Style Transfer with Diffusion Models

Module: Generative AI | Difficulty: Advanced

Diffusion Style Transfer

Gram Matrix Style Loss

where is the Gram matrix.

Prompt-Based Style Transfer

def gram_matrix(features):
    B, C, H, W = features.shape
    F = features.view(B, C, -1)
    G = torch.bmm(F, F.transpose(1, 2))
    return G / (C * H * W)

def style_loss(gen_feat, style_feat):
    return ((gram_matrix(gen_feat) - gram_matrix(style_feat))**2).mean()

Research Insight: Diffusion-based style transfer outperforms traditional methods because it uses learned perceptual features (from CLIP) rather than hand-crafted features (from VGG). However, it struggles with preserving spatial structure.