Update app.py

Fix dtype inconsistency bug to enable half-precision inference

app.py

@@ -19,7 +19,7 @@ latent_scale_factor = 0.18215  # Same as in DiTTrainer
 global_progress = 0
 
 # Enable half precision inference
-USE_HALF_PRECISION = False
+USE_HALF_PRECISION = True
 
 def load_dit_model(dit_size):
     """Load DiT model of specified size"""
@@ -99,7 +99,9 @@ class DiffusionSampler:
             if torch.cuda.is_available():
                 torch.cuda.manual_seed(seed)
                 torch.cuda.manual_seed_all(seed)
-        
+
+        if self.use_half:
+            model.half()
         model.to(self.device)
         model.eval()
         
@@ -185,9 +187,9 @@ def generate_random_seed():
     return random.randint(0, 2**32 - 1)
 
 MODEL_SAMPLE_LIMITS = {
-    "S": {"min":1, "max": 18, "default": 4},
-    "B": {"min":1, "max": 9, "default": 4},
-    "L": {"min":1, "max": 3, "default": 1}
+    "S": {"min":1, "max": 16, "default": 4},
+    "B": {"min":1, "max": 12, "default": 3},
+    "L": {"min":1, "max": 4, "default": 1}
 }
 
 def update_sample_slider(dit_size):