dribnet/clipit 📝🔢 → ❓
About
Image generation with CLIP + VQGAN / PixelDraw
Example Output
Output
[object Object]
Performance Metrics
All Input Parameters
{
"aspect": "widescreen",
"prompts": "sunset river snow mountain",
"quality": "draft"
}
Input Parameters
- aspect
- widescreen or square aspect
- prompts
- Text prompts
- quality
- quality
- display_every
- Display image iterations. For reference, the total number of iterations is determined by the quality chosen above: draft=200, normal=300, better and best=500
Output Schema
Example Execution Logs
Working with z of shape (1, 256, 16, 16) = 65536 dimensions. Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth 0%| | 0.00/528M [00:00<?, ?B/s] 1%| | 3.15M/528M [00:00<00:16, 33.0MB/s] 1%|1 | 5.95M/528M [00:00<00:17, 31.8MB/s] 4%|4 | 22.4M/528M [00:00<00:12, 42.1MB/s] 9%|8 | 44.9M/528M [00:00<00:09, 55.9MB/s] 13%|#3 | 69.2M/528M [00:00<00:06, 73.0MB/s] 17%|#7 | 91.7M/528M [00:00<00:04, 92.1MB/s] 21%|## | 109M/528M [00:00<00:04, 103MB/s] 25%|##4 | 130M/528M [00:00<00:03, 123MB/s] 29%|##8 | 152M/528M [00:00<00:02, 143MB/s] 34%|###3 | 177M/528M [00:01<00:02, 165MB/s] 38%|###8 | 203M/528M [00:01<00:01, 187MB/s] 43%|####3 | 228M/528M [00:01<00:01, 204MB/s] 48%|####7 | 252M/528M [00:01<00:01, 217MB/s] 52%|#####2 | 275M/528M [00:01<00:01, 215MB/s] 57%|#####6 | 299M/528M [00:01<00:01, 225MB/s] 61%|######1 | 322M/528M [00:01<00:00, 228MB/s] 65%|######5 | 345M/528M [00:01<00:00, 215MB/s] 70%|######9 | 369M/528M [00:01<00:00, 225MB/s] 74%|#######4 | 391M/528M [00:02<00:00, 202MB/s] 78%|#######7 | 412M/528M [00:02<00:00, 157MB/s] 83%|########2 | 437M/528M [00:02<00:00, 179MB/s] 88%|########7 | 463M/528M [00:02<00:00, 200MB/s] 93%|#########2| 490M/528M [00:02<00:00, 218MB/s] 97%|#########7| 513M/528M [00:02<00:00, 219MB/s] 100%|##########| 528M/528M [00:02<00:00, 205MB/s] loaded pretrained LPIPS loss from taming/modules/autoencoder/lpips/vgg.pth VQLPIPSWithDiscriminator running with hinge loss. Restored from models/vqgan_imagenet_f16_16384.ckpt 0%| | 0.00/338M [00:00<?, ?iB/s] 0%|1 | 1.12M/338M [00:00<00:30, 11.6MiB/s] 5%|#7 | 15.7M/338M [00:00<00:21, 16.0MiB/s] 12%|####4 | 39.6M/338M [00:00<00:14, 22.3MiB/s] 19%|####### | 62.8M/338M [00:00<00:09, 30.6MiB/s] 23%|########8 | 79.0M/338M [00:00<00:06, 40.6MiB/s] 30%|###########2 | 99.8M/338M [00:00<00:04, 53.7MiB/s] 36%|##############1 | 122M/338M [00:00<00:03, 69.8MiB/s] 43%|################6 | 144M/338M [00:00<00:02, 88.2MiB/s] 48%|###################3 | 164M/338M [00:00<00:01, 107MiB/s] 54%|#####################6 | 183M/338M [00:01<00:01, 123MiB/s] 60%|########################1 | 204M/338M [00:01<00:00, 142MiB/s] 68%|########################### | 228M/338M [00:01<00:00, 164MiB/s] 74%|#############################7 | 251M/338M [00:01<00:00, 180MiB/s] 81%|################################2 | 272M/338M [00:01<00:00, 190MiB/s] 87%|##################################7 | 294M/338M [00:01<00:00, 185MiB/s] 93%|#####################################3 | 315M/338M [00:01<00:00, 196MiB/s] 100%|#######################################8| 336M/338M [00:01<00:00, 203MiB/s] 100%|########################################| 338M/338M [00:01<00:00, 203MiB/s] Using device: cuda:0 Optimising using: Adam Using text prompts: ['sunset river snow mountain'] Using seed: 14723532543356836858 0it [00:00, ?it/s] /root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/nn/functional.py:3451: 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: 0.952009, losses: 0.952009 0it [00:00, ?it/s] 1it [00:00, 3.26it/s] 2it [00:00, 3.60it/s] 3it [00:00, 3.89it/s] 4it [00:00, 4.11it/s] 5it [00:01, 4.31it/s] 6it [00:01, 4.45it/s] 7it [00:01, 4.57it/s] 8it [00:01, 4.65it/s] 9it [00:01, 4.73it/s] 10it [00:02, 4.78it/s] iter: 10, loss: 0.872961, losses: 0.872961 10it [00:02, 4.78it/s] 11it [00:02, 4.13it/s] 12it [00:02, 4.35it/s] 13it [00:02, 4.50it/s] 14it [00:03, 4.62it/s] 15it [00:03, 4.66it/s] 16it [00:03, 4.71it/s] 17it [00:03, 4.76it/s] 18it [00:03, 4.81it/s] 19it [00:04, 4.78it/s] 20it [00:04, 4.83it/s] iter: 20, loss: 0.816933, losses: 0.816933 20it [00:04, 4.83it/s] 21it [00:05, 2.72it/s] 22it [00:05, 3.13it/s] 23it [00:05, 3.52it/s] 24it [00:05, 3.82it/s] 25it [00:05, 4.06it/s] 26it [00:06, 4.29it/s] 27it [00:06, 4.49it/s] 28it [00:06, 4.58it/s] 29it [00:06, 4.67it/s] 30it [00:06, 4.76it/s] iter: 30, loss: 0.793794, losses: 0.793794 30it [00:07, 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Version Details
- Version ID
fce706432e4003efc9e6a62c60631a90fb87a1aa121f8396f2f602a1c46e3676- Version Created
- September 28, 2021