readme: ---
  license: apache-2.0
  ---
  
  [Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the 🤗 Transformers and 🤗 Diffusers libraries and Habana's Gaudi processor (HPU). It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks.
  Learn more about how to take advantage of the power of Habana HPUs to train Transformers models at [hf.co/hardware/habana](https://huggingface.co/hardware/habana).
  
  ## Stable Diffusion HPU configuration
  
  This model only contains the `GaudiConfig` file for running **Stable Diffusion 1** (e.g. [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)) or **Stable Diffusion 2** (e.g. [stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2)) on Habana's Gaudi processors (HPU).
  
  **This model contains no model weights, only a GaudiConfig.**
  
  This enables to specify:
  - `use_habana_mixed_precision`: whether to use Habana Mixed Precision (HMP)
      - `hmp_opt_level`: optimization level for HMP, see [here](https://docs.habana.ai/en/latest/PyTorch/PyTorch_Mixed_Precision/PT_Mixed_Precision.html#configuration-options) for a detailed explanation
      - `hmp_bf16_ops`: list of operators that should run in bf16
      - `hmp_fp32_ops`: list of operators that should run in fp32
      - `hmp_is_verbose`: verbosity
  
  ## Usage
  
  The `GaudiStableDiffusionPipeline` (`GaudiDDIMScheduler`) is instantiated the same way as the `StableDiffusionPipeline` (`DDIMScheduler`) in the 🤗 Diffusers library.
  The only difference is that there are a few new training arguments specific to HPUs.
  
  Here is an example with one prompt:
  ```python
  from optimum.habana import GaudiConfig
  from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline
  
  
  model_name = "stabilityai/stable-diffusion-2"
  
  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",
  )
  
  outputs = generator(
      ["An image of a squirrel in Picasso style"],
      num_images_per_prompt=16,
      batch_size=4,
  )
  ```
  
  Check out the [documentation](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion) and [this example](https://github.com/huggingface/optimum-habana/tree/main/examples/stable-diffusion) for more advanced usage.
  