dreamfinetune 0.4.2

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StableDiffusionInpaintingFineTune

This project provides a toolkit for fine-tuning the Stable Diffusion model for inpainting tasks (image restoration based on a mask) using PyTorch and Hugging Face Diffusers libraries.

Requirements

Before starting, you need to install the following libraries:

• torch
• diffusers
• transformers
• accelerate
• huggingface_hub
• PIL
• numpy
• tqdm

Description

StableDiffusionInpaintingFineTune

This class is responsible for fine-tuning the Stable Diffusion model for the inpainting task. It supports training both the text encoder and the UNet model and uses various settings to control the training process.

Constructor
^^^^^^^^^^^

.. code-block:: python

   __init__(self, pretrained_model_name_or_path, resolution, center_crop, ...)

- **pretrained_model_name_or_path**: The path or name of the pre-trained model.
- **resolution**: The resolution of the images.
- **center_crop**: Whether to apply center cropping during data preparation.
- **train_text_encoder**: Whether to train the text encoder.
- **dataset**: The dataset object.
- **learning_rate**: The initial learning rate.
- **max_training_steps**: The maximum number of training steps.
- **save_steps**: The number of steps between saving checkpoints.
- **train_batch_size**: The batch size.
- **gradient_accumulation_steps**: The number of steps to accumulate gradients.
- **mixed_precision**: Use of mixed precision ("fp16", "bf16", or None).
- **gradient_checkpointing**: Use of gradient checkpointing.
- **use_8bit_adam**: Use of the 8-bit Adam optimizer.
- **seed**: The random seed for reproducibility.
- **output_dir**: The directory for saving results.
- **push_to_hub**: Whether to upload the results to the Hugging Face Hub.
- **repo_id**: The repository ID on Hugging Face Hub.

Methods
^^^^^^^

- **prepare_mask_and_masked_image(image, mask)**: Prepares the mask and masked image.
- **random_mask(im_shape, ratio=1, mask_full_image=False)**: Generates a random mask.
- **load_args_for_training()**: Loads the necessary components of the model for training.
- **collate_fn(examples)**: Forms a batch of data for the model.
- **__call__(self, *args, **kwargs)**: The main method for running the training process.

Usage
-----

To start training, you should create an instance of the ``StableDiffusionInpaintingFineTune`` class and call its ``__call__`` method, passing the necessary arguments.

.. code-block:: python

   model = StableDiffusionInpaintingFineTune(
       pretrained_model_name_or_path="path_to_model",
       resolution=512,
       center_crop=True,
       ...
   )

   model()

License
-------

The project is distributed under the MIT License.