71 lines
3.3 KiB
Plaintext
71 lines
3.3 KiB
Plaintext
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<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# How to use Stable Diffusion on Habana Gaudi
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🤗 Diffusers is compatible with Habana Gaudi through 🤗 [Optimum Habana](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion).
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## Requirements
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- Optimum Habana 1.3 or later, [here](https://huggingface.co/docs/optimum/habana/installation) is how to install it.
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- SynapseAI 1.7.
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## Inference Pipeline
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To generate images with Stable Diffusion 1 and 2 on Gaudi, you need to instantiate two instances:
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- A pipeline with [`GaudiStableDiffusionPipeline`](https://huggingface.co/docs/optimum/habana/package_reference/stable_diffusion_pipeline). This pipeline supports *text-to-image generation*.
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- 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.
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When initializing the pipeline, you have to specify `use_habana=True` to deploy it on HPUs.
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Furthermore, in order to get the fastest possible generations you should enable **HPU graphs** with `use_hpu_graphs=True`.
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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).
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```python
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from optimum.habana import GaudiConfig
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from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline
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model_name = "stabilityai/stable-diffusion-2-base"
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scheduler = GaudiDDIMScheduler.from_pretrained(model_name, subfolder="scheduler")
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pipeline = GaudiStableDiffusionPipeline.from_pretrained(
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model_name,
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scheduler=scheduler,
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use_habana=True,
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use_hpu_graphs=True,
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gaudi_config="Habana/stable-diffusion",
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)
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```
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You can then call the pipeline to generate images by batches from one or several prompts:
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```python
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outputs = pipeline(
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prompt=[
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"High quality photo of an astronaut riding a horse in space",
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"Face of a yellow cat, high resolution, sitting on a park bench",
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],
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num_images_per_prompt=10,
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batch_size=4,
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)
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```
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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.
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## Benchmark
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Here are the latencies for Habana Gaudi 1 and Gaudi 2 with the [Habana/stable-diffusion](https://huggingface.co/Habana/stable-diffusion) Gaudi configuration (mixed precision bf16/fp32):
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| | Latency | Batch size |
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| ------- |:-------:|:----------:|
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| Gaudi 1 | 4.37s | 4/8 |
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| Gaudi 2 | 1.19s | 4/8 |
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