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replicate/dreambooth:79c69b72
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_prompt |
string
|
The prompt with identifier specifying the instance
|
|
| class_prompt |
string
|
The prompt to specify images in the same class as provided instance images.
|
|
| instance_data |
string
|
A ZIP file containing the training data of instance images
|
|
| class_data |
string
|
A ZIP file containing the training data of class images. Images will be generated if you do not provide.
|
|
| save_sample_prompt |
string
|
The prompt used to generate sample outputs to save.
|
|
| save_sample_negative_prompt |
string
|
The negative prompt used to generate sample outputs to save.
|
|
| n_save_sample |
integer
|
4
|
The number of samples to save.
|
| save_guidance_scale |
number
|
7.5
|
CFG for save sample.
|
| save_infer_steps |
integer
|
50
|
The number of inference steps for save sample.
|
| pad_tokens |
boolean
|
False
|
Flag to pad tokens to length 77.
|
| with_prior_preservation |
boolean
|
True
|
Flag to add prior preservation loss.
|
| prior_loss_weight |
number
|
1
|
Weight of prior preservation loss.
|
| num_class_images |
integer
|
50
|
Minimal class images for prior preservation loss. If not have enough images, additional images will be sampled with class_prompt.
|
| 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.
|
| center_crop |
boolean
|
False
|
Whether to center crop images before resizing to 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.
|
| sample_batch_size |
integer
|
4
|
Batch size (per device) for sampling images.
|
| num_train_epochs |
integer
|
1
|
None
|
| max_train_steps |
integer
|
500
|
Total number of training steps to perform. If provided, overrides num_train_epochs.
|
| gradient_accumulation_steps |
integer
|
1
|
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.
|
| learning_rate |
number
|
0.000001
|
Initial learning rate (after the potential warmup period) to use.
|
| scale_lr |
boolean
|
False
|
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.
|
| use_8bit_adam |
boolean
|
False
|
Whether or not to use 8-bit Adam from bitsandbytes.
|
| adam_beta1 |
number
|
0.9
|
The beta1 parameter for the Adam optimizer.
|
| adam_beta2 |
number
|
0.999
|
The beta2 parameter for the Adam optimizer.
|
| adam_weight_decay |
number
|
0.01
|
Weight decay to use
|
| adam_epsilon |
number
|
0.00000001
|
Epsilon value for the Adam optimizer
|
| max_grad_norm |
number
|
1
|
Max gradient norm.
|
| save_interval |
integer
|
10000
|
Save weights every N steps.
|
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'}