colab-enterprise/gen-ai-demo/Video-Generation-GenAI.ipynb (3,895 lines of code) (raw):
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"##################################################################################\n",
"# Copyright 2024 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License.\n",
"###################################################################################"
]
},
{
"cell_type": "markdown",
"id": "RFQISQhBaSVD",
"metadata": {
"id": "RFQISQhBaSVD"
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"source": [
"# Data beans video feed generation\n",
"\n",
"This notebook generate synthetic videos for the truck video feed.\n",
" - Iterates over the `COND_IMAGES_DIR`, create the videos and put the resulting file on the `VIDEO_GEN_PATH` folder.\n",
" - This notebook requires `CUDA` (GPU)"
]
},
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"source": [
"! pip install diffusers\n",
"! pip install transformers\n",
"! pip install accelerate\n",
"! pip3 install torch torchvision torchaudio\n",
"! pip3 install opencv-python"
]
},
{
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"source": [
"# Restart kernel after installs so that your environment can access the new packages\n",
"import IPython\n",
"import time\n",
"\n",
"app = IPython.Application.instance()\n",
"app.kernel.do_shutdown(True)"
]
},
{
"cell_type": "code",
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"source": [
"import torch\n",
"import os\n",
"import shutil\n",
"from diffusers import StableVideoDiffusionPipeline\n",
"from diffusers.utils import load_image, export_to_video\n",
"from pathlib import Path"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ROUwk_KZbPPg",
"metadata": {
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"height": 193
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"source": [
"#Ensure GPU is available\n",
"assert torch.cuda.is_available()\n",
"torch.cuda.empty_cache()"
]
},
{
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"COND_IMAGES_DIR = \"images\"\n",
"VIDEO_GEN_PATH = \"videos\"\n",
"os.makedirs(COND_IMAGES_DIR, exist_ok=True)\n",
"os.makedirs(VIDEO_GEN_PATH, exist_ok=True)"
]
},
{
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"outputId": "f5e990bd-0d66-415b-8ca4-f01ec420cc01"
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"outputs": [],
"source": [
"from google.colab import files\n",
"uploaded = files.upload()"
]
},
{
"cell_type": "code",
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"id": "EXIklILsduq6",
"metadata": {
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"source": [
"for filename in uploaded.keys():\n",
" shutil.copy(filename, COND_IMAGES_DIR)"
]
},
{
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"def _gen_video(cond_image, video_path):\n",
" pipe = StableVideoDiffusionPipeline.from_pretrained(\"stabilityai/stable-video-diffusion-img2vid-xt\", torch_dtype=torch.float16, variant=\"fp16\")\n",
" pipe.enable_model_cpu_offload()\n",
" pipe.unet.enable_forward_chunking()\n",
"\n",
" image = load_image(cond_image)\n",
" image = image.resize((476, 476))\n",
"\n",
" generator = torch.manual_seed(42)\n",
" frames = pipe(image, decode_chunk_size=8, generator=generator).frames[0]\n",
"\n",
" export_to_video(frames, video_path, fps=25)\n"
]
},
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"source": [
"def generate_videos():\n",
" for image in os.listdir(COND_IMAGES_DIR):\n",
" if not image.startswith(\".\"):\n",
" image_filename = os.path.join(COND_IMAGES_DIR,image)\n",
" video_filename = os.path.join(VIDEO_GEN_PATH,os.path.splitext(image)[0] + \".mp4\")\n",
" _gen_video(image_filename,video_filename)"
]
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