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edenartlab /sdxl-lora-trainer:95542d9a

Playground API

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
name
string
Name of new LORA concept
lora_training_urls
string
Training images for new LORA concept (can be images or a .zip file of images)
mode
string
concept
face / style or concept (default)
seed
integer
Random seed for reproducible training. Leave empty to use a random seed
resolution
integer
896
Square pixel resolution which your images will be resized to for training
train_batch_size
integer
2
Batch size (per device) for training
num_train_epochs
integer
10000
Number of epochs to loop through your training dataset
max_train_steps
integer
600
Number of individual training steps. Takes precedence over num_train_epochs
checkpointing_steps
integer
10000
Number of steps between saving checkpoints. Set to very very high number to disable checkpointing, because you don't need one.
is_lora
boolean
True
Whether to use LoRA training. If set to False, will use Full fine tuning
unet_learning_rate
number
0.000001
Learning rate for the U-Net. We recommend this value to be somewhere between `1e-6` to `1e-5`.
ti_lr
number
0.0003
Scaling of learning rate for training textual inversion embeddings. Don't alter unless you know what you're doing.
lora_lr
number
0.0001
Scaling of learning rate for training LoRA embeddings. Don't alter unless you know what you're doing.
ti_weight_decay
number
0.00001
weight decay for textual inversion embeddings. Don't alter unless you know what you're doing.
lora_weight_decay
number
0.0001
weight decay for LoRa. Don't alter unless you know what you're doing.
lora_rank
integer
4
Rank of LoRA embeddings. For faces 4 is good, for complex objects you might try 6 or 8
lr_scheduler
string (enum)
constant

Options:

constant, linear

Learning rate scheduler to use for training
lr_warmup_steps
integer
50
Number of warmup steps for lr schedulers with warmups.
token_string
string
TOK
A unique string that will be trained to refer to the concept in the input images. Can be anything, but TOK works well
caption_prefix
string
a photo of TOK,
Text which will be used as prefix during automatic captioning. Must contain the `token_string`. For example, if caption text is 'a photo of TOK', automatic captioning will expand to 'a photo of TOK under a bridge', 'a photo of TOK holding a cup', etc.
mask_target_prompts
string
Prompt that describes part of the image that you will find important. For example, if you are fine-tuning your pet, `photo of a dog` will be a good prompt. Prompt-based masking is used to focus the fine-tuning process on the important/salient parts of the image
crop_based_on_salience
boolean
True
If you want to crop the image to `target_size` based on the important parts of the image, set this to True. If you want to crop the image based on face detection, set this to False
use_face_detection_instead
boolean
False
If you want to use face detection instead of CLIPSeg for masking. For face applications, we recommend using this option.
clipseg_temperature
number
1
How blurry you want the CLIPSeg mask to be. We recommend this value be something between `0.5` to `1.0`. If you want to have more sharp mask (but thus more errorful), you can decrease this value.
left_right_flip_augmentation
boolean
True
Add left-right flipped version of each img to the training data, recommended for most cases. If you are learning a face, you prob want to disable this
verbose
boolean
True
verbose output
run_name
string
1693687184
Subdirectory where all files will be saved
run_local
boolean
False
for debugging locally

Output schema

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

Schema 
{'items': {'properties': {'attributes': {'title': 'Attributes',
                                         'type': 'object'},
                          'files': {'items': {'format': 'uri',
                                              'type': 'string'},
                                    'title': 'Files',
                                    'type': 'array'},
                          'isFinal': {'default': False,
                                      'title': 'Isfinal',
                                      'type': 'boolean'},
                          'name': {'title': 'Name', 'type': 'string'},
                          'progress': {'title': 'Progress', 'type': 'number'},
                          'thumbnails': {'default': [],
                                         'items': {'format': 'uri',
                                                   'type': 'string'},
                                         'title': 'Thumbnails',
                                         'type': 'array'}},
           'required': ['files'],
           'title': 'CogOutput',
           'type': 'object'},
 'title': 'Output',
 'type': 'array',
 'x-cog-array-type': 'iterator'}