> For the complete documentation index, see [llms.txt](https://boinc-ai.gitbook.io/diffusers/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://boinc-ai.gitbook.io/diffusers/optimization-special-hardware/habana-gaudi.md).

# Habana Gaudi

## How to use Stable Diffusion on Habana Gaudi

 🌍 Diffusers is compatible with Habana Gaudi through 🌍 [Optimum Habana](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion).

### Requirements

* Optimum Habana 1.6 or later, [here](https://huggingface.co/docs/optimum/habana/installation) is how to install it.
* SynapseAI 1.10.

### Inference Pipeline

To generate images with Stable Diffusion 1 and 2 on Gaudi, you need to instantiate two instances:

* A pipeline with [`GaudiStableDiffusionPipeline`](https://huggingface.co/docs/optimum/habana/package_reference/stable_diffusion_pipeline). This pipeline supports *text-to-image generation*.
* A scheduler with [`GaudiDDIMScheduler`](https://huggingface.co/docs/optimum/habana/package_reference/stable_diffusion_pipeline#optimum.habana.diffusers.GaudiDDIMScheduler). This scheduler has been optimized for Habana Gaudi.

When initializing the pipeline, you have to specify `use_habana=True` to deploy it on HPUs. Furthermore, in order to get the fastest possible generations you should enable **HPU graphs** with `use_hpu_graphs=True`. Finally, you will need to specify a [Gaudi configuration](https://huggingface.co/docs/optimum/habana/package_reference/gaudi_config) which can be downloaded from the [Hugging Face Hub](https://huggingface.co/Habana).

Copied

```
from optimum.habana import GaudiConfig
from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline

model_name = "stabilityai/stable-diffusion-2-base"
scheduler = GaudiDDIMScheduler.from_pretrained(model_name, subfolder="scheduler")
pipeline = GaudiStableDiffusionPipeline.from_pretrained(
    model_name,
    scheduler=scheduler,
    use_habana=True,
    use_hpu_graphs=True,
    gaudi_config="Habana/stable-diffusion-2",
)
```

You can then call the pipeline to generate images by batches from one or several prompts:

Copied

```
outputs = pipeline(
    prompt=[
        "High quality photo of an astronaut riding a horse in space",
        "Face of a yellow cat, high resolution, sitting on a park bench",
    ],
    num_images_per_prompt=10,
    batch_size=4,
)
```

For more information, check out Optimum Habana’s [documentation](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion) and the [example](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion) provided in the official Github repository.

### Benchmark

Here are the latencies for Habana first-generation Gaudi and Gaudi2 with the [Habana/stable-diffusion](https://huggingface.co/Habana/stable-diffusion) and [Habana/stable-diffusion-2](https://huggingface.co/Habana/stable-diffusion-2) Gaudi configurations (mixed precision bf16/fp32):

* [Stable Diffusion v1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) (512x512 resolution):

|                        | Latency (batch size = 1) | Throughput (batch size = 8) |
| ---------------------- | :----------------------: | :-------------------------: |
| first-generation Gaudi |           3.80s          |        0.308 images/s       |
| Gaudi2                 |           1.33s          |        1.081 images/s       |

* [Stable Diffusion v2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) (768x768 resolution):

|                        | Latency (batch size = 1) |            Throughput           |
| ---------------------- | :----------------------: | :-----------------------------: |
| first-generation Gaudi |           10.2s          | 0.108 images/s (batch size = 4) |
| Gaudi2                 |           3.17s          | 0.379 images/s (batch size = 8) |