Prediction

Model

csslc/ccsr:b66be6a6

ku43wdtbeicfhxbrc3odcehdx4

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

2m 10s

Created

almost 2 years ago

Webhook

–

Input

image: https://replicate.delivery/pbxt/KEsL8qx725guGDgsIH3NX0G8TElQ9Fkp9JuzM0GWwCWXUi3u/19.jpg
sr_scale: 4
steps: 45
tile_diffusion: false
tile_diffusion_size: 512
tile_diffusion_stride: 256
tile_vae: false
vae_decoder_tile_size: 224
vae_encoder_tile_size: 1024
color_fix_type: adain
t_max: 0.6667
t_min: 0.3333

{
  "color_fix_type": "adain",
  "image": "https://replicate.delivery/pbxt/KEsL8qx725guGDgsIH3NX0G8TElQ9Fkp9JuzM0GWwCWXUi3u/19.jpg",
  "sr_scale": 4,
  "steps": 45,
  "t_max": 0.6667,
  "t_min": 0.3333,
  "tile_diffusion": false,
  "tile_diffusion_size": 512,
  "tile_diffusion_stride": 256,
  "tile_vae": false,
  "vae_decoder_tile_size": 224,
  "vae_encoder_tile_size": 1024
}

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_4Ud**********************************

This is your API token. Keep it to yourself.

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

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

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

const output = await replicate.run(
  "csslc/ccsr:b66be6a6d779e57163b0489329f50ea4b56bcbcc7b3d48ef39bae6b812a7f947",
  {
    input: {
      color_fix_type: "adain",
      image: "https://replicate.delivery/pbxt/KEsL8qx725guGDgsIH3NX0G8TElQ9Fkp9JuzM0GWwCWXUi3u/19.jpg",
      sr_scale: 4,
      steps: 45,
      t_max: 0.6667,
      t_min: 0.3333,
      tile_diffusion: false,
      tile_diffusion_size: 512,
      tile_diffusion_stride: 256,
      tile_vae: false,
      vae_decoder_tile_size: 224,
      vae_encoder_tile_size: 1024
    }
  }
);

// 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=r8_4Ud**********************************

This is your API token. Keep it to yourself.

Import the client:

import replicate

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

output = replicate.run(
    "csslc/ccsr:b66be6a6d779e57163b0489329f50ea4b56bcbcc7b3d48ef39bae6b812a7f947",
    input={
        "color_fix_type": "adain",
        "image": "https://replicate.delivery/pbxt/KEsL8qx725guGDgsIH3NX0G8TElQ9Fkp9JuzM0GWwCWXUi3u/19.jpg",
        "sr_scale": 4,
        "steps": 45,
        "t_max": 0.6667,
        "t_min": 0.3333,
        "tile_diffusion": False,
        "tile_diffusion_size": 512,
        "tile_diffusion_stride": 256,
        "tile_vae": False,
        "vae_decoder_tile_size": 224,
        "vae_encoder_tile_size": 1024
    }
)

# To access the file URL:
print(output.url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output.read())

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=r8_4Ud**********************************

This is your API token. Keep it to yourself.

Run csslc/ccsr 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": "csslc/ccsr:b66be6a6d779e57163b0489329f50ea4b56bcbcc7b3d48ef39bae6b812a7f947",
    "input": {
      "color_fix_type": "adain",
      "image": "https://replicate.delivery/pbxt/KEsL8qx725guGDgsIH3NX0G8TElQ9Fkp9JuzM0GWwCWXUi3u/19.jpg",
      "sr_scale": 4,
      "steps": 45,
      "t_max": 0.6667,
      "t_min": 0.3333,
      "tile_diffusion": false,
      "tile_diffusion_size": 512,
      "tile_diffusion_stride": 256,
      "tile_vae": false,
      "vae_decoder_tile_size": 224,
      "vae_encoder_tile_size": 1024
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "id": "ku43wdtbeicfhxbrc3odcehdx4",
  "model": "csslc/ccsr",
  "version": "b66be6a6d779e57163b0489329f50ea4b56bcbcc7b3d48ef39bae6b812a7f947",
  "input": {
    "color_fix_type": "adain",
    "image": "https://replicate.delivery/pbxt/KEsL8qx725guGDgsIH3NX0G8TElQ9Fkp9JuzM0GWwCWXUi3u/19.jpg",
    "sr_scale": 4,
    "steps": 45,
    "t_max": 0.6667,
    "t_min": 0.3333,
    "tile_diffusion": false,
    "tile_diffusion_size": 512,
    "tile_diffusion_stride": 256,
    "tile_vae": false,
    "vae_decoder_tile_size": 224,
    "vae_encoder_tile_size": 1024
  },
  "logs": "ControlLDM: Running in eps-prediction mode\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.\nDiffusionWrapper has 865.91 M params.\nmaking attention of type 'vanilla-xformers' with 512 in_channels\nbuilding MemoryEfficientAttnBlock with 512 in_channels...\nWorking with z of shape (1, 4, 32, 32) = 4096 dimensions.\nmaking attention of type 'vanilla-xformers' with 512 in_channels\nbuilding MemoryEfficientAttnBlock with 512 in_channels...\nopen_clip_pytorch_model.bin:   0%|          | 0.00/3.94G [00:00<?, ?B/s]\nopen_clip_pytorch_model.bin:   0%|          | 10.5M/3.94G [00:00<02:40, 24.4MB/s]\nopen_clip_pytorch_model.bin:   1%|          | 41.9M/3.94G [00:00<00:44, 87.9MB/s]\nopen_clip_pytorch_model.bin:   2%|▏         | 73.4M/3.94G [00:00<00:28, 136MB/s] \nopen_clip_pytorch_model.bin:   3%|▎         | 105M/3.94G [00:00<00:22, 167MB/s] \nopen_clip_pytorch_model.bin:   3%|▎         | 136M/3.94G [00:00<00:19, 190MB/s]\nopen_clip_pytorch_model.bin:   4%|▍         | 168M/3.94G [00:01<00:18, 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The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\nwarnings.warn(msg)\nloaded pretrained LPIPS loss from taming/modules/autoencoder/lpips/vgg.pth\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads.\nSetting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads.\nUsing seed: 60190\nGlobal seed set to 60190\ntimesteps used in spaced sampler:\n[0, 23, 45, 68, 91, 114, 136, 159, 182, 204, 227, 250, 272, 295, 318, 341, 363, 386, 409, 431, 454, 477, 499, 522, 545, 568, 590, 613, 636, 658, 681, 704, 727, 749, 772, 795, 817, 840, 863, 885, 908, 931, 954, 976, 999]\nSpaced Sampler:   0%|          | 0/45 [00:00<?, ?it/s]/src/model/q_sampler.py:467: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\ntao_index = torch.tensor(torch.round(index * t_max), dtype=torch.int64)\nSpaced Sampler:   0%|          | 0/45 [00:00<?, ?it/s]\u001b[A\nSpaced Sampler:   0%|          | 0/45 [00:00<?, ?it/s]\nSpaced Sampler:   2%|▏         | 1/45 [00:00<00:08,  5.21it/s]\u001b[A\nSpaced Sampler:   4%|▍         | 2/45 [00:00<00:08,  5.23it/s]\u001b[A\nSpaced Sampler:   7%|▋         | 3/45 [00:00<00:08,  5.23it/s]\u001b[A\nSpaced Sampler:   9%|▉         | 4/45 [00:00<00:07,  5.23it/s]\u001b[A\nSpaced Sampler:  11%|█         | 5/45 [00:00<00:07,  5.23it/s]\u001b[A\nSpaced Sampler:  13%|█▎        | 6/45 [00:01<00:07,  5.22it/s]\u001b[A\nSpaced Sampler:  16%|█▌        | 7/45 [00:01<00:07,  5.22it/s]\u001b[A\nSpaced Sampler:  18%|█▊        | 8/45 [00:01<00:07,  5.22it/s]\u001b[A\nSpaced Sampler:  20%|██        | 9/45 [00:01<00:06,  5.21it/s]\u001b[A\nSpaced Sampler:  22%|██▏       | 10/45 [00:01<00:06,  5.22it/s]\u001b[A\nSpaced Sampler:  24%|██▍       | 11/45 [00:02<00:06,  5.22it/s]\u001b[A\nSpaced Sampler:  27%|██▋       | 12/45 [00:02<00:06,  5.22it/s]\u001b[A\nSpaced Sampler:  29%|██▉       | 13/45 [00:02<00:06,  5.22it/s]\u001b[A\nSpaced Sampler:  31%|███       | 14/45 [00:02<00:05,  5.22it/s]\u001b[A\nSpaced Sampler:  33%|███▎      | 15/45 [00:02<00:05,  5.22it/s]\u001b[A\nSpaced Sampler:  33%|███▎      | 15/45 [00:02<00:05,  5.22it/s]",
  "output": "https://replicate.delivery/pbxt/uuPXTeVX8h30Wq4Bp16TJcCbI0Vf5M5sUO2nVucR4mjruMNSA/out.png",
  "data_removed": false,
  "error": null,
  "source": "web",
  "status": "succeeded",
  "created_at": "2024-01-17T00:02:49.77581Z",
  "started_at": "2024-01-17T00:04:14.550557Z",
  "completed_at": "2024-01-17T00:05:00.216889Z",
  "urls": {
    "cancel": "https://api.replicate.com/v1/predictions/ku43wdtbeicfhxbrc3odcehdx4/cancel",
    "get": "https://api.replicate.com/v1/predictions/ku43wdtbeicfhxbrc3odcehdx4"
  },
  "metrics": {
    "predict_time": 45.666332,
    "total_time": 130.441079
  }
}

Generated in

45.7 seconds

Tweak it Report

ControlLDM: Running in eps-prediction mode Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. DiffusionWrapper has 865.91 M params. making attention of type 'vanilla-xformers' with 512 in_channels building MemoryEfficientAttnBlock with 512 in_channels... Working with z of shape (1, 4, 32, 32) = 4096 dimensions. making attention of type 'vanilla-xformers' with 512 in_channels building MemoryEfficientAttnBlock with 512 in_channels... open_clip_pytorch_model.bin: 0%| | 0.00/3.94G [00:00<?, ?B/s] open_clip_pytorch_model.bin: 0%| | 10.5M/3.94G [00:00<02:40, 24.4MB/s] open_clip_pytorch_model.bin: 1%| | 41.9M/3.94G [00:00<00:44, 87.9MB/s] open_clip_pytorch_model.bin: 2%|▏ | 73.4M/3.94G [00:00<00:28, 136MB/s] open_clip_pytorch_model.bin: 3%|▎ | 105M/3.94G [00:00<00:22, 167MB/s] open_clip_pytorch_model.bin: 3%|▎ | 136M/3.94G [00:00<00:19, 190MB/s] open_clip_pytorch_model.bin: 4%|▍ | 168M/3.94G [00:01<00:18, 207MB/s] open_clip_pytorch_model.bin: 5%|▌ | 199M/3.94G [00:01<00:16, 221MB/s] open_clip_pytorch_model.bin: 6%|▌ | 231M/3.94G [00:01<00:16, 224MB/s] open_clip_pytorch_model.bin: 7%|▋ | 262M/3.94G [00:01<00:15, 232MB/s] open_clip_pytorch_model.bin: 8%|▊ | 304M/3.94G [00:01<00:14, 251MB/s] open_clip_pytorch_model.bin: 9%|▊ | 336M/3.94G 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The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights. warnings.warn(msg) loaded pretrained LPIPS loss from taming/modules/autoencoder/lpips/vgg.pth Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is None and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 320, context_dim is 1024 and using 5 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is None and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 640, context_dim is 1024 and using 10 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is None and using 20 heads. Setting up MemoryEfficientCrossAttention. Query dim is 1280, context_dim is 1024 and using 20 heads. Using seed: 60190 Global seed set to 60190 timesteps used in spaced sampler: [0, 23, 45, 68, 91, 114, 136, 159, 182, 204, 227, 250, 272, 295, 318, 341, 363, 386, 409, 431, 454, 477, 499, 522, 545, 568, 590, 613, 636, 658, 681, 704, 727, 749, 772, 795, 817, 840, 863, 885, 908, 931, 954, 976, 999] Spaced Sampler: 0%| | 0/45 [00:00<?, ?it/s]/src/model/q_sampler.py:467: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor). tao_index = torch.tensor(torch.round(index * t_max), dtype=torch.int64) Spaced Sampler: 0%| | 0/45 [00:00<?, ?it/s][A Spaced Sampler: 0%| | 0/45 [00:00<?, ?it/s] Spaced Sampler: 2%|▏ | 1/45 [00:00<00:08, 5.21it/s][A Spaced Sampler: 4%|▍ | 2/45 [00:00<00:08, 5.23it/s][A Spaced Sampler: 7%|▋ | 3/45 [00:00<00:08, 5.23it/s][A Spaced Sampler: 9%|▉ | 4/45 [00:00<00:07, 5.23it/s][A Spaced Sampler: 11%|█ | 5/45 [00:00<00:07, 5.23it/s][A Spaced Sampler: 13%|█▎ | 6/45 [00:01<00:07, 5.22it/s][A Spaced Sampler: 16%|█▌ | 7/45 [00:01<00:07, 5.22it/s][A Spaced Sampler: 18%|█▊ | 8/45 [00:01<00:07, 5.22it/s][A Spaced Sampler: 20%|██ | 9/45 [00:01<00:06, 5.21it/s][A Spaced Sampler: 22%|██▏ | 10/45 [00:01<00:06, 5.22it/s][A Spaced Sampler: 24%|██▍ | 11/45 [00:02<00:06, 5.22it/s][A Spaced Sampler: 27%|██▋ | 12/45 [00:02<00:06, 5.22it/s][A Spaced Sampler: 29%|██▉ | 13/45 [00:02<00:06, 5.22it/s][A Spaced Sampler: 31%|███ | 14/45 [00:02<00:05, 5.22it/s][A Spaced Sampler: 33%|███▎ | 15/45 [00:02<00:05, 5.22it/s][A Spaced Sampler: 33%|███▎ | 15/45 [00:02<00:05, 5.22it/s]