Style the `scripts` directory (#250)

Style scripts

Makefile

@@ -3,7 +3,7 @@
 # make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
 export PYTHONPATH = src
 
-check_dirs := examples tests src utils
+check_dirs := examples scripts src tests utils
 
 modified_only_fixup:
 	$(eval modified_py_files := $(shell python utils/get_modified_files.py $(check_dirs)))

scripts/change_naming_configs_and_checkpoints.py

@@ -15,12 +15,15 @@
 """ Conversion script for the LDM checkpoints. """
 
 import argparse
-import os
 import json
+import os
+
 import torch
-from diffusers import UNet2DModel, UNet2DConditionModel
+
+from diffusers import UNet2DConditionModel, UNet2DModel
 from transformers.file_utils import has_file
 
+
 do_only_config = False
 do_only_weights = True
 do_only_renaming = False
@@ -37,9 +40,7 @@ if __name__ == "__main__":
         help="The config json file corresponding to the architecture.",
     )
 
-    parser.add_argument(
-        "--dump_path", default=None, type=str, required=True, help="Path to the output model."
-    )
+    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
 
     args = parser.parse_args()
 

scripts/conversion_ldm_uncond.py

 import argparse
 
-import OmegaConf
 import torch
 
-from diffusers import UNetLDMModel, VQModel, LDMPipeline, DDIMScheduler
+import OmegaConf
+from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
+
 
 def convert_ldm_original(checkpoint_path, config_path, output_path):
     config = OmegaConf.load(config_path)
@@ -53,4 +54,3 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
-

scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py

@@ -16,8 +16,10 @@
 
 import argparse
 import json
+
 import torch
-from diffusers import UNet2DModel
+
+from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNet2DModel
 
 
 def convert_ncsnpp_checkpoint(checkpoint, config):

scripts/generate_logits.py

-from huggingface_hub import HfApi
-from transformers.file_utils import has_file
-from diffusers import UNet2DModel
 import random
+
 import torch
+
+from diffusers import UNet2DModel
+from huggingface_hub import HfApi
+
+
 api = HfApi()
 
 results = {}
-results["google_ddpm_cifar10_32"] = torch.tensor([-0.7515, -1.6883,  0.2420,  0.0300,  0.6347,  1.3433, -1.1743, -3.7467,
+# fmt: off
+results["google_ddpm_cifar10_32"] = torch.tensor([
+    -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
     1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
     -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
-         0.3186,  0.2721, -0.9759, -1.2461,  2.6257,  1.3557])
-results["google_ddpm_ema_bedroom_256"] = torch.tensor([-2.3639, -2.5344,  0.0054, -0.6674,  1.5990,  1.0158,  0.3124, -2.1436,
+    0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
+])
+results["google_ddpm_ema_bedroom_256"] = torch.tensor([
+    -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
     1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
     -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
-         2.4204, -0.9752, -1.2546,  0.8027,  3.2758,  3.1365])
-results["CompVis_ldm_celebahq_256"] = torch.tensor([-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
+    2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
+])
+results["CompVis_ldm_celebahq_256"] = torch.tensor([
+    -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
     -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
     -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
-         0.0250,  0.1290, -0.1543,  0.6316,  1.0460,  1.4943])
-results["google_ncsnpp_ffhq_1024"] = torch.tensor([ 0.0911,  0.1107,  0.0182,  0.0435, -0.0805, -0.0608,  0.0381,  0.2172,
+    0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
+])
+results["google_ncsnpp_ffhq_1024"] = torch.tensor([
+    0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
     -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
     0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-        -0.0707,  0.0642,  0.0220, -0.0134, -0.1333, -0.1505])
-results["google_ncsnpp_bedroom_256"] = torch.tensor([ 0.1321,  0.1337,  0.0440,  0.0622, -0.0591, -0.0370,  0.0503,  0.2133,
+    -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
+])
+results["google_ncsnpp_bedroom_256"] = torch.tensor([
+    0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
     -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
     0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-        -0.0665,  0.0659,  0.0383, -0.0005, -0.1266, -0.1386])
-results["google_ncsnpp_celebahq_256"] = torch.tensor([ 0.1154,  0.1218,  0.0307,  0.0526, -0.0711, -0.0541,  0.0366,  0.2078,
+    -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
+])
+results["google_ncsnpp_celebahq_256"] = torch.tensor([
+    0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
     -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
     0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-        -0.0681,  0.0661,  0.0331, -0.0046, -0.1268, -0.1431])
-results["google_ncsnpp_church_256"] = torch.tensor([ 0.1192,  0.1240,  0.0414,  0.0606, -0.0557, -0.0412,  0.0430,  0.2042,
+    -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
+])
+results["google_ncsnpp_church_256"] = torch.tensor([
+    0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
     -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
     0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-        -0.0645,  0.0626,  0.0341, -0.0010, -0.1199, -0.1390])
-results["google_ncsnpp_ffhq_256"] = torch.tensor([ 0.1075,  0.1074,  0.0205,  0.0431, -0.0774, -0.0607,  0.0298,  0.2042,
+    -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
+])
+results["google_ncsnpp_ffhq_256"] = torch.tensor([
+    0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
     -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
     0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-        -0.0712,  0.0605,  0.0250, -0.0099, -0.1316, -0.1473])
-results["google_ddpm_cat_256"] = torch.tensor([-1.4572, -2.0481, -0.0414, -0.6005,  1.4136,  0.5848,  0.4028, -2.7330,
+    -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
+])
+results["google_ddpm_cat_256"] = torch.tensor([
+    -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
     1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
     -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
     1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
-results["google_ddpm_celebahq_256"] = torch.tensor([-1.3690, -1.9720, -0.4090, -0.6966,  1.4660,  0.9938, -0.1385, -2.7324,
+results["google_ddpm_celebahq_256"] = torch.tensor([
+    -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
     0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
     -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
-         1.6430, -0.9090, -0.9392, -0.0126,  2.4268,  2.3266])
-results["google_ddpm_ema_celebahq_256"] = torch.tensor([-1.3525, -1.9628, -0.3956, -0.6860,  1.4664,  1.0014, -0.1259, -2.7212,
+    1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
+])
+results["google_ddpm_ema_celebahq_256"] = torch.tensor([
+    -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
     0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
     -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
-         1.6505, -0.8958, -0.9298, -0.0151,  2.4257,  2.3355])
-results["google_ddpm_church_256"] = torch.tensor([-2.0585, -2.7897, -0.2850, -0.8940,  1.9052,  0.5702,  0.6345, -3.8959,
+    1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
+])
+results["google_ddpm_church_256"] = torch.tensor([
+    -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
     1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
     -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
-         3.4981, -0.6255, -1.1644, -0.1591,  3.7097,  3.2066])
-results["google_ddpm_bedroom_256"] = torch.tensor([-2.3139, -2.5594, -0.0197, -0.6785,  1.7001,  1.1606,  0.3075, -2.1740,
+    3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
+])
+results["google_ddpm_bedroom_256"] = torch.tensor([
+    -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
     1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
     -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
-         2.3779, -1.0623, -1.2468,  0.8959,  3.3367,  3.2243])
-results["google_ddpm_ema_church_256"] = torch.tensor([-2.0628, -2.7667, -0.2089, -0.8263,  2.0539,  0.5992,  0.6495, -3.8336,
+    2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
+])
+results["google_ddpm_ema_church_256"] = torch.tensor([
+    -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
     1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
     -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
-         3.3396, -0.7328, -1.0417,  0.0383,  3.7093,  3.2343])
-results["google_ddpm_ema_cat_256"] = torch.tensor([-1.4574, -2.0569, -0.0473, -0.6117,  1.4018,  0.5769,  0.4129, -2.7344,
+    3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
+])
+results["google_ddpm_ema_cat_256"] = torch.tensor([
+    -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
     1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
     -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
-         1.8471, -0.5839, -0.5605, -0.0464,  2.3250,  2.1219])
+    1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
+])
+# fmt: on
 
 models = api.list_models(filter="diffusers")
 for mod in models:
@@ -75,7 +109,7 @@ for mod in models:
         print(f"Started running {mod.modelId}!!!")
 
         if mod.modelId.startswith("CompVis"):
-            model = UNet2DModel.from_pretrained(local_checkpoint, subfolder = "unet")
+            model = UNet2DModel.from_pretrained(local_checkpoint, subfolder="unet")
         else:
             model = UNet2DModel.from_pretrained(local_checkpoint)
 
@@ -85,7 +119,9 @@ for mod in models:
         noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
         time_step = torch.tensor([10] * noise.shape[0])
         with torch.no_grad():
-            logits = model(noise, time_step)['sample']
+            logits = model(noise, time_step)["sample"]
 
-        assert torch.allclose(logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1e-3)
+        assert torch.allclose(
+            logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1e-3
+        )
         print(f"{mod.modelId} has passed succesfully!!!")