SyncTweedies: A General Generative Framework Based on Synchronized Diffusions

• URL: http://arxiv.org/abs/2403.14370v4
• Date: Mon, 04 Nov 2024 01:25:37 GMT
• Title: SyncTweedies: A General Generative Framework Based on Synchronized Diffusions
• Authors: Jaihoon Kim, Juil Koo, Kyeongmin Yeo, Minhyuk Sung,
• Abstract summary: We present exhaustive investigation into all possible scenarios for synchronizing multiple diffusion processes through a canonical space. We reveal a previously unexplored case: averaging the outputs of Tweedie's formula while conducting denoising in multiple instance spaces. In our experiments generating visual content aforementioned, we demonstrate the superior quality of generation by SyncTweedies compared to other synchronization methods.
• Score: 11.292617528150291
• License:
• Abstract: We introduce a general framework for generating diverse visual content, including ambiguous images, panorama images, mesh textures, and Gaussian splat textures, by synchronizing multiple diffusion processes. We present exhaustive investigation into all possible scenarios for synchronizing multiple diffusion processes through a canonical space and analyze their characteristics across applications. In doing so, we reveal a previously unexplored case: averaging the outputs of Tweedie's formula while conducting denoising in multiple instance spaces. This case also provides the best quality with the widest applicability to downstream tasks. We name this case SyncTweedies. In our experiments generating visual content aforementioned, we demonstrate the superior quality of generation by SyncTweedies compared to other synchronization methods, optimization-based and iterative-update-based methods.

Related papers

• StochSync: Stochastic Diffusion Synchronization for Image Generation in Arbitrary Spaces [11.517082612850443]
  We propose a method for generating images in arbitrary spaces using a pretrained image diffusion model. The zero-shot method combines the strengths of both image conditioning and 3D mesh-based methods.
  arXiv  Detail & Related papers  (2025-01-26T08:22:44Z)
• Optical-Flow Guided Prompt Optimization for Coherent Video Generation [51.430833518070145]
  We propose a framework called MotionPrompt that guides the video generation process via optical flow. We optimize learnable token embeddings during reverse sampling steps by using gradients from a trained discriminator applied to random frame pairs. This approach allows our method to generate visually coherent video sequences that closely reflect natural motion dynamics, without compromising the fidelity of the generated content.
  arXiv  Detail & Related papers  (2024-11-23T12:26:52Z)
• ViewFusion: Towards Multi-View Consistency via Interpolated Denoising [48.02829400913904]
  We introduce ViewFusion, a training-free algorithm that can be seamlessly integrated into existing pre-trained diffusion models. Our approach adopts an auto-regressive method that implicitly leverages previously generated views as context for the next view generation. Our framework successfully extends single-view conditioned models to work in multiple-view conditional settings without any additional fine-tuning.
  arXiv  Detail & Related papers  (2024-02-29T04:21:38Z)
• Contextualized Diffusion Models for Text-Guided Image and Video Generation [67.69171154637172]
  Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. We propose a novel and general contextualized diffusion model (ContextDiff) by incorporating the cross-modal context encompassing interactions and alignments between text condition and visual sample. We generalize our model to both DDPMs and DDIMs with theoretical derivations, and demonstrate the effectiveness of our model in evaluations with two challenging tasks: text-to-image generation, and text-to-video editing.
  arXiv  Detail & Related papers  (2024-02-26T15:01:16Z)
• Synchformer: Efficient Synchronization from Sparse Cues [100.89656994681934]
  Our contributions include a novel audio-visual synchronization model, and training that decouples extraction from synchronization modelling. This approach achieves state-of-the-art performance in both dense and sparse settings. We also extend synchronization model training to AudioSet a million-scale 'in-the-wild' dataset, investigate evidence attribution techniques for interpretability, and explore a new capability for synchronization models: audio-visual synchronizability.
  arXiv  Detail & Related papers  (2024-01-29T18:59:55Z)
• Collaborative Score Distillation for Consistent Visual Synthesis [70.29294250371312]
  Collaborative Score Distillation (CSD) is based on the Stein Variational Gradient Descent (SVGD) We show the effectiveness of CSD in a variety of tasks, encompassing the visual editing of panorama images, videos, and 3D scenes. Our results underline the competency of CSD as a versatile method for enhancing inter-sample consistency, thereby broadening the applicability of text-to-image diffusion models.
  arXiv  Detail & Related papers  (2023-07-04T17:31:50Z)
• VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation [88.49030739715701]
  This work presents a decomposed diffusion process via resolving the per-frame noise into a base noise that is shared among all frames and a residual noise that varies along the time axis. Experiments on various datasets confirm that our approach, termed as VideoFusion, surpasses both GAN-based and diffusion-based alternatives in high-quality video generation.
  arXiv  Detail & Related papers  (2023-03-15T02:16:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.