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edenartlab /sdxl-lora-trainer:9ddd8d5e

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
unnamed
Name of new LORA concept
lora_training_urls
string
Training images for new LORA concept (can be image urls or an url to a .zip file of images)
concept_mode
string (enum)
style

Options:

style, face, object

What are you trying to learn?
sd_model_version
string (enum)
sdxl

Options:

sdxl, sd15

SDXL gives much better LoRa's if you just need static images. If you want to make AnimateDiff animations, train an SD15 lora.
max_train_steps
integer
300
Number of training steps. Increasing this usually leads to overfitting, only viable if you have > 100 training imgs. For faces you may want to reduce to eg 300
checkpointing_steps
integer
10000
Save a checkpoint every n steps (The final checkpoint will always be saved)
resolution
integer
512
Square pixel resolution which your images will be resized to for training, highly recommended: 512 or 768
unet_lr
number
0.0003
final learning rate of unet (after warmup), increasing this usually leads to strong overfitting
ti_lr
number
0.001
Learning rate for training textual inversion embeddings. Don't alter unless you know what you're doing.
lora_rank
integer
16
Rank of LoRA embeddings for the unet.
n_tokens
integer
3

Min: 1

Max: 4

How many new tokens to train (highly recommended to leave this at 2)
train_batch_size
integer
4
Batch size (per device) for training (dont increase unless running on a BIG GPU)
n_sample_imgs
integer
4
Number of sample images in validation grid
validation_img_size
integer
1024
Resolution of sample images in validation grid
sample_imgs_lora_scale
number
Scale factor for LoRa when generating sample images. If not provided, will be set automatically
seed
integer
Random seed for reproducible training. Leave empty to use a random seed

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': {'default': [],
                                    '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'}},
           'title': 'CogOutput',
           'type': 'object'},
 'title': 'Output',
 'type': 'array',
 'x-cog-array-type': 'iterator'}