# The Challenges of Image Generation Models in Generating Multi-Component Images

arXiv: https://arxiv.org/abs/2311.13620 · HTML: https://mlsanity.com/paper/2311.13620/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 37 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 30 of 31)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [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)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks](https://mlsanity.com/paper/1711.10485/) (2017)
- [Compositional Visual Generation with Composable Diffusion Models](https://mlsanity.com/paper/2206.01714/) (2022)
- [StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets](https://mlsanity.com/paper/2202.00273/) (2022)
- [CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers](https://mlsanity.com/paper/2204.14217/) (2022)
- [Generating Images with Sparse Representations](https://mlsanity.com/paper/2103.03841/) (2021)
- [How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models](https://mlsanity.com/paper/2102.08921/) (2021)
- [MirrorGAN: Learning Text-to-image Generation by Redescription](https://mlsanity.com/paper/1903.05854/) (2019)
- [Effectively Unbiased FID and Inception Score and where to find them](https://mlsanity.com/paper/1911.07023/) (2019)
- [High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling](https://mlsanity.com/paper/2005.11742/) (2020)
- [Specifying Object Attributes and Relations in Interactive Scene Generation](https://mlsanity.com/paper/1909.05379/) (2019)
- [Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation](https://mlsanity.com/paper/2304.01816/) (2023)
- [TIGEr: Text-to-Image Grounding for Image Caption Evaluation](https://mlsanity.com/paper/1909.02050/) (2019)
- [Dual Contradistinctive Generative Autoencoder](https://mlsanity.com/paper/2011.10063/) (2020)
- [Discovering the Hidden Vocabulary of DALLE-2](https://mlsanity.com/paper/2206.00169/) (2022)
- [Generating Multiple Objects at Spatially Distinct Locations](https://mlsanity.com/paper/1901.00686/) (2019)
- [Context-Aware Layout to Image Generation with Enhanced Object Appearance](https://mlsanity.com/paper/2103.11897/) (2021)
- [Image Inpainting Guided by Coherence Priors of Semantics and Textures](https://mlsanity.com/paper/2012.08054/) (2020)
- [Adversarial Attacks on Image Generation With Made-Up Words](https://mlsanity.com/paper/2208.04135/) (2022)

## Built on by (most cited first, 3 of 3)

- [Collaborative Control for Geometry-Conditioned PBR Image Generation](https://mlsanity.com/paper/2402.05919/) (2024)
- [From Noise to Nuance: Advances in Deep Generative Image Models](https://mlsanity.com/paper/2412.09656/) (2024)
- [Dressing the Imagination: A Dataset for AI-Powered Translation of Text into Fashion Outfits and A Novel NeRA Adapter for Enhanced Feature Adaptation](https://mlsanity.com/paper/2411.13901/) (2024)

## Abstract (arXiv)

Recent advances in text-to-image generators have led to substantial capabilities in image generation. However, the complexity of prompts acts as a bottleneck in the quality of images generated. A particular under-explored facet is the ability of generative models to create high-quality images comprising multiple components given as a prior. In this paper, we propose and validate a metric called Components Inclusion Score (CIS) to evaluate the extent to which a model can correctly generate multiple components. Our results reveal that the evaluated models struggle to incorporate all the visual elements from prompts with multiple components (8.53% drop in CIS per component for all evaluated models). We also identify a significant decline in the quality of the images and context awareness within an image as the number of components increased (15.91% decrease in inception Score and 9.62% increase in Frechet Inception Distance). To remedy this issue, we fine-tuned Stable Diffusion V2 on a custom-created test dataset with multiple components, outperforming its vanilla counterpart. To conclude, these findings reveal a critical limitation in existing text-to-image generators, shedding light on the challenge of generating multiple components within a single image using a complex prompt.