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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 /> | |
| [](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() | |