Tariq Berrada — accepted AI-conference papers

2025

Boosting Latent Diffusion with Perceptual Objectives

Tariq Berrada, Pietro Astolfi, Melissa Hall, Marton Havasi, Yohann Benchetrit, Adriana Romero-Soriano +3

ICLR 2025poster

Latent diffusion models (LDMs) power state-of-the-art high-resolution generative image models. LDMs learn the data distribution in the latent space of an autoencoder (AE) and produce images by mapping the generated latents into RGB image space using the AE decoder. While this approach allows for eff…

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2025

Entropy Rectifying Guidance for Diffusion and Flow Models

Tariq Berrada, Adriana Romero-Soriano, Michal Drozdzal, Jakob Verbeek, Karteek Alahari

NeurIPS 2025poster

Guidance techniques are commonly used in diffusion and flow models to improve image quality and input consistency for conditional generative tasks such as class-conditional and text-to-image generation. In particular, classifier-free guidance (CFG) is the most widely adopted guidance technique. It r…

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2024

On improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models

Tariq Berrada, Pietro Astolfi, Melissa Hall, Reyhane Askari Hemmat, Yohann Benchetrit, Marton Havasi +5

NeurIPS 2024poster

Large-scale training of latent diffusion models (LDMs) has enabled unprecedented quality in image generation. However, large-scale end-to-end training of these models is computationally costly, and hence most research focuses either on finetuning pretrained models or experiments at smaller scales…

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