Fine-tune SDXL Model
Welcome to our guide for fine-tuning SDXL! Here you'll find comprehensive walkthroughs that'll assist you with launching, tracking, and understanding the billing process related to fine-tuning SDXL on the MonsterAPI Platform.
Demo Colab Notebooks with Python Client
| Service Name | Colab Notebook |
|---|---|
| SDXL Finetuning |  |
Below, you'll find a list of topics with essential information about each step of the process.
This guide walks you through the process of initiating a fine-tuning job for SDXL. From opening the fine-tuning portal and creating a new job, to choosing a model and dataset selection, and finally submitting your job, this guide covers all steps in detail.
Users can provide their dataset in one of the following ways:
- Option 1 - S3 Presigned Link
Provide a secure pre-signed URL to grant access to your dataset stored in an S3 bucket. - Option 2 - Upload a Custom Dataset
Upload a custom dataset of your choice, packaged as a zip file. - Option 3 - Manual Bucket Access
For hands-on management, provide AWS Access and Secret Keys, along with specific details such as the S3 Bucket Name and Object Key.
Monitor the progress of your fine-tuning job from launching to completion. Understand the different stages and download your fine-tuned model once the job is complete.
Learn about the cost associated with fine-tuning jobs, including how jobs are billed, the cost per minute, and what happens if your account runs out of credits. Guidance on maintaining an active payment method and subscription plan to avoid job termination is also provided.
To start fine-tuning SDXL, click here.
Visit each page for detailed information on each process. If you have any questions or need further assistance, feel free to reach out to our support team.
For any queries, don't hesitate to contact MonsterAPI Support.
Updated about 1 month ago