Update gradio-app.py

gradio-app.py

@@ -36,45 +36,6 @@ pipe.vae = pipe.vae.cuda()
 pipe.unet.to(memory_format=torch.channels_last)
 pipe.set_progress_bar_config(disable=True)
 
-if TORCH_COMPILE:
-    #optmode = 'max-autotune'
-    #optmode = 'reduce-overhead'
-    #pipe.text_encoder = torch.compile(pipe.text_encoder, mode=optmode)
-    #pipe.tokenizer = torch.compile(pipe.tokenizer, mode=optmode)
-    #pipe.unet = torch.compile(pipe.unet, mode=optmode)
-    #pipe.vae = torch.compile(pipe.vae, mode=optmode)
-    doCompile = True
-    if doCompile:
-        from sfast.compilers.diffusion_pipeline_compiler import (compile, compile_unet, compile_vae, CompilationConfig)
-        config = CompilationConfig.Default()
-
-        try:
-            import xformers
-            config.enable_xformers = True
-        except ImportError:
-            print('xformers not installed, skipping')
-        try:
-            import triton
-            config.enable_triton = True
-        except ImportError:
-            print('Triton not installed, skipping')
-
-        config.enable_cuda_graph = True
-        config.enable_jit = True
-        config.enable_jit_freeze = True
-        config.trace_scheduler = False#True   CHECK THIS AGAIN
-        config.enable_cnn_optimization = True
-        config.preserve_parameters = False
-        config.prefer_lowp_gemm = True
-        config.enable_fused_linear_geglu = True
-
-        torch.jit.optimize_for_inference = True
-        torch.jit.enable_onednn_fusion = True
-        torch.jit.set_fusion_strategy([('STATIC', 1), ('DYNAMIC', 1)])
-
-        for p in pipe.text_encoder.parameters(): p.requires_grad=False
-        for p in pipe.vae.decoder.parameters(): p.requires_grad=False
-        for p in pipe.unet.parameters(): p.requires_grad=False
 
 def predict(prompt1, prompt2, merge_ratio, guidance, steps, sharpness, seed=1231231):
     torch.manual_seed(seed)