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import gradio as gr
import torch
from diffusers import DDColorPipeline
from PIL import Image
import numpy as np
import os
# Model loading with optimized settings
cache_dir = "./model_cache"
os.makedirs(cache_dir, exist_ok=True)
# Load model once at startup
pipe = DDColorPipeline.from_pretrained(
"camenduru/cv_ddcolor_image-colorization",
torch_dtype=torch.float16,
cache_dir=cache_dir
).to("cuda")
def colorize_image(input_image):
"""Process B&W image and return colorized version"""
# Ensure image is in grayscale mode
if input_image.mode != 'L':
input_image = input_image.convert('L')
# Resize to model's expected input size (based on DDColor paper)
target_size = (256, 256)
resized_image = input_image.resize(target_size)
# Convert to numpy array for pipeline input
grayscale_array = np.array(resized_image)
# Generate colorized image
with torch.inference_mode():
result = pipe(grayscale_array).images[0]
return result
# Custom CSS for vintage styling
custom_css = """
#output-image {max-width: 100%; border: 2px solid #ccc; border-radius: 8px;}
"""
# UI Layout
with gr.Blocks(theme="soft", css=custom_css) as demo:
gr.Markdown("""
# 📸 Vintage Photo Colorizer
Transform old black & white photos into vibrant color images using DDColor AI.
## How to Use
1. Upload a grayscale image (or color image will be converted to B&W)
2. Click "Colorize" to process
3. Download your new colorized photo!
""")
with gr.Row():
input_img = gr.Image(label="Upload Black & White Image", type="pil")
colorize_btn = gr.Button("🎨 Colorize Photo", variant="primary")
output_img = gr.Image(label="Colorized Image", elem_id="output-image")
colorize_btn.click(
fn=colorize_image,
inputs=[input_img],
outputs=[output_img]
)
gr.Markdown("""
### Powered by [DDColor](https://huggingface.co/papers/2212.11613 )
*Dual Decoders for Photo-Realistic Image Colorization*
""")
demo.launch() |