tonyhopkins994
/
test
- Public
- 234 runs
Run tonyhopkins994/test 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
|
Living Room, high quality, best quality, highres, high resolution, highly detailed, realistic, ultrarealistic, photorealistic, 4K, 8K
|
Input prompt
|
| negative_prompt |
string
|
blurry, distorted, low quality, worst quality, unrealistic, sketch, cartoon, artificial
|
Negative Prompt
|
| seed |
string
|
-1
|
Random generation seed
|
| num_inference_steps |
integer
|
30
Min: 1 Max: 500 |
Number of denoising steps
|
| guidance_scale |
number
|
7.5
Min: 1 Max: 50 |
Scale for classifier-free guidance
|
| strength |
number
|
1
Max: 1 |
Inpainting strength; 0 equates to no change, 1 equates to complete destruction of initial image
|
| num_outputs |
integer
|
1
Min: 1 Max: 6 |
Number of images to output
|
| upscale |
string
(enum)
|
None
Options: None, 2x, 3x |
Upscale resolution
|
| input_image |
string
|
Source image for inpainting, base64 encoded string
|
|
| mask_image |
string
|
Image mask for inpainting, base64 encoded string
|
|
| ip_adapter_scale |
number
|
0.5
Max: 1 |
IP adapter guidance strength
|
| ip_adapter_image |
string
|
Input image for IP adapter, base64 encoded string
|
|
| normal_image |
string
|
Input image for normal controlnet, base64 encoded string
|
|
| depth_image |
string
|
Input image for depth controlnet, base64 encoded string
|
|
| mlsd_image |
string
|
Input image for mlsd controlnet, base64 encoded string
|
|
| seg_image |
string
|
Input image for segmentation controlnet, base64 encoded string
|
{
"type": "object",
"title": "Input",
"properties": {
"seed": {
"type": "string",
"title": "Seed",
"default": "-1",
"x-order": 2,
"description": "Random generation seed"
},
"prompt": {
"type": "string",
"title": "Prompt",
"default": "Living Room, high quality, best quality, highres, high resolution, highly detailed, realistic, ultrarealistic, photorealistic, 4K, 8K",
"x-order": 0,
"description": "Input prompt"
},
"upscale": {
"enum": [
"None",
"2x",
"3x"
],
"type": "string",
"title": "upscale",
"description": "Upscale resolution",
"default": "None",
"x-order": 7
},
"strength": {
"type": "number",
"title": "Strength",
"default": 1,
"maximum": 1,
"minimum": 0,
"x-order": 5,
"description": "Inpainting strength; 0 equates to no change, 1 equates to complete destruction of initial image"
},
"seg_image": {
"type": "string",
"title": "Seg Image",
"x-order": 15,
"description": "Input image for segmentation controlnet, base64 encoded string"
},
"mask_image": {
"type": "string",
"title": "Mask Image",
"x-order": 9,
"description": "Image mask for inpainting, base64 encoded string"
},
"mlsd_image": {
"type": "string",
"title": "Mlsd Image",
"x-order": 14,
"description": "Input image for mlsd controlnet, base64 encoded string"
},
"depth_image": {
"type": "string",
"title": "Depth Image",
"x-order": 13,
"description": "Input image for depth controlnet, base64 encoded string"
},
"input_image": {
"type": "string",
"title": "Input Image",
"x-order": 8,
"description": "Source image for inpainting, base64 encoded string"
},
"num_outputs": {
"type": "integer",
"title": "Num Outputs",
"default": 1,
"maximum": 6,
"minimum": 1,
"x-order": 6,
"description": "Number of images to output"
},
"normal_image": {
"type": "string",
"title": "Normal Image",
"x-order": 12,
"description": "Input image for normal controlnet, base64 encoded string"
},
"guidance_scale": {
"type": "number",
"title": "Guidance Scale",
"default": 7.5,
"maximum": 50,
"minimum": 1,
"x-order": 4,
"description": "Scale for classifier-free guidance"
},
"negative_prompt": {
"type": "string",
"title": "Negative Prompt",
"default": "blurry, distorted, low quality, worst quality, unrealistic, sketch, cartoon, artificial",
"x-order": 1,
"description": "Negative Prompt"
},
"ip_adapter_image": {
"type": "string",
"title": "Ip Adapter Image",
"x-order": 11,
"description": "Input image for IP adapter, base64 encoded string"
},
"ip_adapter_scale": {
"type": "number",
"title": "Ip Adapter Scale",
"default": 0.5,
"maximum": 1,
"minimum": 0,
"x-order": 10,
"description": "IP adapter guidance strength"
},
"num_inference_steps": {
"type": "integer",
"title": "Num Inference Steps",
"default": 30,
"maximum": 500,
"minimum": 1,
"x-order": 3,
"description": "Number of denoising steps"
}
}
}
Output schema
The shape of the response you’ll get when you run this model with an API.
Schema
{
"type": "array",
"items": {
"type": "string"
},
"title": "Output"
}