Pros of GPEN

• Focuses on face restoration and enhancement, offering more specialized results for facial images
• Provides pre-trained models for different resolutions, allowing flexibility in output quality
• Includes a user-friendly GUI for easier use by non-technical users

Cons of GPEN

• Limited to face-specific applications, unlike AnimeGANv2's broader anime-style transformation
• Requires more computational resources due to its complex architecture
• Less customizable for artistic style transfer compared to AnimeGANv2

Code Comparison

GPEN:

from gpen import FaceRestoration

model = FaceRestoration(model_path='path/to/model.pth')
restored_face = model.process(input_image)

AnimeGANv2:

from AnimeGANv2 import AnimeGANv2

model = AnimeGANv2(checkpoint='path/to/checkpoint')
anime_image = model.convert(input_image)

Both repositories provide easy-to-use interfaces for their respective tasks. GPEN focuses on face restoration with a single process method, while AnimeGANv2 offers a convert method for anime-style transformation. GPEN's code structure is more oriented towards face-specific processing, whereas AnimeGANv2 is designed for general image conversion to anime style.

Pros of Real-ESRGAN

• More versatile, capable of enhancing both real-world images and anime-style content
• Offers better performance on a wider range of image types, including heavily compressed or low-quality images
• Provides pre-trained models for various use cases, making it easier to get started

Cons of Real-ESRGAN

• Requires more computational resources due to its more complex architecture
• May introduce artifacts or over-smoothing in some cases, especially with extreme upscaling factors

Code Comparison

Real-ESRGAN:

from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer

model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
upsampler = RealESRGANer(scale=4, model_path='weights/RealESRGAN_x4plus.pth', model=model, tile=0, tile_pad=10, pre_pad=0)

AnimeGANv2:

from net import generator
import tensorflow as tf

checkpoint_dir = './checkpoint/generator_Hayao_weight'
generator = generator.Generator()
ckpt = tf.train.Checkpoint(generator=generator)
ckpt.restore(tf.train.latest_checkpoint(checkpoint_dir)).expect_partial()

Both repositories focus on image enhancement, but Real-ESRGAN is more versatile and generally performs better on a wider range of images. AnimeGANv2 is specifically designed for anime-style image generation and may produce better results for that particular use case.

Pros of ailab

• More comprehensive AI lab with multiple projects and tools
• Larger community and corporate backing from Bilibili
• Broader scope beyond just anime-style image generation

Cons of ailab

• Less focused on anime-specific image generation
• May have a steeper learning curve due to broader scope
• Documentation might be more complex or less tailored to anime enthusiasts

Code Comparison

AnimeGANv2:

def load_test_data(image_path, size=[256,256]):
    img = cv2.imread(image_path).astype(np.float32)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = preprocessing(img, size)
    img = np.expand_dims(img, axis=0)
    return img

ailab:

def preprocess_image(image_path, target_size=(224, 224)):
    img = Image.open(image_path)
    img = img.resize(target_size)
    img_array = np.array(img) / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    return img_array

Both repositories provide image preprocessing functions, but AnimeGANv2 is more tailored for anime-style conversion, while ailab's function is more general-purpose for various AI tasks.

Pros of first-order-model

• Focuses on motion transfer and animation, allowing for more dynamic results
• Supports a wider range of input types, including videos and images
• Has a more extensive documentation and usage examples

Cons of first-order-model

• Requires more computational resources and training time
• May produce less consistent results across different input types
• Has a steeper learning curve for implementation and fine-tuning

Code Comparison

first-order-model:

from demo import load_checkpoints
generator, kp_detector = load_checkpoints(config_path='config/vox-256.yaml', 
                                          checkpoint_path='vox-cpk.pth.tar')

AnimeGANv2:

from net import generator
G = generator.Generator()
G.load_state_dict(torch.load('weights/generator.pth', map_location=device))

The first-order-model code demonstrates loading checkpoints for both the generator and keypoint detector, while AnimeGANv2 focuses on loading the generator weights. This reflects the different approaches and complexities of the two projects.

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

• Focuses on restoring and enhancing old, damaged photos
• Provides a complete pipeline for photo restoration, including face enhancement
• Offers pre-trained models for immediate use

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

• More complex setup and dependencies compared to AnimeGANv2
• Limited to photo restoration, not suitable for stylization or animation

Code Comparison

AnimeGANv2:

def conv2d(inputs, filters, kernel_size=3, strides=1, padding='SAME', name='conv'):
    return tf.layers.conv2d(inputs=inputs, filters=filters, kernel_size=kernel_size, strides=strides,
                            padding=padding, name=name, kernel_initializer=tf.contrib.layers.xavier_initializer())

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

def conv_block(input, num_filters, kernel_size, stride, padding='same', use_bias=True):
    x = Conv2D(num_filters, kernel_size=kernel_size, strides=stride, padding=padding, use_bias=use_bias)(input)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)
    return x

Both projects use convolutional layers, but Bringing-Old-Photos-Back-to-Life includes batch normalization and LeakyReLU activation, which may contribute to better stability and performance in photo restoration tasks.

AnimeGANv2 is more focused on stylization and anime-style image generation, while Bringing-Old-Photos-Back-to-Life specializes in photo restoration and enhancement. The choice between them depends on the specific use case and desired output.

Pros of ESRGAN

• More versatile, capable of enhancing various types of images beyond anime
• Provides pre-trained models for different use cases
• Extensive documentation and examples for implementation

Cons of ESRGAN

• Requires more computational resources for training and inference
• May produce artifacts in some cases, especially with extreme upscaling

Code Comparison

AnimeGANv2:

def load_test_data(image_path, size):
    img = cv2.imread(image_path).astype(np.float32)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = preprocessing(img, size)
    return img

ESRGAN:

def preprocess(img):
    img = np.transpose(img, (2, 0, 1))
    img = torch.from_numpy(img).float()
    img = img.unsqueeze(0)
    return img

Both repositories focus on image enhancement, but AnimeGANv2 specializes in transforming real-world images into anime-style artwork, while ESRGAN is designed for general image super-resolution. AnimeGANv2 may be more suitable for specific anime-related projects, whereas ESRGAN offers broader applicability across various image types and resolutions.