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Save time, resources and money with Latent Diffusion based image generation.

This article shows a novel approach to training a generative model for image generation at reduced training times using latents and using a pre-trained ImageNet latent classifier as a component of the loss function.

The image generation model was trained from an initialised (not pre-trained) state remarkably was less than 10 hours on a single desktop consumer NVIDIA card.

The article delves into a novel approach to training generative models for image generation with reduced training times by leveraging latent representations and perceptual latent loss. It highlights the use of a pre-trained ImageNet latent classifier within the loss function to train a diffusion model. This method encodes over 14 million ImageNet images into latent representations using a Variational Autoencoder (VAE), dramatically reducing computational overhead. The latent classification model is trained to refine these representations, and the activations from this model are incorporated into a U-Net diffusion model to iteratively generate high-quality images from noise. The article demonstrates the efficiency and effectiveness of this technique using examples like celebrity and bedroom images, with significant improvements over traditional methods.

The concept of perceptual latent loss is emphasized as a key innovation. Unlike conventional loss functions like MSE or MAE, this method incorporates activations from a pre-trained classifier into the loss function to achieve higher-quality outputs. Denoising Diffusion Implicit Models (DDIM) are employed for faster iterative refinement compared to the traditional Denoising Diffusion Probabilistic Models (DDPM). By progressively denoising latent representations, the model navigates from random noise back to the manifold of plausible images. This approach requires fewer steps, making it computationally efficient. The integration of DDIM and perceptual latent loss enhances the generative model's ability to produce visually coherent and detailed images.

Lastly, the article explores the broader implications of this technique, particularly in how latent representations improve memory and computation efficiency while maintaining image quality. The methodology’s success is showcased through comparisons between models trained with and without perceptual latent loss, with the former producing significantly better results. By introducing latent activations into the perceptual loss function and leveraging U-Net architectures, the approach bridges the gap between computational efficiency and high-quality image generation. This work sets a foundation for further innovations in generative AI, particularly in optimizing the training processes and enhancing output quality with limited computational resources.

Read the full article here:

Latent Diffusion and Perceptual Latent Loss

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