cloneofsimo/hotshot-xl-lora-controlnet:f7b9a444

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cloneofsimo /hotshot-xl-lora-controlnet:f7b9a444

Playground API

Input

prompt

string

Shift + Return to add a new line

The main prompt that guides the image generation.

Default: "Hi there doggo!"

negative_prompt

string

Shift + Return to add a new line

A negative prompt to avoid certain features in the generated images.

Default: ""

width

integer

The width of the generated images.

Default: 672

height

integer

The height of the generated images.

Default: 384

original_width

integer

The width of the `original_size` of images. If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).

Default: 1920

original_height

integer

The `original_size height` of the images.

Default: 1080

target_width

integer

The `target_size width` of the images.

Default: 512

target_height

integer

The `target_size height` of the images.

Default: 512

steps

integer

The number of steps for the prediction.

Default: 30

video_length

integer

The length of the video in frames.

Default: 8

video_duration

integer

The duration of the video in milliseconds.

Default: 1000

lora

string

Shift + Return to add a new line

The URL for LoRA weights.

control_type

string

Shift + Return to add a new line

The type of control net to use for conditional generation.

gif_path

string

Shift + Return to add a new line

The path to the GIF for conditional generation.

control_guidance_start

number

The start of the control guidance.

Default: 0

control_guidance_end

number

The end of the control guidance.

Default: 1

controlnet_conditioning_scale

number

The scale of the controlnet conditioning.

Default: 0.7

seed

integer

The seed for the random number generator.

Default: 455

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

npx create-replicate --model=cloneofsimo/hotshot-xl-lora-controlnet

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 cloneofsimo/hotshot-xl-lora-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cloneofsimo/hotshot-xl-lora-controlnet:f7b9a4447c4e22770461a31ebe1a3145d6cfc23a1e647bed27a612bff159a566",
  {
    input: {
      seed: 455,
      steps: 30,
      width: 672,
      height: 384,
      prompt: "Hi there doggo!",
      target_width: 512,
      video_length: 8,
      target_height: 512,
      original_width: 1920,
      video_duration: 1000,
      negative_prompt: "",
      original_height: 1080,
      control_guidance_end: 1,
      control_guidance_start: 0,
      controlnet_conditioning_scale: 0.7
    }
  }
);
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 cloneofsimo/hotshot-xl-lora-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cloneofsimo/hotshot-xl-lora-controlnet:f7b9a4447c4e22770461a31ebe1a3145d6cfc23a1e647bed27a612bff159a566",
    input={
        "seed": 455,
        "steps": 30,
        "width": 672,
        "height": 384,
        "prompt": "Hi there doggo!",
        "target_width": 512,
        "video_length": 8,
        "target_height": 512,
        "original_width": 1920,
        "video_duration": 1000,
        "negative_prompt": "",
        "original_height": 1080,
        "control_guidance_end": 1,
        "control_guidance_start": 0,
        "controlnet_conditioning_scale": 0.7
    }
)
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 cloneofsimo/hotshot-xl-lora-controlnet 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": "f7b9a4447c4e22770461a31ebe1a3145d6cfc23a1e647bed27a612bff159a566",
    "input": {
      "seed": 455,
      "steps": 30,
      "width": 672,
      "height": 384,
      "prompt": "Hi there doggo!",
      "target_width": 512,
      "video_length": 8,
      "target_height": 512,
      "original_width": 1920,
      "video_duration": 1000,
      "negative_prompt": "",
      "original_height": 1080,
      "control_guidance_end": 1,
      "control_guidance_start": 0,
      "controlnet_conditioning_scale": 0.7
    }
  }' \
  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.