nightmareai/arf-svox2 🖼️❓🔢 → 🖼️
About
Artistic Radiance Fields - Transfer the style of an image to a 3D scene (NeRF)
Example Output
Output
Performance Metrics
698.70s
Prediction Time
1009.12s
Total Time
All Input Parameters
{
"image": "https://replicate.delivery/mgxm/065ea769-0fe4-472b-961f-19e3d5a80902/941d4f18-97f8-4bdf-a0e6-bec0acd53301.png",
"scene": "room",
"num_epoches": "5"
}
Input Parameters
- image (required)
- Style image
- scene
- Pretrained NeRF scene to style
- num_epoches
- Epoches for running style transfer
Output Schema
Output
Example Execution Logs
Downloading checkpoint for room
Detected LLFF dataset
Using pre-scaled images from /root/.cache/arf/data/llff/room/images_4
Loaded LLFF data /root/.cache/arf/data/llff/room 10.706691140704915 91.66782174279389
recentered (3, 4)
[1.7806027 6.0023217]
Overriding offset 250-> 250
dmin = 1.780603, dmax = 6.002322, invz = 0, offset = 250
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z_bounds from LLFF: [1.7806026935577393, 6.002321720123291] (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/room/ckpt.npz'
ic| grid.basis_dim: 1
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)
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
ic| "Style image: ": 'Style image: '
args.style: '/tmp/tmpviwijbsm941d4f18-97f8-4bdf-a0e6-bec0acd53301.png'
style_img.shape: torch.Size([504, 420, 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: 26671680
batches_per_epoch: 5335
batch_size: 5000
optim_type: 'ray'
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4579 22.9125 : 86%|████████▌ | 4576/5335 [00:52<00:05, 127.19it/s]
4579 22.9125 : 86%|████████▌ | 4592/5335 [00:52<00:05, 134.53it/s]
4599 25.2750 : 86%|████████▌ | 4592/5335 [00:52<00:05, 134.53it/s]
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4619 25.6228 : 87%|████████▋ | 4630/5335 [00:52<00:04, 159.59it/s]
4639 26.5839 : 87%|████████▋ | 4630/5335 [00:52<00:04, 159.59it/s]
4639 26.5839 : 87%|████████▋ | 4650/5335 [00:52<00:04, 169.57it/s]
4659 25.9116 : 87%|████████▋ | 4650/5335 [00:52<00:04, 169.57it/s]
4659 25.9116 : 88%|████████▊ | 4669/5335 [00:52<00:03, 174.87it/s]
4679 24.2891 : 88%|████████▊ | 4669/5335 [00:52<00:03, 174.87it/s]
4679 24.2891 : 88%|████████▊ | 4688/5335 [00:52<00:03, 176.25it/s]
4699 24.8238 : 88%|████████▊ | 4688/5335 [00:52<00:03, 176.25it/s]
4699 24.8238 : 88%|████████▊ | 4706/5335 [00:52<00:03, 169.15it/s]
4719 26.1178 : 88%|████████▊ | 4706/5335 [00:53<00:03, 169.15it/s]
4719 26.1178 : 89%|████████▊ | 4724/5335 [00:53<00:03, 159.02it/s]
4739 27.9142 : 89%|████████▊ | 4724/5335 [00:53<00:03, 159.02it/s]
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4739 27.9142 : 89%|████████▉ | 4756/5335 [00:53<00:04, 127.70it/s]
4759 26.5263 : 89%|████████▉ | 4756/5335 [00:53<00:04, 127.70it/s]
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4839 25.2130 : 91%|█████████ | 4853/5335 [00:54<00:04, 119.14it/s]
4859 24.8576 : 91%|█████████ | 4853/5335 [00:54<00:04, 119.14it/s]
4859 24.8576 : 91%|█████████ | 4866/5335 [00:54<00:04, 108.31it/s]
4859 24.8576 : 91%|█████████▏| 4878/5335 [00:54<00:04, 101.75it/s]
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4979 24.6053 : 93%|█████████▎| 4971/5335 [00:55<00:03, 97.82it/s]
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4979 24.6053 : 94%|█████████▎| 4991/5335 [00:55<00:03, 89.77it/s]
4999 24.9791 : 94%|█████████▎| 4991/5335 [00:55<00:03, 89.77it/s]
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4999 24.9791 : 94%|█████████▍| 5018/5335 [00:56<00:04, 70.37it/s]
5019 25.1175 : 94%|█████████▍| 5018/5335 [00:56<00:04, 70.37it/s]
5019 25.1175 : 94%|█████████▍| 5026/5335 [00:56<00:04, 67.84it/s]
5019 25.1175 : 94%|█████████▍| 5034/5335 [00:56<00:04, 70.11it/s]
5039 27.5497 : 94%|█████████▍| 5034/5335 [00:56<00:04, 70.11it/s]
5039 27.5497 : 95%|█████████▍| 5044/5335 [00:56<00:03, 76.34it/s]
5039 27.5497 : 95%|█████████▍| 5055/5335 [00:56<00:03, 84.41it/s]
5059 25.4145 : 95%|█████████▍| 5055/5335 [00:56<00:03, 84.41it/s]
5059 25.4145 : 95%|█████████▍| 5067/5335 [00:56<00:02, 92.43it/s]
5079 26.6469 : 95%|█████████▍| 5067/5335 [00:56<00:02, 92.43it/s]
5079 26.6469 : 95%|█████████▌| 5080/5335 [00:56<00:02, 101.42it/s]
5079 26.6469 : 95%|█████████▌| 5093/5335 [00:56<00:02, 108.19it/s]
5099 27.8850 : 95%|█████████▌| 5093/5335 [00:56<00:02, 108.19it/s]
5099 27.8850 : 96%|█████████▌| 5106/5335 [00:56<00:02, 113.36it/s]
5099 27.8850 : 96%|█████████▌| 5119/5335 [00:57<00:01, 117.02it/s]
5119 26.5010 : 96%|█████████▌| 5119/5335 [00:57<00:01, 117.02it/s]
5119 26.5010 : 96%|█████████▌| 5131/5335 [00:57<00:01, 116.87it/s]
5139 24.4466 : 96%|█████████▌| 5131/5335 [00:57<00:01, 116.87it/s]
5139 24.4466 : 96%|█████████▋| 5143/5335 [00:57<00:01, 112.66it/s]
5139 24.4466 : 97%|█████████▋| 5155/5335 [00:57<00:01, 104.00it/s]
5159 24.9924 : 97%|█████████▋| 5155/5335 [00:57<00:01, 104.00it/s]
5159 24.9924 : 97%|█████████▋| 5166/5335 [00:57<00:01, 95.70it/s]
5159 24.9924 : 97%|█████████▋| 5176/5335 [00:57<00:01, 89.05it/s]
5179 25.8780 : 97%|█████████▋| 5176/5335 [00:57<00:01, 89.05it/s]
5179 25.8780 : 97%|█████████▋| 5186/5335 [00:57<00:01, 88.96it/s]
5179 25.8780 : 97%|█████████▋| 5198/5335 [00:57<00:01, 95.92it/s]
5199 27.1582 : 97%|█████████▋| 5198/5335 [00:57<00:01, 95.92it/s]
5199 27.1582 : 98%|█████████▊| 5211/5335 [00:58<00:01, 104.68it/s]
5219 26.3454 : 98%|█████████▊| 5211/5335 [00:58<00:01, 104.68it/s]
5219 26.3454 : 98%|█████████▊| 5225/5335 [00:58<00:00, 114.38it/s]
5239 27.0302 : 98%|█████████▊| 5225/5335 [00:58<00:00, 114.38it/s]
5239 27.0302 : 98%|█████████▊| 5240/5335 [00:58<00:00, 124.02it/s]
5239 27.0302 : 99%|█████████▊| 5256/5335 [00:58<00:00, 131.86it/s]
5259 26.0069 : 99%|█████████▊| 5256/5335 [00:58<00:00, 131.86it/s]
5259 26.0069 : 99%|█████████▉| 5271/5335 [00:58<00:00, 136.82it/s]
5279 24.9670 : 99%|█████████▉| 5271/5335 [00:58<00:00, 136.82it/s]
5279 24.9670 : 99%|█████████▉| 5286/5335 [00:58<00:00, 138.57it/s]
5299 25.1446 : 99%|█████████▉| 5286/5335 [00:58<00:00, 138.57it/s]
5299 25.1446 : 99%|█████████▉| 5300/5335 [00:58<00:00, 135.27it/s]
5299 25.1446 : 100%|█████████▉| 5314/5335 [00:58<00:00, 125.96it/s]
5319 24.7315 : 100%|█████████▉| 5314/5335 [00:58<00:00, 125.96it/s]
5319 24.7315 : 100%|█████████▉| 5327/5335 [00:58<00:00, 116.44it/s]
5319 24.7315 : 100%|██████████| 5335/5335 [00:59<00:00, 90.42it/s]
ic| "Training epoch: ": 'Training epoch: '
epoch_id: 1
epoch_size: 26671680
batches_per_epoch: 5335
batch_size: 5000
optim_type: 'ray'
0%| | 0/5335 [00:00<?, ?it/s]
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0%| | 6/5335 [00:00<03:13, 27.51it/s]
0%| | 11/5335 [00:00<02:26, 36.29it/s]
0%| | 16/5335 [00:00<02:11, 40.48it/s]
5354 19.5023 : 0%| | 16/5335 [00:00<02:11, 40.48it/s]
5354 19.5023 : 0%| | 21/5335 [00:00<02:03, 43.00it/s]
5354 19.5023 : 1%| | 27/5335 [00:00<01:56, 45.72it/s]
5354 19.5023 : 1%| | 33/5335 [00:00<01:51, 47.54it/s]
5354 19.5023 : 1%| | 39/5335 [00:00<01:43, 51.03it/s]
5374 22.0903 : 1%| | 39/5335 [00:00<01:43, 51.03it/s]
5374 22.0903 : 1%| | 46/5335 [00:00<01:34, 56.08it/s]
5374 22.0903 : 1%| | 54/5335 [00:01<01:25, 61.77it/s]
5394 22.4892 : 1%| | 54/5335 [00:01<01:25, 61.77it/s]
5394 22.4892 : 1%| | 62/5335 [00:01<01:20, 65.69it/s]
5394 22.4892 : 1%|▏ | 70/5335 [00:01<01:15, 69.61it/s]
5394 22.4892 : 1%|▏ | 78/5335 [00:01<01:12, 72.54it/s]
5414 22.9025 : 1%|▏ | 78/5335 [00:01<01:12, 72.54it/s]
5414 22.9025 : 2%|▏ | 86/5335 [00:01<01:10, 74.44it/s]
5414 22.9025 : 2%|▏ | 94/5335 [00:01<01:09, 75.75it/s]
5434 22.8703 : 2%|▏ | 94/5335 [00:01<01:09, 75.75it/s]
5434 22.8703 : 2%|▏ | 102/5335 [00:01<01:08, 76.74it/s]
5434 22.8703 : 2%|▏ | 110/5335 [00:01<01:07, 77.51it/s]
5434 22.8703 : 2%|▏ | 118/5335 [00:01<01:08, 75.79it/s]
5454 21.3938 : 2%|▏ | 118/5335 [00:01<01:08, 75.79it/s]
5454 21.3938 : 2%|▏ | 126/5335 [00:02<01:12, 71.99it/s]
5454 21.3938 : 3%|▎ | 134/5335 [00:02<01:17, 66.73it/s]
5474 21.1332 : 3%|▎ | 134/5335 [00:02<01:17, 66.73it/s]
5474 21.1332 : 3%|▎ | 141/5335 [00:02<01:23, 62.57it/s]
5474 21.1332 : 3%|▎ | 148/5335 [00:02<01:26, 60.13it/s]
5474 21.1332 : 3%|▎ | 155/5335 [00:02<01:25, 60.23it/s]
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ic| "Training epoch: ": 'Training epoch: '
epoch_id: 2
epoch_size: 35
batches_per_epoch: 35
batch_size: 1
optim_type: 'image'
0%| | 0/35 [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: 35
batches_per_epoch: 35
batch_size: 1
optim_type: 'image'
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ic| "Training epoch: ": 'Training epoch: '
epoch_id: 4
epoch_size: 35
batches_per_epoch: 35
batch_size: 1
optim_type: 'image'
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ic| "Training epoch: ": 'Training epoch: '
epoch_id: 5
epoch_size: 35
batches_per_epoch: 35
batch_size: 1
optim_type: 'image'
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ic| "Training epoch: ": 'Training epoch: '
epoch_id: 6
epoch_size: 35
batches_per_epoch: 35
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/room/images_4
Loaded LLFF data /root/.cache/arf/data/llff/room 10.706691140704915 91.66782174279389
recentered (3, 4)
[1.7806027 6.0023217]
Overriding offset 250-> 250
dmin = 1.780603, dmax = 6.002322, invz = 0, offset = 250
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z_bounds from LLFF: [1.7806026935577393, 6.002321720123291] (not used)
scene_radius [1.496031746031746, 1.6613756613756614, 1.0]
Writing to /root/.cache/arf/ckpt_arf/llff/room/6a105837-ea37-4dd4-b598-503144108999/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).
Version Details
- Version ID
7bb81e4046b96358001e6275e36eee3e7228fe9f1a0e1515a5f553cbbcad07e7- Version Created
- June 24, 2022