{"id":6195,"date":"2026-04-08T14:51:36","date_gmt":"2026-04-08T06:51:36","guid":{"rendered":"https:\/\/crepal.ai\/blog\/?p=6195"},"modified":"2026-04-08T14:51:39","modified_gmt":"2026-04-08T06:51:39","slug":"aivideo-hunyuan-image-to-video-tutorial","status":"publish","type":"post","link":"https:\/\/crepal.ai\/blog\/aivideo\/aivideo-hunyuan-image-to-video-tutorial\/","title":{"rendered":"Hunyuan Image to Video: How to Use Tencent’s AI Model"},"content":{"rendered":"\n

Hi, I’m Dora \u2014 and I’ll be real with you: I almost skipped Hunyuan entirely.<\/p>\n\n\n\n

If you have ever been stuck with animating a product shot into a short video clip through a paid tool but turned out being frustrated by a plasticky and woring motion, try Hunyuan I2V<\/a><\/strong>, and you would see the output loop three times in a row, just like I did.<\/p>\n\n\n\n

This is Dora, who almost skipped Hunyuan entirely. Now, however, I am still here to share with you guys my dozens of tests across the original I2V model and the newer HunyuanVideo-1.5 distilled version, and everything henceforward.<\/p>\n\n\n\n

What Is Hunyuan Image to Video?<\/h2>\n\n\n\n

Hunyuan Image to Video (I2V) is Tencent’s open-source AI model that animates a static image into a short video clip, guided by a text prompt. Tencent officially released HunyuanVideo-I2V on March 6, 2025, alongside LoRA training code for customizable special effects.<\/p>\n\n\n\n

Under the hood, it’s not a simple frame-interpolation trick. The model uses a “token replace” technique to reconstruct and incorporate reference image information into the video generation process, while a pre-trained Multimodal Large Language Model (MLLM) with a Decoder-Only architecture acts as the text encoder \u2014 letting it understand both image content and the caption simultaneously. That’s why it handles semantic motion prompts (like “the wind picks up, her scarf starts to flutter”) better than simpler tools.<\/p>\n\n\n\n

The newer HunyuanVideo-1.5<\/strong> raised the bar further. It delivers top-tier quality with only 8.3 billion parameters, significantly lowering the barrier to usage, and runs smoothly on consumer-grade GPUs. A step-distilled I2V variant is especially exciting: it generates videos in 8 or 12 steps, reducing end-to-end generation time by 75%, so a single RTX 4090 can generate videos within 75 seconds.<\/p>\n\n\n\n

For creators who’ve been locked out of high-quality AI video because of cost or hardware requirements, that’s a genuine shift.<\/p>\n\n\n\n

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Where to Access Hunyuan<\/h2>\n\n\n\n

Hugging Face<\/h3>\n\n\n\n

The fastest way to experiment without installing anything. Both the original HunyuanVideo-I2V model weights<\/a> and the HunyuanVideo-1.5 weights<\/a> are hosted on Hugging Face \u2014 and both are integrated with the Diffusers library, so you can run inference in a Python notebook without touching ComfyUI.<\/p>\n\n\n\n

Quick install:<\/p>\n\n\n\n

pip install diffusers transformers accelerate<\/code><\/pre>\n\n\n\n
Then in Python:<\/p>\n\n\n\n
from diffusers import HunyuanVideoImageToVideoPipeline\nimport torch\n\npipe = HunyuanVideoImageToVideoPipeline.from_pretrained(\n    \"tencent\/HunyuanVideo-1.5\",\n    torch_dtype=torch.bfloat16\n).to(\"cuda\")\n\nvideo = pipe(\n    image=\"your_input.jpg\",\n    prompt=\"camera slowly zooms out, soft morning light\",\n    num_inference_steps=12,  # distilled model: 8 or 12 steps recommended\n    height=480,\n    width=832,\n).frames[0]<\/code><\/pre>\n\n\n\n
Fair warning: you’ll want at least 24GB VRAM for the 480p distilled model. The full 720p original needs 60GB minimum.<\/p>\n\n\n\n
ComfyUI<\/h3>\n\n\n\n
This is where most creators actually live with Hunyuan, and the community support here is exceptional. ComfyUI now natively supports the HunyuanVideo-I2V model, and community developers Kijai and city96 have updated their custom nodes to support it as well. There are three main flavors:<\/p>\n\n\n\n
Version<\/td>Plugin Required<\/td>Best For<\/td><\/tr>
Comfy-Org\/HunyuanVideo_repackaged<\/td>None (native)<\/td>Beginners, clean setup<\/td><\/tr>
Kijai\/HunyuanVideo_comfy<\/td>ComfyUI-HunyuanVideoWrapper<\/td>Lower VRAM (FP8 inference)<\/td><\/tr>
city96\/HunyuanVideo-I2V-gguf<\/td>ComfyUI-GGUF<\/td>Very low VRAM, GGUF format<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n