How to fine-tune FLUX.1
Fine-tuning FLUX.1 on Replicate is a straightforward process that can be done either through the web interface or programmatically via the API. Let’s walk through both methods.
Prepare your training data
To start fine-tuning, you’ll need a collection of images that represent the concept you want to teach the model. These images should be diverse enough to cover different aspects of the concept. For example, if you’re fine-tuning on a specific character, include images in various settings, poses, and lighting. Here are some guidelines:
Use 12-20 images for best results
Use large images if possible
Use JPEG or PNG formats
(Optional) Create a corresponding .txt file for each image with the same name, containing the caption
Once you have your images (and optional captions), zip them up into a single file.
Start the training process
https://replicate.com/blog/fine-tune-flux
Fine-tuning FLUX.1 on Replicate is a straightforward process that can be done either through the web interface or programmatically via the API. Let’s walk through both methods.
Prepare your training data
To start fine-tuning, you’ll need a collection of images that represent the concept you want to teach the model. These images should be diverse enough to cover different aspects of the concept. For example, if you’re fine-tuning on a specific character, include images in various settings, poses, and lighting. Here are some guidelines:
Use 12-20 images for best results
Use large images if possible
Use JPEG or PNG formats
(Optional) Create a corresponding .txt file for each image with the same name, containing the caption
Once you have your images (and optional captions), zip them up into a single file.
Start the training process
https://replicate.com/blog/fine-tune-flux
Replicate
Fine-tune FLUX.1 with your own images
We've added fine-tuning (LoRA) support to FLUX.1 image generation models. You can train FLUX.1 on your own images with one line of code using Replicate's API.