Watermarked images in training data cause generative models to reproduce watermark artifacts in their outputs. The LVM-Image-Watermark Filter (DLC) component scores each image for watermark probability and removes images that exceed the threshold you set, keeping watermark artifacts out of your image generation model training pipelines.
Supported computing resources
How it works
For each image, the component calculates a watermark probability score. Images with a score above the Watermark Probability threshold are filtered out. The remaining images and their metadata are written to the output path you specify.
Typical pipeline position:
Read File Data (or any image preprocessing component)
→ LVM-Image-Watermark Filter (DLC)
→ Image generation model training componentInputs and outputs
Input ports
The component accepts input from any of the following:
Read File Data component — reads the Object Storage Service (OSS) path where training data is stored
Image Data OSS Path parameter — specify an OSS directory or an image metadata file directly (see the parameter table below)
Any image data preprocessing component
Output port
Filtering results are written to the OSS path specified by Output File OSS Path. The output includes three files:
| File | Description |
|---|---|
{name}.jsonl | The filtered dataset. The filename is set by Output Filename. Pass this file to the downstream training component. |
{name}_stats.jsonl | The state file. |
dj_run_yaml.yaml | The parameter configuration file used when the algorithm runs. |
Configure the component
Configure the LVM-Image-Watermark Filter (DLC) component in Machine Learning Designer. The following table describes all parameters.
| Tab | Parameter | Required | Description | Default |
|---|---|---|---|---|
| Field Settings | Image Data OSS Path | No | The OSS directory where image data is stored, or an image metadata file. If no upstream component is connected on the first run, select the OSS directory manually. The component generates meta.jsonl in the parent directory of the path you specify. On subsequent runs, select meta.jsonl directly instead of the directory. | — |
| Field Settings | Output File OSS Path | Yes | The OSS directory where filtering results are stored. See the output file descriptions above. | — |
| Field Settings | Output Filename | Yes | The filename for the filtered dataset (the {name}.jsonl output file). | result.jsonl |
| Parameter Settings | Watermark Probability | Yes | The maximum watermark probability. Images with a score above this value are filtered out. Lower the threshold to remove more images; raise it to keep more. | 0.8 |
| Execution Tuning | Select Resource Group — Public Resource Group | No | The instance type (CPU or GPU) and virtual private cloud (VPC) to use. You must select a GPU instance type for the algorithm. | — |
| Execution Tuning | Select Resource Group — Dedicated resource group | No | The number of vCPUs, memory, shared memory, and GPUs to allocate. | — |
| Execution Tuning | Maximum Running Duration (seconds) | No | The maximum time the component can run. The job is terminated if this limit is exceeded. | — |
You must select a GPU instance type when configuring the resource group.