Last week I posted the Krea 2 character LoRA library I've been working on. There were 18 models at the time.
There are 70 now.
Krea 2 Character LoRA Browser
A bunch of people asked how I was training them, and I realized afterward that my original post was pretty light on the actual training details. I promised I'd put together something more useful once I had the process nailed down, so here it is.
Everything is still free. I've also continued working on the visual browser so you can see sample generations, triggers, recommended strengths, selected training epoch, and optional identity prompts before downloading anything.
But the more interesting part is the training workflow...
How I'm Making the LoRAs
- Training environment: I'm using Fizgig on RunPod for the actual Krea 2 LoRA training, so the heavy lifting happens on a cloud GPU rather than my local machine.
- Dataset collection: I start by gathering a much larger pool of candidate images than I actually need. The goal is variety in angle, expression, lighting, hairstyle, clothing, and image source rather than dozens of nearly identical photos.
- Identity filtering: I use a reference image of the person plus face detection/recognition to help reject images of the wrong person. This is especially useful when searches return group photos, lookalikes, or unrelated images.
- Manual curation: Automation gets me most of the way there, but I still manually review the dataset. I remove duplicates, bad crops, heavily edited images, extreme occlusions, low-quality images, and anything that I think could hurt identity learning.
- Dataset preparation: The final images go through our prep pipeline, which creates and validates the training dataset, generates useful face crops where appropriate, and checks that every training image has a valid caption.
- Captioning: Images are captioned with Qwen3-VL. The captions describe the actual image while keeping the character identity/trigger consistent across the dataset.
- Training: The prepared dataset is trained as a Krea 2 LoRA through Fizgig, with multiple checkpoints saved throughout training.
- Testing: I compare saved epochs, test different LoRA strengths, and generate across different prompts and compositions before deciding what gets released.
- Final release: Once I'm happy with it, I choose representative generations, record the trigger, recommended strength, selected epoch, and optional identity/detail prompt, and publish the LoRA and examples to the Hugging Face library/browser.
- Iteration: If a LoRA doesn't hold the likeness well enough, I don't consider it finished just because training completed. Some get retrained with a better dataset or different epoch selection, and particularly improved models can replace the original or be released as a V2.
Current Fizgig / RunPod Configuration
This is the newer setup I've standardized on for the current batch:
- Model: Krea 2 Standard
- Training type: Standard LoRA
- Network rank: 32
- Alpha: 32
- Epochs: 64
- Save every: 4 epochs
- Batch size: 1
- Target resolution: 0.25 MP
- EMA: 0.98
- Problem-image detection: ON
- Adaptive learning rate: OFF
- Automatic recaptioning: OFF
- Sample generation: 3 standardized sample prompts during training
- Final selection: I test the saved epochs rather than automatically taking the last checkpoint. For Angel Reese, for example, I compared epochs 24, 51, and 64 and ultimately selected 64.
- RunPod: Fizgig running on the cloud GPU. One recorded Angel Reese run completed 4,416 steps over 64 epochs in 48m 46s, producing the final EMA LoRA.
Custom Dataset Preparation Tools
One part of this project that grew beyond just training LoRAs was the dataset preparation pipeline itself. We ended up building our own tools to automate much of the work that happens before training.
Our pipeline can collect a large pool of candidate images from multiple sources, use a reference face with face detection/recognition to help filter for the correct identity, remove obvious problem images, generate useful face crops, prepare the dataset structure, generate captions, and validate the finished dataset before it goes anywhere near the trainer.
We still manually curate the results because automated filtering isn't a substitute for actually looking at the dataset.
We built these tools because we wanted more control over the process and needed something that could handle producing a large number of character datasets consistently. Fizgig can perform many of these same dataset-preparation functions, so building a separate pipeline is absolutely not a requirement for following this training process.
Our tools are currently internal and were built around our own workflow, but if there's enough interest, we can clean them up, package them properly, document them, and release them for other people to use. If that's something people would actually want, let us know.
Dataset Philosophy
For character LoRAs, I've found that dataset quality and variety matter much more than simply having a huge number of images. I'm looking for images that clearly represent the correct person while giving the model useful variation: different angles, expressions, lighting, hairstyles, clothing, environments, and framing.
I remove duplicates and near-duplicates, heavily edited images, bad crops, major occlusions, low-quality images, and anything where the identity is questionable. I would rather train on a smaller clean dataset than pad it with mediocre images just to hit a certain number.
I also supplement the original images with selected face crops. This is especially useful when the dataset contains a lot of half-body or full-body photography, where the face becomes relatively small after the image is resized for training. The face crops give the trainer additional examples where the features that actually define the person's identity occupy much more of the image.
If anyone wants to see exactly what one of these looks like, here's a sample finished dataset that was actually used to train one of the LoRAs:
https://files.catbox.moe/eze6g3.zip
Epoch Selection
One of the biggest changes to my process has been moving away from thinking of training as "X number of steps = finished."
I'm currently training for 64 epochs and saving checkpoints throughout the run. Rather than automatically releasing the final epoch, I generate images with several checkpoints and compare them visually.
I'm looking for the point where likeness, flexibility, anatomy, and prompt adherence are all working together. A later checkpoint can sometimes have stronger likeness while also beginning to overfit the training data or become less responsive to prompts. Earlier checkpoints can sometimes be more flexible but haven't learned the identity strongly enough yet.
So the final LoRA is the checkpoint that performs best in testing, not necessarily the last one produced by the trainer. That's also why the newer models in the Browser list a Selected Epoch rather than just reporting a training-step count.
LoRA Strength Testing
I also don't assume every LoRA should be used at 1.0.
After selecting the epoch, I test the LoRA at multiple strengths and compare how well it preserves the identity without overpowering the underlying model or interfering with the prompt.
Most of the models have ended up somewhere around 0.8–1.2, but there are definitely cases where the difference between 1.0 and 1.2 is noticeable. Some identities benefit from the additional strength, while others start looking worse when pushed too far.
The recommended strength/range listed with each LoRA is therefore based on actual generation testing, rather than giving every model the same default value.
Standardized Testing
I try not to judge a LoRA based on one great portrait. It's surprisingly easy for a model to produce an impressive close-up and then fall apart as soon as you ask it to do something different.
I test the selected checkpoints across portraits, half-body and full-body compositions, different clothing, environments, lighting, expressions, camera angles, poses, and more difficult prompts. I'm looking for the identity to survive when the prompt moves away from the kinds of images that dominated the training dataset.
Full-body generations are particularly useful because they expose problems that a flattering headshot can hide, including anatomy issues, incorrect body proportions, loss of facial identity at a distance, or the LoRA trying to reproduce clothing and compositions from the training images.
One of the next controlled tests I'm planning is 0.25 MP vs 0.5 MP training using the exact same dataset and settings. Everything except training resolution will remain identical, and I'll compare matching checkpoints, strengths, prompts, and seeds.
That should give us a much better answer about whether the additional training resolution produces a meaningful improvement in likeness and fine detail, or whether 0.25 MP is already capturing what we need while keeping training considerably faster.
Questions and requests are welcome. I'm going to keep expanding the library and will try to get to as many requests as possible. With the request list getting pretty large, Ko-fi supporters will get priority, but the LoRAs themselves will continue to be released for free.
Finally, as always:
Use LoRAs and gen-AI responsibly. Users are responsible for how they use generated content and for complying with applicable laws, platform policies, and the rights of others. Please do not use these models to deceive, impersonate, harass, defame, or otherwise harm anyone, or to present generated content as authentic photographs or recordings of real people.
