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| # Copyright 2023 ByteDance and/or its affiliates. | |
| # | |
| # Copyright (2023) MagicAnimate Authors | |
| # | |
| # ByteDance, its affiliates and licensors retain all intellectual | |
| # property and proprietary rights in and to this material, related | |
| # documentation and any modifications thereto. Any use, reproduction, | |
| # disclosure or distribution of this material and related documentation | |
| # without an express license agreement from ByteDance or | |
| # its affiliates is strictly prohibited. | |
| import argparse | |
| import imageio | |
| import numpy as np | |
| import gradio as gr | |
| import spaces | |
| import os | |
| from PIL import Image | |
| from subprocess import PIPE, run | |
| from demo.animate import MagicAnimate | |
| from huggingface_hub import snapshot_download | |
| snapshot_download(repo_id="runwayml/stable-diffusion-v1-5", local_dir="./stable-diffusion-v1-5") | |
| snapshot_download(repo_id="stabilityai/sd-vae-ft-mse", local_dir="./sd-vae-ft-mse") | |
| snapshot_download(repo_id="zcxu-eric/MagicAnimate", local_dir="./MagicAnimate") | |
| animator = MagicAnimate() | |
| def animate(reference_image, motion_sequence_state, seed=1, steps=25, guidance_scale=7.5): | |
| return animator(reference_image, motion_sequence_state, seed, steps, guidance_scale) | |
| with gr.Blocks() as demo: | |
| gr.HTML( | |
| """ | |
| <div style="display: flex; justify-content: center; align-items: center; text-align: center;"> | |
| <a href="https://github.com/magic-research/magic-animate" style="margin-right: 20px; text-decoration: none; display: flex; align-items: center;"> | |
| </a> | |
| <div> | |
| <h1 >MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model</h1> | |
| <h5 style="margin: 0;">If you like our project, please give us a star ✨ on Github for the latest update.</h5> | |
| <div style="display: flex; justify-content: center; align-items: center; text-align: center;> | |
| <a href="https://arxiv.org/abs/2311.16498"><img src="https://img.shields.io/badge/Arxiv-2311.16498-red"></a> | |
| <a href='https://showlab.github.io/magicanimate'><img src='https://img.shields.io/badge/Project_Page-MagicAnimate-green' alt='Project Page'></a> | |
| <a href='https://github.com/magic-research/magic-animate'><img src='https://img.shields.io/badge/Github-Code-blue'></a> | |
| </div> | |
| </div> | |
| </div> | |
| """) | |
| animation = gr.Video(format="mp4", label="Animation Results", autoplay=True) | |
| with gr.Row(): | |
| reference_image = gr.Image(label="Reference Image") | |
| motion_sequence = gr.Video(format="mp4", label="Motion Sequence",max_length=5) | |
| with gr.Column(): | |
| random_seed = gr.Textbox(label="Random seed", value=1, info="default: -1") | |
| sampling_steps = gr.Textbox(label="Sampling steps", value=25, info="default: 25") | |
| guidance_scale = gr.Textbox(label="Guidance scale", value=7.5, info="default: 7.5") | |
| submit = gr.Button("Animate") | |
| def read_video(video): | |
| reader = imageio.get_reader(video) | |
| fps = reader.get_meta_data()['fps'] | |
| return video | |
| def read_image(image, size=512): | |
| return np.array(Image.fromarray(image).resize((size, size))) | |
| # when user uploads a new video | |
| motion_sequence.upload( | |
| read_video, | |
| motion_sequence, | |
| motion_sequence, | |
| queue=False | |
| ) | |
| # when `first_frame` is updated | |
| reference_image.upload( | |
| read_image, | |
| reference_image, | |
| reference_image, | |
| queue=False | |
| ) | |
| # when the `submit` button is clicked | |
| submit.click( | |
| animate, | |
| [reference_image, motion_sequence, random_seed, sampling_steps, guidance_scale], | |
| animation | |
| ) | |
| cached_examples: dict[str, tuple[list[str], str]] = { | |
| "monalisa": (["inputs/applications/source_image/monalisa.png", "inputs/applications/driving/densepose/running.mp4"], "inputs/applications/output/monalisa.png"), | |
| "demo4": (["inputs/applications/source_image/demo4.png", "inputs/applications/driving/densepose/demo4.mp4"], "inputs/applications/output/demo4.png"), | |
| "dalle2.": (["inputs/applications/source_image/dalle2.jpeg", "inputs/applications/driving/densepose/running2.mp4"], "inputs/applications/output/dalle2.jpeg"), | |
| "dalle8.": (["inputs/applications/source_image/dalle8.jpeg", "inputs/applications/driving/densepose/dancing2.mp4"], "inputs/applications/output/dalle8.jpeg"), | |
| "multi1_source": (["inputs/applications/source_image/multi1_source.png", "inputs/applications/driving/densepose/multi_dancing.mp4"], "inputs/applications/output/multi1_source.png"), | |
| } | |
| # Examples | |
| gr.Markdown("## Examples") | |
| gr.Examples( | |
| fn=lambda image, video: cached_examples[image['path'].split('/')[-1].removesuffix('.png')], | |
| examples=[inputs for inputs, output in cached_examples.values()], | |
| inputs=[reference_image, motion_sequence], | |
| outputs=animation, | |
| cache_examples=True, | |
| preprocess=False, | |
| ) | |
| # demo.queue(max_size=15, api_open=False) | |
| demo.launch(share=True, show_api=False) | |