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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "fZ_xQvU70UQc"
},
"source": [
"# Tune-A-Video\n",
"\n",
"**[Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://arxiv.org/abs/2212.11565)** \n",
"[Jay Zhangjie Wu](https://zhangjiewu.github.io/), \n",
"[Yixiao Ge](https://geyixiao.com/), \n",
"[Xintao Wang](https://xinntao.github.io/), \n",
"[Stan Weixian Lei](), \n",
"[Yuchao Gu](https://ycgu.site/), \n",
"[Wynne Hsu](https://www.comp.nus.edu.sg/~whsu/), \n",
"[Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en), \n",
"[Xiaohu Qie](https://scholar.google.com/citations?user=mk-F69UAAAAJ&hl=en), \n",
"[Mike Zheng Shou](https://sites.google.com/view/showlab) \n",
"\n",
"[](https://tuneavideo.github.io/)\n",
"[](https://arxiv.org/abs/2212.11565)\n",
"[](https://huggingface.co/spaces/Tune-A-Video-library/Tune-A-Video-Training-UI)\n",
"[](https://github.com/showlab/Tune-A-Video)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wnTMyW41cC1E"
},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XU7NuMAA2drw"
},
"outputs": [],
"source": [
"#@markdown Check type of GPU and VRAM available.\n",
"!nvidia-smi --query-gpu=name,memory.total,memory.free --format=csv,noheader"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "D1PRgre3Gt5U"
},
"outputs": [],
"source": [
"#@title Install requirements\n",
"\n",
"!git clone https://github.com/showlab/Tune-A-Video.git /content/Tune-A-Video\n",
"%cd /content/Tune-A-Video \n",
"# %pip install -r requirements.txt\n",
"%pip install -q -U --pre triton\n",
"%pip install -q diffusers[torch]==0.11.1 transformers==4.26.0 bitsandbytes==0.35.4 \\\n",
"decord accelerate omegaconf einops ftfy gradio imageio-ffmpeg xformers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "m6I6kZNG3Inb"
},
"outputs": [],
"source": [
"#@title Download pretrained model\n",
"\n",
"#@markdown Name/Path of the initial model.\n",
"MODEL_NAME = \"CompVis/stable-diffusion-v1-4\" #@param {type:\"string\"}\n",
"\n",
"#@markdown If model should be download from a remote repo. Untick it if the model is loaded from a local path.\n",
"download_pretrained_model = True #@param {type:\"boolean\"}\n",
"if download_pretrained_model:\n",
" !git lfs install\n",
" !git clone https://huggingface.co/$MODEL_NAME checkpoints/$MODEL_NAME\n",
" MODEL_NAME = f\"./checkpoints/{MODEL_NAME}\"\n",
"print(f\"[*] MODEL_NAME={MODEL_NAME}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qn5ILIyDJIcX"
},
"source": [
"## Usage\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "REmFAHfz9Y_X"
},
"source": [
"### Training\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Rxg0y5MBudmd"
},
"outputs": [],
"source": [
"#@markdown If model weights should be saved directly in google drive (takes around 4-5 GB).\n",
"save_to_gdrive = False #@param {type:\"boolean\"}\n",
"if save_to_gdrive:\n",
" from google.colab import drive\n",
" drive.mount('/content/drive')\n",
"\n",
"#@markdown Enter the directory name to save model at.\n",
"\n",
"OUTPUT_DIR = \"outputs/man-skiing\" #@param {type:\"string\"}\n",
"if save_to_gdrive:\n",
" OUTPUT_DIR = \"/content/drive/MyDrive/\" + OUTPUT_DIR\n",
"\n",
"print(f\"[*] Weights will be saved at {OUTPUT_DIR}\")\n",
"\n",
"!mkdir -p $OUTPUT_DIR\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "32gYIDDR1aCp"
},
"outputs": [],
"source": [
"#@markdown Upload your video by running this cell.\n",
"\n",
"#@markdown OR\n",
"\n",
"#@markdown You can use the file manager on the left panel to upload (drag and drop) to `data` folder.\n",
"\n",
"import os\n",
"from google.colab import files\n",
"import shutil\n",
"\n",
"uploaded = files.upload()\n",
"for filename in uploaded.keys():\n",
" dst_path = os.path.join(\"data\", filename)\n",
" shutil.move(filename, dst_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wGGFFpNcR2d_"
},
"outputs": [],
"source": [
"#@markdown Train config\n",
"\n",
"from omegaconf import OmegaConf\n",
"\n",
"CONFIG_NAME = \"configs/man-skiing.yaml\" #@param {type:\"string\"}\n",
"\n",
"train_video_path = \"data/man-skiing.mp4\" #@param {type:\"string\"}\n",
"train_prompt = \"a man is skiing\" #@param {type:\"string\"}\n",
"video_length = 8 #@param {type:\"number\"}\n",
"width = 512 #@param {type:\"number\"}\n",
"height = 512 #@param {type:\"number\"}\n",
"learning_rate = 3e-5 #@param {type:\"number\"}\n",
"train_steps = 300 #@param {type:\"number\"}\n",
"\n",
"config = {\n",
" \"pretrained_model_path\": MODEL_NAME,\n",
" \"output_dir\": OUTPUT_DIR,\n",
" \"train_data\": {\n",
" \"video_path\": train_video_path,\n",
" \"prompt\": train_prompt,\n",
" \"n_sample_frames\": video_length,\n",
" \"width\": width,\n",
" \"height\": height,\n",
" \"sample_start_idx\": 0,\n",
" \"sample_frame_rate\": 2,\n",
" },\n",
" \"validation_data\": {\n",
" \"prompts\": [\n",
" \"mickey mouse is skiing on the snow\",\n",
" \"spider man is skiing on the beach, cartoon style\",\n",
" \"wonder woman, wearing a cowboy hat, is skiing\",\n",
" \"a man, wearing pink clothes, is skiing at sunset\",\n",
" ],\n",
" \"video_length\": video_length,\n",
" \"width\": width,\n",
" \"height\": height,\n",
" \"num_inference_steps\": 20,\n",
" \"guidance_scale\": 12.5,\n",
" \"use_inv_latent\": True,\n",
" \"num_inv_steps\": 50,\n",
" },\n",
" \"learning_rate\": learning_rate,\n",
" \"train_batch_size\": 1,\n",
" \"max_train_steps\": train_steps,\n",
" \"checkpointing_steps\": 1000,\n",
" \"validation_steps\": 100,\n",
" \"trainable_modules\": [\n",
" \"attn1.to_q\",\n",
" \"attn2.to_q\",\n",
" \"attn_temp\",\n",
" ],\n",
" \"seed\": 33,\n",
" \"mixed_precision\": \"fp16\",\n",
" \"use_8bit_adam\": False,\n",
" \"gradient_checkpointing\": True,\n",
" \"enable_xformers_memory_efficient_attention\": True,\n",
"}\n",
"\n",
"OmegaConf.save(config, CONFIG_NAME)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jjcSXTp-u-Eg"
},
"outputs": [],
"source": [
"!accelerate launch train_tuneavideo.py --config=$CONFIG_NAME"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ToNG4fd_dTbF"
},
"source": [
"### Inference"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "91bsSFv2Punm"
},
"outputs": [],
"source": [
"import torch\n",
"from torch import autocast\n",
"from diffusers import DDIMScheduler\n",
"from tuneavideo.pipelines.pipeline_tuneavideo import TuneAVideoPipeline\n",
"from tuneavideo.models.unet import UNet3DConditionModel\n",
"from tuneavideo.util import save_videos_grid\n",
"\n",
"\n",
"unet = UNet3DConditionModel.from_pretrained(OUTPUT_DIR, subfolder='unet', torch_dtype=torch.float16).to('cuda')\n",
"scheduler = DDIMScheduler.from_pretrained(MODEL_NAME, subfolder='scheduler')\n",
"pipe = TuneAVideoPipeline.from_pretrained(MODEL_NAME, unet=unet, scheduler=scheduler, torch_dtype=torch.float16).to(\"cuda\")\n",
"pipe.enable_xformers_memory_efficient_attention()\n",
"pipe.enable_vae_slicing()\n",
"\n",
"g_cuda = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "oIzkltjpVO_f"
},
"outputs": [],
"source": [
"#@markdown Can set random seed here for reproducibility.\n",
"g_cuda = torch.Generator(device='cuda')\n",
"seed = 1234 #@param {type:\"number\"}\n",
"g_cuda.manual_seed(seed)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "K6xoHWSsbcS3",
"scrolled": false
},
"outputs": [],
"source": [
"#@markdown Run for generating videos.\n",
"\n",
"prompt = \"iron man is skiing\" #@param {type:\"string\"}\n",
"negative_prompt = \"\" #@param {type:\"string\"}\n",
"use_inv_latent = True #@param {type:\"boolean\"}\n",
"inv_latent_path = \"\" #@param {type:\"string\"}\n",
"num_samples = 1 #@param {type:\"number\"}\n",
"guidance_scale = 12.5 #@param {type:\"number\"}\n",
"num_inference_steps = 50 #@param {type:\"number\"}\n",
"video_length = 8 #@param {type:\"number\"}\n",
"height = 512 #@param {type:\"number\"}\n",
"width = 512 #@param {type:\"number\"}\n",
"\n",
"ddim_inv_latent = None\n",
"if use_inv_latent and inv_latent_path == \"\":\n",
" from natsort import natsorted\n",
" from glob import glob\n",
" import os\n",
" inv_latent_path = natsorted(glob(f\"{OUTPUT_DIR}/inv_latents/*\"))[-1]\n",
" ddim_inv_latent = torch.load(inv_latent_path).to(torch.float16)\n",
" print(f\"DDIM inversion latent loaded from {inv_latent_path}\")\n",
"\n",
"with autocast(\"cuda\"), torch.inference_mode():\n",
" videos = pipe(\n",
" prompt, \n",
" latents=ddim_inv_latent,\n",
" video_length=video_length, \n",
" height=height, \n",
" width=width, \n",
" negative_prompt=negative_prompt,\n",
" num_videos_per_prompt=num_samples,\n",
" num_inference_steps=num_inference_steps, \n",
" guidance_scale=guidance_scale,\n",
" generator=g_cuda\n",
" ).videos\n",
"\n",
"save_dir = \"./results\" #@param {type:\"string\"}\n",
"save_path = f\"{save_dir}/{prompt}.gif\"\n",
"save_videos_grid(videos, save_path)\n",
"\n",
"# display\n",
"from IPython.display import Image, display\n",
"display(Image(filename=save_path))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jXgi8HM4c-DA"
},
"outputs": [],
"source": [
"#@markdown Free runtime memory\n",
"exit()"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.13-final"
},
"vscode": {
"interpreter": {
"hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
}
}
},
"nbformat": 4,
"nbformat_minor": 0
} |