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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 torch |
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from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler |
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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, |
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int(os.getenv('DEFAULT_NUM_FRAMES', '16'))) |
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pipe = DiffusionPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b', |
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torch_dtype=torch.float16, |
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variant='fp16') |
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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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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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def generate(prompt: str, seed: int, num_frames: int, |
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num_inference_steps: int) -> str: |
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if seed == -1: |
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seed = random.randint(0, 1000000) |
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generator = torch.Generator().manual_seed(seed) |
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frames = pipe(prompt, |
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num_inference_steps=num_inference_steps, |
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num_frames=num_frames, |
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generator=generator).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, 16, 25], |
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['A panda eating bamboo on a rock.', 0, 16, 25], |
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['Spiderman is surfing.', 0, 16, 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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with gr.Group(): |
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with gr.Box(): |
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with gr.Row(elem_id='prompt-container').style(equal_height=True): |
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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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elem_id='prompt-text-input').style(container=False) |
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run_button = gr.Button('Generate video').style( |
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full_width=False) |
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result = gr.Video(label='Result', show_label=False, elem_id='gallery') |
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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=-1, |
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maximum=1000000, |
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step=1, |
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value=-1, |
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info='If set to -1, a different seed will be used each time.') |
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num_frames = gr.Slider( |
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label='Number of frames', |
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minimum=16, |
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maximum=MAX_NUM_FRAMES, |
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step=1, |
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value=16, |
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info= |
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'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(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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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(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=os.getenv('SYSTEM') == 'spaces') |
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prompt.submit(fn=generate, inputs=inputs, outputs=result) |
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run_button.click(fn=generate, inputs=inputs, outputs=result) |
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demo.queue(api_open=False, max_size=15).launch() |
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