8 citations · 13 across the 12 of their papers we have counts for
13 papers
UnStep: Training-Free Acceleration of Causal Video Diffusion with Fewer Steps Than Distillation
Youssef Mansour, Enis Simsar, Fadime Sener +4
Distilling bidirectional multi-step video diffusion transformers into few-step causal models has become a common approach for streaming video generation. While these few-step stude…
Sparse Forcing: Native Trainable Sparse Attention for Real-time Autoregressive Diffusion Video Generation
Boxun Xu, Yuming Du, Zichang Liu +7
We introduce Sparse Forcing, a training-and-inference paradigm for autoregressive video diffusion models that improves long-horizon generation quality while reducing decoding laten…
SneakPeek: Future-Guided Instructional Streaming Video Generation
Cheeun Hong, German Barquero, Fadime Sener +6
Instructional video generation is an emerging task that aims to synthesize coherent demonstrations of procedural activities from textual descriptions. Such capability has broad imp…
Autoregressive Distillation of Diffusion Transformers
Yeongmin Kim, Sotiris Anagnostidis, Yuming Du +6
Diffusion models with transformer architectures have demonstrated promising capabilities in generating high-fidelity images and scalability for high resolution. However, iterative…
FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute
Sotiris Anagnostidis, Gregor Bachmann, Yeongmin Kim +7
Despite their remarkable performance, modern Diffusion Transformers are hindered by substantial resource requirements during inference, stemming from the fixed and large amount of…
Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment
Gregor Bachmann, Sotiris Anagnostidis, Albert Pumarola +6
The performance of large language models (LLMs) is closely linked to their underlying size, leading to ever-growing networks and hence slower inference. Speculative decoding has be…
Movie Gen: A Cast of Media Foundation Models
Adam Polyak, Amit Zohar, Andrew Brown +85
We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabili…
Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation
Jonas Kohler, Albert Pumarola, Edgar Schönfeld +4
Diffusion models are a powerful generative framework, but come with expensive inference. Existing acceleration methods often compromise image quality or fail under complex conditio…
Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models
Neta Shaul, Uriel Singer, Ricky T. Q. Chen +4
This paper introduces Bespoke Non-Stationary (BNS) Solvers, a solver distillation approach to improve sample efficiency of Diffusion and Flow models. BNS solvers are based on a fam…
Animated Stickers: Bringing Stickers to Life with Video Diffusion
David Yan, Winnie Zhang, Luxin Zhang +15
We introduce animated stickers, a video diffusion model which generates an animation conditioned on a text prompt and static sticker image. Our model is built on top of the state-o…
fMPI: Fast Novel View Synthesis in the Wild with Layered Scene Representations
Jonas Kohler, Nicolas Griffiths Sanchez, Luca Cavalli +4
In this study, we propose two novel input processing paradigms for novel view synthesis (NVS) methods based on layered scene representations that significantly improve their runtim…
Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models
Angela Castillo, Jonas Kohler, Juan C. Pérez +5
This paper presents a comprehensive study on the role of Classifier-Free Guidance (CFG) in text-conditioned diffusion models from the perspective of inference efficiency. In partic…
Bespoke Solvers for Generative Flow Models
Neta Shaul, Juan Perez, Ricky T. Q. Chen +3
Diffusion or flow-based models are powerful generative paradigms that are notoriously hard to sample as samples are defined as solutions to high-dimensional Ordinary or Stochastic…