dribnet/pixray-genesis 📝❓ → 🖼️

▶️ 160.7K runs 📅 Nov 2021 ⚙️ Cog 0.4.4

text-to-image

About

Example Output

Output

[object Object][object Object][object Object][object Object]

Performance Metrics

57.13s Total Time

All Input Parameters

{
  "title": "gradients",
  "quality": "draft",
  "optional_settings": "\n"
}

Input Parameters

title Type: stringDefault:
quality Default: draft
optional_settings Type: stringDefault:

Output Schema

Output

Type: array • Items Type: string • Items Format: uri

Example Execution Logs

---> Pixray Genesis Init
Using seed:
5563025599011693809
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:
['gradients']

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: 0.906, losses: 0.86, 0.0459 (-0=>0.9061)

0it [00:00, ?it/s]
iter: 10, loss: 0.859, losses: 0.815, 0.0443 (-1=>0.8542)

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iter: 20, loss: 0.84, losses: 0.795, 0.0444 (-3=>0.8383)

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iter: 30, loss: 0.831, losses: 0.787, 0.044 (-1=>0.8276)

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iter: 40, loss: 0.821, losses: 0.777, 0.0439 (-0=>0.8214)

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iter: 50, loss: 0.817, losses: 0.774, 0.0434 (-1=>0.8086)

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iter: 60, loss: 0.806, losses: 0.762, 0.0437 (-5=>0.804)

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iter: 70, loss: 0.807, losses: 0.763, 0.044 (-1=>0.7982)

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Dropping learning rate

0it [00:09, ?it/s]

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iter: 80, loss: 0.802, losses: 0.757, 0.0446 (-3=>0.7963)

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iter: 90, loss: 0.8, losses: 0.756, 0.0443 (-13=>0.7963)

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iter: 100, finished (-1=>0.7956)

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Version Details

Version ID: 2556070ce036747c9faf76b9c2c388046348f8d50cf33ba825e0eb7f62ff37ff
Version Created: October 27, 2022

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