# Video Generation Comparison

Compare the leading open-source video generation models for deployment on Clore.ai GPU servers.

{% hint style="info" %}
**AI video generation** has exploded in 2024-2025. This guide compares the top open-source models — Hunyuan Video, Wan2.1, CogVideoX, Mochi 1, and LTX-Video — covering quality, speed, VRAM requirements, and use cases.
{% endhint %}

***

## Quick Decision Matrix

|                    | Hunyuan Video | Wan2.1     | CogVideoX  | Mochi 1    | LTX-Video  |
| ------------------ | ------------- | ---------- | ---------- | ---------- | ---------- |
| **Developer**      | Tencent       | Alibaba    | Zhipu AI   | Genmo      | LightRicks |
| **Quality**        | ⭐⭐⭐⭐⭐         | ⭐⭐⭐⭐⭐      | ⭐⭐⭐⭐       | ⭐⭐⭐⭐       | ⭐⭐⭐        |
| **Speed**          | Slow          | Medium     | Medium     | Medium     | **Fast**   |
| **Min VRAM**       | 24GB          | 16GB       | 16GB       | 24GB       | **8GB**    |
| **Max resolution** | 1280×720      | 1280×720   | 1440×960   | 848×480    | 1216×704   |
| **Max length**     | 5s            | 5s         | 6s         | 5.4s       | 2min       |
| **License**        | CLA           | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 |
| **GitHub stars**   | 10K+          | 7K+        | 6K+        | 4K+        | 5K+        |

***

## Overview

### Hunyuan Video

Tencent's Hunyuan Video is widely considered the best open-source video generation model as of early 2025. It uses a transformer-based architecture with exceptional motion quality.

**Key specs**: 13B parameters, 5s at 720p, requires 24GB+ VRAM

### Wan2.1

Alibaba's Wan (Wenying) 2.1 is a strong competitor to Hunyuan, offering similar quality with lower minimum VRAM requirements. Available in 1.3B and 14B parameter variants.

**Key specs**: 1.3B (lite) or 14B, 5s at 720p, 16GB+ VRAM for 1.3B

### CogVideoX

Zhipu AI's CogVideoX focuses on precise text-following and coherent long-form video. It's particularly strong for cinematic content and story-driven generation.

**Key specs**: 5B/10B parameters, 6s at 1440×960, 16GB+ VRAM

### Mochi 1

Genmo's Mochi 1 is known for smooth, fluid motion and realistic physics. It uses a novel AsymmDiT architecture. Available fully open-source (weights + training code).

**Key specs**: 10B parameters, 5.4s at 848×480, 24GB VRAM

### LTX-Video

LightRick's LTX-Video prioritizes inference speed above all. It can generate video in real-time or near-real-time on modern GPUs — ideal for interactive applications.

**Key specs**: 2B parameters, up to 2 minutes of video, 8GB VRAM

***

## Quality Comparison

### EvalCrafter Benchmark (2025)

{% hint style="info" %}
Quality is subjective. These scores reflect community consensus from VBench and EvalCrafter benchmarks.
{% endhint %}

| Model         | VBench Score | Motion Quality | Text Alignment | Aesthetic  |
| ------------- | ------------ | -------------- | -------------- | ---------- |
| Hunyuan Video | **83.2**     | **Excellent**  | Excellent      | Excellent  |
| Wan2.1 (14B)  | **82.8**     | Excellent      | Excellent      | Excellent  |
| CogVideoX-5B  | 79.6         | Good           | **Very Good**  | Good       |
| Mochi 1       | 77.4         | Very Good      | Good           | Good       |
| LTX-Video     | 71.2         | Good           | Good           | Acceptable |

### Qualitative Strengths

| Model         | Best At                            | Weaknesses                  |
| ------------- | ---------------------------------- | --------------------------- |
| Hunyuan Video | Overall quality, cinematography    | Very slow, VRAM hungry      |
| Wan2.1        | Balance of quality/efficiency, I2V | Occasionally over-saturated |
| CogVideoX     | Long-form narrative, text accuracy | Less dynamic motion         |
| Mochi 1       | Fluid motion, physics              | Lower resolution limit      |
| LTX-Video     | Speed, long videos                 | Quality gap vs others       |

***

## Speed Benchmarks

### Generation Time (A100 80GB, single GPU)

| Model         | 480p 5s    | 720p 5s   | 1080p 5s |
| ------------- | ---------- | --------- | -------- |
| Hunyuan Video | 45 min     | \~3 hours | ❌ OOM    |
| Wan2.1 (14B)  | 15 min     | 45 min    | ❌ OOM    |
| Wan2.1 (1.3B) | 3 min      | 8 min     | ❌ OOM    |
| CogVideoX-5B  | 10 min     | 25 min    | ❌ OOM    |
| Mochi 1       | 8 min      | ❌ OOM     | ❌ OOM    |
| LTX-Video     | **45 sec** | **3 min** | 8 min    |

{% hint style="warning" %}
**Times are approximate** and vary with sampler steps (20-50), guidance scale, and hardware. Use fewer steps for previews.
{% endhint %}

### With Optimization (TeaCache / FORA / Step Distillation)

Optimized inference can reduce generation time significantly:

| Model         | With Cache      | Speedup |
| ------------- | --------------- | ------- |
| Hunyuan Video | \~15 min (720p) | 4×      |
| Wan2.1        | \~12 min (720p) | \~4×    |
| CogVideoX     | \~8 min (720p)  | \~3×    |
| LTX-Video     | \~45s (720p)    | 4×      |

***

## VRAM Requirements

### Minimum VRAM by Model and Resolution

| Model         | 480p    | 720p  | 1080p |
| ------------- | ------- | ----- | ----- |
| Hunyuan Video | 24GB    | 40GB+ | ❌     |
| Wan2.1 (14B)  | 24GB    | 40GB+ | ❌     |
| Wan2.1 (1.3B) | **8GB** | 16GB  | 24GB  |
| CogVideoX-5B  | 16GB    | 24GB  | ❌     |
| CogVideoX-2B  | **8GB** | 16GB  | ❌     |
| Mochi 1       | 24GB    | ❌     | ❌     |
| LTX-Video     | **8GB** | 12GB  | 24GB  |

### Memory Optimization Techniques

#### Quantization

```python
# CogVideoX with 8-bit quantization (halves VRAM)
from diffusers import CogVideoXPipeline
import torch

pipe = CogVideoXPipeline.from_pretrained(
    "THUDM/CogVideoX-5b",
    torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload()  # Further reduces VRAM
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()
```

#### CPU Offloading

```python
# Wan2.1 with CPU offload for lower VRAM
from diffusers import WanPipeline

pipe = WanPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
    torch_dtype=torch.bfloat16
)
pipe.enable_model_cpu_offload()
```

***

## Hunyuan Video: Deep Dive

### Architecture

* **13B DiT** (Diffusion Transformer) parameters
* Full attention over all spatial and temporal tokens
* Trained on 1B+ video clips

### Deployment on Clore.ai

```bash
# Clone and install
git clone https://github.com/Tencent/HunyuanVideo
cd HunyuanVideo
pip install -r requirements.txt

# Download weights (~87GB)
huggingface-cli download tencent/HunyuanVideo --local-dir ./weights

# Generate
python sample_video.py \
  --video-size 720 1280 \
  --video-length 129 \
  --infer-steps 50 \
  --prompt "A majestic eagle soaring over snow-capped mountains" \
  --flow-shift 7.0 \
  --embedded-cfg-scale 6.0 \
  --save-path ./outputs
```

### Via ComfyUI

```bash
# Install HunyuanVideo nodes for ComfyUI
cd ComfyUI/custom_nodes
git clone https://github.com/kijai/ComfyUI-HunyuanVideoWrapper
pip install -r ComfyUI-HunyuanVideoWrapper/requirements.txt
```

**Best for**: Highest quality cinematic video generation, no VRAM constraints

***

## Wan2.1: Deep Dive

### Architecture

* **Two variants**: Wan2.1-T2V-1.3B and Wan2.1-T2V-14B
* **Image-to-Video** (I2V) model also available
* Strong multilingual (Chinese + English) prompts

### Deployment on Clore.ai

```python
from diffusers import WanPipeline
from diffusers.utils import export_to_video
import torch

# 1.3B model — fits in 8-16GB VRAM
pipe = WanPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
    torch_dtype=torch.bfloat16,
)
pipe.to("cuda")

output = pipe(
    prompt="A serene Japanese garden with cherry blossoms falling",
    negative_prompt="low quality, blurry",
    height=480,
    width=832,
    num_frames=81,
    num_inference_steps=50,
    guidance_scale=5.0,
).frames[0]

export_to_video(output, "wan_video.mp4", fps=16)
```

### Image-to-Video with Wan2.1

```python
from diffusers import WanImageToVideoPipeline
from PIL import Image

pipe = WanImageToVideoPipeline.from_pretrained(
    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers",
    torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()

image = Image.open("input.jpg")
output = pipe(
    image=image,
    prompt="The person walks forward confidently",
    num_frames=81,
).frames[0]
```

**Best for**: Balance of quality and efficiency, I2V, multilingual

***

## CogVideoX: Deep Dive

### Architecture

* **Expert Transformer** with 3D full attention
* **5B and 10B** parameter variants
* CogView3 image encoder for visual quality

### Deployment on Clore.ai

```python
from diffusers import CogVideoXPipeline
from diffusers.utils import export_to_video
import torch

pipe = CogVideoXPipeline.from_pretrained(
    "THUDM/CogVideoX-5b",
    torch_dtype=torch.bfloat16
)
pipe.to("cuda")
pipe.vae.enable_slicing()
pipe.vae.enable_tiling()

video = pipe(
    prompt="A time-lapse of a city at night with light trails from cars",
    num_videos_per_prompt=1,
    num_inference_steps=50,
    num_frames=49,
    guidance_scale=6,
    generator=torch.Generator(device="cuda").manual_seed(42),
).frames[0]

export_to_video(video, "cogvideo.mp4", fps=8)
```

**Best for**: Precise text-to-video, narrative content, long-form generation

***

## Mochi 1: Deep Dive

### Architecture

* **AsymmDiT** — asymmetric diffusion transformer
* Focus on temporal consistency and fluid motion
* Fully open-source including training code

### Deployment on Clore.ai

```bash
pip install mochi-preview

python -c "
from mochi_preview.pipelines import DecoderModelFactory, DitModelFactory, MochiSingleGPUPipeline, T5ModelFactory
import tempfile
from pathlib import Path

pipeline = MochiSingleGPUPipeline(
    text_encoder_factory=T5ModelFactory(),
    dit_factory=DitModelFactory(model_path='./weights/mochi-dit.safetensors'),
    decoder_factory=DecoderModelFactory(model_path='./weights/mochi-vae.safetensors'),
    cpu_offload=True,
    decode_type='tiled_full',
)

video = pipeline(
    height=480, width=848,
    num_frames=163,
    num_inference_steps=64,
    sigma_schedule_type='linear_quadratic',
    cfg_schedule_type='linear',
    conditioning_args={'prompt': 'A dolphin leaping through ocean waves at sunset'},
)
"
```

**Best for**: Fluid motion, realistic physics, research use cases

***

## LTX-Video: Deep Dive

### Architecture

* **2B parameter** DiT — smaller, faster
* Native **long video** support (up to 2 minutes)
* Designed for real-time or near-real-time generation

### Deployment on Clore.ai

```python
from diffusers import LTXPipeline
from diffusers.utils import export_to_video
import torch

pipe = LTXPipeline.from_pretrained(
    "Lightricks/LTX-Video",
    torch_dtype=torch.bfloat16
)
pipe.to("cuda")

video = pipe(
    prompt="A butterfly landing on a flower in a summer garden",
    negative_prompt="worst quality, inconsistent motion, blurry",
    width=704,
    height=480,
    num_frames=161,
    decode_timestep=0.03,
    decode_noise_scale=0.025,
    num_inference_steps=50,
).frames[0]

export_to_video(video, "ltx_video.mp4", fps=24)
```

**Best for**: Fast generation, interactive applications, long videos, limited VRAM (8GB)

***

## Feature Comparison

### Capabilities Overview

| Feature           | Hunyuan | Wan2.1 | CogVideoX | Mochi | LTX |
| ----------------- | ------- | ------ | --------- | ----- | --- |
| Text-to-Video     | ✅       | ✅      | ✅         | ✅     | ✅   |
| Image-to-Video    | ✅       | ✅      | ✅         | ❌     | ✅   |
| Video-to-Video    | ❌       | ❌      | ✅         | ❌     | ✅   |
| ControlNet        | Partial | ❌      | ✅         | ❌     | ❌   |
| LoRA support      | ✅       | ✅      | ✅         | ❌     | ✅   |
| ComfyUI nodes     | ✅       | ✅      | ✅         | ✅     | ✅   |
| Long video (>10s) | ❌       | ❌      | Partial   | ❌     | ✅   |
| Chinese prompts   | ✅       | ✅      | ✅         | ❌     | ❌   |

***

## Clore.ai GPU Recommendations

### For Each Model

| Model         | Minimum GPU     | Recommended  | Ideal       |
| ------------- | --------------- | ------------ | ----------- |
| Hunyuan Video | RTX 3090 (24GB) | A6000 (48GB) | A100 (80GB) |
| Wan2.1 14B    | RTX 3090 (24GB) | A6000 (48GB) | A100 (80GB) |
| Wan2.1 1.3B   | RTX 3080 (10GB) | RTX 3090     | RTX 4090    |
| CogVideoX-5B  | RTX 3090 (24GB) | A6000 (48GB) | A100        |
| CogVideoX-2B  | RTX 3080 (10GB) | RTX 3090     | RTX 4090    |
| Mochi 1       | RTX 3090 (24GB) | A6000 (48GB) | A100        |
| LTX-Video     | RTX 3080 (10GB) | RTX 4080     | RTX 4090    |

### Cost Estimate per Video

```
Hunyuan Video (720p, 5s) on A100 80GB (~$1.50/hr):
  Time: ~45 min → Cost: ~$1.12 per video

Wan2.1-1.3B (480p, 5s) on RTX 3090 (~$0.50/hr):
  Time: ~3 min → Cost: ~$0.025 per video

LTX-Video (720p, 5s) on RTX 4090 (~$0.60/hr):
  Time: ~3 min → Cost: ~$0.030 per video
```

***

## When to Use Which

### Decision Guide

```
Maximum quality (no cost limit)?
  → Hunyuan Video on A100

Best quality/cost balance?
  → Wan2.1 14B on A6000

Limited VRAM (8-12GB)?
  → LTX-Video or Wan2.1 1.3B

Need fast generation?
  → LTX-Video

Need Image-to-Video?
  → Wan2.1 I2V or CogVideoX

Need long videos (>10s)?
  → LTX-Video

Research/fine-tuning?
  → Mochi 1 (open training code) or CogVideoX

ComfyUI workflow?
  → All supported, Hunyuan/Wan best nodes
```

***

## Useful Links

* [Hunyuan Video GitHub](https://github.com/Tencent/HunyuanVideo)
* [Wan2.1 on HuggingFace](https://huggingface.co/Wan-AI)
* [CogVideoX GitHub](https://github.com/THUDM/CogVideo)
* [Mochi 1 GitHub](https://github.com/genmoai/mochi)
* [LTX-Video GitHub](https://github.com/Lightricks/LTX-Video)
* [Video Generation Leaderboard](https://huggingface.co/spaces/ArtificialAnalysis/video-generation-arena-leaderboard)

***

## Summary

| Model             | Use When                                   |
| ----------------- | ------------------------------------------ |
| **Hunyuan Video** | Best quality matters most, A100+ available |
| **Wan2.1**        | Best balance of quality and efficiency     |
| **CogVideoX**     | Precise text-to-video, long narrative      |
| **Mochi 1**       | Fluid motion, physics, open research       |
| **LTX-Video**     | Speed, low VRAM, long videos               |

The open-source video generation ecosystem moves fast. For most Clore.ai deployments, **Wan2.1** (1.3B for budget, 14B for quality) offers the best combination of quality, speed, and resource efficiency.