inference.py

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()