FLUX LoRA Training Simplified: From Zero to Hero with Kohya SS GUI (8GB GPU, Windows) Tutorial Guide

Ultimate Kohya GUI FLUX LoRA training tutorial. This tutorial is product of non-stop 9 days research and training. I have trained over 73 FLUX LoRA models and analyzed all to prepare this tutorial video. The research still going on and hopefully the results will be significantly improved and latest configs and findings will be shared. Please watch the tutorial without skipping any part. If you are a beginner user or an expert user, this tutorial covers all for you.

🔗 Full Instructions and Links Written Post (the one used in the tutorial) ⤵️

0:00 Full FLUX LoRA Training Tutorial
3:37 Guide on downloading and extracting Kohya GUI
4:22 System requirements: Python, FFmpeg, CUDA, C++ tools, and Git
5:40 Verifying installations using the command prompt
6:20 Kohya GUI installation process and error-checking
6:59 Setting the Accelerate option in Kohya GUI, with a discussion of choices
7:50 Use of the bat file update to upgrade libraries and scripts
8:42 Speed differences between Torch 2.4.0 and 2.5, particularly on Windows and Linux
10:14 Kohya GUI interface and selecting LoRA training mode
10:33 LoRA vs. DreamBooth training, with pros and cons
11:03 Emphasis on extensive research, with over 72 training sessions
11:50 Ongoing research on hyperparameters and future updates
12:30 Selecting configurations based on GPU VRAM size
13:05 Different configurations and their impact on training quality
14:22 "Better colors" configuration for improved image coloring
15:58 Setting the pre-trained model path and links for downloading models
16:42 Significance of training images and potential errors
17:08 Dataset preparation, emphasizing image captioning, cropping, and resizing
17:54 Repeating and regularization images for balanced datasets
18:25 Impact of regularization images and their optional use in FLUX training
19:00 Instance and class prompts and their importance in training
19:58 Setting the destination directory for saving training data
20:26 Preparing training data in Kohya GUI and generated folder structure
21:10 Joy Caption for batch captioning images, with key features
21:52 Joy Caption interface for batch captioning
22:39 Impact of captioning on likeness, with tips for training styles
23:26 Adding an activation token to prompts
23:54 Image caption editor for manual caption editing
24:53 Batch edit options in the caption editor
25:34 Verifying captions for activation token inclusion
26:06 Kohya GUI and copying info to respective fields
27:01 "Train images image" folder path and its relevance
28:10 Setting different repeating numbers for multiple concepts
28:45 Setting the output name for generated checkpoints
29:03 Parameters: epochs, training dataset, and VAE path
29:21 Epochs and recommended numbers based on images
30:11 Training dataset quality, including diversity
31:00 Importance of image focus, sharpness, and lighting
31:42 Saving checkpoints at specific intervals
32:11 Caption file extension option (default: TXT)
33:20 VAE path setting and selecting the appropriate VA.saveTensor file
33:59 Clip large model setting and selecting the appropriate file
34:20 T5 XXL setting and selecting the appropriate file
34:51 Saving and reloading configurations in Kohya GUI
35:36 Ongoing research on clip large training and VRAM usage
36:06 Checking VRAM usage before training and tips to reduce it
37:39 Starting training in Kohya GUI and explanation of messages
38:48 Messages during training: steps, batch size, and regularization factor
39:59 How to set virtual RAM memory to prevent errors
40:34 Checkpoint saving process and their location
41:11 Output directory setting and changing it for specific locations
42:00 Checkpoint size and saving them in FP16 format for smaller files
43:21 Swarm UI for using trained models and its features
44:02 Moving LoRA files to the Swarm UI folder
44:41 Speed up Swarm UI on RTX 4000 series GPUs
45:13 Generating images using FLUX in Swarm UI
46:12 Generating an image without a LoRA using test prompts
46:55 VRAM usage with FLUX and using multiple GPUs for faster generation
47:54 Using LoRAs in Swarm UI and selecting a LoRA
48:27 Generating an image using a LoRA in Swarm UI
49:01 Optional in-painting face feature in Swarm UI
49:46 Overfitting in FLUX training and training image quality
51:59 Finding the best checkpoint using the Grid Generator tool in Swarm UI
52:55 Grid Generator tool for selecting LoRAs and prompts
53:59 Generating the grid and expected results
56:57 Analyzing grid results in Swarm UI
57:56 Finding the best LoRA checkpoint based on grid results
58:56 Generating images with wildcards in Swarm UI
1:00:05 Save models on Hugging Face with a link to a tutorial
1:00:05 Training SDXL and SD1.5 models using Kohya GUI
1:03:20 Using regularization images for SDXL training
1:05:30 Saving checkpoints during SDXL training
1:06:15 Extracting LoRAs from SDXL models

[SECourses]

🔗 Full Instructions and Links Written Post (the one used in the tutorial) ⤵

🔗 SECourses Discord Channel to Get Full Support ⤵

🔗 FLUX SwarmUI Instructions Post (public no need login) ⤵

🔗 FLUX Models 1-Click Robust Auto Downloader Scripts ⤵

🔗 Main Windows SwarmUI Tutorial (Watch To Learn How to Use) ⤵

🔗 Cloud SwarmUI Tutorial (Massed Compute - RunPod - Kaggle) ⤵

🔗 SECourses Reddit ⤵

🔗 SECourses GitHub ⤵

[flintandsteel]

Perfect timing! Yesterday I spent hours on your blog post, finally training my first very slow Flux Lora last night! The whole time I was wishing you had a video on all of this. LOL. Thanks!

[ronnykhalil]

what a resource!! thank you for always deep diving and bringing back the treasure

[theironneon]

Thank you so much! i've been watching your channel and keep waiting for this update

[devnull_]

Thanks! I haven't yet gotten my setup fully configured, but this is very extensive. I did do quite a bit of LoRA, DB and TI training with SD 1.5, but then lost interest, so this is really nice to get back to speed. I don't quite know if anyone could complain about this kind of stuff, you've put a ton of your precious time into this.

[johnnyapbeats]

Your effort is amazing, only by captioning a 1hour video you won my like and subscribe. Looking forward to seeing more amazing tutorials from you!

[KnowtHurts]

Furkan, thanks for the configs for full model training. I haven't thoroughly tested Lora yet and you already have configs for the full model. Thanks a lot! I really appreciate your hard work, man🙏

[marhensa]

22:38 I read some intensive research on Reddit that captioning images in Flux LoRA should be avoided if you are only training for facial likeness (not style, pattern, or photo angle), captioning faces only makes it worse.. I tried it, and it turned out better for me with my simple dataset (2400 steps: 10 pictures, 10 repeats, 24 epochs).

[SouthbayCreations]

Fantastic video! Thank you for sharing the knowledge!

[anshulsingh]

I wanna make Lora but damn this video is long. It will take me days to do all these

[KnowtHurts]

Furkan, thanks again for all the knowledge you share with the community! You are number 1 creator in AI space, in my opinion. I trained my first model today and results are great. I am looking forward to full model training.

I trained 30 images and found out an interesting thing in my test grid: at 175 epochs model slightly opens her mouth much more than at 150 and 200 epochs🙂

Overall 125-200 epochs are all good but there are instances where 50 and 75 epochs do really well too. Flux is really impressive. Imo, it is better than Midjourney.

[AgustinCaniglia]

Your videos are very good I will for sure try your settings..

[moonusaco]

Your videos are getting much better. Much easier to understand. I know you have allot to say and it's exciting. =] The slower pace is easier to listen to, much appreciated... =]

[tazztone]

just in time as i was looking for a guide

[KnowtHurts]

Guys, if you have this error, let it be, it should not affect training (people already asked about this but I leave my comment also so those who have this issue find their answer faster):

ERROR: Could not find a version that satisfies the requirement xformers==0.0.27.post2 (from versions: none)
ERROR: No matching distribution found for xformers==0.0.27.post2

[BabylonBaller]

Just signed up to support you brother. It is very much worth it, saved me a ton of valuable time.

[pk.]

Wow im impressed, this is a lot of information, amazing and thank you for this 💪

[harshdurgude]

These are great tutorials does anyone know any other channels like this which are focused on text or diffusion models

[Archilives_UE_Ai]

Thanks for sharing useful information about Vram

[YogeshMali-ly]

Please make a video on how to train LoRA model for flux in colab using our own dataset that contains multiple subfolder for each character. Please show how to do it with kohya without its gui