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#!/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 | |
MAX_NUM_FRAMES = int(os.getenv('MAX_NUM_FRAMES', '200')) | |
DEFAULT_NUM_FRAMES = min(MAX_NUM_FRAMES, | |
int(os.getenv('DEFAULT_NUM_FRAMES', '16'))) | |
pipe = DiffusionPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b', | |
torch_dtype=torch.float16, | |
variant='fp16') | |
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
pipe.enable_model_cpu_offload() | |
pipe.enable_vae_slicing() | |
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: | |
if seed == -1: | |
seed = random.randint(0, 1000000) | |
generator = torch.Generator().manual_seed(seed) | |
frames = pipe(prompt, | |
num_inference_steps=num_inference_steps, | |
num_frames=num_frames, | |
generator=generator).frames | |
return to_video(frames, 8) | |
examples = [ | |
['An astronaut riding a horse.', 0, 16, 25], | |
['A panda eating bamboo on a rock.', 0, 16, 25], | |
['Spiderman is surfing.', 0, 16, 25], | |
] | |
with gr.Blocks(css='style.css') as demo: | |
gr.Markdown(DESCRIPTION) | |
with gr.Group(): | |
with gr.Box(): | |
with gr.Row(elem_id='prompt-container').style(equal_height=True): | |
prompt = gr.Text( | |
label='Prompt', | |
show_label=False, | |
max_lines=1, | |
placeholder='Enter your prompt', | |
elem_id='prompt-text-input').style(container=False) | |
run_button = gr.Button('Generate video').style( | |
full_width=False) | |
result = gr.Video(label='Result', show_label=False, elem_id='gallery') | |
with gr.Accordion('Advanced options', open=False): | |
seed = gr.Slider( | |
label='Seed', | |
minimum=-1, | |
maximum=1000000, | |
step=1, | |
value=-1, | |
info='If set to -1, a different seed will be used each time.') | |
num_frames = gr.Slider( | |
label='Number of frames', | |
minimum=16, | |
maximum=MAX_NUM_FRAMES, | |
step=1, | |
value=16, | |
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('SYSTEM') == 'spaces') | |
prompt.submit(fn=generate, inputs=inputs, outputs=result) | |
run_button.click(fn=generate, inputs=inputs, outputs=result) | |
demo.queue(api_open=False, max_size=15).launch() | |