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aiunivers /utopianpony-inpainting:72ce888e

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
image
string
RGB image to inpaint
mask
string
Mask: white=replace (edit), black=keep (preserve original)
prompt
string
Positive prompt
negative_prompt
string
low quality, blurry, distorted, watermark, logo, text, artifacts, chromatic aberration
Negative prompt (defaults include artifact suppression)
steps
integer
40

Min: 1

Max: 150

Inference steps
guidance_scale
number
7.5

Max: 30

CFG guidance scale
scheduler
string
dpm
Scheduler (euler|euler_a|dpm)
strength
number
0.55

Min: 0.05

Max: 1

Inpaint strength (higher = more change; lower for subtle edits)
seed
integer
Random seed (blank = random)
enable_safety_checker
boolean
False
Enable safety checker (off by default)
guidance_rescale
number
0

Max: 1

Guidance rescale to mitigate over-saturation / burn from high CFG (0 = off)
mask_threshold
integer
0

Max: 255

Threshold (0-255) to binarize mask after optional inversion (0 = skip)
mask_feather
integer
0

Max: 128

Feather (Gaussian blur radius) applied AFTER binarization for softer edges (0 = none)
invert_mask
boolean
False
Invert mask BEFORE threshold (use if your white/black semantics are reversed)
karras
boolean
True
Use Karras sigmas for smoother scheduler noise (where supported)
preserve_composition
boolean
True
Pass SDXL original/target size hints to better preserve aspect & framing
lora_paths
string
Comma-separated paths (local or under weights/) to LoRA .safetensors for SDXL (applied sequentially)
lora_scale
number
0.8

Max: 2

Base scale for all LoRAs (individual scaling not yet exposed)
upscale_factor
number
1

Min: 1

Max: 2.5

Optional latent second-pass upscale factor ( >1 enables high-res re-inpaint pass)
upscale_steps
integer
15

Min: 5

Max: 60

Steps for high-res second pass (only used if upscale_factor>1)

Output schema

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

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