replicategithubwc/zeroscope:890dddde – Run with an API on Replicate

Version

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replicategithubwc /zeroscope:890dddde

Input

prompt

string

Shift + Return to add a new line

astronaut riding a horse on mars, beautiful, 8k, perfect, award winning, national geographicastronaut riding a horse on mars, beautiful, 8k, perfect, award winning, national geographic

Input prompt

Default: "astronaut riding a horse on mars, beautiful, 8k, perfect, award winning, national geographic"

negative_prompt

string

Shift + Return to add a new line

very blue, dust, noisy, washed out, ugly, distorted, brokenvery blue, dust, noisy, washed out, ugly, distorted, broken

Specify things to not see in the output

Default: "very blue, dust, noisy, washed out, ugly, distorted, broken"

width

integer

Width of output image

Default: 1024

height

integer

Height of output image

Default: 576

num_frames

integer

Number of frames for the output video

Default: 24

fps

integer

fps for the output video

Default: 24

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 12.5

scheduler

string

Choose a scheduler.

Default: "K_EULER_ANCESTRAL"

init_weight

number

(minimum: 0, maximum: 1)

Strength of upscale

Default: 0.6

seed

integer

Random seed. Leave blank to randomize the seed

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

npx create-replicate --model=replicategithubwc/zeroscope

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 replicategithubwc/zeroscope using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "replicategithubwc/zeroscope:890ddddef39053199faef0c55e4d78dd6ea144cd3412655b24e616da2fe714b3",
  {
    input: {
      fps: 24,
      width: 1024,
      height: 576,
      prompt: "astronaut riding a horse on mars, beautiful, 8k, perfect, award winning, national geographic",
      scheduler: "K_EULER_ANCESTRAL",
      num_frames: 24,
      init_weight: 0.6,
      guidance_scale: 12.5,
      negative_prompt: "very blue, dust, noisy, washed out, ugly, distorted, broken",
      num_inference_steps: 50
    }
  }
);

// 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 replicategithubwc/zeroscope using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "replicategithubwc/zeroscope:890ddddef39053199faef0c55e4d78dd6ea144cd3412655b24e616da2fe714b3",
    input={
        "fps": 24,
        "width": 1024,
        "height": 576,
        "prompt": "astronaut riding a horse on mars, beautiful, 8k, perfect, award winning, national geographic",
        "scheduler": "K_EULER_ANCESTRAL",
        "num_frames": 24,
        "init_weight": 0.6,
        "guidance_scale": 12.5,
        "negative_prompt": "very blue, dust, noisy, washed out, ugly, distorted, broken",
        "num_inference_steps": 50
    }
)
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 replicategithubwc/zeroscope 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": "890ddddef39053199faef0c55e4d78dd6ea144cd3412655b24e616da2fe714b3",
    "input": {
      "fps": 24,
      "width": 1024,
      "height": 576,
      "prompt": "astronaut riding a horse on mars, beautiful, 8k, perfect, award winning, national geographic",
      "scheduler": "K_EULER_ANCESTRAL",
      "num_frames": 24,
      "init_weight": 0.6,
      "guidance_scale": 12.5,
      "negative_prompt": "very blue, dust, noisy, washed out, ugly, distorted, broken",
      "num_inference_steps": 50
    }
  }' \
  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/replicategithubwc/zeroscope@sha256:890ddddef39053199faef0c55e4d78dd6ea144cd3412655b24e616da2fe714b3 \
  -i 'fps=24' \
  -i 'width=1024' \
  -i 'height=576' \
  -i 'prompt="astronaut riding a horse on mars, beautiful, 8k, perfect, award winning, national geographic"' \
  -i 'scheduler="K_EULER_ANCESTRAL"' \
  -i 'num_frames=24' \
  -i 'init_weight=0.6' \
  -i 'guidance_scale=12.5' \
  -i 'negative_prompt="very blue, dust, noisy, washed out, ugly, distorted, broken"' \
  -i 'num_inference_steps=50'

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/replicategithubwc/zeroscope@sha256:890ddddef39053199faef0c55e4d78dd6ea144cd3412655b24e616da2fe714b3
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "fps": 24,
      "width": 1024,
      "height": 576,
      "prompt": "astronaut riding a horse on mars, beautiful, 8k, perfect, award winning, national geographic",
      "scheduler": "K_EULER_ANCESTRAL",
      "num_frames": 24,
      "init_weight": 0.6,
      "guidance_scale": 12.5,
      "negative_prompt": "very blue, dust, noisy, washed out, ugly, distorted, broken",
      "num_inference_steps": 50
    }
  }' \
  http://localhost:5000/predictions

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

Output

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