StableDiffusion3Inpaint model

[source]

StableDiffusion3Inpaint class

keras_hub.models.StableDiffusion3Inpaint(backbone, preprocessor, **kwargs)

An end-to-end Stable Diffusion 3 model for inpaint generation.

This model has a generate() method, which generates images based on a combination of a reference image, mask and a text prompt.

Arguments

• backbone: A keras_hub.models.StableDiffusion3Backbone instance.
• preprocessor: A keras_hub.models.StableDiffusion3TextToImagePreprocessor instance.

Examples

Use generate() to do image generation.

reference_image = np.ones((1024, 1024, 3), dtype="float32")
reference_mask = np.ones((1024, 1024), dtype="float32")
inpaint = keras_hub.models.StableDiffusion3Inpaint.from_preset(
    "stable_diffusion_3_medium", height=512, width=512
)
inpaint.generate(
    reference_image,
    reference_mask,
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
)

# Generate with batched prompts.
reference_images = np.ones((2, 512, 512, 3), dtype="float32")
reference_mask = np.ones((2, 1024, 1024), dtype="float32")
inpaint.generate(
    reference_images,
    reference_mask,
    ["cute wallpaper art of a cat", "cute wallpaper art of a dog"]
)

# Generate with different `num_steps`, `guidance_scale` and `strength`.
inpaint.generate(
    reference_image,
    reference_mask,
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    num_steps=50,
    guidance_scale=5.0,
    strength=0.6,
)

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from_preset method

StableDiffusion3Inpaint.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,
)

backbone property

keras_hub.models.StableDiffusion3Inpaint.backbone

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

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generate method

StableDiffusion3Inpaint.generate(
    inputs, num_steps=50, guidance_scale=7.0, strength=0.6, 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 self.backbone.height and self.backbone.width, 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.
• guidance_scale: 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.
• 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.
• seed: optional int. Used as a random seed.

preprocessor property

keras_hub.models.StableDiffusion3Inpaint.preprocessor

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