FastPhotoStyle

Pros of deep-photo-styletransfer

• Produces high-quality results with detailed preservation of content structure
• Offers more control over the stylization process through various parameters
• Suitable for academic research and experimentation in photo-realistic style transfer

Cons of deep-photo-styletransfer

• Slower processing time compared to FastPhotoStyle
• Requires more computational resources and technical expertise to set up and use
• Less user-friendly for non-technical users or those seeking quick results

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

FastPhotoStyle:

from photo_style import stylize
content_image = load_image(args.content)
style_image = load_image(args.style)
output = stylize(content_image, style_image)

FastPhotoStyle offers a more streamlined API and easier integration, while deep-photo-styletransfer provides more granular control over the process. FastPhotoStyle is generally faster and more accessible for practical applications, whereas deep-photo-styletransfer may be preferred for research or when fine-tuning the style transfer process is required.

Pros of fast-style-transfer

• Simpler implementation, making it easier for beginners to understand and use
• Faster training process, allowing for quicker experimentation with different styles
• Supports both image and video stylization

Cons of fast-style-transfer

• Generally produces lower quality results compared to FastPhotoStyle
• Less control over the stylization process and output
• Limited to a single style per model, requiring separate training for each style

Code Comparison

FastPhotoStyle:

from photo_wct import PhotoWCT
p_wct = PhotoWCT()
p_wct.load_state_dict(torch.load('models/photo_wct.pth'))
stylized = p_wct.transfer(content, style, csF=1.0)

fast-style-transfer:

import style
stylizer = style.StyleTransfer(model_path)
stylized = stylizer.transfer_style(content_image)

Both repositories focus on neural style transfer, but FastPhotoStyle offers more advanced features and generally produces higher quality results at the cost of increased complexity. fast-style-transfer is simpler to use and faster to train, making it more accessible for beginners or those looking for quick stylization results.

Pros of fast-neural-style

• Simpler implementation, making it easier to understand and modify
• Faster training and inference times for basic style transfer tasks
• Supports both training new models and using pre-trained models

Cons of fast-neural-style

• Limited to artistic style transfer, lacking photorealistic capabilities
• Less advanced features compared to FastPhotoStyle
• May produce lower quality results for complex style transfers

Code Comparison

FastPhotoStyle:

from photo_style import stylize
stylized = stylize(content, style, content_masks, style_masks)

fast-neural-style:

local style_img = image.load('examples/inputs/starry_night.jpg', 3)
local content_img = image.load('examples/inputs/stanford_quad.jpg', 3)
local output = net:forward({content_img, style_img})

FastPhotoStyle offers a more streamlined API with mask support, while fast-neural-style uses a Lua-based implementation with separate content and style inputs. FastPhotoStyle's approach is more suitable for photorealistic style transfer, whereas fast-neural-style is better for artistic style transfer.

Pros of AdaIN-style

• Faster training and inference times due to simpler architecture
• More flexible in handling various artistic styles
• Produces smoother and more coherent stylized images

Cons of AdaIN-style

• Less photorealistic results compared to FastPhotoStyle
• May struggle with preserving fine details in complex scenes
• Limited control over specific style transfer parameters

Code Comparison

AdaIN-style:

def calc_mean_std(feat, eps=1e-5):
    size = feat.size()
    assert (len(size) == 4)
    N, C = size[:2]
    feat_var = feat.view(N, C, -1).var(dim=2) + eps
    feat_std = feat_var.sqrt().view(N, C, 1, 1)
    feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
    return feat_mean, feat_std

FastPhotoStyle:

void PhotoWCT::Whiten(const float* ContentIN, float* ContentOUT, const int N, const int C, const int H, const int W)
{
    const int HW = H * W;
    Eigen::MatrixXf Content = Eigen::Map<const Eigen::MatrixXf>(ContentIN, C, HW);
    Eigen::JacobiSVD<Eigen::MatrixXf> svd(Content, Eigen::ComputeThinU | Eigen::ComputeThinV);
    Eigen::MatrixXf WhitenContent = svd.matrixU() * Eigen::MatrixXf::Identity(C, HW);
    Eigen::Map<Eigen::MatrixXf>(ContentOUT, C, HW) = WhitenContent;
}