diff --git a/comfy/text_encoders/hunyuan_video.py b/comfy/text_encoders/hunyuan_video.py
index 1d814aad..dbb259e5 100644
--- a/comfy/text_encoders/hunyuan_video.py
+++ b/comfy/text_encoders/hunyuan_video.py
@@ -42,7 +42,7 @@ class HunyuanVideoTokenizer:
         self.llama_template = """<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: 1. The main content and theme of the video.2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.4. background environment, light, style and atmosphere.5. camera angles, movements, and transitions used in the video:<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"""  # 95 tokens
         self.llama = LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=1)
 
-    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, image_embeds=None, **kwargs):
+    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, image_embeds=None, image_interleave=1, **kwargs):
         out = {}
         out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
 
@@ -56,7 +56,7 @@ class HunyuanVideoTokenizer:
             for i in range(len(r)):
                 if r[i][0] == 128257:
                     if image_embeds is not None and embed_count < image_embeds.shape[0]:
-                        r[i] = ({"type": "embedding", "data": image_embeds[embed_count], "original_type": "image"},) + r[i][1:]
+                        r[i] = ({"type": "embedding", "data": image_embeds[embed_count], "original_type": "image", "image_interleave": image_interleave},) + r[i][1:]
                         embed_count += 1
         out["llama"] = llama_text_tokens
         return out
@@ -92,10 +92,10 @@ class HunyuanVideoClipModel(torch.nn.Module):
         llama_out, llama_pooled, llama_extra_out = self.llama.encode_token_weights(token_weight_pairs_llama)
 
         template_end = 0
-        image_start = None
-        image_end = None
+        extra_template_end = 0
         extra_sizes = 0
         user_end = 9999999999999
+        images = []
 
         tok_pairs = token_weight_pairs_llama[0]
         for i, v in enumerate(tok_pairs):
@@ -112,22 +112,28 @@ class HunyuanVideoClipModel(torch.nn.Module):
                 else:
                     if elem.get("original_type") == "image":
                         elem_size = elem.get("data").shape[0]
-                        if image_start is None:
+                        if template_end > 0:
+                            if user_end == -1:
+                                extra_template_end += elem_size - 1
+                        else:
                             image_start = i + extra_sizes
                             image_end = i + elem_size + extra_sizes
-                        extra_sizes += elem_size - 1
+                            images.append((image_start, image_end, elem.get("image_interleave", 1)))
+                            extra_sizes += elem_size - 1
 
         if llama_out.shape[1] > (template_end + 2):
             if tok_pairs[template_end + 1][0] == 271:
                 template_end += 2
-        llama_output = llama_out[:, template_end + extra_sizes:user_end + extra_sizes]
-        llama_extra_out["attention_mask"] = llama_extra_out["attention_mask"][:, template_end + extra_sizes:user_end + extra_sizes]
+        llama_output = llama_out[:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end]
+        llama_extra_out["attention_mask"] = llama_extra_out["attention_mask"][:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end]
         if llama_extra_out["attention_mask"].sum() == torch.numel(llama_extra_out["attention_mask"]):
             llama_extra_out.pop("attention_mask")  # attention mask is useless if no masked elements
 
-        if image_start is not None:
-            image_output = llama_out[:, image_start: image_end]
-            llama_output = torch.cat([image_output[:, ::2], llama_output], dim=1)
+        if len(images) > 0:
+            out = []
+            for i in images:
+                out.append(llama_out[:, i[0]: i[1]: i[2]])
+            llama_output = torch.cat(out + [llama_output], dim=1)
 
         l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
         return llama_output, l_pooled, llama_extra_out
diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py
index 4f700bbe..56aef9b0 100644
--- a/comfy_extras/nodes_hunyuan.py
+++ b/comfy_extras/nodes_hunyuan.py
@@ -57,14 +57,15 @@ class TextEncodeHunyuanVideo_ImageToVideo:
             "clip": ("CLIP", ),
             "clip_vision_output": ("CLIP_VISION_OUTPUT", ),
             "prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            "image_interleave": ("INT", {"default": 2, "min": 1, "max": 512, "tooltip": "How much the image influences things vs the text prompt. Higher number means more influence from the text prompt."}),
             }}
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "encode"
 
     CATEGORY = "advanced/conditioning"
 
-    def encode(self, clip, clip_vision_output, prompt):
-        tokens = clip.tokenize(prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, image_embeds=clip_vision_output.mm_projected)
+    def encode(self, clip, clip_vision_output, prompt, image_interleave):
+        tokens = clip.tokenize(prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, image_embeds=clip_vision_output.mm_projected, image_interleave=image_interleave)
         return (clip.encode_from_tokens_scheduled(tokens), )