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@@ -262,11 +262,9 @@ This section compares the UNet Diffusion model and the UNet Model in terms of th
 
 **UNet Model**: In contrast, the UNet Model operates in a single step, using standard image-to-image translation techniques to generate denoised images. This allows for faster inference, making it more suitable for real-time applications. However, the UNet Model struggles to predict the noise effectively, which leads to incomplete denoising. As a result, the denoised images are often not fully cleaned and may still contain visible noise, resulting in unrecognizable content.
 
-The **UNet2DModel (Diffusion Model)** excels in denoising quality due to its iterative process, which allows for more accurate and efficient noise removal. However, it is computationally expensive and less suited for real-time applications. On the other hand, the **UNet Model** is more efficient in terms of speed, making it suitable for time-sensitive tasks, but it fails to effectively predict and remove noise. This leads to suboptimal denoising, where the images are not adequately cleaned, and the content remains partially distorted. This explains the difference in results: the **UNet Diffusion model** produces cleaner, artifact-free images, while the **UNet Model** struggles with noise removal, leaving visible artifacts in the output.
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 ### Conclusion
-In this section, we outlined the architecture and training process for a diffusion model using the UNet2DModel (Diffusion Model). This model is trained to perform image denoising, progressively refining noisy images into clean ones. We compared it with the UNet, highlighting the key differences and how they are tailored to their respective tasks.
 
+The **UNet2DModel (Diffusion Model)** excels in denoising quality due to its iterative process, which allows for more accurate and efficient noise removal. However, it is computationally expensive and less suited for real-time applications. On the other hand, the **UNet Model** is more efficient in terms of speed, making it suitable for time-sensitive tasks, but it fails to effectively predict and remove noise. This leads to suboptimal denoising, where the images are not adequately cleaned, and the content remains partially distorted. This explains the difference in results: the **UNet Diffusion model** produces cleaner, artifact-free images, while the **UNet Model** struggles with noise removal, leaving visible artifacts in the output.
 
 
 ## Part 4: What About Those Beautiful Images?