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import gradio as gr
import numpy as np
from PIL import Image
import cv2
from moviepy.editor import VideoFileClip
from share_btn import community_icon_html, loading_icon_html, share_js
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import export_to_video
pipe_xl = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_XL", torch_dtype=torch.float16, revision="refs/pr/17")
pipe_xl.vae.enable_slicing()
pipe_xl.scheduler = DPMSolverMultistepScheduler.from_config(pipe_xl.scheduler.config)
pipe_xl.enable_model_cpu_offload()
pipe_xl.to("cuda")
def convert_mp4_to_frames(video_path):
# Read the video file
video = cv2.VideoCapture(video_path)
frames = []
# Iterate through each frame
while True:
# Read a frame
ret, frame = video.read()
# If the frame was not successfully read, then we have reached the end of the video
if not ret:
break
# Convert BGR to RGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Append the frame to the list of frames
frames.append(frame)
# Release the video object
video.release()
# Convert the list of frames to a numpy array
frames = np.array(frames)
return frames
def infer(prompt, video_in):
video = convert_mp4_to_frames(video_in)
video_resized = [Image.fromarray(frame).resize((1024, 576)) for frame in video]
video_frames = pipe_xl(prompt, video=video_resized, strength=0.6).frames
video_path = export_to_video(video_frames, output_video_path="xl_result.mp4")
return "xl_result.mp4", gr.Group.update(visible=True)
css = """
#col-container {max-width: 510px; margin-left: auto; margin-right: auto;}
a {text-decoration-line: underline; font-weight: 600;}
.animate-spin {
animation: spin 1s linear infinite;
}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
#share-btn-container {
display: flex;
padding-left: 0.5rem !important;
padding-right: 0.5rem !important;
background-color: #000000;
justify-content: center;
align-items: center;
border-radius: 9999px !important;
max-width: 13rem;
}
#share-btn-container:hover {
background-color: #060606;
}
#share-btn {
all: initial;
color: #ffffff;
font-weight: 600;
cursor:pointer;
font-family: 'IBM Plex Sans', sans-serif;
margin-left: 0.5rem !important;
padding-top: 0.5rem !important;
padding-bottom: 0.5rem !important;
right:0;
}
#share-btn * {
all: unset;
}
#share-btn-container div:nth-child(-n+2){
width: auto !important;
min-height: 0px !important;
}
#share-btn-container .wrap {
display: none !important;
}
#share-btn-container.hidden {
display: none!important;
}
img[src*='#center'] {
display: block;
margin: auto;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(
"""
<h1 style="text-align: center;">Zeroscope Text-to-Video</h1>
<p style="text-align: center;">
A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. <br />
</p>
[](https://huggingface.co/spaces/fffiloni/zeroscope?duplicate=true)
"""
)
video_in = gr.Video(type="numpy", source="upload")
prompt_in = gr.Textbox(label="Prompt", placeholder="Darth Vader is surfing on waves", elem_id="prompt-in")
#inference_steps = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=40, interactive=False)
submit_btn = gr.Button("Submit")
video_result = gr.Video(label="Video Output", elem_id="video-output")
with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
community_icon = gr.HTML(community_icon_html)
loading_icon = gr.HTML(loading_icon_html)
share_button = gr.Button("Share to community", elem_id="share-btn")
submit_btn.click(fn=infer,
inputs=[prompt_in, video_in],
outputs=[video_result, share_group])
share_button.click(None, [], [], _js=share_js)
demo.queue(max_size=12).launch()
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