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tiger-ai-lab /anyv2v:3c7b5bc5
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 |
|---|---|---|---|
| video |
string
|
Input video
|
|
| edited_first_frame |
string
|
Provide the edited first frame of the input video. This is optional, leave it blank and provide the prompt below to use the default pipeline that edits the frist frame with instructpix2pix
|
|
| instruct_pix2pix_prompt |
string
|
turn man into robot
|
The first step invovles using timbrooks/instruct-pix2pix to edit the first frame. Specify the prompt for editing the first frame. This will be ignored if edited_first_frame above is provided.
|
| editing_prompt |
string
|
a man doing exercises for the body and mind
|
Describe the input video
|
| editing_negative_prompt |
string
|
Distorted, discontinuous, Ugly, blurry, low resolution, motionless, static, disfigured, disconnected limbs, Ugly faces, incomplete arms
|
Things not to see int the edited video
|
| num_inference_steps |
integer
|
50
Min: 1 Max: 500 |
Number of denoising steps
|
| guidance_scale |
number
|
9
Min: 1 Max: 20 |
Scale for classifier-free guidance
|
| pnp_f_t |
number
|
1
Max: 1 |
Specifies the proportion of time steps in the DDIM sampling process where the convolutional injection is applied. A higher value improves motion consistency. 1.0 indicates injection at every time step
|
| pnp_spatial_attn_t |
number
|
1
Max: 1 |
Specifies the proportion of time steps in the DDIM sampling process where the spatial attention injection is applied. A higher value improves motion consistency. 1.0 indicates injection at every time step
|
| pnp_temp_attn_t |
number
|
1
Max: 1 |
Specifies the proportion of time steps in the DDIM sampling process where the temporal attention injection is applied. A higher value improves motion consistency. 1.0 indicates injection at every time step
|
| ddim_init_latents_t_idx |
integer
|
0
|
This parameter determines the time step index at which to begin sampling from the initial DDIM inversed latents, with a range of [0, num_inference_steps-1]. In the context of a DDIM sampling process where the sampling step is 50, the scheduler progresses through the time steps in the sequence [981, 961, 941, ..., 1]. Therefore, setting ddim_init_latents_t_idx to 0 initiates the sampling from t=981, whereas setting it to 1 starts the process at t=961. A higher index enhances motion consistency with the source video but may lead to flickering and cause the edited video to diverge from the edited first frame.
|
| ddim_inversion_steps |
integer
|
100
|
Number of ddim inversion steps
|
| seed |
integer
|
Random seed. Leave blank to randomize the seed
|
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'}