|
22 | 22 | from distutils.util import strtobool |
23 | 23 |
|
24 | 24 | import torch |
| 25 | +import numpy as np |
25 | 26 |
|
26 | 27 | from diffusers import GaussianDDPMScheduler, UNetModel |
27 | 28 | from diffusers.pipeline_utils import DiffusionPipeline |
|
35 | 36 | def get_random_generator(seed): |
36 | 37 | seed = 1234 |
37 | 38 | random.seed(seed) |
38 | | - os.environ[‘PYTHONHASHSEED’] = str(seed) |
| 39 | + os.environ["PYTHONHASHSEED"] = str(seed) |
39 | 40 | np.random.seed(seed) |
40 | 41 | torch.manual_seed(seed) |
41 | 42 | torch.cuda.manual_seed(seed) |
@@ -176,6 +177,7 @@ def test_sample(self): |
176 | 177 |
|
177 | 178 | assert image.shape == (1, 3, 256, 256) |
178 | 179 | image_slice = image[0, -1, -3:, -3:].cpu() |
| 180 | + import ipdb; ipdb.set_trace() |
179 | 181 | assert (image_slice - torch.tensor([[-0.0598, -0.0611, -0.0506], [-0.0726, 0.0220, 0.0103], [-0.0723, -0.1310, -0.2458]])).abs().sum() < 1e-3 |
180 | 182 |
|
181 | 183 | def test_sample_fast(self): |
@@ -216,6 +218,7 @@ def test_sample_fast(self): |
216 | 218 |
|
217 | 219 | assert image.shape == (1, 3, 256, 256) |
218 | 220 | image_slice = image[0, -1, -3:, -3:].cpu() |
| 221 | + import ipdb; ipdb.set_trace() |
219 | 222 | assert (image_slice - torch.tensor([[0.1746, 0.5125, -0.7920], [-0.5734, -0.2910, -0.1984], [0.4090, -0.7740, -0.3941]])).abs().sum() < 1e-3 |
220 | 223 |
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221 | 224 |
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