dribnet/pixray-text2pixel-0x42 📝 → ❓
About
Uses pixray to generate an image from text prompt
Example Output
Output
[object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object][object Object]
Performance Metrics
354.13s
Prediction Time
810.32s
Total Time
All Input Parameters
{
"aspect": "widescreen",
"prompts": "No bots. No Spam. Be Kind. ❤️",
"quality": "better"
}
Input Parameters
- aspect
- wide or narrow
- prompts
- description of what to draw
- quality
- speed vs quality
Output Schema
Example Execution Logs
---> 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]
Version Details
- Version ID
d22600e086598aacf688b94b15eae093985e77352b0e4bb9ecf31b8214b9c7cc- Version Created
- December 9, 2021