peter942/steerable-motion:443f102a – Run with an API on Replicate

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peter942 /steerable-motion:443f102a

Playground API

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_frames_per_keyframe

integer

Linear Frames per keyframe

Default: 16

dynamic_frames_per_keyframe

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_value

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)"

interpolation_type

string

Interpolation type

Default: "ease-in-out"

soft_scaled_cn_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

ip_adapter_model_weight

number

IP adapter image weight

Default: 1

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:443f102a9d608ec715635ff28640249d2e32b779f6b0b71e20c40c13e6ff6866",
  {
    input: {
      ckpt: "Counterfeit-V3.0_fp32.safetensors",
      buffer: 4,
      motion_scale: 0.8,
      output_format: "video/h264-mp4",
      image_dimension: "512x512",
      negative_prompt: "(worst quality, low quality:1.2)",
      image_prompt_list: "0_:16_:24_:36_:48_:60_:72_:84_:96_:108_",
      interpolation_type: "ease-in-out",
      stmfnet_multiplier: 2,
      ip_adapter_model_weight: 1,
      linear_cn_strength_value: "(0.0,1.0)",
      soft_scaled_cn_multiplier: 0.87,
      dynamic_cn_strength_values: "(0.0,1.0),(0.0,1.0),(0.0,1.0),(0.0,1.0)",
      linear_frames_per_keyframe: 16,
      type_of_frame_distribution: "linear",
      dynamic_frames_per_keyframe: "0,10,26,40,46",
      type_of_key_frame_influence: "linear",
      linear_key_frame_influence_value: 0.9,
      type_of_cn_strength_distribution: "dynamic",
      dynamic_key_frame_influence_value: "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:443f102a9d608ec715635ff28640249d2e32b779f6b0b71e20c40c13e6ff6866",
    input={
        "ckpt": "Counterfeit-V3.0_fp32.safetensors",
        "buffer": 4,
        "motion_scale": 0.8,
        "output_format": "video/h264-mp4",
        "image_dimension": "512x512",
        "negative_prompt": "(worst quality, low quality:1.2)",
        "image_prompt_list": "0_:16_:24_:36_:48_:60_:72_:84_:96_:108_",
        "interpolation_type": "ease-in-out",
        "stmfnet_multiplier": 2,
        "ip_adapter_model_weight": 1,
        "linear_cn_strength_value": "(0.0,1.0)",
        "soft_scaled_cn_multiplier": 0.87,
        "dynamic_cn_strength_values": "(0.0,1.0),(0.0,1.0),(0.0,1.0),(0.0,1.0)",
        "linear_frames_per_keyframe": 16,
        "type_of_frame_distribution": "linear",
        "dynamic_frames_per_keyframe": "0,10,26,40,46",
        "type_of_key_frame_influence": "linear",
        "linear_key_frame_influence_value": 0.9,
        "type_of_cn_strength_distribution": "dynamic",
        "dynamic_key_frame_influence_value": "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": "443f102a9d608ec715635ff28640249d2e32b779f6b0b71e20c40c13e6ff6866",
    "input": {
      "ckpt": "Counterfeit-V3.0_fp32.safetensors",
      "buffer": 4,
      "motion_scale": 0.8,
      "output_format": "video/h264-mp4",
      "image_dimension": "512x512",
      "negative_prompt": "(worst quality, low quality:1.2)",
      "image_prompt_list": "0_:16_:24_:36_:48_:60_:72_:84_:96_:108_",
      "interpolation_type": "ease-in-out",
      "stmfnet_multiplier": 2,
      "ip_adapter_model_weight": 1,
      "linear_cn_strength_value": "(0.0,1.0)",
      "soft_scaled_cn_multiplier": 0.87,
      "dynamic_cn_strength_values": "(0.0,1.0),(0.0,1.0),(0.0,1.0),(0.0,1.0)",
      "linear_frames_per_keyframe": 16,
      "type_of_frame_distribution": "linear",
      "dynamic_frames_per_keyframe": "0,10,26,40,46",
      "type_of_key_frame_influence": "linear",
      "linear_key_frame_influence_value": 0.9,
      "type_of_cn_strength_distribution": "dynamic",
      "dynamic_key_frame_influence_value": "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.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/peter942/steerable-motion@sha256:443f102a9d608ec715635ff28640249d2e32b779f6b0b71e20c40c13e6ff6866 \
  -i 'ckpt="Counterfeit-V3.0_fp32.safetensors"' \
  -i 'buffer=4' \
  -i 'motion_scale=0.8' \
  -i 'output_format="video/h264-mp4"' \
  -i 'image_dimension="512x512"' \
  -i 'negative_prompt="(worst quality, low quality:1.2)"' \
  -i 'image_prompt_list="0_:16_:24_:36_:48_:60_:72_:84_:96_:108_"' \
  -i 'interpolation_type="ease-in-out"' \
  -i 'stmfnet_multiplier=2' \
  -i 'ip_adapter_model_weight=1' \
  -i 'linear_cn_strength_value="(0.0,1.0)"' \
  -i 'soft_scaled_cn_multiplier=0.87' \
  -i 'dynamic_cn_strength_values="(0.0,1.0),(0.0,1.0),(0.0,1.0),(0.0,1.0)"' \
  -i 'linear_frames_per_keyframe=16' \
  -i 'type_of_frame_distribution="linear"' \
  -i 'dynamic_frames_per_keyframe="0,10,26,40,46"' \
  -i 'type_of_key_frame_influence="linear"' \
  -i 'linear_key_frame_influence_value=0.9' \
  -i 'type_of_cn_strength_distribution="dynamic"' \
  -i 'dynamic_key_frame_influence_value="0.5,0.5,2.0,0.5"'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/peter942/steerable-motion@sha256:443f102a9d608ec715635ff28640249d2e32b779f6b0b71e20c40c13e6ff6866
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "ckpt": "Counterfeit-V3.0_fp32.safetensors",
      "buffer": 4,
      "motion_scale": 0.8,
      "output_format": "video/h264-mp4",
      "image_dimension": "512x512",
      "negative_prompt": "(worst quality, low quality:1.2)",
      "image_prompt_list": "0_:16_:24_:36_:48_:60_:72_:84_:96_:108_",
      "interpolation_type": "ease-in-out",
      "stmfnet_multiplier": 2,
      "ip_adapter_model_weight": 1,
      "linear_cn_strength_value": "(0.0,1.0)",
      "soft_scaled_cn_multiplier": 0.87,
      "dynamic_cn_strength_values": "(0.0,1.0),(0.0,1.0),(0.0,1.0),(0.0,1.0)",
      "linear_frames_per_keyframe": 16,
      "type_of_frame_distribution": "linear",
      "dynamic_frames_per_keyframe": "0,10,26,40,46",
      "type_of_key_frame_influence": "linear",
      "linear_key_frame_influence_value": 0.9,
      "type_of_cn_strength_distribution": "dynamic",
      "dynamic_key_frame_influence_value": "0.5,0.5,2.0,0.5"
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

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