Hunyuan Video

Generate high-quality videos with Tencent's Hunyuan Video on Clore.ai

Generate high-quality videos with Tencent's open-source Hunyuan Video.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Renting on CLORE.AI

1. Visit CLORE.AI Marketplace

2. Filter by GPU type, VRAM, and price

3. Choose On-Demand (fixed rate) or Spot (bid price)

4. Configure your order:

   • Select Docker image

   • Set ports (TCP for SSH, HTTP for web UIs)

   • Add environment variables if needed

   • Enter startup command

5. Select payment: CLORE, BTC, or USDT/USDC

6. Create order and wait for deployment

Access Your Server

• Find connection details in My Orders

• Web interfaces: Use the HTTP port URL

• SSH: ssh -p <port> root@<proxy-address>

What is Hunyuan Video?

Hunyuan Video from Tencent offers:

• High-quality text-to-video generation

• 5+ second video clips

• 720p resolution

• Open-source and commercially usable

Resources

• Model: tencent/HunyuanVideo

• GitHub: Tencent/HunyuanVideo

• Paper: HunyuanVideo Paper

Recommended Hardware

Component	Minimum	Recommended	Optimal
GPU	RTX 4090 24GB	A100 40GB	A100 80GB
VRAM	24GB	40GB	80GB
CPU	8 cores	16 cores	32 cores
RAM	32GB	64GB	128GB
Storage	100GB NVMe	200GB NVMe	500GB NVMe
Internet	500 Mbps	1 Gbps	1 Gbps

Quick Deploy on CLORE.AI

Docker Image:

pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel

Ports:

22/tcp
7860/http

Command:

git clone https://github.com/Tencent/HunyuanVideo.git && \
cd HunyuanVideo && \
pip install -r requirements.txt && \
python sample_video.py --prompt "A cat walking in a garden"

Accessing Your Service

After deployment, find your http_pub URL in My Orders:

1. Go to My Orders page

2. Click on your order

3. Find the http_pub URL (e.g., abc123.clorecloud.net)

Use https://YOUR_HTTP_PUB_URL instead of localhost in examples below.

Installation

git clone https://github.com/Tencent/HunyuanVideo.git
cd HunyuanVideo
pip install -r requirements.txt

# Download models
python download_models.py

What You Can Create

Marketing Content

• Product showcase videos

• Social media clips

• Promotional animations

Creative Projects

• Music video concepts

• Short film prototypes

• Art installations

Education & Training

• Explainer video drafts

• Training material concepts

• Visualization of concepts

Basic Usage

import torch
from diffusers import HunyuanVideoPipeline
from diffusers.utils import export_to_video

pipe = HunyuanVideoPipeline.from_pretrained(
    "tencent/HunyuanVideo",
    torch_dtype=torch.float16
)
pipe.to("cuda")
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()

prompt = "A majestic eagle soaring over snow-capped mountains, cinematic lighting, 4K"

video_frames = pipe(
    prompt=prompt,
    num_frames=45,
    num_inference_steps=50,
    guidance_scale=7.0
).frames[0]

export_to_video(video_frames, "eagle.mp4", fps=15)

Advanced Generation

import torch
from diffusers import HunyuanVideoPipeline
from diffusers.utils import export_to_video

pipe = HunyuanVideoPipeline.from_pretrained(
    "tencent/HunyuanVideo",
    torch_dtype=torch.float16
)
pipe.to("cuda")
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()
pipe.vae.enable_slicing()

video_frames = pipe(
    prompt="Time-lapse of a flower blooming, macro photography, detailed petals",
    negative_prompt="blurry, low quality, distorted, ugly",
    num_frames=45,
    height=544,
    width=960,
    num_inference_steps=50,
    guidance_scale=7.0,
    generator=torch.Generator("cuda").manual_seed(42)
).frames[0]

export_to_video(video_frames, "flower_bloom.mp4", fps=15)

Prompt Examples

Nature & Landscapes

prompts = [
    "Aurora borealis dancing over a frozen lake, time-lapse, ethereal",
    "Ocean waves crashing on volcanic black sand beach, slow motion",
    "Thunderstorm over a wheat field, dramatic lighting, 4K",
    "Cherry blossoms falling in a Japanese garden, spring, peaceful"
]

Sci-Fi & Fantasy

prompts = [
    "Spaceship launching from a futuristic city, cinematic, detailed",
    "Dragon flying through clouds at sunset, epic, fantasy",
    "Robot walking through neon-lit streets, cyberpunk, rain",
    "Magical portal opening in an ancient forest, mystical lights"
]

Abstract & Artistic

prompts = [
    "Ink drops diffusing in water, macro, colorful, abstract",
    "Geometric shapes morphing and transforming, motion graphics",
    "Light painting in darkness, long exposure effect, vibrant"
]

Batch Generation

import os
import torch
from diffusers import HunyuanVideoPipeline
from diffusers.utils import export_to_video

pipe = HunyuanVideoPipeline.from_pretrained("tencent/HunyuanVideo", torch_dtype=torch.float16)
pipe.to("cuda")
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()

prompts = [
    "Underwater coral reef with colorful fish swimming",
    "City traffic time-lapse at night, light trails",
    "Butterfly emerging from cocoon, nature documentary"
]

output_dir = "./videos"
os.makedirs(output_dir, exist_ok=True)

for i, prompt in enumerate(prompts):
    print(f"Generating {i+1}/{len(prompts)}: {prompt[:50]}...")

    video_frames = pipe(
        prompt=prompt,
        num_frames=45,
        num_inference_steps=50,
        guidance_scale=7.0
    ).frames[0]

    export_to_video(video_frames, f"{output_dir}/video_{i:03d}.mp4", fps=15)

Gradio Interface

import gradio as gr
import torch
from diffusers import HunyuanVideoPipeline
from diffusers.utils import export_to_video
import tempfile

pipe = HunyuanVideoPipeline.from_pretrained("tencent/HunyuanVideo", torch_dtype=torch.float16)
pipe.to("cuda")
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()

def generate(prompt, negative_prompt, num_frames, steps, guidance, seed):
    generator = torch.Generator("cuda").manual_seed(seed) if seed > 0 else None

    video_frames = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_frames=num_frames,
        num_inference_steps=steps,
        guidance_scale=guidance,
        generator=generator
    ).frames[0]

    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as f:
        export_to_video(video_frames, f.name, fps=15)
        return f.name

demo = gr.Interface(
    fn=generate,
    inputs=[
        gr.Textbox(label="Prompt", lines=3),
        gr.Textbox(label="Negative Prompt", value="blurry, low quality"),
        gr.Slider(16, 60, value=45, step=1, label="Frames"),
        gr.Slider(20, 100, value=50, step=5, label="Steps"),
        gr.Slider(3, 12, value=7, step=0.5, label="Guidance"),
        gr.Number(value=-1, label="Seed")
    ],
    outputs=gr.Video(label="Generated Video"),
    title="Hunyuan Video - Text to Video on CLORE.AI"
)

demo.launch(server_name="0.0.0.0", server_port=7860)

Performance

Resolution	Frames	GPU	Time
544x960	45	RTX 4090	~5 min
544x960	45	A100 40GB	~3 min
544x960	45	A100 80GB	~2 min
720x1280	45	A100 80GB	~4 min

Common Problems & Solutions

Out of Memory

Problem: CUDA out of memory on 24GB GPU

Solutions:

# Enable all memory optimizations
pipe.enable_model_cpu_offload()
pipe.enable_sequential_cpu_offload()  # More aggressive
pipe.vae.enable_tiling()
pipe.vae.enable_slicing()

# Reduce frames and resolution
video = pipe(prompt, num_frames=24, height=480, width=720).frames[0]

Slow Generation

Problem: Takes too long to generate

Solutions:

• Reduce num_inference_steps (30-40 still gives good results)

• Reduce num_frames (24 frames = 1.6s at 15fps)

• Use A100 GPU for faster processing

• Ensure you have NVMe storage for model loading

Poor Video Quality

Problem: Blurry or inconsistent motion

Solutions:

• Increase num_inference_steps to 75-100

• Adjust guidance_scale (6-8 works best)

• Write more detailed prompts

• Add negative prompts to avoid issues

Video Artifacts

Problem: Flickering or temporal inconsistencies

Solutions:

• Use consistent seed for reproducibility

• Avoid prompts with rapid motion

• Post-process with video stabilization

Troubleshooting

Out of memory

• Hunyuan requires 24GB+ VRAM minimum

• Use A100 40GB/80GB for best results

• Reduce video length/resolution

Video generation fails

• Check all model files downloaded correctly

• Ensure enough disk space (100GB+)

• Verify CUDA and PyTorch compatibility

Poor video quality

• Increase inference steps

• Use more descriptive prompts

• Check input resolution matches expected

Slow generation

• Video generation is compute-intensive

• Use A100/H100 for faster results

• Consider shorter clips first

Cost Estimate

Typical CLORE.AI marketplace rates (as of 2024):

GPU	Hourly Rate	Daily Rate	4-Hour Session
RTX 3060	~$0.03	~$0.70	~$0.12
RTX 3090	~$0.06	~$1.50	~$0.25
RTX 4090	~$0.10	~$2.30	~$0.40
A100 40GB	~$0.17	~$4.00	~$0.70
A100 80GB	~$0.25	~$6.00	~$1.00

Prices vary by provider and demand. Check CLORE.AI Marketplace for current rates.

Save money:

• Use the Spot market for interruptible work — about a third of servers price spot below on-demand (median ~13% off), the rest match it

• Pay with CLORE tokens

• Compare prices across different providers

Next Steps

• CogVideoX - Alternative T2V

• Wan2.1 Video - Another T2V option

• AnimateDiff - Image animation