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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 |
|---|---|---|---|
| prompt |
string
|
Prompt - using compel, use +++ to increase words weight:: doc: https://github.com/damian0815/compel/tree/main/doc || https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#attention-weighting
|
|
| negative_prompt |
string
|
Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality
|
Negative prompt - using compel, use +++ to increase words weight//// negative-embeddings available ///// FastNegativeV2 , boring_e621_v4 , verybadimagenegative_v1 || to use them, write their keyword in negative prompt
|
| num_inference_steps |
integer
|
20
|
Steps to run denoising
|
| guidance_scale |
number
|
7
Min: 0.1 Max: 30 |
Scale for classifier-free guidance
|
| seed |
integer
|
Seed
|
|
| disable_safety_check |
boolean
|
False
|
Disable safety check. Use at your own risk!
|
| max_width |
integer
|
512
|
Max width/Resolution of image
|
| max_height |
integer
|
512
|
Max height/Resolution of image
|
| scheduler |
string
(enum)
|
DDIM
Options: DDIM, DPMSolverMultistep, HeunDiscrete, K_EULER_ANCESTRAL, K_EULER, PNDM |
Choose a scheduler.
|
| lineart_image |
string
|
Control image for lineart adapter
|
|
| lineart_conditioning_scale |
number
|
1
|
Conditioning scale for canny controlnet
|
| depth_image |
string
|
Control image for depth controlnet
|
|
| depth_conditioning_scale |
number
|
1
|
Conditioning scale for depth controlnet
|
| ip_adapter_image |
string
|
IP Adapter image
|
|
| ip_adapter_weight |
number
|
1
|
IP Adapter weight
|
| lightning_lora_weight |
number
|
0
|
disabled on 0
|
| micro_detail_lora_weight |
number
|
0
|
disabled on 0
|
Output schema
The shape of the response you’ll get when you run this model with an API.
Schema
{'items': {'format': 'uri', 'type': 'string'},
'title': 'Output',
'type': 'array'}