afiaka87/clip-guided-diffusion:a9650e4b – Run with an API on Replicate

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afiaka87 /clip-guided-diffusion:a9650e4b

Playground API

Input

prompt

*string

Shift + Return to add a new line

Text prompt to use.

init_image

file

an image to blend with diffusion before clip guidance begins. Uses half as many timesteps.

respace

string

Number of timesteps. Fewer is faster, but less accurate.

Default: "250"

clip_guidance_scale

integer

(minimum: 0, maximum: 2500)

Scale for CLIP spherical distance loss. Values will need tinkering for different settings.

Default: 1000

tv_scale

number

(minimum: 0, maximum: 250)

Scale for a denoising loss that effects the last half of the diffusion process. 0, 100, 150 and 200

Default: 50

range_scale

number

(minimum: 0, maximum: 250)

Controls how far out of RGB range values may get.

Default: 50

sat_scale

number

(minimum: 0, maximum: 128)

Controls how much saturation is allowed. Use for ddim. From @nshepperd.

Default: 0

use_augmentations

boolean

Whether to use augmentation during prediction. May help with ddim and respacing <= 100.

Default: false

use_magnitude

boolean

Use the magnitude of the loss. May help (only) with ddim and respacing <= 100

Default: false

seed

integer

Seed for reproducibility

Default: 0

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

npx create-replicate --model=afiaka87/clip-guided-diffusion

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 afiaka87/clip-guided-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "afiaka87/clip-guided-diffusion:a9650e4b263abd9e00d3e45bd991b32e458ba7df1b6413e98b88562440200260",
  {
    input: {
      seed: 0,
      respace: "250",
      tv_scale: 50,
      sat_scale: 0,
      range_scale: 50,
      use_magnitude: false,
      use_augmentations: false,
      clip_guidance_scale: 1000
    }
  }
);
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 afiaka87/clip-guided-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "afiaka87/clip-guided-diffusion:a9650e4b263abd9e00d3e45bd991b32e458ba7df1b6413e98b88562440200260",
    input={
        "seed": 0,
        "respace": "250",
        "tv_scale": 50,
        "sat_scale": 0,
        "range_scale": 50,
        "use_magnitude": False,
        "use_augmentations": False,
        "clip_guidance_scale": 1000
    }
)

# The afiaka87/clip-guided-diffusion model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/afiaka87/clip-guided-diffusion/api#output-schema
    print(item)

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 afiaka87/clip-guided-diffusion 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": "a9650e4b263abd9e00d3e45bd991b32e458ba7df1b6413e98b88562440200260",
    "input": {
      "seed": 0,
      "respace": "250",
      "tv_scale": 50,
      "sat_scale": 0,
      "range_scale": 50,
      "use_magnitude": false,
      "use_augmentations": false,
      "clip_guidance_scale": 1000
    }
  }' \
  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/afiaka87/clip-guided-diffusion@sha256:a9650e4b263abd9e00d3e45bd991b32e458ba7df1b6413e98b88562440200260 \
  -i 'seed=0' \
  -i 'respace="250"' \
  -i 'tv_scale=50' \
  -i 'sat_scale=0' \
  -i 'range_scale=50' \
  -i 'use_magnitude=false' \
  -i 'use_augmentations=false' \
  -i 'clip_guidance_scale=1000'

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/afiaka87/clip-guided-diffusion@sha256:a9650e4b263abd9e00d3e45bd991b32e458ba7df1b6413e98b88562440200260
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 0,
      "respace": "250",
      "tv_scale": 50,
      "sat_scale": 0,
      "range_scale": 50,
      "use_magnitude": false,
      "use_augmentations": false,
      "clip_guidance_scale": 1000
    }
  }' \
  http://localhost:5000/predictions

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

Output

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