125 lines
4.1 KiB
Python
125 lines
4.1 KiB
Python
#!/usr/bin/env python
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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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from gradio.themes.utils import sizes
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theme = gr.themes.Default(radius_size=sizes.radius_none).set(
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block_label_text_color = '#4D63FF',
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block_title_text_color = '#4D63FF',
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button_primary_text_color = '#4D63FF',
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button_primary_background_fill='#FFFFFF',
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button_primary_border_color='#4D63FF',
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button_primary_background_fill_hover='#EDEFFF',
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)
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css = "footer {visibility: hidden}"
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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(theme=theme, css=css) as demo:
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gr.Markdown("""
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<div align='center' ><font size='60'>通过文本合成视频</font></div>
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""")
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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='输入提示',
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elem_id='prompt-text-input').style(container=False)
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run_button = gr.Button('生成视频').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('高级选项', 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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label="示例")
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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(server_name="0.0.0.0")
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