This tutorial covers vanilla text-to-image fine-tuning using LoRA. LoRA brings about stylistic variations by introducing subtle modifications to the corresponding model file. Closed. Where did you get the train_dreambooth_lora_sdxl. The options are almost the same as cache_latents. LoRA is a type of performance-efficient fine-tuning, or PEFT, that is much cheaper to accomplish than full model fine-tuning. py”。 portrait of male HighCWu ControlLoRA 使用Canny边缘控制的模式 . Additional comment actions. LoRA: A faster way to fine-tune Stable Diffusion. 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 . Tried to allocate 26. 🧠43 Generative AI and Fine Tuning / Training Tutorials Including Stable Diffusion, SDXL, DeepFloyd IF, Kandinsky and more. Lecture 18: How Use Stable Diffusion, SDXL, ControlNet, LoRAs For FREE Without A GPU On Kaggle Like Google Colab. If you want to use a model from the HF Hub instead, specify the model URL and token. 0 efficiently. parser. Train LoRAs for subject/style images 2. I am using the following command with the latest repo on github. Now. In the last few days I've upgraded all my Loras for SD XL to a better configuration with smaller files. Kohya SS will open. The team also shows that LoRA is compatible with Dreambooth, a method that allows users to “teach” new concepts to a Stable Diffusion model, and summarize the advantages of applying LoRA on. We’ve built an API that lets you train DreamBooth models and run predictions on them in the cloud. 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. 1. center_crop, encoder. 5, SD 2. 9 repository, this is an official method, no funny business ;) its easy to get one though, in your account settings, copy your read key from there. You can. LCM train scripts crash due to missing unet_time_cond_proj_dim argument bug Something isn't working #5829. Dreambooth LoRA > Source Model tab. image grid of some input, regularization and output samples. weight is the emphasis applied to the LoRA model. Reload to refresh your session. . Describe the bug I get the following issue when trying to resume from checkpoint. In this video, I'll show you how to train LORA SDXL 1. ipynb and kohya-LoRA-dreambooth. Update on LoRA : enabling super fast dreambooth : you can now fine tune text encoders to gain much more fidelity, just like the original Dreambooth. By reading this article, you will learn to do Dreambooth fine-tuning of Stable Diffusion XL 0. Improved the download link function from outside huggingface using aria2c. こんにちはとりにくです。皆さんLoRA学習やっていますか？ 私はそこらへんの興味が薄く、とりあえず雑に自分の絵柄やフォロワの絵柄を学習させてみて満足していたのですが、ようやく本腰入れはじめました。 というのもコピー機学習法なる手法――生成される絵になるべく影響を与えず. You can train your model with just a few images, and the training process takes about 10-15 minutes. fit(train_dataset, epochs=epoch s, callbacks=[ckpt_callback]) Experiments and inference. Possible to train dreambooth model locally on 8GB Vram? I was playing around with training loras using kohya-ss. In this notebook, we show how to fine-tune Stable Diffusion XL (SDXL) with DreamBooth and LoRA on a T4 GPU. 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. DreamBooth is a method to personalize text-to-image models like Stable Diffusion given just a few (3-5) images of a subject. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"dev","path":"dev","contentType":"directory"},{"name":"drive","path":"drive","contentType. 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. The LR Scheduler settings allow you to control how LR changes during training. py で、二つのText Encoderそれぞれに独立した学習率が指定できるように. Jul 27, 2023. Premium Premium Full Finetune | 200 Images. like below . py \\ --pretrained_model_name_or_path= $MODEL_NAME \\ --instance_data_dir= $INSTANCE_DIR \\ --output_dir= $OUTPUT_DIR \\ --instance_prompt= \" a photo of sks dog \" \\ --resolution=512 \\ --train_batch_size=1 \\ --gradient_accumulation_steps=1 \\ --checkpointing_steps=100 \\ --learning. --full_bf16 option is added. LoRA_Easy_Training_Scripts. You can train SDXL on your own images with one line of code using the Replicate API. Automate any workflow. Even for simple training like a person, I'm training the whole checkpoint with dream trainer and extract a lora after. There are multiple ways to fine-tune SDXL, such as Dreambooth, LoRA diffusion (Originally for LLMs), and Textual. Successfully merging a pull request may close this issue. py and it outputs a bin file, how are you supposed to transform it to a . 5 model and the somewhat less popular v2. py is a script for SDXL fine-tuning. Let me show you how to train LORA SDXL locally with the help of Kohya ss GUI. py cannot resume training from checkpoint ! ! model freezed ! ! bug Something isn't working #5840 opened Nov 17, 2023 by yuxu915. JoePenna’s Dreambooth requires a minimum of 24GB of VRAM so the lowest T4 GPU (Standard) that is usually given. . 10: brew install [email protected] costed money and now for SDXL it costs even more money. Resources:AutoTrain Advanced - Training Colab -. Inference TODO. They train fast and can be used to train on all different aspects of a data set (character, concept, style). 📷 9. 5 model and the somewhat less popular v2. What's the difference between them? i also see there's a train_dreambooth_lora_sdxl. The problem is that in the. 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 is a combination of two techniques: Dreambooth and LoRA. Describe the bug. I was looking at that figuring out all the argparse commands. Much of the following still also applies to training on top of the older SD1. The Notebook is currently setup for A100 using Batch 30. The LoRA loading function was generating slightly faulty results yesterday, according to my test. The batch size determines how many images the model processes simultaneously. It costs about $2. py script shows how to implement the training procedure and adapt it for Stable Diffusion XL . 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 another fine-tuning technique that lets you train your model on a concept like a character or style. • 4 mo. LyCORIS / LORA / DreamBooth tutorial. DreamBooth is a method by Google AI that has been notably implemented into models like Stable Diffusion. 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. We will use Kaggle free notebook to do Kohya S. dreambooth is much superior. . 0. check this post for a tutorial. Double the number of steps to get almost the same training as the original Diffusers version and XavierXiao's. 0, which just released this week. py. 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. Trains run twice a week between Melbourne and Dimboola. 0. 25. Plan and track work. Collaborate outside of code. LoRA Type: Standard. DocumentationHypernetworks & LORA Prone to overfitting easily, which means it won't transfer your character's exact design to different models For LORA, some people are able to get decent results on weak GPUs. 0:00 Introduction to easy tutorial of using RunPod. Dreamboothing with LoRA . Ensure enable buckets is checked, if images are of different sizes. github. How to train LoRA on SDXL; This is a long one, so use the table of contents to navigate! Table Of Contents . 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 . Because there are two text encoders with SDXL, the results may not be predictable. Go to the Dreambooth tab. Generating samples during training seems to consume massive amounts of VRam. The generated Ugly Sonic images from the trained LoRA are much better and more coherent over a variety of prompts, to put it mildly. This script uses dreambooth technique, but with posibillity to train style via captions for all images (not just single concept). Generate Stable Diffusion images at breakneck speed. It is a much larger model compared to its predecessors. How to Do SDXL Training For FREE with Kohya LoRA - Kaggle - NO GPU Required - Pwns Google Colab. Using T4 you might reduce to 8. Settings used in Jar Jar Binks LoRA training. The training is based on image-caption pairs datasets using SDXL 1. I'd have to try with all the memory attentions but it will most likely be damn slow. 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. 5. Dreambooth is a technique to teach new concepts to Stable Diffusion using a specialized form of fine-tuning. 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. harrywang commented on Feb 21. probably even default settings works. This is a guide on how to train a good quality SDXL 1. By saving each epoch, I was able to test the LoRA at various stages of training and find the best one. ago. In --init_word, specify the string of the copy source token when initializing embeddings. 13:26 How to use png info to re-generate same image. load_lora_weights(". safetensors format so I can load it just like pipe. github. I’ve trained a. py script shows how to implement the ControlNet training procedure and adapt it for Stable Diffusion XL. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 5 checkpoints are still much better atm imo. In addition to a vew minor formatting and QoL additions, I've added Stable Diffusion V2 as the default training option and optimized the training settings to reflect what I've found to be the best general ones. Note: When using LoRA we can use a much higher learning rate compared to non-LoRA fine-tuning. The default is constant_with_warmup with 0 warmup steps. py --pretrained_model_name_or_path= $MODEL_NAME --instance_data_dir= $INSTANCE_DIR --output_dir=. For additional details on PEFT, please check this blog post or the diffusers LoRA documentation. git clone into RunPod’s workspace. 1st, does the google colab fast-stable diffusion support training dreambooth on SDXL? 2nd, I see there's a train_dreambooth. Share Sort by: Best. GL. They’re used to restore the class when your trained concept bleeds into it. 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. py gives the following. FurkanGozukara opened this issue Jul 10, 2023 · 3 comments Comments. Thanks to KohakuBlueleaf! SDXL 0. sdxl_train. ControlNet, SDXL are supported as well. The service departs Melbourne at 08:05 in the morning, which arrives into. The results were okay'ish, not good, not bad, but also not satisfying. Last time I checked DB needed at least 11gb, so you cant dreambooth locally. py file to your working directory. I have only tested it a bit,. 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. I'm also not using gradient checkpointing as it's slows things down. com github. It adds pairs of rank-decomposition weight matrices (called update matrices) to existing weights, and only trains those newly added weights. Comfy UI now supports SSD-1B. 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. Toggle navigation. 0! In addition to that, we will also learn how to generate images. See the help message for the usage. See the help message for the usage. hempires. 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. July 21, 2023: This Colab notebook now supports SDXL 1. Training. Whether comfy is better depends on how many steps in your workflow you want to automate. Let me show you how to train LORA SDXL locally with the help of Kohya ss GUI. 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. I've done a lot of experimentation on SD1. pt files from models trained with train_text_encoder gives very bad results after using monkeypatch to generate images. DreamBooth is a method by Google AI that has been notably implemented into models like Stable Diffusion. py (for finetuning) trains U-Net only by default, and can train both U-Net and Text Encoder with --train_text_encoder option. Or for a default accelerate configuration without answering questions about your environment dreambooth_trainer. . Words that the tokenizer already has (common words) cannot be used. train_dataset = DreamBoothDataset( instance_data_root=args. Beware random updates will often break it, often not through the extension maker’s fault. There are 18 high quality and very interesting style Loras that you can use for personal or commercial use. Reload to refresh your session. Use the checkpoint merger in auto1111. DreamBooth is a method to personalize text2image models like stable diffusion given just a few (3~5) images of a subject. 5. e train_dreambooth_sdxl. 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. Train a LCM LoRA on the model. IE: 20 images 2020 samples = 1 epoch 2 epochs to get a super rock solid train = 4040 samples. py, but it also supports DreamBooth dataset. This tutorial is based on the diffusers package, which does not support image-caption datasets for. 0 (SDXL 1. It is able to train on SDXL yes, check the SDXL branch of kohya scripts. 0 model! April 21, 2023: Google has blocked usage of Stable Diffusion with a free account. py script shows how to implement the. And later down: CUDA out of memory. py 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. 5, SD 2. py is a script for LoRA training for SDXL. 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. $25. 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. . {"payload":{"allShortcutsEnabled":false,"fileTree":{"examples/dreambooth":{"items":[{"name":"README. 5 as the original set of ControlNet models were trained from it. In this tutorial, I show how to install the Dreambooth extension of Automatic1111 Web UI from scratch. 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. 5 if you have the luxury of 24GB VRAM). 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. 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. class_data_dir if args. 5 Dreambooth training I always use 3000 steps for 8-12 training images for a single concept. However, ControlNet can be trained to. Create your own models fine-tuned on faces or styles using the latest version of Stable Diffusion. Mixed Precision: bf16. 0. SDXL bridges the gap a little as people are getting great results with LoRA for person likeness, but full model training is still going to get you that little bit closer. Cosine: starts off fast and slows down as it gets closer to finishing. md. LoRA is faster and cheaper than DreamBooth. Im using automatic1111 and I run the initial prompt with sdxl but the lora I made with sd1. 8:52 How to prepare training dataset folders for Kohya LoRA / DreamBooth training. Train SDXL09 Lora with Colab. For example 40 images, 15 epoch, 10-20 repeats and with minimal tweakings on rate works. 1. ago. 2 GB and pruning has not been a thing yet. Training Config. Moreover, DreamBooth, LoRA, Kohya, Google Colab, Kaggle, Python and more. e. Create a new model. The `train_dreambooth. py in consumer GPUs like T4 or V100. I ha. add_argument ( "--learning_rate_text", type = float, default = 5e-4, help = "Initial learning rate (after the potential warmup period) to use. Reload to refresh your session. The train_dreambooth_lora. For you information, DreamBooth is a method to personalize text-to-image models with just a few images of a subject (around 3–5). accelerat…32 DIM should be your ABSOLUTE MINIMUM for SDXL at the current moment. Next step is to perform LoRA Folder preparation. DreamBooth : 24 GB settings, uses around 17 GB. {"payload":{"allShortcutsEnabled":false,"fileTree":{"examples/text_to_image":{"items":[{"name":"README. safetensord或Diffusers版模型的目录> --dataset. -class_prompt - denotes a prompt without the unique identifier/instance. I wrote a simple script, SDXL Resolution Calculator: Simple tool for determining Recommended SDXL Initial Size and Upscale Factor for Desired Final Resolution. Highly recommend downgrading to xformers 14 to reduce black outputs. Making models to train from (like, a dreambooth for the style of a series, then train the characters from that dreambooth). 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. 💡 Note: For now, we only allow. It can be run on RunPod. Train a LCM LoRA on the model. This is the ultimate LORA step-by-step training guide,. Top 8% Rank by size. Furkan Gözükara PhD. Same training dataset. I'm capping my VRAM when I'm finetuning at 1024 with batch size 2-4 and I have 24gb. LCM LoRA for SDXL 1. Run a script to generate our custom subject, in this case the sweet, Gal Gadot. This example assumes that you have basic familiarity with Diffusion models and how to. 0001. Prodigy also can be used for SDXL LoRA training and LyCORIS training, and I read that it has good success rate at it. Basically everytime I try to train via dreambooth in a1111, the generation of class images works without any issue, but training causes issues. I've trained some LORAs using Kohya-ss but wasn't very satisfied with my results, so I'm interested in. Styles in general. Train ZipLoRA 3. Due to this, the parameters are not being backpropagated and updated. They train fast and can be used to train on all different aspects of a data set (character, concept, style). --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. Review the model in Model Quick Pick. Similar to DreamBooth, LoRA lets. ZipLoRA-pytorch. The service departs Dimboola at 13:34 in the afternoon, which arrives into Ballarat at. 34:18 How to do SDXL LoRA training if you don't have a strong GPU. Das ganze machen wir mit Hilfe von Dreambooth und Koh. py. 5 based custom models or do Stable Diffusion XL (SDXL) LoRA training but… 2 min read · Oct 8 See all from Furkan Gözükara. The usage is almost the. SDXL > Become A Master Of SDXL Training With Kohya SS LoRAs - Combine Power Of Automatic1111 & SDXL LoRAs SD 1. Keep in mind you will need more than 12gb of system ram, so select "high system ram option" if you do not use A100. I the past I was training 1. Uncensored Chat API Uncensored Chat API alows you to create chatbots that can talk about anything. It is the successor to the popular v1. This tutorial is based on Unet fine-tuning via LoRA instead of doing a full-fledged. Are you on the correct tab, the first tab is for dreambooth, the second tab is for LoRA (Dreambooth LoRA) (if you don't have an option to change the LoRA type, or set the network size ( start with 64, and alpha=64, and convolutional network size / alpha =32 ) ) you are in the wrong tab. load_lora_weights(". ceil(len (train_dataloader) / args. Basically it trains part. SDXL LoRA Extraction does that Work? · Issue #1286 · bmaltais/kohya_ss · GitHub. For LoRa, the LR defaults are 1e-4 for UNET and 5e-5 for Text. The usage is almost the same as train_network. 0 delivering up to 60% more speed in inference and fine-tuning and 50% smaller in size. Moreover, I will investigate and make a workflow about celebrity name based training hopefully. While for smaller datasets like lambdalabs/pokemon-blip-captions, it might not be a problem, it can definitely lead to memory problems when the script is used on a larger dataset. In Image folder to caption, enter /workspace/img. Remember that the longest part of this will be when it's installing the 4gb torch and torchvision libraries. Step 2: Use the LoRA in prompt. train_dreambooth_ziplora_sdxl. You switched accounts on another tab or window. How would I get the equivalent using 10 images, repeats, steps and epochs for Lora?To get started with the Fast Stable template, connect to Jupyter Lab. (Cmd BAT / SH + PY on GitHub) 1 / 5. It can be different from the filename. and it works extremely well. Notifications. 9of9 Valentine Kozin guest. People are training with too many images on very low learning rates and are still getting shit results. 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. py'. It would be neat to extend the SDXL dreambooth Lora script with an example of how to train the refiner. 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. Kohya LoRA, DreamBooth, Fine Tuning, SDXL, Automatic1111 Web UI. Looks like commit b4053de has broken as LoRA Extended training as diffusers 0. Lora Models. pyDreamBooth fine-tuning with LoRA. To reiterate, Joe Penna branch of Dreambooth-Stable-Diffusion contains Jupyter notebooks designed to help train your personal embedding. Image by the author. The train_controlnet_sdxl. r/StableDiffusion. 0. py is a script for SDXL fine-tuning. This tutorial is based on the diffusers package, which does not support image-caption datasets for. That makes it easier to troubleshoot later to get everything working on a different model. A few short months later, Simo Ryu has created a new image generation model that applies a. Reload to refresh your session. you can try lowering the learn rate to 3e-6 for example and increase the steps. Conclusion This script is a comprehensive example of. . 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>. nohup accelerate launch train_dreambooth_lora_sdxl. . It is the successor to the popular v1. A Colab Notebook For LoRA Training (Dreambooth Method) [ ] Notebook Name Description Link V14; Kohya LoRA Dreambooth. py script shows how to implement the training procedure and adapt it for Stable Diffusion XL. It's more experimental than main branch, but has served as my dev branch for the time. Conclusion This script is a comprehensive example of. Here is a quick breakdown of what each of those parameters means: -instance_prompt - the prompt we would type to generate. Stay subscribed for all. Our training examples use Stable Diffusion 1. JAPANESE GUARDIAN - This was the simplest possible workflow and probably shouldn't have worked (it didn't before) but the final output is 8256x8256 all within Automatic1111. 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. Also, by using LoRA, it's possible to run train_text_to_image_lora. Reply reply2. Already have an account? Another question: convert_lora_safetensor_to_diffusers. py. size ()) Verify Dimensionality: Ensure that model_pred has the correct. After investigation, it seems like it is an issue on diffusers side. Our experiments are based on this repository and are inspired by this blog post from Hugging Face. Maybe try 8bit adam?Go to the Dreambooth tab. Trying to train with SDXL. 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. Describe the bug. I came across photoai. Minimum 30 images imo. And make sure to checkmark “SDXL Model” if you are training. How to add it to the diffusers pipeline?Now you can fine-tune SDXL DreamBooth (LoRA) in Hugging Face Spaces!. attn1. 5. . With the new update, Dreambooth extension is unable to train LoRA extended models. 25 participants. LoRA is a type of performance-efficient fine-tuning, or PEFT, that is much cheaper to accomplish than full. 0. You switched accounts on another tab or window. Train a LCM LoRA on the model. ) Cloud - Kaggle - Free. LoRA is compatible with Dreambooth and the process is similar to fine-tuning, with a couple of advantages: ; Training is faster. sdxl_train_network. You can take a dozen or so images of the same item and get SD to "learn" what it is. Styles in general. DreamBooth is a method by Google AI that has been notably implemented into models like Stable Diffusion. Using the class images thing in a very specific way. md","path":"examples/text_to_image/README. Raw output, ADetailer not used, 1024x1024, 20 steps, DPM++ 2M SDE Karras. 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. Upto 70% speed up on RTX 4090. The defaults you see i have used to train a bunch of Lora, feel free to experiment. py. zipfile_url: " Invalid string " unzip_to: " Invalid string " Show code. I'm planning to reintroduce dreambooth to fine-tune in a different way.