tstramer/papercut-diffusion:57552920 – Run with an API on Replicate

Version

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tstramer /papercut-diffusion:57552920

Playground API

Input

prompt

string

Shift + Return to add a new line

Input prompt

Default: ""

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

init_image

file

Inital image to generate variations of. Will be resized to the specified width and height

mask

file

Black and white image to use as mask for inpainting over init_image. Black pixels are inpainted and white pixels are preserved. Tends to work better with prompt strength of 0.5-0.7. Consider using https://replicate.com/andreasjansson/stable-diffusion-inpainting instead.

prompt_strength

number

Prompt strength when using init image. 1.0 corresponds to full destruction of information in init image

Default: 0.8

num_outputs

integer

(minimum: 1, maximum: 10)

Number of images to output. If the NSFW filter is triggered, you may get fewer outputs than this.

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: 7.5

scheduler

string

Choose a scheduler. If you use an init image, PNDM will be used

Default: "K-LMS"

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=tstramer/papercut-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";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run tstramer/papercut-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "tstramer/papercut-diffusion:575529206bfb4bedcc8efb6e45a5fd344f1769edef09d318b57dabbfe0fd0a75",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "Papercut lion",
      scheduler: "K-LMS",
      num_outputs: 1,
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      num_inference_steps: 150
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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

output = replicate.run(
    "tstramer/papercut-diffusion:575529206bfb4bedcc8efb6e45a5fd344f1769edef09d318b57dabbfe0fd0a75",
    input={
        "width": 512,
        "height": 512,
        "prompt": "Papercut lion",
        "scheduler": "K-LMS",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "prompt_strength": 0.8,
        "num_inference_steps": 150
    }
)
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 tstramer/papercut-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": "tstramer/papercut-diffusion:575529206bfb4bedcc8efb6e45a5fd344f1769edef09d318b57dabbfe0fd0a75",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "Papercut lion",
      "scheduler": "K-LMS",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 150
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2022-11-22T06:21:57.711196Z",
  "created_at": "2022-11-22T06:21:45.739818Z",
  "data_removed": false,
  "error": null,
  "id": "7iulhx5ebndmxnr6bvo63o2uki",
  "input": {
    "width": 512,
    "height": 512,
    "prompt": "Papercut lion",
    "scheduler": "K-LMS",
    "num_outputs": 1,
    "guidance_scale": 7.5,
    "prompt_strength": 0.8,
    "num_inference_steps": "150"
  },
  "logs": "Using seed: 45353\n  0%|          | 0/150 [00:00<?, ?it/s]\n  1%|▏         | 2/150 [00:00<00:11, 13.23it/s]\n  3%|▎         | 4/150 [00:00<00:10, 13.65it/s]\n  4%|▍         | 6/150 [00:00<00:10, 13.76it/s]\n  5%|▌         | 8/150 [00:00<00:10, 13.85it/s]\n  7%|▋         | 10/150 [00:00<00:10, 13.95it/s]\n  8%|▊         | 12/150 [00:00<00:10, 13.10it/s]\n  9%|▉         | 14/150 [00:01<00:10, 13.35it/s]\n 11%|█         | 16/150 [00:01<00:09, 13.56it/s]\n 12%|█▏        | 18/150 [00:01<00:09, 13.62it/s]\n 13%|█▎        | 20/150 [00:01<00:09, 13.76it/s]\n 15%|█▍        | 22/150 [00:01<00:09, 13.82it/s]\n 16%|█▌        | 24/150 [00:01<00:09, 13.79it/s]\n 17%|█▋        | 26/150 [00:01<00:09, 13.54it/s]\n 19%|█▊        | 28/150 [00:02<00:08, 13.66it/s]\n 20%|██        | 30/150 [00:02<00:08, 13.68it/s]\n 21%|██▏       | 32/150 [00:02<00:08, 13.80it/s]\n 23%|██▎       | 34/150 [00:02<00:08, 13.89it/s]\n 24%|██▍       | 36/150 [00:02<00:08, 13.81it/s]\n 25%|██▌       | 38/150 [00:02<00:08, 13.87it/s]\n 27%|██▋       | 40/150 [00:02<00:08, 13.49it/s]\n 28%|██▊       | 42/150 [00:03<00:07, 13.62it/s]\n 29%|██▉       | 44/150 [00:03<00:07, 13.77it/s]\n 31%|███       | 46/150 [00:03<00:07, 13.79it/s]\n 32%|███▏      | 48/150 [00:03<00:07, 13.90it/s]\n 33%|███▎      | 50/150 [00:03<00:07, 13.93it/s]\n 35%|███▍      | 52/150 [00:03<00:07, 13.77it/s]\n 36%|███▌      | 54/150 [00:03<00:07, 13.35it/s]\n 37%|███▋      | 56/150 [00:04<00:06, 13.55it/s]\n 39%|███▊      | 58/150 [00:04<00:06, 13.70it/s]\n 40%|████      | 60/150 [00:04<00:06, 13.44it/s]\n 41%|████▏     | 62/150 [00:04<00:06, 13.40it/s]\n 43%|████▎     | 64/150 [00:04<00:06, 13.52it/s]\n 44%|████▍     | 66/150 [00:04<00:06, 13.67it/s]\n 45%|████▌     | 68/150 [00:04<00:06, 13.64it/s]\n 47%|████▋     | 70/150 [00:05<00:05, 13.80it/s]\n 48%|████▊     | 72/150 [00:05<00:05, 13.94it/s]\n 49%|████▉     | 74/150 [00:05<00:05, 13.87it/s]\n 51%|█████     | 76/150 [00:05<00:05, 13.90it/s]\n 52%|█████▏    | 78/150 [00:05<00:05, 13.96it/s]\n 53%|█████▎    | 80/150 [00:05<00:05, 13.79it/s]\n 55%|█████▍    | 82/150 [00:05<00:04, 13.71it/s]\n 56%|█████▌    | 84/150 [00:06<00:04, 13.74it/s]\n 57%|█████▋    | 86/150 [00:06<00:04, 13.73it/s]\n 59%|█████▊    | 88/150 [00:06<00:04, 13.83it/s]\n 60%|██████    | 90/150 [00:06<00:04, 13.90it/s]\n 61%|██████▏   | 92/150 [00:06<00:04, 13.87it/s]\n 63%|██████▎   | 94/150 [00:06<00:04, 13.83it/s]\n 64%|██████▍   | 96/150 [00:06<00:03, 13.84it/s]\n 65%|██████▌   | 98/150 [00:07<00:03, 13.82it/s]\n 67%|██████▋   | 100/150 [00:07<00:03, 13.87it/s]\n 68%|██████▊   | 102/150 [00:07<00:03, 13.94it/s]\n 69%|██████▉   | 104/150 [00:07<00:03, 13.91it/s]\n 71%|███████   | 106/150 [00:07<00:03, 13.97it/s]\n 72%|███████▏  | 108/150 [00:07<00:03, 13.82it/s]\n 73%|███████▎  | 110/150 [00:08<00:02, 13.73it/s]\n 75%|███████▍  | 112/150 [00:08<00:02, 13.61it/s]\n 76%|███████▌  | 114/150 [00:08<00:02, 13.67it/s]\n 77%|███████▋  | 116/150 [00:08<00:02, 13.71it/s]\n 79%|███████▊  | 118/150 [00:08<00:02, 13.82it/s]\n 80%|████████  | 120/150 [00:08<00:02, 13.80it/s]\n 81%|████████▏ | 122/150 [00:08<00:02, 13.75it/s]\n 83%|████████▎ | 124/150 [00:09<00:01, 13.79it/s]\n 84%|████████▍ | 126/150 [00:09<00:01, 13.65it/s]\n 85%|████████▌ | 128/150 [00:09<00:01, 13.70it/s]\n 87%|████████▋ | 130/150 [00:09<00:01, 13.68it/s]\n 88%|████████▊ | 132/150 [00:09<00:01, 13.60it/s]\n 89%|████████▉ | 134/150 [00:09<00:01, 13.69it/s]\n 91%|█████████ | 136/150 [00:09<00:01, 13.64it/s]\n 92%|█████████▏| 138/150 [00:10<00:00, 13.68it/s]\n 93%|█████████▎| 140/150 [00:10<00:00, 13.74it/s]\n 95%|█████████▍| 142/150 [00:10<00:00, 13.88it/s]\n 96%|█████████▌| 144/150 [00:10<00:00, 13.92it/s]\n 97%|█████████▋| 146/150 [00:10<00:00, 13.96it/s]\n 99%|█████████▊| 148/150 [00:10<00:00, 13.99it/s]\n100%|██████████| 150/150 [00:10<00:00, 13.76it/s]\n100%|██████████| 150/150 [00:10<00:00, 13.74it/s]",
  "metrics": {
    "predict_time": 11.938234,
    "total_time": 11.971378
  },
  "output": [
    "https://replicate.delivery/pbxt/OqM8jYYdsW7XF5AWmteM0SvfX6k5DADVvUU4TK9BtW0FyhCQA/out-0.png"
  ],
  "started_at": "2022-11-22T06:21:45.772962Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/7iulhx5ebndmxnr6bvo63o2uki",
    "cancel": "https://api.replicate.com/v1/predictions/7iulhx5ebndmxnr6bvo63o2uki/cancel"
  },
  "version": "575529206bfb4bedcc8efb6e45a5fd344f1769edef09d318b57dabbfe0fd0a75"
}

Generated in

11.9 seconds

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