► Keras 3 API documentation / KerasCV / Models / Tasks / StableDiffusion image-generation model

StableDiffusion image-generation model

[source]

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

[source]

StableDiffusionV2 class

keras_cv.models.StableDiffusionV2(img_height=512, img_width=512, jit_compile=True)

Keras implementation of Stable Diffusion v2.

Note that the StableDiffusion API, as well as the APIs of the sub-components of StableDiffusionV2 (e.g. ImageEncoder, DiffusionModelV2) 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 StableDiffusionV2
from PIL import Image

model = StableDiffusionV2(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

[source]

Decoder class

keras_cv.models.stable_diffusion.Decoder(
    img_height, img_width, name=None, download_weights=True
)

Sequential groups a linear stack of layers into a Model.

Examples

model = keras.Sequential()
model.add(keras.Input(shape=(16,)))
model.add(keras.layers.Dense(8))

# Note that you can also omit the initial `Input`.
# In that case the model doesn't have any weights until the first call
# to a training/evaluation method (since it isn't yet built):
model = keras.Sequential()
model.add(keras.layers.Dense(8))
model.add(keras.layers.Dense(4))
# model.weights not created yet

# Whereas if you specify an `Input`, the model gets built
# continuously as you are adding layers:
model = keras.Sequential()
model.add(keras.Input(shape=(16,)))
model.add(keras.layers.Dense(8))
len(model.weights)  # Returns "2"

# When using the delayed-build pattern (no input shape specified), you can
# choose to manually build your model by calling
# `build(batch_input_shape)`:
model = keras.Sequential()
model.add(keras.layers.Dense(8))
model.add(keras.layers.Dense(4))
model.build((None, 16))
len(model.weights)  # Returns "4"

# Note that when using the delayed-build pattern (no input shape specified),
# the model gets built the first time you call `fit`, `eval`, or `predict`,
# or the first time you call the model on some input data.
model = keras.Sequential()
model.add(keras.layers.Dense(8))
model.add(keras.layers.Dense(1))
model.compile(optimizer='sgd', loss='mse')
# This builds the model for the first time:
model.fit(x, y, batch_size=32, epochs=10)

[source]

DiffusionModel class

keras_cv.models.stable_diffusion.DiffusionModel(
    img_height, img_width, max_text_length, name=None, download_weights=True
)

A model grouping layers into an object with training/inference features.

There are three ways to instantiate a Model:

With the "Functional API"

You start from Input, you chain layer calls to specify the model's forward pass, and finally you create your model from inputs and outputs:

inputs = keras.Input(shape=(37,))
x = keras.layers.Dense(32, activation="relu")(inputs)
outputs = keras.layers.Dense(5, activation="softmax")(x)
model = keras.Model(inputs=inputs, outputs=outputs)

Note: Only dicts, lists, and tuples of input tensors are supported. Nested inputs are not supported (e.g. lists of list or dicts of dict).

A new Functional API model can also be created by using the intermediate tensors. This enables you to quickly extract sub-components of the model.

Example

inputs = keras.Input(shape=(None, None, 3))
processed = keras.layers.RandomCrop(width=128, height=128)(inputs)
conv = keras.layers.Conv2D(filters=32, kernel_size=3)(processed)
pooling = keras.layers.GlobalAveragePooling2D()(conv)
feature = keras.layers.Dense(10)(pooling)

full_model = keras.Model(inputs, feature)
backbone = keras.Model(processed, conv)
activations = keras.Model(conv, feature)

Note that the backbone and activations models are not created with keras.Input objects, but with the tensors that originate from keras.Input objects. Under the hood, the layers and weights will be shared across these models, so that user can train the full_model, and use backbone or activations to do feature extraction. The inputs and outputs of the model can be nested structures of tensors as well, and the created models are standard Functional API models that support all the existing APIs.

By subclassing the Model class

In that case, you should define your layers in __init__() and you should implement the model's forward pass in call().

class MyModel(keras.Model):
    def __init__(self):
        super().__init__()
        self.dense1 = keras.layers.Dense(32, activation="relu")
        self.dense2 = keras.layers.Dense(5, activation="softmax")

    def call(self, inputs):
        x = self.dense1(inputs)
        return self.dense2(x)

model = MyModel()

If you subclass Model, you can optionally have a training argument (boolean) in call(), which you can use to specify a different behavior in training and inference:

class MyModel(keras.Model):
    def __init__(self):
        super().__init__()
        self.dense1 = keras.layers.Dense(32, activation="relu")
        self.dense2 = keras.layers.Dense(5, activation="softmax")
        self.dropout = keras.layers.Dropout(0.5)

    def call(self, inputs, training=False):
        x = self.dense1(inputs)
        x = self.dropout(x, training=training)
        return self.dense2(x)

model = MyModel()

Once the model is created, you can config the model with losses and metrics with model.compile(), train the model with model.fit(), or use the model to do prediction with model.predict().

With the Sequential class

In addition, keras.Sequential is a special case of model where the model is purely a stack of single-input, single-output layers.

model = keras.Sequential([
    keras.Input(shape=(None, None, 3)),
    keras.layers.Conv2D(filters=32, kernel_size=3),
])

[source]

ImageEncoder class

keras_cv.models.stable_diffusion.ImageEncoder(download_weights=True)

ImageEncoder is the VAE Encoder for StableDiffusion.

[source]

NoiseScheduler class

keras_cv.models.stable_diffusion.NoiseScheduler(
    train_timesteps=1000,
    beta_start=0.0001,
    beta_end=0.02,
    beta_schedule="linear",
    variance_type="fixed_small",
    clip_sample=True,
)

Arguments

train_timesteps: number of diffusion steps used to train the model.
beta_start: the starting `beta` value of inference.
beta_end: the final `beta` value.
beta_schedule: the beta schedule, a mapping from a beta range to a
    sequence of betas for stepping the model. Choose from `linear` or
    `quadratic`.
variance_type: options to clip the variance used when adding noise to
    the de-noised sample. Choose from `fixed_small`, `fixed_small_log`,
    `fixed_large`, `fixed_large_log`, `learned` or `learned_range`.
clip_sample: option to clip predicted sample between -1 and 1 for
    numerical stability.

[source]

SimpleTokenizer class

keras_cv.models.stable_diffusion.SimpleTokenizer(bpe_path=None)

[source]

TextEncoder class

keras_cv.models.stable_diffusion.TextEncoder(
    max_length, vocab_size=49408, name=None, download_weights=True
)

A model grouping layers into an object with training/inference features.

There are three ways to instantiate a Model:

With the "Functional API"

You start from Input, you chain layer calls to specify the model's forward pass, and finally you create your model from inputs and outputs:

inputs = keras.Input(shape=(37,))
x = keras.layers.Dense(32, activation="relu")(inputs)
outputs = keras.layers.Dense(5, activation="softmax")(x)
model = keras.Model(inputs=inputs, outputs=outputs)

Note: Only dicts, lists, and tuples of input tensors are supported. Nested inputs are not supported (e.g. lists of list or dicts of dict).

A new Functional API model can also be created by using the intermediate tensors. This enables you to quickly extract sub-components of the model.

Example

inputs = keras.Input(shape=(None, None, 3))
processed = keras.layers.RandomCrop(width=128, height=128)(inputs)
conv = keras.layers.Conv2D(filters=32, kernel_size=3)(processed)
pooling = keras.layers.GlobalAveragePooling2D()(conv)
feature = keras.layers.Dense(10)(pooling)

full_model = keras.Model(inputs, feature)
backbone = keras.Model(processed, conv)
activations = keras.Model(conv, feature)

Note that the backbone and activations models are not created with keras.Input objects, but with the tensors that originate from keras.Input objects. Under the hood, the layers and weights will be shared across these models, so that user can train the full_model, and use backbone or activations to do feature extraction. The inputs and outputs of the model can be nested structures of tensors as well, and the created models are standard Functional API models that support all the existing APIs.

By subclassing the Model class

In that case, you should define your layers in __init__() and you should implement the model's forward pass in call().

class MyModel(keras.Model):
    def __init__(self):
        super().__init__()
        self.dense1 = keras.layers.Dense(32, activation="relu")
        self.dense2 = keras.layers.Dense(5, activation="softmax")

    def call(self, inputs):
        x = self.dense1(inputs)
        return self.dense2(x)

model = MyModel()

If you subclass Model, you can optionally have a training argument (boolean) in call(), which you can use to specify a different behavior in training and inference:

class MyModel(keras.Model):
    def __init__(self):
        super().__init__()
        self.dense1 = keras.layers.Dense(32, activation="relu")
        self.dense2 = keras.layers.Dense(5, activation="softmax")
        self.dropout = keras.layers.Dropout(0.5)

    def call(self, inputs, training=False):
        x = self.dense1(inputs)
        x = self.dropout(x, training=training)
        return self.dense2(x)

model = MyModel()

Once the model is created, you can config the model with losses and metrics with model.compile(), train the model with model.fit(), or use the model to do prediction with model.predict().

With the Sequential class

In addition, keras.Sequential is a special case of model where the model is purely a stack of single-input, single-output layers.

model = keras.Sequential([
    keras.Input(shape=(None, None, 3)),
    keras.layers.Conv2D(filters=32, kernel_size=3),
])

[source]

TextEncoderV2 class

keras_cv.models.stable_diffusion.TextEncoderV2(
    max_length, vocab_size=49408, name=None, download_weights=True
)

A model grouping layers into an object with training/inference features.

There are three ways to instantiate a Model:

With the "Functional API"

You start from Input, you chain layer calls to specify the model's forward pass, and finally you create your model from inputs and outputs:

inputs = keras.Input(shape=(37,))
x = keras.layers.Dense(32, activation="relu")(inputs)
outputs = keras.layers.Dense(5, activation="softmax")(x)
model = keras.Model(inputs=inputs, outputs=outputs)

Note: Only dicts, lists, and tuples of input tensors are supported. Nested inputs are not supported (e.g. lists of list or dicts of dict).

A new Functional API model can also be created by using the intermediate tensors. This enables you to quickly extract sub-components of the model.

Example

inputs = keras.Input(shape=(None, None, 3))
processed = keras.layers.RandomCrop(width=128, height=128)(inputs)
conv = keras.layers.Conv2D(filters=32, kernel_size=3)(processed)
pooling = keras.layers.GlobalAveragePooling2D()(conv)
feature = keras.layers.Dense(10)(pooling)

full_model = keras.Model(inputs, feature)
backbone = keras.Model(processed, conv)
activations = keras.Model(conv, feature)

Note that the backbone and activations models are not created with keras.Input objects, but with the tensors that originate from keras.Input objects. Under the hood, the layers and weights will be shared across these models, so that user can train the full_model, and use backbone or activations to do feature extraction. The inputs and outputs of the model can be nested structures of tensors as well, and the created models are standard Functional API models that support all the existing APIs.

By subclassing the Model class

In that case, you should define your layers in __init__() and you should implement the model's forward pass in call().

class MyModel(keras.Model):
    def __init__(self):
        super().__init__()
        self.dense1 = keras.layers.Dense(32, activation="relu")
        self.dense2 = keras.layers.Dense(5, activation="softmax")

    def call(self, inputs):
        x = self.dense1(inputs)
        return self.dense2(x)

model = MyModel()

If you subclass Model, you can optionally have a training argument (boolean) in call(), which you can use to specify a different behavior in training and inference:

class MyModel(keras.Model):
    def __init__(self):
        super().__init__()
        self.dense1 = keras.layers.Dense(32, activation="relu")
        self.dense2 = keras.layers.Dense(5, activation="softmax")
        self.dropout = keras.layers.Dropout(0.5)

    def call(self, inputs, training=False):
        x = self.dense1(inputs)
        x = self.dropout(x, training=training)
        return self.dense2(x)

model = MyModel()

Once the model is created, you can config the model with losses and metrics with model.compile(), train the model with model.fit(), or use the model to do prediction with model.predict().

With the Sequential class

In addition, keras.Sequential is a special case of model where the model is purely a stack of single-input, single-output layers.

model = keras.Sequential([
    keras.Input(shape=(None, None, 3)),
    keras.layers.Conv2D(filters=32, kernel_size=3),
])
StableDiffusion image-generation model
StableDiffusion class
StableDiffusionV2 class
Decoder class
DiffusionModel class
◆ With the "Functional API"
◆ By subclassing the Model class
◆ With the Sequential class
ImageEncoder class
NoiseScheduler class
Arguments
SimpleTokenizer class
TextEncoder class
◆ With the "Functional API"
◆ By subclassing the Model class
◆ With the Sequential class
TextEncoderV2 class
◆ With the "Functional API"
◆ By subclassing the Model class
◆ With the Sequential class