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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): | |
#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.Markdown( | |
""" | |
<h1 style="text-align: center;">Zeroscope Text-to-Video</h1> | |
A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. <br /> | |
This zeroscope_v2_576w model was trained using 9,923 clips and 29,769 tagged frames at 24 frames, 576x320 resolution.<br /> | |
[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg)](https://huggingface.co/spaces/fffiloni/zeroscope?duplicate=true) | |
""" | |
) | |
prompt_in = gr.Textbox(label="Prompt", placeholder="Darth Vader is surfing on waves") | |
#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") | |
submit_btn.click(fn=infer, | |
inputs=[prompt_in], | |
outputs=[video_result]) | |
demo.queue(max_size=12).launch() | |