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# Suggested reading list | ||
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## Fundamental papers | ||
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1. **Denoising Diffusion Probabilistic Models** | ||
- https://arxiv.org/abs/2006.11239 | ||
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2. **Improved Denoising Diffusion Probabilistic Models** | ||
- https://arxiv.org/abs/2102.09672 | ||
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3. Generative Modeling by Estimating Gradients of the Data Distribution | ||
- https://arxiv.org/abs/1907.05600 | ||
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4. **Score-Based Generative Modeling through Stochastic Differential Equations** | ||
- https://arxiv.org/abs/2011.13456 | ||
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5. **Denoising Diffusion Implicit Models** | ||
- https://arxiv.org/abs/2010.02502 | ||
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6. **Diffusion Models Beat GANs on Image Synthesis** | ||
- https://arxiv.org/abs/2105.05233 | ||
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7. **Elucidating the Design Space of Diffusion-Based Generative Models** | ||
- https://arxiv.org/abs/2206.00364 | ||
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8. **High-Resolution Image Synthesis with Latent Diffusion Models** | ||
- https://arxiv.org/abs/2112.10752 | ||
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9. **SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis** | ||
- https://www.youtube.com/watch?v=kkYaikeLJd | ||
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## Inversion | ||
1. Null-text Inversion for Editing Real Images using Guided Diffusion Models | ||
- https://arxiv.org/abs/2211.09794 | ||
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## Text-based image editing | ||
1. Prompt-to-Prompt Image Editing with Cross Attention Control | ||
- https://arxiv.org/abs/2208.01626 | ||
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2. Adding Conditional Control to Text-to-Image Diffusion Models | ||
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3. **InstructPix2Pix: Learning to Follow Image Editing Instructions** | ||
- https://arxiv.org/abs/2211.09800 | ||
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## SD finetuning | ||
1. **An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion** | ||
- https://arxiv.org/abs/2208.01618 | ||
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2. **DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation** | ||
- https://arxiv.org/abs/2208.12242 | ||
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3. **LoRA: Low-Rank Adaptation of Large Language Models** | ||
- https://arxiv.org/abs/2106.09685 | ||
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4. Key-Locked Rank One Editing for Text-to-Image Personalization | ||
- https://arxiv.org/abs/2305.01644 | ||
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## Image-based editing | ||
1. **SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations** | ||
- https://arxiv.org/abs/2108.01073 | ||
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2. Palette: Image-to-Image Diffusion Models | ||
- https://arxiv.org/abs/2111.05826 | ||
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## SD-based video synthesis | ||
1. **DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion** | ||
- https://arxiv.org/abs/2304.06025 | ||
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## Super-resolution | ||
1. **Image Super-Resolution via Iterative Refinement** | ||
- https://arxiv.org/abs/2104.07636 | ||
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## Garment Try-on | ||
1. **TryOnDiffusion: A Tale of Two UNets** | ||
- https://arxiv.org/abs/2306.08276 | ||
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## Subject-swapping | ||
1. **Photoswap: Personalized Subject Swapping in Images** | ||
- https://arxiv.org/abs/2305.18286 | ||
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## Fast sampling | ||
1. Progressive Distillation for Fast Sampling of Diffusion Models | ||
- https://arxiv.org/abs/2202.00512 | ||
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2. On Distillation of Guided Diffusion Models | ||
- https://arxiv.org/abs/2210.03142 | ||
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>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! | ||
**Happy reading!** | ||
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