First Ever SDXL Training With Kohya LoRA - Stable Diffusion XL Training Will Replace Older Models
How to install #Kohya SS GUI trainer and do #LoRA training with Stable Diffusion XL (#SDXL) this is the video you are looking for. I have shown how to install Kohya from scratch. The best parameters to do LoRA training with SDXL. How to use Kohya SDXL LoRAs with ComfyUI. How to do checkpoint comparison with SDXL LoRAs and many more cool stuff.
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Learn step-by-step how to install Kohya GUI and do SDXL Stable Diffusion X-Large training from scratch. See example images of raw Stable Diffusion X-Large outputs after LoRA training. Find out how to optimize parameters, do checkpoint comparisons, generate hundreds of images, and use DeepFace AI to sort images by similarity. See how to leverage inpainting to boost image quality. Discover techniques to create stylized images with a realistic base. Get solutions to train on low VRAM GPUs or even CPUs. Resources include model links, prompt examples, parameter settings, workflows, and more. Whether experienced with AI image generation or just starting out, this in-depth tutorial will help take your Stable Diffusion skills to the next level.
Learn every step to install Kohya GUI from scratch and train the new Stable Diffusion X-Large (SDXL) model for state-of-the-art image generation. This in-depth tutorial will guide you to set up repositories, prepare datasets, optimize training parameters, and leverage techniques like LoRA and inpainting to achieve photorealistic results.
See examples of raw SDXL model outputs after custom training using real photos. Find out how to tune settings like learning rate, optimizers, batch size, and network rank to improve image quality and training speed. Learn to generate hundreds of samples and automatically sort them by similarity using DeepFace AI to easily cherrypick the best images.
Get solutions to train SDXL even with limited VRAM - use gradient checkpointing or offload training to Google Colab or RunPod. See how to create stylized images while retaining a photorealistic face using strength tuning and prompt engineering.
Whether you’re experienced with AI image generation or just starting out, this extensive tutorial will take your Stable Diffusion abilities to the next level.
0:00 Introduction to SDXL LoRA training tutorial
2:50 How to install Kohya GUI trainer
4:58 How to start Kohya GUI trainer after the installation
5:35 Beginning to show all SDXL LoRA training setup and parameters on Kohya trainer
5:51 How to download SDXL model to use as a base training model
6:20 How to prepare training data with Kohya GUI
7:06 What is repeating parameter of Kohya training
7:42 How to set classification images and use which images as regularization images
8:52 How to prepare training dataset folders for Kohya LoRA / DreamBooth training
9:35 What are ohwx rare token and man class token
10:02 Why I am using real images as classification images for realism
10:35 Don’t forget copy folders info
11:06 Best parameters for SDXL Kohya LoRA training
14:45 Explanation of Network Rank Dimension for LoRA
15:31 What is Network Alpha for LoRA training
16:18 How to save settings / configuration in Kohya GUI trainer
16:46 The importance and usage of print training command button
18:05 Why did I get out of VRAM error (OOM)
19:02 What does it / second means you see in Kohya trainer or on SD web UIs
19:55 Where to see number of steps it will take
20:35 Where I shared my training command and how you can use it
20:46 How you can do training with my settings
21:40 How to use trained SDXL LoRA models with ComfyUI
23:00 How to do checkpoint comparison with Kohya LoRA SDXL in ComfyUI
27:05 How to generate amazing images after finding best training checkpoint
28:03 How to utilize AI to find best generated images very easily
31:42 How to find best images in certain looking direction / pose
32:03 How to inpaint generated image to improve quality and similarity
34:18 How to do SDXL LoRA training if you don’t have a strong GPU
35:10 How to get stylized images such as GTA5 style images of yourself
39:00 Please help me. Thank you so much
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