Prediction

Model

zylim0702/controlnet-v1-1-multi:211486c3

tntmvudbhpfncycnoocai6z7xa

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

8.7s

Created

over 2 years ago

Webhook

–

Input

image: https://replicate.delivery/pbxt/JREI44b9KCW78ynS9sH9je7wCckmEHcSF3EXwJBlhDhbh0jH/dog.png
prompt: a dog in a bright sunshine jungle, hard lighting
autogenerated_prompt: true
structure: canny
num_samples: 1
ddim_steps: 20
strength: 1
scale: 9
preprocessor_resolution: 512
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
low_threshold: 100
high_threshold: 200
image_upscaler: false

{
  "a_prompt": "Best quality, extremely detailed",
  "autogenerated_prompt": true,
  "ddim_steps": 20,
  "high_threshold": 200,
  "image": "https://replicate.delivery/pbxt/JREI44b9KCW78ynS9sH9je7wCckmEHcSF3EXwJBlhDhbh0jH/dog.png",
  "image_upscaler": false,
  "low_threshold": 100,
  "n_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
  "num_samples": 1,
  "preprocessor_resolution": 512,
  "prompt": "a dog in a bright sunshine jungle, hard lighting",
  "scale": 9,
  "strength": 1,
  "structure": "canny"
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_aLP**********************************

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 zylim0702/controlnet-v1-1-multi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "zylim0702/controlnet-v1-1-multi:211486c3a33e26c7513c3ae4db00621f155bff401d3a241e260995e04bbbd88a",
  {
    input: {
      a_prompt: "Best quality, extremely detailed",
      autogenerated_prompt: true,
      ddim_steps: 20,
      high_threshold: 200,
      image: "https://replicate.delivery/pbxt/JREI44b9KCW78ynS9sH9je7wCckmEHcSF3EXwJBlhDhbh0jH/dog.png",
      image_upscaler: false,
      low_threshold: 100,
      n_prompt: "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      num_samples: 1,
      preprocessor_resolution: 512,
      prompt: "a dog in a bright sunshine jungle, hard lighting",
      scale: 9,
      strength: 1,
      structure: "canny"
    }
  }
);

// 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_aLP**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run zylim0702/controlnet-v1-1-multi using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "zylim0702/controlnet-v1-1-multi:211486c3a33e26c7513c3ae4db00621f155bff401d3a241e260995e04bbbd88a",
    input={
        "a_prompt": "Best quality, extremely detailed",
        "autogenerated_prompt": True,
        "ddim_steps": 20,
        "high_threshold": 200,
        "image": "https://replicate.delivery/pbxt/JREI44b9KCW78ynS9sH9je7wCckmEHcSF3EXwJBlhDhbh0jH/dog.png",
        "image_upscaler": False,
        "low_threshold": 100,
        "n_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
        "num_samples": 1,
        "preprocessor_resolution": 512,
        "prompt": "a dog in a bright sunshine jungle, hard lighting",
        "scale": 9,
        "strength": 1,
        "structure": "canny"
    }
)

# 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_aLP**********************************

This is your API token. Keep it to yourself.

Run zylim0702/controlnet-v1-1-multi 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": "zylim0702/controlnet-v1-1-multi:211486c3a33e26c7513c3ae4db00621f155bff401d3a241e260995e04bbbd88a",
    "input": {
      "a_prompt": "Best quality, extremely detailed",
      "autogenerated_prompt": true,
      "ddim_steps": 20,
      "high_threshold": 200,
      "image": "https://replicate.delivery/pbxt/JREI44b9KCW78ynS9sH9je7wCckmEHcSF3EXwJBlhDhbh0jH/dog.png",
      "image_upscaler": false,
      "low_threshold": 100,
      "n_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      "num_samples": 1,
      "preprocessor_resolution": 512,
      "prompt": "a dog in a bright sunshine jungle, hard lighting",
      "scale": 9,
      "strength": 1,
      "structure": "canny"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "tntmvudbhpfncycnoocai6z7xa",
  "model": "zylim0702/controlnet-v1-1-multi",
  "version": "211486c3a33e26c7513c3ae4db00621f155bff401d3a241e260995e04bbbd88a",
  "input": {
    "a_prompt": "Best quality, extremely detailed",
    "autogenerated_prompt": true,
    "ddim_steps": 20,
    "high_threshold": 200,
    "image": "https://replicate.delivery/pbxt/JREI44b9KCW78ynS9sH9je7wCckmEHcSF3EXwJBlhDhbh0jH/dog.png",
    "image_upscaler": false,
    "low_threshold": 100,
    "n_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
    "num_samples": 1,
    "preprocessor_resolution": 512,
    "prompt": "a dog in a bright sunshine jungle, hard lighting",
    "scale": 9,
    "strength": 1,
    "structure": "canny"
  },
  "logs": "0%|          | 0/55 [00:00<?, ?it/s]\n 56%|█████▋    | 31/55 [00:00<00:00, 304.70it/s]\n100%|██████████| 55/55 [00:00<00:00, 309.42it/s]\na dog in a bright sunshine jungle, hard lighting, there is a dog sitting on a bench in a field, sitting on a bench, sitting on bench, sit on a bench, sitting on a park bench, happy dog, portrait, benches, portrait shot, award - winning pet photography, portrait image, sittin, a wooden, four legged, medium portrait, on a sunny day, at a park, peaceful mood\nUsing seed: 25123\nThe following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['sunny day, at a park, peaceful mood']\nThe following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['sunny day, at a park, peaceful mood']\n  0%|          | 0/20 [00:00<?, ?it/s]\n  5%|▌         | 1/20 [00:00<00:05,  3.44it/s]\n 10%|█         | 2/20 [00:00<00:05,  3.43it/s]\n 15%|█▌        | 3/20 [00:00<00:04,  3.42it/s]\n 20%|██        | 4/20 [00:01<00:04,  3.41it/s]\n 25%|██▌       | 5/20 [00:01<00:04,  3.41it/s]\n 30%|███       | 6/20 [00:01<00:04,  3.41it/s]\n 35%|███▌      | 7/20 [00:02<00:03,  3.41it/s]\n 40%|████      | 8/20 [00:02<00:03,  3.41it/s]\n 45%|████▌     | 9/20 [00:02<00:03,  3.41it/s]\n 50%|█████     | 10/20 [00:02<00:02,  3.41it/s]\n 55%|█████▌    | 11/20 [00:03<00:02,  3.40it/s]\n 60%|██████    | 12/20 [00:03<00:02,  3.40it/s]\n 65%|██████▌   | 13/20 [00:03<00:02,  3.40it/s]\n 70%|███████   | 14/20 [00:04<00:01,  3.40it/s]\n 75%|███████▌  | 15/20 [00:04<00:01,  3.40it/s]\n 80%|████████  | 16/20 [00:04<00:01,  3.40it/s]\n 85%|████████▌ | 17/20 [00:04<00:00,  3.41it/s]\n 90%|█████████ | 18/20 [00:05<00:00,  3.42it/s]\n 95%|█████████▌| 19/20 [00:05<00:00,  3.42it/s]\n100%|██████████| 20/20 [00:05<00:00,  3.42it/s]\n100%|██████████| 20/20 [00:05<00:00,  3.41it/s]",
  "output": [
    "https://replicate.delivery/pbxt/kARY8cHjYeSXdyeeXaOsKlECvnhwD0ijD7MHYvP7X3NZZzeFB/out-0.png"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2023-08-31T01:13:09.043216Z",
  "started_at": "2023-08-31T01:13:08.95776Z",
  "completed_at": "2023-08-31T01:13:17.775722Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/tntmvudbhpfncycnoocai6z7xa/cancel",
    "get": "https://api.replicate.com/v1/predictions/tntmvudbhpfncycnoocai6z7xa"
  },
  "metrics": {
    "predict_time": 8.817962,
    "total_time": 8.732506
  }
}

Generated in

8.8 seconds

Tweak it Report

0%| | 0/55 [00:00<?, ?it/s] 56%|█████▋ | 31/55 [00:00<00:00, 304.70it/s] 100%|██████████| 55/55 [00:00<00:00, 309.42it/s] a dog in a bright sunshine jungle, hard lighting, there is a dog sitting on a bench in a field, sitting on a bench, sitting on bench, sit on a bench, sitting on a park bench, happy dog, portrait, benches, portrait shot, award - winning pet photography, portrait image, sittin, a wooden, four legged, medium portrait, on a sunny day, at a park, peaceful mood Using seed: 25123 The following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['sunny day, at a park, peaceful mood'] The following part of your input was truncated because CLIP can only handle sequences up to 77 tokens: ['sunny day, at a park, peaceful mood'] 0%| | 0/20 [00:00<?, ?it/s] 5%|▌ | 1/20 [00:00<00:05, 3.44it/s] 10%|█ | 2/20 [00:00<00:05, 3.43it/s] 15%|█▌ | 3/20 [00:00<00:04, 3.42it/s] 20%|██ | 4/20 [00:01<00:04, 3.41it/s] 25%|██▌ | 5/20 [00:01<00:04, 3.41it/s] 30%|███ | 6/20 [00:01<00:04, 3.41it/s] 35%|███▌ | 7/20 [00:02<00:03, 3.41it/s] 40%|████ | 8/20 [00:02<00:03, 3.41it/s] 45%|████▌ | 9/20 [00:02<00:03, 3.41it/s] 50%|█████ | 10/20 [00:02<00:02, 3.41it/s] 55%|█████▌ | 11/20 [00:03<00:02, 3.40it/s] 60%|██████ | 12/20 [00:03<00:02, 3.40it/s] 65%|██████▌ | 13/20 [00:03<00:02, 3.40it/s] 70%|███████ | 14/20 [00:04<00:01, 3.40it/s] 75%|███████▌ | 15/20 [00:04<00:01, 3.40it/s] 80%|████████ | 16/20 [00:04<00:01, 3.40it/s] 85%|████████▌ | 17/20 [00:04<00:00, 3.41it/s] 90%|█████████ | 18/20 [00:05<00:00, 3.42it/s] 95%|█████████▌| 19/20 [00:05<00:00, 3.42it/s] 100%|██████████| 20/20 [00:05<00:00, 3.42it/s] 100%|██████████| 20/20 [00:05<00:00, 3.41it/s]