Spaces:
Running
on
Zero
Running
on
Zero
| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import os | |
| import random | |
| import tempfile | |
| import gradio as gr | |
| import imageio | |
| import numpy as np | |
| import spaces | |
| import torch | |
| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler | |
| DESCRIPTION = "# zeroscope v2" | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
| MAX_NUM_FRAMES = int(os.getenv("MAX_NUM_FRAMES", "200")) | |
| DEFAULT_NUM_FRAMES = min(MAX_NUM_FRAMES, int(os.getenv("DEFAULT_NUM_FRAMES", "24"))) | |
| MAX_SEED = np.iinfo(np.int32).max | |
| if torch.cuda.is_available(): | |
| 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() | |
| pipe.enable_vae_slicing() | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| def to_video(frames: np.ndarray, fps: int) -> str: | |
| frames = np.clip((frames * 255), 0, 255).astype(np.uint8) | |
| out_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) | |
| writer = imageio.get_writer(out_file.name, format="FFMPEG", fps=fps) | |
| for frame in frames: | |
| writer.append_data(frame) | |
| writer.close() | |
| return out_file.name | |
| def generate( | |
| prompt: str, | |
| seed: int, | |
| num_frames: int, | |
| num_inference_steps: int, | |
| progress=gr.Progress(track_tqdm=True), | |
| ) -> str: | |
| generator = torch.Generator().manual_seed(seed) | |
| frames = pipe( | |
| prompt, | |
| num_inference_steps=num_inference_steps, | |
| num_frames=num_frames, | |
| width=576, | |
| height=320, | |
| generator=generator, | |
| ).frames[0] | |
| return to_video(frames, 8) | |
| examples = [ | |
| ["An astronaut riding a horse", 0, 24, 25], | |
| ["A panda eating bamboo on a rock", 0, 24, 25], | |
| ["Spiderman is surfing", 0, 24, 25], | |
| ] | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.Markdown(DESCRIPTION) | |
| gr.DuplicateButton( | |
| value="Duplicate Space for private use", | |
| elem_id="duplicate-button", | |
| visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", | |
| ) | |
| with gr.Group(): | |
| with gr.Row(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| run_button = gr.Button("Generate video", scale=0) | |
| result = gr.Video(label="Result", show_label=False) | |
| with gr.Accordion("Advanced options", open=False): | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| num_frames = gr.Slider( | |
| label="Number of frames", | |
| minimum=24, | |
| maximum=MAX_NUM_FRAMES, | |
| step=1, | |
| value=24, | |
| info="Note that the content of the video also changes when you change the number of frames.", | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=10, | |
| maximum=50, | |
| step=1, | |
| value=25, | |
| ) | |
| inputs = [ | |
| prompt, | |
| seed, | |
| num_frames, | |
| num_inference_steps, | |
| ] | |
| gr.Examples( | |
| examples=examples, | |
| inputs=inputs, | |
| outputs=result, | |
| fn=generate, | |
| ) | |
| gr.on( | |
| triggers=[prompt.submit, run_button.click], | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=generate, | |
| inputs=inputs, | |
| outputs=result, | |
| api_name="run", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=10).launch() | |