Description
In this video, we take a look at a DALL-E for text-to-image generation. What is it? Why do we have it? How does it look?
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[1 π] Slides: https://link.excalidraw.com/p/readonly/NXtiUh19HjH4BuC2IQ6V
[2 π] DALL-E main paper: https://arxiv.org/pdf/2102.12092
[3 π] DALL-E blog page: https://openai.com/index/dall-e/
[4 π] Evolution of auto encoders: https://youtu.be/XyWNmHZi1oA?si=0X5iE2FKfToDaRNM
[5 π] Colab notebook I put together to understand the gumbel distribution, gumbel max trick and Gumbel Softmax Relaxation: https://colab.research.google.com/drive/1KSKB3AIUzyMnpym8HeSVZCxOtzS-DI9u#scrollTo=1af4a395
[6 π] Nice mathematical proof to show gumbel max trick: [https://github.com/priyammaz/PyTorch-Adventures/blob/main/PyTorch for Generation/AutoEncoders/Intro to AutoEncoders/gumbel_softmax_quantizer.ipynb](https://github.com/priyammaz/PyTorch-Adventures/blob/main/PyTorch%20for%20Generation/AutoEncoders/Intro%20to%20AutoEncoders/gumbel_softmax_quantizer.ipynb)
[7 π] Attention is all you need paper: https://arxiv.org/pdf/1706.03762
[8 π] Image is worth 16 x 16 words paper: https://arxiv.org/pdf/2010.11929
[9 π] Improving generative language understanding paper: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
[10 π] Learning Bounded Context-Free-Grammar via LSTM and the Transformer:
Difference and Explanations paper: https://arxiv.org/pdf/2112.09174
[11 π] DALL-E architecture code: https://github.com/openai/DALL-E/blob/master/dall_e/encoder.py
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CHAPTERS
00:00 What is DALL-E?
00:33 Why DALL-E with historical context
03:35 Components of DALL-E: dVAE and GPT
04:39 Stage 1: discrete VAE training
08:00 Stage 2: GPT training
11:38 Inference
13:36 dVAE encoder
15:58 dVAE image tokenizer
17:33 dVAE decoder
18:14 dVAE loss
20:56 Gumbel Distribution
23:20 Gumbel Max Trick
27:27 Gumbel Softmax Relaxation
29:20 Quiz Time
30:17 Summary