license: apache-2.0
Optimum Habana is the interface between the Hugging Face 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 and deploy Transformers and Diffusers models at hf.co/hardware/habana.
Stable Diffusion HPU configuration
This model only contains the GaudiConfig file for running Stable Diffusion v1 (e.g. runwayml/stable-diffusion-v1-5) on Habana's Gaudi processors (HPU).
This model contains no model weights, only a GaudiConfig.
This enables to specify:
use_torch_autocast: whether to use Torch Autocast for managing mixed precision
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.
It is strongly recommended to train this model doing bf16 mixed-precision training for optimal performance and accuracy.
Here is an example with one prompt:
from optimum.habana import GaudiConfig
from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline
model_name = "runwayml/stable-diffusion-v1-5"
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 = pipeline(
["An image of a squirrel in Picasso style"],
num_images_per_prompt=16,
batch_size=4,
)
Check out the documentation and this example for more advanced usage.