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nightmareai /majesty-diffusion:4e30be5a
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 |
|---|---|---|---|
| clip_prompt |
string
|
The portrait of a Majestic Princess, trending on artstation
|
Prompt for CLIP guidance
|
| latent_prompt |
string
|
The portrait of a Majestic Princess, trending on artstation
|
Prompt for latent diffusion
|
| progressive_output |
boolean
|
True
|
Progressive output (slows down generation)
|
| model |
string
(enum)
|
finetuned
Options: original, finetuned, ongo, erlich |
Latent diffusion model (ongo and erlich may need to download, taking extra time)
|
| latent_negatives |
string
|
|
Negative prompts for Latent Diffusion
|
| height |
integer
(enum)
|
256
Options: 128, 192, 256, 320, 384 |
Output height (output will be scaled up 1.5x with default settings)
|
| width |
integer
(enum)
|
256
Options: 128, 192, 256, 320, 384 |
Output width (output will be scaled up 1.5x with default settings)
|
| init_image |
string
|
Initial image
|
|
| init_mask |
string
|
A mask same width and height as the original image with the color black indicating where to inpaint
|
|
| init_scale |
integer
|
1000
|
Controls how much the init image should influence the final result. Experiment with values around 1000
|
| init_brightness |
number
|
0
|
Init image brightness
|
| latent_scale |
integer
|
12
|
The `latent_diffusion_guidance_scale` will determine how much the `latent_prompts` affect the image. Lower help with text interpretation, higher help with composition. Try values between 0-15. If you see too much text, lower it
|
| clip_scale |
integer
|
16000
|
CLIP guidance scale
|
| aesthetic_loss_scale |
integer
|
400
|
Aesthetic loss scale
|
| starting_timestep |
number
|
0.9
|
Starting timestep
|
| custom_settings |
string
|
Advanced settings file (will override any settings from above if present)
|
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',
'x-cog-array-type': 'iterator'}