Spaces:
Runtime error
Runtime error
File size: 3,701 Bytes
62f5cce adfea12 fc652c4 62f5cce fc652c4 62f5cce fc652c4 965498b 6b357fe 62f5cce fc652c4 62f5cce fc652c4 62f5cce fc652c4 adfea12 fc652c4 62f5cce fc652c4 62f5cce a9c0e29 62f5cce fc652c4 62f5cce e2e7f03 6b357fe 25423bd e2e7f03 dd86f00 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 |
#!/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()
|