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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):
#prompt = "Darth Vader is surfing on waves"
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 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;}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(
"""
<h1 style="text-align: center;">Zeroscope Text-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 />
[](https://huggingface.co/spaces/fffiloni/zeroscope?duplicate=true)
</p>
"""
)
prompt_in = gr.Textbox(label="Prompt", placeholder="Darth Vader is surfing on waves", elem_id="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")
submit_btn.click(fn=infer,
inputs=[prompt_in],
outputs=[video_result, share_group])
demo.queue(max_size=12).launch()
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