GridShow: Omni Visual Generation

Cong Wan Xiangyang Luo Zijian Cai Yiren Song Yunlong Zhao Yifan Bai^† Yuhang He Yihong Gong

Abstract

In this paper, we introduce GRID, a novel paradigm that reframes a broad range of visual generation tasks as the problem of arranging grids, akin to film strips. At its core, GRID transforms temporal sequences into grid layouts, enabling image generation models to process visual sequences holistically. To achieve both layout consistency and motion coherence, we develop a parallel flow-matching training strategy that combines layout matching and temporal losses, guided by a coarse-to-fine schedule that evolves from basic layouts to precise motion control. Our approach demonstrates remarkable efficiency, achieving up to 35 $\times$ faster inference speeds while using $<\frac{1}{1000}$ of the computational resources compared to specialized models. Extensive experiments show that GRID exhibits exceptional versatility across diverse visual generation tasks, from Text-to-Video to 3D Editing, while maintaining its foundational image generation capabilities. This dual strength in both expanded applications and preserved core competencies establishes GRID as an efficient and versatile omni-solution for visual generation. Our code is available at: https://github.com/Should-AI-Lab/GRID.

Visual Grid Generation

⁰⁰footnotetext:

\dagger

Project Lead

Refer to caption — Figure 1: Framework Overview: We transforms temporal and view sequences into structured layout spaces, enabling consistent synthesis for diverse generation and interpolation tasks from text and image/frame inputs.

1 Introduction

Film strips demonstrate an elegant approach in visual arts: by arranging temporal sequences into structured grids, allowing time-based narratives to be displayed in layouts while maintaining their narrative coherence and visual connections. This organization does more than preserve chronological order - it enables efficient content manipulation, comparison, and editing. Drawing inspiration from this intuitive yet powerful organizational principle, we propose a fundamental question: Can we directly reframe various visual generation tasks as how to layout, where key visual elements (such as multiple viewpoints or video frames) are treated as grid “layout”?

To answer this, a natural starting point emerges from the recent breakthroughs in text-to-image generation. Models like (Esser et al., 2024; Baldridge et al., 2024; Betker et al., 2023) have shown remarkable capabilities in understanding and generating complex spatial relationships. This leads us to a straightforward initial attempt: we test their ability to generate grid-arranged multi-view layouts through simple prompting (Figure 8). However, our experiments reveal that the current models, despite their advanced capabilities, fall short in two fundamental aspects (detailed in Section A.1):

•

Layout Control: They fail to maintain both consistent grid structures and visual appearances across layouts.
•

Motion Coherence: When given specific motion instructions (e.g., “rotate clockwise”), they cannot reliably create sequential movements across layouts.

To address these, we introduce GRID, which reformulates temporal sequences as grid layouts, allowing image generation models to process the entire sequence holistically and learn both spatial relationships and motion patterns.

Building on this grid-based framework, we develop a parallel flow-matching training strategy that leverages large-scale web datasets, where video frames are arranged in grid layouts. The model learns to simultaneously generate all frames in these structured layouts through a base parallel matching loss, achieving consistent visual appearances and proper grid arrangements. This approach naturally utilizes the models’ self-attention mechanisms to capture and maintain spatial relationships across the entire layout.

For precise motion control, we further incorporate dedicated temporal loss and motion-annotated datasets during fine-tuning. The temporal loss ensures smooth transitions between adjacent frames, while the motion annotations help learn specific patterns like “rotate clockwise”. These components are balanced through a coarse-to-fine training schedule to achieve both fluid motion and consistent spatial structure.

Through our carefully designed training paradigm, GRID achieves remarkable efficiency gains, demonstrating a substantial 6-35 $\times$ acceleration in inference speed compared to specialized expert models, while requiring merely $\frac{1}{1000}$ of the training computational resources. Our framework exhibits exceptional versatility, achieving competitive or superior performance across a diverse spectrum of generation tasks, including Text-to-Video, Image-to-Video, and Multi-view generations, with performance improvements of up to 23 $\%$ . Furthermore, we extend the capabilities of GRID to encompass Video Style Transfer, Video Restoration, and 3D Editing tasks, while preserving its original strong image generation capabilities for image tasks such as image editing and style transfer. This unique combination of expanded capabilities and preserved foundational strengths establishes GRID as a omni-solution for visual generation.

Our main contributions are summarized as follows:

•

Novel Grid-based Framework: We introduce a new paradigm that reformulates temporal sequences as grid layouts, enabling holistic processing of visual sequences through image generation models.
•

Coarse-to-fine Training Strategy: We develop a parallel flow-matching strategy combining layout matching and temporal coherence losses, with a coarse-to-fine training schedule that evolves from basic layouts to more precise motion control.
•

Omni Generation: We demonstrate strong performance across multiple visual generation tasks while maintaining low computational costs. Our method achieves results comparable to task-specific approaches, despite using a single, efficient framework.

2 Layout Generation

Inspired by film strips that organize temporal sequences into structured grids, we present GRID, a grid layout-driven framework that reformulates multiple visual generation tasks through grid-based representation. Our GRID consists of three key components: 1) Grid Representation, which enables layout-based video organization for comprehensive visual generation; 2) Parallel Flow Matching, which ensures temporal coherence in successive grids; and 3) Coarse-to-fine Training, which enhances motion control capabilities. The framework architecture is illustrated in Figure 2 (left).

2.1 Grid Representation

Existing text-to-image models, with inherent attention mechanisms, enable image manipulation and editing by generating new content from partial image information and semantic instructions, which inspires us to extend this capability to temporal generation by introducing a novel input paradigm, termed Grid Representation, that generates temporal content from keyframe visuals and semantic instructions.

Consider a general visual generation task that transforms an input condition $c_{content}$ (such as a text description $T$ ) into a sequence of images $(I_{0},...,I_{f})$ . We propose a grid layout specification $c_{\text{layout}}$ that arranges temporal frames into a structured grid within a single image, where each cell $(i,j)$ contains a specific image $I_{ij}$ . As shown in Figure 2 (right), when this grid structure is input into a conventional text-to-image model, the model’s inherent attention mechanisms naturally extend their functionality to process this spatial arrangement as:

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Self-attention Expansion: The standard self-attention mechanism $(I,I)$ (yellow block) expands into two distinct components:
- –
  
  Intra-frame attention $(I_{i},I_{i})$ : Maintains feature learning within individual grid cells
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  Cross-frame attention $(I_{i},I_{j})$ : Enables temporal relationships between different grid cells
•

Cross-attention Extension: The text-image cross-attention $(I,T)$ (pink block) extends naturally to provide uniform text conditioning across all frame positions

Our approach demonstrates that thoughtful problem restructuring can be more effective than architectural modifications. By reorganizing the input space into a grid representation, standard text-to-image models can naturally handle temporal generation without architectural changes (see Appendix A.2 for detailed attention mechanism analysis). This grid-based design offers two key advantages: First, it enables parallel generation of all frames and eliminates the error accumulation problems common in autoregressive approaches (Tian et al., 2024). Second, by leveraging the inherent consistency priors within pretrained image generation models, our approach effectively transfers their learned spatial consistency to temporal and multi-view coherence. This crucial advantage avoids the need for extensive pretraining on massive video datasets, as the grid representation naturally extends existing image-level understanding to sequence generation. Additionally, through flexible layout conditioning ( $c_{\text{layout}}$ ), our model shows strong generalization capabilities beyond training constraints (Section A.5), suggesting a promising solution to the fixed-length limitations of existing methods. Additionally, our grid representation supports diverse input types, including multi-view images and multi-frame sequences, laying the foundation for a comprehensive omni-generation model that bridges image and video domains.

2.2 Parallel Flow Matching

To fully leverage the potential of our grid representation, we employ parallel flow matching (Esser et al., 2024) to ensure temporal coherence across consecutive grids. For each training sample $\mathbf{I}=(I_{ij})$ , we generate a corresponding text representation by integrating layout specifications with content descriptions: $c^{\prime}=[c_{\text{layout}},c_{\text{content}}]$ . Here, $c_{\text{layout}}$ encodes the spatial structure (e.g., a sequence arranged in $m\times n$ grids), while $c_{\text{content}}$ captures the visual content as well as the temporal relationships between frames.

Parallel Flow Evolution with Global Awareness. Our grid representation integrates seamlessly with flow matching by organizing temporal frames into a unified grid image $\mathbf{I}$ . This enables parallel evolution of frames through the following process:

\mathbf{I}_{t}=(1-t)\mathbf{I}+t\epsilon,\quad t\sim\mathcal{U}(0,1),\quad\epsilon\sim\mathcal{N}(0,I)

(1)

Unlike autoregressive approaches that generate frames sequentially, our formulation allows all frames to evolve simultaneously from noise to target distribution through the model’s native prediction process:

f:(\mathbf{I}_{t},t,c^{\prime})\rightarrow\epsilon-\mathbf{I}

(2)

In this formulation, each frame $(I_{ij})_{t}$ interacts with others within the grid, enabling mutual influence. This interaction naturally enforces temporal consistency across the entire sequence.

2.3 Coarse-to-Fine Training

Training models for temporal understanding in grid representation demands extensive video data to achieve key capabilities like identity preservation and motion consistency - essential features for video and multi-view generation that text-to-image models typically lack. This training process faces two main challenges from mixed quality of available data: the abundance of low-quality internet videos, and high computational costs of processing high-resolution footage. We tackle these limitations through a coarse-to-fine training strategy that combines two key components: data curriculum and loss dynamic. This dual approach optimizes both training efficiency and model performance, enabling effective use of diverse data sources while minimizing computational overhead. Our strategy enhances the capabilities of our flow-based framework without sacrificing training efficiency.

Data Curriculum. Our training strategy follows a Coarse-to-Fine approach, starting with foundational learning and advancing to refinement:

•

Coarse Phase: In the initial phase, we utilize large-scale Internet datasets, including WebVid, TikTok, and Objaverse, which are designed with uniform $c_{\text{layout}}$ specifications. Although the content descriptions ( $c_{\text{content}}$ ) are automatically generated by GLM-4V-9B (Du et al., 2022) and may lack precise control details, the vast scale and diversity of this data—albeit at lower resolutions—provide a strong basis for developing robust spatial understanding and basic layout structures.
•

Fine Phase: Building on the foundational knowledge from the coarse phase, we transition to training with carefully curated, high-resolution samples. These samples are paired with detailed descriptions generated by GPT-4 (OpenAI, 2023), offering explicit spatial and temporal instructions. As shown in Figure 2, these high-quality captions facilitate fine-grained control over complex layout variations, enabling the model to handle intricate spatial and temporal dynamics effectively.

Loss Dynamic. Alongside the data curriculum, we gradually incorporate temporal supervision by employing a dynamic training objective:

\mathcal{L}_{\text{total}}=\mathcal{L}_{\text{base}}+\alpha\mathcal{L}_{\text{flow}}

(3)

The base loss ensures accurate appearance generation for each grid position:

\mathcal{L}_{\text{base}}=\mathbb{E}_{t,\epsilon}[|\epsilon-\epsilon_{\theta}(\mathbf{I},t,c^{\prime})|^{2}]

(4)

The flow loss captures temporal dynamics through directional changes between adjacent positions:

	$\displaystyle\Delta\epsilon^{ij}$	$\displaystyle=\begin{cases}\epsilon^{ij}-\epsilon^{i,j-1}&\text{if }j>0\\ \epsilon^{i,0}-\epsilon^{i-1,n}&\text{if }j=0\end{cases}$		(5)
	$\displaystyle\Delta\epsilon_{\theta}^{ij}$	$\displaystyle=\begin{cases}\epsilon_{\theta}^{ij}-\epsilon_{\theta}^{i,j-1}&\text{if }j>0\\ \epsilon_{\theta}^{i,0}-\epsilon_{\theta}^{i-1,n}&\text{if }j=0\end{cases}$		(5)

\mathcal{L}_{\text{flow}}=\mathbb{E}_{t,\epsilon}[|\Delta\epsilon-\Delta\epsilon_{\theta}(\mathbf{I},t,c^{\prime})|^{2}]

(6)

The weight $\alpha$ gradually increases from 0 to a preset upper bound, allowing the model to first establish precise content generation capabilities before focusing on temporal dynamics. This staged evolution of the loss function complements our data curriculum, enabling the model to effectively learn both the spatial and temporal aspects of generation in a coordinated manner.

2.4 Omni Inference

We propose an omni-inference framework designed to handle a wide range of generation tasks using a reference-guided grid layout initialization. The core idea of our approach is to unify different generation tasks by employing a well-structured initialization process combined with controlled grid noise injection. At the same time, we ensure consistency with the reference through the use of a binary mask.

Given a reference image $I_{\text{ref}}$ or key frames $(I_{0},...,I_{m-1})$ , we construct a grid structure $\mathbf{I}=(I_{ij})_{m\times n}$ . For single-image expansion and frame interpolation tasks, we initialize the grid as:

I_{ij}=\begin{cases}I_{\text{ref}}&\text{expansion}\\ (1-\frac{j}{n})I_{i,0}+\frac{j}{n}I_{i+1,0}&\text{interpolation}\end{cases}

(7)

The generation process requires both flexibility and reference consistency. To achieve this balance, we introduce controlled grid noise injection instead of starting from pure noise:

\mathbf{I}_{T}=(1-T)\mathbf{I}+T\epsilon,\quad\epsilon\sim\mathcal{N}(0,I)

(8)

where $T$ denotes the starting time. This noise injection enables diverse generation while retaining the initialization structure.

To maintain reference consistency during generation, we employ a binary mask $M\in\{0,1\}^{m\times n}$ :

M_{ij}=\begin{cases}0&\text{if }(i,j)\text{ contains reference frame}\\ 1&\text{otherwise}\end{cases}

(9)

This mask modulates the update process:

\mathbf{I}_{t}=(1-M)\odot\mathbf{I}_{\text{ref}}+M\odot\mathbf{I}_{t}

(10)

ensuring reference frames remain unchanged while allowing other regions to evolve.

The noise level $T$ plays a key role in balancing generation quality. A large $T$ leads to pure noise with poor reference consistency, while a small $T$ yields near-duplicates. Our experiments show $T\in[0.8,1.0]$ a good balance between diversity and fidelity.

3 Experiments

3.1 Experimental Setup

Datasets

We train our model separately for video generation and multi-view generation tasks, both following a two-stage strategy: (1) For coarse-level training, we combine video clips from WebVid (Bain et al., 2021), and TikTok (Jafarian & Park, 2022) arranged in 8×8 and 4×4 grid layouts for video generation, and 30K sequences from Objaverse (Deitke et al., 2023) in 4×6 grids for multi-view generation. Each sequence is paired with automated captions and GLM-generated annotations emphasizing spatial and temporal relationships, using the sequence’s inherent attributes (e.g., category labels) and visual content as queries. (2) For fine-grained control, we construct high-quality datasets of 1K sequences with structured annotations for each task. We first manually create exemplar annotations to establish a consistent format, then use these as few-shot examples for GPT-4o to generate precise control instructions while maintaining annotation consistency across the dataset.

Implementation Details

We implement GRID based on the FLUX-dev, initializing from its pretrained weights. For video generation training, we adopt LoRA with ranks of 16 and 256, training for 10K steps with batch size 4 across 8 A800 GPUs using AdamW optimizer (learning rate 1e-4). The temporal loss weight $\alpha$ starts from 0 and gradually increases to a maximum of 0.5. For multi-view generation, we train on 30K sequences for 1.5K steps using LoRA rank 256 and Ours-EF using LoRA rank 16. During inference, we use a guidance scale of 3.5 and sampling step of 20.

Evaluation Protocol

We evaluate our model on three distinct generation tasks: (1) Text-to-video generation on UCF-101 dataset (Soomro et al., 2012), evaluated using FVD (Unterthiner et al., 2019) (I3D backbone) and IS (Xu et al., 2018). We evaluate both 16-frame and 64-frame generation settings; (2) Image-to-video generation on a randomly sampled subset of 100 TikTok videos, measured by FVD and CLIP_img score; (3) Multi-view generation on Objaverse, where we evaluate on 30 randomly selected objects with 24 frames per sequence at different viewpoints to assess 4D generation capabilities. We compute FVD, CLIP metrics, following (Liang et al., 2024).

3.2 Main Results

Table 1: Comprehensive Generation Results. Comparison across different generation tasks. Our model demonstrates superior efficiency while maintaining competitive quality across varying sequence lengths. Notably, while existing methods (AnimateDiffv3, VideoCrafter2) are limited to 16-frame generation, our approach scales efficiently to 64-frame sequences with only linear time increase. Underlined values indicate the best results among our variants, and bold values show the best across all methods. Test Time indicates the average sampling time per sequence.

	Text-to-Video (16-frame)			Text-to-Video (64-frame)			Image-to-Video
Method	FVD $\downarrow$	IS $\uparrow$	Time(s) $\downarrow$	FVD $\downarrow$	IS $\uparrow$	Time(s) $\downarrow$	FVD $\downarrow$	CLIP ${}_{img}\uparrow$	Para ${}_{train}\downarrow$
AnimateDiffv3	464.1	35.24	12	-	-	-	250.9	0.9229	419M
VideoCrafter2	424.2	32.00	15	-	-	-	-	-	919M
OpenSora1.2	472.0	39.07	12	1000.5	37.11	66	-	-	1.5B
CogVideo5b	301.1	36.27	48	740.1	34.82	132	122.5	0.9185	5B
Ours(Stage1)	482.1	32.46	7.2	1003.2	32.48	24	115.5	0.9598	160M
Ours(Stage1+2)	438.3	36.56	7.2	994.6	36.47	24	104.6	0.9695
Ours(Full)	418.9	37.34	7.2	721.6	36.63	24	93.7	0.9709

Table 2: Quantitative comparison of Multi-view Generation Results on Text-to-Multyview, Image-to-Multyview and Multyview interpolation Tasks. Time indicates the whole time cost during inference

Task	Method	CLIP-F↑	CLIP-O↑	FVD↓	Time↓
Text 2MV	4DFY	0.8092	0.6163	390.4	3h
	Ours-EF	0.9060	0.6189	355.6	6m
	Ours	0.9427	0.6247	324.3	6m
Image 2MV	STAG4D	0.88	0.64	475.4	3h30m
	Ours-EF	0.9392	0.6580	333.7	6m
	Ours	0.9486	0.6554	350.6	6m
Interp olation	Ours-EF	0.9356	0.7223	348.2	6m
Interp olation	Ours	0.9543	0.7415	295.1	6m

We compare our approach with several state-of-the-art methods from well-established video/multyview generation model series, all of which represent the current frontiers in their respective domains.

Text-to-Video Generation

As shown in Table 1, we evaluate our approach on both short (16-frame) and long (64-frame) generation tasks. For 64-frame generation, our full model pushes FVD to 721.6. For 16-frame, we significantly reduce computational costs, achieving 6.7× faster inference (7.2s vs 48s) compared to CogVideo. This efficiency advantage becomes more pronounced in 64-frame generation, where our approach maintains 5.5× faster inference speed. Our staged training demonstrates clear benefits: Stage1 (trained with coarse annotations and $\mathcal{L}_{base}$ only) achieves a FVD of 482.1, which reduces to 438.3 with fine-grained annotations, and further reduces to 418.9 with $\mathcal{L}_{flow}$ .

Image-to-Video Generation

We evaluate on the TikTok dataset, which contains 100 high-quality short videos with diverse motion patterns after our processing and annotation process. For image-guided video synthesis, our method achieves breakthrough performance by reducing FVD to 93.7, a 23% improvement over the second-best result. The CLIP_img score also sees a substantial boost to 0.9709.

Multi-view Generation

We evaluate multi-view generation capabilities on selected Objaverse test set, which contains 30 3D objects from multiple viewpoints. As shown in Table 2, our method achieves transformative improvements in text-to-multi-view synthesis. We improve CLIP-F score by 13.4 points to 0.9427 and reduce FVD by 17% to 324.3, while dramatically cutting inference time from 3 hours to just 6 minutes compared to 4DFY (Bahmani et al., 2024). For image-to-multi-view tasks, we push the CLIP-F score up by 6.9 points to 0.9486 while achieving 35× faster inference compared to STAG4D (Zeng et al., 2024).

3.3 Extension Capabilities

Beyond the primary generation tasks, we demonstrate GRID’s strong zero-shot generalization capabilities across diverse video and multi-view applications without any task-specific training or architectural modifications. The layout-based design enables natural adaptation to various downstream tasks through prompt engineering alone.

Video Style Transfer

We explore zero-shot style transfer capabilities. As shown in Figure 6, our model successfully transfers distinctive features from fox, red panda, and tiger while maintaining the original temporal coherence and motion patterns.

Video Restoration

Our architecture’s multi-scale processing capability enables effective video restoration without explicit training. Figure 12 shows our model’s performance in recovering high-quality videos from severely degraded inputs (with Gaussian blur and block masking).

3D Editing

We further showcase the model’s understanding of 3D structure through zero-shot appearance editing. As demonstrated in Figure 7, given a sequence of human poses, our model can generate diverse variations by controlling attributes like hair color and clothing style through simple text. This capability stems from our layout-based training strategy, which naturally captures the relationship between spatial arrangement and semantic attributes.

These extensions validate the strong generalization potential of our approach. By leveraging the grid-based architecture and rich layout representations learned during training, GRID adapts to various downstream tasks without modifications, offering a versatile foundation for video and multi-view applications. More potential applications are demonstrated in Appendix A.7. Furthermore, since our method preserves FLUX’s original architecture, it retains all image generation capabilities of the base model, as shown in Appendix A.7.4.

4 Related Work

Text-to-Image Generation

Diffusion models (Sohl-Dickstein et al., 2015; Ho et al., 2020) have fundamentally transformed image generation by employing iterative denoising processes to synthesize high-quality outputs. Subsequent advancements (Rombach et al., 2022; Podell et al., 2023; Ramesh et al., 2022; Saharia et al., 2022) have refined this paradigm leveraging latent spaces with significantly reduced computational costs. Diffusion Transformers (DiT) (Peebles & Xie, 2023) further advanced this area by replacing the U-Net architecture with transformer-based designs. This architectural shift improved training efficiency, paving the way for more scalable and versatile generative frameworks. Building on these, flow matching (Lipman et al., 2022; Esser et al., 2024) reformulates the generation process as a straight-path trajectory between data and noise distributions. More recently, FLUX (BlackForest, 2024), has combined the strengths of DiT and flow matching to achieve efficient and high-quality image generation. These models also integrate powerful language models (Raffel et al., 2020) and joint text-image attention mechanisms. This multimodal understanding has unlocked new possibilities for instruction-following and creative applications. Beyond generating high-quality images, text-to-image models demonstrate a strong spatial understanding that can be naturally extended to temporal dimensions through layout representations, enabling diverse downstream tasks.

Task-Specific Generation

Diffusion-based approaches have shown remarkable progress in generalized video generation tasks (Ho et al., 2022; Blattmann et al., 2023b; Zhang et al., 2023; Blattmann et al., 2023a; He et al., 2023; Zhou et al., 2022; Wang et al., 2023a; Ge et al., 2023; Wang et al., 2023c, b; Singer et al., 2022; Zhang et al., 2023; Zeng et al., 2023). Notable works like VideoLDM (Blattmann et al., 2023b), Animatediff (Guo et al., 2023), and SVD (Chai et al., 2023) advance temporal modeling through specialized architectures. In the multi-view domain, various approaches (Watson et al., 2022; Liu et al., 2023a; Shi et al., 2023b; Long et al., 2024; Shi et al., 2023a; Lu et al., 2024; Li et al., 2023; Liu et al., 2023b; Li et al., 2024; Yang et al., 2024) focus on cross-view consistency through different attention mechanisms and feature space alignments. Recent 4D generation methods (Ren et al., 2023; Liang et al., 2024; Xie et al., 2024b; Sun et al., 2024; Wu et al., 2024) further extend to joint spatial-temporal synthesis, though often facing efficiency challenges or requiring multi-step generation. While these methods achieve remarkable results, they are typically tailored to specific tasks, relying on specialized architectures for image, video, or multi-view generation. Additionally, methods like VideoPoet (Kondratyuk et al., 2023) employ complex cross-modal alignment mechanisms to bridge different generation modes. In contrast, our approach introduces layout generation, an omni framework that transforms temporal and spatial generation into layout representations. This enables seamless multi-modal generation, to address a wide range of tasks through straightforward modifications to input representations, without the need for complex cross-modal alignment mechanisms.

5 Conclusion

We present GRID, a unified layout-based framework bridging video and multi-view generation through efficient grid representation. Our two-stage training strategy enables both robust generation and precise control, while the temporal refinement mechanism enhances motion coherence. Experiments demonstrate significant computational efficiency gains while maintaining competitive performance across tasks. The framework’s strong zero-shot generalization capabilities further enable adaptation to diverse applications without task-specific training, suggesting a promising direction for efficient visual sequence generation.

Impact Statement

This paper introduces research aimed at advancing visual sequence generation through an efficient layout-based framework. However, we must emphasize the potential risks associated with this technology, particularly in facial manipulation applications (Xie et al., 2024a; Luo et al., 2024), where our method could be misused to compromise identity security. Nevertheless, recent advances in adversarial perturbation protection mechanisms (Wan et al., 2024) provide solutions to help users protect their personal data against unauthorized model fine-tuning and malicious content generation. Therefore, we call for attention to these risks and encourage the adoption of defensive techniques to ensure the protection of personal content while advancing the development of generative AI technologies.

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Appendix A Appendix

A.1 Why Flux? Zero-shot Analysis of Foundation Models

To better understand the layout capabilities of existing models before fine-tuning, we conducted a comprehensive zero-shot evaluation comparing three state-of-the-art models: DALLE-3, Flux, and Imagen3. Figure 8 presents their generation results, with each row corresponding to DALLE-3 (top), Flux (middle), and Imagen3 (bottom) respectively.

Our analysis reveals varying degrees of grid layout understanding across models. While all models demonstrate basic grid comprehension, they exhibit different strengths and limitations. For motion control, we observe that precise directional instructions (e.g., clockwise rotation) often result in random orientations across all models, indicating limited spatial-temporal control capabilities.

In terms of grid structure accuracy, DALLE-3 shows inconsistent interpretation of specific layout requirements (e.g., 4×4 or 4×6 grids), while Flux and Imagen3 demonstrate better adherence to specified grid configurations. Notably, Flux exhibits superior understanding of spatial arrangements.

Content consistency across grid cells varies significantly. Both Imagen3 and DALLE-3 show noticeable variations in object appearance across frames, while Flux maintains better consistency in object characteristics throughout the sequence. This superior consistency, combined with its open-source nature, motivated our choice of Flux as the base model for our framework.

A.2 Why is it Natural for GRID to Leverage Built-in Attention Mechanism

Video generation fundamentally requires three key capabilities: spatial understanding within frames, temporal consistency between frames, and semantic control across the entire sequence. Traditional approaches tackle these requirements by implementing separate attention modules, as shown in Figure 9(a). While this modular design directly addresses each requirement, it introduces architectural complexity and potential inconsistencies between modules.

Our key insight is that these seemingly distinct requirements can be unified through spatial reformulation. By organizing temporal sequences into grid layouts, we transform temporal relationships into spatial ones, allowing FLUX’s native attention mechanism to naturally handle all requirements through a single, coherent process.

This unification works through two complementary mechanisms, as illustrated in Figure 9(b). First, the original image self-attention $(I,I)$ automatically extends across the grid structure. When processing grid cells containing different temporal frames, this self-attention naturally splits into inner-frame attention $(I_{i},I_{i})$ and cross-frame attention $(I_{i},I_{j})$ . The inner-frame component maintains spatial understanding within each frame, while the cross-frame component captures temporal relationships - effectively handling both spatial and temporal coherence through a single mechanism.

Second, the text-image cross-attention $(T,[I_{i}]_{i=0}^{f})$ operates globally across all grid cells, enabling unified semantic control. This global operation ensures that textual instructions consistently influence all frames, maintaining semantic coherence throughout the sequence. The grid layout allows this semantic guidance to naturally incorporate both content and temporal specifications, as the attention mechanism can reference the spatial relationships between grid cells.

This reformulation fundamentally changes how temporal information is processed. Rather than treating temporal relationships as a separate problem requiring specialized mechanisms, we transform them into spatial relationships that existing attention mechanisms are already optimized to handle. This approach not only simplifies the architecture but also provides more robust temporal understanding, as it leverages the well-established capabilities of spatial attention mechanisms.

The elegance of this solution lies in its ability to achieve complex temporal processing without architectural modifications. By thoughtfully restructuring the problem space, we enable standard attention mechanisms to naturally extend their capabilities, demonstrating how strategic problem reformulation can be more powerful than architectural elaboration.

A.3 Comparison with Existing Approaches and Computational Efficiency Analysis

Current approaches to video generation can be categorized into two distinct paradigms, each with fundamental limitations in terms of architectural design and computational requirements. We provide a detailed analysis of these approaches and contrast them with our method:

Paradigm 1: Image Models as Single-Frame Generators Methods like SVD and AnimateDiff utilize pre-trained text-to-image models as frame generators while introducing separate modules for motion learning. This approach presents several fundamental limitations:

First, these methods require complex architectural additions for temporal modeling, introducing significant parameter overhead without leveraging the inherent capabilities of pre-trained image models. For instance, AnimateDiff introduces temporal attention layers that must be trained from scratch, while SVD requires separate motion estimation networks.

Second, the sequential nature of frame generation in these approaches leads to substantial computational overhead during inference. This sequential processing not only impacts generation speed but also limits the model’s ability to maintain long-term temporal consistency, as each frame is generated with limited context from previous frames.

Paradigm 2: End-to-End Video Architectures Recent approaches like Sora, CogVideo, and Huanyuan Video attempt to solve video generation through end-to-end training of video-specific architectures. While theoretically promising, these methods face severe practical constraints:

The computational requirements are particularly striking:

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CogVideo requires approximately 35M video clips and an additional 2B filtered images from LAION-5B and COYO-700M datasets
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Open-Sora necessitates more than 35M videos for training
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These models typically demand multiple 80GB GPUs with sequence parallelism just for inference
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Training typically requires thousands of GPU-days, making reproduction and iteration challenging for most research teams

Our Grid-based Framework: A Resource-Efficient Alternative In contrast, GRID achieves competitive performance through a fundamentally different approach:

1. Architectural Efficiency: Our grid-based framework requires only 160M additional parameters while maintaining competitive performance. This efficiency stems from:

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Treating temporal sequences as spatial layouts, enabling parallel processing
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Leveraging existing image generation capabilities without architectural complexity
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Efficient parameter sharing across temporal and spatial dimensions

2. Data Efficiency: We achieve remarkable data efficiency improvements:

\text{Data Reduction}\approx\frac{>35M\text{ videos (previous methods)}}{<35K\text{ videos (our method)}}=1000\times

(11)

This efficiency is achieved through:

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Strategic use of grid-based training that maximizes information extraction from each video
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Effective transfer learning from pre-trained image models
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Focused training on essential video-specific components

3. Computational Accessibility: Our approach enables high-quality video generation while maintaining accessibility for research environments with limited computational resources:

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Training can be completed on standard research GPUs
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Inference requires significantly less memory compared to end-to-end approaches
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The model maintains strong performance across both video and image tasks

This comprehensive analysis demonstrates that our approach not only addresses the limitations of existing methods but also achieves substantial improvements in computational efficiency while maintaining competitive performance. The significant reductions in data requirements and computational resources make our method particularly valuable for practical applications and research environments with limited resources.

A.4 Distinction from In-Context LoRA

Recent work IC-LoRA (Huang et al., 2024b, a) also utilizes grid-based layouts for image generation, which might superficially appear similar to our approach. However, a careful analysis reveals fundamental differences in both theoretical foundation and technical implementation.

Different Theoretical Foundations: The core principle of IC-LoRA is to use grid layouts as a prompt engineering technique, where multiple images are arranged together to provide in-context examples for task adaptation. This is essentially an extension of in-context learning from language models to visual domain. Their grid layout serves merely as a presentation format for example-based learning.

In contrast, our approach fundamentally re-conceptualizes temporal sequences into spatial layouts. Rather than using grids for example presentation, we treat them as an inherent representation of temporal information, where spatial relationships in the grid directly correspond to temporal relationships in the sequence. This enables our model to learn and generate temporal dynamics in a holistic manner.

Distinct Technical Objectives: IC-LoRA’s technical implementation focuses on task adaptation through example pairs. Their method relies on LoRA-based fine-tuning and natural language prompts to define relationships between grid elements. However, this approach has inherent limitations in handling temporal dynamics, as it treats each grid element independently without explicit modeling of their temporal relationships.

Our method, on the other hand, is specifically designed for temporal sequence generation. We introduce parallel flow-matching and dedicated temporal loss functions that explicitly model motion patterns and temporal coherence. This allows our approach to capture and generate complex temporal dynamics that are beyond the capability of example-based methods like IC-LoRA.

Different Application Scopes: While IC-LoRA excels at static, example-based generation tasks through prompt engineering, it struggles with temporal sequence generation due to its fundamental design limitations. Our method, specifically designed for temporal modeling, naturally handles both static and dynamic visual generation tasks while maintaining precise control over temporal dynamics.

This analysis demonstrates that despite the superficial similarity in using grid layouts, our approach represents a fundamentally different direction in visual generation. We independently developed our method to address the specific challenges of temporal sequence generation, resulting in distinct technical contributions that go beyond the capabilities of example-based frameworks like IC-LoRA.

These crucial differences are evidenced by our method’s superior performance in temporal tasks and its ability to maintain consistent motion patterns across sequences - capabilities that are fundamentally beyond the scope of IC-LoRA’s example-based approach.

A.5 Inference Details

For extension tasks (style transfer, restoration, and editing), we modify the omni-inference framework to process full sequences while maintaining temporal coherence. Unlike the reference-guided generation that requires partial initialization and masking, these tasks operate on complete sequences with controlled noise injection for appearance modification.

Given an input sequence represented as a grid structure $\mathbf{I}=(I_{ij})_{m\times n}$ , we initialize the generation process with noise-injected states:

\mathbf{I}_{T}=(1-T)\mathbf{I}+T\epsilon,\quad\epsilon\sim\mathcal{N}(0,I)

(12)

where $T\in[0.8,0.9]$ represents a lower noise level compared to the reference-guided generation. This lower $T$ value helps preserve the original temporal structure while allowing sufficient flexibility for appearance modifications.

A.6 Post-Processing Pipeline

For multi-view generation results, we employ a two-stage enhancement process. First, the generated sequences are processed as video frames to ensure temporal consistency. Subsequently, we apply super-resolution using Real-ESRGAN (Wang et al., ) with anime-video-v3 weights, upscaling from 256×256 resolution to 1024×1024. This enhancement pipeline significantly improves visual quality while maintaining temporal coherence.

Table 3 shows parts of our inference prompts for multyview generation. We basically follow this prompt format.

Common Format	A 24-frame sequence arranged in a 4x6 grid. Each frame captures a 3D model of [subject] from a different angle, rotating 360 degrees. The sequence begins with a front view and progresses through a complete clockwise rotation
Category	Subject Description
Creative Fusion	a skyscraper with knitted wool surface and cable-knit details
	a mechanical hummingbird with clockwork wings and steampunk gears hovering near a neon flower
	a bonsai tree with spiral galaxies and nebulae blooming from its twisted branches
	a phoenix crafted entirely from woven bamboo strips with intricate basketwork details glowing from within
	a jellyfish with a transparent porcelain bell decorated in blue-and-white patterns and ink-brush tentacles
	a coral reef made entirely of rainbow-hued blown glass with intricate marine life formations
	an urban street where buildings are shaped as giant functional musical instruments including a violin apartment and piano mall
	a butterfly with stained glass wings depicting medieval scenes catching sunlight
	a floating city where traditional Chinese pavilions rest on clouds made of flowing silk fabric in pastel colors
	a lion composed of moving gears and pistons that transforms between mechanical and organic forms
	a garden where geometric crystal formations grow and branch like plants with rainbow refractions
	a tree whose trunk is a twisting pagoda with branches of miniature traditional buildings and roof tile leaves
	a phoenix-dragon hybrid creature covered in mirrored scales that create fractal reflections
	a celestial teapot with constellation etchings pouring a stream of stars and nebulae
	an origami landscape where paper mountains continuously fold and unfold to reveal geometric cities and rivers
	a sphere where traditional Chinese ink and wash paintings flow continuously between day and night scenes
Natural Creatures	a Velociraptor in hunting pose with detailed scales and feathers
	a Mammoth with detailed fur and tusks
	a chameleon changing colors with detailed scales
	a white tiger in mid-stride with flowing muscles
	a Pterodactyl with spread wings in flight pose
	an orangutan showing intelligent behavior
	a polar bear with detailed fur texture

Table 3: Prompt format for 360° object rotation generation. All prompts follow the same structural template, varying only in the subject description. The subjects are categorized into creative fusion designs that combine different artistic elements and concepts, and natural creatures that focus on realistic animal representations.

A.7 Potential Applications

Our framework demonstrates significant potential beyond its primary applications.

A.7.1 Creative Multi-view Generation

As shown in Figure 10, our method exhibits remarkable flexibility in combining different conceptual elements to create novel multi-view compositions. The grid-based layout allows for intuitive arrangement and manipulation of various visual elements, enabling creative expressions that would be challenging for traditional approaches. This capability suggests promising applications in creative design, artistic visualization, and content creation.

A.7.2 Flexible Frame Extension

Notably, our model demonstrates strong generalization capability in sequence length. Despite being trained on 4×4 (16-frame) driving scenarios, the model can effectively generate 4×8 (32-frame) sequences by simply adjusting the $c_{l}ayout$ prompt at inference time. As shown in Figure 11, the extended sequences maintain temporal consistency and visual quality comparable to the original training length. This flexibility suggests that our layout-based approach naturally accommodates variable-length generation without requiring explicit retraining, opening possibilities for dynamic content generation across different temporal scales.

A.7.3 Future Extension to Video Understanding

Our layout-based framework shows potential in transforming traditional video understanding tasks into image-domain problems. Unlike conventional autoregressive approaches (Bai et al., 2024) that process frames sequentially, our method arranges frames in a grid layout, enabling parallel processing and global temporal modeling. This approach could benefit various video understanding tasks: for video-text retrieval, the layout representation allows direct comparison between video content and text embeddings across all frames simultaneously; for video question answering, it enables the model to attend to relevant frames across the entire sequence without sequential constraints; for video tracking and other analysis tasks, it avoids error accumulation common in traditional sequential processing. While we have not conducted specific experiments in these directions, our framework’s ability to convert temporal relationships into spatial ones through layouts offers a promising alternative to conventional video understanding paradigms, potentially enabling more efficient and effective multi-modal video analysis.

A.7.4 Maintained Image Generation Ability

Our framework preserves the original Flux model’s image generation capabilities while extending its functionality to handle video sequences. As demonstrated in Figure 13, the model maintains high-quality performance on various image generation tasks such as text-to-image synthesis, image editing, and style transfer. This preservation of original capabilities alongside newly acquired video generation abilities creates a versatile model that can seamlessly handle both single-image and multi-frame tasks. The ability to maintain original image generation quality while adding new functionality demonstrates the effectiveness of our training approach and the robustness of the layout-based framework.

A.8 Limitations

Our approach faces two primary limitations. First, the grid-based layout design inherently constrains frame resolution due to limitations of the based Text-to-Image models when processing multiple frames simultaneously. Second, our training strategy, based on lora finetuning, shows limitations in text-to-video generation tasks that significantly deviate from the base model’s capabilities. Combined with our relatively small training dataset, this makes it challenging to achieve competitive performance in open-world video generation scenarios requiring complex motion understanding.

A.9 Multyview Camera Parameters

Building upon the dataset opensourced by Diffusion4D (Liang et al., 2024), Table 4 presents camera trajectory parameters, which serve as the foundation for consistent 4D content generation and subsequent reconstruction tasks.

Our camera configuration follows precise mathematical relationships, with cameras positioned at 15-degree intervals along a circle of radius 2 units in the horizontal plane. The systematic progression of coordinate bases ensures optimal coverage while maintaining consistent inter-frame relationships. Each camera’s orientation is defined by orthogonal basis vectors, with the Y vector consistently aligned with the negative Z-axis to establish stable up-direction reference.

Frame	X Vector	Y Vector	Z Vector	Origin
1	[1.0, 0.0, 0.0]	[-0.0, 0.0, -1.0]	[-0.0, 1.0, 0.0]	[0.0, -2.0, 0.0]
2	[0.96, 0.27, -0.0]	[0.0, -0.0, -1.0]	[-0.27, 0.96, -0.0]	[0.54, -1.93, 0.0]
3	[-0.92, 0.4, -0.0]	[0.0, 0.0, -1.0]	[-0.4, -0.92, -0.0]	[0.8, 1.83, 0.0]
4	[-0.99, 0.14, -0.0]	[0.0, 0.0, -1.0]	[-0.14, -0.99, -0.0]	[0.27, 1.98, 0.0]
5	[-0.99, -0.14, 0.0]	[-0.0, 0.0, -1.0]	[0.14, -0.99, -0.0]	[-0.27, 1.98, 0.0]
6	[-0.92, -0.4, 0.0]	[-0.0, 0.0, -1.0]	[0.4, -0.92, -0.0]	[-0.8, 1.83, 0.0]
7	[-0.78, -0.63, 0.0]	[-0.0, -0.0, -1.0]	[0.63, -0.78, 0.0]	[-1.26, 1.55, 0.0]
8	[-0.58, -0.82, -0.0]	[0.0, 0.0, -1.0]	[0.82, -0.58, 0.0]	[-1.63, 1.15, 0.0]
9	[-0.33, -0.94, -0.0]	[0.0, -0.0, -1.0]	[0.94, -0.33, 0.0]	[-1.88, 0.67, 0.0]
10	[-0.07, -1.0, -0.0]	[0.0, 0.0, -1.0]	[1.0, -0.07, 0.0]	[-2.0, 0.14, 0.0]
11	[0.2, -0.98, 0.0]	[0.0, -0.0, -1.0]	[0.98, 0.2, 0.0]	[-1.96, -0.41, 0.0]
12	[0.46, -0.89, 0.0]	[0.0, -0.0, -1.0]	[0.89, 0.46, 0.0]	[-1.78, -0.92, 0.0]
13	[0.85, 0.52, 0.0]	[-0.0, 0.0, -1.0]	[-0.52, 0.85, 0.0]	[1.04, -1.71, 0.0]
14	[0.68, -0.73, -0.0]	[-0.0, 0.0, -1.0]	[0.73, 0.68, 0.0]	[-1.46, -1.37, 0.0]
15	[0.85, -0.52, -0.0]	[0.0, 0.0, -1.0]	[0.52, 0.85, 0.0]	[-1.04, -1.71, 0.0]
16	[0.96, -0.27, 0.0]	[-0.0, -0.0, -1.0]	[0.27, 0.96, -0.0]	[-0.54, -1.93, 0.0]
17	[1.0, -0.0, 0.0]	[0.0, 0.0, -1.0]	[0.0, 1.0, 0.0]	[-0.0, -2.0, 0.0]
18	[0.68, 0.73, 0.0]	[0.0, 0.0, -1.0]	[-0.73, 0.68, 0.0]	[1.46, -1.37, 0.0]
19	[0.46, 0.89, -0.0]	[-0.0, -0.0, -1.0]	[-0.89, 0.46, 0.0]	[1.78, -0.92, 0.0]
20	[0.2, 0.98, -0.0]	[-0.0, -0.0, -1.0]	[-0.98, 0.2, 0.0]	[1.96, -0.41, 0.0]
21	[-0.07, 1.0, 0.0]	[-0.0, 0.0, -1.0]	[-1.0, -0.07, 0.0]	[2.0, 0.14, 0.0]
22	[-0.33, 0.94, 0.0]	[-0.0, -0.0, -1.0]	[-0.94, -0.33, 0.0]	[1.88, 0.67, 0.0]
23	[-0.58, 0.82, 0.0]	[-0.0, 0.0, -1.0]	[-0.82, -0.58, 0.0]	[1.63, 1.15, 0.0]
24	[-0.78, 0.63, -0.0]	[0.0, -0.0, -1.0]	[-0.63, -0.78, 0.0]	[1.26, 1.55, 0.0]

Table 4: Camera Parameters for 24 Frames