Video Generation Comparison

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

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.

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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+

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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

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Quality Comparison

EvalCrafter Benchmark (2025)

Quality is subjective. These scores reflect community consensus from VBench and EvalCrafter benchmarks.

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

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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

Times are approximate and vary with sampler steps (20-50), guidance scale, and hardware. Use fewer steps for previews.

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×

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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

# 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

# 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()

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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

# 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

# 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

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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

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

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

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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

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

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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

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

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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

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)

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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	✅	✅	✅	❌	❌

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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 ([bare metal](https://clore.ai/bare-metal)):
  Time: ~45 min → Cost: ~$1.12 per video

Wan2.1-1.3B (480p, 5s) on RTX 3090 ($0.07–0.21/hr):
  Time: ~3 min → Cost: ~$0.025 per video

LTX-Video (720p, 5s) on RTX 4090 ($0.14–0.42/hr):
  Time: ~3 min → Cost: ~$0.030 per video

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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

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Useful Links

• Hunyuan Video GitHub

• Wan2.1 on HuggingFace

• CogVideoX GitHub

• Mochi 1 GitHub

• LTX-Video GitHub

• Video Generation Leaderboard

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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.