Pros of neural-style (jcjohnson)

• More extensive documentation and usage instructions
• Supports multiple GPUs for faster processing
• Includes pre-trained VGG models for convenience

Cons of neural-style (jcjohnson)

• Less actively maintained (last update in 2018)
• Requires older versions of Torch and CUDA
• Limited to specific image sizes due to VGG network constraints

Code Comparison

neural-style (jcjohnson):

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')
cmd:option('-image_size', 512, 'Maximum height / width of generated image')

neural-style (anishathalye):

parser.add_argument('--content', type=str,
                    dest='content', help='content image',
                    metavar='CONTENT', required=True)
parser.add_argument('--styles', type=str,
                    dest='styles', nargs='+', help='style images',
                    metavar='STYLE', required=True)
parser.add_argument('--output', type=str,
                    dest='output', help='output path',
                    metavar='OUTPUT', required=True)

Both repositories implement neural style transfer, but they differ in programming language and command-line argument structure. The jcjohnson version uses Lua and Torch, while the anishathalye version is implemented in Python, making it more accessible to a wider range of developers.

Pros of fast-style-transfer

• Significantly faster processing time for style transfer
• Real-time style transfer capability for video streams
• Pre-trained models available for immediate use

Cons of fast-style-transfer

• Limited flexibility in adjusting style parameters
• Potentially lower quality output compared to neural-style
• Requires training a separate model for each style

Code Comparison

neural-style:

def stylize(content_image, style_image, content_weight, style_weight):
    # Optimization-based approach
    # Iteratively updates the output image
    # ...

fast-style-transfer:

def stylize(content_image, model):
    # Feed-forward network approach
    # Single pass through a pre-trained model
    # ...

The key difference is that neural-style uses an optimization-based approach, iteratively updating the output image, while fast-style-transfer uses a feed-forward network that can generate stylized images in a single pass through a pre-trained model.

neural-style offers more flexibility and potentially higher quality results but at the cost of longer processing times. fast-style-transfer sacrifices some quality and flexibility for significantly faster processing, making it suitable for real-time applications.

Pros of deep-photo-styletransfer

• Focuses on photorealistic style transfer, maintaining the structure and details of the original photo
• Implements a photorealistic smoothing step to reduce artifacts and improve output quality
• Utilizes semantic segmentation to better preserve content boundaries

Cons of deep-photo-styletransfer

• More complex implementation and setup compared to neural-style
• Requires additional inputs like segmentation masks for optimal results
• May have longer processing times due to the additional smoothing step

Code Comparison

neural-style:

def stylize(content_image, style_image, content_weight, style_weight):
    # Simple implementation using VGG19 for feature extraction
    model = vgg19.VGG19(weights='imagenet', include_top=False)
    # ... (content and style loss calculation)
    # Optimization step

deep-photo-styletransfer:

function [stylized_im] = deep_photo_style_transfer(content_im, style_im, content_seg, style_seg)
    % Photorealistic style transfer with segmentation and smoothing
    [feat_content, feat_style] = vgg19_forward(content_im, style_im);
    % ... (content and style loss calculation with segmentation)
    % Photorealistic smoothing step
    stylized_im = wls_smoothing(raw_output, content_im);
end

The code snippets highlight the differences in approach, with deep-photo-styletransfer incorporating segmentation and smoothing for more photorealistic results.

Pros of FastPhotoStyle

• Faster processing time for style transfer
• Better preservation of content structure and details
• Supports both photo-realistic and artistic style transfer

Cons of FastPhotoStyle

• Requires more computational resources (GPU)
• Less flexibility in style manipulation compared to Neural Style
• More complex setup and dependencies

Code Comparison

Neural Style:

content_image = imread(args.content_image)
style_image = imread(args.style_image)
network = build_vgg19(content_image)
content_features = get_content_features(network, content_image)
style_features = get_style_features(network, style_image)
output = optimize_image(content_features, style_features)

FastPhotoStyle:

content = load_image(args.content)
style = load_image(args.style)
content_seg = load_seg(args.content_seg)
style_seg = load_seg(args.style_seg)
output = stylize(content, style, content_seg, style_seg)

Both repositories focus on neural style transfer, but FastPhotoStyle aims for more photo-realistic results and faster processing. Neural Style offers more artistic flexibility but may be slower. FastPhotoStyle requires segmentation maps for optimal results, while Neural Style operates directly on images. The code structure reflects these differences, with FastPhotoStyle incorporating segmentation and Neural Style focusing on feature extraction and optimization.

Pros of neural-style-tf

• Implemented in TensorFlow, offering potential performance benefits and GPU acceleration
• Supports multiple style images for blended styles
• Includes additional features like color preservation and masked styling

Cons of neural-style-tf

• May require more setup due to TensorFlow dependencies
• Potentially more complex codebase for users unfamiliar with TensorFlow

Code Comparison

neural-style (Lua/Torch):

local img = image.load(params.content_image, 3)
local img = image.scale(img, params.image_size, 'bilinear')
local content_image = preprocess(img):float()

neural-style-tf (Python/TensorFlow):

content_image = scipy.misc.imread(content_image)
content_image = scipy.misc.imresize(content_image, (image_height, image_width))
content_image = content_image.astype(np.float32)

Both repositories implement the neural style transfer algorithm, but neural-style-tf offers a more modern TensorFlow implementation with additional features. The choice between them may depend on your familiarity with Lua/Torch vs. Python/TensorFlow and specific requirements for style transfer tasks.