Dwayne Johnson aka The Rock FLUX Dev Fine-Tuning / DreamBooth Model for Educational and Research Purposes - Dwayne Johnson aka The Rock FLUX Dev LoRA Model for Educational and Research Purposes - Full Tutorial
Type | |
Stats | 44 |
Reviews | (17) |
Published | Nov 2, 2024 |
Base Model | |
Training | Steps: 4,760 Epochs: 170 |
Trigger Words | ohwx man |
Hash | AutoV2 7CBFAC7158 |
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 :
I have trained both FLUX LoRA and Fine-Tuning / DreamBooth model.
Activation token / trigger word : ohwx man
Each training was up to 200 epochs and once every 10 epoch checkpoints saved and shared on below Hugging Face Repo : https://huggingface.co/MonsterMMORPG/Model_Training_Experiments_As_A_Baseline
This model contains experimental results comparing Fine-Tuning / DreamBooth and LoRA training approaches.
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
Key Observations
LoRA demonstrates excellent realism but shows more obvious overfitting when generating stylized images.
Fine-Tuning / DreamBooth is better than LoRA as expected.
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