Star 6. down_blocks. ZipLoRA-pytorch. sdx_train. py . Its APIs can change in future. py gives the following error: RuntimeError: Given groups=1, wei. LORA Source Model. DreamBooth DreamBooth is a method to personalize text-to-image models like Stable Diffusion given just a few (3-5) images of a subject. This yes, is a large and strong opinionated YELL from me - you'll get a 100mb lora, unlike SD 1. Last year, DreamBooth was released. LoRA Type: Standard. . From there, you can run the automatic1111 notebook, which will launch the UI for automatic, or you can directly train dreambooth using one of the dreambooth notebooks. We’ve added fine-tuning (Dreambooth, Textual Inversion and LoRA) support to SDXL 1. 0 Base with VAE Fix (0. . I don’t have this issue if I use thelastben or kohya sdxl Lora notebook. Kohya LoRA, DreamBooth, Fine Tuning, SDXL, Automatic1111 Web UI, LLMs, GPT, TTS. Plan and track work. 10: brew install [email protected] costed money and now for SDXL it costs even more money. tool guide. Training data is used to change weights in the model so it will be capable of rendering images similar to the training data, but care needs to be taken that it does not "override" existing data. Train Models Train models with your own data and use them in production in minutes. Dreambooth is the best training method for Stable Diffusion. DreamBooth and LoRA enable fine-tuning SDXL model for niche purposes with limited data. But when I use acceleration launch, it fails when the number of steps reaches "checkpointing_steps". )r/StableDiffusion • 28 min. I have only tested it a bit,. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"dev","path":"dev","contentType":"directory"},{"name":"drive","path":"drive","contentType. He must apparently already have access to the model cause some of the code and README details make it sound like that. At the moment, what is the best way to train stable diffusion to depict a particular human's likeness? * 1. Here we use 1e-4 instead of the usual 1e-5. nohup accelerate launch train_dreambooth_lora_sdxl. Hi u/Jc_105, the guide I linked contains instructions on setting up bitsnbytes and xformers for Windows without the use of WSL (Windows Subsystem for Linux. In Image folder to caption, enter /workspace/img. 在官方库下载train_dreambooth_lora_sdxl. Last time I checked DB needed at least 11gb, so you cant dreambooth locally. In this tutorial, I show how to install the Dreambooth extension of Automatic1111 Web UI from scratch. 256/1 or 128/1, I dont know). Furkan Gözükara PhD. Download and Initialize Kohya. Use LORA: "Unchecked" Train Imagic Only: "Unchecked" Generate Classification Images Using. buckjohnston. How To Do SDXL LoRA Training On RunPod With Kohya SS GUI Trainer & Use LoRAs With Automatic1111 UI. Codespaces. The Notebook is currently setup for A100 using Batch 30. 🧠43 Generative AI and Fine Tuning / Training Tutorials Including Stable Diffusion, SDXL, DeepFloyd IF, Kandinsky and more. pt files from models trained with train_text_encoder gives very bad results after using monkeypatch to generate images. It is a combination of two techniques: Dreambooth and LoRA. . Of course they are, they are doing it wrong. dev441」が公開されてその問題は解決したようです。. What's the difference between them? i also see there's a train_dreambooth_lora_sdxl. Train a LCM LoRA on the model. First Ever SDXL Training With Kohya LoRA - Stable Diffusion XL Training Will Replace Older Models - Full Tutorial youtube upvotes · comments. I get errors using kohya-ss which don't specify it being vram related but I assume it is. py, but it also supports DreamBooth dataset. In this guide we saw how to fine-tune SDXL model to generate custom dog photos using just 5 images for training. training_utils'" And indeed it's not in the file in the sites-packages. The usage is almost the same as fine_tune. JoePenna’s Dreambooth requires a minimum of 24GB of VRAM so the lowest T4 GPU (Standard) that is usually given. Train a LCM LoRA on the model. 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. 5 and if your inputs are clean. Train LoRAs for subject/style images 2. kohya_ss supports training for LoRA, Textual Inversion but this guide will just focus on the Dreambooth method. py is a script for LoRA training for SDXL. DreamBooth is a method by Google AI that has been notably implemented into models like Stable Diffusion. cuda. LoRA: It can be trained with higher "learning_rate" than Dreambooth and can fit the style of the training images in the shortest time compared to other methods. Describe the bug I trained dreambooth with lora and sd-xl for 1000 steps, then I try to continue traning resume from the 500th step, however, it seems like the training starts without the 1000's checkpoint, i. Dreambooth allows you to train up to 3 concepts at a time, so this is possible. If I train SDXL LoRa using train_dreambooth_lora_sdxl. . ) Cloud - Kaggle - Free. The abstract from the paper is: We present SDXL, a latent diffusion model for text-to. See the help message for the usage. It is the successor to the popular v1. It is said that Lora is 95% as good as. さっそくVRAM 12GBのRTX 3080でDreamBoothが実行可能か調べてみました。. Please keep the following points in mind:</p> <ul dir="auto"> <li>SDXL has two text. It has a UI written in pyside6 to help streamline the process of training models. View code ZipLoRA-pytorch Installation Usage 1. However, extracting the LORA from dreambooth checkpoint does work well when you also install Kohya. 4. Prodigy also can be used for SDXL LoRA training and LyCORIS training, and I read that it has good success rate at it. LoRA is compatible with network. Runpod/Stable Horde/Leonardo is your friend at this point. (Cmd BAT / SH + PY on GitHub) 1 / 5. It was a way to train Stable Diffusion on your own objects or styles. I am looking for step-by-step solutions to train face models (subjects) on Dreambooth using an RTX 3060 card, preferably using the AUTOMATIC1111 Dreambooth extension (since it's the only one that makes it easier using something like Lora or xformers), that produces results on the highest accuracy to the training images as possible. You can train a model with as few as three images and the training process takes less than half an hour. Training. num_class_images, tokenizer=tokenizer, size=args. Tools Help Share Connect T4 Fine-tuning Stable Diffusion XL with DreamBooth and LoRA on a free-tier Colab Notebook 🧨 In this notebook, we show how to fine-tune Stable. Photos of obscure objects, animals or even the likeness of a specific person can be inserted into SD’s image model to improve accuracy even beyond what textual inversion is capable of, with training completed in less than an hour on a 3090. Dreambooth is a technique to teach new concepts to Stable Diffusion using a specialized form of fine-tuning. But to answer your question, I haven't tried it, and don't really know if you should beyond what I read. ago. Using T4 you might reduce to 8. BLIP is a pre-training framework for unified vision-language understanding and generation, which achieves state-of-the-art results on a wide range of vision-language tasks. Comfy is better at automating workflow, but not at anything else. To add a LoRA with weight in AUTOMATIC1111 Stable Diffusion WebUI, use the following syntax in the prompt or the negative prompt: <lora: name: weight>. train_dreambooth_lora_sdxl. Kohya SS is FAST. You can take a dozen or so images of the same item and get SD to "learn" what it is. With the new update, Dreambooth extension is unable to train LoRA extended models. Training commands. training_utils'" And indeed it's not in the file in the sites-packages. Steps to reproduce: create model click settings performance wizardThe usage is almost the same as fine_tune. Trains run twice a week between Dimboola and Melbourne. Dreamboothing with LoRA . The service departs Dimboola at 13:34 in the afternoon, which arrives into. with_prior_preservation else None, class_prompt=args. py 脚本，拿它就能使用 SDXL 基本模型来训练 LoRA；这个脚本还是开箱即用的，不过我稍微调了下参数。 不夸张地说，训练好的 LoRA 在各种提示词下生成的 Ugly Sonic 图像都更好看、更有条理。Options for Learning LoRA . Stable Diffusion（diffusers）におけるLoRAの実装は、 AttnProcsLayers としておこなれています（ 参考 ）。. As a result, the entire ecosystem have to be rebuilt again before the consumers can make use of SDXL 1. dim() to be true, but got false (see below) Reproduction Run the tutorial at ex. Then I merged the two large models obtained, and carried out hierarchical weight adjustment. Ensure enable buckets is checked, if images are of different sizes. It can be used to fine-tune models, or train LoRAs and Textual-Inversion embeddings. I can suggest you these videos. ;. 5 as the original set of ControlNet models were trained from it. 1. py训练脚本。将该文件放在工作目录中。 如果你使用的是旧版本的diffusers，它将由于版本不匹配而报告错误。但是你可以通过在脚本中找到check_min_version函数并注释它来轻松解决这个问题，如下所示: # check_min_version("0. py is a script for SDXL fine-tuning. ceil(len (train_dataloader) / args. once they get epic realism in xl i'll probably give a dreambooth checkpoint a go although the long training time is a bit of a turnoff for me as well for sdxl - it's just much faster to iterate on 1. We will use Kaggle free notebook to do Kohya S. resolution, center_crop=args. checkpionts remain the same as the middle checkpoint). In Prefix to add to WD14 caption, write your TRIGGER followed by a comma and then your CLASS followed by a comma like so: "lisaxl, girl, ". NOTE: You need your Huggingface Read Key to access the SDXL 0. . Although LoRA was initially designed as a technique for reducing the number of trainable parameters in large-language models, the technique can also be applied to. LoRA vs Dreambooth. Hopefully full DreamBooth tutorial coming soon to the SECourses. ipynb and kohya-LoRA-dreambooth. The train_dreambooth_lora. SDXL LoRA Extraction does that Work? · Issue #1286 · bmaltais/kohya_ss · GitHub. Some of my results have been really good though. There are multiple ways to fine-tune SDXL, such as Dreambooth, LoRA diffusion (Originally for LLMs), and Textual. The default is constant_with_warmup with 0 warmup steps. py. Access the notebook here => fast+DreamBooth colab. You signed out in another tab or window. class_prompt, class_num=args. LoRA is a type of performance-efficient fine-tuning, or PEFT, that is much cheaper to accomplish than full model fine-tuning. Update on LoRA : enabling super fast dreambooth : you can now fine tune text encoders to gain much more fidelity, just like the original Dreambooth. Learning: While you can train on any model of your choice, I have found that training on the base stable-diffusion-v1-5 model from runwayml (the default), produces the most translatable results that can be implemented on other models that are derivatives. py script for training a LoRA using the SDXL base model which works out of the box although I tweaked the parameters a bit. Ever since SDXL came out and first tutorials how to train loras were out, I tried my luck getting a likeness of myself out of it. beam_search :A tag already exists with the provided branch name. Get Enterprise Plan NEW. In diesem Video zeige ich euch, wie ihr euer eigenes LoRA Modell für Stable Diffusion trainieren könnt. It was a way to train Stable Diffusion on your objects or styles. Suggested upper and lower bounds: 5e-7 (lower) and 5e-5 (upper) Can be constant or cosine. py script shows how to implement the training procedure and adapt it for Stable Diffusion XL . For v1. accelerate launch train_dreambooth_lora. Furthermore, SDXL full DreamBooth training is also on my research and workflow preparation list. Describe the bug wrt train_dreambooth_lora_sdxl. fit(train_dataset, epochs=epoch s, callbacks=[ckpt_callback]) Experiments and inference. probably even default settings works. $25. Given ∼ 3 − 5 images of a subject we fine tune a text-to-image diffusion in two steps: (a) fine tuning the low-resolution text-to-image model with the input images paired with a text prompt containing a unique identifier and the name of the class the subject belongs to (e. . Image by the author. DreamBooth. py and it outputs a bin file, how are you supposed to transform it to a . sdxl_train_network. Then dreambooth will train for that many more steps ( depending on how many images you are training on). Follow the setting below under LoRA > Tools > Deprecated > Dreambooth/LoRA Folder preparation and press “Prepare. Also tried turning on and off various options such as memory attention (default/xformers), precision (fp16/bf16), using extended Lora or not and choosing different base models (SD 1. image grid of some input, regularization and output samples. Training Config. How Use Stable Diffusion, SDXL, ControlNet, LoRAs For FREE Without A GPU On Kaggle Like. Train Batch Size: 2 As we are using ThinkDiffusion we can set the batch size to 2, but if you are on a lower end GPU, then you should leave this as 1. So I had a feeling that the Dreambooth TI creation would produce similarly higher quality outputs. 🎁#stablediffusion #sdxl #stablediffusiontutorial Stable Diffusion SDXL Lora Training Tutorial📚 Commands to install sd-scripts 📝to install Kohya GUI from scratch, train Stable Diffusion X-Large (SDXL) model, optimize parameters, and generate high-quality images with this in-depth tutorial from SE Courses. LoRA brings about stylistic variations by introducing subtle modifications to the corresponding model file. LoRA uses lesser VRAM but very hard to get correct configuration atm. Because there are two text encoders with SDXL, the results may not be predictable. 5 Dreambooth training I always use 3000 steps for 8-12 training images for a single concept. This tutorial is based on Unet fine-tuning via LoRA instead of doing a full-fledged. Minimum 30 images imo. I'm planning to reintroduce dreambooth to fine-tune in a different way. Generative AI has. This guide demonstrates how to use LoRA, a low-rank approximation technique, to fine-tune DreamBooth with the CompVis/stable-diffusion-v1-4 model. Nice thanks for the input I’m gonna give it a try. 3. 5k. Styles in general. This training process has been tested on an Nvidia GPU with 8GB of VRAM. 「xformers==0. I’ve trained a few already myself. Just training. Using techniques like 8-bit Adam, fp16 training or gradient accumulation, it is possible to train on 16 GB GPUs like the ones provided by Google Colab or Kaggle. . py is a script for LoRA training for SDXL. See the help message for the usage. LoRA is compatible with Dreambooth and the process is similar to fine-tuning, with a couple of advantages: Training is faster. Create your own models fine-tuned on faces or styles using the latest version of Stable Diffusion. File "E:DreamboothTrainingstable-diffusion-webuiextensionssd_dreambooth_extensiondreambooth rain_dreambooth. People are training with too many images on very low learning rates and are still getting shit results. The defaults you see i have used to train a bunch of Lora, feel free to experiment. In short, the LoRA training model makes it easier to train Stable Diffusion (as well as many other models such as LLaMA and other GPT models) on different concepts, such as characters or a specific style. Teach the model the new concept (fine-tuning with Dreambooth) Execute this this sequence of cells to run the training process. SDXL LoRA training, cannot resume from checkpoint #4566. AttnProcsLayersの実装は こちら にあり、やっていることは 単純にAttentionの部分を別途学習しているだけ ということです。. 0 delivering up to 60% more speed in inference and fine-tuning and 50% smaller in size. 5 and Liberty). 4 file. About the number of steps . Your LoRA will be heavily influenced by the. Tried to allocate 26. DreamBooth fine-tuning with LoRA This guide demonstrates how to use LoRA, a low-rank approximation technique, to fine-tune DreamBooth with the CompVis/stable-diffusion-v1-4 model. The author of sd-scripts, kohya-ss, provides the following recommendations for training SDXL: Please. Write better code with AI. You can try replacing the 3rd model with whatever you used as a base model in your training. py is a script for SDXL fine-tuning. r/DreamBooth. Reload to refresh your session. Low-Rank Adaptation of Large Language Models (LoRA) is a training method that accelerates the training of large models while consuming less memory. 5 of my wifes face works much better than the ones Ive made with sdxl so I enabled independent. b. runwayml/stable-diffusion-v1-5. I wanted to try a dreambooth model, but I am having a hard time finding out if its even possible to do locally on 8GB vram. sd-diffusiondb-canny-model-control-lora, on 100 openpose pictures, 30k training. class_data_dir if args. In Kohya_SS GUI use Dreambooth LoRA tab > LyCORIS/LoCon. py, but it also supports DreamBooth dataset. Resources:AutoTrain Advanced - Training Colab - Kohya LoRA Dreambooth: LoRA Training (Dreambooth method) Kohya LoRA Fine-Tuning: LoRA Training (Fine-tune method) Kohya Trainer: Native Training: Kohya Dreambooth: Dreambooth Training: Cagliostro Colab UI NEW: A Customizable Stable Diffusion Web UI [ ] Stability AI released SDXL model 1. In this video, I'll show you how to train LORA SDXL 1. thank you for valuable replyI am using kohya-ss scripts with bmaltais GUI for my LoRA training, not d8ahazard dreambooth A1111 extension, which is another popular option. I tried the sdxl lora training script in the diffusers repo and it worked great in diffusers but when I tried to use it in comfyui it didn’t look anything like the sample images I was getting in diffusers, not sure. 9 using Dreambooth LoRA; Thanks. AttnProcsLayersの実装は こちら にあり、やっていることは 単純にAttentionの部分を別途学習しているだけ ということです。. If you want to use a model from the HF Hub instead, specify the model URL and token. Even for simple training like a person, I'm training the whole checkpoint with dream trainer and extract a lora after. 21. This repo based on diffusers lib and TheLastBen code. 5 Models > Generate Studio Quality Realistic Photos By Kohya LoRA Stable Diffusion Training - Full TutorialYes, you use the LORA on any model later, but it just makes everything easier to have ONE known good model that it will work with. Select the Training tab. Describe the bug. 1st, does the google colab fast-stable diffusion support training dreambooth on SDXL? 2nd, I see there's a train_dreambooth. The whole process may take from 15 min to 2 hours. Additional comment actions. Then this is the tutorial you were looking for. ; Use the LoRA with any SDXL diffusion model and the LCM scheduler; bingo! Start Training. LoRA : 12 GB settings - 32 Rank, uses less than 12 GB. I'm also not using gradient checkpointing as it's slows things down. py, when will there be a pure dreambooth version of sdxl? i. Das ganze machen wir mit Hilfe von Dreambooth und Koh. Échale que mínimo para lo que viene necesitas una de 12 o 16 para Loras, para Dreambooth o 3090 o 4090, no hay más. LORA yes. 13:26 How to use png info to re-generate same image. Train a LCM LoRA on the model. Before running the scripts, make sure to install the library's training dependencies. Again, train at 512 is already this difficult, and not to forget that SDXL is 1024px model, which is (1024/512)^4=16 times more difficult than the above results. 0. 9 via LoRA. train_dataset = DreamBoothDataset( instance_data_root=args. Just to show a small sample on how powerful this is. 2U/edX stock price falls by 50%{"payload":{"allShortcutsEnabled":false,"fileTree":{"examples":{"items":[{"name":"community","path":"examples/community","contentType":"directory"},{"name. It was updated to use the sdxl 1. Next step is to perform LoRA Folder preparation. Lets say you want to train on dog and cat pictures, that would normally require you to split the training. py'. - Try to inpaint the face over the render generated by RealisticVision. Name the output with -inpaint. 6 and check add to path on the first page of the python installer. Not sure if it's related, I tried to run the webUI with both venv and conda, the outcome is exactly the same. dreambooth is much superior. The problem is that in the. There are multiple ways to fine-tune SDXL, such as Dreambooth, LoRA diffusion (Originally for LLMs), and Textual. 8 GB LoRA Training - Fix CUDA & xformers For DreamBooth and Textual Inversion in Automatic1111 SD UI. Here is my launch script: accelerate launch --mixed_precision="fp16" train_dreambooth_lora_sdxl. I'd have to try with all the memory attentions but it will most likely be damn slow. Let’s say you want to do DreamBooth training of Stable Diffusion 1. However with: xformers ON, gradient checkpointing ON (less quality), batch size 1-4, DIM/Alpha controlled (Prob. This is an implementation of ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs by using 🤗diffusers. It can be used as a tool for image captioning, for example, astronaut riding a horse in space. Dimboola railway station is located on the Western standard gauge line in Victoria, Australia. Automate any workflow. I do prefer to train LORA using Kohya in the end but the there’s less feedback. 0. From my experience, bmaltais implementation is. ) Cloud - Kaggle - Free. Conclusion This script is a comprehensive example of. md","path":"examples/dreambooth/README. safetensord或Diffusers版模型的目录> --dataset. The service departs Dimboola at 13:34 in the afternoon, which arrives into Ballarat at. 3 does not work with LoRA extended training. I tried 10 times to train lore on Kaggle and google colab, and each time the training results were terrible even after 5000 training steps on 50 images. . 10. Copy link FurkanGozukara commented Jul 10, 2023. You can train your model with just a few images, and the training process takes about 10-15 minutes. Training text encoder in kohya_ss SDXL Dreambooth. attn1. Finetune a Stable Diffusion model with LoRA. 4 billion. 0. Using V100 you should be able to run batch 12. The DreamBooth API described below still works, but you can achieve better results at a higher resolution using SDXL. For you information, DreamBooth is a method to personalize text-to-image models with just a few images of a subject (around 3–5). dev0")This will only work if you have enough compute credits or a Colab Pro subscription. x models. But fear not! If you're. 1. . KeyError: 'unet. This tutorial covers vanilla text-to-image fine-tuning using LoRA. Hi, I am trying to train dreambooth sdxl but keep running out of memory when trying it for 1024px resolution. 2 GB and pruning has not been a thing yet. View code ZipLoRA-pytorch Installation Usage 1. If you don't have a strong GPU for Stable Diffusion XL training then this is the tutorial you are looking for. ipynb and kohya-LoRA-dreambooth. py and train_dreambooth_lora. lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator. . But all of this is actually quite extensively detailed in the stable-diffusion-webui's wiki. If you've ev. The results were okay'ish, not good, not bad, but also not satisfying. DreamBooth is a method by Google AI that has been notably implemented into models like Stable Diffusion. bmaltais/kohya_ss. the image we are attempting to fine tune. 0. Become A Master Of SDXL Training With Kohya SS LoRAs - Combine Power Of Automatic1111 & SDXL LoRAs - 85 Minutes - Fully Edited And Chaptered - 73 Chapters - Manually Corrected - Subtitles. Now. For specific characters or concepts, I still greatly prefer LoRA above LoHA/LoCon, since I don't want the style to bleed into the character/concept. If i export to safetensors and try in comfyui it warnings about layers not being loaded and the results don’t look anything like when using diffusers code. 0」をベースにするとよいと思います。 ただしプリセットそのままでは学習に時間がかかりすぎるなどの不都合があったので、私の場合は下記のようにパラメータを変更し. chunk operation, print the size or shape of model_pred to ensure it has the expected dimensions. If you've ever. Kohya SS will open. zipfile_url: " Invalid string " unzip_to: " Invalid string " Show code. LCM LoRA for Stable Diffusion 1. This will be a collection of my Test LoRA models trained on SDXL 0. 5, SD 2. Mixed Precision: bf16. This guide demonstrates how to use LoRA, a low-rank approximation technique, to fine-tune DreamBooth with the CompVis/stable-diffusion-v1-4 model. 0 (UPDATED) 1. How to Fine-tune SDXL 0. Tools Help Share Connect T4 Fine-tuning Stable Diffusion XL with DreamBooth and LoRA on a free-tier Colab Notebook 🧨 In this notebook, we show how to fine-tune Stable Diffusion XL (SDXL). Yep, as stated Kohya can train SDXL LoRas just fine. Use "add diff". However, ControlNet can be trained to. The train_dreambooth_lora. Looks like commit b4053de has broken as LoRA Extended training as diffusers 0. Melbourne to Dimboola train times. The LR Scheduler settings allow you to control how LR changes during training. Closed. E. In --init_word, specify the string of the copy source token when initializing embeddings. AutoTrain Advanced: faster and easier training and deployments of state-of-the-art machine learning models. Here is what I found when baking Loras in the oven: Character Loras can already have good results with 1500-3000 steps. I came across photoai. Conclusion. . 30 images might be rigid. From what I've been told, LoRA training on SDXL at batch size 1 took 13. • 8 mo. Also, inference at 8GB GPU is possible but needs to modify the webui’s lowvram codes to make the strategy even more aggressive (and slow). py script from? The one I found in the diffusers package's examples/dreambooth directory fails with "ImportError: cannot import name 'unet_lora_state_dict' from diffusers. This document covers basic info regarding my DreamBooth installation, all the scripts I use and will provide links to all the needed tools and external. 0 base model as of yesterday. Highly recommend downgrading to xformers 14 to reduce black outputs. you can try lowering the learn rate to 3e-6 for example and increase the steps. 💡 Note: For now, we only allow. latent-consistency/lcm-lora-sdxl. . Overview Create a dataset for training Adapt a model to a new task Unconditional image generation Textual Inversion DreamBooth Text-to-image Low-Rank Adaptation of Large Language Models (LoRA) ControlNet InstructPix2Pix Training Custom Diffusion T2I-Adapters Reinforcement learning training with DDPO. train_dreambooth_lora_sdxl. One last thing you need to do before training your model is telling the Kohya GUI where the folders you created in the first step are located on your hard drive. The usage is. ago. Just like the title says. Remember that the longest part of this will be when it's installing the 4gb torch and torchvision libraries. I’ve trained a. 1.