |
| " # Overlay the masked image after the image is generated\n", | |
| " if args.init_sample is not None:\n", | |
| " img_original = args.init_sample\n", | |
| " elif init_image is not None:\n", | |
| " img_original = init_image\n", | |
| " else:\n", | |
| " raise Exception(\"Cannot overlay the masked image without an init image to overlay\")\n", | |
| "\n", | |
| " mask_fullres = prepare_mask(args.mask_file if mask_image is None else mask_image, \n", | |
| " img_original.shape, \n", | |
| " args.mask_contrast_adjust, \n", | |
| " args.mask_brightness_adjust)\n", | |
| " mask_fullres = mask_fullres[:,:3,:,:]\n", | |
| " mask_fullres = repeat(mask_fullres, '1 ... -> b ...', b=batch_size)\n", | |
| "\n", | |
| " mask_fullres[mask_fullres < mask_fullres.max()] = 0\n", | |
| " mask_fullres = gaussian_filter(mask_fullres, args.mask_overlay_blur)\n", | |
| " mask_fullres = torch.Tensor(mask_fullres).to(device)\n", | |
| "\n", | |
| " x_samples = img_original * mask_fullres + x_samples * ((mask_fullres * -1.0) + 1)\n", | |
| "\n", | |
| "\n", | |
| " if return_sample:\n", | |
| " results.append(x_samples.clone())\n", | |
| "\n", | |
| " x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)\n", | |
| "\n", | |
| " if return_c:\n", | |
| " results.append(c.clone())\n", | |
| "\n", | |
| " for x_sample in x_samples:\n", | |
| " x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')\n", | |
| " image = Image.fromarray(x_sample.astype(np.uint8))\n", | |
| " results.append(image)\n", | |
| " return results" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "CIUJ7lWI4v53" | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Using config: ./stable-diffusion/configs/stable-diffusion/v1-inference.yaml\n", | |
| "/workspace/models/sd-v1-4.ckpt exists\n", | |
| "\n", | |
| "...checking sha256\n", | |
| "hash is correct\n", | |
| "\n", | |
| "Using ckpt: /workspace/models/sd-v1-4.ckpt\n", | |
| "Loading model from /workspace/models/sd-v1-4.ckpt\n", | |
| "Global Step: 470000\n", | |
| "LatentDiffusion: Running in eps-prediction mode\n", | |
| "DiffusionWrapper has 859.52 M params.\n", | |
| "making attention of type 'vanilla' with 512 in_channels\n", | |
| "Working with z of shape (1, 4, 32, 32) = 4096 dimensions.\n", | |
| "making attention of type 'vanilla' with 512 in_channels\n" | |
| ] | |
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| "Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.4.self_attn.q_proj.bias', 'vision_model.encoder.layers.17.self_attn.v_proj.bias', 'vision_model.encoder.layers.21.self_attn.out_proj.bias', 'vision_model.encoder.layers.22.layer_norm1.weight', 'vision_model.encoder.layers.21.self_attn.v_proj.bias', 'vision_model.encoder.layers.22.mlp.fc1.weight', 'vision_model.encoder.layers.16.mlp.fc1.bias', 'vision_model.encoder.layers.21.mlp.fc2.bias', 'vision_model.encoder.layers.8.mlp.fc2.bias', 'vision_model.encoder.layers.14.self_attn.k_proj.weight', 'vision_model.encoder.layers.3.self_attn.k_proj.weight', 'vision_model.encoder.layers.8.self_attn.k_proj.bias', 'vision_model.encoder.layers.19.self_attn.v_proj.weight', 'vision_model.encoder.layers.3.mlp.fc1.weight', 'vision_model.encoder.layers.16.self_attn.k_proj.weight', 'vision_model.encoder.layers.3.layer_norm2.bias', 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'vision_model.encoder.layers.8.self_attn.k_proj.weight', 'vision_model.encoder.layers.22.self_attn.k_proj.weight', 'vision_model.encoder.layers.21.layer_norm2.bias', 'vision_model.encoder.layers.5.self_attn.v_proj.weight', 'vision_model.encoder.layers.19.self_attn.v_proj.bias', 'vision_model.encoder.layers.6.mlp.fc2.bias', 'vision_model.encoder.layers.7.mlp.fc2.bias', 'vision_model.encoder.layers.18.layer_norm2.bias', 'vision_model.encoder.layers.16.self_attn.k_proj.bias', 'vision_model.encoder.layers.3.self_attn.v_proj.weight']\n", | |
| "- This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", | |
| "- This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#@markdown **Select and Load Model**\n", | |
| "\n", | |
| "model_config = \"v1-inference.yaml\" #@param [\"custom\",\"v1-inference.yaml\"]\n", | |
| "model_checkpoint = \"sd-v1-4.ckpt\" #@param [\"custom\",\"sd-v1-4-full-ema.ckpt\",\"sd-v1-4.ckpt\",\"sd-v1-3-full-ema.ckpt\",\"sd-v1-3.ckpt\",\"sd-v1-2-full-ema.ckpt\",\"sd-v1-2.ckpt\",\"sd-v1-1-full-ema.ckpt\",\"sd-v1-1.ckpt\", \"robo-diffusion-v1.ckpt\",\"waifu-diffusion-v1-3.ckpt\"]\n", | |
| "if model_checkpoint == \"waifu-diffusion-v1-3.ckpt\":\n", | |
| " model_checkpoint = \"model-epoch05-float16.ckpt\"\n", | |
| "custom_config_path = \"\" #@param {type:\"string\"}\n", | |
| "custom_checkpoint_path = \"\" #@param {type:\"string\"}\n", | |
| "\n", | |
| "load_on_run_all = True #@param {type: 'boolean'}\n", | |
| "half_precision = True # check\n", | |
| "check_sha256 = True #@param {type:\"boolean\"}\n", | |
| "\n", | |
| "model_map = {\n", | |
| " \"sd-v1-4-full-ema.ckpt\": {\n", | |
| " 'sha256': '14749efc0ae8ef0329391ad4436feb781b402f4fece4883c7ad8d10556d8a36a',\n", | |
| " 'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/blob/main/sd-v1-4-full-ema.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"sd-v1-4.ckpt\": {\n", | |
| " 'sha256': 'fe4efff1e174c627256e44ec2991ba279b3816e364b49f9be2abc0b3ff3f8556',\n", | |
| " 'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"sd-v1-3-full-ema.ckpt\": {\n", | |
| " 'sha256': '54632c6e8a36eecae65e36cb0595fab314e1a1545a65209f24fde221a8d4b2ca',\n", | |
| " 'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-3-original/blob/main/sd-v1-3-full-ema.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"sd-v1-3.ckpt\": {\n", | |
| " 'sha256': '2cff93af4dcc07c3e03110205988ff98481e86539c51a8098d4f2236e41f7f2f',\n", | |
| " 'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-3-original/resolve/main/sd-v1-3.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"sd-v1-2-full-ema.ckpt\": {\n", | |
| " 'sha256': 'bc5086a904d7b9d13d2a7bccf38f089824755be7261c7399d92e555e1e9ac69a',\n", | |
| " 'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/blob/main/sd-v1-2-full-ema.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"sd-v1-2.ckpt\": {\n", | |
| " 'sha256': '3b87d30facd5bafca1cbed71cfb86648aad75d1c264663c0cc78c7aea8daec0d',\n", | |
| " 'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/resolve/main/sd-v1-2.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"sd-v1-1-full-ema.ckpt\": {\n", | |
| " 'sha256': 'efdeb5dc418a025d9a8cc0a8617e106c69044bc2925abecc8a254b2910d69829',\n", | |
| " 'url':'https://huggingface.co/CompVis/stable-diffusion-v-1-1-original/resolve/main/sd-v1-1-full-ema.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"sd-v1-1.ckpt\": {\n", | |
| " 'sha256': '86cd1d3ccb044d7ba8db743d717c9bac603c4043508ad2571383f954390f3cea',\n", | |
| " 'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-1-original/resolve/main/sd-v1-1.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"robo-diffusion-v1.ckpt\": {\n", | |
| " 'sha256': '244dbe0dcb55c761bde9c2ac0e9b46cc9705ebfe5f1f3a7cc46251573ea14e16',\n", | |
| " 'url': 'https://huggingface.co/nousr/robo-diffusion/resolve/main/models/robo-diffusion-v1.ckpt',\n", | |
| " 'requires_login': False,\n", | |
| " },\n", | |
| " \"model-epoch05-float16.ckpt\": {\n", | |
| " 'sha256': '26cf2a2e30095926bb9fd9de0c83f47adc0b442dbfdc3d667d43778e8b70bece',\n", | |
| " 'url': 'https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/model-epoch05-float16.ckpt',\n", | |
| " 'requires_login': False,\n", | |
| " },\n", | |
| "}\n", | |
| "\n", | |
| "# config path\n", | |
| "ckpt_config_path = custom_config_path if model_config == \"custom\" else os.path.join(models_path, model_config)\n", | |
| "if os.path.exists(ckpt_config_path):\n", | |
| " print(f\"{ckpt_config_path} exists\")\n", | |
| "else:\n", | |
| " ckpt_config_path = \"./stable-diffusion/configs/stable-diffusion/v1-inference.yaml\"\n", | |
| "print(f\"Using config: {ckpt_config_path}\")\n", | |
| "\n", | |
| "# checkpoint path or download\n", | |
| "ckpt_path = custom_checkpoint_path if model_checkpoint == \"custom\" else os.path.join(models_path, model_checkpoint)\n", | |
| "ckpt_valid = True\n", | |
| "if os.path.exists(ckpt_path):\n", | |
| " print(f\"{ckpt_path} exists\")\n", | |
| "elif 'url' in model_map[model_checkpoint]:\n", | |
| " url = model_map[model_checkpoint]['url']\n", | |
| "\n", | |
| " # CLI dialogue to authenticate download\n", | |
| " if model_map[model_checkpoint]['requires_login']:\n", | |
| " print(\"This model requires an authentication token\")\n", | |
| " print(\"Please ensure you have accepted its terms of service before continuing.\")\n", | |
| "\n", | |
| " username = input(\"What is your huggingface username?:\")\n", | |
| " token = input(\"What is your huggingface token?:\")\n", | |
| "\n", | |
| " _, path = url.split(\"https://\")\n", | |
| "\n", | |
| " url = f\"https://{username}:{token}@{path}\"\n", | |
| "\n", | |
| " # contact server for model\n", | |
| " print(f\"Attempting to download {model_checkpoint}...this may take a while\")\n", | |
| " ckpt_request = requests.get(url)\n", | |
| " request_status = ckpt_request.status_code\n", | |
| "\n", | |
| " # inform user of errors\n", | |
| " if request_status == 403:\n", | |
| " raise ConnectionRefusedError(\"You have not accepted the license for this model.\")\n", | |
| " elif request_status == 404:\n", | |
| " raise ConnectionError(\"Could not make contact with server\")\n", | |
| " elif request_status != 200:\n", | |
| " raise ConnectionError(f\"Some other error has ocurred - response code: {request_status}\")\n", | |
| "\n", | |
| " # write to model path\n", | |
| " with open(os.path.join(models_path, model_checkpoint), 'wb') as model_file:\n", | |
| " model_file.write(ckpt_request.content)\n", | |
| "else:\n", | |
| " print(f\"Please download model checkpoint and place in {os.path.join(models_path, model_checkpoint)}\")\n", | |
| " ckpt_valid = False\n", | |
| "\n", | |
| "if check_sha256 and model_checkpoint != \"custom\" and ckpt_valid:\n", | |
| " import hashlib\n", | |
| " print(\"\\n...checking sha256\")\n", | |
| " with open(ckpt_path, \"rb\") as f:\n", | |
| " bytes = f.read() \n", | |
| " hash = hashlib.sha256(bytes).hexdigest()\n", | |
| " del bytes\n", | |
| " if model_map[model_checkpoint][\"sha256\"] == hash:\n", | |
| " print(\"hash is correct\\n\")\n", | |
| " else:\n", | |
| " print(\"hash in not correct\\n\")\n", | |
| " ckpt_valid = False\n", | |
| "\n", | |
| "if ckpt_valid:\n", | |
| " print(f\"Using ckpt: {ckpt_path}\")\n", | |
| "\n", | |
| "def load_model_from_config(config, ckpt, verbose=False, device='cuda', half_precision=True):\n", | |
| " map_location = \"cuda\" #@param [\"cpu\", \"cuda\"]\n", | |
| " print(f\"Loading model from {ckpt}\")\n", | |
| " pl_sd = torch.load(ckpt, map_location=map_location)\n", | |
| " if \"global_step\" in pl_sd:\n", | |
| " print(f\"Global Step: {pl_sd['global_step']}\")\n", | |
| " sd = pl_sd[\"state_dict\"]\n", | |
| " model = instantiate_from_config(config.model)\n", | |
| " m, u = model.load_state_dict(sd, strict=False)\n", | |
| " if len(m) > 0 and verbose:\n", | |
| " print(\"missing keys:\")\n", | |
| " print(m)\n", | |
| " if len(u) > 0 and verbose:\n", | |
| " print(\"unexpected keys:\")\n", | |
| " print(u)\n", | |
| "\n", | |
| " if half_precision:\n", | |
| " model = model.half().to(device)\n", | |
| " else:\n", | |
| " model = model.to(device)\n", | |
| " model.eval()\n", | |
| " return model\n", | |
| "\n", | |
| "if load_on_run_all and ckpt_valid:\n", | |
| " local_config = OmegaConf.load(f\"{ckpt_config_path}\")\n", | |
| " model = load_model_from_config(local_config, f\"{ckpt_path}\", half_precision=half_precision)\n", | |
| " device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", | |
| " model = model.to(device)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ov3r4RD1tzsT" | |
| }, | |
| "source": [ | |
| "# Settings" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "0j7rgxvLvfay" | |
| }, | |
| "source": [ | |
| "### Animation Settings" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "8HJN2TE3vh-J" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "\n", | |
| "def DeforumAnimArgs():\n", | |
| "\n", | |
| " #@markdown ####**Animation:**\n", | |
| " animation_mode = '3D' #@param ['None', '2D', '3D', 'Video Input', 'Interpolation'] {type:'string'}\n", | |
| " max_frames = 10000 #@param {type:\"number\"}\n", | |
| " border = 'replicate' #@param ['wrap', 'replicate'] {type:'string'}\n", | |
| "\n", | |
| " #@markdown ####**Motion Parameters:**\n", | |
| " angle = \"0:(0)\"#@param {type:\"string\"}\n", | |
| " zoom = \"0:(1.04)\"#@param {type:\"string\"}\n", | |
| " translation_x = \"0:(0),67:(1.992),118:(1.183),186:(-1.675),237:(0.406),288:(-0.079),356:(0.838),407:(-3.645),458:(0.628),526:(0.777),577:(3.62),628:(-0.34),696:(-0.414),747:(2.905),798:(0.447),866:(-1.415),917:(3.624),968:(-0.505),1036:(-1.888),1087:(2.281),1138:(1.017),1206:(2.136),1257:(0.811),1308:(-0.463),1435:(1.992),1486:(1.183),1554:(-1.675),1605:(0.406),1656:(-0.079),1724:(0.838),1775:(-3.645),1826:(0.628),1894:(0.777),1945:(3.62),1996:(-0.34),2064:(-0.414),2115:(2.905),2166:(0.447),2234:(-1.415),2285:(3.624),2336:(-0.505),2404:(-1.888),2455:(2.281),2506:(1.017),2574:(2.136),2625:(0.811),2676:(-0.463),2803:(1.992),2854:(1.183),2922:(-1.675),2973:(0.406),3024:(-0.079),3092:(0.838),3143:(-3.645),3194:(0.628),3262:(0.777),3313:(3.62),3364:(-0.34),3432:(-0.414),3483:(2.905),3534:(0.447),3602:(-1.415),3653:(3.624),3704:(-0.505),3772:(-1.888),3823:(2.281),3874:(1.017),3942:(2.136),3993:(0.811),4044:(-0.463),4171:(1.992),4222:(1.183),4290:(-1.675),4341:(0.406),4392:(-0.079),4460:(0.838),4511:(-3.645),4562:(0.628),4630:(0.777),4681:(3.62),4732:(-0.34),4800:(-0.414),4851:(2.905),4902:(0.447),4970:(-1.415),5021:(3.624),5072:(-0.505),5140:(-1.888),5191:(2.281),5242:(1.017),5310:(2.136),5361:(0.811),5412:(-0.463),5539:(1.992),5590:(1.183),5658:(-1.675),5709:(0.406),5760:(-0.079),5828:(0.838),5879:(-3.645),5930:(0.628),5998:(0.777),6049:(3.62),6100:(-0.34),6168:(-0.414),6219:(2.905),6270:(0.447),6338:(-1.415),6389:(3.624),6440:(-0.505),6508:(-1.888),6559:(2.281),6610:(1.017),6678:(2.136),6729:(0.811),6780:(-0.463),6907:(1.992),6958:(1.183),7026:(-1.675),7077:(0.406),7128:(-0.079),7196:(0.838),7247:(-3.645),7298:(0.628),7366:(0.777),7417:(3.62),7468:(-0.34),7536:(-0.414),7587:(2.905),7638:(0.447),7706:(-1.415),7757:(3.624),7808:(-0.505),7876:(-1.888),7927:(2.281),7978:(1.017),8046:(2.136),8097:(0.811),8148:(-0.463),8275:(1.992),8326:(1.183),8394:(-1.675),8445:(0.406),8496:(-0.079),8564:(0.838),8615:(-3.645),8666:(0.628),8734:(0.777),8785:(3.62),8836:(-0.34),8904:(-0.414),8955:(2.905),9006:(0.447),9074:(-1.415),9125:(3.624),9176:(-0.505),9244:(-1.888),9295:(2.281),9346:(1.017),9414:(2.136),9465:(0.811),9516:(-0.463),9643:(1.992),9694:(1.183),9762:(-1.675),9813:(0.406),9864:(-0.079),9932:(0.838),9983:(-3.645),\"#@param {type:\"string\"}\n", | |
| " translation_y = \"0:(0),67:(3.683),118:(-0.508),186:(-0.468),237:(1.695),288:(-1.081),356:(-0.096),407:(-2.627),458:(-0.389),526:(-1.648),577:(-2.304),628:(-1.851),696:(0.953),747:(0.639),798:(0.538),866:(-1.853),917:(-3.285),968:(0.453),1036:(-2.352),1087:(-0.56),1138:(2.318),1206:(-0.448),1257:(3.43),1308:(-1.339),1435:(3.683),1486:(-0.508),1554:(-0.468),1605:(1.695),1656:(-1.081),1724:(-0.096),1775:(-2.627),1826:(-0.389),1894:(-1.648),1945:(-2.304),1996:(-1.851),2064:(0.953),2115:(0.639),2166:(0.538),2234:(-1.853),2285:(-3.285),2336:(0.453),2404:(-2.352),2455:(-0.56),2506:(2.318),2574:(-0.448),2625:(3.43),2676:(-1.339),2803:(3.683),2854:(-0.508),2922:(-0.468),2973:(1.695),3024:(-1.081),3092:(-0.096),3143:(-2.627),3194:(-0.389),3262:(-1.648),3313:(-2.304),3364:(-1.851),3432:(0.953),3483:(0.639),3534:(0.538),3602:(-1.853),3653:(-3.285),3704:(0.453),3772:(-2.352),3823:(-0.56),3874:(2.318),3942:(-0.448),3993:(3.43),4044:(-1.339),4171:(3.683),4222:(-0.508),4290:(-0.468),4341:(1.695),4392:(-1.081),4460:(-0.096),4511:(-2.627),4562:(-0.389),4630:(-1.648),4681:(-2.304),4732:(-1.851),4800:(0.953),4851:(0.639),4902:(0.538),4970:(-1.853),5021:(-3.285),5072:(0.453),5140:(-2.352),5191:(-0.56),5242:(2.318),5310:(-0.448),5361:(3.43),5412:(-1.339),5539:(3.683),5590:(-0.508),5658:(-0.468),5709:(1.695),5760:(-1.081),5828:(-0.096),5879:(-2.627),5930:(-0.389),5998:(-1.648),6049:(-2.304),6100:(-1.851),6168:(0.953),6219:(0.639),6270:(0.538),6338:(-1.853),6389:(-3.285),6440:(0.453),6508:(-2.352),6559:(-0.56),6610:(2.318),6678:(-0.448),6729:(3.43),6780:(-1.339),6907:(3.683),6958:(-0.508),7026:(-0.468),7077:(1.695),7128:(-1.081),7196:(-0.096),7247:(-2.627),7298:(-0.389),7366:(-1.648),7417:(-2.304),7468:(-1.851),7536:(0.953),7587:(0.639),7638:(0.538),7706:(-1.853),7757:(-3.285),7808:(0.453),7876:(-2.352),7927:(-0.56),7978:(2.318),8046:(-0.448),8097:(3.43),8148:(-1.339),8275:(3.683),8326:(-0.508),8394:(-0.468),8445:(1.695),8496:(-1.081),8564:(-0.096),8615:(-2.627),8666:(-0.389),8734:(-1.648),8785:(-2.304),8836:(-1.851),8904:(0.953),8955:(0.639),9006:(0.538),9074:(-1.853),9125:(-3.285),9176:(0.453),9244:(-2.352),9295:(-0.56),9346:(2.318),9414:(-0.448),9465:(3.43),9516:(-1.339),9643:(3.683),9694:(-0.508),9762:(-0.468),9813:(1.695),9864:(-1.081),9932:(-0.096),9983:(-2.627),\"#@param {type:\"string\"}\n", | |
| " translation_z = \"0:(0),67:(6.357),118:(4.734),186:(3.685),237:(7.083),288:(4.088),356:(4.456),407:(6.888),458:(3.815),526:(4.452),577:(6.471),628:(4.18),696:(3.92),747:(6.231),798:(4.687),866:(3.937),917:(7.649),968:(4.087),1036:(3.981),1087:(6.217),1138:(4.68),1206:(3.765),1257:(7.029),1308:(4.001),1435:(6.357),1486:(4.734),1554:(3.685),1605:(7.083),1656:(4.088),1724:(4.456),1775:(6.888),1826:(3.815),1894:(4.452),1945:(6.471),1996:(4.18),2064:(3.92),2115:(6.231),2166:(4.687),2234:(3.937),2285:(7.649),2336:(4.087),2404:(3.981),2455:(6.217),2506:(4.68),2574:(3.765),2625:(7.029),2676:(4.001),2803:(6.357),2854:(4.734),2922:(3.685),2973:(7.083),3024:(4.088),3092:(4.456),3143:(6.888),3194:(3.815),3262:(4.452),3313:(6.471),3364:(4.18),3432:(3.92),3483:(6.231),3534:(4.687),3602:(3.937),3653:(7.649),3704:(4.087),3772:(3.981),3823:(6.217),3874:(4.68),3942:(3.765),3993:(7.029),4044:(4.001),4171:(6.357),4222:(4.734),4290:(3.685),4341:(7.083),4392:(4.088),4460:(4.456),4511:(6.888),4562:(3.815),4630:(4.452),4681:(6.471),4732:(4.18),4800:(3.92),4851:(6.231),4902:(4.687),4970:(3.937),5021:(7.649),5072:(4.087),5140:(3.981),5191:(6.217),5242:(4.68),5310:(3.765),5361:(7.029),5412:(4.001),5539:(6.357),5590:(4.734),5658:(3.685),5709:(7.083),5760:(4.088),5828:(4.456),5879:(6.888),5930:(3.815),5998:(4.452),6049:(6.471),6100:(4.18),6168:(3.92),6219:(6.231),6270:(4.687),6338:(3.937),6389:(7.649),6440:(4.087),6508:(3.981),6559:(6.217),6610:(4.68),6678:(3.765),6729:(7.029),6780:(4.001),6907:(6.357),6958:(4.734),7026:(3.685),7077:(7.083),7128:(4.088),7196:(4.456),7247:(6.888),7298:(3.815),7366:(4.452),7417:(6.471),7468:(4.18),7536:(3.92),7587:(6.231),7638:(4.687),7706:(3.937),7757:(7.649),7808:(4.087),7876:(3.981),7927:(6.217),7978:(4.68),8046:(3.765),8097:(7.029),8148:(4.001),8275:(6.357),8326:(4.734),8394:(3.685),8445:(7.083),8496:(4.088),8564:(4.456),8615:(6.888),8666:(3.815),8734:(4.452),8785:(6.471),8836:(4.18),8904:(3.92),8955:(6.231),9006:(4.687),9074:(3.937),9125:(7.649),9176:(4.087),9244:(3.981),9295:(6.217),9346:(4.68),9414:(3.765),9465:(7.029),9516:(4.001),9643:(6.357),9694:(4.734),9762:(3.685),9813:(7.083),9864:(4.088),9932:(4.456),9983:(6.888),\"#@param {type:\"string\"}\n", | |
| " rotation_3d_x = \"0:(0),67:(0.09),118:(-0.242),186:(-0.238),237:(-0.001),288:(0.232),356:(0.115),407:(0.184),458:(0.035),526:(-0.276),577:(-0.086),628:(0.239),696:(-0.043),747:(0.069),798:(0.02),866:(0.148),917:(-0.047),968:(0.257),1036:(-0.247),1087:(0.17),1138:(0.231),1206:(-0.036),1257:(-0.086),1308:(-0.003),1435:(0.09),1486:(-0.242),1554:(-0.238),1605:(-0.001),1656:(0.232),1724:(0.115),1775:(0.184),1826:(0.035),1894:(-0.276),1945:(-0.086),1996:(0.239),2064:(-0.043),2115:(0.069),2166:(0.02),2234:(0.148),2285:(-0.047),2336:(0.257),2404:(-0.247),2455:(0.17),2506:(0.231),2574:(-0.036),2625:(-0.086),2676:(-0.003),2803:(0.09),2854:(-0.242),2922:(-0.238),2973:(-0.001),3024:(0.232),3092:(0.115),3143:(0.184),3194:(0.035),3262:(-0.276),3313:(-0.086),3364:(0.239),3432:(-0.043),3483:(0.069),3534:(0.02),3602:(0.148),3653:(-0.047),3704:(0.257),3772:(-0.247),3823:(0.17),3874:(0.231),3942:(-0.036),3993:(-0.086),4044:(-0.003),4171:(0.09),4222:(-0.242),4290:(-0.238),4341:(-0.001),4392:(0.232),4460:(0.115),4511:(0.184),4562:(0.035),4630:(-0.276),4681:(-0.086),4732:(0.239),4800:(-0.043),4851:(0.069),4902:(0.02),4970:(0.148),5021:(-0.047),5072:(0.257),5140:(-0.247),5191:(0.17),5242:(0.231),5310:(-0.036),5361:(-0.086),5412:(-0.003),5539:(0.09),5590:(-0.242),5658:(-0.238),5709:(-0.001),5760:(0.232),5828:(0.115),5879:(0.184),5930:(0.035),5998:(-0.276),6049:(-0.086),6100:(0.239),6168:(-0.043),6219:(0.069),6270:(0.02),6338:(0.148),6389:(-0.047),6440:(0.257),6508:(-0.247),6559:(0.17),6610:(0.231),6678:(-0.036),6729:(-0.086),6780:(-0.003),6907:(0.09),6958:(-0.242),7026:(-0.238),7077:(-0.001),7128:(0.232),7196:(0.115),7247:(0.184),7298:(0.035),7366:(-0.276),7417:(-0.086),7468:(0.239),7536:(-0.043),7587:(0.069),7638:(0.02),7706:(0.148),7757:(-0.047),7808:(0.257),7876:(-0.247),7927:(0.17),7978:(0.231),8046:(-0.036),8097:(-0.086),8148:(-0.003),8275:(0.09),8326:(-0.242),8394:(-0.238),8445:(-0.001),8496:(0.232),8564:(0.115),8615:(0.184),8666:(0.035),8734:(-0.276),8785:(-0.086),8836:(0.239),8904:(-0.043),8955:(0.069),9006:(0.02),9074:(0.148),9125:(-0.047),9176:(0.257),9244:(-0.247),9295:(0.17),9346:(0.231),9414:(-0.036),9465:(-0.086),9516:(-0.003),9643:(0.09),9694:(-0.242),9762:(-0.238),9813:(-0.001),9864:(0.232),9932:(0.115),9983:(0.184),\"#@param {type:\"string\"}\n", | |
| " rotation_3d_y = \"0:(0),67:(-0.46),118:(-0.276),186:(0.003),237:(0.221),288:(0.276),356:(0.012),407:(-0.282),458:(-0.182),526:(-0.141),577:(0.21),628:(-0.084),696:(0.004),747:(-0.415),798:(-0.258),866:(0.122),917:(0.15),968:(0.002),1036:(0.298),1087:(-0.044),1138:(0.181),1206:(0.197),1257:(0.43),1308:(-0.077),1435:(-0.46),1486:(-0.276),1554:(0.003),1605:(0.221),1656:(0.276),1724:(0.012),1775:(-0.282),1826:(-0.182),1894:(-0.141),1945:(0.21),1996:(-0.084),2064:(0.004),2115:(-0.415),2166:(-0.258),2234:(0.122),2285:(0.15),2336:(0.002),2404:(0.298),2455:(-0.044),2506:(0.181),2574:(0.197),2625:(0.43),2676:(-0.077),2803:(-0.46),2854:(-0.276),2922:(0.003),2973:(0.221),3024:(0.276),3092:(0.012),3143:(-0.282),3194:(-0.182),3262:(-0.141),3313:(0.21),3364:(-0.084),3432:(0.004),3483:(-0.415),3534:(-0.258),3602:(0.122),3653:(0.15),3704:(0.002),3772:(0.298),3823:(-0.044),3874:(0.181),3942:(0.197),3993:(0.43),4044:(-0.077),4171:(-0.46),4222:(-0.276),4290:(0.003),4341:(0.221),4392:(0.276),4460:(0.012),4511:(-0.282),4562:(-0.182),4630:(-0.141),4681:(0.21),4732:(-0.084),4800:(0.004),4851:(-0.415),4902:(-0.258),4970:(0.122),5021:(0.15),5072:(0.002),5140:(0.298),5191:(-0.044),5242:(0.181),5310:(0.197),5361:(0.43),5412:(-0.077),5539:(-0.46),5590:(-0.276),5658:(0.003),5709:(0.221),5760:(0.276),5828:(0.012),5879:(-0.282),5930:(-0.182),5998:(-0.141),6049:(0.21),6100:(-0.084),6168:(0.004),6219:(-0.415),6270:(-0.258),6338:(0.122),6389:(0.15),6440:(0.002),6508:(0.298),6559:(-0.044),6610:(0.181),6678:(0.197),6729:(0.43),6780:(-0.077),6907:(-0.46),6958:(-0.276),7026:(0.003),7077:(0.221),7128:(0.276),7196:(0.012),7247:(-0.282),7298:(-0.182),7366:(-0.141),7417:(0.21),7468:(-0.084),7536:(0.004),7587:(-0.415),7638:(-0.258),7706:(0.122),7757:(0.15),7808:(0.002),7876:(0.298),7927:(-0.044),7978:(0.181),8046:(0.197),8097:(0.43),8148:(-0.077),8275:(-0.46),8326:(-0.276),8394:(0.003),8445:(0.221),8496:(0.276),8564:(0.012),8615:(-0.282),8666:(-0.182),8734:(-0.141),8785:(0.21),8836:(-0.084),8904:(0.004),8955:(-0.415),9006:(-0.258),9074:(0.122),9125:(0.15),9176:(0.002),9244:(0.298),9295:(-0.044),9346:(0.181),9414:(0.197),9465:(0.43),9516:(-0.077),9643:(-0.46),9694:(-0.276),9762:(0.003),9813:(0.221),9864:(0.276),9932:(0.012),9983:(-0.282),\"#@param {type:\"string\"}\n", | |
| " rotation_3d_z = \"0:(0)\"#@param {type:\"string\"}\n", | |
| " flip_2d_perspective = False #@param {type:\"boolean\"}\n", | |
| " perspective_flip_theta = \"0:(0)\"#@param {type:\"string\"}\n", | |
| " perspective_flip_phi = \"0:(t%15)\"#@param {type:\"string\"}\n", | |
| " perspective_flip_gamma = \"0:(0)\"#@param {type:\"string\"}\n", | |
| " perspective_flip_fv = \"0:(53)\"#@param {type:\"string\"}\n", | |
| " noise_schedule = \"0: (0.02)\"#@param {type:\"string\"}\n", | |
| " strength_schedule = \"0:(0.72), 30:(0.78)\"#@param {type:\"string\"}\n", | |
| " contrast_schedule = \"0: (1.0)\"#@param {type:\"string\"}\n", | |
| "\n", | |
| " #@markdown ####**Coherence:**\n", | |
| " color_coherence = 'Match Frame 0 LAB' #@param ['None', 'Match Frame 0 HSV', 'Match Frame 0 LAB', 'Match Frame 0 RGB'] {type:'string'}\n", | |
| " diffusion_cadence = '1' #@param ['1','2','3','4','5','6','7','8'] {type:'string'}\n", | |
| "\n", | |
| " #@markdown ####**3D Depth Warping:**\n", | |
| " use_depth_warping = True #@param {type:\"boolean\"}\n", | |
| " midas_weight = 0.3#@param {type:\"number\"}\n", | |
| " near_plane = 200\n", | |
| " far_plane = 10000\n", | |
| " fov = 65#@param {type:\"number\"}\n", | |
| " padding_mode = 'border'#@param ['border', 'reflection', 'zeros'] {type:'string'}\n", | |
| " sampling_mode = 'bicubic'#@param ['bicubic', 'bilinear', 'nearest'] {type:'string'}\n", | |
| " save_depth_maps = False #@param {type:\"boolean\"}\n", | |
| "\n", | |
| " #@markdown ####**Video Input:**\n", | |
| " video_init_path ='/content/video_in.mp4'#@param {type:\"string\"}\n", | |
| " extract_nth_frame = 1#@param {type:\"number\"}\n", | |
| " overwrite_extracted_frames = True #@param {type:\"boolean\"}\n", | |
| " use_mask_video = False #@param {type:\"boolean\"}\n", | |
| " video_mask_path ='/content/video_in.mp4'#@param {type:\"string\"}\n", | |
| "\n", | |
| " #@markdown ####**Interpolation:**\n", | |
| " interpolate_key_frames = False #@param {type:\"boolean\"}\n", | |
| " interpolate_x_frames = 4 #@param {type:\"number\"}\n", | |
| " \n", | |
| " #@markdown ####**Resume Animation:**\n", | |
| " resume_from_timestring = False #@param {type:\"boolean\"}\n", | |
| " resume_timestring = \"20220829210106\" #@param {type:\"string\"}\n", | |
| "\n", | |
| " return locals()\n", | |
| "\n", | |
| "class DeformAnimKeys():\n", | |
| " def __init__(self, anim_args):\n", | |
| " self.angle_series = get_inbetweens(parse_key_frames(anim_args.angle), anim_args.max_frames)\n", | |
| " self.zoom_series = get_inbetweens(parse_key_frames(anim_args.zoom), anim_args.max_frames)\n", | |
| " self.translation_x_series = get_inbetweens(parse_key_frames(anim_args.translation_x), anim_args.max_frames)\n", | |
| " self.translation_y_series = get_inbetweens(parse_key_frames(anim_args.translation_y), anim_args.max_frames)\n", | |
| " self.translation_z_series = get_inbetweens(parse_key_frames(anim_args.translation_z), anim_args.max_frames)\n", | |
| " self.rotation_3d_x_series = get_inbetweens(parse_key_frames(anim_args.rotation_3d_x), anim_args.max_frames)\n", | |
| " self.rotation_3d_y_series = get_inbetweens(parse_key_frames(anim_args.rotation_3d_y), anim_args.max_frames)\n", | |
| " self.rotation_3d_z_series = get_inbetweens(parse_key_frames(anim_args.rotation_3d_z), anim_args.max_frames)\n", | |
| " self.perspective_flip_theta_series = get_inbetweens(parse_key_frames(anim_args.perspective_flip_theta), anim_args.max_frames)\n", | |
| " self.perspective_flip_phi_series = get_inbetweens(parse_key_frames(anim_args.perspective_flip_phi), anim_args.max_frames)\n", | |
| " self.perspective_flip_gamma_series = get_inbetweens(parse_key_frames(anim_args.perspective_flip_gamma), anim_args.max_frames)\n", | |
| " self.perspective_flip_fv_series = get_inbetweens(parse_key_frames(anim_args.perspective_flip_fv), anim_args.max_frames)\n", | |
| " self.noise_schedule_series = get_inbetweens(parse_key_frames(anim_args.noise_schedule), anim_args.max_frames)\n", | |
| " self.strength_schedule_series = get_inbetweens(parse_key_frames(anim_args.strength_schedule), anim_args.max_frames)\n", | |
| " self.contrast_schedule_series = get_inbetweens(parse_key_frames(anim_args.contrast_schedule), anim_args.max_frames)\n", | |
| "\n", | |
| "\n", | |
| "def get_inbetweens(key_frames, max_frames, integer=False, interp_method='Linear'):\n", | |
| " import numexpr\n", | |
| " key_frame_series = pd.Series([np.nan for a in range(max_frames)])\n", | |
| " \n", | |
| " for i in range(0, max_frames):\n", | |
| " if i in key_frames:\n", | |
| " value = key_frames[i]\n", | |
| " value_is_number = check_is_number(value)\n", | |
| " # if it's only a number, leave the rest for the default interpolation\n", | |
| " if value_is_number:\n", | |
| " t = i\n", | |
| " key_frame_series[i] = value\n", | |
| " if not value_is_number:\n", | |
| " t = i\n", | |
| " key_frame_series[i] = numexpr.evaluate(value)\n", | |
| " key_frame_series = key_frame_series.astype(float)\n", | |
| " \n", | |
| " if interp_method == 'Cubic' and len(key_frames.items()) <= 3:\n", | |
| " interp_method = 'Quadratic' \n", | |
| " if interp_method == 'Quadratic' and len(key_frames.items()) <= 2:\n", | |
| " interp_method = 'Linear'\n", | |
| " \n", | |
| " key_frame_series[0] = key_frame_series[key_frame_series.first_valid_index()]\n", | |
| " key_frame_series[max_frames-1] = key_frame_series[key_frame_series.last_valid_index()]\n", | |
| " key_frame_series = key_frame_series.interpolate(method=interp_method.lower(), limit_direction='both')\n", | |
| " if integer:\n", | |
| " return key_frame_series.astype(int)\n", | |
| " return key_frame_series\n", | |
| "\n", | |
| "def parse_key_frames(string, prompt_parser=None):\n", | |
| " # because math functions (i.e. sin(t)) can utilize brackets \n", | |
| " # it extracts the value in form of some stuff\n", | |
| " # which has previously been enclosed with brackets and\n", | |
| " # with a comma or end of line existing after the closing one\n", | |
| " pattern = r'((?P<frame>[0-9]+):[\\s]*\\((?P<param>[\\S\\s]*?)\\)([,][\\s]?|[\\s]?$))'\n", | |
| " frames = dict()\n", | |
| " for match_object in re.finditer(pattern, string):\n", | |
| " frame = int(match_object.groupdict()['frame'])\n", | |
| " param = match_object.groupdict()['param']\n", | |
| " if prompt_parser:\n", | |
| " frames[frame] = prompt_parser(param)\n", | |
| " else:\n", | |
| " frames[frame] = param\n", | |
| " if frames == {} and len(string) != 0:\n", | |
| " raise RuntimeError('Key Frame string not correctly formatted')\n", | |
| " return frames" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "63UOJvU3xdPS" | |
| }, | |
| "source": [ | |
| "### Prompts\n", | |
| "`animation_mode: None` batches on list of *prompts*. `animation_mode: 2D` uses *animation_prompts* key frame sequence" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "2ujwkGZTcGev" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "\n", | |
| "prompts = [\n", | |
| " \"a beautiful forest by Asher Brown Durand, trending on Artstation\", # the first prompt I want\n", | |
| " \"a beautiful portrait of a woman by Artgerm, trending on Artstation\", # the second prompt I want\n", | |
| " #\"this prompt I don't want it I commented it out\",\n", | |
| " #\"a nousr robot, trending on Artstation\", # use \"nousr robot\" with the robot diffusion model (see model_checkpoint setting)\n", | |
| " #\"touhou 1girl komeiji_koishi portrait, green hair\", # waifu diffusion prompts can use danbooru tag groups (see model_checkpoint)\n", | |
| " #\"this prompt has weights if prompt weighting enabled:2 can also do negative:-2\", # (see prompt_weighting)\n", | |
| "]\n", | |
| "\n", | |
| "animation_prompts = {\n", | |
| " 0: \"photo inside a cavern of a raw mutilated carcass suffering human face screaming in pain, severely burned flesh burnt bbq kabob, tearing flesh bleeding, partially hidden behind a rock, with black eyes, open mouth and big teeth\",\n", | |
| " #20: \"a beautiful banana, trending on Artstation\",\n", | |
| " #30: \"a beautiful coconut, trending on Artstation\",\n", | |
| " #40: \"a beautiful durian, trending on Artstation\",\n", | |
| "}" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "s8RAo2zI-vQm" | |
| }, | |
| "source": [ | |
| "# Run" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "qH74gBWDd2oq" | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", 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| "<PIL.Image.Image image mode=RGB size=512x512 at 0x7F108C2B2AD0>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Rendering animation frame 665 of 10000\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| "Global seed set to 1989635121\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "photo inside a cavern of a raw mutilated carcass suffering human face screaming in pain, severely burned flesh burnt bbq kabob, tearing flesh bleeding, partially hidden behind a rock, with black eyes, open mouth and big teeth 1989635121\n", | |
| "Angle: 0.0 Zoom: 1.04\n", | |
| "Tx: -0.38026470588235295 Ty: -0.32529411764705896 Tz: 4.0385294117647055\n", | |
| "Rx: 0.08555882352941177 Ry: -0.03611764705882353 Rz: 0.0\n" | |
| ] | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": [ | |
| " 79%|███████▊ | 11/14 [00:01<00:00, 10.45it/s]" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "#@markdown **Load Settings**\n", | |
| "override_settings_with_file = False #@param {type:\"boolean\"}\n", | |
| "custom_settings_file = \"/content/drive/MyDrive/Settings.txt\"#@param {type:\"string\"}\n", | |
| "\n", | |
| "def DeforumArgs():\n", | |
| " #@markdown **Image Settings**\n", | |
| " W = 512 #@param\n", | |
| " H = 512 #@param\n", | |
| " W, H = map(lambda x: x - x % 64, (W, H)) # resize to integer multiple of 64\n", | |
| "\n", | |
| " #@markdown **Sampling Settings**\n", | |
| " seed = -1 #@param\n", | |
| " sampler = 'euler_ancestral' #@param [\"klms\",\"dpm2\",\"dpm2_ancestral\",\"heun\",\"euler\",\"euler_ancestral\",\"plms\", \"ddim\"]\n", | |
| " steps = 65 #@param\n", | |
| " scale = 19 #@param\n", | |
| " ddim_eta = 0.0 #@param\n", | |
| " dynamic_threshold = None\n", | |
| " static_threshold = None \n", | |
| "\n", | |
| " #@markdown **Save & Display Settings**\n", | |
| " save_samples = True #@param {type:\"boolean\"}\n", | |
| " save_settings = True #@param {type:\"boolean\"}\n", | |
| " display_samples = True #@param {type:\"boolean\"}\n", | |
| " save_sample_per_step = False #@param {type:\"boolean\"}\n", | |
| " show_sample_per_step = False #@param {type:\"boolean\"}\n", | |
| "\n", | |
| " #@markdown **Prompt Settings**\n", | |
| " prompt_weighting = True #@param {type:\"boolean\"}\n", | |
| " normalize_prompt_weights = True #@param {type:\"boolean\"}\n", | |
| " log_weighted_subprompts = False #@param {type:\"boolean\"}\n", | |
| "\n", | |
| " #@markdown **Batch Settings**\n", | |
| " n_batch = 1 #@param\n", | |
| " batch_name = \"decomp2_cavern4_1\" #@param {type:\"string\"}\n", | |
| " filename_format = \"{timestring}_{index}_{prompt}.png\" #@param [\"{timestring}_{index}_{seed}.png\",\"{timestring}_{index}_{prompt}.png\"]\n", | |
| " seed_behavior = \"iter\" #@param [\"iter\",\"fixed\",\"random\"]\n", | |
| " make_grid = False #@param {type:\"boolean\"}\n", | |
| " grid_rows = 2 #@param \n", | |
| " outdir = get_output_folder(output_path, batch_name)\n", | |
| "\n", | |
| " #@markdown **Init Settings**\n", | |
| " use_init = False #@param {type:\"boolean\"}\n", | |
| " strength = 0.0 #@param {type:\"number\"}\n", | |
| " strength_0_no_init = True # Set the strength to 0 automatically when no init image is used\n", | |
| " init_image = \"https://cdn.pixabay.com/photo/2022/07/30/13/10/green-longhorn-beetle-7353749_1280.jpg\" #@param {type:\"string\"}\n", | |
| " # Whiter areas of the mask are areas that change more\n", | |
| " use_mask = False #@param {type:\"boolean\"}\n", | |
| " use_alpha_as_mask = False # use the alpha channel of the init image as the mask\n", | |
| " mask_file = \"https://www.filterforge.com/wiki/images/archive/b/b7/20080927223728%21Polygonal_gradient_thumb.jpg\" #@param {type:\"string\"}\n", | |
| " invert_mask = False #@param {type:\"boolean\"}\n", | |
| " # Adjust mask image, 1.0 is no adjustment. Should be positive numbers.\n", | |
| " mask_brightness_adjust = 1.0 #@param {type:\"number\"}\n", | |
| " mask_contrast_adjust = 1.0 #@param {type:\"number\"}\n", | |
| " # Overlay the masked image at the end of the generation so it does not get degraded by encoding and decoding\n", | |
| " overlay_mask = True # {type:\"boolean\"}\n", | |
| " # Blur edges of final overlay mask, if used. Minimum = 0 (no blur)\n", | |
| " mask_overlay_blur = 5 # {type:\"number\"}\n", | |
| "\n", | |
| " n_samples = 1 # doesnt do anything\n", | |
| " precision = 'autocast' \n", | |
| " C = 4\n", | |
| " f = 8\n", | |
| "\n", | |
| " prompt = \"\"\n", | |
| " timestring = \"\"\n", | |
| " init_latent = None\n", | |
| " init_sample = None\n", | |
| " init_c = None\n", | |
| "\n", | |
| " return locals()\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "def next_seed(args):\n", | |
| " if args.seed_behavior == 'iter':\n", | |
| " args.seed += 1\n", | |
| " elif args.seed_behavior == 'fixed':\n", | |
| " pass # always keep seed the same\n", | |
| " else:\n", | |
| " args.seed = random.randint(0, 2**32 - 1)\n", | |
| " return args.seed\n", | |
| "\n", | |
| "def render_image_batch(args):\n", | |
| " args.prompts = {k: f\"{v:05d}\" for v, k in enumerate(prompts)}\n", | |
| " \n", | |
| " # create output folder for the batch\n", | |
| " os.makedirs(args.outdir, exist_ok=True)\n", | |
| " if args.save_settings or args.save_samples:\n", | |
| " print(f\"Saving to {os.path.join(args.outdir, args.timestring)}_*\")\n", | |
| "\n", | |
| " # save settings for the batch\n", | |
| " if args.save_settings:\n", | |
| " filename = os.path.join(args.outdir, f\"{args.timestring}_settings.txt\")\n", | |
| " with open(filename, \"w+\", encoding=\"utf-8\") as f:\n", | |
| " json.dump(dict(args.__dict__), f, ensure_ascii=False, indent=4)\n", | |
| "\n", | |
| " index = 0\n", | |
| " \n", | |
| " # function for init image batching\n", | |
| " init_array = []\n", | |
| " if args.use_init:\n", | |
| " if args.init_image == \"\":\n", | |
| " raise FileNotFoundError(\"No path was given for init_image\")\n", | |
| " if args.init_image.startswith('http://') or args.init_image.startswith('https://'):\n", | |
| " init_array.append(args.init_image)\n", | |
| " elif not os.path.isfile(args.init_image):\n", | |
| " if args.init_image[-1] != \"/\": # avoids path error by adding / to end if not there\n", | |
| " args.init_image += \"/\" \n", | |
| " for image in sorted(os.listdir(args.init_image)): # iterates dir and appends images to init_array\n", | |
| " if image.split(\".\")[-1] in (\"png\", \"jpg\", \"jpeg\"):\n", | |
| " init_array.append(args.init_image + image)\n", | |
| " else:\n", | |
| " init_array.append(args.init_image)\n", | |
| " else:\n", | |
| " init_array = [\"\"]\n", | |
| "\n", | |
| " # when doing large batches don't flood browser with images\n", | |
| " clear_between_batches = args.n_batch >= 32\n", | |
| "\n", | |
| " for iprompt, prompt in enumerate(prompts): \n", | |
| " args.prompt = prompt\n", | |
| " print(f\"Prompt {iprompt+1} of {len(prompts)}\")\n", | |
| " print(f\"{args.prompt}\")\n", | |
| "\n", | |
| " all_images = []\n", | |
| "\n", | |
| " for batch_index in range(args.n_batch):\n", | |
| " if clear_between_batches and batch_index % 32 == 0: \n", | |
| " display.clear_output(wait=True) \n", | |
| " print(f\"Batch {batch_index+1} of {args.n_batch}\")\n", | |
| " \n", | |
| " for image in init_array: # iterates the init images\n", | |
| " args.init_image = image\n", | |
| " results = generate(args)\n", | |
| " for image in results:\n", | |
| " if args.make_grid:\n", | |
| " all_images.append(T.functional.pil_to_tensor(image))\n", | |
| " if args.save_samples:\n", | |
| " if args.filename_format == \"{timestring}_{index}_{prompt}.png\":\n", | |
| " filename = f\"{args.timestring}_{index:05}_{sanitize(prompt)[:160]}.png\"\n", | |
| " else:\n", | |
| " filename = f\"{args.timestring}_{index:05}_{args.seed}.png\"\n", | |
| " image.save(os.path.join(args.outdir, filename))\n", | |
| " if args.display_samples:\n", | |
| " display.display(image)\n", | |
| " index += 1\n", | |
| " args.seed = next_seed(args)\n", | |
| "\n", | |
| " #print(len(all_images))\n", | |
| " if args.make_grid:\n", | |
| " grid = make_grid(all_images, nrow=int(len(all_images)/args.grid_rows))\n", | |
| " grid = rearrange(grid, 'c h w -> h w c').cpu().numpy()\n", | |
| " filename = f\"{args.timestring}_{iprompt:05d}_grid_{args.seed}.png\"\n", | |
| " grid_image = Image.fromarray(grid.astype(np.uint8))\n", | |
| " grid_image.save(os.path.join(args.outdir, filename))\n", | |
| " display.clear_output(wait=True) \n", | |
| " display.display(grid_image)\n", | |
| "\n", | |
| "\n", | |
| "def render_animation(args, anim_args):\n", | |
| " # animations use key framed prompts\n", | |
| " args.prompts = animation_prompts\n", | |
| "\n", | |
| " # expand key frame strings to values\n", | |
| " keys = DeformAnimKeys(anim_args)\n", | |
| "\n", | |
| " # resume animation\n", | |
| " start_frame = 0\n", | |
| " if anim_args.resume_from_timestring:\n", | |
| " for tmp in os.listdir(args.outdir):\n", | |
| " if tmp.split(\"_\")[0] == anim_args.resume_timestring:\n", | |
| " start_frame += 1\n", | |
| " start_frame = start_frame - 1\n", | |
| "\n", | |
| " # create output folder for the batch\n", | |
| " os.makedirs(args.outdir, exist_ok=True)\n", | |
| " print(f\"Saving animation frames to {args.outdir}\")\n", | |
| "\n", | |
| " # save settings for the batch\n", | |
| " settings_filename = os.path.join(args.outdir, f\"{args.timestring}_settings.txt\")\n", | |
| " with open(settings_filename, \"w+\", encoding=\"utf-8\") as f:\n", | |
| " s = {**dict(args.__dict__), **dict(anim_args.__dict__)}\n", | |
| " json.dump(s, f, ensure_ascii=False, indent=4)\n", | |
| " \n", | |
| " # resume from timestring\n", | |
| " if anim_args.resume_from_timestring:\n", | |
| " args.timestring = anim_args.resume_timestring\n", | |
| "\n", | |
| " # expand prompts out to per-frame\n", | |
| " prompt_series = pd.Series([np.nan for a in range(anim_args.max_frames)])\n", | |
| " for i, prompt in animation_prompts.items():\n", | |
| " prompt_series[i] = prompt\n", | |
| " prompt_series = prompt_series.ffill().bfill()\n", | |
| "\n", | |
| " # check for video inits\n", | |
| " using_vid_init = anim_args.animation_mode == 'Video Input'\n", | |
| "\n", | |
| " # load depth model for 3D\n", | |
| " predict_depths = (anim_args.animation_mode == '3D' and anim_args.use_depth_warping) or anim_args.save_depth_maps\n", | |
| " if predict_depths:\n", | |
| " depth_model = DepthModel(device)\n", | |
| " depth_model.load_midas(models_path)\n", | |
| " if anim_args.midas_weight < 1.0:\n", | |
| " depth_model.load_adabins()\n", | |
| " else:\n", | |
| " depth_model = None\n", | |
| " anim_args.save_depth_maps = False\n", | |
| "\n", | |
| " # state for interpolating between diffusion steps\n", | |
| " turbo_steps = 1 if using_vid_init else int(anim_args.diffusion_cadence)\n", | |
| " turbo_prev_image, turbo_prev_frame_idx = None, 0\n", | |
| " turbo_next_image, turbo_next_frame_idx = None, 0\n", | |
| "\n", | |
| " # resume animation\n", | |
| " prev_sample = None\n", | |
| " color_match_sample = None\n", | |
| " if anim_args.resume_from_timestring:\n", | |
| " last_frame = start_frame-1\n", | |
| " if turbo_steps > 1:\n", | |
| " last_frame -= last_frame%turbo_steps\n", | |
| " path = os.path.join(args.outdir,f\"{args.timestring}_{last_frame:05}.png\")\n", | |
| " img = cv2.imread(path)\n", | |
| " img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", | |
| " prev_sample = sample_from_cv2(img)\n", | |
| " if anim_args.color_coherence != 'None':\n", | |
| " color_match_sample = img\n", | |
| " if turbo_steps > 1:\n", | |
| " turbo_next_image, turbo_next_frame_idx = sample_to_cv2(prev_sample, type=np.float32), last_frame\n", | |
| " turbo_prev_image, turbo_prev_frame_idx = turbo_next_image, turbo_next_frame_idx\n", | |
| " start_frame = last_frame+turbo_steps\n", | |
| "\n", | |
| " args.n_samples = 1\n", | |
| " frame_idx = start_frame\n", | |
| " while frame_idx < anim_args.max_frames:\n", | |
| " print(f\"Rendering animation frame {frame_idx} of {anim_args.max_frames}\")\n", | |
| " noise = keys.noise_schedule_series[frame_idx]\n", | |
| " strength = keys.strength_schedule_series[frame_idx]\n", | |
| " contrast = keys.contrast_schedule_series[frame_idx]\n", | |
| " depth = None\n", | |
| " \n", | |
| " # emit in-between frames\n", | |
| " if turbo_steps > 1:\n", | |
| " tween_frame_start_idx = max(0, frame_idx-turbo_steps)\n", | |
| " for tween_frame_idx in range(tween_frame_start_idx, frame_idx):\n", | |
| " tween = float(tween_frame_idx - tween_frame_start_idx + 1) / float(frame_idx - tween_frame_start_idx)\n", | |
| " print(f\" creating in between frame {tween_frame_idx} tween:{tween:0.2f}\")\n", | |
| "\n", | |
| " advance_prev = turbo_prev_image is not None and tween_frame_idx > turbo_prev_frame_idx\n", | |
| " advance_next = tween_frame_idx > turbo_next_frame_idx\n", | |
| "\n", | |
| " if depth_model is not None:\n", | |
| " assert(turbo_next_image is not None)\n", | |
| " depth = depth_model.predict(turbo_next_image, anim_args)\n", | |
| "\n", | |
| " if anim_args.animation_mode == '2D':\n", | |
| " if advance_prev:\n", | |
| " turbo_prev_image = anim_frame_warp_2d(turbo_prev_image, args, anim_args, keys, tween_frame_idx)\n", | |
| " if advance_next:\n", | |
| " turbo_next_image = anim_frame_warp_2d(turbo_next_image, args, anim_args, keys, tween_frame_idx)\n", | |
| " else: # '3D'\n", | |
| " if advance_prev:\n", | |
| " turbo_prev_image = anim_frame_warp_3d(turbo_prev_image, depth, anim_args, keys, tween_frame_idx)\n", | |
| " if advance_next:\n", | |
| " turbo_next_image = anim_frame_warp_3d(turbo_next_image, depth, anim_args, keys, tween_frame_idx)\n", | |
| " turbo_prev_frame_idx = turbo_next_frame_idx = tween_frame_idx\n", | |
| "\n", | |
| " if turbo_prev_image is not None and tween < 1.0:\n", | |
| " img = turbo_prev_image*(1.0-tween) + turbo_next_image*tween\n", | |
| " else:\n", | |
| " img = turbo_next_image\n", | |
| "\n", | |
| " filename = f\"{args.timestring}_{tween_frame_idx:05}.png\"\n", | |
| " cv2.imwrite(os.path.join(args.outdir, filename), cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_RGB2BGR))\n", | |
| " if anim_args.save_depth_maps:\n", | |
| " depth_model.save(os.path.join(args.outdir, f\"{args.timestring}_depth_{tween_frame_idx:05}.png\"), depth)\n", | |
| " if turbo_next_image is not None:\n", | |
| " prev_sample = sample_from_cv2(turbo_next_image)\n", | |
| "\n", | |
| " # apply transforms to previous frame\n", | |
| " if prev_sample is not None:\n", | |
| " if anim_args.animation_mode == '2D':\n", | |
| " prev_img = anim_frame_warp_2d(sample_to_cv2(prev_sample), args, anim_args, keys, frame_idx)\n", | |
| " else: # '3D'\n", | |
| " prev_img_cv2 = sample_to_cv2(prev_sample)\n", | |
| " depth = depth_model.predict(prev_img_cv2, anim_args) if depth_model else None\n", | |
| " prev_img = anim_frame_warp_3d(prev_img_cv2, depth, anim_args, keys, frame_idx)\n", | |
| "\n", | |
| " # apply color matching\n", | |
| " if anim_args.color_coherence != 'None':\n", | |
| " if color_match_sample is None:\n", | |
| " color_match_sample = prev_img.copy()\n", | |
| " else:\n", | |
| " prev_img = maintain_colors(prev_img, color_match_sample, anim_args.color_coherence)\n", | |
| "\n", | |
| " # apply scaling\n", | |
| " contrast_sample = prev_img * contrast\n", | |
| " # apply frame noising\n", | |
| " noised_sample = add_noise(sample_from_cv2(contrast_sample), noise)\n", | |
| "\n", | |
| " # use transformed previous frame as init for current\n", | |
| " args.use_init = True\n", | |
| " if half_precision:\n", | |
| " args.init_sample = noised_sample.half().to(device)\n", | |
| " else:\n", | |
| " args.init_sample = noised_sample.to(device)\n", | |
| " args.strength = max(0.0, min(1.0, strength))\n", | |
| "\n", | |
| " # grab prompt for current frame\n", | |
| " args.prompt = prompt_series[frame_idx]\n", | |
| " print(f\"{args.prompt} {args.seed}\")\n", | |
| " if not using_vid_init:\n", | |
| " print(f\"Angle: {keys.angle_series[frame_idx]} Zoom: {keys.zoom_series[frame_idx]}\")\n", | |
| " print(f\"Tx: {keys.translation_x_series[frame_idx]} Ty: {keys.translation_y_series[frame_idx]} Tz: {keys.translation_z_series[frame_idx]}\")\n", | |
| " print(f\"Rx: {keys.rotation_3d_x_series[frame_idx]} Ry: {keys.rotation_3d_y_series[frame_idx]} Rz: {keys.rotation_3d_z_series[frame_idx]}\")\n", | |
| "\n", | |
| " # grab init image for current frame\n", | |
| " if using_vid_init:\n", | |
| " init_frame = os.path.join(args.outdir, 'inputframes', f\"{frame_idx+1:05}.jpg\") \n", | |
| " print(f\"Using video init frame {init_frame}\")\n", | |
| " args.init_image = init_frame\n", | |
| " if anim_args.use_mask_video:\n", | |
| " mask_frame = os.path.join(args.outdir, 'maskframes', f\"{frame_idx+1:05}.jpg\")\n", | |
| " args.mask_file = mask_frame\n", | |
| "\n", | |
| " # sample the diffusion model\n", | |
| " sample, image = generate(args, frame_idx, return_latent=False, return_sample=True)\n", | |
| " if not using_vid_init:\n", | |
| " prev_sample = sample\n", | |
| "\n", | |
| " if turbo_steps > 1:\n", | |
| " turbo_prev_image, turbo_prev_frame_idx = turbo_next_image, turbo_next_frame_idx\n", | |
| " turbo_next_image, turbo_next_frame_idx = sample_to_cv2(sample, type=np.float32), frame_idx\n", | |
| " frame_idx += turbo_steps\n", | |
| " else: \n", | |
| " filename = f\"{args.timestring}_{frame_idx:05}.png\"\n", | |
| " image.save(os.path.join(args.outdir, filename))\n", | |
| " if anim_args.save_depth_maps:\n", | |
| " if depth is None:\n", | |
| " depth = depth_model.predict(sample_to_cv2(sample), anim_args)\n", | |
| " depth_model.save(os.path.join(args.outdir, f\"{args.timestring}_depth_{frame_idx:05}.png\"), depth)\n", | |
| " frame_idx += 1\n", | |
| "\n", | |
| " display.clear_output(wait=True)\n", | |
| " display.display(image)\n", | |
| "\n", | |
| " args.seed = next_seed(args)\n", | |
| "\n", | |
| "def vid2frames(video_path, frames_path, n=1, overwrite=True): \n", | |
| " if not os.path.exists(frames_path) or overwrite: \n", | |
| " try:\n", | |
| " for f in pathlib.Path(video_in_frame_path).glob('*.jpg'):\n", | |
| " f.unlink()\n", | |
| " except:\n", | |
| " pass\n", | |
| " assert os.path.exists(video_path), f\"Video input {video_path} does not exist\"\n", | |
| " \n", | |
| " vidcap = cv2.VideoCapture(video_path)\n", | |
| " success,image = vidcap.read()\n", | |
| " count = 0\n", | |
| " t=1\n", | |
| " success = True\n", | |
| " while success:\n", | |
| " if count % n == 0:\n", | |
| " cv2.imwrite(frames_path + os.path.sep + f\"{t:05}.jpg\" , image) # save frame as JPEG file\n", | |
| " t += 1\n", | |
| " success,image = vidcap.read()\n", | |
| " count += 1\n", | |
| " print(\"Converted %d frames\" % count)\n", | |
| " else: print(\"Frames already unpacked\")\n", | |
| "\n", | |
| "def render_input_video(args, anim_args):\n", | |
| " # create a folder for the video input frames to live in\n", | |
| " video_in_frame_path = os.path.join(args.outdir, 'inputframes') \n", | |
| " os.makedirs(video_in_frame_path, exist_ok=True)\n", | |
| " \n", | |
| " # save the video frames from input video\n", | |
| " print(f\"Exporting Video Frames (1 every {anim_args.extract_nth_frame}) frames to {video_in_frame_path}...\")\n", | |
| " vid2frames(anim_args.video_init_path, video_in_frame_path, anim_args.extract_nth_frame, anim_args.overwrite_extracted_frames)\n", | |
| "\n", | |
| " # determine max frames from length of input frames\n", | |
| " anim_args.max_frames = len([f for f in pathlib.Path(video_in_frame_path).glob('*.jpg')])\n", | |
| " args.use_init = True\n", | |
| " print(f\"Loading {anim_args.max_frames} input frames from {video_in_frame_path} and saving video frames to {args.outdir}\")\n", | |
| "\n", | |
| " if anim_args.use_mask_video:\n", | |
| " # create a folder for the mask video input frames to live in\n", | |
| " mask_in_frame_path = os.path.join(args.outdir, 'maskframes') \n", | |
| " os.makedirs(mask_in_frame_path, exist_ok=True)\n", | |
| "\n", | |
| " # save the video frames from mask video\n", | |
| " print(f\"Exporting Video Frames (1 every {anim_args.extract_nth_frame}) frames to {mask_in_frame_path}...\")\n", | |
| " vid2frames(anim_args.video_mask_path, mask_in_frame_path, anim_args.extract_nth_frame, anim_args.overwrite_extracted_frames)\n", | |
| " args.use_mask = True\n", | |
| " args.overlay_mask = True\n", | |
| "\n", | |
| " render_animation(args, anim_args)\n", | |
| "\n", | |
| "def render_interpolation(args, anim_args):\n", | |
| " # animations use key framed prompts\n", | |
| " args.prompts = animation_prompts\n", | |
| "\n", | |
| " # create output folder for the batch\n", | |
| " os.makedirs(args.outdir, exist_ok=True)\n", | |
| " print(f\"Saving animation frames to {args.outdir}\")\n", | |
| "\n", | |
| " # save settings for the batch\n", | |
| " settings_filename = os.path.join(args.outdir, f\"{args.timestring}_settings.txt\")\n", | |
| " with open(settings_filename, \"w+\", encoding=\"utf-8\") as f:\n", | |
| " s = {**dict(args.__dict__), **dict(anim_args.__dict__)}\n", | |
| " json.dump(s, f, ensure_ascii=False, indent=4)\n", | |
| " \n", | |
| " # Interpolation Settings\n", | |
| " args.n_samples = 1\n", | |
| " args.seed_behavior = 'fixed' # force fix seed at the moment bc only 1 seed is available\n", | |
| " prompts_c_s = [] # cache all the text embeddings\n", | |
| "\n", | |
| " print(f\"Preparing for interpolation of the following...\")\n", | |
| "\n", | |
| " for i, prompt in animation_prompts.items():\n", | |
| " args.prompt = prompt\n", | |
| "\n", | |
| " # sample the diffusion model\n", | |
| " results = generate(args, return_c=True)\n", | |
| " c, image = results[0], results[1]\n", | |
| " prompts_c_s.append(c) \n", | |
| " \n", | |
| " # display.clear_output(wait=True)\n", | |
| " display.display(image)\n", | |
| " \n", | |
| " args.seed = next_seed(args)\n", | |
| "\n", | |
| " display.clear_output(wait=True)\n", | |
| " print(f\"Interpolation start...\")\n", | |
| "\n", | |
| " frame_idx = 0\n", | |
| "\n", | |
| " if anim_args.interpolate_key_frames:\n", | |
| " for i in range(len(prompts_c_s)-1):\n", | |
| " dist_frames = list(animation_prompts.items())[i+1][0] - list(animation_prompts.items())[i][0]\n", | |
| " if dist_frames <= 0:\n", | |
| " print(\"key frames duplicated or reversed. interpolation skipped.\")\n", | |
| " return\n", | |
| " else:\n", | |
| " for j in range(dist_frames):\n", | |
| " # interpolate the text embedding\n", | |
| " prompt1_c = prompts_c_s[i]\n", | |
| " prompt2_c = prompts_c_s[i+1] \n", | |
| " args.init_c = prompt1_c.add(prompt2_c.sub(prompt1_c).mul(j * 1/dist_frames))\n", | |
| "\n", | |
| " # sample the diffusion model\n", | |
| " results = generate(args)\n", | |
| " image = results[0]\n", | |
| "\n", | |
| " filename = f\"{args.timestring}_{frame_idx:05}.png\"\n", | |
| " image.save(os.path.join(args.outdir, filename))\n", | |
| " frame_idx += 1\n", | |
| "\n", | |
| " display.clear_output(wait=True)\n", | |
| " display.display(image)\n", | |
| "\n", | |
| " args.seed = next_seed(args)\n", | |
| "\n", | |
| " else:\n", | |
| " for i in range(len(prompts_c_s)-1):\n", | |
| " for j in range(anim_args.interpolate_x_frames+1):\n", | |
| " # interpolate the text embedding\n", | |
| " prompt1_c = prompts_c_s[i]\n", | |
| " prompt2_c = prompts_c_s[i+1] \n", | |
| " args.init_c = prompt1_c.add(prompt2_c.sub(prompt1_c).mul(j * 1/(anim_args.interpolate_x_frames+1)))\n", | |
| "\n", | |
| " # sample the diffusion model\n", | |
| " results = generate(args)\n", | |
| " image = results[0]\n", | |
| "\n", | |
| " filename = f\"{args.timestring}_{frame_idx:05}.png\"\n", | |
| " image.save(os.path.join(args.outdir, filename))\n", | |
| " frame_idx += 1\n", | |
| "\n", | |
| " display.clear_output(wait=True)\n", | |
| " display.display(image)\n", | |
| "\n", | |
| " args.seed = next_seed(args)\n", | |
| "\n", | |
| " # generate the last prompt\n", | |
| " args.init_c = prompts_c_s[-1]\n", | |
| " results = generate(args)\n", | |
| " image = results[0]\n", | |
| " filename = f\"{args.timestring}_{frame_idx:05}.png\"\n", | |
| " image.save(os.path.join(args.outdir, filename))\n", | |
| "\n", | |
| " display.clear_output(wait=True)\n", | |
| " display.display(image)\n", | |
| " args.seed = next_seed(args)\n", | |
| "\n", | |
| " #clear init_c\n", | |
| " args.init_c = None\n", | |
| "\n", | |
| "\n", | |
| "args_dict = DeforumArgs()\n", | |
| "anim_args_dict = DeforumAnimArgs()\n", | |
| "\n", | |
| "if override_settings_with_file:\n", | |
| " print(f\"reading custom settings from {custom_settings_file}\")\n", | |
| " if not os.path.isfile(custom_settings_file):\n", | |
| " print('The custom settings file does not exist. The in-notebook settings will be used instead')\n", | |
| " else:\n", | |
| " with open(custom_settings_file, \"r\") as f:\n", | |
| " jdata = json.loads(f.read())\n", | |
| " animation_prompts = jdata[\"prompts\"]\n", | |
| " for i, k in enumerate(args_dict):\n", | |
| " if k in jdata:\n", | |
| " args_dict[k] = jdata[k]\n", | |
| " else:\n", | |
| " print(f\"key {k} doesn't exist in the custom settings data! using the default value of {args_dict[k]}\")\n", | |
| " for i, k in enumerate(anim_args_dict):\n", | |
| " if k in jdata:\n", | |
| " anim_args_dict[k] = jdata[k]\n", | |
| " else:\n", | |
| " print(f\"key {k} doesn't exist in the custom settings data! using the default value of {anim_args_dict[k]}\")\n", | |
| " print(args_dict)\n", | |
| " print(anim_args_dict)\n", | |
| "\n", | |
| "args = SimpleNamespace(**args_dict)\n", | |
| "anim_args = SimpleNamespace(**anim_args_dict)\n", | |
| "\n", | |
| "args.timestring = time.strftime('%Y%m%d%H%M%S')\n", | |
| "args.strength = max(0.0, min(1.0, args.strength))\n", | |
| "\n", | |
| "if args.seed == -1:\n", | |
| " args.seed = random.randint(0, 2**32 - 1)\n", | |
| "if not args.use_init:\n", | |
| " args.init_image = None\n", | |
| "if args.sampler == 'plms' and (args.use_init or anim_args.animation_mode != 'None'):\n", | |
| " print(f\"Init images aren't supported with PLMS yet, switching to KLMS\")\n", | |
| " args.sampler = 'klms'\n", | |
| "if args.sampler != 'ddim':\n", | |
| " args.ddim_eta = 0\n", | |
| "\n", | |
| "if anim_args.animation_mode == 'None':\n", | |
| " anim_args.max_frames = 1\n", | |
| "elif anim_args.animation_mode == 'Video Input':\n", | |
| " args.use_init = True\n", | |
| "\n", | |
| "# clean up unused memory\n", | |
| "gc.collect()\n", | |
| "torch.cuda.empty_cache()\n", | |
| "\n", | |
| "# dispatch to appropriate renderer\n", | |
| "if anim_args.animation_mode == '2D' or anim_args.animation_mode == '3D':\n", | |
| " render_animation(args, anim_args)\n", | |
| "elif anim_args.animation_mode == 'Video Input':\n", | |
| " render_input_video(args, anim_args)\n", | |
| "elif anim_args.animation_mode == 'Interpolation':\n", | |
| " render_interpolation(args, anim_args)\n", | |
| "else:\n", | |
| " render_image_batch(args) " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "4zV0J_YbMCTx", | |
| "tags": [] | |
| }, | |
| "source": [ | |
| "# Create video from frames" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "no2jP8HTMBM0", | |
| "jupyter": { | |
| "source_hidden": true | |
| }, | |
| "tags": [] | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "skip_video_for_run_all = True #@param {type: 'boolean'}\n", | |
| "fps = 12 #@param {type:\"number\"}\n", | |
| "#@markdown **Manual Settings**\n", | |
| "use_manual_settings = False #@param {type:\"boolean\"}\n", | |
| "image_path = \"/content/drive/MyDrive/AI/StableDiffusion/2022-09/20220903000939_%05d.png\" #@param {type:\"string\"}\n", | |
| "mp4_path = \"/content/drive/MyDrive/AI/StableDiffu'/content/drive/MyDrive/AI/StableDiffusion/2022-09/sion/2022-09/20220903000939.mp4\" #@param {type:\"string\"}\n", | |
| "render_steps = True #@param {type: 'boolean'}\n", | |
| "path_name_modifier = \"x0_pred\" #@param [\"x0_pred\",\"x\"]\n", | |
| "\n", | |
| "\n", | |
| "if skip_video_for_run_all == True:\n", | |
| " print('Skipping video creation, uncheck skip_video_for_run_all if you want to run it')\n", | |
| "else:\n", | |
| " import os\n", | |
| " import subprocess\n", | |
| " from base64 import b64encode\n", | |
| "\n", | |
| " print(f\"{image_path} -> {mp4_path}\")\n", | |
| "\n", | |
| " if use_manual_settings:\n", | |
| " max_frames = \"200\" #@param {type:\"string\"}\n", | |
| " else:\n", | |
| " if render_steps: # render steps from a single image\n", | |
| " fname = f\"{path_name_modifier}_%05d.png\"\n", | |
| " all_step_dirs = [os.path.join(args.outdir, d) for d in os.listdir(args.outdir) if os.path.isdir(os.path.join(args.outdir,d))]\n", | |
| " newest_dir = max(all_step_dirs, key=os.path.getmtime)\n", | |
| " image_path = os.path.join(newest_dir, fname)\n", | |
| " print(f\"Reading images from {image_path}\")\n", | |
| " mp4_path = os.path.join(newest_dir, f\"{args.timestring}_{path_name_modifier}.mp4\")\n", | |
| " max_frames = str(args.steps)\n", | |
| " else: # render images for a video\n", | |
| " image_path = os.path.join(args.outdir, f\"{args.timestring}_%05d.png\")\n", | |
| " mp4_path = os.path.join(args.outdir, f\"{args.timestring}.mp4\")\n", | |
| " max_frames = str(anim_args.max_frames)\n", | |
| "\n", | |
| " # make video\n", | |
| " cmd = [\n", | |
| " 'ffmpeg',\n", | |
| " '-y',\n", | |
| " '-vcodec', 'png',\n", | |
| " '-r', str(fps),\n", | |
| " '-start_number', str(0),\n", | |
| " '-i', image_path,\n", | |
| " '-frames:v', max_frames,\n", | |
| " '-c:v', 'libx264',\n", | |
| " '-vf',\n", | |
| " f'fps={fps}',\n", | |
| " '-pix_fmt', 'yuv420p',\n", | |
| " '-crf', '17',\n", | |
| " '-preset', 'veryfast',\n", | |
| " '-pattern_type', 'sequence',\n", | |
| " mp4_path\n", | |
| " ]\n", | |
| " process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", | |
| " stdout, stderr = process.communicate()\n", | |
| " if process.returncode != 0:\n", | |
| " print(stderr)\n", | |
| " raise RuntimeError(stderr)\n", | |
| "\n", | |
| " mp4 = open(mp4_path,'rb').read()\n", | |
| " data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", | |
| " display.display( display.HTML(f'<video controls loop><source src=\"{data_url}\" type=\"video/mp4\"></video>') )" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "TxIOPT0G5Lx1" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#@markdown **Model and Output Paths**\n", | |
| "# ask for the link\n", | |
| "print(\"Local Path Variables:\\n\")\n", | |
| "\n", | |
| "models_path = \"/workspace/models\" #@param {type:\"string\"}\n", | |
| "output_path = \"/workspace/output\" #@param {type:\"string\"}\n", | |
| "\n", | |
| "#@markdown **Google Drive Path Variables (Optional)**\n", | |
| "mount_google_drive = False #@param {type:\"boolean\"}\n", | |
| "force_remount = False\n", | |
| "\n", | |
| "if mount_google_drive:\n", | |
| " from google.colab import drive # type: ignore\n", | |
| " try:\n", | |
| " drive_path = \"/content/drive\"\n", | |
| " drive.mount(drive_path,force_remount=force_remount)\n", | |
| " models_path_gdrive = \"/content/drive/MyDrive/AI/models\" #@param {type:\"string\"}\n", | |
| " output_path_gdrive = \"/content/drive/MyDrive/AI/StableDiffusion\" #@param {type:\"string\"}\n", | |
| " models_path = models_path_gdrive\n", | |
| " output_path = output_path_gdrive\n", | |
| " except:\n", | |
| " print(\"...error mounting drive or with drive path variables\")\n", | |
| " print(\"...reverting to default path variables\")\n", | |
| "\n", | |
| "import os\n", | |
| "os.makedirs(models_path, exist_ok=True)\n", | |
| "os.makedirs(output_path, exist_ok=True)\n", | |
| "\n", | |
| "print(f\"models_path: {models_path}\")\n", | |
| "print(f\"output_path: {output_path}\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "VRNl2mfepEIe" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#@markdown **Setup Environment**\n", | |
| "\n", | |
| "setup_environment = True #@param {type:\"boolean\"}\n", | |
| "print_subprocess = False #@param {type:\"boolean\"}\n", | |
| "\n", | |
| "if setup_environment:\n", | |
| " import subprocess, time\n", | |
| " print(\"Setting up environment...\")\n", | |
| " start_time = time.time()\n", | |
| " all_process = [\n", | |
| " ['pip', 'install', 'torch==1.12.1+cu113', 'torchvision==0.13.1+cu113', '--extra-index-url', 'https://download.pytorch.org/whl/cu113'],\n", | |
| " ['pip', 'install', 'omegaconf==2.2.3', 'einops==0.4.1', 'pytorch-lightning==1.7.4', 'torchmetrics==0.9.3', 'torchtext==0.13.1', 'transformers==4.21.2', 'kornia==0.6.7'],\n", | |
| " ['git', 'clone', 'https://github.com/deforum/stable-diffusion'],\n", | |
| " ['pip', 'install', '-e', 'git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers'],\n", | |
| " ['pip', 'install', '-e', 'git+https://github.com/openai/CLIP.git@main#egg=clip'],\n", | |
| " ['pip', 'install', 'accelerate', 'ftfy', 'jsonmerge', 'matplotlib', 'resize-right', 'timm', 'torchdiffeq'],\n", | |
| " ['git', 'clone', 'https://github.com/shariqfarooq123/AdaBins.git'],\n", | |
| " ['git', 'clone', 'https://github.com/isl-org/MiDaS.git'],\n", | |
| " ['git', 'clone', 'https://github.com/MSFTserver/pytorch3d-lite.git'],\n", | |
| " ]\n", | |
| " for process in all_process:\n", | |
| " running = subprocess.run(process,stdout=subprocess.PIPE).stdout.decode('utf-8')\n", | |
| " if print_subprocess:\n", | |
| " print(running)\n", | |
| " \n", | |
| " print(subprocess.run(['git', 'clone', 'https://github.com/deforum/k-diffusion/'], stdout=subprocess.PIPE).stdout.decode('utf-8'))\n", | |
| " with open('k-diffusion/k_diffusion/__init__.py', 'w') as f:\n", | |
| " f.write('')\n", | |
| "\n", | |
| " end_time = time.time()\n", | |
| " print(f\"Environment set up in {end_time-start_time:.0f} seconds\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "81qmVZbrm4uu" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#@markdown **Python Definitions**\n", | |
| "import json\n", | |
| "from IPython import display\n", | |
| "\n", | |
| "import gc, math, os, pathlib, subprocess, sys, time\n", | |
| "import cv2\n", | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "import random\n", | |
| "import requests\n", | |
| "import torch\n", | |
| "import torch.nn as nn\n", | |
| "import torchvision.transforms as T\n", | |
| "import torchvision.transforms.functional as TF\n", | |
| "from contextlib import contextmanager, nullcontext\n", | |
| "from einops import rearrange, repeat\n", | |
| "from omegaconf import OmegaConf\n", | |
| "from PIL import Image\n", | |
| "from pytorch_lightning import seed_everything\n", | |
| "from skimage.exposure import match_histograms\n", | |
| "from torchvision.utils import make_grid\n", | |
| "from tqdm import tqdm, trange\n", | |
| "from types import SimpleNamespace\n", | |
| "from torch import autocast\n", | |
| "import re\n", | |
| "from scipy.ndimage import gaussian_filter\n", | |
| "\n", | |
| "sys.path.extend([\n", | |
| " 'src/taming-transformers',\n", | |
| " 'src/clip',\n", | |
| " 'stable-diffusion/',\n", | |
| " 'k-diffusion',\n", | |
| " 'pytorch3d-lite',\n", | |
| " 'AdaBins',\n", | |
| " 'MiDaS',\n", | |
| "])\n", | |
| "\n", | |
| "import py3d_tools as p3d\n", | |
| "\n", | |
| "from helpers import DepthModel, sampler_fn\n", | |
| "from k_diffusion.external import CompVisDenoiser\n", | |
| "from ldm.util import instantiate_from_config\n", | |
| "from ldm.models.diffusion.ddim import DDIMSampler\n", | |
| "from ldm.models.diffusion.plms import PLMSSampler\n", | |
| "\n", | |
| "def sanitize(prompt):\n", | |
| " whitelist = set('abcdefghijklmnopqrstuvwxyz ABCDEFGHIJKLMNOPQRSTUVWXYZ')\n", | |
| " tmp = ''.join(filter(whitelist.__contains__, prompt))\n", | |
| " return tmp.replace(' ', '_')\n", | |
| "\n", | |
| "from functools import reduce\n", | |
| "def construct_RotationMatrixHomogenous(rotation_angles):\n", | |
| " assert(type(rotation_angles)==list and len(rotation_angles)==3)\n", | |
| " RH = np.eye(4,4)\n", | |
| " cv2.Rodrigues(np.array(rotation_angles), RH[0:3, 0:3])\n", | |
| " return RH\n", | |
| "\n", | |
| "# https://en.wikipedia.org/wiki/Rotation_matrix\n", | |
| "def getRotationMatrixManual(rotation_angles):\n", | |
| "\t\n", | |
| " rotation_angles = [np.deg2rad(x) for x in rotation_angles]\n", | |
| " \n", | |
| " phi = rotation_angles[0] # around x\n", | |
| " gamma = rotation_angles[1] # around y\n", | |
| " theta = rotation_angles[2] # around z\n", | |
| " \n", | |
| " # X rotation\n", | |
| " Rphi = np.eye(4,4)\n", | |
| " sp = np.sin(phi)\n", | |
| " cp = np.cos(phi)\n", | |
| " Rphi[1,1] = cp\n", | |
| " Rphi[2,2] = Rphi[1,1]\n", | |
| " Rphi[1,2] = -sp\n", | |
| " Rphi[2,1] = sp\n", | |
| " \n", | |
| " # Y rotation\n", | |
| " Rgamma = np.eye(4,4)\n", | |
| " sg = np.sin(gamma)\n", | |
| " cg = np.cos(gamma)\n", | |
| " Rgamma[0,0] = cg\n", | |
| " Rgamma[2,2] = Rgamma[0,0]\n", | |
| " Rgamma[0,2] = sg\n", | |
| " Rgamma[2,0] = -sg\n", | |
| " \n", | |
| " # Z rotation (in-image-plane)\n", | |
| " Rtheta = np.eye(4,4)\n", | |
| " st = np.sin(theta)\n", | |
| " ct = np.cos(theta)\n", | |
| " Rtheta[0,0] = ct\n", | |
| " Rtheta[1,1] = Rtheta[0,0]\n", | |
| " Rtheta[0,1] = -st\n", | |
| " Rtheta[1,0] = st\n", | |
| " \n", | |
| " R = reduce(lambda x,y : np.matmul(x,y), [Rphi, Rgamma, Rtheta]) \n", | |
| " \n", | |
| " return R\n", | |
| "\n", | |
| "\n", | |
| "def getPoints_for_PerspectiveTranformEstimation(ptsIn, ptsOut, W, H, sidelength):\n", | |
| " \n", | |
| " ptsIn2D = ptsIn[0,:]\n", | |
| " ptsOut2D = ptsOut[0,:]\n", | |
| " ptsOut2Dlist = []\n", | |
| " ptsIn2Dlist = []\n", | |
| " \n", | |
| " for i in range(0,4):\n", | |
| " ptsOut2Dlist.append([ptsOut2D[i,0], ptsOut2D[i,1]])\n", | |
| " ptsIn2Dlist.append([ptsIn2D[i,0], ptsIn2D[i,1]])\n", | |
| " \n", | |
| " pin = np.array(ptsIn2Dlist) + [W/2.,H/2.]\n", | |
| " pout = (np.array(ptsOut2Dlist) + [1.,1.]) * (0.5*sidelength)\n", | |
| " pin = pin.astype(np.float32)\n", | |
| " pout = pout.astype(np.float32)\n", | |
| " \n", | |
| " return pin, pout\n", | |
| "\n", | |
| "def warpMatrix(W, H, theta, phi, gamma, scale, fV):\n", | |
| " \n", | |
| " # M is to be estimated\n", | |
| " M = np.eye(4, 4)\n", | |
| " \n", | |
| " fVhalf = np.deg2rad(fV/2.)\n", | |
| " d = np.sqrt(W*W+H*H)\n", | |
| " sideLength = scale*d/np.cos(fVhalf)\n", | |
| " h = d/(2.0*np.sin(fVhalf))\n", | |
| " n = h-(d/2.0);\n", | |
| " f = h+(d/2.0);\n", | |
| " \n", | |
| " # Translation along Z-axis by -h\n", | |
| " T = np.eye(4,4)\n", | |
| " T[2,3] = -h\n", | |
| " \n", | |
| " # Rotation matrices around x,y,z\n", | |
| " R = getRotationMatrixManual([phi, gamma, theta])\n", | |
| " \n", | |
| " \n", | |
| " # Projection Matrix \n", | |
| " P = np.eye(4,4)\n", | |
| " P[0,0] = 1.0/np.tan(fVhalf)\n", | |
| " P[1,1] = P[0,0]\n", | |
| " P[2,2] = -(f+n)/(f-n)\n", | |
| " P[2,3] = -(2.0*f*n)/(f-n)\n", | |
| " P[3,2] = -1.0\n", | |
| " \n", | |
| " # pythonic matrix multiplication\n", | |
| " F = reduce(lambda x,y : np.matmul(x,y), [P, T, R]) \n", | |
| " \n", | |
| " # shape should be 1,4,3 for ptsIn and ptsOut since perspectiveTransform() expects data in this way. \n", | |
| " # In C++, this can be achieved by Mat ptsIn(1,4,CV_64FC3);\n", | |
| " ptsIn = np.array([[\n", | |
| " [-W/2., H/2., 0.],[ W/2., H/2., 0.],[ W/2.,-H/2., 0.],[-W/2.,-H/2., 0.]\n", | |
| " ]])\n", | |
| " ptsOut = np.array(np.zeros((ptsIn.shape), dtype=ptsIn.dtype))\n", | |
| " ptsOut = cv2.perspectiveTransform(ptsIn, F)\n", | |
| " \n", | |
| " ptsInPt2f, ptsOutPt2f = getPoints_for_PerspectiveTranformEstimation(ptsIn, ptsOut, W, H, sideLength)\n", | |
| " \n", | |
| " # check float32 otherwise OpenCV throws an error\n", | |
| " assert(ptsInPt2f.dtype == np.float32)\n", | |
| " assert(ptsOutPt2f.dtype == np.float32)\n", | |
| " M33 = cv2.getPerspectiveTransform(ptsInPt2f,ptsOutPt2f)\n", | |
| "\n", | |
| " return M33, sideLength\n", | |
| "\n", | |
| "def anim_frame_warp_2d(prev_img_cv2, args, anim_args, keys, frame_idx):\n", | |
| " angle = keys.angle_series[frame_idx]\n", | |
| " zoom = keys.zoom_series[frame_idx]\n", | |
| " translation_x = keys.translation_x_series[frame_idx]\n", | |
| " translation_y = keys.translation_y_series[frame_idx]\n", | |
| "\n", | |
| " center = (args.W // 2, args.H // 2)\n", | |
| " trans_mat = np.float32([[1, 0, translation_x], [0, 1, translation_y]])\n", | |
| " rot_mat = cv2.getRotationMatrix2D(center, angle, zoom)\n", | |
| " trans_mat = np.vstack([trans_mat, [0,0,1]])\n", | |
| " rot_mat = np.vstack([rot_mat, [0,0,1]])\n", | |
| " if anim_args.flip_2d_perspective:\n", | |
| " perspective_flip_theta = keys.perspective_flip_theta_series[frame_idx]\n", | |
| " perspective_flip_phi = keys.perspective_flip_phi_series[frame_idx]\n", | |
| " perspective_flip_gamma = keys.perspective_flip_gamma_series[frame_idx]\n", | |
| " perspective_flip_fv = keys.perspective_flip_fv_series[frame_idx]\n", | |
| " M,sl = warpMatrix(args.W, args.H, perspective_flip_theta, perspective_flip_phi, perspective_flip_gamma, 1., perspective_flip_fv);\n", | |
| " post_trans_mat = np.float32([[1, 0, (args.W-sl)/2], [0, 1, (args.H-sl)/2]])\n", | |
| " post_trans_mat = np.vstack([post_trans_mat, [0,0,1]])\n", | |
| " bM = np.matmul(M, post_trans_mat)\n", | |
| " xform = np.matmul(bM, rot_mat, trans_mat)\n", | |
| " else:\n", | |
| " xform = np.matmul(rot_mat, trans_mat)\n", | |
| "\n", | |
| " return cv2.warpPerspective(\n", | |
| " prev_img_cv2,\n", | |
| " xform,\n", | |
| " (prev_img_cv2.shape[1], prev_img_cv2.shape[0]),\n", | |
| " borderMode=cv2.BORDER_WRAP if anim_args.border == 'wrap' else cv2.BORDER_REPLICATE\n", | |
| " )\n", | |
| "\n", | |
| "def anim_frame_warp_3d(prev_img_cv2, depth, anim_args, keys, frame_idx):\n", | |
| " TRANSLATION_SCALE = 1.0/200.0 # matches Disco\n", | |
| " translate_xyz = [\n", | |
| " -keys.translation_x_series[frame_idx] * TRANSLATION_SCALE, \n", | |
| " keys.translation_y_series[frame_idx] * TRANSLATION_SCALE, \n", | |
| " -keys.translation_z_series[frame_idx] * TRANSLATION_SCALE\n", | |
| " ]\n", | |
| " rotate_xyz = [\n", | |
| " math.radians(keys.rotation_3d_x_series[frame_idx]), \n", | |
| " math.radians(keys.rotation_3d_y_series[frame_idx]), \n", | |
| " math.radians(keys.rotation_3d_z_series[frame_idx])\n", | |
| " ]\n", | |
| " rot_mat = p3d.euler_angles_to_matrix(torch.tensor(rotate_xyz, device=device), \"XYZ\").unsqueeze(0)\n", | |
| " result = transform_image_3d(prev_img_cv2, depth, rot_mat, translate_xyz, anim_args)\n", | |
| " torch.cuda.empty_cache()\n", | |
| " return result\n", | |
| "\n", | |
| "def add_noise(sample: torch.Tensor, noise_amt: float) -> torch.Tensor:\n", | |
| " return sample + torch.randn(sample.shape, device=sample.device) * noise_amt\n", | |
| "\n", | |
| "def get_output_folder(output_path, batch_folder):\n", | |
| " out_path = os.path.join(output_path,time.strftime('%Y-%m'))\n", | |
| " if batch_folder != \"\":\n", | |
| " out_path = os.path.join(out_path, batch_folder)\n", | |
| " os.makedirs(out_path, exist_ok=True)\n", | |
| " return out_path\n", | |
| "\n", | |
| "def load_img(path, shape, use_alpha_as_mask=False):\n", | |
| " # use_alpha_as_mask: Read the alpha channel of the image as the mask image\n", | |
| " if path.startswith('http://') or path.startswith('https://'):\n", | |
| " image = Image.open(requests.get(path, stream=True).raw)\n", | |
| " else:\n", | |
| " image = Image.open(path)\n", | |
| "\n", | |
| " if use_alpha_as_mask:\n", | |
| " image = image.convert('RGBA')\n", | |
| " else:\n", | |
| " image = image.convert('RGB')\n", | |
| "\n", | |
| " image = image.resize(shape, resample=Image.LANCZOS)\n", | |
| "\n", | |
| " mask_image = None\n", | |
| " if use_alpha_as_mask:\n", | |
| " # Split alpha channel into a mask_image\n", | |
| " red, green, blue, alpha = Image.Image.split(image)\n", | |
| " mask_image = alpha.convert('L')\n", | |
| " image = image.convert('RGB')\n", | |
| "\n", | |
| " image = np.array(image).astype(np.float16) / 255.0\n", | |
| " image = image[None].transpose(0, 3, 1, 2)\n", | |
| " image = torch.from_numpy(image)\n", | |
| " image = 2.*image - 1.\n", | |
| "\n", | |
| " return image, mask_image\n", | |
| "\n", | |
| "def load_mask_latent(mask_input, shape):\n", | |
| " # mask_input (str or PIL Image.Image): Path to the mask image or a PIL Image object\n", | |
| " # shape (list-like len(4)): shape of the image to match, usually latent_image.shape\n", | |
| " \n", | |
| " if isinstance(mask_input, str): # mask input is probably a file name\n", | |
| " if mask_input.startswith('http://') or mask_input.startswith('https://'):\n", | |
| " mask_image = Image.open(requests.get(mask_input, stream=True).raw).convert('RGBA')\n", | |
| " else:\n", | |
| " mask_image = Image.open(mask_input).convert('RGBA')\n", | |
| " elif isinstance(mask_input, Image.Image):\n", | |
| " mask_image = mask_input\n", | |
| " else:\n", | |
| " raise Exception(\"mask_input must be a PIL image or a file name\")\n", | |
| "\n", | |
| " mask_w_h = (shape[-1], shape[-2])\n", | |
| " mask = mask_image.resize(mask_w_h, resample=Image.LANCZOS)\n", | |
| " mask = mask.convert(\"L\")\n", | |
| " return mask\n", | |
| "\n", | |
| "def prepare_mask(mask_input, mask_shape, mask_brightness_adjust=1.0, mask_contrast_adjust=1.0):\n", | |
| " # mask_input (str or PIL Image.Image): Path to the mask image or a PIL Image object\n", | |
| " # shape (list-like len(4)): shape of the image to match, usually latent_image.shape\n", | |
| " # mask_brightness_adjust (non-negative float): amount to adjust brightness of the iamge, \n", | |
| " # 0 is black, 1 is no adjustment, >1 is brighter\n", | |
| " # mask_contrast_adjust (non-negative float): amount to adjust contrast of the image, \n", | |
| " # 0 is a flat grey image, 1 is no adjustment, >1 is more contrast\n", | |
| " \n", | |
| " mask = load_mask_latent(mask_input, mask_shape)\n", | |
| "\n", | |
| " # Mask brightness/contrast adjustments\n", | |
| " if mask_brightness_adjust != 1:\n", | |
| " mask = TF.adjust_brightness(mask, mask_brightness_adjust)\n", | |
| " if mask_contrast_adjust != 1:\n", | |
| " mask = TF.adjust_contrast(mask, mask_contrast_adjust)\n", | |
| "\n", | |
| " # Mask image to array\n", | |
| " mask = np.array(mask).astype(np.float32) / 255.0\n", | |
| " mask = np.tile(mask,(4,1,1))\n", | |
| " mask = np.expand_dims(mask,axis=0)\n", | |
| " mask = torch.from_numpy(mask)\n", | |
| "\n", | |
| " if args.invert_mask:\n", | |
| " mask = ( (mask - 0.5) * -1) + 0.5\n", | |
| " \n", | |
| " mask = np.clip(mask,0,1)\n", | |
| " return mask\n", | |
| "\n", | |
| "def maintain_colors(prev_img, color_match_sample, mode):\n", | |
| " if mode == 'Match Frame 0 RGB':\n", | |
| " return match_histograms(prev_img, color_match_sample, multichannel=True)\n", | |
| " elif mode == 'Match Frame 0 HSV':\n", | |
| " prev_img_hsv = cv2.cvtColor(prev_img, cv2.COLOR_RGB2HSV)\n", | |
| " color_match_hsv = cv2.cvtColor(color_match_sample, cv2.COLOR_RGB2HSV)\n", | |
| " matched_hsv = match_histograms(prev_img_hsv, color_match_hsv, multichannel=True)\n", | |
| " return cv2.cvtColor(matched_hsv, cv2.COLOR_HSV2RGB)\n", | |
| " else: # Match Frame 0 LAB\n", | |
| " prev_img_lab = cv2.cvtColor(prev_img, cv2.COLOR_RGB2LAB)\n", | |
| " color_match_lab = cv2.cvtColor(color_match_sample, cv2.COLOR_RGB2LAB)\n", | |
| " matched_lab = match_histograms(prev_img_lab, color_match_lab, multichannel=True)\n", | |
| " return cv2.cvtColor(matched_lab, cv2.COLOR_LAB2RGB)\n", | |
| "\n", | |
| "\n", | |
| "#\n", | |
| "# Callback functions\n", | |
| "#\n", | |
| "class SamplerCallback(object):\n", | |
| " # Creates the callback function to be passed into the samplers for each step\n", | |
| " def __init__(self, args, mask=None, init_latent=None, sigmas=None, sampler=None,\n", | |
| " verbose=False):\n", | |
| " self.sampler_name = args.sampler\n", | |
| " self.dynamic_threshold = args.dynamic_threshold\n", | |
| " self.static_threshold = args.static_threshold\n", | |
| " self.mask = mask\n", | |
| " self.init_latent = init_latent \n", | |
| " self.sigmas = sigmas\n", | |
| " self.sampler = sampler\n", | |
| " self.verbose = verbose\n", | |
| "\n", | |
| " self.batch_size = args.n_samples\n", | |
| " self.save_sample_per_step = args.save_sample_per_step\n", | |
| " self.show_sample_per_step = args.show_sample_per_step\n", | |
| " self.paths_to_image_steps = [os.path.join( args.outdir, f\"{args.timestring}_{index:02}_{args.seed}\") for index in range(args.n_samples) ]\n", | |
| "\n", | |
| " if self.save_sample_per_step:\n", | |
| " for path in self.paths_to_image_steps:\n", | |
| " os.makedirs(path, exist_ok=True)\n", | |
| "\n", | |
| " self.step_index = 0\n", | |
| "\n", | |
| " self.noise = None\n", | |
| " if init_latent is not None:\n", | |
| " self.noise = torch.randn_like(init_latent, device=device)\n", | |
| "\n", | |
| " self.mask_schedule = None\n", | |
| " if sigmas is not None and len(sigmas) > 0:\n", | |
| " self.mask_schedule, _ = torch.sort(sigmas/torch.max(sigmas))\n", | |
| " elif len(sigmas) == 0:\n", | |
| " self.mask = None # no mask needed if no steps (usually happens because strength==1.0)\n", | |
| "\n", | |
| " if self.sampler_name in [\"plms\",\"ddim\"]: \n", | |
| " if mask is not None:\n", | |
| " assert sampler is not None, \"Callback function for stable-diffusion samplers requires sampler variable\"\n", | |
| "\n", | |
| " if self.sampler_name in [\"plms\",\"ddim\"]: \n", | |
| " # Callback function formated for compvis latent diffusion samplers\n", | |
| " self.callback = self.img_callback_\n", | |
| " else: \n", | |
| " # Default callback function uses k-diffusion sampler variables\n", | |
| " self.callback = self.k_callback_\n", | |
| "\n", | |
| " self.verbose_print = print if verbose else lambda *args, **kwargs: None\n", | |
| "\n", | |
| " def view_sample_step(self, latents, path_name_modifier=''):\n", | |
| " if self.save_sample_per_step or self.show_sample_per_step:\n", | |
| " samples = model.decode_first_stage(latents)\n", | |
| " if self.save_sample_per_step:\n", | |
| " fname = f'{path_name_modifier}_{self.step_index:05}.png'\n", | |
| " for i, sample in enumerate(samples):\n", | |
| " sample = sample.double().cpu().add(1).div(2).clamp(0, 1)\n", | |
| " sample = torch.tensor(np.array(sample))\n", | |
| " grid = make_grid(sample, 4).cpu()\n", | |
| " TF.to_pil_image(grid).save(os.path.join(self.paths_to_image_steps[i], fname))\n", | |
| " if self.show_sample_per_step:\n", | |
| " print(path_name_modifier)\n", | |
| " self.display_images(samples)\n", | |
| " return\n", | |
| "\n", | |
| " def display_images(self, images):\n", | |
| " images = images.double().cpu().add(1).div(2).clamp(0, 1)\n", | |
| " images = torch.tensor(np.array(images))\n", | |
| " grid = make_grid(images, 4).cpu()\n", | |
| " display.display(TF.to_pil_image(grid))\n", | |
| " return\n", | |
| "\n", | |
| " # The callback function is applied to the image at each step\n", | |
| " def dynamic_thresholding_(self, img, threshold):\n", | |
| " # Dynamic thresholding from Imagen paper (May 2022)\n", | |
| " s = np.percentile(np.abs(img.cpu()), threshold, axis=tuple(range(1,img.ndim)))\n", | |
| " s = np.max(np.append(s,1.0))\n", | |
| " torch.clamp_(img, -1*s, s)\n", | |
| " torch.FloatTensor.div_(img, s)\n", | |
| "\n", | |
| " # Callback for samplers in the k-diffusion repo, called thus:\n", | |
| " # callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})\n", | |
| " def k_callback_(self, args_dict):\n", | |
| " self.step_index = args_dict['i']\n", | |
| " if self.dynamic_threshold is not None:\n", | |
| " self.dynamic_thresholding_(args_dict['x'], self.dynamic_threshold)\n", | |
| " if self.static_threshold is not None:\n", | |
| " torch.clamp_(args_dict['x'], -1*self.static_threshold, self.static_threshold)\n", | |
| " if self.mask is not None:\n", | |
| " init_noise = self.init_latent + self.noise * args_dict['sigma']\n", | |
| " is_masked = torch.logical_and(self.mask >= self.mask_schedule[args_dict['i']], self.mask != 0 )\n", | |
| " new_img = init_noise * torch.where(is_masked,1,0) + args_dict['x'] * torch.where(is_masked,0,1)\n", | |
| " args_dict['x'].copy_(new_img)\n", | |
| "\n", | |
| " self.view_sample_step(args_dict['denoised'], \"x0_pred\")\n", | |
| "\n", | |
| " # Callback for Compvis samplers\n", | |
| " # Function that is called on the image (img) and step (i) at each step\n", | |
| " def img_callback_(self, img, i):\n", | |
| " self.step_index = i\n", | |
| " # Thresholding functions\n", | |
| " if self.dynamic_threshold is not None:\n", | |
| " self.dynamic_thresholding_(img, self.dynamic_threshold)\n", | |
| " if self.static_threshold is not None:\n", | |
| " torch.clamp_(img, -1*self.static_threshold, self.static_threshold)\n", | |
| " if self.mask is not None:\n", | |
| " i_inv = len(self.sigmas) - i - 1\n", | |
| " init_noise = self.sampler.stochastic_encode(self.init_latent, torch.tensor([i_inv]*self.batch_size).to(device), noise=self.noise)\n", | |
| " is_masked = torch.logical_and(self.mask >= self.mask_schedule[i], self.mask != 0 )\n", | |
| " new_img = init_noise * torch.where(is_masked,1,0) + img * torch.where(is_masked,0,1)\n", | |
| " img.copy_(new_img)\n", | |
| "\n", | |
| " self.view_sample_step(img, \"x\")\n", | |
| "\n", | |
| "def sample_from_cv2(sample: np.ndarray) -> torch.Tensor:\n", | |
| " sample = ((sample.astype(float) / 255.0) * 2) - 1\n", | |
| " sample = sample[None].transpose(0, 3, 1, 2).astype(np.float16)\n", | |
| " sample = torch.from_numpy(sample)\n", | |
| " return sample\n", | |
| "\n", | |
| "def sample_to_cv2(sample: torch.Tensor, type=np.uint8) -> np.ndarray:\n", | |
| " sample_f32 = rearrange(sample.squeeze().cpu().numpy(), \"c h w -> h w c\").astype(np.float32)\n", | |
| " sample_f32 = ((sample_f32 * 0.5) + 0.5).clip(0, 1)\n", | |
| " sample_int8 = (sample_f32 * 255)\n", | |
| " return sample_int8.astype(type)\n", | |
| "\n", | |
| "def transform_image_3d(prev_img_cv2, depth_tensor, rot_mat, translate, anim_args):\n", | |
| " # adapted and optimized version of transform_image_3d from Disco Diffusion https://github.com/alembics/disco-diffusion \n", | |
| " w, h = prev_img_cv2.shape[1], prev_img_cv2.shape[0]\n", | |
| "\n", | |
| " aspect_ratio = float(w)/float(h)\n", | |
| " near, far, fov_deg = anim_args.near_plane, anim_args.far_plane, anim_args.fov\n", | |
| " persp_cam_old = p3d.FoVPerspectiveCameras(near, far, aspect_ratio, fov=fov_deg, degrees=True, device=device)\n", | |
| " persp_cam_new = p3d.FoVPerspectiveCameras(near, far, aspect_ratio, fov=fov_deg, degrees=True, R=rot_mat, T=torch.tensor([translate]), device=device)\n", | |
| "\n", | |
| " # range of [-1,1] is important to torch grid_sample's padding handling\n", | |
| " y,x = torch.meshgrid(torch.linspace(-1.,1.,h,dtype=torch.float32,device=device),torch.linspace(-1.,1.,w,dtype=torch.float32,device=device))\n", | |
| " z = torch.as_tensor(depth_tensor, dtype=torch.float32, device=device)\n", | |
| " xyz_old_world = torch.stack((x.flatten(), y.flatten(), z.flatten()), dim=1)\n", | |
| "\n", | |
| " xyz_old_cam_xy = persp_cam_old.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]\n", | |
| " xyz_new_cam_xy = persp_cam_new.get_full_projection_transform().transform_points(xyz_old_world)[:,0:2]\n", | |
| "\n", | |
| " offset_xy = xyz_new_cam_xy - xyz_old_cam_xy\n", | |
| " # affine_grid theta param expects a batch of 2D mats. Each is 2x3 to do rotation+translation.\n", | |
| " identity_2d_batch = torch.tensor([[1.,0.,0.],[0.,1.,0.]], device=device).unsqueeze(0)\n", | |
| " # coords_2d will have shape (N,H,W,2).. which is also what grid_sample needs.\n", | |
| " coords_2d = torch.nn.functional.affine_grid(identity_2d_batch, [1,1,h,w], align_corners=False)\n", | |
| " offset_coords_2d = coords_2d - torch.reshape(offset_xy, (h,w,2)).unsqueeze(0)\n", | |
| "\n", | |
| " image_tensor = rearrange(torch.from_numpy(prev_img_cv2.astype(np.float32)), 'h w c -> c h w').to(device)\n", | |
| " new_image = torch.nn.functional.grid_sample(\n", | |
| " image_tensor.add(1/512 - 0.0001).unsqueeze(0), \n", | |
| " offset_coords_2d, \n", | |
| " mode=anim_args.sampling_mode, \n", | |
| " padding_mode=anim_args.padding_mode, \n", | |
| " align_corners=False\n", | |
| " )\n", | |
| "\n", | |
| " # convert back to cv2 style numpy array\n", | |
| " result = rearrange(\n", | |
| " new_image.squeeze().clamp(0,255), \n", | |
| " 'c h w -> h w c'\n", | |
| " ).cpu().numpy().astype(prev_img_cv2.dtype)\n", | |
| " return result\n", | |
| "\n", | |
| "def check_is_number(value):\n", | |
| " float_pattern = r'^(?=.)([+-]?([0-9]*)(\\.([0-9]+))?)$'\n", | |
| " return re.match(float_pattern, value)\n", | |
| "\n", | |
| "# prompt weighting with colons and number coefficients (like 'bacon:0.75 eggs:0.25')\n", | |
| "# borrowed from https://github.com/kylewlacy/stable-diffusion/blob/0a4397094eb6e875f98f9d71193e350d859c4220/ldm/dream/conditioning.py\n", | |
| "# and https://github.com/raefu/stable-diffusion-automatic/blob/unstablediffusion/modules/processing.py\n", | |
| "def get_uc_and_c(prompts, model, args, frame = 0):\n", | |
| " prompt = prompts[0] # they are the same in a batch anyway\n", | |
| "\n", | |
| " # get weighted sub-prompts\n", | |
| " negative_subprompts, positive_subprompts = split_weighted_subprompts(\n", | |
| " prompt, frame, not args.normalize_prompt_weights\n", | |
| " )\n", | |
| "\n", | |
| " uc = get_learned_conditioning(model, negative_subprompts, \"\", args, -1)\n", | |
| " c = get_learned_conditioning(model, positive_subprompts, prompt, args, 1)\n", | |
| "\n", | |
| " return (uc, c)\n", | |
| "\n", | |
| "def get_learned_conditioning(model, weighted_subprompts, text, args, sign = 1):\n", | |
| " if len(weighted_subprompts) < 1:\n", | |
| " log_tokenization(text, model, args.log_weighted_subprompts, sign)\n", | |
| " c = model.get_learned_conditioning(args.n_samples * [text])\n", | |
| " else:\n", | |
| " c = None\n", | |
| " for subtext, subweight in weighted_subprompts:\n", | |
| " log_tokenization(subtext, model, args.log_weighted_subprompts, sign * subweight)\n", | |
| " if c is None:\n", | |
| " c = model.get_learned_conditioning(args.n_samples * [subtext])\n", | |
| " c *= subweight\n", | |
| " else:\n", | |
| " c.add_(model.get_learned_conditioning(args.n_samples * [subtext]), alpha=subweight)\n", | |
| " \n", | |
| " return c\n", | |
| "\n", | |
| "def parse_weight(match, frame = 0)->float:\n", | |
| " import numexpr\n", | |
| " w_raw = match.group(\"weight\")\n", | |
| " if w_raw == None:\n", | |
| " return 1\n", | |
| " if check_is_number(w_raw):\n", | |
| " return float(w_raw)\n", | |
| " else:\n", | |
| " t = frame\n", | |
| " if len(w_raw) < 3:\n", | |
| " print('the value inside `-characters cannot represent a math function')\n", | |
| " return 1\n", | |
| " return float(numexpr.evaluate(w_raw[1:-1]))\n", | |
| "\n", | |
| "def normalize_prompt_weights(parsed_prompts):\n", | |
| " if len(parsed_prompts) == 0:\n", | |
| " return parsed_prompts\n", | |
| " weight_sum = sum(map(lambda x: x[1], parsed_prompts))\n", | |
| " if weight_sum == 0:\n", | |
| " print(\n", | |
| " \"Warning: Subprompt weights add up to zero. Discarding and using even weights instead.\")\n", | |
| " equal_weight = 1 / max(len(parsed_prompts), 1)\n", | |
| " return [(x[0], equal_weight) for x in parsed_prompts]\n", | |
| " return [(x[0], x[1] / weight_sum) for x in parsed_prompts]\n", | |
| "\n", | |
| "def split_weighted_subprompts(text, frame = 0, skip_normalize=False):\n", | |
| " \"\"\"\n", | |
| " grabs all text up to the first occurrence of ':'\n", | |
| " uses the grabbed text as a sub-prompt, and takes the value following ':' as weight\n", | |
| " if ':' has no value defined, defaults to 1.0\n", | |
| " repeats until no text remaining\n", | |
| " \"\"\"\n", | |
| " prompt_parser = re.compile(\"\"\"\n", | |
| " (?P<prompt> # capture group for 'prompt'\n", | |
| " (?:\\\\\\:|[^:])+ # match one or more non ':' characters or escaped colons '\\:'\n", | |
| " ) # end 'prompt'\n", | |
| " (?: # non-capture group\n", | |
| " :+ # match one or more ':' characters\n", | |
| " (?P<weight>(( # capture group for 'weight'\n", | |
| " -?\\d+(?:\\.\\d+)? # match positive or negative integer or decimal number\n", | |
| " )|( # or\n", | |
| " `[\\S\\s]*?`# a math function\n", | |
| " )))? # end weight capture group, make optional\n", | |
| " \\s* # strip spaces after weight\n", | |
| " | # OR\n", | |
| " $ # else, if no ':' then match end of line\n", | |
| " ) # end non-capture group\n", | |
| " \"\"\", re.VERBOSE)\n", | |
| " negative_prompts = []\n", | |
| " positive_prompts = []\n", | |
| " for match in re.finditer(prompt_parser, text):\n", | |
| " w = parse_weight(match, frame)\n", | |
| " if w < 0:\n", | |
| " # negating the sign as we'll feed this to uc\n", | |
| " negative_prompts.append((match.group(\"prompt\").replace(\"\\\\:\", \":\"), -w))\n", | |
| " elif w > 0:\n", | |
| " positive_prompts.append((match.group(\"prompt\").replace(\"\\\\:\", \":\"), w))\n", | |
| "\n", | |
| " if skip_normalize:\n", | |
| " return (negative_prompts, positive_prompts)\n", | |
| " return (normalize_prompt_weights(negative_prompts), normalize_prompt_weights(positive_prompts))\n", | |
| "\n", | |
| "# shows how the prompt is tokenized\n", | |
| "# usually tokens have '</w>' to indicate end-of-word,\n", | |
| "# but for readability it has been replaced with ' '\n", | |
| "def log_tokenization(text, model, log=False, weight=1):\n", | |
| " if not log:\n", | |
| " return\n", | |
| " tokens = model.cond_stage_model.tokenizer._tokenize(text)\n", | |
| " tokenized = \"\"\n", | |
| " discarded = \"\"\n", | |
| " usedTokens = 0\n", | |
| " totalTokens = len(tokens)\n", | |
| " for i in range(0, totalTokens):\n", | |
| " token = tokens[i].replace('</w>', ' ')\n", | |
| " # alternate color\n", | |
| " s = (usedTokens % 6) + 1\n", | |
| " if i < model.cond_stage_model.max_length:\n", | |
| " tokenized = tokenized + f\"\\x1b[0;3{s};40m{token}\"\n", | |
| " usedTokens += 1\n", | |
| " else: # over max token length\n", | |
| " discarded = discarded + f\"\\x1b[0;3{s};40m{token}\"\n", | |
| " print(f\"\\n>> Tokens ({usedTokens}), Weight ({weight:.2f}):\\n{tokenized}\\x1b[0m\")\n", | |
| " if discarded != \"\":\n", | |
| " print(\n", | |
| " f\">> Tokens Discarded ({totalTokens-usedTokens}):\\n{discarded}\\x1b[0m\"\n", | |
| " )\n", | |
| "\n", | |
| "def generate(args, frame = 0, return_latent=False, return_sample=False, return_c=False):\n", | |
| " seed_everything(args.seed)\n", | |
| " os.makedirs(args.outdir, exist_ok=True)\n", | |
| "\n", | |
| " sampler = PLMSSampler(model) if args.sampler == 'plms' else DDIMSampler(model)\n", | |
| " model_wrap = CompVisDenoiser(model)\n", | |
| " batch_size = args.n_samples\n", | |
| " prompt = args.prompt\n", | |
| " assert prompt is not None\n", | |
| " data = [batch_size * [prompt]]\n", | |
| " precision_scope = autocast if args.precision == \"autocast\" else nullcontext\n", | |
| "\n", | |
| " init_latent = None\n", | |
| " mask_image = None\n", | |
| " init_image = None\n", | |
| " if args.init_latent is not None:\n", | |
| " init_latent = args.init_latent\n", | |
| " elif args.init_sample is not None:\n", | |
| " with precision_scope(\"cuda\"):\n", | |
| " init_latent = model.get_first_stage_encoding(model.encode_first_stage(args.init_sample))\n", | |
| " elif args.use_init and args.init_image != None and args.init_image != '':\n", | |
| " init_image, mask_image = load_img(args.init_image, \n", | |
| " shape=(args.W, args.H), \n", | |
| " use_alpha_as_mask=args.use_alpha_as_mask)\n", | |
| " init_image = init_image.to(device)\n", | |
| " init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)\n", | |
| " with precision_scope(\"cuda\"):\n", | |
| " init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image)) # move to latent space \n", | |
| "\n", | |
| " if not args.use_init and args.strength > 0 and args.strength_0_no_init:\n", | |
| " print(\"\\nNo init image, but strength > 0. Strength has been auto set to 0, since use_init is False.\")\n", | |
| " print(\"If you want to force strength > 0 with no init, please set strength_0_no_init to False.\\n\")\n", | |
| " args.strength = 0\n", | |
| "\n", | |
| " # Mask functions\n", | |
| " if args.use_mask:\n", | |
| " assert args.mask_file is not None or mask_image is not None, \"use_mask==True: An mask image is required for a mask. Please enter a mask_file or use an init image with an alpha channel\"\n", | |
| " assert args.use_init, \"use_mask==True: use_init is required for a mask\"\n", | |
| " assert init_latent is not None, \"use_mask==True: An latent init image is required for a mask\"\n", | |
| "\n", | |
| "\n", | |
| " mask = prepare_mask(args.mask_file if mask_image is None else mask_image, \n", | |
| " init_latent.shape, \n", | |
| " args.mask_contrast_adjust, \n", | |
| " args.mask_brightness_adjust)\n", | |
| " \n", | |
| " if (torch.all(mask == 0) or torch.all(mask == 1)) and args.use_alpha_as_mask:\n", | |
| " raise Warning(\"use_alpha_as_mask==True: Using the alpha channel from the init image as a mask, but the alpha channel is blank.\")\n", | |
| " \n", | |
| " mask = mask.to(device)\n", | |
| " mask = repeat(mask, '1 ... -> b ...', b=batch_size)\n", | |
| " else:\n", | |
| " mask = None\n", | |
| "\n", | |
| " assert not ( (args.use_mask and args.overlay_mask) and (args.init_sample is None and init_image is None)), \"Need an init image when use_mask == True and overlay_mask == True\"\n", | |
| " \n", | |
| " t_enc = int((1.0-args.strength) * args.steps)\n", | |
| "\n", | |
| " # Noise schedule for the k-diffusion samplers (used for masking)\n", | |
| " k_sigmas = model_wrap.get_sigmas(args.steps)\n", | |
| " k_sigmas = k_sigmas[len(k_sigmas)-t_enc-1:]\n", | |
| "\n", | |
| " if args.sampler in ['plms','ddim']:\n", | |
| " sampler.make_schedule(ddim_num_steps=args.steps, ddim_eta=args.ddim_eta, ddim_discretize='fill', verbose=False)\n", | |
| "\n", | |
| " callback = SamplerCallback(args=args,\n", | |
| " mask=mask, \n", | |
| " init_latent=init_latent,\n", | |
| " sigmas=k_sigmas,\n", | |
| " sampler=sampler,\n", | |
| " verbose=False).callback \n", | |
| "\n", | |
| " results = []\n", | |
| " with torch.no_grad():\n", | |
| " with precision_scope(\"cuda\"):\n", | |
| " with model.ema_scope():\n", | |
| " for prompts in data:\n", | |
| " if isinstance(prompts, tuple):\n", | |
| " prompts = list(prompts)\n", | |
| " if args.prompt_weighting:\n", | |
| " uc, c = get_uc_and_c(prompts, model, args, frame)\n", | |
| " else:\n", | |
| " uc = model.get_learned_conditioning(batch_size * [\"\"])\n", | |
| " c = model.get_learned_conditioning(prompts)\n", | |
| "\n", | |
| "\n", | |
| " if args.scale == 1.0:\n", | |
| " uc = None\n", | |
| " if args.init_c != None:\n", | |
| " c = args.init_c\n", | |
| "\n", | |
| " if args.sampler in [\"klms\",\"dpm2\",\"dpm2_ancestral\",\"heun\",\"euler\",\"euler_ancestral\"]:\n", | |
| " samples = sampler_fn(\n", | |
| " c=c, \n", | |
| " uc=uc, \n", | |
| " args=args, \n", | |
| " model_wrap=model_wrap, \n", | |
| " init_latent=init_latent, \n", | |
| " t_enc=t_enc, \n", | |
| " device=device, \n", | |
| " cb=callback)\n", | |
| " else:\n", | |
| " # args.sampler == 'plms' or args.sampler == 'ddim':\n", | |
| " if init_latent is not None and args.strength > 0:\n", | |
| " z_enc = sampler.stochastic_encode(init_latent, torch.tensor([t_enc]*batch_size).to(device))\n", | |
| " else:\n", | |
| " z_enc = torch.randn([args.n_samples, args.C, args.H // args.f, args.W // args.f], device=device)\n", | |
| " if args.sampler == 'ddim':\n", | |
| " samples = sampler.decode(z_enc, \n", | |
| " c, \n", | |
| " t_enc, \n", | |
| " unconditional_guidance_scale=args.scale,\n", | |
| " unconditional_conditioning=uc,\n", | |
| " img_callback=callback)\n", | |
| " elif args.sampler == 'plms': # no \"decode\" function in plms, so use \"sample\"\n", | |
| " shape = [args.C, args.H // args.f, args.W // args.f]\n", | |
| " samples, _ = sampler.sample(S=args.steps,\n", | |
| " conditioning=c,\n", | |
| " batch_size=args.n_samples,\n", | |
| " shape=shape,\n", | |
| " verbose=False,\n", | |
| " unconditional_guidance_scale=args.scale,\n", | |
| " unconditional_conditioning=uc,\n", | |
| " eta=args.ddim_eta,\n", | |
| " x_T=z_enc,\n", | |
| " img_callback=callback)\n", | |
| " else:\n", | |
| " raise Exception(f\"Sampler {args.sampler} not recognised.\")\n", | |
| "\n", | |
| " \n", | |
| " if return_latent:\n", | |
| " results.append(samples.clone())\n", | |
| "\n", | |
| " x_samples = model.decode_first_stage(samples)\n", | |
| "\n", | |
| " if args.use_mask and args.overlay_mask:\n", | |
| " # Overlay the masked image after the image is generated\n", | |
| " if args.init_sample is not None:\n", | |
| " img_original = args.init_sample\n", | |
| " elif init_image is not None:\n", | |
| " img_original = init_image\n", | |
| " else:\n", | |
| " raise Exception(\"Cannot overlay the masked image without an init image to overlay\")\n", | |
| "\n", | |
| " mask_fullres = prepare_mask(args.mask_file if mask_image is None else mask_image, \n", | |
| " img_original.shape, \n", | |
| " args.mask_contrast_adjust, \n", | |
| " args.mask_brightness_adjust)\n", | |
| " mask_fullres = mask_fullres[:,:3,:,:]\n", | |
| " mask_fullres = repeat(mask_fullres, '1 ... -> b ...', b=batch_size)\n", | |
| "\n", | |
| " mask_fullres[mask_fullres < mask_fullres.max()] = 0\n", | |
| " mask_fullres = gaussian_filter(mask_fullres, args.mask_overlay_blur)\n", | |
| " mask_fullres = torch.Tensor(mask_fullres).to(device)\n", | |
| "\n", | |
| " x_samples = img_original * mask_fullres + x_samples * ((mask_fullres * -1.0) + 1)\n", | |
| "\n", | |
| "\n", | |
| " if return_sample:\n", | |
| " results.append(x_samples.clone())\n", | |
| "\n", | |
| " x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)\n", | |
| "\n", | |
| " if return_c:\n", | |
| " results.append(c.clone())\n", | |
| "\n", | |
| " for x_sample in x_samples:\n", | |
| " x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')\n", | |
| " image = Image.fromarray(x_sample.astype(np.uint8))\n", | |
| " results.append(image)\n", | |
| " return results" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "CIUJ7lWI4v53" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#@markdown **Select and Load Model**\n", | |
| "\n", | |
| "model_config = \"v1-inference.yaml\" #@param [\"custom\",\"v1-inference.yaml\"]\n", | |
| "model_checkpoint = \"sd-v1-4.ckpt\" #@param [\"custom\",\"sd-v1-4-full-ema.ckpt\",\"sd-v1-4.ckpt\",\"sd-v1-3-full-ema.ckpt\",\"sd-v1-3.ckpt\",\"sd-v1-2-full-ema.ckpt\",\"sd-v1-2.ckpt\",\"sd-v1-1-full-ema.ckpt\",\"sd-v1-1.ckpt\", \"robo-diffusion-v1.ckpt\",\"waifu-diffusion-v1-3.ckpt\"]\n", | |
| "if model_checkpoint == \"waifu-diffusion-v1-3.ckpt\":\n", | |
| " model_checkpoint = \"model-epoch05-float16.ckpt\"\n", | |
| "custom_config_path = \"\" #@param {type:\"string\"}\n", | |
| "custom_checkpoint_path = \"\" #@param {type:\"string\"}\n", | |
| "\n", | |
| "load_on_run_all = True #@param {type: 'boolean'}\n", | |
| "half_precision = True # check\n", | |
| "check_sha256 = True #@param {type:\"boolean\"}\n", | |
| "\n", | |
| "model_map = {\n", | |
| " \"sd-v1-4-full-ema.ckpt\": {\n", | |
| " 'sha256': '14749efc0ae8ef0329391ad4436feb781b402f4fece4883c7ad8d10556d8a36a',\n", | |
| " 'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/blob/main/sd-v1-4-full-ema.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"sd-v1-4.ckpt\": {\n", | |
| " 'sha256': 'fe4efff1e174c627256e44ec2991ba279b3816e364b49f9be2abc0b3ff3f8556',\n", | |
| " 'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"sd-v1-3-full-ema.ckpt\": {\n", | |
| " 'sha256': '54632c6e8a36eecae65e36cb0595fab314e1a1545a65209f24fde221a8d4b2ca',\n", | |
| " 'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-3-original/blob/main/sd-v1-3-full-ema.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"sd-v1-3.ckpt\": {\n", | |
| " 'sha256': '2cff93af4dcc07c3e03110205988ff98481e86539c51a8098d4f2236e41f7f2f',\n", | |
| " 'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-3-original/resolve/main/sd-v1-3.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"sd-v1-2-full-ema.ckpt\": {\n", | |
| " 'sha256': 'bc5086a904d7b9d13d2a7bccf38f089824755be7261c7399d92e555e1e9ac69a',\n", | |
| " 'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/blob/main/sd-v1-2-full-ema.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"sd-v1-2.ckpt\": {\n", | |
| " 'sha256': '3b87d30facd5bafca1cbed71cfb86648aad75d1c264663c0cc78c7aea8daec0d',\n", | |
| " 'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-2-original/resolve/main/sd-v1-2.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"sd-v1-1-full-ema.ckpt\": {\n", | |
| " 'sha256': 'efdeb5dc418a025d9a8cc0a8617e106c69044bc2925abecc8a254b2910d69829',\n", | |
| " 'url':'https://huggingface.co/CompVis/stable-diffusion-v-1-1-original/resolve/main/sd-v1-1-full-ema.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"sd-v1-1.ckpt\": {\n", | |
| " 'sha256': '86cd1d3ccb044d7ba8db743d717c9bac603c4043508ad2571383f954390f3cea',\n", | |
| " 'url': 'https://huggingface.co/CompVis/stable-diffusion-v-1-1-original/resolve/main/sd-v1-1.ckpt',\n", | |
| " 'requires_login': True,\n", | |
| " },\n", | |
| " \"robo-diffusion-v1.ckpt\": {\n", | |
| " 'sha256': '244dbe0dcb55c761bde9c2ac0e9b46cc9705ebfe5f1f3a7cc46251573ea14e16',\n", | |
| " 'url': 'https://huggingface.co/nousr/robo-diffusion/resolve/main/models/robo-diffusion-v1.ckpt',\n", | |
| " 'requires_login': False,\n", | |
| " },\n", | |
| " \"model-epoch05-float16.ckpt\": {\n", | |
| " 'sha256': '26cf2a2e30095926bb9fd9de0c83f47adc0b442dbfdc3d667d43778e8b70bece',\n", | |
| " 'url': 'https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/model-epoch05-float16.ckpt',\n", | |
| " 'requires_login': False,\n", | |
| " },\n", | |
| "}\n", | |
| "\n", | |
| "# config path\n", | |
| "ckpt_config_path = custom_config_path if model_config == \"custom\" else os.path.join(models_path, model_config)\n", | |
| "if os.path.exists(ckpt_config_path):\n", | |
| " print(f\"{ckpt_config_path} exists\")\n", | |
| "else:\n", | |
| " ckpt_config_path = \"./stable-diffusion/configs/stable-diffusion/v1-inference.yaml\"\n", | |
| "print(f\"Using config: {ckpt_config_path}\")\n", | |
| "\n", | |
| "# checkpoint path or download\n", | |
| "ckpt_path = custom_checkpoint_path if model_checkpoint == \"custom\" else os.path.join(models_path, model_checkpoint)\n", | |
| "ckpt_valid = True\n", | |
| "if os.path.exists(ckpt_path):\n", | |
| " print(f\"{ckpt_path} exists\")\n", | |
| "elif 'url' in model_map[model_checkpoint]:\n", | |
| " url = model_map[model_checkpoint]['url']\n", | |
| "\n", | |
| " # CLI dialogue to authenticate download\n", | |
| " if model_map[model_checkpoint]['requires_login']:\n", | |
| " print(\"This model requires an authentication token\")\n", | |
| " print(\"Please ensure you have accepted its terms of service before continuing.\")\n", | |
| "\n", | |
| " username = input(\"What is your huggingface username?:\")\n", | |
| " token = input(\"What is your huggingface token?:\")\n", | |
| "\n", | |
| " _, path = url.split(\"https://\")\n", | |
| "\n", | |
| " url = f\"https://{username}:{token}@{path}\"\n", | |
| "\n", | |
| " # contact server for model\n", | |
| " print(f\"Attempting to download {model_checkpoint}...this may take a while\")\n", | |
| " ckpt_request = requests.get(url)\n", | |
| " request_status = ckpt_request.status_code\n", | |
| "\n", | |
| " # inform user of errors\n", | |
| " if request_status == 403:\n", | |
| " raise ConnectionRefusedError(\"You have not accepted the license for this model.\")\n", | |
| " elif request_status == 404:\n", | |
| " raise ConnectionError(\"Could not make contact with server\")\n", | |
| " elif request_status != 200:\n", | |
| " raise ConnectionError(f\"Some other error has ocurred - response code: {request_status}\")\n", | |
| "\n", | |
| " # write to model path\n", | |
| " with open(os.path.join(models_path, model_checkpoint), 'wb') as model_file:\n", | |
| " model_file.write(ckpt_request.content)\n", | |
| "else:\n", | |
| " print(f\"Please download model checkpoint and place in {os.path.join(models_path, model_checkpoint)}\")\n", | |
| " ckpt_valid = False\n", | |
| "\n", | |
| "if check_sha256 and model_checkpoint != \"custom\" and ckpt_valid:\n", | |
| " import hashlib\n", | |
| " print(\"\\n...checking sha256\")\n", | |
| " with open(ckpt_path, \"rb\") as f:\n", | |
| " bytes = f.read() \n", | |
| " hash = hashlib.sha256(bytes).hexdigest()\n", | |
| " del bytes\n", | |
| " if model_map[model_checkpoint][\"sha256\"] == hash:\n", | |
| " print(\"hash is correct\\n\")\n", | |
| " else:\n", | |
| " print(\"hash in not correct\\n\")\n", | |
| " ckpt_valid = False\n", | |
| "\n", | |
| "if ckpt_valid:\n", | |
| " print(f\"Using ckpt: {ckpt_path}\")\n", | |
| "\n", | |
| "def load_model_from_config(config, ckpt, verbose=False, device='cuda', half_precision=True):\n", | |
| " map_location = \"cuda\" #@param [\"cpu\", \"cuda\"]\n", | |
| " print(f\"Loading model from {ckpt}\")\n", | |
| " pl_sd = torch.load(ckpt, map_location=map_location)\n", | |
| " if \"global_step\" in pl_sd:\n", | |
| " print(f\"Global Step: {pl_sd['global_step']}\")\n", | |
| " sd = pl_sd[\"state_dict\"]\n", | |
| " model = instantiate_from_config(config.model)\n", | |
| " m, u = model.load_state_dict(sd, strict=False)\n", | |
| " if len(m) > 0 and verbose:\n", | |
| " print(\"missing keys:\")\n", | |
| " print(m)\n", | |
| " if len(u) > 0 and verbose:\n", | |
| " print(\"unexpected keys:\")\n", | |
| " print(u)\n", | |
| "\n", | |
| " if half_precision:\n", | |
| " model = model.half().to(device)\n", | |
| " else:\n", | |
| " model = model.to(device)\n", | |
| " model.eval()\n", | |
| " return model\n", | |
| "\n", | |
| "if load_on_run_all and ckpt_valid:\n", | |
| " local_config = OmegaConf.load(f\"{ckpt_config_path}\")\n", | |
| " model = load_model_from_config(local_config, f\"{ckpt_path}\", half_precision=half_precision)\n", | |
| " device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", | |
| " model = model.to(device)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "ov3r4RD1tzsT" | |
| }, | |
| "source": [ | |
| "# Settings" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "0j7rgxvLvfay" | |
| }, | |
| "source": [ | |
| "### Animation Settings" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "8HJN2TE3vh-J" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "\n", | |
| "def DeforumAnimArgs():\n", | |
| "\n", | |
| " #@markdown ####**Animation:**\n", | |
| " animation_mode = 'None' #@param ['None', '2D', '3D', 'Video Input', 'Interpolation'] {type:'string'}\n", | |
| " max_frames = 1000 #@param {type:\"number\"}\n", | |
| " border = 'replicate' #@param ['wrap', 'replicate'] {type:'string'}\n", | |
| "\n", | |
| " #@markdown ####**Motion Parameters:**\n", | |
| " angle = \"0:(0)\"#@param {type:\"string\"}\n", | |
| " zoom = \"0:(1.04)\"#@param {type:\"string\"}\n", | |
| " translation_x = \"0:(10*sin(2*3.14*t/10))\"#@param {type:\"string\"}\n", | |
| " translation_y = \"0:(0)\"#@param {type:\"string\"}\n", | |
| " translation_z = \"0:(10)\"#@param {type:\"string\"}\n", | |
| " rotation_3d_x = \"0:(0)\"#@param {type:\"string\"}\n", | |
| " rotation_3d_y = \"0:(0)\"#@param {type:\"string\"}\n", | |
| " rotation_3d_z = \"0:(0)\"#@param {type:\"string\"}\n", | |
| " flip_2d_perspective = False #@param {type:\"boolean\"}\n", | |
| " perspective_flip_theta = \"0:(0)\"#@param {type:\"string\"}\n", | |
| " perspective_flip_phi = \"0:(t%15)\"#@param {type:\"string\"}\n", | |
| " perspective_flip_gamma = \"0:(0)\"#@param {type:\"string\"}\n", | |
| " perspective_flip_fv = \"0:(53)\"#@param {type:\"string\"}\n", | |
| " noise_schedule = \"0: (0.02)\"#@param {type:\"string\"}\n", | |
| " strength_schedule = \"0: (0.65)\"#@param {type:\"string\"}\n", | |
| " contrast_schedule = \"0: (1.0)\"#@param {type:\"string\"}\n", | |
| "\n", | |
| " #@markdown ####**Coherence:**\n", | |
| " color_coherence = 'Match Frame 0 LAB' #@param ['None', 'Match Frame 0 HSV', 'Match Frame 0 LAB', 'Match Frame 0 RGB'] {type:'string'}\n", | |
| " diffusion_cadence = '1' #@param ['1','2','3','4','5','6','7','8'] {type:'string'}\n", | |
| "\n", | |
| " #@markdown ####**3D Depth Warping:**\n", | |
| " use_depth_warping = True #@param {type:\"boolean\"}\n", | |
| " midas_weight = 0.3#@param {type:\"number\"}\n", | |
| " near_plane = 200\n", | |
| " far_plane = 10000\n", | |
| " fov = 40#@param {type:\"number\"}\n", | |
| " padding_mode = 'border'#@param ['border', 'reflection', 'zeros'] {type:'string'}\n", | |
| " sampling_mode = 'bicubic'#@param ['bicubic', 'bilinear', 'nearest'] {type:'string'}\n", | |
| " save_depth_maps = False #@param {type:\"boolean\"}\n", | |
| "\n", | |
| " #@markdown ####**Video Input:**\n", | |
| " video_init_path ='/content/video_in.mp4'#@param {type:\"string\"}\n", | |
| " extract_nth_frame = 1#@param {type:\"number\"}\n", | |
| " overwrite_extracted_frames = True #@param {type:\"boolean\"}\n", | |
| " use_mask_video = False #@param {type:\"boolean\"}\n", | |
| " video_mask_path ='/content/video_in.mp4'#@param {type:\"string\"}\n", | |
| "\n", | |
| " #@markdown ####**Interpolation:**\n", | |
| " interpolate_key_frames = False #@param {type:\"boolean\"}\n", | |
| " interpolate_x_frames = 4 #@param {type:\"number\"}\n", | |
| " \n", | |
| " #@markdown ####**Resume Animation:**\n", | |
| " resume_from_timestring = False #@param {type:\"boolean\"}\n", | |
| " resume_timestring = \"20220829210106\" #@param {type:\"string\"}\n", | |
| "\n", | |
| " return locals()\n", | |
| "\n", | |
| "class DeformAnimKeys():\n", | |
| " def __init__(self, anim_args):\n", | |
| " self.angle_series = get_inbetweens(parse_key_frames(anim_args.angle), anim_args.max_frames)\n", | |
| " self.zoom_series = get_inbetweens(parse_key_frames(anim_args.zoom), anim_args.max_frames)\n", | |
| " self.translation_x_series = get_inbetweens(parse_key_frames(anim_args.translation_x), anim_args.max_frames)\n", | |
| " self.translation_y_series = get_inbetweens(parse_key_frames(anim_args.translation_y), anim_args.max_frames)\n", | |
| " self.translation_z_series = get_inbetweens(parse_key_frames(anim_args.translation_z), anim_args.max_frames)\n", | |
| " self.rotation_3d_x_series = get_inbetweens(parse_key_frames(anim_args.rotation_3d_x), anim_args.max_frames)\n", | |
| " self.rotation_3d_y_series = get_inbetweens(parse_key_frames(anim_args.rotation_3d_y), anim_args.max_frames)\n", | |
| " self.rotation_3d_z_series = get_inbetweens(parse_key_frames(anim_args.rotation_3d_z), anim_args.max_frames)\n", | |
| " self.perspective_flip_theta_series = get_inbetweens(parse_key_frames(anim_args.perspective_flip_theta), anim_args.max_frames)\n", | |
| " self.perspective_flip_phi_series = get_inbetweens(parse_key_frames(anim_args.perspective_flip_phi), anim_args.max_frames)\n", | |
| " self.perspective_flip_gamma_series = get_inbetweens(parse_key_frames(anim_args.perspective_flip_gamma), anim_args.max_frames)\n", | |
| " self.perspective_flip_fv_series = get_inbetweens(parse_key_frames(anim_args.perspective_flip_fv), anim_args.max_frames)\n", | |
| " self.noise_schedule_series = get_inbetweens(parse_key_frames(anim_args.noise_schedule), anim_args.max_frames)\n", | |
| " self.strength_schedule_series = get_inbetweens(parse_key_frames(anim_args.strength_schedule), anim_args.max_frames)\n", | |
| " self.contrast_schedule_series = get_inbetweens(parse_key_frames(anim_args.contrast_schedule), anim_args.max_frames)\n", | |
| "\n", | |
| "\n", | |
| "def get_inbetweens(key_frames, max_frames, integer=False, interp_method='Linear'):\n", | |
| " import numexpr\n", | |
| " key_frame_series = pd.Series([np.nan for a in range(max_frames)])\n", | |
| " \n", | |
| " for i in range(0, max_frames):\n", | |
| " if i in key_frames:\n", | |
| " value = key_frames[i]\n", | |
| " value_is_number = check_is_number(value)\n", | |
| " # if it's only a number, leave the rest for the default interpolation\n", | |
| " if value_is_number:\n", | |
| " t = i\n", | |
| " key_frame_series[i] = value\n", | |
| " if not value_is_number:\n", | |
| " t = i\n", | |
| " key_frame_series[i] = numexpr.evaluate(value)\n", | |
| " key_frame_series = key_frame_series.astype(float)\n", | |
| " \n", | |
| " if interp_method == 'Cubic' and len(key_frames.items()) <= 3:\n", | |
| " interp_method = 'Quadratic' \n", | |
| " if interp_method == 'Quadratic' and len(key_frames.items()) <= 2:\n", | |
| " interp_method = 'Linear'\n", | |
| " \n", | |
| " key_frame_series[0] = key_frame_series[key_frame_series.first_valid_index()]\n", | |
| " key_frame_series[max_frames-1] = key_frame_series[key_frame_series.last_valid_index()]\n", | |
| " key_frame_series = key_frame_series.interpolate(method=interp_method.lower(), limit_direction='both')\n", | |
| " if integer:\n", | |
| " return key_frame_series.astype(int)\n", | |
| " return key_frame_series\n", | |
| "\n", | |
| "def parse_key_frames(string, prompt_parser=None):\n", | |
| " # because math functions (i.e. sin(t)) can utilize brackets \n", | |
| " # it extracts the value in form of some stuff\n", | |
| " # which has previously been enclosed with brackets and\n", | |
| " # with a comma or end of line existing after the closing one\n", | |
| " pattern = r'((?P<frame>[0-9]+):[\\s]*\\((?P<param>[\\S\\s]*?)\\)([,][\\s]?|[\\s]?$))'\n", | |
| " frames = dict()\n", | |
| " for match_object in re.finditer(pattern, string):\n", | |
| " frame = int(match_object.groupdict()['frame'])\n", | |
| " param = match_object.groupdict()['param']\n", | |
| " if prompt_parser:\n", | |
| " frames[frame] = prompt_parser(param)\n", | |
| " else:\n", | |
| " frames[frame] = param\n", | |
| " if frames == {} and len(string) != 0:\n", | |
| " raise RuntimeError('Key Frame string not correctly formatted')\n", | |
| " return frames" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "63UOJvU3xdPS" | |
| }, | |
| "source": [ | |
| "### Prompts\n", | |
| "`animation_mode: None` batches on list of *prompts*. `animation_mode: 2D` uses *animation_prompts* key frame sequence" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "2ujwkGZTcGev" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "\n", | |
| "prompts = [\n", | |
| " \"a beautiful forest by Asher Brown Durand, trending on Artstation\", # the first prompt I want\n", | |
| " \"a beautiful portrait of a woman by Artgerm, trending on Artstation\", # the second prompt I want\n", | |
| " #\"this prompt I don't want it I commented it out\",\n", | |
| " #\"a nousr robot, trending on Artstation\", # use \"nousr robot\" with the robot diffusion model (see model_checkpoint setting)\n", | |
| " #\"touhou 1girl komeiji_koishi portrait, green hair\", # waifu diffusion prompts can use danbooru tag groups (see model_checkpoint)\n", | |
| " #\"this prompt has weights if prompt weighting enabled:2 can also do negative:-2\", # (see prompt_weighting)\n", | |
| "]\n", | |
| "\n", | |
| "animation_prompts = {\n", | |
| " 0: \"a beautiful apple, trending on Artstation\",\n", | |
| " 20: \"a beautiful banana, trending on Artstation\",\n", | |
| " 30: \"a beautiful coconut, trending on Artstation\",\n", | |
| " 40: \"a beautiful durian, trending on Artstation\",\n", | |
| "}" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "s8RAo2zI-vQm" | |
| }, | |
| "source": [ | |
| "# Run" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "qH74gBWDd2oq" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#@markdown **Load Settings**\n", | |
| "override_settings_with_file = False #@param {type:\"boolean\"}\n", | |
| "custom_settings_file = \"/content/drive/MyDrive/Settings.txt\"#@param {type:\"string\"}\n", | |
| "\n", | |
| "def DeforumArgs():\n", | |
| " #@markdown **Image Settings**\n", | |
| " W = 512 #@param\n", | |
| " H = 512 #@param\n", | |
| " W, H = map(lambda x: x - x % 64, (W, H)) # resize to integer multiple of 64\n", | |
| "\n", | |
| " #@markdown **Sampling Settings**\n", | |
| " seed = -1 #@param\n", | |
| " sampler = 'klms' #@param [\"klms\",\"dpm2\",\"dpm2_ancestral\",\"heun\",\"euler\",\"euler_ancestral\",\"plms\", \"ddim\"]\n", | |
| " steps = 50 #@param\n", | |
| " scale = 7 #@param\n", | |
| " ddim_eta = 0.0 #@param\n", | |
| " dynamic_threshold = None\n", | |
| " static_threshold = None \n", | |
| "\n", | |
| " #@markdown **Save & Display Settings**\n", | |
| " save_samples = True #@param {type:\"boolean\"}\n", | |
| " save_settings = True #@param {type:\"boolean\"}\n", | |
| " display_samples = True #@param {type:\"boolean\"}\n", | |
| " save_sample_per_step = False #@param {type:\"boolean\"}\n", | |
| " show_sample_per_step = False #@param {type:\"boolean\"}\n", | |
| "\n", | |
| " #@markdown **Prompt Settings**\n", | |
| " prompt_weighting = False #@param {type:\"boolean\"}\n", | |
| " normalize_prompt_weights = True #@param {type:\"boolean\"}\n", | |
| " log_weighted_subprompts = False #@param {type:\"boolean\"}\n", | |
| "\n", | |
| " #@markdown **Batch Settings**\n", | |
| " n_batch = 1 #@param\n", | |
| " batch_name = \"StableFun\" #@param {type:\"string\"}\n", | |
| " filename_format = \"{timestring}_{index}_{prompt}.png\" #@param [\"{timestring}_{index}_{seed}.png\",\"{timestring}_{index}_{prompt}.png\"]\n", | |
| " seed_behavior = \"iter\" #@param [\"iter\",\"fixed\",\"random\"]\n", | |
| " make_grid = False #@param {type:\"boolean\"}\n", | |
| " grid_rows = 2 #@param \n", | |
| " outdir = get_output_folder(output_path, batch_name)\n", | |
| "\n", | |
| " #@markdown **Init Settings**\n", | |
| " use_init = False #@param {type:\"boolean\"}\n", | |
| " strength = 0.0 #@param {type:\"number\"}\n", | |
| " strength_0_no_init = True # Set the strength to 0 automatically when no init image is used\n", | |
| " init_image = \"https://cdn.pixabay.com/photo/2022/07/30/13/10/green-longhorn-beetle-7353749_1280.jpg\" #@param {type:\"string\"}\n", | |
| " # Whiter areas of the mask are areas that change more\n", | |
| " use_mask = False #@param {type:\"boolean\"}\n", | |
| " use_alpha_as_mask = False # use the alpha channel of the init image as the mask\n", | |
| " mask_file = \"https://www.filterforge.com/wiki/images/archive/b/b7/20080927223728%21Polygonal_gradient_thumb.jpg\" #@param {type:\"string\"}\n", | |
| " invert_mask = False #@param {type:\"boolean\"}\n", | |
| " # Adjust mask image, 1.0 is no adjustment. Should be positive numbers.\n", | |
| " mask_brightness_adjust = 1.0 #@param {type:\"number\"}\n", | |
| " mask_contrast_adjust = 1.0 #@param {type:\"number\"}\n", | |
| " # Overlay the masked image at the end of the generation so it does not get degraded by encoding and decoding\n", | |
| " overlay_mask = True # {type:\"boolean\"}\n", | |
| " # Blur edges of final overlay mask, if used. Minimum = 0 (no blur)\n", | |
| " mask_overlay_blur = 5 # {type:\"number\"}\n", | |
| "\n", | |
| " n_samples = 1 # doesnt do anything\n", | |
| " precision = 'autocast' \n", | |
| " C = 4\n", | |
| " f = 8\n", | |
| "\n", | |
| " prompt = \"\"\n", | |
| " timestring = \"\"\n", | |
| " init_latent = None\n", | |
| " init_sample = None\n", | |
| " init_c = None\n", | |
| "\n", | |
| " return locals()\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "def next_seed(args):\n", | |
| " if args.seed_behavior == 'iter':\n", | |
| " args.seed += 1\n", | |
| " elif args.seed_behavior == 'fixed':\n", | |
| " pass # always keep seed the same\n", | |
| " else:\n", | |
| " args.seed = random.randint(0, 2**32 - 1)\n", | |
| " return args.seed\n", | |
| "\n", | |
| "def render_image_batch(args):\n", | |
| " args.prompts = {k: f\"{v:05d}\" for v, k in enumerate(prompts)}\n", | |
| " \n", | |
| " # create output folder for the batch\n", | |
| " os.makedirs(args.outdir, exist_ok=True)\n", | |
| " if args.save_settings or args.save_samples:\n", | |
| " print(f\"Saving to {os.path.join(args.outdir, args.timestring)}_*\")\n", | |
| "\n", | |
| " # save settings for the batch\n", | |
| " if args.save_settings:\n", | |
| " filename = os.path.join(args.outdir, f\"{args.timestring}_settings.txt\")\n", | |
| " with open(filename, \"w+\", encoding=\"utf-8\") as f:\n", | |
| " json.dump(dict(args.__dict__), f, ensure_ascii=False, indent=4)\n", | |
| "\n", | |
| " index = 0\n", | |
| " \n", | |
| " # function for init image batching\n", | |
| " init_array = []\n", | |
| " if args.use_init:\n", | |
| " if args.init_image == \"\":\n", | |
| " raise FileNotFoundError(\"No path was given for init_image\")\n", | |
| " if args.init_image.startswith('http://') or args.init_image.startswith('https://'):\n", | |
| " init_array.append(args.init_image)\n", | |
| " elif not os.path.isfile(args.init_image):\n", | |
| " if args.init_image[-1] != \"/\": # avoids path error by adding / to end if not there\n", | |
| " args.init_image += \"/\" \n", | |
| " for image in sorted(os.listdir(args.init_image)): # iterates dir and appends images to init_array\n", | |
| " if image.split(\".\")[-1] in (\"png\", \"jpg\", \"jpeg\"):\n", | |
| " init_array.append(args.init_image + image)\n", | |
| " else:\n", | |
| " init_array.append(args.init_image)\n", | |
| " else:\n", | |
| " init_array = [\"\"]\n", | |
| "\n", | |
| " # when doing large batches don't flood browser with images\n", | |
| " clear_between_batches = args.n_batch >= 32\n", | |
| "\n", | |
| " for iprompt, prompt in enumerate(prompts): \n", | |
| " args.prompt = prompt\n", | |
| " print(f\"Prompt {iprompt+1} of {len(prompts)}\")\n", | |
| " print(f\"{args.prompt}\")\n", | |
| "\n", | |
| " all_images = []\n", | |
| "\n", | |
| " for batch_index in range(args.n_batch):\n", | |
| " if clear_between_batches and batch_index % 32 == 0: \n", | |
| " display.clear_output(wait=True) \n", | |
| " print(f\"Batch {batch_index+1} of {args.n_batch}\")\n", | |
| " \n", | |
| " for image in init_array: # iterates the init images\n", | |
| " args.init_image = image\n", | |
| " results = generate(args)\n", | |
| " for image in results:\n", | |
| " if args.make_grid:\n", | |
| " all_images.append(T.functional.pil_to_tensor(image))\n", | |
| " if args.save_samples:\n", | |
| " if args.filename_format == \"{timestring}_{index}_{prompt}.png\":\n", | |
| " filename = f\"{args.timestring}_{index:05}_{sanitize(prompt)[:160]}.png\"\n", | |
| " else:\n", | |
| " filename = f\"{args.timestring}_{index:05}_{args.seed}.png\"\n", | |
| " image.save(os.path.join(args.outdir, filename))\n", | |
| " if args.display_samples:\n", | |
| " display.display(image)\n", | |
| " index += 1\n", | |
| " args.seed = next_seed(args)\n", | |
| "\n", | |
| " #print(len(all_images))\n", | |
| " if args.make_grid:\n", | |
| " grid = make_grid(all_images, nrow=int(len(all_images)/args.grid_rows))\n", | |
| " grid = rearrange(grid, 'c h w -> h w c').cpu().numpy()\n", | |
| " filename = f\"{args.timestring}_{iprompt:05d}_grid_{args.seed}.png\"\n", | |
| " grid_image = Image.fromarray(grid.astype(np.uint8))\n", | |
| " grid_image.save(os.path.join(args.outdir, filename))\n", | |
| " display.clear_output(wait=True) \n", | |
| " display.display(grid_image)\n", | |
| "\n", | |
| "\n", | |
| "def render_animation(args, anim_args):\n", | |
| " # animations use key framed prompts\n", | |
| " args.prompts = animation_prompts\n", | |
| "\n", | |
| " # expand key frame strings to values\n", | |
| " keys = DeformAnimKeys(anim_args)\n", | |
| "\n", | |
| " # resume animation\n", | |
| " start_frame = 0\n", | |
| " if anim_args.resume_from_timestring:\n", | |
| " for tmp in os.listdir(args.outdir):\n", | |
| " if tmp.split(\"_\")[0] == anim_args.resume_timestring:\n", | |
| " start_frame += 1\n", | |
| " start_frame = start_frame - 1\n", | |
| "\n", | |
| " # create output folder for the batch\n", | |
| " os.makedirs(args.outdir, exist_ok=True)\n", | |
| " print(f\"Saving animation frames to {args.outdir}\")\n", | |
| "\n", | |
| " # save settings for the batch\n", | |
| " settings_filename = os.path.join(args.outdir, f\"{args.timestring}_settings.txt\")\n", | |
| " with open(settings_filename, \"w+\", encoding=\"utf-8\") as f:\n", | |
| " s = {**dict(args.__dict__), **dict(anim_args.__dict__)}\n", | |
| " json.dump(s, f, ensure_ascii=False, indent=4)\n", | |
| " \n", | |
| " # resume from timestring\n", | |
| " if anim_args.resume_from_timestring:\n", | |
| " args.timestring = anim_args.resume_timestring\n", | |
| "\n", | |
| " # expand prompts out to per-frame\n", | |
| " prompt_series = pd.Series([np.nan for a in range(anim_args.max_frames)])\n", | |
| " for i, prompt in animation_prompts.items():\n", | |
| " prompt_series[i] = prompt\n", | |
| " prompt_series = prompt_series.ffill().bfill()\n", | |
| "\n", | |
| " # check for video inits\n", | |
| " using_vid_init = anim_args.animation_mode == 'Video Input'\n", | |
| "\n", | |
| " # load depth model for 3D\n", | |
| " predict_depths = (anim_args.animation_mode == '3D' and anim_args.use_depth_warping) or anim_args.save_depth_maps\n", | |
| " if predict_depths:\n", | |
| " depth_model = DepthModel(device)\n", | |
| " depth_model.load_midas(models_path)\n", | |
| " if anim_args.midas_weight < 1.0:\n", | |
| " depth_model.load_adabins()\n", | |
| " else:\n", | |
| " depth_model = None\n", | |
| " anim_args.save_depth_maps = False\n", | |
| "\n", | |
| " # state for interpolating between diffusion steps\n", | |
| " turbo_steps = 1 if using_vid_init else int(anim_args.diffusion_cadence)\n", | |
| " turbo_prev_image, turbo_prev_frame_idx = None, 0\n", | |
| " turbo_next_image, turbo_next_frame_idx = None, 0\n", | |
| "\n", | |
| " # resume animation\n", | |
| " prev_sample = None\n", | |
| " color_match_sample = None\n", | |
| " if anim_args.resume_from_timestring:\n", | |
| " last_frame = start_frame-1\n", | |
| " if turbo_steps > 1:\n", | |
| " last_frame -= last_frame%turbo_steps\n", | |
| " path = os.path.join(args.outdir,f\"{args.timestring}_{last_frame:05}.png\")\n", | |
| " img = cv2.imread(path)\n", | |
| " img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", | |
| " prev_sample = sample_from_cv2(img)\n", | |
| " if anim_args.color_coherence != 'None':\n", | |
| " color_match_sample = img\n", | |
| " if turbo_steps > 1:\n", | |
| " turbo_next_image, turbo_next_frame_idx = sample_to_cv2(prev_sample, type=np.float32), last_frame\n", | |
| " turbo_prev_image, turbo_prev_frame_idx = turbo_next_image, turbo_next_frame_idx\n", | |
| " start_frame = last_frame+turbo_steps\n", | |
| "\n", | |
| " args.n_samples = 1\n", | |
| " frame_idx = start_frame\n", | |
| " while frame_idx < anim_args.max_frames:\n", | |
| " print(f\"Rendering animation frame {frame_idx} of {anim_args.max_frames}\")\n", | |
| " noise = keys.noise_schedule_series[frame_idx]\n", | |
| " strength = keys.strength_schedule_series[frame_idx]\n", | |
| " contrast = keys.contrast_schedule_series[frame_idx]\n", | |
| " depth = None\n", | |
| " \n", | |
| " # emit in-between frames\n", | |
| " if turbo_steps > 1:\n", | |
| " tween_frame_start_idx = max(0, frame_idx-turbo_steps)\n", | |
| " for tween_frame_idx in range(tween_frame_start_idx, frame_idx):\n", | |
| " tween = float(tween_frame_idx - tween_frame_start_idx + 1) / float(frame_idx - tween_frame_start_idx)\n", | |
| " print(f\" creating in between frame {tween_frame_idx} tween:{tween:0.2f}\")\n", | |
| "\n", | |
| " advance_prev = turbo_prev_image is not None and tween_frame_idx > turbo_prev_frame_idx\n", | |
| " advance_next = tween_frame_idx > turbo_next_frame_idx\n", | |
| "\n", | |
| " if depth_model is not None:\n", | |
| " assert(turbo_next_image is not None)\n", | |
| " depth = depth_model.predict(turbo_next_image, anim_args)\n", | |
| "\n", | |
| " if anim_args.animation_mode == '2D':\n", | |
| " if advance_prev:\n", | |
| " turbo_prev_image = anim_frame_warp_2d(turbo_prev_image, args, anim_args, keys, tween_frame_idx)\n", | |
| " if advance_next:\n", | |
| " turbo_next_image = anim_frame_warp_2d(turbo_next_image, args, anim_args, keys, tween_frame_idx)\n", | |
| " else: # '3D'\n", | |
| " if advance_prev:\n", | |
| " turbo_prev_image = anim_frame_warp_3d(turbo_prev_image, depth, anim_args, keys, tween_frame_idx)\n", | |
| " if advance_next:\n", | |
| " turbo_next_image = anim_frame_warp_3d(turbo_next_image, depth, anim_args, keys, tween_frame_idx)\n", | |
| " turbo_prev_frame_idx = turbo_next_frame_idx = tween_frame_idx\n", | |
| "\n", | |
| " if turbo_prev_image is not None and tween < 1.0:\n", | |
| " img = turbo_prev_image*(1.0-tween) + turbo_next_image*tween\n", | |
| " else:\n", | |
| " img = turbo_next_image\n", | |
| "\n", | |
| " filename = f\"{args.timestring}_{tween_frame_idx:05}.png\"\n", | |
| " cv2.imwrite(os.path.join(args.outdir, filename), cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_RGB2BGR))\n", | |
| " if anim_args.save_depth_maps:\n", | |
| " depth_model.save(os.path.join(args.outdir, f\"{args.timestring}_depth_{tween_frame_idx:05}.png\"), depth)\n", | |
| " if turbo_next_image is not None:\n", | |
| " prev_sample = sample_from_cv2(turbo_next_image)\n", | |
| "\n", | |
| " # apply transforms to previous frame\n", | |
| " if prev_sample is not None:\n", | |
| " if anim_args.animation_mode == '2D':\n", | |
| " prev_img = anim_frame_warp_2d(sample_to_cv2(prev_sample), args, anim_args, keys, frame_idx)\n", | |
| " else: # '3D'\n", | |
| " prev_img_cv2 = sample_to_cv2(prev_sample)\n", | |
| " depth = depth_model.predict(prev_img_cv2, anim_args) if depth_model else None\n", | |
| " prev_img = anim_frame_warp_3d(prev_img_cv2, depth, anim_args, keys, frame_idx)\n", | |
| "\n", | |
| " # apply color matching\n", | |
| " if anim_args.color_coherence != 'None':\n", | |
| " if color_match_sample is None:\n", | |
| " color_match_sample = prev_img.copy()\n", | |
| " else:\n", | |
| " prev_img = maintain_colors(prev_img, color_match_sample, anim_args.color_coherence)\n", | |
| "\n", | |
| " # apply scaling\n", | |
| " contrast_sample = prev_img * contrast\n", | |
| " # apply frame noising\n", | |
| " noised_sample = add_noise(sample_from_cv2(contrast_sample), noise)\n", | |
| "\n", | |
| " # use transformed previous frame as init for current\n", | |
| " args.use_init = True\n", | |
| " if half_precision:\n", | |
| " args.init_sample = noised_sample.half().to(device)\n", | |
| " else:\n", | |
| " args.init_sample = noised_sample.to(device)\n", | |
| " args.strength = max(0.0, min(1.0, strength))\n", | |
| "\n", | |
| " # grab prompt for current frame\n", | |
| " args.prompt = prompt_series[frame_idx]\n", | |
| " print(f\"{args.prompt} {args.seed}\")\n", | |
| " if not using_vid_init:\n", | |
| " print(f\"Angle: {keys.angle_series[frame_idx]} Zoom: {keys.zoom_series[frame_idx]}\")\n", | |
| " print(f\"Tx: {keys.translation_x_series[frame_idx]} Ty: {keys.translation_y_series[frame_idx]} Tz: {keys.translation_z_series[frame_idx]}\")\n", | |
| " print(f\"Rx: {keys.rotation_3d_x_series[frame_idx]} Ry: {keys.rotation_3d_y_series[frame_idx]} Rz: {keys.rotation_3d_z_series[frame_idx]}\")\n", | |
| "\n", | |
| " # grab init image for current frame\n", | |
| " if using_vid_init:\n", | |
| " init_frame = os.path.join(args.outdir, 'inputframes', f\"{frame_idx+1:05}.jpg\") \n", | |
| " print(f\"Using video init frame {init_frame}\")\n", | |
| " args.init_image = init_frame\n", | |
| " if anim_args.use_mask_video:\n", | |
| " mask_frame = os.path.join(args.outdir, 'maskframes', f\"{frame_idx+1:05}.jpg\")\n", | |
| " args.mask_file = mask_frame\n", | |
| "\n", | |
| " # sample the diffusion model\n", | |
| " sample, image = generate(args, frame_idx, return_latent=False, return_sample=True)\n", | |
| " if not using_vid_init:\n", | |
| " prev_sample = sample\n", | |
| "\n", | |
| " if turbo_steps > 1:\n", | |
| " turbo_prev_image, turbo_prev_frame_idx = turbo_next_image, turbo_next_frame_idx\n", | |
| " turbo_next_image, turbo_next_frame_idx = sample_to_cv2(sample, type=np.float32), frame_idx\n", | |
| " frame_idx += turbo_steps\n", | |
| " else: \n", | |
| " filename = f\"{args.timestring}_{frame_idx:05}.png\"\n", | |
| " image.save(os.path.join(args.outdir, filename))\n", | |
| " if anim_args.save_depth_maps:\n", | |
| " if depth is None:\n", | |
| " depth = depth_model.predict(sample_to_cv2(sample), anim_args)\n", | |
| " depth_model.save(os.path.join(args.outdir, f\"{args.timestring}_depth_{frame_idx:05}.png\"), depth)\n", | |
| " frame_idx += 1\n", | |
| "\n", | |
| " display.clear_output(wait=True)\n", | |
| " display.display(image)\n", | |
| "\n", | |
| " args.seed = next_seed(args)\n", | |
| "\n", | |
| "def vid2frames(video_path, frames_path, n=1, overwrite=True): \n", | |
| " if not os.path.exists(frames_path) or overwrite: \n", | |
| " try:\n", | |
| " for f in pathlib.Path(video_in_frame_path).glob('*.jpg'):\n", | |
| " f.unlink()\n", | |
| " except:\n", | |
| " pass\n", | |
| " assert os.path.exists(video_path), f\"Video input {video_path} does not exist\"\n", | |
| " \n", | |
| " vidcap = cv2.VideoCapture(video_path)\n", | |
| " success,image = vidcap.read()\n", | |
| " count = 0\n", | |
| " t=1\n", | |
| " success = True\n", | |
| " while success:\n", | |
| " if count % n == 0:\n", | |
| " cv2.imwrite(frames_path + os.path.sep + f\"{t:05}.jpg\" , image) # save frame as JPEG file\n", | |
| " t += 1\n", | |
| " success,image = vidcap.read()\n", | |
| " count += 1\n", | |
| " print(\"Converted %d frames\" % count)\n", | |
| " else: print(\"Frames already unpacked\")\n", | |
| "\n", | |
| "def render_input_video(args, anim_args):\n", | |
| " # create a folder for the video input frames to live in\n", | |
| " video_in_frame_path = os.path.join(args.outdir, 'inputframes') \n", | |
| " os.makedirs(video_in_frame_path, exist_ok=True)\n", | |
| " \n", | |
| " # save the video frames from input video\n", | |
| " print(f\"Exporting Video Frames (1 every {anim_args.extract_nth_frame}) frames to {video_in_frame_path}...\")\n", | |
| " vid2frames(anim_args.video_init_path, video_in_frame_path, anim_args.extract_nth_frame, anim_args.overwrite_extracted_frames)\n", | |
| "\n", | |
| " # determine max frames from length of input frames\n", | |
| " anim_args.max_frames = len([f for f in pathlib.Path(video_in_frame_path).glob('*.jpg')])\n", | |
| " args.use_init = True\n", | |
| " print(f\"Loading {anim_args.max_frames} input frames from {video_in_frame_path} and saving video frames to {args.outdir}\")\n", | |
| "\n", | |
| " if anim_args.use_mask_video:\n", | |
| " # create a folder for the mask video input frames to live in\n", | |
| " mask_in_frame_path = os.path.join(args.outdir, 'maskframes') \n", | |
| " os.makedirs(mask_in_frame_path, exist_ok=True)\n", | |
| "\n", | |
| " # save the video frames from mask video\n", | |
| " print(f\"Exporting Video Frames (1 every {anim_args.extract_nth_frame}) frames to {mask_in_frame_path}...\")\n", | |
| " vid2frames(anim_args.video_mask_path, mask_in_frame_path, anim_args.extract_nth_frame, anim_args.overwrite_extracted_frames)\n", | |
| " args.use_mask = True\n", | |
| " args.overlay_mask = True\n", | |
| "\n", | |
| " render_animation(args, anim_args)\n", | |
| "\n", | |
| "def render_interpolation(args, anim_args):\n", | |
| " # animations use key framed prompts\n", | |
| " args.prompts = animation_prompts\n", | |
| "\n", | |
| " # create output folder for the batch\n", | |
| " os.makedirs(args.outdir, exist_ok=True)\n", | |
| " print(f\"Saving animation frames to {args.outdir}\")\n", | |
| "\n", | |
| " # save settings for the batch\n", | |
| " settings_filename = os.path.join(args.outdir, f\"{args.timestring}_settings.txt\")\n", | |
| " with open(settings_filename, \"w+\", encoding=\"utf-8\") as f:\n", | |
| " s = {**dict(args.__dict__), **dict(anim_args.__dict__)}\n", | |
| " json.dump(s, f, ensure_ascii=False, indent=4)\n", | |
| " \n", | |
| " # Interpolation Settings\n", | |
| " args.n_samples = 1\n", | |
| " args.seed_behavior = 'fixed' # force fix seed at the moment bc only 1 seed is available\n", | |
| " prompts_c_s = [] # cache all the text embeddings\n", | |
| "\n", | |
| " print(f\"Preparing for interpolation of the following...\")\n", | |
| "\n", | |
| " for i, prompt in animation_prompts.items():\n", | |
| " args.prompt = prompt\n", | |
| "\n", | |
| " # sample the diffusion model\n", | |
| " results = generate(args, return_c=True)\n", | |
| " c, image = results[0], results[1]\n", | |
| " prompts_c_s.append(c) \n", | |
| " \n", | |
| " # display.clear_output(wait=True)\n", | |
| " display.display(image)\n", | |
| " \n", | |
| " args.seed = next_seed(args)\n", | |
| "\n", | |
| " display.clear_output(wait=True)\n", | |
| " print(f\"Interpolation start...\")\n", | |
| "\n", | |
| " frame_idx = 0\n", | |
| "\n", | |
| " if anim_args.interpolate_key_frames:\n", | |
| " for i in range(len(prompts_c_s)-1):\n", | |
| " dist_frames = list(animation_prompts.items())[i+1][0] - list(animation_prompts.items())[i][0]\n", | |
| " if dist_frames <= 0:\n", | |
| " print(\"key frames duplicated or reversed. interpolation skipped.\")\n", | |
| " return\n", | |
| " else:\n", | |
| " for j in range(dist_frames):\n", | |
| " # interpolate the text embedding\n", | |
| " prompt1_c = prompts_c_s[i]\n", | |
| " prompt2_c = prompts_c_s[i+1] \n", | |
| " args.init_c = prompt1_c.add(prompt2_c.sub(prompt1_c).mul(j * 1/dist_frames))\n", | |
| "\n", | |
| " # sample the diffusion model\n", | |
| " results = generate(args)\n", | |
| " image = results[0]\n", | |
| "\n", | |
| " filename = f\"{args.timestring}_{frame_idx:05}.png\"\n", | |
| " image.save(os.path.join(args.outdir, filename))\n", | |
| " frame_idx += 1\n", | |
| "\n", | |
| " display.clear_output(wait=True)\n", | |
| " display.display(image)\n", | |
| "\n", | |
| " args.seed = next_seed(args)\n", | |
| "\n", | |
| " else:\n", | |
| " for i in range(len(prompts_c_s)-1):\n", | |
| " for j in range(anim_args.interpolate_x_frames+1):\n", | |
| " # interpolate the text embedding\n", | |
| " prompt1_c = prompts_c_s[i]\n", | |
| " prompt2_c = prompts_c_s[i+1] \n", | |
| " args.init_c = prompt1_c.add(prompt2_c.sub(prompt1_c).mul(j * 1/(anim_args.interpolate_x_frames+1)))\n", | |
| "\n", | |
| " # sample the diffusion model\n", | |
| " results = generate(args)\n", | |
| " image = results[0]\n", | |
| "\n", | |
| " filename = f\"{args.timestring}_{frame_idx:05}.png\"\n", | |
| " image.save(os.path.join(args.outdir, filename))\n", | |
| " frame_idx += 1\n", | |
| "\n", | |
| " display.clear_output(wait=True)\n", | |
| " display.display(image)\n", | |
| "\n", | |
| " args.seed = next_seed(args)\n", | |
| "\n", | |
| " # generate the last prompt\n", | |
| " args.init_c = prompts_c_s[-1]\n", | |
| " results = generate(args)\n", | |
| " image = results[0]\n", | |
| " filename = f\"{args.timestring}_{frame_idx:05}.png\"\n", | |
| " image.save(os.path.join(args.outdir, filename))\n", | |
| "\n", | |
| " display.clear_output(wait=True)\n", | |
| " display.display(image)\n", | |
| " args.seed = next_seed(args)\n", | |
| "\n", | |
| " #clear init_c\n", | |
| " args.init_c = None\n", | |
| "\n", | |
| "\n", | |
| "args_dict = DeforumArgs()\n", | |
| "anim_args_dict = DeforumAnimArgs()\n", | |
| "\n", | |
| "if override_settings_with_file:\n", | |
| " print(f\"reading custom settings from {custom_settings_file}\")\n", | |
| " if not os.path.isfile(custom_settings_file):\n", | |
| " print('The custom settings file does not exist. The in-notebook settings will be used instead')\n", | |
| " else:\n", | |
| " with open(custom_settings_file, \"r\") as f:\n", | |
| " jdata = json.loads(f.read())\n", | |
| " animation_prompts = jdata[\"prompts\"]\n", | |
| " for i, k in enumerate(args_dict):\n", | |
| " if k in jdata:\n", | |
| " args_dict[k] = jdata[k]\n", | |
| " else:\n", | |
| " print(f\"key {k} doesn't exist in the custom settings data! using the default value of {args_dict[k]}\")\n", | |
| " for i, k in enumerate(anim_args_dict):\n", | |
| " if k in jdata:\n", | |
| " anim_args_dict[k] = jdata[k]\n", | |
| " else:\n", | |
| " print(f\"key {k} doesn't exist in the custom settings data! using the default value of {anim_args_dict[k]}\")\n", | |
| " print(args_dict)\n", | |
| " print(anim_args_dict)\n", | |
| "\n", | |
| "args = SimpleNamespace(**args_dict)\n", | |
| "anim_args = SimpleNamespace(**anim_args_dict)\n", | |
| "\n", | |
| "args.timestring = time.strftime('%Y%m%d%H%M%S')\n", | |
| "args.strength = max(0.0, min(1.0, args.strength))\n", | |
| "\n", | |
| "if args.seed == -1:\n", | |
| " args.seed = random.randint(0, 2**32 - 1)\n", | |
| "if not args.use_init:\n", | |
| " args.init_image = None\n", | |
| "if args.sampler == 'plms' and (args.use_init or anim_args.animation_mode != 'None'):\n", | |
| " print(f\"Init images aren't supported with PLMS yet, switching to KLMS\")\n", | |
| " args.sampler = 'klms'\n", | |
| "if args.sampler != 'ddim':\n", | |
| " args.ddim_eta = 0\n", | |
| "\n", | |
| "if anim_args.animation_mode == 'None':\n", | |
| " anim_args.max_frames = 1\n", | |
| "elif anim_args.animation_mode == 'Video Input':\n", | |
| " args.use_init = True\n", | |
| "\n", | |
| "# clean up unused memory\n", | |
| "gc.collect()\n", | |
| "torch.cuda.empty_cache()\n", | |
| "\n", | |
| "# dispatch to appropriate renderer\n", | |
| "if anim_args.animation_mode == '2D' or anim_args.animation_mode == '3D':\n", | |
| " render_animation(args, anim_args)\n", | |
| "elif anim_args.animation_mode == 'Video Input':\n", | |
| " render_input_video(args, anim_args)\n", | |
| "elif anim_args.animation_mode == 'Interpolation':\n", | |
| " render_interpolation(args, anim_args)\n", | |
| "else:\n", | |
| " render_image_batch(args) " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "4zV0J_YbMCTx" | |
| }, | |
| "source": [ | |
| "# Create video from frames" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "cellView": "form", | |
| "id": "no2jP8HTMBM0" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "skip_video_for_run_all = True #@param {type: 'boolean'}\n", | |
| "fps = 12 #@param {type:\"number\"}\n", | |
| "#@markdown **Manual Settings**\n", | |
| "use_manual_settings = False #@param {type:\"boolean\"}\n", | |
| "image_path = \"/content/drive/MyDrive/AI/StableDiffusion/2022-09/20220903000939_%05d.png\" #@param {type:\"string\"}\n", | |
| "mp4_path = \"/content/drive/MyDrive/AI/StableDiffu'/content/drive/MyDrive/AI/StableDiffusion/2022-09/sion/2022-09/20220903000939.mp4\" #@param {type:\"string\"}\n", | |
| "render_steps = True #@param {type: 'boolean'}\n", | |
| "path_name_modifier = \"x0_pred\" #@param [\"x0_pred\",\"x\"]\n", | |
| "\n", | |
| "\n", | |
| "if skip_video_for_run_all == True:\n", | |
| " print('Skipping video creation, uncheck skip_video_for_run_all if you want to run it')\n", | |
| "else:\n", | |
| " import os\n", | |
| " import subprocess\n", | |
| " from base64 import b64encode\n", | |
| "\n", | |
| " print(f\"{image_path} -> {mp4_path}\")\n", | |
| "\n", | |
| " if use_manual_settings:\n", | |
| " max_frames = \"200\" #@param {type:\"string\"}\n", | |
| " else:\n", | |
| " if render_steps: # render steps from a single image\n", | |
| " fname = f\"{path_name_modifier}_%05d.png\"\n", | |
| " all_step_dirs = [os.path.join(args.outdir, d) for d in os.listdir(args.outdir) if os.path.isdir(os.path.join(args.outdir,d))]\n", | |
| " newest_dir = max(all_step_dirs, key=os.path.getmtime)\n", | |
| " image_path = os.path.join(newest_dir, fname)\n", | |
| " print(f\"Reading images from {image_path}\")\n", | |
| " mp4_path = os.path.join(newest_dir, f\"{args.timestring}_{path_name_modifier}.mp4\")\n", | |
| " max_frames = str(args.steps)\n", | |
| " else: # render images for a video\n", | |
| " image_path = os.path.join(args.outdir, f\"{args.timestring}_%05d.png\")\n", | |
| " mp4_path = os.path.join(args.outdir, f\"{args.timestring}.mp4\")\n", | |
| " max_frames = str(anim_args.max_frames)\n", | |
| "\n", | |
| " # make video\n", | |
| " cmd = [\n", | |
| " 'ffmpeg',\n", | |
| " '-y',\n", | |
| " '-vcodec', 'png',\n", | |
| " '-r', str(fps),\n", | |
| " '-start_number', str(0),\n", | |
| " '-i', image_path,\n", | |
| " '-frames:v', max_frames,\n", | |
| " '-c:v', 'libx264',\n", | |
| " '-vf',\n", | |
| " f'fps={fps}',\n", | |
| " '-pix_fmt', 'yuv420p',\n", | |
| " '-crf', '17',\n", | |
| " '-preset', 'veryfast',\n", | |
| " '-pattern_type', 'sequence',\n", | |
| " mp4_path\n", | |
| " ]\n", | |
| " process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n", | |
| " stdout, stderr = process.communicate()\n", | |
| " if process.returncode != 0:\n", | |
| " print(stderr)\n", | |
| " raise RuntimeError(stderr)\n", | |
| "\n", | |
| " mp4 = open(mp4_path,'rb').read()\n", | |
| " data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", | |
| " display.display( display.HTML(f'<video controls loop><source src=\"{data_url}\" type=\"video/mp4\"></video>') )" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "accelerator": "GPU", | |
| "colab": { | |
| "collapsed_sections": [], | |
| "include_colab_link": true, | |
| "private_outputs": true, | |
| "provenance": [] | |
| }, | |
| "gpuClass": "standard", | |
| "kernelspec": { | |
| "display_name": "Python 3", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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