for text guided image variation. When combined with an unCLIP prior, it can also be used for full text to image generation. The abstract from the paper is: Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples.  Tips Stable unCLIP takes  noise_level as input during inference which determines how much noise is added to the image embeddings. A higher noise_level increases variation in the final un-noised images. By default, we do not add any additional noise to the image embeddings (noise_level = 0).  Text-to-Image Generation Stable unCLIP can be leveraged for text-to-image generation by pipelining it with the prior model of KakaoBrain’s open source DALL-E 2 replication Karlo:   Copied import torch