The Pallada arriving in Victoria Harbour in grand entrance format with her crew atop the yardarms. 4070 uses less power, performance is similar, VRAM 12 GB. I've a 1060gtx. 5 so SDXL could be seen as SD 3. It has incredibly minor upgrades that most people can't justify losing their entire mod list for. I run it following their docs and the sample validation images look great but I’m struggling to use it outside of the diffusers code. I have the same GPU, 32gb ram and i9-9900k, but it takes about 2 minutes per image on SDXL with A1111. It is primarily used to generate detailed images conditioned on text descriptions, though it can also be applied to other tasks such as inpainting, outpainting, and generating image-to-image translations guided by a text prompt. Lora fine-tuning SDXL 1024x1024 on 12GB vram! It's possible, on a 3080Ti! I think I did literally every trick I could find, and it peaks at 11. The release of SDXL 0. Available now on github:. I don't have anything else running that would be making meaningful use of my GPU. i'm running on 6gb vram, i've switched from a1111 to comfyui for sdxl for a 1024x1024 base + refiner takes around 2m. A GeForce RTX GPU with 12GB of RAM for Stable Diffusion at a great price. 4070 solely for the Ada architecture. SDXL LoRA Training Tutorial ; Start training your LoRAs with Kohya GUI version with best known settings ; First Ever SDXL Training With Kohya LoRA - Stable Diffusion XL Training Will Replace Older Models ComfyUI Tutorial and Other SDXL Tutorials ; If you are interested in using ComfyUI checkout below tutorial When it comes to AI models like Stable Diffusion XL, having more than enough VRAM is important. you can use SDNext and set the diffusers to use sequential CPU offloading, it loads the part of the model its using while it generates the image, because of that you only end up using around 1-2GB of vram. DreamBooth is a method to personalize text2image models like stable diffusion given just a few (3~5) images of a subject. #SDXL is currently in beta and in this video I will show you how to use it install it on your PC. worst quality, low quality, bad quality, lowres, blurry, out of focus, deformed, ugly, fat, obese, poorly drawn face, poorly drawn eyes, poorly drawn eyelashes, bad. You're asked to pick which image you like better of the two. Around 7 seconds per iteration. Next. Finally, change the LoRA_Dim to 128 and ensure the the Save_VRAM variable is key to switch to. This is my repository with the updated source and a sample launcher. Also, SDXL was not trained on only 1024x1024 images. Your image will open in the img2img tab, which you will automatically navigate to. 9 delivers ultra-photorealistic imagery, surpassing previous iterations in terms of sophistication and visual quality. com はじめに今回の学習は「DreamBooth fine-tuning of the SDXL UNet via LoRA」として紹介されています。いわゆる通常のLoRAとは異なるようです。16GBで動かせるということはGoogle Colabで動かせるという事だと思います。自分は宝の持ち腐れのRTX 4090をここぞとばかりに使いました。 touch-sp. bat. Next). This will be using the optimized model we created in section 3. I noticed it said it was using 42gb of vram even after I enabled all performance optimizations and it. Epoch와 Max train epoch는 동일한 값을 입력해야하며, 보통은 6 이하로 잡음. 1. This comes to ≈ 270. I am using a modest graphics card (2080 8GB VRAM), which should be sufficient for training a LoRA with a 1. 5. bat and my webui. Even after spending an entire day trying to make SDXL 0. The kandinsky model needs just a bit more processing power and VRAM than 2. SDXL 1. A_Tomodachi. I used a collection for these as 1. I also tried with --xformers -. You buy 100 compute units for $9. 5 model and the somewhat less popular v2. The settings below are specifically for the SDXL model, although Stable Diffusion 1. • 15 days ago. Run the Automatic1111 WebUI with the Optimized Model. Anyone else with a 6GB VRAM GPU that can confirm or deny how long it should take? 58 images of varying sizes but all resized down to no greater than 512x512, 100 steps each, so 5800 steps. 0 with lowvram flag but my images come deepfried, I searched for possible solutions but whats left is that 8gig VRAM simply isnt enough for SDLX 1. Cannot be used with --lowvram/Sequential CPU offloading. Fine-tuning Stable Diffusion XL with DreamBooth and LoRA on a free-tier Colab Notebook 🧨. The interface uses a set of default settings that are optimized to give the best results when using SDXL models. 1. So I set up SD and Kohya_SS gui, used AItrepeneur's low VRAM config, but training is taking an eternity. 0, 2. Kohya_ss has started to integrate code for SDXL training support in his sdxl branch. 2023. I've found ComfyUI is way more memory efficient than Automatic1111 (and 3-5x faster, as of 1. bat as outlined above and prepped a set of images for 384p and voila. Additionally, “ braces ” has been tagged a few times. I disabled bucketing and enabled "Full bf16" and now my VRAM usage is 15GB and it runs WAY faster. Minimal training probably around 12 VRAM. This tutorial should work on all devices including Windows,. ComfyUIでSDXLを動かすメリット. The 24gb VRAM offered by a 4090 are enough to run this training config using my setup. SDXL 1024x1024 pixel DreamBooth training vs 512x512 pixel results comparison - DreamBooth is full fine tuning with only difference of prior preservation loss - 17 GB VRAM sufficient I just did my first 512x512 pixels Stable Diffusion XL (SDXL) DreamBooth training with my best hyper parameters. 6. So, to. However, there’s a promising solution that has emerged, allowing users to run SDXL on 6GB VRAM systems through the utilization of Comfy UI, an interface that streamlines the process and optimizes memory. 1 it/s. It works by associating a special word in the prompt with the example images. 5 which are also much faster to iterate on and test atm. Tick the box for FULL BF16 training if you are using Linux or managed to get BitsAndBytes 0. First training at 300 steps with a preview every 100 steps is. I’ve trained a few already myself. The abstract from the paper is: We present SDXL, a latent diffusion model for text-to. In the AI world, we can expect it to be better. SDXL 1. And even having Gradient Checkpointing on (decreasing quality). 1. 4 participants. Supported models: Stable Diffusion 1. Checked out the last april 25th green bar commit. Inside the /image folder, create a new folder called /10_projectname. Normally, images are "compressed" each time they are loaded, but you can. It uses something like 14GB just before training starts, so there's no way to starte SDXL training on older drivers. It has enough VRAM to use ALL features of stable diffusion. I have only 12GB of vram so I can only train unet (--network_train_unet_only) with batch size 1 and dim 128. 9 and Stable Diffusion 1. . For anyone else seeing this, I had success as well on a GTX 1060 with 6GB VRAM. 5, one image at a time and takes less than 45 seconds per image, But, for other things, or for generating more than one image in batch, I have to lower the image resolution to 480 px x 480 px or to 384 px x 384 px. . Dreambooth in 11GB of VRAM. If you remember SDv1, the early training for that took over 40GiB of VRAM - now you can train it on a potato, thanks to mass community-driven optimization. Create photorealistic and artistic images using SDXL. They give me hope that model trainers will be able to unleash amazing images of future models but NOT one image I’ve seen has been wow out of SDXL. Repeats can be. 9 may be run on a recent consumer GPU with only the following requirements: a computer running Windows 10 or 11 or Linux, 16GB of RAM, and an Nvidia GeForce RTX 20 graphics card (or higher standard) with at least 8GB of VRAM. 1) images have better composition and coherence compared to SD1. Local Interfaces for SDXL. 5 locally on my RTX 3080 ti Windows 10, I've gotten good results and it only takes me a couple hours. 1. Now you can set any count of images and Colab will generate as many as you set On Windows - WIP Prerequisites . I tried the official codes from Stability without much modifications, and also tried to reduce the VRAM consumption using all my knowledges. I just tried to train an SDXL model today using your extension, 4090 here. How to use Kohya SDXL LoRAs with ComfyUI. 5x), but I can't get the refiner to work. 5 = Skyrim SE, the version the vast majority of modders make mods for and PC players play on. I can train lora model in b32abdd version using rtx3050 4g laptop with --xformers --shuffle_caption --use_8bit_adam --network_train_unet_only --mixed_precision="fp16" but when I update to 82713e9 version (which is lastest) I just out of m. 5, and their main competitor: MidJourney. How To Do SDXL LoRA Training On RunPod With Kohya SS GUI Trainer & Use LoRAs With Automatic1111 UI. It is a much larger model compared to its predecessors. You want to use Stable Diffusion, use image generative AI models for free, but you can't pay online services or you don't have a strong computer. 示例展示 SDXL-Lora 文生图. I have a gtx 1650 and I'm using A1111's client. • 1 yr. 9. 8GB of system RAM usage and 10661/12288MB of VRAM usage on my 3080 Ti 12GB. Probably manually and with a lot of VRAM, there is nothing fundamentally different in SDXL, it run with comfyui out of the box. Barely squeaks by on 48GB VRAM. 9 loras with only 8GBs. r/StableDiffusion. 画像生成AI界隈で非常に注目されており、既にAUTOMATIC1111で使用することが可能です。. No branches or pull requests. DreamBooth is a method to personalize text-to-image models like Stable Diffusion given just a few (3-5) images of a subject. 7 GB out of 24 GB) but doesn't dip into "shared GPU memory usage" (using regular RAM). BEAR IN MIND This is day-zero of SDXL training - we haven't released anything to the public yet. 8-1. DreamBooth is a training technique that updates the entire diffusion model by training on just a few images of a subject or style. I get more well-mutated hands (less artifacts) often with proportionally abnormally large palms and/or finger sausage sections ;) Hand proportions are often. Thanks @JeLuf. . - Farmington Hills, MI (Suburb of Detroit) 22710 Haggerty Road, Suite 190 Farmington Hills, MI 48335 . Moreover, I will investigate and make a workflow about celebrity name based. Stability AI has released the latest version of its text-to-image algorithm, SDXL 1. 1. If you’re training on a GPU with limited vRAM, you should try enabling the gradient_checkpointing and mixed_precision parameters in the. And may be kill explorer process. OneTrainer. It's definitely possible. AnimateDiff, based on this research paper by Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, and Bo Dai, is a way to add limited motion to Stable Diffusion generations. Notes: ; The train_text_to_image_sdxl. I can run SD XL - both base and refiner steps - using InvokeAI or Comfyui - without any issues. These are the 8 images displayed in a grid: LCM LoRA generations with 1 to 8 steps. It can generate novel images from text descriptions and produces. I'm sharing a few I made along the way together with some detailed information on how I run things, I hope. Next, the Training_Epochs count allows us to extend how many total times the training process looks at each individual image. 99. • 1 mo. I can train lora model in b32abdd version using rtx3050 4g laptop with --xformers --shuffle_caption --use_8bit_adam --network_train_unet_only --mixed_precision="fp16" but when I update to 82713e9 version (which is lastest) I just out of m. May be even lowering desktop resolution and switch off 2nd monitor if you have it. Below you will find comparison between 1024x1024 pixel training vs 512x512 pixel training. like there are for 1. 12 samples/sec Image was as expected (to the pixel) ANALYSIS. It's possible to train XL lora on 8gb in reasonable time. Most LoRAs that I know of so far are only for the base model. With swinlr to upscale 1024x1024 up to 4-8 times. 43:36 How to do training on your second GPU with Kohya SS. But it took FOREVER with 12GB VRAM. matteogeniaccio. Tick the box for FULL BF16 training if you are using Linux or managed to get BitsAndBytes 0. I was playing around with training loras using kohya-ss. Roop, base for faceswap extension, was discontinued on 20. All generations are made at 1024x1024 pixels. (UPDATED) Please note that if you are using the Rapid machine on ThinkDiffusion, then the training batch size should be set to 1 as it has lower vRam; 2. py is a script for SDXL fine-tuning. Apply your skills to various domains such as art, design, entertainment, education, and more. 32 DIM should be your ABSOLUTE MINIMUM for SDXL at the current moment. I haven't had a ton of success up until just yesterday. . json workflows) and a bunch of "CUDA out of memory" errors on Vlad (even with the. Same gpu here. 5 loras at rank 128. However, one of the main limitations of the model is that it requires a significant amount of. Res 1024X1024. 5 based custom models or do Stable Diffusion XL (SDXL) LoRA training but… 2 min read · Oct 8 See all from Furkan Gözükara. Most ppl use ComfyUI which is supposed to be more optimized than A1111 but for some reason, for me, A1111 is more faster, and I love the external network browser to organize my Loras. Once publicly released, it will require a system with at least 16GB of RAM and a GPU with 8GB of. 7gb of vram and generates an image in 16 seconds for sde karras 30 steps. I don't believe there is any way to process stable diffusion images with the ram memory installed in your PC. I assume that smaller lower res sdxl models would work even on 6gb gpu's. The chart above evaluates user preference for SDXL (with and without refinement) over SDXL 0. Rank 8, 16, 32, 64, 96 VRAM usages are tested and. SDXL 1. In this tutorial, we will discuss how to run Stable Diffusion XL on low VRAM GPUS (less than 8GB VRAM). 9 dreambooth parameters to find how to get good results with few steps. Development. 0. It took ~45 min and a bit more than 16GB vram on a 3090 (less vram might be possible with a batch size of 1 and gradient_accumulation_step=2)Option 2: MEDVRAM. 5, SD 2. Getting a 512x704 image out every 4 to 5 seconds. 122. The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance. Reload to refresh your session. 5 and 2. 36+ working on your system. Personalized text-to-image generation with. Say goodbye to frustrations. sudo apt-get update. It allows the model to generate contextualized images of the subject in different scenes, poses, and views. It'll process a primary subject and leave. Based on a local experiment with GeForce RTX 4090 GPU (24GB), the VRAM consumption is as follows: 512 resolution — 11GB for training, 19GB when saving checkpoint; 1024 resolution — 17GB for training,. 9 can be run on a modern consumer GPU. Yep, as stated Kohya can train SDXL LoRas just fine. Checked out the last april 25th green bar commit. Resources. Below the image, click on " Send to img2img ". Make the following changes: In the Stable Diffusion checkpoint dropdown, select the refiner sd_xl_refiner_1. You definitely didn't try all possible settings. The largest consumer GPU has 24 GB of VRAM. Shop for the AORUS Radeon™ RX 7900 XTX ELITE Edition w/ 24GB GDDR6 VRAM, Dual DisplayPort v2. 0-RC , its taking only 7. 54 GiB free VRAM when you tried to upscale Reply Thenamesarealltaken_. AUTOMATIC1111 has fixed high VRAM issue in Pre-release version 1. 5 Models > Generate Studio Quality Realistic Photos By Kohya LoRA Stable Diffusion Training - Full Tutorial I'm not an expert but since is 1024 X 1024, I doubt It will work in a 4gb vram card. Best. This is the ultimate LORA step-by-step training guide, and I have to say this b. SDXL Lora training with 8GB VRAM. Oh I almost forgot to mention that I am using H10080G, the best graphics card in the world. Train costed money and now for SDXL it costs even more money. 🎁#stablediffusion #sdxl #stablediffusiontutorial Stable Diffusion SDXL Lora Training Tutorial📚 Commands to install sd-scripts 📝requirements. In addition, I think it may work either on 8GB VRAM. AdamW and AdamW8bit are the most commonly used optimizers for LoRA training. VRAM使用量が少なくて済む. Used torch. Click to see where Colab generated images will be saved . Other reports claimed ability to generate at least native 1024x1024 with just 4GB VRAM. nazihater3000. bat" file. ~1. BLIP Captioning. pull down the repo. It is a much larger model. Most of the work is to make it train with low VRAM configs. From the testing above, it’s easy to see how the RTX 4060 Ti 16GB is the best-value graphics card for AI image generation you can buy right now. Anyways, a single A6000 will be also faster than the RTX 3090/4090 since it can do higher batch sizes. So this is SDXL Lora + RunPod training which probably will be something that the majority will be running currently. Install SD. Stable Diffusion XL（SDXL）とは？. . I followed some online tutorials but run in to a problem that I think a lot of people encountered and that is really really long training time. This interface should work with 8GB VRAM GPUs, but 12GB. Master SDXL training with Kohya SS LoRAs in this 1-2 hour tutorial by SE Courses. Then this is the tutorial you were looking for. 9 testing in the meantime ;)TLDR; Despite its powerful output and advanced model architecture, SDXL 0. Resizing. Set classifier free guidance (CFG) to zero after 8 steps. I can generate images without problem if I use medVram or lowVram, but I wanted to try and train an embedding, but no matter how low I set the settings it just threw out of VRAM errors. Based on a local experiment with GeForce RTX 4090 GPU (24GB), the VRAM consumption is as follows: 512 resolution — 11GB for training, 19GB when saving checkpoint; 1024 resolution — 17GB for training, 19GB when saving checkpoint; Let’s proceed to the next section for the installation process. It was really not worth the effort. Do you have any use for someone like me? I can assist in user guides or with captioning conventions. Learn to install Automatic1111 Web UI, use LoRAs, and train models with minimal VRAM. Reload to refresh your session. This versatile model can generate distinct images without imposing any specific “feel,” granting users complete artistic freedom. I'm training embeddings at 384 x 384, and actually getting previews loaded without errors. DeepSpeed is a deep learning framework for optimizing extremely big (up to 1T parameter) networks that can offload some variable from GPU VRAM to CPU RAM. 1, SDXL and inpainting models; Model formats: diffusers and ckpt models; Training methods: Full fine-tuning, LoRA, embeddings; Masked Training: Let the training focus on just certain parts of the. Ultimate guide to the LoRA training. r/StableDiffusion. This workflow uses both models, SDXL1. 0004 lr instead of 0. Also, as counterintuitive as it might seem, don't generate low resolution images, test it with 1024x1024 at. 0 is weeks away. This reduces VRAM usage A LOT!!! Almost half. This exciting development paves the way for seamless stable diffusion and Lora training in the world of AI art. 9 is able to be run on a modern consumer GPU, needing only a Windows 10 or 11, or Linux operating system, with 16GB RAM, an Nvidia GeForce RTX 20 graphics card (equivalent or higher standard) equipped with a minimum of 8GB of VRAM. The 3060 is insane for it's class, it has so much Vram in comparisson to the 3070 and 3080. Fast ~18 steps, 2 seconds images, with Full Workflow Included! No controlnet, No inpainting, No LoRAs, No editing, No eye or face restoring, Not Even Hires Fix! Raw output, pure and simple TXT2IMG. You switched accounts on another tab or window. For running it after install run below command and use 3001 connect button on MyPods interface ; If it doesn't start at the first time execute again🧠43 Generative AI and Fine Tuning / Training Tutorials Including Stable Diffusion, SDXL, DeepFloyd IF, Kandinsky and more. when i train lora thr Zero-2 stage of deepspeed and offload optimizer states and parameters to CPU, torch. 109. Higher rank will use more VRAM and slow things down a bit, or a lot if you're close to the VRAM limit and there's lots of swapping to regular RAM, so maybe try training ranks in the 16-64 range. 5 SD checkpoint. Kohya GUI has support for SDXL training for about two weeks now so yes, training is possible (as long as you have enough VRAM). 1. The model can generate large (1024×1024) high-quality images. By default, doing a full fledged fine-tuning requires about 24 to 30GB VRAM. Alternatively, use 🤗 Accelerate to gain full control over the training loop. 🧨 Diffusers3. 3. 512x1024 same settings - 14-17 seconds. 0 is 768 X 768 and have problems with low end cards. ) Cloud - RunPod - Paid. 5 based custom models or do Stable Diffusion XL (SDXL) LoRA training but… 2 min read · Oct 8 See all from Furkan Gözükara. 3b. SDXL includes a refiner model specialized in denoising low-noise stage images to generate higher-quality images from the base model. Some limitations in training but can still get it work at reduced resolutions. VRAM spends 77G. 5 is version 1. Describe the bug. How To Do Stable Diffusion LORA Training By Using Web UI On Different Models - Tested SD 1. I'm using a 2070 Super with 8gb VRAM. 0 offers better design capabilities as compared to V1. When it comes to additional VRAM and Stable Diffusion, the sky is the limit --- Stable Diffusion will gladly use every gigabyte of VRAM available on an RTX 4090. Customizing the model has also been simplified with SDXL 1. 12GB VRAM – this is the recommended VRAM for working with SDXL. Faster training with larger VRAM (the larger the batch size the faster the learning rate can be used). 5 model. 5. But the same problem happens once you save the state, vram usage jumps to 17GB and at this point, it never releases it. 0-RC , its taking only 7. By design, the extension should clear all prior VRAM usage before training, and then restore SD back to "normal" when training is complete. In this tutorial, we will use a cheap cloud GPU service provider RunPod to use both Stable Diffusion Web UI Automatic1111 and Stable Diffusion trainer Kohya SS GUI to train SDXL LoRAs. However you could try adding "--xformers" to your "set COMMANDLINE_ARGS" line in your. For the second command, if you don't use the option --cache_text_encoder_outputs, Text Encoders are on VRAM, and it uses a lot of VRAM. Using the Pick-a-Pic dataset of 851K crowdsourced pairwise preferences, we fine-tune the base model of the state-of-the-art Stable Diffusion XL (SDXL)-1. Fooocusis a Stable Diffusion interface that is designed to reduce the complexity of other SD interfaces like ComfyUI, by making the image generation process only require a single prompt. . Don't forget your FULL MODELS on SDXL are 6. 6). Even less VRAM usage - Less than 2 GB for 512x512 images on ‘low’ VRAM usage setting (SD 1. For LoRA, 2-3 epochs of learning is sufficient. . For now I can say that on initial loading of the training the system RAM spikes to about 71. Of course there are settings that are depended on the the model you are training on, Like the resolution (1024,1024 on SDXL) I suggest to set a very long training time and test the lora meanwhile you are still training, when it starts to become overtrain stop the training and test the different versions to pick the best one for your needs. Describe alternatives you've consideredAccording to the resource panel, the configuration uses around 11. For this run I used airbrushed style artwork from retro game and VHS covers. 5, 2. Simplest solution is to just switch to ComfyUI. Please follow our guide here 4. How to install #Kohya SS GUI trainer and do #LoRA training with Stable Diffusion XL (#SDXL) this is the video you are looking for. #stablediffusion #A1111 #AI #Lora #koyass #sd #sdxl #refiner #art #lowvram #lora This video introduces how A1111 can be updated to use SDXL 1. 其他注意事项：SDXL 训练请勿开启 validation 选项。如果还遇到显存不足的情况，请参考 #4-训练显存优化。 2. . Switch to the advanced sub tab. In the above example, your effective batch size becomes 4. Joviex. Higher rank will use more VRAM and slow things down a bit, or a lot if you're close to the VRAM limit and there's lots of swapping to regular RAM, so maybe try training ranks in the 16-64 range. Most items can be left default, but we want to change a few. CANUCKS ANNOUNCE 2023 TRAINING CAMP IN VICTORIA. 512 is a fine default. I uploaded that model to my dropbox and run the following command in a jupyter cell to upload it to the GPU (you may do the same): import urllib. It was updated to use the sdxl 1. SDXL Kohya LoRA Training With 12 GB VRAM Having GPUs - Tested On RTX 3060. SDXL+ Controlnet on 6GB VRAM GPU : any success? I tried on ComfyUI to apply an open pose SD XL controlnet to no avail with my 6GB graphic card. SDXL consists of a much larger UNet and two text encoders that make the cross-attention context quite larger than the previous variants. Finally had some breakthroughs in SDXL training. Hey all, I'm looking to train Stability AI's new SDXL Lora model using Google Colab. although your results with base sdxl dreambooth look fantastic so far!It is if you have less then 16GB and are using ComfyUI because it aggressively offloads stuff to RAM from VRAM as you gen to save on memory. With 6GB of VRAM, a batch size of 2 would be barely possible. The Stability AI SDXL 1. 9 doesn't seem to work with less than 1024×1024, and so it uses around 8-10 gb vram even at the bare minimum for 1 image batch due to the model being loaded itself as well The max I can do on 24gb vram is 6 image batch of 1024×1024. 10 is the number of times each image will be trained per epoch. SDXL 1. --However, this assumes training won't require much more VRAM than SD 1. 1. It could be training models quickly but instead it can only train on one card… Seems backwards. This all still looks like midjourney v 4 back in November before the training was completed by users voting. Object training: 4e-6 for about 150-300 epochs or 1e-6 for about 600 epochs. This is on a remote linux machine running Linux Mint over xrdp so the VRAM usage by the window manager is only 60MB. Open the provided URL in your browser to access the Stable Diffusion SDXL application. 1 awards. 5 and 30 steps, and 6-20 minutes (it varies wildly) with SDXL. I train for about 20-30 steps per image and check the output by compiling to a safetesnors file, and then using live txt2img and multiple prompts containing the trigger and class and the tags that were in the training. Conclusion! . I am very newbie at this. I think the minimum. Here are the changes to make in Kohya for SDXL LoRA training⌚ timestamps:00:00 - intro00:14 - update Kohya02:55 - regularization images10:25 - prepping your.