Prediction

Model

cjwbw/scalecrafter:3b367f93

4hllaalbfkhnlzkzwbqfpvznnu

Status

Succeeded

Source

Web

Hardware

A100 (80GB)

Total duration

41.4s

Created

about 2 years ago

Webhook

–

Input

prompt: a professional photograph of an astronaut riding a horse
width: 2048
height: 2048
num_inference_steps: 50

{
  "height": 2048,
  "num_inference_steps": 50,
  "prompt": "a professional photograph of an astronaut riding a horse",
  "width": 2048
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_eeq**********************************

This is your API token. Keep it to yourself.

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

const output = await replicate.run(
  "cjwbw/scalecrafter:3b367f9321bd18ca8b29ef9bf9507348d822c2cfcd5764b9414eff879f086b59",
  {
    input: {
      height: 2048,
      num_inference_steps: 50,
      prompt: "a professional photograph of an astronaut riding a horse",
      width: 2048
    }
  }
);

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

// To write the file to disk:
fs.writeFile("my-image.png", 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=r8_eeq**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

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

output = replicate.run(
    "cjwbw/scalecrafter:3b367f9321bd18ca8b29ef9bf9507348d822c2cfcd5764b9414eff879f086b59",
    input={
        "height": 2048,
        "num_inference_steps": 50,
        "prompt": "a professional photograph of an astronaut riding a horse",
        "width": 2048
    }
)

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

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

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=r8_eeq**********************************

This is your API token. Keep it to yourself.

Run cjwbw/scalecrafter 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": "cjwbw/scalecrafter:3b367f9321bd18ca8b29ef9bf9507348d822c2cfcd5764b9414eff879f086b59",
    "input": {
      "height": 2048,
      "num_inference_steps": 50,
      "prompt": "a professional photograph of an astronaut riding a horse",
      "width": 2048
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "4hllaalbfkhnlzkzwbqfpvznnu",
  "model": "cjwbw/scalecrafter",
  "version": "3b367f9321bd18ca8b29ef9bf9507348d822c2cfcd5764b9414eff879f086b59",
  "input": {
    "height": 2048,
    "num_inference_steps": 50,
    "prompt": "a professional photograph of an astronaut riding a horse",
    "width": 2048
  },
  "logs": "Using seed: 43181\nReading dilation settings\ndown_blocks.3.resnets.0.conv1 : 2.0\ndown_blocks.3.resnets.0.conv2 : 2.0\ndown_blocks.3.resnets.1.conv1 : 2.0\ndown_blocks.3.resnets.1.conv2 : 2.0\nup_blocks.0.resnets.0.conv1 : 2.0\nup_blocks.0.resnets.0.conv2 : 2.0\nup_blocks.0.resnets.1.conv1 : 2.0\nup_blocks.0.resnets.1.conv2 : 2.0\nup_blocks.0.resnets.2.conv1 : 2.0\nup_blocks.0.resnets.2.conv2 : 2.0\nup_blocks.0.upsamplers.0.conv : 2.0\nmid_block.resnets.0.conv1 : 2.0\nmid_block.resnets.0.conv2 : 2.0\nmid_block.resnets.1.conv1 : 2.0\nmid_block.resnets.1.conv2 : 2.0\n  0%|          | 0/50 [00:00<?, ?it/s]\n  2%|▏         | 1/50 [00:00<00:36,  1.35it/s]\n  4%|▍         | 2/50 [00:01<00:35,  1.35it/s]\n  6%|▌         | 3/50 [00:02<00:34,  1.35it/s]\n  8%|▊         | 4/50 [00:02<00:34,  1.35it/s]\n 10%|█         | 5/50 [00:03<00:33,  1.35it/s]\n 12%|█▏        | 6/50 [00:04<00:32,  1.34it/s]\n 14%|█▍        | 7/50 [00:05<00:32,  1.34it/s]\n 16%|█▌        | 8/50 [00:05<00:31,  1.34it/s]\n 18%|█▊        | 9/50 [00:06<00:30,  1.34it/s]\n 20%|██        | 10/50 [00:07<00:29,  1.34it/s]\n 22%|██▏       | 11/50 [00:08<00:29,  1.34it/s]\n 24%|██▍       | 12/50 [00:08<00:28,  1.34it/s]\n 26%|██▌       | 13/50 [00:09<00:27,  1.34it/s]\n 28%|██▊       | 14/50 [00:10<00:26,  1.34it/s]\n 30%|███       | 15/50 [00:11<00:26,  1.34it/s]\n 32%|███▏      | 16/50 [00:11<00:25,  1.34it/s]\n 34%|███▍      | 17/50 [00:12<00:24,  1.34it/s]\n 36%|███▌      | 18/50 [00:13<00:23,  1.34it/s]\n 38%|███▊      | 19/50 [00:14<00:23,  1.34it/s]\n 40%|████      | 20/50 [00:14<00:22,  1.34it/s]\n 42%|████▏     | 21/50 [00:15<00:21,  1.34it/s]\n 44%|████▍     | 22/50 [00:16<00:20,  1.34it/s]\n 46%|████▌     | 23/50 [00:17<00:20,  1.34it/s]\n 48%|████▊     | 24/50 [00:17<00:19,  1.34it/s]\n 50%|█████     | 25/50 [00:18<00:18,  1.34it/s]\n 52%|█████▏    | 26/50 [00:19<00:17,  1.34it/s]\n 54%|█████▍    | 27/50 [00:20<00:17,  1.34it/s]\n 56%|█████▌    | 28/50 [00:20<00:16,  1.34it/s]\n 58%|█████▊    | 29/50 [00:21<00:15,  1.34it/s]\n 60%|██████    | 30/50 [00:22<00:14,  1.34it/s]\n 62%|██████▏   | 31/50 [00:23<00:14,  1.34it/s]\n 64%|██████▍   | 32/50 [00:23<00:13,  1.34it/s]\n 66%|██████▌   | 33/50 [00:24<00:12,  1.34it/s]\n 68%|██████▊   | 34/50 [00:25<00:11,  1.34it/s]\n 70%|███████   | 35/50 [00:26<00:11,  1.34it/s]\n 72%|███████▏  | 36/50 [00:26<00:10,  1.34it/s]\n 74%|███████▍  | 37/50 [00:27<00:09,  1.34it/s]\n 76%|███████▌  | 38/50 [00:28<00:08,  1.34it/s]\n 78%|███████▊  | 39/50 [00:29<00:08,  1.34it/s]\n 80%|████████  | 40/50 [00:29<00:07,  1.34it/s]\n 82%|████████▏ | 41/50 [00:30<00:06,  1.34it/s]\n 84%|████████▍ | 42/50 [00:31<00:05,  1.34it/s]\n 86%|████████▌ | 43/50 [00:32<00:05,  1.34it/s]\n 88%|████████▊ | 44/50 [00:32<00:04,  1.34it/s]\n 90%|█████████ | 45/50 [00:33<00:03,  1.34it/s]\n 92%|█████████▏| 46/50 [00:34<00:02,  1.34it/s]\n 94%|█████████▍| 47/50 [00:35<00:02,  1.34it/s]\n 96%|█████████▌| 48/50 [00:35<00:01,  1.34it/s]\n 98%|█████████▊| 49/50 [00:36<00:00,  1.34it/s]\n100%|██████████| 50/50 [00:37<00:00,  1.34it/s]\n100%|██████████| 50/50 [00:37<00:00,  1.34it/s]",
  "output": "https://replicate.delivery/pbxt/46puDNY7c9oKKVHo5haYa5zcNK31fOgmycBSGVCBnXxOGw3IA/out.png",
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-10-18T21:41:09.383528Z",
  "started_at": "2023-10-18T21:41:09.363368Z",
  "completed_at": "2023-10-18T21:41:50.808384Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/4hllaalbfkhnlzkzwbqfpvznnu/cancel",
    "get": "https://api.replicate.com/v1/predictions/4hllaalbfkhnlzkzwbqfpvznnu"
  },
  "metrics": {
    "predict_time": 41.445016,
    "total_time": 41.424856
  }
}

Generated in

41.5 seconds

Tweak it Report

Using seed: 43181 Reading dilation settings down_blocks.3.resnets.0.conv1 : 2.0 down_blocks.3.resnets.0.conv2 : 2.0 down_blocks.3.resnets.1.conv1 : 2.0 down_blocks.3.resnets.1.conv2 : 2.0 up_blocks.0.resnets.0.conv1 : 2.0 up_blocks.0.resnets.0.conv2 : 2.0 up_blocks.0.resnets.1.conv1 : 2.0 up_blocks.0.resnets.1.conv2 : 2.0 up_blocks.0.resnets.2.conv1 : 2.0 up_blocks.0.resnets.2.conv2 : 2.0 up_blocks.0.upsamplers.0.conv : 2.0 mid_block.resnets.0.conv1 : 2.0 mid_block.resnets.0.conv2 : 2.0 mid_block.resnets.1.conv1 : 2.0 mid_block.resnets.1.conv2 : 2.0 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:36, 1.35it/s] 4%|▍ | 2/50 [00:01<00:35, 1.35it/s] 6%|▌ | 3/50 [00:02<00:34, 1.35it/s] 8%|▊ | 4/50 [00:02<00:34, 1.35it/s] 10%|█ | 5/50 [00:03<00:33, 1.35it/s] 12%|█▏ | 6/50 [00:04<00:32, 1.34it/s] 14%|█▍ | 7/50 [00:05<00:32, 1.34it/s] 16%|█▌ | 8/50 [00:05<00:31, 1.34it/s] 18%|█▊ | 9/50 [00:06<00:30, 1.34it/s] 20%|██ | 10/50 [00:07<00:29, 1.34it/s] 22%|██▏ | 11/50 [00:08<00:29, 1.34it/s] 24%|██▍ | 12/50 [00:08<00:28, 1.34it/s] 26%|██▌ | 13/50 [00:09<00:27, 1.34it/s] 28%|██▊ | 14/50 [00:10<00:26, 1.34it/s] 30%|███ | 15/50 [00:11<00:26, 1.34it/s] 32%|███▏ | 16/50 [00:11<00:25, 1.34it/s] 34%|███▍ | 17/50 [00:12<00:24, 1.34it/s] 36%|███▌ | 18/50 [00:13<00:23, 1.34it/s] 38%|███▊ | 19/50 [00:14<00:23, 1.34it/s] 40%|████ | 20/50 [00:14<00:22, 1.34it/s] 42%|████▏ | 21/50 [00:15<00:21, 1.34it/s] 44%|████▍ | 22/50 [00:16<00:20, 1.34it/s] 46%|████▌ | 23/50 [00:17<00:20, 1.34it/s] 48%|████▊ | 24/50 [00:17<00:19, 1.34it/s] 50%|█████ | 25/50 [00:18<00:18, 1.34it/s] 52%|█████▏ | 26/50 [00:19<00:17, 1.34it/s] 54%|█████▍ | 27/50 [00:20<00:17, 1.34it/s] 56%|█████▌ | 28/50 [00:20<00:16, 1.34it/s] 58%|█████▊ | 29/50 [00:21<00:15, 1.34it/s] 60%|██████ | 30/50 [00:22<00:14, 1.34it/s] 62%|██████▏ | 31/50 [00:23<00:14, 1.34it/s] 64%|██████▍ | 32/50 [00:23<00:13, 1.34it/s] 66%|██████▌ | 33/50 [00:24<00:12, 1.34it/s] 68%|██████▊ | 34/50 [00:25<00:11, 1.34it/s] 70%|███████ | 35/50 [00:26<00:11, 1.34it/s] 72%|███████▏ | 36/50 [00:26<00:10, 1.34it/s] 74%|███████▍ | 37/50 [00:27<00:09, 1.34it/s] 76%|███████▌ | 38/50 [00:28<00:08, 1.34it/s] 78%|███████▊ | 39/50 [00:29<00:08, 1.34it/s] 80%|████████ | 40/50 [00:29<00:07, 1.34it/s] 82%|████████▏ | 41/50 [00:30<00:06, 1.34it/s] 84%|████████▍ | 42/50 [00:31<00:05, 1.34it/s] 86%|████████▌ | 43/50 [00:32<00:05, 1.34it/s] 88%|████████▊ | 44/50 [00:32<00:04, 1.34it/s] 90%|█████████ | 45/50 [00:33<00:03, 1.34it/s] 92%|█████████▏| 46/50 [00:34<00:02, 1.34it/s] 94%|█████████▍| 47/50 [00:35<00:02, 1.34it/s] 96%|█████████▌| 48/50 [00:35<00:01, 1.34it/s] 98%|█████████▊| 49/50 [00:36<00:00, 1.34it/s] 100%|██████████| 50/50 [00:37<00:00, 1.34it/s] 100%|██████████| 50/50 [00:37<00:00, 1.34it/s]