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+# Suggested reading list
+This document contains the suggested reading list of papers pertaining to Diffusion Models
+## Fundamental papers
+
+1. **Auto-Encoding Variational Bayes**
+    - https://arxiv.org/pdf/1312.6114
+
+2. **Denoising Diffusion Probabilistic Models**
+    - https://arxiv.org/abs/2006.11239
+
+3. **Improved Denoising Diffusion Probabilistic Models**
+   - https://arxiv.org/abs/2102.09672
+
+4. **Generative Modeling by Estimating Gradients of the Data Distribution**
+   - https://arxiv.org/abs/1907.05600
+
+5. **Score-Based Generative Modeling through Stochastic Differential Equations**
+   - https://arxiv.org/abs/2011.13456
+
+6. **Denoising Diffusion Implicit Models**
+   - https://arxiv.org/abs/2010.02502
+
+7. **Diffusion Models Beat GANs on Image Synthesis**
+   - https://arxiv.org/abs/2105.05233
+
+8. **Elucidating the Design Space of Diffusion-Based Generative Models**
+    - https://arxiv.org/abs/2206.00364
+
+9. **Classifier-Free Diffusion Guidance**
+    - https://arxiv.org/abs/2207.12598
+
+10. **High-Resolution Image Synthesis with Latent Diffusion Models**
+    - https://arxiv.org/abs/2112.10752
+
+11. **SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis**
+    - https://www.youtube.com/watch?v=kkYaikeLJd
+
+
+## Inversion
+1. Null-text Inversion for Editing Real Images using Guided Diffusion Models
+   - https://arxiv.org/abs/2211.09794
+
+
+## Text-based image editing
+1. Prompt-to-Prompt Image Editing with Cross Attention Control
+   - https://arxiv.org/abs/2208.01626
+
+2. Adding Conditional Control to Text-to-Image Diffusion Models
+   - https://arxiv.org/abs/2302.05543
+
+3. **InstructPix2Pix: Learning to Follow Image Editing Instructions**
+   - https://arxiv.org/abs/2211.09800
+
+
+## SD finetuning and controlled generation
+1. **An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion**
+    - https://arxiv.org/abs/2208.01618
+
+2.  **DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation**
+    - https://arxiv.org/abs/2208.12242
+
+3. **LoRA: Low-Rank Adaptation of Large Language Models**
+    - https://arxiv.org/abs/2106.09685
+  
+4. Key-Locked Rank One Editing for Text-to-Image Personalization
+    - https://arxiv.org/abs/2305.01644
+
+5. **T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models**
+    - https://arxiv.org/abs/2302.08453
+
+
+## Image-based editing
+1. **SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations**
+    - https://arxiv.org/abs/2108.01073
+
+2. **Palette: Image-to-Image Diffusion Models**
+   - https://arxiv.org/abs/2111.05826
+
+
+## SD-based video synthesis
+1. **DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion**
+   - https://arxiv.org/abs/2304.06025
+
+
+## Super-resolution
+1. **Image Super-Resolution via Iterative Refinement**
+    - https://arxiv.org/abs/2104.07636
+
+
+## Garments Try-on
+1. **TryOnDiffusion: A Tale of Two UNets**
+    - https://arxiv.org/abs/2306.08276
+
+
+## Subject-swapping
+1. **Photoswap: Personalized Subject Swapping in Images**
+    - https://arxiv.org/abs/2305.18286
+
+
+## Fast sampling
+1.  **Progressive Distillation for Fast Sampling of Diffusion Models**
+    - https://arxiv.org/abs/2202.00512
+
+2.  On Distillation of Guided Diffusion Models
+    - https://arxiv.org/abs/2210.03142
+
+## Video Synthesis
+1. **Video Diffusion Models**
+    - https://arxiv.org/abs/2204.03458
+2. **DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion**
+    - https://arxiv.org/abs/2304.06025
+3. **DisCo: Disentangled Control for Referring Human Dance Generation in Real World**
+    - https://arxiv.org/abs/2307.00040
+4. **Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation**
+    - https://arxiv.org/abs/2212.11565
+
+
+>Note: 
+> 1. Bold highlighted papers are must read for solid understanding of diffusion models and its possible applications.
+> 2. Moreover, this is not exhaustive but suggested list. If any one of you find an interesting paper or has any suggestions. They are more than welcome!
+
+
+
+