Prediction

Model

fermatresearch/high-resolution-controlnet-tile:13c637d3

4nt04qw3exrgm0cgx9stm0nek8

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

68.5s

Created

over 1 year ago by fermatresearch

Webhook

–

Input

prompt: a woman wearing a colorful suit
image: https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg
resolution: 2048
resemblance: 0.85
creativity: 0.4
hdr: 0
scheduler: DDIM
steps: 8
guidance_scale: 0
negative_prompt: Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant
guess_mode: false
tile_size: 512
lora_sharpness_strength: 1
lora_details_strength: 0.75
format: jpg

{
  "creativity": 0.4,
  "format": "jpg",
  "guess_mode": false,
  "guidance_scale": 0,
  "hdr": 0,
  "image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
  "lora_details_strength": 0.75,
  "lora_sharpness_strength": 1,
  "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant",
  "prompt": "a woman wearing a colorful suit",
  "resemblance": 0.85,
  "resolution": 2048,
  "scheduler": "DDIM",
  "steps": 8,
  "tile_size": 512
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_cxf**********************************

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

const output = await replicate.run(
  "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76",
  {
    input: {
      creativity: 0.4,
      format: "jpg",
      guess_mode: false,
      guidance_scale: 0,
      hdr: 0,
      image: "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
      lora_details_strength: 0.75,
      lora_sharpness_strength: 1,
      negative_prompt: "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant",
      prompt: "a woman wearing a colorful suit",
      resemblance: 0.85,
      resolution: 2048,
      scheduler: "DDIM",
      steps: 8,
      tile_size: 512
    }
  }
);

// 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_cxf**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

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

output = replicate.run(
    "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76",
    input={
        "creativity": 0.4,
        "format": "jpg",
        "guess_mode": False,
        "guidance_scale": 0,
        "hdr": 0,
        "image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
        "lora_details_strength": 0.75,
        "lora_sharpness_strength": 1,
        "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant",
        "prompt": "a woman wearing a colorful suit",
        "resemblance": 0.85,
        "resolution": 2048,
        "scheduler": "DDIM",
        "steps": 8,
        "tile_size": 512
    }
)

# 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_cxf**********************************

This is your API token. Keep it to yourself.

Run fermatresearch/high-resolution-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": "fermatresearch/high-resolution-controlnet-tile:13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76",
    "input": {
      "creativity": 0.4,
      "format": "jpg",
      "guess_mode": false,
      "guidance_scale": 0,
      "hdr": 0,
      "image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
      "lora_details_strength": 0.75,
      "lora_sharpness_strength": 1,
      "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant",
      "prompt": "a woman wearing a colorful suit",
      "resemblance": 0.85,
      "resolution": 2048,
      "scheduler": "DDIM",
      "steps": 8,
      "tile_size": 512
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "4nt04qw3exrgm0cgx9stm0nek8",
  "model": "fermatresearch/high-resolution-controlnet-tile",
  "version": "13c637d35ebfe5e44a5297c370134b0f6921d0ee73f103ccfee7349dcaf59c76",
  "input": {
    "creativity": 0.4,
    "format": "jpg",
    "guess_mode": false,
    "guidance_scale": 0,
    "hdr": 0,
    "image": "https://replicate.delivery/pbxt/LKXd5RrqR3YITx6LYu6HFOIHgQl8EdHZV2qQcLhPoI4deTpO/f84e7869-32ca-444b-a720-19e4325f4347.jpg",
    "lora_details_strength": 0.75,
    "lora_sharpness_strength": 1,
    "negative_prompt": "Teeth, tooth, open mouth, longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, mutant",
    "prompt": "a woman wearing a colorful suit",
    "resemblance": 0.85,
    "resolution": 2048,
    "scheduler": "DDIM",
    "steps": 8,
    "tile_size": 512
  },
  "logs": "Using seed: 33864\nThe config attributes {'solver_order': 2, 'algorithm_type': 'deis', 'solver_type': 'logrho', 'lower_order_final': True, 'use_karras_sigmas': False} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  6.35it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.64it/s]\n100%|██████████| 3/3 [00:00<00:00,  9.96it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.80it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.21it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.66it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  3.28it/s]\n 67%|██████▋   | 2/3 [00:00<00:00,  4.88it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.80it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.23it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.72it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.08it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.54it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.72it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.08it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.54it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  3.28it/s]\n 67%|██████▋   | 2/3 [00:00<00:00,  4.87it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.78it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.21it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.77it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.16it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.61it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  6.38it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.61it/s]\n100%|██████████| 3/3 [00:00<00:00,  9.94it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.78it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.15it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.61it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  6.34it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.58it/s]\n100%|██████████| 3/3 [00:00<00:00,  9.91it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.71it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.04it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.51it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  3.28it/s]\n 67%|██████▋   | 2/3 [00:00<00:00,  4.86it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.76it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.20it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  3.27it/s]\n 67%|██████▋   | 2/3 [00:00<00:00,  4.86it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.75it/s]\n100%|██████████| 3/3 [00:00<00:00,  5.19it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.70it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.04it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.50it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  6.35it/s]\n100%|██████████| 3/3 [00:00<00:00, 10.59it/s]\n100%|██████████| 3/3 [00:00<00:00,  9.92it/s]\n  0%|          | 0/3 [00:00<?, ?it/s]\n 33%|███▎      | 1/3 [00:00<00:00,  4.77it/s]\n100%|██████████| 3/3 [00:00<00:00,  8.14it/s]\n100%|██████████| 3/3 [00:00<00:00,  7.60it/s]\n  0%|          | 0/2 [00:00<?, ?it/s]\n 50%|█████     | 1/2 [00:01<00:01,  1.95s/it]\n100%|██████████| 2/2 [00:02<00:00,  1.39s/it]\n100%|██████████| 2/2 [00:02<00:00,  1.47s/it]",
  "output": "https://replicate.delivery/pbxt/uVYXPohfE3U5WC67kSr4TahCjwuu1hJJZz3f633PfMwQtDYmA/output.jpg",
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2024-07-25T15:12:37.239Z",
  "started_at": "2024-07-25T15:13:11.319777Z",
  "completed_at": "2024-07-25T15:13:45.687896Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/4nt04qw3exrgm0cgx9stm0nek8/cancel",
    "get": "https://api.replicate.com/v1/predictions/4nt04qw3exrgm0cgx9stm0nek8",
    "web": "https://replicate.com/p/4nt04qw3exrgm0cgx9stm0nek8"
  },
  "metrics": {
    "predict_time": 34.368119126,
    "total_time": 68.448896
  }
}

Generated in

34.4 seconds

Tweak it Report

Using seed: 33864 The config attributes {'solver_order': 2, 'algorithm_type': 'deis', 'solver_type': 'logrho', 'lower_order_final': True, 'use_karras_sigmas': False} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file. 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 6.35it/s] 100%|██████████| 3/3 [00:00<00:00, 10.64it/s] 100%|██████████| 3/3 [00:00<00:00, 9.96it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.80it/s] 100%|██████████| 3/3 [00:00<00:00, 8.21it/s] 100%|██████████| 3/3 [00:00<00:00, 7.66it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.28it/s] 67%|██████▋ | 2/3 [00:00<00:00, 4.88it/s] 100%|██████████| 3/3 [00:00<00:00, 5.80it/s] 100%|██████████| 3/3 [00:00<00:00, 5.23it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.72it/s] 100%|██████████| 3/3 [00:00<00:00, 8.08it/s] 100%|██████████| 3/3 [00:00<00:00, 7.54it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.72it/s] 100%|██████████| 3/3 [00:00<00:00, 8.08it/s] 100%|██████████| 3/3 [00:00<00:00, 7.54it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.28it/s] 67%|██████▋ | 2/3 [00:00<00:00, 4.87it/s] 100%|██████████| 3/3 [00:00<00:00, 5.78it/s] 100%|██████████| 3/3 [00:00<00:00, 5.21it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.77it/s] 100%|██████████| 3/3 [00:00<00:00, 8.16it/s] 100%|██████████| 3/3 [00:00<00:00, 7.61it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 6.38it/s] 100%|██████████| 3/3 [00:00<00:00, 10.61it/s] 100%|██████████| 3/3 [00:00<00:00, 9.94it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.78it/s] 100%|██████████| 3/3 [00:00<00:00, 8.15it/s] 100%|██████████| 3/3 [00:00<00:00, 7.61it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 6.34it/s] 100%|██████████| 3/3 [00:00<00:00, 10.58it/s] 100%|██████████| 3/3 [00:00<00:00, 9.91it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.71it/s] 100%|██████████| 3/3 [00:00<00:00, 8.04it/s] 100%|██████████| 3/3 [00:00<00:00, 7.51it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.28it/s] 67%|██████▋ | 2/3 [00:00<00:00, 4.86it/s] 100%|██████████| 3/3 [00:00<00:00, 5.76it/s] 100%|██████████| 3/3 [00:00<00:00, 5.20it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 3.27it/s] 67%|██████▋ | 2/3 [00:00<00:00, 4.86it/s] 100%|██████████| 3/3 [00:00<00:00, 5.75it/s] 100%|██████████| 3/3 [00:00<00:00, 5.19it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.70it/s] 100%|██████████| 3/3 [00:00<00:00, 8.04it/s] 100%|██████████| 3/3 [00:00<00:00, 7.50it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 6.35it/s] 100%|██████████| 3/3 [00:00<00:00, 10.59it/s] 100%|██████████| 3/3 [00:00<00:00, 9.92it/s] 0%| | 0/3 [00:00<?, ?it/s] 33%|███▎ | 1/3 [00:00<00:00, 4.77it/s] 100%|██████████| 3/3 [00:00<00:00, 8.14it/s] 100%|██████████| 3/3 [00:00<00:00, 7.60it/s] 0%| | 0/2 [00:00<?, ?it/s] 50%|█████ | 1/2 [00:01<00:01, 1.95s/it] 100%|██████████| 2/2 [00:02<00:00, 1.39s/it] 100%|██████████| 2/2 [00:02<00:00, 1.47s/it]