nightmareai/arf-svox2:7bb81e40 – Run with an API on Replicate

Version

You're looking at a specific version of this model. Jump to the model overview.

nightmareai /arf-svox2:7bb81e40

Playground API

Input

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

npx create-replicate --model=nightmareai/arf-svox2

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 nightmareai/arf-svox2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "nightmareai/arf-svox2:7bb81e4046b96358001e6275e36eee3e7228fe9f1a0e1515a5f553cbbcad07e7",
  {
    input: {
      image: "https://replicate.delivery/mgxm/bb0cc2e1-7ef1-4633-9b0b-a5064994d801/9e661f3d-e3d1-4995-bbab-b9ac48b367fa.png",
      scene: "orchids",
      num_epoches: 5
    }
  }
);

// 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 nightmareai/arf-svox2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "nightmareai/arf-svox2:7bb81e4046b96358001e6275e36eee3e7228fe9f1a0e1515a5f553cbbcad07e7",
    input={
        "image": "https://replicate.delivery/mgxm/bb0cc2e1-7ef1-4633-9b0b-a5064994d801/9e661f3d-e3d1-4995-bbab-b9ac48b367fa.png",
        "scene": "orchids",
        "num_epoches": 5
    }
)
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 nightmareai/arf-svox2 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": "nightmareai/arf-svox2:7bb81e4046b96358001e6275e36eee3e7228fe9f1a0e1515a5f553cbbcad07e7",
    "input": {
      "image": "https://replicate.delivery/mgxm/bb0cc2e1-7ef1-4633-9b0b-a5064994d801/9e661f3d-e3d1-4995-bbab-b9ac48b367fa.png",
      "scene": "orchids",
      "num_epoches": 5
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2022-06-24T03:07:15.329275Z",
  "created_at": "2022-06-24T02:46:34.459972Z",
  "data_removed": false,
  "error": null,
  "id": "xdqpa66u6ve6bcknypme4on7me",
  "input": {
    "image": "https://replicate.delivery/mgxm/bb0cc2e1-7ef1-4633-9b0b-a5064994d801/9e661f3d-e3d1-4995-bbab-b9ac48b367fa.png",
    "scene": "orchids",
    "num_epoches": "5"
  },
  "logs": "Downloading checkpoint for orchids\nDetected LLFF dataset\nUsing pre-scaled images from /root/.cache/arf/data/llff/orchids/images_4\nLoaded LLFF data /root/.cache/arf/data/llff/orchids 14.48833973008133 124.51196423745613\nrecentered (3, 4)\n[1.4028997 8.9214735]\nOverriding offset 250-> 250\ndmin = 1.402900, dmax = 8.921474, invz = 0, offset = 250\n\n  0%|          | 0/21 [00:00<?, ?it/s]\n 10%|▉         | 2/21 [00:00<00:01, 15.48it/s]\n 24%|██▍       | 5/21 [00:00<00:00, 21.27it/s]\n 38%|███▊      | 8/21 [00:00<00:00, 23.77it/s]\n 52%|█████▏    | 11/21 [00:00<00:00, 24.88it/s]\n 67%|██████▋   | 14/21 [00:00<00:00, 25.77it/s]\n 81%|████████  | 17/21 [00:00<00:00, 26.16it/s]\n 95%|█████████▌| 20/21 [00:00<00:00, 26.31it/s]\n100%|██████████| 21/21 [00:00<00:00, 25.02it/s]\nz_bounds from LLFF: [1.4028997421264648, 8.921473503112793] (not used)\nscene_radius [1.496031746031746, 1.6613756613756614, 1.0]\n Generating rays, scaling factor 1\nic| \"Loaded ckpt: \": 'Loaded ckpt: '\n    args.init_ckpt: '/root/.cache/arf/ckpt_svox2/llff/orchids/ckpt.npz'\nic| grid.basis_dim: 1\nopt_style.py:508: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n  style_img = imageio.imread(args.style).astype(np.float32) / 255.0\nRender options RenderOptions(backend='cuvol', background_brightness=0.5, step_size=0.5, sigma_thresh=1e-08, stop_thresh=1e-07, last_sample_opaque=False, near_clip=0.0, use_spheric_clip=False, random_sigma_std=0.0, random_sigma_std_background=0.0)\nic| \"Style image: \": 'Style image: '\n    args.style: '/tmp/tmpbn7uaxrx9e661f3d-e3d1-4995-bbab-b9ac48b367fa.png'\n    style_img.shape: torch.Size([504, 336, 3])\nDownloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n\n  0%|          | 0.00/528M [00:00<?, ?B/s]\n  2%|▏         | 9.88M/528M [00:00<00:05, 104MB/s]\n  7%|▋         | 34.8M/528M [00:00<00:02, 197MB/s]\n 11%|█▏        | 60.1M/528M [00:00<00:02, 228MB/s]\n 16%|█▋        | 86.2M/528M [00:00<00:01, 246MB/s]\n 21%|██        | 110M/528M [00:00<00:02, 197MB/s]\n 25%|██▍       | 130M/528M [00:00<00:02, 198MB/s]\n 28%|██▊       | 149M/528M [00:00<00:02, 198MB/s]\n 33%|███▎      | 174M/528M [00:00<00:01, 215MB/s]\n 38%|███▊      | 199M/528M [00:00<00:01, 230MB/s]\n 42%|████▏     | 221M/528M [00:01<00:01, 231MB/s]\n 46%|████▌     | 244M/528M [00:01<00:01, 224MB/s]\n 50%|█████     | 266M/528M [00:01<00:01, 226MB/s]\n 55%|█████▍    | 289M/528M [00:01<00:01, 230MB/s]\n 59%|█████▉    | 313M/528M [00:01<00:00, 237MB/s]\n 64%|██████▍   | 337M/528M [00:01<00:00, 243MB/s]\n 68%|██████▊   | 361M/528M [00:01<00:00, 230MB/s]\n 73%|███████▎  | 383M/528M [00:01<00:00, 229MB/s]\n 77%|███████▋  | 405M/528M [00:01<00:00, 229MB/s]\n 81%|████████  | 427M/528M [00:02<00:00, 212MB/s]\n 85%|████████▍ | 448M/528M [00:02<00:00, 215MB/s]\n 89%|████████▉ | 470M/528M [00:02<00:00, 218MB/s]\n 93%|█████████▎| 491M/528M [00:02<00:00, 206MB/s]\n 97%|█████████▋| 513M/528M [00:02<00:00, 212MB/s]\n100%|██████████| 528M/528M [00:02<00:00, 216MB/s]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 0\n    epoch_size: 16003008\n    batches_per_epoch: 3201\n    batch_size: 5000\n    optim_type: 'ray'\n\n  0%|          | 0/3201 [00:00<?, ?it/s]\n  0%|          | 4/3201 [00:00<01:33, 34.20it/s]\n  0%|          | 9/3201 [00:00<01:15, 42.48it/s]\n  0%|          | 16/3201 [00:00<01:00, 52.80it/s]\n19 12.6900 :   0%|          | 16/3201 [00:00<01:00, 52.80it/s]\n19 12.6900 :   1%|          | 23/3201 [00:00<00:54, 58.80it/s]\n19 12.6900 :   1%|          | 31/3201 [00:00<00:49, 63.69it/s]\n19 12.6900 :   1%|          | 38/3201 [00:00<00:48, 65.51it/s]\n39 12.9441 :   1%|          | 38/3201 [00:00<00:48, 65.51it/s]\n39 12.9441 :   1%|▏         | 45/3201 [00:00<00:47, 66.02it/s]\n39 12.9441 :   2%|▏         | 52/3201 [00:00<00:48, 64.80it/s]\n39 12.9441 :   2%|▏         | 59/3201 [00:00<00:49, 63.29it/s]\n59 12.9714 :   2%|▏         | 59/3201 [00:00<00:49, 63.29it/s]\n59 12.9714 :   2%|▏         | 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21.4729 :  59%|█████▉    | 1899/3201 [00:23<00:16, 80.95it/s]\n1899 21.4729 :  60%|█████▉    | 1908/3201 [00:23<00:16, 80.74it/s]\n1899 21.4729 :  60%|█████▉    | 1917/3201 [00:23<00:15, 81.07it/s]\n1919 22.7881 :  60%|█████▉    | 1917/3201 [00:23<00:15, 81.07it/s]\n1919 22.7881 :  60%|██████    | 1926/3201 [00:23<00:15, 81.58it/s]\n1919 22.7881 :  60%|██████    | 1935/3201 [00:23<00:15, 83.05it/s]\n1939 22.9330 :  60%|██████    | 1935/3201 [00:23<00:15, 83.05it/s]\n1939 22.9330 :  61%|██████    | 1944/3201 [00:23<00:15, 83.72it/s]\n1939 22.9330 :  61%|██████    | 1953/3201 [00:24<00:14, 83.59it/s]\n1959 24.5427 :  61%|██████    | 1953/3201 [00:24<00:14, 83.59it/s]\n1959 24.5427 :  61%|██████▏   | 1962/3201 [00:24<00:15, 82.03it/s]\n1959 24.5427 :  62%|██████▏   | 1971/3201 [00:24<00:15, 79.69it/s]\n1959 24.5427 :  62%|██████▏   | 1979/3201 [00:24<00:15, 78.53it/s]\n1979 19.4006 :  62%|██████▏   | 1979/3201 [00:24<00:15, 78.53it/s]\n1979 19.4006 :  62%|██████▏   | 1987/3201 [00:24<00:15, 76.46it/s]\n1979 19.4006 :  62%|██████▏   | 1995/3201 [00:24<00:16, 74.50it/s]\n1999 19.7991 :  62%|██████▏   | 1995/3201 [00:24<00:16, 74.50it/s]\n1999 19.7991 :  63%|██████▎   | 2003/3201 [00:24<00:16, 73.22it/s]\n1999 19.7991 :  63%|██████▎   | 2011/3201 [00:24<00:16, 72.24it/s]\n1999 19.7991 :  63%|██████▎   | 2019/3201 [00:24<00:16, 70.90it/s]\n2019 20.1750 :  63%|██████▎   | 2019/3201 [00:25<00:16, 70.90it/s]\n2019 20.1750 :  63%|██████▎   | 2027/3201 [00:25<00:17, 68.72it/s]\n2019 20.1750 :  64%|██████▎   | 2034/3201 [00:25<00:17, 66.27it/s]\n2039 18.9449 :  64%|██████▎   | 2034/3201 [00:25<00:17, 66.27it/s]\n2039 18.9449 :  64%|██████▍   | 2041/3201 [00:25<00:17, 64.90it/s]\n2039 18.9449 :  64%|██████▍   | 2048/3201 [00:25<00:17, 65.12it/s]\n2039 18.9449 :  64%|██████▍   | 2055/3201 [00:25<00:17, 65.52it/s]\n2059 21.9417 :  64%|██████▍   | 2055/3201 [00:25<00:17, 65.52it/s]\n2059 21.9417 :  64%|██████▍   | 2062/3201 [00:25<00:17, 65.87it/s]\n2059 21.9417 :  65%|██████▍   | 2069/3201 [00:25<00:17, 65.88it/s]\n2059 21.9417 :  65%|██████▍   | 2076/3201 [00:25<00:17, 66.07it/s]\n2079 21.2382 :  65%|██████▍   | 2076/3201 [00:25<00:17, 66.07it/s]\n2079 21.2382 :  65%|██████▌   | 2083/3201 [00:25<00:16, 67.15it/s]\n2079 21.2382 :  65%|██████▌   | 2090/3201 [00:26<00:16, 67.61it/s]\n2079 21.2382 :  66%|██████▌   | 2097/3201 [00:26<00:16, 67.61it/s]\n2099 21.5167 :  66%|██████▌   | 2097/3201 [00:26<00:16, 67.61it/s]\n2099 21.5167 :  66%|██████▌   | 2104/3201 [00:26<00:16, 67.63it/s]\n2099 21.5167 :  66%|██████▌   | 2111/3201 [00:26<00:16, 67.08it/s]\n2099 21.5167 :  66%|██████▌   | 2118/3201 [00:26<00:16, 65.42it/s]\n2119 20.6946 :  66%|██████▌   | 2118/3201 [00:26<00:16, 65.42it/s]\n2119 20.6946 :  66%|██████▋   | 2125/3201 [00:26<00:16, 63.64it/s]\n2119 20.6946 :  67%|██████▋   | 2132/3201 [00:26<00:17, 62.05it/s]\n2139 11.7181 :  67%|██████▋   | 2132/3201 [00:26<00:17, 62.05it/s]\n2139 11.7181 :  67%|██████▋   | 2141/3201 [00:26<00:15, 68.81it/s]\n2139 11.7181 :  67%|██████▋   | 2152/3201 [00:26<00:13, 78.55it/s]\n2159 19.8938 :  67%|██████▋   | 2152/3201 [00:27<00:13, 78.55it/s]\n2159 19.8938 :  68%|██████▊   | 2163/3201 [00:27<00:12, 85.23it/s]\n2159 19.8938 :  68%|██████▊   | 2174/3201 [00:27<00:11, 89.67it/s]\n2179 21.6050 :  68%|██████▊   | 2174/3201 [00:27<00:11, 89.67it/s]\n2179 21.6050 :  68%|██████▊   | 2184/3201 [00:27<00:11, 90.98it/s]\n2179 21.6050 :  69%|██████▊   | 2194/3201 [00:27<00:11, 90.08it/s]\n2199 19.0323 :  69%|██████▊   | 2194/3201 [00:27<00:11, 90.08it/s]\n2199 19.0323 :  69%|██████▉   | 2204/3201 [00:27<00:11, 89.44it/s]\n2199 19.0323 :  69%|██████▉   | 2214/3201 [00:27<00:10, 90.29it/s]\n2219 22.3982 :  69%|██████▉   | 2214/3201 [00:27<00:10, 90.29it/s]\n2219 22.3982 :  69%|██████▉   | 2224/3201 [00:27<00:10, 91.09it/s]\n2219 22.3982 :  70%|██████▉   | 2234/3201 [00:27<00:10, 91.82it/s]\n2239 21.5829 :  70%|██████▉   | 2234/3201 [00:27<00:10, 91.82it/s]\n2239 21.5829 :  70%|███████   | 2244/3201 [00:27<00:10, 92.86it/s]\n2239 21.5829 :  70%|███████   | 2254/3201 [00:28<00:10, 93.50it/s]\n2259 20.9133 :  70%|███████   | 2254/3201 [00:28<00:10, 93.50it/s]\n2259 20.9133 :  71%|███████   | 2264/3201 [00:28<00:10, 92.46it/s]\n2259 20.9133 :  71%|███████   | 2274/3201 [00:28<00:10, 91.30it/s]\n2279 23.0865 :  71%|███████   | 2274/3201 [00:28<00:10, 91.30it/s]\n2279 23.0865 :  71%|███████▏  | 2284/3201 [00:28<00:10, 90.10it/s]\n2279 23.0865 :  72%|███████▏  | 2294/3201 [00:28<00:10, 89.13it/s]\n2299 18.5271 :  72%|███████▏  | 2294/3201 [00:28<00:10, 89.13it/s]\n2299 18.5271 :  72%|███████▏  | 2304/3201 [00:28<00:09, 90.03it/s]\n2299 18.5271 :  72%|███████▏  | 2314/3201 [00:28<00:09, 90.43it/s]\n2319 21.3224 :  72%|███████▏  | 2314/3201 [00:28<00:09, 90.43it/s]\n2319 21.3224 :  73%|███████▎  | 2324/3201 [00:28<00:09, 89.73it/s]\n2319 21.3224 :  73%|███████▎  | 2333/3201 [00:28<00:09, 88.10it/s]\n2339 21.8212 :  73%|███████▎  | 2333/3201 [00:29<00:09, 88.10it/s]\n2339 21.8212 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81.58it/s]\n2439 13.0168 :  76%|███████▌  | 2432/3201 [00:30<00:09, 81.58it/s]\n2439 13.0168 :  76%|███████▋  | 2441/3201 [00:30<00:09, 79.85it/s]\n2439 13.0168 :  77%|███████▋  | 2449/3201 [00:30<00:09, 77.30it/s]\n2439 13.0168 :  77%|███████▋  | 2457/3201 [00:30<00:09, 75.36it/s]\n2459 17.9014 :  77%|███████▋  | 2457/3201 [00:30<00:09, 75.36it/s]\n2459 17.9014 :  77%|███████▋  | 2465/3201 [00:30<00:09, 74.12it/s]\n2459 17.9014 :  77%|███████▋  | 2473/3201 [00:30<00:09, 72.90it/s]\n2479 20.7776 :  77%|███████▋  | 2473/3201 [00:30<00:09, 72.90it/s]\n2479 20.7776 :  78%|███████▊  | 2481/3201 [00:30<00:10, 70.77it/s]\n2479 20.7776 :  78%|███████▊  | 2489/3201 [00:30<00:10, 68.71it/s]\n2479 20.7776 :  78%|███████▊  | 2496/3201 [00:31<00:10, 66.07it/s]\n2499 21.8543 :  78%|███████▊  | 2496/3201 [00:31<00:10, 66.07it/s]\n2499 21.8543 :  78%|███████▊  | 2503/3201 [00:31<00:10, 63.69it/s]\n2499 21.8543 :  78%|███████▊  | 2510/3201 [00:31<00:11, 61.89it/s]\n2499 21.8543 :  79%|███████▊  | 2517/3201 [00:31<00:11, 60.73it/s]\n2519 24.0118 :  79%|███████▊  | 2517/3201 [00:31<00:11, 60.73it/s]\n2519 24.0118 :  79%|███████▉  | 2524/3201 [00:31<00:11, 60.11it/s]\n2519 24.0118 :  79%|███████▉  | 2531/3201 [00:31<00:10, 61.00it/s]\n2519 24.0118 :  79%|███████▉  | 2538/3201 [00:31<00:10, 62.30it/s]\n2539 20.4381 :  79%|███████▉  | 2538/3201 [00:31<00:10, 62.30it/s]\n2539 20.4381 :  80%|███████▉  | 2545/3201 [00:31<00:10, 62.59it/s]\n2539 20.4381 :  80%|███████▉  | 2552/3201 [00:31<00:10, 62.99it/s]\n2539 20.4381 :  80%|███████▉  | 2559/3201 [00:32<00:10, 63.28it/s]\n2559 23.9635 :  80%|███████▉  | 2559/3201 [00:32<00:10, 63.28it/s]\n2559 23.9635 :  80%|████████  | 2566/3201 [00:32<00:10, 62.71it/s]\n2559 23.9635 :  80%|████████  | 2573/3201 [00:32<00:09, 62.97it/s]\n2579 21.3666 :  80%|████████  | 2573/3201 [00:32<00:09, 62.97it/s]\n2579 21.3666 :  81%|████████  | 2580/3201 [00:32<00:09, 63.30it/s]\n2579 21.3666 :  81%|████████  | 2587/3201 [00:32<00:09, 63.28it/s]\n2579 21.3666 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50.11it/s]\n2659 21.2192 :  83%|████████▎ | 2661/3201 [00:33<00:10, 49.65it/s]\n2659 21.2192 :  83%|████████▎ | 2666/3201 [00:34<00:10, 49.47it/s]\n2659 21.2192 :  83%|████████▎ | 2671/3201 [00:34<00:10, 49.04it/s]\n2659 21.2192 :  84%|████████▎ | 2677/3201 [00:34<00:10, 49.54it/s]\n2679 21.2361 :  84%|████████▎ | 2677/3201 [00:34<00:10, 49.54it/s]\n2679 21.2361 :  84%|████████▍ | 2683/3201 [00:34<00:10, 49.98it/s]\n2679 21.2361 :  84%|████████▍ | 2689/3201 [00:34<00:10, 50.31it/s]\n2679 21.2361 :  84%|████████▍ | 2695/3201 [00:34<00:10, 49.88it/s]\n2699 22.3840 :  84%|████████▍ | 2695/3201 [00:34<00:10, 49.88it/s]\n2699 22.3840 :  84%|████████▍ | 2700/3201 [00:34<00:10, 49.89it/s]\n2699 22.3840 :  85%|████████▍ | 2705/3201 [00:34<00:09, 49.71it/s]\n2699 22.3840 :  85%|████████▍ | 2710/3201 [00:34<00:09, 49.58it/s]\n2699 22.3840 :  85%|████████▍ | 2715/3201 [00:35<00:09, 49.13it/s]\n2719 22.4492 :  85%|████████▍ | 2715/3201 [00:35<00:09, 49.13it/s]\n2719 22.4492 :  85%|████████▌ | 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57.70it/s]\n2859 21.3169 :  89%|████████▉ | 2859/3201 [00:37<00:05, 57.70it/s]\n2859 21.3169 :  90%|████████▉ | 2865/3201 [00:37<00:05, 57.61it/s]\n2859 21.3169 :  90%|████████▉ | 2871/3201 [00:37<00:05, 57.61it/s]\n2859 21.3169 :  90%|████████▉ | 2877/3201 [00:37<00:05, 57.57it/s]\n2879 22.3423 :  90%|████████▉ | 2877/3201 [00:38<00:05, 57.57it/s]\n2879 22.3423 :  90%|█████████ | 2883/3201 [00:38<00:05, 57.44it/s]\n2879 22.3423 :  90%|█████████ | 2889/3201 [00:38<00:05, 57.74it/s]\n2879 22.3423 :  90%|█████████ | 2895/3201 [00:38<00:05, 57.80it/s]\n2899 17.5927 :  90%|█████████ | 2895/3201 [00:38<00:05, 57.80it/s]\n2899 17.5927 :  91%|█████████ | 2902/3201 [00:38<00:04, 59.84it/s]\n2899 17.5927 :  91%|█████████ | 2909/3201 [00:38<00:04, 62.69it/s]\n2899 17.5927 :  91%|█████████ | 2916/3201 [00:38<00:04, 64.49it/s]\n2919 20.6155 :  91%|█████████ | 2916/3201 [00:38<00:04, 64.49it/s]\n2919 20.6155 :  91%|█████████▏| 2924/3201 [00:38<00:04, 66.42it/s]\n2919 20.6155 :  92%|█████████▏| 2931/3201 [00:38<00:04, 67.27it/s]\n2919 20.6155 :  92%|█████████▏| 2938/3201 [00:38<00:03, 66.81it/s]\n2939 22.1781 :  92%|█████████▏| 2938/3201 [00:38<00:03, 66.81it/s]\n2939 22.1781 :  92%|█████████▏| 2945/3201 [00:39<00:03, 65.87it/s]\n2939 22.1781 :  92%|█████████▏| 2952/3201 [00:39<00:03, 64.44it/s]\n2939 22.1781 :  92%|█████████▏| 2959/3201 [00:39<00:03, 63.31it/s]\n2959 22.3789 :  92%|█████████▏| 2959/3201 [00:39<00:03, 63.31it/s]\n2959 22.3789 :  93%|█████████▎| 2966/3201 [00:39<00:03, 63.56it/s]\n2959 22.3789 :  93%|█████████▎| 2973/3201 [00:39<00:03, 63.27it/s]\n2979 24.0965 :  93%|█████████▎| 2973/3201 [00:39<00:03, 63.27it/s]\n2979 24.0965 :  93%|█████████▎| 2980/3201 [00:39<00:03, 63.50it/s]\n2979 24.0965 :  93%|█████████▎| 2987/3201 [00:39<00:03, 64.65it/s]\n2979 24.0965 :  94%|█████████▎| 2994/3201 [00:39<00:03, 65.92it/s]\n2999 26.3606 :  94%|█████████▎| 2994/3201 [00:39<00:03, 65.92it/s]\n2999 26.3606 :  94%|█████████▍| 3001/3201 [00:39<00:03, 66.28it/s]\n2999 26.3606 :  94%|█████████▍| 3008/3201 [00:40<00:02, 66.53it/s]\n2999 26.3606 :  94%|█████████▍| 3015/3201 [00:40<00:02, 67.13it/s]\n3019 24.8046 :  94%|█████████▍| 3015/3201 [00:40<00:02, 67.13it/s]\n3019 24.8046 :  94%|█████████▍| 3022/3201 [00:40<00:02, 67.01it/s]\n3019 24.8046 :  95%|█████████▍| 3029/3201 [00:40<00:02, 66.22it/s]\n3019 24.8046 :  95%|█████████▍| 3036/3201 [00:40<00:02, 66.45it/s]\n3039 23.1518 :  95%|█████████▍| 3036/3201 [00:40<00:02, 66.45it/s]\n3039 23.1518 :  95%|█████████▌| 3043/3201 [00:40<00:02, 66.31it/s]\n3039 23.1518 :  95%|█████████▌| 3050/3201 [00:40<00:02, 66.96it/s]\n3039 23.1518 :  96%|█████████▌| 3058/3201 [00:40<00:02, 69.73it/s]\n3059 19.9182 :  96%|█████████▌| 3058/3201 [00:40<00:02, 69.73it/s]\n3059 19.9182 :  96%|█████████▌| 3066/3201 [00:40<00:01, 71.16it/s]\n3059 19.9182 :  96%|█████████▌| 3074/3201 [00:40<00:01, 72.29it/s]\n3079 22.5793 :  96%|█████████▌| 3074/3201 [00:41<00:01, 72.29it/s]\n3079 22.5793 :  96%|█████████▋| 3082/3201 [00:41<00:01, 72.61it/s]\n3079 22.5793 :  97%|█████████▋| 3090/3201 [00:41<00:01, 72.29it/s]\n3079 22.5793 :  97%|█████████▋| 3098/3201 [00:41<00:01, 71.25it/s]\n3099 25.0865 :  97%|█████████▋| 3098/3201 [00:41<00:01, 71.25it/s]\n3099 25.0865 :  97%|█████████▋| 3106/3201 [00:41<00:01, 69.30it/s]\n3099 25.0865 :  97%|█████████▋| 3113/3201 [00:41<00:01, 68.97it/s]\n3119 23.0459 :  97%|█████████▋| 3113/3201 [00:41<00:01, 68.97it/s]\n3119 23.0459 :  98%|█████████▊| 3121/3201 [00:41<00:01, 69.32it/s]\n3119 23.0459 :  98%|█████████▊| 3129/3201 [00:41<00:01, 69.90it/s]\n3119 23.0459 :  98%|█████████▊| 3137/3201 [00:41<00:00, 70.55it/s]\n3139 24.2085 :  98%|█████████▊| 3137/3201 [00:41<00:00, 70.55it/s]\n3139 24.2085 :  98%|█████████▊| 3145/3201 [00:41<00:00, 70.96it/s]\n3139 24.2085 :  99%|█████████▊| 3153/3201 [00:42<00:00, 71.58it/s]\n3159 24.1763 :  99%|█████████▊| 3153/3201 [00:42<00:00, 71.58it/s]\n3159 24.1763 :  99%|█████████▉| 3161/3201 [00:42<00:00, 71.90it/s]\n3159 24.1763 :  99%|█████████▉| 3169/3201 [00:42<00:00, 72.40it/s]\n3159 24.1763 :  99%|█████████▉| 3177/3201 [00:42<00:00, 71.88it/s]\n3179 23.6437 :  99%|█████████▉| 3177/3201 [00:42<00:00, 71.88it/s]\n3179 23.6437 : 100%|█████████▉| 3185/3201 [00:42<00:00, 71.33it/s]\n3179 23.6437 : 100%|█████████▉| 3193/3201 [00:42<00:00, 71.40it/s]\n3199 22.3849 : 100%|█████████▉| 3193/3201 [00:42<00:00, 71.40it/s]\n3199 22.3849 : 100%|██████████| 3201/3201 [00:42<00:00, 71.97it/s]\n3199 22.3849 : 100%|██████████| 3201/3201 [00:42<00:00, 74.86it/s]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 1\n    epoch_size: 16003008\n    batches_per_epoch: 3201\n    batch_size: 5000\n    optim_type: 'ray'\n\n  0%|          | 0/3201 [00:00<?, ?it/s]\n  0%|          | 8/3201 [00:00<00:43, 72.64it/s]\n  0%|          | 16/3201 [00:00<00:45, 70.02it/s]\n3220 19.6978 :   0%|          | 16/3201 [00:00<00:45, 70.02it/s]\n3220 19.6978 :   1%|          | 24/3201 [00:00<00:46, 68.67it/s]\n3220 19.6978 :   1%|          | 31/3201 [00:00<00:46, 68.02it/s]\n3220 19.6978 :   1%|          | 38/3201 [00:00<00:46, 67.69it/s]\n3240 19.8323 :   1%|          | 38/3201 [00:00<00:46, 67.69it/s]\n3240 19.8323 :   1%|▏         | 45/3201 [00:00<00:47, 66.59it/s]\n3240 19.8323 :   2%|▏         | 52/3201 [00:00<00:49, 63.78it/s]\n3240 19.8323 :   2%|▏         | 59/3201 [00:00<00:51, 60.66it/s]\n3260 21.4656 :   2%|▏         | 59/3201 [00:00<00:51, 60.66it/s]\n3260 21.4656 :   2%|▏         | 66/3201 [00:01<00:53, 58.36it/s]\n3260 21.4656 :   2%|▏         | 72/3201 [00:01<00:54, 57.87it/s]\n3260 21.4656 :   2%|▏         | 78/3201 [00:01<00:53, 58.37it/s]\n3280 21.8562 :   2%|▏         | 78/3201 [00:01<00:53, 58.37it/s]\n3280 21.8562 :   3%|▎         | 85/3201 [00:01<00:52, 59.35it/s]\n3280 21.8562 :   3%|▎         | 92/3201 [00:01<00:51, 59.92it/s]\n3280 21.8562 :   3%|▎         | 99/3201 [00:01<00:51, 60.67it/s]\n3300 22.3756 :   3%|▎         | 99/3201 [00:01<00:51, 60.67it/s]\n3300 22.3756 :   3%|▎         | 106/3201 [00:01<00:49, 62.18it/s]\n3300 22.3756 :   4%|▎         | 113/3201 [00:01<00:48, 64.28it/s]\n3320 21.8797 :   4%|▎         | 113/3201 [00:01<00:48, 64.28it/s]\n3320 21.8797 :   4%|▎         | 120/3201 [00:01<00:47, 64.64it/s]\n3320 21.8797 :   4%|▍         | 127/3201 [00:02<00:48, 63.72it/s]\n3320 21.8797 :   4%|▍         | 134/3201 [00:02<00:49, 62.41it/s]\n3340 21.1491 :   4%|▍         | 134/3201 [00:02<00:49, 62.41it/s]\n3340 21.1491 :   4%|▍         | 141/3201 [00:02<00:50, 60.94it/s]\n3340 21.1491 :   5%|▍         | 148/3201 [00:02<00:49, 61.06it/s]\n3340 21.1491 :   5%|▍         | 155/3201 [00:02<00:49, 62.03it/s]\n3360 23.0528 :   5%|▍         | 155/3201 [00:02<00:49, 62.03it/s]\n3360 23.0528 :   5%|▌         | 163/3201 [00:02<00:47, 64.61it/s]\n3360 23.0528 :   5%|▌         | 171/3201 [00:02<00:45, 66.90it/s]\n3360 23.0528 :   6%|▌         | 179/3201 [00:02<00:43, 68.91it/s]\n3380 24.5076 :   6%|▌         | 179/3201 [00:02<00:43, 68.91it/s]\n3380 24.5076 :   6%|▌         | 187/3201 [00:02<00:43, 69.73it/s]\n3380 24.5076 :   6%|▌         | 194/3201 [00:03<00:43, 68.98it/s]\n3400 23.9446 :   6%|▌         | 194/3201 [00:03<00:43, 68.98it/s]\n3400 23.9446 :   6%|▋         | 201/3201 [00:03<00:44, 67.58it/s]\n3400 23.9446 :   6%|▋         | 208/3201 [00:03<00:45, 65.21it/s]\n3400 23.9446 :   7%|▋         | 215/3201 [00:03<00:46, 63.92it/s]\n3420 24.2935 :   7%|▋         | 215/3201 [00:03<00:46, 63.92it/s]\n3420 24.2935 :   7%|▋         | 222/3201 [00:03<00:47, 63.21it/s]\n3420 24.2935 :   7%|▋         | 229/3201 [00:03<00:47, 62.95it/s]\n3420 24.2935 :   7%|▋         | 236/3201 [00:03<00:46, 63.85it/s]\n3440 25.6467 :   7%|▋         | 236/3201 [00:03<00:46, 63.85it/s]\n3440 25.6467 :   8%|▊         | 243/3201 [00:03<00:45, 64.84it/s]\n3440 25.6467 :   8%|▊         | 250/3201 [00:03<00:45, 65.46it/s]\n3440 25.6467 :   8%|▊         | 257/3201 [00:04<00:44, 66.32it/s]\n3460 24.6063 :   8%|▊         | 257/3201 [00:04<00:44, 66.32it/s]\n3460 24.6063 :   8%|▊       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Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n  warnings.warn(\n/root/.pyenv/versions/3.8.8/lib/python3.8/site-packages/torch/nn/functional.py:3657: UserWarning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and now uses scale_factor directly, instead of relying on the computed output size. If you wish to restore the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.\n  warnings.warn(\n/root/.pyenv/versions/3.8.8/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n\n  5%|▍         | 1/21 [00:02<00:50,  2.53s/it]\n 10%|▉         | 2/21 [00:05<00:47,  2.50s/it]\n 14%|█▍        | 3/21 [00:06<00:36,  2.02s/it]\n 19%|█▉        | 4/21 [00:09<00:38,  2.25s/it]\n 24%|██▍       | 5/21 [00:11<00:37,  2.33s/it]\n 29%|██▊       | 6/21 [00:13<00:32,  2.18s/it]\n 33%|███▎      | 7/21 [00:15<00:31,  2.29s/it]\n 38%|███▊      | 8/21 [00:18<00:30,  2.34s/it]\n 43%|████▎     | 9/21 [00:20<00:28,  2.36s/it]\n 48%|████▊     | 10/21 [00:23<00:26,  2.44s/it]\n 52%|█████▏    | 11/21 [00:25<00:23,  2.37s/it]\n 57%|█████▋    | 12/21 [00:28<00:21,  2.42s/it]\n 62%|██████▏   | 13/21 [00:30<00:19,  2.42s/it]\n 67%|██████▋   | 14/21 [00:33<00:18,  2.58s/it]\n 71%|███████▏  | 15/21 [00:35<00:13,  2.27s/it]\n 76%|███████▌  | 16/21 [00:36<00:10,  2.15s/it]\n 81%|████████  | 17/21 [00:40<00:10,  2.53s/it]\n 86%|████████▌ | 18/21 [00:42<00:07,  2.43s/it]\n 90%|█████████ | 19/21 [00:45<00:05,  2.73s/it]\n6421 0.3690 0.0142 0.0029 :  90%|█████████ | 19/21 [00:48<00:05,  2.73s/it]\n6421 0.3690 0.0142 0.0029 :  95%|█████████▌| 20/21 [00:48<00:02,  2.57s/it]\n6421 0.3690 0.0142 0.0029 : 100%|██████████| 21/21 [00:51<00:00,  2.68s/it]\n6421 0.3690 0.0142 0.0029 : 100%|██████████| 21/21 [00:51<00:00,  2.43s/it]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 3\n    epoch_size: 21\n    batches_per_epoch: 21\n    batch_size: 1\n    optim_type: 'image'\n\n  0%|          | 0/21 [00:00<?, ?it/s]\n  5%|▍         | 1/21 [00:02<00:45,  2.29s/it]\n 10%|▉         | 2/21 [00:04<00:47,  2.48s/it]\n 14%|█▍        | 3/21 [00:07<00:45,  2.55s/it]\n 19%|█▉        | 4/21 [00:10<00:43,  2.54s/it]\n 24%|██▍       | 5/21 [00:12<00:40,  2.51s/it]\n 29%|██▊       | 6/21 [00:14<00:33,  2.25s/it]\n 33%|███▎      | 7/21 [00:17<00:36,  2.64s/it]\n 38%|███▊      | 8/21 [00:19<00:28,  2.21s/it]\n 43%|████▎     | 9/21 [00:20<00:23,  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?it/s]\n  5%|▍         | 1/21 [00:02<00:59,  2.96s/it]\n 10%|▉         | 2/21 [00:04<00:43,  2.28s/it]\n 14%|█▍        | 3/21 [00:08<00:50,  2.82s/it]\n 19%|█▉        | 4/21 [00:09<00:38,  2.27s/it]\n 24%|██▍       | 5/21 [00:12<00:37,  2.35s/it]\n 29%|██▊       | 6/21 [00:15<00:38,  2.56s/it]\n 33%|███▎      | 7/21 [00:17<00:34,  2.45s/it]\n 38%|███▊      | 8/21 [00:19<00:31,  2.46s/it]\n 43%|████▎     | 9/21 [00:21<00:25,  2.14s/it]\n 48%|████▊     | 10/21 [00:24<00:26,  2.40s/it]\n 52%|█████▏    | 11/21 [00:26<00:22,  2.24s/it]\n 57%|█████▋    | 12/21 [00:28<00:20,  2.27s/it]\n 62%|██████▏   | 13/21 [00:31<00:19,  2.48s/it]\n 67%|██████▋   | 14/21 [00:33<00:17,  2.50s/it]\n 71%|███████▏  | 15/21 [00:36<00:14,  2.48s/it]\n 76%|███████▌  | 16/21 [00:39<00:13,  2.63s/it]\n 81%|████████  | 17/21 [00:40<00:09,  2.30s/it]\n 86%|████████▌ | 18/21 [00:43<00:07,  2.40s/it]\n 90%|█████████ | 19/21 [00:45<00:04,  2.23s/it]\n6463 0.3366 0.0199 0.0037 :  90%|█████████ | 19/21 [00:46<00:04,  2.23s/it]\n6463 0.3366 0.0199 0.0037 :  95%|█████████▌| 20/21 [00:46<00:01,  1.99s/it]\n6463 0.3366 0.0199 0.0037 : 100%|██████████| 21/21 [00:49<00:00,  2.06s/it]\n6463 0.3366 0.0199 0.0037 : 100%|██████████| 21/21 [00:49<00:00,  2.33s/it]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 5\n    epoch_size: 21\n    batches_per_epoch: 21\n    batch_size: 1\n    optim_type: 'image'\n\n  0%|          | 0/21 [00:00<?, ?it/s]\n  5%|▍         | 1/21 [00:02<00:47,  2.39s/it]\n 10%|▉         | 2/21 [00:05<00:48,  2.54s/it]\n 14%|█▍        | 3/21 [00:07<00:44,  2.45s/it]\n 19%|█▉        | 4/21 [00:10<00:42,  2.53s/it]\n 24%|██▍       | 5/21 [00:12<00:40,  2.53s/it]\n 29%|██▊       | 6/21 [00:14<00:32,  2.17s/it]\n 33%|███▎      | 7/21 [00:15<00:29,  2.08s/it]\n 38%|███▊      | 8/21 [00:18<00:28,  2.16s/it]\n 43%|████▎     | 9/21 [00:21<00:29,  2.42s/it]\n 48%|████▊     | 10/21 [00:23<00:26,  2.42s/it]\n 52%|█████▏    | 11/21 [00:25<00:22,  2.26s/it]\n 57%|█████▋    | 12/21 [00:28<00:20,  2.33s/it]\n 62%|██████▏   | 13/21 [00:29<00:16,  2.06s/it]\n 67%|██████▋   | 14/21 [00:32<00:16,  2.33s/it]\n 71%|███████▏  | 15/21 [00:34<00:12,  2.10s/it]\n 76%|███████▌  | 16/21 [00:35<00:09,  1.94s/it]\n 81%|████████  | 17/21 [00:37<00:07,  1.92s/it]\n 86%|████████▌ | 18/21 [00:40<00:06,  2.23s/it]\n 90%|█████████ | 19/21 [00:41<00:04,  2.03s/it]\n6484 0.3352 0.0212 0.0028 :  90%|█████████ | 19/21 [00:44<00:04,  2.03s/it]\n6484 0.3352 0.0212 0.0028 :  95%|█████████▌| 20/21 [00:44<00:02,  2.22s/it]\n6484 0.3352 0.0212 0.0028 : 100%|██████████| 21/21 [00:46<00:00,  1.98s/it]\n6484 0.3352 0.0212 0.0028 : 100%|██████████| 21/21 [00:46<00:00,  2.19s/it]\nic| \"Training epoch: \": 'Training epoch: '\n    epoch_id: 6\n    epoch_size: 21\n    batches_per_epoch: 21\n    batch_size: 1\n    optim_type: 'image'\n\n  0%|          | 0/21 [00:00<?, ?it/s]\n  5%|▍         | 1/21 [00:01<00:37,  1.86s/it]\n 10%|▉         | 2/21 [00:04<00:39,  2.08s/it]\n 14%|█▍        | 3/21 [00:06<00:39,  2.20s/it]\n 19%|█▉        | 4/21 [00:09<00:45,  2.70s/it]\n 24%|██▍       | 5/21 [00:12<00:42,  2.64s/it]\n 29%|██▊       | 6/21 [00:14<00:39,  2.61s/it]\n 33%|███▎      | 7/21 [00:17<00:38,  2.72s/it]\n 38%|███▊      | 8/21 [00:19<00:30,  2.32s/it]\n 43%|████▎     | 9/21 [00:20<00:24,  2.05s/it]\n 48%|████▊     | 10/21 [00:22<00:21,  1.95s/it]\n 52%|█████▏    | 11/21 [00:25<00:20,  2.10s/it]\n 57%|█████▋    | 12/21 [00:27<00:20,  2.26s/it]\n 62%|██████▏   | 13/21 [00:29<00:17,  2.25s/it]\n 67%|██████▋   | 14/21 [00:32<00:16,  2.31s/it]\n 71%|███████▏  | 15/21 [00:34<00:14,  2.38s/it]\n 76%|███████▌  | 16/21 [00:36<00:10,  2.19s/it]\n 81%|████████  | 17/21 [00:39<00:09,  2.43s/it]\n 86%|████████▌ | 18/21 [00:41<00:06,  2.24s/it]\n 90%|█████████ | 19/21 [00:43<00:04,  2.09s/it]\n6505 0.3296 0.0203 0.0030 :  90%|█████████ | 19/21 [00:45<00:04,  2.09s/it]\n6505 0.3296 0.0203 0.0030 :  95%|█████████▌| 20/21 [00:45<00:02,  2.25s/it]\n6505 0.3296 0.0203 0.0030 : 100%|██████████| 21/21 [00:47<00:00,  2.14s/it]\n6505 0.3296 0.0203 0.0030 : 100%|██████████| 21/21 [00:47<00:00,  2.27s/it]\nSetting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]\nLoading model from: /root/.pyenv/versions/3.8.8/lib/python3.8/site-packages/lpips/weights/v0.1/vgg.pth\nDetected LLFF dataset\nUsing pre-scaled images from /root/.cache/arf/data/llff/orchids/images_4\nLoaded LLFF data /root/.cache/arf/data/llff/orchids 14.48833973008133 124.51196423745613\nrecentered (3, 4)\n[1.4028997 8.9214735]\nOverriding offset 250-> 250\ndmin = 1.402900, dmax = 8.921474, invz = 0, offset = 250\n\n  0%|          | 0/4 [00:00<?, ?it/s]\n 50%|█████     | 2/4 [00:00<00:00, 16.83it/s]\n100%|██████████| 4/4 [00:00<00:00, 20.10it/s]\n\n  0%|          | 0/120 [00:00<?, ?it/s]\n100%|██████████| 120/120 [00:00<00:00, 93275.85it/s]\nz_bounds from LLFF: [1.4028997421264648, 8.921473503112793] (not used)\nscene_radius [1.496031746031746, 1.6613756613756614, 1.0]\nWriting to 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1.89it/s]\n 98%|█████████▊| 118/120 [01:03<00:01,  1.92it/s]\n 99%|█████████▉| 119/120 [01:04<00:00,  1.96it/s]\n100%|██████████| 120/120 [01:04<00:00,  2.00it/s]\n100%|██████████| 120/120 [01:04<00:00,  1.86it/s]\nIMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=8, resizing from (1008, 756) to (1008, 760) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to 1 (risking incompatibility).",
  "metrics": {
    "predict_time": 570.285809,
    "total_time": 1240.869303
  },
  "output": "https://replicate.delivery/mgxm/d2b84d4e-b401-4655-b550-1cec927c1fa3/test_renders_path.mp4",
  "started_at": "2022-06-24T02:57:45.043466Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/xdqpa66u6ve6bcknypme4on7me",
    "cancel": "https://api.replicate.com/v1/predictions/xdqpa66u6ve6bcknypme4on7me/cancel"
  },
  "version": "7bb81e4046b96358001e6275e36eee3e7228fe9f1a0e1515a5f553cbbcad07e7"
}

Generated in

9 minutes 30 seconds

Tweak itReport

Downloading checkpoint for orchids
Detected LLFF dataset
Using pre-scaled images from /root/.cache/arf/data/llff/orchids/images_4
Loaded LLFF data /root/.cache/arf/data/llff/orchids 14.48833973008133 124.51196423745613
recentered (3, 4)
[1.4028997 8.9214735]
Overriding offset 250-> 250
dmin = 1.402900, dmax = 8.921474, invz = 0, offset = 250

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z_bounds from LLFF: [1.4028997421264648, 8.921473503112793] (not used)
scene_radius [1.496031746031746, 1.6613756613756614, 1.0]
 Generating rays, scaling factor 1
ic| "Loaded ckpt: ": 'Loaded ckpt: '
    args.init_ckpt: '/root/.cache/arf/ckpt_svox2/llff/orchids/ckpt.npz'
ic| grid.basis_dim: 1
opt_style.py:508: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning dissapear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.
  style_img = imageio.imread(args.style).astype(np.float32) / 255.0
Render options RenderOptions(backend='cuvol', background_brightness=0.5, step_size=0.5, sigma_thresh=1e-08, stop_thresh=1e-07, last_sample_opaque=False, near_clip=0.0, use_spheric_clip=False, random_sigma_std=0.0, random_sigma_std_background=0.0)
ic| "Style image: ": 'Style image: '
    args.style: '/tmp/tmpbn7uaxrx9e661f3d-e3d1-4995-bbab-b9ac48b367fa.png'
    style_img.shape: torch.Size([504, 336, 3])
Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth

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ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 0
    epoch_size: 16003008
    batches_per_epoch: 3201
    batch_size: 5000
    optim_type: 'ray'

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1039 21.7927 :  33%|███▎      | 1047/3201 [00:14<00:21, 100.04it/s]
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1279 20.7603 :  41%|████      | 1297/3201 [00:17<00:22, 83.73it/s]
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1319 23.1769 :  41%|████      | 1315/3201 [00:17<00:22, 84.59it/s]
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1319 23.1769 :  42%|████▏     | 1333/3201 [00:17<00:21, 86.72it/s]
1339 21.6849 :  42%|████▏     | 1333/3201 [00:17<00:21, 86.72it/s]
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1379 17.4973 :  43%|████▎     | 1369/3201 [00:18<00:21, 85.76it/s]
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1519 22.6124 :  48%|████▊     | 1525/3201 [00:19<00:17, 98.31it/s]
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1539 19.7035 :  48%|████▊     | 1551/3201 [00:20<00:14, 112.28it/s]
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1679 19.0798 :  53%|█████▎    | 1685/3201 [00:21<00:13, 113.68it/s]
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1699 21.5608 :  53%|█████▎    | 1709/3201 [00:21<00:13, 111.49it/s]
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1719 21.8924 :  54%|█████▍    | 1721/3201 [00:21<00:13, 109.33it/s]
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1739 21.5251 :  54%|█████▍    | 1732/3201 [00:21<00:13, 105.37it/s]
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1759 22.4120 :  55%|█████▍    | 1754/3201 [00:21<00:13, 103.79it/s]
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1779 23.2911 :  55%|█████▌    | 1776/3201 [00:22<00:13, 104.13it/s]
1779 23.2911 :  56%|█████▌    | 1787/3201 [00:22<00:13, 105.75it/s]
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1799 22.8980 :  56%|█████▌    | 1798/3201 [00:22<00:13, 106.96it/s]
1799 22.8980 :  57%|█████▋    | 1809/3201 [00:22<00:13, 105.91it/s]
1819 22.3961 :  57%|█████▋    | 1809/3201 [00:22<00:13, 105.91it/s]
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1819 22.3961 :  57%|█████▋    | 1831/3201 [00:22<00:13, 100.89it/s]
1839 19.3529 :  57%|█████▋    | 1831/3201 [00:22<00:13, 100.89it/s]
1839 19.3529 :  58%|█████▊    | 1842/3201 [00:22<00:14, 96.63it/s]
1839 19.3529 :  58%|█████▊    | 1852/3201 [00:22<00:14, 93.40it/s]
1859 20.9672 :  58%|█████▊    | 1852/3201 [00:22<00:14, 93.40it/s]
1859 20.9672 :  58%|█████▊    | 1862/3201 [00:22<00:14, 90.72it/s]
1859 20.9672 :  58%|█████▊    | 1872/3201 [00:23<00:15, 87.20it/s]
1879 21.3375 :  58%|█████▊    | 1872/3201 [00:23<00:15, 87.20it/s]
1879 21.3375 :  59%|█████▉    | 1881/3201 [00:23<00:15, 83.29it/s]
1879 21.3375 :  59%|█████▉    | 1890/3201 [00:23<00:16, 81.37it/s]
1879 21.3375 :  59%|█████▉    | 1899/3201 [00:23<00:16, 80.95it/s]
1899 21.4729 :  59%|█████▉    | 1899/3201 [00:23<00:16, 80.95it/s]
1899 21.4729 :  60%|█████▉    | 1908/3201 [00:23<00:16, 80.74it/s]
1899 21.4729 :  60%|█████▉    | 1917/3201 [00:23<00:15, 81.07it/s]
1919 22.7881 :  60%|█████▉    | 1917/3201 [00:23<00:15, 81.07it/s]
1919 22.7881 :  60%|██████    | 1926/3201 [00:23<00:15, 81.58it/s]
1919 22.7881 :  60%|██████    | 1935/3201 [00:23<00:15, 83.05it/s]
1939 22.9330 :  60%|██████    | 1935/3201 [00:23<00:15, 83.05it/s]
1939 22.9330 :  61%|██████    | 1944/3201 [00:23<00:15, 83.72it/s]
1939 22.9330 :  61%|██████    | 1953/3201 [00:24<00:14, 83.59it/s]
1959 24.5427 :  61%|██████    | 1953/3201 [00:24<00:14, 83.59it/s]
1959 24.5427 :  61%|██████▏   | 1962/3201 [00:24<00:15, 82.03it/s]
1959 24.5427 :  62%|██████▏   | 1971/3201 [00:24<00:15, 79.69it/s]
1959 24.5427 :  62%|██████▏   | 1979/3201 [00:24<00:15, 78.53it/s]
1979 19.4006 :  62%|██████▏   | 1979/3201 [00:24<00:15, 78.53it/s]
1979 19.4006 :  62%|██████▏   | 1987/3201 [00:24<00:15, 76.46it/s]
1979 19.4006 :  62%|██████▏   | 1995/3201 [00:24<00:16, 74.50it/s]
1999 19.7991 :  62%|██████▏   | 1995/3201 [00:24<00:16, 74.50it/s]
1999 19.7991 :  63%|██████▎   | 2003/3201 [00:24<00:16, 73.22it/s]
1999 19.7991 :  63%|██████▎   | 2011/3201 [00:24<00:16, 72.24it/s]
1999 19.7991 :  63%|██████▎   | 2019/3201 [00:24<00:16, 70.90it/s]
2019 20.1750 :  63%|██████▎   | 2019/3201 [00:25<00:16, 70.90it/s]
2019 20.1750 :  63%|██████▎   | 2027/3201 [00:25<00:17, 68.72it/s]
2019 20.1750 :  64%|██████▎   | 2034/3201 [00:25<00:17, 66.27it/s]
2039 18.9449 :  64%|██████▎   | 2034/3201 [00:25<00:17, 66.27it/s]
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3099 25.0865 :  97%|█████████▋| 3098/3201 [00:41<00:01, 71.25it/s]
3099 25.0865 :  97%|█████████▋| 3106/3201 [00:41<00:01, 69.30it/s]
3099 25.0865 :  97%|█████████▋| 3113/3201 [00:41<00:01, 68.97it/s]
3119 23.0459 :  97%|█████████▋| 3113/3201 [00:41<00:01, 68.97it/s]
3119 23.0459 :  98%|█████████▊| 3121/3201 [00:41<00:01, 69.32it/s]
3119 23.0459 :  98%|█████████▊| 3129/3201 [00:41<00:01, 69.90it/s]
3119 23.0459 :  98%|█████████▊| 3137/3201 [00:41<00:00, 70.55it/s]
3139 24.2085 :  98%|█████████▊| 3137/3201 [00:41<00:00, 70.55it/s]
3139 24.2085 :  98%|█████████▊| 3145/3201 [00:41<00:00, 70.96it/s]
3139 24.2085 :  99%|█████████▊| 3153/3201 [00:42<00:00, 71.58it/s]
3159 24.1763 :  99%|█████████▊| 3153/3201 [00:42<00:00, 71.58it/s]
3159 24.1763 :  99%|█████████▉| 3161/3201 [00:42<00:00, 71.90it/s]
3159 24.1763 :  99%|█████████▉| 3169/3201 [00:42<00:00, 72.40it/s]
3159 24.1763 :  99%|█████████▉| 3177/3201 [00:42<00:00, 71.88it/s]
3179 23.6437 :  99%|█████████▉| 3177/3201 [00:42<00:00, 71.88it/s]
3179 23.6437 : 100%|█████████▉| 3185/3201 [00:42<00:00, 71.33it/s]
3179 23.6437 : 100%|█████████▉| 3193/3201 [00:42<00:00, 71.40it/s]
3199 22.3849 : 100%|█████████▉| 3193/3201 [00:42<00:00, 71.40it/s]
3199 22.3849 : 100%|██████████| 3201/3201 [00:42<00:00, 71.97it/s]
3199 22.3849 : 100%|██████████| 3201/3201 [00:42<00:00, 74.86it/s]
ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 1
    epoch_size: 16003008
    batches_per_epoch: 3201
    batch_size: 5000
    optim_type: 'ray'

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3260 21.4656 :   2%|▏         | 59/3201 [00:00<00:51, 60.66it/s]
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3560 25.1776 :  11%|█▏        | 361/3201 [00:05<00:44, 64.34it/s]
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3560 25.1776 :  12%|█▏        | 375/3201 [00:05<00:44, 62.91it/s]
3580 22.0617 :  12%|█▏        | 375/3201 [00:05<00:44, 62.91it/s]
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3580 22.0617 :  12%|█▏        | 389/3201 [00:06<00:43, 64.05it/s]
3580 22.0617 :  12%|█▏        | 396/3201 [00:06<00:43, 65.02it/s]
3600 25.2638 :  12%|█▏        | 396/3201 [00:06<00:43, 65.02it/s]
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3680 25.1801 :  15%|█▍        | 475/3201 [00:07<00:43, 63.39it/s]
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3800 24.3812 :  19%|█▊        | 595/3201 [00:09<00:47, 54.31it/s]
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4020 25.6341 :  25%|██▌       | 815/3201 [00:12<00:30, 77.13it/s]
4020 25.6341 :  26%|██▌       | 823/3201 [00:12<00:31, 75.85it/s]
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4020 25.6341 :  26%|██▌       | 839/3201 [00:12<00:31, 75.85it/s]
4040 23.7984 :  26%|██▌       | 839/3201 [00:12<00:31, 75.85it/s]
4040 23.7984 :  26%|██▋       | 847/3201 [00:13<00:30, 76.52it/s]
4040 23.7984 :  27%|██▋       | 855/3201 [00:13<00:30, 77.31it/s]
4060 26.2141 :  27%|██▋       | 855/3201 [00:13<00:30, 77.31it/s]
4060 26.2141 :  27%|██▋       | 864/3201 [00:13<00:29, 78.34it/s]
4060 26.2141 :  27%|██▋       | 873/3201 [00:13<00:29, 80.19it/s]
4080 24.2419 :  27%|██▋       | 873/3201 [00:13<00:29, 80.19it/s]
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4080 24.2419 :  28%|██▊       | 891/3201 [00:13<00:28, 80.74it/s]
4100 24.8735 :  28%|██▊       | 891/3201 [00:13<00:28, 80.74it/s]
4100 24.8735 :  28%|██▊       | 900/3201 [00:13<00:29, 78.64it/s]
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4120 23.7020 :  29%|██▉       | 927/3201 [00:14<00:26, 84.51it/s]
4120 23.7020 :  29%|██▉       | 938/3201 [00:14<00:25, 89.28it/s]
4140 23.4685 :  29%|██▉       | 938/3201 [00:14<00:25, 89.28it/s]
4140 23.4685 :  30%|██▉       | 948/3201 [00:14<00:24, 91.46it/s]
4140 23.4685 :  30%|██▉       | 958/3201 [00:14<00:24, 92.54it/s]
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4160 24.7562 :  30%|███       | 968/3201 [00:14<00:24, 90.59it/s]
4160 24.7562 :  31%|███       | 978/3201 [00:14<00:24, 89.05it/s]
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4180 24.8512 :  31%|███       | 987/3201 [00:14<00:24, 88.76it/s]
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4220 24.7862 :  32%|███▏      | 1017/3201 [00:15<00:23, 92.07it/s]
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4820 26.3163 :  50%|█████     | 1616/3201 [00:21<00:14, 109.66it/s]
4820 26.3163 :  51%|█████     | 1628/3201 [00:21<00:14, 111.11it/s]
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4860 24.4944 :  52%|█████▏    | 1652/3201 [00:22<00:13, 112.11it/s]
4860 24.4944 :  52%|█████▏    | 1664/3201 [00:22<00:13, 110.70it/s]
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4880 23.0004 :  52%|█████▏    | 1676/3201 [00:22<00:13, 109.44it/s]
4880 23.0004 :  53%|█████▎    | 1687/3201 [00:22<00:13, 108.49it/s]
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4960 25.0170 :  55%|█████▍    | 1752/3201 [00:23<00:14, 98.72it/s]
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4980 25.5811 :  56%|█████▌    | 1783/3201 [00:23<00:14, 100.42it/s]
4980 25.5811 :  56%|█████▌    | 1794/3201 [00:23<00:13, 102.02it/s]
5000 24.3996 :  56%|█████▌    | 1794/3201 [00:23<00:13, 102.02it/s]
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5020 24.8549 :  57%|█████▋    | 1816/3201 [00:23<00:13, 100.94it/s]
5020 24.8549 :  57%|█████▋    | 1827/3201 [00:23<00:13, 99.01it/s]
5020 24.8549 :  57%|█████▋    | 1837/3201 [00:23<00:14, 94.40it/s]
5040 22.6000 :  57%|█████▋    | 1837/3201 [00:23<00:14, 94.40it/s]
5040 22.6000 :  58%|█████▊    | 1847/3201 [00:23<00:14, 90.81it/s]
5040 22.6000 :  58%|█████▊    | 1857/3201 [00:24<00:15, 87.57it/s]
5060 24.0438 :  58%|█████▊    | 1857/3201 [00:24<00:15, 87.57it/s]
5060 24.0438 :  58%|█████▊    | 1866/3201 [00:24<00:15, 85.22it/s]
5060 24.0438 :  59%|█████▊    | 1875/3201 [00:24<00:16, 81.72it/s]
5080 24.1035 :  59%|█████▊    | 1875/3201 [00:24<00:16, 81.72it/s]
5080 24.1035 :  59%|█████▉    | 1884/3201 [00:24<00:16, 78.39it/s]
5080 24.1035 :  59%|█████▉    | 1892/3201 [00:24<00:16, 77.77it/s]
5100 24.3221 :  59%|█████▉    | 1892/3201 [00:24<00:16, 77.77it/s]
5100 24.3221 :  59%|█████▉    | 1900/3201 [00:24<00:16, 77.54it/s]
5100 24.3221 :  60%|█████▉    | 1908/3201 [00:24<00:16, 77.37it/s]
5100 24.3221 :  60%|█████▉    | 1916/3201 [00:24<00:16, 77.45it/s]
5120 26.6220 :  60%|█████▉    | 1916/3201 [00:24<00:16, 77.45it/s]
5120 26.6220 :  60%|██████    | 1924/3201 [00:24<00:16, 77.88it/s]
5120 26.6220 :  60%|██████    | 1933/3201 [00:25<00:15, 79.39it/s]
5140 24.9975 :  60%|██████    | 1933/3201 [00:25<00:15, 79.39it/s]
5140 24.9975 :  61%|██████    | 1942/3201 [00:25<00:15, 80.19it/s]
5140 24.9975 :  61%|██████    | 1951/3201 [00:25<00:15, 80.36it/s]
5160 26.5609 :  61%|██████    | 1951/3201 [00:25<00:15, 80.36it/s]
5160 26.5609 :  61%|██████    | 1960/3201 [00:25<00:15, 79.39it/s]
5160 26.5609 :  61%|██████▏   | 1968/3201 [00:25<00:15, 77.08it/s]
5160 26.5609 :  62%|██████▏   | 1976/3201 [00:25<00:16, 75.34it/s]
5180 24.2770 :  62%|██████▏   | 1976/3201 [00:25<00:16, 75.34it/s]
5180 24.2770 :  62%|██████▏   | 1984/3201 [00:25<00:16, 73.76it/s]
5180 24.2770 :  62%|██████▏   | 1992/3201 [00:25<00:16, 71.72it/s]
5200 23.0097 :  62%|██████▏   | 1992/3201 [00:25<00:16, 71.72it/s]
5200 23.0097 :  62%|██████▏   | 2000/3201 [00:25<00:17, 70.28it/s]
5200 23.0097 :  63%|██████▎   | 2008/3201 [00:26<00:17, 69.37it/s]
5200 23.0097 :  63%|██████▎   | 2015/3201 [00:26<00:17, 67.72it/s]
5220 23.8698 :  63%|██████▎   | 2015/3201 [00:26<00:17, 67.72it/s]
5220 23.8698 :  63%|██████▎   | 2022/3201 [00:26<00:17, 66.11it/s]
5220 23.8698 :  63%|██████▎   | 2029/3201 [00:26<00:18, 64.23it/s]
5220 23.8698 :  64%|██████▎   | 2036/3201 [00:26<00:18, 62.10it/s]
5240 22.4548 :  64%|██████▎   | 2036/3201 [00:26<00:18, 62.10it/s]
5240 22.4548 :  64%|██████▍   | 2043/3201 [00:26<00:18, 61.27it/s]
5240 22.4548 :  64%|██████▍   | 2050/3201 [00:26<00:18, 61.34it/s]
5240 22.4548 :  64%|██████▍   | 2057/3201 [00:26<00:18, 61.88it/s]
5260 25.1397 :  64%|██████▍   | 2057/3201 [00:26<00:18, 61.88it/s]
5260 25.1397 :  64%|██████▍   | 2064/3201 [00:27<00:18, 61.82it/s]
5260 25.1397 :  65%|██████▍   | 2071/3201 [00:27<00:18, 61.67it/s]
5260 25.1397 :  65%|██████▍   | 2078/3201 [00:27<00:18, 62.29it/s]
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5280 24.0596 :  65%|██████▌   | 2092/3201 [00:27<00:17, 63.81it/s]
5280 24.0596 :  66%|██████▌   | 2099/3201 [00:27<00:17, 63.91it/s]
5300 22.6564 :  66%|██████▌   | 2099/3201 [00:27<00:17, 63.91it/s]
5300 22.6564 :  66%|██████▌   | 2106/3201 [00:27<00:17, 63.67it/s]
5300 22.6564 :  66%|██████▌   | 2113/3201 [00:27<00:17, 62.31it/s]
5320 22.6525 :  66%|██████▌   | 2113/3201 [00:27<00:17, 62.31it/s]
5320 22.6525 :  66%|██████▌   | 2120/3201 [00:27<00:17, 60.58it/s]
5320 22.6525 :  66%|██████▋   | 2127/3201 [00:28<00:18, 59.15it/s]
5320 22.6525 :  67%|██████▋   | 2133/3201 [00:28<00:18, 57.86it/s]
5340 19.8258 :  67%|██████▋   | 2133/3201 [00:28<00:18, 57.86it/s]
5340 19.8258 :  67%|██████▋   | 2142/3201 [00:28<00:15, 66.36it/s]
5340 19.8258 :  67%|██████▋   | 2152/3201 [00:28<00:13, 75.42it/s]
5360 21.5081 :  67%|██████▋   | 2152/3201 [00:28<00:13, 75.42it/s]
5360 21.5081 :  68%|██████▊   | 2162/3201 [00:28<00:12, 80.84it/s]
5360 21.5081 :  68%|██████▊   | 2172/3201 [00:28<00:11, 86.02it/s]
5380 22.7156 :  68%|██████▊   | 2172/3201 [00:28<00:11, 86.02it/s]
5380 22.7156 :  68%|██████▊   | 2182/3201 [00:28<00:11, 87.49it/s]
5380 22.7156 :  68%|██████▊   | 2191/3201 [00:28<00:11, 86.06it/s]
5400 23.0208 :  68%|██████▊   | 2191/3201 [00:28<00:11, 86.06it/s]
5400 23.0208 :  69%|██████▊   | 2200/3201 [00:28<00:11, 84.91it/s]
5400 23.0208 :  69%|██████▉   | 2209/3201 [00:28<00:11, 85.60it/s]
5400 23.0208 :  69%|██████▉   | 2218/3201 [00:29<00:11, 85.86it/s]
5420 23.8782 :  69%|██████▉   | 2218/3201 [00:29<00:11, 85.86it/s]
5420 23.8782 :  70%|██████▉   | 2227/3201 [00:29<00:11, 86.44it/s]
5420 23.8782 :  70%|██████▉   | 2237/3201 [00:29<00:10, 87.75it/s]
5440 23.0223 :  70%|██████▉   | 2237/3201 [00:29<00:10, 87.75it/s]
5440 23.0223 :  70%|███████   | 2246/3201 [00:29<00:10, 88.31it/s]
5440 23.0223 :  70%|███████   | 2255/3201 [00:29<00:10, 88.36it/s]
5460 22.0570 :  70%|███████   | 2255/3201 [00:29<00:10, 88.36it/s]
5460 22.0570 :  71%|███████   | 2264/3201 [00:29<00:10, 88.23it/s]
5460 22.0570 :  71%|███████   | 2273/3201 [00:29<00:10, 86.71it/s]
5480 24.3108 :  71%|███████   | 2273/3201 [00:29<00:10, 86.71it/s]
5480 24.3108 :  71%|███████▏  | 2282/3201 [00:29<00:10, 85.33it/s]
5480 24.3108 :  72%|███████▏  | 2291/3201 [00:29<00:10, 84.07it/s]
5500 21.9869 :  72%|███████▏  | 2291/3201 [00:30<00:10, 84.07it/s]
5500 21.9869 :  72%|███████▏  | 2300/3201 [00:30<00:10, 84.45it/s]
5500 21.9869 :  72%|███████▏  | 2309/3201 [00:30<00:10, 85.48it/s]
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ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 2
    epoch_size: 21
    batches_per_epoch: 21
    batch_size: 1
    optim_type: 'image'

  0%|          | 0/21 [00:00<?, ?it/s]/root/.pyenv/versions/3.8.8/lib/python3.8/site-packages/torch/nn/functional.py:3609: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
  warnings.warn(
/root/.pyenv/versions/3.8.8/lib/python3.8/site-packages/torch/nn/functional.py:3657: UserWarning: The default behavior for interpolate/upsample with float scale_factor changed in 1.6.0 to align with other frameworks/libraries, and now uses scale_factor directly, instead of relying on the computed output size. If you wish to restore the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details.
  warnings.warn(
/root/.pyenv/versions/3.8.8/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)
  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)

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ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 3
    epoch_size: 21
    batches_per_epoch: 21
    batch_size: 1
    optim_type: 'image'

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ic| "Training epoch: ": 'Training epoch: '
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    epoch_size: 21
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    batch_size: 1
    optim_type: 'image'

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ic| "Training epoch: ": 'Training epoch: '
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    epoch_size: 21
    batches_per_epoch: 21
    batch_size: 1
    optim_type: 'image'

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ic| "Training epoch: ": 'Training epoch: '
    epoch_id: 6
    epoch_size: 21
    batches_per_epoch: 21
    batch_size: 1
    optim_type: 'image'

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Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
Loading model from: /root/.pyenv/versions/3.8.8/lib/python3.8/site-packages/lpips/weights/v0.1/vgg.pth
Detected LLFF dataset
Using pre-scaled images from /root/.cache/arf/data/llff/orchids/images_4
Loaded LLFF data /root/.cache/arf/data/llff/orchids 14.48833973008133 124.51196423745613
recentered (3, 4)
[1.4028997 8.9214735]
Overriding offset 250-> 250
dmin = 1.402900, dmax = 8.921474, invz = 0, offset = 250

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z_bounds from LLFF: [1.4028997421264648, 8.921473503112793] (not used)
scene_radius [1.496031746031746, 1.6613756613756614, 1.0]
Writing to /root/.cache/arf/ckpt_arf/llff/orchids/04f8c1bb-ff70-4ac6-a27b-cbf851608503/test_renders_path

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IMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=8, resizing from (1008, 756) to (1008, 760) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to 1 (risking incompatibility).