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Update Intel Gaudi doc (#11479)
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com> Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
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@@ -208,7 +208,7 @@
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- local: optimization/mps
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title: Metal Performance Shaders (MPS)
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- local: optimization/habana
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title: Habana Gaudi
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title: Intel Gaudi
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- local: optimization/neuron
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title: AWS Neuron
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title: Optimized hardware
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@@ -10,67 +10,22 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
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specific language governing permissions and limitations under the License.
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-->
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# Habana Gaudi
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# Intel Gaudi
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🤗 Diffusers is compatible with Habana Gaudi through 🤗 [Optimum](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion). Follow the [installation](https://docs.habana.ai/en/latest/Installation_Guide/index.html) guide to install the SynapseAI and Gaudi drivers, and then install Optimum Habana:
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The Intel Gaudi AI accelerator family includes [Intel Gaudi 1](https://habana.ai/products/gaudi/), [Intel Gaudi 2](https://habana.ai/products/gaudi2/), and [Intel Gaudi 3](https://habana.ai/products/gaudi3/). Each server is equipped with 8 devices, known as Habana Processing Units (HPUs), providing 128GB of memory on Gaudi 3, 96GB on Gaudi 2, and 32GB on the first-gen Gaudi. For more details on the underlying hardware architecture, check out the [Gaudi Architecture](https://docs.habana.ai/en/latest/Gaudi_Overview/Gaudi_Architecture.html) overview.
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```bash
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python -m pip install --upgrade-strategy eager optimum[habana]
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Diffusers pipelines can take advantage of HPU acceleration, even if a pipeline hasn't been added to [Optimum for Intel Gaudi](https://huggingface.co/docs/optimum/main/en/habana/index) yet, with the [GPU Migration Toolkit](https://docs.habana.ai/en/latest/PyTorch/PyTorch_Model_Porting/GPU_Migration_Toolkit/GPU_Migration_Toolkit.html).
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Call `.to("hpu")` on your pipeline to move it to a HPU device as shown below for Flux:
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```py
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import torch
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from diffusers import DiffusionPipeline
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pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
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pipeline.to("hpu")
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image = pipeline("An image of a squirrel in Picasso style").images[0]
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```
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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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- [`~optimum.habana.diffusers.GaudiStableDiffusionPipeline`], a pipeline for text-to-image generation.
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- [`~optimum.habana.diffusers.GaudiDDIMScheduler`], a Gaudi-optimized scheduler.
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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`.
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Finally, specify a [`~optimum.habana.GaudiConfig`] which can be downloaded from the [Habana](https://huggingface.co/Habana) organization on the Hub.
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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-2",
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)
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```
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Now you can 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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We benchmarked Habana's first-generation Gaudi and Gaudi2 with the [Habana/stable-diffusion](https://huggingface.co/Habana/stable-diffusion) and [Habana/stable-diffusion-2](https://huggingface.co/Habana/stable-diffusion-2) Gaudi configurations (mixed precision bf16/fp32) to demonstrate their performance.
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For [Stable Diffusion v1.5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) on 512x512 images:
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| | Latency (batch size = 1) | Throughput |
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| ---------------------- |:------------------------:|:---------------------------:|
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| first-generation Gaudi | 3.80s | 0.308 images/s (batch size = 8) |
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| Gaudi2 | 1.33s | 1.081 images/s (batch size = 8) |
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For [Stable Diffusion v2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) on 768x768 images:
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| | Latency (batch size = 1) | Throughput |
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| ---------------------- |:------------------------:|:-------------------------------:|
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| first-generation Gaudi | 10.2s | 0.108 images/s (batch size = 4) |
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| Gaudi2 | 3.17s | 0.379 images/s (batch size = 8) |
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> [!TIP]
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> For Gaudi-optimized diffusion pipeline implementations, we recommend using [Optimum for Intel Gaudi](https://huggingface.co/docs/optimum/main/en/habana/index).
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@@ -175,7 +175,7 @@
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- local: optimization/mps
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title: Metal Performance Shaders (MPS)
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- local: optimization/habana
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title: Habana Gaudi
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title: Intel Gaudi
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title: 최적화된 하드웨어
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title: 추론 가속화와 메모리 줄이기
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- sections:
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@@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
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specific language governing permissions and limitations under the License.
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-->
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# Habana Gaudi에서 Stable Diffusion을 사용하는 방법
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# Intel Gaudi에서 Stable Diffusion을 사용하는 방법
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🤗 Diffusers는 🤗 [Optimum Habana](https://huggingface.co/docs/optimum/habana/usage_guides/stable_diffusion)를 통해서 Habana Gaudi와 호환됩니다.
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