replicategithubwc
/
controlnet-sdxl
- Public
- 1.6K runs
Run replicategithubwc/controlnet-sdxl 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 |
|---|---|---|---|
| image |
string
|
Input image for img2img or inpaint mode
|
|
| prompt |
string
|
aerial view, a futuristic research complex in a bright foggy jungle, hard lighting
|
Input prompt
|
| negative_prompt |
string
|
low quality, bad quality, sketches
|
Input Negative Prompt
|
| model_type |
string
(enum)
|
canny
Options: canny, openpose, scribble |
ControlNet model type to use
|
| width |
integer
|
1024
|
Width of output image
|
| height |
integer
|
1024
|
Height of output image
|
| num_outputs |
integer
|
1
Min: 1 Max: 4 |
Number of images to output.
|
| num_inference_steps |
integer
|
50
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)
|
K_EULER
Options: DDIM, DPMSolverMultistep, HeunDiscrete, KarrasDPM, K_EULER_ANCESTRAL, K_EULER, PNDM |
scheduler
|
| condition_scale |
number
|
0.5
Max: 1 |
controlnet conditioning scale for generalization
|
| adapter_conditioning_factor |
number
|
1
Max: 1 |
Factor to scale image by
|
| low_threshold |
integer
|
100
Min: 1 Max: 255 |
Canny line detection low threshold
|
| high_threshold |
integer
|
200
Min: 1 Max: 255 |
Canny line detection high threshold
|
| seed |
integer
|
0
|
Random seed. Set to 0 to randomize the seed
|
{
"type": "object",
"title": "Input",
"properties": {
"seed": {
"type": "integer",
"title": "Seed",
"default": 0,
"x-order": 14,
"description": "Random seed. Set to 0 to randomize the seed"
},
"image": {
"type": "string",
"title": "Image",
"format": "uri",
"x-order": 0,
"description": "Input image for img2img or inpaint mode"
},
"width": {
"type": "integer",
"title": "Width",
"default": 1024,
"x-order": 4,
"description": "Width of output image"
},
"height": {
"type": "integer",
"title": "Height",
"default": 1024,
"x-order": 5,
"description": "Height of output image"
},
"prompt": {
"type": "string",
"title": "Prompt",
"default": "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
"x-order": 1,
"description": "Input prompt"
},
"scheduler": {
"enum": [
"DDIM",
"DPMSolverMultistep",
"HeunDiscrete",
"KarrasDPM",
"K_EULER_ANCESTRAL",
"K_EULER",
"PNDM"
],
"type": "string",
"title": "scheduler",
"description": "scheduler",
"default": "K_EULER",
"x-order": 9
},
"model_type": {
"enum": [
"canny",
"openpose",
"scribble"
],
"type": "string",
"title": "model_type",
"description": "ControlNet model type to use",
"default": "canny",
"x-order": 3
},
"num_outputs": {
"type": "integer",
"title": "Num Outputs",
"default": 1,
"maximum": 4,
"minimum": 1,
"x-order": 6,
"description": "Number of images to output."
},
"low_threshold": {
"type": "integer",
"title": "Low Threshold",
"default": 100,
"maximum": 255,
"minimum": 1,
"x-order": 12,
"description": "Canny line detection low threshold"
},
"guidance_scale": {
"type": "number",
"title": "Guidance Scale",
"default": 7.5,
"maximum": 20,
"minimum": 1,
"x-order": 8,
"description": "Scale for classifier-free guidance"
},
"high_threshold": {
"type": "integer",
"title": "High Threshold",
"default": 200,
"maximum": 255,
"minimum": 1,
"x-order": 13,
"description": "Canny line detection high threshold"
},
"condition_scale": {
"type": "number",
"title": "Condition Scale",
"default": 0.5,
"maximum": 1,
"minimum": 0,
"x-order": 10,
"description": "controlnet conditioning scale for generalization"
},
"negative_prompt": {
"type": "string",
"title": "Negative Prompt",
"default": "low quality, bad quality, sketches",
"x-order": 2,
"description": "Input Negative Prompt"
},
"num_inference_steps": {
"type": "integer",
"title": "Num Inference Steps",
"default": 50,
"maximum": 500,
"minimum": 1,
"x-order": 7,
"description": "Number of denoising steps"
},
"adapter_conditioning_factor": {
"type": "number",
"title": "Adapter Conditioning Factor",
"default": 1,
"maximum": 1,
"minimum": 0,
"x-order": 11,
"description": "Factor to scale image by"
}
}
}
Output schema
The shape of the response you’ll get when you run this model with an API.
{
"type": "array",
"items": {
"type": "string",
"format": "uri"
},
"title": "Output"
}