# A Survey of Diffusion Based Image Generation Models: Issues and Their Solutions

arXiv: https://arxiv.org/abs/2308.13142 · HTML: https://mlsanity.com/paper/2308.13142/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 73 of its 83 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 73)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2304.05977/) (2023)

## Built on by (most cited first, 25 of 25)

- [Diffusion Model-Based Image Editing: A Survey](https://mlsanity.com/paper/2402.17525/) (2024)
- [ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning](https://mlsanity.com/paper/2405.19237/) (2024)
- [Diffusion Model-Based Video Editing: A Survey](https://mlsanity.com/paper/2407.07111/) (2024)
- [Diffusion Model for Planning: A Systematic Literature Review](https://mlsanity.com/paper/2408.10266/) (2024)
- [Generalizing Alignment Paradigm of Text-to-Image Generation with Preferences through f-divergence Minimization](https://mlsanity.com/paper/2409.09774/) (2024)
- [From Image to Video: An Empirical Study of Diffusion Representations](https://mlsanity.com/paper/2502.07001/) (2025)
- [What You See Is What Matters: A Novel Visual and Physics-Based Metric for Evaluating Video Generation Quality](https://mlsanity.com/paper/2411.13609/) (2024)
- [Physics-Inspired Generative Models in Medical Imaging: A Review](https://mlsanity.com/paper/2407.10856/) (2024)
- [Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data](https://mlsanity.com/paper/2404.16637/) (2024)
- [The Fabrication of Reality and Fantasy: Scene Generation with LLM-Assisted Prompt Interpretation](https://mlsanity.com/paper/2407.12579/) (2024)
- [Toward Scene Graph and Layout Guided Complex 3D Scene Generation](https://mlsanity.com/paper/2412.20473/) (2024)
- [TextDiffSeg: Text-guided Latent Diffusion Model for 3d Medical Images Segmentation](https://mlsanity.com/paper/2504.11825/) (2025)
- [REDEditing: Relationship-Driven Precise Backdoor Poisoning on Text-to-Image Diffusion Models](https://mlsanity.com/paper/2504.14554/) (2025)
- [FeatInv: Spatially resolved mapping from feature space to input space using conditional diffusion models](https://mlsanity.com/paper/2505.21032/) (2025)
- [FacEnhance: Facial Expression Enhancing with Recurrent DDPMs](https://mlsanity.com/paper/2406.09040/) (2024)
- [TextTIGER: Text-based Intelligent Generation with Entity Prompt Refinement for Text-to-Image Generation](https://mlsanity.com/paper/2504.18269/) (2025)
- [Range-Edit: Semantic Mask Guided Outdoor LiDAR Scene Editing](https://mlsanity.com/paper/2511.17269/) (2025)
- [AMVICC: A Novel Benchmark for Cross-Modal Failure Mode Profiling for VLMs and IGMs](https://mlsanity.com/paper/2601.17037/) (2026)
- [Track Anything Behind Everything: Zero-Shot Amodal Video Object Segmentation](https://mlsanity.com/paper/2411.19210/) (2024)
- [ASMR: Augmenting Life Scenario using Large Generative Models for Robotic Action Reflection](https://mlsanity.com/paper/2506.13956/) (2025)
- [Detecting AI-Generated Images via Diffusion Snap-Back Reconstruction: A Forensic Approach](https://mlsanity.com/paper/2511.00352/) (2025)
- [Polaris: Scaling Up Instruction-Guided Image Generation Towards Millions of Personalized Style Needs](https://mlsanity.com/paper/2606.01858/) (2026)
- [IMUG-Bench: Benchmarking Unified Multimodal Models on Interleaved Understanding and Generation](https://mlsanity.com/paper/2606.09169/) (2026)
- [When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors](https://mlsanity.com/paper/2610.13765/) (2026)
- [Your Discrete Graph Diffusion Model is Secretly Memorizing](https://mlsanity.com/paper/2610.13786/) (2026)

## Abstract (arXiv)

Recently, there has been significant progress in the development of large models. Following the success of ChatGPT, numerous language models have been introduced, demonstrating remarkable performance. Similar advancements have also been observed in image generation models, such as Google's Imagen model, OpenAI's DALL-E 2, and stable diffusion models, which have exhibited impressive capabilities in generating images. However, similar to large language models, these models still encounter unresolved challenges. Fortunately, the availability of open-source stable diffusion models and their underlying mathematical principles has enabled the academic community to extensively analyze the performance of current image generation models and make improvements based on this stable diffusion framework. This survey aims to examine the existing issues and the current solutions pertaining to image generation models.