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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
|
Number of diffusion steps
|
| guidance_scale |
number
|
7.5
|
Scale for classifier-free guidance
|
| seed |
integer
|
None
|
|
| width |
integer
|
768
|
None
|
| height |
integer
|
768
|
None
|
| sizing_strategy |
None
|
width/height
|
Decide how to resize images – use width/height, resize based on input image or control image
|
| num_outputs |
integer
|
1
|
Number of outputs
|
| control_image |
string
|
Control image
|
|
| image |
string
|
Optional img2img
|
|
| mask_image |
string
|
Optional mask for inpainting
|
|
| prompt_strength |
number
|
0.8
|
Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image
|
| controlnet_conditioning_scale |
number
|
0.75
|
How strong the controlnet conditioning is
|
| controlnet_start |
number
|
0.0
|
When controlnet conditioning starts
|
| controlnet_end |
number
|
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'}