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jd7h /luciddreamer:fbf8e0df
Input
Run this model in Node.js with one line of code:
npx create-replicate --model=jd7h/luciddreamer
or set up a project from scratch
Install Replicate’s Node.js client library:
npm install replicateSet 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 jd7h/luciddreamer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"jd7h/luciddreamer:fbf8e0dfef4ca0c0de45cf1afbf12c81667ee29fd79852852262aee4f167fbf5",
{
input: {
cfg: 7.5,
prompt: "A dog on a skateboard, hair waving in the wind, HDR, photorealistic, 8K",
iterations: 1000,
neg_prompt: "unrealistic, blurry, low quality, out of focus, ugly, low contrast, dull, low resolution, distorted, boring",
init_prompt: "dog"
}
}
);
console.log(output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:
pip install replicateSet 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 replicateRun jd7h/luciddreamer using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"jd7h/luciddreamer:fbf8e0dfef4ca0c0de45cf1afbf12c81667ee29fd79852852262aee4f167fbf5",
input={
"cfg": 7.5,
"prompt": "A dog on a skateboard, hair waving in the wind, HDR, photorealistic, 8K",
"iterations": 1000,
"neg_prompt": "unrealistic, blurry, low quality, out of focus, ugly, low contrast, dull, low resolution, distorted, boring",
"init_prompt": "dog"
}
)
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 jd7h/luciddreamer 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": "fbf8e0dfef4ca0c0de45cf1afbf12c81667ee29fd79852852262aee4f167fbf5",
"input": {
"cfg": 7.5,
"prompt": "A dog on a skateboard, hair waving in the wind, HDR, photorealistic, 8K",
"iterations": 1000,
"neg_prompt": "unrealistic, blurry, low quality, out of focus, ugly, low contrast, dull, low resolution, distorted, boring",
"init_prompt": "dog"
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Add a payment method to run this model.
Sign in with GitHub
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terms of service and privacy policy
Output
{
"completed_at": "2023-12-22T16:32:55.487744Z",
"created_at": "2023-12-22T16:14:30.678052Z",
"data_removed": false,
"error": null,
"id": "3atbyrtb3q54kierm4sx5u3bsa",
"input": {
"cfg": 7.5,
"prompt": "A dog on a skateboard, hair waving in the wind, HDR, photorealistic, 8K",
"iterations": 1000,
"neg_prompt": "unrealistic, blurry, low quality, out of focus, ugly, low contrast, dull, low resolution, distorted, boring",
"init_prompt": "dog"
},
"logs": "Using seed: 2212729883\nTest iter: [1, 200, 400, 600, 800, 1000]\nSave iter: [500, 1000]\nOptimizing\nOutput folder: ./output/Replicate [22/12 16:16:11]\nTensorboard not available: not logging progress [22/12 16:16:11]\nReading Test Transforms [22/12 16:16:11]\ncreating base model...[22/12 16:16:12]\n 0%| | 0.00/890M [00:00<?, ?iB/s]\n 1%|▍ | 10.9M/890M [00:00<00:08, 115MiB/s]\n 3%|▉ | 22.7M/890M [00:00<00:07, 120MiB/s]\n 4%|█▍ | 34.2M/890M [00:00<00:07, 114MiB/s]\n 5%|█▉ | 45.1M/890M [00:00<00:08, 106MiB/s]\n 6%|██▌ | 57.7M/890M [00:00<00:07, 115MiB/s]\n 8%|███ | 68.7M/890M [00:00<00:07, 111MiB/s]\n 9%|███▍ | 79.4M/890M [00:00<00:07, 111MiB/s]\n 10%|███▉ | 90.0M/890M [00:00<00:07, 110MiB/s]\n 12%|████▋ | 103M/890M [00:00<00:07, 118MiB/s]\n 13%|█████▏ | 115M/890M [00:01<00:06, 120MiB/s]\n 14%|█████▋ | 127M/890M [00:01<00:06, 123MiB/s]\n 16%|██████▎ | 140M/890M [00:01<00:06, 125MiB/s]\n 17%|██████▊ | 152M/890M [00:01<00:06, 114MiB/s]\n 18%|███████▎ | 163M/890M [00:01<00:07, 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[22/12 16:16:28]\ndownloading base checkpoint... [22/12 16:16:33]\n 0%| | 0.00/161M [00:00<?, ?iB/s]\n 6%|▌ | 9.18M/161M [00:00<00:01, 90.8MiB/s]\n 11%|█▏ | 18.3M/161M [00:00<00:01, 74.8MiB/s]\n 16%|█▌ | 26.2M/161M [00:00<00:01, 76.4MiB/s]\n 21%|██ | 34.0M/161M [00:00<00:01, 76.3MiB/s]\n 26%|██▌ | 42.3M/161M [00:00<00:01, 78.6MiB/s]\n 31%|███ | 50.3M/161M [00:00<00:01, 78.7MiB/s]\n 37%|███▋ | 59.1M/161M [00:00<00:01, 81.6MiB/s]\n 42%|████▏ | 67.4M/161M [00:00<00:01, 81.8MiB/s]\n 47%|████▋ | 76.0M/161M [00:00<00:01, 82.6MiB/s]\n 52%|█████▏ | 84.2M/161M [00:01<00:00, 80.5MiB/s]\n 57%|█████▋ | 92.3M/161M [00:01<00:00, 75.6MiB/s]\n 62%|██████▏ | 99.9M/161M [00:01<00:00, 73.4MiB/s]\n 68%|██████▊ | 109M/161M [00:01<00:00, 77.9MiB/s] \n 73%|███████▎ | 117M/161M [00:01<00:00, 78.7MiB/s]\n 78%|███████▊ | 126M/161M [00:01<00:00, 81.0MiB/s]\n 83%|████████▎ | 134M/161M [00:01<00:00, 73.2MiB/s]\n 88%|████████▊ | 141M/161M [00:01<00:00, 68.7MiB/s]\n 92%|█████████▏| 149M/161M [00:01<00:00, 70.8MiB/s]\n 97%|█████████▋| 157M/161M [00:02<00:00, 71.5MiB/s]\n100%|██████████| 161M/161M [00:02<00:00, 76.3MiB/s]\ndownloading upsampler checkpoint... [22/12 16:16:35]\n 0%| | 0.00/162M [00:00<?, ?iB/s]\n 6%|▌ | 8.98M/162M [00:00<00:01, 84.4MiB/s]\n 11%|█ | 17.7M/162M [00:00<00:01, 85.6MiB/s]\n 16%|█▌ | 26.3M/162M [00:00<00:01, 68.1MiB/s]\n 21%|██▏ | 34.6M/162M [00:00<00:01, 72.9MiB/s]\n 26%|██▋ | 42.8M/162M [00:00<00:01, 76.0MiB/s]\n 31%|███▏ | 50.7M/162M [00:00<00:01, 72.0MiB/s]\n 36%|███▋ | 58.7M/162M [00:00<00:01, 74.6MiB/s]\n 41%|████ | 66.3M/162M [00:00<00:01, 73.9MiB/s]\n 46%|████▋ | 75.0M/162M [00:00<00:01, 77.9MiB/s]\n 51%|█████ | 82.9M/162M [00:01<00:01, 76.4MiB/s]\n 56%|█████▋ | 91.5M/162M [00:01<00:00, 79.1MiB/s]\n 61%|██████▏ | 99.4M/162M [00:01<00:00, 76.0MiB/s]\n 66%|██████▌ | 107M/162M [00:01<00:00, 75.1MiB/s] \n 71%|███████ | 115M/162M [00:01<00:00, 72.0MiB/s]\n 75%|███████▌ | 122M/162M [00:01<00:00, 67.8MiB/s]\n 79%|███████▉ | 129M/162M [00:01<00:00, 65.5MiB/s]\n 84%|████████▎ | 136M/162M [00:01<00:00, 66.2MiB/s]\n 89%|████████▉ | 144M/162M [00:01<00:00, 70.9MiB/s]\n 94%|█████████▍| 153M/162M [00:02<00:00, 76.2MiB/s]\n 99%|█████████▉| 160M/162M [00:02<00:00, 62.2MiB/s]\n100%|██████████| 162M/162M [00:02<00:00, 71.7MiB/s]\n0it [00:00, ?it/s]\n1it [00:01, 1.13s/it]\n4it [00:01, 3.98it/s]\n7it [00:01, 7.11it/s]\n10it [00:01, 10.06it/s]\n13it [00:01, 12.68it/s]\n16it [00:01, 14.89it/s]\n19it [00:01, 16.66it/s]\n22it [00:02, 18.06it/s]\n25it [00:02, 19.11it/s]\n28it [00:02, 19.89it/s]\n31it [00:02, 20.46it/s]\n34it [00:02, 20.85it/s]\n37it [00:02, 21.15it/s]\n40it [00:02, 21.35it/s]\n43it [00:03, 21.48it/s]\n46it [00:03, 21.57it/s]\n49it [00:03, 21.63it/s]\n52it [00:03, 21.66it/s]\n55it [00:03, 21.69it/s]\n58it [00:03, 21.74it/s]\n61it [00:03, 21.78it/s]\n64it [00:04, 21.79it/s]\n67it [00:04, 16.75it/s]\n69it [00:04, 11.47it/s]\n71it [00:05, 9.06it/s]\n73it [00:05, 7.75it/s]\n74it [00:05, 7.28it/s]\n75it [00:05, 6.86it/s]\n76it [00:05, 6.51it/s]\n77it [00:06, 6.23it/s]\n78it [00:06, 6.03it/s]\n79it [00:06, 5.87it/s]\n80it [00:06, 5.76it/s]\n81it [00:06, 5.68it/s]\n82it [00:07, 5.62it/s]\n83it [00:07, 5.58it/s]\n84it [00:07, 5.54it/s]\n85it [00:07, 5.52it/s]\n86it [00:07, 5.50it/s]\n87it [00:07, 5.50it/s]\n88it [00:08, 5.49it/s]\n89it [00:08, 5.48it/s]\n90it [00:08, 5.48it/s]\n91it [00:08, 5.48it/s]\n92it [00:08, 5.47it/s]\n93it [00:09, 5.47it/s]\n94it [00:09, 5.47it/s]\n95it [00:09, 5.47it/s]\n96it [00:09, 5.46it/s]\n97it [00:09, 5.45it/s]\n98it [00:09, 5.45it/s]\n99it [00:10, 5.46it/s]\n100it [00:10, 5.46it/s]\n101it [00:10, 5.46it/s]\n102it [00:10, 5.45it/s]\n103it [00:10, 5.45it/s]\n104it [00:11, 5.42it/s]\n105it [00:11, 5.44it/s]\n106it [00:11, 5.45it/s]\n107it [00:11, 5.44it/s]\n108it [00:11, 5.44it/s]\n109it [00:11, 5.44it/s]\n110it [00:12, 5.43it/s]\n111it [00:12, 5.43it/s]\n112it [00:12, 5.43it/s]\n113it [00:12, 5.43it/s]\n114it [00:12, 5.43it/s]\n115it [00:13, 5.42it/s]\n116it [00:13, 5.42it/s]\n117it [00:13, 5.42it/s]\n118it [00:13, 5.42it/s]\n119it [00:13, 5.42it/s]\n120it [00:14, 5.42it/s]\n121it [00:14, 5.41it/s]\n122it [00:14, 5.42it/s]\n123it [00:14, 5.42it/s]\n124it [00:14, 5.42it/s]\n125it [00:14, 5.42it/s]\n126it [00:15, 5.42it/s]\n127it [00:15, 5.42it/s]\n128it [00:15, 5.42it/s]\n129it [00:15, 5.42it/s]\n130it [00:15, 8.29it/s]\nGenerating random point cloud (81920)... [22/12 16:16:53]\nNumber of points at initialisation : 81920 [22/12 16:16:54]\ntrain_process is in : ./output/Replicate/train_process/ [22/12 16:16:54]\n[INFO] loading stable diffusion... [22/12 16:16:57]\n[INFO] loaded stable diffusion! [22/12 16:16:59]\ntest views is in : ./output/Replicate/test_six_views/1_iteration [22/12 16:17:15]\n[ITER 1] Eval Done! [22/12 16:17:15]\nvideos is in : ./output/Replicate/videos/1_iteration [22/12 16:17:15]\nGenerating Video using 240 different view points[22/12 16:17:21]\n[ITER 1] Video Save Done! [22/12 16:17:24]\nTraining progress: 0%| | 0/1000 [00:00<?, ?it/s]\nTraining progress: 0%| | 0/1000 [00:32<?, ?it/s, Loss=1.0017186]\nTraining progress: 1%| | 10/1000 [00:32<53:16, 3.23s/it, Loss=1.0017186]\nTraining progress: 1%| | 10/1000 [00:40<53:16, 3.23s/it, Loss=1.0077757]\nTraining progress: 2%|▏ | 20/1000 [00:40<29:57, 1.83s/it, Loss=1.0077757]\nTraining progress: 2%|▏ | 20/1000 [00:49<29:57, 1.83s/it, Loss=1.0079711]\nTraining progress: 3%|▎ | 30/1000 [00:49<22:13, 1.37s/it, Loss=1.0079711]\nTraining progress: 3%|▎ | 30/1000 [00:57<22:13, 1.37s/it, Loss=1.0078438]\nTraining progress: 4%|▍ | 40/1000 [00:57<18:36, 1.16s/it, Loss=1.0078438]\nTraining progress: 4%|▍ | 40/1000 [01:05<18:36, 1.16s/it, Loss=1.0082182]\nTraining progress: 5%|▌ | 50/1000 [01:05<16:07, 1.02s/it, Loss=1.0082182]\nTraining progress: 5%|▌ | 50/1000 [01:13<16:07, 1.02s/it, Loss=1.0086312]\nTraining progress: 6%|▌ | 60/1000 [01:13<14:56, 1.05it/s, Loss=1.0086312]\nTraining progress: 6%|▌ | 60/1000 [01:21<14:56, 1.05it/s, Loss=1.0084402]\nTraining progress: 7%|▋ | 70/1000 [01:21<14:01, 1.11it/s, Loss=1.0084402]\nTraining progress: 7%|▋ | 70/1000 [01:29<14:01, 1.11it/s, Loss=1.0085526]\nTraining progress: 8%|▊ | 80/1000 [01:29<13:33, 1.13it/s, Loss=1.0085526]\nTraining progress: 8%|▊ | 80/1000 [01:38<13:33, 1.13it/s, Loss=1.0085533]\nTraining progress: 9%|▉ | 90/1000 [01:38<13:09, 1.15it/s, Loss=1.0085533]\nTraining progress: 9%|▉ | 90/1000 [01:48<13:09, 1.15it/s, Loss=1.0085533]\nTraining progress: 10%|█ | 100/1000 [01:48<13:34, 1.11it/s, Loss=1.0085533]\nTraining progress: 10%|█ | 100/1000 [01:56<13:34, 1.11it/s, Loss=1.0088473]\nTraining progress: 11%|█ | 110/1000 [01:56<12:57, 1.15it/s, Loss=1.0088473]\nTraining progress: 11%|█ | 110/1000 [02:04<12:57, 1.15it/s, Loss=1.0088198]\nTraining progress: 12%|█▏ | 120/1000 [02:04<12:33, 1.17it/s, Loss=1.0088198]\nTraining progress: 12%|█▏ | 120/1000 [02:12<12:33, 1.17it/s, Loss=1.0089299]\nTraining progress: 13%|█▎ | 130/1000 [02:12<12:04, 1.20it/s, Loss=1.0089299]\nTraining progress: 13%|█▎ | 130/1000 [02:20<12:04, 1.20it/s, Loss=1.0090425]\nTraining progress: 14%|█▍ | 140/1000 [02:20<11:48, 1.21it/s, Loss=1.0090425]\nTraining progress: 14%|█▍ | 140/1000 [02:28<11:48, 1.21it/s, Loss=1.0093045]\nTraining progress: 15%|█▌ | 150/1000 [02:28<11:36, 1.22it/s, Loss=1.0093045]\nTraining progress: 15%|█▌ | 150/1000 [02:36<11:36, 1.22it/s, Loss=1.0089162]\nTraining progress: 16%|█▌ | 160/1000 [02:36<11:19, 1.24it/s, Loss=1.0089162]\nTraining progress: 16%|█▌ | 160/1000 [02:44<11:19, 1.24it/s, Loss=1.0091812]\nTraining progress: 17%|█▋ | 170/1000 [02:44<11:13, 1.23it/s, Loss=1.0091812]\nTraining progress: 17%|█▋ | 170/1000 [02:52<11:13, 1.23it/s, Loss=1.0082853]\nTraining progress: 18%|█▊ | 180/1000 [02:52<11:06, 1.23it/s, Loss=1.0082853]\nTraining progress: 18%|█▊ | 180/1000 [03:00<11:06, 1.23it/s, Loss=1.0082300]\nTraining progress: 19%|█▉ | 190/1000 [03:00<10:48, 1.25it/s, Loss=1.0082300]\nTraining progress: 19%|█▉ | 190/1000 [03:09<10:48, 1.25it/s, Loss=1.0085656]\ntest views is in : ./output/Replicate/test_six_views/200_iteration[22/12 16:20:08]\n[ITER 200] Eval Done! [22/12 16:20:09]\nvideos is in : ./output/Replicate/videos/200_iteration[22/12 16:20:09]\nGenerating Video using 240 different view points[22/12 16:20:15]\n[ITER 200] Video Save Done! [22/12 16:20:17]\nTraining progress: 20%|██ | 200/1000 [03:09<11:08, 1.20it/s, Loss=1.0085656]\nTraining progress: 20%|██ | 200/1000 [03:27<11:08, 1.20it/s, Loss=1.0087679]\nTraining progress: 21%|██ | 210/1000 [03:27<14:44, 1.12s/it, Loss=1.0087679]\nTraining progress: 21%|██ | 210/1000 [03:35<14:44, 1.12s/it, Loss=1.0089540]\nTraining progress: 22%|██▏ | 220/1000 [03:35<13:32, 1.04s/it, Loss=1.0089540]\nTraining progress: 22%|██▏ | 220/1000 [03:44<13:32, 1.04s/it, Loss=1.0088401]\nTraining progress: 23%|██▎ | 230/1000 [03:44<12:39, 1.01it/s, Loss=1.0088401]\nTraining progress: 23%|██▎ | 230/1000 [03:52<12:39, 1.01it/s, Loss=1.0080839]\nTraining progress: 24%|██▍ | 240/1000 [03:52<12:04, 1.05it/s, Loss=1.0080839]\nTraining progress: 24%|██▍ | 240/1000 [04:01<12:04, 1.05it/s, Loss=1.0084546]\nTraining progress: 25%|██▌ | 250/1000 [04:01<11:26, 1.09it/s, Loss=1.0084546]\nTraining progress: 25%|██▌ | 250/1000 [04:09<11:26, 1.09it/s, Loss=1.0087927]\nTraining progress: 26%|██▌ | 260/1000 [04:09<10:56, 1.13it/s, Loss=1.0087927]\nTraining progress: 26%|██▌ | 260/1000 [04:17<10:56, 1.13it/s, Loss=1.0085855]\nTraining progress: 27%|██▋ | 270/1000 [04:17<10:30, 1.16it/s, Loss=1.0085855]\nTraining progress: 27%|██▋ | 270/1000 [04:26<10:30, 1.16it/s, Loss=1.0083972]\nTraining progress: 28%|██▊ | 280/1000 [04:26<10:20, 1.16it/s, Loss=1.0083972]\nTraining progress: 28%|██▊ | 280/1000 [04:34<10:20, 1.16it/s, Loss=1.0090715]\nTraining progress: 29%|██▉ | 290/1000 [04:34<10:08, 1.17it/s, Loss=1.0090715]\nTraining progress: 29%|██▉ | 290/1000 [04:44<10:08, 1.17it/s, Loss=1.0085778]\nTraining progress: 30%|███ | 300/1000 [04:44<10:18, 1.13it/s, Loss=1.0085778]\nTraining progress: 30%|███ | 300/1000 [04:52<10:18, 1.13it/s, Loss=1.0078171]\nTraining progress: 31%|███ | 310/1000 [04:52<09:56, 1.16it/s, Loss=1.0078171]\nTraining progress: 31%|███ | 310/1000 [05:01<09:56, 1.16it/s, Loss=1.0089422]\nTraining progress: 32%|███▏ | 320/1000 [05:01<10:04, 1.12it/s, Loss=1.0089422]\nTraining progress: 32%|███▏ | 320/1000 [05:10<10:04, 1.12it/s, Loss=1.0096785]\nTraining progress: 33%|███▎ | 330/1000 [05:10<09:47, 1.14it/s, Loss=1.0096785]\nTraining progress: 33%|███▎ | 330/1000 [05:18<09:47, 1.14it/s, Loss=1.0101533]\nTraining progress: 34%|███▍ | 340/1000 [05:18<09:31, 1.15it/s, Loss=1.0101533]\nTraining progress: 34%|███▍ | 340/1000 [05:26<09:31, 1.15it/s, Loss=1.0105944]\nTraining progress: 35%|███▌ | 350/1000 [05:26<09:15, 1.17it/s, Loss=1.0105944]\nTraining progress: 35%|███▌ | 350/1000 [05:35<09:15, 1.17it/s, Loss=1.0102142]\nTraining progress: 36%|███▌ | 360/1000 [05:35<09:04, 1.18it/s, Loss=1.0102142]\nTraining progress: 36%|███▌ | 360/1000 [05:44<09:04, 1.18it/s, Loss=1.0089403]\nTraining progress: 37%|███▋ | 370/1000 [05:44<08:59, 1.17it/s, Loss=1.0089403]\nTraining progress: 37%|███▋ | 370/1000 [05:52<08:59, 1.17it/s, Loss=1.0095749]\nTraining progress: 38%|███▊ | 380/1000 [05:52<08:44, 1.18it/s, Loss=1.0095749]\nTraining progress: 38%|███▊ | 380/1000 [06:00<08:44, 1.18it/s, Loss=1.0104323]\nTraining progress: 39%|███▉ | 390/1000 [06:00<08:28, 1.20it/s, Loss=1.0104323]\nTraining progress: 39%|███▉ | 390/1000 [06:09<08:28, 1.20it/s, Loss=1.0101397]\ntest views is in : ./output/Replicate/test_six_views/400_iteration [22/12 16:23:08]\n[ITER 400] Eval Done! [22/12 16:23:09]\nvideos is in : ./output/Replicate/videos/400_iteration [22/12 16:23:09]\nGenerating Video using 240 different view points[22/12 16:23:15]\n[ITER 400] Video Save Done! [22/12 16:23:18]\nTraining progress: 40%|████ | 400/1000 [06:09<08:33, 1.17it/s, Loss=1.0101397]\nTraining progress: 40%|████ | 400/1000 [06:27<08:33, 1.17it/s, Loss=1.0088887]\nTraining progress: 41%|████ | 410/1000 [06:27<11:11, 1.14s/it, Loss=1.0088887]\nTraining progress: 41%|████ | 410/1000 [06:36<11:11, 1.14s/it, Loss=1.0095410]\nTraining progress: 42%|████▏ | 420/1000 [06:36<10:23, 1.08s/it, Loss=1.0095410]\nTraining progress: 42%|████▏ | 420/1000 [06:45<10:23, 1.08s/it, Loss=1.0092835]\nTraining progress: 43%|████▎ | 430/1000 [06:45<09:41, 1.02s/it, Loss=1.0092835]\nTraining progress: 43%|████▎ | 430/1000 [06:54<09:41, 1.02s/it, Loss=1.0091632]\nTraining progress: 44%|████▍ | 440/1000 [06:54<09:09, 1.02it/s, Loss=1.0091632]\nTraining progress: 44%|████▍ | 440/1000 [07:03<09:09, 1.02it/s, Loss=1.0088365]\nTraining progress: 45%|████▌ | 450/1000 [07:03<08:45, 1.05it/s, Loss=1.0088365]\nTraining progress: 45%|████▌ | 450/1000 [07:11<08:45, 1.05it/s, Loss=1.0097462]\nTraining progress: 46%|████▌ | 460/1000 [07:11<08:13, 1.09it/s, Loss=1.0097462]\nTraining progress: 46%|████▌ | 460/1000 [07:19<08:13, 1.09it/s, Loss=1.0092854]\nTraining progress: 47%|████▋ | 470/1000 [07:19<07:49, 1.13it/s, Loss=1.0092854]\nTraining progress: 47%|████▋ | 470/1000 [07:28<07:49, 1.13it/s, Loss=1.0093460]\nTraining progress: 48%|████▊ | 480/1000 [07:28<07:39, 1.13it/s, Loss=1.0093460]\nTraining progress: 48%|████▊ | 480/1000 [07:37<07:39, 1.13it/s, Loss=1.0090952]\nscale up theta_range to: [60, 90] [22/12 16:24:43]\nscale up radius_range to: [4.9399999999999995, 5.225] [22/12 16:24:43]\nscale up phi_range to: [-180, 180] [22/12 16:24:43]\nscale up fovy_range to: [0.24, 0.6] [22/12 16:24:43]\nTraining progress: 49%|████▉ | 490/1000 [07:37<07:25, 1.14it/s, Loss=1.0090952]\nTraining progress: 49%|████▉ | 490/1000 [07:45<07:25, 1.14it/s, Loss=1.0091098]\n[ITER 500] Saving Gaussians [22/12 16:24:45]\nTraining progress: 50%|█████ | 500/1000 [07:45<07:20, 1.14it/s, Loss=1.0091098]\nTraining progress: 50%|█████ | 500/1000 [07:57<07:20, 1.14it/s, Loss=1.0093191]\nTraining progress: 51%|█████ | 510/1000 [07:57<07:47, 1.05it/s, Loss=1.0093191]\nTraining progress: 51%|█████ | 510/1000 [08:05<07:47, 1.05it/s, Loss=1.0097375]\nTraining progress: 52%|█████▏ | 520/1000 [08:05<07:20, 1.09it/s, Loss=1.0097375]\nTraining progress: 52%|█████▏ | 520/1000 [08:14<07:20, 1.09it/s, Loss=1.0100511]\nTraining progress: 53%|█████▎ | 530/1000 [08:14<07:02, 1.11it/s, Loss=1.0100511]\nTraining progress: 53%|█████▎ | 530/1000 [08:23<07:02, 1.11it/s, Loss=1.0102542]\nTraining progress: 54%|█████▍ | 540/1000 [08:23<06:58, 1.10it/s, Loss=1.0102542]\nTraining progress: 54%|█████▍ | 540/1000 [08:32<06:58, 1.10it/s, Loss=1.0098752]\nTraining progress: 55%|█████▌ | 550/1000 [08:32<06:42, 1.12it/s, Loss=1.0098752]\nTraining progress: 55%|█████▌ | 550/1000 [08:40<06:42, 1.12it/s, Loss=1.0098831]\nTraining progress: 56%|█████▌ | 560/1000 [08:40<06:26, 1.14it/s, Loss=1.0098831]\nTraining progress: 56%|█████▌ | 560/1000 [08:49<06:26, 1.14it/s, Loss=1.0099793]\nTraining progress: 57%|█████▋ | 570/1000 [08:49<06:14, 1.15it/s, Loss=1.0099793]\nTraining progress: 57%|█████▋ | 570/1000 [08:57<06:14, 1.15it/s, Loss=1.0098103]\nTraining progress: 58%|█████▊ | 580/1000 [08:57<06:02, 1.16it/s, Loss=1.0098103]\nTraining progress: 58%|█████▊ | 580/1000 [09:06<06:02, 1.16it/s, Loss=1.0090983]\nTraining progress: 59%|█████▉ | 590/1000 [09:06<05:56, 1.15it/s, Loss=1.0090983]\nTraining progress: 59%|█████▉ | 590/1000 [09:15<05:56, 1.15it/s, Loss=1.0095025]\ntest views is in : ./output/Replicate/test_six_views/600_iteration [22/12 16:26:14]\n[ITER 600] Eval Done! [22/12 16:26:15]\nvideos is in : ./output/Replicate/videos/600_iteration[22/12 16:26:15]\nGenerating Video using 240 different view points[22/12 16:26:21]\n[ITER 600] Video Save Done! [22/12 16:26:24]\nTraining progress: 60%|██████ | 600/1000 [09:15<05:55, 1.13it/s, Loss=1.0095025]\nTraining progress: 60%|██████ | 600/1000 [09:34<05:55, 1.13it/s, Loss=1.0120878]\nTraining progress: 61%|██████ | 610/1000 [09:34<07:41, 1.18s/it, Loss=1.0120878]\nTraining progress: 61%|██████ | 610/1000 [09:43<07:41, 1.18s/it, Loss=1.0099001]\nTraining progress: 62%|██████▏ | 620/1000 [09:43<06:56, 1.10s/it, Loss=1.0099001]\nTraining progress: 62%|██████▏ | 620/1000 [09:51<06:56, 1.10s/it, Loss=1.0100498]\nTraining progress: 63%|██████▎ | 630/1000 [09:51<06:19, 1.03s/it, Loss=1.0100498]\nTraining progress: 63%|██████▎ | 630/1000 [10:01<06:19, 1.03s/it, Loss=1.0104325]\nTraining progress: 64%|██████▍ | 640/1000 [10:01<06:01, 1.01s/it, Loss=1.0104325]\nTraining progress: 64%|██████▍ | 640/1000 [10:10<06:01, 1.01s/it, Loss=1.0113608]\nTraining progress: 65%|██████▌ | 650/1000 [10:10<05:38, 1.03it/s, Loss=1.0113608]\nTraining progress: 65%|██████▌ | 650/1000 [10:19<05:38, 1.03it/s, Loss=1.0094696]\nTraining progress: 66%|██████▌ | 660/1000 [10:19<05:19, 1.06it/s, Loss=1.0094696]\nTraining progress: 66%|██████▌ | 660/1000 [10:27<05:19, 1.06it/s, Loss=1.0095921]\nTraining progress: 67%|██████▋ | 670/1000 [10:27<05:04, 1.08it/s, Loss=1.0095921]\nTraining progress: 67%|██████▋ | 670/1000 [10:36<05:04, 1.08it/s, Loss=1.0090344]\nTraining progress: 68%|██████▊ | 680/1000 [10:36<04:49, 1.11it/s, Loss=1.0090344]\nTraining progress: 68%|██████▊ | 680/1000 [10:45<04:49, 1.11it/s, Loss=1.0103945]\nTraining progress: 69%|██████▉ | 690/1000 [10:45<04:35, 1.12it/s, Loss=1.0103945]\nTraining progress: 69%|██████▉ | 690/1000 [10:54<04:35, 1.12it/s, Loss=1.0091865]\nTraining progress: 70%|███████ | 700/1000 [10:54<04:32, 1.10it/s, Loss=1.0091865]\nTraining progress: 70%|███████ | 700/1000 [11:02<04:32, 1.10it/s, Loss=1.0109836]\nTraining progress: 71%|███████ | 710/1000 [11:02<04:17, 1.13it/s, Loss=1.0109836]\nTraining progress: 71%|███████ | 710/1000 [11:11<04:17, 1.13it/s, Loss=1.0101475]\nTraining progress: 72%|███████▏ | 720/1000 [11:11<04:08, 1.13it/s, Loss=1.0101475]\nTraining progress: 72%|███████▏ | 720/1000 [11:20<04:08, 1.13it/s, Loss=1.0097659]\nTraining progress: 73%|███████▎ | 730/1000 [11:20<04:01, 1.12it/s, Loss=1.0097659]\nTraining progress: 73%|███████▎ | 730/1000 [11:29<04:01, 1.12it/s, Loss=1.0101017]\nTraining progress: 74%|███████▍ | 740/1000 [11:29<03:51, 1.12it/s, Loss=1.0101017]\nTraining progress: 74%|███████▍ | 740/1000 [11:37<03:51, 1.12it/s, Loss=1.0095184]\nTraining progress: 75%|███████▌ | 750/1000 [11:37<03:37, 1.15it/s, Loss=1.0095184]\nTraining progress: 75%|███████▌ | 750/1000 [11:47<03:37, 1.15it/s, Loss=1.0111929]\nTraining progress: 76%|███████▌ | 760/1000 [11:47<03:33, 1.12it/s, Loss=1.0111929]\nTraining progress: 76%|███████▌ | 760/1000 [11:55<03:33, 1.12it/s, Loss=1.0105028]\nTraining progress: 77%|███████▋ | 770/1000 [11:55<03:21, 1.14it/s, Loss=1.0105028]\nTraining progress: 77%|███████▋ | 770/1000 [12:04<03:21, 1.14it/s, Loss=1.0102045]\nTraining progress: 78%|███████▊ | 780/1000 [12:04<03:10, 1.16it/s, Loss=1.0102045]\nTraining progress: 78%|███████▊ | 780/1000 [12:12<03:10, 1.16it/s, Loss=1.0098310]\nTraining progress: 79%|███████▉ | 790/1000 [12:12<03:01, 1.16it/s, Loss=1.0098310]\nTraining progress: 79%|███████▉ | 790/1000 [12:22<03:01, 1.16it/s, Loss=1.0101976]\ntest views is in : ./output/Replicate/test_six_views/800_iteration [22/12 16:29:21]\n[ITER 800] Eval Done! [22/12 16:29:22]\nvideos is in : ./output/Replicate/videos/800_iteration [22/12 16:29:22]\nGenerating Video using 240 different view points[22/12 16:29:28]\n[ITER 800] Video Save Done! [22/12 16:29:31]\nTraining progress: 80%|████████ | 800/1000 [12:22<02:57, 1.12it/s, Loss=1.0101976]\nTraining progress: 80%|████████ | 800/1000 [12:41<02:57, 1.12it/s, Loss=1.0115836]\nTraining progress: 81%|████████ | 810/1000 [12:41<03:45, 1.19s/it, Loss=1.0115836]\nTraining progress: 81%|████████ | 810/1000 [12:50<03:45, 1.19s/it, Loss=1.0098869]\nTraining progress: 82%|████████▏ | 820/1000 [12:50<03:18, 1.10s/it, Loss=1.0098869]\nTraining progress: 82%|████████▏ | 820/1000 [12:59<03:18, 1.10s/it, Loss=1.0102600]\nTraining progress: 83%|████████▎ | 830/1000 [12:59<02:57, 1.05s/it, Loss=1.0102600]\nTraining progress: 83%|████████▎ | 830/1000 [13:08<02:57, 1.05s/it, Loss=1.0097042]\nTraining progress: 84%|████████▍ | 840/1000 [13:08<02:39, 1.01it/s, Loss=1.0097042]\nTraining progress: 84%|████████▍ | 840/1000 [13:16<02:39, 1.01it/s, Loss=1.0103961]\nTraining progress: 85%|████████▌ | 850/1000 [13:16<02:24, 1.04it/s, Loss=1.0103961]\nTraining progress: 85%|████████▌ | 850/1000 [13:26<02:24, 1.04it/s, Loss=1.0108702]\nTraining progress: 86%|████████▌ | 860/1000 [13:26<02:13, 1.05it/s, Loss=1.0108702]\nTraining progress: 86%|████████▌ | 860/1000 [13:34<02:13, 1.05it/s, Loss=1.0092877]\nTraining progress: 87%|████████▋ | 870/1000 [13:34<02:00, 1.07it/s, Loss=1.0092877]\nTraining progress: 87%|████████▋ | 870/1000 [13:43<02:00, 1.07it/s, Loss=1.0103984]\nTraining progress: 88%|████████▊ | 880/1000 [13:43<01:49, 1.10it/s, Loss=1.0103984]\nTraining progress: 88%|████████▊ | 880/1000 [13:52<01:49, 1.10it/s, Loss=1.0098017]\nTraining progress: 89%|████████▉ | 890/1000 [13:52<01:39, 1.11it/s, Loss=1.0098017]\nTraining progress: 89%|████████▉ | 890/1000 [14:01<01:39, 1.11it/s, Loss=1.0112966]\nTraining progress: 90%|█████████ | 900/1000 [14:01<01:31, 1.09it/s, Loss=1.0112966]\nTraining progress: 90%|█████████ | 900/1000 [14:11<01:31, 1.09it/s, Loss=1.0114046]\nTraining progress: 91%|█████████ | 910/1000 [14:11<01:22, 1.09it/s, Loss=1.0114046]\nTraining progress: 91%|█████████ | 910/1000 [14:20<01:22, 1.09it/s, Loss=1.0115868]\nTraining progress: 92%|█████████▏| 920/1000 [14:20<01:12, 1.10it/s, Loss=1.0115868]\nTraining progress: 92%|█████████▏| 920/1000 [14:29<01:12, 1.10it/s, Loss=1.0097545]\nTraining progress: 93%|█████████▎| 930/1000 [14:29<01:03, 1.10it/s, Loss=1.0097545]\nTraining progress: 93%|█████████▎| 930/1000 [14:37<01:03, 1.10it/s, Loss=1.0098565]\nTraining progress: 94%|█████████▍| 940/1000 [14:37<00:53, 1.12it/s, Loss=1.0098565]\nTraining progress: 94%|█████████▍| 940/1000 [14:46<00:53, 1.12it/s, Loss=1.0107825]\nTraining progress: 95%|█████████▌| 950/1000 [14:46<00:43, 1.14it/s, Loss=1.0107825]\nTraining progress: 95%|█████████▌| 950/1000 [14:54<00:43, 1.14it/s, Loss=1.0096509]\nTraining progress: 96%|█████████▌| 960/1000 [14:54<00:34, 1.14it/s, Loss=1.0096509]\nTraining progress: 96%|█████████▌| 960/1000 [15:04<00:34, 1.14it/s, Loss=1.0093848]\nTraining progress: 97%|█████████▋| 970/1000 [15:04<00:26, 1.12it/s, Loss=1.0093848]\nTraining progress: 97%|█████████▋| 970/1000 [15:13<00:26, 1.12it/s, Loss=1.0099435]\nTraining progress: 98%|█████████▊| 980/1000 [15:13<00:17, 1.12it/s, Loss=1.0099435]\nTraining progress: 98%|█████████▊| 980/1000 [15:21<00:17, 1.12it/s, Loss=1.0103432]\nscale up theta_range to: [60, 90][22/12 16:32:28]\nscale up radius_range to: [4.693, 5.0] [22/12 16:32:28]\nscale up phi_range to: [-180, 180] [22/12 16:32:28]\nscale up fovy_range to: [0.18, 0.6] [22/12 16:32:28]\nTraining progress: 99%|█████████▉| 990/1000 [15:21<00:08, 1.13it/s, Loss=1.0103432]\nTraining progress: 99%|█████████▉| 990/1000 [15:31<00:08, 1.13it/s, Loss=1.0096740]\nTraining progress: 100%|██████████| 1000/1000 [15:31<00:00, 1.11it/s, Loss=1.0096740]\nTraining progress: 100%|██████████| 1000/1000 [15:31<00:00, 1.07it/s, Loss=1.0096740]\ntest views is in : ./output/Replicate/test_six_views/1000_iteration[22/12 16:32:30]\n[ITER 1000] Eval Done! [22/12 16:32:31]\nvideos is in : ./output/Replicate/videos/1000_iteration [22/12 16:32:31]\nGenerating Video using 240 different view points[22/12 16:32:37]\n[ITER 1000] Video Save Done! [22/12 16:32:42]\n[ITER 1000] Saving Gaussians [22/12 16:32:42]\nTraining complete.[22/12 16:32:52]",
"metrics": {
"predict_time": 1004.990021,
"total_time": 1104.809692
},
"output": [
"https://replicate.delivery/pbxt/dGlbDMx6U2LGJxucqINW2G8IwyjmnsEEC2IJdwsVxbkNsNhE/video_rgb_1000.mp4",
"https://replicate.delivery/pbxt/abd77ehLGfiKvENIKMkoPsKgLUEJwnW9MXY48MFhr6l3w2ESA/video_rgb.mp4"
],
"started_at": "2023-12-22T16:16:10.497723Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/3atbyrtb3q54kierm4sx5u3bsa",
"cancel": "https://api.replicate.com/v1/predictions/3atbyrtb3q54kierm4sx5u3bsa/cancel"
},
"version": "fbf8e0dfef4ca0c0de45cf1afbf12c81667ee29fd79852852262aee4f167fbf5"
}Generated in
Using seed: 2212729883
Test iter: [1, 200, 400, 600, 800, 1000]
Save iter: [500, 1000]
Optimizing
Output folder: ./output/Replicate [22/12 16:16:11]
Tensorboard not available: not logging progress [22/12 16:16:11]
Reading Test Transforms [22/12 16:16:11]
creating base model...[22/12 16:16:12]
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creating upsample model... [22/12 16:16:28]
downloading base checkpoint... [22/12 16:16:33]
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downloading upsampler checkpoint... [22/12 16:16:35]
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Generating random point cloud (81920)... [22/12 16:16:53]
Number of points at initialisation : 81920 [22/12 16:16:54]
train_process is in : ./output/Replicate/train_process/ [22/12 16:16:54]
[INFO] loading stable diffusion... [22/12 16:16:57]
[INFO] loaded stable diffusion! [22/12 16:16:59]
test views is in : ./output/Replicate/test_six_views/1_iteration [22/12 16:17:15]
[ITER 1] Eval Done! [22/12 16:17:15]
videos is in : ./output/Replicate/videos/1_iteration [22/12 16:17:15]
Generating Video using 240 different view points[22/12 16:17:21]
[ITER 1] Video Save Done! [22/12 16:17:24]
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test views is in : ./output/Replicate/test_six_views/200_iteration[22/12 16:20:08]
[ITER 200] Eval Done! [22/12 16:20:09]
videos is in : ./output/Replicate/videos/200_iteration[22/12 16:20:09]
Generating Video using 240 different view points[22/12 16:20:15]
[ITER 200] Video Save Done! [22/12 16:20:17]
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test views is in : ./output/Replicate/test_six_views/400_iteration [22/12 16:23:08]
[ITER 400] Eval Done! [22/12 16:23:09]
videos is in : ./output/Replicate/videos/400_iteration [22/12 16:23:09]
Generating Video using 240 different view points[22/12 16:23:15]
[ITER 400] Video Save Done! [22/12 16:23:18]
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Training progress: 48%|████▊ | 480/1000 [07:37<07:39, 1.13it/s, Loss=1.0090952]
scale up theta_range to: [60, 90] [22/12 16:24:43]
scale up radius_range to: [4.9399999999999995, 5.225] [22/12 16:24:43]
scale up phi_range to: [-180, 180] [22/12 16:24:43]
scale up fovy_range to: [0.24, 0.6] [22/12 16:24:43]
Training progress: 49%|████▉ | 490/1000 [07:37<07:25, 1.14it/s, Loss=1.0090952]
Training progress: 49%|████▉ | 490/1000 [07:45<07:25, 1.14it/s, Loss=1.0091098]
[ITER 500] Saving Gaussians [22/12 16:24:45]
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Training progress: 50%|█████ | 500/1000 [07:57<07:20, 1.14it/s, Loss=1.0093191]
Training progress: 51%|█████ | 510/1000 [07:57<07:47, 1.05it/s, Loss=1.0093191]
Training progress: 51%|█████ | 510/1000 [08:05<07:47, 1.05it/s, Loss=1.0097375]
Training progress: 52%|█████▏ | 520/1000 [08:05<07:20, 1.09it/s, Loss=1.0097375]
Training progress: 52%|█████▏ | 520/1000 [08:14<07:20, 1.09it/s, Loss=1.0100511]
Training progress: 53%|█████▎ | 530/1000 [08:14<07:02, 1.11it/s, Loss=1.0100511]
Training progress: 53%|█████▎ | 530/1000 [08:23<07:02, 1.11it/s, Loss=1.0102542]
Training progress: 54%|█████▍ | 540/1000 [08:23<06:58, 1.10it/s, Loss=1.0102542]
Training progress: 54%|█████▍ | 540/1000 [08:32<06:58, 1.10it/s, Loss=1.0098752]
Training progress: 55%|█████▌ | 550/1000 [08:32<06:42, 1.12it/s, Loss=1.0098752]
Training progress: 55%|█████▌ | 550/1000 [08:40<06:42, 1.12it/s, Loss=1.0098831]
Training progress: 56%|█████▌ | 560/1000 [08:40<06:26, 1.14it/s, Loss=1.0098831]
Training progress: 56%|█████▌ | 560/1000 [08:49<06:26, 1.14it/s, Loss=1.0099793]
Training progress: 57%|█████▋ | 570/1000 [08:49<06:14, 1.15it/s, Loss=1.0099793]
Training progress: 57%|█████▋ | 570/1000 [08:57<06:14, 1.15it/s, Loss=1.0098103]
Training progress: 58%|█████▊ | 580/1000 [08:57<06:02, 1.16it/s, Loss=1.0098103]
Training progress: 58%|█████▊ | 580/1000 [09:06<06:02, 1.16it/s, Loss=1.0090983]
Training progress: 59%|█████▉ | 590/1000 [09:06<05:56, 1.15it/s, Loss=1.0090983]
Training progress: 59%|█████▉ | 590/1000 [09:15<05:56, 1.15it/s, Loss=1.0095025]
test views is in : ./output/Replicate/test_six_views/600_iteration [22/12 16:26:14]
[ITER 600] Eval Done! [22/12 16:26:15]
videos is in : ./output/Replicate/videos/600_iteration[22/12 16:26:15]
Generating Video using 240 different view points[22/12 16:26:21]
[ITER 600] Video Save Done! [22/12 16:26:24]
Training progress: 60%|██████ | 600/1000 [09:15<05:55, 1.13it/s, Loss=1.0095025]
Training progress: 60%|██████ | 600/1000 [09:34<05:55, 1.13it/s, Loss=1.0120878]
Training progress: 61%|██████ | 610/1000 [09:34<07:41, 1.18s/it, Loss=1.0120878]
Training progress: 61%|██████ | 610/1000 [09:43<07:41, 1.18s/it, Loss=1.0099001]
Training progress: 62%|██████▏ | 620/1000 [09:43<06:56, 1.10s/it, Loss=1.0099001]
Training progress: 62%|██████▏ | 620/1000 [09:51<06:56, 1.10s/it, Loss=1.0100498]
Training progress: 63%|██████▎ | 630/1000 [09:51<06:19, 1.03s/it, Loss=1.0100498]
Training progress: 63%|██████▎ | 630/1000 [10:01<06:19, 1.03s/it, Loss=1.0104325]
Training progress: 64%|██████▍ | 640/1000 [10:01<06:01, 1.01s/it, Loss=1.0104325]
Training progress: 64%|██████▍ | 640/1000 [10:10<06:01, 1.01s/it, Loss=1.0113608]
Training progress: 65%|██████▌ | 650/1000 [10:10<05:38, 1.03it/s, Loss=1.0113608]
Training progress: 65%|██████▌ | 650/1000 [10:19<05:38, 1.03it/s, Loss=1.0094696]
Training progress: 66%|██████▌ | 660/1000 [10:19<05:19, 1.06it/s, Loss=1.0094696]
Training progress: 66%|██████▌ | 660/1000 [10:27<05:19, 1.06it/s, Loss=1.0095921]
Training progress: 67%|██████▋ | 670/1000 [10:27<05:04, 1.08it/s, Loss=1.0095921]
Training progress: 67%|██████▋ | 670/1000 [10:36<05:04, 1.08it/s, Loss=1.0090344]
Training progress: 68%|██████▊ | 680/1000 [10:36<04:49, 1.11it/s, Loss=1.0090344]
Training progress: 68%|██████▊ | 680/1000 [10:45<04:49, 1.11it/s, Loss=1.0103945]
Training progress: 69%|██████▉ | 690/1000 [10:45<04:35, 1.12it/s, Loss=1.0103945]
Training progress: 69%|██████▉ | 690/1000 [10:54<04:35, 1.12it/s, Loss=1.0091865]
Training progress: 70%|███████ | 700/1000 [10:54<04:32, 1.10it/s, Loss=1.0091865]
Training progress: 70%|███████ | 700/1000 [11:02<04:32, 1.10it/s, Loss=1.0109836]
Training progress: 71%|███████ | 710/1000 [11:02<04:17, 1.13it/s, Loss=1.0109836]
Training progress: 71%|███████ | 710/1000 [11:11<04:17, 1.13it/s, Loss=1.0101475]
Training progress: 72%|███████▏ | 720/1000 [11:11<04:08, 1.13it/s, Loss=1.0101475]
Training progress: 72%|███████▏ | 720/1000 [11:20<04:08, 1.13it/s, Loss=1.0097659]
Training progress: 73%|███████▎ | 730/1000 [11:20<04:01, 1.12it/s, Loss=1.0097659]
Training progress: 73%|███████▎ | 730/1000 [11:29<04:01, 1.12it/s, Loss=1.0101017]
Training progress: 74%|███████▍ | 740/1000 [11:29<03:51, 1.12it/s, Loss=1.0101017]
Training progress: 74%|███████▍ | 740/1000 [11:37<03:51, 1.12it/s, Loss=1.0095184]
Training progress: 75%|███████▌ | 750/1000 [11:37<03:37, 1.15it/s, Loss=1.0095184]
Training progress: 75%|███████▌ | 750/1000 [11:47<03:37, 1.15it/s, Loss=1.0111929]
Training progress: 76%|███████▌ | 760/1000 [11:47<03:33, 1.12it/s, Loss=1.0111929]
Training progress: 76%|███████▌ | 760/1000 [11:55<03:33, 1.12it/s, Loss=1.0105028]
Training progress: 77%|███████▋ | 770/1000 [11:55<03:21, 1.14it/s, Loss=1.0105028]
Training progress: 77%|███████▋ | 770/1000 [12:04<03:21, 1.14it/s, Loss=1.0102045]
Training progress: 78%|███████▊ | 780/1000 [12:04<03:10, 1.16it/s, Loss=1.0102045]
Training progress: 78%|███████▊ | 780/1000 [12:12<03:10, 1.16it/s, Loss=1.0098310]
Training progress: 79%|███████▉ | 790/1000 [12:12<03:01, 1.16it/s, Loss=1.0098310]
Training progress: 79%|███████▉ | 790/1000 [12:22<03:01, 1.16it/s, Loss=1.0101976]
test views is in : ./output/Replicate/test_six_views/800_iteration [22/12 16:29:21]
[ITER 800] Eval Done! [22/12 16:29:22]
videos is in : ./output/Replicate/videos/800_iteration [22/12 16:29:22]
Generating Video using 240 different view points[22/12 16:29:28]
[ITER 800] Video Save Done! [22/12 16:29:31]
Training progress: 80%|████████ | 800/1000 [12:22<02:57, 1.12it/s, Loss=1.0101976]
Training progress: 80%|████████ | 800/1000 [12:41<02:57, 1.12it/s, Loss=1.0115836]
Training progress: 81%|████████ | 810/1000 [12:41<03:45, 1.19s/it, Loss=1.0115836]
Training progress: 81%|████████ | 810/1000 [12:50<03:45, 1.19s/it, Loss=1.0098869]
Training progress: 82%|████████▏ | 820/1000 [12:50<03:18, 1.10s/it, Loss=1.0098869]
Training progress: 82%|████████▏ | 820/1000 [12:59<03:18, 1.10s/it, Loss=1.0102600]
Training progress: 83%|████████▎ | 830/1000 [12:59<02:57, 1.05s/it, Loss=1.0102600]
Training progress: 83%|████████▎ | 830/1000 [13:08<02:57, 1.05s/it, Loss=1.0097042]
Training progress: 84%|████████▍ | 840/1000 [13:08<02:39, 1.01it/s, Loss=1.0097042]
Training progress: 84%|████████▍ | 840/1000 [13:16<02:39, 1.01it/s, Loss=1.0103961]
Training progress: 85%|████████▌ | 850/1000 [13:16<02:24, 1.04it/s, Loss=1.0103961]
Training progress: 85%|████████▌ | 850/1000 [13:26<02:24, 1.04it/s, Loss=1.0108702]
Training progress: 86%|████████▌ | 860/1000 [13:26<02:13, 1.05it/s, Loss=1.0108702]
Training progress: 86%|████████▌ | 860/1000 [13:34<02:13, 1.05it/s, Loss=1.0092877]
Training progress: 87%|████████▋ | 870/1000 [13:34<02:00, 1.07it/s, Loss=1.0092877]
Training progress: 87%|████████▋ | 870/1000 [13:43<02:00, 1.07it/s, Loss=1.0103984]
Training progress: 88%|████████▊ | 880/1000 [13:43<01:49, 1.10it/s, Loss=1.0103984]
Training progress: 88%|████████▊ | 880/1000 [13:52<01:49, 1.10it/s, Loss=1.0098017]
Training progress: 89%|████████▉ | 890/1000 [13:52<01:39, 1.11it/s, Loss=1.0098017]
Training progress: 89%|████████▉ | 890/1000 [14:01<01:39, 1.11it/s, Loss=1.0112966]
Training progress: 90%|█████████ | 900/1000 [14:01<01:31, 1.09it/s, Loss=1.0112966]
Training progress: 90%|█████████ | 900/1000 [14:11<01:31, 1.09it/s, Loss=1.0114046]
Training progress: 91%|█████████ | 910/1000 [14:11<01:22, 1.09it/s, Loss=1.0114046]
Training progress: 91%|█████████ | 910/1000 [14:20<01:22, 1.09it/s, Loss=1.0115868]
Training progress: 92%|█████████▏| 920/1000 [14:20<01:12, 1.10it/s, Loss=1.0115868]
Training progress: 92%|█████████▏| 920/1000 [14:29<01:12, 1.10it/s, Loss=1.0097545]
Training progress: 93%|█████████▎| 930/1000 [14:29<01:03, 1.10it/s, Loss=1.0097545]
Training progress: 93%|█████████▎| 930/1000 [14:37<01:03, 1.10it/s, Loss=1.0098565]
Training progress: 94%|█████████▍| 940/1000 [14:37<00:53, 1.12it/s, Loss=1.0098565]
Training progress: 94%|█████████▍| 940/1000 [14:46<00:53, 1.12it/s, Loss=1.0107825]
Training progress: 95%|█████████▌| 950/1000 [14:46<00:43, 1.14it/s, Loss=1.0107825]
Training progress: 95%|█████████▌| 950/1000 [14:54<00:43, 1.14it/s, Loss=1.0096509]
Training progress: 96%|█████████▌| 960/1000 [14:54<00:34, 1.14it/s, Loss=1.0096509]
Training progress: 96%|█████████▌| 960/1000 [15:04<00:34, 1.14it/s, Loss=1.0093848]
Training progress: 97%|█████████▋| 970/1000 [15:04<00:26, 1.12it/s, Loss=1.0093848]
Training progress: 97%|█████████▋| 970/1000 [15:13<00:26, 1.12it/s, Loss=1.0099435]
Training progress: 98%|█████████▊| 980/1000 [15:13<00:17, 1.12it/s, Loss=1.0099435]
Training progress: 98%|█████████▊| 980/1000 [15:21<00:17, 1.12it/s, Loss=1.0103432]
scale up theta_range to: [60, 90][22/12 16:32:28]
scale up radius_range to: [4.693, 5.0] [22/12 16:32:28]
scale up phi_range to: [-180, 180] [22/12 16:32:28]
scale up fovy_range to: [0.18, 0.6] [22/12 16:32:28]
Training progress: 99%|█████████▉| 990/1000 [15:21<00:08, 1.13it/s, Loss=1.0103432]
Training progress: 99%|█████████▉| 990/1000 [15:31<00:08, 1.13it/s, Loss=1.0096740]
Training progress: 100%|██████████| 1000/1000 [15:31<00:00, 1.11it/s, Loss=1.0096740]
Training progress: 100%|██████████| 1000/1000 [15:31<00:00, 1.07it/s, Loss=1.0096740]
test views is in : ./output/Replicate/test_six_views/1000_iteration[22/12 16:32:30]
[ITER 1000] Eval Done! [22/12 16:32:31]
videos is in : ./output/Replicate/videos/1000_iteration [22/12 16:32:31]
Generating Video using 240 different view points[22/12 16:32:37]
[ITER 1000] Video Save Done! [22/12 16:32:42]
[ITER 1000] Saving Gaussians [22/12 16:32:42]
Training complete.[22/12 16:32:52]