Update app.py

app.py

@@ -6,18 +6,13 @@
 # copies of the Software, and to permit persons to whom the Software is
 import spaces
 import os
-
 import random
 import uuid
 import gradio as gr
 import numpy as np
 from PIL import Image
 import torch
-import torch._dynamo
-
-import hidet
-import diffusers
-
+#import diffusers
 from diffusers import AutoencoderKL, StableDiffusionXLPipeline
 from diffusers import EulerAncestralDiscreteScheduler
 from typing import Tuple
@@ -31,8 +26,8 @@ torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
 torch.backends.cudnn.allow_tf32 = False
 torch.backends.cudnn.deterministic = False
 torch.backends.cudnn.benchmark = False
-#torch.backends.cuda.preferred_blas_library="cublas"
-# torch.backends.cuda.preferred_linalg_library="cusolver"
+torch.backends.cuda.preferred_blas_library="cublas"
+torch.backends.cuda.preferred_linalg_library="cusolver"
 torch.set_float32_matmul_precision("highest")
 
 FTP_HOST = "1ink.us"
@@ -112,30 +107,10 @@ def load_and_prepare_model():
     pipe.safety_checker=None
     #pipe.unet.to(memory_format=torch.channels_last)
     #pipe.enable_vae_tiling()
-    #pipe.unet = pipe.unet.to(memory_format=torch.contiguous_format)
-    #pipe.unet = torch.compile(pipe.unet, backend="hidet")
     pipe.to(device=device, dtype=torch.bfloat16)
 
     return pipe
     
-          # for compile
-hidet.option.parallel_build(True)
-#hidet.option.parallel_tune(-1,4.0)
-torch._dynamo.config.suppress_errors = True
-torch._dynamo.disallow_in_graph(diffusers.models.attention.BasicTransformerBlock)
-# more search
-hidet.torch.dynamo_config.search_space(1)
-#hidet.torch.dynamo_config.dump_graph_ir("./local_graph")
-hidet.option.cache_dir("local_cache")
-# automatically transform the model to use float16 data type
-hidet.torch.dynamo_config.use_fp16(True)
-# use float16 data type as the accumulate data type in operators with reduction
-hidet.torch.dynamo_config.use_fp16_reduction(True)
-# use tensorcore
-hidet.torch.dynamo_config.use_tensor_core()
-# Preload and compile both models
-hidet.torch.dynamo_config.steal_weights(False)
-
 pipe = load_and_prepare_model()
 
 MAX_SEED = np.iinfo(np.int64).max