kayces
/
contronet
- Public
- 14 runs
Run kayces/contronet 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 |
|---|---|---|---|
| image |
string
|
Input image
|
|
| prompt |
string
|
Prompt for the model
|
|
| num_samples |
string
(enum)
|
1
Options: 1, 4 |
Number of samples (higher values may OOM)
|
| image_resolution |
string
(enum)
|
512
Options: 256, 512, 768 |
Image resolution to be generated
|
| low_threshold |
integer
|
100
Min: 1 Max: 255 |
Canny line detection low threshold
|
| high_threshold |
integer
|
200
Min: 1 Max: 255 |
Canny line detection high threshold
|
| ddim_steps |
integer
|
20
|
Steps
|
| scale |
number
|
9
Min: 0.1 Max: 30 |
Scale for classifier-free guidance
|
| seed |
integer
|
Seed
|
|
| eta |
number
|
0
|
Controls the amount of noise that is added to the input data during the denoising diffusion process. Higher value -> more noise
|
| a_prompt |
string
|
best quality, extremely detailed
|
Additional text to be appended to prompt
|
| n_prompt |
string
|
longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality
|
Negative Prompt
|
| detect_resolution |
integer
|
512
Min: 128 Max: 1024 |
Resolution at which detection method will be applied)
|
{
"type": "object",
"title": "Input",
"required": [
"image",
"prompt"
],
"properties": {
"eta": {
"type": "number",
"title": "Eta",
"default": 0,
"x-order": 9,
"description": "Controls the amount of noise that is added to the input data during the denoising diffusion process. Higher value -> more noise"
},
"seed": {
"type": "integer",
"title": "Seed",
"x-order": 8,
"description": "Seed"
},
"image": {
"type": "string",
"title": "Image",
"format": "uri",
"x-order": 0,
"description": "Input image"
},
"scale": {
"type": "number",
"title": "Scale",
"default": 9,
"maximum": 30,
"minimum": 0.1,
"x-order": 7,
"description": "Scale for classifier-free guidance"
},
"prompt": {
"type": "string",
"title": "Prompt",
"x-order": 1,
"description": "Prompt for the model"
},
"a_prompt": {
"type": "string",
"title": "A Prompt",
"default": "best quality, extremely detailed",
"x-order": 10,
"description": "Additional text to be appended to prompt"
},
"n_prompt": {
"type": "string",
"title": "N Prompt",
"default": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
"x-order": 11,
"description": "Negative Prompt"
},
"ddim_steps": {
"type": "integer",
"title": "Ddim Steps",
"default": 20,
"x-order": 6,
"description": "Steps"
},
"num_samples": {
"enum": [
"1",
"4"
],
"type": "string",
"title": "num_samples",
"description": "Number of samples (higher values may OOM)",
"default": "1",
"x-order": 2
},
"low_threshold": {
"type": "integer",
"title": "Low Threshold",
"default": 100,
"maximum": 255,
"minimum": 1,
"x-order": 4,
"description": "Canny line detection low threshold"
},
"high_threshold": {
"type": "integer",
"title": "High Threshold",
"default": 200,
"maximum": 255,
"minimum": 1,
"x-order": 5,
"description": "Canny line detection high threshold"
},
"image_resolution": {
"enum": [
"256",
"512",
"768"
],
"type": "string",
"title": "image_resolution",
"description": "Image resolution to be generated",
"default": "512",
"x-order": 3
},
"detect_resolution": {
"type": "integer",
"title": "Detect Resolution",
"default": 512,
"maximum": 1024,
"minimum": 128,
"x-order": 12,
"description": "Resolution at which detection method will be applied)"
}
}
}
Output schema
The shape of the response you’ll get when you run this model with an API.
{
"type": "array",
"items": {
"type": "string",
"format": "uri"
},
"title": "Output"
}