Inpaint

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Inpaint class

keras_hub.models.Inpaint()

Base class for image-to-image tasks.

Inpaint tasks wrap a keras_hub.models.Backbone and a keras_hub.models.Preprocessor to create a model that can be used for generation and generative fine-tuning.

Inpaint tasks provide an additional, high-level generate() function which can be used to generate image by token with a (image, mask, string) in, image out signature.

All Inpaint tasks include a from_preset() constructor which can be used to load a pre-trained config and weights.

Example

# Load a Stable Diffusion 3 backbone with pre-trained weights.
reference_image = np.ones((1024, 1024, 3), dtype="float32")
reference_mask = np.ones((1024, 1024), dtype="float32")
inpaint = keras_hub.models.Inpaint.from_preset(
    "stable_diffusion_3_medium",
)
inpaint.generate(
    reference_image,
    reference_mask,
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
)

# Load a Stable Diffusion 3 backbone at bfloat16 precision.
inpaint = keras_hub.models.Inpaint.from_preset(
    "stable_diffusion_3_medium",
    dtype="bfloat16",
)
inpaint.generate(
    reference_image,
    reference_mask,
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
)

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from_preset method

Inpaint.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:

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 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

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

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compile method

Inpaint.compile(optimizer="auto", loss="auto", metrics="auto", **kwargs)

Configures the Inpaint task for training.

The Inpaint task extends the default compilation signature of keras.Model.compile with defaults for optimizer, loss, and metrics. To override these defaults, pass any value to these arguments during compilation.

Arguments

• optimizer: "auto", an optimizer name, or a keras.Optimizer instance. Defaults to "auto", which uses the default optimizer for the given model and task. See keras.Model.compile and keras.optimizers for more info on possible optimizer values.
• loss: "auto", a loss name, or a keras.losses.Loss instance. Defaults to "auto", where a keras.losses.MeanSquaredError loss will be applied. See keras.Model.compile and keras.losses for more info on possible loss values.
• metrics: "auto", or a list of metrics to be evaluated by the model during training and testing. Defaults to "auto", where a keras.metrics.MeanSquaredError will be applied to track the loss of the model during training. See keras.Model.compile and keras.metrics for more info on possible metrics values.
• **kwargs: See keras.Model.compile for a full list of arguments supported by the compile method.

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save_to_preset method

Inpaint.save_to_preset(preset_dir, max_shard_size=10)

Save task to a preset directory.

Arguments

• preset_dir: The path to the local model preset directory.
• max_shard_size: int or float. Maximum size in GB for each sharded file. If None, no sharding will be done. Defaults to 10.

preprocessor property

keras_hub.models.Inpaint.preprocessor

A keras_hub.models.Preprocessor layer used to preprocess input.

backbone property

keras_hub.models.Inpaint.backbone

A keras_hub.models.Backbone model with the core architecture.

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generate method

Inpaint.generate(inputs, num_steps, strength, guidance_scale=None, seed=None)

Generate image based on the provided inputs.

Typically, inputs is a dict with "images" "masks" and "prompts" keys. "images" are reference images within a value range of [-1.0, 1.0], which will be resized to height and width from self.backbone.image_shape, then encoded into latent space by the VAE encoder. "masks" are mask images with a boolean dtype, where white pixels are repainted while black pixels are preserved. "prompts" are strings that will be tokenized and encoded by the text encoder.

Some models support a "negative_prompts" key, which helps steer the model away from generating certain styles and elements. To enable this, add "negative_prompts" to the input dict.

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

• inputs: python data, tensor data, or a tf.data.Dataset. The format must be one of the following:
  • A dict with "images", "masks", "prompts" and/or "negative_prompts" keys.
  • A tf.data.Dataset with "images", "masks", "prompts" and/or "negative_prompts" keys.
• num_steps: int. The number of diffusion steps to take.
• strength: float. Indicates the extent to which the reference images are transformed. Must be between 0.0 and 1.0. When strength=1.0, images is essentially ignore and added noise is maximum and the denoising process runs for the full number of iterations specified in num_steps.
• guidance_scale: Optional float. The classifier free guidance scale defined in Classifier-Free Diffusion Guidance. A higher scale encourages generating images more closely related to the prompts, typically at the cost of lower image quality. Note that some models don't utilize classifier-free guidance.
• seed: optional int. Used as a random seed.