pipelines-beta/arcn2 | Run with an API on Replicate

pipelines-beta / arcn2

Cold

Public
3 runs
H100
Fine-tune

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

The text prompt describing your video scene.

Default: ""

lora_url

string

Shift + Return to add a new line

A URL pointing to your LoRA .safetensors file or a Hugging Face repo (e.g. 'user/repo' - uses the first .safetensors file).

Default: ""

lora_strength

number

(minimum: -10, maximum: 10)

Scale/strength for your LoRA.

Default: 1

scheduler

string

Algorithm used to generate the video frames.

Default: "DPMSolverMultistepScheduler"

steps

integer

(minimum: 1, maximum: 150)

Number of diffusion steps.

Default: 50

guidance_scale

number

(minimum: 0, maximum: 30)

Overall influence of text vs. model.

Default: 6

flow_shift

integer

(minimum: 0, maximum: 20)

Video continuity factor (flow).

Default: 9

num_frames

integer

(minimum: 1, maximum: 1440)

How many frames (duration) in the resulting video.

Default: 33

width

integer

(minimum: 64, maximum: 1536)

Width for the generated video.

Default: 640

height

integer

(minimum: 64, maximum: 1024)

Height for the generated video.

Default: 360

denoise_strength

number

(minimum: 0, maximum: 2)

Controls how strongly noise is applied each step.

Default: 1

force_offload

boolean

Whether to force model layers offloaded to CPU.

Default: true

frame_rate

integer

(minimum: 1, maximum: 60)

Video frame rate.

Default: 16

crf

integer

(minimum: 0, maximum: 51)

CRF (quality) for H264 encoding. Lower values = higher quality.

Default: 19

enhance_weight

number

(minimum: 0, maximum: 2)

Strength of the video enhancement effect.

Default: 0.3

enhance_single

boolean

Apply enhancement to individual frames.

Default: true

enhance_double

boolean

Apply enhancement across frame pairs.

Default: true

enhance_start

number

(minimum: 0, maximum: 1)

When to start enhancement in the video. Must be less than enhance_end.

Default: 0

enhance_end

number

(minimum: 0, maximum: 1)

When to end enhancement in the video. Must be greater than enhance_start.

Default: 1

seed

integer

Set a seed for reproducibility. Random by default.

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

npx create-replicate --model=pipelines-beta/arcn2

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";
import fs from "node:fs";

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

Run pipelines-beta/arcn2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "pipelines-beta/arcn2:6912f585cf8ebe9328a32a8ba201d2aa5cf2c58da50e75a342048693f3386c6e",
  {
    input: {
      crf: 19,
      steps: 50,
      width: 640,
      height: 360,
      prompt: "",
      lora_url: "",
      scheduler: "DPMSolverMultistepScheduler",
      flow_shift: 9,
      frame_rate: 16,
      num_frames: 33,
      enhance_end: 1,
      enhance_start: 0,
      force_offload: true,
      lora_strength: 1,
      enhance_double: true,
      enhance_single: true,
      enhance_weight: 0.3,
      guidance_scale: 6,
      denoise_strength: 1
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", 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 pipelines-beta/arcn2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pipelines-beta/arcn2:6912f585cf8ebe9328a32a8ba201d2aa5cf2c58da50e75a342048693f3386c6e",
    input={
        "crf": 19,
        "steps": 50,
        "width": 640,
        "height": 360,
        "prompt": "",
        "lora_url": "",
        "scheduler": "DPMSolverMultistepScheduler",
        "flow_shift": 9,
        "frame_rate": 16,
        "num_frames": 33,
        "enhance_end": 1,
        "enhance_start": 0,
        "force_offload": True,
        "lora_strength": 1,
        "enhance_double": True,
        "enhance_single": True,
        "enhance_weight": 0.3,
        "guidance_scale": 6,
        "denoise_strength": 1
    }
)
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 pipelines-beta/arcn2 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": "pipelines-beta/arcn2:6912f585cf8ebe9328a32a8ba201d2aa5cf2c58da50e75a342048693f3386c6e",
    "input": {
      "crf": 19,
      "steps": 50,
      "width": 640,
      "height": 360,
      "prompt": "",
      "lora_url": "",
      "scheduler": "DPMSolverMultistepScheduler",
      "flow_shift": 9,
      "frame_rate": 16,
      "num_frames": 33,
      "enhance_end": 1,
      "enhance_start": 0,
      "force_offload": true,
      "lora_strength": 1,
      "enhance_double": true,
      "enhance_single": true,
      "enhance_weight": 0.3,
      "guidance_scale": 6,
      "denoise_strength": 1
    }
  }' \
  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 runs on Nvidia H100 GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.