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import gradio as gr
import cv2
import numpy as np
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image
# Load the anime-style diffusion model
pipe = AutoPipelineForImage2Image.from_pretrained(
"nitrosocke/Arcane-Diffusion",
safety_checker=None,
)
pipe.to("cuda")
# Function to process a single frame
def process_frame(frame, prompt):
# Convert frame from BGR (OpenCV format) to RGB (expected by the model)
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Load the frame as an image for the diffusion model
image = load_image(frame_rgb)
# Apply the anime-style transformation
result = pipe(prompt=prompt, image=image, strength=0.75).images[0]
# Convert back to numpy array
return np.array(result)
# Function to convert the entire video
def video_to_anime(video_path, prompt="Arcane style"):
# Read the input video using OpenCV
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
frames = []
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frames.append(frame)
cap.release()
# Process each frame to anime style
processed_frames = [process_frame(frame, prompt) for frame in frames]
# Write the output video using OpenCV
height, width, _ = processed_frames[0].shape
fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Codec for MP4
output_path = "output.mp4"
out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
for frame in processed_frames:
# Convert back to BGR for OpenCV
frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
out.write(frame_bgr)
out.release()
return output_path
# Create the Gradio interface
iface = gr.Interface(
fn=video_to_anime,
inputs=[
gr.Video(label="Input Video"),
gr.Textbox(label="Style Prompt", default="Arcane style")
],
outputs=gr.Video(label="Output Video"),
title="Video to Anime Converter",
description="Upload a video and convert it to anime style!"
)
# Launch the interface
iface.launch() |