# Hierarchical Text-Conditional Image Generation with CLIP Latents

**Authors:** Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen  
**arXiv:** [2204.06125](https://arxiv.org/abs/2204.06125)  
**Date:** 2022-04-13  
**Summary page:** https://www.summarizepaper.com/en/paper/2204.06125v1/

## Key points

- Authors explore the use of contrastive models like CLIP for image generation
- Propose a two-stage model with a prior generating CLIP image embeddings and a decoder producing images based on these embeddings
- Explicitly generating image representations enhances diversity, photorealism, and caption similarity
- Decoders can generate variations preserving semantics and style while altering non-essential details
- Joint embedding space of CLIP enables language-guided image manipulations in a zero-shot manner
- Utilize diffusion models for the decoder and experiment with autoregressive and diffusion models for the prior, finding diffusion more efficient with higher-quality samples
- Compare their model unCLIP to GLIDE in terms of photorealism, caption similarity, and sample diversity through human evaluations
- Emphasize human judgment in assessing generation capabilities beyond metrics like FID
- Showcase samples from unCLIP at varying guidance scales to demonstrate quality improvements without compromising semantic diversity

## Summary

In their paper titled "Hierarchical Text-Conditional Image Generation with CLIP Latents," authors Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen explore the use of contrastive models like CLIP to learn robust image representations that capture both semantics and style. They propose a two-stage model consisting of a prior that generates a CLIP image embedding based on a text caption, and a decoder that produces an image conditioned on this embedding. The authors demonstrate that explicitly generating image representations enhances image diversity while maintaining photorealism and caption similarity. Furthermore, the decoders conditioned on image representations are capable of generating variations of an image that preserve its semantics and style while altering non-essential details. The joint embedding space of CLIP enables language-guided image manipulations in a zero-shot manner. The authors utilize diffusion models for the decoder and experiment with autoregressive and diffusion models for the prior, finding the latter to be more computationally efficient and yielding higher-quality samples. In their evaluation section, the authors compare their model, unCLIP, to GLIDE in terms of photorealism, caption similarity, and sample diversity through systematic human evaluations. They highlight the importance of human judgment in assessing generation capabilities beyond metrics like FID. The study follows established protocols for evaluating photorealism and caption similarity by presenting users with pairs of images for comparison. The authors also showcase samples from unCLIP at varying guidance scales to demonstrate improvements in quality without compromising semantic diversity. This approach allows for realistic lighting and shadows in generated images while maintaining content consistency across different guidance scales. Overall, the study underscores the potential of leveraging CLIP embeddings for hierarchical text-conditional image generation, showcasing advancements in generating diverse and realistic images guided by textual prompts.