diff --git a/nodes.py b/nodes.py
index 42f7013..94d024a 100644
--- a/nodes.py
+++ b/nodes.py
@@ -542,18 +542,8 @@ class CogVideoXFunSampler:
                 "positive": ("CONDITIONING", ),
                 "negative": ("CONDITIONING", ),
                 "video_length": ("INT", {"default": 49, "min": 5, "max": 49, "step": 4}),
-                "base_resolution": (
-                    [ 
-                        256,
-                        320,
-                        384,
-                        448,
-                        512,
-                        768,
-                        960,
-                        1024,
-                    ], {"default": 768}
-                ),
+                "width": ("INT", {"default": 720, "min": 256, "max": 1024, "step": 4}),
+                "height": ("INT", {"default": 480, "min": 256, "max": 1024, "step": 4}),
                 "seed": ("INT", {"default": 43, "min": 0, "max": 0xffffffffffffffff}),
                 "steps": ("INT", {"default": 50, "min": 1, "max": 200, "step": 1}),
                 "cfg": ("FLOAT", {"default": 6.0, "min": 1.0, "max": 20.0, "step": 0.01}),
@@ -584,7 +574,7 @@ class CogVideoXFunSampler:
     FUNCTION = "process"
     CATEGORY = "CogVideoWrapper"
 
-    def process(self, pipeline,  positive, negative, video_length, base_resolution, seed, steps, cfg, scheduler, 
+    def process(self, pipeline,  positive, negative, video_length, width, height, seed, steps, cfg, scheduler, 
                 start_img=None, end_img=None, opt_empty_latent=None):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
@@ -597,8 +587,6 @@ class CogVideoXFunSampler:
 
         mm.soft_empty_cache()
 
-        aspect_ratio_sample_size = {key : [x / 512 * base_resolution for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
-
         if start_img is not None:
             start_img = [to_pil(_start_img) for _start_img in start_img] if start_img is not None else None
             end_img = [to_pil(_end_img) for _end_img in end_img] if end_img is not None else None
@@ -607,9 +595,6 @@ class CogVideoXFunSampler:
         else:
             original_width = opt_empty_latent["samples"][0].shape[-1] * 8
             original_height = opt_empty_latent["samples"][0].shape[-2] * 8
-        closest_size, closest_ratio = get_closest_ratio(original_height, original_width, ratios=aspect_ratio_sample_size)
-        height, width = [int(x / 16) * 16 for x in closest_size]
-        print(f"Closest size: {width}x{height}")
         
         # Load Sampler
         if scheduler == "DPM++":
@@ -668,18 +653,8 @@ class CogVideoXFunVid2VidSampler:
                 "positive": ("CONDITIONING", ),
                 "negative": ("CONDITIONING", ),
                 "video_length": ("INT", {"default": 49, "min": 5, "max": 49, "step": 4}),
-                "base_resolution": (
-                    [ 
-                        256,
-                        320,
-                        384,
-                        448,
-                        512,
-                        768,
-                        960,
-                        1024,
-                    ], {"default": 768}
-                ),
+                "width": ("INT", {"default": 720, "min": 256, "max": 1024, "step": 4}),
+                "height": ("INT", {"default": 480, "min": 256, "max": 1024, "step": 4}),
                 "seed": ("INT", {"default": 43, "min": 0, "max": 0xffffffffffffffff}),
                 "steps": ("INT", {"default": 50, "min": 1, "max": 200, "step": 1}),
                 "cfg": ("FLOAT", {"default": 6.0, "min": 1.0, "max": 20.0, "step": 0.01}),
@@ -707,7 +682,7 @@ class CogVideoXFunVid2VidSampler:
     FUNCTION = "process"
     CATEGORY = "CogVideoWrapper"
 
-    def process(self, pipeline, positive, negative, video_length, base_resolution, seed, steps, cfg, denoise_strength, scheduler, validation_video):
+    def process(self, pipeline, positive, negative, video_length, width, height, seed, steps, cfg, denoise_strength, scheduler, validation_video):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
         pipe = pipeline["pipe"]
@@ -718,11 +693,8 @@ class CogVideoXFunVid2VidSampler:
         mm.soft_empty_cache()
 
         # Count most suitable height and width
-        aspect_ratio_sample_size    = {key : [x / 512 * base_resolution for x in ASPECT_RATIO_512[key]] for key in ASPECT_RATIO_512.keys()}
         validation_video = np.array(validation_video.cpu().numpy() * 255, np.uint8)
         original_width, original_height = Image.fromarray(validation_video[0]).size
-        closest_size, closest_ratio = get_closest_ratio(original_height, original_width, ratios=aspect_ratio_sample_size)
-        height, width = [int(x / 16) * 16 for x in closest_size]
         
         base_path = pipeline["base_path"]