jerray
/
realistic-vision-v5
- Public
- 70 runs
Run jerray/realistic-vision-v5 with an API
Use one of our client libraries to get started quickly. Clicking on a library will take you to the Playground tab where you can tweak different inputs, see the results, and copy the corresponding code to use in your own project.
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 |
|---|---|---|---|
| face_image |
string
|
Input face image
|
|
| control_image |
string
|
Control image
|
|
| prompt |
string
|
a photo of an astronaut riding a horse on mars
|
Input prompt
|
| negative_prompt |
string
|
Specify things to not see in the output
|
|
| clip_skip |
integer
|
1
Min: 1 |
None
|
| width |
integer
(enum)
|
512
Options: 128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024 |
Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits
|
| height |
integer
(enum)
|
512
Options: 128, 256, 384, 448, 512, 576, 640, 704, 768, 832, 896, 960, 1024 |
Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits
|
| num_outputs |
integer
|
1
Min: 1 Max: 4 |
Number of images to output.
|
| num_inference_steps |
integer
|
30
Min: 1 Max: 500 |
Number of denoising steps
|
| guidance_scale |
number
|
7.5
Min: 1 Max: 20 |
Scale for classifier-free guidance
|
| scheduler |
string
(enum)
|
DPMSolverMultistep
Options: PNDM, KLMS, DDIM, K_EULER, K_EULER_ANCESTRAL, DPMSolverMultistep, DPM++ SDE Karras, DPM++ 2M Karras |
Choose a scheduler.
|
| canny_low_threshold |
integer
|
100
Min: 1 Max: 255 |
Canny line detection low threshold
|
| canny_high_threshold |
integer
|
200
Min: 1 Max: 255 |
Canny line detection high threshold
|
| controlnet_conditioning_scale |
number
|
1
Max: 2 |
Control Weight
|
| control_guidance_start |
number
|
0
Max: 1 |
The percentage of total steps at which the controlnet starts applying
|
| control_guidance_end |
number
|
1
Max: 1 |
The percentage of total steps at which the controlnet stops applying
|
| resize_mode |
string
(enum)
|
fill
Options: fill, crop, cover |
fill - The image is resized to fill the given dimension. cover - The image keeps its aspect ratio and fills the given dimension. The image will be clipped to fit. crop - The image keeps its aspect ratio and scales to the target size.
|
| seed |
integer
|
Random seed. Leave blank to randomize the seed
|
|
| lora_model |
string
(enum)
|
Options: adventurers_v1, add_detail, thick_impasto_painting |
An enumeration.
|
| cross_attention_scale |
number
|
0.8
Max: 1 |
A scale value of 0 is the same as not using your LoRA weights and you’re only using the base model weights, and a scale value of 1 means you’re only using the fully finetuned LoRA weights.
|
| restore_face_upscale |
integer
|
1
Min: 1 Max: 4 |
Restore face upscaling
|
| restore_face_upsample |
boolean
|
True
|
Restore face upsampling
|
| restore_face_background_enhance |
boolean
|
True
|
Restore face background enhance
|
| codeformer_fidelity |
number
|
0.7
Max: 1 |
Codeformer fidelity
|
{
"type": "object",
"title": "Input",
"required": [
"face_image"
],
"properties": {
"seed": {
"type": "integer",
"title": "Seed",
"x-order": 17,
"description": "Random seed. Leave blank to randomize the seed"
},
"width": {
"enum": [
128,
256,
384,
448,
512,
576,
640,
704,
768,
832,
896,
960,
1024
],
"type": "integer",
"title": "width",
"description": "Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits",
"default": 512,
"x-order": 5
},
"height": {
"enum": [
128,
256,
384,
448,
512,
576,
640,
704,
768,
832,
896,
960,
1024
],
"type": "integer",
"title": "height",
"description": "Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits",
"default": 512,
"x-order": 6
},
"prompt": {
"type": "string",
"title": "Prompt",
"default": "a photo of an astronaut riding a horse on mars",
"x-order": 2,
"description": "Input prompt"
},
"clip_skip": {
"type": "integer",
"title": "Clip Skip",
"default": 1,
"minimum": 1,
"x-order": 4
},
"scheduler": {
"enum": [
"PNDM",
"KLMS",
"DDIM",
"K_EULER",
"K_EULER_ANCESTRAL",
"DPMSolverMultistep",
"DPM++ SDE Karras",
"DPM++ 2M Karras"
],
"type": "string",
"title": "scheduler",
"description": "Choose a scheduler.",
"default": "DPMSolverMultistep",
"x-order": 10
},
"face_image": {
"type": "string",
"title": "Face Image",
"format": "uri",
"x-order": 0,
"description": "Input face image"
},
"lora_model": {
"enum": [
"adventurers_v1",
"add_detail",
"thick_impasto_painting"
],
"type": "string",
"title": "lora_model",
"description": "An enumeration.",
"x-order": 18
},
"num_outputs": {
"type": "integer",
"title": "Num Outputs",
"default": 1,
"maximum": 4,
"minimum": 1,
"x-order": 7,
"description": "Number of images to output."
},
"resize_mode": {
"enum": [
"fill",
"crop",
"cover"
],
"type": "string",
"title": "resize_mode",
"description": "fill - The image is resized to fill the given dimension. cover - The image keeps its aspect ratio and fills the given dimension. The image will be clipped to fit. crop - The image keeps its aspect ratio and scales to the target size.",
"default": "fill",
"x-order": 16
},
"control_image": {
"type": "string",
"title": "Control Image",
"format": "uri",
"x-order": 1,
"description": "Control image"
},
"guidance_scale": {
"type": "number",
"title": "Guidance Scale",
"default": 7.5,
"maximum": 20,
"minimum": 1,
"x-order": 9,
"description": "Scale for classifier-free guidance"
},
"negative_prompt": {
"type": "string",
"title": "Negative Prompt",
"x-order": 3,
"description": "Specify things to not see in the output"
},
"canny_low_threshold": {
"type": "integer",
"title": "Canny Low Threshold",
"default": 100,
"maximum": 255,
"minimum": 1,
"x-order": 11,
"description": "Canny line detection low threshold"
},
"codeformer_fidelity": {
"type": "number",
"title": "Codeformer Fidelity",
"default": 0.7,
"maximum": 1,
"minimum": 0,
"x-order": 23,
"description": "Codeformer fidelity"
},
"num_inference_steps": {
"type": "integer",
"title": "Num Inference Steps",
"default": 30,
"maximum": 500,
"minimum": 1,
"x-order": 8,
"description": "Number of denoising steps"
},
"canny_high_threshold": {
"type": "integer",
"title": "Canny High Threshold",
"default": 200,
"maximum": 255,
"minimum": 1,
"x-order": 12,
"description": "Canny line detection high threshold"
},
"control_guidance_end": {
"type": "number",
"title": "Control Guidance End",
"default": 1,
"maximum": 1,
"minimum": 0,
"x-order": 15,
"description": "The percentage of total steps at which the controlnet stops applying"
},
"restore_face_upscale": {
"type": "integer",
"title": "Restore Face Upscale",
"default": 1,
"maximum": 4,
"minimum": 1,
"x-order": 20,
"description": "Restore face upscaling"
},
"cross_attention_scale": {
"type": "number",
"title": "Cross Attention Scale",
"default": 0.8,
"maximum": 1,
"minimum": 0,
"x-order": 19,
"description": " A scale value of 0 is the same as not using your LoRA weights and you\u2019re only using the base model weights, and a scale value of 1 means you\u2019re only using the fully finetuned LoRA weights."
},
"restore_face_upsample": {
"type": "boolean",
"title": "Restore Face Upsample",
"default": true,
"x-order": 21,
"description": "Restore face upsampling"
},
"control_guidance_start": {
"type": "number",
"title": "Control Guidance Start",
"default": 0,
"maximum": 1,
"minimum": 0,
"x-order": 14,
"description": "The percentage of total steps at which the controlnet starts applying"
},
"controlnet_conditioning_scale": {
"type": "number",
"title": "Controlnet Conditioning Scale",
"default": 1,
"maximum": 2,
"minimum": 0,
"x-order": 13,
"description": "Control Weight"
},
"restore_face_background_enhance": {
"type": "boolean",
"title": "Restore Face Background Enhance",
"default": true,
"x-order": 22,
"description": "Restore face background enhance"
}
}
}
Output schema
The shape of the response you’ll get when you run this model with an API.
Schema
{
"type": "array",
"items": {
"type": "string",
"format": "uri"
},
"title": "Output"
}