CodeFormer

Pros of GFPGAN

• Faster inference time, especially for batch processing
• Better handling of extreme facial degradations
• More extensive pre-trained models available

Cons of GFPGAN

• Less flexibility in terms of customization options
• May produce over-smoothed results in some cases
• Limited to facial restoration, while CodeFormer has broader applications

Code Comparison

GFPGAN:

from gfpgan import GFPGANer

restorer = GFPGANer(model_path='experiments/pretrained_models/GFPGANv1.3.pth', upscale=2)
restored_img, _ = restorer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)

CodeFormer:

from basicsr.utils import img2tensor, tensor2img
from torchvision.transforms.functional import normalize

input_img = img2tensor(img / 255., bgr2rgb=True, float32=True)
normalize(input_img, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
with torch.no_grad():
    output = net(input_img.unsqueeze(0), w=w, adain=True)[0]
    restored_img = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))

Both repositories focus on facial restoration and enhancement, but they differ in their approaches and use cases. GFPGAN is generally faster and better suited for extreme degradations, while CodeFormer offers more flexibility and broader applications beyond facial restoration.

Pros of Real-ESRGAN

• Focuses on general image restoration and enhancement
• Provides pre-trained models for various use cases
• Offers both CPU and GPU support for inference

Cons of Real-ESRGAN

• Less specialized in face restoration compared to CodeFormer
• May produce artifacts in some cases, especially with faces
• Limited control over specific aspects of the restoration process

Code Comparison

CodeFormer:

from codeformer import CodeFormer
model = CodeFormer()
restored_img = model.restore(input_img, w=0.7)

Real-ESRGAN:

from realesrgan import RealESRGAN
model = RealESRGAN(device='cuda')
restored_img = model.predict(input_img)

Both repositories offer easy-to-use interfaces for image restoration. CodeFormer provides more control over the restoration process with its w parameter, while Real-ESRGAN focuses on simplicity and general-purpose enhancement. CodeFormer is more specialized for face restoration, whereas Real-ESRGAN is designed for broader image enhancement tasks.

Pros of Bringing-Old-Photos-Back-to-Life

• Specialized in restoring old and damaged photos
• Includes face enhancement and colorization features
• Comprehensive pipeline for handling various photo degradations

Cons of Bringing-Old-Photos-Back-to-Life

• Less versatile for general image restoration tasks
• May require more computational resources due to multiple processing steps
• Limited to specific use case of old photo restoration

Code Comparison

Bringing-Old-Photos-Back-to-Life:

from bringing_old_photos_back_to_life import restore_old_photo

restored_image = restore_old_photo(input_image)

CodeFormer:

from codeformer import restore_image

restored_image = restore_image(input_image, fidelity_weight=0.5)

CodeFormer offers a simpler API with a single function call and adjustable fidelity weight, while Bringing-Old-Photos-Back-to-Life likely involves multiple steps in its restoration pipeline.

CodeFormer is more versatile, handling various image restoration tasks beyond just old photos. It uses a unified model for multiple purposes, potentially offering better performance on a wider range of images. However, Bringing-Old-Photos-Back-to-Life may excel specifically in restoring old and damaged photos due to its specialized focus.

Pros of RePaint

• Focuses specifically on inpainting tasks, potentially offering more specialized results in this area
• Utilizes a diffusion-based approach, which can be effective for generating high-quality, diverse outputs
• Implements a novel "repaint" algorithm for improved inpainting results

Cons of RePaint

• More limited in scope compared to CodeFormer, focusing primarily on inpainting rather than multiple image restoration tasks
• May require more computational resources due to the iterative nature of diffusion models
• Less emphasis on face restoration specifically, which is a key feature of CodeFormer

Code Comparison

CodeFormer:

from basicsr.utils import imwrite, img2tensor, tensor2img
from torchvision.transforms.functional import normalize

img = img2tensor(img, bgr2rgb=True, float32=True)
normalize(img, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)

RePaint:

from PIL import Image
import numpy as np

image = np.array(Image.open(image_path))
mask = np.array(Image.open(mask_path).convert("L"))

The code snippets show different approaches to image processing and loading. CodeFormer uses PyTorch-based utilities for tensor conversion and normalization, while RePaint uses PIL and NumPy for image loading and manipulation.

Pros of IOPaint

• Offers a user-friendly web interface for inpainting tasks
• Supports multiple inpainting models, including LaMa and SD
• Provides real-time preview and adjustable brush sizes

Cons of IOPaint

• Limited to inpainting functionality, lacking face restoration features
• May require more manual input for complex image editing tasks
• Potentially slower processing times for large images

Code Comparison

IOPaint (Python):

@app.route("/inpaint", methods=["POST"])
def inpaint():
    image = request.files["image"]
    mask = request.files["mask"]
    model = request.form["model"]
    result = process_inpainting(image, mask, model)
    return jsonify({"result": result})

CodeFormer (Python):

def restore_face(img, codeformer, face_helper, device):
    cropped_faces, restored_faces, restored_img = face_helper.enhance(
        img, has_aligned=False, only_center_face=False, paste_back=True)
    return restored_img

The code snippets demonstrate the different focus areas of each project. IOPaint emphasizes web-based inpainting with multiple model options, while CodeFormer concentrates on face restoration functionality.