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import os
import cv2
import glob
import json
import datetime
import requests
import gradio as gr
from tool_for_end2end import *
os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
DATADIR = './temp'
_HEADER_ = '''
<div style="text-align: center; max-width: 650px; margin: 0 auto;">
<h1 style="font-size: 2.5rem; font-weight: 700; margin-bottom: 1rem; display: contents;">Tencent HunyuanVideo-Avatar Demo</h1>
</div>
'''
# flask url
URL = "http://127.0.0.1:80/predict2"
def post_and_get(audio_input, id_image, prompt):
now = datetime.datetime.now().isoformat()
imgdir = os.path.join(DATADIR, 'reference')
videodir = os.path.join(DATADIR, 'video')
imgfile = os.path.join(imgdir, now + '.png')
output_video_path = os.path.join(videodir, now + '.mp4')
os.makedirs(imgdir, exist_ok=True)
os.makedirs(videodir, exist_ok=True)
cv2.imwrite(imgfile, id_image[:,:,::-1])
proxies = {
"http": None,
"https": None,
}
files = {
"image_buffer": encode_image_to_base64(imgfile),
"audio_buffer": encode_wav_to_base64(audio_input),
"text": prompt,
"save_fps": 25,
}
r = requests.get(URL, data = json.dumps(files), proxies=proxies)
ret_dict = json.loads(r.text)
print(ret_dict["info"])
save_video_base64_to_local(
video_path=None,
base64_buffer=ret_dict["content"][0]["buffer"],
output_video_path=output_video_path)
return output_video_path
def create_demo():
with gr.Blocks() as demo:
gr.Markdown(_HEADER_)
with gr.Tab('语音数字人驱动'):
with gr.Row():
with gr.Column(scale=1):
with gr.Group():
prompt = gr.Textbox(label="Prompt", value="a man is speaking.")
audio_input = gr.Audio(sources=["upload"],
type="filepath",
label="Upload Audio",
elem_classes="media-upload",
scale=1)
id_image = gr.Image(label="Input reference image", height=480)
with gr.Column(scale=2):
with gr.Group():
output_image = gr.Video(label="Generated Video")
with gr.Column(scale=1):
generate_btn = gr.Button("Generate")
generate_btn.click(fn=post_and_get,
inputs=[audio_input, id_image, prompt],
outputs=[output_image],
)
# quick_prompts = [[x] for x in glob.glob('./assets/images/*.png')]
# example_quick_prompts = gr.Dataset(samples=quick_prompts, label='Other object', samples_per_page=1000, components=[id_image])
# example_quick_prompts.click(lambda x: x[0], inputs=example_quick_prompts, outputs=id_image, show_progress=False, queue=False)
# with gr.Row(), gr.Column():
# gr.Markdown("## Examples")
# example_inps = [
# [
# 'A woman is drinking coffee at a café.',
# './assets/images/seg_woman_01.png',
# 1280, 720, 30, 129, 7.5, 13, 1024,
# "assets/videos/seg_woman_01.mp4"
# ],
# [
# 'In a cubicle of an office building, a woman focuses intently on the computer screen, typing rapidly on the keyboard, surrounded by piles of documents.',
# './assets/images/seg_woman_03.png',
# 1280, 720, 30, 129, 7.5, 13, 1025,
# "./assets/videos/seg_woman_03.mp4"
# ],
# [
# 'A man walks across an ancient stone bridge holding an umbrella, raindrops tapping against it.',
# './assets/images/seg_man_01.png',
# 1280, 720, 30, 129, 7.5, 13, 1025,
# "./assets/videos/seg_man_01.mp4"
# ],
# [
# 'During a train journey, a man admires the changing scenery through the window.',
# './assets/images/seg_man_02.png',
# 1280, 720, 30, 129, 7.5, 13, 1026,
# "./assets/videos/seg_man_02.mp4"
# ]
# ]
# gr.Examples(examples=example_inps, inputs=[prompt, id_image, width, height, num_steps, num_frames, guidance, flow_shift, seed, output_image],)
return demo
if __name__ == "__main__":
allowed_paths = ['/']
demo = create_demo()
demo.launch(server_name='0.0.0.0', server_port=8080, share=True, allowed_paths=allowed_paths)
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