fix: use correct mean and std for sampling

src/tiny_ddpm/sample.py

@@ -1,10 +1,10 @@
-from typing import Dict, Union
+from typing import Dict
 import torch
 from tqdm import tqdm
 import matplotlib.pyplot as plt
 import numpy as np
 from train import extract, get_diffusion_params
-from train import TIMESTEPS, IMAGE_SIZE, CHANNELS, DDIM_TIMESTEPS
+from train import TIMESTEPS, IMAGE_SIZE, CHANNELS, DDIM_TIMESTEPS, NORM_MEAN, NORM_STD
 from model import UNet
 
 torch.manual_seed(1)
@@ -132,12 +132,13 @@ def ddim_sample_images(
     return x
 
 
-def show_images(images: Union[torch.Tensor, np.array], title=""):
+def show_images(images: torch.Tensor, title=""):
     """Display a batch of images in a grid"""
-    if isinstance(images, torch.Tensor):
+    mean = torch.tensor(NORM_MEAN).to(device).view(1, 3, 1, 1).expand_as(images)
+    std = torch.tensor(NORM_STD).to(device).view(1, 3, 1, 1).expand_as(images)
+    images = (images * std + mean).clip(0, 1)
     images = images.detach().cpu().numpy()
 
-    images = (images * 0.25 + 0.5).clip(0, 1)
     for idx in range(min(16, len(images))):
         plt.subplot(4, 4, idx + 1)
         plt.imshow(np.transpose(images[idx], (1, 2, 0)))
@@ -157,7 +158,7 @@ if __name__ == "__main__":
 
     model.eval()
     generated_images = (
-        ddpm_sample_images(  # change to ddim_sample_images here to enable DDIM
+        ddim_sample_images(  # change to ddim_sample_images here to enable DDIM
             model=model,
             image_size=IMAGE_SIZE,
             batch_size=16,

src/tiny_ddpm/train.py

@@ -17,6 +17,9 @@ CHANNELS = 3
 TIMESTEPS = 1000
 DDIM_TIMESTEPS = 100
 
+NORM_MEAN = (0.4914, 0.4822, 0.4465)
+NORM_STD = (0.2470, 0.2435, 0.2616)
+
 
 def count_parameters(model):
     return sum(p.numel() for p in model.parameters() if p.requires_grad)
@@ -27,7 +30,7 @@ transform = transforms.Compose(
         transforms.Resize(IMAGE_SIZE),
         transforms.RandomHorizontalFlip(0.5),
         transforms.ToTensor(),
-        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
+        transforms.Normalize(NORM_MEAN, NORM_STD),
     ]
 )