pablodawson
/
panohead
Geometry-Aware 3D Full-Head Synthesis in 360 degrees
Prediction
pablodawson/panohead:7035cfb06dc997def6c41434d45eeedde52a67c4f19e047e077689d90ad9e04eID6hct7ojbcavet44dmfixk7pcoiStatusSucceededSourceWebHardwareT4Total durationCreatedInput
- face_image
{ "face_image": "https://replicate.delivery/pbxt/J5ov8Sr82CN0exHXHmOebgVVhEg2oPcxzw5xjl9ZxIEiYTgl/_125556748_gettyimages-1240422158.jpg" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run pablodawson/panohead using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "pablodawson/panohead:7035cfb06dc997def6c41434d45eeedde52a67c4f19e047e077689d90ad9e04e", { input: { face_image: "https://replicate.delivery/pbxt/J5ov8Sr82CN0exHXHmOebgVVhEg2oPcxzw5xjl9ZxIEiYTgl/_125556748_gettyimages-1240422158.jpg" } } ); 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 replicateImport the client:import replicateRun pablodawson/panohead using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "pablodawson/panohead:7035cfb06dc997def6c41434d45eeedde52a67c4f19e047e077689d90ad9e04e", input={ "face_image": "https://replicate.delivery/pbxt/J5ov8Sr82CN0exHXHmOebgVVhEg2oPcxzw5xjl9ZxIEiYTgl/_125556748_gettyimages-1240422158.jpg" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run pablodawson/panohead 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": "7035cfb06dc997def6c41434d45eeedde52a67c4f19e047e077689d90ad9e04e", "input": { "face_image": "https://replicate.delivery/pbxt/J5ov8Sr82CN0exHXHmOebgVVhEg2oPcxzw5xjl9ZxIEiYTgl/_125556748_gettyimages-1240422158.jpg" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
preview_video
training_video
{ "completed_at": "2023-06-30T19:56:26.820574Z", "created_at": "2023-06-30T19:43:39.435690Z", "data_removed": false, "error": null, "id": "6hct7ojbcavet44dmfixk7pcoi", "input": { "face_image": "https://replicate.delivery/pbxt/J5ov8Sr82CN0exHXHmOebgVVhEg2oPcxzw5xjl9ZxIEiYTgl/_125556748_gettyimages-1240422158.jpg" }, "logs": "Cropping...\n 0%| | 0/1 [00:00<?, ?it/s]\n100%|██████████| 1/1 [00:03<00:00, 3.31s/it]\n100%|██████████| 1/1 [00:03<00:00, 3.31s/it]\nresults: 1\nProjection...\nLoading networks from \"/src/PanoHead/models/easy-khair-180-gpc0.8-trans10-025000.pkl\"...\nprojecting: [0] /src/stage/tmpckxtr00i_125556748_gettyimages-1240422158.jpg\ncamera matrix: torch.Size([1, 25])\nComputing W midpoint and stddev using 10000 samples...\nSetting up PyTorch plugin \"bias_act_plugin\"... Done.\nSetting up PyTorch plugin \"upfirdn2d_plugin\"... Done.\n/root/.pyenv/versions/3.8.17/lib/python3.8/site-packages/torch/nn/modules/module.py:1501: UserWarning: operator() profile_node %106 : int = prim::profile_ivalue(%104)\ndoes not have profile information (Triggered internally at ../third_party/nvfuser/csrc/graph_fuser.cpp:104.)\nreturn forward_call(*args, **kwargs)\nstep: 1/300 mse: 0.26 perc: 0.53 w_norm: 0.00 noise: 0.00\nstep: 2/300 mse: 0.29 perc: 0.51 w_norm: 0.00 noise: 0.00\nstep: 3/300 mse: 0.23 perc: 0.50 w_norm: 0.00 noise: 0.00\nstep: 4/300 mse: 0.19 perc: 0.44 w_norm: 0.00 noise: 0.00\nstep: 5/300 mse: 0.18 perc: 0.47 w_norm: 0.00 noise: 0.00\nstep: 6/300 mse: 0.18 perc: 0.46 w_norm: 0.00 noise: 0.00\nstep: 7/300 mse: 0.11 perc: 0.41 w_norm: 0.00 noise: 0.00\nstep: 8/300 mse: 0.11 perc: 0.41 w_norm: 0.00 noise: 0.00\nstep: 9/300 mse: 0.11 perc: 0.43 w_norm: 0.01 noise: 0.00\nstep: 10/300 mse: 0.11 perc: 0.42 w_norm: 0.01 noise: 0.00\nstep: 11/300 mse: 0.10 perc: 0.43 w_norm: 0.01 noise: 0.00\nstep: 12/300 mse: 0.11 perc: 0.41 w_norm: 0.02 noise: 0.00\nstep: 13/300 mse: 0.10 perc: 0.41 w_norm: 0.02 noise: 0.00\nstep: 14/300 mse: 0.12 perc: 0.41 w_norm: 0.03 noise: 0.00\nstep: 15/300 mse: 0.12 perc: 0.42 w_norm: 0.03 noise: 0.00\nstep: 16/300 mse: 0.11 perc: 0.42 w_norm: 0.04 noise: 0.00\nstep: 17/300 mse: 0.10 perc: 0.39 w_norm: 0.04 noise: 0.00\nstep: 18/300 mse: 0.09 perc: 0.40 w_norm: 0.04 noise: 0.00\nstep: 19/300 mse: 0.08 perc: 0.39 w_norm: 0.05 noise: 0.00\nstep: 20/300 mse: 0.10 perc: 0.38 w_norm: 0.05 noise: 0.00\nstep: 21/300 mse: 0.10 perc: 0.40 w_norm: 0.06 noise: 0.00\nstep: 22/300 mse: 0.09 perc: 0.40 w_norm: 0.06 noise: 0.00\nstep: 23/300 mse: 0.10 perc: 0.39 w_norm: 0.06 noise: 0.00\nstep: 24/300 mse: 0.11 perc: 0.39 w_norm: 0.07 noise: 0.00\nstep: 25/300 mse: 0.11 perc: 0.43 w_norm: 0.07 noise: 0.00\nstep: 26/300 mse: 0.10 perc: 0.38 w_norm: 0.07 noise: 0.00\nstep: 27/300 mse: 0.10 perc: 0.38 w_norm: 0.08 noise: 0.00\nstep: 28/300 mse: 0.10 perc: 0.38 w_norm: 0.08 noise: 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0.09\nstep: 266/300 mse: 0.01 perc: 0.09\nstep: 267/300 mse: 0.01 perc: 0.09\nstep: 268/300 mse: 0.01 perc: 0.08\nstep: 269/300 mse: 0.01 perc: 0.08\nstep: 270/300 mse: 0.01 perc: 0.08\nstep: 271/300 mse: 0.01 perc: 0.08\nstep: 272/300 mse: 0.01 perc: 0.08\nstep: 273/300 mse: 0.01 perc: 0.08\nstep: 274/300 mse: 0.01 perc: 0.08\nstep: 275/300 mse: 0.01 perc: 0.08\nstep: 276/300 mse: 0.01 perc: 0.08\nstep: 277/300 mse: 0.01 perc: 0.08\nstep: 278/300 mse: 0.01 perc: 0.08\nstep: 279/300 mse: 0.01 perc: 0.08\nstep: 280/300 mse: 0.01 perc: 0.08\nstep: 281/300 mse: 0.01 perc: 0.08\nstep: 282/300 mse: 0.01 perc: 0.08\nstep: 283/300 mse: 0.01 perc: 0.08\nstep: 284/300 mse: 0.01 perc: 0.08\nstep: 285/300 mse: 0.01 perc: 0.08\nstep: 286/300 mse: 0.01 perc: 0.08\nstep: 287/300 mse: 0.01 perc: 0.08\nstep: 288/300 mse: 0.01 perc: 0.08\nstep: 289/300 mse: 0.01 perc: 0.08\nstep: 290/300 mse: 0.01 perc: 0.08\nstep: 291/300 mse: 0.01 perc: 0.08\nstep: 292/300 mse: 0.01 perc: 0.08\nstep: 293/300 mse: 0.01 perc: 0.08\nstep: 294/300 mse: 0.01 perc: 0.08\nstep: 295/300 mse: 0.01 perc: 0.08\nstep: 296/300 mse: 0.01 perc: 0.08\nstep: 297/300 mse: 0.01 perc: 0.08\nstep: 298/300 mse: 0.01 perc: 0.08\nstep: 299/300 mse: 0.01 perc: 0.08\nstep: 300/300 mse: 0.01 perc: 0.08\nElapsed: 311.2 s\nSaving optimization progress video \"/src/output/easy-khair-180-gpc0.8-trans10-025000.pkl/0/proj.mp4\"\nCreate video...\nLoading networks from \"/src/output/easy-khair-180-gpc0.8-trans10-025000.pkl/0/fintuned_generator.pkl\"...\nSetting up PyTorch plugin \"bias_act_plugin\"... Done.\nSetting up PyTorch plugin \"upfirdn2d_plugin\"... 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10.12it/s]\n 88%|████████▊ | 210/240 [00:20<00:02, 10.06it/s]\n 88%|████████▊ | 212/240 [00:20<00:02, 9.94it/s]\n 89%|████████▉ | 214/240 [00:20<00:02, 9.99it/s]\n 90%|████████▉ | 215/240 [00:21<00:02, 9.89it/s]\n 90%|█████████ | 217/240 [00:21<00:02, 10.00it/s]\n 91%|█████████ | 218/240 [00:21<00:02, 9.94it/s]\n 92%|█████████▏| 220/240 [00:21<00:01, 10.09it/s]\n 92%|█████████▎| 222/240 [00:21<00:01, 9.91it/s]\n 93%|█████████▎| 223/240 [00:21<00:01, 9.89it/s]\n 94%|█████████▍| 225/240 [00:22<00:01, 10.00it/s]\n 95%|█████████▍| 227/240 [00:22<00:01, 9.72it/s]\n 95%|█████████▌| 228/240 [00:22<00:01, 9.10it/s]\n 95%|█████████▌| 229/240 [00:22<00:01, 9.03it/s]\n 96%|█████████▌| 230/240 [00:22<00:01, 8.78it/s]\n 96%|█████████▋| 231/240 [00:22<00:00, 9.05it/s]\n 97%|█████████▋| 232/240 [00:22<00:00, 9.05it/s]\n 97%|█████████▋| 233/240 [00:22<00:00, 9.17it/s]\n 98%|█████████▊| 234/240 [00:23<00:00, 9.36it/s]\n 98%|█████████▊| 236/240 [00:23<00:00, 9.84it/s]\n 99%|█████████▉| 237/240 [00:23<00:00, 9.83it/s]\n100%|█████████▉| 239/240 [00:23<00:00, 9.97it/s]\n100%|██████████| 240/240 [00:23<00:00, 9.91it/s]\n100%|██████████| 240/240 [00:23<00:00, 10.14it/s]", "metrics": { "predict_time": 396.092014, "total_time": 767.384884 }, "output": { "ply_out": null, "preview_video": "https://replicate.delivery/pbxt/1O5QYzE78FJiGVOmz33pYAEuajefHIM9RrJhlAVZEBPpVOLRA/post.mp4", "training_video": "https://replicate.delivery/pbxt/bCe7APyvFftlKUDAdwsYbRJjJAjH8B2w1OBleGXZtfEkW5sEB/proj.mp4" }, "started_at": "2023-06-30T19:49:50.728560Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/6hct7ojbcavet44dmfixk7pcoi", "cancel": "https://api.replicate.com/v1/predictions/6hct7ojbcavet44dmfixk7pcoi/cancel" }, "version": "7035cfb06dc997def6c41434d45eeedde52a67c4f19e047e077689d90ad9e04e" }Generated inCropping... 0%| | 0/1 [00:00<?, ?it/s] 100%|██████████| 1/1 [00:03<00:00, 3.31s/it] 100%|██████████| 1/1 [00:03<00:00, 3.31s/it] results: 1 Projection... Loading networks from "/src/PanoHead/models/easy-khair-180-gpc0.8-trans10-025000.pkl"... projecting: [0] /src/stage/tmpckxtr00i_125556748_gettyimages-1240422158.jpg camera matrix: torch.Size([1, 25]) Computing W midpoint and stddev using 10000 samples... Setting up PyTorch plugin "bias_act_plugin"... Done. Setting up PyTorch plugin "upfirdn2d_plugin"... Done. /root/.pyenv/versions/3.8.17/lib/python3.8/site-packages/torch/nn/modules/module.py:1501: UserWarning: operator() profile_node %106 : int = prim::profile_ivalue(%104) does not have profile information (Triggered internally at ../third_party/nvfuser/csrc/graph_fuser.cpp:104.) return forward_call(*args, **kwargs) step: 1/300 mse: 0.26 perc: 0.53 w_norm: 0.00 noise: 0.00 step: 2/300 mse: 0.29 perc: 0.51 w_norm: 0.00 noise: 0.00 step: 3/300 mse: 0.23 perc: 0.50 w_norm: 0.00 noise: 0.00 step: 4/300 mse: 0.19 perc: 0.44 w_norm: 0.00 noise: 0.00 step: 5/300 mse: 0.18 perc: 0.47 w_norm: 0.00 noise: 0.00 step: 6/300 mse: 0.18 perc: 0.46 w_norm: 0.00 noise: 0.00 step: 7/300 mse: 0.11 perc: 0.41 w_norm: 0.00 noise: 0.00 step: 8/300 mse: 0.11 perc: 0.41 w_norm: 0.00 noise: 0.00 step: 9/300 mse: 0.11 perc: 0.43 w_norm: 0.01 noise: 0.00 step: 10/300 mse: 0.11 perc: 0.42 w_norm: 0.01 noise: 0.00 step: 11/300 mse: 0.10 perc: 0.43 w_norm: 0.01 noise: 0.00 step: 12/300 mse: 0.11 perc: 0.41 w_norm: 0.02 noise: 0.00 step: 13/300 mse: 0.10 perc: 0.41 w_norm: 0.02 noise: 0.00 step: 14/300 mse: 0.12 perc: 0.41 w_norm: 0.03 noise: 0.00 step: 15/300 mse: 0.12 perc: 0.42 w_norm: 0.03 noise: 0.00 step: 16/300 mse: 0.11 perc: 0.42 w_norm: 0.04 noise: 0.00 step: 17/300 mse: 0.10 perc: 0.39 w_norm: 0.04 noise: 0.00 step: 18/300 mse: 0.09 perc: 0.40 w_norm: 0.04 noise: 0.00 step: 19/300 mse: 0.08 perc: 0.39 w_norm: 0.05 noise: 0.00 step: 20/300 mse: 0.10 perc: 0.38 w_norm: 0.05 noise: 0.00 step: 21/300 mse: 0.10 perc: 0.40 w_norm: 0.06 noise: 0.00 step: 22/300 mse: 0.09 perc: 0.40 w_norm: 0.06 noise: 0.00 step: 23/300 mse: 0.10 perc: 0.39 w_norm: 0.06 noise: 0.00 step: 24/300 mse: 0.11 perc: 0.39 w_norm: 0.07 noise: 0.00 step: 25/300 mse: 0.11 perc: 0.43 w_norm: 0.07 noise: 0.00 step: 26/300 mse: 0.10 perc: 0.38 w_norm: 0.07 noise: 0.00 step: 27/300 mse: 0.10 perc: 0.38 w_norm: 0.08 noise: 0.00 step: 28/300 mse: 0.10 perc: 0.38 w_norm: 0.08 noise: 0.00 step: 29/300 mse: 0.10 perc: 0.40 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Loading networks from "/src/output/easy-khair-180-gpc0.8-trans10-025000.pkl/0/fintuned_generator.pkl"... Setting up PyTorch plugin "bias_act_plugin"... Done. Setting up PyTorch plugin "upfirdn2d_plugin"... 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