ApplyRifleXRoPE_WanVideo

experimental

__init__.py

@@ -182,6 +182,7 @@ NODE_CONFIG = {
     "VAELoaderKJ": {"class": VAELoaderKJ, "name": "VAELoader KJ"},
     "ScheduledCFGGuidance": {"class": ScheduledCFGGuidance, "name": "Scheduled CFG Guidance"},
     "ApplyRifleXRoPE_HunuyanVideo": {"class": ApplyRifleXRoPE_HunuyanVideo, "name": "Apply RifleXRoPE HunuyanVideo"},
+    "ApplyRifleXRoPE_WanVideo": {"class": ApplyRifleXRoPE_WanVideo, "name": "Apply RifleXRoPE WanVideo"},
 
     #instance diffusion
     "CreateInstanceDiffusionTracking": {"class": CreateInstanceDiffusionTracking},

nodes/nodes.py

@@ -2494,6 +2494,42 @@ cfg input can be a list of floats matching step count, or a single float for all
         return (guider, )
     
 
+class ApplyRifleXRoPE_WanVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "latent": ("LATENT", {"tooltip": "Only used to get the latent count"}),
+                "k": ("INT", {"default": 4, "min": 1, "max": 100, "step": 1, "tooltip": "Index of intrinsic frequency"}),
+            } 
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+    CATEGORY = "KJNodes/experimental"
+    EXPERIMENTAL = True
+    DESCRIPTION = "Extends the potential frame count of HunyuanVideo using this method: https://github.com/thu-ml/RIFLEx"
+
+    def patch(self, model, latent, k):
+        model_class = model.model.diffusion_model
+        
+        model_clone = model.clone()
+        num_frames = latent["samples"].shape[2]
+        d = model_class.dim // model_class.num_heads
+
+        rope_embedder = EmbedND_RifleX(
+            d, 
+            10000.0, 
+            [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)],
+            num_frames,
+            k
+            )
+        
+        model_clone.add_object_patch(f"diffusion_model.rope_embedder", rope_embedder)
+                    
+        return (model_clone, )
+    
 class ApplyRifleXRoPE_HunuyanVideo:
     @classmethod
     def INPUT_TYPES(s):
@@ -2513,7 +2549,6 @@ class ApplyRifleXRoPE_HunuyanVideo:
 
     def patch(self, model, latent, k):
         model_class = model.model.diffusion_model
-        print(model_class.pe_embedder)
         
         model_clone = model.clone()
         num_frames = latent["samples"].shape[2]
@@ -2549,6 +2584,23 @@ def rope_riflex(pos, dim, theta, L_test, k):
     out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
     return out.to(dtype=torch.float32, device=pos.device)
 
+class EmbedND_RifleX(nn.Module):
+    def __init__(self, dim, theta, axes_dim, num_frames, k):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+        self.num_frames = num_frames
+        self.k = k
+
+    def forward(self, ids):
+        n_axes = ids.shape[-1]
+        emb = torch.cat(
+            [rope_riflex(ids[..., i], self.axes_dim[i], self.theta, self.num_frames, self.k) for i in range(n_axes)],
+            dim=-3,
+        )
+        return emb.unsqueeze(1)
+    
 class EmbedND_RifleX(nn.Module):
     def __init__(self, dim, theta, axes_dim, num_frames, k):
         super().__init__()