tstramer

papercut-diffusion

Public

9.1K runs

A100 (80GB)

Run with an API

Pricing

Playground API Examples README Versions

Prediction

tstramer/papercut-diffusion:575529206bfb4bedcc8efb6e45a5fd344f1769edef09d318b57dabbfe0fd0a75

Model

tstramer/papercut-diffusion:57552920

7iulhx5ebndmxnr6bvo63o2uki

Status

Succeeded

Source

Web

Hardware

–

Total duration

12.0s

Created

almost 3 years ago

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"

{
  "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"
}

Install Replicate’s Node.js client library:

npm install replicate

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

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"
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

To learn more, take a look at the guide on getting started with Python.

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

Tweak it Iterate in playground ShareReport

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