Diffusion models have become increasingly popular in the field of image generation. These models use a stochastic process to generate images by iteratively diffusing noise until it resembles the desired image. In recent years, several research papers have been published on diffusion models for image generation. In this article, we will provide a comprehensive list of the best six research papers on diffusion models for image generation.
1. “Improved Techniques for Training Score-Based Generative Models” by Nils Thuerey, et al.
This paper proposes a new training method for score-based generative models that use diffusion processes. The authors introduce a new loss function that improves the stability and convergence of the training process. They also propose a new sampling method that reduces the computational cost of generating high-quality images.
2. “Diffusion Probabilistic Models for Image Generation” by Takeru Miyato, et al.
This paper introduces a new diffusion probabilistic model for image generation. The authors propose a new diffusion process that uses a time-dependent diffusion coefficient to control the rate of diffusion. They also introduce a new sampling method that improves the quality of generated images.
3. “Generative Modeling by Estimating Gradients of the Data Distribution” by Ilya Sutskever, et al.
This paper proposes a new generative model that uses diffusion processes to estimate gradients of the data distribution. The authors introduce a new training method that uses a reverse-time diffusion process to estimate the gradients. They also propose a new sampling method that improves the quality of generated images.
4. “Diffusion Models Beat GANs on Image Synthesis” by Jonathan Ho, et al.
This paper compares diffusion models with generative adversarial networks (GANs) on image synthesis tasks. The authors show that diffusion models outperform GANs on several metrics, including image quality and diversity. They also propose a new sampling method that improves the quality of generated images.
5. “Diffusion Models for Text Generation” by Yiping Song, et al.
This paper extends diffusion models to text generation tasks. The authors propose a new diffusion process that uses a time-dependent diffusion coefficient to control the rate of diffusion. They also introduce a new sampling method that improves the quality of generated text.
6. “Diffusion Models for Video Generation” by Ting Chen, et al.
This paper extends diffusion models to video generation tasks. The authors propose a new diffusion process that uses a spatiotemporal diffusion coefficient to control the rate of diffusion. They also introduce a new sampling method that improves the quality of generated videos.
In conclusion, diffusion models have become a popular approach for image generation in recent years. The six research papers listed above provide valuable insights into the development and application of diffusion models for image generation, text generation, and video generation. These papers introduce new training methods, sampling methods, and diffusion processes that improve the quality and diversity of generated images, texts, and videos. Researchers and practitioners in the field of image generation can benefit from studying these papers and applying their findings to their own work.
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