Model reference · open weights

HunyuanVideo-1.5-720p_i2v

Video hunyuanvideo-community Image→video 1 build Its own licence terms 962 dl/mo

HunyuanVideo-1.5-720p_i2v is an open-weight video model from hunyuanvideo-community. HunyuanVideo-1.5-Diffusers-720p_i2v_distilled (FP32) weighs 27.1 GB; the smallest configuration that runs it is RTX 4090 24 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • HunyuanVideo-1.5-720p_i2v is an image-to-video model with 8.3B parameters developed by hunyuanvideo-community.
  • It generates 720p videos from input images and text prompts, utilizing attention masks for variable-length sequences.
  • The model is distributed under an "other" license and requires an attention backend that handles padding efficiently for optimal performance.

Summary of the hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v_distilled model card, 2026-10-01

What it is

Released byhunyuanvideo-community
Released2025-11-29
Parameters8.3B
VRAM27.1 GB for the weights

What it runs on

Memory and cards for HunyuanVideo-1.5-Diffusers-720p_i2v_distilled (FP32)

27.1 GBweights, file size
16.7 GBbiggest part
537 MBruntime overhead
CardWeightsMemory
RTX 3060 12 GB … RTX 4060 Ti 16 GBdoes not fit
RTX 3090 24 GBfits (encoders offloaded)23.4 GB
RTX 4090 24 GBfits (encoders offloaded)23.4 GB
RTX 5090 32 GBtight31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What hunyuanvideo-community says about HunyuanVideo-1.5-720p_i2v

Read the model card

Hunyuan1.5 use attention masks with variable-length sequences. For best performance, we recommend using an attention backend that handles padding efficiently.

We recommend installing kernels (pip install kernels) to access prebuilt attention kernels

You can check our documentation to learn more about all the different attention backends we support.

import torch

dtype = torch.bfloat16
device = "cuda:0"
from diffusers import HunyuanVideo15ImageToVideoPipeline, attention_backend
from diffusers.utils import export_to_video, load_image

pipe = HunyuanVideo15ImageToVideoPipeline.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v_distilled", torch_dtype=dtype)
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()

generator = torch.Generator(device=device).manual_seed(1)
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG")
prompt="Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
with attention_backend("_flash_3_hub"): # or `"flash_hub" if you're not using Hopper GPUs
    video = pipe(
        prompt=prompt,
        image=image,
        generator=generator,
        num_frames=121,
        num_inference_steps=50,
    ).frames[0]
export_to_video(video, "output.mp4", fps=24)

to use default attention backend,

import torch

dtype = torch.bfloat16
device = "cuda:0"
from diffusers import HunyuanVideo15ImageToVideoPipeline
from diffusers.utils import export_to_video, load_image

pipe = HunyuanVideo15ImageToVideoPipeline.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v_distilled", torch_dtype=dtype)
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()

generator = torch.Generator(device=device).manual_seed(1)
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG")
prompt="Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."

video = pipe(
    prompt=prompt,
    image=image,
    generator=generator,
    num_frames=121,
    num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=24)

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

Running it yourself

Run it on a rented GPU

Rent a machine by the hour. Runs as it is with diffusers, on the machine, in Python.

# on your rented machine: pip install diffusers transformers accelerate ftfy
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video

pipe = DiffusionPipeline.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v_distilled", torch_dtype=torch.bfloat16).to("cuda")
frames = pipe(prompt="a drone shot over a forest at sunrise").frames[0]
export_to_video(frames, "/workspace/out.mp4", fps=16)
Renting a GPU: connect, tunnels, Python
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