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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()
caption = gr.load(name="spaces/fffiloni/CoCa-clone")
def create_image_caption(image_init):
cap = caption(image_init, "Nucleus sampling", 1.2, 0.5, 5, 20, fn_index=0)
print("cap: " + cap)
return cap
def infer(image_init):
prompt = create_image_caption(image_init)
video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
video_path = export_to_video(video_frames)
print(video_path)
return prompt, 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;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(
"""
<h1 style="text-align: center;">Zeroscope Image-to-Video</h1>
<p style="text-align: center;">
A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. <br />
This demo is a variation that lets you upload an image as reference for video generation.
</p>
[](https://huggingface.co/spaces/fffiloni/zeroscope-img-to-video?duplicate=true)
"""
)
image_init = gr.Image(label="Image Init",type="filepath", source="upload", elem_id="image-init")
#inference_steps = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=40, interactive=False)
submit_btn = gr.Button("Submit")
coca_cap = gr.Textbox(label="Caption", placeholder="CoCa Caption will be displayed here", elem_id="coca-cap-in")
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")
submit_btn.click(fn=infer,
inputs=[image_init],
outputs=[coca_cap, video_result, share_group])
share_button.click(None, [], [], _js=share_js)
demo.queue(max_size=12).launch()
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