Update class reference to support newest diffuser >= v.0.26.1

_test.py

@@ -1,38 +0,0 @@
-import time
-import torch
-from diffusers import (StableDiffusionPipeline,
-                       EulerAncestralDiscreteScheduler)
-from sfast.compilers.diffusion_pipeline_compiler import (compile,
-                                                         CompilationConfig)
-
-def load_model():
-    model = StableDiffusionPipeline.from_pretrained(
-        'runwayml/stable-diffusion-v1-5',
-        torch_dtype=torch.float16)
-
-    model.scheduler = EulerAncestralDiscreteScheduler.from_config(
-        model.scheduler.config)
-    model.safety_checker = None
-    model.to(torch.device('cuda'))
-    return model
-
-model = load_model()
-
-config = CompilationConfig.Default()
-# xformers and Triton are suggested for achieving best performance.
-try:
-    import xformers
-    config.enable_xformers = True
-except ImportError:
-    print('xformers not installed, skip')
-try:
-    import triton
-    config.enable_triton = True
-except ImportError:
-    print('Triton not installed, skip')
-# CUDA Graph is suggested for small batch sizes and small resolutions to reduce CPU overhead.
-# But it can increase the amount of GPU memory used.
-# For StableVideoDiffusionPipeline it is not needed.
-config.enable_cuda_graph = True
-
-model = compile(model, config)