tonyhopkins994/cnplus-prod

tonyhopkins994 / cnplus-prod

Public
21 runs

Run tonyhopkins994/cnplus-prod 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	Input prompt
negative_prompt	string	blurry, distorted	Negative Prompt
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
num_outputs	integer	1 Min: 1 Max: 6	Number of images to output
ip_adapter_scale	number	0.5	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

{
  "type": "object",
  "title": "Input",
  "properties": {
    "prompt": {
      "type": "string",
      "title": "Prompt",
      "default": "Living Room",
      "x-order": 0,
      "description": "Input prompt"
    },
    "mlsd_image": {
      "type": "string",
      "title": "Mlsd Image",
      "x-order": 9,
      "description": "Input image for mlsd controlnet, base64 encoded string"
    },
    "depth_image": {
      "type": "string",
      "title": "Depth Image",
      "x-order": 8,
      "description": "Input image for depth controlnet, base64 encoded string"
    },
    "num_outputs": {
      "type": "integer",
      "title": "Num Outputs",
      "default": 1,
      "maximum": 6,
      "minimum": 1,
      "x-order": 4,
      "description": "Number of images to output"
    },
    "normal_image": {
      "type": "string",
      "title": "Normal Image",
      "x-order": 7,
      "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": 3,
      "description": "Scale for classifier-free guidance"
    },
    "negative_prompt": {
      "type": "string",
      "title": "Negative Prompt",
      "default": "blurry, distorted",
      "x-order": 1,
      "description": "Negative Prompt"
    },
    "ip_adapter_image": {
      "type": "string",
      "title": "Ip Adapter Image",
      "x-order": 6,
      "description": "Input image for IP adapter, base64 encoded string"
    },
    "ip_adapter_scale": {
      "type": "number",
      "title": "Ip Adapter Scale",
      "default": 0.5,
      "x-order": 5,
      "description": "IP adapter guidance strength"
    },
    "num_inference_steps": {
      "type": "integer",
      "title": "Num Inference Steps",
      "default": 30,
      "maximum": 500,
      "minimum": 1,
      "x-order": 2,
      "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"
}