diff --git a/comfy/text_encoders/qwen35.py b/comfy/text_encoders/qwen35.py
index f684edd57..c15a5bf7b 100644
--- a/comfy/text_encoders/qwen35.py
+++ b/comfy/text_encoders/qwen35.py
@@ -213,7 +213,10 @@ class GatedDeltaNet(nn.Module):
         mixed_qkv = mixed_qkv.transpose(1, 2)  # [B, seq_len, conv_dim]
         query, key, value = mixed_qkv.split([self.key_dim, self.key_dim, self.value_dim], dim=-1)
         beta = b.sigmoid()
-        g = -self.A_log.float().exp() * F.softplus(a.float() + self.dt_bias.float())
+
+        A_log = comfy.model_management.cast_to_device(self.A_log, x.device, torch.float32)
+        dt_bias = comfy.model_management.cast_to_device(self.dt_bias, x.device, torch.float32)
+        g = -A_log.exp() * F.softplus(a.float() + dt_bias)
 
         # Delta rule
         if use_recurrent:
@@ -476,9 +479,15 @@ class Qwen35VisionRotaryEmbedding(nn.Module):
         inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
         self.register_buffer("inv_freq", inv_freq, persistent=False)
 
-    def forward(self, seqlen):
-        seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
-        freqs = torch.outer(seq, self.inv_freq)
+    def forward(self, seqlen, device=None, dtype=None):
+        if device is None:
+            device = self.inv_freq.device
+        if dtype is None:
+            dtype = self.inv_freq.dtype
+
+        inv_freq = comfy.model_management.cast_to_device(self.inv_freq, device, dtype)
+        seq = torch.arange(seqlen, device=device, dtype=dtype)
+        freqs = torch.outer(seq, inv_freq)
         return freqs
 
 
@@ -567,12 +576,11 @@ class Qwen35VisionModel(nn.Module):
         ])
         self.merger = Qwen35VisionPatchMerger(self.hidden_size, self.spatial_merge_size, config["out_hidden_size"], device=device, dtype=dtype, ops=ops)
 
-    def rot_pos_emb(self, grid_thw):
+    def rot_pos_emb(self, grid_thw, device):
         merge_size = self.spatial_merge_size
         grid_thw_list = grid_thw.tolist()
         max_hw = max(max(h, w) for _, h, w in grid_thw_list)
-        freq_table = self.rotary_pos_emb(max_hw)
-        device = freq_table.device
+        freq_table = self.rotary_pos_emb(max_hw, device=device, dtype=torch.float32)
         total_tokens = sum(int(t * h * w) for t, h, w in grid_thw_list)
         pos_ids = torch.empty((total_tokens, 2), dtype=torch.long, device=device)
         offset = 0
@@ -653,7 +661,7 @@ class Qwen35VisionModel(nn.Module):
         x = self.patch_embed(x)
         pos_embeds = self.fast_pos_embed_interpolate(grid_thw).to(x.device)
         x = x + pos_embeds
-        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+        rotary_pos_emb = self.rot_pos_emb(grid_thw, device=x.device)
         seq_len = x.shape[0]
         x = x.reshape(seq_len, -1)
         rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)