Spaces:
Sleeping
Sleeping
| #!/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 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.enable_model_cpu_offload() | |
| else: | |
| pipe = DiffusionPipeline.from_pretrained('cerspense/zeroscope_v2_576w') | |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
| 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: list[np.ndarray], fps: int) -> str: | |
| 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) -> 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 | |
| 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.Box(): | |
| 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, | |
| cache_examples=os.getenv('CACHE_EXAMPLES') == '1') | |
| prompt.submit( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| ).then( | |
| fn=generate, | |
| inputs=inputs, | |
| outputs=result, | |
| api_name='run', | |
| ) | |
| run_button.click( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| ).then( | |
| fn=generate, | |
| inputs=inputs, | |
| outputs=result, | |
| ) | |
| demo.queue(max_size=10).launch() | |