peter942/steerable-motion – Run with an API on Replicate

peter942 / steerable-motion

Cold

Public
237 runs
A100 (80GB)

Run with an API

Playground API Examples README Versions

Input

image_list

*file

Image list (.zip file)

image_prompt_list

string

Shift + Return to add a new line

Image prompt list

Default: "0_:16_:24_:36_:48_:60_:72_:84_:96_:108_"

negative_prompt

string

Shift + Return to add a new line

Negative Prompt

Default: "(worst quality, low quality:1.2)"

type_of_frame_distribution

string

Frame distribution

Default: "linear"

linear_frame_distribution_value

integer

Linear Frames per keyframe

Default: 16

dynamic_frame_distribution_values

string

Shift + Return to add a new line

Dynamic Frames per keyframe

Default: "0,10,26,40,46"

type_of_key_frame_influence

string

Frame distribution

Default: "linear"

linear_key_frame_influence_value

number

Linear keyframe influence

Default: 0.9

dynamic_key_frame_influence_values

string

Shift + Return to add a new line

Dynamic keyframe influence

Default: "0.5,0.5,2.0,0.5"

buffer

integer

Buffer

Default: 4

type_of_cn_strength_distribution

string

Type of CN strength distribution

Default: "dynamic"

linear_cn_strength_value

string

Shift + Return to add a new line

Linear CN strength influence

Default: "(0.0,1.0)"

dynamic_cn_strength_values

string

Shift + Return to add a new line

Dynamic CN strength influence

Default: "(0.0,1.0),(0.0,1.0),(0.0,1.0),(0.0,1.0)"

soft_scaled_cn_weights_multiplier

number

cn multiplier

Default: 0.87

stmfnet_multiplier

number

stmfnet multiplier

Default: 2

ckpt

string

Select a Module

Default: "Counterfeit-V3.0_fp32.safetensors"

motion_scale

number

Motion scale

Default: 0.8

relative_ipadapter_strength

number

IP adapter image weight

Default: 1

relative_ipadapter_influence

number

IP adapter image weight

Default: 1

ipadapter_noise

number

IP adapter noise

Default: 0

image_dimension

string

Select image dimenstions

Default: "512x512"

output_format

string

Output format

Default: "video/h264-mp4"

Run this model in Node.js with one line of code:

npx create-replicate --model=peter942/steerable-motion

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run peter942/steerable-motion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "peter942/steerable-motion:aa308181b5df669f20e56411b74ebafd7c01f82f7fe2a34a4b9382d4bd8155ba",
  {
    input: {
      ckpt: "Counterfeit-V3.0_fp32.safetensors",
      buffer: 4,
      motion_scale: 0.8,
      output_format: "video/h264-mp4",
      image_dimension: "512x512",
      ipadapter_noise: 0,
      negative_prompt: "(worst quality, low quality:1.2)",
      image_prompt_list: "0_:16_:24_:36_:48_:60_:72_:84_:96_:108_",
      stmfnet_multiplier: 2,
      linear_cn_strength_value: "(0.0,1.0)",
      dynamic_cn_strength_values: "(0.0,1.0),(0.0,1.0),(0.0,1.0),(0.0,1.0)",
      type_of_frame_distribution: "linear",
      relative_ipadapter_strength: 1,
      type_of_key_frame_influence: "linear",
      relative_ipadapter_influence: 1,
      linear_frame_distribution_value: 16,
      linear_key_frame_influence_value: 0.9,
      type_of_cn_strength_distribution: "dynamic",
      dynamic_frame_distribution_values: "0,10,26,40,46",
      soft_scaled_cn_weights_multiplier: 0.87,
      dynamic_key_frame_influence_values: "0.5,0.5,2.0,0.5"
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run peter942/steerable-motion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "peter942/steerable-motion:aa308181b5df669f20e56411b74ebafd7c01f82f7fe2a34a4b9382d4bd8155ba",
    input={
        "ckpt": "Counterfeit-V3.0_fp32.safetensors",
        "buffer": 4,
        "motion_scale": 0.8,
        "output_format": "video/h264-mp4",
        "image_dimension": "512x512",
        "ipadapter_noise": 0,
        "negative_prompt": "(worst quality, low quality:1.2)",
        "image_prompt_list": "0_:16_:24_:36_:48_:60_:72_:84_:96_:108_",
        "stmfnet_multiplier": 2,
        "linear_cn_strength_value": "(0.0,1.0)",
        "dynamic_cn_strength_values": "(0.0,1.0),(0.0,1.0),(0.0,1.0),(0.0,1.0)",
        "type_of_frame_distribution": "linear",
        "relative_ipadapter_strength": 1,
        "type_of_key_frame_influence": "linear",
        "relative_ipadapter_influence": 1,
        "linear_frame_distribution_value": 16,
        "linear_key_frame_influence_value": 0.9,
        "type_of_cn_strength_distribution": "dynamic",
        "dynamic_frame_distribution_values": "0,10,26,40,46",
        "soft_scaled_cn_weights_multiplier": 0.87,
        "dynamic_key_frame_influence_values": "0.5,0.5,2.0,0.5"
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run peter942/steerable-motion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "aa308181b5df669f20e56411b74ebafd7c01f82f7fe2a34a4b9382d4bd8155ba",
    "input": {
      "ckpt": "Counterfeit-V3.0_fp32.safetensors",
      "buffer": 4,
      "motion_scale": 0.8,
      "output_format": "video/h264-mp4",
      "image_dimension": "512x512",
      "ipadapter_noise": 0,
      "negative_prompt": "(worst quality, low quality:1.2)",
      "image_prompt_list": "0_:16_:24_:36_:48_:60_:72_:84_:96_:108_",
      "stmfnet_multiplier": 2,
      "linear_cn_strength_value": "(0.0,1.0)",
      "dynamic_cn_strength_values": "(0.0,1.0),(0.0,1.0),(0.0,1.0),(0.0,1.0)",
      "type_of_frame_distribution": "linear",
      "relative_ipadapter_strength": 1,
      "type_of_key_frame_influence": "linear",
      "relative_ipadapter_influence": 1,
      "linear_frame_distribution_value": 16,
      "linear_key_frame_influence_value": 0.9,
      "type_of_cn_strength_distribution": "dynamic",
      "dynamic_frame_distribution_values": "0,10,26,40,46",
      "soft_scaled_cn_weights_multiplier": 0.87,
      "dynamic_key_frame_influence_values": "0.5,0.5,2.0,0.5"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

No output yet! Press "Submit" to start a prediction.

Run time and cost

This model costs approximately $0.49 to run on Replicate, or 2 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 6 minutes. The predict time for this model varies significantly based on the inputs.

Readme

This model doesn't have a readme.