The document discusses various advancements in generative models, specifically focusing on latent diffusion models and their applications in high-resolution image synthesis. It highlights key components of stable diffusion, including the use of a high-quality decoder and frozen language models, alongside the importance of extensive training datasets and computational resources. The source is open-sourced and improves upon previous models like VQ-GAN and VAE.

1. Stable Diffusion path
presented by
Vitaly Bondar
Generative DL enthusiast
johngull @ gmail

2. Autoencoder
Illustration: Lilian Weng
LeCun, Y. Modeles connexionistes de l’apprentissage ` . Ph.D. thesis, 1987

3. Variational autoencoder (VAE)
Illustrations:
Lilian Weng, D. Kingma
Kingma, Welling. Auto-Encoding Variational Bayes. 2013

4. Variational autoencoder (VAE)
Kingma, Welling. Auto-Encoding Variational Bayes. 2013

5. Vector Quantized Variational Autoencoder (VQ-VAE)
Oord et al. Neural Discrete Representation Learning, 2017

6. Vector Quantized Variational Autoencoder (VQ-VAE)
Oord et al. Neural Discrete Representation Learning, 2017

7. Vector Quantized Variational Autoencoder (VQ-VAE)
Oord et al. Neural Discrete Representation Learning, 2017

8. VQ-GAN
Esser, Rombach et al. Taming Transformers for High-Resolution Image Synthesis, 2020

9. VQ-GAN
Esser, Rombach et al. Taming Transformers for High-Resolution Image Synthesis, 2020

10. VQ-GAN
Esser, Rombach et al. Taming Transformers for High-Resolution Image Synthesis, 2020

11. Recap: diffusion models
Sohl-Dickstein et al. Deep Unsupervised Learning using Nonequilibrium Thermodynamics, 2015;
Yang & Ermon, 2019; DDPM; Ho et al. 2020; … ?

12. Latent diffussions
Rombach, Blattmann et al. High-Resolution Image Synthesis with Latent Diffusion Models, 2021

13. Latent diffussions
Training phases:
● Autoencoder
○ Loss: Patch-based GAN loss + Perceptual loss
○ Regularization: KL-loss (close to VAE) OR quantization in Decoder (like VQ-GAN)
● Various generative tasks
○ Loss: classical diffusion L2 restoration loss
○ All trainings done on single A100
Rombach, Blattmann et al. High-Resolution Image Synthesis with Latent Diffusion Models, 2021

14. Latent diffussions
Downsampling for 4-16x: speedup of generative training without sampling quality loss
Rombach, Blattmann et al. High-Resolution Image Synthesis with Latent Diffusion Models, 2021

15. Latent diffussions
Rombach, Blattmann et al. High-Resolution Image Synthesis with Latent Diffusion Models, 2021

16. Latent diffussions

17. Latent diffussions

18. Latent diffussions

19. Latent diffussions

20. Imagen (important influencer)
Saharia, Chan. Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding, 2022

21. Imagen (important influencer)
Saharia, Chan. Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding, 2022

22. Imagen (important influencer)

23. Stable diffusion
● No paper (core team is from Latent diffusion authors)
● Open source: https://github.com/CompVis/stable-diffusion
● Well improved and well trained latent diffusion model

24. Stable diffusion
Key components:
● High quality decoder from VQ-GAN
● Diffusion in latent space
● Frozen language model (CLIP ViT-L/14 embeddings)
● Classifier-free guidance
● A lot of data (LAION-5B and its subsets)
● A lot of compute power

25. Stable diffusion