- Denoising Diffusion Probabilistic Models; Ho et al. - https://arxiv.org/pdf/2006.11239.pdf
- Elucidating the Design Space of Diffusion-Based Generative Models; Karras et al. - https://arxiv.org/pdf/2206.00364.pdf
- Improved Denoising Diffusion Probabilistic Models; Nichol, Dhariwal - https://arxiv.org/abs/2102.09672
- Generative Modeling by Estimating Gradients of the Data Distribution; Song, Ermon - https://arxiv.org/pdf/1907.05600.pdf
- Score-Based Generative Modeling through Stochastic Differential Equations; Song et al. - https://arxiv.org/pdf/2011.13456.pdf
- Consistency Models; Song et al. - https://arxiv.org/pdf/2303.01469.pdf
- High-Resolution Image Synthesis with Latent Diffusion Models; Rombach et al. - https://arxiv.org/pdf/2112.10752.pdf
- Diffusion Models Beat GANs on Image Synthesis; Nichol, Dhariwal - https://arxiv.org/pdf/2105.05233.pdf (Classifier Guidance)
- Classifier-Free diffusion guidance; Ho, Salimans - https://arxiv.org/pdf/2207.12598.pdf
- Adding Conditional Control to Text-to-Image Diffusion Models; Zhang,Agrawala https://arxiv.org/pdf/2302.05543.pdf
- Denoising Diffusion Implicit Models; Song et al. - https://arxiv.org/pdf/2010.02502.pdf
- Pseudo Numerical Methods for Diffusion Models on Manifolds; Liu et al. - https://arxiv.org/pdf/2202.09778.pdf
- DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models; Lu et al. - https://arxiv.org/pdf/2211.01095v1.pdf
- An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion; Gal et al. - https://arxiv.org/pdf/2208.01618v1.pdf
- DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation; Ruiz et al. - https://arxiv.org/pdf/2208.12242v1.pdf
- LoRA: Low-Rank Adaptation of Large Language Models; Hu et al. - https://arxiv.org/pdf/2106.09685.pdf
- Diffusion Models: A Comprehensive Survey of Methods and Applications; Yang et al. - https://arxiv.org/pdf/2209.00796v9.pdf
- What the DAAM: Interpreting Stable Diffusion Using Cross Attention; Tang et al. - https://arxiv.org/pdf/2210.04885v5.pdf
- Is synthetic data from Generative models ready for image recognition; He et al. - https://arxiv.org/pdf/2210.07574v2.pdf
- Compositional Visual Generation with Composable Diffusion Models; Lie et al. - https://arxiv.org/pdf/2206.01714.pdf
- PAIR-Diffusion: Object Level Image editing with structure and appearance; Goel et al. - https://arxiv.org/pdf/2303.17546v1.pdf