Furkan Gözükara

Posted on Nov 25

FLUX 2 vs FLUX SRPO, New FLUX Training Kohya SS GUI Premium App With Presets & Features

Full tutorial link > https://www.youtube.com/watch?v=RQHmyJVOHXo

FLUX 2 has been published and I have compared it to the very best FLUX base model known as FLUX SRPO. Moreover, we have updated our FLUX Training APP and presets to the next level. Massive speed up gaings with 0 quality loss and lots of new features. I will show all of the new features we have with new SECourses Kohya SS GUI Premium app and compare FLUX SRPO trained model results with FLUX 2.

This below image blocked on BFL site so only exists in local image as comparison

00:00:00 Introduction to New FLUX Training Improvements and Local Training Showcase

00:00:24 Understanding FLUX SRPO Model: High Realism with Minimal VRAM Requirements

00:00:38 Updated Configurations for Training Realism on 6GB VRAM GPUs Locally

00:01:07 FLUX 2 Announcement and Setting Up Comparisons with BFL Playground

00:01:45 FLUX 2 Dev Model Technical Specs: 32 Billion Parameters and Hardware Challenges

00:02:11 Overview of Changes in SECourses Premium Kohya Trainer Version 35

00:02:46 Development Updates: GUI Improvements and Full Torch Compile Support

00:03:13 LoRA Presets Update: VRAM Optimization and Speed Improvements via Torch Compile

00:03:27 Introducing On-the-Fly FP8 Scaled LoRA Training Support

00:03:42 Quality Comparison Analysis: BF16 vs FP8 Scaled Weights LoRA

00:04:24 VRAM Usage and Speed Analysis: Block Swap Count Reduction with FP8 Scaled

00:05:12 Why 32GB GPUs Don't Need FP8 Scaled: Fitting Completely with Torch Compile

00:05:36 DreamBooth Training Specifics: BF16 Mixed Precision and Tab Selection

00:06:18 New 80GB GPU Configuration: Significant Training Speed Up and Cost Analysis

00:07:39 New Feature: FLUX FP8 Converter Tool for DreamBooth Models

00:08:10 Verifying Quality of Converted FP8 Scaled Models vs Original BF16

00:09:03 New Image Pre-processing Tool: Visualizing How Kohya Sees Your Dataset

00:09:34 Demonstration of Pre-processing: Identifying Padding and Orientation Issues

00:10:38 Additional Features: Memory Efficient Loading and CPU Text Encoder Caching

00:11:23 New Automated Model Downloader Tool: Installation and Model Selection Guide

00:12:35 FLUX 2 vs FLUX 1 Context: Can the New Model Replace Existing Workflows?

00:13:03 Setting Up the Generation Comparison: SRPO Fine-tune vs Base vs FLUX 2 Pro

00:14:13 Analyzing First Comparison Results: Portrait Quality and Realism Assessment

00:15:02 Second Comparison Test: Handling Unrealistic Elements and Animals in Prompts

00:15:33 Importance of Resolution and Platform Choice for FLUX 2 Quality

00:16:24 Analyzing Second Results: Prompt Adherence and Realism in FLUX 2 vs SRPO

00:17:07 Testing the Prompt on Nano Banana Pro: Quality Assessment

00:17:39 Comparison with Seedream 4 Model and Final Thoughts on FLUX 2 Potential

00:18:50 Current Recommendation: Why Qwen Image Realism is the Temporary King

In this video, I demonstrate the newest improvements and features added to our SECourses Premium Kohya Trainer (v35). We have achieved massive performance gains and VRAM reductions, allowing for high-quality FLUX training on GPUs with as little as 6GB of VRAM using the FLUX SRPO model.
I also break down the brand new FLUX 2 announcement! We perform a side-by-side comparison between my locally trained FLUX SRPO model, the base FLUX 1 Dev, and the newly released FLUX 2 Pro model to see if it’s time to switch workflows.

Full Torch Compile Support: Now enabled across all presets for faster training speeds.
FP8 Scaled LoRA Training: On-the-fly conversion that drastically reduces VRAM usage (0 block swaps on 24GB cards) with zero quality loss.
New Tools: Introducing the "FLUX FP8 Converter" to shrink DreamBooth models to 11.1GB and a new "Image Pre-processing" tool to visualize exactly how Kohya sees your dataset.
Hardware Optimization: New presets ranging from 6GB VRAM consumer cards up to 80GB A100/H100 configurations for lightning-fast training.
FLUX 2 Analysis: A realistic look at the 32-billion parameter giant and how it compares to our optimized FLUX 1 workflow.