dribnet/pixray-text2pixel-0x42
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pixray-text2pixel-0x42
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dribnet/pixray-text2pixel-0x42
Uses pixray to generate an image from text prompt
dribnet/clipit
Image generation with CLIP + VQGAN / PixelDraw
dribnet/pixray-vqgan
dribnet/pixray-pixel
dribnet/pixray-text2image
Uses pixray to generate an image from text prompt
dribnet/pixray-text2pixel
Turn any description into pixel art
dribnet/pixray
Pixray with custom settings
dribnet/pixray-genesis
dribnet/pixray-api
Uses pixray with raw settings.
dribnet/pixray-tiler
Turn any description into wallpaper tiles
dribnet/bex-research-portfolio-code
dribnet/pixray-tiler-future
dribnet/8bidoug
A pixray tool for 24x24 pixelart
dribnet/homage1
Homage to the Pixel: text prompt to 6 color squares
Prediction
dribnet/pixray-text2pixel-0x42:d22600e086598aacf688b94b15eae093985e77352b0e4bb9ecf31b8214b9c7ccInput
- aspect
- widescreen
- prompts
- No bots. No Spam. Be Kind. ❤️
- quality
- better
{ "aspect": "widescreen", "prompts": "No bots. No Spam. Be Kind. ❤️", "quality": "better" }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-text2pixel-0x42 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "dribnet/pixray-text2pixel-0x42:d22600e086598aacf688b94b15eae093985e77352b0e4bb9ecf31b8214b9c7cc", { input: { aspect: "widescreen", prompts: "No bots. No Spam. Be Kind. ❤️", quality: "better" } } ); 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-text2pixel-0x42 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "dribnet/pixray-text2pixel-0x42:d22600e086598aacf688b94b15eae093985e77352b0e4bb9ecf31b8214b9c7cc", input={ "aspect": "widescreen", "prompts": "No bots. No Spam. Be Kind. ❤️", "quality": "better" } ) # The dribnet/pixray-text2pixel-0x42 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-text2pixel-0x42/api#output-schema print(item)To learn more, take a look at the guide on getting started with Python.
Run dribnet/pixray-text2pixel-0x42 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-text2pixel-0x42:d22600e086598aacf688b94b15eae093985e77352b0e4bb9ecf31b8214b9c7cc", "input": { "aspect": "widescreen", "prompts": "No bots. No Spam. Be Kind. ❤️", "quality": "better" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2021-12-09T23:16:23.390059Z", "created_at": "2021-12-09T23:02:53.066894Z", "data_removed": false, "error": null, "id": "an6xkrotebdlbjvpudtbpvgn44", "input": { "aspect": "widescreen", "prompts": "No bots. No Spam. Be Kind. ❤️", "quality": "better" }, "logs": "---> BasePixrayPredictor Predict\nUsing seed:\n17099965292127082386\nreusing cached copy of model\nmodels/vqgan_imagenet_f16_16384.ckpt\nUsing device:\ncuda:0\nOptimising using:\nAdam\nUsing text prompts:\n['No bots. No Spam. Be Kind. ❤️']\n\n0it [00:00, ?it/s]\niter: 0, loss: 3.03, losses: 1.01, 0.0759, 0.922, 0.0467, 0.927, 0.0476 (-0=>3.031)\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(\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 10, loss: 2.9, losses: 0.987, 0.0776, 0.853, 0.0491, 0.882, 0.0475 (-0=>2.897)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 20, loss: 2.83, losses: 0.95, 0.0811, 0.836, 0.0506, 0.866, 0.0474 (-0=>2.831)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 30, loss: 2.8, losses: 0.933, 0.0829, 0.829, 0.0524, 0.858, 0.047 (-0=>2.802)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 40, loss: 2.82, losses: 0.946, 0.0821, 0.835, 0.0502, 0.858, 0.0466 (-6=>2.797)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 50, loss: 2.81, losses: 0.946, 0.081, 0.832, 0.0503, 0.85, 0.0476 (-4=>2.774)\n\n0it [00:00, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 60, loss: 2.8, losses: 0.942, 0.0829, 0.828, 0.0508, 0.848, 0.047 (-6=>2.766)\n\n0it [00:00, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 70, loss: 2.74, losses: 0.924, 0.0836, 0.811, 0.0536, 0.823, 0.0495 (-0=>2.744)\n\n0it [00:00, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 80, loss: 2.73, losses: 0.92, 0.0824, 0.806, 0.053, 0.823, 0.0484 (-0=>2.733)\n\n0it [00:00, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 90, loss: 2.79, losses: 0.941, 0.0845, 0.827, 0.0501, 0.836, 0.0485 (-6=>2.712)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 100, loss: 2.75, losses: 0.934, 0.0837, 0.811, 0.0534, 0.822, 0.0502 (-8=>2.71)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 110, loss: 2.75, losses: 0.939, 0.0839, 0.814, 0.0523, 0.811, 0.0523 (-1=>2.69)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 120, loss: 2.73, losses: 0.932, 0.0875, 0.805, 0.054, 0.803, 0.0529 (-4=>2.677)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 130, loss: 2.66, losses: 0.906, 0.0857, 0.773, 0.0582, 0.779, 0.0553 (-0=>2.657)\n\n0it [00:00, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 140, loss: 2.64, losses: 0.901, 0.0876, 0.77, 0.0575, 0.769, 0.0555 (-0=>2.64)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 150, loss: 2.62, losses: 0.888, 0.0889, 0.764, 0.0594, 0.765, 0.0577 (-0=>2.623)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 160, loss: 2.7, losses: 0.924, 0.0864, 0.786, 0.0551, 0.793, 0.054 (-10=>2.623)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 170, loss: 2.62, losses: 0.889, 0.0884, 0.764, 0.0586, 0.766, 0.0565 (-0=>2.623)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 180, loss: 2.62, losses: 0.887, 0.0894, 0.758, 0.0599, 0.772, 0.0571 (-10=>2.623)\n\n0it [00:00, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 190, loss: 2.69, losses: 0.914, 0.0882, 0.784, 0.0573, 0.787, 0.0549 (-20=>2.623)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 200, loss: 2.69, losses: 0.91, 0.0882, 0.787, 0.0567, 0.788, 0.0562 (-30=>2.623)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 210, loss: 2.69, losses: 0.92, 0.0874, 0.786, 0.0578, 0.786, 0.0567 (-8=>2.611)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 220, loss: 2.7, losses: 0.926, 0.086, 0.793, 0.055, 0.792, 0.053 (-18=>2.611)\n\n0it [00:01, ?it/s]\nDropping learning rate\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 230, loss: 2.69, losses: 0.913, 0.0869, 0.79, 0.0564, 0.788, 0.0572 (-3=>2.608)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 240, loss: 2.68, losses: 0.912, 0.0865, 0.786, 0.0566, 0.781, 0.056 (-2=>2.6)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 250, loss: 2.7, losses: 0.915, 0.0866, 0.791, 0.056, 0.791, 0.0559 (-2=>2.589)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 260, loss: 2.69, losses: 0.916, 0.0875, 0.791, 0.0558, 0.789, 0.0553 (-4=>2.584)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 270, loss: 2.66, losses: 0.903, 0.0867, 0.776, 0.0581, 0.775, 0.0576 (-14=>2.584)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 280, loss: 2.64, losses: 0.894, 0.0885, 0.771, 0.0604, 0.765, 0.0585 (-24=>2.584)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 290, loss: 2.67, losses: 0.907, 0.0886, 0.782, 0.0586, 0.777, 0.0581 (-34=>2.584)\n\n0it [00:01, ?it/s]\n\n0it [00:11, ?it/s]\n\n0it [00:00, ?it/s]\niter: 300, finished (-44=>2.584)\n\n0it [00:00, ?it/s]\n\n0it [00:00, ?it/s]", "metrics": { "predict_time": 354.132628, "total_time": 810.323165 }, "output": [ { "file": "https://replicate.delivery/mgxm/c8a14106-b2b5-4cea-a544-1ee981906d53/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/d8abc932-9a15-463b-adba-f360957cbf3c/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/45d81a3a-7d92-4854-ad50-dfb722ca2f8c/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/1a24659d-673c-403e-898a-e890301936d9/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/2aed8101-92ac-47cf-a163-fd575f363c9a/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/eaafadf5-8f66-48da-8e1a-bc2e095ac30f/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/8ba75fdc-27ed-46cb-a8ad-ce05aef457cc/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/24a26c7a-76f7-49d6-8e49-cf16b37a2716/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/cfb23953-db88-4a5b-86f9-de09b4c522aa/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/22628133-060d-47c7-ba8d-ad3955291d27/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/2788d274-4191-453f-a8d8-b00962a77dd2/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/0276a98b-8b0e-46b1-b4bb-99d54fcbc391/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/6d3094a7-3cb6-408b-934c-36eaa484bd9d/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/b3b8229b-67f6-44cd-8a3c-d05da2e4a34e/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/2aa6c916-d6d0-4e28-845a-82af9e40ac6a/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/e683c5b1-09cd-4e73-9494-e0d9a678bbb9/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/8c695985-f61f-4619-bb46-39c34d0ad3ea/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/647210a9-8fbf-4d85-a812-05609e1e49db/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/d1c6179f-458a-4ba0-bc44-d9569411d1fd/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/e52fd62c-26a1-4841-9ec1-af3e0a64874b/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/00c8d6e7-bef0-444f-9cd3-9baa9836f6b0/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/eb88e34f-0cc7-4bb1-946b-24f5fd6a8850/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/87f421b8-4a60-4fd7-8851-ecfad32d80f5/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/18a8a02d-f0d3-425e-9638-90e88a01d1f3/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/2fd28ba8-68e7-4003-99c7-4083448bf864/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/b97b9d26-4d91-4f09-bfb5-6133b6b58432/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/6b4dfb49-f851-45e6-8c09-83e62782efb0/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/4e35db82-f63a-4ab7-9302-47b9a43110b1/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/60e2c4bb-e8e0-40ee-be60-b018d4f466f8/tempfile.png" }, { "file": "https://replicate.delivery/mgxm/3f86ab3b-225c-4056-b39e-50c26ee85a71/tempfile.png" } ], "started_at": "2021-12-09T23:10:29.257431Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/an6xkrotebdlbjvpudtbpvgn44", "cancel": "https://api.replicate.com/v1/predictions/an6xkrotebdlbjvpudtbpvgn44/cancel" }, "version": "d22600e086598aacf688b94b15eae093985e77352b0e4bb9ecf31b8214b9c7cc" }Generated in---> BasePixrayPredictor Predict Using seed: 17099965292127082386 reusing cached copy of model models/vqgan_imagenet_f16_16384.ckpt Using device: cuda:0 Optimising using: Adam Using text prompts: ['No bots. No Spam. Be Kind. ❤️'] 0it [00:00, ?it/s] iter: 0, loss: 3.03, losses: 1.01, 0.0759, 0.922, 0.0467, 0.927, 0.0476 (-0=>3.031) 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( 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 10, loss: 2.9, losses: 0.987, 0.0776, 0.853, 0.0491, 0.882, 0.0475 (-0=>2.897) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 20, loss: 2.83, losses: 0.95, 0.0811, 0.836, 0.0506, 0.866, 0.0474 (-0=>2.831) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 30, loss: 2.8, losses: 0.933, 0.0829, 0.829, 0.0524, 0.858, 0.047 (-0=>2.802) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 40, loss: 2.82, losses: 0.946, 0.0821, 0.835, 0.0502, 0.858, 0.0466 (-6=>2.797) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 50, loss: 2.81, losses: 0.946, 0.081, 0.832, 0.0503, 0.85, 0.0476 (-4=>2.774) 0it [00:00, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 60, loss: 2.8, losses: 0.942, 0.0829, 0.828, 0.0508, 0.848, 0.047 (-6=>2.766) 0it [00:00, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 70, loss: 2.74, losses: 0.924, 0.0836, 0.811, 0.0536, 0.823, 0.0495 (-0=>2.744) 0it [00:00, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 80, loss: 2.73, losses: 0.92, 0.0824, 0.806, 0.053, 0.823, 0.0484 (-0=>2.733) 0it [00:00, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 90, loss: 2.79, losses: 0.941, 0.0845, 0.827, 0.0501, 0.836, 0.0485 (-6=>2.712) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 100, loss: 2.75, losses: 0.934, 0.0837, 0.811, 0.0534, 0.822, 0.0502 (-8=>2.71) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 110, loss: 2.75, losses: 0.939, 0.0839, 0.814, 0.0523, 0.811, 0.0523 (-1=>2.69) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 120, loss: 2.73, losses: 0.932, 0.0875, 0.805, 0.054, 0.803, 0.0529 (-4=>2.677) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 130, loss: 2.66, losses: 0.906, 0.0857, 0.773, 0.0582, 0.779, 0.0553 (-0=>2.657) 0it [00:00, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 140, loss: 2.64, losses: 0.901, 0.0876, 0.77, 0.0575, 0.769, 0.0555 (-0=>2.64) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 150, loss: 2.62, losses: 0.888, 0.0889, 0.764, 0.0594, 0.765, 0.0577 (-0=>2.623) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 160, loss: 2.7, losses: 0.924, 0.0864, 0.786, 0.0551, 0.793, 0.054 (-10=>2.623) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 170, loss: 2.62, losses: 0.889, 0.0884, 0.764, 0.0586, 0.766, 0.0565 (-0=>2.623) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 180, loss: 2.62, losses: 0.887, 0.0894, 0.758, 0.0599, 0.772, 0.0571 (-10=>2.623) 0it [00:00, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 190, loss: 2.69, losses: 0.914, 0.0882, 0.784, 0.0573, 0.787, 0.0549 (-20=>2.623) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 200, loss: 2.69, losses: 0.91, 0.0882, 0.787, 0.0567, 0.788, 0.0562 (-30=>2.623) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 210, loss: 2.69, losses: 0.92, 0.0874, 0.786, 0.0578, 0.786, 0.0567 (-8=>2.611) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 220, loss: 2.7, losses: 0.926, 0.086, 0.793, 0.055, 0.792, 0.053 (-18=>2.611) 0it [00:01, ?it/s] Dropping learning rate 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 230, loss: 2.69, losses: 0.913, 0.0869, 0.79, 0.0564, 0.788, 0.0572 (-3=>2.608) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 240, loss: 2.68, losses: 0.912, 0.0865, 0.786, 0.0566, 0.781, 0.056 (-2=>2.6) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 250, loss: 2.7, losses: 0.915, 0.0866, 0.791, 0.056, 0.791, 0.0559 (-2=>2.589) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 260, loss: 2.69, losses: 0.916, 0.0875, 0.791, 0.0558, 0.789, 0.0553 (-4=>2.584) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 270, loss: 2.66, losses: 0.903, 0.0867, 0.776, 0.0581, 0.775, 0.0576 (-14=>2.584) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 280, loss: 2.64, losses: 0.894, 0.0885, 0.771, 0.0604, 0.765, 0.0585 (-24=>2.584) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 290, loss: 2.67, losses: 0.907, 0.0886, 0.782, 0.0586, 0.777, 0.0581 (-34=>2.584) 0it [00:01, ?it/s] 0it [00:11, ?it/s] 0it [00:00, ?it/s] iter: 300, finished (-44=>2.584) 0it [00:00, ?it/s] 0it [00:00, ?it/s]
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