Stable Diffusion is a text-to-image latent diffusion model. Check out this blog post for more information.
To generate images with Stable Diffusion on Gaudi, you need to instantiate two instances:
GaudiStableDiffusionPipeline
. This pipeline supports text-to-image generation.GaudiDDIMScheduler
. This scheduler has been optimized for Gaudi.When initializing the pipeline, you have to specify use_habana=True
to deploy it on HPUs.
Furthermore, to get the fastest possible generations you should enable HPU graphs with use_hpu_graphs=True
.
Finally, you will need to specify a Gaudi configuration which can be downloaded from the Hugging Face Hub.
from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline
model_name = "CompVis/stable-diffusion-v1-4"
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",
)
You can then call the pipeline to generate images from one or several prompts:
outputs = pipeline(
prompt=["High quality photo of an astronaut riding a horse in space", "Face of a yellow cat, high resolution, sitting on a park bench"],
num_images_per_prompt=10,
batch_size=4,
output_type="pil",
)
Generated images can be returned as either PIL images or NumPy arrays, depending on the output_type
option.
Check out the example provided in the official Github repository.
Stable Diffusion 2 can be used with the exact same classes. Here is an example:
from optimum.habana.diffusers import GaudiDDIMScheduler, GaudiStableDiffusionPipeline
model_name = "stabilityai/stable-diffusion-2-1"
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-2",
)
outputs = pipeline(
["An image of a squirrel in Picasso style"],
num_images_per_prompt=10,
batch_size=2,
height=768,
width=768,
)
There are two different checkpoints for Stable Diffusion 2:
The Stable Diffusion upscaler diffusion model was created by the researchers and engineers from CompVis, Stability AI, and LAION. It is used to enhance the resolution of input images by a factor of 4.
See here for more information.
To generate RGB and depth images with Stable Diffusion Upscale on Gaudi, you need to instantiate two instances:
GaudiStableDiffusionUpscalePipeline
.GaudiDDIMScheduler
. This scheduler has been optimized for Gaudi.When initializing the pipeline, you have to specify use_habana=True
to deploy it on HPUs.
Furthermore, to get the fastest possible generations you should enable HPU graphs with use_hpu_graphs=True
.
Finally, you will need to specify a Gaudi configuration which can be downloaded from the Hugging Face Hub.
import requests
from io import BytesIO
from optimum.habana.diffusers import (
GaudiDDIMScheduler,
GaudiStableDiffusionUpscalePipeline,
)
from optimum.habana.utils import set_seed
from PIL import Image
set_seed(42)
model_name_upscale = "stabilityai/stable-diffusion-x4-upscaler"
scheduler = GaudiDDIMScheduler.from_pretrained(model_name_upscale, subfolder="scheduler")
url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
response = requests.get(url)
low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
low_res_img = low_res_img.resize((128, 128))
low_res_img.save("low_res_cat.png")
prompt = "a white cat"
pipeline = GaudiStableDiffusionUpscalePipeline.from_pretrained(
model_name_upscale,
scheduler=scheduler,
use_habana=True,
use_hpu_graphs=True,
gaudi_config="Habana/stable-diffusion",
)
upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
upscaled_image.save("upsampled_cat.png")
To accelerate your Stable Diffusion pipeline, you can run it in full bfloat16 precision.
This will also save memory.
You just need to pass torch_dtype=torch.bfloat16
to from_pretrained
when instantiating your pipeline.
Here is how to do it:
import torch
pipeline = GaudiStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
scheduler=scheduler,
use_habana=True,
use_hpu_graphs=True,
gaudi_config="Habana/stable-diffusion",
torch_dtype=torch.bfloat16
)
Textual Inversion is a method to personalize text2image models like Stable Diffusion on your own images using just 3-5 examples.
You can find here an example script that implements this training method.
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