1. Setup and Initialization

The process began on September 8, 2025, at 06:01:09 AM, when the training job was submitted. The system, running on an NVIDIA CUDA device, quickly created a dedicated workspace for the job.

The training was configured using the kohya engine, a popular tool for fine-tuning Stable Diffusion models. The system loaded key settings from a training_config.toml file, including:

• Learning Rate: A unetLR of 0.0005 and a textEncoderLR of 0.00005, which controls how quickly the model's parameters are updated.

• Optimizer: Adafactor was chosen as the optimization algorithm.

• Model Type: The networks.lora module was imported, confirming that a Low-Rank Adaptation (Lora) model was being trained.

• Dataset: The dataset consisted of 34 images, each with a corresponding .txt caption file. The captions were formatted as simple tags.

2. Training Execution

The training loop began, with the system calculating its total steps and batches.

• Batches and Epochs: The training used a trainBatchSize of 4 images per step. With 34 images and a num_repeats of 58, the total number of training images was 34 * 58 = 1972. This resulted in 495 batches per epoch and a total of 9900 optimization steps for 20 epochs.

• Data Augmentation: To prevent the model from overfitting to the small dataset, data augmentation techniques were used. flip_aug (horizontal flipping of images) and shuffle_caption (randomizing the order of tags) were enabled.

• Monitoring Progress: The stdout logs showed the training progressing epoch by epoch, saving a safetensors checkpoint file after each one. For example, saving checkpoint: ...000001.safetensors was logged after the first epoch.

3. Checkpointing and Finalization

As the training progressed, the system continuously saved its work.

• Intermediate Models: The worker logs show that the system was uploading intermediate models to the orchestrator. For example, at one point, it logged that it had "uploaded 7 models and 21 sample images so far."

• Sample Image Generation: The logs also show the model generating sample images at various points using the prompts provided in the sample-images.json file. The prompts included phrases like "my father" and "my father and his friend just1n, kissing."

The process concluded on September 8, 2025, at 02:31:17 PM, when the status was updated to Ready, indicating that the model was fully trained and the final safetensors file was available for use.