Current State (As-Is)
Civitai offers an LoRA trainer for Stable Diffusion where users can upload images and train LoRA models using the platform’s resources. The training process consumes a site currency called "Buzz," which users spend to start and run training jobs.
Users can upload images and configure training settings before starting.
Once the training begins, there is no way to cancel or stop the process.
Training runs for the full number of specified epochs, consuming the full Buzz amount, even if the user notices early on that the LoRA is already performing well or if an issue arises.
Previews and downloads are available per epoch, allowing users to check progress.
Ideal State (To-Be)
A more flexible system should allow users to stop training when necessary. This would:
Prevent wasted resources for both users (Buzz) and Civitai (computational power).
Allow users to correct errors early, such as wrong tags, incorrect captions, or poor image selection.
Give users more control over their training, leading to better results and a more efficient workflow.
Improve user satisfaction, as they would not feel forced to complete a training session they already know is flawed.
Suggested Improvements
To make the LoRA training process more efficient and user-friendly, Civitai should implement the following features:
1. Cancellation Option with Partial Refund
Users should be able to cancel a training session within the first few epochs.
Partial refund of Buzz should be provided based on the number of epochs completed.
This prevents unnecessary loss when users realize their training has an issue.
2. Adaptive Buzz Usage
Instead of charging the full Buzz amount upfront, Civitai could implement incremental charging.
Buzz would be deducted per completed epoch, allowing users to stop early without wasting their full budget.
Why This Matters
Without an abort feature, the current system forces users to waste Buzz and computational resources, even when it is clear early on that a training session is flawed. This discourages users from experimenting and optimizing their models, leading to frustration and inefficiency.
Additionally, the current system results in unnecessary waste:
Users are forced to spend more Buzz than needed, even if the ideal LoRA model is reached early.
Civitai's server capacity is wasted on computations that are no longer needed.
Failed training sessions consume resources without producing useful results.
Implementing cancel and adaptive Buzz usage features would make Civitai’s LoRA training more user-friendly and prevent unnecessary waste, benefiting both the platform and its users.
Call to Action
Civitai should consider integrating these improvements to create a more efficient, flexible, and cost-effective training environment. This will not only enhance user satisfaction but also optimize resource usage on their platform.
What do you think? Does this make sense? Let me know in the comments if you have additional ideas or suggestions!