DALL·E 2 Explained - model architecture, results and comparison

DALL·E 2 Explained - model architecture, results and comparison
Dalle-2 or unCLIP is an image generation model that leverages the diffusion model to generate images from text embeddings. Here is a video that explains the DALLE-2 paper. More specifically, the model architecture, the results and comparison to other state-of-the-art models like GLIDE.
Paper Title
Hierarchical Text-Conditional Image Generation with CLIP Latents
Paper Abstract
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.
Paper Link
arxiv.org/abs/2204.06125
Website
openai.com/dall-e-2
Video Outline
0:00​ - Introduction
1:05 - Method / Model of CLIP
1:53 - Method / Model of unCLIP
3:02 - Decoder Architecture
4:04 - Prior Architecture
5:59 - Image Manipulation
7:49 - Image Interpolation
8:26 - Languge Guided Manipulation
9:14 - Importance of the Prior
10:07 - Results / Human Evaluation
AI Bites
TH-cam: / aibites​
Twitter: / ai_bites​
Patreon: / ai_bites​
Github: github.com/ai-bites​
Vision Transformers (ViT): • Vision Transformer (Vi...
Data Efficient Image Transformer (DeiT): • DeiT - Data-efficient ...
📚 📚 📚 BOOKS I HAVE READ, REFER AND RECOMMEND 📚 📚 📚
📖 Deep Learning by Ian Goodfellow - amzn.to/3Wnyixv
📙 Pattern Recognition and Machine Learning by Christopher M. Bishop - amzn.to/3ZVnQQA
📗 Machine Learning: A Probabilistic Perspective by Kevin Murphy - amzn.to/3kAqThb
📘 Multiple View Geometry in Computer Vision by R Hartley and A Zisserman - amzn.to/3XKVOWi
Music: www.bensound.com
#machinelearning #deeplearning #aibites

[@oc1655]

this is excellent. i just can't understand why companies (e.g., openai in this case) cannot add small notational hints to make their method more understandable. your diagram at around 2:50 is better than the 2 page gibberish actual paper presents. great work!

[@rezarawassizadeh4601]

Thank you for this easy to understand, to my understanding CLIP is not separate from Prior. Prior includes the frozen CLIP model that constructs image embedding.

[@liji9354]

Thank you so much! this is super helpful!

[@salomeshunamon4737]

How would you say DALLE2 compares to Stable Diffusion architecturally? Would you consider Stable Diffusion a latent diffusion model, denoising diffusion model or something else?

[@salomeshunamon4737]

Another question :) I see in the table that humans evaluated the output and rated the photos by photorealism and prompt accuracy, but what is diversity?

[@AIBites]

so diversity is how different the output images look. For example, if you want images of winter, then the generated images should not always show hibernating trees without leaves but also show snow