add sdxl and lora-lcm optimization (#12444)

* add sdxl and lora-lcm optimization

* fix openjourney speed drop

python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion/lora-lcm.py

@@ -17,16 +17,18 @@
 
 import torch
 from diffusers import DiffusionPipeline, LCMScheduler
-import ipex_llm
+from ipex_llm import optimize_model
 import argparse
 import time
 
 
 def main(args):
     pipe = DiffusionPipeline.from_pretrained(
         args.repo_id_or_model_path,
-        torch_dtype=torch.bfloat16,
-    ).to("xpu")
+        torch_dtype=torch.float16,
+    )
+    pipe = optimize_model(pipe, low_bit=None)
+    pipe.to("xpu")
 
     # set scheduler
     pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

python/llm/example/GPU/HuggingFace/Multimodal/StableDiffusion/sdxl.py

@@ -17,7 +17,7 @@
 
 from diffusers import AutoPipelineForText2Image
 import torch
-import ipex_llm
+from ipex_llm import optimize_model
 import numpy as np
 from PIL import Image
 import argparse
@@ -27,9 +27,11 @@
 def main(args):
     pipeline_text2image = AutoPipelineForText2Image.from_pretrained(
         args.repo_id_or_model_path, 
-        torch_dtype=torch.bfloat16, 
+        torch_dtype=torch.float16, 
         use_safetensors=True
-    ).to("xpu")
+    )
+    pipeline_text2image = optimize_model(pipeline_text2image, low_bit=None)
+    pipeline_text2image.to("xpu")
 
     with torch.inference_mode():
         # warmup

python/llm/src/ipex_llm/transformers/models/sd.py

@@ -111,7 +111,7 @@ def __call__(
             # padding head_dim 40 to 64
             query, key, value = padding_qkv_hd(query, key, value, 40, 64)
 
-            if use_sdp_non_causal(head_dim, query.device, query.dtype):
+            if use_sdp_non_causal(query.size(-1), query.device, query.dtype):
                 import xe_addons
                 hidden_states = xe_addons.sdp_non_causal(query, key.contiguous(),
                                                          value.contiguous(), attention_mask)