Model_Training_Experiments_As_A_Baseline

MonsterMMORPG/Model_Training_Experiments_As_A_Baseline

Model_Training_Experiments_As_A_Baseline is a machine learning model from MonsterMMORPG. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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Updated Nov 5, 2024

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Hugging Face

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At a glance

Access: Public
Created: Oct 31, 2024
Updated: Nov 5, 2024
SHA: f66675c7

Model card

From the Hugging Face model README

Model Training Experiments and Full Guide and Tutorial: Fine-Tuning vs LoRA Comparison

This repository contains experimental results comparing Fine-Tuning/DreamBooth and LoRA training approaches.

I am sharing how I trained this model with full details and even the dataset: please read entire post very carefully.

This model is purely trained for educational and research purposes only for SFW and ethical image generation.

The workflow and the config used in this tutorial can be used to train clothing, items, animals, pets, objects, styles, simply anything.

The uploaded images have SwarmUI metadata and can be re-generated exactly. For generations FP16 model used but FP8 should yield almost same quality. Don't forget to have used yolo face masking model in prompts.

How To Use

Download model into diffusion_models of the SwarmUI. Then you need to use Clip-L and T5-XXL models as well. I recommend T5-XXL FP16 or Scaled FP8 version.

A newest fully public tutorial here for how to use : https://youtu.be/-zOKhoO9a5s

Additional Resources

Installers and Config Files : https://www.patreon.com/posts/112099700
FLUX Fine-Tuning / DreamBooth Zero-to-Hero Tutorial : https://youtu.be/FvpWy1x5etM
FLUX LoRA Training Zero-to-Hero Tutorial : https://youtu.be/nySGu12Y05k)
Complete Dataset, Training Config Json Files and Testing Prompts : https://www.patreon.com/posts/114972274
Click below link to download all trained LoRA and Fine-Tuning / DreamBooth checkpoints for free
https://huggingface.co/MonsterMMORPG/Model_Training_Experiments_As_A_Baseline/tree/main

Environment Setup

Kohya GUI Version: 021c6f5ae3055320a56967284e759620c349aa56
Torch: 2.5.1
xFormers: 0.0.28.post3

Dataset Information

Resolution: 1024x1024
Dataset Size: 28 images
Captions: "ohwx man" (nothing else)
Activation Token/Trigger Word: "ohwx man"

Fine-Tuning / DreamBooth Experiment

Configuration

Config File: 48GB_GPU_28200MB_6.4_second_it_Tier_1.json
Training: Up to 200 epochs with consistent config
Optimal Result: Epoch 170 (subjective assessment)

Results

LoRA Experiment

Configuration

Config File: Rank_1_29500MB_8_85_Second_IT.json
Training: Up to 200 epochs
Optimal Result: Epoch 160 (subjective assessment)

Results

Comparison Results

LoRA Epochs Comparison

LoRA 90 vs 160 vs Fine-Tuning 170 Comparison

Precision Testing

Compared different precision formats in LoRA training:

FP8 vs FP16 vs FP32 LoRA configurations
View Precision Comparison Grid

Model Variant Analysis

Tested various model variants with LoRA (FP32 Version):

FP8 FLUX DEV Base
FP8 Scaled
GGUF 8
FLUX DEV
View Model Variants Comparison Grid
Works best with FP16 DEV base model, then GGUF 8 base model and then FP8 raw base model and FP8 scaled model sometimes works better sometimes worse

Key Observations

LoRA demonstrates excellent realism but shows more obvious overfitting when generating stylized images.
Fine-Tuning / DreamBooth is better than LoRA as expected.
FP8 almost yields perfect quality as FP32 with LoRA
I have used Kohya GUI to convert FP32 saved LoRAs into FP16 and FP8
Here full public article : https://www.patreon.com/posts/115376830

Model Naming Convention

Fine-Tuning Models

Dwayne_Johnson_FLUX_Fine_Tuning-000010.safetensors
- 10 epochs
- 280 steps (28 images × 10 epochs)
- Batch size: 1
- Resolution: 1024x1024
Dwayne_Johnson_FLUX_Fine_Tuning-000020.safetensors
- 20 epochs
- 560 steps (28 images × 20 epochs)
- Batch size: 1
- Resolution: 1024x1024

LoRA Models

Dwayne_Johnson_FLUX_LoRA-000010.safetensors
- 10 epochs
- 280 steps (28 images × 10 epochs)
- Batch size: 1
- Resolution: 1024x1024
Dwayne_Johnson_FLUX_LoRA-000020.safetensors
- 20 epochs
- 560 steps (28 images × 20 epochs)
- Batch size: 1
- Resolution: 1024x1024

Some Example Images - They have MetaData on CivitAI

CivitAI : https://civitai.com/models/911087

image/png

Created: Oct 31, 2024
Updated: Nov 5, 2024