yan-ops/ai_test | Run with an API on Replicate

Input

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

npx create-replicate --model=yan-ops/ai_test

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 yan-ops/ai_test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "yan-ops/ai_test:764920580aa3dcf73a04deca1552375ed2d92a1ca501d36a27e7796e5d87a01b",
  {
    input: {
      model: "control_v11p_sd15_openpose",
      width: 640,
      height: 800,
      cfg_scale: 1.8,
      batch_size: 1,
      det_thresh: 0.1,
      request_id: "",
      sampling_steps: 7,
      negative_prompt: "",
      sampling_method: "Euler A",
      denoising_strength: 0.6,
      token_merging_ratio: 0.5,
      control_guidance_end: 1,
      control_guidance_start: 0,
      controlnet_conditioning_scale: 0.3
    }
  }
);

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 yan-ops/ai_test using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "yan-ops/ai_test:764920580aa3dcf73a04deca1552375ed2d92a1ca501d36a27e7796e5d87a01b",
    input={
        "model": "control_v11p_sd15_openpose",
        "width": 640,
        "height": 800,
        "cfg_scale": 1.8,
        "batch_size": 1,
        "det_thresh": 0.1,
        "request_id": "",
        "sampling_steps": 7,
        "negative_prompt": "",
        "sampling_method": "Euler A",
        "denoising_strength": 0.6,
        "token_merging_ratio": 0.5,
        "control_guidance_end": 1,
        "control_guidance_start": 0,
        "controlnet_conditioning_scale": 0.3
    }
)
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 yan-ops/ai_test 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": "yan-ops/ai_test:764920580aa3dcf73a04deca1552375ed2d92a1ca501d36a27e7796e5d87a01b",
    "input": {
      "model": "control_v11p_sd15_openpose",
      "width": 640,
      "height": 800,
      "cfg_scale": 1.8,
      "batch_size": 1,
      "det_thresh": 0.1,
      "request_id": "",
      "sampling_steps": 7,
      "negative_prompt": "",
      "sampling_method": "Euler A",
      "denoising_strength": 0.6,
      "token_merging_ratio": 0.5,
      "control_guidance_end": 1,
      "control_guidance_start": 0,
      "controlnet_conditioning_scale": 0.3
    }
  }' \
  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/yan-ops/ai_test@sha256:764920580aa3dcf73a04deca1552375ed2d92a1ca501d36a27e7796e5d87a01b \
  -i 'model="control_v11p_sd15_openpose"' \
  -i 'width=640' \
  -i 'height=800' \
  -i 'cfg_scale=1.8' \
  -i 'batch_size=1' \
  -i 'det_thresh=0.1' \
  -i 'request_id=""' \
  -i 'sampling_steps=7' \
  -i 'negative_prompt=""' \
  -i 'sampling_method="Euler A"' \
  -i 'denoising_strength=0.6' \
  -i 'token_merging_ratio=0.5' \
  -i 'control_guidance_end=1' \
  -i 'control_guidance_start=0' \
  -i 'controlnet_conditioning_scale=0.3'

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/yan-ops/ai_test@sha256:764920580aa3dcf73a04deca1552375ed2d92a1ca501d36a27e7796e5d87a01b
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "model": "control_v11p_sd15_openpose",
      "width": 640,
      "height": 800,
      "cfg_scale": 1.8,
      "batch_size": 1,
      "det_thresh": 0.1,
      "request_id": "",
      "sampling_steps": 7,
      "negative_prompt": "",
      "sampling_method": "Euler A",
      "denoising_strength": 0.6,
      "token_merging_ratio": 0.5,
      "control_guidance_end": 1,
      "control_guidance_start": 0,
      "controlnet_conditioning_scale": 0.3
    }
  }' \
  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 runs on Nvidia L40S GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.