tonyhopkins994 / test

  • Public
  • 227 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
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

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"
}