Update app.py

app.py

@@ -50,41 +50,22 @@ def generate(
     apply_refiner: bool = False,
     model = 'stabilityai/stable-diffusion-xl-base-1.0',
     vaecall = 'madebyollin/sdxl-vae-fp16-fix',
-    lora = 'pierroromeu/lora-trained-xl-folder',
+    lora = 'amazonaws-la/juliette',
 ) -> PIL.Image.Image:
     if torch.cuda.is_available():
-        vae = AutoencoderKL.from_pretrained(vaecall, torch_dtype=torch.float16)
-        # Substitua o valor dinâmico antes de chamar from_pretrained
-        pipe_model_name = model
-        pipe = DiffusionPipeline.from_pretrained(
-            pipe_model_name,
-            vae=vae,
-            torch_dtype=torch.float16,
-            use_safetensors=True,
-        )
-        if ENABLE_REFINER:
-            refiner_model_name = model
-            refiner = DiffusionPipeline.from_pretrained(
-                refiner_model_name,
-                vae=vae,
-                torch_dtype=torch.float16,
-                use_safetensors=True,
-            )
+        pipe = DiffusionPipeline.from_pretrained(model, torch_dtype=torch.float16)
+
+        pipe.load_lora_weights(lora)
                                    
         if ENABLE_CPU_OFFLOAD:
             pipe.enable_model_cpu_offload()
-            if ENABLE_REFINER:
-                refiner.enable_model_cpu_offload()
+            
         else:
             pipe.to(device)
-            if ENABLE_REFINER:
-                refiner.to(device)
 
         if USE_TORCH_COMPILE:
             pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-            if ENABLE_REFINER:
-                refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
-
+        
     generator = torch.Generator().manual_seed(seed)
 
     if not use_negative_prompt: