Pros of neural-style

• More versatile, capable of applying various artistic styles beyond photo-realistic transfers
• Simpler implementation, making it easier to understand and modify
• Wider community adoption and support

Cons of neural-style

• Less effective at preserving photorealistic details in the output
• May produce more artifacts and unrealistic textures in the stylized image
• Slower processing time for high-resolution images

Code Comparison

neural-style:

local content_image = image.load(params.content_image, 3)
local style_image = image.load(params.style_image, 3)
local content_layers = params.content_layers or {21}
local style_layers = params.style_layers or {2, 7, 12, 17, 22}

deep-photo-styletransfer:

content_image = imread(content_image_path);
style_image = imread(style_image_path);
content_seg = imread(content_seg_path);
style_seg = imread(style_seg_path);

The code snippets show that neural-style uses Lua and focuses on specifying content and style layers, while deep-photo-styletransfer uses MATLAB and includes segmentation maps for more precise control over the style transfer process.

Pros of fast-style-transfer

• Significantly faster processing time, allowing for real-time style transfer
• Supports video style transfer
• Easier to set up and use, with fewer dependencies

Cons of fast-style-transfer

• Generally lower quality results compared to deep-photo-styletransfer
• Less control over the style transfer process
• Limited to pre-trained styles, while deep-photo-styletransfer allows for more customization

Code Comparison

deep-photo-styletransfer:

net = loadjson('models/vgg19.json');
net = vl_simplenn_tidy(net);
content = imread('input/in1.png');
style = imread('style/tar1.png');

fast-style-transfer:

import tensorflow as tf
from style import stylize
content_image = tf.io.read_file('input/content.jpg')
stylized_image = stylize(content_image, 'models/wave.ckpt')

The code snippets show that deep-photo-styletransfer uses MATLAB and requires more setup, while fast-style-transfer uses Python and TensorFlow, offering a more streamlined approach. deep-photo-styletransfer loads both content and style images separately, allowing for more flexibility, whereas fast-style-transfer uses pre-trained style models for quicker processing.

Pros of FastPhotoStyle

• Significantly faster processing time, enabling real-time style transfer
• Improved preservation of content structure and details
• Support for high-resolution images

Cons of FastPhotoStyle

• May produce less artistic results compared to Deep Photo Style Transfer
• Requires more computational resources (GPU) for optimal performance

Code Comparison

Deep Photo Style Transfer:

def wct_core(cont_feat, styl_feat):
    cFSize = cont_feat.size()
    c_mean = torch.mean(cont_feat,1) 
    c_mean = c_mean.unsqueeze(1).expand_as(cont_feat)
    cont_feat = cont_feat - c_mean

FastPhotoStyle:

def wct_core(content, style, alpha=1.0):
    c_c, c_h, c_w = content.size(0), content.size(1), content.size(2)
    content = content.view(c_c, -1)
    c_mean = torch.mean(content, 1, keepdim=True)
    content = content - c_mean

Both repositories implement the Whitening and Coloring Transform (WCT) algorithm, but FastPhotoStyle's implementation is optimized for speed and efficiency. The core functionality remains similar, with slight differences in tensor manipulation and dimension handling.

Pros of Deep-Image-Analogy

• Focuses on semantic-aware image analogy, allowing for more precise style transfer between semantically similar regions
• Supports bidirectional style transfer, enabling both A→B and B→A transformations
• Utilizes a coarse-to-fine approach, resulting in more detailed and refined outputs

Cons of Deep-Image-Analogy

• May require more computational resources due to its complex architecture and bidirectional processing
• Limited to transferring styles between images with similar semantic content, potentially reducing versatility
• Might produce less artistic or abstract results compared to deep-photo-styletransfer

Code Comparison

Deep-Image-Analogy:

[nn_field_A2B, nn_field_B2A] = NNF_Search(A_feats, B_feats, params);
[A_prime, B_prime] = Reconstruct(A, B, nn_field_A2B, nn_field_B2A);

deep-photo-styletransfer:

local content_image = image.load(params.content_image, 3)
local style_image = image.load(params.style_image, 3)
local output_image = stylize(content_image, style_image, params)

The code snippets highlight the different approaches: Deep-Image-Analogy focuses on bidirectional nearest neighbor field search and reconstruction, while deep-photo-styletransfer uses a more straightforward stylization process.

Pros of pytorch-CycleGAN-and-pix2pix

• Supports multiple image-to-image translation tasks, including style transfer, object transfiguration, and season transfer
• Implements both CycleGAN and pix2pix models, offering more flexibility for different use cases
• Built with PyTorch, providing easier customization and integration with modern deep learning workflows

Cons of pytorch-CycleGAN-and-pix2pix

• May produce less photorealistic results for style transfer compared to deep-photo-styletransfer
• Requires paired datasets for pix2pix, which can be more challenging to obtain than single style images

Code Comparison

deep-photo-styletransfer (Lua/Torch):

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

pytorch-CycleGAN-and-pix2pix (Python/PyTorch):

class CycleGANModel(BaseModel):
    def __init__(self, opt):
        BaseModel.__init__(self, opt)
        self.loss_names = ['D_A', 'G_A', 'cycle_A', 'idt_A', 'D_B', 'G_B', 'cycle_B', 'idt_B']
        self.visual_names = ['real_A', 'fake_B', 'rec_A', 'real_B', 'fake_A', 'rec_B']

Pros of style-transfer

• Simpler implementation, making it easier to understand and modify
• Faster processing time for style transfer
• More flexible in terms of input image types and styles

Cons of style-transfer

• Less photorealistic results compared to deep-photo-styletransfer
• May produce artifacts or distortions in the output image
• Limited control over specific style transfer parameters

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
local style_layers = params.style_layers

style-transfer:

content_image = scipy.misc.imread(args.content_image)
style_image = scipy.misc.imread(args.style_image)
content_layer = 'conv4_2'
style_layers = ['conv1_1', 'conv2_1', 'conv3_1', 'conv4_1', 'conv5_1']

The code snippets show differences in image loading and layer selection. deep-photo-styletransfer uses Lua and allows for more customizable layer selection, while style-transfer uses Python and has a simpler, predefined layer structure.