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) ⤵️ ▶️ https://www.patreon.com/posts/click-to-open-post-used-in-tutorial-110879657 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 9:54 Starting Kohya GUI via the gui.bat or automatic starter file 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 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 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