put in everything

inference.py

@@ -0,0 +1,49 @@
+import torch
+import torch.optim as optim
+import torch.nn as nn
+from tqdm import tqdm
+import numpy as np
+from PIL import Image
+import requests
+import io
+
+from model import Unet, ConditionalUnet, GaussianDiffusion, DiffusionImageAPI
+
+def inference1():
+  # new image from web page
+  image = requests.get("https://picsum.photos/120/80").content
+  return Image.open(io.BytesIO(image))
+
+def inference():
+  model = Unet(
+    image_channels=3,
+  )
+  model.load_state_dict(torch.load("./model_final.pt"))
+
+  diffusion = GaussianDiffusion(
+    model=model,
+    noise_steps=1000,
+    beta_0=1e-4,
+    beta_T=0.02,
+    image_size=(120, 80),
+  )
+
+  imageAPI = DiffusionImageAPI(diffusion)
+
+  images, versions = diffusion.sample(1)
+  images = []
+  for image in versions:
+    images.append(imageAPI.tensor_to_image(image.squeeze(0)))
+  
+  #print(len(images))
+  #print(images[0])
+  ## make gif out of pillow images
+  #images[0].save('./gif_output/versions.gif',
+  #               save_all=True,
+  #               append_images=images[1:],
+  #               duration=100,
+  #               loop=0)
+  return images[-1]
+
+if __name__ == "__main__":
+  inference().show()