This code accomplishes the following main workflows:
1. **Environmental preparedness**：
- Install the required Python packages, e.g. `simple-aesthetics-predictor`, `data-juicer`, `peft`, `lightning`, `pandas`, `torchvision` and others.
- Uninstalling and reinstalling certain versions of packages, e.g., uninstalling `pytorch-lightning` before installing other dependencies.
2. **Data set loading and pre-processing**：
- Load the file named `AI-ModelScope/'.lowres_anime` dataset and extract the image data from it.
- Save the image data to disk and create a file containing the path to the image and the corresponding text label (here it is uniformly labeled as"second dimension (math.)") of the metadata file ``.
3. **Data cleansing and filtering**：
- Create a configuration file `data_juicer_config.yaml` that defines the rules for data cleaning, including image size filtering and aspect ratio filtering.
- Use of `data-The `juicer` tool processes the dataset and outputs the filtered results to the `` file.
4. **Data set organization and feature extraction** .：
- Read the cleaned dataset, organize it into DataFrame format and save it as a CSV file``.
- The embedding vectors of the image and text are extracted using the CLIP model and the similarity scores between them are computed.
5. **Constructing a PyTorch dataset** .：
- Create a custom PyTorch dataset class `CustomDataset` for loading image and text data.
- Use this dataset class to create a data loader `DataLoader` for subsequent training use.
6. **Image generation**：
- Use the `StableDiffusionPipeline` model to generate a series of quadratic style images based on different prompts (prompters) and save them to disk.
- Stitch the resulting images together to form one large image and resize it.
Below is a more detailed explanation of each section:
### Environment preparation
This section is responsible for making sure that all necessary Python packages are properly installed.
### Dataset loading and preprocessing
This part loads the dataset from `MsDataset` and performs basic processing on the images, such as converting color modes and saving to disk. Also, a metadata entry containing paths and tags is created for each image.
### Data Cleaning and Filtering
This section defines a configuration file to specify the criteria for data cleansing and calls the `data-juicer` tool to perform these operations.
### Data set organization and feature extraction
This part organizes the processed dataset into an easy-to-use format and calculates the similarity between images and text using the CLIP model.
### Building a PyTorch dataset
This part creates a PyTorch dataset class for easy loading and preprocessing of data, as well as creating a data loader.
### Image generation
This part uses `StableDiffusionPipeline` to generate images based on the given prompts and stitch multiple images together to form the final output.
The workflow of the entire script starts with dataset loading and preprocessing, followed by data cleaning, followed by feature extraction and dataset construction, and finally using the generative model to generate new images based on the prompts.