Prediction

Model

fermatresearch/magic-style-transfer:3b5fa5d3

l362oidbq3lho4epawqurablle

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

14.0s

Created

over 1 year ago

Webhook

–

Input

prompt: shot in the style of sksfer, words "MARC AGUILAR" by Van Gogh, colorful pattern
image: https://replicate.delivery/pbxt/KbVQ4ItrGWdUk9jN7KdNo5nr7h9CxSvJncYIhKtDlpA61nyl/Screenshot%202024-03-20%20at%2019.09.12.png
ip_image: https://replicate.delivery/pbxt/KbVQ45hsJ2OSfsMuV7FUWjCodQCIosVO3LofwKHfO7rghXGs/339009%402x.jpg
condition_depth_scale: 0.09
condition_canny_scale: 0.9
lora_scale: 0.3
ip_scale: 0.83
strength: 1
negative_prompt: Empty string
background_color: #A2A2A2
resizing_scale: 1
num_inference_steps: 30
num_outputs: 1
scheduler: K_EULER
guidance_scale: 7.5
apply_watermark: true

{
  "apply_watermark": true,
  "background_color": "#A2A2A2",
  "condition_canny_scale": 0.9,
  "condition_depth_scale": 0.09,
  "guidance_scale": 7.5,
  "image": "https://replicate.delivery/pbxt/KbVQ4ItrGWdUk9jN7KdNo5nr7h9CxSvJncYIhKtDlpA61nyl/Screenshot%202024-03-20%20at%2019.09.12.png",
  "ip_image": "https://replicate.delivery/pbxt/KbVQ45hsJ2OSfsMuV7FUWjCodQCIosVO3LofwKHfO7rghXGs/339009%402x.jpg",
  "ip_scale": 0.83,
  "lora_scale": 0.3,
  "negative_prompt": "",
  "num_inference_steps": 30,
  "num_outputs": 1,
  "prompt": "shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern",
  "resizing_scale": 1,
  "scheduler": "K_EULER",
  "strength": 1
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_7v0**********************************

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/magic-style-transfer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fermatresearch/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca",
  {
    input: {
      apply_watermark: true,
      background_color: "#A2A2A2",
      condition_canny_scale: 0.9,
      condition_depth_scale: 0.09,
      guidance_scale: 7.5,
      image: "https://replicate.delivery/pbxt/KbVQ4ItrGWdUk9jN7KdNo5nr7h9CxSvJncYIhKtDlpA61nyl/Screenshot%202024-03-20%20at%2019.09.12.png",
      ip_image: "https://replicate.delivery/pbxt/KbVQ45hsJ2OSfsMuV7FUWjCodQCIosVO3LofwKHfO7rghXGs/339009%402x.jpg",
      ip_scale: 0.83,
      lora_scale: 0.3,
      negative_prompt: "",
      num_inference_steps: 30,
      num_outputs: 1,
      prompt: "shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern",
      resizing_scale: 1,
      scheduler: "K_EULER",
      strength: 1
    }
  }
);

// 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_7v0**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run fermatresearch/magic-style-transfer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fermatresearch/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca",
    input={
        "apply_watermark": True,
        "background_color": "#A2A2A2",
        "condition_canny_scale": 0.9,
        "condition_depth_scale": 0.09,
        "guidance_scale": 7.5,
        "image": "https://replicate.delivery/pbxt/KbVQ4ItrGWdUk9jN7KdNo5nr7h9CxSvJncYIhKtDlpA61nyl/Screenshot%202024-03-20%20at%2019.09.12.png",
        "ip_image": "https://replicate.delivery/pbxt/KbVQ45hsJ2OSfsMuV7FUWjCodQCIosVO3LofwKHfO7rghXGs/339009%402x.jpg",
        "ip_scale": 0.83,
        "lora_scale": 0.3,
        "negative_prompt": "",
        "num_inference_steps": 30,
        "num_outputs": 1,
        "prompt": "shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern",
        "resizing_scale": 1,
        "scheduler": "K_EULER",
        "strength": 1
    }
)

# 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_7v0**********************************

This is your API token. Keep it to yourself.

Run fermatresearch/magic-style-transfer 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/magic-style-transfer:3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca",
    "input": {
      "apply_watermark": true,
      "background_color": "#A2A2A2",
      "condition_canny_scale": 0.9,
      "condition_depth_scale": 0.09,
      "guidance_scale": 7.5,
      "image": "https://replicate.delivery/pbxt/KbVQ4ItrGWdUk9jN7KdNo5nr7h9CxSvJncYIhKtDlpA61nyl/Screenshot%202024-03-20%20at%2019.09.12.png",
      "ip_image": "https://replicate.delivery/pbxt/KbVQ45hsJ2OSfsMuV7FUWjCodQCIosVO3LofwKHfO7rghXGs/339009%402x.jpg",
      "ip_scale": 0.83,
      "lora_scale": 0.3,
      "negative_prompt": "",
      "num_inference_steps": 30,
      "num_outputs": 1,
      "prompt": "shot in the style of sksfer, words \\"MARC AGUILAR\\" by Van Gogh, colorful pattern",
      "resizing_scale": 1,
      "scheduler": "K_EULER",
      "strength": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "l362oidbq3lho4epawqurablle",
  "model": "fermatresearch/magic-style-transfer",
  "version": "3b5fa5d360c361090f11164292e45cc5d14cea8d089591d47c580cac9ec1c7ca",
  "input": {
    "apply_watermark": true,
    "background_color": "#A2A2A2",
    "condition_canny_scale": 0.9,
    "condition_depth_scale": 0.09,
    "guidance_scale": 7.5,
    "image": "https://replicate.delivery/pbxt/KbVQ4ItrGWdUk9jN7KdNo5nr7h9CxSvJncYIhKtDlpA61nyl/Screenshot%202024-03-20%20at%2019.09.12.png",
    "ip_image": "https://replicate.delivery/pbxt/KbVQ45hsJ2OSfsMuV7FUWjCodQCIosVO3LofwKHfO7rghXGs/339009%402x.jpg",
    "ip_scale": 0.83,
    "lora_scale": 0.3,
    "negative_prompt": "",
    "num_inference_steps": 30,
    "num_outputs": 1,
    "prompt": "shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern",
    "resizing_scale": 1,
    "scheduler": "K_EULER",
    "strength": 1
  },
  "logs": "Using seed: 55864\nPrompt: shot in the style of sksfer, words \"MARC AGUILAR\" by Van Gogh, colorful pattern\nOriginal width:1420, height:774\nAspect Ratio: 1.83\nnew_width:1344, new_height:768\nYou have 2 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts.\n  0%|          | 0/30 [00:00<?, ?it/s]\n  3%|▎         | 1/30 [00:00<00:09,  2.97it/s]\n  7%|▋         | 2/30 [00:00<00:10,  2.77it/s]\n 10%|█         | 3/30 [00:01<00:10,  2.65it/s]\n 13%|█▎        | 4/30 [00:01<00:09,  2.61it/s]\n 17%|█▋        | 5/30 [00:01<00:09,  2.59it/s]\n 20%|██        | 6/30 [00:02<00:09,  2.57it/s]\n 23%|██▎       | 7/30 [00:02<00:08,  2.56it/s]\n 27%|██▋       | 8/30 [00:03<00:08,  2.55it/s]\n 30%|███       | 9/30 [00:03<00:08,  2.55it/s]\n 33%|███▎      | 10/30 [00:03<00:07,  2.54it/s]\n 37%|███▋      | 11/30 [00:04<00:07,  2.54it/s]\n 40%|████      | 12/30 [00:04<00:07,  2.54it/s]\n 43%|████▎     | 13/30 [00:05<00:06,  2.54it/s]\n 47%|████▋     | 14/30 [00:05<00:06,  2.54it/s]\n 50%|█████     | 15/30 [00:05<00:05,  2.54it/s]\n 53%|█████▎    | 16/30 [00:06<00:05,  2.54it/s]\n 57%|█████▋    | 17/30 [00:06<00:05,  2.54it/s]\n 60%|██████    | 18/30 [00:07<00:04,  2.53it/s]\n 63%|██████▎   | 19/30 [00:07<00:04,  2.53it/s]\n 67%|██████▋   | 20/30 [00:07<00:03,  2.53it/s]\n 70%|███████   | 21/30 [00:08<00:03,  2.53it/s]\n 73%|███████▎  | 22/30 [00:08<00:03,  2.53it/s]\n 77%|███████▋  | 23/30 [00:08<00:02,  2.53it/s]\n 80%|████████  | 24/30 [00:09<00:02,  2.53it/s]\n 83%|████████▎ | 25/30 [00:09<00:01,  2.53it/s]\n 87%|████████▋ | 26/30 [00:10<00:01,  2.53it/s]\n 90%|█████████ | 27/30 [00:10<00:01,  2.53it/s]\n 93%|█████████▎| 28/30 [00:10<00:00,  2.53it/s]\n 97%|█████████▋| 29/30 [00:11<00:00,  2.53it/s]\n100%|██████████| 30/30 [00:11<00:00,  2.52it/s]\n100%|██████████| 30/30 [00:11<00:00,  2.55it/s]",
  "output": [
    "https://replicate.delivery/pbxt/U8K8kORxUV76CJeWFZSPIu8AX2fPLZreCEzY9VD7Z7J1EbElA/out-0.png"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2024-03-20T18:10:21.157751Z",
  "started_at": "2024-03-20T18:10:21.175419Z",
  "completed_at": "2024-03-20T18:10:35.114254Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/l362oidbq3lho4epawqurablle/cancel",
    "get": "https://api.replicate.com/v1/predictions/l362oidbq3lho4epawqurablle"
  },
  "metrics": {
    "predict_time": 13.938835,
    "total_time": 13.956503
  }
}

Generated in

13.9 seconds

Tweak it Report

Using seed: 55864 Prompt: shot in the style of sksfer, words "MARC AGUILAR" by Van Gogh, colorful pattern Original width:1420, height:774 Aspect Ratio: 1.83 new_width:1344, new_height:768 You have 2 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts. 0%| | 0/30 [00:00<?, ?it/s] 3%|▎ | 1/30 [00:00<00:09, 2.97it/s] 7%|▋ | 2/30 [00:00<00:10, 2.77it/s] 10%|█ | 3/30 [00:01<00:10, 2.65it/s] 13%|█▎ | 4/30 [00:01<00:09, 2.61it/s] 17%|█▋ | 5/30 [00:01<00:09, 2.59it/s] 20%|██ | 6/30 [00:02<00:09, 2.57it/s] 23%|██▎ | 7/30 [00:02<00:08, 2.56it/s] 27%|██▋ | 8/30 [00:03<00:08, 2.55it/s] 30%|███ | 9/30 [00:03<00:08, 2.55it/s] 33%|███▎ | 10/30 [00:03<00:07, 2.54it/s] 37%|███▋ | 11/30 [00:04<00:07, 2.54it/s] 40%|████ | 12/30 [00:04<00:07, 2.54it/s] 43%|████▎ | 13/30 [00:05<00:06, 2.54it/s] 47%|████▋ | 14/30 [00:05<00:06, 2.54it/s] 50%|█████ | 15/30 [00:05<00:05, 2.54it/s] 53%|█████▎ | 16/30 [00:06<00:05, 2.54it/s] 57%|█████▋ | 17/30 [00:06<00:05, 2.54it/s] 60%|██████ | 18/30 [00:07<00:04, 2.53it/s] 63%|██████▎ | 19/30 [00:07<00:04, 2.53it/s] 67%|██████▋ | 20/30 [00:07<00:03, 2.53it/s] 70%|███████ | 21/30 [00:08<00:03, 2.53it/s] 73%|███████▎ | 22/30 [00:08<00:03, 2.53it/s] 77%|███████▋ | 23/30 [00:08<00:02, 2.53it/s] 80%|████████ | 24/30 [00:09<00:02, 2.53it/s] 83%|████████▎ | 25/30 [00:09<00:01, 2.53it/s] 87%|████████▋ | 26/30 [00:10<00:01, 2.53it/s] 90%|█████████ | 27/30 [00:10<00:01, 2.53it/s] 93%|█████████▎| 28/30 [00:10<00:00, 2.53it/s] 97%|█████████▋| 29/30 [00:11<00:00, 2.53it/s] 100%|██████████| 30/30 [00:11<00:00, 2.52it/s] 100%|██████████| 30/30 [00:11<00:00, 2.55it/s]