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cloneofsimo /lora-advanced-training:59d71170
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 |
|---|---|---|---|
| instance_data |
string
|
A ZIP file containing your training images (JPG, PNG, etc. size not restricted). These images contain your 'subject' that you want the trained model to embed in the output domain for later generating customized scenes beyond the training images. For best results, use images without noise or unrelated objects in the background.
|
|
| class_data |
string
|
An optional ZIP file containing the training data of class images. This corresponds to `class_prompt` above, also with the purpose of keeping the model generalizable. By default, the pretrained stable-diffusion model will generate N images (determined by the `num_class_images` you set) based on the `class_prompt` provided. But to save time or to have your preferred specific set of `class_data`, you can also provide them in a ZIP file.
|
|
| seed |
integer
|
1337
|
A seed for reproducible training
|
| resolution |
integer
|
512
|
The resolution for input images. All the images in the train/validation dataset will be resized to this resolution.
|
| train_text_encoder |
boolean
|
True
|
Whether to train the text encoder
|
| train_batch_size |
integer
|
1
|
Batch size (per device) for the training dataloader.
|
| gradient_accumulation_steps |
integer
|
4
|
Number of updates steps to accumulate before performing a backward/update pass.
|
| gradient_checkpointing |
boolean
|
False
|
Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.
|
| scale_lr |
boolean
|
True
|
Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.
|
| lr_scheduler |
string
(enum)
|
constant
Options: linear, cosine, cosine_with_restarts, polynomial, constant, constant_with_warmup |
The scheduler type to use
|
| lr_warmup_steps |
integer
|
0
|
Number of steps for the warmup in the lr scheduler.
|
| clip_ti_decay |
boolean
|
True
|
Whether or not to clip the TI decay to be between 0 and 1.
|
| color_jitter |
boolean
|
True
|
Whether or not to use color jitter.
|
| continue_inversion |
boolean
|
False
|
Whether or not to continue an inversion.
|
| continue_inversion_lr |
number
|
0.0001
|
The learning rate for continuing an inversion.
|
| device |
string
|
cuda:0
|
The device to use. Can be 'cuda' or 'cpu'.
|
| initializer_tokens |
string
|
The tokens to use for the initializer. If not provided, will randomly initialize from gaussian N(0,0.017^2)
|
|
| learning_rate_text |
number
|
0.00001
|
The learning rate for the text encoder.
|
| learning_rate_ti |
number
|
0.0005
|
The learning rate for the TI.
|
| learning_rate_unet |
number
|
0.0001
|
The learning rate for the unet.
|
| lora_rank |
integer
|
4
|
The rank for the LORA.
|
| lora_dropout_p |
number
|
0.1
|
Dropout at lora
|
| lora_scale |
number
|
1
|
Scale for the LORA.
|
| lr_scheduler_lora |
string
(enum)
|
constant
Options: linear, cosine, cosine_with_restarts, polynomial, constant, constant_with_warmup |
The scheduler type to use
|
| lr_warmup_steps_lora |
integer
|
0
|
Number of steps for the warmup in the lr scheduler.
|
| max_train_steps_ti |
integer
|
500
|
The maximum number of training steps for the TI.
|
| max_train_steps_tuning |
integer
|
1000
|
The maximum number of training steps for the tuning.
|
| perform_inversion |
boolean
|
True
|
Whether to perform inversion during training.
|
| placeholder_token_at_data |
string
|
Whether or not to use a placeholder token at the data.
|
|
| placeholder_tokens |
string
|
<s1>|<s2>
|
The placeholder tokens to use for the initializer. If not provided, will use the first tokens of the data.
|
| use_face_segmentation_condition |
boolean
|
False
|
Whether or not to use the face segmentation condition.
|
| use_template |
string
(enum)
|
object
Options: object, style, none |
The template to use for the inversion.
|
| weight_decay_lora |
number
|
0.001
|
The weight decay for the LORA loss.
|
| weight_decay_ti |
number
|
0
|
The weight decay for the TI.
|
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'}