evilstreak/clipdraw 📝🔢 → 🖼️
About
Generate art from text prompts. Based on kvfrans/clipdraw.
Example Output
Prompt:
"Watercolor painting of an underwater submarine."
Output
Performance Metrics
288.61s
Prediction Time
293.78s
Total Time
All Input Parameters
{
"prompt": "Watercolor painting of an underwater submarine.",
"num_paths": 256,
"num_iterations": 600,
"display_frequency": 10,
"distance_from_centre_power": 2,
"distance_from_centre_weight": 0.001,
"distance_from_centre_threshold": 75
}
Input Parameters
- prompt
- prompt for generating image
- num_paths
- number of paths/curves
- num_iterations
- number of iterations
- display_frequency
- display frequency of intermediate images
- distance_from_centre_power
- power the distance from centre is raised to
- distance_from_centre_weight
- weight the distance from centre penalty is multiplied by
- distance_from_centre_threshold
- distance at which points start generating penalties to loss
Output Schema
Output
Example Execution Logs
/root/.pyenv/versions/3.8.13/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py:876: UserWarning: Argument fill/fillcolor is not supported for Tensor input. Fill value is zero
warnings.warn("Argument fill/fillcolor is not supported for Tensor input. Fill value is zero")
iteration: 0, render:loss: 5.265625
iteration: 10, render:loss: 2.767578125
iteration: 20, render:loss: 1.107421875
iteration: 30, render:loss: 0.0966796875
iteration: 40, render:loss: -0.5224609375
iteration: 50, render:loss: -0.89892578125
iteration: 60, render:loss: -1.0595703125
iteration: 70, render:loss: -1.29296875
iteration: 80, render:loss: -1.4404296875
iteration: 90, render:loss: -1.4287109375
iteration: 100, render:loss: -1.56640625
iteration: 110, render:loss: -1.541015625
iteration: 120, render:loss: -1.587890625
iteration: 130, render:loss: -1.67578125
iteration: 140, render:loss: -1.64453125
iteration: 150, render:loss: -1.7158203125
iteration: 160, render:loss: -1.7373046875
iteration: 170, render:loss: -1.74609375
iteration: 180, render:loss: -1.7265625
iteration: 190, render:loss: -1.7255859375
iteration: 200, render:loss: -1.7373046875
iteration: 210, render:loss: -1.7431640625
iteration: 220, render:loss: -1.7451171875
iteration: 230, render:loss: -1.7607421875
iteration: 240, render:loss: -1.7685546875
iteration: 250, render:loss: -1.7470703125
iteration: 260, render:loss: -1.7724609375
iteration: 270, render:loss: -1.787109375
iteration: 280, render:loss: -1.763671875
iteration: 290, render:loss: -1.7802734375
iteration: 300, render:loss: -1.775390625
iteration: 310, render:loss: -1.7880859375
iteration: 320, render:loss: -1.84375
iteration: 330, render:loss: -1.796875
iteration: 340, render:loss: -1.791015625
iteration: 350, render:loss: -1.818359375
iteration: 360, render:loss: -1.8525390625
iteration: 370, render:loss: -1.892578125
iteration: 380, render:loss: -1.8544921875
iteration: 390, render:loss: -1.8896484375
iteration: 400, render:loss: -1.9033203125
iteration: 410, render:loss: -1.8642578125
iteration: 420, render:loss: -1.8837890625
iteration: 430, render:loss: -1.8681640625
iteration: 440, render:loss: -1.8837890625
iteration: 450, render:loss: -1.9345703125
iteration: 460, render:loss: -1.8701171875
iteration: 470, render:loss: -1.9306640625
iteration: 480, render:loss: -1.962890625
iteration: 490, render:loss: -1.9423828125
iteration: 500, render:loss: -1.9580078125
iteration: 510, render:loss: -1.9638671875
iteration: 520, render:loss: -1.9638671875
iteration: 530, render:loss: -1.962890625
iteration: 540, render:loss: -1.9833984375
iteration: 550, render:loss: -1.9541015625
iteration: 560, render:loss: -1.9814453125
iteration: 570, render:loss: -1.9267578125
iteration: 580, render:loss: -1.98046875
iteration: 590, render:loss: -1.9609375
iteration: 599, render:loss: -2.0078125
Version Details
- Version ID
9b6a753e2d3471530a5c11b3c77b9f64eaf3b8a2c6810bba487dadd0c687a230- Version Created
- May 26, 2022