Update app.py

app.py

@@ -16,10 +16,10 @@ login(token=hf_token)
 
 torch.set_float32_matmul_precision("high")
 
-torch._inductor.config.conv_1x1_as_mm = True
-torch._inductor.config.coordinate_descent_tuning = True
-torch._inductor.config.epilogue_fusion = False
-torch._inductor.config.coordinate_descent_check_all_directions = True
+#torch._inductor.config.conv_1x1_as_mm = True
+#torch._inductor.config.coordinate_descent_tuning = True
+#torch._inductor.config.epilogue_fusion = False
+#torch._inductor.config.coordinate_descent_check_all_directions = True
 
 
 # Load LoRAs from JSON file
@@ -27,14 +27,14 @@ with open('loras.json', 'r') as f:
     loras = json.load(f)
     
 # Initialize the base model
-base_model = "stabilityai/stable-diffusion-3.5-large"
+#base_model = "stabilityai/stable-diffusion-3.5-large"
 pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-3.5-large", torch_dtype=torch.bfloat16)
 
-pipe.transformer.to(memory_format=torch.channels_last)
-pipe.vae.to(memory_format=torch.channels_last)
+#pipe.transformer.to(memory_format=torch.channels_last)
+#pipe.vae.to(memory_format=torch.channels_last)
 
-pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
-pipe.vae.decode = torch.compile(pipe.vae.decode, mode="max-autotune", fullgraph=True)
+#pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
+#pipe.vae.decode = torch.compile(pipe.vae.decode, mode="max-autotune", fullgraph=True)
 
 MAX_SEED = 2**32-1