diff --git a/comfy/ldm/hunyuan_video/model.py b/comfy/ldm/hunyuan_video/model.py
index ca289c5bd..7732182a4 100644
--- a/comfy/ldm/hunyuan_video/model.py
+++ b/comfy/ldm/hunyuan_video/model.py
@@ -41,6 +41,7 @@ class HunyuanVideoParams:
     qkv_bias: bool
     guidance_embed: bool
     byt5: bool
+    meanflow: bool
 
 
 class SelfAttentionRef(nn.Module):
@@ -256,6 +257,11 @@ class HunyuanVideo(nn.Module):
         else:
             self.byt5_in = None
 
+        if params.meanflow:
+            self.time_r_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
+        else:
+            self.time_r_in = None
+
         if final_layer:
             self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations)
 
@@ -282,6 +288,14 @@ class HunyuanVideo(nn.Module):
         img = self.img_in(img)
         vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype))
 
+        if self.time_r_in is not None:
+            w = torch.where(transformer_options['sigmas'][0] == transformer_options['sample_sigmas'])[0]  # This most likely could be improved
+            if len(w) > 0:
+                timesteps_r = transformer_options['sample_sigmas'][w[0] + 1]
+                timesteps_r = timesteps_r.unsqueeze(0).to(device=timesteps.device, dtype=timesteps.dtype)
+                vec_r = self.time_r_in(timestep_embedding(timesteps_r, 256, time_factor=1000.0).to(img.dtype))
+                vec = (vec + vec_r) / 2
+
         if ref_latent is not None:
             ref_latent_ids = self.img_ids(ref_latent)
             ref_latent = self.img_in(ref_latent)
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index dbcbe5f5a..fe983cede 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -142,12 +142,20 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["in_channels"] = in_w.shape[1] #SkyReels img2video has 32 input channels
         dit_config["patch_size"] = list(in_w.shape[2:])
         dit_config["out_channels"] = out_w.shape[0] // math.prod(dit_config["patch_size"])
-        if '{}vector_in.in_layer.weight'.format(key_prefix) in state_dict:
+        if any(s.startswith('{}vector_in.'.format(key_prefix)) for s in state_dict_keys):
             dit_config["vec_in_dim"] = 768
-            dit_config["axes_dim"] = [16, 56, 56]
         else:
             dit_config["vec_in_dim"] = None
+
+        if len(dit_config["patch_size"]) == 2:
             dit_config["axes_dim"] = [64, 64]
+        else:
+            dit_config["axes_dim"] = [16, 56, 56]
+
+        if any(s.startswith('{}time_r_in.'.format(key_prefix)) for s in state_dict_keys):
+            dit_config["meanflow"] = True
+        else:
+            dit_config["meanflow"] = False
 
         dit_config["context_in_dim"] = state_dict['{}txt_in.input_embedder.weight'.format(key_prefix)].shape[1]
         dit_config["hidden_size"] = in_w.shape[0]