cjwbw/supermarionation:94b2a93e – Run with an API on Replicate

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cjwbw /supermarionation:94b2a93e

Playground API

Input

prompt

string

Shift + Return to add a new line

Input prompt

Default: ""

negative_prompt

string

Shift + Return to add a new line

The prompt or prompts not to guide the image generation (what you do not want to see in the generation). Ignored when not using guidance.

image

file

Input image for img2img generation

width

integer

Width of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 512

height

integer

Height of output image. Maximum size is 1024x768 or 768x1024 because of memory limits

Default: 512

num_outputs

integer

Number of images to output

Default: 1

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: 7

scheduler

string

Choose a scheduler.

Default: "K_EULER"

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=cjwbw/supermarionation

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

const output = await replicate.run(
  "cjwbw/supermarionation:94b2a93e5211618a1e3255a7e4d37add0ce4fd629ddbbd06dd35eaaccf16186d",
  {
    input: {
      image: "https://replicate.delivery/pbxt/IPYBr61UJghgxqHNIP8Cw1Ky06v2gVQBtMrQoSOqyR0fCuWT/bradd.jpeg",
      width: 512,
      height: 512,
      prompt: "Brad Pitt in supermarionation style",
      scheduler: "K_EULER",
      num_outputs: 1,
      guidance_scale: 7,
      num_inference_steps: 50
    }
  }
);
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 cjwbw/supermarionation using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cjwbw/supermarionation:94b2a93e5211618a1e3255a7e4d37add0ce4fd629ddbbd06dd35eaaccf16186d",
    input={
        "image": "https://replicate.delivery/pbxt/IPYBr61UJghgxqHNIP8Cw1Ky06v2gVQBtMrQoSOqyR0fCuWT/bradd.jpeg",
        "width": 512,
        "height": 512,
        "prompt": "Brad Pitt in supermarionation style",
        "scheduler": "K_EULER",
        "num_outputs": 1,
        "guidance_scale": 7,
        "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 cjwbw/supermarionation 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": "94b2a93e5211618a1e3255a7e4d37add0ce4fd629ddbbd06dd35eaaccf16186d",
    "input": {
      "image": "https://replicate.delivery/pbxt/IPYBr61UJghgxqHNIP8Cw1Ky06v2gVQBtMrQoSOqyR0fCuWT/bradd.jpeg",
      "width": 512,
      "height": 512,
      "prompt": "Brad Pitt in supermarionation style",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "guidance_scale": 7,
      "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/cjwbw/supermarionation@sha256:94b2a93e5211618a1e3255a7e4d37add0ce4fd629ddbbd06dd35eaaccf16186d \
  -i 'image="https://replicate.delivery/pbxt/IPYBr61UJghgxqHNIP8Cw1Ky06v2gVQBtMrQoSOqyR0fCuWT/bradd.jpeg"' \
  -i 'width=512' \
  -i 'height=512' \
  -i 'prompt="Brad Pitt in supermarionation style"' \
  -i 'scheduler="K_EULER"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=7' \
  -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/cjwbw/supermarionation@sha256:94b2a93e5211618a1e3255a7e4d37add0ce4fd629ddbbd06dd35eaaccf16186d
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "image": "https://replicate.delivery/pbxt/IPYBr61UJghgxqHNIP8Cw1Ky06v2gVQBtMrQoSOqyR0fCuWT/bradd.jpeg",
      "width": 512,
      "height": 512,
      "prompt": "Brad Pitt in supermarionation style",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "guidance_scale": 7,
      "num_inference_steps": 50
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2023-03-03T17:42:04.461841Z",
  "created_at": "2023-03-03T17:41:47.699046Z",
  "data_removed": false,
  "error": null,
  "id": "iryyq7ljkngc5fkzkosknzpfg4",
  "input": {
    "image": "https://replicate.delivery/pbxt/IPYBr61UJghgxqHNIP8Cw1Ky06v2gVQBtMrQoSOqyR0fCuWT/bradd.jpeg",
    "width": 512,
    "height": 512,
    "prompt": "Brad Pitt in supermarionation style",
    "scheduler": "K_EULER",
    "num_outputs": 1,
    "guidance_scale": 7,
    "num_inference_steps": 50
  },
  "logs": "Using seed: 15667\n  0%|          | 0/40 [00:00<?, ?it/s]\n  2%|▎         | 1/40 [00:00<00:14,  2.70it/s]\n  5%|▌         | 2/40 [00:00<00:13,  2.72it/s]\n  8%|▊         | 3/40 [00:01<00:13,  2.72it/s]\n 10%|█         | 4/40 [00:01<00:13,  2.73it/s]\n 12%|█▎        | 5/40 [00:01<00:12,  2.73it/s]\n 15%|█▌        | 6/40 [00:02<00:12,  2.72it/s]\n 18%|█▊        | 7/40 [00:02<00:12,  2.71it/s]\n 20%|██        | 8/40 [00:02<00:11,  2.71it/s]\n 22%|██▎       | 9/40 [00:03<00:11,  2.72it/s]\n 25%|██▌       | 10/40 [00:03<00:11,  2.73it/s]\n 28%|██▊       | 11/40 [00:04<00:10,  2.72it/s]\n 30%|███       | 12/40 [00:04<00:10,  2.72it/s]\n 32%|███▎      | 13/40 [00:04<00:09,  2.71it/s]\n 35%|███▌      | 14/40 [00:05<00:09,  2.72it/s]\n 38%|███▊      | 15/40 [00:05<00:09,  2.72it/s]\n 40%|████      | 16/40 [00:05<00:08,  2.72it/s]\n 42%|████▎     | 17/40 [00:06<00:08,  2.72it/s]\n 45%|████▌     | 18/40 [00:06<00:08,  2.71it/s]\n 48%|████▊     | 19/40 [00:06<00:07,  2.71it/s]\n 50%|█████     | 20/40 [00:07<00:07,  2.71it/s]\n 52%|█████▎    | 21/40 [00:07<00:07,  2.71it/s]\n 55%|█████▌    | 22/40 [00:08<00:06,  2.71it/s]\n 57%|█████▊    | 23/40 [00:08<00:06,  2.70it/s]\n 60%|██████    | 24/40 [00:08<00:05,  2.70it/s]\n 62%|██████▎   | 25/40 [00:09<00:05,  2.70it/s]\n 65%|██████▌   | 26/40 [00:09<00:05,  2.69it/s]\n 68%|██████▊   | 27/40 [00:09<00:04,  2.69it/s]\n 70%|███████   | 28/40 [00:10<00:04,  2.69it/s]\n 72%|███████▎  | 29/40 [00:10<00:04,  2.68it/s]\n 75%|███████▌  | 30/40 [00:11<00:03,  2.69it/s]\n 78%|███████▊  | 31/40 [00:11<00:03,  2.69it/s]\n 80%|████████  | 32/40 [00:11<00:02,  2.68it/s]\n 82%|████████▎ | 33/40 [00:12<00:02,  2.68it/s]\n 85%|████████▌ | 34/40 [00:12<00:02,  2.68it/s]\n 88%|████████▊ | 35/40 [00:12<00:01,  2.68it/s]\n 90%|█████████ | 36/40 [00:13<00:01,  2.68it/s]\n 92%|█████████▎| 37/40 [00:13<00:01,  2.68it/s]\n 95%|█████████▌| 38/40 [00:14<00:00,  2.67it/s]\n 98%|█████████▊| 39/40 [00:14<00:00,  2.67it/s]\n100%|██████████| 40/40 [00:14<00:00,  2.66it/s]\n100%|██████████| 40/40 [00:14<00:00,  2.70it/s]",
  "metrics": {
    "predict_time": 16.682007,
    "total_time": 16.762795
  },
  "output": [
    "https://replicate.delivery/pbxt/ZeU0zaMIaEyDAqdo3eDTCFjtyo1jgBZJIiVan2ad33drNeHhA/out-0.png"
  ],
  "started_at": "2023-03-03T17:41:47.779834Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/iryyq7ljkngc5fkzkosknzpfg4",
    "cancel": "https://api.replicate.com/v1/predictions/iryyq7ljkngc5fkzkosknzpfg4/cancel"
  },
  "version": "94b2a93e5211618a1e3255a7e4d37add0ce4fd629ddbbd06dd35eaaccf16186d"
}

Generated in

16.7 seconds

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