LPH-VTON uses a single denoising process with staged handover from structure-biased to texture-biased diffusion models to improve both geometric alignment and textural fidelity in virtual try-on.
In: Proceedings of the IEEE/CVF International Conference on Computer Vision
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InstantID enables zero-shot identity-preserving image generation from one facial image via a novel IdentityNet that combines strong semantic and weak spatial conditioning with text prompts in diffusion models.
A wavelet diffusion GAN for SISR reduces diffusion timesteps via the diffusion GAN paradigm and applies DWT for dimensionality reduction, claiming faster training/inference and higher fidelity than prior methods on CelebA-HQ.
citing papers explorer
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LPH-VTON: Resolving the Structure-Texture Dilemma of Virtual Try-On via Latent Process Handover
LPH-VTON uses a single denoising process with staged handover from structure-biased to texture-biased diffusion models to improve both geometric alignment and textural fidelity in virtual try-on.
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InstantID: Zero-shot Identity-Preserving Generation in Seconds
InstantID enables zero-shot identity-preserving image generation from one facial image via a novel IdentityNet that combines strong semantic and weak spatial conditioning with text prompts in diffusion models.
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A Wavelet Diffusion GAN for Image Super-Resolution
A wavelet diffusion GAN for SISR reduces diffusion timesteps via the diffusion GAN paradigm and applies DWT for dimensionality reduction, claiming faster training/inference and higher fidelity than prior methods on CelebA-HQ.