Update README.md

README.md

@@ -16,14 +16,14 @@ tags:
 base_model: CompVis/stable-diffusion-v1-4
 ---
 
-# Model Card for Model ID
-
+# Model Card for Renaissance Stable Diffusion
 <!-- Provide a quick summary of what the model is/does. -->
 This model uses the KerasCV implementation of stability.ai's text-to-image model. Unlike other open-source alternatives like Hugging Face's Diffusers, KerasCV offers advantages such as XLA compilation and mixed precision support, resulting in state-of-the-art generation speed.
 
 ## Model Details
+If you'd like to see more regarding our process, results, or additional information about this project, please navigate to the Wiki section of this repository also available [here](https://github.com/martingasparyan/Fine-Tune-Stable-Diffusion/wiki).
 
-### Model Description
+#### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
 This model can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H) to generate high-quality Reniassance portraits from textual prompts. 
@@ -39,11 +39,14 @@ This model uses the KerasCV implementation of stability.ai's text-to-image model
 
 
 ## To Generate your own Examples:
-Install Dependencies
+### 1. Install Dependencies
+```python
 !pip install keras-cv==0.6.0 -q
 !pip install -U tensorflow -q
 !pip install keras-core -q
-Imports
+```
+### 2. Imports
+```python
 from textwrap import wrap
 import os
 import keras_cv
@@ -58,21 +61,30 @@ from keras_cv.models.stable_diffusion.image_encoder import ImageEncoder
 from keras_cv.models.stable_diffusion.noise_scheduler import NoiseScheduler
 from keras_cv.models.stable_diffusion.text_encoder import TextEncoder
 from tensorflow import keras
-Create a base Stable diffusion Model
+```
+### 3. Create a base Stable diffusion Model
+```python
 my_base_model = keras_cv.models.StableDiffusion(img_width=512, img_height=512)
-Load Weights from our h5 model which is hosted on Hugging Face here:
+```
+### 4. Load Weights from the h5 model which is hosted on Hugging Face:
+```python
 my_base_model.diffusion_model.load_weights('/path/to/file/renaissance_model.h5')
-Create a variable to hold the values of the to-be-generated image such as prompt, batch size, iterations, and seed
+```
+### 5. Create a variable to hold the values of the to-be-generated image such as prompt, batch size, iterations, and seed
+```python
 img = my_base_model.text_to_image(
        prompt="A woman with an enigmatic smile against a dark background",
        batch_size=1,  # How many images to generate at once
        num_steps=25,  # Number of iterations (controls image quality)
        seed=123,  # Set this to always get the same image from the same prompt
     )
-Display using the function:
+```
+### 6. Display using the function:
+```python
 def plot_images(images):
     plt.figure(figsize=(5, 5))
     plt.imshow(images)
     plt.axis("off")
     
-plot_images(img)
+plot_images(img)
+```