Results 1 to 10 of about 5,000,249 (354)
Retarding Sub- and Accelerating Super-Diffusion Governed by Distributed Order Fractional Diffusion Equations [PDF]
, 2002 We propose diffusion-like equations with time and space fractional derivatives of the distributed order for the kinetic description of anomalous diffusion and relaxation phenomena, whose diffusion exponent varies with time and which, correspondingly, can not be viewed as self-affine random processes possessing a unique Hurst exponent.
A. Ayache +29 more
arxiv +3 more sources
On diffusion approximation with discontinuous coefficients [PDF]
arXiv, 2002 Convergence of stochastic processes with jumps to diffusion processes is investigated in the case when the limit process has discontinuous coefficients. An example is given in which the diffusion approximation of a queueing model yields a diffusion process with discontinuous diffusion and drift coefficients.
Krylov, N. V., Liptser, R.
arxiv +6 more sources
High-Resolution Image Synthesis with Latent Diffusion Models [PDF]
Computer Vision and Pattern Recognition, 2021 By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism
Robin Rombach +4 more
semanticscholar +1 more source
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding [PDF]
Neural Information Processing Systems, 2022 We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large transformer language models in understanding text and hinges on the strength ...
Chitwan Saharia +13 more
semanticscholar +1 more source
Adding Conditional Control to Text-to-Image Diffusion Models [PDF]
IEEE International Conference on Computer Vision, 2023 We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers ...
Lvmin Zhang, Anyi Rao, Maneesh Agrawala
semanticscholar +1 more source
Classifier-Free Diffusion Guidance [PDF]
arXiv.org, 2022 Classifier guidance is a recently introduced method to trade off mode coverage and sample fidelity in conditional diffusion models post training, in the same spirit as low temperature sampling or truncation in other types of generative models. Classifier
Jonathan Ho
semanticscholar +1 more source
DreamFusion: Text-to-3D using 2D Diffusion [PDF]
International Conference on Learning Representations, 2022 Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D data and efficient architectures for ...
Ben Poole +3 more
semanticscholar +1 more source
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation [PDF]
Computer Vision and Pattern Recognition, 2022 Large text-to-image models achieved a remarkable leap in the evolution of AI, enabling high-quality and diverse synthesis of images from a given text prompt.
Nataniel Ruiz +5 more
semanticscholar +1 more source
Elucidating the Design Space of Diffusion-Based Generative Models [PDF]
Neural Information Processing Systems, 2022 We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices.
Tero Karras +3 more
semanticscholar +1 more source
Scalable Diffusion Models with Transformers [PDF]
IEEE International Conference on Computer Vision, 2022 We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our
William S. Peebles, Saining Xie
semanticscholar +1 more source