pixray / api
bare pixray for API use
Prediction
pixray/api:6addca4edde007986704548c11ec6e606ffc6121ebe66e2ba021475ee586fc09ModelIDhb5z7ctvynftdoixyjh534w4quStatusSucceededSourceWebHardware–Total durationCreatedInput
- settings
- prompts: 'a day glo acrylic portrait of a cyberpunk empress, by Yasutomo Oka.' quality: best drawer: vqgan aspect: square vqgan_model: wikiart_16384 custom_loss: aesthetic
{ "settings": "prompts: 'a day glo acrylic portrait of a cyberpunk empress, by Yasutomo Oka.'\nquality: best\ndrawer: vqgan\naspect: square\nvqgan_model: wikiart_16384\ncustom_loss: aesthetic\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 pixray/api using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "pixray/api:6addca4edde007986704548c11ec6e606ffc6121ebe66e2ba021475ee586fc09", { input: { settings: "prompts: 'a day glo acrylic portrait of a cyberpunk empress, by Yasutomo Oka.'\nquality: best\ndrawer: vqgan\naspect: square\nvqgan_model: wikiart_16384\ncustom_loss: aesthetic\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 pixray/api using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "pixray/api:6addca4edde007986704548c11ec6e606ffc6121ebe66e2ba021475ee586fc09", input={ "settings": "prompts: 'a day glo acrylic portrait of a cyberpunk empress, by Yasutomo Oka.'\nquality: best\ndrawer: vqgan\naspect: square\nvqgan_model: wikiart_16384\ncustom_loss: aesthetic\n" } ) # The 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/pixray/api/api#output-schema print(item)
To learn more, take a look at the guide on getting started with Python.
Run 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": "pixray/api:6addca4edde007986704548c11ec6e606ffc6121ebe66e2ba021475ee586fc09", "input": { "settings": "prompts: \'a day glo acrylic portrait of a cyberpunk empress, by Yasutomo Oka.\'\\nquality: best\\ndrawer: vqgan\\naspect: square\\nvqgan_model: wikiart_16384\\ncustom_loss: aesthetic\\n" } }' \ https://api.replicate.com/v1/predictions
To learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-02-07T23:21:38.406843Z", "created_at": "2022-02-07T23:05:38.293169Z", "data_removed": false, "error": null, "id": "hb5z7ctvynftdoixyjh534w4qu", "input": { "settings": "prompts: 'a day glo acrylic portrait of a cyberpunk empress, by Yasutomo Oka.'\nquality: best\ndrawer: vqgan\naspect: square\nvqgan_model: wikiart_16384\ncustom_loss: aesthetic\n" }, "logs": "---> BasePixrayPredictor Predict\nUsing seed:\n11914490315746453492\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_wikiart_16384.ckpt\nLoaded CLIP RN50x4: 288x288 and 178.30M params\nLoaded CLIP ViT-B/32: 224x224 and 151.28M params\nLoaded CLIP ViT-B/16: 224x224 and 149.62M params\nUsing device:\ncuda:0\nOptimising using:\nAdam\nUsing text prompts:\n['a day glo acrylic portrait of a cyberpunk empress, by Yasutomo Oka.']\nusing custom losses: aesthetic\n\n0it [00:00, ?it/s]\niter: 0, loss: 3.66, losses: 0.843, 0.0817, 0.98, 0.0605, 0.962, 0.0651, 0.67 (-0=>3.663)\n\n0it [00:01, ?it/s]\niter: 10, loss: 3.07, losses: 0.711, 0.0857, 0.839, 0.0636, 0.816, 0.0634, 0.489 (-0=>3.068)\n\n0it [00:26, ?it/s]\n\n0it [00:50, ?it/s]\n\n0it [00:00, ?it/s]\niter: 20, loss: 2.75, losses: 0.646, 0.0879, 0.778, 0.0662, 0.741, 0.0637, 0.368 (-1=>2.728)\n\n0it [00:01, ?it/s]\niter: 30, loss: 2.59, losses: 0.572, 0.0926, 0.728, 0.0686, 0.702, 0.0645, 0.363 (-0=>2.59)\n\n0it [00:28, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 40, loss: 2.51, losses: 0.555, 0.0916, 0.717, 0.0697, 0.692, 0.0672, 0.319 (-2=>2.475)\n\n0it [00:01, ?it/s]\niter: 50, loss: 2.44, losses: 0.543, 0.0893, 0.708, 0.0702, 0.669, 0.0684, 0.292 (-3=>2.401)\n\n0it [00:28, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 60, loss: 2.38, losses: 0.534, 0.0881, 0.682, 0.0704, 0.662, 0.0671, 0.273 (-1=>2.305)\n\n0it [00:01, ?it/s]\niter: 70, loss: 2.3, losses: 0.509, 0.0895, 0.671, 0.0727, 0.651, 0.0691, 0.238 (-5=>2.282)\n\n0it [00:28, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 80, loss: 2.28, losses: 0.504, 0.0912, 0.66, 0.0709, 0.635, 0.07, 0.25 (-4=>2.264)\n\n0it [00:01, ?it/s]\niter: 90, loss: 2.25, losses: 0.488, 0.0919, 0.66, 0.073, 0.623, 0.0719, 0.238 (-7=>2.207)\n\n0it [00:28, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 100, loss: 2.27, losses: 0.496, 0.0932, 0.658, 0.0729, 0.624, 0.0717, 0.255 (-17=>2.207)\n\n0it [00:01, ?it/s]\niter: 110, loss: 2.28, losses: 0.487, 0.0932, 0.667, 0.0716, 0.631, 0.0702, 0.255 (-8=>2.189)\n\n0it [00:28, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 120, loss: 2.16, losses: 0.486, 0.0916, 0.633, 0.074, 0.609, 0.0727, 0.195 (-0=>2.16)\n\n0it [00:01, ?it/s]\niter: 130, loss: 2.18, losses: 0.477, 0.0904, 0.641, 0.0747, 0.605, 0.0747, 0.222 (-10=>2.16)\n\n0it [00:28, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 140, loss: 2.2, losses: 0.467, 0.0919, 0.647, 0.0748, 0.617, 0.0745, 0.223 (-20=>2.16)\n\n0it [00:01, ?it/s]\niter: 150, loss: 2.19, losses: 0.473, 0.0926, 0.641, 0.0736, 0.61, 0.0746, 0.221 (-6=>2.156)\n\n0it [00:28, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 160, loss: 2.18, losses: 0.484, 0.0902, 0.641, 0.0727, 0.614, 0.0736, 0.206 (-3=>2.136)\n\n0it [00:01, ?it/s]\niter: 170, loss: 2.18, losses: 0.474, 0.0916, 0.638, 0.0722, 0.618, 0.0739, 0.21 (-6=>2.128)\n\n0it [00:28, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 180, loss: 2.21, losses: 0.462, 0.0913, 0.652, 0.0741, 0.625, 0.0746, 0.228 (-16=>2.128)\n\n0it [00:01, ?it/s]\niter: 190, loss: 2.19, losses: 0.473, 0.0909, 0.637, 0.074, 0.622, 0.0736, 0.219 (-7=>2.124)\n\n0it [00:28, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 200, loss: 2.15, losses: 0.462, 0.0917, 0.636, 0.0755, 0.611, 0.0747, 0.198 (-2=>2.094)\n\n0it [00:01, ?it/s]\niter: 210, loss: 2.19, losses: 0.461, 0.0909, 0.645, 0.0737, 0.618, 0.074, 0.225 (-12=>2.094)\n\n0it [00:28, ?it/s]\n---> BasePixrayPredictor Predict\nUsing seed:\n3383883393288688316\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_wikiart_16384.ckpt\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 220, loss: 2.14, losses: 0.468, 0.0934, 0.629, 0.0744, 0.615, 0.0755, 0.18 (-22=>2.094)\n\n0it [00:01, ?it/s]\nLoaded CLIP RN50x4: 288x288 and 178.30M params\nLoaded CLIP ViT-B/32: 224x224 and 151.28M params\nLoaded CLIP ViT-B/16: 224x224 and 149.62M params\nCaught SIGTERM, exiting...\nUsing device:\ncuda:0\nOptimising using:\nAdam\nUsing text prompts:\n['a day glo acrylic portrait of a cyberpunk empress, by Yasutomo Oka.']\nusing custom losses: aesthetic\n\n0it [00:00, ?it/s]\niter: 0, loss: 3.7, losses: 0.837, 0.0829, 0.982, 0.0609, 0.96, 0.0644, 0.713 (-0=>3.7)\n\n0it [00:01, ?it/s]\niter: 230, loss: 2.17, losses: 0.466, 0.0911, 0.639, 0.074, 0.617, 0.0744, 0.206 (-3=>2.08)\n\n0it [00:28, ?it/s]\niter: 10, loss: 2.79, losses: 0.663, 0.0885, 0.791, 0.0675, 0.783, 0.0647, 0.328 (-0=>2.785)\n\n0it [00:27, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 240, loss: 2.12, losses: 0.468, 0.0919, 0.631, 0.0749, 0.605, 0.0761, 0.178 (-13=>2.08)\n\n0it [00:01, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 20, loss: 2.53, losses: 0.585, 0.0894, 0.71, 0.0687, 0.726, 0.0664, 0.287 (-0=>2.533)\n\n0it [00:01, ?it/s]\niter: 250, loss: 2.14, losses: 0.468, 0.0914, 0.629, 0.073, 0.607, 0.075, 0.201 (-23=>2.08)\n\n0it [00:28, ?it/s]\niter: 30, loss: 2.38, losses: 0.519, 0.0932, 0.678, 0.0723, 0.665, 0.0712, 0.281 (-1=>2.379)\n\n0it [00:28, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 260, loss: 2.19, losses: 0.477, 0.0916, 0.638, 0.0751, 0.609, 0.0741, 0.22 (-33=>2.08)\n\n0it [00:01, ?it/s]\nDropping learning rate\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 40, loss: 2.25, losses: 0.479, 0.0956, 0.659, 0.0732, 0.64, 0.0729, 0.231 (-0=>2.25)\n\n0it [00:01, ?it/s]\niter: 270, loss: 2.07, losses: 0.451, 0.0942, 0.624, 0.076, 0.587, 0.077, 0.159 (-0=>2.069)\n\n0it [00:28, ?it/s]\niter: 50, loss: 2.21, losses: 0.478, 0.0951, 0.65, 0.0719, 0.627, 0.0723, 0.22 (-0=>2.215)\n\n0it [00:28, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 280, loss: 2.07, losses: 0.462, 0.0913, 0.611, 0.077, 0.592, 0.0763, 0.162 (-9=>2.051)\n\n0it [00:01, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 60, loss: 2.22, losses: 0.482, 0.0947, 0.641, 0.0727, 0.629, 0.0734, 0.226 (-1=>2.198)\n\n0it [00:01, ?it/s]\niter: 290, loss: 2.04, losses: 0.446, 0.0906, 0.612, 0.0752, 0.59, 0.0778, 0.144 (-0=>2.036)\n\n0it [00:28, ?it/s]\niter: 70, loss: 2.18, losses: 0.463, 0.0964, 0.646, 0.0734, 0.621, 0.0748, 0.202 (-2=>2.149)\n\n0it [00:28, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 300, loss: 2.11, losses: 0.443, 0.0911, 0.628, 0.074, 0.613, 0.075, 0.187 (-10=>2.036)\n\n0it [00:01, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 80, loss: 2.18, losses: 0.47, 0.0946, 0.64, 0.0733, 0.613, 0.0755, 0.212 (-4=>2.104)\n\n0it [00:01, ?it/s]\niter: 310, loss: 2.13, losses: 0.468, 0.0939, 0.632, 0.0744, 0.603, 0.0755, 0.184 (-20=>2.036)\n\n0it [00:28, ?it/s]\niter: 90, loss: 2.08, losses: 0.451, 0.0943, 0.617, 0.0746, 0.598, 0.076, 0.174 (-0=>2.085)\n\n0it [00:28, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 320, loss: 2.09, losses: 0.473, 0.093, 0.615, 0.0746, 0.595, 0.076, 0.163 (-30=>2.036)\n\n0it [00:01, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 100, loss: 2.12, losses: 0.446, 0.0965, 0.626, 0.0738, 0.602, 0.078, 0.201 (-10=>2.085)\n\n0it [00:01, ?it/s]\niter: 330, loss: 2.09, losses: 0.453, 0.091, 0.623, 0.0753, 0.596, 0.0769, 0.177 (-40=>2.036)\n\n0it [00:28, ?it/s]\niter: 110, loss: 2.18, losses: 0.465, 0.0956, 0.631, 0.0748, 0.616, 0.0743, 0.225 (-20=>2.085)\n\n0it [00:28, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 340, loss: 2.07, losses: 0.46, 0.0909, 0.608, 0.0768, 0.587, 0.0764, 0.174 (-50=>2.036)\n\n0it [00:01, ?it/s]\n\n0it [00:53, ?it/s]\n\n0it [00:00, ?it/s]\niter: 120, loss: 2.13, losses: 0.449, 0.0964, 0.632, 0.074, 0.603, 0.0759, 0.199 (-2=>2.066)\n\n0it [00:01, ?it/s]\niter: 350, finished (-60=>2.036)\n\n0it [00:27, ?it/s]\n\n0it [00:27, ?it/s]", "metrics": { "predict_time": 959.955818, "total_time": 960.113674 }, "output": [ { "file": "https://replicate.delivery/mgxm/b4f9b0f6-75e6-4ba2-a0d0-d9dce8b69052/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/011e40b5-4e1f-4202-9b71-2eadabeab48f/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/0119cfaa-bb01-4829-9e46-77e196779d01/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/847355e3-ce3e-41bb-aea0-49201a7cf7e3/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/519aa258-57fe-4424-8e40-b3a3c7e4e34e/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/a2f4ca8e-ebfd-4438-bac5-dbb28f107bd5/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/710eac9e-5c56-44a9-912b-366cde72d690/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/a0e646eb-4659-46de-b458-042d1e045f64/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/a17d7c2f-de2f-4b58-bbbf-c67ae7943e36/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/7f8831a8-5232-41d0-bec3-56bd0034e48f/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/8705a4a2-1069-4dff-84ae-22fa575a261b/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/3ff0e75d-c23d-4a21-8e98-26715e882c53/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/84f6dc4d-87af-44f9-9d92-524bbd9c3d51/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/acacde31-c2c8-4cef-b809-1ce25afd1b32/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/cebd49fb-0441-4cc5-ab08-d4b4fbd72808/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/8bdce1a1-5447-4e07-9c0c-6701f168f49e/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/34211b18-54e9-478d-8512-5f7d2912c0a5/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/261457bf-d315-4b0b-81bd-d861db3d97e0/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/9bd04b8b-98c7-49f8-8770-546d5cb65922/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/7e0198ed-4b94-4339-b1ae-2e17b371cd4a/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/379e8de5-567c-4732-96a1-3697813fe6a2/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/0195547c-b501-49ba-96b8-f000f957a587/tempfile.png" } ], "started_at": "2022-02-07T23:05:38.451025Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/hb5z7ctvynftdoixyjh534w4qu", "cancel": "https://api.replicate.com/v1/predictions/hb5z7ctvynftdoixyjh534w4qu/cancel" }, "version": "6addca4edde007986704548c11ec6e606ffc6121ebe66e2ba021475ee586fc09" }
Generated in---> BasePixrayPredictor Predict Using seed: 11914490315746453492 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_wikiart_16384.ckpt Loaded CLIP RN50x4: 288x288 and 178.30M params Loaded CLIP ViT-B/32: 224x224 and 151.28M params Loaded CLIP ViT-B/16: 224x224 and 149.62M params Using device: cuda:0 Optimising using: Adam Using text prompts: ['a day glo acrylic portrait of a cyberpunk empress, by Yasutomo Oka.'] using custom losses: aesthetic 0it [00:00, ?it/s] iter: 0, loss: 3.66, losses: 0.843, 0.0817, 0.98, 0.0605, 0.962, 0.0651, 0.67 (-0=>3.663) 0it [00:01, ?it/s] iter: 10, loss: 3.07, losses: 0.711, 0.0857, 0.839, 0.0636, 0.816, 0.0634, 0.489 (-0=>3.068) 0it [00:26, ?it/s] 0it [00:50, ?it/s] 0it [00:00, ?it/s] iter: 20, loss: 2.75, losses: 0.646, 0.0879, 0.778, 0.0662, 0.741, 0.0637, 0.368 (-1=>2.728) 0it [00:01, ?it/s] iter: 30, loss: 2.59, losses: 0.572, 0.0926, 0.728, 0.0686, 0.702, 0.0645, 0.363 (-0=>2.59) 0it [00:28, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 40, loss: 2.51, losses: 0.555, 0.0916, 0.717, 0.0697, 0.692, 0.0672, 0.319 (-2=>2.475) 0it [00:01, ?it/s] iter: 50, loss: 2.44, losses: 0.543, 0.0893, 0.708, 0.0702, 0.669, 0.0684, 0.292 (-3=>2.401) 0it [00:28, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 60, loss: 2.38, losses: 0.534, 0.0881, 0.682, 0.0704, 0.662, 0.0671, 0.273 (-1=>2.305) 0it [00:01, ?it/s] iter: 70, loss: 2.3, losses: 0.509, 0.0895, 0.671, 0.0727, 0.651, 0.0691, 0.238 (-5=>2.282) 0it [00:28, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 80, loss: 2.28, losses: 0.504, 0.0912, 0.66, 0.0709, 0.635, 0.07, 0.25 (-4=>2.264) 0it [00:01, ?it/s] iter: 90, loss: 2.25, losses: 0.488, 0.0919, 0.66, 0.073, 0.623, 0.0719, 0.238 (-7=>2.207) 0it [00:28, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 100, loss: 2.27, losses: 0.496, 0.0932, 0.658, 0.0729, 0.624, 0.0717, 0.255 (-17=>2.207) 0it [00:01, ?it/s] iter: 110, loss: 2.28, losses: 0.487, 0.0932, 0.667, 0.0716, 0.631, 0.0702, 0.255 (-8=>2.189) 0it [00:28, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 120, loss: 2.16, losses: 0.486, 0.0916, 0.633, 0.074, 0.609, 0.0727, 0.195 (-0=>2.16) 0it [00:01, ?it/s] iter: 130, loss: 2.18, losses: 0.477, 0.0904, 0.641, 0.0747, 0.605, 0.0747, 0.222 (-10=>2.16) 0it [00:28, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 140, loss: 2.2, losses: 0.467, 0.0919, 0.647, 0.0748, 0.617, 0.0745, 0.223 (-20=>2.16) 0it [00:01, ?it/s] iter: 150, loss: 2.19, losses: 0.473, 0.0926, 0.641, 0.0736, 0.61, 0.0746, 0.221 (-6=>2.156) 0it [00:28, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 160, loss: 2.18, losses: 0.484, 0.0902, 0.641, 0.0727, 0.614, 0.0736, 0.206 (-3=>2.136) 0it [00:01, ?it/s] iter: 170, loss: 2.18, losses: 0.474, 0.0916, 0.638, 0.0722, 0.618, 0.0739, 0.21 (-6=>2.128) 0it [00:28, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 180, loss: 2.21, losses: 0.462, 0.0913, 0.652, 0.0741, 0.625, 0.0746, 0.228 (-16=>2.128) 0it [00:01, ?it/s] iter: 190, loss: 2.19, losses: 0.473, 0.0909, 0.637, 0.074, 0.622, 0.0736, 0.219 (-7=>2.124) 0it [00:28, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 200, loss: 2.15, losses: 0.462, 0.0917, 0.636, 0.0755, 0.611, 0.0747, 0.198 (-2=>2.094) 0it [00:01, ?it/s] iter: 210, loss: 2.19, losses: 0.461, 0.0909, 0.645, 0.0737, 0.618, 0.074, 0.225 (-12=>2.094) 0it [00:28, ?it/s] ---> BasePixrayPredictor Predict Using seed: 3383883393288688316 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_wikiart_16384.ckpt 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 220, loss: 2.14, losses: 0.468, 0.0934, 0.629, 0.0744, 0.615, 0.0755, 0.18 (-22=>2.094) 0it [00:01, ?it/s] Loaded CLIP RN50x4: 288x288 and 178.30M params Loaded CLIP ViT-B/32: 224x224 and 151.28M params Loaded CLIP ViT-B/16: 224x224 and 149.62M params Caught SIGTERM, exiting... Using device: cuda:0 Optimising using: Adam Using text prompts: ['a day glo acrylic portrait of a cyberpunk empress, by Yasutomo Oka.'] using custom losses: aesthetic 0it [00:00, ?it/s] iter: 0, loss: 3.7, losses: 0.837, 0.0829, 0.982, 0.0609, 0.96, 0.0644, 0.713 (-0=>3.7) 0it [00:01, ?it/s] iter: 230, loss: 2.17, losses: 0.466, 0.0911, 0.639, 0.074, 0.617, 0.0744, 0.206 (-3=>2.08) 0it [00:28, ?it/s] iter: 10, loss: 2.79, losses: 0.663, 0.0885, 0.791, 0.0675, 0.783, 0.0647, 0.328 (-0=>2.785) 0it [00:27, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 240, loss: 2.12, losses: 0.468, 0.0919, 0.631, 0.0749, 0.605, 0.0761, 0.178 (-13=>2.08) 0it [00:01, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 20, loss: 2.53, losses: 0.585, 0.0894, 0.71, 0.0687, 0.726, 0.0664, 0.287 (-0=>2.533) 0it [00:01, ?it/s] iter: 250, loss: 2.14, losses: 0.468, 0.0914, 0.629, 0.073, 0.607, 0.075, 0.201 (-23=>2.08) 0it [00:28, ?it/s] iter: 30, loss: 2.38, losses: 0.519, 0.0932, 0.678, 0.0723, 0.665, 0.0712, 0.281 (-1=>2.379) 0it [00:28, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 260, loss: 2.19, losses: 0.477, 0.0916, 0.638, 0.0751, 0.609, 0.0741, 0.22 (-33=>2.08) 0it [00:01, ?it/s] Dropping learning rate 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 40, loss: 2.25, losses: 0.479, 0.0956, 0.659, 0.0732, 0.64, 0.0729, 0.231 (-0=>2.25) 0it [00:01, ?it/s] iter: 270, loss: 2.07, losses: 0.451, 0.0942, 0.624, 0.076, 0.587, 0.077, 0.159 (-0=>2.069) 0it [00:28, ?it/s] iter: 50, loss: 2.21, losses: 0.478, 0.0951, 0.65, 0.0719, 0.627, 0.0723, 0.22 (-0=>2.215) 0it [00:28, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 280, loss: 2.07, losses: 0.462, 0.0913, 0.611, 0.077, 0.592, 0.0763, 0.162 (-9=>2.051) 0it [00:01, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 60, loss: 2.22, losses: 0.482, 0.0947, 0.641, 0.0727, 0.629, 0.0734, 0.226 (-1=>2.198) 0it [00:01, ?it/s] iter: 290, loss: 2.04, losses: 0.446, 0.0906, 0.612, 0.0752, 0.59, 0.0778, 0.144 (-0=>2.036) 0it [00:28, ?it/s] iter: 70, loss: 2.18, losses: 0.463, 0.0964, 0.646, 0.0734, 0.621, 0.0748, 0.202 (-2=>2.149) 0it [00:28, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 300, loss: 2.11, losses: 0.443, 0.0911, 0.628, 0.074, 0.613, 0.075, 0.187 (-10=>2.036) 0it [00:01, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 80, loss: 2.18, losses: 0.47, 0.0946, 0.64, 0.0733, 0.613, 0.0755, 0.212 (-4=>2.104) 0it [00:01, ?it/s] iter: 310, loss: 2.13, losses: 0.468, 0.0939, 0.632, 0.0744, 0.603, 0.0755, 0.184 (-20=>2.036) 0it [00:28, ?it/s] iter: 90, loss: 2.08, losses: 0.451, 0.0943, 0.617, 0.0746, 0.598, 0.076, 0.174 (-0=>2.085) 0it [00:28, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 320, loss: 2.09, losses: 0.473, 0.093, 0.615, 0.0746, 0.595, 0.076, 0.163 (-30=>2.036) 0it [00:01, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 100, loss: 2.12, losses: 0.446, 0.0965, 0.626, 0.0738, 0.602, 0.078, 0.201 (-10=>2.085) 0it [00:01, ?it/s] iter: 330, loss: 2.09, losses: 0.453, 0.091, 0.623, 0.0753, 0.596, 0.0769, 0.177 (-40=>2.036) 0it [00:28, ?it/s] iter: 110, loss: 2.18, losses: 0.465, 0.0956, 0.631, 0.0748, 0.616, 0.0743, 0.225 (-20=>2.085) 0it [00:28, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 340, loss: 2.07, losses: 0.46, 0.0909, 0.608, 0.0768, 0.587, 0.0764, 0.174 (-50=>2.036) 0it [00:01, ?it/s] 0it [00:53, ?it/s] 0it [00:00, ?it/s] iter: 120, loss: 2.13, losses: 0.449, 0.0964, 0.632, 0.074, 0.603, 0.0759, 0.199 (-2=>2.066) 0it [00:01, ?it/s] iter: 350, finished (-60=>2.036) 0it [00:27, ?it/s] 0it [00:27, ?it/s]
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