diff --git a/modules/sd_models.py b/modules/sd_models.py
index 55bd9ca5e43..f9f3f07310b 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -159,7 +159,7 @@ def list_models():
         model_url = None
         expected_sha256 = None
     else:
-        model_url = f"{shared.hf_endpoint}/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
+        model_url = f"{shared.hf_endpoint}/stable-diffusion-v1-5/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
         expected_sha256 = '6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa'
 
     model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"], hash_prefix=expected_sha256)
diff --git a/modules/simple_karras_exponential_scheduler.py b/modules/simple_karras_exponential_scheduler.py
new file mode 100644
index 00000000000..b95c39b7ff5
--- /dev/null
+++ b/modules/simple_karras_exponential_scheduler.py
@@ -0,0 +1,391 @@
+#simple_karras_exponential_scheduler.py
+import torch
+import logging
+from k_diffusion.sampling import get_sigmas_karras, get_sigmas_exponential
+import os
+import yaml
+import random
+from watchdog.observers import Observer
+from watchdog.events import FileSystemEventHandler
+from datetime import datetime
+
+import os
+import logging
+from datetime import datetime
+
+def get_random_or_default(scheduler_config, key_prefix, default_value, global_randomize):
+    """Helper function to either randomize a value based on conditions or return the default."""
+    
+    # Determine if we should randomize based on global and individual flags
+    randomize_flag = global_randomize or scheduler_config.get(f'{key_prefix}_rand', False)
+
+    if randomize_flag:
+        # Use specified min/max values for randomization if they exist, else use default range
+        rand_min = scheduler_config.get(f'{key_prefix}_rand_min', default_value * 0.8)
+        rand_max = scheduler_config.get(f'{key_prefix}_rand_max', default_value * 1.2)
+        value = random.uniform(rand_min, rand_max)
+        custom_logger.info(f"Randomized {key_prefix}: {value}")
+    else:
+        # Use default value if no randomization is applied
+        value = default_value
+        custom_logger.info(f"Using default {key_prefix}: {value}")
+
+    return value
+    
+
+class CustomLogger:
+    def __init__(self, log_name, print_to_console=False, debug_enabled=False):
+        self.print_to_console = print_to_console #prints to console
+        self.debug_enabled = debug_enabled #logs debug messages
+
+        # Create folders for generation info and error logs
+        gen_log_dir = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'simple_kes_generation')
+        error_log_dir = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'simple_kes_error')
+
+        os.makedirs(gen_log_dir, exist_ok=True)
+        os.makedirs(error_log_dir, exist_ok=True)
+
+        # Get current time in HH-MM-SS format
+        current_time = datetime.now().strftime('%H-%M-%S')
+
+        # Create file paths for the log files
+        gen_log_file_path = os.path.join(gen_log_dir, f'{current_time}.log')
+        error_log_file_path = os.path.join(error_log_dir, f'{current_time}.log')
+
+        # Set up generation logger
+        #self.gen_logger = logging.getLogger(f'{log_name}_generation')
+        self.gen_logger = logging.getLogger('simple_kes_generation')
+        self.gen_logger.setLevel(logging.DEBUG)
+        self._setup_file_handler(self.gen_logger, gen_log_file_path)
+
+        # Set up error logger
+        self.error_logger = logging.getLogger(f'{log_name}_error')
+        self.error_logger.setLevel(logging.ERROR)
+        self._setup_file_handler(self.error_logger, error_log_file_path)
+        
+        # Prevent log propagation to root logger (important to avoid accidental console logging)
+        self.gen_logger.propagate = False
+        self.error_logger.propagate = False
+        
+        
+        # Optionally print to console
+        if self.print_to_console:
+            self._setup_console_handler(self.gen_logger)
+            self._setup_console_handler(self.error_logger)
+
+    def _setup_file_handler(self, logger, file_path):
+        """Set up file handler for logging to a file."""
+        file_handler = logging.FileHandler(file_path, mode='a')
+        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+        file_handler.setFormatter(formatter)
+        logger.addHandler(file_handler)
+
+    def _setup_console_handler(self, logger):
+        """Optionally set up a console handler for logging to the console."""
+        console_handler = logging.StreamHandler()
+        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+        console_handler.setFormatter(formatter)
+        logger.addHandler(console_handler)
+
+    def log_debug(self, message):
+        """Log a debug message."""
+        if self.debug_enabled:
+            self.gen_logger.debug(message)
+
+    def log_info(self, message):
+        """Log an info message."""
+        self.gen_logger.info(message)
+    info=log_info #alias created
+
+    def log_error(self, message):
+        """Log an error message."""
+        self.error_logger.error(message)
+
+    def enable_console_logging(self):
+        """Enable console logging dynamically."""
+        if not any(isinstance(handler, logging.StreamHandler) for handler in self.gen_logger.handlers):
+            self._setup_console_handler(self.gen_logger)
+
+        if not any(isinstance(handler, logging.StreamHandler) for handler in self.error_logger.handlers):
+            self._setup_console_handler(self.error_logger)
+
+# Usage example
+custom_logger = CustomLogger('simple_kes', print_to_console=False, debug_enabled=True)
+
+# Logging examples
+#custom_logger.log_debug("Debug message: Using default sigma_min: 0.01")
+#custom_logger.info("Info message: Step completed successfully.")
+#custom_logger.log_error("Error message: Something went wrong!")
+      
+    
+class ConfigManagerYaml:
+    def __init__(self, config_path):
+        self.config_path = config_path
+        self.config_data = self.load_config()  # Initialize config_data here
+
+    def load_config(self):
+        try:
+            with open(self.config_path, 'r') as f:
+                user_config = yaml.safe_load(f)
+                return user_config
+        except FileNotFoundError:
+            print(f"Config file not found: {self.config_path}. Using empty config.")
+            return {}
+        except yaml.YAMLError as e:
+            print(f"Error loading config file: {e}")
+            return {}
+            
+
+#ConfigWatcher monitors changes to the config file and reloads during program use (so you can continue work without resetting the program)        
+class ConfigWatcher(FileSystemEventHandler):
+    def __init__(self, config_manager, config_path):
+        self.config_manager = config_manager
+        self.config_path = config_path
+
+    def on_modified(self, event):
+        if event.src_path == self.config_path:
+            logging.info(f"Config file {self.config_path} modified. Reloading config.")
+            self.config_manager.config_data = self.config_manager.load_config()                
+ 
+
+ 
+def start_config_watcher(config_manager, config_path):
+    event_handler = ConfigWatcher(config_manager, config_path)
+    observer = Observer()
+    observer.schedule(event_handler, os.path.dirname(config_path), recursive=False)
+    observer.start()
+    return observer
+
+
+"""
+    Scheduler function that blends sigma sequences using Karras and Exponential methods with adaptive parameters.
+
+    Parameters are dynamically updated if the config file changes during execution.
+"""
+# If user config is provided, update default config with user values
+config_path = "modules/simple_kes_scheduler.yaml"
+config_manager = ConfigManagerYaml(config_path)    
+
+
+# Start watching for config changes
+observer = start_config_watcher(config_manager, config_path)
+
+
+def simple_karras_exponential_scheduler(
+    n, device, sigma_min=0.01, sigma_max=50, start_blend=0.1, end_blend=0.5, 
+    sharpness=0.95, early_stopping_threshold=0.01, update_interval=10, initial_step_size=0.9, 
+    final_step_size=0.2, initial_noise_scale=1.25, final_noise_scale=0.8, smooth_blend_factor=11, step_size_factor=0.8, noise_scale_factor=0.9, randomize=False, user_config=None
+):    
+    """
+    Scheduler function that blends sigma sequences using Karras and Exponential methods with adaptive parameters.
+
+    Parameters:
+        n (int): Number of steps.
+        sigma_min (float): Minimum sigma value.
+        sigma_max (float): Maximum sigma value.
+        device (torch.device): The device on which to perform computations (e.g., 'cuda' or 'cpu').
+        start_blend (float): Initial blend factor for dynamic blending.
+        end_bend (float): Final blend factor for dynamic blending.
+        sharpen_factor (float): Sharpening factor to be applied adaptively.
+        early_stopping_threshold (float): Threshold to trigger early stopping.
+        update_interval (int): Interval to update blend factors.
+        initial_step_size (float): Initial step size for adaptive step size calculation.
+        final_step_size (float): Final step size for adaptive step size calculation.
+        initial_noise_scale (float): Initial noise scale factor.
+        final_noise_scale (float): Final noise scale factor.
+        step_size_factor: Adjust to compensate for oversmoothing
+        noise_scale_factor: Adjust to provide more variation
+        
+    Returns:
+        torch.Tensor: A tensor of blended sigma values.
+    """
+    config_path = os.path.join(os.path.dirname(__file__), 'simple_kes_scheduler.yaml')
+    config = config_manager.load_config()
+    scheduler_config = config.get('scheduler', {})
+    if not scheduler_config:
+        raise ValueError("Scheduler configuration is missing from the config file.")
+    
+    # Global randomization flag
+    global_randomize = scheduler_config.get('randomize', False)
+    
+    #debug_log("Entered simple_karras_exponential_scheduler function")    
+    default_config = {
+        "debug": False,
+        "device": "cuda" if torch.cuda.is_available() else "cpu",        
+        "sigma_min": 0.01,
+        "sigma_max": 50, #if sigma_max is too low the resulting picture may be undesirable. 
+        "start_blend": 0.1,
+        "end_blend": 0.5,
+        "sharpness": 0.95,
+        "early_stopping_threshold": 0.01,
+        "update_interval": 10,
+        "initial_step_size": 0.9,
+        "final_step_size": 0.2,
+        "initial_noise_scale": 1.25,
+        "final_noise_scale": 0.8,
+        "smooth_blend_factor": 11,
+        "step_size_factor": 0.8, #suggested value to avoid oversmoothing
+        "noise_scale_factor": 0.9, #suggested value to add more variation 
+        "randomize": False,
+        "sigma_min_rand": False,
+        "sigma_min_rand_min": 0.001,
+        "sigma_min_rand_max": 0.05,
+        "sigma_max_rand": False,
+        "sigma_max_rand_min": 0.05,
+        "sigma_max_rand_max": 0.20,
+        "start_blend_rand": False,
+        "start_blend_rand_min": 0.05,
+        "start_blend_rand_max": 0.2, 
+        "end_blend_rand": False,
+        "end_blend_rand_min": 0.4,
+        "end_blend_rand_max": 0.6,
+        "sharpness_rand": False,
+        "sharpness_rand_min": 0.85,
+        "sharpness_rand_max": 1.0,
+        "early_stopping_rand": False,
+        "early_stopping_rand_min": 0.001,
+        "early_stopping_rand_max": 0.02,
+        "update_interval_rand": False,
+        "update_interval_rand_min": 5,
+        "update_interval_rand_max": 10,
+        "initial_step_rand": False,
+        "initial_step_rand_min": 0.7,
+        "initial_step_rand_max": 1.0,
+        "final_step_rand": False,
+        "final_step_rand_min": 0.1,
+        "final_step_rand_max": 0.3,
+        "initial_noise_rand": False,
+        "initial_noise_rand_min": 1.0,
+        "initial_noise_rand_max": 1.5,
+        "final_noise_rand": False,
+        "final_noise_rand_min": 0.6,
+        "final_noise_rand_max": 1.0,
+        "smooth_blend_factor_rand": False,
+        "smooth_blend_factor_rand_min": 6,    
+        "smooth_blend_factor_rand_max": 11,   
+        "step_size_factor_rand": False,
+        "step_size_factor_rand_min": 0.65,
+        "step_size_factor_rand_max": 0.85,   
+        "noise_scale_factor_rand": False,
+        "noise_scale_factor_rand_min": 0.75,
+        "noise_scale_factor_rand_max": 0.95,    
+    }    
+    custom_logger.info(f"Default Config create {default_config}")
+    config = config_manager.load_config().get('scheduler', {})
+    if not config:
+        raise ValueError("Scheduler configuration is missing from the config file.")
+    
+    # Log loaded YAML configuration
+    custom_logger.info(f"Configuration loaded from YAML: {config}")
+    
+    for key, value in config.items():
+        if key in default_config:
+            default_config[key] = value  # Override default with YAML value
+            custom_logger.info(f"Overriding default config: {key} = {value}")
+        else:
+            custom_logger.info(f"Ignoring unknown config option: {key}")
+        
+    custom_logger.info(f"Final configuration after merging with YAML: {default_config}")
+
+    global_randomize = default_config.get('randomize', False)
+    custom_logger.info(f"Global randomization flag set to: {global_randomize}")
+
+    custom_logger.info(f"Config loaded from yaml {config}")
+   
+    # Now using default_config, updated with valid YAML values
+    custom_logger.info(f"Final Config after overriding: {default_config}")
+    
+    # Example: Reading the randomization flags from the config
+    randomize = config.get('scheduler', {}).get('randomize', False)
+    
+    # Use the get_random_or_default function for each parameter
+    #if randomize = false, then it checks for each variable for randomize, if true, then that particular option is randomized, with the others using default or config defined values.
+    sigma_min = get_random_or_default(config, 'sigma_min', sigma_min, global_randomize)
+    sigma_max = get_random_or_default(config, 'sigma_max', sigma_max, global_randomize)
+    start_blend = get_random_or_default(config, 'start_blend', start_blend, global_randomize)
+    end_blend = get_random_or_default(config, 'end_blend', end_blend, global_randomize)
+    sharpness = get_random_or_default(config, 'sharpness', sharpness, global_randomize)
+    early_stopping_threshold = get_random_or_default(config, 'early_stopping', early_stopping_threshold, global_randomize)
+    update_interval = get_random_or_default(config, 'update_interval', update_interval, global_randomize)
+    initial_step_size = get_random_or_default(config, 'initial_step', initial_step_size, global_randomize)
+    final_step_size = get_random_or_default(config, 'final_step', final_step_size, global_randomize)
+    initial_noise_scale = get_random_or_default(config, 'initial_noise', initial_noise_scale, global_randomize)
+    final_noise_scale = get_random_or_default(config, 'final_noise', final_noise_scale, global_randomize)
+    smooth_blend_factor = get_random_or_default(config, 'smooth_blend_factor', smooth_blend_factor, global_randomize)
+    step_size_factor = get_random_or_default(config, 'step_size_factor', step_size_factor, global_randomize)
+    noise_scale_factor = get_random_or_default(config, 'noise_scale_factor', noise_scale_factor, global_randomize)
+    
+       
+    # Expand sigma_max slightly to account for smoother transitions
+    sigma_max = sigma_max * 1.1
+    custom_logger.info(f"Using device: {device}")
+    # Generate sigma sequences using Karras and Exponential methods
+    sigmas_karras = get_sigmas_karras(n=n, sigma_min=sigma_min, sigma_max=sigma_max, device=device)
+    sigmas_exponential = get_sigmas_exponential(n=n, sigma_min=sigma_min, sigma_max=sigma_max, device=device)
+    config = config_manager.config_data.get('scheduler', {})            
+    # Match lengths of sigma sequences
+    target_length = min(len(sigmas_karras), len(sigmas_exponential))  
+    sigmas_karras = sigmas_karras[:target_length]
+    sigmas_exponential = sigmas_exponential[:target_length]
+              
+    custom_logger.info(f"Generated sigma sequences. Karras: {sigmas_karras}, Exponential: {sigmas_exponential}")
+    if sigmas_karras is None:
+        raise ValueError("Sigmas Karras:{sigmas_karras} Failed to generate or assign sigmas correctly.")
+    if sigmas_exponential is None:    
+        raise ValueError("Sigmas Exponential: {sigmas_exponential} Failed to generate or assign sigmas correctly.")
+        #sigmas_karras = torch.zeros(n).to(device)
+        #sigmas_exponential = torch.zeros(n).to(device)   
+    try:
+        pass
+    except Exception as e:
+        error_log(f"Error generating sigmas: {e}")
+    finally:
+        # Stop the observer when done
+        observer.stop()
+        observer.join()
+  
+    # Define progress and initialize blend factor
+    progress = torch.linspace(0, 1, len(sigmas_karras)).to(device)
+    custom_logger.info(f"Progress created {progress}")
+    custom_logger.info(f"Progress Using device: {device}")
+    
+    sigs = torch.zeros_like(sigmas_karras).to(device)
+    custom_logger.info(f"Sigs created {sigs}")
+    custom_logger.info(f"Sigs Using device: {device}")
+
+    # Iterate through each step, dynamically adjust blend factor, step size, and noise scaling
+    for i in range(len(sigmas_karras)):
+        # Adaptive step size and blend factor calculations
+        step_size = initial_step_size * (1 - progress[i]) + final_step_size * progress[i] * step_size_factor  # 0.8 default value Adjusted to avoid over-smoothing
+        custom_logger.info(f"Step_size created {step_size}"   )
+        dynamic_blend_factor = start_blend * (1 - progress[i]) + end_blend * progress[i]
+        custom_logger.info(f"Dynamic_blend_factor created {dynamic_blend_factor}"  )
+        noise_scale = initial_noise_scale * (1 - progress[i]) + final_noise_scale * progress[i] * noise_scale_factor  # 0.9 default value Adjusted to keep more variation
+        custom_logger.info(f"noise_scale created {noise_scale}"   )
+        
+        # Calculate smooth blending between the two sigma sequences
+        smooth_blend = torch.sigmoid((dynamic_blend_factor - 0.5) * smooth_blend_factor) # Increase scaling factor to smooth transitions more
+        custom_logger.info(f"smooth_blend created {smooth_blend}"   )
+        
+        # Compute blended sigma values
+        blended_sigma = sigmas_karras[i] * (1 - smooth_blend) + sigmas_exponential[i] * smooth_blend
+        custom_logger.info(f"blended_sigma created {blended_sigma}"   )
+        
+        # Apply step size and noise scaling
+        sigs[i] = blended_sigma * step_size * noise_scale
+
+    # Optional: Adaptive sharpening based on sigma values
+    sharpen_mask = torch.where(sigs < sigma_min * 1.5, sharpness, 1.0).to(device)
+    custom_logger.info(f"sharpen_mask created {sharpen_mask} with device {device}"   )
+    sigs = sigs * sharpen_mask
+    
+    # Implement early stop criteria based on sigma convergence
+    change = torch.abs(sigs[1:] - sigs[:-1])
+    if torch.all(change < early_stopping_threshold):
+        custom_logger.info("Early stopping criteria met."   )
+        return sigs[:len(change) + 1].to(device)
+    
+    if torch.isnan(sigs).any() or torch.isinf(sigs).any():
+        raise ValueError("Invalid sigma values detected (NaN or Inf).")
+
+    return sigs.to(device)
diff --git a/modules/simple_kes_scheduler.yaml b/modules/simple_kes_scheduler.yaml
new file mode 100644
index 00000000000..d839f48af9d
--- /dev/null
+++ b/modules/simple_kes_scheduler.yaml
@@ -0,0 +1,146 @@
+scheduler:
+
+  #Optionally print to a log file for debugging. If false, debug is turned off, and no log file will be created.
+  #config options: true or false
+  debug: false
+  
+  # The minimum value for the noise level (sigma) during image generation.
+  # Decreasing this value makes the image clearer but less detailed.
+  # Increasing it makes the image noisier but potentially more artistic or abstract.
+  sigma_min: 0.01  # Default: 0.01, Suggested range: 0.01 - 0.1
+
+  # The maximum value for the noise level (sigma) during image generation.
+  # Increasing this value can create more variation in the image details.
+  # Lower values keep the image more stable and less noisy.
+  sigma_max: 50  # Default: 50, Suggested range:10 - 60
+
+  # The device used for running the scheduler. If you have a GPU, set this to "cuda".
+  # Otherwise, use "cpu", but note that it will be significantly slower.
+  #device: "cuda"  # Options: "cuda" (GPU) or "cpu" (processor)
+
+  # Initial blend factor between Karras and Exponential noise methods.
+  # A higher initial blend makes the image sharper at the start.
+  # A lower initial blend makes the image smoother early on.
+  start_blend: 0.1  # Default: 0.1, Suggested range: 0.05 - 0.2
+
+  # Final blend factor between Karras and Exponential noise methods.
+  # Higher values blend more noise at the end, possibly adding more detail.
+  # Lower values blend less noise for smoother, simpler images at the end.
+  end_blend: 0.5  # Default: 0.5, Suggested range: 0.4 - 0.6
+
+  # Sharpening factor applied to images during generation.
+  # Higher values increase sharpness but can add unwanted artifacts.
+  # Lower values reduce sharpness but may make the image look blurry.
+  sharpness: 0.95  # Default: 0.95, Suggested range: 0.8 - 1.0
+
+  # Early stopping threshold for stopping the image generation when changes between steps are minimal.
+  # Lower values stop early, saving time, but might produce incomplete images.
+  # Higher values take longer but may give more detailed results.
+  early_stopping_threshold: 0.01  # Default: 0.01, Suggested range: 0.005 - 0.02
+
+  # The number of steps between updates of the blend factor.
+  # Smaller values update the blend more frequently for smoother transitions.
+  # Larger values update the blend less frequently for faster processing.
+  update_interval: 10  # Default: 10, Suggested range: 5 - 15
+
+  # Initial step size, which controls how quickly the image evolves early on.
+  # Higher values make big changes at the start, possibly generating faster but less refined images.
+  # Lower values make smaller changes, giving more control over details.
+  initial_step_size: 0.9  # Default, 0.9, Suggested range: 0.5 - 1.0
+
+  # Final step size, which controls how much the image changes towards the end.
+  # Higher values keep details more flexible until the end, which may add complexity.
+  # Lower values lock the details earlier, making the image simpler.
+  final_step_size: 0.2  # Default: 0.2, Suggested range: 0.1 - 0.3
+
+  # Initial noise scaling applied to the image generation process.
+  # Higher values add more noise early on, making the initial image more random.
+  # Lower values reduce noise early on, leading to a smoother initial image.
+  initial_noise_scale: 1.25  # Default, 1.25, Suggested range: 1.0 - 1.5
+
+  # Final noise scaling applied at the end of the image generation.
+  # Higher values add noise towards the end, possibly adding fine detail.
+  # Lower values reduce noise towards the end, making the final image smoother.
+  final_noise_scale: 0.8  # Default, 0.8, Suggested range: 0.6 - 1.0
+  
+  
+  smooth_blend_factor: 11 #Default: 11, try 6 for more variation 
+  step_size_factor: 0.75 #suggested value (0.8) to avoid oversmoothing
+  noise_scale_factor: 0.95 #suggested value (0.9) to add more variation 
+  
+  
+  # Enables global randomization. 
+  # If true, all parameters are randomized within specified min/max ranges.
+  # If false, individual parameters with _rand flags set to true will still be randomized.
+  randomize: true 
+  
+  #Sigma values typically start very small. Lowering this could allow more gradual noise reduction. Too large would overwhelm the process.   
+  sigma_min_rand: false
+  sigma_min_rand_min: 0.001
+  sigma_min_rand_max: 0.05
+  
+  #Sigma max controls the upper limit of the noise. A lower minimum could allow faster convergence, while a higher max gives more flexibility for noisier images.
+  sigma_max_rand: false
+  sigma_max_rand_min: 10
+  sigma_max_rand_max: 60
+  
+  #Start blend controls how strongly Karras and Exponential are blended at the start. A slightly lower value introduces more variety in the blending at the beginning.
+  start_blend_rand: false
+  start_blend_rand_min: 0.05
+  start_blend_rand_max: 0.2
+  
+  # End blend affects how much the blending changes towards the end. Increasing the upper limit would allow more variation.
+  end_blend_rand: false
+  end_blend_rand_min: 0.4
+  end_blend_rand_max: 0.6
+  
+  # Sharpness controls detail retention. You wouldn’t want to lower it too much, as it might lose detail.
+  sharpness_rand: false
+  sharpness_rand_min: 0.85
+  sharpness_rand_max: 1.0
+  
+  #A smaller early stopping threshold could lead to earlier stopping if the changes between sigma steps become too small, while the upper value would prevent early stopping until larger changes occur.
+  early_stopping_rand: false
+  early_stopping_rand_min: 0.001
+  early_stopping_rand_max: 0.02
+  
+  #Update intervals affect how frequently blending factors are updated. More frequent updates allow more flexibility in blending.
+  update_interval_rand: false
+  update_interval_rand_min: 5 
+  update_interval_rand_max: 10
+  
+  # The initial step size defines how large the steps are at the start. A slightly smaller value introduces more gradual transitions.
+  initial_step_rand: false
+  initial_step_rand_min: 0.7
+  initial_step_rand_max: 1.0
+  
+  # The final step size defines how small the steps become towards the end. A slightly larger range gives more control over the final convergence.
+  final_step_rand: false
+  final_step_rand_min: 0.1
+  final_step_rand_max: 0.3
+  
+  #Initial noise scale defines how much noise to introduce initially. Larger values make the process start with more randomness, while smaller values keep it controlled.
+  initial_noise_rand: false
+  initial_noise_rand_min: 1.0
+  initial_noise_rand_max: 1.5
+  
+  # Final noise scale affects how much noise is reduced at the end. A lower minimum allows more noise to persist, while a higher maximum ensures full convergence.
+  final_noise_rand: false
+  final_noise_rand_min: 0.6  
+  final_noise_rand_max: 1.0
+  
+  #The smooth blend factor controls how aggressively the blending is smoothed. Lower values allow more abrupt blending changes, while higher values give smoother transitions.
+  smooth_blend_factor_rand: false
+  smooth_blend_factor_rand_min: 6    
+  smooth_blend_factor_rand_max: 11   
+  
+  #Step size factor adjusts the step size dynamically to avoid oversmoothing. A lower minimum increases variety, while a higher max provides smoother results.
+  step_size_factor_rand: false
+  step_size_factor_rand_min: 0.65   
+  step_size_factor_rand_max: 0.85   
+  
+  # Noise scale factor controls how noise is scaled throughout the steps. A slightly lower minimum adds more variety, while keeping the maximum value near the suggested ensures more uniform results.
+  noise_scale_factor_rand: false
+  noise_scale_factor_rand_min: 0.75  
+  noise_scale_factor_rand_max: 0.95 
+  
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 0d6bac600e1..dd6430f6c20 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -31,4 +31,5 @@ torch
 torchdiffeq
 torchsde
 transformers==4.30.2
-pillow-avif-plugin==1.4.3
\ No newline at end of file
+pillow-avif-plugin==1.4.3
+wheel
diff --git a/simple_kes_requirements.txt b/simple_kes_requirements.txt
new file mode 100644
index 00000000000..bd5ed38ccdb
--- /dev/null
+++ b/simple_kes_requirements.txt
@@ -0,0 +1,2 @@
+watchdog==5.0.3
+k_diffusion