blendto
/
sdxl-controlnet-xformers
- Public
- 3K runs
Run blendto/sdxl-controlnet-xformers 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 |
|---|---|---|---|
| prompt1 |
string
|
Input prompt
|
|
| prompt2 |
string
|
Input prompt
|
|
| prompt3 |
string
|
Input prompt
|
|
| prompt4 |
string
|
Input prompt
|
|
| negative_prompt |
string
|
|
Input Negative Prompt
|
| image1 |
string
|
Input image for img2img or inpaint mode
|
|
| image2 |
string
|
Input image for img2img or inpaint mode
|
|
| image3 |
string
|
Input image for img2img or inpaint mode
|
|
| image4 |
string
|
Input image for img2img or inpaint mode
|
|
| width1 |
integer
|
1024
|
Width of output image
|
| height1 |
integer
|
1024
|
Height of output image
|
| height2 |
integer
|
1024
|
Height of output image
|
| width2 |
integer
|
1024
|
Width of output image
|
| height3 |
integer
|
1024
|
Height of output image
|
| width3 |
integer
|
1024
|
Width of output image
|
| height4 |
integer
|
1024
|
Height of output image
|
| width4 |
integer
|
1024
|
Width of output image
|
| scheduler |
string
(enum)
|
K_EULER
Options: DDIM, DPMSolverMultistep, HeunDiscrete, KarrasDPM, K_EULER_ANCESTRAL, K_EULER, PNDM, UNIPC |
scheduler
|
| num_inference_steps |
integer
|
50
Min: 1 Max: 500 |
Number of denoising steps
|
| guidance_scale |
number
|
7.5
Min: 1 Max: 50 |
Scale for classifier-free guidance
|
| seed |
integer
|
Random seed. Leave blank to randomize the seed
|
|
| controlnet_conditioning_scale |
number
|
0.6
Max: 1 |
ControlNet conditioning scale. Only applicable on trained models.
|
{
"type": "object",
"title": "Input",
"properties": {
"seed": {
"type": "integer",
"title": "Seed",
"x-order": 20,
"description": "Random seed. Leave blank to randomize the seed"
},
"image1": {
"type": "string",
"title": "Image1",
"format": "uri",
"x-order": 5,
"description": "Input image for img2img or inpaint mode"
},
"image2": {
"type": "string",
"title": "Image2",
"format": "uri",
"x-order": 6,
"description": "Input image for img2img or inpaint mode"
},
"image3": {
"type": "string",
"title": "Image3",
"format": "uri",
"x-order": 7,
"description": "Input image for img2img or inpaint mode"
},
"image4": {
"type": "string",
"title": "Image4",
"format": "uri",
"x-order": 8,
"description": "Input image for img2img or inpaint mode"
},
"width1": {
"type": "integer",
"title": "Width1",
"default": 1024,
"x-order": 9,
"description": "Width of output image"
},
"width2": {
"type": "integer",
"title": "Width2",
"default": 1024,
"x-order": 12,
"description": "Width of output image"
},
"width3": {
"type": "integer",
"title": "Width3",
"default": 1024,
"x-order": 14,
"description": "Width of output image"
},
"width4": {
"type": "integer",
"title": "Width4",
"default": 1024,
"x-order": 16,
"description": "Width of output image"
},
"height1": {
"type": "integer",
"title": "Height1",
"default": 1024,
"x-order": 10,
"description": "Height of output image"
},
"height2": {
"type": "integer",
"title": "Height2",
"default": 1024,
"x-order": 11,
"description": "Height of output image"
},
"height3": {
"type": "integer",
"title": "Height3",
"default": 1024,
"x-order": 13,
"description": "Height of output image"
},
"height4": {
"type": "integer",
"title": "Height4",
"default": 1024,
"x-order": 15,
"description": "Height of output image"
},
"prompt1": {
"type": "string",
"title": "Prompt1",
"x-order": 0,
"description": "Input prompt"
},
"prompt2": {
"type": "string",
"title": "Prompt2",
"x-order": 1,
"description": "Input prompt"
},
"prompt3": {
"type": "string",
"title": "Prompt3",
"x-order": 2,
"description": "Input prompt"
},
"prompt4": {
"type": "string",
"title": "Prompt4",
"x-order": 3,
"description": "Input prompt"
},
"scheduler": {
"enum": [
"DDIM",
"DPMSolverMultistep",
"HeunDiscrete",
"KarrasDPM",
"K_EULER_ANCESTRAL",
"K_EULER",
"PNDM",
"UNIPC"
],
"type": "string",
"title": "scheduler",
"description": "scheduler",
"default": "K_EULER",
"x-order": 17
},
"guidance_scale": {
"type": "number",
"title": "Guidance Scale",
"default": 7.5,
"maximum": 50,
"minimum": 1,
"x-order": 19,
"description": "Scale for classifier-free guidance"
},
"negative_prompt": {
"type": "string",
"title": "Negative Prompt",
"default": "",
"x-order": 4,
"description": "Input Negative Prompt"
},
"num_inference_steps": {
"type": "integer",
"title": "Num Inference Steps",
"default": 50,
"maximum": 500,
"minimum": 1,
"x-order": 18,
"description": "Number of denoising steps"
},
"controlnet_conditioning_scale": {
"type": "number",
"title": "Controlnet Conditioning Scale",
"default": 0.6,
"maximum": 1,
"minimum": 0,
"x-order": 21,
"description": "ControlNet conditioning scale. Only applicable on trained models."
}
}
}
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"
}