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from __future__ import annotations |
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import os |
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import random |
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import tempfile |
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import gradio as gr |
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import imageio |
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import numpy as np |
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import spaces |
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import torch |
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from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler |
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DESCRIPTION = "# zeroscope v2" |
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if not torch.cuda.is_available(): |
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" |
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MAX_NUM_FRAMES = int(os.getenv("MAX_NUM_FRAMES", "200")) |
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DEFAULT_NUM_FRAMES = min(MAX_NUM_FRAMES, int(os.getenv("DEFAULT_NUM_FRAMES", "24"))) |
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MAX_SEED = np.iinfo(np.int32).max |
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CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1" |
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if torch.cuda.is_available(): |
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pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dtype=torch.float16) |
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pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) |
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pipe.enable_model_cpu_offload() |
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pipe.enable_vae_slicing() |
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else: |
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pipe = None |
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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return seed |
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def to_video(frames: list[np.ndarray], fps: int) -> str: |
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out_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) |
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writer = imageio.get_writer(out_file.name, format="FFMPEG", fps=fps) |
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for frame in frames: |
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writer.append_data(frame) |
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writer.close() |
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return out_file.name |
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@spaces.GPU |
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def generate( |
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prompt: str, |
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seed: int, |
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num_frames: int, |
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num_inference_steps: int, |
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) -> str: |
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generator = torch.Generator().manual_seed(seed) |
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frames = pipe( |
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prompt, |
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num_inference_steps=num_inference_steps, |
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num_frames=num_frames, |
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width=576, |
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height=320, |
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generator=generator, |
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).frames |
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return to_video(frames, 8) |
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examples = [ |
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["An astronaut riding a horse", 0, 24, 25], |
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["A panda eating bamboo on a rock", 0, 24, 25], |
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["Spiderman is surfing", 0, 24, 25], |
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] |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION) |
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gr.DuplicateButton( |
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value="Duplicate Space for private use", |
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elem_id="duplicate-button", |
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visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", |
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) |
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with gr.Box(): |
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with gr.Row(): |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt", |
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container=False, |
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) |
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run_button = gr.Button("Generate video", scale=0) |
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result = gr.Video(label="Result", show_label=False) |
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with gr.Accordion("Advanced options", open=False): |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=0, |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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num_frames = gr.Slider( |
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label="Number of frames", |
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minimum=24, |
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maximum=MAX_NUM_FRAMES, |
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step=1, |
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value=24, |
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info="Note that the content of the video also changes when you change the number of frames.", |
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) |
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num_inference_steps = gr.Slider( |
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label="Number of inference steps", |
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minimum=10, |
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maximum=50, |
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step=1, |
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value=25, |
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) |
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inputs = [ |
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prompt, |
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seed, |
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num_frames, |
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num_inference_steps, |
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] |
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gr.Examples( |
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examples=examples, |
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inputs=inputs, |
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outputs=result, |
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fn=generate, |
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cache_examples=CACHE_EXAMPLES, |
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) |
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prompt.submit( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=generate, |
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inputs=inputs, |
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outputs=result, |
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api_name="run", |
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) |
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run_button.click( |
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fn=randomize_seed_fn, |
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inputs=[seed, randomize_seed], |
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outputs=seed, |
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queue=False, |
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api_name=False, |
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).then( |
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fn=generate, |
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inputs=inputs, |
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outputs=result, |
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api_name=False, |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=10).launch() |
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