fofr/sdxl-lcm-video2video | Run with an API on Replicate

fofr / sdxl-lcm-video2video

Cold

Public
152 runs
L40S

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

Input prompt

Default: "An astronaut riding a rainbow unicorn"

negative_prompt

string

Shift + Return to add a new line

Negative Prompt

Default: ""

video

*file

Video to split into frames

fps

integer

(minimum: 1)

Number of images per second of video, when not exporting all frames

Default: 8

extract_all_frames

boolean

Get every frame of the video. Ignores fps. Slow for large videos.

Default: false

max_width

integer

(minimum: 1)

Maximum width of the video. Maintains aspect ratio.

Default: 512

num_inference_steps

integer

(minimum: 1, maximum: 30)

Number of denoising steps

Default: 4

guidance_scale

number

(minimum: 0, maximum: 5)

Scale for classifier-free guidance

Default: 1.1

prompt_strength

number

(minimum: 0, maximum: 1)

Prompt strength. 1.0 corresponds to full destruction of information in image

Default: 0.5

seed

integer

Random seed. Leave blank to randomize the seed

lora_scale

number

(minimum: 0, maximum: 1)

LoRA additive scale. Only applicable on trained models.

Default: 0.6

lora_weights

string

Shift + Return to add a new line

Replicate LoRA weights to use. Leave blank to use the default weights.

controlnet_1

string

Controlnet

Default: "none"

controlnet_1_conditioning_scale

number

(minimum: 0, maximum: 4)

How strong the controlnet conditioning is

Default: 0.75

controlnet_1_start

number

(minimum: 0, maximum: 1)

When controlnet conditioning starts

Default: 0

controlnet_1_end

number

(minimum: 0, maximum: 1)

When controlnet conditioning ends

Default: 1

controlnet_2

string

Controlnet

Default: "none"

controlnet_2_conditioning_scale

number

(minimum: 0, maximum: 4)

How strong the controlnet conditioning is

Default: 0.75

controlnet_2_start

number

(minimum: 0, maximum: 1)

When controlnet conditioning starts

Default: 0

controlnet_2_end

number

(minimum: 0, maximum: 1)

When controlnet conditioning ends

Default: 1

controlnet_3

string

Controlnet

Default: "none"

controlnet_3_conditioning_scale

number

(minimum: 0, maximum: 4)

How strong the controlnet conditioning is

Default: 0.75

controlnet_3_start

number

(minimum: 0, maximum: 1)

When controlnet conditioning starts

Default: 0

controlnet_3_end

number

(minimum: 0, maximum: 1)

When controlnet conditioning ends

Default: 1

return_frames

boolean

Return a tar file with all the frames alongside the video

Default: false

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

npx create-replicate --model=fofr/sdxl-lcm-video2video

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 fofr/sdxl-lcm-video2video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fofr/sdxl-lcm-video2video:b960f1c3a124d3e254035c8849cf136fd3fc72a711ddde152f93d83b477bd6da",
  {
    input: {
      fps: 8,
      prompt: "An astronaut riding a rainbow unicorn",
      max_width: 512,
      lora_scale: 0.6,
      controlnet_1: "none",
      controlnet_2: "none",
      controlnet_3: "none",
      return_frames: false,
      guidance_scale: 1.1,
      negative_prompt: "",
      prompt_strength: 0.5,
      controlnet_1_end: 1,
      controlnet_2_end: 1,
      controlnet_3_end: 1,
      controlnet_1_start: 0,
      controlnet_2_start: 0,
      controlnet_3_start: 0,
      extract_all_frames: false,
      num_inference_steps: 4,
      controlnet_1_conditioning_scale: 0.75,
      controlnet_2_conditioning_scale: 0.75,
      controlnet_3_conditioning_scale: 0.75
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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 fofr/sdxl-lcm-video2video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fofr/sdxl-lcm-video2video:b960f1c3a124d3e254035c8849cf136fd3fc72a711ddde152f93d83b477bd6da",
    input={
        "fps": 8,
        "prompt": "An astronaut riding a rainbow unicorn",
        "max_width": 512,
        "lora_scale": 0.6,
        "controlnet_1": "none",
        "controlnet_2": "none",
        "controlnet_3": "none",
        "return_frames": False,
        "guidance_scale": 1.1,
        "negative_prompt": "",
        "prompt_strength": 0.5,
        "controlnet_1_end": 1,
        "controlnet_2_end": 1,
        "controlnet_3_end": 1,
        "controlnet_1_start": 0,
        "controlnet_2_start": 0,
        "controlnet_3_start": 0,
        "extract_all_frames": False,
        "num_inference_steps": 4,
        "controlnet_1_conditioning_scale": 0.75,
        "controlnet_2_conditioning_scale": 0.75,
        "controlnet_3_conditioning_scale": 0.75
    }
)
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 fofr/sdxl-lcm-video2video 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": "fofr/sdxl-lcm-video2video:b960f1c3a124d3e254035c8849cf136fd3fc72a711ddde152f93d83b477bd6da",
    "input": {
      "fps": 8,
      "prompt": "An astronaut riding a rainbow unicorn",
      "max_width": 512,
      "lora_scale": 0.6,
      "controlnet_1": "none",
      "controlnet_2": "none",
      "controlnet_3": "none",
      "return_frames": false,
      "guidance_scale": 1.1,
      "negative_prompt": "",
      "prompt_strength": 0.5,
      "controlnet_1_end": 1,
      "controlnet_2_end": 1,
      "controlnet_3_end": 1,
      "controlnet_1_start": 0,
      "controlnet_2_start": 0,
      "controlnet_3_start": 0,
      "extract_all_frames": false,
      "num_inference_steps": 4,
      "controlnet_1_conditioning_scale": 0.75,
      "controlnet_2_conditioning_scale": 0.75,
      "controlnet_3_conditioning_scale": 0.75
    }
  }' \
  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/fofr/sdxl-lcm-video2video@sha256:b960f1c3a124d3e254035c8849cf136fd3fc72a711ddde152f93d83b477bd6da \
  -i 'fps=8' \
  -i 'prompt="An astronaut riding a rainbow unicorn"' \
  -i 'max_width=512' \
  -i 'lora_scale=0.6' \
  -i 'controlnet_1="none"' \
  -i 'controlnet_2="none"' \
  -i 'controlnet_3="none"' \
  -i 'return_frames=false' \
  -i 'guidance_scale=1.1' \
  -i 'negative_prompt=""' \
  -i 'prompt_strength=0.5' \
  -i 'controlnet_1_end=1' \
  -i 'controlnet_2_end=1' \
  -i 'controlnet_3_end=1' \
  -i 'controlnet_1_start=0' \
  -i 'controlnet_2_start=0' \
  -i 'controlnet_3_start=0' \
  -i 'extract_all_frames=false' \
  -i 'num_inference_steps=4' \
  -i 'controlnet_1_conditioning_scale=0.75' \
  -i 'controlnet_2_conditioning_scale=0.75' \
  -i 'controlnet_3_conditioning_scale=0.75'

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/fofr/sdxl-lcm-video2video@sha256:b960f1c3a124d3e254035c8849cf136fd3fc72a711ddde152f93d83b477bd6da
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "fps": 8,
      "prompt": "An astronaut riding a rainbow unicorn",
      "max_width": 512,
      "lora_scale": 0.6,
      "controlnet_1": "none",
      "controlnet_2": "none",
      "controlnet_3": "none",
      "return_frames": false,
      "guidance_scale": 1.1,
      "negative_prompt": "",
      "prompt_strength": 0.5,
      "controlnet_1_end": 1,
      "controlnet_2_end": 1,
      "controlnet_3_end": 1,
      "controlnet_1_start": 0,
      "controlnet_2_start": 0,
      "controlnet_3_start": 0,
      "extract_all_frames": false,
      "num_inference_steps": 4,
      "controlnet_1_conditioning_scale": 0.75,
      "controlnet_2_conditioning_scale": 0.75,
      "controlnet_3_conditioning_scale": 0.75
    }
  }' \
  http://localhost:5000/predictions

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

Output

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

Run time and cost

This model costs approximately $0.11 to run on Replicate, or 9 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 L40S GPU hardware. Predictions typically complete within 112 seconds. The predict time for this model varies significantly based on the inputs.

Readme

This model doesn't have a readme.