DreamBooth is a method by Google AI that has been notably implemented into models like Stable Diffusion. Don't forget your FULL MODELS on SDXL are 6. For additional details on PEFT, please check this blog post or the diffusers LoRA documentation. ago. DreamBooth with Stable Diffusion V2. To train a dreambooth model, please select an appropriate model from the hub. py. We ran various experiments with a slightly modified version of this example. Moreover, I will investigate and make a workflow about celebrity name based training hopefully. 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. LoRA is a type of performance-efficient fine-tuning, or PEFT, that is much cheaper to accomplish than full model fine-tuning. . 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. We recommend DreamBooth for generating images of people. During the production process of this version, I conducted comparative tests by integrating Filmgirl Lora into the base model and using Filmgirl Lora's training set for Dreambooth training. sd-diffusiondb-canny-model-control-lora, on 100 openpose pictures, 30k training. Access the notebook here => fast+DreamBooth colab. The options are almost the same as cache_latents. x and SDXL LoRAs. I can suggest you these videos. Location within Victoria. Possible to train dreambooth model locally on 8GB Vram? I was playing around with training loras using kohya-ss. You need as few as three training images and it takes about 20 minutes (depending on how many iterations that you use). accelerat…32 DIM should be your ABSOLUTE MINIMUM for SDXL at the current moment. The same goes for SD 2. The validation images are all black, and they are not nude just all black images. To do so, just specify <code>--train_text_encoder</code> while launching training. Just an FYI. Conclusion This script is a comprehensive example of. py script shows how to implement the ControlNet training procedure and adapt it for Stable Diffusion XL. I've trained some LORAs using Kohya-ss but wasn't very satisfied with my results, so I'm interested in. 2. 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. LoRAs are extremely small (8MB, or even below!) dreambooth models and can be dynamically loaded. e train_dreambooth_sdxl. 5s. Keep in mind you will need more than 12gb of system ram, so select "high system ram option" if you do not use A100. e. DreamBooth training, including U-Net and Text Encoder; Fine-tuning (native training), including U-Net and Text Encoder. py script pre-computes text embeddings and the VAE encodings and keeps them in memory. ipynb and kohya-LoRA-dreambooth. 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. I ha. The train_dreambooth_lora. - Try to inpaint the face over the render generated by RealisticVision. Stay subscribed for all. Yep, as stated Kohya can train SDXL LoRas just fine. Describe the bug. . io. pip uninstall torchaudio. Sign up ProductI found that is easier to train in SDXL and is probably due the base is way better than 1. py'. Using T4 you might reduce to 8. It is the successor to the popular v1. 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. I'm using the normal stuff: xformers, gradient checkpointing, cache latents to disk, bf16. 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. . The same just happened to Lora training recently as well and now it OOMs even on 512x512 sets with. py is a script for LoRA training for SDXL. 9 VAE throughout this experiment. How to train an SDXL LoRA (Koyha with Runpod) This guide will cover training an SDXL LoRA. 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. OutOfMemoryError: CUDA out of memory. 30 images might be rigid. Fork 860. Will investigate training only unet without text encoder. Not sure how youtube videos show they train SDXL Lora. 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. Solution of DreamBooth in dreambooth. To reiterate, Joe Penna branch of Dreambooth-Stable-Diffusion contains Jupyter notebooks designed to help train your personal embedding. training_utils'" And indeed it's not in the file in the sites-packages. Let’s say you want to do DreamBooth training of Stable Diffusion 1. py is a script for LoRA training for SDXL. Closed. They’re used to restore the class when your trained concept bleeds into it. xiankgx opened this issue on Aug 10 · 3 comments · Fixed by #4632. Not sure if it's related, I tried to run the webUI with both venv and conda, the outcome is exactly the same. down_blocks. with_prior_preservation else None, class_prompt=args. py and it outputs a bin file, how are you supposed to transform it to a . 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. The results indicated that employing an existing token did indeed accelerated the training process, yet, the (facial) resemblance produced is not at par with that of unique token. Because there are two text encoders with SDXL, the results may not be predictable. train lora in sd xl-- 使用扣除背景的图训练~ conda activate sd. so far. It would be neat to extend the SDXL dreambooth Lora script with an example of how to train the refiner. I wrote the guide before LORA was a thing, but I brought it up. 20. 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. /loras", weight_name="Theovercomer8. Raw output, ADetailer not used, 1024x1024, 20 steps, DPM++ 2M SDE Karras. If you've ever. View code ZipLoRA-pytorch Installation Usage 1. The train_dreambooth_lora_sdxl. Its APIs can change in future. さっそくVRAM 12GBのRTX 3080でDreamBoothが実行可能か調べてみました。. Share Sort by: Best. py' and sdxl_train. Y fíjate que muchas veces te hablo de batch size UNO, que eso tarda la vida. The Notebook is currently setup for A100 using Batch 30. Here is my launch script: accelerate launch --mixed_precision="fp16" train_dreambooth_lora_sdxl. Although LoRA was initially. This video shows you how to get it works on Microsoft Windows so now everyone with a 12GB 3060 can train at home too :) Circle filling dataset . Yae Miko. py, but it also supports DreamBooth dataset. Just to show a small sample on how powerful this is. 0 as the base model. Using the LCM LoRA, we get great results in just ~6s (4 steps). For example, set it to 256 to. DreamBooth is a method by Google AI that has been notably implemented into models like Stable Diffusion. ControlNet, SDXL are supported as well. Trying to train with SDXL. How to Fine-tune SDXL 0. It save network as Lora, and may be merged in model back. ) Automatic1111 Web UI - PC - Free. By the way, if you’re not familiar with Google Colab, it is a free cloud-based service for machine. Use the checkpoint merger in auto1111. . 0. --full_bf16 option is added. However, ControlNet can be trained to. Standard Optimal Dreambooth/LoRA | 50 Images. 10. Here is what I found when baking Loras in the oven: Character Loras can already have good results with 1500-3000 steps. 6 and check add to path on the first page of the python installer. LoRAs are extremely small (8MB, or even below!) dreambooth models and can be dynamically loaded. The LoRA loading function was generating slightly faulty results yesterday, according to my test. Reload to refresh your session. Share and showcase results, tips, resources, ideas, and more. 🧠43 Generative AI and Fine Tuning / Training Tutorials Including Stable Diffusion, SDXL, DeepFloyd IF, Kandinsky and more. py:92 in train │. py script, it initializes two text encoder parameters but its require_grad is False. Stability AI released SDXL model 1. So if I have 10 images, I would train for 1200 steps. 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. . The validation images are all black, and they are not nude just all black images. py, when will there be a pure dreambooth version of sdxl? i. Stable Diffusion XL (SDXL) is one of the latest and most powerful AI image generation models, capable of creating high. 0 base model. (up to 1024/1024), might be even higher for SDXL, your model becomes more flexible at running at random aspects ratios or even just set up your subject as. ) Cloud - Kaggle - Free. 00 MiB (GP. . 0 in July 2023. ※本記事のLoRAは、あまり性能が良いとは言えませんのでご了承ください（お試しで学習方法を学びたい、程度であれば現在でも有効ですが、古い記事なので操作方法が変わっている可能性があります）。別のLoRAについて記事を公開した際は、こちらでお知らせします。 ※DreamBoothのextensionが. Some popular models you can start training on are: Stable Diffusion v1. It is a combination of two techniques: Dreambooth and LoRA. With dreambooth you are actually training the model itself versus textual inversion where you are simply finding a set of words that match you item the closest. Conclusion This script is a comprehensive example of. the image we are attempting to fine tune. However with: xformers ON, gradient checkpointing ON (less quality), batch size 1-4, DIM/Alpha controlled (Prob. 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. Finetune a Stable Diffusion model with LoRA. It adds pairs of rank-decomposition weight matrices (called update matrices) to existing weights, and only trains those newly added weights. The options are almost the same as cache_latents. Moreover, DreamBooth, LoRA, Kohya, Google Colab, Kaggle, Python and more. DreamBooth : 24 GB settings, uses around 17 GB. py is a script for LoRA training for SDXL. py'. See the help message for the usage. Review the model in Model Quick Pick. 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 . dim() >= src. Fine-tuning Stable Diffusion XL with DreamBooth and LoRA on a free-tier Colab Notebook 🧨. Das ganze machen wir mit Hilfe von Dreambooth und Koh. The usage is almost the. 0. But to answer your question, I haven't tried it, and don't really know if you should beyond what I read. 🎁#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. 75 GiB total capacity; 14. Let me show you how to train LORA SDXL locally with the help of Kohya ss GUI. 💡 Note: For now, we only allow. Step 2: Use the LoRA in prompt. )r/StableDiffusion • 28 min. Dreambooth has a lot of new settings now that need to be defined clearly in order to make it work. How to install #Kohya SS GUI trainer and do #LoRA training with Stable Diffusion XL (#SDXL) this is the video you are looking for. For LoRa, the LR defaults are 1e-4 for UNET and 5e-5 for Text. Simplified cells to create the train_folder_directory and reg_folder_directory folders in kohya-dreambooth. 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. DreamBooth is a way to train Stable Diffusion on a particular object or style, creating your own version of the model that generates those objects or styles. You can train SDXL on your own images with one line of code using the Replicate API. 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. You signed out in another tab or window. Open the terminal and dive into the folder using the. safetensors")? Also, is such LoRa from dreambooth supposed to work in ComfyUI?Describe the bug. textual inversion is great for lower vram. Tried to train on 14 images. The training is based on image-caption pairs datasets using SDXL 1. 0 (UPDATED) 1. Dreambooth allows you to "teach" new concepts to a Stable Diffusion model. README. 0! In addition to that, we will also learn how to generate images. I was looking at that figuring out all the argparse commands. My favorite is 100-200 images with 4 or 2 repeats with various pose and angles. This is the ultimate LORA step-by-step training guide, and I have to say this b. 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. Under the "Create Model" sub-tab, enter a new model name and select the source checkpoint to train from. Furkan Gözükara PhD. ControlNet training example for Stable Diffusion XL (SDXL) . Most of the times I just get black squares as preview images, and the loss goes to nan after some 20 epochs 130 steps. • 8 mo. Settings used in Jar Jar Binks LoRA training. In the following code snippet from lora_gui. Saved searches Use saved searches to filter your results more quicklyDreambooth works similarly to textual inversion but by a different mechanism. Extract LoRA files. py' and sdxl_train. r/DreamBooth. 🚀LCM update brings SDXL and SSD-1B to the game 🎮正好 Hugging Face 提供了一个 train_dreambooth_lora_sdxl. So 9600 or 10000 steps would suit 96 images much better. ; We only need a few images of the subject we want to train (5 or 10 are usually enough). LoRA is compatible with network. dreambooth is much superior. Then this is the tutorial you were looking for. Installation: Install Homebrew. 9of9 Valentine Kozin guest. Cosine: starts off fast and slows down as it gets closer to finishing. . These models allow for the use of smaller appended models to fine-tune diffusion models. 5 based custom models or do Stable Diffusion XL (SDXL) LoRA training but… 2 min read · Oct 8 See all from Furkan Gözükara. Dreambooth, train Stable Diffusion V2 with images up to 1024px on free Colab (T4), testing + feedback needed I just pushed an update to the colab making it possible to train the new v2 models up to 1024px with a simple trick, this needs a lot of testing to get the right settings, so any feedback would be great for the community. . py and it outputs a bin file, how are you supposed to transform it to a . py script for training a LoRA using the SDXL base model which works out of the box although I tweaked the parameters a bit. It can be run on RunPod. 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. If you've ev. . The service departs Dimboola at 13:34 in the afternoon, which arrives into. I get errors using kohya-ss which don't specify it being vram related but I assume it is. How to train LoRAs on SDXL model with least amount of VRAM using settings. Select the Training tab. LoRA : 12 GB settings - 32 Rank, uses less than 12 GB. Reload to refresh your session. First edit app2. Train and deploy a DreamBooth model. You switched accounts on another tab or window. How To Do Stable Diffusion LORA Training By Using Web UI On Different Models - Tested SD 1. AutoTrain Advanced: faster and easier training and deployments of state-of-the-art machine learning models. Trains run twice a week between Dimboola and Ballarat. Go to the Dreambooth tab. This guide demonstrates how to use LoRA, a low-rank approximation technique, to fine-tune DreamBooth with the CompVis/stable-diffusion-v1-4 model. access_token = "hf. I'll post a full workflow once I find the best params but the first pic as a magician was the best image I ever generated and I really wanted to share!Lora seems to be a lightweight training technique used to adapt large language models (LLMs) to specific tasks or domains. py. Successfully merging a pull request may close this issue. Name the output with -inpaint. It seems to be a good idea to choose something that has a similar concept to what you want to learn. It also shows a warning:Updated Film Grian version 2. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"dev","path":"dev","contentType":"directory"},{"name":"drive","path":"drive","contentType. 50. Code. it was taking too long (and i'm technical) so I just built an app that lets you train SD/SDXL LoRAs in your browser, save configuration settings as templates to use later, and quickly test your results with in-app inference. x? * Dreambooth or LoRA? Describe the bug when i train lora thr Zero-2 stage of deepspeed and offload optimizer states and parameters to CPU, torch. dim() to be true, but got false (see below) Reproduction Run the tutorial at ex. And make sure to checkmark “SDXL Model” if you are training. 5. The train_dreambooth_lora_sdxl. Dreamboothing with LoRA Dreambooth allows you to "teach" new concepts to a Stable Diffusion model. I used SDXL 1. 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. The train_dreambooth_lora_sdxl. It was a way to train Stable Diffusion on your objects or styles. LoRA is compatible with Dreambooth and the process is similar to fine-tuning, with a couple of advantages: Training is faster. Fine-tuning allows you to train SDXL on a particular object or style, and create a new model that generates images of those objects or styles. It will rebuild your venv folder based on that version of python. probably even default settings works. 5, SD 2. Create a new model. Taking Diffusers Beyond Images. Suggested upper and lower bounds: 5e-7 (lower) and 5e-5 (upper) Can be constant or cosine. People are training with too many images on very low learning rates and are still getting shit results. KeyError: 'unet. Create a folder on your machine — I named mine “training”. I’ve trained a. In Kohya_ss GUI, go to the LoRA page. 0. 1st DreamBooth vs 2nd LoRA. class_prompt, class_num=args. It costs about $2. 5 if you have the luxury of 24GB VRAM). We will use Kaggle free notebook to do Kohya S. This method should be preferred for training models with multiple subjects and styles. 1. 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. py scripts. Just to show a small sample on how powerful this is. add_argument ( "--learning_rate_text", type = float, default = 5e-4, help = "Initial learning rate (after the potential warmup period) to use. py script shows how to implement the training procedure and adapt it for Stable Diffusion XL . overclockd. Train LoRAs for subject/style images 2. 35:10 How to get stylized images such as GTA5. In this case have used Dimensions=8, Alphas=4. 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. To save memory, the number of training steps per step is half that of train_drebooth. g. Higher resolution requires higher memory during training. py, specify the name of the module to be trained in the --network_module option. weight is the emphasis applied to the LoRA model. Share and showcase results, tips, resources, ideas, and more. Just like the title says. accelerate launch train_dreambooth_lora. . 9 using Dreambooth LoRA; Thanks. 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, ". 1. 1. Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sourcesaccelerate launch /home/ubuntu/content/diffusers/examples/dreambooth/train_dreambooth_rnpd_sdxl_lora. it starts from the beginn. Styles in general. You can even do it for free on a google collab with some limitations. Open the Google Colab notebook. The DreamBooth API described below still works, but you can achieve better results at a higher resolution using SDXL. Hello, I am getting much better results using the --train_text_encoder flag with the Dreambooth script. sdxlをベースにしたloraの作り方！ 最新モデルを使って自分の画風を学習させてみよう【Stable Diffusion XL】 今回はLoRAを使った学習に関する話題で、タイトルの通り Stable Diffusion XL（SDXL）をベースにしたLoRAモデルの作り方 をご紹介するという内容になっています。I just extracted a base dimension rank 192 & alpha 192 rank LoRA from my Stable Diffusion XL (SDXL) U-NET + Text Encoder DreamBooth trained… 2 min read · Nov 7 Karlheinz AgsteinerObject training: 4e-6 for about 150-300 epochs or 1e-6 for about 600 epochs. This tutorial covers vanilla text-to-image fine-tuning using LoRA. x models. pyDreamBooth fine-tuning with LoRA. 0 as the base model. Kohya LoRA, DreamBooth, Fine Tuning, SDXL, Automatic1111 Web UI, LLMs, GPT, TTS. 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. , “A [V] dog”), in parallel,. So, I wanted to know when is better training a LORA and when just training a simple Embedding. 0. LoRA_Easy_Training_Scripts. The general rule is that you need x100 training images for the number of steps. This repo based on diffusers lib and TheLastBen code. ipynb. Or for a default accelerate configuration without answering questions about your environment dreambooth_trainer. 19. sdxl_train_network. It can be different from the filename. DreamBooth is a method by Google AI that has been notably implemented into models like Stable Diffusion. 10'000 steps under 15 minutes. 5. Computer Engineer. The default is constant_with_warmup with 0 warmup steps. SDXLで学習を行う際のパラメータ設定はKohya_ss GUIのプリセット「SDXL – LoRA adafactor v1. SDXL consists of a much larger UNet and two text encoders that make the cross-attention context quite larger than the previous variants. Trains run twice a week between Dimboola and Melbourne. The. sdxl_train_network. Train 1'200 steps under 3 minutes. Copy link FurkanGozukara commented Jul 10, 2023. From my experience, bmaltais implementation is. Kohya SS is FAST. 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]. 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. chunk operation, print the size or shape of model_pred to ensure it has the expected dimensions. New comments cannot be posted. 0 base model as of yesterday. 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. 2U/edX stock price falls by 50%{"payload":{"allShortcutsEnabled":false,"fileTree":{"examples":{"items":[{"name":"community","path":"examples/community","contentType":"directory"},{"name. I do this for one reason, my first model experiment were done with dreambooth techinque, in that case you had an option called "stop text encoder training". I wanted to research the impact of regularization images and captions when training a Lora on a subject in Stable Diffusion XL 1. I'm planning to reintroduce dreambooth to fine-tune in a different way. These libraries are common to both Shivam and the LORA repo, however I think only LORA can claim to train with 6GB of VRAM. Running locally with PyTorch Installing the dependencies . The generated Ugly Sonic images from the trained LoRA are much better and more coherent over a variety of prompts, to put it mildly. Making models to train from (like, a dreambooth for the style of a series, then train the characters from that dreambooth). Closed. 1. xiankgx opened this issue on Aug 10 · 3 comments · Fixed by #4632. ## Running locally with PyTorch ### Installing. • 3 mo. Here we use 1e-4 instead of the usual 1e-5. Yep, as stated Kohya can train SDXL LoRas just fine. Codespaces. If you don't have a strong GPU for Stable Diffusion XL training then this is the tutorial you are looking for. 4. 0:00 Introduction to easy tutorial of using RunPod to do SDXL training Updated for SDXL 1. 4 while keeping all other dependencies at latest, and this problem did not happen, so the break should be fully within the diffusers repo and probably within the past couple days. For single image training, I can produce a LORA in 90 seconds with my 3060, from Toms hardware a 4090 is around 4 times faster than what I have, possibly even faster. 無料版ColabでDreamBoothとLoRAでSDXLをファインチューニング 「SDXL」の高いメモリ要件は、ダウンストリームアプリケーションで使用する場合、制限的であるように思われることがよくあります。3. 19. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"dev","path":"dev","contentType":"directory"},{"name":"drive","path":"drive","contentType. DreamBooth is a method to personalize text-to-image models like Stable Diffusion given just a few (3-5) images of a subject. I couldn't even get my machine with the 1070 8Gb to even load SDXL (suspect the 16gb of vram was hamstringing it). if you have 10GB vram do dreambooth. ) Automatic1111 Web UI - PC - FreeRegularisation images are generated from the class that your new concept belongs to, so I made 500 images using ‘artstyle’ as the prompt with SDXL base model. 9 VAE) 15 images x 67 repeats @ 1 batch = 1005 steps x 2 Epochs = 2,010 total steps. Training. Similar to DreamBooth, LoRA lets. /loras", weight_name="lora. The Notebook is currently setup for A100 using Batch 30. If I train SDXL LoRa using train_dreambooth_lora_sdxl. Upto 70% speed up on RTX 4090. 5 model and the somewhat less popular v2. This guide demonstrates how to use LoRA, a low-rank approximation technique, to fine-tune DreamBooth with the CompVis/stable-diffusion-v1-4 model. md","path":"examples/text_to_image/README. Image by the author. The LoRA model will be saved to your Google Drive under AI_PICS > Lora if Use_Google_Drive is selected. Style Loras is something I've been messing with lately. sdxl_train_network.