cleanup

nodes.py

@@ -3775,63 +3775,6 @@ class WanVideoEncode:
  
         return ({"samples": latents, "noise_mask": mask},)
 
-class WanVideoLatentReScale:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-                    "samples": ("LATENT",),
-                    "direction": (["comfy_to_wrapper", "wrapper_to_comfy"], {"tooltip": "Direction to rescale latents, from comfy to wrapper or vice versa"}),
-                }
-                }
-
-    RETURN_TYPES = ("LATENT",)
-    RETURN_NAMES = ("samples",)
-    FUNCTION = "encode"
-    CATEGORY = "WanVideoWrapper"
-    DESCRIPTION = "Rescale latents to match the expected range for encoding or decoding. Can be used to "
-
-    def encode(self, samples, direction):
-        samples = samples.copy()
-        latents = samples["samples"]
-
-        if latents.shape[1] == 48:
-            mean = [
-                    -0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557,
-                    -0.1382, 0.0542, 0.2813, 0.0891, 0.1570, -0.0098, 0.0375, -0.1825,
-                    -0.2246, -0.1207, -0.0698, 0.5109, 0.2665, -0.2108, -0.2158, 0.2502,
-                    -0.2055, -0.0322, 0.1109, 0.1567, -0.0729, 0.0899, -0.2799, -0.1230,
-                    -0.0313, -0.1649, 0.0117, 0.0723, -0.2839, -0.2083, -0.0520, 0.3748,
-                    0.0152, 0.1957, 0.1433, -0.2944, 0.3573, -0.0548, -0.1681, -0.0667,
-                ]
-            std = [
-                    0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.4990, 0.4818, 0.5013,
-                    0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978,
-                    0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659,
-                    0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093,
-                    0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887,
-                    0.3971, 1.0600, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744
-                ]
-        else:
-            mean = [
-                -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508,
-                0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921
-            ]
-            std = [
-                2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743,
-                3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160
-            ]
-        mean = torch.tensor(mean).view(1, latents.shape[1], 1, 1, 1)
-        std = torch.tensor(std).view(1, latents.shape[1], 1, 1, 1)
-        inv_std = (1.0 / std).view(1, latents.shape[1], 1, 1, 1)
-        if direction == "comfy_to_wrapper":
-            latents = (latents - mean.to(latents)) * inv_std.to(latents)
-        elif direction == "wrapper_to_comfy":
-            latents = latents / inv_std.to(latents) + mean.to(latents)
-
-        samples["samples"] = latents
-
-        return (samples,)
-
 NODE_CLASS_MAPPINGS = {
     "WanVideoSampler": WanVideoSampler,
     "WanVideoDecode": WanVideoDecode,
@@ -3860,7 +3803,6 @@ NODE_CLASS_MAPPINGS = {
     "WanVideoBlockList": WanVideoBlockList,
     "WanVideoTextEncodeCached": WanVideoTextEncodeCached,
     "WanVideoAddExtraLatent": WanVideoAddExtraLatent,
-    "WanVideoLatentReScale": WanVideoLatentReScale,
     "WanVideoScheduler": WanVideoScheduler,
     "WanVideoAddStandInLatent": WanVideoAddStandInLatent,
     "WanVideoAddControlEmbeds": WanVideoAddControlEmbeds,
@@ -3895,7 +3837,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "WanVideoBlockList": "WanVideo Block List",
     "WanVideoTextEncodeCached": "WanVideo TextEncode Cached",
     "WanVideoAddExtraLatent": "WanVideo Add Extra Latent",
-    "WanVideoLatentReScale": "WanVideo Latent ReScale",
     "WanVideoAddStandInLatent": "WanVideo Add StandIn Latent",
     "WanVideoAddControlEmbeds": "WanVideo Add Control Embeds",
     "WanVideoRoPEFunction": "WanVideo RoPE Function"

nodes_utility.py

@@ -267,7 +267,7 @@ class WanVideoLatentReScale:
     RETURN_NAMES = ("samples",)
     FUNCTION = "encode"
     CATEGORY = "WanVideoWrapper"
-    DESCRIPTION = "Rescale latents to match the expected range for encoding or decoding. Can be used to "
+    DESCRIPTION = "Rescale latents to match the expected range for encoding or decoding between native ComfyUI VAE and the WanVideoWrapper VAE."
 
     def encode(self, samples, direction):
         samples = samples.copy()