I've trained 1. sdxl_train. It then looks like it is processing the images, but then throws: 0/6400 [00:00<?, ?it/s]OOM Detected, reducing batch/grad size to 0/1. Using V100 you should be able to run batch 12. 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. 2. By reading this article, you will learn to do Dreambooth fine-tuning of Stable Diffusion XL 0. prepare(lora_layers, optimizer, train_dataloader, lr_scheduler) # We need to recalculate our total training steps as the size of the training dataloader may have changed. Get Enterprise Plan NEW. Train SDXL09 Lora with Colab. . . Stability AI released SDXL model 1. Instant dev environments. py is a script for LoRA training for SDXL. By saving each epoch, I was able to test the LoRA at various stages of training and find the best one. io. Already have an account? Another question: convert_lora_safetensor_to_diffusers. Let’s say you want to do DreamBooth training of Stable Diffusion 1. DreamBooth is a method by Google AI that has been notably implemented into models like Stable Diffusion. The default is constant_with_warmup with 0 warmup steps. The service departs Dimboola at 13:34 in the afternoon, which arrives into. Update, August 2023: We've added fine-tuning support to SDXL, the latest version of Stable Diffusion. . 4 billion. py . Some people have been using it with a few of their photos to place themselves in fantastic situations, while others are using it to incorporate new styles. You switched accounts on another tab or window. This guide will show you how to finetune DreamBooth. Segmind Stable Diffusion Image Generation with Custom Objects. Dreambooth allows you to "teach" new concepts to a Stable Diffusion model. Image by the author. . 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. 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. Using the class images thing in a very specific way. 75 GiB total capacity; 14. Keep in mind you will need more than 12gb of system ram, so select "high system ram option" if you do not use A100. Cheaper image generation services. The options are almost the same as cache_latents. LoRA is faster and cheaper than DreamBooth. This prompt is used for generating "class images" for. Share and showcase results, tips, resources, ideas, and more. This code cell will download your dataset and automatically extract it to the train_data_dir if the unzip_to variable is empty. For example, set it to 256 to. How to install #Kohya SS GUI trainer and do #LoRA training with Stable Diffusion XL (#SDXL) this is the video you are looking for. Any way to run it in less memory. 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. 0. 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’ve trained a. The. If not mentioned, settings was left default, or requires configuration based on your own hardware; Training against SDXL 1. Inside a new Jupyter notebook, execute this git command to clone the code repository into the pod’s workspace. Install 3. LoRA is a type of performance-efficient fine-tuning, or PEFT, that is much cheaper to accomplish than full model fine-tuning. 9 VAE throughout this experiment. Just to show a small sample on how powerful this is. 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 ）。. 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. The train_controlnet_sdxl. Sign up ProductI found that is easier to train in SDXL and is probably due the base is way better than 1. py is a script for SDXL fine-tuning. LORA Dreambooth'd myself in SDXL (great similarity & flexibility) I'm trying to get results as good as normal dreambooth training and I'm getting pretty close. 9 using Dreambooth LoRA; Thanks. 1. Toggle navigation. Each version is a different LoRA, there are no Trigger words as this is not using Dreambooth. 5 model is the latest version of the official v1 model. 0 as the base model. 🚀LCM update brings SDXL and SSD-1B to the game 🎮正好 Hugging Face 提供了一个 train_dreambooth_lora_sdxl. resolution, center_crop=args. Thanks for this awesome project! When I run the script "train_dreambooth_lora. Dreambooth is another fine-tuning technique that lets you train your model on a concept like a character or style. Computer Engineer. Basic Fast Dreambooth | 10 Images. ) Cloud - Kaggle - Free. Y fíjate que muchas veces te hablo de batch size UNO, que eso tarda la vida. It is able to train on SDXL yes, check the SDXL branch of kohya scripts. Go to training section. This yes, is a large and strong opinionated YELL from me - you'll get a 100mb lora, unlike SD 1. Here are two examples of how you can use your imported LoRa models in your Stable Diffusion prompts: Prompt: (masterpiece, top quality, best quality), pixel, pixel art, bunch of red roses <lora:pixel_f2:0. We’ve built an API that lets you train DreamBooth models and run predictions on them in the cloud. Comfy is better at automating workflow, but not at anything else. But to answer your question, I haven't tried it, and don't really know if you should beyond what I read. Just like the title says. You can also download your fine-tuned LoRA weights to use. 4. py. Steps to reproduce: create model click settings performance wizardThe usage is almost the same as fine_tune. Any way to run it in less memory. 4 file. Hi, I was wondering how do you guys train text encoder in kohya dreambooth (NOT Lora) gui for Sdxl? There are options: stop text encoder training. Styles in general. There are 18 high quality and very interesting style Loras that you can use for personal or commercial use. Another question is, is it possible to pass negative prompt into SDXL? The text was updated successfully, but these errors were encountered:LoRA are basically an embedding that applies like a hypernetwork with decently close to dreambooth quality. This is the ultimate LORA step-by-step training guide, and I have to say this b. Training. . 10: brew install [email protected] costed money and now for SDXL it costs even more money. Select the LoRA tab. Describe the bug. The usage is almost the. py is a script for LoRA training for SDXL. ControlNet, SDXL are supported as well. We re-uploaded it to be compatible with datasets here. Using T4 you might reduce to 8. To access Jupyter Lab notebook make sure pod is fully started then Press Connect. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"dev","path":"dev","contentType":"directory"},{"name":"drive","path":"drive","contentType. harrywang commented on Feb 21. FurkanGozukara opened this issue Jul 10, 2023 · 3 comments Comments. Unbeatable Dreambooth Speed. 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. It adds pairs of rank-decomposition weight matrices (called update matrices) to existing weights, and only trains those newly added weights. Constant: same rate throughout training. name is the name of the LoRA model. . Find and fix vulnerabilities. 5 using dreambooth to depict the likeness of a particular human a few times. In diesem Video zeige ich euch, wie ihr euer eigenes LoRA Modell für Stable Diffusion trainieren könnt. xiankgx opened this issue on Aug 10 · 3 comments · Fixed by #4632. Same training dataset. I also am curious if there's any combination of settings that people have gotten full fine-tune/dreambooth (not LORA) training to work for 24GB VRAM cards. 19. Lecture 18: How Use Stable Diffusion, SDXL, ControlNet, LoRAs For FREE Without A GPU On Kaggle Like Google Colab. Update on LoRA : enabling super fast dreambooth : you can now fine tune text encoders to gain much more fidelity, just like the original Dreambooth. 0 as the base model. Certainly depends on what you are trying to do, art styles and faces obviously are a lot more represented in the actual model and things that SD already do well, compared to trying to train on very obscure things. 0. 75 (checked, did not edit values) -no sanity prompt ConceptsDreambooth on Windows with LOW VRAM! Yes, it's that brand new one with even LOWER VRAM requirements! Also much faster thanks to xformers. 0. I do prefer to train LORA using Kohya in the end but the there’s less feedback. 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. Training. You can try replacing the 3rd model with whatever you used as a base model in your training. View All. The LoRA model will be saved to your Google Drive under AI_PICS > Lora if Use_Google_Drive is selected. Old scripts can be found here If you want to train on SDXL, then go here. Our training examples use Stable Diffusion 1. Thanks to KohakuBlueleaf! ;. bin with the diffusers inference code. Turned out about the 5th or 6th epoch was what I went with. weight is the emphasis applied to the LoRA model. Access 100+ Dreambooth And Stable Diffusion Models using simple and fast API. 5 with Dreambooth, comparing the use of unique token with that of existing close token. Follow the setting below under LoRA > Tools > Deprecated > Dreambooth/LoRA Folder preparation and press “Prepare. dim() >= src. DreamBooth is a method to personalize text2image models like stable diffusion given just a few (3~5) images of a subject. For v1. All of the details, tips and tricks of Kohya trainings. Furthermore, SDXL full DreamBooth training is also on my research and workflow preparation list. 2U/edX stock price falls by 50%{"payload":{"allShortcutsEnabled":false,"fileTree":{"examples":{"items":[{"name":"community","path":"examples/community","contentType":"directory"},{"name. SDXL output SD 1. Just training the base model isn't feasible for accurately generating images of subjects such as people, animals, etc. 5k. </li> </ul> <h3. 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. 📷 9. Closed. Last time I checked DB needed at least 11gb, so you cant dreambooth locally. I asked fine tuned model to generate my image as a cartoon. 長らくDiffusersのDreamBoothでxFormersがうまく機能しない時期がありました。. I rolled the diffusers along with train_dreambooth_lora_sdxl. 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. Write better code with AI. 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. Manage code changes. Maybe try 8bit adam?Go to the Dreambooth tab. Runpod/Stable Horde/Leonardo is your friend at this point. 211 upvotes · 65 comments. Taking Diffusers Beyond Images. Get solutions to train SDXL even with limited VRAM — use gradient checkpointing or offload training to Google Colab or RunPod. Use LORA: "Unchecked" Train Imagic Only: "Unchecked" Generate Classification Images Using. For a few reasons: I use Kohya SS to create LoRAs all the time and it works really well. The usage is almost the same as train_network. In Kohya_ss GUI, go to the LoRA page. this is lora not dreambooth with dreambooth minimum is 10 GB and you cant train both unet and text encoder at the same time i have amazing tutorials playlist if you are interested in Stable Diffusion Tutorials, Automatic1111 and Google Colab Guides, DreamBooth, Textual Inversion / Embedding, LoRA, AI Upscaling, Pix2Pix, Img2ImgLoRA stands for Low-Rank Adaptation. LoRa uses a separate set of Learning Rate fields because the LR values are much higher for LoRa than normal dreambooth. --max_train_steps=2400 --save_interval=800 For the class images, I have used the 200 from the following:Do DreamBooth working with SDXL atm? #634. ; There's no need to use the sks word to train Dreambooth. driftjohnson. My results have been hit-and-miss. I create the model (I don't touch any settings, just select my source checkpoint), put the file path in the Concepts>>Concept 1>>Dataset Directory field, and then click Train . 2 GB and pruning has not been a thing yet. Top 8% Rank by size. Double the number of steps to get almost the same training as the original Diffusers version and XavierXiao's. 5 as the original set of ControlNet models were trained from it. Making models to train from (like, a dreambooth for the style of a series, then train the characters from that dreambooth). load_lora_weights(". Your LoRA will be heavily influenced by the. 1st DreamBooth vs 2nd 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. Under the "Create Model" sub-tab, enter a new model name and select the source checkpoint to train from. . -class_prompt - denotes a prompt without the unique identifier/instance. Segmind has open-sourced its latest marvel, the SSD-1B model. How Use Stable Diffusion, SDXL, ControlNet, LoRAs For FREE Without A GPU On Kaggle Like. - Change models to my Dreambooth model of the subject, that was created using Protogen/1. Removed the download and generate regularization images function from kohya-dreambooth. Now that your images and folders are prepared, you are ready to train your own custom SDXL LORA model with Kohya. ai – Pixel art style LoRA. OutOfMemoryError: CUDA out of memory. JoePenna’s Dreambooth requires a minimum of 24GB of VRAM so the lowest T4 GPU (Standard) that is usually given. You can increase the size of the LORA to at least to 256mb at the moment, not even including locon. 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. But for Dreambooth single alone expect to 20-23 GB VRAM MIN. The LR Scheduler settings allow you to control how LR changes during training. Run a script to generate our custom subject, in this case the sweet, Gal Gadot. Or for a default accelerate configuration without answering questions about your environment It would be neat to extend the SDXL dreambooth Lora script with an example of how to train the refiner. This method should be preferred for training models with multiple subjects and styles. Use the square-root of your typical Dimensions and Alphas for Network and Convolution. Dreambooth has a lot of new settings now that need to be defined clearly in order to make it work. All expe. In --init_word, specify the string of the copy source token when initializing embeddings. Moreover, DreamBooth, LoRA, Kohya, Google Colab, Kaggle, Python and more. Share and showcase results, tips, resources, ideas, and more. I now use EveryDream2 to train. But nothing else really so i was wondering which settings should i change?Checkpoint model (trained via Dreambooth or similar): another 4gb file that you load instead of the stable-diffusion-1. center_crop, encoder. 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。. This video is about sdxl dreambooth tutorial , In this video, I'll dive deep about stable diffusion xl, commonly referred to as SDXL or SDXL1. It has been a while since programmers using Diffusers can’t have the LoRA loaded in an easy way. safetensors")? Also, is such LoRa from dreambooth supposed to work in ComfyUI?Describe the bug. pip uninstall xformers. LCM train scripts crash due to missing unet_time_cond_proj_dim argument bug Something isn't working #5829. But I heard LoRA sucks compared to dreambooth. Dimboola railway station is located on the Western standard gauge line in Victoria, Australia. I suspect that the text encoder's weights are still not saved properly. Next step is to perform LoRA Folder preparation. 🧠43 Generative AI and Fine Tuning / Training Tutorials Including Stable Diffusion, SDXL, DeepFloyd IF, Kandinsky and more. Hello, I am getting much better results using the --train_text_encoder flag with the Dreambooth script. LoRA is a type of performance-efficient fine-tuning, or PEFT, that is much cheaper to accomplish than full. Also, by using LoRA, it's possible to run train_text_to_image_lora. Premium Premium Full Finetune | 200 Images. No difference whatsoever. Prodigy also can be used for SDXL LoRA training and LyCORIS training, and I read that it has good success rate at it. Using T4 you might reduce to 8. py' and sdxl_train. I am using the following command with the latest repo on github. LoRA is compatible with Dreambooth and the process is similar to fine-tuning, with a couple of advantages: Training is faster. So, we fine-tune both using LoRA. And later down: CUDA out of memory. LoRA were never the best way, Dreambooth with text encoder always came out more accurate (and more specifically joepenna repo for v1. 3 does not work with LoRA extended training. DreamBooth training example for Stable Diffusion XL (SDXL) DreamBooth is a method to personalize text2image models like stable diffusion given just a few (3~5) images of a subject. For example, you can use SDXL (base), or any fine-tuned or dreamboothed version you like. Let’s say you want to do DreamBooth training of Stable Diffusion 1. 0) using Dreambooth. Tried to allocate 26. Both GUIs do the same thing. Trains run twice a week between Dimboola and Melbourne. How To Do SDXL LoRA Training On RunPod With Kohya SS GUI Trainer & Use LoRAs With Automatic1111 UI. train_dataset = DreamBoothDataset( instance_data_root=args. I am using the following command with the latest repo on github. Low-Rank Adaptation of Large Language Models (LoRA) is a training method that accelerates the training of large models while consuming less memory. Steps to reproduce the problem. Comfy UI now supports SSD-1B. You signed in with another tab or window. Star 6. Set the presets dropdown to: SDXL - LoRA prodigy AI_now v1. 9 via LoRA. How to use trained LoRA model with SDXL? Do DreamBooth working with SDXL atm? #634. /loras", weight_name="Theovercomer8. py, but it also supports DreamBooth dataset. A few short months later, Simo Ryu has created a new image generation model that applies a. Last year, DreamBooth was released. 0 LoRa with good likeness, diversity and flexibility using my tried and true settings which I discovered through countless euros and time spent on training throughout the past 10 months. 0. 2. py", line. . Making models to train from (like, a dreambooth for the style of a series, then train the characters from that dreambooth). bmaltais/kohya_ss. Similar to DreamBooth, LoRA lets. Hi, I am trying to train dreambooth sdxl but keep running out of memory when trying it for 1024px resolution. --full_bf16 option is added. dim() to be true, but got false (see below) Reproduction Run the tutorial at ex. Saved searches Use saved searches to filter your results more quicklyDreambooth works similarly to textual inversion but by a different mechanism. md","path":"examples/text_to_image/README. Stay subscribed for all. Standard Optimal Dreambooth/LoRA | 50 Images. Available at HF and Civitai. Or for a default accelerate configuration without answering questions about your environment DreamBooth was proposed in DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation by Ruiz et al. KeyError: 'unet. Train LoRAs for subject/style images 2. The usage is. 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. ;. md. Now. beam_search :A tag already exists with the provided branch name. 5 where you're gonna get like a 70mb Lora. py and train_lora_dreambooth. py, line 408, in…So the best practice to achieve multiple epochs (AND MUCH BETTER RESULTS) is to count your photos, times that by 101 to get the epoch, and set your max steps to be X epochs. To reiterate, Joe Penna branch of Dreambooth-Stable-Diffusion contains Jupyter notebooks designed to help train your personal embedding. You can disable this in Notebook settingsSDXL 1. py. Of course they are, they are doing it wrong. overclockd. 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. py script for training a LoRA using the SDXL base model which works out of the box although I tweaked the parameters a bit. 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. I was the idea that LORA is used when you want to train multiple concepts, and the Embedding is used for training one single concept. 5 models and remembered they, too, were more flexible than mere loras. sdxl_train_network. Outputs will not be saved. LoRAs are extremely small (8MB, or even below!) dreambooth models and can be dynamically loaded. py script shows how to implement the ControlNet training procedure and adapt it for Stable Diffusion XL. instance_prompt, class_data_root=args. 00 MiB (GPU 0; 14. py (because the target image and the regularization image are divided into different batches instead of the same batch). The general rule is that you need x100 training images for the number of steps. 00001 unet learning rate -constant_with_warmup LR scheduler -other settings from all the vids, 8bit AdamW, fp16, xformers -Scale prior loss to 0. This is a guide on how to train a good quality SDXL 1. num_update_steps_per_epoch = math. Styles in general. The results were okay'ish, not good, not bad, but also not satisfying. 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. Just to show a small sample on how powerful this is. The following steps explain how to train a basic Pokemon Style LoRA using the lambdalabs/pokemon-blip-captions dataset, and how to use it in InvokeAI. Select the Training tab. (Open this block if you are interested in how this process works under the hood or if you want to change advanced training settings or hyperparameters) [ ] ↳ 6 cells. {"payload":{"allShortcutsEnabled":false,"fileTree":{"examples/dreambooth":{"items":[{"name":"README. It is a combination of two techniques: Dreambooth and LoRA. SDXL LoRA training, cannot resume from checkpoint #4566. This tutorial covers vanilla text-to-image fine-tuning using LoRA. │ E:kohyasdxl_train. For specific instructions on using the Dreambooth solution, please refer to the Dreambooth README. 12:53 How to use SDXL LoRA models with Automatic1111 Web UI. Describe the bug When resume training from a middle lora checkpoint, it stops update the model( i. 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. Train 1'200 steps under 3 minutes. Under the "Create Model" sub-tab, enter a new model name and select the source checkpoint to train from. It is a much larger model compared to its predecessors. Train and deploy a DreamBooth model on Replicate With just a handful of images and a single API call, you can train a model, publish it to. buckjohnston. lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator. I highly doubt you’ll ever have enough training images to stress that storage space. However, I ideally want to train my own models using dreambooth, and I do not want to use collab, or pay for something like Runpod. You can train a model with as few as three images and the training process takes less than half an hour. With the new update, Dreambooth extension is unable to train LoRA extended models. A simple usecase for [filewords] in Dreambooth would be like this. This video is about sdxl dreambooth tutorial , In this video, I'll dive deep about stable diffusion xl, commonly referred to as SDXL or SDXL1. 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). train_dreambooth_lora_sdxl. Generated by Finetuned SDXL. Create 1024x1024 images in 2. py converts safetensors to diffusers format. 1. Step 2: Use the LoRA in prompt. Now. I get great results when using the output . Where’s the best place to train the models and use the APIs to connect them to my apps?Fortunately, Hugging Face provides a train_dreambooth_lora_sdxl. This might be common knowledge, however, the resources I. Looks like commit b4053de has broken as LoRA Extended training as diffusers 0. If you've ever. 25. I use the Kohya-GUI trainer by bmaltais for all my models and I always rent a RTX 4090 GPU on vast. It is suitable for training on large files such as full cpkt or safetensors models [1], and can reduce the number of trainable parameters while maintaining model quality [2]. train lora in sd xl-- 使用扣除背景的图训练~ conda activate sd. 0 in July 2023. sdxl_train_network. py . safetensord或Diffusers版模型的目录> --dataset. There are multiple ways to fine-tune SDXL, such as Dreambooth, LoRA diffusion (Originally for LLMs), and Textual. They train fast and can be used to train on all different aspects of a data set (character, concept, style). tool guide. Share Sort by: Best. so far. Dreambooth model on up to 10 images (uncaptioned) Dreambooth AND LoRA model on up to 50 images (manually captioned) Fully fine-tuned model & LoRA with specialized settings, up to 200 manually. . If you want to use a model from the HF Hub instead, specify the model URL and token. How to train LoRA on SDXL; This is a long one, so use the table of contents to navigate! Table Of Contents . 21 Online. py . There are two ways to go about training the Dreambooth method: Token+class Method: Trains to associate the subject or concept with a specific token. All of these are considered for. Automate any workflow. ControlNet training example for Stable Diffusion XL (SDXL) . Style Loras is something I've been messing with lately. 5 based custom models or do Stable Diffusion XL (SDXL) LoRA training but… 2 min read · Oct 8 See all from Furkan Gözükara. yes but the 1. You signed out in another tab or window. py --pretrained_model_name_or_path= $MODEL_NAME --instance_data_dir= $INSTANCE_DIR --output_dir=. But if your txt files simply have cat and dog written in them, you can then in the concept setting build a prompt like: a photo of a [filewords]In the brief guide on the kohya-ss github, they recommend not training the text encoder. In this video, I'll show you how to train LORA SDXL 1. Usually there are more class images than training images, so it is required to repeat training images to use all regularization images in the epoch. cuda. It can be run on RunPod.