raywind/voxelart – API reference

raywind / voxelart

Voxel Art

Public
35 runs

Run raywind/voxelart 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		Prompt to guide the image generation
negative_prompt	string		The prompt or prompts not to guide the image generation (what you do not want to see in the generation). Ignored when not using guidance.
num_outputs	integer (enum)	1 Options: 1, 4	Number of images to output
num_inference_steps	integer	20 Min: 1 Max: 500	The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
guidance_scale	number	7.5 Min: 1 Max: 20	Scale for classifier-free guidance. Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality.
scheduler	string (enum)	DPMSolverMultistep Options: DDIM, K_EULER, DPMSolverMultistep, K_EULER_ANCESTRAL, PNDM, KLMS	Choose a scheduler.
seed	integer		Random seed. Leave blank to randomize the seed

{
  "type": "object",
  "title": "Input",
  "required": [
    "prompt"
  ],
  "properties": {
    "seed": {
      "type": "integer",
      "title": "Seed",
      "x-order": 6,
      "description": "Random seed. Leave blank to randomize the seed"
    },
    "prompt": {
      "type": "string",
      "title": "Prompt",
      "x-order": 0,
      "description": "Prompt to guide the image generation"
    },
    "scheduler": {
      "enum": [
        "DDIM",
        "K_EULER",
        "DPMSolverMultistep",
        "K_EULER_ANCESTRAL",
        "PNDM",
        "KLMS"
      ],
      "type": "string",
      "title": "scheduler",
      "description": "Choose a scheduler.",
      "default": "DPMSolverMultistep",
      "x-order": 5
    },
    "num_outputs": {
      "enum": [
        1,
        4
      ],
      "type": "integer",
      "title": "num_outputs",
      "description": "Number of images to output",
      "default": 1,
      "x-order": 2
    },
    "guidance_scale": {
      "type": "number",
      "title": "Guidance Scale",
      "default": 7.5,
      "maximum": 20,
      "minimum": 1,
      "x-order": 4,
      "description": "Scale for classifier-free guidance. Higher guidance scale encourages to generate images that are closely linked to the text prompt, usually at the expense of lower image quality."
    },
    "negative_prompt": {
      "type": "string",
      "title": "Negative Prompt",
      "x-order": 1,
      "description": "The prompt or prompts not to guide the image generation (what you do not want to see in the generation). Ignored when not using guidance."
    },
    "num_inference_steps": {
      "type": "integer",
      "title": "Num Inference Steps",
      "default": 20,
      "maximum": 500,
      "minimum": 1,
      "x-order": 3,
      "description": "The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference."
    }
  }
}

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"
}