Prediction

lucataco/controlnet-tile:f688ff774c27a4843c819c9264c0f949925970bb278669ed9140364c8389869c

Model

lucataco/controlnet-tile:f688ff77

ID

v5tigvdbnzgkeidvqzxs6jh4ge

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

10.0s

Created

almost 2 years ago

Input

image
scale: 16
strength: 1
num_inference_steps: 32

{
  "image": "https://replicate.delivery/pbxt/JwoCrAGhRpzYM5f2hfwQq0wxm62dBfv5NJIXG7bRtIC0cJiJ/dog_sm.png",
  "scale": 16,
  "strength": 1,
  "num_inference_steps": 32
}

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

const output = await replicate.run(
  "lucataco/controlnet-tile:f688ff774c27a4843c819c9264c0f949925970bb278669ed9140364c8389869c",
  {
    input: {
      image: "https://replicate.delivery/pbxt/JwoCrAGhRpzYM5f2hfwQq0wxm62dBfv5NJIXG7bRtIC0cJiJ/dog_sm.png",
      scale: 16,
      strength: 1,
      num_inference_steps: 32
    }
  }
);

// 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

Import the client:

import replicate

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

output = replicate.run(
    "lucataco/controlnet-tile:f688ff774c27a4843c819c9264c0f949925970bb278669ed9140364c8389869c",
    input={
        "image": "https://replicate.delivery/pbxt/JwoCrAGhRpzYM5f2hfwQq0wxm62dBfv5NJIXG7bRtIC0cJiJ/dog_sm.png",
        "scale": 16,
        "strength": 1,
        "num_inference_steps": 32
    }
)

# 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.

Run lucataco/controlnet-tile 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/controlnet-tile:f688ff774c27a4843c819c9264c0f949925970bb278669ed9140364c8389869c",
    "input": {
      "image": "https://replicate.delivery/pbxt/JwoCrAGhRpzYM5f2hfwQq0wxm62dBfv5NJIXG7bRtIC0cJiJ/dog_sm.png",
      "scale": 16,
      "strength": 1,
      "num_inference_steps": 32
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Loading...

{
  "completed_at": "2023-11-27T02:29:27.887683Z",
  "created_at": "2023-11-27T02:29:17.881994Z",
  "data_removed": false,
  "error": null,
  "id": "v5tigvdbnzgkeidvqzxs6jh4ge",
  "input": {
    "image": "https://replicate.delivery/pbxt/JwoCrAGhRpzYM5f2hfwQq0wxm62dBfv5NJIXG7bRtIC0cJiJ/dog_sm.png",
    "scale": 16,
    "strength": 1,
    "num_inference_steps": 32
  },
  "logs": "Using seed: 1885155035\nResizing to 1024\n  0%|          | 0/32 [00:00<?, ?it/s]\n  3%|▎         | 1/32 [00:00<00:14,  2.21it/s]\n  6%|▋         | 2/32 [00:00<00:09,  3.12it/s]\n  9%|▉         | 3/32 [00:00<00:08,  3.58it/s]\n 12%|█▎        | 4/32 [00:01<00:07,  3.85it/s]\n 16%|█▌        | 5/32 [00:01<00:06,  4.03it/s]\n 19%|█▉        | 6/32 [00:01<00:06,  4.14it/s]\n 22%|██▏       | 7/32 [00:01<00:05,  4.21it/s]\n 25%|██▌       | 8/32 [00:02<00:05,  4.26it/s]\n 28%|██▊       | 9/32 [00:02<00:05,  4.29it/s]\n 31%|███▏      | 10/32 [00:02<00:05,  4.31it/s]\n 34%|███▍      | 11/32 [00:02<00:04,  4.33it/s]\n 38%|███▊      | 12/32 [00:02<00:04,  4.34it/s]\n 41%|████      | 13/32 [00:03<00:04,  4.35it/s]\n 44%|████▍     | 14/32 [00:03<00:04,  4.34it/s]\n 47%|████▋     | 15/32 [00:03<00:03,  4.35it/s]\n 50%|█████     | 16/32 [00:03<00:03,  4.35it/s]\n 53%|█████▎    | 17/32 [00:04<00:03,  4.35it/s]\n 56%|█████▋    | 18/32 [00:04<00:03,  4.35it/s]\n 59%|█████▉    | 19/32 [00:04<00:02,  4.35it/s]\n 62%|██████▎   | 20/32 [00:04<00:02,  4.35it/s]\n 66%|██████▌   | 21/32 [00:05<00:02,  4.36it/s]\n 69%|██████▉   | 22/32 [00:05<00:02,  4.35it/s]\n 72%|███████▏  | 23/32 [00:05<00:02,  4.35it/s]\n 75%|███████▌  | 24/32 [00:05<00:01,  4.35it/s]\n 78%|███████▊  | 25/32 [00:05<00:01,  4.35it/s]\n 81%|████████▏ | 26/32 [00:06<00:01,  4.35it/s]\n 84%|████████▍ | 27/32 [00:06<00:01,  4.35it/s]\n 88%|████████▊ | 28/32 [00:06<00:00,  4.35it/s]\n 91%|█████████ | 29/32 [00:06<00:00,  4.35it/s]\n 94%|█████████▍| 30/32 [00:07<00:00,  4.35it/s]\n 97%|█████████▋| 31/32 [00:07<00:00,  4.35it/s]\n100%|██████████| 32/32 [00:07<00:00,  4.35it/s]\n100%|██████████| 32/32 [00:07<00:00,  4.23it/s]",
  "metrics": {
    "predict_time": 9.971312,
    "total_time": 10.005689
  },
  "output": "https://replicate.delivery/pbxt/Lb5ati7OIcZyJ50QbbBZ4yeAEkBsF1Ih6zbBrSjdHV3DiNeRA/output.png",
  "started_at": "2023-11-27T02:29:17.916371Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/v5tigvdbnzgkeidvqzxs6jh4ge",
    "cancel": "https://api.replicate.com/v1/predictions/v5tigvdbnzgkeidvqzxs6jh4ge/cancel"
  },
  "version": "f688ff774c27a4843c819c9264c0f949925970bb278669ed9140364c8389869c"
}

Generated in

10.0 seconds

Tweak it Iterate in playground Share Report

Using seed: 1885155035
Resizing to 1024
  0%|          | 0/32 [00:00<?, ?it/s]
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100%|██████████| 32/32 [00:07<00:00,  4.23it/s]

Prediction

lucataco/controlnet-tile:f688ff774c27a4843c819c9264c0f949925970bb278669ed9140364c8389869c

Model

lucataco/controlnet-tile:f688ff77

ID

n2xbqalb6jq467mm4v4qzmxoou

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

14.8s

Created

almost 2 years ago

Input

image
scale: 2
strength: 0.15
num_inference_steps: 32

{
  "image": "https://replicate.delivery/pbxt/JwoDHQ0gIgBPeiXQPiAI0s7NVFkRxDLwXie51LIhDBYWIvBe/ship.jpg",
  "scale": 2,
  "strength": 0.15,
  "num_inference_steps": 32
}

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

const output = await replicate.run(
  "lucataco/controlnet-tile:f688ff774c27a4843c819c9264c0f949925970bb278669ed9140364c8389869c",
  {
    input: {
      image: "https://replicate.delivery/pbxt/JwoDHQ0gIgBPeiXQPiAI0s7NVFkRxDLwXie51LIhDBYWIvBe/ship.jpg",
      scale: 2,
      strength: 0.15,
      num_inference_steps: 32
    }
  }
);

// 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

Import the client:

import replicate

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

output = replicate.run(
    "lucataco/controlnet-tile:f688ff774c27a4843c819c9264c0f949925970bb278669ed9140364c8389869c",
    input={
        "image": "https://replicate.delivery/pbxt/JwoDHQ0gIgBPeiXQPiAI0s7NVFkRxDLwXie51LIhDBYWIvBe/ship.jpg",
        "scale": 2,
        "strength": 0.15,
        "num_inference_steps": 32
    }
)

# 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.

Run lucataco/controlnet-tile 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/controlnet-tile:f688ff774c27a4843c819c9264c0f949925970bb278669ed9140364c8389869c",
    "input": {
      "image": "https://replicate.delivery/pbxt/JwoDHQ0gIgBPeiXQPiAI0s7NVFkRxDLwXie51LIhDBYWIvBe/ship.jpg",
      "scale": 2,
      "strength": 0.15,
      "num_inference_steps": 32
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Loading...

Generated in

14.7 seconds

Tweak it Iterate in playground Share Report

Prediction

lucataco/controlnet-tile:f688ff774c27a4843c819c9264c0f949925970bb278669ed9140364c8389869c

Model

lucataco/controlnet-tile:f688ff77

ID

val22ftbhvxmwy2wxjqigjciru

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

15.3s

Created

almost 2 years ago

Input

image
scale: 2
strength: 0.15
num_inference_steps: 32

{
  "image": "https://replicate.delivery/pbxt/JwoE4MuPxHRUaAfRxRwXa3ZzEgJMTaD7dDkR3vowOYQv79cy/mouse.png",
  "scale": 2,
  "strength": 0.15,
  "num_inference_steps": 32
}

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

const output = await replicate.run(
  "lucataco/controlnet-tile:f688ff774c27a4843c819c9264c0f949925970bb278669ed9140364c8389869c",
  {
    input: {
      image: "https://replicate.delivery/pbxt/JwoE4MuPxHRUaAfRxRwXa3ZzEgJMTaD7dDkR3vowOYQv79cy/mouse.png",
      scale: 2,
      strength: 0.15,
      num_inference_steps: 32
    }
  }
);

// 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

Import the client:

import replicate

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

output = replicate.run(
    "lucataco/controlnet-tile:f688ff774c27a4843c819c9264c0f949925970bb278669ed9140364c8389869c",
    input={
        "image": "https://replicate.delivery/pbxt/JwoE4MuPxHRUaAfRxRwXa3ZzEgJMTaD7dDkR3vowOYQv79cy/mouse.png",
        "scale": 2,
        "strength": 0.15,
        "num_inference_steps": 32
    }
)

# 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.

Run lucataco/controlnet-tile 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/controlnet-tile:f688ff774c27a4843c819c9264c0f949925970bb278669ed9140364c8389869c",
    "input": {
      "image": "https://replicate.delivery/pbxt/JwoE4MuPxHRUaAfRxRwXa3ZzEgJMTaD7dDkR3vowOYQv79cy/mouse.png",
      "scale": 2,
      "strength": 0.15,
      "num_inference_steps": 32
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Loading...

Generated in

15.2 seconds

Tweak it Iterate in playground Share Report

Prediction

Input

Output

Prediction

Input

Output

Prediction

Input

Output

Logs (v5tigvdbnzgkeidvqzxs6jh4ge)

Logs (n2xbqalb6jq467mm4v4qzmxoou)

Logs (val22ftbhvxmwy2wxjqigjciru)