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# HunyuanVideo-1.5

HunyuanVideo-1.5 is a lightweight yet powerful video generation model that achieves state-of-the-art visual quality and motion coherence with only 8.3 billion parameters, enabling efficient inference on consumer-grade GPUs. This achievement is built upon several key components, including meticulous data curation, an advanced DiT architecture with selective and sliding tile attention (SSTA), enhanced bilingual understanding through glyph-aware text encoding, progressive pre-training and post-training, and an efficient video super-resolution network. Leveraging these designs, we developed a unified framework capable of high-quality text-to-video and image-to-video generation across multiple durations and resolutions. Extensive experiments demonstrate that this compact and proficient model establishes a new state-of-the-art among open-source models.

You can find all the original HunyuanVideo checkpoints under the [Tencent](https://huggingface.co/tencent) organization.

> [!TIP]
> Click on the HunyuanVideo models in the right sidebar for more examples of video generation tasks.
>
> The examples below use a checkpoint from [hunyuanvideo-community](https://huggingface.co/hunyuanvideo-community) because the weights are stored in a layout compatible with Diffusers.

The example below demonstrates how to generate a video optimized for memory or inference speed.

<hfoptions id="usage">
<hfoption id="memory">

Refer to the [Reduce memory usage](../../optimization/memory) guide for more details about the various memory saving techniques.


```py
import torch
from diffusers import AutoModel, HunyuanVideo15Pipeline
from diffusers.utils import export_to_video


pipeline = HunyuanVideo15Pipeline.from_pretrained(
    "HunyuanVideo-1.5-Diffusers-480p_t2v",
    dtype=torch.bfloat16,
)

# model-offloading and tiling
pipeline.enable_model_cpu_offload()
pipeline.vae.enable_tiling()

prompt = "A fluffy teddy bear sits on a bed of soft pillows surrounded by children's toys."
video = pipeline(prompt=prompt, num_frames=61, num_inference_steps=30).frames[0]
export_to_video(video, "output.mp4", fps=15)
```

## Notes

- HunyuanVideo1.5 use attention masks with variable-length sequences. For best performance, we recommend using an attention backend that handles padding efficiently.

    - **H100/H800:** `_flash_3_hub` or `_flash_3_varlen_hub`
    - **A100/A800/RTX 4090:** `flash_hub` or `flash_varlen_hub`
    - **Other GPUs:** `sage_hub`

Refer to the [Attention backends](../../optimization/attention_backends) guide for more details about using a different backend.


```py
pipe.transformer.set_attention_backend("flash_hub")  # or your preferred backend
```

- [`HunyuanVideo15Pipeline`] use guider and does not take `guidance_scale` parameter at runtime. 

You can check the default guider configuration using `pipe.guider`:

```py
>>> pipe.guider 
ClassifierFreeGuidance {
  "_class_name": "ClassifierFreeGuidance",
  "_diffusers_version": "0.36.0.dev0",
  "enabled": true,
  "guidance_rescale": 0.0,
  "guidance_scale": 6.0,
  "start": 0.0,
  "stop": 1.0,
  "use_original_formulation": false
}

State:
  step: None
  num_inference_steps: None
  timestep: None
  count_prepared: 0
  enabled: True
  num_conditions: 2
```

To update guider configuration, you can run `pipe.guider = pipe.guider.new(...)`

```py
pipe.guider = pipe.guider.new(guidance_scale=5.0)
```

Read more on Guider [here](../../using-diffusers/guiders).



## HunyuanVideo15Pipeline

[[autodoc]] HunyuanVideo15Pipeline
  - all
  - __call__

## HunyuanVideo15ImageToVideoPipeline

[[autodoc]] HunyuanVideo15ImageToVideoPipeline
  - all
  - __call__

## HunyuanVideo15PipelineOutput

[[autodoc]] pipelines.hunyuan_video1_5.pipeline_output.HunyuanVideo15PipelineOutput
