Spaces:
Sleeping
Sleeping
File size: 4,448 Bytes
8eb5d1d 2cd1841 8eb5d1d 7642b02 8eb5d1d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
#!/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()
|