From 3c84b404a8424a28b8e732a22794c1151e1b7495 Mon Sep 17 00:00:00 2001
From: lucidrains <lucidrains@gmail.com>
Date: Mon, 10 Nov 2025 15:51:33 -0800
Subject: [PATCH] rnn layer needs to be hyper connected too

---
 dreamer4/dreamer4.py | 20 +++++++-------------
 pyproject.toml       |  2 +-
 2 files changed, 8 insertions(+), 14 deletions(-)

diff --git a/dreamer4/dreamer4.py b/dreamer4/dreamer4.py
index d6583f6..639fb7f 100644
--- a/dreamer4/dreamer4.py
+++ b/dreamer4/dreamer4.py
@@ -1471,7 +1471,7 @@ class AxialSpaceTimeTransformer(Module):
         special_attend_only_itself = False,  # this is set to True for the video tokenizer decoder (latents can only attend to itself while spatial modalities attend to the latents and everything)
         final_norm = True,
         value_residual = True,               # https://arxiv.org/abs/2410.17897 - but with learned mixing from OSS
-        rnn_time = False
+        rnn_time = True
     ):
         super().__init__()
         assert depth >= time_block_every, f'depth must be at least {time_block_every}'
@@ -1531,10 +1531,7 @@ class AxialSpaceTimeTransformer(Module):
                 hyper_conn(branch = SwiGLUFeedforward(dim = dim, **ff_kwargs))
             ]))
 
-            rnn_layers.append(ModuleList([
-                nn.RMSNorm(dim),
-                nn.GRU(dim, dim, batch_first = True)
-            ]) if is_time_block and rnn_time else None)
+            rnn_layers.append(hyper_conn(branch = nn.Sequential(nn.RMSNorm(dim), nn.GRU(dim, dim, batch_first = True))) if is_time_block and rnn_time else None)
 
         self.layers = ModuleList(layers)
         self.rnn_layers = ModuleList(rnn_layers)
@@ -1618,22 +1615,19 @@ class AxialSpaceTimeTransformer(Module):
 
         tokens = self.expand_streams(tokens)
 
-        for (pre_attn_rearrange, post_attn_rearrange, attn, ff), maybe_rnn_modules, layer_is_time in zip(self.layers, self.rnn_layers, self.is_time):
+        for (pre_attn_rearrange, post_attn_rearrange, attn, ff), maybe_rnn, layer_is_time in zip(self.layers, self.rnn_layers, self.is_time):
 
             tokens = pre_attn_rearrange(tokens)
 
             # maybe rnn for time
 
-            if layer_is_time and exists(maybe_rnn_modules):
-                rnn_prenorm, rnn = maybe_rnn_modules
+            if layer_is_time and exists(maybe_rnn):
 
-                rnn_input, inverse_pack_time = pack_one(tokens, '* t d')
+                tokens, inverse_pack_batch = pack_one(tokens, '* t d')
 
-                rnn_out, rnn_hiddens = rnn(rnn_prenorm(rnn_input)) # todo, handle rnn cache
+                tokens, rnn_hiddens = maybe_rnn(tokens) # todo, handle rnn cache
 
-                rnn_out = inverse_pack_time(rnn_out)
-
-                tokens = rnn_out + tokens
+                tokens = inverse_pack_batch(tokens)
 
             # when is a axial time attention block, should be causal
 
diff --git a/pyproject.toml b/pyproject.toml
index 477e76b..2aacd30 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "dreamer4"
-version = "0.1.16"
+version = "0.1.17"
 description = "Dreamer 4"
 authors = [
     { name = "Phil Wang", email = "lucidrains@gmail.com" }