FluxTextToImagePreprocessor layer

[source]

FluxTextToImagePreprocessor class

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

Flux text-to-image model preprocessor.

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

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

Arguments

• clip_l_preprocessor: A keras_hub.models.CLIPPreprocessor instance.
• t5_preprocessor: A optional keras_hub.models.T5Preprocessor instance.

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from_preset method

FluxTextToImagePreprocessor.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",
)