---
title: HunyuanVideo 1.5 – Open-Source Video Synthesis Model
url: https://www.emergentmind.com/topics/hunyuanvideo-1-5
type: topic
---

# HunyuanVideo 1.5 – Open-Source Video Synthesis Model

HunyuanVideo 1.5 is an open-source video generation model from Tencent, advancing the state of the art in visual quality, motion coherence, and efficiency in the domain of text-to-video and image-to-video synthesis. With 8.3 billion parameters, HunyuanVideo 1.5 introduces a highly optimized architecture, novel attention mechanisms, glyph-aware text encoding, progressive multi-stage training, and a cascaded video super-resolution network, establishing new performance benchmarks among open-source video generation systems. The model and its code base are available at https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5 [2511.18870].

## 1. System Architecture and Pipeline

HunyuanVideo 1.5 utilizes a unified, two-stage pipeline that enables high-fidelity, temporally consistent video synthesis while maintaining computational efficiency. The pipeline consists of:

- An 8.3B parameter Diffusion Transformer (DiT) core that operates on 3D causal VAE latents.
- An initial video synthesis step generating 5–10 second video latents at 480p–720p resolution.
- A latent-space Video Super-Resolution (VSR) network that further upsamples content to 1080p.

This architecture is directly focused on supporting both text-to-video (T2V) and image-to-video (I2V) generation within a single framework. Peak memory usage is held at 13.6 GB for 720p × 121 frames, ensuring inference feasibility on consumer GPUs.

**Pipeline Structure:**
```
Text/Image Prompt
      │
  VAE Encoder
      │
  8.3B DiT (with SSTA)
      │
Latent Video (480–720p)
      │
  VSR Network
      │
  1080p Video
```
The 3D causal VAE provides ×16 spatial and ×4 temporal compression, facilitating efficient and scalable latent modeling [2511.18870].

## 2. Data Acquisition and Filtering

HunyuanVideo 1.5's success critically depends on large-scale, multi-stage data curation:

- **Video Data:** Over 10 million hours of raw video form the basis for pre-training; post-segmentation and filtering (using PySceneDetect, transition classifiers), ≈800 million high-quality clips remain. Duration is fixed at 2–10 seconds per clip.
- **Image Data:** 5 billion images are pre-filtered from an initial 10 billion pool for staged T2I bootstrapping.
- **Filtering Pipeline Steps:**
  1. De-duplication, removal of padding and stitch artifacts, and exclusion of low-motion samples.
  2. Visual quality assessment (sharpness, detail, dynamic range, noise).
  3. Aesthetic filtering based on Dover scores.

**Captioning:**
Structured, multi-component captions are generated: for video—narrative, shot type, angle, lighting, style, color, and atmosphere; for I2V—foreground/background transitions; for T2V/I2V—natural-language tokens for recognized camera motion.

Post-training, RL-based fine-tuning (OPA-DPO) balances descriptive richness and hallucination in captions, with camera motion recognized and encoded as conditional tokens [2511.18870].

## 3. Model Design: DiT and Attention Innovations

HunyuanVideo 1.5 adopts a DiT backbone with advanced attention mechanisms to simultaneously handle large context lengths, spatiotemporal correlations, and diverse generative conditioning.

- **Core Hyperparameters:**
  - 54 dual-stream DiT blocks, model dimension 2048, FFN dimension 8192.
  - 16 attention heads with head dimension 128.

- **Selective and Sliding Tile Attention (SSTA):**
  SSTA substantially reduces attention cost by combining:
  - **Selective attention:** Top-$k$ block selection based on importance scores (Equation: $Score_i = \lambda Score_s - \beta Score_r$) derived from Q–K similarity and K–K redundancy.
  - **Sliding Tile Attention:** Local 3D windowed masks reinforce sparsity, controlling receptive field size.
  The resulting mask enables flexible, data-adaptive sparse attention, yielding up to 1.87× speedup on 10s 720p sequences versus FlashAttention-3.

- **3D Causal VAE:** 
  The VAE reduces the input spatial/temporal size efficiently, supporting the DiT regime and enabling latent upsampling for final output.

## 4. Text Encoding and Conditioning

A specialized dual-channel text encoding scheme optimizes cross-lingual generation fidelity:

- **Qwen2.5-VL Multimodal Encoder:** Captures global semantics and high-level action or scene information.
- **Glyph-ByT5 Encoder:** Extracts fine-grained glyph features for both Chinese and English, improving token-level discrimination.
- Conditioning is performed by concatenated embedding streams, augmented with learnable tokens to indicate task type (T2V, I2V, or T2I). An additional cross-channel alignment loss during multi-task pre-training aligns VL/Glyph semantic spaces:
  $$
  \mathcal{L}_{align} = \| f_{VL}(t) - f_{glyph}(t) \|_2^2
  $$
This dual approach yields ∼17% absolute improvements in instruction following, with strong bilingual prompting and zero-shot generalization [2511.18870].

## 5. Progressive Training and Post-Training Regimen

The training strategy is highly staged, blending image and video tasks for maximal transfer and stability:

- **Pre-training:** Eight progressive stages with dynamic curriculum mixing image and video data at increasing resolutions and durations. Initial T2I stages warm up DiT weights, preventing catastrophic forgetting and enhancing structural stability.
- **Continued Training (CT):** Separate CT runs for T2V and I2V on 1M premium video clips each.
- **Supervised Fine-Tuning (SFT):** Final stabilization using rigorously filtered, high-aesthetic video clips to maximize output realism.
- **RLHF (for I2V):** Online RL (MixGRPO solver) with reward models over textual and visual alignment, image fidelity, and motion realism. For T2V, offline Direct Preference Optimization (DPO) on ranked data, then online RL.
- **Losses:**
  - Denoising flow-matching:
    $$
    \mathcal{L}_{denoise} = \mathbb{E}_{x_0,\epsilon,t}[ \| \epsilon - \epsilon_\theta(z_t, t, cond) \|^2 ]
    $$
  - For VSR: pixel-wise, perceptual (VGG-based), and flow-matching objectives.

The optimizer Muon is employed for faster convergence compared to AdamW (weight decay = 0.01) [2511.18870].

## 6. Video Super-Resolution and Output

To achieve 1080p output from lower-resolution latents, HunyuanVideo 1.5 applies a cascaded VSR network:

- Low-resolution latents (480–720p) plus noise are processed by the DiT-based VSR, which aligns low-to-high resolution in latent space via a dedicated spatial upsampler.
- The VAE decoder then reconstructs full-resolution frames.

Losses combine pixel-level differences and VGG feature-space perceptual terms, maintaining spatial and temporal consistency.

## 7. Comparative Evaluation and Open Source Release

**Quantitative Benchmarks:**
HunyuanVideo 1.5 achieves superior or competitive performance across major open and closed-source systems in both T2V and I2V tasks.

| Dimension                | HY1.5 (T2V, 720p) | Best Open Baseline | Best Closed Baseline |
|--------------------------|-------------------|--------------------|---------------------|
| Instruction Following    | 61.57             | 50.03 (Kling2.1)   | 73.77 (Veo3)        |
| Aesthetic Quality        | 63.30             | 68.22 (Seedance)   | 67.98 (Veo3)        |
| Visual Quality           | 57.35             | 60.20 (Seedance)   | 58.64 (Veo3)        |
| Structural Stability     | 79.75             | 73.75 (Wan2.2)     | 75.62 (Veo3)        |
| Motion Effects           | 57.67             | 58.59 (Kling2.1)   | 60.81 (Veo3)        |

**Speed and Memory:**
- 720p × 121 frames: ~2.0s/step (dense); ~1.6s/step (sparse) on 8×H800 GPUs.
- 50-step generation: 28.33s (dense), 26.41s (sparse), with 13.6 GB GPU usage for 720p sequences.

**Open-Source Release:**
Code and weights are available at https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5, including PyTorch APIs for text-to-video, image-to-video, super-resolution, and attention sparsity toggling [2511.18870].

## 8. Context, Significance, and Implications

HunyuanVideo 1.5 decisively advances the open-source video generation field, reducing the gap to closed-source models in both motion realism and text-video alignment. Key contributions include efficient SSTA for scalable attention over long video sequences, glyph-aware dual-channel encoding for robust bilingual comprehension, and a transparent, highly scalable training regimen anchored by progressive curation and curriculum.

The model’s design suggests promising directions for further research: the SSTA mechanism, joint pre-training with structured, multilingual captions, and the dual-channel language encoder establish new templates for scalable, general-purpose video generation systems. HunyuanVideo 1.5 also lowers the technical and resource barrier for academic and industrial video synthesis applications, providing a reproducible, performant foundation for the next generation of open-source generative models [2511.18870][2412.03603].

Source: https://www.emergentmind.com/topics/hunyuanvideo-1-5