diff --git a/model_loading.py b/model_loading.py
index 1123a4f..d8ad72f 100644
--- a/model_loading.py
+++ b/model_loading.py
@@ -147,6 +147,7 @@ class DownloadAndLoadCogVideoModel:
                         "alibaba-pai/CogVideoX-Fun-V1.1-2b-Pose",
                         "alibaba-pai/CogVideoX-Fun-V1.1-5b-Pose",
                         "alibaba-pai/CogVideoX-Fun-V1.1-5b-Control",
+                        "alibaba-pai/CogVideoX-Fun-V1.5-5b-InP",
                         "feizhengcong/CogvideoX-Interpolation",
                         "NimVideo/cogvideox-2b-img2vid"
                     ],
@@ -215,7 +216,7 @@ class DownloadAndLoadCogVideoModel:
         download_path = folder_paths.get_folder_paths("CogVideo")[0]
         
         if "Fun" in model:
-            if not "1.1" in model:
+            if not "1.1" and not "1.5" in model:
                 repo_id = "kijai/CogVideoX-Fun-pruned"
                 if "2b" in model:
                     base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", "CogVideoX-Fun-2b-InP") # location of the official model
@@ -225,7 +226,7 @@ class DownloadAndLoadCogVideoModel:
                     base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", "CogVideoX-Fun-5b-InP") # location of the official model
                     if not os.path.exists(base_path):
                         base_path = os.path.join(download_path, "CogVideoX-Fun-5b-InP")
-            elif "1.1" in model:
+            else:
                 repo_id = model
                 base_path = os.path.join(folder_paths.models_dir, "CogVideoX_Fun", (model.split("/")[-1])) # location of the official model
                 if not os.path.exists(base_path):
@@ -278,7 +279,7 @@ class DownloadAndLoadCogVideoModel:
         transformer = CogVideoXTransformer3DModel.from_pretrained(base_path, subfolder=subfolder, attention_mode=attention_mode)
         transformer = transformer.to(dtype).to(transformer_load_device)
 
-        if "1.5" in model:
+        if "1.5" in model and not "fun" in model:
             transformer.config.sample_height = 300
             transformer.config.sample_width = 300
 
diff --git a/nodes.py b/nodes.py
index 3819b54..08997b0 100644
--- a/nodes.py
+++ b/nodes.py
@@ -360,8 +360,8 @@ class CogVideoImageEncodeFunInP:
         masked_image_latents = masked_image_latents.permute(0, 2, 1, 3, 4)  # B, T, C, H, W
 
         mask = torch.zeros_like(masked_image_latents[:, :, :1, :, :])
-        if end_image is not None:
-            mask[:, -1, :, :, :] = 0
+        #if end_image is not None:
+        #    mask[:, -1, :, :, :] = 0
         mask[:, 0, :, :, :] = vae_scaling_factor
 
         final_latents = masked_image_latents * vae_scaling_factor
@@ -623,7 +623,7 @@ class CogVideoSampler:
             image_conds = image_cond_latents["samples"]
             image_cond_start_percent = image_cond_latents.get("start_percent", 0.0)
             image_cond_end_percent = image_cond_latents.get("end_percent", 1.0)
-            if "1.5" in model_name or "1_5" in model_name:
+            if ("1.5" in model_name or "1_5" in model_name) and not "fun" in model_name.lower():
                 image_conds = image_conds / 0.7 # needed for 1.5 models
         else:
             if not "fun" in model_name.lower():
diff --git a/pipeline_cogvideox.py b/pipeline_cogvideox.py
index 8b3a71d..0db9671 100644
--- a/pipeline_cogvideox.py
+++ b/pipeline_cogvideox.py
@@ -471,6 +471,8 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
 
         # 5.5.
         if image_cond_latents is not None:
+            image_cond_frame_count = image_cond_latents.size(1)
+            patch_size_t = self.transformer.config.patch_size_t
             if image_cond_latents.shape[1] == 2:
                 logger.info("More than one image conditioning frame received, interpolating")
                 padding_shape = (
@@ -482,8 +484,8 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
                 )
                 latent_padding = torch.zeros(padding_shape, device=device, dtype=self.vae_dtype)
                 image_cond_latents = torch.cat([image_cond_latents[:, 0, :, :, :].unsqueeze(1), latent_padding, image_cond_latents[:, -1, :, :, :].unsqueeze(1)], dim=1)
-                if self.transformer.config.patch_size_t is not None:
-                    first_frame = image_cond_latents[:, : image_cond_latents.size(1) % self.transformer.config.patch_size_t, ...]
+                if patch_size_t:
+                    first_frame = image_cond_latents[:, : image_cond_frame_count % patch_size_t, ...]
                     image_cond_latents = torch.cat([first_frame, image_cond_latents], dim=1)
 
                 logger.info(f"image cond latents shape: {image_cond_latents.shape}")
@@ -500,13 +502,19 @@ class CogVideoXPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin):
                     latent_padding = torch.zeros(padding_shape, device=device, dtype=self.vae_dtype)
                     image_cond_latents = torch.cat([image_cond_latents, latent_padding], dim=1)
                     # Select the first frame along the second dimension
-                    if self.transformer.config.patch_size_t is not None:
-                        first_frame = image_cond_latents[:, : image_cond_latents.size(1) % self.transformer.config.patch_size_t, ...]
+                    if patch_size_t:
+                        first_frame = image_cond_latents[:, : image_cond_frame_count % patch_size_t, ...]
                         image_cond_latents = torch.cat([first_frame, image_cond_latents], dim=1)
                 else:
                     image_cond_latents = image_cond_latents.repeat(1, latents.shape[1], 1, 1, 1)
             else:
                 logger.info(f"Received {image_cond_latents.shape[1]} image conditioning frames")
+                if fun_mask is not None and patch_size_t:
+                    logger.info(f"1.5 model received {fun_mask.shape[1]} masks")
+                    first_frame = image_cond_latents[:, : image_cond_frame_count % patch_size_t, ...]
+                    image_cond_latents = torch.cat([first_frame, image_cond_latents], dim=1)
+                    fun_mask_first_frame = fun_mask[:, : image_cond_frame_count % patch_size_t, ...]
+                    fun_mask = torch.cat([fun_mask_first_frame, fun_mask], dim=1)
             image_cond_latents = image_cond_latents.to(self.vae_dtype)
 
         # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline