diff --git a/pyproject.toml b/pyproject.toml
index 910ebad..eedb2df 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,25 +10,25 @@ python = "^3.10"
 torch = {version = "*", source = "pytorch-gpu-src"}
 torchvision = {version = "*", source = "pytorch-gpu-src"}
 torchaudio = {version = "*", source = "pytorch-gpu-src"}
-omegaconf = "2.3.0"
+omegaconf = ">=2.3.0"
 diffusers = "0.31.0"
 compel = "2.0.2"
-invisible-watermark = "^0.2.0"
-transformers = "^4.41.2"
-accelerate = "^0.31.0"
-safetensors = "0.4.3"
-mediapy = "^1.1.9"
+invisible-watermark = "0.2.0"
+transformers = ">=4.41.2"
+accelerate = ">=0.31.0"
+safetensors = ">=0.4.3"
+mediapy = ">=1.1.9"
 ipywidgets = "7.7.1"
-controlnet-aux = "^0.0.6"
-mediapipe = "0.10.1"
-pytorch-lightning = "^2.0.9.post0"
-ultralytics = "^8.0.0"
-huggingface_hub = "^0.23.1"
-peft = "^0.11.1"
-torchsde = "^0.2.6"
-onnxruntime = "^1.18.0"
-insightface = "^0.7.3"
-opencv-contrib-python = "^4.8.0.76"
+controlnet-aux = "0.0.6"
+mediapipe = ">=0.10.1"
+pytorch-lightning = ">=2.0.9.post0"
+ultralytics = ">=8.0.0"
+huggingface_hub = ">=0.23.1"
+peft = ">=0.11.1"
+torchsde = ">=0.2.6"
+onnxruntime = ">=1.18.0"
+insightface = ">=0.7.3"
+opencv-contrib-python = ">=4.8.0.76"
 sentencepiece = "*"
 numpy = "<=1.26.4"
 lark = "*"
diff --git a/stablepy/diffusers_vanilla/model.py b/stablepy/diffusers_vanilla/model.py
index cd384f2..b83a576 100644
--- a/stablepy/diffusers_vanilla/model.py
+++ b/stablepy/diffusers_vanilla/model.py
@@ -50,6 +50,7 @@
     check_variant_file,
     cachebox,
     release_resources,
+    validate_and_update_params,
 )
 from .lora_loader import lora_mix_load, load_no_fused_lora
 from .inpainting_canvas import draw, make_inpaint_condition
@@ -233,6 +234,32 @@ def switch_pipe_class(
         verbose_info=False,
     ):
 
+        if hasattr(self.pipe, "set_pag_applied_layers"):
+            if not hasattr(self.pipe, "text_encoder_2"):
+                self.pipe = StableDiffusionPipeline(
+                    vae=self.pipe.vae,
+                    text_encoder=self.pipe.text_encoder,
+                    tokenizer=self.pipe.tokenizer,
+                    unet=self.pipe.unet,
+                    scheduler=self.pipe.scheduler,
+                    safety_checker=self.pipe.safety_checker,
+                    feature_extractor=self.pipe.feature_extractor,
+                    image_encoder=self.pipe.image_encoder,
+                    requires_safety_checker=self.pipe.config.requires_safety_checker,
+                )
+            else:
+                self.pipe = StableDiffusionXLPipeline(
+                    vae=self.pipe.vae,
+                    text_encoder=self.pipe.text_encoder,
+                    text_encoder_2=self.pipe.text_encoder_2,
+                    tokenizer=self.pipe.tokenizer,
+                    tokenizer_2=self.pipe.tokenizer_2,
+                    unet=self.pipe.unet,
+                    scheduler=self.pipe.scheduler,
+                    feature_extractor=self.pipe.feature_extractor,
+                    image_encoder=self.pipe.image_encoder,
+                )
+
         tk = "base"
         model_components = dict(
             vae=self.pipe.vae,
@@ -1284,6 +1311,7 @@ def __call__(
         image_previews: bool = False,
         xformers_memory_efficient_attention: bool = False,
         gui_active: bool = False,
+        **kwargs,
     ):
 
         """
@@ -2468,6 +2496,7 @@ def _execution_device(self):
             hires_params_config,
             hires_pipe,
             metadata,
+            kwargs,
         )
 
     def post_processing(
@@ -2653,6 +2682,7 @@ def start_work(
         hires_params_config,
         hires_pipe,
         metadata,
+        kwargs,
     ):
         for i in range(loop_generation):
             # number seed
@@ -2696,6 +2726,8 @@ def start_work(
                 pipe_params_config["output_type"] = "latent"
                 # self.pipe.__class__._execution_device = property(_execution_device)
 
+            validate_and_update_params(self.pipe.__class__, kwargs, pipe_params_config)
+
             try:
                 images = self.pipe(
                     **pipe_params_config,
@@ -2778,6 +2810,7 @@ def start_stream(
         hires_params_config,
         hires_pipe,
         metadata,
+        kwargs,
     ):
         for i in range(loop_generation):
             # number seed
@@ -2821,6 +2854,8 @@ def start_stream(
                 # self.pipe.__class__._execution_device = property(_execution_device)
                 self.metadata = metadata
 
+            validate_and_update_params(self.pipe.__class__, kwargs, pipe_params_config)
+
             try:
                 logger.debug("Start stream")
                 # self.stream_config(5)
diff --git a/stablepy/diffusers_vanilla/utils.py b/stablepy/diffusers_vanilla/utils.py
index dc3af49..3f2d5d9 100644
--- a/stablepy/diffusers_vanilla/utils.py
+++ b/stablepy/diffusers_vanilla/utils.py
@@ -14,6 +14,7 @@
 import hashlib
 from collections import OrderedDict
 import gc
+import inspect
 
 
 def generate_lora_tags(names_list, scales_list):
@@ -319,6 +320,29 @@ def latents_to_rgb(latents, latent_resize, vae_decoding, pipe):
     return resized_image
 
 
+def validate_and_update_params(cls, kwargs, config):
+    """
+    Validates kwargs against the parameters of a given class's `__call__` method
+    and updates the provided configuration dictionary.
+
+    Args:
+        cls: The class whose `__call__` method parameters are used for validation.
+        kwargs (dict): The keyword arguments to validate.
+        config (dict): The dictionary to update with valid parameters.
+    """
+    if kwargs:
+        # logger.debug(kwargs)
+        valid_params = inspect.signature(cls.__call__).parameters.keys()
+        for name_param, value_param in kwargs.items():
+            if name_param in valid_params:
+                config.update({name_param: value_param})
+                logger.debug(f"Parameter added: '{name_param}': {value_param}.")
+            else:
+                logger.error(
+                    f"The pipeline '{cls.__name__}' had an invalid parameter"
+                    f" removed: '{name_param}'.")
+
+
 def cachebox(max_cache_size=None, hash_func=hashlib.md5):
     """Alternative to lru_cache"""