|
|
|
|
|
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>' |
|
if (SPACE_ID := os.getenv('SPACE_ID')) is not None: |
|
DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></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) |
|
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() |
|
|
