gmcolab
/
img2img_blend
- Public
- 173 runs
Run gmcolab/img2img_blend 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 |
|---|---|---|---|
| prompt |
string
|
Input prompt
|
|
| negative_prompt |
string
|
semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, poorly drawn, lowres, bad quality, worst quality, unrealistic, overexposed, underexposed
|
Negative prompt
|
| image |
string
|
Image generated with controlnet, overlaid with the original object
|
|
| noback_image |
string
|
The original object image, with removed background
|
|
| prompt_strength |
number
|
0.1
|
Prompt strength when providing the image. 1.0 corresponds to full destruction of information in init image
|
| num_outputs |
integer
|
1
Min: 1 Max: 8 |
Number of images to output. Higher number of outputs may OOM.
|
| num_inference_steps |
integer
|
20
Min: 1 Max: 500 |
Number of denoising steps
|
| guidance_scale |
number
|
1
Min: 1 Max: 20 |
Scale for classifier-free guidance
|
| scheduler |
string
(enum)
|
K_EULER_ANCESTRAL
Options: DDIM, K_EULER, DPMSolverMultistep, K_EULER_ANCESTRAL, PNDM, KLMS |
Choose a scheduler.
|
| seed |
integer
|
Random seed. Leave blank to randomize the seed
|
{
"type": "object",
"title": "Input",
"required": [
"prompt",
"image",
"noback_image"
],
"properties": {
"seed": {
"type": "integer",
"title": "Seed",
"x-order": 9,
"description": "Random seed. Leave blank to randomize the seed"
},
"image": {
"type": "string",
"title": "Image",
"format": "uri",
"x-order": 2,
"description": "Image generated with controlnet, overlaid with the original object"
},
"prompt": {
"type": "string",
"title": "Prompt",
"x-order": 0,
"description": "Input prompt"
},
"scheduler": {
"enum": [
"DDIM",
"K_EULER",
"DPMSolverMultistep",
"K_EULER_ANCESTRAL",
"PNDM",
"KLMS"
],
"type": "string",
"title": "scheduler",
"description": "Choose a scheduler.",
"default": "K_EULER_ANCESTRAL",
"x-order": 8
},
"num_outputs": {
"type": "integer",
"title": "Num Outputs",
"default": 1,
"maximum": 8,
"minimum": 1,
"x-order": 5,
"description": "Number of images to output. Higher number of outputs may OOM."
},
"noback_image": {
"type": "string",
"title": "Noback Image",
"format": "uri",
"x-order": 3,
"description": "The original object image, with removed background"
},
"guidance_scale": {
"type": "number",
"title": "Guidance Scale",
"default": 1,
"maximum": 20,
"minimum": 1,
"x-order": 7,
"description": "Scale for classifier-free guidance"
},
"negative_prompt": {
"type": "string",
"title": "Negative Prompt",
"default": "semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, poorly drawn, lowres, bad quality, worst quality, unrealistic, overexposed, underexposed",
"x-order": 1,
"description": "Negative prompt"
},
"prompt_strength": {
"type": "number",
"title": "Prompt Strength",
"default": 0.1,
"x-order": 4,
"description": "Prompt strength when providing the image. 1.0 corresponds to full destruction of information in init image"
},
"num_inference_steps": {
"type": "integer",
"title": "Num Inference Steps",
"default": 20,
"maximum": 500,
"minimum": 1,
"x-order": 6,
"description": "Number of denoising steps"
}
}
}
Output schema
The shape of the response you’ll get when you run this model with an API.
{
"type": "array",
"items": {
"type": "string",
"format": "uri"
},
"title": "Output"
}