Developer guides / KerasCV / High-performance image generation using Stable Diffusion in KerasCV

High-performance image generation using Stable Diffusion in KerasCV

Authors: fchollet, lukewood, divamgupta
Date created: 2022/09/25
Last modified: 2022/09/25
Description: Generate new images using KerasCV's StableDiffusion model.

View in Colab GitHub source


In this guide, we will show how to generate novel images based on a text prompt using the KerasCV implementation of's text-to-image model, Stable Diffusion.

Stable Diffusion is a powerful, open-source text-to-image generation model. While there exist multiple open-source implementations that allow you to easily create images from textual prompts, KerasCV's offers a few distinct advantages. These include XLA compilation and mixed precision support, which together achieve state-of-the-art generation speed.

In this guide, we will explore KerasCV's Stable Diffusion implementation, show how to use these powerful performance boosts, and explore the performance benefits that they offer.

To get started, let's install a few dependencies and sort out some imports:

!pip install —upgrade keras-cv
import time
import keras_cv
from tensorflow import keras
import matplotlib.pyplot as plt


Unlike most tutorials, where we first explain a topic then show how to implement it, with text-to-image generation it is easier to show instead of tell.

Check out the power of keras_cv.models.StableDiffusion().

First, we construct a model:

model = keras_cv.models.StableDiffusion(img_width=512, img_height=512)

Next, we give it a prompt:

images = model.text_to_image("photograph of an astronaut riding a horse", batch_size=3)

def plot_images(images):
    plt.figure(figsize=(20, 20))
    for i in range(len(images)):
        ax = plt.subplot(1, len(images), i + 1)

25/25 [==============================] - 19s 317ms/step


Pretty incredible!

But that's not all this model can do. Let's try a more complex prompt:

images = model.text_to_image(
    "cute magical flying dog, fantasy art, "
    "golden color, high quality, highly detailed, elegant, sharp focus, "
    "concept art, character concepts, digital painting, mystery, adventure",
25/25 [==============================] - 8s 316ms/step


The possibilities are literally endless (or at least extend to the boundaries of Stable Diffusion's latent manifold).

Wait, how does this even work?

Unlike what you might expect at this point, Stable Diffusion doesn't actually run on magic. It's a kind of "latent diffusion model". Let's dig into what that means.

You may be familiar with the idea of super-resolution: it's possible to train a deep learning model to denoise an input image — and thereby turn it into a higher-resolution version. The deep learning model doesn't do this by magically recovering the information that's missing from the noisy, low-resolution input — rather, the model uses its training data distribution to hallucinate the visual details that would be most likely given the input. To learn more about super-resolution, you can check out the following tutorials:


When you push this idea to the limit, you may start asking — what if we just run such a model on pure noise? The model would then "denoise the noise" and start hallucinating a brand new image. By repeating the process multiple times, you can get turn a small patch of noise into an increasingly clear and high-resolution artificial picture.

This is the key idea of latent diffusion, proposed in High-Resolution Image Synthesis with Latent Diffusion Models in 2020. To understand diffusion in depth, you can check the tutorial Denoising Diffusion Implicit Models.

Denoising diffusion

Now, to go from latent diffusion to a text-to-image system, you still need to add one key feature: the ability to control the generated visual contents via prompt keywords. This is done via "conditioning", a classic deep learning technique which consists of concatenating to the noise patch a vector that represents a bit of text, then training the model on a dataset of {image: caption} pairs.

This gives rise to the Stable Diffusion architecture. Stable Diffusion consists of three parts:

  • A text encoder, which turns your prompt into a latent vector.
  • A diffusion model, which repeatedly "denoises" a 64x64 latent image patch.
  • A decoder, which turns the final 64x64 latent patch into a higher-resolution 512x512 image.

First, your text prompt gets projected into a latent vector space by the text encoder, which is simply a pretrained, frozen language model. Then that prompt vector is concatenated to a randomly generated noise patch, which is repeatedly "denoised" by the diffusion model over a series of "steps" (the more steps you run the clearer and nicer your image will be — the default value is 50 steps).

Finally, the 64x64 latent image is sent through the decoder to properly render it in high resolution.

The Stable Diffusion architecture

All-in-all, it's a pretty simple system — the Keras implementation fits in four files that represent less than 500 lines of code in total:

But this relatively simple system starts looking like magic once you train on billions of pictures and their captions. As Feynman said about the universe: "It's not complicated, it's just a lot of it!"

Perks of KerasCV

With several implementations of Stable Diffusion publicly available why should you use keras_cv.models.StableDiffusion?

Aside from the easy-to-use API, KerasCV's Stable Diffusion model comes with some powerful advantages, including:

  • Graph mode execution
  • XLA compilation through jit_compile=True
  • Support for mixed precision computation

When these are combined, the KerasCV Stable Diffusion model runs orders of magnitude faster than naive implementations. This section shows how to enable all of these features, and the resulting performance gain yielded from using them.

For the purposes of comparison, we ran benchmarks comparing the runtime of the HuggingFace diffusers implementation of Stable Diffusion against the KerasCV implementation. Both implementations were tasked to generate 3 images with a step count of 50 for each image. In this benchmark, we used a Tesla T4 GPU.

All of our benchmarks are open source on GitHub, and may be re-run on Colab to reproduce the results. The results from the benchmark are displayed in the table below:

GPU Model Runtime
Tesla T4 KerasCV (Warm Start) 28.97s
Tesla T4 diffusers (Warm Start) 41.33s
Tesla V100 KerasCV (Warm Start) 12.45
Tesla V100 diffusers (Warm Start) 12.72

30% improvement in execution time on the Tesla T4!. While the improvement is much lower on the V100, we generally expect the results of the benchmark to consistently favor the KerasCV across all NVIDIA GPUs.

For the sake of completeness, both cold-start and warm-start generation times are reported. Cold-start execution time includes the one-time cost of model creation and compilation, and is therefore negligible in a production environment (where you would reuse the same model instance many times). Regardless, here are the cold-start numbers:

GPU Model Runtime
Tesla T4 KerasCV (Cold Start) 83.47s
Tesla T4 diffusers (Cold Start) 46.27s
Tesla V100 KerasCV (Cold Start) 76.43
Tesla V100 diffusers (Cold Start) 13.90

While the runtime results from running this guide may vary, in our testing the KerasCV implementation of Stable Diffusion is significantly faster than its PyTorch counterpart. This may be largely attributed to XLA compilation.

Note: The performance benefits of each optimization can vary significantly between hardware setups.

To get started, let's first benchmark our unoptimized model:

benchmark_result = []
start = time.time()
images = model.text_to_image(
    "A cute otter in a rainbow whirlpool holding shells, watercolor",
end = time.time()
benchmark_result.append(["Standard", end - start])

print(f"Standard model: {(end - start):.2f} seconds")
keras.backend.clear_session()  # Clear session to preserve memory.
25/25 [==============================] - 8s 316ms/step
Standard model: 8.17 seconds


Mixed precision

"Mixed precision" consists of performing computation using float16 precision, while storing weights in the float32 format. This is done to take advantage of the fact that float16 operations are backed by significantly faster kernels than their float32 counterparts on modern NVIDIA GPUs.

Enabling mixed precision computation in Keras (and therefore for keras_cv.models.StableDiffusion) is as simple as calling:

INFO:tensorflow:Mixed precision compatibility check (mixed_float16): OK
Your GPU will likely run quickly with dtype policy mixed_float16 as it has compute capability of at least 7.0. Your GPU: NVIDIA A100-SXM4-40GB, compute capability 8.0

That's all. Out of the box - it just works.

model = keras_cv.models.StableDiffusion()

print("Compute dtype:", model.diffusion_model.compute_dtype)
    "Variable dtype:",
Compute dtype: float16
Variable dtype: float32

As you can see, the model constructed above now uses mixed precision computation; leveraging the speed of float16 operations for computation, while storing variables in float32 precision.

# Warm up model to run graph tracing before benchmarking.
model.text_to_image("warming up the model", batch_size=3)

start = time.time()
images = model.text_to_image(
    "a cute magical flying dog, fantasy art, "
    "golden color, high quality, highly detailed, elegant, sharp focus, "
    "concept art, character concepts, digital painting, mystery, adventure",
end = time.time()
benchmark_result.append(["Mixed Precision", end - start])

print(f"Mixed precision model: {(end - start):.2f} seconds")
25/25 [==============================] - 15s 226ms/step
25/25 [==============================] - 6s 226ms/step
Mixed precision model: 6.02 seconds


XLA Compilation

TensorFlow comes with the XLA: Accelerated Linear Algebra compiler built-in. keras_cv.models.StableDiffusion supports a jit_compile argument out of the box. Setting this argument to True enables XLA compilation, resulting in a significant speed-up.

Let's use this below:

# Set back to the default for benchmarking purposes.

model = keras_cv.models.StableDiffusion(jit_compile=True)
# Before we benchmark the model, we run inference once to make sure the TensorFlow
# graph has already been traced.
images = model.text_to_image("An avocado armchair", batch_size=3)
25/25 [==============================] - 36s 245ms/step


Let's benchmark our XLA model:

start = time.time()
images = model.text_to_image(
    "A cute otter in a rainbow whirlpool holding shells, watercolor",
end = time.time()
benchmark_result.append(["XLA", end - start])

print(f"With XLA: {(end - start):.2f} seconds")
25/25 [==============================] - 6s 245ms/step
With XLA: 6.27 seconds


On an A100 GPU, we get about a 2x speedup. Fantastic!

Putting it all together

So, how do you assemble the world's most performant stable diffusion inference pipeline (as of September 2022).

With these two lines of code:

model = keras_cv.models.StableDiffusion(jit_compile=True)

And to use it...

# Let's make sure to warm up the model
images = model.text_to_image(
    "Teddy bears conducting machine learning research",
25/25 [==============================] - 39s 157ms/step


Exactly how fast is it? Let's find out!

start = time.time()
images = model.text_to_image(
    "A mysterious dark stranger visits the great pyramids of egypt, "
    "high quality, highly detailed, elegant, sharp focus, "
    "concept art, character concepts, digital painting",
end = time.time()
benchmark_result.append(["XLA + Mixed Precision", end - start])

print(f"XLA + mixed precision: {(end - start):.2f} seconds")
25/25 [==============================] - 4s 158ms/step
XLA + mixed precision: 4.25 seconds


Let's check out the results:

print("{:<20} {:<20}".format("Model", "Runtime"))
for result in benchmark_result:
    name, runtime = result
    print("{:<20} {:<20}".format(name, runtime))
Model                 Runtime             
Standard              8.17177152633667    
Mixed Precision       6.022329568862915   
XLA                   6.265935659408569   
XLA + Mixed Precision 4.252242088317871   

It only took our fully-optimized model four seconds to generate three novel images from a text prompt on an A100 GPU.


KerasCV offers a state-of-the-art implementation of Stable Diffusion — and through the use of XLA and mixed precision, it delivers the fastest Stable Diffusion pipeline available as of September 2022.

Normally, at the end of a tutorial we leave you with some future directions to continue in to learn. This time, we leave you with one idea:

Go run your own prompts through the model! It is an absolute blast!

If you have your own NVIDIA GPU, or a M1 MacBookPro, you can also run the model locally on your machine. (Note that when running on a M1 MacBookPro, you should not enable mixed precision, as it is not yet well supported by Apple's Metal runtime.)