dribnet/pixray-tiler ✓📝 → 🖼️
About
Turn any description into wallpaper tiles
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
703.51s
Total Time
All Input Parameters
{
"prompts": "colorful marble texture",
"pixelart": true
}
Input Parameters
- mirror
- shifted pattern?
- prompts
- text prompt
- pixelart
- pixelart style?
- settings
- yaml settings
Output Schema
Output
Example Execution Logs
---> BasePixrayPredictor Predict Using seed: 9379428758220384575 Running pixeldrawer with 64x64 grid All CLIP models already loaded: ['RN50', 'ViT-B/32', 'ViT-B/16'] --> RN50 normal encoding colorful marble texture #pixelart --> ViT-B/32 normal encoding colorful marble texture #pixelart --> ViT-B/16 normal encoding colorful marble texture #pixelart Using device: cuda:0 Optimising using: Adam Using text prompts: ['colorful marble texture #pixelart'] 0it [00:00, ?it/s] iter: 0, loss: 2.71, losses: 0, 0.903, 0.0783, 0.808, 0.0476, 0.828, 0.0496 (-0=>2.715) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 10, loss: 2.53, losses: 0, 0.863, 0.0774, 0.739, 0.0482, 0.759, 0.0484 (-0=>2.535) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 20, loss: 2.44, losses: 0, 0.841, 0.0782, 0.7, 0.05, 0.723, 0.0483 (-0=>2.441) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 30, loss: 2.35, losses: 0, 0.803, 0.0771, 0.682, 0.0489, 0.69, 0.048 (-0=>2.349) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 40, loss: 2.37, losses: 0, 0.807, 0.0786, 0.683, 0.0485, 0.704, 0.0478 (-2=>2.322) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 50, loss: 2.34, losses: 0, 0.794, 0.0789, 0.674, 0.0495, 0.699, 0.0484 (-8=>2.315) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 60, loss: 2.31, losses: 0, 0.777, 0.0796, 0.667, 0.0494, 0.688, 0.0484 (-2=>2.281) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 70, loss: 2.31, losses: 0, 0.773, 0.079, 0.667, 0.0493, 0.69, 0.0493 (-4=>2.264) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 80, loss: 2.29, losses: 0, 0.763, 0.0803, 0.659, 0.0501, 0.683, 0.0499 (-8=>2.248) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 90, loss: 2.23, losses: 0, 0.738, 0.0787, 0.652, 0.0488, 0.663, 0.0489 (-0=>2.229) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 100, loss: 2.25, losses: 0, 0.753, 0.0792, 0.652, 0.0501, 0.671, 0.05 (-10=>2.229) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 110, loss: 2.27, losses: 0, 0.761, 0.0793, 0.654, 0.0494, 0.678, 0.0492 (-6=>2.221) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 120, loss: 2.26, losses: 0, 0.76, 0.0795, 0.655, 0.0489, 0.67, 0.049 (-16=>2.221) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 130, loss: 2.3, losses: 0, 0.774, 0.0778, 0.665, 0.0488, 0.687, 0.0486 (-8=>2.199) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 140, loss: 2.28, losses: 0, 0.763, 0.0793, 0.655, 0.0508, 0.68, 0.0504 (-18=>2.199) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 150, loss: 2.19, losses: 0, 0.721, 0.0778, 0.642, 0.05, 0.649, 0.0512 (-4=>2.189) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 160, loss: 2.23, losses: 0, 0.748, 0.0805, 0.643, 0.0506, 0.661, 0.0504 (-14=>2.189) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 170, loss: 2.22, losses: 0, 0.743, 0.0792, 0.644, 0.0501, 0.656, 0.0502 (-8=>2.176) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 180, loss: 2.17, losses: 0, 0.717, 0.078, 0.632, 0.0524, 0.637, 0.0522 (-0=>2.168) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 190, loss: 2.23, losses: 0, 0.742, 0.0798, 0.646, 0.0505, 0.664, 0.0506 (-10=>2.168) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 200, loss: 2.21, losses: 0, 0.74, 0.0779, 0.643, 0.0493, 0.653, 0.0507 (-4=>2.146) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 210, loss: 2.24, losses: 0, 0.747, 0.0793, 0.652, 0.0497, 0.665, 0.0501 (-4=>2.141) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 220, loss: 2.19, losses: 0, 0.732, 0.0775, 0.64, 0.0494, 0.642, 0.0506 (-14=>2.141) 0it [00:00, ?it/s] Dropping learning rate 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 230, loss: 2.27, losses: 0, 0.761, 0.0801, 0.661, 0.0499, 0.67, 0.0507 (-4=>2.162) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 240, loss: 2.24, losses: 0, 0.747, 0.079, 0.647, 0.0503, 0.665, 0.0506 (-2=>2.146) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 250, loss: 2.16, losses: 0, 0.707, 0.0794, 0.634, 0.0515, 0.634, 0.0523 (-2=>2.142) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 260, loss: 2.2, losses: 0, 0.73, 0.0807, 0.641, 0.0518, 0.647, 0.0515 (-12=>2.142) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 270, loss: 2.21, losses: 0, 0.73, 0.079, 0.645, 0.0495, 0.653, 0.051 (-22=>2.142) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 280, loss: 2.15, losses: 0, 0.71, 0.0778, 0.631, 0.0496, 0.626, 0.0539 (-32=>2.142) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 290, loss: 2.14, losses: 0, 0.696, 0.0792, 0.629, 0.0515, 0.631, 0.0521 (-0=>2.138) 0it [00:00, ?it/s] 0it [00:15, ?it/s] 0it [00:00, ?it/s] iter: 300, finished (-10=>2.138) 0it [00:00, ?it/s] 0it [00:00, ?it/s]
Version Details
- Version ID
416d8c260a1181579a2dae2898ede45f91726ddaed13d2da7ea1482f3bf8c931- Version Created
- October 27, 2022