afiaka87/ldm-autoedit – Run with an API on Replicate

afiaka87 / ldm-autoedit

Cold

Public
1.5K runs
T4

Run with an API

Playground API Examples README Versions

Input

text

string

Shift + Return to add a new line

(required) Text to use for the model's prediction.

Default: ""

edit

file

(optional) an existing image you want to edit

negative

string

Shift + Return to add a new line

(optional) Negate the model's prediction for this text from the model's prediction for the target text.

Default: ""

aesthetic_rating

integer

(minimum: 0, maximum: 9)

Number between 0 and 9 representing the aesthetic rating. Will initialize the prompt CLIP embed with the respective aesthetic embed.

Default: 9

aesthetic_weight

number

(minimum: 0, maximum: 1)

Weight of the aesthetic embedding in the average prompt embedding.

Default: 0.5

batch_size

integer

(minimum: 1, maximum: 32)

Batch size.

Default: 1

width

integer

Target width

Default: 256

height

integer

Target height

Default: 256

iterations

integer

(minimum: 1, maximum: 30)

Number of iterations to run the model for.

Default: 10

starting_radius

number

(minimum: 0.1)

size of noise blur at the start of editing (larger = coarser changes)

Default: 5

ending_radius

number

(minimum: 0.1, maximum: 5)

size of noise blur at the end of editing (smaller = editing fine details)

Default: 0.1

starting_threshold

number

(minimum: 0.05, maximum: 1)

how much of the image to replace at the start of editing (1 = inpaint the entire image)

Default: 0.6

ending_threshold

number

(minimum: 0.1, maximum: 1)

how much of the image to replace at the end of editing

Default: 0.5

guidance_scale

number

(minimum: -10, maximum: 100)

Controls how much the image should look like the prompt

Default: 5

seed

integer

(minimum: -1)

(optional) Seed for the random number generator.

Default: -1

Run this model in Node.js with one line of code:

npx create-replicate --model=afiaka87/ldm-autoedit

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run afiaka87/ldm-autoedit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "afiaka87/ldm-autoedit:d0d04e11830081fcc1066cc5e6306e4ffdcf35c0d95d12c6cbc2b869f17466d0",
  {
    input: {
      seed: -1,
      text: "new york city completely flooded",
      width: 256,
      height: 256,
      negative: "",
      batch_size: 8,
      iterations: 20,
      ending_radius: 0.1,
      guidance_scale: 5,
      starting_radius: 3,
      aesthetic_rating: 9,
      aesthetic_weight: 0.5,
      ending_threshold: 0.5,
      starting_threshold: 0.6
    }
  }
);

console.log(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=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run afiaka87/ldm-autoedit using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "afiaka87/ldm-autoedit:d0d04e11830081fcc1066cc5e6306e4ffdcf35c0d95d12c6cbc2b869f17466d0",
    input={
        "seed": -1,
        "text": "new york city completely flooded",
        "width": 256,
        "height": 256,
        "negative": "",
        "batch_size": 8,
        "iterations": 20,
        "ending_radius": 0.1,
        "guidance_scale": 5,
        "starting_radius": 3,
        "aesthetic_rating": 9,
        "aesthetic_weight": 0.5,
        "ending_threshold": 0.5,
        "starting_threshold": 0.6
    }
)

# The afiaka87/ldm-autoedit model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/afiaka87/ldm-autoedit/api#output-schema
    print(item)

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=<paste-your-token-here>

Find your API token in your account settings.

Run afiaka87/ldm-autoedit 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": "afiaka87/ldm-autoedit:d0d04e11830081fcc1066cc5e6306e4ffdcf35c0d95d12c6cbc2b869f17466d0",
    "input": {
      "seed": -1,
      "text": "new york city completely flooded",
      "width": 256,
      "height": 256,
      "negative": "",
      "batch_size": 8,
      "iterations": 20,
      "ending_radius": 0.1,
      "guidance_scale": 5,
      "starting_radius": 3,
      "aesthetic_rating": 9,
      "aesthetic_weight": 0.5,
      "ending_threshold": 0.5,
      "starting_threshold": 0.6
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2022-06-17T22:12:45.575809Z",
  "created_at": "2022-06-17T19:14:48.140184Z",
  "data_removed": false,
  "error": null,
  "id": "lnri7jpouzdd7c73npp7bt5k5y",
  "input": {
    "seed": -1,
    "text": "new york city completely flooded",
    "width": 256,
    "height": 256,
    "batch_size": "8",
    "iterations": "20",
    "ending_radius": 0.1,
    "guidance_scale": "5",
    "starting_radius": 3,
    "aesthetic_rating": 9,
    "aesthetic_weight": "0.5",
    "ending_threshold": 0.5,
    "starting_threshold": 0.6
  },
  "logs": "Using seed 319970566\nRunning simulation for new york city completely flooded\nEncoding text embeddings with new york city completely flooded dimensions\nUsing aesthetic embedding 9 with weight 0.5\nUsing inpaint model but no image is provided. Initializing with zeros.\nPopulation: 00, batch: 00, score: 0.2321 - first iteration\nPopulation: 00, batch: 01, score: 0.2690 - first iteration\nPopulation: 00, batch: 02, score: 0.2435 - first iteration\nPopulation: 00, batch: 03, score: 0.2721 - first iteration\nPopulation: 00, batch: 04, score: 0.2456 - first iteration\nPopulation: 00, batch: 05, score: 0.2513 - first iteration\nPopulation: 00, batch: 06, score: 0.2099 - first iteration\nPopulation: 00, batch: 07, score: 0.2464 - first iteration\nPopulation: 01, batch: 00, score: 0.2197 - no improvement\nPopulation: 01, batch: 01, score: 0.2195 - no improvement\nPopulation: 01, batch: 02, score: 0.2059 - no improvement\nPopulation: 01, batch: 03, score: 0.2742 - new best, replacing\nPopulation: 01, batch: 04, score: 0.1948 - no improvement\nPopulation: 01, batch: 05, score: 0.2332 - no improvement\nPopulation: 01, batch: 06, score: 0.2409 - new best, replacing\nPopulation: 01, batch: 07, score: 0.1885 - no improvement\nPopulation: 02, batch: 00, score: 0.2307 - no improvement\nPopulation: 02, batch: 01, score: 0.2659 - no improvement\nPopulation: 02, batch: 02, score: 0.2015 - no improvement\nPopulation: 02, batch: 03, score: 0.2596 - no improvement\nPopulation: 02, batch: 04, score: 0.2528 - new best, replacing\nPopulation: 02, batch: 05, score: 0.2577 - new best, replacing\nPopulation: 02, batch: 06, score: 0.2172 - no improvement\nPopulation: 02, batch: 07, score: 0.2784 - new best, replacing\nPopulation: 03, batch: 00, score: 0.2575 - new best, replacing\nPopulation: 03, batch: 01, score: 0.2551 - no improvement\nPopulation: 03, batch: 02, score: 0.2123 - no improvement\nPopulation: 03, batch: 03, score: 0.2359 - no improvement\nPopulation: 03, batch: 04, score: 0.1640 - no improvement\nPopulation: 03, batch: 05, score: 0.2039 - no improvement\nPopulation: 03, batch: 06, score: 0.2175 - no improvement\nPopulation: 03, batch: 07, score: 0.2194 - no improvement\nPopulation: 04, batch: 00, score: 0.2356 - no improvement\nPopulation: 04, batch: 01, score: 0.2373 - no improvement\nPopulation: 04, batch: 02, score: 0.2436 - new best, replacing\nPopulation: 04, batch: 03, score: 0.1799 - no improvement\nPopulation: 04, batch: 04, score: 0.2512 - no improvement\nPopulation: 04, batch: 05, score: 0.2531 - no improvement\nPopulation: 04, batch: 06, score: 0.2440 - new best, replacing\nPopulation: 04, batch: 07, score: 0.2075 - no improvement\nPopulation: 05, batch: 00, score: 0.2042 - no improvement\nPopulation: 05, batch: 01, score: 0.2293 - no improvement\nPopulation: 05, batch: 02, score: 0.2215 - no improvement\nPopulation: 05, batch: 03, score: 0.1763 - no improvement\nPopulation: 05, batch: 04, score: 0.1872 - no improvement\nPopulation: 05, batch: 05, score: 0.2393 - no improvement\nPopulation: 05, batch: 06, score: 0.1795 - no improvement\nPopulation: 05, batch: 07, score: 0.2669 - no improvement\nPopulation: 06, batch: 00, score: 0.2417 - no improvement\nPopulation: 06, batch: 01, score: 0.2546 - no improvement\nPopulation: 06, batch: 02, score: 0.2241 - no improvement\nPopulation: 06, batch: 03, score: 0.2616 - no improvement\nPopulation: 06, batch: 04, score: 0.2387 - no improvement\nPopulation: 06, batch: 05, score: 0.2262 - no improvement\nPopulation: 06, batch: 06, score: 0.1805 - no improvement\nPopulation: 06, batch: 07, score: 0.2203 - no improvement\nPopulation: 07, batch: 00, score: 0.2594 - new best, replacing\nPopulation: 07, batch: 01, score: 0.2134 - no improvement\nPopulation: 07, batch: 02, score: 0.2345 - no improvement\nPopulation: 07, batch: 03, score: 0.1960 - no improvement\nPopulation: 07, batch: 04, score: 0.2309 - no improvement\nPopulation: 07, batch: 05, score: 0.1765 - no improvement\nPopulation: 07, batch: 06, score: 0.2479 - new best, replacing\nPopulation: 07, batch: 07, score: 0.2632 - no improvement\nPopulation: 08, batch: 00, score: 0.2128 - no improvement\nPopulation: 08, batch: 01, score: 0.2006 - no improvement\nPopulation: 08, batch: 02, score: 0.2634 - new best, replacing\nPopulation: 08, batch: 03, score: 0.2098 - no improvement\nPopulation: 08, batch: 04, score: 0.2537 - new best, replacing\nPopulation: 08, batch: 05, score: 0.2092 - no improvement\nPopulation: 08, batch: 06, score: 0.1991 - no improvement\nPopulation: 08, batch: 07, score: 0.2331 - no improvement\nPopulation: 09, batch: 00, score: 0.2534 - no improvement\nPopulation: 09, batch: 01, score: 0.2258 - no improvement\nPopulation: 09, batch: 02, score: 0.2439 - no improvement\nPopulation: 09, batch: 03, score: 0.2231 - no improvement\nPopulation: 09, batch: 04, score: 0.2115 - no improvement\nPopulation: 09, batch: 05, score: 0.1948 - no improvement\nPopulation: 09, batch: 06, score: 0.2480 - new best, replacing\nPopulation: 09, batch: 07, score: 0.2123 - no improvement\nPopulation: 10, batch: 00, score: 0.2416 - no improvement\nPopulation: 10, batch: 01, score: 0.1903 - no improvement\nPopulation: 10, batch: 02, score: 0.1687 - no improvement\nPopulation: 10, batch: 03, score: 0.2367 - no improvement\nPopulation: 10, batch: 04, score: 0.1970 - no improvement\nPopulation: 10, batch: 05, score: 0.2277 - no improvement\nPopulation: 10, batch: 06, score: 0.2293 - no improvement\nPopulation: 10, batch: 07, score: 0.2269 - no improvement\nPopulation: 11, batch: 00, score: 0.2344 - no improvement\nPopulation: 11, batch: 01, score: 0.1815 - no improvement\nPopulation: 11, batch: 02, score: 0.2390 - no improvement\nPopulation: 11, batch: 03, score: 0.2152 - no improvement\nPopulation: 11, batch: 04, score: 0.2705 - new best, replacing\nPopulation: 11, batch: 05, score: 0.2594 - new best, replacing\nPopulation: 11, batch: 06, score: 0.2681 - new best, replacing\nPopulation: 11, batch: 07, score: 0.2214 - no improvement\nPopulation: 12, batch: 00, score: 0.1791 - no improvement\nPopulation: 12, batch: 01, score: 0.1462 - no improvement\nPopulation: 12, batch: 02, score: 0.2466 - no improvement\nPopulation: 12, batch: 03, score: 0.2606 - no improvement\nPopulation: 12, batch: 04, score: 0.2558 - no improvement\nPopulation: 12, batch: 05, score: 0.2077 - no improvement\nPopulation: 12, batch: 06, score: 0.2274 - no improvement\nPopulation: 12, batch: 07, score: 0.1566 - no improvement\nPopulation: 13, batch: 00, score: 0.1646 - no improvement\nPopulation: 13, batch: 01, score: 0.2091 - no improvement\nPopulation: 13, batch: 02, score: 0.2259 - no improvement\nPopulation: 13, batch: 03, score: 0.1968 - no improvement\nPopulation: 13, batch: 04, score: 0.1815 - no improvement\nPopulation: 13, batch: 05, score: 0.2707 - new best, replacing\nPopulation: 13, batch: 06, score: 0.2093 - no improvement\nPopulation: 13, batch: 07, score: 0.2379 - no improvement\nPopulation: 14, batch: 00, score: 0.2417 - no improvement\nPopulation: 14, batch: 01, score: 0.2123 - no improvement\nPopulation: 14, batch: 02, score: 0.2621 - no improvement\nPopulation: 14, batch: 03, score: 0.2265 - no improvement\nPopulation: 14, batch: 04, score: 0.2324 - no improvement\nPopulation: 14, batch: 05, score: 0.2448 - no improvement\nPopulation: 14, batch: 06, score: 0.2493 - no improvement\nPopulation: 14, batch: 07, score: 0.2735 - no improvement\nPopulation: 15, batch: 00, score: 0.2594 - no improvement\nPopulation: 15, batch: 01, score: 0.1612 - no improvement\nPopulation: 15, batch: 02, score: 0.2433 - no improvement\nPopulation: 15, batch: 03, score: 0.2404 - no improvement\nPopulation: 15, batch: 04, score: 0.2012 - no improvement\nPopulation: 15, batch: 05, score: 0.2447 - no improvement\nPopulation: 15, batch: 06, score: 0.2301 - no improvement\nPopulation: 15, batch: 07, score: 0.1743 - no improvement\nPopulation: 16, batch: 00, score: 0.2655 - new best, replacing\nPopulation: 16, batch: 01, score: 0.1834 - no improvement\nPopulation: 16, batch: 02, score: 0.2349 - no improvement\nPopulation: 16, batch: 03, score: 0.2386 - no improvement\nPopulation: 16, batch: 04, score: 0.2124 - no improvement\nPopulation: 16, batch: 05, score: 0.2237 - no improvement\nPopulation: 16, batch: 06, score: 0.2070 - no improvement\nPopulation: 16, batch: 07, score: 0.2414 - no improvement\nPopulation: 17, batch: 00, score: 0.2654 - no improvement\nPopulation: 17, batch: 01, score: 0.2449 - no improvement\nPopulation: 17, batch: 02, score: 0.2614 - no improvement\nPopulation: 17, batch: 03, score: 0.2317 - no improvement\nPopulation: 17, batch: 04, score: 0.2586 - no improvement\nPopulation: 17, batch: 05, score: 0.2151 - no improvement\nPopulation: 17, batch: 06, score: 0.2692 - new best, replacing\nPopulation: 17, batch: 07, score: 0.2458 - no improvement\nPopulation: 18, batch: 00, score: 0.2488 - no improvement\nPopulation: 18, batch: 01, score: 0.1992 - no improvement\nPopulation: 18, batch: 02, score: 0.2390 - no improvement\nPopulation: 18, batch: 03, score: 0.1938 - no improvement\nPopulation: 18, batch: 04, score: 0.2575 - no improvement\nPopulation: 18, batch: 05, score: 0.2330 - no improvement\nPopulation: 18, batch: 06, score: 0.2420 - no improvement\nPopulation: 18, batch: 07, score: 0.2627 - no improvement\nPopulation: 19, batch: 00, score: 0.2351 - no improvement\nPopulation: 19, batch: 01, score: 0.2673 - no improvement\nPopulation: 19, batch: 02, score: 0.2246 - no improvement\nPopulation: 19, batch: 03, score: 0.2070 - no improvement\nPopulation: 19, batch: 04, score: 0.2719 - new best, replacing\nPopulation: 19, batch: 05, score: 0.2245 - no improvement\nPopulation: 19, batch: 06, score: 0.2218 - no improvement\nPopulation: 19, batch: 07, score: 0.2362 - no improvement",
  "metrics": {
    "predict_time": 221.867528,
    "total_time": 10677.435625
  },
  "output": [
    [
      "https://replicate.delivery/mgxm/e665ea1a-2291-41b4-ad02-5bd2a3ce7697/new_york_city_completely_flooded_iter_000_batch_000_score_0.png",
      "https://replicate.delivery/mgxm/a5183755-db4d-44e3-b950-2d64d840ec15/new_york_city_completely_flooded_iter_000_batch_001_score_0.png",
      "https://replicate.delivery/mgxm/17df4351-ae6d-4c53-aee6-579a21a32fb6/new_york_city_completely_flooded_iter_000_batch_002_score_0.png",
      "https://replicate.delivery/mgxm/812f647d-c5fe-4672-ad0e-c4cdf045576f/new_york_city_completely_flooded_iter_000_batch_003_score_0.png",
      "https://replicate.delivery/mgxm/8db89ed7-0034-4657-9e5e-7fa4a3a5964e/new_york_city_completely_flooded_iter_000_batch_004_score_0.png",
      "https://replicate.delivery/mgxm/1d4ee48e-2e2a-4568-9243-c6e129bbfc56/new_york_city_completely_flooded_iter_000_batch_005_score_0.png",
      "https://replicate.delivery/mgxm/d4a55fad-5c0e-45cb-89ab-fcd89ac91102/new_york_city_completely_flooded_iter_000_batch_006_score_0.png",
      "https://replicate.delivery/mgxm/b298499d-32b6-4fca-9582-eff01abe73c5/new_york_city_completely_flooded_iter_000_batch_007_score_0.png"
    ],
    [
      "https://replicate.delivery/mgxm/6e1b5fa0-c66b-4e52-875d-b40c2e265c42/new_york_city_completely_flooded_iter_001_batch_000_score_0.png",
      "https://replicate.delivery/mgxm/162e1cb5-a321-4c98-a89e-bd3f1e4f51fb/new_york_city_completely_flooded_iter_001_batch_001_score_0.png",
      "https://replicate.delivery/mgxm/ab577251-7a25-4d87-958e-ac852c86d07b/new_york_city_completely_flooded_iter_001_batch_002_score_0.png",
      "https://replicate.delivery/mgxm/952fa8c6-b547-4ca4-9df1-2ff7612cb1d5/new_york_city_completely_flooded_iter_001_batch_003_score_0.png",
      "https://replicate.delivery/mgxm/d3fb6148-986e-41bb-9e5d-eb3dd0d18c52/new_york_city_completely_flooded_iter_001_batch_004_score_0.png",
      "https://replicate.delivery/mgxm/0bffded0-39ef-4e37-8d28-2a1c83b83a59/new_york_city_completely_flooded_iter_001_batch_005_score_0.png",
      "https://replicate.delivery/mgxm/ba04b106-3840-4dfd-9a72-75e503ad752a/new_york_city_completely_flooded_iter_001_batch_006_score_0.png",
      "https://replicate.delivery/mgxm/fdf5f05a-541a-4944-9427-859a315c74e8/new_york_city_completely_flooded_iter_001_batch_007_score_0.png"
    ],
    [
      "https://replicate.delivery/mgxm/74e7a921-88eb-4502-848e-07f5774585e5/new_york_city_completely_flooded_iter_002_batch_000_score_0.png",
      "https://replicate.delivery/mgxm/7477b0ad-a8a3-4697-be5a-7bb307f8e627/new_york_city_completely_flooded_iter_002_batch_001_score_0.png",
      "https://replicate.delivery/mgxm/8cb11309-ef0c-48b1-9047-822a10c31a30/new_york_city_completely_flooded_iter_002_batch_002_score_0.png",
      "https://replicate.delivery/mgxm/8f59e0b8-3962-47aa-b3a3-18261c495717/new_york_city_completely_flooded_iter_002_batch_003_score_0.png",
      "https://replicate.delivery/mgxm/1c92a2fd-8524-43c4-921c-e1614d7a1a17/new_york_city_completely_flooded_iter_002_batch_004_score_0.png",
      "https://replicate.delivery/mgxm/4e79affc-8ed6-4887-8c63-8d31b740edcd/new_york_city_completely_flooded_iter_002_batch_005_score_0.png",
      "https://replicate.delivery/mgxm/5273f895-890d-4731-880a-4ae025c3c137/new_york_city_completely_flooded_iter_002_batch_006_score_0.png",
      "https://replicate.delivery/mgxm/42300edf-5a11-4825-970e-78be3a6fb3b8/new_york_city_completely_flooded_iter_002_batch_007_score_0.png"
    ],
    [
      "https://replicate.delivery/mgxm/e04c672d-452b-40d7-a47e-d09776371b8d/new_york_city_completely_flooded_iter_003_batch_000_score_0.png",
      "https://replicate.delivery/mgxm/e4a61aac-d617-413a-b413-b00043b1a547/new_york_city_completely_flooded_iter_003_batch_001_score_0.png",
      "https://replicate.delivery/mgxm/4087b5ae-efdd-4063-91d1-a1df7fbe5a9e/new_york_city_completely_flooded_iter_003_batch_002_score_0.png",
      "https://replicate.delivery/mgxm/e6c30a1f-0137-4761-a5ec-1cb7b6bdab40/new_york_city_completely_flooded_iter_003_batch_003_score_0.png",
      "https://replicate.delivery/mgxm/00eebb69-2205-4fb6-92d8-73c285c0f7c7/new_york_city_completely_flooded_iter_003_batch_004_score_0.png",
      "https://replicate.delivery/mgxm/d54be450-9068-4d97-b2d3-d4543238ae14/new_york_city_completely_flooded_iter_003_batch_005_score_0.png",
      "https://replicate.delivery/mgxm/35db94ac-8992-4668-95c5-da6054fa7e68/new_york_city_completely_flooded_iter_003_batch_006_score_0.png",
      "https://replicate.delivery/mgxm/e8bfc8f7-89f5-4634-a3d5-23257bc04bb2/new_york_city_completely_flooded_iter_003_batch_007_score_0.png"
    ],
    [
      "https://replicate.delivery/mgxm/a3bb48fb-7512-47c1-a800-4b0ee298af4c/new_york_city_completely_flooded_iter_004_batch_000_score_0.png",
      "https://replicate.delivery/mgxm/c9d0ab6f-644f-4fd2-be62-10eb3b471ac9/new_york_city_completely_flooded_iter_004_batch_001_score_0.png",
      "https://replicate.delivery/mgxm/a6d95ad0-3119-4288-806d-15e04e8bb78f/new_york_city_completely_flooded_iter_004_batch_002_score_0.png",
      "https://replicate.delivery/mgxm/da4f9e96-228d-483c-8ab8-82071cf54ad8/new_york_city_completely_flooded_iter_004_batch_003_score_0.png",
      "https://replicate.delivery/mgxm/826c05b0-ad03-4d1d-be3e-cbde62da808a/new_york_city_completely_flooded_iter_004_batch_004_score_0.png",
      "https://replicate.delivery/mgxm/41558c9b-bdbf-4119-b092-58f3bad8a1f4/new_york_city_completely_flooded_iter_004_batch_005_score_0.png",
      "https://replicate.delivery/mgxm/ece15757-2d93-4994-9522-9dc786a02128/new_york_city_completely_flooded_iter_004_batch_006_score_0.png",
      "https://replicate.delivery/mgxm/4745e52a-713c-4c37-a4ff-b137c3f68c20/new_york_city_completely_flooded_iter_004_batch_007_score_0.png"
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      "https://replicate.delivery/mgxm/908ae783-e292-4dce-9509-59f02688803d/new_york_city_completely_flooded_iter_019_batch_006_score_0.png",
      "https://replicate.delivery/mgxm/20eed2d1-7700-44ba-bc84-4e514892d4b1/new_york_city_completely_flooded_iter_019_batch_007_score_0.png"
    ]
  ],
  "started_at": "2022-06-17T22:09:03.708281Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/lnri7jpouzdd7c73npp7bt5k5y",
    "cancel": "https://api.replicate.com/v1/predictions/lnri7jpouzdd7c73npp7bt5k5y/cancel"
  },
  "version": "d0d04e11830081fcc1066cc5e6306e4ffdcf35c0d95d12c6cbc2b869f17466d0"
}

Generated in

3 minutes 42 seconds

Tweak itReport

Using seed 319970566
Running simulation for new york city completely flooded
Encoding text embeddings with new york city completely flooded dimensions
Using aesthetic embedding 9 with weight 0.5
Using inpaint model but no image is provided. Initializing with zeros.
Population: 00, batch: 00, score: 0.2321 - first iteration
Population: 00, batch: 01, score: 0.2690 - first iteration
Population: 00, batch: 02, score: 0.2435 - first iteration
Population: 00, batch: 03, score: 0.2721 - first iteration
Population: 00, batch: 04, score: 0.2456 - first iteration
Population: 00, batch: 05, score: 0.2513 - first iteration
Population: 00, batch: 06, score: 0.2099 - first iteration
Population: 00, batch: 07, score: 0.2464 - first iteration
Population: 01, batch: 00, score: 0.2197 - no improvement
Population: 01, batch: 01, score: 0.2195 - no improvement
Population: 01, batch: 02, score: 0.2059 - no improvement
Population: 01, batch: 03, score: 0.2742 - new best, replacing
Population: 01, batch: 04, score: 0.1948 - no improvement
Population: 01, batch: 05, score: 0.2332 - no improvement
Population: 01, batch: 06, score: 0.2409 - new best, replacing
Population: 01, batch: 07, score: 0.1885 - no improvement
Population: 02, batch: 00, score: 0.2307 - no improvement
Population: 02, batch: 01, score: 0.2659 - no improvement
Population: 02, batch: 02, score: 0.2015 - no improvement
Population: 02, batch: 03, score: 0.2596 - no improvement
Population: 02, batch: 04, score: 0.2528 - new best, replacing
Population: 02, batch: 05, score: 0.2577 - new best, replacing
Population: 02, batch: 06, score: 0.2172 - no improvement
Population: 02, batch: 07, score: 0.2784 - new best, replacing
Population: 03, batch: 00, score: 0.2575 - new best, replacing
Population: 03, batch: 01, score: 0.2551 - no improvement
Population: 03, batch: 02, score: 0.2123 - no improvement
Population: 03, batch: 03, score: 0.2359 - no improvement
Population: 03, batch: 04, score: 0.1640 - no improvement
Population: 03, batch: 05, score: 0.2039 - no improvement
Population: 03, batch: 06, score: 0.2175 - no improvement
Population: 03, batch: 07, score: 0.2194 - no improvement
Population: 04, batch: 00, score: 0.2356 - no improvement
Population: 04, batch: 01, score: 0.2373 - no improvement
Population: 04, batch: 02, score: 0.2436 - new best, replacing
Population: 04, batch: 03, score: 0.1799 - no improvement
Population: 04, batch: 04, score: 0.2512 - no improvement
Population: 04, batch: 05, score: 0.2531 - no improvement
Population: 04, batch: 06, score: 0.2440 - new best, replacing
Population: 04, batch: 07, score: 0.2075 - no improvement
Population: 05, batch: 00, score: 0.2042 - no improvement
Population: 05, batch: 01, score: 0.2293 - no improvement
Population: 05, batch: 02, score: 0.2215 - no improvement
Population: 05, batch: 03, score: 0.1763 - no improvement
Population: 05, batch: 04, score: 0.1872 - no improvement
Population: 05, batch: 05, score: 0.2393 - no improvement
Population: 05, batch: 06, score: 0.1795 - no improvement
Population: 05, batch: 07, score: 0.2669 - no improvement
Population: 06, batch: 00, score: 0.2417 - no improvement
Population: 06, batch: 01, score: 0.2546 - no improvement
Population: 06, batch: 02, score: 0.2241 - no improvement
Population: 06, batch: 03, score: 0.2616 - no improvement
Population: 06, batch: 04, score: 0.2387 - no improvement
Population: 06, batch: 05, score: 0.2262 - no improvement
Population: 06, batch: 06, score: 0.1805 - no improvement
Population: 06, batch: 07, score: 0.2203 - no improvement
Population: 07, batch: 00, score: 0.2594 - new best, replacing
Population: 07, batch: 01, score: 0.2134 - no improvement
Population: 07, batch: 02, score: 0.2345 - no improvement
Population: 07, batch: 03, score: 0.1960 - no improvement
Population: 07, batch: 04, score: 0.2309 - no improvement
Population: 07, batch: 05, score: 0.1765 - no improvement
Population: 07, batch: 06, score: 0.2479 - new best, replacing
Population: 07, batch: 07, score: 0.2632 - no improvement
Population: 08, batch: 00, score: 0.2128 - no improvement
Population: 08, batch: 01, score: 0.2006 - no improvement
Population: 08, batch: 02, score: 0.2634 - new best, replacing
Population: 08, batch: 03, score: 0.2098 - no improvement
Population: 08, batch: 04, score: 0.2537 - new best, replacing
Population: 08, batch: 05, score: 0.2092 - no improvement
Population: 08, batch: 06, score: 0.1991 - no improvement
Population: 08, batch: 07, score: 0.2331 - no improvement
Population: 09, batch: 00, score: 0.2534 - no improvement
Population: 09, batch: 01, score: 0.2258 - no improvement
Population: 09, batch: 02, score: 0.2439 - no improvement
Population: 09, batch: 03, score: 0.2231 - no improvement
Population: 09, batch: 04, score: 0.2115 - no improvement
Population: 09, batch: 05, score: 0.1948 - no improvement
Population: 09, batch: 06, score: 0.2480 - new best, replacing
Population: 09, batch: 07, score: 0.2123 - no improvement
Population: 10, batch: 00, score: 0.2416 - no improvement
Population: 10, batch: 01, score: 0.1903 - no improvement
Population: 10, batch: 02, score: 0.1687 - no improvement
Population: 10, batch: 03, score: 0.2367 - no improvement
Population: 10, batch: 04, score: 0.1970 - no improvement
Population: 10, batch: 05, score: 0.2277 - no improvement
Population: 10, batch: 06, score: 0.2293 - no improvement
Population: 10, batch: 07, score: 0.2269 - no improvement
Population: 11, batch: 00, score: 0.2344 - no improvement
Population: 11, batch: 01, score: 0.1815 - no improvement
Population: 11, batch: 02, score: 0.2390 - no improvement
Population: 11, batch: 03, score: 0.2152 - no improvement
Population: 11, batch: 04, score: 0.2705 - new best, replacing
Population: 11, batch: 05, score: 0.2594 - new best, replacing
Population: 11, batch: 06, score: 0.2681 - new best, replacing
Population: 11, batch: 07, score: 0.2214 - no improvement
Population: 12, batch: 00, score: 0.1791 - no improvement
Population: 12, batch: 01, score: 0.1462 - no improvement
Population: 12, batch: 02, score: 0.2466 - no improvement
Population: 12, batch: 03, score: 0.2606 - no improvement
Population: 12, batch: 04, score: 0.2558 - no improvement
Population: 12, batch: 05, score: 0.2077 - no improvement
Population: 12, batch: 06, score: 0.2274 - no improvement
Population: 12, batch: 07, score: 0.1566 - no improvement
Population: 13, batch: 00, score: 0.1646 - no improvement
Population: 13, batch: 01, score: 0.2091 - no improvement
Population: 13, batch: 02, score: 0.2259 - no improvement
Population: 13, batch: 03, score: 0.1968 - no improvement
Population: 13, batch: 04, score: 0.1815 - no improvement
Population: 13, batch: 05, score: 0.2707 - new best, replacing
Population: 13, batch: 06, score: 0.2093 - no improvement
Population: 13, batch: 07, score: 0.2379 - no improvement
Population: 14, batch: 00, score: 0.2417 - no improvement
Population: 14, batch: 01, score: 0.2123 - no improvement
Population: 14, batch: 02, score: 0.2621 - no improvement
Population: 14, batch: 03, score: 0.2265 - no improvement
Population: 14, batch: 04, score: 0.2324 - no improvement
Population: 14, batch: 05, score: 0.2448 - no improvement
Population: 14, batch: 06, score: 0.2493 - no improvement
Population: 14, batch: 07, score: 0.2735 - no improvement
Population: 15, batch: 00, score: 0.2594 - no improvement
Population: 15, batch: 01, score: 0.1612 - no improvement
Population: 15, batch: 02, score: 0.2433 - no improvement
Population: 15, batch: 03, score: 0.2404 - no improvement
Population: 15, batch: 04, score: 0.2012 - no improvement
Population: 15, batch: 05, score: 0.2447 - no improvement
Population: 15, batch: 06, score: 0.2301 - no improvement
Population: 15, batch: 07, score: 0.1743 - no improvement
Population: 16, batch: 00, score: 0.2655 - new best, replacing
Population: 16, batch: 01, score: 0.1834 - no improvement
Population: 16, batch: 02, score: 0.2349 - no improvement
Population: 16, batch: 03, score: 0.2386 - no improvement
Population: 16, batch: 04, score: 0.2124 - no improvement
Population: 16, batch: 05, score: 0.2237 - no improvement
Population: 16, batch: 06, score: 0.2070 - no improvement
Population: 16, batch: 07, score: 0.2414 - no improvement
Population: 17, batch: 00, score: 0.2654 - no improvement
Population: 17, batch: 01, score: 0.2449 - no improvement
Population: 17, batch: 02, score: 0.2614 - no improvement
Population: 17, batch: 03, score: 0.2317 - no improvement
Population: 17, batch: 04, score: 0.2586 - no improvement
Population: 17, batch: 05, score: 0.2151 - no improvement
Population: 17, batch: 06, score: 0.2692 - new best, replacing
Population: 17, batch: 07, score: 0.2458 - no improvement
Population: 18, batch: 00, score: 0.2488 - no improvement
Population: 18, batch: 01, score: 0.1992 - no improvement
Population: 18, batch: 02, score: 0.2390 - no improvement
Population: 18, batch: 03, score: 0.1938 - no improvement
Population: 18, batch: 04, score: 0.2575 - no improvement
Population: 18, batch: 05, score: 0.2330 - no improvement
Population: 18, batch: 06, score: 0.2420 - no improvement
Population: 18, batch: 07, score: 0.2627 - no improvement
Population: 19, batch: 00, score: 0.2351 - no improvement
Population: 19, batch: 01, score: 0.2673 - no improvement
Population: 19, batch: 02, score: 0.2246 - no improvement
Population: 19, batch: 03, score: 0.2070 - no improvement
Population: 19, batch: 04, score: 0.2719 - new best, replacing
Population: 19, batch: 05, score: 0.2245 - no improvement
Population: 19, batch: 06, score: 0.2218 - no improvement
Population: 19, batch: 07, score: 0.2362 - no improvement

Examples

View more examples

Run time and cost

This model runs on Nvidia T4 GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

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Model description

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Intended use

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Ethical considerations

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Caveats and recommendations

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