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Found 3 papers, 3 papers with code
Diversity-aware Channel Pruning for StyleGAN Compression
Specifically, by assessing channel importance based on their sensitivities to latent vector perturbations, our method enhances the diversity of samples in the compressed model.
Towards Squeezing-Averse Virtual Try-On via Sequential Deformation
In this paper, we first investigate a visual quality degradation problem observed in recent high-resolution virtual try-on approach.
Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer
Despite the impressive generative capabilities of diffusion models, existing diffusion model-based style transfer methods require inference-stage optimization (e. g. fine-tuning or textual inversion of style) which is time-consuming, or fails to leverage the generative ability of large-scale diffusion models.
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