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► KerasHub: Pretrained Models / API documentation / Model Architectures / Stable Diffusion 3 / StableDiffusion3TextToImagePreprocessor layer

StableDiffusion3TextToImagePreprocessor layer

[source]

StableDiffusion3TextToImagePreprocessor class

keras_hub.models.StableDiffusion3TextToImagePreprocessor(
    clip_l_preprocessor, clip_g_preprocessor, t5_preprocessor=None, **kwargs
)

Stable Diffusion 3 text-to-image model preprocessor.

This preprocessing layer is meant for use with keras_hub.models.StableDiffusion3TextToImage.

For use with generation, the layer exposes one methods generate_preprocess().

Arguments

Guides and examples using StableDiffusion3TextToImagePreprocessor

[source]

from_preset method

StableDiffusion3TextToImagePreprocessor.from_preset(
    preset, config_file="preprocessor.json", **kwargs
)

Instantiate a keras_hub.models.Preprocessor 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:

  1. a built-in preset identifier like 'bert_base_en'
  2. a Kaggle Models handle like 'kaggle://user/bert/keras/bert_base_en'
  3. a Hugging Face handle like 'hf://user/bert_base_en'
  4. a path to a local preset directory like './bert_base_en'

For any Preprocessor subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

As there are usually multiple preprocessing classes for a given model, this method should be called on a specific subclass like keras_hub.models.BertTextClassifierPreprocessor.from_preset().

Arguments

  • preset: string. A built-in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.

Examples

# Load a preprocessor for Gemma generation.
preprocessor = keras_hub.models.CausalLMPreprocessor.from_preset(
    "gemma_2b_en",
)

# Load a preprocessor for Bert classification.
preprocessor = keras_hub.models.TextClassifierPreprocessor.from_preset(
    "bert_base_en",
)
Preset 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.
stable_diffusion_3.5_medium 3.37B 3 billion parameter, including CLIP L and CLIP G text encoders, MMDiT-X generative model, and VAE autoencoder. Developed by Stability AI.
stable_diffusion_3.5_large 9.05B 9 billion parameter, including CLIP L and CLIP G text encoders, MMDiT generative model, and VAE autoencoder. Developed by Stability AI.
stable_diffusion_3.5_large_turbo 9.05B 9 billion parameter, including CLIP L and CLIP G text encoders, MMDiT generative model, and VAE autoencoder. A timestep-distilled version that eliminates classifier-free guidance and uses fewer steps for generation. Developed by Stability AI.

Guides and examples using from_preset

StableDiffusion3TextToImagePreprocessor layer
StableDiffusion3TextToImagePreprocessor class
from_preset method