StableDiffusion3TextToImage
classkeras_hub.models.StableDiffusion3TextToImage(backbone, preprocessor, **kwargs)
An end-to-end Stable Diffusion 3 model for text-to-image generation.
This model has a generate()
method, which generates image based on a
prompt.
Arguments
keras_hub.models.StableDiffusion3Backbone
instance.keras_hub.models.StableDiffusion3TextToImagePreprocessor
instance.Examples
Use generate()
to do image generation.
text_to_image = keras_hub.models.StableDiffusion3TextToImage.from_preset(
"stable_diffusion_3_medium", height=512, width=512
)
text_to_image.generate(
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
)
# Generate with batched prompts.
text_to_image.generate(
["cute wallpaper art of a cat", "cute wallpaper art of a dog"]
)
# Generate with different `num_steps` and `guidance_scale`.
text_to_image.generate(
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
num_steps=50,
guidance_scale=5.0,
)
# Generate with `negative_prompts`.
text_to_image.generate(
{
"prompts": "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"negative_prompts": "green color",
}
)
from_preset
methodStableDiffusion3TextToImage.from_preset(preset, load_weights=True, **kwargs)
Instantiate a keras_hub.models.Task
from a model preset.
A preset is a directory of configs, weights and other file assets used
to save and load a pre-trained model. The preset
can be passed as
one of:
'bert_base_en'
'kaggle://user/bert/keras/bert_base_en'
'hf://user/bert_base_en'
'./bert_base_en'
For any Task
subclass, you can run cls.presets.keys()
to list all
built-in presets available on the class.
This constructor can be called in one of two ways. Either from a task
specific base class like keras_hub.models.CausalLM.from_preset()
, or
from a model class like keras_hub.models.BertTextClassifier.from_preset()
.
If calling from the a base class, the subclass of the returning object
will be inferred from the config in the preset directory.
Arguments
True
, saved weights will be loaded into
the model architecture. If False
, all weights will be
randomly initialized.Examples
# Load a Gemma generative task.
causal_lm = keras_hub.models.CausalLM.from_preset(
"gemma_2b_en",
)
# Load a Bert classification task.
model = keras_hub.models.TextClassifier.from_preset(
"bert_base_en",
num_classes=2,
)
Preset name | Parameters | Description |
---|---|---|
stable_diffusion_3_medium | 2.99B | 3 billion parameter, including CLIP L and CLIP G text encoders, MMDiT generative model, and VAE autoencoder. Developed by Stability AI. |
backbone
propertykeras_hub.models.StableDiffusion3TextToImage.backbone
A keras_hub.models.Backbone
model with the core architecture.
generate
methodStableDiffusion3TextToImage.generate(
inputs, num_steps=28, guidance_scale=7.0, seed=None
)
Generate image based on the provided inputs
.
Typically, inputs
contains a text description (known as a prompt) used
to guide the image generation.
Some models support a negative_prompts
key, which helps steer the
model away from generating certain styles and elements. To enable this,
pass prompts
and negative_prompts
as a dict:
text_to_image.generate(
{
"prompts": "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
"negative_prompts": "green color",
}
)
If inputs
are a tf.data.Dataset
, outputs will be generated
"batch-by-batch" and concatenated. Otherwise, all inputs will be
processed as batches.
Arguments
tf.data.Dataset
. The format
must be one of the following:tf.data.Dataset
with "prompts" and/or "negative_prompts"
keyspreprocessor
propertykeras_hub.models.StableDiffusion3TextToImage.preprocessor
A keras_hub.models.Preprocessor
layer used to preprocess input.