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expa-ai /cloudflare-hack:1643cc69
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
|
An astronaut riding a rainbow unicorn
|
Input prompt
|
| negative_prompt |
string
|
|
Negative Prompt
|
| segment_human |
boolean
|
False
|
Segment human from original photo as preprocessing.
|
| image |
string
|
Input image for img2img
|
|
| width |
integer
|
768
|
Width of output image
|
| height |
integer
|
768
|
Height of output image
|
| sizing_strategy |
string
(enum)
|
input_image
Options: width_height, input_image, controlnet_1_image, controlnet_2_image, controlnet_3_image, mask_image |
Decide how to resize images – use width/height, resize based on input image or control image
|
| num_outputs |
integer
|
1
Min: 1 Max: 4 |
Number of images to output
|
| scheduler |
string
(enum)
|
K_EULER
Options: DDIM, DPMSolverMultistep, HeunDiscrete, KarrasDPM, K_EULER_ANCESTRAL, K_EULER, PNDM |
scheduler
|
| num_inference_steps |
integer
|
30
Min: 1 Max: 500 |
Number of denoising steps
|
| guidance_scale |
number
|
7.5
Min: 1 Max: 50 |
Scale for classifier-free guidance
|
| prompt_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
|
|
| refine |
string
(enum)
|
no_refiner
Options: no_refiner, base_image_refiner |
Which refine style to use
|
| refine_steps |
integer
|
For base_image_refiner, the number of steps to refine, defaults to num_inference_steps
|
|
| apply_watermark |
boolean
|
True
|
Applies a watermark to enable determining if an image is generated in downstream applications. If you have other provisions for generating or deploying images safely, you can use this to disable watermarking.
|
| 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.
|
|
| upscale |
boolean
|
True
|
Enable tile upscale.
|
| upscale_steps |
integer
|
For the number of steps to upscale, defaults to num_inference_steps
|
|
| hdr |
number
|
0
Max: 1 |
HDR improvement over the original image
|
| apply_brand_bg |
boolean
|
True
|
Applies a brand background.
|
| controlnet_1 |
string
(enum)
|
none
Options: none, edge_canny, illusion, depth_leres, depth_midas, soft_edge_pidi, soft_edge_hed, lineart, lineart_anime, openpose, segment_animeface_v2 |
Controlnet
|
| controlnet_1_image |
string
|
Input image for first controlnet
|
|
| controlnet_1_conditioning_scale |
number
|
0.75
Max: 4 |
How strong the controlnet conditioning is
|
| controlnet_1_start |
number
|
0
Max: 1 |
When controlnet conditioning starts
|
| controlnet_1_end |
number
|
1
Max: 1 |
When controlnet conditioning ends
|
| controlnet_2 |
string
(enum)
|
none
Options: none, edge_canny, illusion, depth_leres, depth_midas, soft_edge_pidi, soft_edge_hed, lineart, lineart_anime, openpose, segment_animeface_v2 |
Controlnet
|
| controlnet_2_image |
string
|
Input image for second controlnet
|
|
| controlnet_2_conditioning_scale |
number
|
0.75
Max: 4 |
How strong the controlnet conditioning is
|
| controlnet_2_start |
number
|
0
Max: 1 |
When controlnet conditioning starts
|
| controlnet_2_end |
number
|
1
Max: 1 |
When controlnet conditioning ends
|
| controlnet_3 |
string
(enum)
|
none
Options: none, edge_canny, illusion, depth_leres, depth_midas, soft_edge_pidi, soft_edge_hed, lineart, lineart_anime, openpose, segment_animeface_v2 |
Controlnet
|
| controlnet_3_image |
string
|
Input image for third controlnet
|
|
| controlnet_3_conditioning_scale |
number
|
0.75
Max: 4 |
How strong the controlnet conditioning is
|
| controlnet_3_start |
number
|
0
Max: 1 |
When controlnet conditioning starts
|
| controlnet_3_end |
number
|
1
Max: 1 |
When controlnet conditioning ends
|
Output schema
The shape of the response you’ll get when you run this model with an API.
{'items': {'format': 'uri', 'type': 'string'},
'title': 'Output',
'type': 'array'}