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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 painting of a 19th century town
|
None
|
negative_prompt |
string
|
ugly, disfigured, low quality, blurry, nsfw
|
The negative prompt to guide image generation.
|
num_inference_steps |
integer
|
40
Min: 20 Max: 100 |
Number of diffusion steps
|
guidance_scale |
number
|
7.5
Min: 0.1 Max: 30.0 |
Scale for classifier-free guidance
|
seed |
integer
|
None
|
|
width |
integer
|
768
|
None
|
height |
integer
|
768
|
None
|
sizing_strategy |
string
(enum)
|
width/height
Options: width/height, input_image, control_image |
Decide how to resize images – use width/height, resize based on input image or control image
|
num_outputs |
integer
|
1
Min: 1 Max: 4 |
Number of outputs
|
control_image |
string
|
Control image
|
|
image |
string
|
Optional img2img
|
|
mask_image |
string
|
Optional mask for inpainting
|
|
prompt_strength |
number
|
0.8
Max: 1.0 |
Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image
|
controlnet_conditioning_scale |
number
|
0.75
Max: 4.0 |
How strong the controlnet conditioning is
|
controlnet_start |
number
|
0.0
Max: 1.0 |
When controlnet conditioning starts
|
controlnet_end |
number
|
1.0
Max: 1.0 |
When controlnet conditioning ends
|
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'}