Enter a unique name for your dataset
This identifier must be unique across all datasets in your configuration.
Regularisation datasets help preserve the base model's identity. You must also include at least one normal image or video dataset.
Add a primary image or video dataset first. Once one is queued, you can enable regularisation for supporting datasets.
Unchecked (default): Add datasets to your current environment's config.
Checked: Create a completely new config file (includes required text embed datasets).
Dataset Type
Image Dataset

For training with still images

Video Dataset

For training with video sequences Model not marked video-capable; continue with caution.

Audio Dataset

For training with audio/speech

Where is your dataset stored?
Configure required settings
Cache Dataset Options
Create separate dedicated datasets for caching embeddings. This allows you to reuse cached data across multiple training datasets.
Creates a dedicated text_embeds dataset that can be shared across multiple image datasets.
Creates a dedicated image_embeds dataset for VAE latent caching. Useful when training multiple models with the same images.
Configure Text Embeddings Cache
This dedicated dataset will store cached text embeddings that can be reused across multiple training sessions.
Unique identifier for this text embeddings cache dataset
Directory where text embeddings will be stored
Tip: All image datasets in your configuration will automatically use this shared text embeddings cache.
Safe to Share: Multiple datasets can safely use the same text embed cache directory. Common captions will be deduplicated, reducing disk space usage.
Configure VAE Latent Cache Backend
The image_embeds dataset provides a storage backend for VAE latents. Each image dataset can point to its own cache directory within this backend.
Unique identifier for this VAE cache backend dataset
Select Storage Backend
Local Storage

Store VAE cache on local filesystem

S3-Compatible

Store VAE cache in S3-compatible storage

S3-Compatible Storage Configuration
Name of the S3 bucket to store VAE cache
S3-compatible endpoint URL (e.g., AWS S3, Cloudflare R2, MinIO, etc.)
S3 access key (optional if using IAM role)
S3 secret key (optional if using IAM role)
Region name (optional, leave empty to use default)
Path where VAE latents will be stored for your image dataset. This will be stored in the image dataset config, not the image_embeds dataset.
S3 prefix where VAE latents will be stored for your image dataset. This will be stored in the image dataset config, not the image_embeds dataset.
Note: Multiple image datasets can share the same image_embeds backend by pointing to different .
Warning: Each dataset must use a unique VAE cache . Sharing the same cache location across datasets will cause data corruption.
Configure image resolution settings
pixel_area (recommended) maintains aspect ratios • pixel uses fixed edge length • area targets direct megapixels
Higher resolutions require more VRAM but produce better quality. 1024 is recommended for most GPUs.
Clamp before downsampling (match resolution type)
Secondary clamp for very large assets
Using default resolution: 1024px, pixel_area
Configure image cropping behavior
When disabled, images will only be resized without cropping
Choose where crops are taken from in the image
Random
Center
Corner
Face
Choose how aspect ratios are handled during cropping
Square
Preserve
Closest
Random
Define aspect ratio buckets (e.g., 0.75 = 3:4, 1.0 = 1:1, 1.33 = 4:3)
Configure caption settings
How captions should be loaded for each image
Parquet Configuration
Path to the parquet or JSONL metadata file
Column containing image IDs/filenames
Column containing image captions
Column with image width (speeds up loading)
Column with image height (speeds up loading)
Fallback column when primary caption is empty
Add a consistent trigger word or phrase to the beginning of all captions (e.g., "a photo of xyz")
Auto-generate conditioning data for ControlNet
ControlNet training detected! Would you like to automatically generate conditioning data during training?
Canny Parameters
Review Your Dataset Configuration
Backend Type:
Dataset Type:
Configuration:
Instance Data Path:
Metadata Backend:
Caption Strategy:
VAE Cache Dir:
Resolution:
Size:
Type:
Cropping:
Captions:
Strategy:
Additional Settings:
Probability:
Repeats:
Datasets in Queue ()
Select Dataset Directory

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