Enhancing Diffusion Models for High-Quality Image Generation

• URL: http://arxiv.org/abs/2412.14422v1
• Date: Thu, 19 Dec 2024 00:23:15 GMT
• Title: Enhancing Diffusion Models for High-Quality Image Generation
• Authors: Jaineet Shah, Michael Gromis, Rickston Pinto,
• Abstract summary: This report presents the comprehensive implementation, evaluation, and optimization of Denoising Diffusion Probabilistic Models (DDPMs) and Denoising Diffusion Implicit Models (DDIMs) During inference, these models take random noise as input and iteratively generate high-quality images as output. The motivation behind this work is the growing demand for efficient and scalable generative AI models that can produce realistic images across diverse datasets.
• Score: 0.0
• License:
• Abstract: This report presents the comprehensive implementation, evaluation, and optimization of Denoising Diffusion Probabilistic Models (DDPMs) and Denoising Diffusion Implicit Models (DDIMs), which are state-of-the-art generative models. During inference, these models take random noise as input and iteratively generate high-quality images as output. The study focuses on enhancing their generative capabilities by incorporating advanced techniques such as Classifier-Free Guidance (CFG), Latent Diffusion Models with Variational Autoencoders (VAE), and alternative noise scheduling strategies. The motivation behind this work is the growing demand for efficient and scalable generative AI models that can produce realistic images across diverse datasets, addressing challenges in applications such as art creation, image synthesis, and data augmentation. Evaluations were conducted on datasets including CIFAR-10 and ImageNet-100, with a focus on improving inference speed, computational efficiency, and image quality metrics like Frechet Inception Distance (FID). Results demonstrate that DDIM + CFG achieves faster inference and superior image quality. Challenges with VAE and noise scheduling are also highlighted, suggesting opportunities for future optimization. This work lays the groundwork for developing scalable, efficient, and high-quality generative AI systems to benefit industries ranging from entertainment to robotics.

Related papers

• Visual Autoregressive Modeling for Image Super-Resolution [14.935662351654601]
  We propose a novel visual autoregressive modeling for ISR framework with the form of next-scale prediction. We collect large-scale data and design a training process to obtain robust generative priors.
  arXiv  Detail & Related papers  (2025-01-31T09:53:47Z)
• Advancing Diffusion Models: Alias-Free Resampling and Enhanced Rotational Equivariance [0.0]
  diffusion models are still challenged by model-induced artifacts and limited stability in image fidelity. We propose the integration of alias-free resampling layers into the UNet architecture of diffusion models. Our experimental results on benchmark datasets, including CIFAR-10, MNIST, and MNIST-M, reveal consistent gains in image quality.
  arXiv  Detail & Related papers  (2024-11-14T04:23:28Z)
• YaART: Yet Another ART Rendering Technology [119.09155882164573]
  This study introduces YaART, a novel production-grade text-to-image cascaded diffusion model aligned to human preferences. We analyze how these choices affect both the efficiency of the training process and the quality of the generated images. We demonstrate that models trained on smaller datasets of higher-quality images can successfully compete with those trained on larger datasets.
  arXiv  Detail & Related papers  (2024-04-08T16:51:19Z)
• SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder [13.453138169497903]
  SeNM-VAE is a semi-supervised noise modeling method that leverages both paired and unpaired datasets to generate realistic degraded data. We employ our method to generate paired training samples for real-world image denoising and super-resolution tasks. Our approach excels in the quality of synthetic degraded images compared to other unpaired and paired noise modeling methods.
  arXiv  Detail & Related papers  (2024-03-26T09:03:40Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning [20.175586324567025]
  Mitigating catastrophic forgetting is a key hurdle in continual learning. A major issue is the deterioration in the quality of generated data compared to the original. We propose a GR-based approach for continual learning that enhances image quality in generators.
  arXiv  Detail & Related papers  (2023-12-10T17:39:42Z)
• Conditional Denoising Diffusion for Sequential Recommendation [62.127862728308045]
  Two prominent generative models, Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs) GANs suffer from unstable optimization, while VAEs are prone to posterior collapse and over-smoothed generations. We present a conditional denoising diffusion model, which includes a sequence encoder, a cross-attentive denoising decoder, and a step-wise diffuser.
  arXiv  Detail & Related papers  (2023-04-22T15:32:59Z)
• IRGen: Generative Modeling for Image Retrieval [82.62022344988993]
  In this paper, we present a novel methodology, reframing image retrieval as a variant of generative modeling. We develop our model, dubbed IRGen, to address the technical challenge of converting an image into a concise sequence of semantic units. Our model achieves state-of-the-art performance on three widely-used image retrieval benchmarks and two million-scale datasets.
  arXiv  Detail & Related papers  (2023-03-17T17:07:36Z)
• DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder [73.1010640692609]
  We propose a VQ-VAE architecture model with a diffusion decoder (DiVAE) to work as the reconstructing component in image synthesis. Our model achieves state-of-the-art results and generates more photorealistic images specifically.
  arXiv  Detail & Related papers  (2022-06-01T10:39:12Z)
• A Generic Approach for Enhancing GANs by Regularized Latent Optimization [79.00740660219256]
  We introduce a generic framework called em generative-model inference that is capable of enhancing pre-trained GANs effectively and seamlessly. Our basic idea is to efficiently infer the optimal latent distribution for the given requirements using Wasserstein gradient flow techniques.
  arXiv  Detail & Related papers  (2021-12-07T05:22:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.