SuperGen: An Efficient Ultra-high-resolution Video Generation System with Sketching and Tiling
On the map: Diffusion models → Video generation
Abstract (arXiv)
Diffusion models have recently achieved remarkable success in generative tasks (e.g., image and video generation), and the demand for high-quality content (e.g., 2K/4K videos) is rapidly increasing across various domains. However, generating ultra-high-resolution videos on existing standard-resolution (e.g., 720p) platforms remains challenging due to the excessive re-training requirements and prohibitively high computational and memory costs. To this end, we introduce SUPERGEN, an efficient tile-based framework for ultra-high-resolution video generation. SUPERGEN features a novel training-free algorithmic innovation with tiling to successfully support a wide range of resolutions without additional training efforts while significantly reducing both memory footprint and computational complexity. Moreover, SUPERGEN incorporates a tile-tailored, adaptive, region-aware caching strategy that accelerates video generation by exploiting redundancy across denoising steps and spatial regions. SUPERGEN also integrates cache-guided, communication-minimized tile parallelism for enhanced throughput and minimized latency. Evaluations show that SUPERGEN maximizes performance gains while achieving high output quality across various benchmarks.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 44 of its 62 references that are in the map. First posted on arXiv 25 Aug 2025.
Builds on
44 of 62 references.
Closest ancestors · 5
Same direction, newest first
- 2025Less is Enough: Training-Free Video Diffusion Acceleration via Runtime-Adaptive Caching3 Jul
- 2024Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model28 Nov
- 2024Adaptive Caching for Faster Video Generation with Diffusion Transformers4 Nov
- 2024FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality25 Oct
- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets25 Nov
Other references · 39
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 2020Denoising Diffusion Implicit Modelscited by 9,781
- 2022Scalable Diffusion Models with Transformerscited by 6,054
- 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
- 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
- 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,708
- 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,868
- 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,822
- 2024HunyuanVideo: A Systematic Framework For Large Video Generative Modelscited by 1,660
27 more
- 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 985
- 2024Open-Sora: Democratizing Efficient Video Production for Allcited by 652
- 2023MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generationcited by 536
- 2023Ring Attention with Blockwise Transformers for Near-Infinite Contextcited by 457
- 2023DeepCache: Accelerating Diffusion Models for Freecited by 359
- 2024Delta-DiT: A Training-Free Acceleration Method Tailored for Diffusion Transformerscited by 175
- 2023Cache Me if You Can: Accelerating Diffusion Models through Block Cachingcited by 151
- 2024Learning-to-Cache: Accelerating Diffusion Transformer via Layer Cachingcited by 142
- 2024FORA: Fast-Forward Caching in Diffusion Transformer Accelerationcited by 137
- 2023DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generationcited by 110
- 2023DemoFusion: Democratising High-Resolution Image Generation With No cited by 107
- 2024DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Modelscited by 98
- 2025EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modelingcited by 89
- 2025SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restorationcited by 62
- 2024xDiT: an Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelismcited by 56
- 2024AV-DiT: Efficient Audio-Visual Diffusion Transformer for Joint Audio and Video Generationcited by 41
- 2025Region-Adaptive Sampling for Diffusion Transformerscited by 33
- 2024Token Caching for Diffusion Transformer Accelerationcited by 30
- 2024Playable Game Generationcited by 26
- 2024MegaFusion: Extend Diffusion Models towards Higher-resolution Image Generation without Further Tuningcited by 25
- 2024DynamicScaler: Seamless and Scalable Video Generation for Panoramic Scenescited by 24
- 2024AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoisingcited by 22
- 2025AB-Cache: Training-Free Acceleration of Diffusion Models via Adams-Bashforth Cached Feature Reusecited by 13
- 2024SpotDiffusion: A Fast Approach For Seamless Panorama Generation Over Timecited by 12
- 2020Deep Learning Techniques for Super-Resolution in Video Gamescited by 5
- 2024Partially Conditioned Patch Parallelism for Accelerated Diffusion Model Inferencecited by 3
- 2025STAR: A Benchmark for Astronomical Star Fields Super-Resolutioncited by 2
Built on by
Citing papers per year: 2026: 6.
6 papers.
- 2026LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Expertscited by 15
- 2026Efficient Video Diffusion Models: Advancements and Challengescited by 7
- 2026Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithmscited by 5
- 2026SDiT: Semantic Region-Adaptive for Diffusion Transformerscited by 3
- 2026ViBe: Ultra-High-Resolution Video Synthesis Born from Pure Imagescited by 3
- 20264KLSDB: A Large-Scale Dataset for 4K Image Restoration and Generationcited by 1
- 20264KLSDB: A Large-Scale Dataset for 4K Image Restoration and Generation23 May 2026
- 2026Efficient Video Diffusion Models: Advancements and Challenges17 Apr 2026
- 2026Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms30 Mar 2026
- 2026ViBe: Ultra-High-Resolution Video Synthesis Born from Pure Images24 Mar 2026
- 2026LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Experts12 Feb 2026
- 2026SDiT: Semantic Region-Adaptive for Diffusion Transformers18 Jan 2026
Similar, not cited
Close in wording, no citation either way
- 2025UltraGen: High-Resolution Video Generation with Hierarchical Attention
- 2025SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer
- 2026Ultra Flash: Scaling Real-Time Streaming Video Generation to High Resolutions
- 2026High-Throughput Generative Video Super-Resolution on a Single Consumer GPU
- 2026Dynamic Video Generation: Shaping Video Generation Across Time and Space
- 2025HiStream: Efficient High-Resolution Video Generation via Redundancy-Eliminated Streaming
- 2025UniCP: A Unified Caching and Pruning Framework for Efficient Video Generation
- 2026Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization