Controlnet - ModelsLab

Request

Send a POST request to below endpoint.

curl

--request POST 'https://modelslab.com/api/v1/enterprise/controlnet' \

To use the load balancer, you need to have more than 1 server. Pass the first server’s API key, and it will handle the load balancing with the other servers.

You can also use multi ControlNet. Just make sure to pass comma saparated controlnet models to the controlnet_model as “canny,depth” and init_image in the request body.

You can also use multi Lora. Just make sure to pass comma saparated lora model ids to the lora_model as "more_details,animie" in the request body.

You can also use this endpoint to inpaint images with ControlNet. Just make sure to pass the link to the mask_image in the request body. and use controlnet_model as inpaint.

Body

json

{  
  "key": "enterprise_api_key",  
  "controlnet_model": "canny",  
  "controlnet_type" :"canny",  
  "model_id": "midjourney",  
  "auto_hint": "yes",  
  "guess_mode" : "no",  
  "prompt": "a model doing photoshoot, ultra high resolution, 4K image",  
  "negative_prompt": null,  
  "init_image": "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_imgvar/input_image_vermeer.png",  
  "mask_image": null,  
  "width": "512",  
  "height": "512",  
  "samples": "1",  
  "scheduler": "UniPCMultistepScheduler",  
  "num_inference_steps": "30",  
  "safety_checker": "no",  
  "enhance_prompt": "yes",  
  "guidance_scale": 7.5,  
  "strength": 0.55,  
  "seed": null,  
  "webhook": null,  
  "track_id": null
}

ControlNet Models

ControlNet API using Controlnet 1.1 as default: Suported controlnet_model:

canny
depth
hed
mlsd
normal
openpose
scribble
segmentation
inpaint
softedge
lineart
shuffle
tile
face_detector
qrcode

Schedulers

This endpoint also supports schedulers. Use the scheduler parameter in the request body to pass a specific scheduler from the list below:

DDPMScheduler
DDIMScheduler
PNDMScheduler
LMSDiscreteScheduler
EulerDiscreteScheduler
EulerAncestralDiscreteScheduler
DPMSolverMultistepScheduler
HeunDiscreteScheduler
KDPM2DiscreteScheduler
DPMSolverSinglestepScheduler
KDPM2AncestralDiscreteScheduler
UniPCMultistepScheduler
DDIMInverseScheduler
DEISMultistepScheduler
IPNDMScheduler
KarrasVeScheduler
ScoreSdeVeScheduler
LCMScheduler

Body Attributes

string

required

Your enterprise API Key used for request authorization.

string

required

The ID of the model to be used. It can be a public model or your trained model.

string

ControlNet model ID. It can be from the models list or user trained.

string

ControlNet model type. It can be from the models list.

string

default:"no"

Auto hint image. Options: yes/no.

string

default:"no"

If set to yes and no prompt is passed, the model will attempt to guess what’s in the init_image and create variations. Options: yes/no.

string

Text prompt describing required image modifications. Make it detailed for best results.

string

Items you don’t want in the image.

string

Link to the initial image.

string

Link to the ControlNet image.

string

Link to the mask image for inpainting.

integer

default:"512"

Width of the image. Maximum: 1024.

integer

default:"512"

Height of the image. Maximum: 1024.

integer

default:"1"

Number of images to return in the response. Maximum: 4.

string

The scheduler to use. See Schedulers.

string

default:"yes"

Enable ToMeSD for faster results. Default: yes.

string

default:"yes"

Use Karras sigmas for improved results. Default: yes.

string

default:"none"

Used in DPMSolverMultistepScheduler. Default: none.

string

Custom VAE to use. Default: null.

string

Strength values for LoRa models (comma-separated). Range: 0.1–1.

string

LoRa models to use (comma-separated). Example: contrast-fix,yae-miko-genshin

integer

default:"21"

Number of denoising steps. Allowed values: 21 or 31.

string

default:"yes"

NSFW checker. If detected, replaces image with blank. Default: yes.

string

default:"yes"

Enhance prompts for better results. Default: yes.

string

default:"yes"

Enable multilingual input. Default: yes.

number

default:"7.5"

Scale for classifier-free guidance.

number

default:"1"

Scale for ControlNet guidance.

number

default:"0.7"

Prompt strength when using init_image. 1.0 fully overrides the init image.

integer

Seed for reproducibility. Pass null for random generation.

string

URL to receive a POST callback when image generation is complete.

string

ID returned in webhook callback to identify the request.

string

default:"no"

Set to yes to upscale the generated image 2x.

integer

Clip skip value. Range: 1–8.

string

default:"no"

Return response as base64. You can also pass init_image, mask_image, and control_image as base64. Default: no.

string

default:"no"

Generate a temporary image link valid for 24 hours. Default: no.

​Request

​Body

​ControlNet Models

​Schedulers

​Body Attributes

Request

Body

ControlNet Models

Schedulers

Body Attributes