diff --git a/modules/models/sd3/sd3_cond.py b/modules/models/sd3/sd3_cond.py
index c61ae0fe6..e25ba1b63 100644
--- a/modules/models/sd3/sd3_cond.py
+++ b/modules/models/sd3/sd3_cond.py
@@ -177,12 +177,13 @@ class SD3Cond(torch.nn.Module):
         self.weights_loaded = False
 
     def forward(self, prompts: list[str]):
-        lg_out, vector_out = self.model_lg(prompts)
+        with devices.without_autocast():
+            lg_out, vector_out = self.model_lg(prompts)
 
-        token_count = lg_out.shape[1]
+            token_count = lg_out.shape[1]
 
-        t5_out = self.model_t5(prompts, token_count=token_count)
-        lgt_out = torch.cat([lg_out, t5_out], dim=-2)
+            t5_out = self.model_t5(prompts, token_count=token_count)
+            lgt_out = torch.cat([lg_out, t5_out], dim=-2)
 
         return {
             'crossattn': lgt_out,
diff --git a/modules/models/sd3/sd3_model.py b/modules/models/sd3/sd3_model.py
index c17fd97e9..336e8d2d4 100644
--- a/modules/models/sd3/sd3_model.py
+++ b/modules/models/sd3/sd3_model.py
@@ -47,8 +47,7 @@ class SD3Inferencer(torch.nn.Module):
         return contextlib.nullcontext()
 
     def get_learned_conditioning(self, batch: list[str]):
-        with devices.without_autocast():
-            return self.cond_stage_model(batch)
+        return self.cond_stage_model(batch)
 
     def apply_model(self, x, t, cond):
         return self.model(x, t, c_crossattn=cond['crossattn'], y=cond['vector'])
diff --git a/modules/sd_models.py b/modules/sd_models.py
index 45575e440..681030442 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -718,16 +718,15 @@ def get_empty_cond(sd_model):
     p = processing.StableDiffusionProcessingTxt2Img()
     extra_networks.activate(p, {})
 
-    if hasattr(sd_model, 'conditioner'):
+    if hasattr(sd_model, 'get_learned_conditioning'):
         d = sd_model.get_learned_conditioning([""])
-        return d['crossattn']
     else:
         d = sd_model.cond_stage_model([""])
 
-        if isinstance(d, dict):
-            d = d['crossattn']
+    if isinstance(d, dict):
+        d = d['crossattn']
 
-        return d
+    return d
 
 
 def send_model_to_cpu(m):