Tutorial Video Link
Info
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.
Resources
Get the SECourses Premium Kohya Trainer DreamBooth / Fine Tuning : [ https://www.patreon.com/posts/Kohya-FLUX-DreamBooth-Trainer-App-112099700 ]
Get the SECourses Premium Kohya Trainer LoRA : [ https://www.patreon.com/posts/Kohya-FLUX-LoRA-Trainer-App-110879657 ]
DreamBooth Training Tutorial: [ https://www.youtube.com/watch?v=FvpWy1x5etM ]
LoRA Training Tutorial: [ https://www.youtube.com/watch?v=nySGu12Y05k ]
Qwen Image Realism Tutorial: [ https://youtu.be/XWzZ2wnzNuQ ]
Join our Discord Community: [ https://discord.com/servers/secourses-Discord-772774097734074388 ]
Some FLUX SRPO Generations With Medium Quality Training Images Dataset (28) vs FLUX 2 PRO Generations — BFL Website Playground Used — 2048x1152 px
First images are FLUX SRPO, second images are FLUX 2 PRO
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This below image blocked on BFL site so only exists in local image as comparison
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Official BFL Playground Link
⏱️ Video Chapters:
0:00 Introduction to New FLUX Training Improvements and Local Training Showcase
0:24 Understanding FLUX SRPO Model: High Realism with Minimal VRAM Requirements
0:38 Updated Configurations for Training Realism on 6GB VRAM GPUs Locally
1:07 FLUX 2 Announcement and Setting Up Comparisons with BFL Playground
1:45 FLUX 2 Dev Model Technical Specs: 32 Billion Parameters and Hardware Challenges
2:11 Overview of Changes in SECourses Premium Kohya Trainer Version 35
2:46 Development Updates: GUI Improvements and Full Torch Compile Support
3:13 LoRA Presets Update: VRAM Optimization and Speed Improvements via Torch Compile
3:27 Introducing On-the-Fly FP8 Scaled LoRA Training Support
3:42 Quality Comparison Analysis: BF16 vs FP8 Scaled Weights LoRA
4:24 VRAM Usage and Speed Analysis: Block Swap Count Reduction with FP8 Scaled
5:12 Why 32GB GPUs Don’t Need FP8 Scaled: Fitting Completely with Torch Compile
5:36 DreamBooth Training Specifics: BF16 Mixed Precision and Tab Selection
6:18 New 80GB GPU Configuration: Significant Training Speed Up and Cost Analysis
7:39 New Feature: FLUX FP8 Converter Tool for DreamBooth Models
8:10 Verifying Quality of Converted FP8 Scaled Models vs Original BF16
9:03 New Image Pre-processing Tool: Visualizing How Kohya Sees Your Dataset
9:34 Demonstration of Pre-processing: Identifying Padding and Orientation Issues
10:38 Additional Features: Memory Efficient Loading and CPU Text Encoder Caching
11:23 New Automated Model Downloader Tool: Installation and Model Selection Guide
12:35 FLUX 2 vs FLUX 1 Context: Can the New Model Replace Existing Workflows?
13:03 Setting Up the Generation Comparison: SRPO Fine-tune vs Base vs FLUX 2 Pro
14:13 Analyzing First Comparison Results: Portrait Quality and Realism Assessment
15:02 Second Comparison Test: Handling Unrealistic Elements and Animals in Prompts
15:33 Importance of Resolution and Platform Choice for FLUX 2 Quality
16:24 Analyzing Second Results: Prompt Adherence and Realism in FLUX 2 vs SRPO
17:07 Testing the Prompt on Nano Banana Pro: Quality Assessment
17:39 Comparison with Seedream 4 Model and Final Thoughts on FLUX 2 Potential
18:50 Current Recommendation: Why Qwen Image Realism is the Temporary King
More Info
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.
🚀 Key Updates Covered:
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.
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