Update README.md

README.md

@@ -45,7 +45,7 @@ The model was trained in 4 phases:
 
 ### Training Techniques
 
-DeciDiffusion 1.0 was trained to be sample efficient, i.e. to produces high-quality results using fewer diffusion timesteps during inference. 
+DeciDiffusion 1.0 was trained to be sample efficient, i.e. to produce high-quality results using fewer diffusion timesteps during inference. 
 The following training techniques were used to that end: 
 
 - **[V-prediction](https://arxiv.org/pdf/2202.00512.pdf)**
@@ -69,7 +69,7 @@ The following techniques were used to shorten training time:
 - **Batch:** 8192
 - **Learning rate:** 1e-4
   
-#### Phase 2-4
+#### Phases 2-4
 - **Hardware:** 8 x 8 x H100 (80gb)
 - **Optimizer:** LAMB
 - **Batch:** 6144
@@ -85,7 +85,7 @@ Given this skepticism about FID’s reliability, we chose to assess DeciDiffusio
 For our study we chose 10 random prompts and for each prompt generated 3 images 
 by Stable Diffusion 1.5 configured to run for 50 iterations and 3 images by DeciDiffusion configured to run for 30 iterations. 
 
-We then presented 30 side by side comparisons to 300 random individuals, who voted based on adherence to the prompt and aesthetic value. The results of these votes are illustrated below. 
+We then presented 30 side by side comparisons to a group of professionals, who voted based on adherence to the prompt and aesthetic value. 
 
 According to the results, DeciDiffusion at 30 iterations exhibits an edge in aesthetics, but when it comes to prompt alignment, it’s on par with Stable Diffusion at 50 iterations.