[Backend]Add stable_diffusion and detection models support for KunlunXin XPU (#954)

* [FlyCV] Bump up FlyCV -> official release 1.0.0

* add valid_xpu for detection

* add paddledetection model support for xpu

* support all detection model in c++ and python

* fix code

* add python stable_diffusion support

Co-authored-by: DefTruth <qiustudent_r@163.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>

examples/multimodal/stable_diffusion/infer.py

@@ -69,10 +69,7 @@ def parse_arguments():
         type=str,
         default='paddle',
         # Note(zhoushunjie): Will support 'tensorrt', 'paddle-tensorrt' soon.
-        choices=[
-            'onnx_runtime',
-            'paddle',
-        ],
+        choices=['onnx_runtime', 'paddle', 'paddle-xpu'],
         help="The inference runtime backend of unet model and text encoder model."
     )
     parser.add_argument(
@@ -178,6 +175,24 @@ def create_trt_runtime(model_dir,
     return fd.Runtime(option)
 
 
+def create_xpu_runtime(model_dir, model_prefix, device_id=0):
+    option = fd.RuntimeOption()
+    option.use_xpu(
+        device_id,
+        l3_workspace_size=(64 * 1024 * 1024 - 4 * 1024),
+        locked=False,
+        autotune=False,
+        autotune_file="",
+        precision="int16",
+        adaptive_seqlen=True,
+        enable_multi_stream=True)
+    option.use_paddle_lite_backend()
+    model_file = os.path.join(model_dir, model_prefix, "inference.pdmodel")
+    params_file = os.path.join(model_dir, model_prefix, "inference.pdiparams")
+    option.set_model_path(model_file, params_file)
+    return fd.Runtime(option)
+
+
 def get_scheduler(args):
     if args.scheduler == "pndm":
         scheduler = PNDMScheduler(
@@ -291,6 +306,20 @@ if __name__ == "__main__":
             dynamic_shape=unet_dynamic_shape,
             device_id=args.device_id)
         print(f"Spend {time.time() - start : .2f} s to load unet model.")
+    elif args.backend == "paddle-xpu":
+        print("=== build text_encoder_runtime")
+        text_encoder_runtime = create_xpu_runtime(
+            args.model_dir,
+            args.text_encoder_model_prefix,
+            device_id=args.device_id)
+        print("=== build vae_decoder_runtime")
+        vae_decoder_runtime = create_xpu_runtime(
+            args.model_dir, args.vae_model_prefix, device_id=args.device_id)
+        print("=== build unet_runtime")
+        start = time.time()
+        unet_runtime = create_xpu_runtime(
+            args.model_dir, args.unet_model_prefix, device_id=args.device_id)
+        print(f"Spend {time.time() - start : .2f} s to load unet model.")
     pipe = StableDiffusionFastDeployPipeline(
         vae_decoder_runtime=vae_decoder_runtime,
         text_encoder_runtime=text_encoder_runtime,