usamaehsan
/
juggernaut-xl-x-adapters-x-lightning
- Public
- 19 runs
Run usamaehsan/juggernaut-xl-x-adapters-x-lightning 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 |
|---|---|---|---|
| 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
|
{
"type": "object",
"title": "Input",
"required": [
"prompt"
],
"properties": {
"seed": {
"type": "integer",
"title": "Seed",
"x-order": 4,
"description": "Seed"
},
"prompt": {
"type": "string",
"title": "Prompt",
"x-order": 0,
"description": "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"
},
"max_width": {
"type": "integer",
"title": "Max Width",
"default": 512,
"x-order": 6,
"description": "Max width/Resolution of image"
},
"scheduler": {
"enum": [
"DDIM",
"DPMSolverMultistep",
"HeunDiscrete",
"K_EULER_ANCESTRAL",
"K_EULER",
"PNDM"
],
"type": "string",
"title": "scheduler",
"description": "Choose a scheduler.",
"default": "DDIM",
"x-order": 8
},
"max_height": {
"type": "integer",
"title": "Max Height",
"default": 512,
"x-order": 7,
"description": "Max height/Resolution of image"
},
"depth_image": {
"type": "string",
"title": "Depth Image",
"format": "uri",
"x-order": 11,
"description": "Control image for depth controlnet"
},
"lineart_image": {
"type": "string",
"title": "Lineart Image",
"format": "uri",
"x-order": 9,
"description": "Control image for lineart adapter"
},
"guidance_scale": {
"type": "number",
"title": "Guidance Scale",
"default": 7,
"maximum": 30,
"minimum": 0.1,
"x-order": 3,
"description": "Scale for classifier-free guidance"
},
"negative_prompt": {
"type": "string",
"title": "Negative Prompt",
"default": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
"x-order": 1,
"description": "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"
},
"ip_adapter_image": {
"type": "string",
"title": "Ip Adapter Image",
"format": "uri",
"x-order": 13,
"description": "IP Adapter image"
},
"ip_adapter_weight": {
"type": "number",
"title": "Ip Adapter Weight",
"default": 1,
"x-order": 14,
"description": "IP Adapter weight"
},
"num_inference_steps": {
"type": "integer",
"title": "Num Inference Steps",
"default": 20,
"x-order": 2,
"description": "Steps to run denoising"
},
"disable_safety_check": {
"type": "boolean",
"title": "Disable Safety Check",
"default": false,
"x-order": 5,
"description": "Disable safety check. Use at your own risk!"
},
"lightning_lora_weight": {
"type": "number",
"title": "Lightning Lora Weight",
"default": 0,
"x-order": 15,
"description": "disabled on 0"
},
"depth_conditioning_scale": {
"type": "number",
"title": "Depth Conditioning Scale",
"default": 1,
"x-order": 12,
"description": "Conditioning scale for depth controlnet"
},
"micro_detail_lora_weight": {
"type": "number",
"title": "Micro Detail Lora Weight",
"default": 0,
"x-order": 16,
"description": "disabled on 0"
},
"lineart_conditioning_scale": {
"type": "number",
"title": "Lineart Conditioning Scale",
"default": 1,
"x-order": 10,
"description": "Conditioning scale for canny controlnet"
}
}
}
Output schema
The shape of the response you’ll get when you run this model with an API.
Schema
{
"type": "array",
"items": {
"type": "string",
"format": "uri"
},
"title": "Output"
}