🤖 AI Model Training Summary
This document details the configuration and results of your completed Low-Rank Adaptation (LoRA) training job, which customized the Stable Diffusion XL (SDXL) model.
1. Job Status and Timeline
Training Start: $\text{October 18, 2025 at 06:39:35 PM}$
Completion Time (Ready): $\text{October 19, 2025 at 04:17:36 AM}$
Total Duration: Approximately 9 hours and 38 minutes.
Job ID: $\text{563759-20251018103932646}$
Base Model Used: SDXL (Stable Diffusion XL)
History Milestones:
Submitted: $\text{10/18/2025 06:39:33 PM}$
Processing: $\text{10/18/2025 06:39:35 PM}$
Ready (Complete): $\text{10/19/2025 04:17:36 AM}$
2. Dataset and Configuration
Dataset Size: $\text{27}$ image files.
Label Count: $\text{27}$ corresponding descriptions (one for each file).
Label Type: Tags (using short keywords for description).
Training Engine: Kohya (a popular training utility).
LoRA Type: lora (standard Low-Rank Adaptation).
Privacy: $\text{Own Rights}$ (You hold the rights to the training data).
3. Key Training Parameters (The Recipe)
These settings determined how the model learned from your $\text{27}$ images.
Learning Control
Maximum Epochs: $\text{20}$ (The dataset was shown to the model 20 times).
Target Steps: $\text{9,990}$ (The total number of learning batches aimed for).
Num Repeats: $\text{74}$ (This increases the total steps to ensure the model focuses heavily on your small dataset).
Train Batch Size: $\text{4}$ (The model processed 4 images at a time during each step).
Shuffle Caption: $\text{true}$ (The order of tags was randomized to improve learning).
Learning Rates (Speed of Learning)
U-Net Learning Rate (unetLR): $\text{0.0005}$ (Rate for the image generation part).
Text Encoder Learning Rate (textEncoderLR): $\text{0.00005}$ (Rate for the text understanding part—slower is typical).
Optimizer Type: Adafactor.
LR Scheduler: cosine_with_restarts (Determines how the learning rate changes over time).
Model Size and Image Processing
Resolution: $\text{1024}$ (Training size in pixels, standard for SDXL).
Network Dimension (networkDim): $\text{32}$ (A measure of the LoRA's capacity or strength).
Network Alpha (networkAlpha): $\text{16}$ (Used with networkDim for LoRA stability).
Enable Bucket: $\text{true}$ (Helps handle images of slightly different aspect ratios efficiently).
Description
The Lives of Chweee1 and Just1n
The AI model had an assignment: learn the faces, forms, and environments of two men, Chweee1 and Just1n. What it learned was a tapestry of their reality—a mix of quiet professional life, the casual comfort of home, and the intense, private dedication to self.
The Story
Chweee1 was a man of quiet focus, his life measured in degrees of intensity. By day, he was the picture of corporate calm, standing in a brightly lit, modern office setting in a grey polo shirt and beige pants, his arms crossed in front of a cityscape background. His black smartwatch was the only thing ticking faster than the fluorescent lights.
But Chweee1 also carried the mind of an artist. His art studio was a chaotic splash of color—a wall covered with framed, colorful paintings and a desk cluttered with brushes and a paint palette. Here, he found a relaxed smile, standing in a black and white striped shirt, comfortable in the creative mess.
Home was where his discipline truly showed. In his modern living room, walls white and furniture gray, he was often found in a sleeveless tank top and gray sweatpants, his muscular build prominent. Sometimes, his friend, Just1n, would visit. Just1n, an older man with gray hair and a goatee, was a monument to dedication, his defined muscles and large biceps almost digital in their perfection, a testament to years of training.
The model learned, too, of Chweee1's most private moments: the flash of a palm tree tattoo, the subtle tan lines, the high-contrast light of a window casting shadows across defined abs, and the complete, unreserved nakedness in his own space. It was a complete profile, from the sfw (safe for work) composure of his public life to the private, nsfw (not safe for work) moments of raw honesty.
The AI, running its thousands of epochs, didn't just see $\text{27}$ files; it learned $\text{27}$ facets of two human lives.
The Training Words (Categorized Tags)
The model was trained using the following descriptors, which it used to categorize the images.
Training Tags Exhaustive List
This is the complete, consolidated list of all tags used to train the LoRA model on the Stable Diffusion XL base.
Subjects & Physical Appearance
$\text{chweee1}$
$\text{just1n}$
$\text{male}$
$\text{Asian}$
$\text{muscular}$
$\text{tan skin}$
$\text{short hair}$
$\text{glasses}$
$\text{black smartwatch}$
$\text{abs}$
$\text{defined muscles}$
$\text{large biceps}$
$\text{veins}$
$\text{chest hair}$
$\text{arm hair}$
$\text{slight tan lines}$
$\text{short black hair}$
$\text{palm tree tattoo}$
$\text{tribal tattoo}$
$\text{gray hair}$
$\text{goatee}$
Clothing & Attire
$\text{gray tank top}$
$\text{sleeveless gray tank top}$
$\text{gray sweatpants}$
$\text{dark gray t-shirt}$
$\text{camo pants}$
$\text{grey polo shirt}$
$\text{beige pants}$
$\text{shirtless}$
$\text{blue boxer briefs}$
$\text{blue underwear}$
$\text{naked}$
$\text{black shorts}$
$\text{black and white striped shirt}$
$\text{black pants}$
Environments & Objects
$\text{living room}$
$\text{white walls}$
$\text{gray couch}$
$\text{wooden table}$
$\text{bedroom}$
$\text{white bed with pillows}$
$\text{wooden coffee table}$
$\text{TV}$
$\text{office setting}$
$\text{cityscape background}$
$\text{office cubicles}$
$\text{art studio}$
$\text{white desk}$
$\text{paintings on wall}$
$\text{abstract painting}$
$\text{green potted plant}$
$\text{glass cabinet with cables}$