Update app.py

app.py

@@ -389,45 +389,17 @@ def load_and_prepare_model():
     #sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler')
     pipe = StableDiffusionXLPipeline.from_pretrained(
         'ford442/RealVisXL_V5.0_BF16',
-        #torch_dtype=torch.bfloat16,
         add_watermarker=False,
-      #  low_cpu_mem_usage = False,
         token = HF_TOKEN,
-      #  scheduler = sched,
     )
-    #sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1) #,use_karras_sigmas=True)
     pipe.vae = vaeXL #.to(torch.bfloat16)
     pipe.scheduler = sched
-    
     pipe.vae.do_resize = False
-    #pipe.vae.vae_scale_factor = 8
     pipe.vae.do_convert_rgb = True
-    
     pipe.vae.set_default_attn_processor()
-    #pipe.to(device)
-    #pipe.to(torch.bfloat16)
     print(f'init noise scale: {pipe.scheduler.init_noise_sigma}')
     pipe.watermark=None
     pipe.safety_checker=None  
-    
-    '''       # Freeze vae and unet
-    pipe.vae.requires_grad_(False)
-    pipe.unet.requires_grad_(False)
-    pipe.text_encoder.requires_grad_(False)
-    pipe.unet.eval()
-    pipe.vae.eval()
-    pipe.text_encoder.eval()
-    '''
-    #pipe.unet = pipe.unet.to(memory_format=torch.contiguous_format)
-    #pipe.load_lora_weights("ford442/sdxl-vae-bf16", weight_name="LoRA/FLUX-dev-lora-add_details.safetensors", low_cpu_mem_usage=False)
-    #pipe.unet.to(memory_format=torch.channels_last)
-    #pipe.enable_vae_tiling()
-    #pipe.unet = torch.compile(pipe.unet, backend="hidet", dynamic=False, mode='max-autotune') #.to(device=device, dtype=torch.bfloat16)
-    #pipe.unet = torch.compile(pipe.unet, backend="hidet", dynamic=False, mode='max-autotune-no-cudagraphs') #.to(device=device, dtype=torch.bfloat16)
-    #pipe.unet = torch.compile(pipe.unet, backend="hidet", dynamic=False, options={'epilogue_fusion': True, 'shape_padding': True}) #.to(device=device, dtype=torch.bfloat16)
-    #pipe.unet = torch.compile(pipe.unet, dynamic=False)
-    #pipe.unet = torch.compile(pipe.unet, backend="hidet", dynamic=False, options={"search_space": 0})
-    #pipe.unet = torch.compile(pipe.unet, backend="torch_tensorrt", dynamic=False, options={"precision": torch.bfloat16,"optimization_level": 4,})
     pipe.to(torch.device('cuda:0'), torch.bfloat16)
 
     return pipe