qr2ai
/
mbrhan
- Public
- 357 runs
Run qr2ai/mbrhan 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
|
Modern skyscraper, glass facade, urban skyline, clear day.
|
Input prompt
|
| suffix_prompt |
string
|
Futuristic Concept, Cutting-Edge Technology, Historical Landmark, Timeless Aesthetics, sharp focus, 8k, uhd, file grain, masterpiece
|
Additional prompt
|
| negative_prompt |
string
|
deformed, animation, anime, cartoon, comic, cropped, out of frame, low res, draft, cgi, low quality render, thumbnail
|
Input Negative Prompt
|
| use_canny |
boolean
|
False
|
Whether to use canny detector for better details
|
| lora_input |
string
|
|
Comma-separated list of LoRA models from Hugging Face or local paths. Leave empty to skip LoRA.
|
| lora_scale |
string
|
|
Comma-separated list of scales for each LoRA model. Must match the number of LoRAs. Leave empty if no LoRA is used.
|
| image |
string
|
Input image for img2img or inpaint mode
|
|
| width |
integer
|
1024
|
Width of output image
|
| height |
integer
|
1024
|
Height of output image
|
| generate_square |
boolean
|
False
|
Whether generate square image, assert height == width
|
| num_outputs |
integer
|
1
Min: 1 Max: 4 |
Number of images to output.
|
| num_inference_steps |
integer
|
35
Min: 1 Max: 500 |
Number of denoising steps
|
| guidance_scale |
number
|
7.5
Min: 1 Max: 50 |
Scale for classifier-free guidance
|
| adapter_conditioning_scale |
number
|
0.9
Max: 2 |
Scale for adapter module
|
| seed |
integer
|
0
|
Random seed. Enter 0 to randomize the seed
|
| sampler |
string
(enum)
|
Euler a
Options: DPM++ 2M Karras, DPM++ 2M SDE Karras, DPM++ Karras SDE, DPM++ Karras, Euler, Euler a |
The sampling method
|
{
"type": "object",
"title": "Input",
"properties": {
"seed": {
"type": "integer",
"title": "Seed",
"default": 0,
"x-order": 14,
"description": "Random seed. Enter 0 to randomize the seed"
},
"image": {
"type": "string",
"title": "Image",
"format": "uri",
"x-order": 6,
"description": "Input image for img2img or inpaint mode"
},
"width": {
"type": "integer",
"title": "Width",
"default": 1024,
"x-order": 7,
"description": "Width of output image"
},
"height": {
"type": "integer",
"title": "Height",
"default": 1024,
"x-order": 8,
"description": "Height of output image"
},
"prompt": {
"type": "string",
"title": "Prompt",
"default": "Modern skyscraper, glass facade, urban skyline, clear day.",
"x-order": 0,
"description": "Input prompt"
},
"sampler": {
"enum": [
"DPM++ 2M Karras",
"DPM++ 2M SDE Karras",
"DPM++ Karras SDE",
"DPM++ Karras",
"Euler",
"Euler a"
],
"type": "string",
"title": "sampler",
"description": "The sampling method",
"default": "Euler a",
"x-order": 15
},
"use_canny": {
"type": "boolean",
"title": "Use Canny",
"default": false,
"x-order": 3,
"description": "Whether to use canny detector for better details"
},
"lora_input": {
"type": "string",
"title": "Lora Input",
"default": "",
"x-order": 4,
"description": "Comma-separated list of LoRA models from Hugging Face or local paths. Leave empty to skip LoRA."
},
"lora_scale": {
"type": "string",
"title": "Lora Scale",
"default": "",
"x-order": 5,
"description": "Comma-separated list of scales for each LoRA model. Must match the number of LoRAs. Leave empty if no LoRA is used."
},
"num_outputs": {
"type": "integer",
"title": "Num Outputs",
"default": 1,
"maximum": 4,
"minimum": 1,
"x-order": 10,
"description": "Number of images to output."
},
"suffix_prompt": {
"type": "string",
"title": "Suffix Prompt",
"default": "Futuristic Concept, Cutting-Edge Technology, Historical Landmark, Timeless Aesthetics, sharp focus, 8k, uhd, file grain, masterpiece",
"x-order": 1,
"description": "Additional prompt"
},
"guidance_scale": {
"type": "number",
"title": "Guidance Scale",
"default": 7.5,
"maximum": 50,
"minimum": 1,
"x-order": 12,
"description": "Scale for classifier-free guidance"
},
"generate_square": {
"type": "boolean",
"title": "Generate Square",
"default": false,
"x-order": 9,
"description": "Whether generate square image, assert height == width"
},
"negative_prompt": {
"type": "string",
"title": "Negative Prompt",
"default": "deformed, animation, anime, cartoon, comic, cropped, out of frame, low res, draft, cgi, low quality render, thumbnail",
"x-order": 2,
"description": "Input Negative Prompt"
},
"num_inference_steps": {
"type": "integer",
"title": "Num Inference Steps",
"default": 35,
"maximum": 500,
"minimum": 1,
"x-order": 11,
"description": "Number of denoising steps"
},
"adapter_conditioning_scale": {
"type": "number",
"title": "Adapter Conditioning Scale",
"default": 0.9,
"maximum": 2,
"minimum": 0,
"x-order": 13,
"description": "Scale for adapter module"
}
}
}
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"
}