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simpletuner /krea2:7979288f

Input schema

The fields you can use to run this model with an API. If you don’t give a value for a field its default value will be used.

Field Type Default value Description
train_data
string
Zip or tar of training images. Matching .txt captions are used when present; missing captions use filename fallbacks.
hf_dataset
string
Hugging Face dataset repo ID to use when no archive is provided (e.g., user/dataset).
pixel_area
integer
1024

Min: 256

Max: 2048

Pixel-area resolution; also applied to min/max/target size and validation resolution.
enable_crop
boolean
True
Enable cropping when preparing samples.
crop_style
None
center
Crop location style when cropping is enabled.
crop_aspect
None
square
Aspect handling for crops: square (1:1) or preserve source aspect.
trigger_word
string
TOK
Subject token used in generated fallback captions and instance prompts.
caption_strategy
None
textfile
Caption strategy for uploaded archives. textfile uses matching .txt captions and generated filename fallbacks; filename derives captions from filenames; instanceprompt uses one generic prompt for every image.
train_batch_size
integer
1

Min: 1

Max: 4

Training batch size (1-4).
learning_rate
number
0.0001

Min: 0.0000001

Max: 0.01

LoRA learning rate.
checkpoints_total_limit
integer
3

Min: 1

Max: 5

Maximum checkpoints to retain (max 5).
checkpoint_epoch_interval
integer
5

Min: 1

Write a checkpoint every N epochs.
num_train_epochs
integer
25

Min: 1

Total training epochs.
max_train_steps
integer
0
Optional max train steps override (0 disables the step limit).
checkpoint_step_interval
integer
0
Save checkpoints and validate every N optimizer updates; 0 uses epoch intervals.
hub_model_id
string
Optional Hugging Face model repo for the LoRA, checkpoints, and validation images.
hf_instance_prompt
string
Use this prompt for every Hugging Face image instead of its caption column.
hf_caption_column
string
caption
Caption column for a Hugging Face dataset.
hf_revision
string
Optional Hugging Face dataset revision.
validation_prompt
string
Prompt used to generate validation images.
lora_rank
integer

Min: 1

Max: 256

Override the adapter rank; alpha matches rank.
lora_dropout
number
0

Max: 0.999

Adapter dropout probability.
seed
integer
42
Training and validation seed.
attention_mechanism
None
kohaku-fa-auto
Automatic Kohaku attention or native Diffusers attention.
gradient_checkpointing_interval
integer

Min: 1

Max: 64

Blocks per checkpointed segment; blank uses the model recipe.
gradient_checkpointing_segment_stride
integer

Min: 1

Max: 64

Start a checkpointed segment every N blocks; blank uses contiguous segments.
dataloader_prefetch
boolean
True
Prepare upcoming training batches in the background.
push_checkpoints_to_hub
boolean
True
Upload intermediate checkpoints when hub_model_id is set.
enable_regional_compile
boolean
True
Compile repeated transformer blocks.
s3_bucket
string
S3-compatible bucket for publishing checkpoints.
s3_region
string
S3 region (optional).
s3_endpoint_url
string
Custom S3 endpoint URL (for non-AWS providers).
s3_base_path
string
Prefix inside the bucket (defaults to cog/krea2/{job_id}).
s3_public_base_url
string
Public base URL to build shareable links (optional).
s3_access_key
string
S3 access key (leave blank to use IAM/instance roles).
s3_secret_key
string
S3 secret key (leave blank to use IAM/instance roles).
hf_token
string
Hugging Face token for model or dataset access.
return_logs
boolean
True
Print the tail of debug.log.

Output schema

The shape of the response you’ll get when you run this model with an API.

Schema
{'format': 'uri', 'title': 'Output', 'type': 'string'}