02.04-image-from-xsem-and-random-xt.md

1. Title & Purpose

# image-from-xsem-and-random-xt

This document explains how to generate an image-from-xsem-and-random-xt.

##  image-from-xsem-and-random-xt

  


  

```python

from PIL import Image

import torch
import torchvision.transforms as transforms

from torchvision.transforms import functional as VF

from templates import ffhq256_autoenc, LitModel

  

device = 'cuda'

conf = ffhq256_autoenc()

model = LitModel(conf)

# Load and preprocess an image
img = Image.open('example.jpg').resize((256, 256)).convert('RGB')


x = VF.to_tensor(img).unsqueeze(0).to(device)
to_tensor = transforms.ToTensor()
xsem = model.encode(x)
xt = model.encode_stochastic(x, cond, T=250)
# Generate random xt
random_x_t = torch.randn_like(model.encode_t(x, t=50))

# Decode image using x_sem and random_x_t
random_xt_image = model.decode(xsem, random_x_t)

4. Expected Output

## Expected Output

- Demonstrates the effect of randomness in image synthesis.

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