Diffusers documentation

How to use Stable Diffusion on Habana Gaudi

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How to use Stable Diffusion on Habana Gaudi

🤗 Diffusers is compatible with Habana Gaudi through 🤗 Optimum Habana.

Requirements

  • Optimum Habana 1.6 or later, here 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:

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 which can be downloaded from the Hugging Face Hub.

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:

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 and the example provided in the official Github repository.

Benchmark

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

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