{
  "completed_at": "2024-01-16T23:53:28.544718Z",
  "created_at": "2024-01-16T23:51:20.863191Z",
  "data_removed": false,
  "error": null,
  "id": "dpcxmxtbzg4lmduurxuupffsrm",
  "input": {
    "image": "https://replicate.delivery/pbxt/KEsAFaE2ccvOWnpi4UvCuw9E55jW366PDRhiTLC32rmNbZbw/49.jpg",
    "steps": 45,
    "t_max": 0.6667,
    "t_min": 0.3333,
    "sr_scale": 4,
    "tile_vae": false,
    "color_fix_type": "adain",
    "tile_diffusion": false,
    "tile_diffusion_size": 512,
    "tile_diffusion_stride": 256,
    "vae_decoder_tile_size": 224,
    "vae_encoder_tile_size": 1024
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  "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<03:05, 21.2MB/s]\nopen_clip_pytorch_model.bin:   1%|          | 41.9M/3.94G [00:00<00:49, 78.1MB/s]\nopen_clip_pytorch_model.bin:   2%|▏         | 73.4M/3.94G [00:00<00:32, 120MB/s] \nopen_clip_pytorch_model.bin:   3%|▎         | 105M/3.94G [00:00<00:25, 152MB/s] \nopen_clip_pytorch_model.bin:   3%|▎         | 136M/3.94G [00:01<00:21, 181MB/s]\nopen_clip_pytorch_model.bin:   4%|▍         | 168M/3.94G [00:01<00:19, 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217MB/s]\n/root/.pyenv/versions/3.11.7/lib/python3.11/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\nwarnings.warn(\n/root/.pyenv/versions/3.11.7/lib/python3.11/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. 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: 10477\nGlobal seed set to 10477\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<00:04,  9.38it/s]\u001b[A\nSpaced Sampler:   4%|▍         | 2/45 [00:00<00:04,  9.46it/s]\u001b[A\nSpaced Sampler:   7%|▋         | 3/45 [00:00<00:04,  9.48it/s]\u001b[A\nSpaced Sampler:   9%|▉         | 4/45 [00:00<00:04,  9.49it/s]\u001b[A\nSpaced Sampler:  11%|█         | 5/45 [00:00<00:04,  9.50it/s]\u001b[A\nSpaced Sampler:  13%|█▎        | 6/45 [00:00<00:04,  9.50it/s]\u001b[A\nSpaced Sampler:  16%|█▌        | 7/45 [00:00<00:03,  9.50it/s]\u001b[A\nSpaced Sampler:  18%|█▊        | 8/45 [00:00<00:03,  9.50it/s]\u001b[A\nSpaced Sampler:  20%|██        | 9/45 [00:00<00:03,  9.50it/s]\u001b[A\nSpaced Sampler:  22%|██▏       | 10/45 [00:01<00:03,  9.49it/s]\u001b[A\nSpaced Sampler:  24%|██▍       | 11/45 [00:01<00:03,  9.50it/s]\u001b[A\nSpaced Sampler:  27%|██▋       | 12/45 [00:01<00:03,  9.49it/s]\u001b[A\nSpaced Sampler:  29%|██▉       | 13/45 [00:01<00:03,  9.49it/s]\u001b[A\nSpaced Sampler:  31%|███       | 14/45 [00:01<00:03,  9.48it/s]\u001b[A\nSpaced Sampler:  33%|███▎      | 15/45 [00:01<00:03,  9.47it/s]\u001b[A\nSpaced Sampler:  33%|███▎      | 15/45 [00:01<00:03,  9.48it/s]",
  "metrics": {
    "predict_time": 43.505712,
    "total_time": 127.681527
  },
  "output": "https://replicate.delivery/pbxt/81Sg8aj3F7IBJRO9t7rnfrj2JDkjWfVjyQFI5hxm3Ky3jMNSA/out.png",
  "started_at": "2024-01-16T23:52:45.039006Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/dpcxmxtbzg4lmduurxuupffsrm",
    "cancel": "https://api.replicate.com/v1/predictions/dpcxmxtbzg4lmduurxuupffsrm/cancel"
  },
  "version": "b66be6a6d779e57163b0489329f50ea4b56bcbcc7b3d48ef39bae6b812a7f947"
}

{
  "completed_at": "2024-01-17T00:05:00.216889Z",
  "created_at": "2024-01-17T00:02:49.775810Z",
  "data_removed": false,
  "error": null,
  "id": "ku43wdtbeicfhxbrc3odcehdx4",
  "input": {
    "image": "https://replicate.delivery/pbxt/KEsL8qx725guGDgsIH3NX0G8TElQ9Fkp9JuzM0GWwCWXUi3u/19.jpg",
    "steps": 45,
    "t_max": 0.6667,
    "t_min": 0.3333,
    "sr_scale": 4,
    "tile_vae": false,
    "color_fix_type": "adain",
    "tile_diffusion": false,
    "tile_diffusion_size": 512,
    "tile_diffusion_stride": 256,
    "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]",
  "metrics": {
    "predict_time": 45.666332,
    "total_time": 130.441079
  },
  "output": "https://replicate.delivery/pbxt/uuPXTeVX8h30Wq4Bp16TJcCbI0Vf5M5sUO2nVucR4mjruMNSA/out.png",
  "started_at": "2024-01-17T00:04:14.550557Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/ku43wdtbeicfhxbrc3odcehdx4",
    "cancel": "https://api.replicate.com/v1/predictions/ku43wdtbeicfhxbrc3odcehdx4/cancel"
  },
  "version": "b66be6a6d779e57163b0489329f50ea4b56bcbcc7b3d48ef39bae6b812a7f947"
}

{
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  "created_at": "2024-01-17T13:01:01.694682Z",
  "data_removed": false,
  "error": null,
  "id": "lajmtw3b3gcoqyfneou4qx2axq",
  "input": {
    "image": "https://replicate.delivery/pbxt/KF4EV8ueusmWq7Dwfr5nuqQZYpVaYDN6CSBgYG4ZDXTrUbY6/19.jpg",
    "steps": 45,
    "t_max": 0.6667,
    "t_min": 0.3333,
    "sr_scale": 4,
    "tile_vae": false,
    "color_fix_type": "adain",
    "tile_diffusion": false,
    "tile_diffusion_size": 512,
    "tile_diffusion_stride": 256,
    "vae_decoder_tile_size": 224,
    "vae_encoder_tile_size": 1024
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  "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<02:38, 24.8MB/s]\nopen_clip_pytorch_model.bin:   1%|          | 31.5M/3.94G [00:00<00:54, 71.3MB/s]\nopen_clip_pytorch_model.bin:   2%|▏         | 62.9M/3.94G [00:00<00:34, 112MB/s] \nopen_clip_pytorch_model.bin:   2%|▏         | 94.4M/3.94G [00:00<00:27, 139MB/s]\nopen_clip_pytorch_model.bin:   3%|▎         | 115M/3.94G [00:00<00:25, 152MB/s] \nopen_clip_pytorch_model.bin:   4%|▎         | 147M/3.94G [00:01<00:20, 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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: 42309\nGlobal seed set to 42309\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,  5.19it/s]\u001b[A\nSpaced Sampler:   4%|▍         | 2/45 [00:00<00:08,  5.21it/s]\u001b[A\nSpaced Sampler:   7%|▋         | 3/45 [00:00<00:08,  5.22it/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,  5.23it/s]\u001b[A\nSpaced Sampler:  16%|█▌        | 7/45 [00:01<00:07,  5.23it/s]\u001b[A\nSpaced Sampler:  18%|█▊        | 8/45 [00:01<00:07,  5.23it/s]\u001b[A\nSpaced Sampler:  20%|██        | 9/45 [00:01<00:06,  5.22it/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,  5.23it/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,  5.23it/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]",
  "metrics": {
    "predict_time": 45.431014,
    "total_time": 195.792931
  },
  "output": "https://replicate.delivery/pbxt/8vl8v13MB5L7KdOlO6Rz2oqAES7xtB9EQIJpHDirZaJUCWjE/out.png",
  "started_at": "2024-01-17T13:03:32.056599Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/lajmtw3b3gcoqyfneou4qx2axq",
    "cancel": "https://api.replicate.com/v1/predictions/lajmtw3b3gcoqyfneou4qx2axq/cancel"
  },
  "version": "b66be6a6d779e57163b0489329f50ea4b56bcbcc7b3d48ef39bae6b812a7f947"
}