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README.md

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-# ERA_Session20
-
-## Objective:
-The purpose of this repository is to understand the architecture of Generative Art & Stable Diffusion
-
-## Repository:
-```
-.
-├── LICENSE
-├── README.md
-├── config.py
-├── diffusion_loss.py
-├── image_generator.py
-├── inference.ipynb
-├── model.py
-├── prediction.py
-├── requirements.txt
-├── symmetry_loss_analysis.py
-└── utils.py
-```
-
-## How to execute this repository?
-
-In `inference.ipynb`, 
-    - add the prompt in the `prompt` variable
-    - configure the required loss function and execute the prediction function
-
-## Results
-
-`prompt = A King riding a horse`
-
-### 1. Without Loss Function
-
-![Alt text](image.png)
-
-### 2. Blue Channel
-
-Computing the average absolute difference between the `blue channel` values of each pixel in the batch and the target value of `0.9`. This allows us to measure how far, on average the blue channel deviates from the desired value of `0.9` across all images in the batch
-
-![Alt text](image-1.png)
-
-### 3. Elastic Deformations
-
-A data augmentation process. Applying the random elastic deformations to get an input image. The Strength and Smoothness of these deformations are controlled by the `alpha` and `sigma` parameters. The process involves generating displacement vectors for each pixel, adding these vectors to an identified grid, and then using the deformed grid to interpolate pixel values from the original image.
-
-![Alt text](image-2.png)
-
-### 4. Saturation
-
-Applied a saturation adjustment to the images, and the error is calculated as the mean absolute pixel-wise difference between the original and the transformed images
-
-![Alt text](image-3.png)