StableDiffusion image-generation model
StableDiffusion class
keras_cv.models.StableDiffusion(img_height=512, img_width=512, jit_compile=True)
Keras implementation of Stable Diffusion.
Note that the StableDiffusion API, as well as the APIs of the sub-components of StableDiffusion (e.g. ImageEncoder, DiffusionModel) should be considered unstable at this point. We do not guarantee backwards compatability for future changes to these APIs.
Stable Diffusion is a powerful image generation model that can be used, among other things, to generate pictures according to a short text description (called a "prompt").
Arguments
- img_height: int, height of the images to generate, in pixel. Note that only multiples of 128 are supported; the value provided will be rounded to the nearest valid value. Defaults to 512.
- img_width: int, width of the images to generate, in pixel. Note that only multiples of 128 are supported; the value provided will be rounded to the nearest valid value. Defaults to 512.
- jit_compile: bool, whether to compile the underlying models to XLA. This can lead to a significant speedup on some systems. Defaults to False.
Example
from keras_cv.models import StableDiffusion
from PIL import Image
model = StableDiffusion(img_height=512, img_width=512, jit_compile=True)
img = model.text_to_image(
prompt="A beautiful horse running through a field",
batch_size=1, # How many images to generate at once
num_steps=25, # Number of iterations (controls image quality)
seed=123, # Set this to always get the same image from the same prompt
)
Image.fromarray(img[0]).save("horse.png")
print("saved at horse.png")
References