Last Updated	Changes
9/18/2023	Updates to Training Data section
3/7/2023	LoRA Trainer Updates

What is the Civitai LoRA Trainer?

LoRA model training is a complicated business! There’s the required scripts, the dependencies; Python, Torch 2.0, all the complex terminology, and once you’ve got to grips with that, there’s the hefty hardware requirements! Training Stable Diffusion 1.5 models can be accomplished with a relatively low amount of VRAM (Video Card Memory), but for SDXL training you’ll need more than most people can supply!

We’ve sidestepped all of these issues by creating a web-based LoRA trainer!

We want LoRA training to be as accessible as possible, so we’ve made the trainer as simple as we could for new users who may never have trained anything, yet also included advanced options for experienced users.

The Trainer is still in a Beta phase, there are a lot of changes to be made, and features to be added, but the core components and functionality are there, and we’re excited to let you all loose!

This Guide will be updated and expanded over time!

Who has Access, & what’s the cost?

The LoRA Trainer is open to all users, and costs a base 500 Buzz for either an SDXL or SD 1.5 Model. Note that LoRA training jobs with very high Epochs and Repeats will require more Buzz, on a sliding scale, but for 90% of training the cost will be 500 Buzz!

How do we access it?

To access the trainer, click on the blue Create icon at the top right of the screen, as if you were going to upload new Content – you’ll see an option to Train a model.

Step 1 – Choosing a model type

From here, the Training Wizard begins, starting with the initial page – the Model Type Selection;

Choosing a model type pre-sets some of the advanced training settings. The model types provide an easy way to set up the configuration for people who’re new to training, but experienced LoRA trainers can tweak these settings before training starts.

Give your model a Name, then click Next to move onto the second step.

Step 2 – Adding and Captioning training data

Now we have to provide our images on which we want to train! It’s as simple as dragging and dropping a zip file of images and corresponding caption.txt files, or even just loose images, into the upload box.

Filename Convention for Zip Files

If you elect to upload an existing dataset, we ask that you provide the data in a zipped file (.zip extension). The file may include only images, or it may include images and text files which include captions, if you’d like to pre-caption your images.

A directory should contain an image file with a name that matches the caption text file. These will be interpreted as a pair in training.

Image + Caption Pairs	Image File Name	Caption Text File Name
Pair One	1.png	1.txt
Pair Two	2.png	2.txt

Windows users can select the images and text files (or images if intending to use the Auto-Captioning feature), right click, and select “Compress to ZIP file” to prepare a dataset for upload.

When you zip this data up, you should only select your image/caption file pairs. Do not include the parent folder when preparing the zip file.

Accepted File Extensions

For images, we accept any of the following image file extensions: .png, .jpg, .jpeg, .webp

For caption files, we only accept .txt file extensions.

Captioning & Managing Image Captions

You’ll then have the ability to edit your captions manually, or use our Auto-tagging/Captioning feature.

Captions are an important component of training a successful model. You can train models without them (see examples below); however, your model will often be more flexible and better overall when you do provide them. To train captionless, leave the boxes below each image blank.

Auto Tagging, (or Auto-Captioning) is performed via the blue Auto Tag button above the Training Data images. This feature can be an invaluable time-saver when you have many images and don’t want to manually enter captions for every image!

The system utilizes WD Tagger 1.4 to “look” at your images, and provide comma separated descriptions of the content. In future, we may offer different Captioning models. Clicking the Auto Tag button will open a window, with the following options;

After captioning is completed (either manually or via the Auto-tagger) there are options to review the captions and make tweaks, as necessary;

Selecting a caption will show all the images to which it is applied, and also provide options for removing, or replacing the caption with a different value, via the Actions button;

Data Ownership & Dataset Sharing

The last step on this page is to confirm that you own the rights to the uploaded images, and glance over our dataset storage policy.

Checking the box for “I want to share my dataset” will make the uploaded images available on your auto-generated Model Card, should you wish to directly publish your new LoRA to Civitai.

Click Next to continue.

Step 3 – Review settings and Submit

The final configuration step requires the selection of a Base Model. At this time we’ve pre-set the specific base models used, but we do allow training against a standard SD 1.5 base, an Anime SD 1.5 model base, Semi-Realistic SD 1.5, Realistic SD 1.5 base, Standard SDXL base, and the Pony Diffusion V6 XL base model.

In future, we plan allow training against a base model of your choice – it’s something we’re looking into.

Advanced users, or anyone who loves the thrill of adventure into the unknown, can expand the Training Parameters under Advanced Settings and go wild!

We have limited some of the settings – you won’t be able to train with every single option currently available in the Kohya scripts, but we’re regularly adding in new options and values.

Under Advanced Settings there’s also an option to supply Sample Image Prompts. By default, the sample images for each Epoch are generated from the captions supplied during Step 2, but if you’d like to supply your own prompts to get a better idea of how the LoRA training is progressing, three fields are provided for custom prompts (three images per Epoch).

Click Submit to begin training!

Step 4 – Waiting

Now for the hard part – waiting while it bakes! After clicking Submit you’ll be taken to your Account page, to a new Training tab, and a pane showing all the LoRAs you’ve trained, or are currently training, on the site. There’s a Status field, and a pretty accurate ETA.

Any issues with the uploaded training images will be highlighted under “Missing Info”.

There’s no need to sit mashing F5 until the LoRA completes – you’ll receive an email when it’s ready to test, or, if you’d like, you can monitor the status in real-time, and see the Epochs appear as they’re available – just click on the LoRA name in the training tab.

Step 5 – Testing

Clicking the link in the email, or the LoRA Name from the training tab to be taken to your results – your Epochs will be listed, with sample images generated for each based on the Sample Image Prompts you supplied during step 3.

🚨 Note that you won’t receive every single Epoch requested! LoRA training uses huge volumes of storage space, and because we train hundreds of models each day, it’s just not practical for us to store them all. We will train to the Epoch specified in the Advanced Settings, but in the review screen, you’ll only be presented with a maximum of 20 Epochs to download.

You may download any of your Epochs to test at any time, but note that Epoch files from models which aren’t accepted (left in Ready status) will be deleted after 30 days.

And you’re done!

Training Job Data Retention

Input Files (Training Datasets) You may upload images and/or text files to be used for training your model. By default, this data will be persisted only until the training job is completed, unless you elect to share your dataset. Note that any captions provided to our system will later be available as metadata on the output model file.

Output Files (Sample Images and Model Files) The images and models that result from completion of training will be retained for 30 days after the completion of the training job.

Read the full Civitai Data Storage Policy here – https://civitai.com/content/training/data-policy

Examples

Below are some example images, trained using the on-site Trainer;

Empire Style – SDXL – Trained on 17 uncaptioned images of Victorian city streets

Big Stompy Robots – SDXL – 15 uncaptioned images of mecha/big robots

Paint Splash Style – SDXL – 30 captioned images

FAQ

• Can I stop training once it has started?
  No, once you pay the Buzz fee and training begins, it can’t be stopped.

• What happens if my training fails?
  We automatically retry each failed run 3 times before we mark the training session as truly “failed”. If your training fails completely, you’ll automatically be refunded the full buzz amount of the run.

• My training is stuck/failed and I didn’t receive a refund!
  If your training is still processing after 48 hours it should be automatically refunded, but if you don’t receive a Buzz refund, please reach out to a Community Manager via Discord or [email protected]

• Where can I learn more about captioning images for training?
  We have a full dataset captioning guide coming soon!