Spaces:
Paused
Paused
File size: 4,587 Bytes
b68324c f9cfa43 b68324c f9cfa43 b68324c f9cfa43 b68324c f9cfa43 b68324c f9cfa43 b68324c 01d5d14 |
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 |
#!/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 spaces
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
DESCRIPTION = 'This space is an API service meant to be used by VideoChain and VideoQuest.\nWant to use this space for yourself? Please use the original code: [https://huggingface.co/spaces/hysts/zeroscope-v2](https://huggingface.co/spaces/hysts/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
SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret')
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
@spaces.GPU
def generate(prompt: str, seed: int, num_frames: int,
num_inference_steps: int,
secret_token: str = '') -> str:
if secret_token != SECRET_TOKEN:
raise gr.Error(
f'Invalid secret token. Please fork the original space if you want to use it for yourself.')
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)
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
secret_token = gr.Text(
label='Secret Token',
max_lines=1,
placeholder='Enter your secret token',
)
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,
secret_token,
]
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=3).launch() |