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Habana Gaudi

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Habana Gaudi

🤗 Diffusers is compatible with Habana Gaudi through 🤗 Optimum. Follow the installation guide to install the SynapseAI and Gaudi drivers, and then install Optimum Habana:

python -m pip install --upgrade-strategy eager optimum[habana]

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

  • GaudiStableDiffusionPipeline, a pipeline for text-to-image generation.
  • GaudiDDIMScheduler, a Gaudi-optimized scheduler.

When you initialize the pipeline, you have to specify use_habana=True to deploy it on HPUs and to get the fastest possible generation, you should enable HPU graphs with use_hpu_graphs=True.

Finally, specify a GaudiConfig which can be downloaded from the Habana organization on the 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(

Now you can call the pipeline to generate images by batches from one or several prompts:

outputs = pipeline(
        "High quality photo of an astronaut riding a horse in space",
        "Face of a yellow cat, high resolution, sitting on a park bench",

For more information, check out 🤗 Optimum Habana’s documentation and the example provided in the official GitHub repository.


We benchmarked Habana’s first-generation Gaudi and Gaudi2 with the Habana/stable-diffusion and Habana/stable-diffusion-2 Gaudi configurations (mixed precision bf16/fp32) to demonstrate their performance.

For Stable Diffusion v1.5 on 512x512 images:

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

For Stable Diffusion v2.1 on 768x768 images:

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)
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