diff --git a/.gitignore b/.gitignore
index 68bc17f..2bc1403 100644
--- a/.gitignore
+++ b/.gitignore
@@ -158,3 +158,4 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+runpod.toml
diff --git a/builder/requirements.txt b/builder/requirements.txt
index ab56d0e..e6b6b0b 100644
--- a/builder/requirements.txt
+++ b/builder/requirements.txt
@@ -1,16 +1,15 @@
 # Required Python packages get listed here, one per line.
 # Reccomended to lock the version number to avoid unexpected changes.
 
-runpod==1.2.1
-# git+https://github.com/runpod/runpod-python.git
+runpod==1.3.0
 
-diffusers>=0.18.3
-transformers==4.31.0
-accelerate==0.21.0
+diffusers==0.21.4
+transformers==4.34.0
+accelerate==0.23.0
 safetensors==0.3.2
 scipy == 1.11.2
 numpy>=1.17
 PyWavelets>=1.1.1
 opencv-python>=4.1.0.25
 invisible-watermark==0.2.0
-xformers==0.0.20
+xformers==0.0.22
diff --git a/src/rp_handler.py b/src/rp_handler.py
index 54b848e..8d2c2fe 100644
--- a/src/rp_handler.py
+++ b/src/rp_handler.py
@@ -25,32 +25,39 @@
 from rp_schemas import INPUT_SCHEMA
 
 
-# -------------------------------- Load Models ------------------------------- #
-def load_base():
-    base_pipe = StableDiffusionXLPipeline.from_pretrained(
-        "stabilityai/stable-diffusion-xl-base-1.0",
-        torch_dtype=torch.float16, variant="fp16", use_safetensors=True, add_watermarker=False
-    ).to("cuda", silence_dtype_warnings=True)
-    base_pipe.enable_xformers_memory_efficient_attention()
-    return base_pipe
-
-
-def load_refiner():
-    refiner_pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
-        "stabilityai/stable-diffusion-xl-refiner-1.0",
-        torch_dtype=torch.float16, variant="fp16", use_safetensors=True, add_watermarker=False
-    ).to("cuda", silence_dtype_warnings=True)
-    refiner_pipe.enable_xformers_memory_efficient_attention()
-    return refiner_pipe
-
-
-with concurrent.futures.ThreadPoolExecutor() as executor:
-    future_base = executor.submit(load_base)
-    future_refiner = executor.submit(load_refiner)
-
-    base = future_base.result()
-    refiner = future_refiner.result()
-
+# ------------------------------- Model Handler ------------------------------ #
+class ModelHandler:
+    def __init__(self):
+        self.base = None
+        self.refiner = None
+        self.load_models()
+
+    def load_base(self):
+        base_pipe = StableDiffusionXLPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-base-1.0",
+            torch_dtype=torch.float16, variant="fp16", use_safetensors=True, add_watermarker=False
+        ).to("cuda", silence_dtype_warnings=True)
+        base_pipe.enable_xformers_memory_efficient_attention()
+        return base_pipe
+
+
+    def load_refiner(self):
+        refiner_pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
+            "stabilityai/stable-diffusion-xl-refiner-1.0",
+            torch_dtype=torch.float16, variant="fp16", use_safetensors=True, add_watermarker=False
+        ).to("cuda", silence_dtype_warnings=True)
+        refiner_pipe.enable_xformers_memory_efficient_attention()
+        return refiner_pipe
+
+    def load_models(self):
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            future_base = executor.submit(self.load_base)
+            future_refiner = executor.submit(self.load_refiner)
+
+            self.base = future_base.result()
+            self.refiner = future_refiner.result()
+
+MODELS = ModelHandler()
 
 # ---------------------------------- Helper ---------------------------------- #
 def _save_and_upload_images(images, job_id):
@@ -103,11 +110,11 @@ def generate_image(job):
 
     generator = torch.Generator("cuda").manual_seed(job_input['seed'])
 
-    base.scheduler = make_scheduler(job_input['scheduler'], base.scheduler.config)
+    MODELS.base.scheduler = make_scheduler(job_input['scheduler'], MODELS.base.scheduler.config)
 
     if starting_image:  # If image_url is provided, run only the refiner pipeline
         init_image = load_image(starting_image).convert("RGB")
-        output = refiner(
+        output = MODELS.refiner(
             prompt=job_input['prompt'],
             num_inference_steps=job_input['refiner_inference_steps'],
             strength=job_input['strength'],
@@ -116,7 +123,7 @@ def generate_image(job):
         ).images
     else:
         # Generate latent image using pipe
-        image = base(
+        image = MODELS.base(
             prompt=job_input['prompt'],
             negative_prompt=job_input['negative_prompt'],
             height=job_input['height'],
@@ -129,7 +136,7 @@ def generate_image(job):
         ).images
 
         # Refine the image using refiner with refiner_inference_steps
-        output = refiner(
+        output = MODELS.refiner(
             prompt=job_input['prompt'],
             num_inference_steps=job_input['refiner_inference_steps'],
             strength=job_input['strength'],