Update app.py

app.py

@@ -43,26 +43,30 @@ def generate(image: Image.Image, edit_prompt: str):
     
     global sampler
     if sampler is None:
-        # A inicialização do sampler agora lê os novos parâmetros do .yaml
         print("Inicializando o XFluxSampler com a configuração completa...")
         sampler = XFluxSampler(
             device=device,
-            ip_loaded=args.use_ip,
-            spatial_condition=args.use_spatial_condition,
-            share_position_embedding=args.share_position_embedding,
-            use_share_weight_referencenet=args.use_share_weight_referencenet,
-            double_block_refnet=args.double_block_refnet,
-            single_block_refnet=args.single_block_refnet
+            ip_loaded=args.get('use_ip', False),
+            spatial_condition=args.get('use_spatial_condition', False),
+            share_position_embedding=args.get('share_position_embedding', False),
+            use_share_weight_referencenet=args.get('use_share_weight_referencenet', False),
+            double_block_refnet=args.get('double_block_refnet', False),
+            single_block_refnet=args.get('single_block_refnet', False)
         )
 
-    img = image_resize(image, 544)
-    w, h = img.size
-    img = img.resize(((w // 32) * 32, (h // 32) * 32))
+    # --- CORREÇÃO: Redimensiona a imagem de entrada para corresponder às dimensões de saída ---
+    target_width = (args.sample_width // 32) * 32
+    target_height = (args.sample_height // 32) * 32
+    img = image.resize((target_width, target_height), Image.Resampling.LANCZOS)
+    
     img = torch.from_numpy((np.array(img) / 127.5) - 1)
     img = img.permute(2, 0, 1).unsqueeze(0).to(device, dtype=dtype)
 
     use_image_conditioning = args.use_spatial_condition or args.use_share_weight_referencenet
     
+    # Gera um seed aleatório se for -1
+    seed = args.seed if args.seed != -1 else np.random.randint(0, 2**32 - 1)
+
     result = sampler(
         prompt=edit_prompt,
         width=args.sample_width,
@@ -70,7 +74,7 @@ def generate(image: Image.Image, edit_prompt: str):
         num_steps=args.sample_steps,
         image_prompt=None,
         true_gs=args.cfg_scale,
-        seed=args.seed if args.seed != -1 else np.random.randint(0, 2**32 - 1),
+        seed=seed,
         ip_scale=args.ip_scale if args.use_ip else 1.0,
         source_image=img if use_image_conditioning else None,
     )