diff --git a/hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py b/hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py
index 389f05d..dc5a693 100644
--- a/hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py
+++ b/hyvideo/diffusion/pipelines/pipeline_hunyuan_video.py
@@ -40,7 +40,7 @@ EXAMPLE_DOC_STRING = """"""
 from ...modules.posemb_layers import get_nd_rotary_pos_embed
 from ....enhance_a_video.globals import enable_enhance, disable_enhance, set_enhance_weight
 
-def get_rotary_pos_embed(transformer, latent_video_length, height, width):
+def get_rotary_pos_embed(transformer, latent_video_length, height, width, k=0):
         target_ndim = 3
         ndim = 5 - 2
         rope_theta = 225
@@ -85,6 +85,8 @@ def get_rotary_pos_embed(transformer, latent_video_length, height, width):
             theta=rope_theta,
             use_real=True,
             theta_rescale_factor=1,
+            num_frames=latent_video_length,
+            k=k,
         )
         return freqs_cos, freqs_sin
 def retrieve_timesteps(
@@ -424,6 +426,7 @@ class HunyuanVideoPipeline(DiffusionPipeline):
         feta_args: Optional[Dict] = None,
         leapfusion_img2vid: Optional[bool] = False,
         image_cond_latents: Optional[torch.Tensor] = None,
+        riflex_freq_index: Optional[int] = None,
         **kwargs,
     ):
         r"""
@@ -611,7 +614,7 @@ class HunyuanVideoPipeline(DiffusionPipeline):
         else:
             # rotary embeddings
             freqs_cos, freqs_sin = get_rotary_pos_embed(
-                self.transformer, latent_video_length, height, width
+                self.transformer, latent_video_length, height, width, k=riflex_freq_index
             )
         if not self.transformer.upcast_rope:
             freqs_cos = freqs_cos.to(self.base_dtype).to(device)
diff --git a/hyvideo/modules/posemb_layers.py b/hyvideo/modules/posemb_layers.py
index 2ac4ce7..0fbbada 100644
--- a/hyvideo/modules/posemb_layers.py
+++ b/hyvideo/modules/posemb_layers.py
@@ -113,6 +113,8 @@ def get_nd_rotary_pos_embed(
     use_real=False,
     theta_rescale_factor: Union[float, List[float]] = 1.0,
     interpolation_factor: Union[float, List[float]] = 1.0,
+    num_frames: int = 129,
+    k: int = 0,
 ):
     """
     This is a n-d version of precompute_freqs_cis, which is a RoPE for tokens with n-d structure.
@@ -163,6 +165,8 @@ def get_nd_rotary_pos_embed(
             use_real=use_real,
             theta_rescale_factor=theta_rescale_factor[i],
             interpolation_factor=interpolation_factor[i],
+            L_test=num_frames,
+            k=k,
         )  # 2 x [WHD, rope_dim_list[i]]
         embs.append(emb)
 
@@ -182,6 +186,8 @@ def get_1d_rotary_pos_embed(
     use_real: bool = False,
     theta_rescale_factor: float = 1.0,
     interpolation_factor: float = 1.0,
+    L_test: int = 100,
+    k: int = 0,
 ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
     """
     Precompute the frequency tensor for complex exponential (cis) with given dimensions.
@@ -215,6 +221,12 @@ def get_1d_rotary_pos_embed(
         theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)
     )  # [D/2]
     # assert interpolation_factor == 1.0, f"interpolation_factor: {interpolation_factor}"
+
+    #RIFLEx https://github.com/thu-ml/RIFLEx
+    if k > 0:
+        freqs[k-1] = 0.9 * 2 * torch.pi / L_test
+
+
     freqs = torch.outer(pos * interpolation_factor, freqs)  # [S, D/2]
     if use_real:
         freqs_cos = freqs.cos().repeat_interleave(2, dim=1)  # [S, D]
diff --git a/nodes.py b/nodes.py
index 24eb0fa..37f94d9 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1155,6 +1155,7 @@ class HyVideoSampler:
                     {
                         "default": 'FlowMatchDiscreteScheduler'
                     }),
+                "riflex_freq_index": ("INT", {"default": 0, "min": 0, "max": 1000, "step": 1, "tooltip": "Frequency index for RIFLEX, disabled when 0, default 4. Allows for new frames to be generated after 129 without looping"}),
             }
         }
 
@@ -1164,7 +1165,8 @@ class HyVideoSampler:
     CATEGORY = "HunyuanVideoWrapper"
 
     def process(self, model, hyvid_embeds, flow_shift, steps, embedded_guidance_scale, seed, width, height, num_frames, 
-                samples=None, denoise_strength=1.0, force_offload=True, stg_args=None, context_options=None, feta_args=None, teacache_args=None, scheduler=None, image_cond_latents=None):
+                samples=None, denoise_strength=1.0, force_offload=True, stg_args=None, context_options=None, feta_args=None, 
+                teacache_args=None, scheduler=None, image_cond_latents=None, riflex_freq_index=0):
         model = model.model
 
         device = mm.get_torch_device()
@@ -1306,6 +1308,7 @@ class HyVideoSampler:
             feta_args=feta_args,
             leapfusion_img2vid = leapfusion_img2vid,
             image_cond_latents = image_cond_latents["samples"] * VAE_SCALING_FACTOR if image_cond_latents is not None else None,
+            riflex_freq_index = riflex_freq_index
         )
 
         print_memory(device)