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import gradio as gr
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, num_inference_steps):
#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
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.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
<div
style="
display: inline-flex;
align-items: center;
gap: 0.8rem;
font-size: 1.75rem;
"
>
<h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
Zeroscope Text-to-Video
</h1>
</div>
<p style="margin-bottom: 10px; font-size: 94%">
A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. <br />
This model was trained using 9,923 clips and 29,769 tagged frames at 24 frames, 576x320 resolution.
</p>
</div>""")
prompt_in = gr.Textbox(label="Prompt", placeholder="Darth Vader is surfing on waves")
inference_steps = gr.Slider(minimum=10, maximum=100, step=1, value=40, interactive=False)
submit_btn = gr.Button("Submit")
video_result = gr.Video(label="Video Output")
submit_btn.click(fn=infer,
inputs=[prompt_in, inference_steps],
outputs=[video_result])
demo.queue(max_size=12).launch()
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