dribnet / pixray-api
Uses pixray with raw settings.
Prediction
dribnet/pixray-api:b35ec81c61e81bd30116b45a70147cbd2e77a3bc72b80edf2b52c22df54a6c92IDndqsu43egnbavkm6vdiitaiukyStatusSucceededSourceWebHardware–Total durationCreatedInput
- settings
- prompts: Godzilla appearing on the one dollar bill. quality: better init_image: "https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg" size: [600, 260] seed: mothra
{ "settings": "prompts: Godzilla appearing on the one dollar bill.\nquality: better\ninit_image: \"https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg\"\nsize: [600, 260]\nseed: mothra\n" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run dribnet/pixray-api using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "dribnet/pixray-api:b35ec81c61e81bd30116b45a70147cbd2e77a3bc72b80edf2b52c22df54a6c92", { input: { settings: "prompts: Godzilla appearing on the one dollar bill.\nquality: better\ninit_image: \"https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg\"\nsize: [600, 260]\nseed: mothra\n" } } ); 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 replicate
Import the client:import replicate
Run dribnet/pixray-api using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "dribnet/pixray-api:b35ec81c61e81bd30116b45a70147cbd2e77a3bc72b80edf2b52c22df54a6c92", input={ "settings": "prompts: Godzilla appearing on the one dollar bill.\nquality: better\ninit_image: \"https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg\"\nsize: [600, 260]\nseed: mothra\n" } ) # The dribnet/pixray-api model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/dribnet/pixray-api/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run dribnet/pixray-api 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": "dribnet/pixray-api:b35ec81c61e81bd30116b45a70147cbd2e77a3bc72b80edf2b52c22df54a6c92", "input": { "settings": "prompts: Godzilla appearing on the one dollar bill.\\nquality: better\\ninit_image: \\"https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg\\"\\nsize: [600, 260]\\nseed: mothra\\n" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2021-12-04T09:20:52.532838Z", "created_at": "2021-12-04T09:15:42.284819Z", "data_removed": false, "error": null, "id": "ndqsu43egnbavkm6vdiitaiuky", "input": { "settings": "prompts: Godzilla appearing on the one dollar bill.\nquality: better\ninit_image: \"https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg\"\nsize: [600, 260]\nseed: mothra\n" }, "logs": "---> BasePixrayPredictor Predict\nUsing seed:\n3540547939\nreusing cached copy of model\nmodels/vqgan_imagenet_f16_16384.ckpt\nAll CLIP models already loaded:\n['RN50', 'ViT-B/32', 'ViT-B/16']\nUsing device:\ncuda:0\nOptimising using:\nAdam\nUsing text prompts:\n['Godzilla appearing on the one dollar bill.']\nUsing initial image https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg (1)\n\n0it [00:00, ?it/s]\niter: 0, loss: 2.72, losses: 0.899, 0.0874, 0.805, 0.0596, 0.808, 0.058 (-0=>2.717)\n\n0it [00:00, ?it/s]\niter: 10, loss: 2.42, losses: 0.813, 0.0866, 0.705, 0.0598, 0.701, 0.0584 (-0=>2.424)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 20, loss: 2.45, losses: 0.824, 0.0849, 0.717, 0.0595, 0.707, 0.0579 (-2=>2.352)\n\n0it [00:00, ?it/s]\niter: 30, loss: 2.35, losses: 0.793, 0.0858, 0.684, 0.0615, 0.667, 0.0599 (-6=>2.314)\n\n0it [00:11, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 40, loss: 2.35, losses: 0.798, 0.0869, 0.679, 0.0618, 0.664, 0.0595 (-4=>2.26)\n\n0it [00:00, ?it/s]\niter: 50, loss: 2.23, losses: 0.752, 0.0898, 0.64, 0.0638, 0.625, 0.0639 (-2=>2.233)\n\n0it [00:11, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 60, loss: 2.33, losses: 0.791, 0.0868, 0.671, 0.0603, 0.658, 0.0608 (-2=>2.224)\n\n0it [00:00, ?it/s]\niter: 70, loss: 2.32, losses: 0.782, 0.0878, 0.668, 0.062, 0.663, 0.0616 (-12=>2.224)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 80, loss: 2.32, losses: 0.774, 0.0868, 0.671, 0.0629, 0.661, 0.0604 (-22=>2.224)\n\n0it [00:00, ?it/s]\niter: 90, loss: 2.29, losses: 0.777, 0.0895, 0.658, 0.0638, 0.64, 0.0627 (-4=>2.211)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 100, loss: 2.29, losses: 0.777, 0.0887, 0.653, 0.0641, 0.647, 0.0631 (-1=>2.194)\n\n0it [00:00, ?it/s]\niter: 110, loss: 2.33, losses: 0.79, 0.0862, 0.671, 0.0606, 0.663, 0.0602 (-11=>2.194)\n\n0it [00:10, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 120, loss: 2.25, losses: 0.762, 0.0896, 0.64, 0.0646, 0.634, 0.0624 (-4=>2.185)\n\n0it [00:00, ?it/s]\niter: 130, loss: 2.24, losses: 0.756, 0.0914, 0.639, 0.0647, 0.63, 0.0626 (-6=>2.177)\n\n0it [00:10, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 140, loss: 2.24, losses: 0.76, 0.0892, 0.633, 0.066, 0.625, 0.0637 (-8=>2.165)\n\n0it [00:00, ?it/s]\niter: 150, loss: 2.16, losses: 0.723, 0.0934, 0.607, 0.0671, 0.601, 0.0655 (-0=>2.157)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 160, loss: 2.17, losses: 0.721, 0.0923, 0.618, 0.0649, 0.608, 0.0636 (-8=>2.155)\n\n0it [00:00, ?it/s]\niter: 170, loss: 2.26, losses: 0.767, 0.0876, 0.645, 0.0633, 0.633, 0.0616 (-18=>2.155)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 180, loss: 2.23, losses: 0.757, 0.0898, 0.632, 0.0645, 0.618, 0.0634 (-4=>2.139)\n\n0it [00:00, ?it/s]\niter: 190, loss: 2.16, losses: 0.725, 0.0929, 0.612, 0.066, 0.602, 0.0664 (-14=>2.139)\n\n0it [00:10, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 200, loss: 2.26, losses: 0.767, 0.0874, 0.646, 0.0622, 0.633, 0.0617 (-24=>2.139)\n\n0it [00:00, ?it/s]\niter: 210, loss: 2.15, losses: 0.72, 0.0911, 0.605, 0.0665, 0.598, 0.0658 (-34=>2.139)\n\n0it [00:10, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 220, loss: 2.22, losses: 0.752, 0.0895, 0.628, 0.0667, 0.615, 0.0648 (-44=>2.139)\n\n0it [00:00, ?it/s]\nDropping learning rate\niter: 230, loss: 2.26, losses: 0.764, 0.0907, 0.641, 0.0652, 0.635, 0.0639 (-3=>2.154)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 240, loss: 2.14, losses: 0.717, 0.0917, 0.602, 0.0669, 0.6, 0.0645 (-5=>2.14)\n\n0it [00:00, ?it/s]\niter: 250, loss: 2.21, losses: 0.745, 0.0914, 0.618, 0.067, 0.624, 0.0647 (-3=>2.133)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 260, loss: 2.24, losses: 0.758, 0.0893, 0.637, 0.065, 0.624, 0.0639 (-13=>2.133)\n\n0it [00:00, ?it/s]\niter: 270, loss: 2.18, losses: 0.739, 0.0923, 0.612, 0.0691, 0.606, 0.0653 (-4=>2.121)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 280, loss: 2.21, losses: 0.746, 0.0918, 0.621, 0.0658, 0.621, 0.0639 (-14=>2.121)\n\n0it [00:00, ?it/s]\niter: 290, loss: 2.14, losses: 0.721, 0.0901, 0.603, 0.0658, 0.592, 0.065 (-24=>2.121)\n\n0it [00:10, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 300, finished (-4=>2.118)\n\n0it [00:00, ?it/s]\n\n0it [00:00, ?it/s]", "metrics": { "predict_time": 309.903421, "total_time": 310.248019 }, "output": [ { "file": "https://replicate.delivery/mgxm/115e508d-d7e0-463e-b63c-9bfc479b70a3/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/3b134e33-0085-4672-b1bd-fdb755ecc41a/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/a3c06312-1812-4b08-8b44-cd2aeda0f059/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/249ad3f7-9e49-4849-a759-dbcc06a1961f/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/802d88d5-87d6-4cc3-8f61-b0251699708f/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/7c8ffe4e-d18a-4811-85e8-873b5684f6db/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/e87fd805-9a61-4a22-9fc5-cd7e96f13668/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/915bbfb5-07c4-4d30-81a2-7c95af78d068/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/9bcb8a1c-81f5-4d8f-82a3-eb02d4fd9296/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/d1c7e23b-7c2e-4348-a1be-0536d448ac6b/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/0a5ca4bd-043e-447b-a69b-34decedf5786/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/79fc9338-46b5-41c3-88cf-3aaa4eccbff5/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/b3fe93c2-049b-4536-8732-88433b47728e/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/eda54ed9-fb91-4668-ae29-b0909a0dc2c9/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/01df22b2-a717-48c6-be5c-36312e134c12/tempfile.png" } ], "started_at": "2021-12-04T09:15:42.629417Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ndqsu43egnbavkm6vdiitaiuky", "cancel": "https://api.replicate.com/v1/predictions/ndqsu43egnbavkm6vdiitaiuky/cancel" }, "version": "b35ec81c61e81bd30116b45a70147cbd2e77a3bc72b80edf2b52c22df54a6c92" }
Generated in---> BasePixrayPredictor Predict Using seed: 3540547939 reusing cached copy of model models/vqgan_imagenet_f16_16384.ckpt All CLIP models already loaded: ['RN50', 'ViT-B/32', 'ViT-B/16'] Using device: cuda:0 Optimising using: Adam Using text prompts: ['Godzilla appearing on the one dollar bill.'] Using initial image https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg (1) 0it [00:00, ?it/s] iter: 0, loss: 2.72, losses: 0.899, 0.0874, 0.805, 0.0596, 0.808, 0.058 (-0=>2.717) 0it [00:00, ?it/s] iter: 10, loss: 2.42, losses: 0.813, 0.0866, 0.705, 0.0598, 0.701, 0.0584 (-0=>2.424) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 20, loss: 2.45, losses: 0.824, 0.0849, 0.717, 0.0595, 0.707, 0.0579 (-2=>2.352) 0it [00:00, ?it/s] iter: 30, loss: 2.35, losses: 0.793, 0.0858, 0.684, 0.0615, 0.667, 0.0599 (-6=>2.314) 0it [00:11, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 40, loss: 2.35, losses: 0.798, 0.0869, 0.679, 0.0618, 0.664, 0.0595 (-4=>2.26) 0it [00:00, ?it/s] iter: 50, loss: 2.23, losses: 0.752, 0.0898, 0.64, 0.0638, 0.625, 0.0639 (-2=>2.233) 0it [00:11, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 60, loss: 2.33, losses: 0.791, 0.0868, 0.671, 0.0603, 0.658, 0.0608 (-2=>2.224) 0it [00:00, ?it/s] iter: 70, loss: 2.32, losses: 0.782, 0.0878, 0.668, 0.062, 0.663, 0.0616 (-12=>2.224) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 80, loss: 2.32, losses: 0.774, 0.0868, 0.671, 0.0629, 0.661, 0.0604 (-22=>2.224) 0it [00:00, ?it/s] iter: 90, loss: 2.29, losses: 0.777, 0.0895, 0.658, 0.0638, 0.64, 0.0627 (-4=>2.211) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 100, loss: 2.29, losses: 0.777, 0.0887, 0.653, 0.0641, 0.647, 0.0631 (-1=>2.194) 0it [00:00, ?it/s] iter: 110, loss: 2.33, losses: 0.79, 0.0862, 0.671, 0.0606, 0.663, 0.0602 (-11=>2.194) 0it [00:10, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 120, loss: 2.25, losses: 0.762, 0.0896, 0.64, 0.0646, 0.634, 0.0624 (-4=>2.185) 0it [00:00, ?it/s] iter: 130, loss: 2.24, losses: 0.756, 0.0914, 0.639, 0.0647, 0.63, 0.0626 (-6=>2.177) 0it [00:10, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 140, loss: 2.24, losses: 0.76, 0.0892, 0.633, 0.066, 0.625, 0.0637 (-8=>2.165) 0it [00:00, ?it/s] iter: 150, loss: 2.16, losses: 0.723, 0.0934, 0.607, 0.0671, 0.601, 0.0655 (-0=>2.157) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 160, loss: 2.17, losses: 0.721, 0.0923, 0.618, 0.0649, 0.608, 0.0636 (-8=>2.155) 0it [00:00, ?it/s] iter: 170, loss: 2.26, losses: 0.767, 0.0876, 0.645, 0.0633, 0.633, 0.0616 (-18=>2.155) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 180, loss: 2.23, losses: 0.757, 0.0898, 0.632, 0.0645, 0.618, 0.0634 (-4=>2.139) 0it [00:00, ?it/s] iter: 190, loss: 2.16, losses: 0.725, 0.0929, 0.612, 0.066, 0.602, 0.0664 (-14=>2.139) 0it [00:10, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 200, loss: 2.26, losses: 0.767, 0.0874, 0.646, 0.0622, 0.633, 0.0617 (-24=>2.139) 0it [00:00, ?it/s] iter: 210, loss: 2.15, losses: 0.72, 0.0911, 0.605, 0.0665, 0.598, 0.0658 (-34=>2.139) 0it [00:10, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 220, loss: 2.22, losses: 0.752, 0.0895, 0.628, 0.0667, 0.615, 0.0648 (-44=>2.139) 0it [00:00, ?it/s] Dropping learning rate iter: 230, loss: 2.26, losses: 0.764, 0.0907, 0.641, 0.0652, 0.635, 0.0639 (-3=>2.154) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 240, loss: 2.14, losses: 0.717, 0.0917, 0.602, 0.0669, 0.6, 0.0645 (-5=>2.14) 0it [00:00, ?it/s] iter: 250, loss: 2.21, losses: 0.745, 0.0914, 0.618, 0.067, 0.624, 0.0647 (-3=>2.133) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 260, loss: 2.24, losses: 0.758, 0.0893, 0.637, 0.065, 0.624, 0.0639 (-13=>2.133) 0it [00:00, ?it/s] iter: 270, loss: 2.18, losses: 0.739, 0.0923, 0.612, 0.0691, 0.606, 0.0653 (-4=>2.121) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 280, loss: 2.21, losses: 0.746, 0.0918, 0.621, 0.0658, 0.621, 0.0639 (-14=>2.121) 0it [00:00, ?it/s] iter: 290, loss: 2.14, losses: 0.721, 0.0901, 0.603, 0.0658, 0.592, 0.065 (-24=>2.121) 0it [00:10, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 300, finished (-4=>2.118) 0it [00:00, ?it/s] 0it [00:00, ?it/s]
Prediction
dribnet/pixray-api:30a982e43193c50480874788bbddcbb3f58397b74c3a580b8f5d3786ab778adeInput
- settings
- # find palettes at https://lospec.com/palette-list palette: https://lospec.com/palette-list/cl8uds-32x.png filters: lookup prompts: "robots at sunset" quality: better seed: god_mode
{ "settings": "# find palettes at https://lospec.com/palette-list\npalette: https://lospec.com/palette-list/cl8uds-32x.png\nfilters: lookup\nprompts: \"robots at sunset\"\nquality: better\nseed: god_mode\n" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run dribnet/pixray-api using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "dribnet/pixray-api:30a982e43193c50480874788bbddcbb3f58397b74c3a580b8f5d3786ab778ade", { input: { settings: "# find palettes at https://lospec.com/palette-list\npalette: https://lospec.com/palette-list/cl8uds-32x.png\nfilters: lookup\nprompts: \"robots at sunset\"\nquality: better\nseed: god_mode\n" } } ); 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 replicate
Import the client:import replicate
Run dribnet/pixray-api using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "dribnet/pixray-api:30a982e43193c50480874788bbddcbb3f58397b74c3a580b8f5d3786ab778ade", input={ "settings": "# find palettes at https://lospec.com/palette-list\npalette: https://lospec.com/palette-list/cl8uds-32x.png\nfilters: lookup\nprompts: \"robots at sunset\"\nquality: better\nseed: god_mode\n" } ) # The dribnet/pixray-api model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/dribnet/pixray-api/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run dribnet/pixray-api 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": "dribnet/pixray-api:30a982e43193c50480874788bbddcbb3f58397b74c3a580b8f5d3786ab778ade", "input": { "settings": "# find palettes at https://lospec.com/palette-list\\npalette: https://lospec.com/palette-list/cl8uds-32x.png\\nfilters: lookup\\nprompts: \\"robots at sunset\\"\\nquality: better\\nseed: god_mode\\n" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2021-11-28T10:06:55.200045Z", "created_at": "2021-11-28T10:00:49.325906Z", "data_removed": false, "error": null, "id": "le76voshdvfjzajwbzhavclkla", "input": { "settings": "# find palettes at https://lospec.com/palette-list\npalette: https://lospec.com/palette-list/cl8uds-32x.png\nfilters: lookup\nprompts: \"robots at sunset\"\nquality: better\nseed: god_mode\n" }, "logs": "---> BasePixrayPredictor Predict\nFound 8 colors in https://lospec.com/palette-list/cl8uds-32x.png\nUsing seed:\n392990954\nWorking with z of shape (1, 256, 16, 16) = 65536 dimensions.\nloaded pretrained LPIPS loss from taming/modules/autoencoder/lpips/vgg.pth\nVQLPIPSWithDiscriminator running with hinge loss.\nRestored from models/vqgan_imagenet_f16_16384.ckpt\ncolor table has 8 entries like [[0.6470588235294118, 0.7176470588235294, 0.8313725490196079], [0.9882352941176471, 0.6901960784313725, 0.5490196078431373], [0.6039215686274509, 0.6705882352941176, 0.788235294117647], [0.5607843137254902, 0.6274509803921569, 0.7490196078431373], [0.9372549019607843, 0.615686274509804, 0.4980392156862745]]\nUsing device:\ncuda:0\nOptimising using:\nAdam\nUsing text prompts:\n['robots at sunset']\n\n0it [00:00, ?it/s]\n/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:3631: 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.\n warnings.warn(\n/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.)\n return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\niter: 0, loss: 3.4, losses: 0.395, 0.994, 0.0795, 0.915, 0.047, 0.916, 0.0481 (-0=>3.395)\n\n0it [00:00, ?it/s]\niter: 10, loss: 2.84, losses: 0.0314, 0.945, 0.0797, 0.85, 0.0455, 0.839, 0.047 (-0=>2.838)\n\n0it [00:11, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 20, loss: 2.8, losses: 0.0259, 0.935, 0.0811, 0.838, 0.0445, 0.831, 0.0458 (-0=>2.802)\n\n0it [00:00, ?it/s]\niter: 30, loss: 2.79, losses: 0.0302, 0.931, 0.0805, 0.835, 0.0446, 0.823, 0.0454 (-2=>2.789)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 40, loss: 2.78, losses: 0.031, 0.926, 0.081, 0.83, 0.0443, 0.82, 0.0458 (-3=>2.76)\n\n0it [00:00, ?it/s]\niter: 50, loss: 2.76, losses: 0.0377, 0.918, 0.0814, 0.825, 0.0437, 0.814, 0.0452 (-13=>2.76)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 60, loss: 2.75, losses: 0.0688, 0.9, 0.0821, 0.813, 0.0428, 0.801, 0.0451 (-6=>2.732)\n\n0it [00:00, ?it/s]\niter: 70, loss: 2.7, losses: 0.0738, 0.879, 0.0798, 0.798, 0.0439, 0.777, 0.0456 (-0=>2.697)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 80, loss: 2.75, losses: 0.138, 0.877, 0.0817, 0.791, 0.0444, 0.776, 0.0466 (-10=>2.697)\n\n0it [00:00, ?it/s]\niter: 90, loss: 2.76, losses: 0.128, 0.893, 0.0822, 0.795, 0.044, 0.772, 0.0458 (-20=>2.697)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 100, loss: 2.74, losses: 0.143, 0.88, 0.0824, 0.783, 0.0459, 0.759, 0.0481 (-6=>2.672)\n\n0it [00:00, ?it/s]\niter: 110, loss: 2.71, losses: 0.116, 0.874, 0.0821, 0.782, 0.0443, 0.762, 0.047 (-16=>2.672)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 120, loss: 2.72, losses: 0.147, 0.862, 0.0833, 0.778, 0.0459, 0.755, 0.0483 (-4=>2.661)\n\n0it [00:00, ?it/s]\niter: 130, loss: 2.65, losses: 0.152, 0.843, 0.0834, 0.744, 0.0468, 0.734, 0.0485 (-6=>2.652)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 140, loss: 2.69, losses: 0.145, 0.852, 0.0822, 0.767, 0.0457, 0.748, 0.0485 (-6=>2.633)\n\n0it [00:00, ?it/s]\niter: 150, loss: 2.68, losses: 0.173, 0.849, 0.0832, 0.749, 0.046, 0.738, 0.047 (-8=>2.628)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 160, loss: 2.73, losses: 0.17, 0.859, 0.0837, 0.773, 0.0461, 0.751, 0.0474 (-18=>2.628)\n\n0it [00:00, ?it/s]\niter: 170, loss: 2.73, losses: 0.166, 0.87, 0.0834, 0.765, 0.0466, 0.756, 0.0472 (-28=>2.628)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 180, loss: 2.68, losses: 0.164, 0.848, 0.0844, 0.744, 0.0476, 0.746, 0.0485 (-38=>2.628)\n\n0it [00:00, ?it/s]\niter: 190, loss: 2.6, losses: 0.156, 0.818, 0.0838, 0.727, 0.0475, 0.72, 0.0484 (-0=>2.6)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 200, loss: 2.7, losses: 0.177, 0.851, 0.0837, 0.752, 0.047, 0.739, 0.0484 (-10=>2.6)\n\n0it [00:00, ?it/s]\niter: 210, loss: 2.7, losses: 0.175, 0.85, 0.0843, 0.751, 0.0473, 0.746, 0.0487 (-20=>2.6)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 220, loss: 2.7, losses: 0.166, 0.858, 0.0853, 0.751, 0.0464, 0.743, 0.0481 (-30=>2.6)\n\n0it [00:00, ?it/s]\nDropping learning rate\niter: 230, loss: 2.6, losses: 0.153, 0.824, 0.0858, 0.722, 0.048, 0.717, 0.0488 (-0=>2.599)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 240, loss: 2.65, losses: 0.149, 0.843, 0.0851, 0.74, 0.0471, 0.738, 0.0493 (-1=>2.574)\n\n0it [00:00, ?it/s]\niter: 250, loss: 2.65, losses: 0.151, 0.842, 0.0842, 0.744, 0.0474, 0.736, 0.0486 (-11=>2.574)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 260, loss: 2.64, losses: 0.154, 0.836, 0.0851, 0.737, 0.0476, 0.731, 0.0481 (-21=>2.574)\n\n0it [00:00, ?it/s]\niter: 270, loss: 2.65, losses: 0.145, 0.843, 0.0853, 0.742, 0.047, 0.735, 0.0482 (-4=>2.562)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 280, loss: 2.61, losses: 0.16, 0.825, 0.0853, 0.721, 0.049, 0.716, 0.049 (-14=>2.562)\n\n0it [00:00, ?it/s]\niter: 290, loss: 2.64, losses: 0.155, 0.835, 0.0849, 0.735, 0.0479, 0.734, 0.0484 (-24=>2.562)\n\n0it [00:12, ?it/s]\n\n0it [00:22, ?it/s]\n\n0it [00:00, ?it/s]\niter: 300, finished (-34=>2.562)\n\n0it [00:00, ?it/s]\n\n0it [00:00, ?it/s]", "metrics": { "total_time": 365.874139 }, "output": [ { "file": "https://replicate.delivery/mgxm/908a2b66-f39c-45b5-80e0-2c3bd1eb89e6/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/c1639b8a-8d93-47b3-b99a-154f64effefc/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/0c1f6a24-8065-4f0e-959c-39a33d9c1ac3/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/4ecde433-c878-47b9-8d34-2208a1fcddfe/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/ef7a3e70-62a2-4a97-965a-2c458d952f60/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/ec99a125-29cd-4bd1-ad8c-efd52cdb37bd/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/cb02501c-13a7-4886-a130-93893136bc0b/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/f37939e4-4f3e-4fe9-987a-655259370aac/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/194d00ed-685e-40a6-befc-b4e5634ea7a9/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/5d69d8ad-6c23-4217-8d6a-3e4d298b3e8a/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/556c728b-e8f0-45cc-abf1-593571e6fbf7/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/5d4ba414-7230-4344-aeff-380712b9ae0c/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/e86c93b8-644c-48ac-a047-bc586d664e5e/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/fd4faa26-6bee-4f32-aeb9-30ef070c854c/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/d1649fbe-e5cb-4374-871b-1c5f51bbd8d2/tempfile.png" } ], "started_at": "2021-11-30T22:00:25.336971Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/le76voshdvfjzajwbzhavclkla", "cancel": "https://api.replicate.com/v1/predictions/le76voshdvfjzajwbzhavclkla/cancel" }, "version": "30a982e43193c50480874788bbddcbb3f58397b74c3a580b8f5d3786ab778ade" }
---> BasePixrayPredictor Predict Found 8 colors in https://lospec.com/palette-list/cl8uds-32x.png Using seed: 392990954 Working with z of shape (1, 256, 16, 16) = 65536 dimensions. loaded pretrained LPIPS loss from taming/modules/autoencoder/lpips/vgg.pth VQLPIPSWithDiscriminator running with hinge loss. Restored from models/vqgan_imagenet_f16_16384.ckpt color table has 8 entries like [[0.6470588235294118, 0.7176470588235294, 0.8313725490196079], [0.9882352941176471, 0.6901960784313725, 0.5490196078431373], [0.6039215686274509, 0.6705882352941176, 0.788235294117647], [0.5607843137254902, 0.6274509803921569, 0.7490196078431373], [0.9372549019607843, 0.615686274509804, 0.4980392156862745]] Using device: cuda:0 Optimising using: Adam Using text prompts: ['robots at sunset'] 0it [00:00, ?it/s] /root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:3631: 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/3.8.12/lib/python3.8/site-packages/torch/functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2157.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] iter: 0, loss: 3.4, losses: 0.395, 0.994, 0.0795, 0.915, 0.047, 0.916, 0.0481 (-0=>3.395) 0it [00:00, ?it/s] iter: 10, loss: 2.84, losses: 0.0314, 0.945, 0.0797, 0.85, 0.0455, 0.839, 0.047 (-0=>2.838) 0it [00:11, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 20, loss: 2.8, losses: 0.0259, 0.935, 0.0811, 0.838, 0.0445, 0.831, 0.0458 (-0=>2.802) 0it [00:00, ?it/s] iter: 30, loss: 2.79, losses: 0.0302, 0.931, 0.0805, 0.835, 0.0446, 0.823, 0.0454 (-2=>2.789) 0it [00:12, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 40, loss: 2.78, losses: 0.031, 0.926, 0.081, 0.83, 0.0443, 0.82, 0.0458 (-3=>2.76) 0it [00:00, ?it/s] iter: 50, loss: 2.76, losses: 0.0377, 0.918, 0.0814, 0.825, 0.0437, 0.814, 0.0452 (-13=>2.76) 0it [00:12, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 60, loss: 2.75, losses: 0.0688, 0.9, 0.0821, 0.813, 0.0428, 0.801, 0.0451 (-6=>2.732) 0it [00:00, ?it/s] iter: 70, loss: 2.7, losses: 0.0738, 0.879, 0.0798, 0.798, 0.0439, 0.777, 0.0456 (-0=>2.697) 0it [00:12, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 80, loss: 2.75, losses: 0.138, 0.877, 0.0817, 0.791, 0.0444, 0.776, 0.0466 (-10=>2.697) 0it [00:00, ?it/s] iter: 90, loss: 2.76, losses: 0.128, 0.893, 0.0822, 0.795, 0.044, 0.772, 0.0458 (-20=>2.697) 0it [00:12, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 100, loss: 2.74, losses: 0.143, 0.88, 0.0824, 0.783, 0.0459, 0.759, 0.0481 (-6=>2.672) 0it [00:00, ?it/s] iter: 110, loss: 2.71, losses: 0.116, 0.874, 0.0821, 0.782, 0.0443, 0.762, 0.047 (-16=>2.672) 0it [00:12, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 120, loss: 2.72, losses: 0.147, 0.862, 0.0833, 0.778, 0.0459, 0.755, 0.0483 (-4=>2.661) 0it [00:00, ?it/s] iter: 130, loss: 2.65, losses: 0.152, 0.843, 0.0834, 0.744, 0.0468, 0.734, 0.0485 (-6=>2.652) 0it [00:12, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 140, loss: 2.69, losses: 0.145, 0.852, 0.0822, 0.767, 0.0457, 0.748, 0.0485 (-6=>2.633) 0it [00:00, ?it/s] iter: 150, loss: 2.68, losses: 0.173, 0.849, 0.0832, 0.749, 0.046, 0.738, 0.047 (-8=>2.628) 0it [00:12, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 160, loss: 2.73, losses: 0.17, 0.859, 0.0837, 0.773, 0.0461, 0.751, 0.0474 (-18=>2.628) 0it [00:00, ?it/s] iter: 170, loss: 2.73, losses: 0.166, 0.87, 0.0834, 0.765, 0.0466, 0.756, 0.0472 (-28=>2.628) 0it [00:12, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 180, loss: 2.68, losses: 0.164, 0.848, 0.0844, 0.744, 0.0476, 0.746, 0.0485 (-38=>2.628) 0it [00:00, ?it/s] iter: 190, loss: 2.6, losses: 0.156, 0.818, 0.0838, 0.727, 0.0475, 0.72, 0.0484 (-0=>2.6) 0it [00:12, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 200, loss: 2.7, losses: 0.177, 0.851, 0.0837, 0.752, 0.047, 0.739, 0.0484 (-10=>2.6) 0it [00:00, ?it/s] iter: 210, loss: 2.7, losses: 0.175, 0.85, 0.0843, 0.751, 0.0473, 0.746, 0.0487 (-20=>2.6) 0it [00:12, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 220, loss: 2.7, losses: 0.166, 0.858, 0.0853, 0.751, 0.0464, 0.743, 0.0481 (-30=>2.6) 0it [00:00, ?it/s] Dropping learning rate iter: 230, loss: 2.6, losses: 0.153, 0.824, 0.0858, 0.722, 0.048, 0.717, 0.0488 (-0=>2.599) 0it [00:12, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 240, loss: 2.65, losses: 0.149, 0.843, 0.0851, 0.74, 0.0471, 0.738, 0.0493 (-1=>2.574) 0it [00:00, ?it/s] iter: 250, loss: 2.65, losses: 0.151, 0.842, 0.0842, 0.744, 0.0474, 0.736, 0.0486 (-11=>2.574) 0it [00:12, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 260, loss: 2.64, losses: 0.154, 0.836, 0.0851, 0.737, 0.0476, 0.731, 0.0481 (-21=>2.574) 0it [00:00, ?it/s] iter: 270, loss: 2.65, losses: 0.145, 0.843, 0.0853, 0.742, 0.047, 0.735, 0.0482 (-4=>2.562) 0it [00:12, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 280, loss: 2.61, losses: 0.16, 0.825, 0.0853, 0.721, 0.049, 0.716, 0.049 (-14=>2.562) 0it [00:00, ?it/s] iter: 290, loss: 2.64, losses: 0.155, 0.835, 0.0849, 0.735, 0.0479, 0.734, 0.0484 (-24=>2.562) 0it [00:12, ?it/s] 0it [00:22, ?it/s] 0it [00:00, ?it/s] iter: 300, finished (-34=>2.562) 0it [00:00, ?it/s] 0it [00:00, ?it/s]
Prediction
dribnet/pixray-api:b35ec81c61e81bd30116b45a70147cbd2e77a3bc72b80edf2b52c22df54a6c92Input
- settings
- prompts: Godzilla appearing on the one dollar bill. quality: better init_image: "https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg" size: [600, 260] seed: god_mode
{ "settings": "prompts: Godzilla appearing on the one dollar bill.\nquality: better\ninit_image: \"https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg\"\nsize: [600, 260]\nseed: god_mode\n" }
Install Replicate’s Node.js client library:npm install replicate
Import and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });
Run dribnet/pixray-api using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "dribnet/pixray-api:b35ec81c61e81bd30116b45a70147cbd2e77a3bc72b80edf2b52c22df54a6c92", { input: { settings: "prompts: Godzilla appearing on the one dollar bill.\nquality: better\ninit_image: \"https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg\"\nsize: [600, 260]\nseed: god_mode\n" } } ); 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 replicate
Import the client:import replicate
Run dribnet/pixray-api using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "dribnet/pixray-api:b35ec81c61e81bd30116b45a70147cbd2e77a3bc72b80edf2b52c22df54a6c92", input={ "settings": "prompts: Godzilla appearing on the one dollar bill.\nquality: better\ninit_image: \"https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg\"\nsize: [600, 260]\nseed: god_mode\n" } ) # The dribnet/pixray-api model can stream output as it's running. # The predict method returns an iterator, and you can iterate over that output. for item in output: # https://replicate.com/dribnet/pixray-api/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run dribnet/pixray-api 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": "dribnet/pixray-api:b35ec81c61e81bd30116b45a70147cbd2e77a3bc72b80edf2b52c22df54a6c92", "input": { "settings": "prompts: Godzilla appearing on the one dollar bill.\\nquality: better\\ninit_image: \\"https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg\\"\\nsize: [600, 260]\\nseed: god_mode\\n" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2021-12-04T09:11:07.914351Z", "created_at": "2021-12-04T09:05:40.411352Z", "data_removed": false, "error": null, "id": "bombcp2qvrgivl545eokcd7nd4", "input": { "settings": "prompts: Godzilla appearing on the one dollar bill.\nquality: better\ninit_image: \"https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg\"\nsize: [600, 260]\nseed: god_mode\n" }, "logs": "---> BasePixrayPredictor Predict\nUsing seed:\n392990954\nWorking with z of shape (1, 256, 16, 16) = 65536 dimensions.\nloaded pretrained LPIPS loss from taming/modules/autoencoder/lpips/vgg.pth\nVQLPIPSWithDiscriminator running with hinge loss.\nRestored from models/vqgan_imagenet_f16_16384.ckpt\nUsing device:\ncuda:0\nOptimising using:\nAdam\nUsing text prompts:\n['Godzilla appearing on the one dollar bill.']\nUsing initial image https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg (1)\n\n0it [00:00, ?it/s]\n/root/.pyenv/versions/3.8.12/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.\n warnings.warn(\niter: 0, loss: 2.75, losses: 0.902, 0.0861, 0.823, 0.0593, 0.822, 0.0576 (-0=>2.75)\n\n0it [00:00, ?it/s]\niter: 10, loss: 2.57, losses: 0.865, 0.0893, 0.751, 0.0587, 0.749, 0.0586 (-2=>2.571)\n\n0it [00:10, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 20, loss: 2.36, losses: 0.793, 0.0893, 0.68, 0.0623, 0.67, 0.0628 (-0=>2.357)\n\n0it [00:00, ?it/s]\niter: 30, loss: 2.4, losses: 0.805, 0.0858, 0.706, 0.0615, 0.683, 0.0599 (-5=>2.332)\n\n0it [00:11, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 40, loss: 2.35, losses: 0.792, 0.0862, 0.692, 0.0608, 0.662, 0.0613 (-3=>2.29)\n\n0it [00:00, ?it/s]\niter: 50, loss: 2.34, losses: 0.794, 0.088, 0.678, 0.0632, 0.66, 0.0609 (-13=>2.29)\n\n0it [00:11, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 60, loss: 2.3, losses: 0.776, 0.0905, 0.66, 0.0654, 0.64, 0.0658 (-5=>2.231)\n\n0it [00:00, ?it/s]\niter: 70, loss: 2.23, losses: 0.742, 0.0938, 0.629, 0.0694, 0.627, 0.0681 (-0=>2.23)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 80, loss: 2.32, losses: 0.789, 0.0879, 0.665, 0.0659, 0.648, 0.0647 (-10=>2.23)\n\n0it [00:00, ?it/s]\niter: 90, loss: 2.29, losses: 0.778, 0.0892, 0.653, 0.064, 0.637, 0.0639 (-4=>2.205)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 100, loss: 2.27, losses: 0.767, 0.0901, 0.643, 0.0667, 0.632, 0.0666 (-6=>2.189)\n\n0it [00:00, ?it/s]\niter: 110, loss: 2.31, losses: 0.784, 0.0884, 0.661, 0.0648, 0.651, 0.064 (-16=>2.189)\n\n0it [00:10, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 120, loss: 2.29, losses: 0.772, 0.0893, 0.655, 0.0659, 0.642, 0.0642 (-26=>2.189)\n\n0it [00:00, ?it/s]\niter: 130, loss: 2.18, losses: 0.734, 0.0904, 0.613, 0.068, 0.605, 0.0668 (-0=>2.178)\n\n0it [00:10, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 140, loss: 2.28, losses: 0.779, 0.0879, 0.65, 0.0628, 0.641, 0.0639 (-6=>2.169)\n\n0it [00:00, ?it/s]\niter: 150, loss: 2.18, losses: 0.727, 0.0941, 0.61, 0.0682, 0.61, 0.0669 (-2=>2.167)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 160, loss: 2.27, losses: 0.776, 0.0869, 0.647, 0.0634, 0.633, 0.0626 (-4=>2.156)\n\n0it [00:00, ?it/s]\niter: 170, loss: 2.24, losses: 0.761, 0.0868, 0.634, 0.0647, 0.626, 0.0639 (-14=>2.156)\n\n0it [00:10, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 180, loss: 2.25, losses: 0.764, 0.09, 0.636, 0.0667, 0.627, 0.0653 (-24=>2.156)\n\n0it [00:00, ?it/s]\niter: 190, loss: 2.16, losses: 0.727, 0.0908, 0.61, 0.0685, 0.597, 0.0675 (-34=>2.156)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 200, loss: 2.23, losses: 0.752, 0.0912, 0.636, 0.0653, 0.623, 0.0635 (-1=>2.153)\n\n0it [00:00, ?it/s]\niter: 210, loss: 2.28, losses: 0.778, 0.0874, 0.643, 0.0634, 0.647, 0.0615 (-8=>2.149)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 220, loss: 2.28, losses: 0.77, 0.087, 0.655, 0.0619, 0.645, 0.0613 (-18=>2.149)\n\n0it [00:00, ?it/s]\nDropping learning rate\niter: 230, loss: 2.15, losses: 0.725, 0.0895, 0.608, 0.0669, 0.598, 0.0657 (-0=>2.154)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 240, loss: 2.26, losses: 0.772, 0.088, 0.641, 0.0636, 0.635, 0.0628 (-1=>2.142)\n\n0it [00:00, ?it/s]\niter: 250, loss: 2.23, losses: 0.756, 0.0895, 0.633, 0.0647, 0.619, 0.0642 (-8=>2.114)\n\n0it [00:10, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 260, loss: 2.2, losses: 0.75, 0.0886, 0.626, 0.0648, 0.61, 0.0648 (-2=>2.111)\n\n0it [00:00, ?it/s]\niter: 270, loss: 2.23, losses: 0.761, 0.0874, 0.635, 0.0653, 0.622, 0.0641 (-12=>2.111)\n\n0it [00:10, ?it/s]\n\n0it [00:20, ?it/s]\n\n0it [00:00, ?it/s]\niter: 280, loss: 2.14, losses: 0.721, 0.0932, 0.598, 0.0703, 0.589, 0.0678 (-22=>2.111)\n\n0it [00:00, ?it/s]\niter: 290, loss: 2.2, losses: 0.748, 0.091, 0.622, 0.0681, 0.606, 0.0671 (-32=>2.111)\n\n0it [00:10, ?it/s]\n\n0it [00:19, ?it/s]\n\n0it [00:00, ?it/s]\niter: 300, finished (-42=>2.111)\n\n0it [00:00, ?it/s]\n\n0it [00:00, ?it/s]", "metrics": { "predict_time": 326.251477, "total_time": 327.502999 }, "output": [ { "file": "https://replicate.delivery/mgxm/1ce4c5ae-c5f4-4352-9941-36e5be7a444e/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/fe5257f4-c22a-4bb6-9d6c-8938ddfde88a/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/a7dd599f-e318-450f-a6f6-c5f628abab93/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/f7556689-d8e3-406d-a22b-9a1d00d1c408/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/d3c10484-f214-4d06-a793-0b88e778d6ad/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/8fe112a7-556b-440b-adfb-a9ecf3429c37/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/bf2eb2b4-80d2-46c5-86d0-8962c172c917/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/3ce0b4c4-b40c-4638-9b41-35f301050a68/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/7ca2ef4e-395e-4c58-9a27-4644e6bc2c61/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/35346f1f-c4eb-47f3-a70a-1fc9eed2b810/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/0c82f3de-283c-4ed3-ad2e-dbf8904f75e5/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/a42a24be-5176-42c2-ae6e-4512e9506795/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/fd75ff03-b552-4346-b8e7-dca6d3c0cbbc/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/e16e0df4-9123-4327-98e4-9e67e96c78bf/tempfile.png" } ], "started_at": "2021-12-04T09:05:41.662874Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/bombcp2qvrgivl545eokcd7nd4", "cancel": "https://api.replicate.com/v1/predictions/bombcp2qvrgivl545eokcd7nd4/cancel" }, "version": "b35ec81c61e81bd30116b45a70147cbd2e77a3bc72b80edf2b52c22df54a6c92" }
Generated in---> BasePixrayPredictor Predict Using seed: 392990954 Working with z of shape (1, 256, 16, 16) = 65536 dimensions. loaded pretrained LPIPS loss from taming/modules/autoencoder/lpips/vgg.pth VQLPIPSWithDiscriminator running with hinge loss. Restored from models/vqgan_imagenet_f16_16384.ckpt Using device: cuda:0 Optimising using: Adam Using text prompts: ['Godzilla appearing on the one dollar bill.'] Using initial image https://upload.wikimedia.org/wikipedia/commons/2/23/US_one_dollar_bill%2C_obverse%2C_series_2009.jpg (1) 0it [00:00, ?it/s] /root/.pyenv/versions/3.8.12/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( iter: 0, loss: 2.75, losses: 0.902, 0.0861, 0.823, 0.0593, 0.822, 0.0576 (-0=>2.75) 0it [00:00, ?it/s] iter: 10, loss: 2.57, losses: 0.865, 0.0893, 0.751, 0.0587, 0.749, 0.0586 (-2=>2.571) 0it [00:10, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 20, loss: 2.36, losses: 0.793, 0.0893, 0.68, 0.0623, 0.67, 0.0628 (-0=>2.357) 0it [00:00, ?it/s] iter: 30, loss: 2.4, losses: 0.805, 0.0858, 0.706, 0.0615, 0.683, 0.0599 (-5=>2.332) 0it [00:11, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 40, loss: 2.35, losses: 0.792, 0.0862, 0.692, 0.0608, 0.662, 0.0613 (-3=>2.29) 0it [00:00, ?it/s] iter: 50, loss: 2.34, losses: 0.794, 0.088, 0.678, 0.0632, 0.66, 0.0609 (-13=>2.29) 0it [00:11, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 60, loss: 2.3, losses: 0.776, 0.0905, 0.66, 0.0654, 0.64, 0.0658 (-5=>2.231) 0it [00:00, ?it/s] iter: 70, loss: 2.23, losses: 0.742, 0.0938, 0.629, 0.0694, 0.627, 0.0681 (-0=>2.23) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 80, loss: 2.32, losses: 0.789, 0.0879, 0.665, 0.0659, 0.648, 0.0647 (-10=>2.23) 0it [00:00, ?it/s] iter: 90, loss: 2.29, losses: 0.778, 0.0892, 0.653, 0.064, 0.637, 0.0639 (-4=>2.205) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 100, loss: 2.27, losses: 0.767, 0.0901, 0.643, 0.0667, 0.632, 0.0666 (-6=>2.189) 0it [00:00, ?it/s] iter: 110, loss: 2.31, losses: 0.784, 0.0884, 0.661, 0.0648, 0.651, 0.064 (-16=>2.189) 0it [00:10, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 120, loss: 2.29, losses: 0.772, 0.0893, 0.655, 0.0659, 0.642, 0.0642 (-26=>2.189) 0it [00:00, ?it/s] iter: 130, loss: 2.18, losses: 0.734, 0.0904, 0.613, 0.068, 0.605, 0.0668 (-0=>2.178) 0it [00:10, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 140, loss: 2.28, losses: 0.779, 0.0879, 0.65, 0.0628, 0.641, 0.0639 (-6=>2.169) 0it [00:00, ?it/s] iter: 150, loss: 2.18, losses: 0.727, 0.0941, 0.61, 0.0682, 0.61, 0.0669 (-2=>2.167) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 160, loss: 2.27, losses: 0.776, 0.0869, 0.647, 0.0634, 0.633, 0.0626 (-4=>2.156) 0it [00:00, ?it/s] iter: 170, loss: 2.24, losses: 0.761, 0.0868, 0.634, 0.0647, 0.626, 0.0639 (-14=>2.156) 0it [00:10, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 180, loss: 2.25, losses: 0.764, 0.09, 0.636, 0.0667, 0.627, 0.0653 (-24=>2.156) 0it [00:00, ?it/s] iter: 190, loss: 2.16, losses: 0.727, 0.0908, 0.61, 0.0685, 0.597, 0.0675 (-34=>2.156) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 200, loss: 2.23, losses: 0.752, 0.0912, 0.636, 0.0653, 0.623, 0.0635 (-1=>2.153) 0it [00:00, ?it/s] iter: 210, loss: 2.28, losses: 0.778, 0.0874, 0.643, 0.0634, 0.647, 0.0615 (-8=>2.149) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 220, loss: 2.28, losses: 0.77, 0.087, 0.655, 0.0619, 0.645, 0.0613 (-18=>2.149) 0it [00:00, ?it/s] Dropping learning rate iter: 230, loss: 2.15, losses: 0.725, 0.0895, 0.608, 0.0669, 0.598, 0.0657 (-0=>2.154) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 240, loss: 2.26, losses: 0.772, 0.088, 0.641, 0.0636, 0.635, 0.0628 (-1=>2.142) 0it [00:00, ?it/s] iter: 250, loss: 2.23, losses: 0.756, 0.0895, 0.633, 0.0647, 0.619, 0.0642 (-8=>2.114) 0it [00:10, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 260, loss: 2.2, losses: 0.75, 0.0886, 0.626, 0.0648, 0.61, 0.0648 (-2=>2.111) 0it [00:00, ?it/s] iter: 270, loss: 2.23, losses: 0.761, 0.0874, 0.635, 0.0653, 0.622, 0.0641 (-12=>2.111) 0it [00:10, ?it/s] 0it [00:20, ?it/s] 0it [00:00, ?it/s] iter: 280, loss: 2.14, losses: 0.721, 0.0932, 0.598, 0.0703, 0.589, 0.0678 (-22=>2.111) 0it [00:00, ?it/s] iter: 290, loss: 2.2, losses: 0.748, 0.091, 0.622, 0.0681, 0.606, 0.0671 (-32=>2.111) 0it [00:10, ?it/s] 0it [00:19, ?it/s] 0it [00:00, ?it/s] iter: 300, finished (-42=>2.111) 0it [00:00, ?it/s] 0it [00:00, ?it/s]
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