iccv23-diffusers-demo/zeroscope-v2

1#!/usr/bin/env python2 3from __future__ import annotations4 5import os6import random7import tempfile8 9import gradio as gr10import imageio11import numpy as np12import spaces13import torch14from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler15 16DESCRIPTION = "# zeroscope v2"17if not torch.cuda.is_available():18    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"19 20MAX_NUM_FRAMES = int(os.getenv("MAX_NUM_FRAMES", "200"))21DEFAULT_NUM_FRAMES = min(MAX_NUM_FRAMES, int(os.getenv("DEFAULT_NUM_FRAMES", "24")))22MAX_SEED = np.iinfo(np.int32).max23CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"24 25if torch.cuda.is_available():26    pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16)27    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)28    pipe.enable_model_cpu_offload()29    pipe.enable_vae_slicing()30else:31    pipe = None32 33 34def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:35    if randomize_seed:36        seed = random.randint(0, MAX_SEED)37    return seed38 39 40def to_video(frames: list[np.ndarray], fps: int) -> str:41    out_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)42    writer = imageio.get_writer(out_file.name, format="FFMPEG", fps=fps)43    for frame in frames:44        writer.append_data(frame)45    writer.close()46    return out_file.name47 48 49@spaces.GPU50def generate(51    prompt: str,52    seed: int,53    num_frames: int,54    num_inference_steps: int,55) -> str:56    generator = torch.Generator().manual_seed(seed)57    frames = pipe(58        prompt,59        num_inference_steps=num_inference_steps,60        num_frames=num_frames,61        width=576,62        height=320,63        generator=generator,64    ).frames65    return to_video(frames, 8)66 67 68examples = [69    ["An astronaut riding a horse", 0, 24, 25],70    ["A panda eating bamboo on a rock", 0, 24, 25],71    ["Spiderman is surfing", 0, 24, 25],72]73 74with gr.Blocks(css="style.css") as demo:75    gr.Markdown(DESCRIPTION)76    gr.DuplicateButton(77        value="Duplicate Space for private use",78        elem_id="duplicate-button",79        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",80    )81    with gr.Box():82        with gr.Row():83            prompt = gr.Text(84                label="Prompt",85                show_label=False,86                max_lines=1,87                placeholder="Enter your prompt",88                container=False,89            )90            run_button = gr.Button("Generate video", scale=0)91        result = gr.Video(label="Result", show_label=False)92        with gr.Accordion("Advanced options", open=False):93            seed = gr.Slider(94                label="Seed",95                minimum=0,96                maximum=MAX_SEED,97                step=1,98                value=0,99            )100            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)101            num_frames = gr.Slider(102                label="Number of frames",103                minimum=24,104                maximum=MAX_NUM_FRAMES,105                step=1,106                value=24,107                info="Note that the content of the video also changes when you change the number of frames.",108            )109            num_inference_steps = gr.Slider(110                label="Number of inference steps",111                minimum=10,112                maximum=50,113                step=1,114                value=25,115            )116 117    inputs = [118        prompt,119        seed,120        num_frames,121        num_inference_steps,122    ]123    gr.Examples(124        examples=examples,125        inputs=inputs,126        outputs=result,127        fn=generate,128        cache_examples=CACHE_EXAMPLES,129    )130 131    prompt.submit(132        fn=randomize_seed_fn,133        inputs=[seed, randomize_seed],134        outputs=seed,135        queue=False,136        api_name=False,137    ).then(138        fn=generate,139        inputs=inputs,140        outputs=result,141        api_name="run",142    )143    run_button.click(144        fn=randomize_seed_fn,145        inputs=[seed, randomize_seed],146        outputs=seed,147        queue=False,148        api_name=False,149    ).then(150        fn=generate,151        inputs=inputs,152        outputs=result,153        api_name=False,154    )155 156if __name__ == "__main__":157    demo.queue(max_size=10).launch()158