ductridev/potat1-50000 – API reference

ductridev / potat1-50000

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79 runs

Run ductridev/potat1-50000 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	anime	The prompt or prompts to guide the video generation.
negative_prompt	string	noise, text, nude	The prompt or prompts not to guide the video generation.
cfg_scale	number	9 Min: 1 Max: 20	Scale for classifier-free guidance (minimum: 1; maximum: 20)
scheduler	string (enum)	DPM++ 2M Karras Options: DPM++ 2M Karras, Euler a	Schedulers(Sampler) define the whole denoising process
height	integer	512 Min: 256	Height of the generated video
width	integer	512 Min: 256	Width of the generated video
num_inference_steps	integer	25 Min: 1 Max: 500	Number of denoising steps (minimum: 1; maximum: 500)
seed	integer		Random seed. Leave blank to randomize the seed
nums_frame	integer	32 Min: 32	Number frames of video
fps	integer	4	fps for the output video

{
  "type": "object",
  "title": "Input",
  "properties": {
    "fps": {
      "type": "integer",
      "title": "Fps",
      "default": 4,
      "x-order": 9,
      "description": "fps for the output video"
    },
    "seed": {
      "type": "integer",
      "title": "Seed",
      "x-order": 7,
      "description": "Random seed. Leave blank to randomize the seed"
    },
    "width": {
      "type": "integer",
      "title": "Width",
      "default": 512,
      "minimum": 256,
      "x-order": 5,
      "description": "Width of the generated video"
    },
    "height": {
      "type": "integer",
      "title": "Height",
      "default": 512,
      "minimum": 256,
      "x-order": 4,
      "description": "Height of the generated video"
    },
    "prompt": {
      "type": "string",
      "title": "Prompt",
      "default": "anime",
      "x-order": 0,
      "description": "The prompt or prompts to guide the video generation."
    },
    "cfg_scale": {
      "type": "number",
      "title": "Cfg Scale",
      "default": 9,
      "maximum": 20,
      "minimum": 1,
      "x-order": 2,
      "description": "Scale for classifier-free guidance (minimum: 1; maximum: 20)"
    },
    "scheduler": {
      "enum": [
        "DPM++ 2M Karras",
        "Euler a"
      ],
      "type": "string",
      "title": "scheduler",
      "description": "Schedulers(Sampler) define the whole denoising process",
      "default": "DPM++ 2M Karras",
      "x-order": 3
    },
    "nums_frame": {
      "type": "integer",
      "title": "Nums Frame",
      "default": 32,
      "minimum": 32,
      "x-order": 8,
      "description": "Number frames of video"
    },
    "negative_prompt": {
      "type": "string",
      "title": "Negative Prompt",
      "default": "noise, text, nude",
      "x-order": 1,
      "description": "The prompt or prompts not to guide the video generation."
    },
    "num_inference_steps": {
      "type": "integer",
      "title": "Num Inference Steps",
      "default": 25,
      "maximum": 500,
      "minimum": 1,
      "x-order": 6,
      "description": "Number of denoising steps (minimum: 1; maximum: 500)"
    }
  }
}

Output schema

The shape of the response you’ll get when you run this model with an API.

Schema

{
  "type": "string",
  "title": "Output",
  "format": "uri"
}