alexgenovese
/
autotrain-dreambooth
Auto Train Dreambooth SDXL | WIP - Not working
Run alexgenovese/autotrain-dreambooth with an API
Use one of our client libraries to get started quickly. Clicking on a library will take you to the Playground tab where you can tweak different inputs, see the results, and copy the corresponding code to use in your own project.
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 |
|---|---|---|---|
| model |
string
|
stabilityai/stable-diffusion-xl-base-1.0
|
HF base model path
|
| prompt |
string
|
a photo of aisudhca bag
|
Instance prompt
|
| train_data_zip |
string
|
Upload image dataset in zip format
|
|
| mixed_precision |
string
(enum)
|
bf16
Options: bf16, fp16 |
FP16 or BF16
|
| train_class_data_zip |
string
|
Upload image dataset in zip format - For Regularization
|
|
| class_prompt |
string
|
Class prompt
|
|
| seed |
integer
|
42
|
Seed
|
| resolution |
integer
(enum)
|
1024
Options: 512, 768, 1024 |
Resolution
|
| center_crop |
boolean
|
False
|
Center crop
|
| train_text_encoder |
boolean
|
True
|
Train text encoder
|
| batch_size |
integer
|
1
|
Train batch size
|
| num_steps |
integer
|
2000
|
Max train steps
|
| checkpointing_steps |
integer
|
500
|
Checkpointing steps
|
| gradient_accumulation |
integer
|
1
|
Gradient accumulation steps
|
| lr |
number
|
0.0004
|
Learning rate
|
| scheduler |
string
(enum)
|
constant
Options: linear, cosine, cosine_with_restarts, polynomial, constant, constant_with_warmup |
Learning rate scheduler
|
| warmup_steps |
integer
|
0
|
Learning rate warmup steps
|
| num_cycles |
integer
|
1
|
Learning rate num cycles
|
| use_8bit_adam |
boolean
|
False
|
Use 8bit adam
|
| xformers |
boolean
|
False
|
Enable xformers memory efficient attention
|
| xl |
boolean
|
True
|
XL
|
{
"type": "object",
"title": "Input",
"required": [
"train_data_zip",
"train_class_data_zip"
],
"properties": {
"lr": {
"type": "number",
"title": "Lr",
"default": 0.0004,
"x-order": 14,
"description": "Learning rate"
},
"xl": {
"type": "boolean",
"title": "Xl",
"default": true,
"x-order": 20,
"description": "XL"
},
"seed": {
"type": "integer",
"title": "Seed",
"default": 42,
"x-order": 6,
"description": "Seed"
},
"model": {
"type": "string",
"title": "Model",
"default": "stabilityai/stable-diffusion-xl-base-1.0",
"x-order": 0,
"description": "HF base model path"
},
"prompt": {
"type": "string",
"title": "Prompt",
"default": "a photo of aisudhca bag",
"x-order": 1,
"description": "Instance prompt"
},
"xformers": {
"type": "boolean",
"title": "Xformers",
"default": false,
"x-order": 19,
"description": "Enable xformers memory efficient attention"
},
"num_steps": {
"type": "integer",
"title": "Num Steps",
"default": 2000,
"x-order": 11,
"description": "Max train steps"
},
"scheduler": {
"enum": [
"linear",
"cosine",
"cosine_with_restarts",
"polynomial",
"constant",
"constant_with_warmup"
],
"type": "string",
"title": "scheduler",
"description": "Learning rate scheduler",
"default": "constant",
"x-order": 15
},
"batch_size": {
"type": "integer",
"title": "Batch Size",
"default": 1,
"x-order": 10,
"description": "Train batch size"
},
"num_cycles": {
"type": "integer",
"title": "Num Cycles",
"default": 1,
"x-order": 17,
"description": "Learning rate num cycles"
},
"resolution": {
"enum": [
512,
768,
1024
],
"type": "integer",
"title": "resolution",
"description": "Resolution",
"default": 1024,
"x-order": 7
},
"center_crop": {
"type": "boolean",
"title": "Center Crop",
"default": false,
"x-order": 8,
"description": "Center crop"
},
"class_prompt": {
"type": "string",
"title": "Class Prompt",
"x-order": 5,
"description": "Class prompt"
},
"warmup_steps": {
"type": "integer",
"title": "Warmup Steps",
"default": 0,
"x-order": 16,
"description": "Learning rate warmup steps"
},
"use_8bit_adam": {
"type": "boolean",
"title": "Use 8Bit Adam",
"default": false,
"x-order": 18,
"description": "Use 8bit adam"
},
"train_data_zip": {
"type": "string",
"title": "Train Data Zip",
"format": "uri",
"x-order": 2,
"description": "Upload image dataset in zip format"
},
"mixed_precision": {
"enum": [
"bf16",
"fp16"
],
"type": "string",
"title": "mixed_precision",
"description": "FP16 or BF16",
"default": "bf16",
"x-order": 3
},
"train_text_encoder": {
"type": "boolean",
"title": "Train Text Encoder",
"default": true,
"x-order": 9,
"description": "Train text encoder"
},
"checkpointing_steps": {
"type": "integer",
"title": "Checkpointing Steps",
"default": 500,
"x-order": 12,
"description": "Checkpointing steps"
},
"train_class_data_zip": {
"type": "string",
"title": "Train Class Data Zip",
"format": "uri",
"x-order": 4,
"description": "Upload image dataset in zip format - For Regularization"
},
"gradient_accumulation": {
"type": "integer",
"title": "Gradient Accumulation",
"default": 1,
"x-order": 13,
"description": "Gradient accumulation steps"
}
}
}
Output schema
The shape of the response you’ll get when you run this model with an API.
Schema
{
"type": "string",
"title": "Output",
"format": "uri"
}