pixray/text2image ❓📝 → 🖼️

▶️ 1.4M runs 📅 Jan 2022 ⚙️ Cog 0.1.2 🔗 GitHub ⚖️ License
text-to-image vqgan-clip

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]

Performance Metrics

301.00s Prediction Time
304.55s Total Time
All Input Parameters 
{
  "prompts": "Manhattan skyline at sunset. #artstation 🌇",
  "settings": "\n"
}
Input Parameters
drawer Default: vqgan
render engine
prompts Type: stringDefault: Cairo skyline at sunset.
text prompt
settings Type: stringDefault:
extra settings in `name: value` format. reference: https://dazhizhong.gitbook.io/pixray-docs/docs/primary-settings
Output Schema

Output

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

Example Execution Logs 
---> BasePixrayPredictor Predict
Using seed:
10184741873048389411
Using device:
cuda:0
Optimising using:
Adam
Using text prompts:
['Manhattan skyline at sunset. #artstation 🌇']

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/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: 3.15, losses: 1.06, 0.0803, 0.967, 0.0479, 0.951, 0.0481 (-0=>3.154)

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/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(

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iter: 10, loss: 3.09, losses: 1.07, 0.0771, 0.942, 0.0435, 0.917, 0.0449 (-0=>3.093)

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iter: 20, loss: 3.09, losses: 1.06, 0.0797, 0.929, 0.0453, 0.928, 0.0467 (-4=>3.032)

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iter: 30, loss: 2.96, losses: 1.01, 0.0821, 0.889, 0.0488, 0.886, 0.0474 (-0=>2.959)

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iter: 40, loss: 2.94, losses:  1, 0.0814, 0.882, 0.0487, 0.872, 0.0482 (-1=>2.891)

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iter: 50, loss: 2.85, losses: 0.975, 0.0859, 0.85, 0.0508, 0.841, 0.0474 (-4=>2.83)

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iter: 60, loss: 2.74, losses: 0.943, 0.0875, 0.813, 0.0516, 0.797, 0.0477 (-0=>2.74)

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iter: 70, loss: 2.72, losses: 0.945, 0.085, 0.804, 0.0501, 0.789, 0.0481 (-4=>2.692)

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iter: 80, loss: 2.64, losses: 0.895, 0.0883, 0.785, 0.0516, 0.769, 0.0486 (-2=>2.636)

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iter: 90, loss: 2.61, losses: 0.884, 0.0883, 0.778, 0.0509, 0.759, 0.0484 (-1=>2.597)

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iter: 100, loss: 2.63, losses: 0.899, 0.0887, 0.781, 0.0492, 0.764, 0.0486 (-11=>2.597)

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iter: 110, loss: 2.6, losses: 0.884, 0.0895, 0.771, 0.0495, 0.753, 0.049 (-5=>2.559)

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iter: 120, loss: 2.56, losses: 0.865, 0.0886, 0.76, 0.0495, 0.746, 0.0485 (-2=>2.557)

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iter: 130, loss: 2.6, losses: 0.884, 0.0881, 0.771, 0.049, 0.758, 0.0481 (-7=>2.556)

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iter: 140, loss: 2.58, losses: 0.875, 0.0883, 0.765, 0.0488, 0.755, 0.0479 (-6=>2.519)

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iter: 150, loss: 2.58, losses: 0.87, 0.0887, 0.764, 0.0487, 0.761, 0.048 (-1=>2.514)

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iter: 160, loss: 2.53, losses: 0.841, 0.09, 0.756, 0.0504, 0.75, 0.0483 (-11=>2.514)

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iter: 170, loss: 2.57, losses: 0.862, 0.089, 0.764, 0.0498, 0.752, 0.0488 (-1=>2.513)

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iter: 180, loss: 2.55, losses: 0.859, 0.0889, 0.758, 0.0493, 0.747, 0.0488 (-11=>2.513)

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iter: 190, loss: 2.56, losses: 0.862, 0.0878, 0.763, 0.0491, 0.755, 0.0485 (-21=>2.513)

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iter: 200, loss: 2.58, losses: 0.875, 0.0885, 0.765, 0.0484, 0.76, 0.0482 (-31=>2.513)

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iter: 210, loss: 2.58, losses: 0.87, 0.0894, 0.765, 0.0493, 0.757, 0.0489 (-5=>2.508)

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iter: 220, loss: 2.58, losses: 0.867, 0.0883, 0.765, 0.0491, 0.762, 0.049 (-15=>2.508)

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Dropping learning rate

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iter: 230, loss: 2.53, losses: 0.846, 0.0904, 0.748, 0.05, 0.748, 0.0493 (-1=>2.506)

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iter: 240, loss: 2.59, losses: 0.88, 0.088, 0.764, 0.0487, 0.761, 0.0482 (-11=>2.506)

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Traceback (most recent call last):
  File "/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/cog/server/redis_queue.py", line 163, in start
    self.handle_message(response_queue, message, cleanup_functions)
  File "/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/cog/server/redis_queue.py", line 232, in handle_message
    result = next(return_value)
  File "/src/cogrun.py", line 132, in predict
    yield from super().predict(settings="pixray_vdiff", prompts=prompts, **ydict)
  File "/src/cogrun.py", line 48, in predict
    run_complete = pixray.do_run(settings, return_display=True)
  File "/src/pixray.py", line 1502, in do_run
    keep_going = train(args, cur_iteration)
  File "/src/pixray.py", line 1367, in train
    loss.backward()
  File "/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/_tensor.py", line 255, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
  File "/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/torch/autograd/__init__.py", line 147, in backward
    Variable._execution_engine.run_backward(
  File "/root/.pyenv/versions/3.8.12/lib/python3.8/site-packages/cog/server/redis_queue.py", line 35, in handle_timeout
    raise TimeoutError(self.error_message)
TimeoutError: Prediction timed out
Version Details
Version ID
5c347a4bfa1d4523a58ae614c2194e15f2ae682b57e3797a5bb468920aa70ebf
Version Created
May 22, 2022
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