{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "QIuzds5LLPyC" }, "source": [ "\"Open\n", "# Stable Video Diffusion (image-to-video) Demo\n", "This notebook is the demo for the new image-to-video model, Stable Video Diffusion, from [Stability AI](https://stability.ai/) **on Colab free plan**.\n", "\n", "This was made by [mkshing](https://twitter.com/mk1stats).\n", "\n", "Visit the following links for the details of Stable Video Diffusion.\n", "* Codebase: https://github.com/Stability-AI/generative-models\n", "* HF: https://huggingface.co/stabilityai/stable-video-diffusion-img2vid\n", " * LICENSE: [STABLE VIDEO DIFFUSION NON-COMMERCIAL COMMUNITY LICENSE AGREEMENT](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid/blob/main/LICENSE)\n", "* Paper: https://stability.ai/research/stable-video-diffusion-scaling-latent-video-diffusion-models-to-large-datasets\n", "\n", "\n", "\n", "## Updates\n", "### 2023.11.27\n", "* Add the other hyper-parameters (`fps_id`, `motion_bucket_id`, `cond_aug`)\n" ] }, { "cell_type": "code", "source": [], "metadata": { "id": "_jO7XtApXOjS" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "aaimSFWfLPgb", "outputId": "cec8dd8a-6ef5-4e4f-9ce6-2ad1c4990309", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[Errno 2] No such file or directory: 'content'\n", "/content\n", "Tue Nov 28 09:53:58 2023 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. 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antlr4-python3-runtime pudb lit urwid_readline\n", "Installing collected packages: wcwidth, tokenizers, sentencepiece, ninja, lit, braceexpand, antlr4-python3-runtime, webdataset, watchdog, validators, urwid, urllib3, tzdata, tensorboardx, smmap, setproctitle, pillow, pathspec, opencv-python, omegaconf, mypy-extensions, lightning-utilities, jedi, ftfy, fire, einops, docker-pycreds, chardet, urwid_readline, sentry-sdk, pydeck, pandas, matplotlib, gitdb, black, pudb, gitpython, wandb, transformers, streamlit, triton, torch, torchvision, torchmetrics, timm, xformers, torchdata, torchaudio, pytorch-lightning, open-clip-torch, kornia, invisible-watermark, fairscale, clip\n", " Attempting uninstall: wcwidth\n", " Found existing installation: wcwidth 0.2.10\n", " Uninstalling wcwidth-0.2.10:\n", " Successfully uninstalled wcwidth-0.2.10\n", " Attempting uninstall: tokenizers\n", " Found existing installation: tokenizers 0.15.0\n", " Uninstalling tokenizers-0.15.0:\n", " Successfully uninstalled tokenizers-0.15.0\n", " Attempting uninstall: urllib3\n", " Found existing installation: urllib3 2.0.7\n", " Uninstalling urllib3-2.0.7:\n", " Successfully uninstalled urllib3-2.0.7\n", " Attempting uninstall: pillow\n", " Found existing installation: Pillow 9.4.0\n", " Uninstalling Pillow-9.4.0:\n", " Successfully uninstalled Pillow-9.4.0\n", " Attempting uninstall: opencv-python\n", " Found existing installation: opencv-python 4.8.0.76\n", " Uninstalling opencv-python-4.8.0.76:\n", " Successfully uninstalled opencv-python-4.8.0.76\n", " Attempting uninstall: chardet\n", " Found existing installation: chardet 5.2.0\n", " Uninstalling chardet-5.2.0:\n", " Successfully uninstalled chardet-5.2.0\n", " Attempting uninstall: pandas\n", " Found existing installation: pandas 1.5.3\n", " Uninstalling pandas-1.5.3:\n", " Successfully uninstalled pandas-1.5.3\n", " Attempting uninstall: matplotlib\n", " Found existing installation: matplotlib 3.7.1\n", " Uninstalling matplotlib-3.7.1:\n", " Successfully uninstalled matplotlib-3.7.1\n", " Attempting uninstall: transformers\n", " Found existing installation: transformers 4.35.2\n", " Uninstalling transformers-4.35.2:\n", " Successfully uninstalled transformers-4.35.2\n", " Attempting uninstall: triton\n", " Found existing installation: triton 2.1.0\n", " Uninstalling triton-2.1.0:\n", " Successfully uninstalled triton-2.1.0\n", " Attempting uninstall: torch\n", " Found existing installation: torch 2.1.0+cu118\n", " Uninstalling torch-2.1.0+cu118:\n", " Successfully uninstalled torch-2.1.0+cu118\n", " Attempting uninstall: torchvision\n", " Found existing installation: torchvision 0.16.0+cu118\n", " Uninstalling torchvision-0.16.0+cu118:\n", " Successfully uninstalled torchvision-0.16.0+cu118\n", " Attempting uninstall: torchdata\n", " Found existing installation: torchdata 0.7.0\n", " Uninstalling torchdata-0.7.0:\n", " Successfully uninstalled torchdata-0.7.0\n", " Attempting uninstall: torchaudio\n", " Found existing installation: torchaudio 2.1.0+cu118\n", " Uninstalling torchaudio-2.1.0+cu118:\n", " Successfully uninstalled torchaudio-2.1.0+cu118\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "lida 0.0.10 requires fastapi, which is not installed.\n", "lida 0.0.10 requires kaleido, which is not installed.\n", "lida 0.0.10 requires python-multipart, which is not installed.\n", "lida 0.0.10 requires uvicorn, which is not installed.\n", "google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.1.3 which is incompatible.\n", "imageio 2.31.6 requires pillow<10.1.0,>=8.3.2, but you have pillow 10.1.0 which is incompatible.\n", "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.0.1+cu118 which is incompatible.\n", "torchtext 0.16.0 requires torchdata==0.7.0, but you have torchdata 0.6.1 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed antlr4-python3-runtime-4.9.3 black-23.7.0 braceexpand-0.1.7 chardet-5.1.0 clip-1.0 docker-pycreds-0.4.0 einops-0.7.0 fairscale-0.4.13 fire-0.5.0 ftfy-6.1.3 gitdb-4.0.11 gitpython-3.1.40 invisible-watermark-0.2.0 jedi-0.19.1 kornia-0.6.9 lightning-utilities-0.10.0 lit-17.0.5 matplotlib-3.8.2 mypy-extensions-1.0.0 ninja-1.11.1.1 omegaconf-2.3.0 open-clip-torch-2.23.0 opencv-python-4.6.0.66 pandas-2.1.3 pathspec-0.11.2 pillow-10.1.0 pudb-2023.1 pydeck-0.8.1b0 pytorch-lightning-2.0.1 sentencepiece-0.1.99 sentry-sdk-1.37.1 setproctitle-1.3.3 smmap-5.0.1 streamlit-1.28.2 tensorboardx-2.6 timm-0.9.12 tokenizers-0.12.1 torch-2.0.1+cu118 torchaudio-2.0.2+cu118 torchdata-0.6.1 torchmetrics-1.2.0 torchvision-0.15.2+cu118 transformers-4.19.1 triton-2.0.0 tzdata-2023.3 urllib3-1.26.18 urwid-2.2.3 urwid_readline-0.13 validators-0.22.0 wandb-0.16.0 watchdog-3.0.0 wcwidth-0.2.12 webdataset-0.2.79 xformers-0.0.22\n" ] }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "PIL", "matplotlib", "mpl_toolkits", "pydevd_plugins", "wcwidth" ] } } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Obtaining file:///content/generative-models\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n", " Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "Building wheels for collected packages: sgm\n", " Building editable for sgm (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for sgm: filename=sgm-0.1.0-py3-none-any.whl size=26737 sha256=afa2dcf74340e017d291435d122d245349fd3d0ccd82e8eab2e319f229829159\n", " Stored in directory: /tmp/pip-ephem-wheel-cache-tdbtt00f/wheels/12/9b/27/03142f4dee9fa0a99f6c146eae81eb66e17b781145ecb05fa5\n", "Successfully built sgm\n", "Installing collected packages: sgm\n", "Successfully installed sgm-0.1.0\n", "Obtaining sdata from git+https://github.com/Stability-AI/datapipelines.git@main#egg=sdata\n", " Cloning 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sha256=e32f9717874d35f7d4b17457be3439ecd7642e24c728fe5eb6e5d0bc480d2dd2\n", " Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n", "Successfully built ffmpy\n", "Installing collected packages: pydub, ffmpy, websockets, typing-extensions, tomlkit, shellingham, semantic-version, python-multipart, orjson, h11, colorama, annotated-types, aiofiles, uvicorn, starlette, pydantic-core, httpcore, pydantic, httpx, gradio-client, fastapi, gradio\n", " Attempting uninstall: typing-extensions\n", " Found existing installation: typing_extensions 4.5.0\n", " Uninstalling typing_extensions-4.5.0:\n", " Successfully uninstalled typing_extensions-4.5.0\n", " Attempting uninstall: pydantic\n", " Found existing installation: pydantic 1.10.13\n", " Uninstalling pydantic-1.10.13:\n", " Successfully uninstalled pydantic-1.10.13\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "lida 0.0.10 requires kaleido, which is not installed.\n", "llmx 0.0.15a0 requires cohere, which is not installed.\n", "llmx 0.0.15a0 requires openai, which is not installed.\n", "llmx 0.0.15a0 requires tiktoken, which is not installed.\n", "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\n", "torchtext 0.16.0 requires torch==2.1.0, but you have torch 2.0.1+cu118 which is incompatible.\n", "torchtext 0.16.0 requires torchdata==0.7.0, but you have torchdata 0.6.1 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed aiofiles-23.2.1 annotated-types-0.6.0 colorama-0.4.6 fastapi-0.104.1 ffmpy-0.3.1 gradio-4.7.1 gradio-client-0.7.0 h11-0.14.0 httpcore-1.0.2 httpx-0.25.2 orjson-3.9.10 pydantic-2.5.2 pydantic-core-2.14.5 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.27.0 tomlkit-0.12.0 typing-extensions-4.8.0 uvicorn-0.24.0.post1 websockets-11.0.3\n" ] } ], "source": [ "#@title Setup\n", "/content\n", "!nvidia-smi\n", "!git clone https://github.com/Stability-AI/generative-models.git\n", "# install required packages from pypi\n", "# !pip3 install -r generative-models/requirements/pt2.txt\n", "# manually install only necesarry packages for colab\n", "!wget https://gist.githubusercontent.com/mkshing/4ad40699756d996ba6b3f7934e6ca532/raw/3f0094272c7a2bd3eb5f1a0db91bed582c9e8f01/requirements.txt\n", "!pip3 install -r requirements.txt\n", "!pip3 install -e generative-models\n", "!pip3 install -e git+https://github.com/Stability-AI/datapipelines.git@main#egg=sdata\n", "!pip3 install gradio" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "zWlfaXvPbR1L" }, "outputs": [], "source": [ "#@title Colab hack for SVD\n", "# !pip uninstall -y numpy\n", "# !pip install -U numpy\n", "!mkdir -p /content/scripts/util/detection\n", "!ln -s /content/generative-models/scripts/util/detection/p_head_v1.npz /content/scripts/util/detection/p_head_v1.npz\n", "!ln -s /content/generative-models/scripts/util/detection/w_head_v1.npz /content/scripts/util/detection/w_head_v1.npz" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "v8O2yR3BLHv6", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "a448e8cd-955f-40b9-960d-c84cf6e7ce32" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "download from https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors to checkpoints/svd_xt.safetensors\n" ] } ], "source": [ "# @title Download weights\n", "import os\n", "import subprocess\n", "version = \"svd_xt\" #@param [\"svd\", \"svd_xt\"]\n", "TYPE2PATH = {\n", " \"svd\": [\"https://huggingface.co/stabilityai/stable-video-diffusion-img2vid/resolve/main/svd.safetensors\", \"checkpoints/svd.safetensors\"],\n", " \"svd_xt\": [\"https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt/resolve/main/svd_xt.safetensors\", \"checkpoints/svd_xt.safetensors\"]\n", "}\n", "download_from, download_to = TYPE2PATH[version]\n", "# @markdown This will take several minutes.
\n", "# @markdown **Reference:**\n", "# @markdown * `svd`: [stabilityai/stable-video-diffusion-img2vid](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid) for 14 frames generation\n", "# @markdown * `svd_xt`: [stabilityai/stable-video-diffusion-img2vid-xt](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt) for 25 frames generation\n", "os.makedirs(\"checkpoints\", exist_ok=True)\n", "if os.path.exists(download_to):\n", " print(\"Already downloaded\")\n", "else:\n", " print(f\"download from {download_from} to {download_to}\")\n", " subprocess.call([\"wget\", download_from, \"-O\", download_to])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "9AZDrh-SUDt2" }, "outputs": [], "source": [ "#@title Load Model\n", "import sys\n", "from omegaconf import OmegaConf\n", "\n", "import torch\n", "\n", "sys.path.append(\"generative-models\")\n", "from sgm.util import default, instantiate_from_config\n", "from scripts.util.detection.nsfw_and_watermark_dectection import DeepFloydDataFiltering\n", "\n", "def load_model(\n", " config: str,\n", " device: str,\n", " num_frames: int,\n", " num_steps: int,\n", "):\n", " config = OmegaConf.load(config)\n", " config.model.params.conditioner_config.params.emb_models[\n", " 0\n", " ].params.open_clip_embedding_config.params.init_device = device\n", " config.model.params.sampler_config.params.num_steps = num_steps\n", " config.model.params.sampler_config.params.guider_config.params.num_frames = (\n", " num_frames\n", " )\n", " with torch.device(device):\n", " model = instantiate_from_config(config.model).to(device).eval().requires_grad_(False)\n", "\n", " filter = DeepFloydDataFiltering(verbose=False, device=device)\n", " return model, filter\n", "\n", "\n", "if version == \"svd\":\n", " num_frames = 14\n", " num_steps = 25\n", " # output_folder = default(output_folder, \"outputs/simple_video_sample/svd/\")\n", " model_config = \"generative-models/scripts/sampling/configs/svd.yaml\"\n", "elif version == \"svd_xt\":\n", " num_frames = 25\n", " num_steps = 30\n", " # output_folder = default(output_folder, \"outputs/simple_video_sample/svd_xt/\")\n", " model_config = \"generative-models/scripts/sampling/configs/svd_xt.yaml\"\n", "else:\n", " raise ValueError(f\"Version {version} does not exist.\")\n", "\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "model, filter = load_model(\n", " model_config,\n", " device,\n", " num_frames,\n", " num_steps,\n", ")\n", "# move models expect unet to cpu\n", "model.conditioner.cpu()\n", "model.first_stage_model.cpu()\n", "# change the dtype of unet\n", "model.model.to(dtype=torch.float16)\n", "torch.cuda.empty_cache()\n", "model = model.requires_grad_(False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "x1-dnq0RT95O" }, "outputs": [], "source": [ "# @title Sampling function\n", "import math\n", "import os\n", "from glob import glob\n", "from pathlib import Path\n", "from typing import Optional\n", "\n", "import cv2\n", "import numpy as np\n", "import torch\n", "from einops import rearrange, repeat\n", "from fire import Fire\n", "\n", "from PIL import Image\n", "from torchvision.transforms import ToTensor\n", "from torchvision.transforms import functional as TF\n", "\n", "from sgm.inference.helpers import embed_watermark\n", "from sgm.util import default, instantiate_from_config\n", "\n", "\n", "def get_unique_embedder_keys_from_conditioner(conditioner):\n", " return list(set([x.input_key for x in conditioner.embedders]))\n", "\n", "\n", "def get_batch(keys, value_dict, N, T, device, dtype=None):\n", " batch = {}\n", " batch_uc = {}\n", "\n", " for key in keys:\n", " if key == \"fps_id\":\n", " batch[key] = (\n", " torch.tensor([value_dict[\"fps_id\"]])\n", " .to(device, dtype=dtype)\n", " .repeat(int(math.prod(N)))\n", " )\n", " elif key == \"motion_bucket_id\":\n", " batch[key] = (\n", " torch.tensor([value_dict[\"motion_bucket_id\"]])\n", " .to(device, dtype=dtype)\n", " .repeat(int(math.prod(N)))\n", " )\n", " elif key == \"cond_aug\":\n", " batch[key] = repeat(\n", " torch.tensor([value_dict[\"cond_aug\"]]).to(device, dtype=dtype),\n", " \"1 -> b\",\n", " b=math.prod(N),\n", " )\n", " elif key == \"cond_frames\":\n", " batch[key] = repeat(value_dict[\"cond_frames\"], \"1 ... -> b ...\", b=N[0])\n", " elif key == \"cond_frames_without_noise\":\n", " batch[key] = repeat(\n", " value_dict[\"cond_frames_without_noise\"], \"1 ... -> b ...\", b=N[0]\n", " )\n", " else:\n", " batch[key] = value_dict[key]\n", "\n", " if T is not None:\n", " batch[\"num_video_frames\"] = T\n", "\n", " for key in batch.keys():\n", " if key not in batch_uc and isinstance(batch[key], torch.Tensor):\n", " batch_uc[key] = torch.clone(batch[key])\n", " return batch, batch_uc\n", "\n", "\n", "\n", "def sample(\n", " input_path: str = \"assets/test_image.png\", # Can either be image file or folder with image files\n", " resize_image: bool = False,\n", " num_frames: Optional[int] = None,\n", " num_steps: Optional[int] = None,\n", " fps_id: int = 6,\n", " motion_bucket_id: int = 127,\n", " cond_aug: float = 0.02,\n", " seed: int = 23,\n", " decoding_t: int = 14, # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.\n", " device: str = \"cuda\",\n", " output_folder: Optional[str] = \"/content/outputs\",\n", " skip_filter: bool = False,\n", "):\n", " \"\"\"\n", " Simple script to generate a single sample conditioned on an image `input_path` or multiple images, one for each\n", " image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.\n", " \"\"\"\n", " torch.manual_seed(seed)\n", "\n", " path = Path(input_path)\n", " all_img_paths = []\n", " if path.is_file():\n", " if any([input_path.endswith(x) for x in [\"jpg\", \"jpeg\", \"png\"]]):\n", " all_img_paths = [input_path]\n", " else:\n", " raise ValueError(\"Path is not valid image file.\")\n", " elif path.is_dir():\n", " all_img_paths = sorted(\n", " [\n", " f\n", " for f in path.iterdir()\n", " if f.is_file() and f.suffix.lower() in [\".jpg\", \".jpeg\", \".png\"]\n", " ]\n", " )\n", " if len(all_img_paths) == 0:\n", " raise ValueError(\"Folder does not contain any images.\")\n", " else:\n", " raise ValueError\n", " all_out_paths = []\n", " for input_img_path in all_img_paths:\n", " with Image.open(input_img_path) as image:\n", " if image.mode == \"RGBA\":\n", " image = image.convert(\"RGB\")\n", " if resize_image and image.size != (1024, 576):\n", " print(f\"Resizing {image.size} to (1024, 576)\")\n", " image = TF.resize(TF.resize(image, 1024), (576, 1024))\n", " w, h = image.size\n", "\n", " if h % 64 != 0 or w % 64 != 0:\n", " width, height = map(lambda x: x - x % 64, (w, h))\n", " image = image.resize((width, height))\n", " print(\n", " f\"WARNING: Your image is of size {h}x{w} which is not divisible by 64. We are resizing to {height}x{width}!\"\n", " )\n", "\n", " image = ToTensor()(image)\n", " image = image * 2.0 - 1.0\n", "\n", " image = image.unsqueeze(0).to(device)\n", " H, W = image.shape[2:]\n", " assert image.shape[1] == 3\n", " F = 8\n", " C = 4\n", " shape = (num_frames, C, H // F, W // F)\n", " if (H, W) != (576, 1024):\n", " print(\n", " \"WARNING: The conditioning frame you provided is not 576x1024. This leads to suboptimal performance as model was only trained on 576x1024. Consider increasing `cond_aug`.\"\n", " )\n", " if motion_bucket_id > 255:\n", " print(\n", " \"WARNING: High motion bucket! This may lead to suboptimal performance.\"\n", " )\n", "\n", " if fps_id < 5:\n", " print(\"WARNING: Small fps value! This may lead to suboptimal performance.\")\n", "\n", " if fps_id > 30:\n", " print(\"WARNING: Large fps value! This may lead to suboptimal performance.\")\n", "\n", " value_dict = {}\n", " value_dict[\"motion_bucket_id\"] = motion_bucket_id\n", " value_dict[\"fps_id\"] = fps_id\n", " value_dict[\"cond_aug\"] = cond_aug\n", " value_dict[\"cond_frames_without_noise\"] = image\n", " value_dict[\"cond_frames\"] = image + cond_aug * torch.randn_like(image)\n", " value_dict[\"cond_aug\"] = cond_aug\n", " # low vram mode\n", " model.conditioner.cpu()\n", " model.first_stage_model.cpu()\n", " torch.cuda.empty_cache()\n", " model.sampler.verbose = True\n", "\n", " with torch.no_grad():\n", " with torch.autocast(device):\n", " model.conditioner.to(device)\n", " batch, batch_uc = get_batch(\n", " get_unique_embedder_keys_from_conditioner(model.conditioner),\n", " value_dict,\n", " [1, num_frames],\n", " T=num_frames,\n", " device=device,\n", " )\n", " c, uc = model.conditioner.get_unconditional_conditioning(\n", " batch,\n", " batch_uc=batch_uc,\n", " force_uc_zero_embeddings=[\n", " \"cond_frames\",\n", " \"cond_frames_without_noise\",\n", " ],\n", " )\n", " model.conditioner.cpu()\n", " torch.cuda.empty_cache()\n", "\n", " # from here, dtype is fp16\n", " for k in [\"crossattn\", \"concat\"]:\n", " uc[k] = repeat(uc[k], \"b ... -> b t ...\", t=num_frames)\n", " uc[k] = rearrange(uc[k], \"b t ... -> (b t) ...\", t=num_frames)\n", " c[k] = repeat(c[k], \"b ... -> b t ...\", t=num_frames)\n", " c[k] = rearrange(c[k], \"b t ... -> (b t) ...\", t=num_frames)\n", " for k in uc.keys():\n", " uc[k] = uc[k].to(dtype=torch.float16)\n", " c[k] = c[k].to(dtype=torch.float16)\n", "\n", " randn = torch.randn(shape, device=device, dtype=torch.float16)\n", "\n", " additional_model_inputs = {}\n", " additional_model_inputs[\"image_only_indicator\"] = torch.zeros(\n", " 2, num_frames\n", " ).to(device, )\n", " additional_model_inputs[\"num_video_frames\"] = batch[\"num_video_frames\"]\n", "\n", " for k in additional_model_inputs:\n", " if isinstance(additional_model_inputs[k], torch.Tensor):\n", " additional_model_inputs[k] = additional_model_inputs[k].to(dtype=torch.float16)\n", "\n", " def denoiser(input, sigma, c):\n", " return model.denoiser(\n", " model.model, input, sigma, c, **additional_model_inputs\n", " )\n", "\n", " samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)\n", " samples_z.to(dtype=model.first_stage_model.dtype)\n", " ##\n", "\n", " model.en_and_decode_n_samples_a_time = decoding_t\n", " model.first_stage_model.to(device)\n", " samples_x = model.decode_first_stage(samples_z)\n", " samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)\n", " model.first_stage_model.cpu()\n", " torch.cuda.empty_cache()\n", "\n", " os.makedirs(output_folder, exist_ok=True)\n", " base_count = len(glob(os.path.join(output_folder, \"*.mp4\")))\n", " video_path = os.path.join(output_folder, f\"{base_count:06d}.mp4\")\n", " writer = cv2.VideoWriter(\n", " video_path,\n", " cv2.VideoWriter_fourcc(*\"MP4V\"),\n", " fps_id + 1,\n", " (samples.shape[-1], samples.shape[-2]),\n", " )\n", "\n", " samples = embed_watermark(samples)\n", " if not skip_filter:\n", " samples = filter(samples)\n", " else:\n", " print(\"WARNING: You have disabled the NSFW/Watermark filter. Please do not expose unfiltered results in services or applications open to the public.\")\n", " vid = (\n", " (rearrange(samples, \"t c h w -> t h w c\") * 255)\n", " .cpu()\n", " .numpy()\n", " .astype(np.uint8)\n", " )\n", " for frame in vid:\n", " frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)\n", " writer.write(frame)\n", " writer.release()\n", " all_out_paths.append(video_path)\n", " return all_out_paths\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5MdVILPlMUDe" }, "outputs": [], "source": [ "# @title Do the Run! markdown Generation takes about 10 mins for `svd_xt` on T4 (Colab free plan). Please be patient...\n", "import gradio as gr\n", "import random\n", "\n", "\n", "def infer(input_path: str, resize_image: bool, n_frames: int, n_steps: int, seed: str, decoding_t: int, fps_id: int, motion_bucket_id: int, cond_aug: float, skip_filter: bool = False) -> str:\n", " if seed == \"random\":\n", " seed = random.randint(0, 2**32)\n", " seed = int(seed)\n", " output_paths = sample(\n", " input_path=input_path,\n", " resize_image=resize_image,\n", " num_frames=n_frames,\n", " num_steps=n_steps,\n", " fps_id=fps_id,\n", " motion_bucket_id=motion_bucket_id,\n", " cond_aug=cond_aug,\n", " seed=seed,\n", " decoding_t=decoding_t, # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.\n", " device=device,\n", " skip_filter=skip_filter,\n", " )\n", " return output_paths[0]\n", "\n", "\n", "with gr.Blocks() as demo:\n", " with gr.Column():\n", " image = gr.Image(label=\"input image\", type=\"filepath\")\n", " resize_image = gr.Checkbox(label=\"resize to optimal size\", value=True)\n", " btn = gr.Button(\"Run\")\n", " with gr.Accordion(label=\"Advanced options\", open=False):\n", " n_frames = gr.Number(precision=0, label=\"number of frames\", value=num_frames)\n", " n_steps = gr.Number(precision=0, label=\"number of steps\", value=num_steps)\n", " seed = gr.Text(value=\"random\", label=\"seed (integer or 'random')\",)\n", " decoding_t = gr.Number(precision=0, label=\"number of frames decoded at a time\", value=2)\n", " fps_id = gr.Number(precision=0, label=\"frames per second\", value=6)\n", " motion_bucket_id = gr.Number(precision=0, value=127, label=\"motion bucket id\")\n", " cond_aug = gr.Number(label=\"condition augmentation factor\", value=0.02)\n", " skip_filter = gr.Checkbox(value=False, label=\"skip nsfw/watermark filter\")\n", " with gr.Column():\n", " video_out = gr.Video(label=\"generated video\")\n", " examples = [\n", " [\"https://user-images.githubusercontent.com/33302880/284758167-367a25d8-8d7b-42d3-8391-6d82813c7b0f.png\"]\n", " ]\n", " inputs = [image, resize_image, n_frames, n_steps, seed, decoding_t, fps_id, motion_bucket_id, cond_aug, skip_filter]\n", " outputs = [video_out]\n", " btn.click(infer, inputs=inputs, outputs=outputs)\n", " gr.Examples(examples=examples, inputs=inputs, outputs=outputs, fn=infer)\n", " demo.queue().launch(debug=True, share=True, show_error=True)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }