Prediction

Model

lucataco/sdxl-controlnet-depth:5e0a5cda

cgekvctbear65ui5vgwiahmn34

Status

Succeeded

Source

Web

Hardware

A40

Total duration

2m 8s

Created

over 2 years ago

Webhook

–

Input

image: https://replicate.delivery/pbxt/JLl0qK0Hjm0GCGhfrmDPQDZkzKHBD98jQ1EjiRiUpC08MzK1/demo.png
prompt: spiderman lecture, photorealistic
num_inference_steps: 30
condition_scale: 0.5
seed: 25087

{
  "condition_scale": 0.5,
  "image": "https://replicate.delivery/pbxt/JLl0qK0Hjm0GCGhfrmDPQDZkzKHBD98jQ1EjiRiUpC08MzK1/demo.png",
  "num_inference_steps": 30,
  "prompt": "spiderman lecture, photorealistic",
  "seed": 25087
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_0Wj**********************************

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 lucataco/sdxl-controlnet-depth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9",
  {
    input: {
      condition_scale: 0.5,
      image: "https://replicate.delivery/pbxt/JLl0qK0Hjm0GCGhfrmDPQDZkzKHBD98jQ1EjiRiUpC08MzK1/demo.png",
      num_inference_steps: 30,
      prompt: "spiderman lecture, photorealistic",
      seed: 25087
    }
  }
);

// 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_0Wj**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run lucataco/sdxl-controlnet-depth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9",
    input={
        "condition_scale": 0.5,
        "image": "https://replicate.delivery/pbxt/JLl0qK0Hjm0GCGhfrmDPQDZkzKHBD98jQ1EjiRiUpC08MzK1/demo.png",
        "num_inference_steps": 30,
        "prompt": "spiderman lecture, photorealistic",
        "seed": 25087
    }
)

# 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_0Wj**********************************

This is your API token. Keep it to yourself.

Run lucataco/sdxl-controlnet-depth 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": "lucataco/sdxl-controlnet-depth:5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9",
    "input": {
      "condition_scale": 0.5,
      "image": "https://replicate.delivery/pbxt/JLl0qK0Hjm0GCGhfrmDPQDZkzKHBD98jQ1EjiRiUpC08MzK1/demo.png",
      "num_inference_steps": 30,
      "prompt": "spiderman lecture, photorealistic",
      "seed": 25087
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "cgekvctbear65ui5vgwiahmn34",
  "model": "lucataco/sdxl-controlnet-depth",
  "version": "5e0a5cda895aa23a1aaa1a9a265220097102448e1b4c42b22a3c6d87c12d41a9",
  "input": {
    "condition_scale": 0.5,
    "image": "https://replicate.delivery/pbxt/JLl0qK0Hjm0GCGhfrmDPQDZkzKHBD98jQ1EjiRiUpC08MzK1/demo.png",
    "num_inference_steps": 30,
    "prompt": "spiderman lecture, photorealistic",
    "seed": 25087
  },
  "logs": "Using seed: 25087\nOriginal width:458, height:458\nAspect Ratio: 1.00\nnew_width:1024, new_height:1024\n  0%|          | 0/30 [00:00<?, ?it/s]\n  3%|▎         | 1/30 [00:00<00:09,  3.05it/s]\n  7%|▋         | 2/30 [00:00<00:08,  3.26it/s]\n 10%|█         | 3/30 [00:00<00:08,  3.33it/s]\n 13%|█▎        | 4/30 [00:01<00:07,  3.34it/s]\n 17%|█▋        | 5/30 [00:01<00:07,  3.36it/s]\n 20%|██        | 6/30 [00:01<00:07,  3.38it/s]\n 23%|██▎       | 7/30 [00:02<00:06,  3.38it/s]\n 27%|██▋       | 8/30 [00:02<00:06,  3.38it/s]\n 30%|███       | 9/30 [00:02<00:06,  3.39it/s]\n 33%|███▎      | 10/30 [00:02<00:05,  3.39it/s]\n 37%|███▋      | 11/30 [00:03<00:05,  3.38it/s]\n 40%|████      | 12/30 [00:03<00:05,  3.35it/s]\n 43%|████▎     | 13/30 [00:03<00:05,  3.37it/s]\n 47%|████▋     | 14/30 [00:04<00:04,  3.37it/s]\n 50%|█████     | 15/30 [00:04<00:04,  3.37it/s]\n 53%|█████▎    | 16/30 [00:04<00:04,  3.38it/s]\n 57%|█████▋    | 17/30 [00:05<00:03,  3.38it/s]\n 60%|██████    | 18/30 [00:05<00:03,  3.38it/s]\n 63%|██████▎   | 19/30 [00:05<00:03,  3.38it/s]\n 67%|██████▋   | 20/30 [00:05<00:02,  3.38it/s]\n 70%|███████   | 21/30 [00:06<00:02,  3.38it/s]\n 73%|███████▎  | 22/30 [00:06<00:02,  3.38it/s]\n 77%|███████▋  | 23/30 [00:06<00:02,  3.38it/s]\n 80%|████████  | 24/30 [00:07<00:01,  3.38it/s]\n 83%|████████▎ | 25/30 [00:07<00:01,  3.38it/s]\n 87%|████████▋ | 26/30 [00:07<00:01,  3.38it/s]\n 90%|█████████ | 27/30 [00:08<00:00,  3.38it/s]\n 93%|█████████▎| 28/30 [00:08<00:00,  3.38it/s]\n 97%|█████████▋| 29/30 [00:08<00:00,  3.38it/s]\n100%|██████████| 30/30 [00:08<00:00,  3.38it/s]\n100%|██████████| 30/30 [00:08<00:00,  3.37it/s]",
  "output": "https://pbxt.replicate.delivery/lvhWUVZ8cqaaDRyNwcoP5SxZnZYO8lG3JXqRmNFAM5Fmc6YE/output.png",
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-09-12T22:45:45.734709Z",
  "started_at": "2023-09-12T22:47:41.267643Z",
  "completed_at": "2023-09-12T22:47:53.749512Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/cgekvctbear65ui5vgwiahmn34/cancel",
    "get": "https://api.replicate.com/v1/predictions/cgekvctbear65ui5vgwiahmn34"
  },
  "metrics": {
    "predict_time": 12.481869,
    "total_time": 128.014803
  }
}

Generated in

12.5 seconds

Tweak it Report

Using seed: 25087 Original width:458, height:458 Aspect Ratio: 1.00 new_width:1024, new_height:1024 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:09, 3.05it/s] 7%|▋ | 2/30 [00:00<00:08, 3.26it/s] 10%|█ | 3/30 [00:00<00:08, 3.33it/s] 13%|█▎ | 4/30 [00:01<00:07, 3.34it/s] 17%|█▋ | 5/30 [00:01<00:07, 3.36it/s] 20%|██ | 6/30 [00:01<00:07, 3.38it/s] 23%|██▎ | 7/30 [00:02<00:06, 3.38it/s] 27%|██▋ | 8/30 [00:02<00:06, 3.38it/s] 30%|███ | 9/30 [00:02<00:06, 3.39it/s] 33%|███▎ | 10/30 [00:02<00:05, 3.39it/s] 37%|███▋ | 11/30 [00:03<00:05, 3.38it/s] 40%|████ | 12/30 [00:03<00:05, 3.35it/s] 43%|████▎ | 13/30 [00:03<00:05, 3.37it/s] 47%|████▋ | 14/30 [00:04<00:04, 3.37it/s] 50%|█████ | 15/30 [00:04<00:04, 3.37it/s] 53%|█████▎ | 16/30 [00:04<00:04, 3.38it/s] 57%|█████▋ | 17/30 [00:05<00:03, 3.38it/s] 60%|██████ | 18/30 [00:05<00:03, 3.38it/s] 63%|██████▎ | 19/30 [00:05<00:03, 3.38it/s] 67%|██████▋ | 20/30 [00:05<00:02, 3.38it/s] 70%|███████ | 21/30 [00:06<00:02, 3.38it/s] 73%|███████▎ | 22/30 [00:06<00:02, 3.38it/s] 77%|███████▋ | 23/30 [00:06<00:02, 3.38it/s] 80%|████████ | 24/30 [00:07<00:01, 3.38it/s] 83%|████████▎ | 25/30 [00:07<00:01, 3.38it/s] 87%|████████▋ | 26/30 [00:07<00:01, 3.38it/s] 90%|█████████ | 27/30 [00:08<00:00, 3.38it/s] 93%|█████████▎| 28/30 [00:08<00:00, 3.38it/s] 97%|█████████▋| 29/30 [00:08<00:00, 3.38it/s] 100%|██████████| 30/30 [00:08<00:00, 3.38it/s] 100%|██████████| 30/30 [00:08<00:00, 3.37it/s]