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zylim0702 /sdxl-lora-customize-training:a4eefb9b
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 |
|---|---|---|---|
| input_images |
string
|
A .zip or .tar file containing the image files that will be used for fine-tuning
|
|
| seed |
integer
|
Random seed for reproducible training. Leave empty to use a random seed
|
|
| resolution |
integer
|
768
|
Square pixel resolution which your images will be resized to for training
|
| train_batch_size |
integer
|
4
|
Batch size (per device) for training
|
| num_train_epochs |
integer
|
4000
|
Number of epochs to loop through your training dataset
|
| max_train_steps |
integer
|
1000
|
Number of individual training steps. Takes precedence over num_train_epochs
|
| 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.
|
| lora_rank |
integer
|
32
|
Rank of LoRA embeddings. Don't alter unless you know what you're doing.
|
| lr_scheduler |
string
(enum)
|
constant
Options: constant, linear |
Learning rate scheduler to use for training
|
| lr_warmup_steps |
integer
|
100
|
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.
|
| verbose |
boolean
|
True
|
verbose output
|
| checkpointing_steps |
integer
|
999999
|
Number of steps between saving checkpoints. Set to very very high number to disable checkpointing, because you don't need one.
|
| input_images_filetype |
string
(enum)
|
infer
Options: zip, tar, infer |
Filetype of the input images. Can be either `zip` or `tar`. By default its `infer`, and it will be inferred from the ext of input file.
|
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'}