Pros of neural-style

• Implemented in Lua and Torch, which can be more efficient for certain deep learning tasks
• Provides detailed documentation and examples for various style transfer techniques
• Supports multiple pre-trained models for different artistic styles

Cons of neural-style

• Limited to single-style transfer, lacking multi-style capabilities
• Requires Torch framework, which has a steeper learning curve compared to PyTorch
• Less active development and community support

Code Comparison

neural-style:

local cmd = torch.CmdLine()
cmd:option('-style_image', 'examples/inputs/seated-nude.jpg', 'Style target image')
cmd:option('-content_image', 'examples/inputs/tubingen.jpg', 'Content target image')
cmd:option('-output_image', 'out.png', 'Output image')

PyTorch-Multi-Style-Transfer:

parser = argparse.ArgumentParser()
parser.add_argument("--content", type=str, default="images/content/venice-boat.jpg")
parser.add_argument("--style", type=str, default="images/9styles.jpg")
parser.add_argument("--output", type=str, default="output.jpg")

PyTorch-Multi-Style-Transfer offers a more modern approach using Python and PyTorch, supporting multi-style transfer and providing easier integration with other deep learning projects. It benefits from PyTorch's dynamic computation graph and extensive ecosystem. However, neural-style may still be preferred for its established codebase and detailed documentation on various style transfer techniques.

Pros of fast-style-transfer

• Implemented in TensorFlow, which may be preferred by some users
• Includes pre-trained models for quick style transfer
• Supports video stylization

Cons of fast-style-transfer

• Limited to single-style transfer per model
• Less flexibility in customizing the style transfer process
• Older implementation (last updated in 2018)

Code Comparison

PyTorch-Multi-Style-Transfer:

style_model = Net(ngf=128)
style_model.load_state_dict(torch.load(args.model))
style_model.to(device)
style_model.eval()

fast-style-transfer:

with tf.Graph().as_default():
    soft_config = tf.ConfigProto(allow_soft_placement=True)
    soft_config.gpu_options.allow_growth = True
    with tf.Session(config=soft_config) as sess:
        batch_shape = (1,) + img.shape
        img_placeholder = tf.placeholder(tf.float32, shape=batch_shape, name='img_placeholder')

Summary

PyTorch-Multi-Style-Transfer offers more flexibility with multi-style transfer and a more recent implementation in PyTorch. fast-style-transfer, while older, provides pre-trained models and video stylization support in TensorFlow. The choice between them depends on the user's preferred framework, desired features, and the need for single or multi-style transfer capabilities.

Pros of deep-photo-styletransfer

• Focuses on photorealistic style transfer, preserving the structure of the original image
• Implements a photorealistic regularization term for improved results
• Provides detailed instructions for setup and usage

Cons of deep-photo-styletransfer

• Implemented in Lua and requires Torch, which is less popular than PyTorch
• Limited to single-style transfer, unlike PyTorch-Multi-Style-Transfer
• Slower processing time compared to PyTorch-Multi-Style-Transfer

Code Comparison

deep-photo-styletransfer:

local content_image = image.load(params.content_image, 3)
local style_image = image.load(params.style_image, 3)
local content_layers = params.content_layers:split(",")
local style_layers = params.style_layers:split(",")

PyTorch-Multi-Style-Transfer:

content_image = utils.tensor_load_rgbimage(args.content_image, size=args.content_size)
style = utils.tensor_load_rgbimage(args.style_image, size=args.style_size)
content_image = content_image.unsqueeze(0)
style = style.unsqueeze(0)

Both repositories provide implementations for neural style transfer, but they differ in their approach and focus. PyTorch-Multi-Style-Transfer offers a more flexible, multi-style approach using PyTorch, while deep-photo-styletransfer specializes in photorealistic style transfer using Lua and Torch.

Pros of FastPhotoStyle

• Faster processing time for style transfer
• Better preservation of content structure and details
• Supports high-resolution image processing

Cons of FastPhotoStyle

• Limited to photorealistic style transfer
• Requires more computational resources
• Less flexibility in artistic style manipulation

Code Comparison

PyTorch-Multi-Style-Transfer:

style_model = Net(ngf=128)
style_model.load_state_dict(torch.load(args.model))
style_model.eval()
output = style_model(content_image, style_image)

FastPhotoStyle:

from photo_style import stylize
output = stylize(content_image, style_image, 
                 content_size=1024, 
                 style_size=1024,
                 alpha=1.0)

The PyTorch-Multi-Style-Transfer code uses a custom neural network model for style transfer, while FastPhotoStyle employs a specialized function for photorealistic style transfer. FastPhotoStyle's approach is more streamlined but less customizable.

FastPhotoStyle is optimized for photorealistic results and faster processing, making it suitable for high-resolution images and applications requiring quick turnaround. However, it may not be ideal for more artistic or abstract style transfers.

PyTorch-Multi-Style-Transfer offers more flexibility in style manipulation and can handle a wider range of artistic styles, but it may be slower and less effective at preserving detailed content structure in photorealistic scenarios.