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asiryan /realism-xl:ff26a1f7

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
score_9, score_8_up, score_7_up, (Western Comics), girl, cute, gothic, seductive, innocent, pale skin, long straight black hair, floating hair, bangs, black goth dress, highly detailed, medieval theme, (fire in the fireplace background), (Full body photo), (depth of field), (dynamic angle), (ultra detailed, high quality texture), (sharp), (artistic image)
Input prompt
negative_prompt
string
score_6, score_5, score_4, bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, jpeg artifacts, deformed, noisy image, poorly drawn face, ugly face, crossed eyes
Negative Input prompt
image
string
Input image for img2img or inpaint mode
mask
string
Input mask for inpaint mode. Black areas will be preserved, white areas will be inpainted.
width
integer
1024
Width of output image
height
integer
1024
Height of output image
num_outputs
integer
1

Min: 1

Max: 4

Number of images to output.
scheduler
string (enum)
K_EULER_ANCESTRAL

Options:

DDIM, DPMSolverMultistep, HeunDiscrete, KarrasDPM, K_EULER_ANCESTRAL, K_EULER, PNDM

scheduler
num_inference_steps
integer
40

Min: 1

Max: 500

Number of denoising steps
guidance_scale
number
3.5

Min: 1

Max: 50

Scale for classifier-free guidance
strength
number
0.8

Max: 1

Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image
seed
integer
Random seed. Leave blank to randomize the seed
lora_scale
number
0.6

Max: 1

LoRA additive scale. Only applicable on trained models.
lora_weights
string
Replicate LoRA weights to use. Leave blank to use the default weights.

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'}