diff --git a/nodes/ksamplers/FL_KsamplerPlus.py b/nodes/ksamplers/FL_KsamplerPlus.py
index de7c9c1..b164126 100644
--- a/nodes/ksamplers/FL_KsamplerPlus.py
+++ b/nodes/ksamplers/FL_KsamplerPlus.py
@@ -131,7 +131,18 @@ class FL_KsamplerPlus:
             if use_sliced_conditioning:
                 batch_size = 1
 
-            b, c, h, w = latent_image["samples"].shape
+            # Handle variable tensor dimensions (4D or 5D)
+            latent_shape = latent_image["samples"].shape
+            if len(latent_shape) == 5:
+                # 5D tensor: [batch, frames, channels, height, width]
+                b, f, c, h, w = latent_shape
+                logging.info(f"Processing 5D latent tensor with shape: {latent_shape}")
+            elif len(latent_shape) == 4:
+                # 4D tensor: [batch, channels, height, width]
+                b, c, h, w = latent_shape
+            else:
+                raise ValueError(f"Unexpected latent tensor shape: {latent_shape}. Expected 4D or 5D tensor.")
+
             base_slice_height = h // y_slices
             base_slice_width = w // x_slices
             overlap_height = int(base_slice_height * overlap)
@@ -145,7 +156,11 @@ class FL_KsamplerPlus:
                 x_start = max(0, x * base_slice_width - overlap_width)
                 x_end = min(w, (x + 1) * base_slice_width + overlap_width)
 
-                section = latent_image["samples"][:, :, y_start:y_end, x_start:x_end].to(device=device)
+                # Handle both 4D and 5D tensor slicing
+                if len(latent_shape) == 5:
+                    section = latent_image["samples"][:, :, :, y_start:y_end, x_start:x_end].to(device=device)
+                else:
+                    section = latent_image["samples"][:, :, y_start:y_end, x_start:x_end].to(device=device)
 
                 if use_sliced_conditioning:
                     region = (x_start * 8, y_start * 8, x_end * 8, y_end * 8)
@@ -188,8 +203,12 @@ class FL_KsamplerPlus:
 
                 # Initialize samples tensor if it hasn't been initialized yet
                 if samples is None:
-                    processed_channels = processed_sections[0].shape[1]
-                    samples = torch.zeros((b, processed_channels, h, w), device=device)
+                    if len(latent_shape) == 5:
+                        processed_channels = processed_sections[0].shape[2]
+                        samples = torch.zeros((b, f, processed_channels, h, w), device=device)
+                    else:
+                        processed_channels = processed_sections[0].shape[1]
+                        samples = torch.zeros((b, processed_channels, h, w), device=device)
 
                 for (_, y_start, y_end, x_start, x_end, _, _), processed_section in zip(batch_sections,
                                                                                         processed_sections):
@@ -207,10 +226,17 @@ class FL_KsamplerPlus:
                     processed_section = processed_section.to(device=device)
                     blend_mask = blend_mask.to(device=device)
 
-                    samples[:, :, y_start:y_end, x_start:x_end] = (
-                            samples[:, :, y_start:y_end, x_start:x_end] * (1 - blend_mask) +
-                            processed_section * blend_mask
-                    )
+                    # Handle both 4D and 5D tensor blending
+                    if len(latent_shape) == 5:
+                        samples[:, :, :, y_start:y_end, x_start:x_end] = (
+                                samples[:, :, :, y_start:y_end, x_start:x_end] * (1 - blend_mask) +
+                                processed_section * blend_mask
+                        )
+                    else:
+                        samples[:, :, y_start:y_end, x_start:x_end] = (
+                                samples[:, :, y_start:y_end, x_start:x_end] * (1 - blend_mask) +
+                                processed_section * blend_mask
+                        )
 
                 if device.type == 'cuda':
                     torch.cuda.empty_cache()
diff --git a/pyproject.toml b/pyproject.toml
index 255b285..c85ca3e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "comfyui_fill-nodes"
 description = "Fill-Nodes is a versatile collection of custom nodes for ComfyUI that extends functionality across multiple domains. Features include advanced image processing (pixelation, slicing, masking), visual effects generation (glitch, halftone, pixel art), comprehensive file handling (PDF creation/extraction, Google Drive integration), AI model interfaces (GPT, DALL-E, Hugging Face), utility nodes for workflow enhancement, and specialized tools for video processing, captioning, and batch operations. The pack provides both practical workflow solutions and creative tools within a unified node collection."
-version = "1.9.5"
+version = "1.9.6"
 license = "LICENSE"
 dependencies = ["diffusers", "librosa", "sounddevice", "glitch_this", "PyOpenGL", "glfw", "scipy>=1.13.1", "requests", "aiohttp", "moviepy", "matplotlib", "reportlab", "openai", "PyPDF2", "pdf2image", "PyMuPDF", "reportlab", "PyPDF2", "ollama", "kornia", "opencv-python", "gdown", "open_clip_torch", "google-genai"]