diff --git a/comfy/sd.py b/comfy/sd.py
index e85f2ed7..2db00fa4 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -341,8 +341,9 @@ class VAE:
                 self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8)
-                self.upscale_index_formula = (lambda a: max(0, a * 6), 8, 8)
+                self.upscale_index_formula = (6, 8, 8)
                 self.downscale_ratio = (lambda a: max(0, math.floor((a + 5) / 6)), 8, 8)
+                self.downscale_index_formula = (6, 8, 8)
                 self.working_dtypes = [torch.float16, torch.float32]
             elif "decoder.up_blocks.0.res_blocks.0.conv1.conv.weight" in sd: #lightricks ltxv
                 tensor_conv1 = sd["decoder.up_blocks.0.res_blocks.0.conv1.conv.weight"]
@@ -357,16 +358,18 @@ class VAE:
                 self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.memory_used_encode = lambda shape, dtype: (70 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                 self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 32, 32)
-                self.upscale_index_formula = (lambda a: max(0, a * 8), 32, 32)
+                self.upscale_index_formula = (8, 32, 32)
                 self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 32, 32)
+                self.downscale_index_formula = (8, 32, 32)
                 self.working_dtypes = [torch.bfloat16, torch.float32]
             elif "decoder.conv_in.conv.weight" in sd:
                 ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
                 ddconfig["conv3d"] = True
                 ddconfig["time_compress"] = 4
                 self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
-                self.upscale_index_formula = (lambda a: max(0, a * 4), 8, 8)
+                self.upscale_index_formula = (4, 8, 8)
                 self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
+                self.downscale_index_formula = (4, 8, 8)
                 self.latent_dim = 3
                 self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1]
                 self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
@@ -453,7 +456,7 @@ class VAE:
 
     def encode_tiled_3d(self, samples, tile_t=9999, tile_x=512, tile_y=512, overlap=(1, 64, 64)):
         encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
-        return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, output_device=self.output_device)
+        return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device)
 
     def decode(self, samples_in):
         pixel_samples = None
@@ -544,7 +547,7 @@ class VAE:
 
         return samples
 
-    def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overlap=None):
+    def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
         dims = self.latent_dim
         pixel_samples = pixel_samples.movedim(-1, 1)
@@ -568,6 +571,12 @@ class VAE:
         elif dims == 2:
             samples = self.encode_tiled_(pixel_samples, **args)
         elif dims == 3:
+            if overlap_t is None:
+                args["overlap"] = (1, overlap, overlap)
+            else:
+                args["overlap"] = (overlap_t, overlap, overlap)
+            if tile_t is not None:
+                args["tile_t"] = tile_t
             samples = self.encode_tiled_3d(pixel_samples, **args)
 
         return samples
diff --git a/nodes.py b/nodes.py
index d6777df4..e95abc40 100644
--- a/nodes.py
+++ b/nodes.py
@@ -337,15 +337,17 @@ class VAEEncodeTiled:
         return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ),
                              "tile_size": ("INT", {"default": 512, "min": 64, "max": 4096, "step": 64}),
                              "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
+                             "temporal_size": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to encode at a time."}),
+                             "temporal_overlap": ("INT", {"default": 8, "min": 4, "max": 4096, "step": 4, "tooltip": "Only used for video VAEs: Amount of frames to overlap."}),
                             }}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "encode"
 
     CATEGORY = "_for_testing"
 
-    def encode(self, vae, pixels, tile_size, overlap):
-        t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, overlap=overlap)
-        return ({"samples":t}, )
+    def encode(self, vae, pixels, tile_size, overlap, temporal_size=64, temporal_overlap=8):
+        t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap)
+        return ({"samples": t}, )
 
 class VAEEncodeForInpaint:
     @classmethod