pyflink.multimodal.expression.ImageExpressionAccessor.watermark_score

ImageExpressionAccessor.watermark_score(*, hf_watermark_model: str = 'amrul-hzz/watermark_detector', model_sharing: Optional[Literal['process', 'shared']] = None, concurrency: Optional[int] = None, batch_size: Optional[int] = None, num_gpus: Optional[float] = None, gpu_type: Optional[str] = None) → pyflink.table.expression.Expression

Score images for watermark likelihood.

Equivalent to image_watermark_score(). See that function for full parameter details.

Parameters

• hf_watermark_model – Hugging Face model used for watermark scoring.

• model_sharing – Optional model sharing mode.

• concurrency – Optional execution concurrency for this operation. None uses the framework default.

• batch_size – Optional inference batch size.

• num_gpus – Optional number of GPUs requested for model inference.

• gpu_type – Optional GPU type requested for model inference.

Returns

A DataFrame expression that produces a DOUBLE watermark likelihood score.