ariel415el / gpdm

Generating Natural Images with Direct Patch Distribution Matching

Cold

Public
5.8K runs
T4
GitHub
Paper
License

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

task

string

(Reshuffle): Start from a noisy and generates samples from the same scene in the reference image. (Retarget): Start from a stretched version of the reference and recreates the scene in another aspect ratio. (Style-transfer): Recreates a content-image with the style of the reference image

Default: "Reshuffle"

num_outputs

integer

How many output images to generate. Generating multiple images at once improves the quality and diversity of the results

Default: 4

num_projections

integer

Number of ramdom projections for SWD. More is better results but is slower and memory inefficient

Default: 64

patch_size

integer

Size of the extracted a patches

Default: 8

height_factor

integer

Change the output's aspect ratio (Retargeting)

Default: 1

width_factor

integer

Change the output's aspect ratio (Retargeting)

Default: 1

reference_image

*file

Preview

The main input image - Style image in style-transfer.

content_image

file

Preview

Only relevant for style-transfer

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

npx create-replicate --model=ariel415el/gpdm

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 ariel415el/gpdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ariel415el/gpdm:ed617f2a049a405b2eaa44944750b41291fd6b69d3092dea962b79d5d0b6407e",
  {
    input: {
      task: "Style-transfer",
      patch_size: 8,
      num_outputs: 1,
      width_factor: 1,
      content_image: "https://replicate.delivery/mgxm/e01a715a-8cc2-45b2-836c-fb929f1a1c34/baiden3_crop.jpg",
      height_factor: 1,
      num_projections: 64,
      reference_image: "https://replicate.delivery/mgxm/f51f0b0f-2710-4aba-ac62-e74857f519bd/wood2_fliped.jpeg"
    }
  }
);

// 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=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run ariel415el/gpdm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ariel415el/gpdm:ed617f2a049a405b2eaa44944750b41291fd6b69d3092dea962b79d5d0b6407e",
    input={
        "task": "Style-transfer",
        "patch_size": 8,
        "num_outputs": 1,
        "width_factor": 1,
        "content_image": "https://replicate.delivery/mgxm/e01a715a-8cc2-45b2-836c-fb929f1a1c34/baiden3_crop.jpg",
        "height_factor": 1,
        "num_projections": 64,
        "reference_image": "https://replicate.delivery/mgxm/f51f0b0f-2710-4aba-ac62-e74857f519bd/wood2_fliped.jpeg"
    }
)
print(output)

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 ariel415el/gpdm 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": "ariel415el/gpdm:ed617f2a049a405b2eaa44944750b41291fd6b69d3092dea962b79d5d0b6407e",
    "input": {
      "task": "Style-transfer",
      "patch_size": 8,
      "num_outputs": 1,
      "width_factor": 1,
      "content_image": "https://replicate.delivery/mgxm/e01a715a-8cc2-45b2-836c-fb929f1a1c34/baiden3_crop.jpg",
      "height_factor": 1,
      "num_projections": 64,
      "reference_image": "https://replicate.delivery/mgxm/f51f0b0f-2710-4aba-ac62-e74857f519bd/wood2_fliped.jpeg"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2022-07-06T14:30:27.971318Z",
  "created_at": "2022-07-06T14:28:08.018868Z",
  "data_removed": false,
  "error": null,
  "id": "jzc7qiwbinbjzgi6gr3gayhau4",
  "input": {
    "task": "Style-transfer",
    "patch_size": 8,
    "num_outputs": 1,
    "width_factor": 1,
    "content_image": "https://replicate.delivery/mgxm/e01a715a-8cc2-45b2-836c-fb929f1a1c34/baiden3_crop.jpg",
    "height_factor": 1,
    "num_projections": 64,
    "reference_image": "https://replicate.delivery/mgxm/f51f0b0f-2710-4aba-ac62-e74857f519bd/wood2_fliped.jpeg"
  },
  "logs": "\nStarting:   0%|          | 0/500 [00:00<?, ?it/s]\nStarting:   0%|          | 1/500 [00:00<03:25,  2.43it/s]\nLvl 0/0, step 1/500:   0%|          | 1/500 [00:00<03:25,  2.43it/s]\nLvl 0/0, step 1/500:   0%|          | 2/500 [00:00<02:43,  3.05it/s]\nLvl 0/0, step 2/500:   0%|          | 2/500 [00:00<02:43,  3.05it/s]\nLvl 0/0, step 2/500:   1%|          | 3/500 [00:00<02:31,  3.28it/s]\nLvl 0/0, step 3/500:   1%|          | 3/500 [00:00<02:31,  3.28it/s]\nLvl 0/0, step 3/500:   1%|          | 4/500 [00:01<02:24,  3.44it/s]\nLvl 0/0, step 4/500:   1%|          | 4/500 [00:01<02:24,  3.44it/s]\nLvl 0/0, step 4/500:   1%|          | 5/500 [00:01<02:20,  3.52it/s]\nLvl 0/0, step 5/500:   1%|          | 5/500 [00:01<02:20,  3.52it/s]\nLvl 0/0, step 5/500:   1%|          | 6/500 [00:01<02:17,  3.58it/s]\nLvl 0/0, step 6/500:   1%|          | 6/500 [00:01<02:17,  3.58it/s]\nLvl 0/0, step 6/500:   1%|▏         | 7/500 [00:02<02:16,  3.62it/s]\nLvl 0/0, step 7/500:   1%|▏         | 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3.64it/s]\nLvl 0/0, step 475/500:  95%|█████████▌| 476/500 [02:12<00:06,  3.64it/s]\nLvl 0/0, step 476/500:  95%|█████████▌| 476/500 [02:12<00:06,  3.64it/s]\nLvl 0/0, step 476/500:  95%|█████████▌| 477/500 [02:12<00:06,  3.63it/s]\nLvl 0/0, step 477/500:  95%|█████████▌| 477/500 [02:12<00:06,  3.63it/s]\nLvl 0/0, step 477/500:  96%|█████████▌| 478/500 [02:12<00:06,  3.63it/s]\nLvl 0/0, step 478/500:  96%|█████████▌| 478/500 [02:12<00:06,  3.63it/s]\nLvl 0/0, step 478/500:  96%|█████████▌| 479/500 [02:12<00:05,  3.62it/s]\nLvl 0/0, step 479/500:  96%|█████████▌| 479/500 [02:12<00:05,  3.62it/s]\nLvl 0/0, step 479/500:  96%|█████████▌| 480/500 [02:13<00:05,  3.61it/s]\nLvl 0/0, step 480/500:  96%|█████████▌| 480/500 [02:13<00:05,  3.61it/s]\nLvl 0/0, step 480/500:  96%|█████████▌| 481/500 [02:13<00:05,  3.61it/s]\nLvl 0/0, step 481/500:  96%|█████████▌| 481/500 [02:13<00:05,  3.61it/s]\nLvl 0/0, step 481/500:  96%|█████████▋| 482/500 [02:13<00:04,  3.60it/s]\nLvl 0/0, step 482/500:  96%|█████████▋| 482/500 [02:13<00:04,  3.60it/s]\nLvl 0/0, step 482/500:  97%|█████████▋| 483/500 [02:13<00:04,  3.60it/s]\nLvl 0/0, step 483/500:  97%|█████████▋| 483/500 [02:13<00:04,  3.60it/s]\nLvl 0/0, step 483/500:  97%|█████████▋| 484/500 [02:14<00:04,  3.59it/s]\nLvl 0/0, step 484/500:  97%|█████████▋| 484/500 [02:14<00:04,  3.59it/s]\nLvl 0/0, step 484/500:  97%|█████████▋| 485/500 [02:14<00:04,  3.60it/s]\nLvl 0/0, step 485/500:  97%|█████████▋| 485/500 [02:14<00:04,  3.60it/s]\nLvl 0/0, step 485/500:  97%|█████████▋| 486/500 [02:14<00:03,  3.61it/s]\nLvl 0/0, step 486/500:  97%|█████████▋| 486/500 [02:14<00:03,  3.61it/s]\nLvl 0/0, step 486/500:  97%|█████████▋| 487/500 [02:15<00:03,  3.61it/s]\nLvl 0/0, step 487/500:  97%|█████████▋| 487/500 [02:15<00:03,  3.61it/s]\nLvl 0/0, step 487/500:  98%|█████████▊| 488/500 [02:15<00:03,  3.60it/s]\nLvl 0/0, step 488/500:  98%|█████████▊| 488/500 [02:15<00:03,  3.60it/s]\nLvl 0/0, step 488/500:  98%|█████████▊| 489/500 [02:15<00:03,  3.60it/s]\nLvl 0/0, step 489/500:  98%|█████████▊| 489/500 [02:15<00:03,  3.60it/s]\nLvl 0/0, step 489/500:  98%|█████████▊| 490/500 [02:15<00:02,  3.60it/s]\nLvl 0/0, step 490/500:  98%|█████████▊| 490/500 [02:15<00:02,  3.60it/s]\nLvl 0/0, step 490/500:  98%|█████████▊| 491/500 [02:16<00:02,  3.58it/s]\nLvl 0/0, step 491/500:  98%|█████████▊| 491/500 [02:16<00:02,  3.58it/s]\nLvl 0/0, step 491/500:  98%|█████████▊| 492/500 [02:16<00:02,  3.58it/s]\nLvl 0/0, step 492/500:  98%|█████████▊| 492/500 [02:16<00:02,  3.58it/s]\nLvl 0/0, step 492/500:  99%|█████████▊| 493/500 [02:16<00:01,  3.59it/s]\nLvl 0/0, step 493/500:  99%|█████████▊| 493/500 [02:16<00:01,  3.59it/s]\nLvl 0/0, step 493/500:  99%|█████████▉| 494/500 [02:17<00:01,  3.59it/s]\nLvl 0/0, step 494/500:  99%|█████████▉| 494/500 [02:17<00:01,  3.59it/s]\nLvl 0/0, step 494/500:  99%|█████████▉| 495/500 [02:17<00:01,  3.58it/s]\nLvl 0/0, step 495/500:  99%|█████████▉| 495/500 [02:17<00:01,  3.58it/s]\nLvl 0/0, step 495/500:  99%|█████████▉| 496/500 [02:17<00:01,  3.58it/s]\nLvl 0/0, step 496/500:  99%|█████████▉| 496/500 [02:17<00:01,  3.58it/s]\nLvl 0/0, step 496/500:  99%|█████████▉| 497/500 [02:17<00:00,  3.58it/s]\nLvl 0/0, step 497/500:  99%|█████████▉| 497/500 [02:17<00:00,  3.58it/s]\nLvl 0/0, step 497/500: 100%|█████████▉| 498/500 [02:18<00:00,  3.58it/s]\nLvl 0/0, step 498/500: 100%|█████████▉| 498/500 [02:18<00:00,  3.58it/s]\nLvl 0/0, step 498/500: 100%|█████████▉| 499/500 [02:18<00:00,  3.58it/s]\nLvl 0/0, step 499/500: 100%|█████████▉| 499/500 [02:18<00:00,  3.58it/s]\nLvl 0/0, step 499/500: 100%|██████████| 500/500 [02:18<00:00,  3.59it/s]\nLvl 0/0, step 500/500: 100%|██████████| 500/500 [02:18<00:00,  3.59it/s]\nLvl 0/0, step 500/500: 100%|██████████| 500/500 [02:18<00:00,  3.60it/s]",
  "metrics": {
    "predict_time": 139.764235,
    "total_time": 139.95245
  },
  "output": "https://replicate.delivery/mgxm/6e6ea32c-abbc-478b-9a31-af5e3b7702c6/output.png",
  "started_at": "2022-07-06T14:28:08.207083Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/jzc7qiwbinbjzgi6gr3gayhau4",
    "cancel": "https://api.replicate.com/v1/predictions/jzc7qiwbinbjzgi6gr3gayhau4/cancel"
  },
  "version": "e07faab395efc0c02c9767395b9efb78599234facfc852b5f75c4db5bd89caf3"
}

Generated in

2 minutes 20 seconds

Tweak it Report


Starting:   0%|          | 0/500 [00:00<?, ?it/s]
Starting:   0%|          | 1/500 [00:00<03:25,  2.43it/s]
Lvl 0/0, step 1/500:   0%|          | 1/500 [00:00<03:25,  2.43it/s]
Lvl 0/0, step 1/500:   0%|          | 2/500 [00:00<02:43,  3.05it/s]
Lvl 0/0, step 2/500:   0%|          | 2/500 [00:00<02:43,  3.05it/s]
Lvl 0/0, step 2/500:   1%|          | 3/500 [00:00<02:31,  3.28it/s]
Lvl 0/0, step 3/500:   1%|          | 3/500 [00:00<02:31,  3.28it/s]
Lvl 0/0, step 3/500:   1%|          | 4/500 [00:01<02:24,  3.44it/s]
Lvl 0/0, step 4/500:   1%|          | 4/500 [00:01<02:24,  3.44it/s]
Lvl 0/0, step 4/500:   1%|          | 5/500 [00:01<02:20,  3.52it/s]
Lvl 0/0, step 5/500:   1%|          | 5/500 [00:01<02:20,  3.52it/s]
Lvl 0/0, step 5/500:   1%|          | 6/500 [00:01<02:17,  3.58it/s]
Lvl 0/0, step 6/500:   1%|          | 6/500 [00:01<02:17,  3.58it/s]
Lvl 0/0, step 6/500:   1%|▏         | 7/500 [00:02<02:16,  3.62it/s]
Lvl 0/0, step 7/500:   1%|▏         | 7/500 [00:02<02:16,  3.62it/s]
Lvl 0/0, step 7/500:   2%|▏         | 8/500 [00:02<02:15,  3.64it/s]
Lvl 0/0, step 8/500:   2%|▏         | 8/500 [00:02<02:15,  3.64it/s]
Lvl 0/0, step 8/500:   2%|▏         | 9/500 [00:02<02:14,  3.64it/s]
Lvl 0/0, step 9/500:   2%|▏         | 9/500 [00:02<02:14,  3.64it/s]
Lvl 0/0, step 9/500:   2%|▏         | 10/500 [00:02<02:14,  3.64it/s]
Lvl 0/0, step 10/500:   2%|▏         | 10/500 [00:02<02:14,  3.64it/s]
Lvl 0/0, step 10/500:   2%|▏         | 11/500 [00:03<02:14,  3.64it/s]
Lvl 0/0, step 11/500:   2%|▏         | 11/500 [00:03<02:14,  3.64it/s]
Lvl 0/0, step 11/500:   2%|▏         | 12/500 [00:03<02:14,  3.62it/s]
Lvl 0/0, step 12/500:   2%|▏         | 12/500 [00:03<02:14,  3.62it/s]
Lvl 0/0, step 12/500:   3%|▎         | 13/500 [00:03<02:14,  3.62it/s]
Lvl 0/0, step 13/500:   3%|▎         | 13/500 [00:03<02:14,  3.62it/s]
Lvl 0/0, step 13/500:   3%|▎         | 14/500 [00:03<02:14,  3.61it/s]
Lvl 0/0, step 14/500:   3%|▎         | 14/500 [00:03<02:14,  3.61it/s]
Lvl 0/0, step 14/500:   3%|▎         | 15/500 [00:04<02:14,  3.61it/s]
Lvl 0/0, step 15/500:   3%|▎         | 15/500 [00:04<02:14,  3.61it/s]
Lvl 0/0, step 15/500:   3%|▎         | 16/500 [00:04<02:14,  3.61it/s]
Lvl 0/0, step 16/500:   3%|▎         | 16/500 [00:04<02:14,  3.61it/s]
Lvl 0/0, step 16/500:   3%|▎         | 17/500 [00:04<02:13,  3.61it/s]
Lvl 0/0, step 17/500:   3%|▎         | 17/500 [00:04<02:13,  3.61it/s]
Lvl 0/0, step 17/500:   4%|▎         | 18/500 [00:05<02:13,  3.60it/s]
Lvl 0/0, step 18/500:   4%|▎         | 18/500 [00:05<02:13,  3.60it/s]
Lvl 0/0, step 18/500:   4%|▍         | 19/500 [00:05<02:13,  3.59it/s]
Lvl 0/0, step 19/500:   4%|▍         | 19/500 [00:05<02:13,  3.59it/s]
Lvl 0/0, step 19/500:   4%|▍         | 20/500 [00:05<02:13,  3.59it/s]
Lvl 0/0, step 20/500:   4%|▍         | 20/500 [00:05<02:13,  3.59it/s]
Lvl 0/0, step 20/500:   4%|▍         | 21/500 [00:05<02:13,  3.58it/s]
Lvl 0/0, step 21/500:   4%|▍         | 21/500 [00:05<02:13,  3.58it/s]
Lvl 0/0, step 21/500:   4%|▍         | 22/500 [00:06<02:13,  3.59it/s]
Lvl 0/0, step 22/500:   4%|▍         | 22/500 [00:06<02:13,  3.59it/s]
Lvl 0/0, step 22/500:   5%|▍         | 23/500 [00:06<02:13,  3.58it/s]
Lvl 0/0, step 23/500:   5%|▍         | 23/500 [00:06<02:13,  3.58it/s]
Lvl 0/0, step 23/500:   5%|▍         | 24/500 [00:06<02:12,  3.58it/s]
Lvl 0/0, step 24/500:   5%|▍         | 24/500 [00:06<02:12,  3.58it/s]
Lvl 0/0, step 24/500:   5%|▌         | 25/500 [00:07<02:12,  3.57it/s]
Lvl 0/0, step 25/500:   5%|▌         | 25/500 [00:07<02:12,  3.57it/s]
Lvl 0/0, step 25/500:   5%|▌         | 26/500 [00:07<02:12,  3.58it/s]
Lvl 0/0, step 26/500:   5%|▌         | 26/500 [00:07<02:12,  3.58it/s]
Lvl 0/0, step 26/500:   5%|▌         | 27/500 [00:07<02:12,  3.58it/s]
Lvl 0/0, step 27/500:   5%|▌         | 27/500 [00:07<02:12,  3.58it/s]
Lvl 0/0, step 27/500:   6%|▌         | 28/500 [00:07<02:11,  3.58it/s]
Lvl 0/0, step 28/500:   6%|▌         | 28/500 [00:07<02:11,  3.58it/s]
Lvl 0/0, step 28/500:   6%|▌         | 29/500 [00:08<02:11,  3.59it/s]
Lvl 0/0, step 29/500:   6%|▌         | 29/500 [00:08<02:11,  3.59it/s]
Lvl 0/0, step 29/500:   6%|▌         | 30/500 [00:08<02:11,  3.59it/s]
Lvl 0/0, step 30/500:   6%|▌         | 30/500 [00:08<02:11,  3.59it/s]
Lvl 0/0, step 30/500:   6%|▌         | 31/500 [00:08<02:11,  3.57it/s]
Lvl 0/0, step 31/500:   6%|▌         | 31/500 [00:08<02:11,  3.57it/s]
Lvl 0/0, step 31/500:   6%|▋         | 32/500 [00:08<02:10,  3.58it/s]
Lvl 0/0, step 32/500:   6%|▋         | 32/500 [00:08<02:10,  3.58it/s]
Lvl 0/0, step 32/500:   7%|▋         | 33/500 [00:09<02:10,  3.57it/s]
Lvl 0/0, step 33/500:   7%|▋         | 33/500 [00:09<02:10,  3.57it/s]
Lvl 0/0, step 33/500:   7%|▋         | 34/500 [00:09<02:09,  3.60it/s]
Lvl 0/0, step 34/500:   7%|▋         | 34/500 [00:09<02:09,  3.60it/s]
Lvl 0/0, step 34/500:   7%|▋         | 35/500 [00:09<02:09,  3.59it/s]
Lvl 0/0, step 35/500:   7%|▋         | 35/500 [00:09<02:09,  3.59it/s]
Lvl 0/0, step 35/500:   7%|▋         | 36/500 [00:10<02:09,  3.59it/s]
Lvl 0/0, step 36/500:   7%|▋         | 36/500 [00:10<02:09,  3.59it/s]
Lvl 0/0, step 36/500:   7%|▋         | 37/500 [00:10<02:08,  3.60it/s]
Lvl 0/0, step 37/500:   7%|▋         | 37/500 [00:10<02:08,  3.60it/s]
Lvl 0/0, step 37/500:   8%|▊         | 38/500 [00:10<02:08,  3.60it/s]
Lvl 0/0, step 38/500:   8%|▊         | 38/500 [00:10<02:08,  3.60it/s]
Lvl 0/0, step 38/500:   8%|▊         | 39/500 [00:10<02:10,  3.54it/s]
Lvl 0/0, step 39/500:   8%|▊         | 39/500 [00:10<02:10,  3.54it/s]
Lvl 0/0, step 39/500:   8%|▊         | 40/500 [00:11<02:10,  3.52it/s]
Lvl 0/0, step 40/500:   8%|▊         | 40/500 [00:11<02:10,  3.52it/s]
Lvl 0/0, step 40/500:   8%|▊         | 41/500 [00:11<02:09,  3.56it/s]
Lvl 0/0, step 41/500:   8%|▊         | 41/500 [00:11<02:09,  3.56it/s]
Lvl 0/0, step 41/500:   8%|▊         | 42/500 [00:11<02:08,  3.57it/s]
Lvl 0/0, step 42/500:   8%|▊         | 42/500 [00:11<02:08,  3.57it/s]
Lvl 0/0, step 42/500:   9%|▊         | 43/500 [00:12<02:09,  3.52it/s]
Lvl 0/0, step 43/500:   9%|▊         | 43/500 [00:12<02:09,  3.52it/s]
Lvl 0/0, step 43/500:   9%|▉         | 44/500 [00:12<02:08,  3.56it/s]
Lvl 0/0, step 44/500:   9%|▉         | 44/500 [00:12<02:08,  3.56it/s]
Lvl 0/0, step 44/500:   9%|▉         | 45/500 [00:12<02:07,  3.58it/s]
Lvl 0/0, step 45/500:   9%|▉         | 45/500 [00:12<02:07,  3.58it/s]
Lvl 0/0, step 45/500:   9%|▉         | 46/500 [00:12<02:06,  3.59it/s]
Lvl 0/0, step 46/500:   9%|▉         | 46/500 [00:12<02:06,  3.59it/s]
Lvl 0/0, step 46/500:   9%|▉         | 47/500 [00:13<02:06,  3.59it/s]
Lvl 0/0, step 47/500:   9%|▉         | 47/500 [00:13<02:06,  3.59it/s]
Lvl 0/0, step 47/500:  10%|▉         | 48/500 [00:13<02:05,  3.60it/s]
Lvl 0/0, step 48/500:  10%|▉         | 48/500 [00:13<02:05,  3.60it/s]
Lvl 0/0, step 48/500:  10%|▉         | 49/500 [00:13<02:05,  3.60it/s]
Lvl 0/0, step 49/500:  10%|▉         | 49/500 [00:13<02:05,  3.60it/s]
Lvl 0/0, step 49/500:  10%|█         | 50/500 [00:14<02:04,  3.60it/s]
Lvl 0/0, step 50/500:  10%|█         | 50/500 [00:14<02:04,  3.60it/s]
Lvl 0/0, step 50/500:  10%|█         | 51/500 [00:14<02:04,  3.60it/s]
Lvl 0/0, step 51/500:  10%|█         | 51/500 [00:14<02:04,  3.60it/s]
Lvl 0/0, step 51/500:  10%|█         | 52/500 [00:14<02:04,  3.59it/s]
Lvl 0/0, step 52/500:  10%|█         | 52/500 [00:14<02:04,  3.59it/s]
Lvl 0/0, step 52/500:  11%|█         | 53/500 [00:14<02:04,  3.60it/s]
Lvl 0/0, step 53/500:  11%|█         | 53/500 [00:14<02:04,  3.60it/s]
Lvl 0/0, step 53/500:  11%|█         | 54/500 [00:15<02:03,  3.62it/s]
Lvl 0/0, step 54/500:  11%|█         | 54/500 [00:15<02:03,  3.62it/s]
Lvl 0/0, step 54/500:  11%|█         | 55/500 [00:15<02:02,  3.63it/s]
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This output was created using a different version of the model, ariel415el/gpdm:e07faab3.

Examples

View more examples

Run time and cost

This model costs approximately $0.053 to run on Replicate, or 18 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 4 minutes. The predict time for this model varies significantly based on the inputs.

Readme

GPDM live demo

A live demo for GPDM, an algorithm introduced in “Generating Natural Images with Direct Patch Distribution Matching”

This demo allows performing the “Reshuffle”, “Retarget” or “Style-transfer” tasks on any uploaded image online with a considerable amount of control over the GPDM algorithm hyper-parameters

Cite

@article{elnekave2022generating, title={Generating natural images with direct Patch Distributions Matching}, author={Elnekave, Ariel and Weiss, Yair}, journal={arXiv preprint arXiv:2203.11862}, year={2022} }