EasyAnimate: Video Generation Framework

• EasyAnimate is a high-performance video generation framework integrating transformer-based architectures and latent compression to produce long-duration, high-resolution videos.
• The framework employs a Hybrid Motion Module and Slice VAE for efficient temporal modeling and consistent video synthesis through a modular, multi-stage training pipeline.
• Open-sourced with comprehensive code, pretrained models, and impressive FID improvements, EasyAnimate advances research in text and image-guided video generation.

EasyAnimate is a high-performance and extensible video generation framework that integrates transformer-based architectures with advanced latent compression and temporal modeling techniques. It is specifically designed to address the challenges of generating long-duration, high-resolution videos with consistent temporal dynamics and appearance. The EasyAnimate ecosystem covers the full pipeline from textual prompt encoding to video inference, offering robust support for both image- and video-guided synthesis through its modular training stages. The framework is open-sourced, providing access to code and pretrained models for research and development (Xu et al., 29 May 2024).

1. Architectural Foundations

EasyAnimate augments the DiT (Diffusion Transformer) backbone by embedding video-specific innovations:

• T5 Text Encoder: Utilized to encode textual prompts, leveraging PixArt-α for semantic richness.
• Slice VAE: Acts as a temporal latent compressor, enabling the encoding and decoding of video sequences divided into manageable segments. This allows efficient handling of long videos (up to 144 frames) by reducing quadratic memory scaling.
• Diffusion Transformer Backbone (DiT): Composed of 12–24 U-ViT style blocks, each augmented with a Hybrid Motion Module for spatio-temporal modeling and deep skip connections to stabilize gradient flow across depth.

During inference, an optional image-guided branch processes reference image and mask features through the VAE, fusing them with the text embedding from the T5 encoder.

2. Hybrid Motion Module and Temporal Modeling

The Hybrid Motion Module is integral to EasyAnimate’s temporal consistency:

• Temporal Self-Attention: Models dependencies across successive frames.
• Global Spatio-Temporal Attention: Captures both local and global correlations, essential for coherent motion in large spatial regions or during broad camera movements.
• Mathematical Formulation:

Let $H \in \mathbb{R}^{T \times N \times d}$ denote the per-frame spatial token features. Two attention branches are computed:

$$\text{TemporalAttention}(H) = \text{softmax}( Q_t K_t^T / \sqrt{d_k} ) V_t$$

$$\text{GlobalAttention}(H) = \text{softmax}( Q_g K_g^T / \sqrt{d_k} ) V_g$$

The fused output is:

$$M(H) = H + \alpha \,\text{TemporalAttention}(H) + \beta \,\text{GlobalAttention}(H)$$

where $\alpha$ and $\beta$ are learnable gates.

Ablation studies indicate that omitting either branch (e.g., setting $\alpha = 0$) impairs motion coherence, especially for global dynamics.

3. Slice VAE: Latent Compression for Long Videos

Slice VAE is introduced to overcome memory bottlenecks in long-sequence generation:

• Temporal Slicing: A video $\{x_1,\ldots,x_T\}$ is divided into $S$ segments, each encoded by a video encoder $q_\phi$ to yield latent $z^{(s)}$.
• Feature Sharing: Decoding for a segment $s$ concatenates features from $z^{(s-1)}, z^{(s)}, z^{(s+1)}$ for local consistency.
• VAE Objective:

$$\mathcal{L}_{\mathrm{VAE}} = \sum_{s=1}^S \mathbb{E}_{q_\phi(z^{(s)}|x^{(s)})}[ -\log p_\theta(x^{(s)}|z^{(s)},z^{(s-1)},z^{(s+1)}) ] + \beta\,\mathrm{KL}(q_\phi(z^{(s)}|x^{(s)}) \| p(z))$$

This structure reduces attention computational complexity from $O(T^2N^2d)$ to $O(T^2N^2d/S)$, where $T$ is sequence length and $S$ the number of slices.

4. Training Pipeline and Data Preparation

EasyAnimate training proceeds through multiple stages, each addressing distinct aspects and increasing video complexity:

• Stage 1 (VAE-Align): Image-only pretraining to stabilize latent space.
• Stage 2 (Motion Pretraining): Motion module is trained on both image and video batches with frozen main DiT weights.
• Stage 3 (Full Video Pretraining): Unfreezes all weights; trains on multi-resolution, multi-frame batches.
• Stage 4 (Hi-Res Finetuning): Small dataset at higher spatial resolutions refines detail and coherence.

Extensive pre-processing (scene cuts, motion filtering via RAFT, OCR-based text filtering, aesthetic scoring, captioning) is employed to curate training data. Training uses batch sizes of up to 1152 and supports LoRA (Low-Rank Adaptation) for efficient fine-tuning.

5. Video Generation Capabilities and Experimental Results

EasyAnimate supports:

• Frame Rate and Resolution Flexibility: “Bucket-based” sampling during training permits a range of configurations; inference allows arbitrary frame count (up to 144) and resolution without retraining.
• Image and Video Guidance: Dual input stream accommodates text prompts, images, and masks.
• Qualitative Performance: Produces sharper, temporally coherent videos, outperforming vanilla DiT and baseline U-Net approaches. Ablation shows 20% FID improvement with Hybrid Motion Module and Slice VAE at 256²/32 frames (FID=14.5 vs baseline 18.2).

Long-duration generation is robust; omission of Slice VAE leads to out-of-memory errors for $T \gtrsim 40$ frames.

6. Implementation, Open Ecosystem, and Extensibility

• Hardware: Optimized for multi-GPU training, e.g., 4 × 80 GB Nvidia A100 for full-resolution training, single A100 40 GB for generation up to 144 frames.
• Framework: PyTorch 2.0, HuggingFace Diffusers/Accelerate, PEFT for LoRA, Hydra-based configuration.
• Repository: All code/scripts, pretrained weights, and YAML configs are provided at https://github.com/aigc-apps/EasyAnimate.

The modular ecosystem adapts to diverse DiT-style models, supports LoRA-based variance for rapid adaptation, and accommodates a range of data sources and video types.

7. Significance, Limitations, and Future Directions

EasyAnimate establishes a versatile text-to-video foundation model architecture, overcoming the challenges of long-sequence consistency and efficient training. Its open-source release and comprehensive training/inference pipeline enable direct reproducibility and extension by the academic community (Xu et al., 29 May 2024).

Limitation-wise, Slice VAE is crucial for scalability—removal results in quadratic memory growth. The main text does not publish full quantitative metrics beyond ablation FIDs, but further evaluation with IS, VBench, or other perceptual metrics would elucidate fine-grained trade-offs.

Prospective research directions include enhanced multi-object handling, refined global attention mechanisms for complex motions, and integration with sketch-based animation (as explored in SketchAnimator (Yang et al., 10 Aug 2025)) and mesh-based feed-forward 4D models (e.g., AnimateAnyMesh (Wu et al., 11 Jun 2025)).