Tags: Diffusion Models - Paper Library

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Collaborative Diffusion Model for Recommender System

Published:5/8/2025

Diffusion ModelsGenerative Recommendation Systems

The CDiff4Rec model addresses key issues in diffusionbased recommender systems by generating pseudousers and utilizing collaborative signals, effectively reconstructing user preferences and surpassing existing models across three public datasets.

A Survey on Personalized Content Synthesis with Diffusion Models

Published:5/9/2024

Personalized Content SynthesisDiffusion ModelsTest-Time Fine-Tuning MethodsPre-Trained Adaptation MethodsObject Personalization

This paper surveys over 150 methods in personalized content synthesis (PCS) using diffusion models, categorizing them into testtime finetuning and pretrained adaptation frameworks, while addressing challenges like overfitting and proposing future research directions.

Consistency Models

Published:3/3/2023

Consistency ModelsDiffusion ModelsImage GenerationCIFAR-10 DatasetImage Inpainting and Colorization

This paper introduces consistency models to address the slow generation speed of diffusion models, enabling fast onestep generation and multistep sampling. They also support zeroshot data editing, outperforming existing techniques on benchmarks like CIFAR10.

HCMA: Hierarchical Cross-model Alignment for Grounded Text-to-Image Generation

Published:5/10/2025

Text-to-Image GenerationHierarchical Cross-Model AlignmentMultimodal GenerationMS-COCO DatasetDiffusion Models

The paper introduces the Hierarchical CrossModal Alignment (HCMA) framework, addressing the conflict between semantic fidelity and spatial control in texttoimage generation. HCMA combines global and local alignment modules to achieve highquality results in complex scenes, sur

Curriculum Conditioned Diffusion for Multimodal Recommendation

Published:4/11/2025

Multimodal Recommendation SystemsDiffusion ModelsKnowledge-Aware Negative SamplingCurriculum Learning FrameworkMultimodal Aligning Module

The Curriculum Conditioned Diffusion framework (CCDRec) addresses data sparsity in multimodal recommendation by integrating diffusion models with negative sampling, enhancing personalization through the exploration of modality correlation. Its effectiveness and robustness are val

CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Published:8/12/2024

Text-to-Video GenerationDiffusion ModelsDiffusion Transformer3D Variational AutoencoderVideo Generation Quality Improvement

CogVideoX is a largescale texttovideo model using a diffusion transformer that generates 10second videos at 16 fps and 768×1360 resolution. It addresses coherence and semantic alignment issues with methods like 3D VAE and expert transformers, achieving significant quality imp

Scalable Diffusion Models with Transformers

Published:12/20/2022

Diffusion ModelsTransformer architectureImage GenerationScalable Diffusion ModelsClass-Conditional Image Generation

This study introduces Diffusion Transformers (DiTs), which replace UNet with a transformer architecture for image generation. Higher Gflops correlate with better performance (lower FID), with the largest model achieving stateoftheart results on ImageNet benchmarks.

Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Published:11/26/2023

Diffusion ModelsVideo Generation ModelsText-to-Video GenerationHigh-Quality Video Fine-TuningVideo Dataset Curation

The paper presents Stable Video Diffusion (SVD), a model for highresolution texttovideo and imagetovideo generation. It evaluates a threestage training process and highlights the importance of wellcurated datasets for highquality video generation, demonstrating strong per

Taming Transformers for High-Resolution Image Synthesis

Published:12/18/2020

Generative Adversarial Policy OptimizationDiffusion ModelsImage Super-resolutionImage Synthesis

This study combines CNN's inductive bias with Transformer expressivity to synthesize highresolution images. It first learns a contextrich vocabulary of image constituents with CNNs, then models their composition using Transformers, achieving stateoftheart results in semantic

Denoising Diffusion Probabilistic Models

Published:6/20/2020

Diffusion ModelsImage SynthesisTraining-Free Acceleration MethodsCIFAR10 DatasetProgressive Lossy Decompression

The paper presents a novel denoising diffusion probabilistic model inspired by nonequilibrium thermodynamics, achieving highquality image synthesis. By training on a weighted variational bound, it establishes a new connection with denoising score matching, attaining competitive

DiffCL: A Diffusion-Based Contrastive Learning Framework with Semantic Alignment for Multimodal Recommendations

Published:1/2/2025

Multimodal Recommendation SystemsDiffusion ModelsContrastive Learning FrameworkSemantic AlignmentGraph-Based Feature Enhancement

DiffCL is a diffusionbased contrastive learning framework for multimodal recommendation that reduces noise via diffusiongenerated views, aligns crossmodal semantics with stable ID embeddings, and alleviates data sparsity using an itemitem graph, enhancing recommendation accur

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