Timeline
Every dated event this book covers, in one place. Each row links to the chapter that teaches it.
| Chapter | Title | Era |
|---|---|---|
| 1 | The Generative Problem | 2006–2013 |
| 2 | Variational Autoencoders | 2013–2015 |
| 3 | The Adversarial Game | 2014 |
| 4 | Taming GAN Training | 2015–2017 |
| 5 | Pixels One at a Time | 2016–2017 |
| 6 | The GAN Golden Age | 2017–2020 |
| 7 | First Words to Pictures | 2015–2018 |
| 8 | Diffusion Foundations | 2015–2020 |
| 9 | Scores, SDEs, and Faster Sampling | 2020–2021 |
| 10 | Guidance | 2021–2022 |
| 11 | CLIP: Bridging Text and Image | 2021 |
| 12 | DALL-E and GLIDE | 2021 |
| 13 | Latent Diffusion and Stable Diffusion | 2021–2022 |
| 14 | The Year Images Went Mainstream | 2022 |
| 15 | Control | 2023 |
| 16 | Personalization and LoRA | 2022–2023 |
| 17 | Editing | 2021–2023 |
| 18 | Few-Step Generation | 2022–2023 |
| 19 | Flow Matching and Transformer Backbones | 2022–2024 |
| 20 | The Current Frontier | 2024–2026 |
| 21 | Evaluation, Provenance, and Safety | Cross-era |
Reading List
Every paper cited across From Noise to Image, 67 in total, in chronological order.
| Year | Paper | Authors | Link |
|---|---|---|---|
| 2006 | A Fast Learning Algorithm for Deep Belief Nets | Hinton, Osindero, and Teh | link |
| 2006 | Reducing the Dimensionality of Data with Neural Networks | Hinton and Salakhutdinov | link |
| 2013 | Auto-Encoding Variational Bayes | Kingma & Welling | 1312.6114 |
| 2014 | Conditional Generative Adversarial Nets | Mirza and Osindero | 1411.1784 |
| 2014 | Generative Adversarial Networks | Goodfellow et al. | 1406.2661 |
| 2014 | Stochastic Backpropagation and Approximate Inference in Deep Generative Models | Rezende, Mohamed & Wierstra | 1401.4082 |
| 2015 | DRAW: A Recurrent Neural Network For Image Generation | Gregor et al. | 1502.04623 |
| 2015 | Deep Unsupervised Learning using Nonequilibrium Thermodynamics | Sohl-Dickstein, Weiss, Maheswaranathan & Ganguli | 1503.03585 |
| 2015 | Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks | Radford, Metz, and Chintala | 1511.06434 |
| 2016 | Conditional Image Generation with PixelCNN Decoders | van den Oord et al. | 1606.05328 |
| 2016 | Improved Techniques for Training GANs | Salimans, Goodfellow, Zaremba, Cheung, Radford, and Chen | 1606.03498 |
| 2016 | Pixel Recurrent Neural Networks | van den Oord et al. | 1601.06759 |
| 2016 | StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks | Zhang et al. | 1612.03242 |
| 2017 | AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks | Xu et al. | 1711.10485 |
| 2017 | GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium | Heusel, Ramsauer, Unterthiner, Nessler, and Hochreiter | 1706.08500 |
| 2017 | Improved Training of Wasserstein GANs | Gulrajani, Ahmed, Arjovsky, Dumoulin, and Courville | 1704.00028 |
| 2017 | Neural Discrete Representation Learning | van den Oord et al. | 1711.00937 |
| 2017 | Progressive Growing of GANs for Improved Quality, Stability, and Variation | Karras et al. | 1710.10196 |
| 2017 | Wasserstein GAN | Arjovsky, Chintala, and Bottou | 1701.07875 |
| 2018 | A Style-Based Generator Architecture for Generative Adversarial Networks | Karras et al. | 1812.04948 |
| 2018 | Glow: Generative Flow with Invertible 1x1 Convolutions | Kingma & Dhariwal | 1807.03039 |
| 2018 | Large Scale GAN Training for High Fidelity Natural Image Synthesis | Brock et al. | 1809.11096 |
| 2019 | Analyzing and Improving the Image Quality of StyleGAN | Karras et al. | 1912.04958 |
| 2019 | Generative Modeling by Estimating Gradients of the Data Distribution | Song & Ermon | 1907.05600 |
| 2020 | Denoising Diffusion Implicit Models | Song, Meng, and Ermon | 2010.02502 |
| 2020 | Denoising Diffusion Probabilistic Models | Ho, Jain & Abbeel | 2006.11239 |
| 2020 | Score-Based Generative Modeling through Stochastic Differential Equations | Song et al. | 2011.13456 |
| 2020 | Taming Transformers for High-Resolution Image Synthesis | Esser, Rombach & Ommer | 2012.09841 |
| 2021 | C2PA Founding Press Release | C2PA | link |
| 2021 | Diffusion Models Beat GANs on Image Synthesis | Dhariwal & Nichol | 2105.05233 |
| 2021 | GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models | Nichol, Dhariwal, Ramesh, Shyam, Mishkin, McGrew, Sutskever & Chen | 2112.10741 |
| 2021 | High-Resolution Image Synthesis with Latent Diffusion Models | Rombach, Blattmann, Lorenz, Esser & Ommer | 2112.10752 |
| 2021 | Improved Denoising Diffusion Probabilistic Models | Nichol and Dhariwal | 2102.09672 |
| 2021 | Learning Transferable Visual Models From Natural Language Supervision | Radford et al. | 2103.00020 |
| 2021 | LoRA: Low-Rank Adaptation of Large Language Models | Hu et al. | 2106.09685 |
| 2021 | SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations | Meng et al. | 2108.01073 |
| 2021 | Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision | Jia et al. | 2102.05918 |
| 2021 | Zero-Shot Text-to-Image Generation | Ramesh, Pavlov, Goh, Gray, Voss, Radford, Chen & Sutskever | 2102.12092 |
| 2022 | An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion | Gal et al. | 2208.01618 |
| 2022 | Classifier-Free Diffusion Guidance | Ho & Salimans | 2207.12598 |
| 2022 | DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation | Ruiz et al. | 2208.12242 |
| 2022 | Flow Matching for Generative Modeling | Lipman, Chen, Ben-Hamu, Nickel & Le | 2210.02747 |
| 2022 | Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow | Liu, Gong & Liu | 2209.03003 |
| 2022 | Hierarchical Text-Conditional Image Generation with CLIP Latents | Ramesh et al. | 2204.06125 |
| 2022 | InstructPix2Pix: Learning to Follow Image Editing Instructions | Brooks, Holynski, and Efros | 2211.09800 |
| 2022 | Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding | Saharia et al. | 2205.11487 |
| 2022 | Progressive Distillation for Fast Sampling of Diffusion Models | Salimans and Ho | 2202.00512 |
| 2022 | Prompt-to-Prompt Image Editing with Cross Attention Control | Hertz et al. | 2208.01626 |
| 2022 | Scalable Diffusion Models with Transformers | Peebles & Xie | 2212.09748 |
| 2022 | Scaling Autoregressive Models for Content-Rich Text-to-Image Generation | Yu et al. | 2206.10789 |
| 2023 | Adding Conditional Control to Text-to-Image Diffusion Models | Zhang, Rao & Agrawala | 2302.05543 |
| 2023 | Adversarial Diffusion Distillation | Sauer, Lorenz, Blattmann, and Rombach | 2311.17042 |
| 2023 | Consistency Models | Song, Dhariwal, Chen, and Sutskever | 2303.01469 |
| 2023 | Extracting Training Data from Diffusion Models | Carlini, Hayes, Nasr, Jagielski, Sehwag, Tramèr, Balle, Ippolito, and Wallace | 2301.13188 |
| 2023 | IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models | Ye et al. | 2308.06721 |
| 2023 | Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference | Luo, Tan, Huang, Li, and Zhao | 2310.04378 |
| 2023 | Muse: Text-To-Image Generation via Masked Generative Transformers | Chang et al. | 2301.00704 |
| 2023 | Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation | Kirstain, Polyak, Singer, Matiana, Penna, and Levy | 2305.01569 |
| 2023 | Rethinking FID: Towards a Better Evaluation Metric for Image Generation | Jayasumana, Ramalingam, Veit, Glasner, Chakrabarti, and Kumar | 2401.09603 |
| 2023 | SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis | Podell et al. | 2307.01952 |
| 2023 | T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models | Mou et al. | 2302.08453 |
| 2023 | The Stable Signature: Rooting Watermarks in Latent Diffusion Models | Fernandez, Couairon, Jégou, Douze, and Furon | 2303.15435 |
| 2024 | Announcing Black Forest Labs and FLUX.1 | Black Forest Labs | link |
| 2024 | Chameleon: Mixed-Modal Early-Fusion Foundation Models | Chameleon Team | 2405.09818 |
| 2024 | Scaling Rectified Flow Transformers for High-Resolution Image Synthesis | Esser et al. | 2403.03206 |
| 2024 | Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction | Tian et al. | 2404.02905 |
| 2025 | Introducing 4o Image Generation | OpenAI | link |