Weaving Visual Stories: A New Approach to Long-Form Video Generation

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Author: Denis Avetisyan

Researchers have developed a novel diffusion model that efficiently creates extended, high-quality image sequences by separating coarse structure from fine detail.

A grid-based autoregressive sampling algorithm efficiently generates arbitrary-length, high-quality image sequences by iteratively refining a coarse grid: initial sampling via a Stage 1 diffusion model is followed by iterative refinement using noised control signals to produce spatiotemporally consistent grid elements, then interpolation generates eight new frames between each pair, and finally, a Stage 2 super-resolution step enhances both spatial and temporal fidelity, achieving a sequence length of <span class="katex-eq" data-katex-display="false">L = \frac{12N - 4}{N}</span> and surpassing state-of-the-art performance.
A grid-based autoregressive sampling algorithm efficiently generates arbitrary-length, high-quality image sequences by iteratively refining a coarse grid: initial sampling via a Stage 1 diffusion model is followed by iterative refinement using noised control signals to produce spatiotemporally consistent grid elements, then interpolation generates eight new frames between each pair, and finally, a Stage 2 super-resolution step enhances both spatial and temporal fidelity, achieving a sequence length of L = \frac{12N - 4}{N} and surpassing state-of-the-art performance.

GriDiT factorizes long image sequence generation into low-resolution coarse prediction and high-resolution refinement using a grid-based representation and autoregressive sampling.

Despite advances in deep learning, modeling long image sequences remains computationally expensive and often suffers from inconsistencies. This paper introduces GriDiT: Factorized Grid-Based Diffusion for Efficient Long Image Sequence Generation, a novel approach that decouples sequence generation into a coarse, low-resolution stage followed by high-resolution refinement. By representing sequences as grids and leveraging diffusion models with self-attention, GriDiT achieves superior synthesis quality, increased efficiency, and improved long-range coherence without architectural modifications. Could this factorization strategy unlock more scalable and effective generative modeling across diverse sequential data modalities?

Deconstructing Temporal Complexity: Introducing GriDiT

The creation of realistic and extended video sequences presents a substantial hurdle for contemporary generative models. Existing techniques often falter when tasked with producing high-fidelity visuals that unfold over many frames, largely due to the exponential increase in computational demands as sequence length grows. Maintaining coherence and visual consistency across these longer durations proves particularly difficult, frequently resulting in flickering, distortions, or a breakdown of realistic motion. This limitation restricts the potential applications of these models in areas such as film production, virtual reality, and advanced simulations, highlighting the need for innovative approaches capable of generating compelling, long-form visual content without sacrificing quality or efficiency.

Generating extended image sequences presents a substantial challenge for current generative models due to an exponential increase in computational demands. Traditional approaches treat each frame as independent, failing to efficiently leverage temporal relationships and leading to inconsistencies-such as flickering or abrupt changes-across the sequence. The computational burden quickly becomes prohibitive as sequence length increases, requiring immense processing power and memory. This limitation hinders the creation of realistically extended animations or simulations, as maintaining both visual fidelity and temporal coherence becomes increasingly difficult with each added frame. Consequently, existing methods often fall short in producing long-form, high-quality video content, necessitating a new approach to decouple computational cost from sequence length and ensure consistent visual narratives.

GriDiT overcomes the challenges of long-form image sequence generation by strategically separating the generative process into two distinct stages: coarse generation and fine-grained refinement. This factorization allows the model to first establish a low-resolution, temporally consistent “skeleton” of the sequence, capturing the broad strokes of motion and content. Subsequently, GriDiT focuses computational resources on enriching this skeleton with high-frequency details, effectively building upon a stable foundation rather than attempting to generate all visual information simultaneously. This decoupling not only enhances temporal coherence across extended sequences but also significantly improves efficiency, as the initial coarse generation requires substantially less processing power than direct high-resolution synthesis. The result is a system capable of producing visually compelling, long-form videos with reduced computational demands and improved stability.

GriDiT significantly eases the computational demands of generating extended image sequences by strategically prioritizing a two-stage approach. Rather than directly attempting to render high-resolution frames, the model initially constructs a low-resolution ‘skeleton’ of the entire sequence. This preliminary step drastically reduces the processing load, as fewer pixels are manipulated during the initial stages. Subsequent refinement then focuses on upscaling and detailing these low-resolution frames, adding fine-grained textures and realism. This factorization not only allows for the generation of substantially longer sequences, but also achieves a demonstrated speedup exceeding two times that of current state-of-the-art methods, opening new possibilities for applications requiring extended, high-fidelity video synthesis.

Our method improves image sequence generation by decoupling dynamics modeling at low resolution from high-resolution frame super-resolution, leveraging the DiT’s self-attention to achieve superior synthesis quality, reduced sampling time, and lower data requirements compared to state-of-the-art approaches that treat sequences as monolithic tensors.
Our method improves image sequence generation by decoupling dynamics modeling at low resolution from high-resolution frame super-resolution, leveraging the DiT’s self-attention to achieve superior synthesis quality, reduced sampling time, and lower data requirements compared to state-of-the-art approaches that treat sequences as monolithic tensors.

Architectural Foundation: DiT and Factorization in Action

GriDiT employs the Diffusion Transformer (DiT) architecture as its foundational element for image generation, utilizing it in a two-stage process. Initially, DiT generates a low-resolution image sequence, establishing the overall composition and content. Subsequently, the same DiT model is applied to refine this initial output, increasing the resolution and adding detail. This dual application of DiT streamlines the generation pipeline, leveraging a single model for both coarse and fine-grained image synthesis, and avoids the need for separate low- and high-resolution models.

The Diffusion Transformer (DiT) utilizes self-attention mechanisms to effectively model complex data distributions by weighting the importance of different data points when generating outputs. Unlike convolutional approaches with fixed receptive fields, self-attention allows each data point to directly attend to all other points in the sequence, capturing long-range dependencies and intricate relationships within the data. This is achieved through the calculation of attention weights based on query, key, and value vectors derived from the input data, enabling the model to dynamically focus on the most relevant information during the diffusion process. The resulting attention maps effectively represent the probabilistic relationships within the data distribution, improving the model’s ability to generate coherent and realistic outputs.

Positional embeddings are integral to GriDiT’s functionality as they provide the model with information regarding the location of each element within the generated image grid. Unlike standard diffusion models that may not inherently understand spatial relationships, GriDiT utilizes 3D positional embeddings to encode both the x, y coordinates defining the image plane and the temporal step within the diffusion process. These embeddings are added to the feature vectors at each layer of the Diffusion Transformer, allowing the model to differentiate between elements at different positions and time steps, thus preserving structural coherence and preventing artifacts during image generation. Without accurate positional encoding, the model would struggle to assemble the generated features into a logically consistent and spatially accurate image.

The integration of a Variational Autoencoder (VAE) into the Diffusion Transformer (DiT) architecture significantly improves latent space representation. This enhancement allows DiT to achieve performance levels comparable to models trained on complete datasets, while requiring only 10% of the training data. The VAE facilitates efficient data compression and reconstruction, enabling the model to generalize effectively from limited examples, particularly beneficial in data-critical domains where acquiring large, labeled datasets is challenging or expensive. This reduction in data dependency is achieved by learning a probabilistic mapping between input data and a lower-dimensional latent space, allowing the model to capture essential features with fewer training instances.

Our two-stage training pipeline utilizes a latent Diffusion Transformer (DiT) to first generate a low-resolution, coarse image sequence organized as a grid of subsampled frames, and then refines individual frames via a conditional DiT-based super-resolution network that recovers details lost during initial <span class="katex-eq" data-katex-display="false">\downarrow K</span> downsampling and subsequent <span class="katex-eq" data-katex-display="false">\uparrow K</span> upsampling, as detailed in section 3.3.
Our two-stage training pipeline utilizes a latent Diffusion Transformer (DiT) to first generate a low-resolution, coarse image sequence organized as a grid of subsampled frames, and then refines individual frames via a conditional DiT-based super-resolution network that recovers details lost during initial \downarrow K downsampling and subsequent \uparrow K upsampling, as detailed in section 3.3.

Scalable Synthesis: Grid-Based Autoregressive Sampling

GriDiT utilizes a grid-based autoregressive sampling strategy for generating image sequences of indefinite length. This approach constructs images iteratively by predicting and filling in grid cells, effectively decomposing the generation process into a series of conditional predictions. Each new cell’s generation is conditioned on previously generated cells, establishing an autoregressive dependency. By operating on a grid representation, the method avoids the limitations of directly generating pixel sequences and facilitates scalable, long-form sequence creation without the typical degradation in coherence observed in other generative models.

Iterative refinement of image grids forms the core of GriDiT’s scalable sequence generation. The process begins with a low-resolution grid which is progressively upsampled and refined through multiple passes. Each iteration builds upon the previous, incrementally increasing detail and coherence. This approach avoids the computational bottlenecks associated with generating entire frames independently, as only the differences between iterations are processed. Consequently, the computational cost scales more favorably with sequence length compared to methods requiring full frame generation at each step, enabling the creation of arbitrarily long and detailed image sequences with improved efficiency.

The GriDiT architecture incorporates inpainting techniques as an integral part of its iterative sampling process to address incomplete or missing frame data during sequence generation. Specifically, after each refinement step, areas of the image grid lacking sufficient information are identified and filled using an inpainting model trained to predict plausible content based on surrounding pixels. This allows the model to effectively extrapolate and maintain visual coherence, even when generating long sequences where initial predictions may deviate from later refined outputs. The inpainting stage operates directly on the grid representation, ensuring that newly generated content seamlessly integrates with existing frames and contributes to a consistent visual narrative throughout the generated sequence.

Image grids function as a structured data representation for autoregressive image sequence generation, enabling consistent outputs over extended lengths. Unlike methods susceptible to cumulative errors, the grid-based approach maintains spatial and temporal coherence by processing and refining image data within a defined structure. Evaluations demonstrate that sequences generated using this method exhibit superior long-range consistency, specifically showing no instances of abrupt or illogical transitions – termed ‘random jumps’ – even when generating sequences extending to 1024 frames. This stability is achieved through the organized processing of image data within the grid, minimizing the propagation of errors common in traditional autoregressive models.

Grid-based autoregressive sampling on the Taichi dataset, using a <span class="katex-eq" data-katex-display="false">K=8</span> control signal, achieves improved spatiotemporal consistency at the expense of per-frame image quality.
Grid-based autoregressive sampling on the Taichi dataset, using a K=8 control signal, achieves improved spatiotemporal consistency at the expense of per-frame image quality.

Expanding the Horizon: Applications and Future Directions

GriDiT’s architectural design allows it to move beyond the creation of typical image sequences, achieving notable success in specialized applications demanding complex data generation. Specifically, the model demonstrates strong capabilities in generating realistic CT volumes – crucial for medical imaging and diagnostics – and crafting compelling SkyTimelapse sequences, capturing extended atmospheric changes with detail. This versatility stems from GriDiT’s ability to effectively model temporal dependencies and maintain visual coherence over extended sequences, proving its adaptability beyond conventional video synthesis and opening avenues for its use in fields requiring high-fidelity, long-form visual data.

The GriDiT model’s capacity for generating extended, coherent visual sequences faced rigorous testing through the Taichi Dataset, a benchmark specifically designed to challenge long-form video generation capabilities. This dataset, known for its complexity and demand for temporal consistency, served as a proving ground for the model’s refinement stage. Results demonstrate GriDiT not only successfully navigates the intricacies of the Taichi Dataset, but also produces high-quality sequences that maintain visual fidelity over extended durations-a crucial element for truly immersive experiences. The model’s performance on this challenging dataset underscores its potential for applications requiring sustained, realistic visual narratives, moving beyond simple short-form content creation.

To enhance the efficiency of GriDiT’s refinement stage, a novel training strategy incorporates lossy compression techniques. This approach deliberately introduces controlled data degradation during training, forcing the model to learn robust feature representations and prioritize essential details for reconstruction. By learning to recover information from compressed inputs, GriDiT becomes significantly more adept at generating high-quality sequences with reduced computational cost. This not only accelerates the sampling process but also demonstrates the model’s capacity to effectively handle imperfect or incomplete information – a crucial capability for real-world applications where data integrity is not always guaranteed. The result is a streamlined refinement process that maintains comparable performance while requiring less data and computational resources.

The development of GriDiT signifies a substantial advancement in generative modeling, promising to reshape the creation of realistic and immersive visual experiences. By achieving a greater than two-fold increase in sampling speed-without sacrificing the quality of generated sequences-the model dramatically lowers the computational cost associated with producing long-form video. This efficiency is further enhanced by GriDiT’s capacity to maintain comparable performance even when trained on reduced datasets, making high-fidelity sequence generation more accessible and practical for a wider range of applications. The implications extend to fields requiring detailed visual simulations, potentially revolutionizing areas like virtual reality, medical imaging, and cinematic content creation through faster iteration and reduced resource demands.

Grid-based autoregressive sampling on the Taichi dataset achieves slightly improved per-frame quality at the cost of reduced spatiotemporal consistency when using a <span class="katex-eq" data-katex-display="false">K=2</span> control signal.
Grid-based autoregressive sampling on the Taichi dataset achieves slightly improved per-frame quality at the cost of reduced spatiotemporal consistency when using a K=2 control signal.

The pursuit of efficient long image sequence generation, as demonstrated by GriDiT, echoes a fundamental principle of mathematical elegance. The paper’s factorization of the generation process – coarse-to-fine resolution and grid-based representation – reveals an inherent logic in tackling complexity. As Geoffrey Hinton once stated, “The way the brain works is deeply connected to the way we do machine learning.” This is particularly evident in GriDiT’s approach to maintaining long-range consistency, mirroring the brain’s ability to build coherent representations from sequential data. The method’s success isn’t merely about achieving high visual fidelity; it’s about a provable architecture that addresses the challenges of autoregressive sampling and computational efficiency, embodying a structured and logical solution.

Future Directions

The pursuit of generating extended image sequences, as demonstrated by GriDiT, inevitably encounters the limitations inherent in discretizing continuous phenomena. While factorization into coarse and fine scales offers a pragmatic improvement in efficiency, it does not fundamentally address the issue of information loss. The true measure of progress will not be in achieving visually pleasing outputs, but in constructing models that demonstrably preserve information across temporal scales-a task demanding a more rigorous mathematical formulation than is currently typical.

Current approaches, including this work, largely rely on autoregressive sampling-a computationally expensive process. The elegance of a truly scalable solution likely resides not in more efficient sampling techniques, but in abandoning it altogether. Exploring non-autoregressive models capable of generating entire sequences in parallel-perhaps through novel applications of operator theory-represents a conceptually more satisfying, albeit significantly more challenging, path. The grid-based representation, while effective, also presents an artificial constraint; future work could investigate whether learned, adaptive representations might yield superior results, provided their computational complexity remains tractable.

Ultimately, the field must move beyond empirical validation and embrace formal verification. A model that merely appears to maintain long-range consistency is insufficient. The goal should be provable consistency-a guarantee that the generated sequence adheres to underlying physical or logical constraints. Such a shift would necessitate a departure from the current emphasis on perceptual realism and a renewed focus on mathematical purity.

Original article: https://arxiv.org/pdf/2512.21276.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-27 18:48