Prediction

Model

jagilley/controlnet-depth2img:922c7bb6

vuqyln4f3ncxjkmrxofxph7woy

Status

Succeeded

Source

Web

Hardware

–

Total duration

2m 31s

Created

almost 3 years ago by jagilley

Webhook

–

Input

image: https://replicate.delivery/pbxt/IKFvJn5EpLuDDsFysOP4B1J9HvKDbMBCwZUK9n6p9mIPoQwG/sd.png
prompt: a stormtrooper giving a lecture at a university
num_samples: 1
image_resolution: 512
ddim_steps: 20
scale: 9
a_prompt: best quality, extremely detailed
n_prompt: longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality
detect_resolution: 512

{
  "a_prompt": "best quality, extremely detailed",
  "ddim_steps": 20,
  "detect_resolution": 512,
  "image": "https://replicate.delivery/pbxt/IKFvJn5EpLuDDsFysOP4B1J9HvKDbMBCwZUK9n6p9mIPoQwG/sd.png",
  "image_resolution": "512",
  "n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
  "num_samples": "1",
  "prompt": "a stormtrooper giving a lecture at a university",
  "scale": 9
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_WlP**********************************

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 jagilley/controlnet-depth2img using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "jagilley/controlnet-depth2img:922c7bb67b87ec32cbc2fd11b1d5f94f0ba4f5519c4dbd02856376444127cc60",
  {
    input: {
      a_prompt: "best quality, extremely detailed",
      ddim_steps: 20,
      detect_resolution: 512,
      image: "https://replicate.delivery/pbxt/IKFvJn5EpLuDDsFysOP4B1J9HvKDbMBCwZUK9n6p9mIPoQwG/sd.png",
      image_resolution: "512",
      n_prompt: "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      num_samples: "1",
      prompt: "a stormtrooper giving a lecture at a university",
      scale: 9
    }
  }
);

// 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=r8_WlP**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run jagilley/controlnet-depth2img using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "jagilley/controlnet-depth2img:922c7bb67b87ec32cbc2fd11b1d5f94f0ba4f5519c4dbd02856376444127cc60",
    input={
        "a_prompt": "best quality, extremely detailed",
        "ddim_steps": 20,
        "detect_resolution": 512,
        "image": "https://replicate.delivery/pbxt/IKFvJn5EpLuDDsFysOP4B1J9HvKDbMBCwZUK9n6p9mIPoQwG/sd.png",
        "image_resolution": "512",
        "n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
        "num_samples": "1",
        "prompt": "a stormtrooper giving a lecture at a university",
        "scale": 9
    }
)

# 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.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_WlP**********************************

This is your API token. Keep it to yourself.

Run jagilley/controlnet-depth2img 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": "jagilley/controlnet-depth2img:922c7bb67b87ec32cbc2fd11b1d5f94f0ba4f5519c4dbd02856376444127cc60",
    "input": {
      "a_prompt": "best quality, extremely detailed",
      "ddim_steps": 20,
      "detect_resolution": 512,
      "image": "https://replicate.delivery/pbxt/IKFvJn5EpLuDDsFysOP4B1J9HvKDbMBCwZUK9n6p9mIPoQwG/sd.png",
      "image_resolution": "512",
      "n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      "num_samples": "1",
      "prompt": "a stormtrooper giving a lecture at a university",
      "scale": 9
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "vuqyln4f3ncxjkmrxofxph7woy",
  "model": "jagilley/controlnet-depth2img",
  "version": "922c7bb67b87ec32cbc2fd11b1d5f94f0ba4f5519c4dbd02856376444127cc60",
  "input": {
    "a_prompt": "best quality, extremely detailed",
    "ddim_steps": 20,
    "detect_resolution": 512,
    "image": "https://replicate.delivery/pbxt/IKFvJn5EpLuDDsFysOP4B1J9HvKDbMBCwZUK9n6p9mIPoQwG/sd.png",
    "image_resolution": "512",
    "n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
    "num_samples": "1",
    "prompt": "a stormtrooper giving a lecture at a university",
    "scale": 9
  },
  "logs": "Global seed set to 625452\nData shape for DDIM sampling is (1, 4, 64, 64), eta 0.0\nRunning DDIM Sampling with 20 timesteps\nDDIM Sampler:   0%|          | 0/20 [00:00<?, ?it/s]\nDDIM Sampler:   5%|▌         | 1/20 [00:00<00:17,  1.10it/s]\nDDIM Sampler:  10%|█         | 2/20 [00:01<00:14,  1.26it/s]\nDDIM Sampler:  15%|█▌        | 3/20 [00:02<00:12,  1.32it/s]\nDDIM Sampler:  20%|██        | 4/20 [00:03<00:11,  1.35it/s]\nDDIM Sampler:  25%|██▌       | 5/20 [00:03<00:10,  1.37it/s]\nDDIM Sampler:  30%|███       | 6/20 [00:04<00:10,  1.38it/s]\nDDIM Sampler:  35%|███▌      | 7/20 [00:05<00:09,  1.39it/s]\nDDIM Sampler:  40%|████      | 8/20 [00:05<00:08,  1.39it/s]\nDDIM Sampler:  45%|████▌     | 9/20 [00:06<00:07,  1.39it/s]\nDDIM Sampler:  50%|█████     | 10/20 [00:07<00:07,  1.39it/s]\nDDIM Sampler:  55%|█████▌    | 11/20 [00:08<00:06,  1.39it/s]\nDDIM Sampler:  60%|██████    | 12/20 [00:08<00:05,  1.39it/s]\nDDIM Sampler:  65%|██████▌   | 13/20 [00:09<00:05,  1.39it/s]\nDDIM Sampler:  70%|███████   | 14/20 [00:10<00:04,  1.39it/s]\nDDIM Sampler:  75%|███████▌  | 15/20 [00:10<00:03,  1.38it/s]\nDDIM Sampler:  80%|████████  | 16/20 [00:11<00:02,  1.38it/s]\nDDIM Sampler:  85%|████████▌ | 17/20 [00:12<00:02,  1.38it/s]\nDDIM Sampler:  90%|█████████ | 18/20 [00:13<00:01,  1.38it/s]\nDDIM Sampler:  95%|█████████▌| 19/20 [00:13<00:00,  1.38it/s]\nDDIM Sampler: 100%|██████████| 20/20 [00:14<00:00,  1.38it/s]\nDDIM Sampler: 100%|██████████| 20/20 [00:14<00:00,  1.37it/s]",
  "output": [
    "https://replicate.delivery/pbxt/tLbfGg8jnegaSEokEekC2x7lZeC6kenBJQ4r3B3kPZHo2c4DC/output_0.png",
    "https://replicate.delivery/pbxt/CCPUFqXwc2LZF5CVU13C8yPE03ENz9gMdnuvXf7Kb2abzhPIA/output_1.png"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-02-16T19:42:23.645033Z",
  "started_at": "2023-02-16T19:44:34.059606Z",
  "completed_at": "2023-02-16T19:44:54.954858Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/vuqyln4f3ncxjkmrxofxph7woy/cancel",
    "get": "https://api.replicate.com/v1/predictions/vuqyln4f3ncxjkmrxofxph7woy"
  },
  "metrics": {
    "predict_time": 20.895252,
    "total_time": 151.309825
  }
}

Generated in

20.9 seconds

Tweak it Report