diff --git a/examples/research_projects/intel_opts/README.md b/examples/research_projects/intel_opts/README.md
index fc606df7d170..6b25679efbe9 100644
--- a/examples/research_projects/intel_opts/README.md
+++ b/examples/research_projects/intel_opts/README.md
@@ -11,6 +11,26 @@ We accelereate the fine-tuning for textual inversion with Intel Extension for Py
 ## Accelerating the inference for Stable Diffusion using Bfloat16
 
 We start the inference acceleration with Bfloat16 using Intel Extension for PyTorch. The [script](inference_bf16.py) is generally designed to support standard Stable Diffusion models with Bfloat16 support.
+```bash
+pip install diffusers transformers accelerate scipy safetensors
+
+export KMP_BLOCKTIME=1
+export KMP_SETTINGS=1
+export KMP_AFFINITY=granularity=fine,compact,1,0
+
+# Intel OpenMP
+export OMP_NUM_THREADS=< Cores to use >
+export LD_PRELOAD=${LD_PRELOAD}:/path/to/lib/libiomp5.so
+# Jemalloc is a recommended malloc implementation that emphasizes fragmentation avoidance and scalable concurrency support.
+export LD_PRELOAD=${LD_PRELOAD}:/path/to/lib/libjemalloc.so
+export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:-1,muzzy_decay_ms:9000000000"
+
+# Launch with default DDIM
+numactl --membind <node N> -C <cpu list> python python inference_bf16.py
+# Launch with DPMSolverMultistepScheduler
+numactl --membind <node N> -C <cpu list> python python inference_bf16.py --dpm
+
+```
 
 ## Accelerating the inference for Stable Diffusion using INT8
 
diff --git a/examples/research_projects/intel_opts/inference_bf16.py b/examples/research_projects/intel_opts/inference_bf16.py
index 8431693a45c8..96ec709f433c 100644
--- a/examples/research_projects/intel_opts/inference_bf16.py
+++ b/examples/research_projects/intel_opts/inference_bf16.py
@@ -1,49 +1,56 @@
+import argparse
+
 import intel_extension_for_pytorch as ipex
 import torch
-from PIL import Image
-
-from diffusers import StableDiffusionPipeline
-
 
-def image_grid(imgs, rows, cols):
-    assert len(imgs) == rows * cols
+from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
 
-    w, h = imgs[0].size
-    grid = Image.new("RGB", size=(cols * w, rows * h))
-    grid_w, grid_h = grid.size
 
-    for i, img in enumerate(imgs):
-        grid.paste(img, box=(i % cols * w, i // cols * h))
-    return grid
+parser = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
+parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
+parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
+args = parser.parse_args()
 
 
-prompt = ["a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"]
-batch_size = 8
-prompt = prompt * batch_size
-
 device = "cpu"
+prompt = "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"
+
 model_id = "path-to-your-trained-model"
-model = StableDiffusionPipeline.from_pretrained(model_id)
-model = model.to(device)
+pipe = StableDiffusionPipeline.from_pretrained(model_id)
+if args.dpm:
+    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to(device)
 
 # to channels last
-model.unet = model.unet.to(memory_format=torch.channels_last)
-model.vae = model.vae.to(memory_format=torch.channels_last)
-model.text_encoder = model.text_encoder.to(memory_format=torch.channels_last)
-model.safety_checker = model.safety_checker.to(memory_format=torch.channels_last)
+pipe.unet = pipe.unet.to(memory_format=torch.channels_last)
+pipe.vae = pipe.vae.to(memory_format=torch.channels_last)
+pipe.text_encoder = pipe.text_encoder.to(memory_format=torch.channels_last)
+if pipe.requires_safety_checker:
+    pipe.safety_checker = pipe.safety_checker.to(memory_format=torch.channels_last)
 
 # optimize with ipex
-model.unet = ipex.optimize(model.unet.eval(), dtype=torch.bfloat16, inplace=True)
-model.vae = ipex.optimize(model.vae.eval(), dtype=torch.bfloat16, inplace=True)
-model.text_encoder = ipex.optimize(model.text_encoder.eval(), dtype=torch.bfloat16, inplace=True)
-model.safety_checker = ipex.optimize(model.safety_checker.eval(), dtype=torch.bfloat16, inplace=True)
+sample = torch.randn(2, 4, 64, 64)
+timestep = torch.rand(1) * 999
+encoder_hidden_status = torch.randn(2, 77, 768)
+input_example = (sample, timestep, encoder_hidden_status)
+try:
+    pipe.unet = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloat16, inplace=True, sample_input=input_example)
+except Exception:
+    pipe.unet = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloat16, inplace=True)
+pipe.vae = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloat16, inplace=True)
+pipe.text_encoder = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloat16, inplace=True)
+if pipe.requires_safety_checker:
+    pipe.safety_checker = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloat16, inplace=True)
 
 # compute
 seed = 666
 generator = torch.Generator(device).manual_seed(seed)
+generate_kwargs = {"generator": generator}
+if args.steps is not None:
+    generate_kwargs["num_inference_steps"] = args.steps
+
 with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
-    images = model(prompt, guidance_scale=7.5, num_inference_steps=50, generator=generator).images
+    image = pipe(prompt, **generate_kwargs).images[0]
 
-    # save image
-    grid = image_grid(images, rows=2, cols=4)
-    grid.save(model_id + ".png")
+# save image
+image.save("generated.png")