Wan 2.1: Architecture: Uses a diffusion transformer and novel Wan-VAE for spatio-temporal 1080P video encoding. Capabilities: Multimodal (text/image-to-video, editing, video-to-audio), bilingual text generation. Efficiency: Runs on 8.19GB VRAM, making it accessible for mid-tier GPUs. Speed: Generates 5-second 480P videos in ~4 minutes (RTX 4090).
HunyuanVideo: Architecture: Leverages a Causal 3D VAE and dual-stream transformer for unified image/video synthesis. Capabilities: Superior text-video alignment, motion diversity, and stability; includes a prompt rewrite model. Hardware: Demands 60–80GB GPU memory (720p), targeting high-end studios. Speed: Optimized via xDiT parallel inference for faster generation, 2-3 minutes per clip at full quality.
Video generation models have advanced significantly, with open-source projects like HunyuanVideo and Wan2.1 pushing the limits of innovation. HunyuanVideo stands out as a groundbreaking open-source video foundation model, competing with top-tier closed-source alternatives. Meanwhile, Wan2.1 provides a robust and comprehensive suite of open video foundation models. Both leverage cutting-edge techniques to produce high-quality videos, enabling extensive customization and optimization.
Start a free trial on Novita AI today. To integrate the Wan 2.1 and Hunyuan Video API, visit our developer docs for more details.
Novita offers highly competitive pricing in the market.
For example, a Wan 2.1 720P 5-second video costs only $0.4 per video
while a similar video on Replicate costs $2.39 per video
Supports generating bilingual text in Chinese and English.
Powered by Wan-VAE, it can encode and decode 1080P videos of any length while preserving temporal consistency.
HunyuanVideo
Open Source: Yes
Capabilities:
Supports Text-to-Video generation.
Includes a prompt rewrite model to optimize and adapt user prompts.
Architecture
Feature
Wan2.1
HunyuanVideo
Architecture
Diffusion transformer paradigm
Causal 3D VAE for spatial-temporally compressed latent space
Latent Space
Spatio-temporal variational autoencoder (VAE) called Wan-VAE
Compresses video and image data into a compact latent space using 3D VAE with CausalConv3D
Text Encoding
T5 Encoder for multilingual text input
Multimodal Large Language Model (MLLM)
Transformer Design
Cross-attention in each transformer block embeds text into the model structure
“Dual-stream to Single-stream” Transformer for unified image and video generation
Wan 2.1, on the other hand, enhances subtitle generation with T5 Encoder and Cross-Attention Mechanism, while supporting robust long video generation using Wan-VAE and the Diffusion Transformer Paradigm.
HunyuanVideo significantly improves text-to-video precision and generation stability through Causal 3D VAE, Prompt Rewrite Model, and Latent Space Compression.
Hardware Requirements
Wan2.1
Wan2.1 is significantly more hardware-efficient, especially for lower-resolution tasks. It is designed to be accessible to users with constrained hardware resources while still supporting high-quality video generation. Key points:
GPU Requirements:
The T2V-1.3B model (Text-to-Video) requires only 8.19GB of VRAM, making it accessible to GPUs like the RTX 3060 or RTX 4060.
Higher resolution models (e.g., 14B models) require more powerful GPUs, such as RTX 3090, RTX 4090, or A100, but these demands are still lower compared to HunyuanVideo.
Model Name
Function
Resolution Support
Model Size
Hardware Demand
Recommended GPU
T2V-14B
Text-to-Video (T2V)
480P / 720P
14B
⭐⭐⭐⭐
A100 / RTX 3090 / RTX 4090
I2V-14B-720P
Image-to-Video (I2V)
720P
14B
⭐⭐⭐⭐
A100 / RTX 3090 / RTX 4090
I2V-14B-480P
Image-to-Video (I2V)
480P
14B
⭐⭐⭐
RTX 3090 / RTX 4070 Ti
T2V-1.3B
Text-to-Video (T2V)
Low Resolution
1.3B
⭐⭐
RTX 3060 / RTX 4060 or higher
HunyuanVideo
HunyuanVideo has higher hardware requirements, as it is designed to handle high-resolution and complex video generation tasks. Below are the key points regarding its hardware demands:
GPU Requirements:
Requires an NVIDIA GPU with CUDA support.
For 720×1280 resolution at 129 frames, at least 60GB of GPU memory is required.
For 544×960 resolution, at least 45GB of GPU memory is needed.
An 80GB GPU (such as NVIDIA A100) is recommended for optimal performance.
HunyuanVideo is designed for high-end hardware, requiring substantial VRAM (45GB–80GB), making it suitable for users with access to high-performance GPUs (e.g., NVIDIA A100 or similar). It is better suited for tasks requiring high-resolution video generation and longer sequences.
Wan2.1 is more accessible to users with standard GPUs, especially for tasks like low-resolution text-to-video generation. The T2V-1.3B model only needs 8.19GB VRAM, making it ideal for users with mid-range GPUs like RTX 3060 or RTX 4060. However, for higher resolutions (720P or larger), more powerful GPUs are recommended.
Output Evalution
1. Video Quality – Resolution
Wan2.1:
Supports both 480P and 720P video generation.
HunyuanVideo:
Evaluated based on Text Alignment, Motion Quality, and Visual Quality.
Supports resolutions up to 720P.
2. Creative
Wan2.1:
Extends prompts to include richer details in generated videos.
Focuses on improving creative outputs by enriching the video generation process.
HunyuanVideo:
Features prompt rewrite modes to better understand user intent.
Enhances visual quality through improved comprehension of prompts.
3. Speed
Wan2.1:
Generates a 5-second 480P video on an RTX 4090 in approximately 4 minutes (without optimization techniques).
HunyuanVideo:
Utilizes parallel inference code powered by xDiT, enabling faster video generation.
Average generation speed: 2-3 minutes per clip at full quality.
Wan2.1: Excels in creative outputs and prompt versatility, making it ideal for users seeking enriched and detailed video generation, though it is slightly slower.
HunyuanVideo: Suitable for users prioritizing video quality, faster generation speeds, and flexibility in video customization.
Application
Wan2.1
Multi-Modal Video Creation
Application: Ideal for creating videos that combine multiple modalities, such as integrating text, images, and other visual elements into a cohesive output.
Reason: Wan 2.1 excels in multi-modal generation, making it suitable for creative and dynamic video content where diverse inputs are required.
Videos with Automatic Subtitle Generation
Application: Perfect for producing videos with automatically generated subtitles, such as tutorials, explainer videos, or social media content.
Reason: Wan 2.1’s ability to generate subtitles directly improves accessibility and saves time in post-production.
Social Media Content with Enhanced Visual Dynamics
Application: Suitable for creating engaging social media videos where multi-modal elements like text overlays and subtitle animations are essential (e.g., TikTok, Instagram).
Reason: Its focus on combining multi-modal inputs allows for visually dynamic and attention-grabbing short videos.
HunyuanVideo
Text-Centric Video Generation
Application: Ideal for videos where the primary focus is on accurately interpreting and visually representing textual content, such as corporate presentations or educational videos.
Reason: Hunyuan’s superior understanding of text ensures precise alignment between the input prompts and the final video output.
Professional Explainer or Instructional Videos
Application: Best for creating clear, concise, and professional explainer videos or instructional guides.
Reason: Hunyuan’s strength in text understanding ensures that complex ideas and instructions are effectively translated into video format.
High-Quality Branding or Marketing Videos
Application: Suitable for crafting high-resolution, professional marketing content where textual prompts guide the storytelling or branding elements.
Reason: Hunyuan’s ability to deeply understand text enables the creation of videos that align closely with branding or campaign messaging.
Simple Version
We’re now testing the two models by inputting the same text prompts to evaluate their understanding of the text and the final output of the videos.
Prompt: A vivid surreal photography, a lively otter jumps into a clear lake in surprise, instantly stirring up layers of ripples. It nimbly pokes its head out of the water, its wet fur clinging to its body, and crystal water drops sliding down its round cheeks. The otter stares forward curiously, with the corners of its mouth slightly raised, as if sharing its happiness with the viewer. The fisheye lens captures this unique perspective, with natural light gently falling and a delicate luster on the water surface. The overall picture presents soft tones, emphasizing the otter’s natural beauty and vivid expression. High-definition texture and mid-ground composition create an immersive atmosphere.
wan 2.1
Hunyuan
Prompt: Backlit art photography, the model stands in the golden glow of dusk, with clear outlines, like a silhouette. The light and transparent silk wrapped around the model, fluttering gently in the breeze, interweaving with the golden light, creating a dreamy halo effect. The model’s expression is calm and her posture is elegant, as if immersed in her own world. The background is a blurred skyline, and the afterglow of the sunset is all over the earth. The high contrast and delicate light and shadow processing show the photographer’s superb skills. Mid-ground, shot from the side against the light, emphasizing the outline and atmosphere
HunyuanVideo and Wan2.1 represent significant advancements in video generation, showcasing innovative architectures, robust capabilities, and high-quality outputs. By leveraging techniques such as 3D VAEs, diffusion transformers, and large-scale data training, these models push the boundaries of visual content creation. Their flexibility in customization and optimization makes them valuable tools for driving innovation across industries like media, education, and advertising.
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