Fast LoRA Training for Flux via Fal API
API
Flux
LoRa Training
1
164
FLUX is great at generating images, but locking in a specific aesthetic or character is easier with a LoRA. Here's how to create your own.
Key Inputs
Load Images from Folder: Paste path to folder containing your image dataset, if you uploaded or are accessing it from your Floyo inputs folder, the input path would look like "input/#input/your_dataset_folder_name"
Flux LoRa Trainer - Trigger Word: Add a trigger word that you will use in your prompts when applying the LoRa in image generation
Quick start recipe
Upload a Zip file of curated images + captions.
Enable is_style and include a unique trigger phrase.
Compare checkpoints with a range of steps to find the sweet spot.
Setting up your Fal.ai API key on Floyo
Sign up for a Fal.ai account
Go to Usage & Billing and add funds to your account
Create an API key, copy it and keep it safe
Run workflow on Floyo
On top-left corner, click on the team drop-down menu and select "Settings"
Select "API Connect" and paste your API key from earlier in the Fal.ai field and save!
Prepping your dataset
Fewer, ultra‑high‑res (≈1024×1024+) images beat many low‑quality ones.
Every image must clearly represent the style or individual and be artifact‑free.
For people, aim for at least 10-20 images in different background (5 headshots, 5 wholebody, 5 halfbody, 5 in other scene)
Captioning
Give the style a unique trigger phrase (so as not be confused with a regular word or term).
For better prompt control, add custom captions that describe content only—leave style cues to the trigger phrase. Create accompanying .txt files with the same name as the image its describing.
If you do add custom captions, be sure to turn on is_style to skip auto‑captioning. It is set to off by default.
Training steps
The default is set to around 2000, but you can train multiple checkpoints (e.g., 500, 1000, 1500, 2000) and pick the one that balances style fidelity with prompt responsiveness.
Too few steps: the character becomes less realistic or the style fades.
Too many steps: model overfits and stops obeying prompts.
Output Path
Will be a URL in the Preview Text Node.
Read more
Nodes & Models
FLUX is great at generating images, but locking in a specific aesthetic or character is easier with a LoRA. Here's how to create your own.
Key Inputs
Load Images from Folder: Paste path to folder containing your image dataset, if you uploaded or are accessing it from your Floyo inputs folder, the input path would look like "input/#input/your_dataset_folder_name"
Flux LoRa Trainer - Trigger Word: Add a trigger word that you will use in your prompts when applying the LoRa in image generation
Quick start recipe
Upload a Zip file of curated images + captions.
Enable is_style and include a unique trigger phrase.
Compare checkpoints with a range of steps to find the sweet spot.
Setting up your Fal.ai API key on Floyo
Sign up for a Fal.ai account
Go to Usage & Billing and add funds to your account
Create an API key, copy it and keep it safe
Run workflow on Floyo
On top-left corner, click on the team drop-down menu and select "Settings"
Select "API Connect" and paste your API key from earlier in the Fal.ai field and save!
Prepping your dataset
Fewer, ultra‑high‑res (≈1024×1024+) images beat many low‑quality ones.
Every image must clearly represent the style or individual and be artifact‑free.
For people, aim for at least 10-20 images in different background (5 headshots, 5 wholebody, 5 halfbody, 5 in other scene)
Captioning
Give the style a unique trigger phrase (so as not be confused with a regular word or term).
For better prompt control, add custom captions that describe content only—leave style cues to the trigger phrase. Create accompanying .txt files with the same name as the image its describing.
If you do add custom captions, be sure to turn on is_style to skip auto‑captioning. It is set to off by default.
Training steps
The default is set to around 2000, but you can train multiple checkpoints (e.g., 500, 1000, 1500, 2000) and pick the one that balances style fidelity with prompt responsiveness.
Too few steps: the character becomes less realistic or the style fades.
Too many steps: model overfits and stops obeying prompts.
Output Path
Will be a URL in the Preview Text Node.
Read more