ltejedor/differentiable-rasterizer-vector-graphics ❓📝🔢🖼️ → 🖼️
About
Controllable generative AI art
Example Output
Prompt:
"red panda"
Output
Performance Metrics
63.18s
Prediction Time
165.40s
Total Time
All Input Parameters
{
"prompt": "red panda",
"initial_positions": [
[
"image_5.png",
-0.194643,
0.121429
],
[
"image_4.png",
0.301786,
-0.217857
],
[
"image_8.png",
0.294643,
0.35
],
[
"image_9.png",
-0.648214,
0.157143
]
]
}
Input Parameters
- loss
- Loss function to optimize the collage. Choose between CLIP and MSE. If MSE is selected, the prompt won't affect the image and target_image is required.
- prompt
- The global prompt for generating the collage.
- patch_url
- An .npy file for the collage patches.
- color_space
- num_patches
- Number of images to start with
- optim_steps
- Number of optimization steps to run during collage generation.
- target_image
- Upload an image file for MSE loss. Required if loss is set to MSE.
- background_red
- background_blue
- initial_colours
- background_green
- initial_positions
- List of lists representing the initial positions of patches.
- initial_transformations
Output Schema
Output
Example Execution Logs
initial positions [['image_5.png', '-0.194643', '0.121429'], ['image_4.png', '0.301786', '-0.217857'], ['image_8.png', '0.294643', '0.35'], ['image_9.png', '-0.648214', '0.157143']] Tiling 1x1 collages Optimisation: Tile size: 448x448 Global size: 448x448 (WxH) High res: Tile size: 896x896 Global size: 896x896 (WxH) Tile 0 prompts: ['a photorealistic sky with sun', 'a photorealistic sky', 'a photorealistic sky with moon', 'a photorealistic tree', 'a photorealistic tree', 'a photorealistic tree', 'a photorealistic field', 'a photorealistic field', 'a photorealistic chicken', 'red panda'] New collage creator for y0, x0 with bg image (not stitch) min 0.0, max 0.0 Using cached version of animals.npy Patch set animals.npy, fixed_scale_patches? True, fixed_scale_coeff=0.5, patch_max_proportion=5 Max size for fixed scale patches: (896,896) <class 'bool'> Patch 0 scaled by 0.50 Patch 1 scaled by 0.50 Patch 2 scaled by 0.50 Patch 3 scaled by 0.50 Patch 4 scaled by 0.50 Patch 5 scaled by 0.50 Patch 6 scaled by 0.50 Patch 7 scaled by 0.50 Patch 8 scaled by 0.50 Patch 9 scaled by 0.50 Patch 10 scaled by 0.50 Patch 11 scaled by 0.50 Patch 12 scaled by 0.50 Patch 13 scaled by 0.50 Patch 14 scaled by 0.50 Patch 15 scaled by 0.50 Patch 16 scaled by 0.50 Patch 17 scaled by 0.50 Patch 18 scaled by 0.50 Patch 19 scaled by 0.50 Patch 20 scaled by 0.50 Patch 21 scaled by 0.50 Patch 22 scaled by 0.50 Patch 23 scaled by 0.50 Patch 24 scaled by 0.50 Patch 25 scaled by 0.50 Patch 26 scaled by 0.50 Patch 27 scaled by 0.50 Patch 28 scaled by 0.50 Patch 29 scaled by 0.50 Patch 30 scaled by 0.50 Patch 31 scaled by 0.50 Patch 32 scaled by 0.50 Patch 33 scaled by 0.50 Patch 34 scaled by 0.50 Patch 35 scaled by 0.50 Patch 36 scaled by 0.50 Patch 37 scaled by 0.50 Patch 38 scaled by 0.50 Patch 39 scaled by 0.50 Patch 40 scaled by 0.50 Patch 41 scaled by 0.50 Patch 42 scaled by 0.50 Patch sizes during optimisation: Patch 0 of shape (172, 136, 4) Patch 1 of shape (192, 122, 4) Patch 2 of shape (144, 334, 4) Patch 3 of shape (142, 126, 4) Patch 4 of shape (152, 182, 4) Patch 5 of shape (148, 146, 4) Patch 6 of shape (206, 194, 4) Patch 7 of shape (148, 138, 4) Patch 8 of shape (182, 260, 4) Patch 9 of shape (159, 262, 4) Patch 10 of shape (174, 226, 4) Patch 11 of shape (157, 158, 4) Patch 12 of shape (96, 193, 4) Patch 13 of shape (254, 145, 4) Patch 14 of shape (176, 116, 4) Patch 15 of shape (166, 178, 4) Patch 16 of shape (151, 209, 4) Patch 17 of shape (129, 208, 4) Patch 18 of shape (158, 161, 4) Patch 19 of shape (122, 192, 4) Patch 20 of shape (143, 230, 4) Patch 21 of shape (195, 149, 4) Patch 22 of shape (159, 178, 4) Patch 23 of shape (71, 184, 4) Patch 24 of shape (126, 218, 4) Patch 25 of shape (134, 177, 4) Patch 26 of shape (146, 162, 4) Patch 27 of shape (156, 170, 4) Patch 28 of shape (154, 233, 4) Patch 29 of shape (233, 166, 4) Patch 30 of shape (57, 201, 4) Patch 31 of shape (148, 219, 4) Patch 32 of shape (159, 218, 4) Patch 33 of shape (146, 228, 4) Patch 34 of shape (249, 136, 4) Patch 35 of shape (170, 146, 4) Patch 36 of shape (161, 133, 4) Patch 37 of shape (150, 172, 4) Patch 38 of shape (163, 206, 4) Patch 39 of shape (154, 194, 4) Patch 40 of shape (184, 417, 4) Patch 41 of shape (134, 218, 4) Patch 42 of shape (180, 256, 4) 43 patches, max (184, 417, 4), min (57, 201, 4) Patch sizes for high-resolution final image: Patch 0 of shape (344, 272, 4) Patch 1 of shape (385, 245, 4) Patch 2 of shape (288, 668, 4) Patch 3 of shape (284, 252, 4) Patch 4 of shape (303, 364, 4) Patch 5 of shape (296, 292, 4) Patch 6 of shape (412, 387, 4) Patch 7 of shape (296, 275, 4) Patch 8 of shape (364, 521, 4) Patch 9 of shape (318, 524, 4) Patch 10 of shape (349, 453, 4) Patch 11 of shape (314, 316, 4) Patch 12 of shape (193, 386, 4) Patch 13 of shape (508, 290, 4) Patch 14 of shape (352, 232, 4) Patch 15 of shape (333, 355, 4) Patch 16 of shape (302, 418, 4) Patch 17 of shape (258, 416, 4) Patch 18 of shape (316, 322, 4) Patch 19 of shape (244, 384, 4) Patch 20 of shape (286, 460, 4) Patch 21 of shape (390, 298, 4) Patch 22 of shape (318, 357, 4) Patch 23 of shape (142, 368, 4) Patch 24 of shape (253, 436, 4) Patch 25 of shape (267, 354, 4) Patch 26 of shape (292, 323, 4) Patch 27 of shape (311, 341, 4) Patch 28 of shape (307, 466, 4) Patch 29 of shape (466, 332, 4) Patch 30 of shape (114, 402, 4) Patch 31 of shape (296, 438, 4) Patch 32 of shape (318, 437, 4) Patch 33 of shape (291, 455, 4) Patch 34 of shape (498, 273, 4) Patch 35 of shape (340, 291, 4) Patch 36 of shape (322, 266, 4) Patch 37 of shape (300, 344, 4) Patch 38 of shape (326, 413, 4) Patch 39 of shape (308, 387, 4) Patch 40 of shape (367, 834, 4) Patch 41 of shape (268, 436, 4) Patch 42 of shape (360, 512, 4) 43 patches, max (367, 834, 4), min (114, 402, 4) Global prompt is red panda Composition prompts ['a photorealistic sky with sun', 'a photorealistic sky', 'a photorealistic sky with moon', 'a photorealistic tree', 'a photorealistic tree', 'a photorealistic tree', 'a photorealistic field', 'a photorealistic field', 'a photorealistic chicken', 'red panda'] PopulationAffineTransforms is_high_res=False, requires_grad=True PopulationColourRGBTransforms for 100 patches, 2 individuals PopulationColourRGBTransforms requires_grad=True Background image of size torch.Size([3, 448, 448]) Starting optimization of collage. Updated patches in 0.337s torch.Size([2, 448, 448, 3]) /src/src/training.py:391: FutureWarning: `torch.nn.utils.clip_grad_norm` is now deprecated in favor of `torch.nn.utils.clip_grad_norm_`. torch.nn.utils.clip_grad_norm(generator.parameters(), image (stitch) min 0.0, max 1.0 Saving temporary image output_20250319_002503//optim_0.png (shape=(448, 896, 3)) [ WARN:0@8.649] global loadsave.cpp:848 imwrite_ Unsupported depth image for selected encoder is fallbacked to CV_8U. Iteration 0, rendering loss -0.812805 0 torch.Size([2, 448, 448, 3]) 1 torch.Size([2, 448, 448, 3]) 2 torch.Size([2, 448, 448, 3]) 3 torch.Size([2, 448, 448, 3]) 4 torch.Size([2, 448, 448, 3]) 5 torch.Size([2, 448, 448, 3]) 6 torch.Size([2, 448, 448, 3]) 7 torch.Size([2, 448, 448, 3]) 8 torch.Size([2, 448, 448, 3]) 9 torch.Size([2, 448, 448, 3]) 10 torch.Size([2, 448, 448, 3]) 11 torch.Size([2, 448, 448, 3]) 12 torch.Size([2, 448, 448, 3]) 13 torch.Size([2, 448, 448, 3]) 14 torch.Size([2, 448, 448, 3]) 15 torch.Size([2, 448, 448, 3]) 16 torch.Size([2, 448, 448, 3]) 17 torch.Size([2, 448, 448, 3]) 18 torch.Size([2, 448, 448, 3]) 19 torch.Size([2, 448, 448, 3]) 20 torch.Size([2, 448, 448, 3]) 21 torch.Size([2, 448, 448, 3]) 22 torch.Size([2, 448, 448, 3]) 23 torch.Size([2, 448, 448, 3]) 24 torch.Size([2, 448, 448, 3]) 25 torch.Size([2, 448, 448, 3]) 26 torch.Size([2, 448, 448, 3]) 27 torch.Size([2, 448, 448, 3]) 28 torch.Size([2, 448, 448, 3]) 29 torch.Size([2, 448, 448, 3]) 30 torch.Size([2, 448, 448, 3]) 31 torch.Size([2, 448, 448, 3]) 32 torch.Size([2, 448, 448, 3]) 33 torch.Size([2, 448, 448, 3]) 34 torch.Size([2, 448, 448, 3]) 35 torch.Size([2, 448, 448, 3]) 36 torch.Size([2, 448, 448, 3]) 37 torch.Size([2, 448, 448, 3]) 38 torch.Size([2, 448, 448, 3]) 39 torch.Size([2, 448, 448, 3]) 40 torch.Size([2, 448, 448, 3]) 41 torch.Size([2, 448, 448, 3]) 42 torch.Size([2, 448, 448, 3]) 43 torch.Size([2, 448, 448, 3]) 44 torch.Size([2, 448, 448, 3]) 45 torch.Size([2, 448, 448, 3]) 46 torch.Size([2, 448, 448, 3]) 47 torch.Size([2, 448, 448, 3]) 48 torch.Size([2, 448, 448, 3]) 49 torch.Size([2, 448, 448, 3]) image (stitch) min 0.0, max 1.0 Saving temporary image output_20250319_002503//optim_50.png (shape=(448, 896, 3)) Iteration 50, rendering loss -0.847153 50 torch.Size([2, 448, 448, 3]) 51 torch.Size([2, 448, 448, 3]) 52 torch.Size([2, 448, 448, 3]) 53 torch.Size([2, 448, 448, 3]) 54 torch.Size([2, 448, 448, 3]) 55 torch.Size([2, 448, 448, 3]) 56 torch.Size([2, 448, 448, 3]) 57 torch.Size([2, 448, 448, 3]) 58 torch.Size([2, 448, 448, 3]) 59 torch.Size([2, 448, 448, 3]) 60 torch.Size([2, 448, 448, 3]) 61 torch.Size([2, 448, 448, 3]) 62 torch.Size([2, 448, 448, 3]) 63 torch.Size([2, 448, 448, 3]) 64 torch.Size([2, 448, 448, 3]) 65 torch.Size([2, 448, 448, 3]) 66 torch.Size([2, 448, 448, 3]) 67 torch.Size([2, 448, 448, 3]) 68 torch.Size([2, 448, 448, 3]) 69 torch.Size([2, 448, 448, 3]) 70 torch.Size([2, 448, 448, 3]) 71 torch.Size([2, 448, 448, 3]) 72 torch.Size([2, 448, 448, 3]) 73 torch.Size([2, 448, 448, 3]) 74 torch.Size([2, 448, 448, 3]) 75 torch.Size([2, 448, 448, 3]) 76 torch.Size([2, 448, 448, 3]) 77 torch.Size([2, 448, 448, 3]) 78 torch.Size([2, 448, 448, 3]) 79 torch.Size([2, 448, 448, 3]) 80 torch.Size([2, 448, 448, 3]) 81 torch.Size([2, 448, 448, 3]) 82 torch.Size([2, 448, 448, 3]) 83 torch.Size([2, 448, 448, 3]) 84 torch.Size([2, 448, 448, 3]) 85 torch.Size([2, 448, 448, 3]) 86 torch.Size([2, 448, 448, 3]) 87 torch.Size([2, 448, 448, 3]) 88 torch.Size([2, 448, 448, 3]) 89 torch.Size([2, 448, 448, 3]) 90 torch.Size([2, 448, 448, 3]) 91 torch.Size([2, 448, 448, 3]) 92 torch.Size([2, 448, 448, 3]) 93 torch.Size([2, 448, 448, 3]) 94 torch.Size([2, 448, 448, 3]) 95 torch.Size([2, 448, 448, 3]) 96 torch.Size([2, 448, 448, 3]) 97 torch.Size([2, 448, 448, 3]) 98 torch.Size([2, 448, 448, 3]) 99 torch.Size([2, 448, 448, 3]) image (stitch) min 0.0, max 1.0 Saving temporary image output_20250319_002503//optim_100.png (shape=(448, 896, 3)) Iteration 100, rendering loss -0.866760 Updated patches in 0.083s 100 torch.Size([2, 448, 448, 3]) 101 torch.Size([2, 448, 448, 3]) 102 torch.Size([2, 448, 448, 3]) 103 torch.Size([2, 448, 448, 3]) 104 torch.Size([2, 448, 448, 3]) 105 torch.Size([2, 448, 448, 3]) 106 torch.Size([2, 448, 448, 3]) 107 torch.Size([2, 448, 448, 3]) 108 torch.Size([2, 448, 448, 3]) 109 torch.Size([2, 448, 448, 3]) 110 torch.Size([2, 448, 448, 3]) 111 torch.Size([2, 448, 448, 3]) 112 torch.Size([2, 448, 448, 3]) 113 torch.Size([2, 448, 448, 3]) 114 torch.Size([2, 448, 448, 3]) 115 torch.Size([2, 448, 448, 3]) 116 torch.Size([2, 448, 448, 3]) 117 torch.Size([2, 448, 448, 3]) 118 torch.Size([2, 448, 448, 3]) 119 torch.Size([2, 448, 448, 3]) 120 torch.Size([2, 448, 448, 3]) 121 torch.Size([2, 448, 448, 3]) 122 torch.Size([2, 448, 448, 3]) 123 torch.Size([2, 448, 448, 3]) 124 torch.Size([2, 448, 448, 3]) 125 torch.Size([2, 448, 448, 3]) 126 torch.Size([2, 448, 448, 3]) 127 torch.Size([2, 448, 448, 3]) 128 torch.Size([2, 448, 448, 3]) 129 torch.Size([2, 448, 448, 3]) 130 torch.Size([2, 448, 448, 3]) 131 torch.Size([2, 448, 448, 3]) 132 torch.Size([2, 448, 448, 3]) 133 torch.Size([2, 448, 448, 3]) 134 torch.Size([2, 448, 448, 3]) 135 torch.Size([2, 448, 448, 3]) 136 torch.Size([2, 448, 448, 3]) 137 torch.Size([2, 448, 448, 3]) 138 torch.Size([2, 448, 448, 3]) 139 torch.Size([2, 448, 448, 3]) 140 torch.Size([2, 448, 448, 3]) 141 torch.Size([2, 448, 448, 3]) 142 torch.Size([2, 448, 448, 3]) 143 torch.Size([2, 448, 448, 3]) 144 torch.Size([2, 448, 448, 3]) 145 torch.Size([2, 448, 448, 3]) 146 torch.Size([2, 448, 448, 3]) 147 torch.Size([2, 448, 448, 3]) 148 torch.Size([2, 448, 448, 3]) 149 torch.Size([2, 448, 448, 3]) image (stitch) min 0.0, max 1.0 Saving temporary image output_20250319_002503//optim_150.png (shape=(448, 896, 3)) Iteration 150, rendering loss -0.922791 150 torch.Size([2, 448, 448, 3]) 151 torch.Size([2, 448, 448, 3]) 152 torch.Size([2, 448, 448, 3]) 153 torch.Size([2, 448, 448, 3]) 154 torch.Size([2, 448, 448, 3]) 155 torch.Size([2, 448, 448, 3]) 156 torch.Size([2, 448, 448, 3]) 157 torch.Size([2, 448, 448, 3]) 158 torch.Size([2, 448, 448, 3]) 159 torch.Size([2, 448, 448, 3]) 160 torch.Size([2, 448, 448, 3]) 161 torch.Size([2, 448, 448, 3]) 162 torch.Size([2, 448, 448, 3]) 163 torch.Size([2, 448, 448, 3]) 164 torch.Size([2, 448, 448, 3]) 165 torch.Size([2, 448, 448, 3]) 166 torch.Size([2, 448, 448, 3]) 167 torch.Size([2, 448, 448, 3]) 168 torch.Size([2, 448, 448, 3]) 169 torch.Size([2, 448, 448, 3]) 170 torch.Size([2, 448, 448, 3]) 171 torch.Size([2, 448, 448, 3]) 172 torch.Size([2, 448, 448, 3]) 173 torch.Size([2, 448, 448, 3]) 174 torch.Size([2, 448, 448, 3]) 175 torch.Size([2, 448, 448, 3]) 176 torch.Size([2, 448, 448, 3]) 177 torch.Size([2, 448, 448, 3]) 178 torch.Size([2, 448, 448, 3]) 179 torch.Size([2, 448, 448, 3]) 180 torch.Size([2, 448, 448, 3]) 181 torch.Size([2, 448, 448, 3]) 182 torch.Size([2, 448, 448, 3]) 183 torch.Size([2, 448, 448, 3]) 184 torch.Size([2, 448, 448, 3]) 185 torch.Size([2, 448, 448, 3]) 186 torch.Size([2, 448, 448, 3]) 187 torch.Size([2, 448, 448, 3]) 188 torch.Size([2, 448, 448, 3]) 189 torch.Size([2, 448, 448, 3]) 190 torch.Size([2, 448, 448, 3]) 191 torch.Size([2, 448, 448, 3]) 192 torch.Size([2, 448, 448, 3]) 193 torch.Size([2, 448, 448, 3]) 194 torch.Size([2, 448, 448, 3]) 195 torch.Size([2, 448, 448, 3]) 196 torch.Size([2, 448, 448, 3]) 197 torch.Size([2, 448, 448, 3]) 198 torch.Size([2, 448, 448, 3]) 199 torch.Size([2, 448, 448, 3]) image (stitch) min 0.0, max 1.0 Saving temporary image output_20250319_002503//optim_200.png (shape=(448, 896, 3)) Iteration 200, rendering loss -0.958267 Updated patches in 0.082s 200 torch.Size([2, 448, 448, 3]) 201 torch.Size([2, 448, 448, 3]) 202 torch.Size([2, 448, 448, 3]) 203 torch.Size([2, 448, 448, 3]) 204 torch.Size([2, 448, 448, 3]) 205 torch.Size([2, 448, 448, 3]) 206 torch.Size([2, 448, 448, 3]) 207 torch.Size([2, 448, 448, 3]) 208 torch.Size([2, 448, 448, 3]) 209 torch.Size([2, 448, 448, 3]) 210 torch.Size([2, 448, 448, 3]) 211 torch.Size([2, 448, 448, 3]) 212 torch.Size([2, 448, 448, 3]) 213 torch.Size([2, 448, 448, 3]) 214 torch.Size([2, 448, 448, 3]) 215 torch.Size([2, 448, 448, 3]) 216 torch.Size([2, 448, 448, 3]) 217 torch.Size([2, 448, 448, 3]) 218 torch.Size([2, 448, 448, 3]) 219 torch.Size([2, 448, 448, 3]) 220 torch.Size([2, 448, 448, 3]) 221 torch.Size([2, 448, 448, 3]) 222 torch.Size([2, 448, 448, 3]) 223 torch.Size([2, 448, 448, 3]) 224 torch.Size([2, 448, 448, 3]) 225 torch.Size([2, 448, 448, 3]) 226 torch.Size([2, 448, 448, 3]) 227 torch.Size([2, 448, 448, 3]) 228 torch.Size([2, 448, 448, 3]) 229 torch.Size([2, 448, 448, 3]) 230 torch.Size([2, 448, 448, 3]) 231 torch.Size([2, 448, 448, 3]) 232 torch.Size([2, 448, 448, 3]) 233 torch.Size([2, 448, 448, 3]) 234 torch.Size([2, 448, 448, 3]) 235 torch.Size([2, 448, 448, 3]) 236 torch.Size([2, 448, 448, 3]) 237 torch.Size([2, 448, 448, 3]) 238 torch.Size([2, 448, 448, 3]) 239 torch.Size([2, 448, 448, 3]) 240 torch.Size([2, 448, 448, 3]) 241 torch.Size([2, 448, 448, 3]) 242 torch.Size([2, 448, 448, 3]) 243 torch.Size([2, 448, 448, 3]) 244 torch.Size([2, 448, 448, 3]) 245 torch.Size([2, 448, 448, 3]) 246 torch.Size([2, 448, 448, 3]) 247 torch.Size([2, 448, 448, 3]) 248 torch.Size([2, 448, 448, 3]) image (stitch) min 0.0, max 1.0 Saving model to output_20250319_002503/... 249 PopulationAffineTransforms is_high_res=True, requires_grad=False PopulationColourRGBTransforms for 100 patches, 1 individuals PopulationColourRGBTransforms requires_grad=False Background image of size torch.Size([3, 896, 896]) Lowest loss: -1.0089111328125 @ index 1: [0, 0] idx [0:896], [0:896] Finished [0, 0] idx [0:896], [0:896] torch.Size([1, 896, 896, 4]) Image has alpha channel PopulationAffineTransforms is_high_res=True, requires_grad=False PopulationColourRGBTransforms for 100 patches, 1 individuals PopulationColourRGBTransforms requires_grad=False Background image of size torch.Size([3, 896, 896]) Lowest loss: -1.0089111328125 @ index 1: Not using background_image [0, 0] idx [0:896], [0:896] Setting alpha to zero outside of patches Finished [0, 0] idx [0:896], [0:896] torch.Size([1, 896, 896, 4]) Image has alpha channel
Version Details
- Version ID
c2405500b0f1671678ef21dab202ecb6ae5ad50d82628e169b3a0cd66a86c6ca- Version Created
- April 3, 2025