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fofr /sdxl-lcm-video2video:b960f1c3
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
|
| video |
string
|
Video to split into frames
|
|
| fps |
integer
|
8
Min: 1 |
Number of images per second of video, when not exporting all frames
|
| extract_all_frames |
boolean
|
False
|
Get every frame of the video. Ignores fps. Slow for large videos.
|
| max_width |
integer
|
512
Min: 1 |
Maximum width of the video. Maintains aspect ratio.
|
| num_inference_steps |
integer
|
4
Min: 1 Max: 30 |
Number of denoising steps
|
| guidance_scale |
number
|
1.1
Max: 5 |
Scale for classifier-free guidance
|
| prompt_strength |
number
|
0.5
Max: 1 |
Prompt strength. 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.
|
|
| controlnet_1 |
string
(enum)
|
none
Options: none, edge_canny, illusion, depth_leres, depth_midas, soft_edge_pidi, soft_edge_hed, lineart, lineart_anime, openpose |
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 |
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 |
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
|
| return_frames |
boolean
|
False
|
Return a tar file with all the frames alongside the video
|
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'}