One of the first implementations used it because it was a. Turned out about the 5th or 6th epoch was what I went with. 1. py. Mastering stable diffusion SDXL Lora training can be a daunting challenge, especially for those passionate about AI art and stable diffusion. Make sure you aren't in the Dreambooth tab, because it looks very similar to the LoRA tab! Source Models Tab. That comes in handy when you need to train Dreambooth models fast. 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. --full_bf16 option is added. LoRA is a type of performance-efficient fine-tuning, or PEFT, that is much cheaper to accomplish than full. To start A1111 UI open. For additional details on PEFT, please check this blog post or the diffusers LoRA documentation. Computer Engineer. In the last few days I've upgraded all my Loras for SD XL to a better configuration with smaller files. 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>. 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. Read my last Reddit post to understand and learn how to implement this model. Segmind has open-sourced its latest marvel, the SSD-1B model. Step 4: Train Your LoRA Model. 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. Melbourne to Dimboola train times. Any way to run it in less memory. 4. 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 . 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. 5 and Liberty). Hi, I am trying to train dreambooth sdxl but keep running out of memory when trying it for 1024px resolution. 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. I have only tested it a bit,. This is an order of magnitude faster, and not having to wait for results is a game-changer. Teach the model the new concept (fine-tuning with Dreambooth) Execute this this sequence of cells to run the training process. ControlNet, SDXL are supported as well. The train_controlnet_sdxl. View code ZipLoRA-pytorch Installation Usage 1. x models. Sd15-inpainting model in the first slot, your model in the 2nd, and the standard sd15 pruned in the 3rd. com github. 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. instance_data_dir, instance_prompt=args. So I had a feeling that the Dreambooth TI creation would produce similarly higher quality outputs. Run a script to generate our custom subject, in this case the sweet, Gal Gadot. So, we fine-tune both using LoRA. Inference TODO. Train a LCM LoRA on the model. py SDXL unet is conditioned on the following from the text_encoders: hidden_states of the penultimate. 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. Generated by Finetuned SDXL. For v1. 🎁#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. 5k. py --pretrained_model_name_or_path=<. Reply reply2. Due to this, the parameters are not being backpropagated and updated. lora, so please specify it. 5 based custom models or do Stable Diffusion XL (SDXL) LoRA training but… 2 min read · Oct 8 See all from Furkan Gözükara. Extract LoRA files instead of full checkpoints to reduce downloaded. In the Kohya interface, go to the Utilities tab, Captioning subtab, then click WD14 Captioning subtab. Back in the terminal, make sure you are in the kohya_ss directory: cd ~/ai/dreambooth/kohya_ss. You can. 0. Describe the bug. Prepare the data for a custom model. 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. resolution — The resolution for input images, all the images in the train/validation datasets will be resized to this. Using V100 you should be able to run batch 12. . LCM LoRA for Stable Diffusion 1. - Change models to my Dreambooth model of the subject, that was created using Protogen/1. I wanted to research the impact of regularization images and captions when training a Lora on a subject in Stable Diffusion XL 1. Stable Diffusion XL (SDXL) is one of the latest and most powerful AI image generation models, capable of creating high. If you want to use a model from the HF Hub instead, specify the model URL and token. 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。. To train a dreambooth model, please select an appropriate model from the hub. 3. . 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。. 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. Installation: Install Homebrew. Stability AI released SDXL model 1. 5 model and the somewhat less popular v2. The options are almost the same as cache_latents. The defaults you see i have used to train a bunch of Lora, feel free to experiment. 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. DreamBooth : 24 GB settings, uses around 17 GB. Using the class images thing in a very specific way. You can train your model with just a few images, and the training process takes about 10-15 minutes. IE: 20 images 2020 samples = 1 epoch 2 epochs to get a super rock solid train = 4040 samples. 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. if you have 10GB vram do dreambooth. 4 billion. Already have an account? Another question: convert_lora_safetensor_to_diffusers. The train_dreambooth_lora. . Will investigate training only unet without text encoder. r/StableDiffusion. You switched accounts on another tab or window. ago • u/Federal-Platypus-793. ) Cloud - Kaggle - Free. py script shows how to implement the training procedure and adapt it for Stable Diffusion XL. Copy link FurkanGozukara commented Jul 10, 2023. Extract LoRA files. It's more experimental than main branch, but has served as my dev branch for the time. Also, by using LoRA, it's possible to run train_text_to_image_lora. sdxl_train_network. 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 . . Describe the bug. 2 GB and pruning has not been a thing yet. safetensors has no affect when using it, only generates SKS gun photos (used "photo of a sks b3e3z" as my prompt). The problem is that in the. DreamBooth DreamBooth is a method to personalize text-to-image models like Stable Diffusion given just a few (3-5) images of a subject. I haven't done any training in months, though I've trained several models and textual inversions successfully in the past. 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. Open the Google Colab notebook. In this video, I'll show you how to train amazing dreambooth models with the newly released SDXL 1. Stable Diffusion XL (SDXL) was proposed in SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis by Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Dreambooth alternatives LORA-based Stable Diffusion Fine Tuning. py script shows how to implement the ControlNet training procedure and adapt it for Stable Diffusion XL. The same just happened to Lora training recently as well and now it OOMs even on 512x512 sets with. Note: When using LoRA we can use a much higher learning rate compared to non-LoRA fine-tuning. Fork 860. Whether comfy is better depends on how many steps in your workflow you want to automate. I'm planning to reintroduce dreambooth to fine-tune in a different way. . name is the name of the LoRA model. It has a UI written in pyside6 to help streamline the process of training models. Of course there are settings that are depended on the the model you are training on, Like the resolution (1024,1024 on SDXL) I suggest to set a very long training time and test the lora meanwhile you are still training, when it starts to become overtrain stop the training and test the different versions to pick the best one for your needs. Pytorch Cityscapes Dataset, train_distribute problem - "Typeerror: path should be string, bytes, pathlike or integer, not NoneType" 4 AttributeError: 'ModifiedTensorBoard' object has no attribute '_train_dir'Hello, I want to use diffusers/train_dreambooth_lora. LoRA were never the best way, Dreambooth with text encoder always came out more accurate (and more specifically joepenna repo for v1. Words that the tokenizer already has (common words) cannot be used. py and train_dreambooth_lora. 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. The following is a list of the common parameters that should be modified based on your use cases: pretrained_model_name_or_path — Path to pretrained model or model identifier from. Keep in mind you will need more than 12gb of system ram, so select "high system ram option" if you do not use A100. Both GUIs do the same thing. Practically speaking, Dreambooth and LoRA are meant to achieve the same thing. Upto 70% speed up on RTX 4090. Before running the scripts, make sure to install the library's training dependencies. Outputs will not be saved. Basically everytime I try to train via dreambooth in a1111, the generation of class images works without any issue, but training causes issues. How to train an SDXL LoRA (Koyha with Runpod) This guide will cover training an SDXL LoRA. In the following code snippet from lora_gui. class_prompt, class_num=args. 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. 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. Closed. • 4 mo. cuda. py, but it also supports DreamBooth dataset. 9of9 Valentine Kozin guest. Stay subscribed for all. Install dependencies that we need to run the training. xiankgx opened this issue on Aug 10 · 3 comments · Fixed by #4632. The LoRA loading function was generating slightly faulty results yesterday, according to my test. ; We only need a few images of the subject we want to train (5 or 10 are usually enough). md","path":"examples/dreambooth/README. py. What's the difference between them? i also see there's a train_dreambooth_lora_sdxl. I asked fine tuned model to generate my image as a cartoon. dev441」が公開されてその問題は解決したようです。. I couldn't even get my machine with the 1070 8Gb to even load SDXL (suspect the 16gb of vram was hamstringing it). My results have been hit-and-miss. 0. If you've ev. 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. The service departs Melbourne at 08:05 in the morning, which arrives into. Finetune a Stable Diffusion model with LoRA. More things will come in the future. It would be neat to extend the SDXL dreambooth Lora script with an example of how to train the refiner. py, specify the name of the module to be trained in the --network_module option. Resources:AutoTrain Advanced - Training Colab -. 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. Reload to refresh your session. I do prefer to train LORA using Kohya in the end but the there’s less feedback. 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. 5 checkpoints are still much better atm imo. This is the written part of the tutorial that describes my process of creating DreamBooth models and their further extractions into LORA and LyCORIS models. The DreamBooth API described below still works, but you can achieve better results at a higher resolution using SDXL. 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. Most of the times I just get black squares as preview images, and the loss goes to nan after some 20 epochs 130 steps. Low-Rank Adaptation of Large Language Models (LoRA) is a training method that accelerates the training of large models while consuming less memory. 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. I have a 8gb 3070 graphics card and a bit over a week ago was able to use LORA to train a model on my graphics card,. The thing is that maybe is true we can train with Dreambooth in SDXL, yes. This prompt is used for generating "class images" for. 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. With the new update, Dreambooth extension is unable to train LoRA extended models. Lets say you want to train on dog and cat pictures, that would normally require you to split the training. Fine-tuning Stable Diffusion XL with DreamBooth and LoRA on a free-tier Colab Notebook 🧨. DreamBooth with Stable Diffusion V2. 以前も記事書きましたが、Attentionとは. You can disable this in Notebook settingsSDXL 1. If you want to use a model from the HF Hub instead, specify the model URL and token. 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. 12:53 How to use SDXL LoRA models with Automatic1111 Web UI. People are training with too many images on very low learning rates and are still getting shit results. In the meantime, I'll share my workaround. DreamBooth is a method by Google AI that has been notably implemented into models like Stable Diffusion. 0. Conclusion. Additionally, I demonstrate my months of work on the realism workflow, which enables you to produce studio-quality images of yourself through #Dreambooth training. Enter the following activate the virtual environment: source venvinactivate. sdx_train. The training is based on image-caption pairs datasets using SDXL 1. 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. The options are almost the same as cache_latents. I have just used the script a couple days ago without problem. x models. I get great results when using the output . Hi can we do masked training for LORA & Dreambooth training?. Settings used in Jar Jar Binks LoRA training. payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"dev","path":"dev","contentType":"directory"},{"name":"drive","path":"drive","contentType. This is the ultimate LORA step-by-step training guide,. DreamBooth is a method to personalize text2image models like stable diffusion given just a few (3~5) images of a subject. Download and Initialize Kohya. You signed out in another tab or window. 💡 Note: For now, we only allow. ai – Pixel art style LoRA. py (because the target image and the regularization image are divided into different batches instead of the same batch). The train_dreambooth_lora. Train LoRAs for subject/style images 2. 4. Head over to the following Github repository and download the train_dreambooth. Yae Miko. How to Fine-tune SDXL 0. When we resume the checkpoint, we load back the unet lora weights. py, but it also supports DreamBooth dataset. You switched accounts on another tab or window. Select LoRA, and LoRA extended. </li> </ul> <h3. . 我们可以在 ControlLoRA 之前注入预训练的 LoRA 模型。 有关详细信息，请参阅“mix_lora_and_control_lora. 256/1 or 128/1, I dont know). py scripts. md","path":"examples/text_to_image/README. How to Do SDXL Training For FREE with Kohya LoRA - Kaggle - NO GPU Required - Pwns Google Colab. 6 or 2. Each version is a different LoRA, there are no Trigger words as this is not using Dreambooth. Let's create our own SDXL LoRA! I have the similar setup with 32gb system with 12gb 3080ti that was taking 24+ hours for around 3000 steps. train_dreambooth_lora_sdxl. From my experience, bmaltais implementation is. Our experiments are based on this repository and are inspired by this blog post from Hugging Face. 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. If you were to instruct the SD model, "Actually, Brad Pitt's. Open comment sort options. e train_dreambooth_sdxl. game character bnha, wearing a red shirt, riding a donkey. Generating samples during training seems to consume massive amounts of VRam. Dimboola to Ballarat train times. io So so smth similar to that notion. How to train LoRA on SDXL; This is a long one, so use the table of contents to navigate! Table Of Contents . I have only tested it a bit,. hopefully i will make an awesome tutorial for best settings of LoRA when i figure them out. In train_network. fit(train_dataset, epochs=epoch s, callbacks=[ckpt_callback]) Experiments and inference. The batch size determines how many images the model processes simultaneously. py file to your working directory. The whole process may take from 15 min to 2 hours. It'll still say XXXX/2020 while training, but when it hits 2020 it'll start. Tried to train on 14 images. sdxl_train. File "E:DreamboothTrainingstable-diffusion-webuiextensionssd_dreambooth_extensiondreambooth rain_dreambooth. 10'000 steps under 15 minutes. Use the square-root of your typical Dimensions and Alphas for Network and Convolution. 1. SDXL output SD 1. Prodigy also can be used for SDXL LoRA training and LyCORIS training, and I read that it has good success rate at it. I want to train the models with my own images and have an api to access the newly generated images. ## Running locally with PyTorch ### Installing. ) Automatic1111 Web UI - PC - Free. We would like to show you a description here but the site won’t allow us. Of course they are, they are doing it wrong. center_crop, encoder. You can try replacing the 3rd model with whatever you used as a base model in your training. ", )Achieve higher levels of image fidelity for tricky subjects, by creating custom trained image models via SD Dreambooth. Dreambooth is the best training method for Stable Diffusion. Available at HF and Civitai. py at main · huggingface/diffusers · GitHub. I'm capping my VRAM when I'm finetuning at 1024 with batch size 2-4 and I have 24gb. Making models to train from (like, a dreambooth for the style of a series, then train the characters from that dreambooth). Last year, DreamBooth was released. In this video, I'll show you how to train amazing dreambooth models with the newly released SDXL 1. Our training examples use Stable Diffusion 1. Here is my launch script: accelerate launch --mixed_precision="fp16" train_dreambooth_lora_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). py”。 portrait of male HighCWu ControlLoRA 使用Canny边缘控制的模式 . Taking Diffusers Beyond Images. instance_prompt, class_data_root=args. Just training. Share Sort by: Best. Just an FYI. For example, we fine-tuned SDXL on images from the Barbie movie and our colleague Zeke. dev0")This will only work if you have enough compute credits or a Colab Pro subscription. Fortunately, Hugging Face provides a train_dreambooth_lora_sdxl. This article discusses how to use the latest LoRA loader from the Diffusers package. Its APIs can change in future. DreamBooth is a method by Google AI that has been notably implemented into models like Stable Diffusion. 5. Follow the setting below under LoRA > Tools > Deprecated > Dreambooth/LoRA Folder preparation and press “Prepare. Access the notebook here => fast+DreamBooth colab. Install Python 3. $25. py, when will there be a pure dreambooth version of sdxl? i. Beware random updates will often break it, often not through the extension maker’s fault. Open the terminal and dive into the folder using the. So far, I've completely stopped using dreambooth as it wouldn't produce the desired results. ; latent-consistency/lcm-lora-sdv1-5. 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. Because there are two text encoders with SDXL, the results may not be predictable. BLIP Captioning. Comfy is better at automating workflow, but not at anything else. Use "add diff". . dim() to be true, but got false (see below) Reproduction Run the tutorial at ex. residentchiefnz. ipynb and kohya-LoRA-dreambooth. It uses successively the following functions load_model_hook, load_lora_into_unet and load_attn_procs. 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. It allows the model to generate contextualized images of the subject in different scenes, poses, and views. That makes it easier to troubleshoot later to get everything working on a different model. sdxl_train. Update, August 2023: We've added fine-tuning support to SDXL, the latest version of Stable Diffusion. ) Automatic1111 Web UI - PC - Free 8 GB LoRA Training - Fix CUDA & xformers For DreamBooth and Textual Inversion in Automatic1111 SD UI. Just to show a small sample on how powerful this is. The Notebook is currently setup for A100 using Batch 30. 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. The usage is almost the. And later down: CUDA out of memory. py' and sdxl_train. safetensors")? Also, is such LoRa from dreambooth supposed to work in ComfyUI?Describe the bug. /loras", weight_name="Theovercomer8. Share and showcase results, tips, resources, ideas, and more. Das ganze machen wir mit Hilfe von Dreambooth und Koh. 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. sdxl_train_network. Name the output with -inpaint. My favorite is 100-200 images with 4 or 2 repeats with various pose and angles. (Cmd BAT / SH + PY on GitHub) 1 / 5. 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. Hello, I am getting much better results using the --train_text_encoder flag with the Dreambooth script. The options are almost the same as cache_latents. Describe the bug When resume training from a middle lora checkpoint, it stops update the model( i. This blog introduces three methods for finetuning SD model with only 5-10 images. 0 (UPDATED) 1. sd-diffusiondb-canny-model-control-lora, on 100 openpose pictures, 30k training. and it works extremely well. Generated by Finetuned SDXL. Where did you get the train_dreambooth_lora_sdxl. py and train_lora_dreambooth. We will use Kaggle free notebook to do Kohya S. Kohya SS is FAST. 9 via LoRA. 5 where you're gonna get like a 70mb Lora. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"dev","path":"dev","contentType":"directory"},{"name":"drive","path":"drive","contentType. kohya_ss supports training for LoRA, Textual Inversion but this guide will just focus on the Dreambooth method. Highly recommend downgrading to xformers 14 to reduce black outputs. Top 8% Rank by size. 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. After I trained LoRA model, I have the following in the output folder and checkpoint subfolder: How to convert them into safetensors. LoRA is a type of performance-efficient fine-tuning, or PEFT, that is much cheaper to accomplish than full model fine-tuning. . This yes, is a large and strong opinionated YELL from me - you'll get a 100mb lora, unlike SD 1. I have recently added the dreambooth extension onto A1111, but when I try, you guessed it, CUDA out of memory. DreamBooth is a method to personalize text2image models like stable diffusion given just a few (3~5) images of a subject. py --pretrained_model_name_or_path= $MODEL_NAME --instance_data_dir= $INSTANCE_DIR --output_dir=. How To Do Stable Diffusion LORA Training By Using Web UI On Different Models - Tested SD 1. I suspect that the text encoder's weights are still not saved properly. This method should be preferred for training models with multiple subjects and styles. I run it following their docs and the sample validation images look great but I’m struggling to use it outside of the diffusers code. {"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":"dev","path":"dev","contentType":"directory"},{"name":"drive","path":"drive","contentType. We recommend DreamBooth for generating images of people. Resources:AutoTrain Advanced - Training Colab - LoRA Dreambooth. py. Without any quality compromise. Training Folder Preparation. In this video, I'll show you how to train LORA SDXL 1. SDXL LoRA training, cannot resume from checkpoint #4566. 5 and. Train 1'200 steps under 3 minutes. This tutorial covers vanilla text-to-image fine-tuning using LoRA. For instance, if you have 10 training images. Now. github. You signed out in another tab or window. So with a consumer grade GPU we can already train a LORA in less than 25 seconds with so-so quality similar to theirs. Dreambooth LoRA training is a method for training large language models (LLMs) to generate images from text descriptions. 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. 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 can take a dozen or so images of the same item and get SD to "learn" what it is. Trains run twice a week between Dimboola and Ballarat.