Wan 2.1 vs Hunyuan Video: Complete Setup Guide

Quick answer: Wan 2.1 is faster to generate with and produces more natural motion, especially for image-to-video. Hunyuan Video is slower per generation but tends to win on image style and visual aesthetics. Both models shown here have since been superseded - see the note below for the current versions.

Note: this guide covers the original Wan 2.1 and the original Hunyuan Video - both have since been superseded by Wan 2.2 / Wan 2.5, and Hunyuan Video 1.5. This comparison is still useful if you're specifically choosing between these two original releases, or troubleshooting an existing setup.

This comparison covers Wan 2.1 and Hunyuan Video - two open-source AI video generation models. Both can be installed in ComfyUI directly, or run through Promptus's CosyFlows without the manual setup below.

Hunyuan Video Installation and Setup

Hunyuan Video supports text-to-video and image-to-video generation. Follow these steps:

Download Required Files: Quantized Q8_0 models for both text and image-to-video (nearly original quality, smaller size). If VRAM is limited, choose smaller quantized variants.

Optional Downloads: LlavaLlama FP8-scaled text encoder for extra VRAM savings; original large model files for maximum quality (only if sufficient VRAM).

Place Files: Put downloaded files into the appropriate ComfyUI model folders. The workflow uses a GGUF unit loader - press CTRL+B in ComfyUI if you prefer the standard diffusion model loader.

Hunyuan Video Configuration Settings

Testing (lower quality, faster): 20 steps, 24 FPS, 49 frames (~2 seconds), resolution below 480p.

High-quality generation: 30 steps, 24 FPS, 121 frames (~5 seconds), 480p or 720p (use a cloud GPU for higher resolutions).

Image-to-video: add image-input nodes and use the LLaVA CLIPVision model for guidance.

Wan 2.1 Installation and Setup

Wan 2.1 offers a simpler workflow. Download Required Files: Q8_0 quantized models for text-to-video and image-to-video; use smaller quantized variants for limited VRAM.

Optional Downloads: a Q8_0 variant for 720p image-to-video (requires higher GPU performance); original large model files for max quality if VRAM permits.

Workflow notes: Wan 2.1 includes a negative-prompt feature to control unwanted elements, and has a less complex node chain than Hunyuan Video.

Wan 2.1 Configuration Settings

Testing (lower quality, faster): 20 steps, 16 FPS, 33 frames (~2 seconds), resolution below 480p.

High-quality generation: 30 steps, 16 FPS, 81 frames (~5 seconds), 480p or 720p.

Image-to-video: uses the ClipVisionH model, with minimal extra nodes compared to text-to-video.

Performance Comparison

These are the relative results from our own local test runs at the settings above - your numbers will vary by GPU and prompt.

Local GPU generation time: Wan 2.1 ran a short test clip in roughly ~400 seconds; Hunyuan Video took roughly ~700 seconds (with --cpu-vae for AMD compatibility) - Hunyuan Video was faster per generation in our runs.

Cloud GPU: 480p videos ran in roughly 25-37 minutes on an L4 GPU; 720p was faster on an L40S.

File sizes: Hunyuan Video's 24 FPS output produced smaller files despite the higher frame rate; Wan 2.1's 16 FPS output ran larger per second due to lower compression.

Video quality: at both 480p and 720p, Wan 2.1 showed more natural movement, particularly for image-to-video. Hunyuan Video was preferred for image style and overall visual aesthetics.

Optimization Recommendations

For Wan 2.1: use 20 steps instead of 30 for faster runs, and start with 2-3 second clips (32-48 frames) to bring generation time down to roughly 10-15 minutes.

For Hunyuan Video: use GPU VAE when available - on AMD GPUs, --cpu-vae helps compatibility but slows generation. Test with quantized models before full-precision files.

Cloud strategy: use an L4 GPU for 480p testing, and upgrade to an L40S for 720p or higher, weighing cloud cost against time saved.

If you'd rather skip the manual ComfyUI setup entirely, Promptus runs both models through ready-made CosyFlows - drag-and-drop workflows that handle model downloads and node configuration for you, with the option to run in the cloud if your local GPU is the bottleneck.