PixelHacker builds upon the latent diffusion architecture by introducing two fixed-size LCG embeddings to separately encode latent foreground and background features. The model employs linear attention to inject these latent features into the denoising process, enabling intermittent structural and semantic multiple interactions. This design encourages the model to learn a data distribution that is both structurally and semantically consistent, resulting in high-quality image inpainting with remarkable consistency in both structure and semantics.
PixelHacker has been extensively evaluated on a wide range of datasets, including Places, CelebA-HQ, and FFHQ, and has demonstrated comprehensive outperformance over state-of-the-art methods. The model's ability to learn a data distribution that is both structurally and semantically consistent makes it a valuable tool for image editing and generation applications. With its advanced features and capabilities, PixelHacker has the potential to revolutionize the field of image inpainting and generation.
