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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
from PIL import Image
from demo.animate import MagicAnimate
animator = MagicAnimate()
def animate(reference_image, motion_sequence_state, seed, steps, guidance_scale):
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")
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
)
# when `first_frame` is updated
reference_image.upload(
read_image,
reference_image,
reference_image
)
# when the `submit` button is clicked
submit.click(
animate,
[reference_image, motion_sequence, random_seed, sampling_steps, guidance_scale],
animation
)
# Examples
gr.Markdown("## Examples")
gr.Examples(
examples=[
["inputs/applications/source_image/monalisa.png", "inputs/applications/driving/densepose/running.mp4"],
["inputs/applications/source_image/demo4.png", "inputs/applications/driving/densepose/demo4.mp4"],
["inputs/applications/source_image/dalle2.jpeg", "inputs/applications/driving/densepose/running2.mp4"],
["inputs/applications/source_image/dalle8.jpeg", "inputs/applications/driving/densepose/dancing2.mp4"],
["inputs/applications/source_image/multi1_source.png", "inputs/applications/driving/densepose/multi_dancing.mp4"],
],
inputs=[reference_image, motion_sequence],
outputs=animation,
)
demo.launch(share=True) |