dribnet / pixray-text2image
Uses pixray to generate an image from text prompt
Prediction
dribnet/pixray-text2image:7eeecc6f78234419555288d44cc7a5bc3eee80519da21c24efef6fbffe4cc3feInput
- aspect
- widescreen
- prompts
- Robots skydiving high above the city
- quality
- normal
{ "aspect": "widescreen", "prompts": "Robots skydiving high above the city", "quality": "normal" }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 dribnet/pixray-text2image using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "dribnet/pixray-text2image:7eeecc6f78234419555288d44cc7a5bc3eee80519da21c24efef6fbffe4cc3fe", { input: { aspect: "widescreen", prompts: "Robots skydiving high above the city", quality: "normal" } } ); 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 dribnet/pixray-text2image using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "dribnet/pixray-text2image:7eeecc6f78234419555288d44cc7a5bc3eee80519da21c24efef6fbffe4cc3fe", input={ "aspect": "widescreen", "prompts": "Robots skydiving high above the city", "quality": "normal" } ) # The dribnet/pixray-text2image 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-text2image/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run dribnet/pixray-text2image 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-text2image:7eeecc6f78234419555288d44cc7a5bc3eee80519da21c24efef6fbffe4cc3fe", "input": { "aspect": "widescreen", "prompts": "Robots skydiving high above the city", "quality": "normal" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2021-12-01T01:31:24.192336Z", "created_at": "2021-12-01T01:27:12.367827Z", "data_removed": false, "error": null, "id": "2bce3gyk7ja7bc6el56h7marn4", "input": { "aspect": "widescreen", "prompts": "Robots skydiving high above the city", "quality": "normal" }, "logs": "---> BasePixrayPredictor Predict\nUsing seed:\n7848817447306549499\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['Robots skydiving high above the city']\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, losses: 0.965, 0.0457, 0.943, 0.0472 (-0=>2.001)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 10, loss: 1.81, losses: 0.862, 0.0465, 0.852, 0.046 (-0=>1.807)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 20, loss: 1.69, losses: 0.806, 0.052, 0.785, 0.0511 (-0=>1.694)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 30, loss: 1.66, losses: 0.782, 0.0555, 0.77, 0.0534 (-6=>1.655)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 40, loss: 1.62, losses: 0.764, 0.056, 0.748, 0.0536 (-2=>1.586)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 50, loss: 1.64, losses: 0.779, 0.0543, 0.755, 0.053 (-8=>1.583)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 60, loss: 1.6, losses: 0.759, 0.056, 0.733, 0.0546 (-4=>1.558)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 70, loss: 1.55, losses: 0.729, 0.0566, 0.706, 0.0551 (-0=>1.547)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 80, loss: 1.55, losses: 0.731, 0.0557, 0.705, 0.0539 (-0=>1.546)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 90, loss: 1.59, losses: 0.754, 0.0568, 0.723, 0.0555 (-6=>1.535)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 100, loss: 1.57, losses: 0.749, 0.057, 0.709, 0.0565 (-2=>1.532)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 110, loss: 1.57, losses: 0.744, 0.0581, 0.708, 0.057 (-2=>1.522)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 120, loss: 1.57, losses: 0.741, 0.0591, 0.709, 0.0576 (-12=>1.522)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 130, loss: 1.52, losses: 0.716, 0.0608, 0.682, 0.0593 (-4=>1.515)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 140, loss: 1.57, losses: 0.744, 0.0588, 0.71, 0.0593 (-14=>1.515)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 150, loss: 1.55, losses: 0.733, 0.0606, 0.698, 0.0588 (-24=>1.515)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 160, loss: 1.49, losses: 0.7, 0.0611, 0.668, 0.061 (-0=>1.491)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 170, loss: 1.53, losses: 0.719, 0.0616, 0.691, 0.0592 (-10=>1.491)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 180, loss: 1.55, losses: 0.728, 0.0591, 0.702, 0.0591 (-20=>1.491)\n\n0it [00:00, ?it/s]\nDropping learning rate\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 190, loss: 1.55, losses: 0.725, 0.062, 0.7, 0.0606 (-2=>1.503)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 200, loss: 1.53, losses: 0.721, 0.0601, 0.694, 0.0592 (-3=>1.495)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 210, loss: 1.48, losses: 0.695, 0.0616, 0.666, 0.0614 (-0=>1.484)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 220, loss: 1.54, losses: 0.723, 0.0609, 0.693, 0.0603 (-6=>1.482)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 230, loss: 1.49, losses: 0.697, 0.062, 0.665, 0.0624 (-16=>1.482)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 240, loss: 1.53, losses: 0.717, 0.0617, 0.692, 0.0604 (-26=>1.482)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 250, finished (-2=>1.481)\n\n0it [00:00, ?it/s]\n\n0it [00:00, ?it/s]", "metrics": { "predict_time": 250.596219, "total_time": 251.824509 }, "output": [ { "file": "https://replicate.delivery/mgxm/4992351b-d1db-4415-b25a-a51a3fe8f8fe/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/469032f7-7a0b-46c8-8c4c-36f70bb37b37/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/2a82daf7-731b-4641-b7de-661a31a49fae/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/a4ecc63b-222f-474f-8ade-a2d7d4464fd4/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/e5fa5aad-ca17-4451-8ed1-9ba5a6cfc6ab/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/d018ef02-8764-449c-baed-02db65187f2e/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/054e7679-b7a8-4fed-88c3-56fd42665954/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/e1f0b3f2-407a-43bf-a735-1e969fd4befd/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/c38e2952-bf2d-4faa-9a03-2ec4de3f3683/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/9f848682-6373-415e-9b6c-26f393f85c91/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/1df4cd14-a0c2-4a97-a5fa-bf22c07f18ea/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/17f4718e-0f3c-482d-8985-2f0338cf2468/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/9f360e10-d2f0-44c9-b413-6dbc157a15b8/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/8b3ca6b3-99a3-4a65-a3ef-e142b0980692/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/a5319666-d2d0-46a8-aebf-f015addbe856/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/772a808b-2d3c-41a0-acd1-1c30106c67ab/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/f0e788fd-cee9-4ba5-8216-e5274035551d/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/2338f84b-d5af-4afe-93ce-a888b97cda7c/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/b7f65950-c1d4-408f-bd53-ebeb0d0c7e72/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/9b500b52-622a-4678-bdd6-0a26f9f9033d/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/431af114-5799-4d87-bae3-92f87cdcd801/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/11f1bce4-9d95-4f5b-ade4-dd24916a6032/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/edf782f5-2fa2-43e9-8e34-bdf4c9933b85/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/cbd88190-7873-4fd9-8bfb-9a89451c0451/tempfile.png" } ], "started_at": "2021-12-01T01:27:13.596117Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/2bce3gyk7ja7bc6el56h7marn4", "cancel": "https://api.replicate.com/v1/predictions/2bce3gyk7ja7bc6el56h7marn4/cancel" }, "version": "7eeecc6f78234419555288d44cc7a5bc3eee80519da21c24efef6fbffe4cc3fe" }Generated in---> BasePixrayPredictor Predict Using seed: 7848817447306549499 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: ['Robots skydiving high above the city'] 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, losses: 0.965, 0.0457, 0.943, 0.0472 (-0=>2.001) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 10, loss: 1.81, losses: 0.862, 0.0465, 0.852, 0.046 (-0=>1.807) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 20, loss: 1.69, losses: 0.806, 0.052, 0.785, 0.0511 (-0=>1.694) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 30, loss: 1.66, losses: 0.782, 0.0555, 0.77, 0.0534 (-6=>1.655) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 40, loss: 1.62, losses: 0.764, 0.056, 0.748, 0.0536 (-2=>1.586) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 50, loss: 1.64, losses: 0.779, 0.0543, 0.755, 0.053 (-8=>1.583) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 60, loss: 1.6, losses: 0.759, 0.056, 0.733, 0.0546 (-4=>1.558) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 70, loss: 1.55, losses: 0.729, 0.0566, 0.706, 0.0551 (-0=>1.547) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 80, loss: 1.55, losses: 0.731, 0.0557, 0.705, 0.0539 (-0=>1.546) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 90, loss: 1.59, losses: 0.754, 0.0568, 0.723, 0.0555 (-6=>1.535) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 100, loss: 1.57, losses: 0.749, 0.057, 0.709, 0.0565 (-2=>1.532) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 110, loss: 1.57, losses: 0.744, 0.0581, 0.708, 0.057 (-2=>1.522) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 120, loss: 1.57, losses: 0.741, 0.0591, 0.709, 0.0576 (-12=>1.522) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 130, loss: 1.52, losses: 0.716, 0.0608, 0.682, 0.0593 (-4=>1.515) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 140, loss: 1.57, losses: 0.744, 0.0588, 0.71, 0.0593 (-14=>1.515) 0it [00:00, ?it/s] 0it [00:09, ?it/s] 0it [00:00, ?it/s] iter: 150, loss: 1.55, losses: 0.733, 0.0606, 0.698, 0.0588 (-24=>1.515) 0it [00:00, ?it/s] 0it [00:09, ?it/s] 0it [00:00, ?it/s] iter: 160, loss: 1.49, losses: 0.7, 0.0611, 0.668, 0.061 (-0=>1.491) 0it [00:00, ?it/s] 0it [00:09, ?it/s] 0it [00:00, ?it/s] iter: 170, loss: 1.53, losses: 0.719, 0.0616, 0.691, 0.0592 (-10=>1.491) 0it [00:00, ?it/s] 0it [00:09, ?it/s] 0it [00:00, ?it/s] iter: 180, loss: 1.55, losses: 0.728, 0.0591, 0.702, 0.0591 (-20=>1.491) 0it [00:00, ?it/s] Dropping learning rate 0it [00:09, ?it/s] 0it [00:00, ?it/s] iter: 190, loss: 1.55, losses: 0.725, 0.062, 0.7, 0.0606 (-2=>1.503) 0it [00:00, ?it/s] 0it [00:09, ?it/s] 0it [00:00, ?it/s] iter: 200, loss: 1.53, losses: 0.721, 0.0601, 0.694, 0.0592 (-3=>1.495) 0it [00:00, ?it/s] 0it [00:09, ?it/s] 0it [00:00, ?it/s] iter: 210, loss: 1.48, losses: 0.695, 0.0616, 0.666, 0.0614 (-0=>1.484) 0it [00:00, ?it/s] 0it [00:09, ?it/s] 0it [00:00, ?it/s] iter: 220, loss: 1.54, losses: 0.723, 0.0609, 0.693, 0.0603 (-6=>1.482) 0it [00:00, ?it/s] 0it [00:09, ?it/s] 0it [00:00, ?it/s] iter: 230, loss: 1.49, losses: 0.697, 0.062, 0.665, 0.0624 (-16=>1.482) 0it [00:00, ?it/s] 0it [00:09, ?it/s] 0it [00:00, ?it/s] iter: 240, loss: 1.53, losses: 0.717, 0.0617, 0.692, 0.0604 (-26=>1.482) 0it [00:00, ?it/s] 0it [00:09, ?it/s] 0it [00:00, ?it/s] iter: 250, finished (-2=>1.481) 0it [00:00, ?it/s] 0it [00:00, ?it/s]Prediction
dribnet/pixray-text2image:7eeecc6f78234419555288d44cc7a5bc3eee80519da21c24efef6fbffe4cc3feIDfsbvxykh3ne7lf5gld5jkre3weStatusSucceededSourceWebHardware–Total durationCreatedInput
- aspect
- widescreen
- prompts
- A child's drawing of robots having a board meeting
- quality
- normal
{ "aspect": "widescreen", "prompts": "A child's drawing of robots having a board meeting", "quality": "normal" }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 dribnet/pixray-text2image using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "dribnet/pixray-text2image:7eeecc6f78234419555288d44cc7a5bc3eee80519da21c24efef6fbffe4cc3fe", { input: { aspect: "widescreen", prompts: "A child's drawing of robots having a board meeting", quality: "normal" } } ); 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 dribnet/pixray-text2image using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "dribnet/pixray-text2image:7eeecc6f78234419555288d44cc7a5bc3eee80519da21c24efef6fbffe4cc3fe", input={ "aspect": "widescreen", "prompts": "A child's drawing of robots having a board meeting", "quality": "normal" } ) # The dribnet/pixray-text2image 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-text2image/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run dribnet/pixray-text2image 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-text2image:7eeecc6f78234419555288d44cc7a5bc3eee80519da21c24efef6fbffe4cc3fe", "input": { "aspect": "widescreen", "prompts": "A child\'s drawing of robots having a board meeting", "quality": "normal" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2021-12-01T00:22:22.057739Z", "created_at": "2021-12-01T00:18:27.005341Z", "data_removed": false, "error": null, "id": "fsbvxykh3ne7lf5gld5jkre3we", "input": { "aspect": "widescreen", "prompts": "A child's drawing of robots having a board meeting", "quality": "normal" }, "logs": "---> BasePixrayPredictor Predict\nUsing seed:\n4786383462674117566\nreusing cached copy of model\nmodels/vqgan_imagenet_f16_16384.ckpt\nUsing device:\ncuda:0\nOptimising using:\nAdam\nUsing text prompts:\n[\"A child's drawing of robots having a board meeting\"]\n\n0it [00:00, ?it/s]\niter: 0, loss: 2.01, losses: 0.97, 0.0455, 0.952, 0.047 (-0=>2.015)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 10, loss: 1.75, losses: 0.849, 0.0475, 0.809, 0.0484 (-0=>1.754)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 20, loss: 1.59, losses: 0.761, 0.0539, 0.716, 0.0545 (-0=>1.586)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 30, loss: 1.57, losses: 0.743, 0.0543, 0.718, 0.0541 (-2=>1.535)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 40, loss: 1.52, losses: 0.716, 0.0545, 0.692, 0.0538 (-4=>1.479)\n\n0it [00:00, ?it/s]\n\n0it [00:09, ?it/s]\n\n0it [00:00, ?it/s]\niter: 50, loss: 1.52, losses: 0.718, 0.0549, 0.687, 0.0554 (-8=>1.461)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 60, loss: 1.47, losses: 0.697, 0.0566, 0.664, 0.0562 (-2=>1.426)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 70, loss: 1.46, losses: 0.693, 0.0569, 0.655, 0.0556 (-6=>1.416)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 80, loss: 1.41, losses: 0.67, 0.0585, 0.624, 0.0576 (-0=>1.411)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 90, loss: 1.46, losses: 0.691, 0.0578, 0.657, 0.0561 (-10=>1.411)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 100, loss: 1.48, losses: 0.696, 0.0577, 0.664, 0.0567 (-9=>1.398)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 110, loss: 1.44, losses: 0.685, 0.0567, 0.642, 0.0562 (-19=>1.398)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 120, loss: 1.38, losses: 0.654, 0.0603, 0.606, 0.0601 (-0=>1.38)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 130, loss: 1.38, losses: 0.653, 0.0594, 0.605, 0.059 (-3=>1.371)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 140, loss: 1.46, losses: 0.692, 0.0583, 0.654, 0.0558 (-13=>1.371)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 150, loss: 1.45, losses: 0.686, 0.0583, 0.651, 0.0562 (-23=>1.371)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 160, loss: 1.47, losses: 0.693, 0.0561, 0.664, 0.056 (-1=>1.371)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 170, loss: 1.42, losses: 0.675, 0.0596, 0.629, 0.0577 (-11=>1.371)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 180, loss: 1.45, losses: 0.686, 0.0567, 0.645, 0.0577 (-6=>1.364)\n\n0it [00:00, ?it/s]\nDropping learning rate\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 190, loss: 1.42, losses: 0.675, 0.0595, 0.625, 0.0577 (-0=>1.417)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 200, loss: 1.43, losses: 0.678, 0.0587, 0.633, 0.058 (-7=>1.369)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 210, loss: 1.43, losses: 0.686, 0.0587, 0.632, 0.0577 (-5=>1.347)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 220, loss: 1.37, losses: 0.647, 0.0615, 0.597, 0.0612 (-15=>1.347)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 230, loss: 1.36, losses: 0.649, 0.0609, 0.587, 0.061 (-25=>1.347)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 240, loss: 1.42, losses: 0.677, 0.0579, 0.626, 0.0571 (-35=>1.347)\n\n0it [00:00, ?it/s]\n\n0it [00:08, ?it/s]\n\n0it [00:00, ?it/s]\niter: 250, finished (-45=>1.347)\n\n0it [00:00, ?it/s]\n\n0it [00:00, ?it/s]", "metrics": { "predict_time": 233.442046, "total_time": 235.052398 }, "output": [ { "file": "https://replicate.delivery/mgxm/3c7c99ec-2def-440b-9e48-f792cf69f305/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/caf41823-cc60-428d-a4ab-b892b62f7d7e/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/0c1e4c42-a0e2-407d-9a1a-a025d39ac349/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/20949036-8c24-43e2-9e37-1c50c03e0f7b/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/48d2daeb-7567-4228-b7e5-f7ae7bd633d6/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/31a061a1-fca2-4e78-82c4-e1bd8385cad8/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/d72242c9-5e54-4874-afc6-207892877095/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/26d5fbff-8aad-400d-9bce-16f5974598c1/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/d5f0c584-269f-4abf-9af7-b83a5decf220/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/604b6a03-74e9-4b04-9b01-1d52a9471be2/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/7d319c4f-8fb1-40cc-b9a7-3ae2bafb4403/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/3dc463fd-fd1d-48f3-94a4-eecf59e6d854/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/03852653-e8b4-482e-bcb5-909978efae54/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/922f1ee4-36e5-447c-8b58-926f2e07eccc/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/e9f9fd81-46e6-4f92-92b1-a5136398c85a/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/468c7940-73be-4dcc-a231-c3183b89245d/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/adc60f6f-8c3b-402b-ad67-d0f2fc7e7a1e/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/fd7c8b4e-9a21-449c-b497-a9146d48a3fa/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/2be4bb7b-3b5e-40d2-b20d-d68e10fab7de/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/6a68c0fe-e85f-414d-a6ec-5264d323fccf/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/bc38d827-d23a-45d3-8dd2-42db185b1736/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/de7b8562-821b-4a93-ad07-8a35b5925349/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/faa7003e-410d-4bd4-9f08-6224b943cbcd/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/67b2df8b-96b2-4775-bb67-13198cf76b41/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/b945a246-e88f-4be1-a8d5-a85dc23a2167/tempfile.png" } ], "started_at": "2021-12-01T00:18:28.615693Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/fsbvxykh3ne7lf5gld5jkre3we", "cancel": "https://api.replicate.com/v1/predictions/fsbvxykh3ne7lf5gld5jkre3we/cancel" }, "version": "7eeecc6f78234419555288d44cc7a5bc3eee80519da21c24efef6fbffe4cc3fe" }Generated in---> BasePixrayPredictor Predict Using seed: 4786383462674117566 reusing cached copy of model models/vqgan_imagenet_f16_16384.ckpt Using device: cuda:0 Optimising using: Adam Using text prompts: ["A child's drawing of robots having a board meeting"] 0it [00:00, ?it/s] iter: 0, loss: 2.01, losses: 0.97, 0.0455, 0.952, 0.047 (-0=>2.015) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 10, loss: 1.75, losses: 0.849, 0.0475, 0.809, 0.0484 (-0=>1.754) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 20, loss: 1.59, losses: 0.761, 0.0539, 0.716, 0.0545 (-0=>1.586) 0it [00:00, ?it/s] 0it [00:09, ?it/s] 0it [00:00, ?it/s] iter: 30, loss: 1.57, losses: 0.743, 0.0543, 0.718, 0.0541 (-2=>1.535) 0it [00:00, ?it/s] 0it [00:09, ?it/s] 0it [00:00, ?it/s] iter: 40, loss: 1.52, losses: 0.716, 0.0545, 0.692, 0.0538 (-4=>1.479) 0it [00:00, ?it/s] 0it [00:09, ?it/s] 0it [00:00, ?it/s] iter: 50, loss: 1.52, losses: 0.718, 0.0549, 0.687, 0.0554 (-8=>1.461) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 60, loss: 1.47, losses: 0.697, 0.0566, 0.664, 0.0562 (-2=>1.426) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 70, loss: 1.46, losses: 0.693, 0.0569, 0.655, 0.0556 (-6=>1.416) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 80, loss: 1.41, losses: 0.67, 0.0585, 0.624, 0.0576 (-0=>1.411) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 90, loss: 1.46, losses: 0.691, 0.0578, 0.657, 0.0561 (-10=>1.411) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 100, loss: 1.48, losses: 0.696, 0.0577, 0.664, 0.0567 (-9=>1.398) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 110, loss: 1.44, losses: 0.685, 0.0567, 0.642, 0.0562 (-19=>1.398) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 120, loss: 1.38, losses: 0.654, 0.0603, 0.606, 0.0601 (-0=>1.38) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 130, loss: 1.38, losses: 0.653, 0.0594, 0.605, 0.059 (-3=>1.371) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 140, loss: 1.46, losses: 0.692, 0.0583, 0.654, 0.0558 (-13=>1.371) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 150, loss: 1.45, losses: 0.686, 0.0583, 0.651, 0.0562 (-23=>1.371) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 160, loss: 1.47, losses: 0.693, 0.0561, 0.664, 0.056 (-1=>1.371) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 170, loss: 1.42, losses: 0.675, 0.0596, 0.629, 0.0577 (-11=>1.371) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 180, loss: 1.45, losses: 0.686, 0.0567, 0.645, 0.0577 (-6=>1.364) 0it [00:00, ?it/s] Dropping learning rate 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 190, loss: 1.42, losses: 0.675, 0.0595, 0.625, 0.0577 (-0=>1.417) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 200, loss: 1.43, losses: 0.678, 0.0587, 0.633, 0.058 (-7=>1.369) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 210, loss: 1.43, losses: 0.686, 0.0587, 0.632, 0.0577 (-5=>1.347) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 220, loss: 1.37, losses: 0.647, 0.0615, 0.597, 0.0612 (-15=>1.347) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 230, loss: 1.36, losses: 0.649, 0.0609, 0.587, 0.061 (-25=>1.347) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 240, loss: 1.42, losses: 0.677, 0.0579, 0.626, 0.0571 (-35=>1.347) 0it [00:00, ?it/s] 0it [00:08, ?it/s] 0it [00:00, ?it/s] iter: 250, finished (-45=>1.347) 0it [00:00, ?it/s] 0it [00:00, ?it/s]
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