How To train A LoRa File In Civitai

Digital art
3 Feb 202413:11

TLDRIn this video, the creator walks through the process of training a LoRa (Low-Rank Adaptation) file on CivitAI. The process involves earning or acquiring Buzz points, which are needed to train a model. The video covers key steps like selecting a style, uploading images, adjusting settings such as epochs and batch size, and experimenting with different outputs. The creator highlights the trial-and-error nature of training, explaining how results vary across different epochs and models. Viewers are encouraged to experiment with prompts and model settings to achieve desired results.

Takeaways

  • 🆓 Civitai offers the option to train a LoRa file for free if you have at least 500 Buzz points.
  • 📊 Buzz points can be earned through ratings, tips, or purchases, and are used to train models.
  • 🎨 You can choose from different options like character, style, or concept when training a LoRa file. The user opted for style.
  • 🖼️ Manually tagging images and adding a trigger word is essential for proper training.
  • ⚙️ The user adjusted advanced training settings like epochs, repeats, and batch size to experiment with the results.
  • 📈 Epoch and training steps significantly affect the quality and final look of the model. More epochs often produce better detail.
  • 👥 Different models, like Darth Vader and Goku, were tested to see how well the trained LoRa file could replicate specific styles.
  • 🧪 Experimentation is key. The user tried different epoch settings to achieve the desired result and learned through trial and error.
  • 👎 Sometimes, models can look overfitted or too sharp, requiring adjustments for a more natural look.
  • 🤖 The user used Fusion B for testing the LoRa files but noted that it currently doesn't allow direct extraction of LoRa models.

Q & A

  • What is the main topic of the video?

    -The video discusses how to train a LoRa (Low-Rank Adaptation) file using CivitAI, including tips on adjustments during the process.

  • What are 'buzz points' in CivitAI?

    -'Buzz points' are a form of currency on CivitAI. Users can earn or purchase them, and they are required to train a LoRa model.

  • What is the minimum number of buzz points needed to train a LoRa model?

    -You need at least 500 buzz points to train a LoRa model in CivitAI.

  • What type of LoRa file does the creator aim to make?

    -The creator aims to make a 'style' LoRa file, focusing on capturing a specific visual style.

  • What are some of the key training settings that can be adjusted?

    -Key settings include the number of epochs, repeats, batch size, steps, resolution, and the LoRa model version (such as standard 1.5 or XL).

  • At which epoch did the creator find the trained model started to achieve the desired look?

    -The creator observed that the desired look started to emerge around epoch 9, with the best results at epoch 16.

  • What does the creator mean by 'overfitting' in the context of LoRa training?

    -'Overfitting' refers to a model becoming too sharp or detailed in a way that it starts to lose its generalization, resulting in images that appear overly processed, with an HDR-like effect.

  • What tool does the creator use for testing and comparison of different epochs?

    -The creator uses 'Diffusion B' to test and compare the results of different epochs to see how the generated images evolve.

  • What challenge did the creator face when replicating the look of certain characters like Bob Marley?

    -The creator struggled to replicate the original look of Bob Marley using the LoRa model, as the generated images became progressively darker and lost detail in the face.

  • What advice does the creator give for users whose models are not working as expected?

    -The creator suggests that users should improve their prompts and experiment with different settings, as a bad prompt can result in unsatisfactory outputs. They also recommend using negative prompts to refine the results.

Outlines

00:00

🎨 Introduction to Training with Civet AI

The creator introduces the video, inviting viewers to like and subscribe. The video covers their attempt to create a LoRA file using Civet AI. They explain that while training a model on Civet AI can be free, users need at least 500 buzz points, which can be earned through ratings or purchased. They outline the steps they'll take to train a model, including clicking the buzz dashboard, discussing a past attempt, and emphasizing that the longest part of the process is writing captions. They walk through selecting training options, such as character, style, or concept, and choosing images for their model.

05:01

⚙️ Training Settings and Customization

The video continues with the creator explaining their choices for model settings, such as sticking with the 'Standard 1.5' model and leaving most settings at default. They experiment with the number of epochs, increasing repeats to 16 and adjusting settings for batch size, steps, and resolution. After configuring the settings, they submit the model for training, discussing how adjustments influence the number of steps and other factors like the optimizer and learning rate. Once submitted, they wait for the training to complete and review the preview results in the next phase.

10:02

🔍 Reviewing Model Results and Testing

In this section, the creator reviews the training results, showing examples of various epochs and comparing the results from different stages. They highlight the importance of experimentation, noting that some images look better at different stages of the process, such as epochs 9 and 16. The creator talks about importing the trained model into Fusion B and using it for further testing, explaining the challenges of overfitting and experimenting with different characters and settings. They also touch on responding to user feedback, emphasizing the importance of proper prompting for better results.

👗 LoRA Model Enhancements and Experimentation

The creator dives deeper into the experimentation process, explaining how LoRA models can enhance various aspects of image generation, such as clothing, hair, and facial details. They explain how certain tools, like 1111, offer features to improve facial accuracy. Moving on to specific model tests, the creator revisits their attempts with the 'dark glow' model and shows different epochs of testing, demonstrating how the look evolved. They compare early and later results, commenting on how changes at different stages affected the quality and details of the images.

💡 Advanced Epoch Experiments

In this paragraph, the creator shares insights from additional model experiments, particularly testing with different characters like Mike Tyson and Bob Marley. They discuss how training results vary depending on the seed number, settings, and the number of epochs. The creator notes that, in some cases, they struggled to replicate the desired look, especially when experimenting with darker tones and losing detail in the face. Despite challenges, they remain optimistic and encourage viewers not to be discouraged if their results don’t come out as expected initially, urging them to continue experimenting.

🔄 Final Thoughts and Viewer Engagement

The creator wraps up the video by reflecting on their various experiments with different models and epochs. They express a preference for epochs 9, 10, 12, and 16 in their testing and discuss the importance of continued trial and error in generating desired results. They encourage viewers to share their own experiences and results, mentioning that while they enjoy creating traditional and cartoon-style art, they are currently retraining one of their models for better outcomes. They also touch on handling NSFW content by using negative prompts. The video concludes with a call to action for viewers to engage with the content and share their results.

Mindmap

Keywords

💡LoRA file

A LoRA file refers to 'Low-Rank Adaptation', a method used to fine-tune AI models by training only a small subset of parameters, making it more efficient. In the video, the creator is explaining the process of making a LoRA file to improve or create unique art styles using the CivitAI platform.

💡CivitAI

CivitAI is a platform where users can train AI models to create specific artistic styles or effects. In the video, the speaker uses CivitAI to train their model, mentioning its features like using buzz points for training and choosing different model types like 'Standard 1.5' or 'XL'.

💡Buzz points

Buzz points are a form of in-platform currency on CivitAI, which can be earned by receiving positive feedback or purchased. These points are needed to train models on the platform, with the speaker mentioning that they have enough to start the training process.

💡Epoch

Epoch refers to a complete cycle through the training data during model training. In the video, the speaker talks about adjusting the number of epochs, which helps refine the model. They note that the visual quality of their generated images improved after reaching higher epoch counts, especially from epoch 9 onward.

💡Training settings

Training settings in this context refer to the parameters the user can adjust during model training, such as the number of epochs, batch size, and learning rate. The speaker experiments with these settings to find the best results for their LoRA file.

💡Standard 1.5 model

The 'Standard 1.5 model' is a version of the AI model used in CivitAI for training. The speaker chooses this version over the XL model due to familiarity and ease of use, intending to test how well it works before potentially switching to another model.

💡Advanced Training settings

Advanced training settings allow further customization during model training, such as adjusting resolution, repeats, and steps. In the video, the speaker explores these options to fine-tune the visual output of their model.

💡Prompting

Prompting involves giving the AI specific instructions or keywords to generate desired outputs. The speaker discusses how improper prompting can lead to poor results, emphasizing that improving prompts can significantly enhance the final images generated by the model.

💡Overfitting

Overfitting occurs when a model becomes too specific to the training data, resulting in unrealistic or overly stylized outputs. The speaker encounters overfitting in some of their tests, where the images become too sharp and unnatural due to excessive training.

💡Diffusion B

Diffusion B is another AI tool mentioned in the video, used by the speaker for model training. The speaker compares results between models trained in Diffusion B and CivitAI, aiming to replicate a specific 'dark glow' style but finding that the results vary based on the tool and settings used.

Highlights

Introduction to training a LoRa file using CivitAI and the importance of buzz points.

The need for at least 500 buzz points to train a model on CivitAI.

Explanation of how to access the buzz dashboard and the rewards system.

Choosing to train a style-based model and using a trigger word for tagging images.

Options to select between Standard 1.5 and XL models for training.

Adjusting advanced training settings like epoch, repeats, and batch size.

The process of submitting the model for training and waiting for results.

Reviewing the generated images in the preview window and comparing different epochs.

Discussion of how epoch adjustments affect image quality and overfitting.

Testing the model in Fusion B to replicate the original look of the dataset.

Insights on how prompting and additional Lora files influence final image quality.

Challenges encountered when trying to replicate certain looks, such as darker face details.

Tips on experimenting with different settings to achieve the desired result.

Personal experiences with training models using famous figures like Mike Tyson and Bob Marley.

Conclusion on the importance of experimentation, prompting, and epoch selection in LoRa training.