Prediction

Model

genericdeag/monkey-user:9bbbfdfb

abx9tj443nrma0cmhravzzxrzr

Status

Succeeded

Source

Web

Hardware

H100

Total duration

25.0s

Created

11 months ago

Webhook

–

Input

prompt: A minimalistic cartoon illustration, a small office with beige walls, a wooden shelf on the left containing a few books, a green potted plant, and a wastebasket, a black-framed picture on the wall labeled "FRIEND" with a red HAL 9000-like glowing eye, a therapist character sitting on a black rolling chair, holding a clipboard with "LOSS: 0.71" written on it, the therapist has a stick-figure-like body and neutral expression, across from them a blob-like anthropomorphic neural network with purple nodes and connecting lines, tired eyes, lying on a brown therapy couch, speech bubble saying "I am so tired of learning, everybody expects me to constantly improve but they never ask if I want to."
aspect_ratio: 4:3
height: 1440
width: 1440
prompt_strength: 0.8
model: dev
num_outputs: 4
num_inference_steps: 28
guidance_scale: 3
output_format: jpg
output_quality: 92
go_fast: false
megapixels: 1
lora_scale: 1
extra_lora_scale: 1

{
  "aspect_ratio": "4:3",
  "extra_lora_scale": 1,
  "go_fast": false,
  "guidance_scale": 3,
  "height": 1440,
  "lora_scale": 1,
  "megapixels": "1",
  "model": "dev",
  "num_inference_steps": 28,
  "num_outputs": 4,
  "output_format": "jpg",
  "output_quality": 92,
  "prompt": "A minimalistic cartoon illustration, a small office with beige walls, a wooden shelf on the left containing a few books, a green potted plant, and a wastebasket, a black-framed picture on the wall labeled \"FRIEND\" with a red HAL 9000-like glowing eye, a therapist character sitting on a black rolling chair, holding a clipboard with \"LOSS: 0.71\" written on it, the therapist has a stick-figure-like body and neutral expression, across from them a blob-like anthropomorphic neural network with purple nodes and connecting lines, tired eyes, lying on a brown therapy couch, speech bubble saying \"I am so tired of learning, everybody expects me to constantly improve but they never ask if I want to.\"",
  "prompt_strength": 0.8,
  "width": 1440
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_BMt**********************************

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 genericdeag/monkey-user using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "genericdeag/monkey-user:9bbbfdfb57fccc75f26ed226e151011cf6e0942166fe6ced97fae0bdd4f97d9d",
  {
    input: {
      aspect_ratio: "4:3",
      extra_lora_scale: 1,
      go_fast: false,
      guidance_scale: 3,
      height: 1440,
      lora_scale: 1,
      megapixels: "1",
      model: "dev",
      num_inference_steps: 28,
      num_outputs: 4,
      output_format: "jpg",
      output_quality: 92,
      prompt: "A minimalistic cartoon illustration, a small office with beige walls, a wooden shelf on the left containing a few books, a green potted plant, and a wastebasket, a black-framed picture on the wall labeled \"FRIEND\" with a red HAL 9000-like glowing eye, a therapist character sitting on a black rolling chair, holding a clipboard with \"LOSS: 0.71\" written on it, the therapist has a stick-figure-like body and neutral expression, across from them a blob-like anthropomorphic neural network with purple nodes and connecting lines, tired eyes, lying on a brown therapy couch, speech bubble saying \"I am so tired of learning, everybody expects me to constantly improve but they never ask if I want to.\"",
      prompt_strength: 0.8,
      width: 1440
    }
  }
);

// 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_BMt**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

Run genericdeag/monkey-user using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "genericdeag/monkey-user:9bbbfdfb57fccc75f26ed226e151011cf6e0942166fe6ced97fae0bdd4f97d9d",
    input={
        "aspect_ratio": "4:3",
        "extra_lora_scale": 1,
        "go_fast": False,
        "guidance_scale": 3,
        "height": 1440,
        "lora_scale": 1,
        "megapixels": "1",
        "model": "dev",
        "num_inference_steps": 28,
        "num_outputs": 4,
        "output_format": "jpg",
        "output_quality": 92,
        "prompt": "A minimalistic cartoon illustration, a small office with beige walls, a wooden shelf on the left containing a few books, a green potted plant, and a wastebasket, a black-framed picture on the wall labeled \"FRIEND\" with a red HAL 9000-like glowing eye, a therapist character sitting on a black rolling chair, holding a clipboard with \"LOSS: 0.71\" written on it, the therapist has a stick-figure-like body and neutral expression, across from them a blob-like anthropomorphic neural network with purple nodes and connecting lines, tired eyes, lying on a brown therapy couch, speech bubble saying \"I am so tired of learning, everybody expects me to constantly improve but they never ask if I want to.\"",
        "prompt_strength": 0.8,
        "width": 1440
    }
)

# 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_BMt**********************************

This is your API token. Keep it to yourself.

Run genericdeag/monkey-user 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": "genericdeag/monkey-user:9bbbfdfb57fccc75f26ed226e151011cf6e0942166fe6ced97fae0bdd4f97d9d",
    "input": {
      "aspect_ratio": "4:3",
      "extra_lora_scale": 1,
      "go_fast": false,
      "guidance_scale": 3,
      "height": 1440,
      "lora_scale": 1,
      "megapixels": "1",
      "model": "dev",
      "num_inference_steps": 28,
      "num_outputs": 4,
      "output_format": "jpg",
      "output_quality": 92,
      "prompt": "A minimalistic cartoon illustration, a small office with beige walls, a wooden shelf on the left containing a few books, a green potted plant, and a wastebasket, a black-framed picture on the wall labeled \\"FRIEND\\" with a red HAL 9000-like glowing eye, a therapist character sitting on a black rolling chair, holding a clipboard with \\"LOSS: 0.71\\" written on it, the therapist has a stick-figure-like body and neutral expression, across from them a blob-like anthropomorphic neural network with purple nodes and connecting lines, tired eyes, lying on a brown therapy couch, speech bubble saying \\"I am so tired of learning, everybody expects me to constantly improve but they never ask if I want to.\\"",
      "prompt_strength": 0.8,
      "width": 1440
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "abx9tj443nrma0cmhravzzxrzr",
  "model": "genericdeag/monkey-user",
  "version": "9bbbfdfb57fccc75f26ed226e151011cf6e0942166fe6ced97fae0bdd4f97d9d",
  "input": {
    "aspect_ratio": "4:3",
    "extra_lora_scale": 1,
    "go_fast": false,
    "guidance_scale": 3,
    "height": 1440,
    "lora_scale": 1,
    "megapixels": "1",
    "model": "dev",
    "num_inference_steps": 28,
    "num_outputs": 4,
    "output_format": "jpg",
    "output_quality": 92,
    "prompt": "A minimalistic cartoon illustration, a small office with beige walls, a wooden shelf on the left containing a few books, a green potted plant, and a wastebasket, a black-framed picture on the wall labeled \"FRIEND\" with a red HAL 9000-like glowing eye, a therapist character sitting on a black rolling chair, holding a clipboard with \"LOSS: 0.71\" written on it, the therapist has a stick-figure-like body and neutral expression, across from them a blob-like anthropomorphic neural network with purple nodes and connecting lines, tired eyes, lying on a brown therapy couch, speech bubble saying \"I am so tired of learning, everybody expects me to constantly improve but they never ask if I want to.\"",
    "prompt_strength": 0.8,
    "width": 1440
  },
  "logs": "2025-01-22 12:56:02.358 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-01-22 12:56:02.359 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  98%|█████████▊| 298/304 [00:00<00:00, 2970.55it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2959.62it/s]\n2025-01-22 12:56:02.462 | SUCCESS  | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.1s\nfree=29065759637504\nDownloading weights\n2025-01-22T12:56:02Z | INFO  | [ Initiating ] chunk_size=150M dest=/tmp/tmp3xx2glem/weights url=https://replicate.delivery/xezq/J0n0syZrnNLIMpU09gi5ECpIWsmQPj72R32C86zUgNI2W6BF/trained_model.tar\n2025-01-22T12:56:03Z | INFO  | [ Complete ] dest=/tmp/tmp3xx2glem/weights size=\"172 MB\" total_elapsed=1.228s url=https://replicate.delivery/xezq/J0n0syZrnNLIMpU09gi5ECpIWsmQPj72R32C86zUgNI2W6BF/trained_model.tar\nDownloaded weights in 1.25s\n2025-01-22 12:56:03.716 | INFO     | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/7ca10f6b1e996242\n2025-01-22 12:56:03.789 | INFO     | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded\n2025-01-22 12:56:03.789 | DEBUG    | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys\n2025-01-22 12:56:03.790 | DEBUG    | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted\nApplying LoRA:   0%|          | 0/304 [00:00<?, ?it/s]\nApplying LoRA:  93%|█████████▎| 283/304 [00:00<00:00, 2810.77it/s]\nApplying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2771.12it/s]\n2025-01-22 12:56:03.900 | SUCCESS  | fp8.lora_loading:load_lora:539 - LoRA applied in 0.18s\nUsing seed: 23215\n0it [00:00, ?it/s]\n1it [00:00,  8.48it/s]\n2it [00:00,  5.96it/s]\n3it [00:00,  5.45it/s]\n4it [00:00,  5.24it/s]\n5it [00:00,  5.12it/s]\n6it [00:01,  5.05it/s]\n7it [00:01,  5.01it/s]\n8it [00:01,  4.98it/s]\n9it [00:01,  4.96it/s]\n10it [00:01,  4.94it/s]\n11it [00:02,  4.93it/s]\n12it [00:02,  4.92it/s]\n13it [00:02,  4.92it/s]\n14it [00:02,  4.91it/s]\n15it [00:02,  4.91it/s]\n16it [00:03,  4.91it/s]\n17it [00:03,  4.90it/s]\n18it [00:03,  4.90it/s]\n19it [00:03,  4.90it/s]\n20it [00:03,  4.90it/s]\n21it [00:04,  4.91it/s]\n22it [00:04,  4.91it/s]\n23it [00:04,  4.91it/s]\n24it [00:04,  4.92it/s]\n25it [00:05,  4.92it/s]\n26it [00:05,  4.91it/s]\n27it [00:05,  4.91it/s]\n28it [00:05,  4.91it/s]\n28it [00:05,  4.99it/s]\n0it [00:00, ?it/s]\n1it [00:00,  4.97it/s]\n2it [00:00,  4.93it/s]\n3it [00:00,  4.92it/s]\n4it [00:00,  4.91it/s]\n5it [00:01,  4.91it/s]\n6it [00:01,  4.90it/s]\n7it [00:01,  4.90it/s]\n8it [00:01,  4.89it/s]\n9it [00:01,  4.89it/s]\n10it [00:02,  4.90it/s]\n11it [00:02,  4.90it/s]\n12it [00:02,  4.89it/s]\n13it [00:02,  4.89it/s]\n14it [00:02,  4.89it/s]\n15it [00:03,  4.89it/s]\n16it [00:03,  4.89it/s]\n17it [00:03,  4.89it/s]\n18it [00:03,  4.90it/s]\n19it [00:03,  4.90it/s]\n20it [00:04,  4.91it/s]\n21it [00:04,  4.90it/s]\n22it [00:04,  4.91it/s]\n23it [00:04,  4.90it/s]\n24it [00:04,  4.91it/s]\n25it [00:05,  4.91it/s]\n26it [00:05,  4.91it/s]\n27it [00:05,  4.91it/s]\n28it [00:05,  4.91it/s]\n28it [00:05,  4.90it/s]\n0it [00:00, ?it/s]\n1it [00:00,  4.96it/s]\n2it [00:00,  4.93it/s]\n3it [00:00,  4.92it/s]\n4it [00:00,  4.91it/s]\n5it [00:01,  4.90it/s]\n6it [00:01,  4.89it/s]\n7it [00:01,  4.89it/s]\n8it [00:01,  4.89it/s]\n9it [00:01,  4.89it/s]\n10it [00:02,  4.89it/s]\n11it [00:02,  4.89it/s]\n12it [00:02,  4.89it/s]\n13it [00:02,  4.89it/s]\n14it [00:02,  4.89it/s]\n15it [00:03,  4.89it/s]\n16it [00:03,  4.89it/s]\n17it [00:03,  4.89it/s]\n18it [00:03,  4.88it/s]\n19it [00:03,  4.88it/s]\n20it [00:04,  4.87it/s]\n21it [00:04,  4.87it/s]\n22it [00:04,  4.87it/s]\n23it [00:04,  4.87it/s]\n24it [00:04,  4.87it/s]\n25it [00:05,  4.87it/s]\n26it [00:05,  4.87it/s]\n27it [00:05,  4.87it/s]\n28it [00:05,  4.87it/s]\n28it [00:05,  4.88it/s]\n0it [00:00, ?it/s]\n1it [00:00,  4.91it/s]\n2it [00:00,  4.88it/s]\n3it [00:00,  4.89it/s]\n4it [00:00,  4.87it/s]\n5it [00:01,  4.87it/s]\n6it [00:01,  4.87it/s]\n7it [00:01,  4.87it/s]\n8it [00:01,  4.87it/s]\n9it [00:01,  4.86it/s]\n10it [00:02,  4.87it/s]\n11it [00:02,  4.87it/s]\n12it [00:02,  4.87it/s]\n13it [00:02,  4.87it/s]\n14it [00:02,  4.87it/s]\n15it [00:03,  4.88it/s]\n16it [00:03,  4.88it/s]\n17it [00:03,  4.87it/s]\n18it [00:03,  4.87it/s]\n19it [00:03,  4.87it/s]\n20it [00:04,  4.88it/s]\n21it [00:04,  4.88it/s]\n22it [00:04,  4.88it/s]\n23it [00:04,  4.87it/s]\n24it [00:04,  4.87it/s]\n25it [00:05,  4.87it/s]\n26it [00:05,  4.87it/s]\n27it [00:05,  4.88it/s]\n28it [00:05,  4.88it/s]\n28it [00:05,  4.87it/s]\nTotal safe images: 4 out of 4",
  "output": [
    "https://replicate.delivery/xezq/lQNg8HAmfZXnLi3abY2upBY9lBzI8FU92H6fmkYXYnC7zpHUA/out-0.jpg",
    "https://replicate.delivery/xezq/rJRZJeKnbrxJa66ouM3nsdU6eNgm846wESjyddjeS6E3nTPoA/out-1.jpg",
    "https://replicate.delivery/xezq/Rhc0NohWSfVYRCYKe3KgczUkwLWnWsRsOZVZbHkWtxB7zpHUA/out-2.jpg",
    "https://replicate.delivery/xezq/S3oBCJLihgJZAVdrivwDuBozHilxsNYfZpHI4jeZtQI7zpHUA/out-3.jpg"
  ],
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2025-01-22T12:56:02.333Z",
  "started_at": "2025-01-22T12:56:02.359649Z",
  "completed_at": "2025-01-22T12:56:27.290079Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/abx9tj443nrma0cmhravzzxrzr/cancel",
    "get": "https://api.replicate.com/v1/predictions/abx9tj443nrma0cmhravzzxrzr",
    "stream": "https://stream.replicate.com/v1/files/bcwr-5wxk3pd6jzyvmo4gd5bpsulq7z264p6ucg35q4yottkine2qmlya",
    "web": "https://replicate.com/p/abx9tj443nrma0cmhravzzxrzr"
  },
  "metrics": {
    "predict_time": 24.930429602,
    "total_time": 24.957079
  }
}

Generated in

24.9 seconds

Tweak it Report

2025-01-22 12:56:02.358 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-01-22 12:56:02.359 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 98%|█████████▊| 298/304 [00:00<00:00, 2970.55it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2959.62it/s] 2025-01-22 12:56:02.462 | SUCCESS | fp8.lora_loading:unload_loras:564 - LoRAs unloaded in 0.1s free=29065759637504 Downloading weights 2025-01-22T12:56:02Z | INFO | [ Initiating ] chunk_size=150M dest=/tmp/tmp3xx2glem/weights url=https://replicate.delivery/xezq/J0n0syZrnNLIMpU09gi5ECpIWsmQPj72R32C86zUgNI2W6BF/trained_model.tar 2025-01-22T12:56:03Z | INFO | [ Complete ] dest=/tmp/tmp3xx2glem/weights size="172 MB" total_elapsed=1.228s url=https://replicate.delivery/xezq/J0n0syZrnNLIMpU09gi5ECpIWsmQPj72R32C86zUgNI2W6BF/trained_model.tar Downloaded weights in 1.25s 2025-01-22 12:56:03.716 | INFO | fp8.lora_loading:convert_lora_weights:498 - Loading LoRA weights for /src/weights-cache/7ca10f6b1e996242 2025-01-22 12:56:03.789 | INFO | fp8.lora_loading:convert_lora_weights:519 - LoRA weights loaded 2025-01-22 12:56:03.789 | DEBUG | fp8.lora_loading:apply_lora_to_model:574 - Extracting keys 2025-01-22 12:56:03.790 | DEBUG | fp8.lora_loading:apply_lora_to_model:581 - Keys extracted Applying LoRA: 0%| | 0/304 [00:00<?, ?it/s] Applying LoRA: 93%|█████████▎| 283/304 [00:00<00:00, 2810.77it/s] Applying LoRA: 100%|██████████| 304/304 [00:00<00:00, 2771.12it/s] 2025-01-22 12:56:03.900 | SUCCESS | fp8.lora_loading:load_lora:539 - LoRA applied in 0.18s Using seed: 23215 0it [00:00, ?it/s] 1it [00:00, 8.48it/s] 2it [00:00, 5.96it/s] 3it [00:00, 5.45it/s] 4it [00:00, 5.24it/s] 5it [00:00, 5.12it/s] 6it [00:01, 5.05it/s] 7it [00:01, 5.01it/s] 8it [00:01, 4.98it/s] 9it [00:01, 4.96it/s] 10it [00:01, 4.94it/s] 11it [00:02, 4.93it/s] 12it [00:02, 4.92it/s] 13it [00:02, 4.92it/s] 14it [00:02, 4.91it/s] 15it [00:02, 4.91it/s] 16it [00:03, 4.91it/s] 17it [00:03, 4.90it/s] 18it [00:03, 4.90it/s] 19it [00:03, 4.90it/s] 20it [00:03, 4.90it/s] 21it [00:04, 4.91it/s] 22it [00:04, 4.91it/s] 23it [00:04, 4.91it/s] 24it [00:04, 4.92it/s] 25it [00:05, 4.92it/s] 26it [00:05, 4.91it/s] 27it [00:05, 4.91it/s] 28it [00:05, 4.91it/s] 28it [00:05, 4.99it/s] 0it [00:00, ?it/s] 1it [00:00, 4.97it/s] 2it [00:00, 4.93it/s] 3it [00:00, 4.92it/s] 4it [00:00, 4.91it/s] 5it [00:01, 4.91it/s] 6it [00:01, 4.90it/s] 7it [00:01, 4.90it/s] 8it [00:01, 4.89it/s] 9it [00:01, 4.89it/s] 10it [00:02, 4.90it/s] 11it [00:02, 4.90it/s] 12it [00:02, 4.89it/s] 13it [00:02, 4.89it/s] 14it [00:02, 4.89it/s] 15it [00:03, 4.89it/s] 16it [00:03, 4.89it/s] 17it [00:03, 4.89it/s] 18it [00:03, 4.90it/s] 19it [00:03, 4.90it/s] 20it [00:04, 4.91it/s] 21it [00:04, 4.90it/s] 22it [00:04, 4.91it/s] 23it [00:04, 4.90it/s] 24it [00:04, 4.91it/s] 25it [00:05, 4.91it/s] 26it [00:05, 4.91it/s] 27it [00:05, 4.91it/s] 28it [00:05, 4.91it/s] 28it [00:05, 4.90it/s] 0it [00:00, ?it/s] 1it [00:00, 4.96it/s] 2it [00:00, 4.93it/s] 3it [00:00, 4.92it/s] 4it [00:00, 4.91it/s] 5it [00:01, 4.90it/s] 6it [00:01, 4.89it/s] 7it [00:01, 4.89it/s] 8it [00:01, 4.89it/s] 9it [00:01, 4.89it/s] 10it [00:02, 4.89it/s] 11it [00:02, 4.89it/s] 12it [00:02, 4.89it/s] 13it [00:02, 4.89it/s] 14it [00:02, 4.89it/s] 15it [00:03, 4.89it/s] 16it [00:03, 4.89it/s] 17it [00:03, 4.89it/s] 18it [00:03, 4.88it/s] 19it [00:03, 4.88it/s] 20it [00:04, 4.87it/s] 21it [00:04, 4.87it/s] 22it [00:04, 4.87it/s] 23it [00:04, 4.87it/s] 24it [00:04, 4.87it/s] 25it [00:05, 4.87it/s] 26it [00:05, 4.87it/s] 27it [00:05, 4.87it/s] 28it [00:05, 4.87it/s] 28it [00:05, 4.88it/s] 0it [00:00, ?it/s] 1it [00:00, 4.91it/s] 2it [00:00, 4.88it/s] 3it [00:00, 4.89it/s] 4it [00:00, 4.87it/s] 5it [00:01, 4.87it/s] 6it [00:01, 4.87it/s] 7it [00:01, 4.87it/s] 8it [00:01, 4.87it/s] 9it [00:01, 4.86it/s] 10it [00:02, 4.87it/s] 11it [00:02, 4.87it/s] 12it [00:02, 4.87it/s] 13it [00:02, 4.87it/s] 14it [00:02, 4.87it/s] 15it [00:03, 4.88it/s] 16it [00:03, 4.88it/s] 17it [00:03, 4.87it/s] 18it [00:03, 4.87it/s] 19it [00:03, 4.87it/s] 20it [00:04, 4.88it/s] 21it [00:04, 4.88it/s] 22it [00:04, 4.88it/s] 23it [00:04, 4.87it/s] 24it [00:04, 4.87it/s] 25it [00:05, 4.87it/s] 26it [00:05, 4.87it/s] 27it [00:05, 4.88it/s] 28it [00:05, 4.88it/s] 28it [00:05, 4.87it/s] Total safe images: 4 out of 4