baldesco/food_sd – API reference

baldesco / food_sd

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31 runs

Run baldesco/food_sd 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	A fantasy landscape, trending on artstation	Input prompt
negative_prompt	string		The prompt NOT to guide the image generation. Ignored when not using guidance
image	string		Inital image to generate variations of.
width	integer (enum)	512 Options: 128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024	Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits
height	integer (enum)	512 Options: 128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024	Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits
prompt_strength	number	0.8	Prompt strength when providing the image. 1.0 corresponds to full destruction of information in init image
num_outputs	integer	1 Min: 1 Max: 8	Number of images to output. Higher number of outputs may OOM.
num_inference_steps	integer	25 Min: 1 Max: 500	Number of denoising steps
guidance_scale	number	7.5 Min: 1 Max: 20	Scale for classifier-free guidance
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
embeddings_txt	string		Plain text file containing one textual inversion embedding.
embeddings_zip	string		Path to a zip file containing textual inversion embedding(s).
hf_token	string		Token to download HuggingFace models

{
  "type": "object",
  "title": "Input",
  "required": [
    "image"
  ],
  "properties": {
    "seed": {
      "type": "integer",
      "title": "Seed",
      "x-order": 10,
      "description": "Random seed. Leave blank to randomize the seed"
    },
    "image": {
      "type": "string",
      "title": "Image",
      "format": "uri",
      "x-order": 2,
      "description": "Inital image to generate variations of."
    },
    "width": {
      "enum": [
        128,
        256,
        384,
        448,
        512,
        576,
        640,
        704,
        768,
        832,
        896,
        960,
        1024
      ],
      "type": "integer",
      "title": "width",
      "description": "Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits",
      "default": 512,
      "x-order": 3
    },
    "height": {
      "enum": [
        128,
        256,
        384,
        448,
        512,
        576,
        640,
        704,
        768,
        832,
        896,
        960,
        1024
      ],
      "type": "integer",
      "title": "height",
      "description": "Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits",
      "default": 512,
      "x-order": 4
    },
    "prompt": {
      "type": "string",
      "title": "Prompt",
      "default": "A fantasy landscape, trending on artstation",
      "x-order": 0,
      "description": "Input prompt"
    },
    "hf_token": {
      "type": "string",
      "title": "Hf Token",
      "x-order": 13,
      "description": "Token to download HuggingFace models"
    },
    "scheduler": {
      "enum": [
        "DDIM",
        "K_EULER",
        "DPMSolverMultistep",
        "K_EULER_ANCESTRAL",
        "PNDM",
        "KLMS"
      ],
      "type": "string",
      "title": "scheduler",
      "description": "Choose a scheduler.",
      "default": "DPMSolverMultistep",
      "x-order": 9
    },
    "num_outputs": {
      "type": "integer",
      "title": "Num Outputs",
      "default": 1,
      "maximum": 8,
      "minimum": 1,
      "x-order": 6,
      "description": "Number of images to output. Higher number of outputs may OOM."
    },
    "embeddings_txt": {
      "type": "string",
      "title": "Embeddings Txt",
      "format": "uri",
      "x-order": 11,
      "description": "Plain text file containing one textual inversion embedding."
    },
    "embeddings_zip": {
      "type": "string",
      "title": "Embeddings Zip",
      "format": "uri",
      "x-order": 12,
      "description": "Path to a zip file containing textual inversion embedding(s)."
    },
    "guidance_scale": {
      "type": "number",
      "title": "Guidance Scale",
      "default": 7.5,
      "maximum": 20,
      "minimum": 1,
      "x-order": 8,
      "description": "Scale for classifier-free guidance"
    },
    "negative_prompt": {
      "type": "string",
      "title": "Negative Prompt",
      "x-order": 1,
      "description": "The prompt NOT to guide the image generation. Ignored when not using guidance"
    },
    "prompt_strength": {
      "type": "number",
      "title": "Prompt Strength",
      "default": 0.8,
      "x-order": 5,
      "description": "Prompt strength when providing the image. 1.0 corresponds to full destruction of information in init image"
    },
    "num_inference_steps": {
      "type": "integer",
      "title": "Num Inference Steps",
      "default": 25,
      "maximum": 500,
      "minimum": 1,
      "x-order": 7,
      "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",
    "format": "uri"
  },
  "title": "Output"
}