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usamaehsan /controlnet-x-majic-mix-realistic-x-ip-adapter:0ee845d7

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
Prompt - using compel, use +++ to increase words weight:: doc: https://github.com/damian0815/compel/tree/main/doc || https://invoke-ai.github.io/InvokeAI/features/PROMPTS/#attention-weighting
negative_prompt
string
Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality
Negative prompt - using compel, use +++ to increase words weight
num_inference_steps
integer
20
Steps to run denoising
guidance_scale
number
7

Min: 0.1

Max: 30

Scale for classifier-free guidance
seed
integer
Seed
eta
number
0
Controls the amount of noise that is added to the input data during the denoising diffusion process. Higher value -> more noise
guess_mode
boolean
False
In this mode, the ControlNet encoder will try best to recognize the content of the input image even if you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
disable_safety_check
boolean
False
Disable safety check. Use at your own risk!
num_outputs
integer
1

Min: 1

Max: 10

Number of images to generate
max_width
integer
512
Max width/Resolution of image
max_height
integer
512
Max height/Resolution of image
scheduler
string (enum)
DDIM

Options:

DDIM, DPMSolverMultistep, HeunDiscrete, K_EULER_ANCESTRAL, K_EULER, KLMS, PNDM, UniPCMultistep

Choose a scheduler.
lineart_image
string
Control image for canny controlnet
lineart_conditioning_scale
number
1
Conditioning scale for canny controlnet
scribble_image
string
Control image for scribble controlnet
scribble_conditioning_scale
number
1
Conditioning scale for scribble controlnet
tile_image
string
Control image for tile controlnet
tile_conditioning_scale
number
1
Conditioning scale for tile controlnet
brightness_image
string
Control image for brightness controlnet
brightness_conditioning_scale
number
1
Conditioning scale for brightness controlnet
inpainting_image
string
Control image for inpainting controlnet
mask_image
string
mask image for inpainting controlnet
inpainting_conditioning_scale
number
1
Conditioning scale for brightness controlnet
sorted_controlnets
string
tile, inpainting, lineart
Comma seperated string of controlnet names, list of names: tile, inpainting, lineart,depth ,scribble , brightness /// example value: tile, inpainting, lineart
ip_adapter_ckpt
string (enum)
ip-adapter_sd15.bin

Options:

ip-adapter_sd15.bin, ip-adapter-plus_sd15.bin, ip-adapter-plus-face_sd15.bin

IP Adapter checkpoint
ip_adapter_image
string
IP Adapter image
ip_adapter_weight
number
1
IP Adapter weight
img2img_image
string
Image2image image
img2img_strength
number
0.5
img2img strength, does not work when inpainting image is given, 0.1-same image, 0.99-complete destruction of image
add_more_detail_lora_scale
number
0.5
Scale/ weight of more_details lora, more scale = more details

Output schema

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

Schema
{'items': {'format': 'uri', 'type': 'string'},
 'title': 'Output',
 'type': 'array'}