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import gradio as gr
from share_btn import community_icon_html, loading_icon_html, share_js
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video
pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
def infer(prompt):
negative_prompt = "text, watermark, copyright, blurry, nsfw"
video_frames = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
video_path = export_to_video(video_frames)
print(video_path)
return video_path, gr.Group.update(visible=True)
css = """
#col-container {max-width: 510px; margin-left: auto; margin-right: auto;}
a {text-decoration-line: underline; font-weight: 600;}
.animate-spin {
animation: spin 1s linear infinite;
}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
#share-btn-container {
display: flex;
padding-left: 0.5rem !important;
padding-right: 0.5rem !important;
background-color: #000000;
justify-content: center;
align-items: center;
border-radius: 9999px !important;
max-width: 13rem;
}
#share-btn-container:hover {
background-color: #060606;
}
#share-btn {
all: initial;
color: #ffffff;
font-weight: 600;
cursor:pointer;
font-family: 'IBM Plex Sans', sans-serif;
margin-left: 0.5rem !important;
padding-top: 0.5rem !important;
padding-bottom: 0.5rem !important;
right:0;
}
#share-btn * {
all: unset;
}
#share-btn-container div:nth-child(-n+2){
width: auto !important;
min-height: 0px !important;
}
#share-btn-container .wrap {
display: none !important;
}
#share-btn-container.hidden {
display: none!important;
}
img[src*='#center'] {
display: block;
margin: auto;
}
.footer {
margin-bottom: 45px;
margin-top: 10px;
text-align: center;
border-bottom: 1px solid #e5e5e5;
}
.footer>p {
font-size: .8rem;
display: inline-block;
padding: 0 10px;
transform: translateY(10px);
background: white;
}
.dark .footer {
border-color: #303030;
}
.dark .footer>p {
background: #0b0f19;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(
"""
<h1 style="text-align: center;">HedgehogAI</h1>
<p style="text-align: center;">
This is a demo version of HedgehogAI, this is a neural network for generating video from text in a fast mode, developed by the CofAI team!<br />
</p>
[](https://huggingface.co/spaces/CofAI/hedgehog?duplicate=true)
"""
)
prompt_in = gr.Textbox(label="Prompt", placeholder="Darth Vader is surfing on waves", elem_id="prompt-in")
#neg_prompt = gr.Textbox(label="Negative prompt", value="text, watermark, copyright, blurry, nsfw", elem_id="neg-prompt-in")
#inference_steps = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=40, interactive=False)
submit_btn = gr.Button("Submit")
video_result = gr.Video(label="Video Output", elem_id="video-output")
with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
community_icon = gr.HTML(community_icon_html)
loading_icon = gr.HTML(loading_icon_html)
share_button = gr.Button("Share to community", elem_id="share-btn")
gr.HTML("""
<div class="footer">
<p>
<a href="https://huggingface.co/cerspense/zeroscope_v2_576w" 🦔 HedgehogAI Text2Video V1.2 model</a> by ☕ CofAI
Demo by <a href="https://hf.co/OptorAI" target="_blank">🐻❄️ OptorAI</a>
</p>
</div>
<div id="may-like-container" style="display: flex;justify-content: center;flex-direction: column;align-items: center;">
<p style="font-size: 0.8em;margin-bottom: 4px;">Powered by: </p>
<div id="may-like" style="display:flex; align-items:center; justify-content: center;height:20px;">
<svg height="20" width="148" style="margin-left:4px">
<a href="https://huggingface.co/spaces/fffiloni/zeroscope-XL" target="_blank">
<image href="https://img.shields.io/badge/🤗 Spaces-Zeroscope XL-blue" src="https://img.shields.io/badge/🤗 Spaces-Image to Music-blue.png" height="20"/>
</a>
</svg>
</div>
</div>
""")
submit_btn.click(fn=infer,
inputs=[prompt_in],
outputs=[video_result, share_group])
share_button.click(None, [], [], _js=share_js)
demo.queue(max_size=12).launch()
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