From 28d1b0a845cff750c37e36fa46999c98e0c7efdf Mon Sep 17 00:00:00 2001
From: ZIFENG278 <zifengzhang18@gmail.com>
Date: Tue, 23 Jan 2024 10:46:36 +0800
Subject: [PATCH] sd lcm 1.0

---
 .gitignore                                    |     4 +-
 flagged/log.csv                               |     3 +
 gr.py                                         |    19 +-
 nohup.out                                     |     0
 prepare.sh                                    |    31 -
 requirements.txt                              |     8 +-
 sd/__init__.py                                |     7 +-
 sd/libuntpu.so                                |   Bin 3401224 -> 0 bytes
 sd/{newtool.py => npuengine.md}               |   299 +-
 sd/npuengine.py                               |    50 +
 sd/npuengine2.md                              |    63 +
 sd/pipeline152device.py                       |   923 +
 sd/pipeline2.py                               |    84 +-
 sd/pipelineXL.py                              |   706 +
 sd/pipelinexl2device.py                       |   773 +
 sd/scheduler.py                               |     2 +-
 sd/untool.py                                  |   440 +-
 test.py                                       |    33 +
 tokenizerV21/merges.txt                       | 48895 +++++++++++++++
 tokenizerV21/special_tokens_map.json          |    24 +
 tokenizerV21/tokenizer_config.json            |    34 +
 tokenizerV21/vocab.json                       | 49410 ++++++++++++++++
 tokenizer_2/merges.txt                        |     0
 tokenizer_2/special_tokens_map.json           |     0
 tokenizer_2/tokenizer_config.json             |     0
 tokenizer_2/vocab.json                        |     0
 ...-1.2.17-py3-none-manylinux2014_aarch64.whl |   Bin 287828 -> 0 bytes
 ...f-1.2.31-py3-none-manylinux2014_x86_64.whl |   Bin 0 -> 305041 bytes
 28 files changed, 101071 insertions(+), 737 deletions(-)
 create mode 100644 flagged/log.csv
 create mode 100644 nohup.out
 delete mode 100644 prepare.sh
 mode change 100644 => 100755 sd/__init__.py
 delete mode 100644 sd/libuntpu.so
 rename sd/{newtool.py => npuengine.md} (65%)
 create mode 100644 sd/npuengine.py
 create mode 100755 sd/npuengine2.md
 create mode 100644 sd/pipeline152device.py
 create mode 100644 sd/pipelineXL.py
 create mode 100644 sd/pipelinexl2device.py
 create mode 100644 test.py
 create mode 100644 tokenizerV21/merges.txt
 create mode 100644 tokenizerV21/special_tokens_map.json
 create mode 100644 tokenizerV21/tokenizer_config.json
 create mode 100644 tokenizerV21/vocab.json
 mode change 100644 => 100755 tokenizer_2/merges.txt
 mode change 100644 => 100755 tokenizer_2/special_tokens_map.json
 mode change 100644 => 100755 tokenizer_2/tokenizer_config.json
 mode change 100644 => 100755 tokenizer_2/vocab.json
 delete mode 100644 tpu_perf-1.2.17-py3-none-manylinux2014_aarch64.whl
 create mode 100644 tpu_perf-1.2.31-py3-none-manylinux2014_x86_64.whl

diff --git a/.gitignore b/.gitignore
index 3bf1682..2d4f3c6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,4 +2,6 @@
 *.png
 *.bmodel
 *.log
-
+#tpu_perf-1.2.31-py3-none-manylinux2014_x86_64.whl
+.venv
+.idea
diff --git a/flagged/log.csv b/flagged/log.csv
new file mode 100644
index 0000000..670f90c
--- /dev/null
+++ b/flagged/log.csv
@@ -0,0 +1,3 @@
+text,output,flag,username,timestamp
+"a girl, 4k","{""path"":""flagged/output/7bdfe86367a3b675d7cf/image.png"",""url"":null,""size"":null,""orig_name"":""image.png"",""mime_type"":null}",,,2023-12-04 17:55:59.243892
+美女,"{""path"":""flagged/output/bdd1c889144ba8f3f558/1701704395.png"",""url"":null,""size"":null,""orig_name"":""1701704395.png"",""mime_type"":null}",,,2023-12-04 23:40:03.485464
diff --git a/gr.py b/gr.py
index a1b6163..05acef2 100644
--- a/gr.py
+++ b/gr.py
@@ -1,12 +1,13 @@
 import gradio as gr
-from sd import StableDiffusionPipeline
+from sd import StableDiffusionPipeline, StableDiffusionXLPipeline, SD2_1_MODELS, SD_XL_MODELS
+from sd import SD15Parallel, SDXL2Parallel
+from sd import UpscaleModel
 from PIL import Image
 import numpy as np
 import os
 import time
 import random
 import torch
-
 def seed_torch(seed=1029):
     seed=seed%4294967296
     random.seed(seed)
@@ -27,22 +28,28 @@ def seed_torch(seed=1029):
 
 
 def generate_image(text):
+    # 这里是一个虚构的图像生成函数
+    # 在实际应用中,您需要替换为实际的图像生成模型代码
+    # 比如调用 DALL-E、Imagen 或其他模型
+    # 这个函数应该根据输入的文本生成并返回图像
     img_pil = pipe(
         prompt=text,
         negative_prompt="low resolution",
         num_inference_steps=4,
         scheduler=scheduler,
-        guidance_scale=0,
-        seeds=[random.randint(0,1000000)]
+        guidance_scale=1.05,
+        seeds=[random.randint(0,100000)]
     )
-    return img_pil #"{}.png".format(time_stamp)
+    time_stamp = str(time.time()).split(".")[0]
+    img_pil.save("{}.png".format(time_stamp))
+    return "{}.png".format(time_stamp)
 
 # 创建 Gradio 接口
 iface = gr.Interface(
     fn=generate_image,                # 指定处理函数
     inputs="text",                    # 输入类型为文本
     outputs="image",                  # 输出类型为图像
-    title="Text-to-Image Generator",  # 界面标题
+    title="SDv1.5 Text-to-Image Generator🛸",  # 界面标题
     description="Generate images from text descriptions."  # 描述
 )
 
diff --git a/nohup.out b/nohup.out
new file mode 100644
index 0000000..e69de29
diff --git a/prepare.sh b/prepare.sh
deleted file mode 100644
index e7aa5e5..0000000
--- a/prepare.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-# wget https://github.com/sophgo/tpu-perf/releases/download/v1.2.17/tpu_perf-1.2.17-py3-none-manylinux2014_aarch64.whl 
-# 判断操作系统是arm还是x86
-if [ $(uname -m) = "x86_64" ]; then
-    echo "Your current operating system is based on x86_64"
-    echo "you need to install the x86_64 version of sophon"
-    echo "check your package now ..."
-    pip3 install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple
-    python3 -c "import sophon.sail" || echo "check package failed" && exit 1
-    echo "check package success"
-else
-    echo "Your current operating system is based on arm64"
-    python3 -m dfn --url http://disk-sophgo-vip.quickconnect.to/sharing/LiM00jkBJ && pip3 install sophon_arm-0.0.0-py3-none-any.whl && rm -rf ./sophon_arm-0.0.0-py3-none-any.whl
-    pip3 install tpu_perf-1.2.17-py3-none-manylinux2014_aarch64.whl
-fi
-
-pip3 install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
-if [ ! -d "./models" ]; then
-    mkdir models
-fi
-if [ ! -d "./models/basic" ]; then
-    mkdir models/basic
-fi
-if [ ! -d "./models/controlnet" ]; then
-    mkdir models/controlnet
-fi
-if [ ! -d "./models/basic/babes20" ]; then
-    python3 -m dfn --url http://disk-sophgo-vip.quickconnect.to/sharing/QWKCQ7Pxa && unzip babes20.zip && rm -rf babes20.zip && mv babes20 models/basic/
-fi
-
-python3 -m dfn --url http://disk-sophgo-vip.quickconnect.to/sharing/63dydmQ6q && mv canny_multize.bmodel models/controlnet/
-python3 -m dfn --url http://disk-sophgo-vip.quickconnect.to/sharing/86Rm1E7cl && mv tile_multize.bmodel models/controlnet/
diff --git a/requirements.txt b/requirements.txt
index 28e94e8..f4afaf9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,10 +3,12 @@ torchsde
 transformers
 tqdm
 diffusers
-flask==2.2.2
+#flask==2.2.2
 pillow
 dfn
 nasops
-flask_cors==4.0.0
+#flask_cors
 lark
-Werkzeug>=2.2.0
\ No newline at end of file
+gradio
+#Werkzeug==2.2.2
+#anyio==3.7.1
diff --git a/sd/__init__.py b/sd/__init__.py
old mode 100644
new mode 100755
index f173cac..4ca2f0a
--- a/sd/__init__.py
+++ b/sd/__init__.py
@@ -1 +1,6 @@
-from .pipeline2 import StableDiffusionPipeline
\ No newline at end of file
+from .pipeline2 import StableDiffusionPipeline, SD2_1_MODELS, SD_XL_MODELS
+from .pipelineXL import StableDiffusionXLPipeline
+from .npuengine import EngineOV 
+from .upscale import UpscaleModel
+from .pipeline152device import SD15Parallel
+from .pipelinexl2device import SDXL2Parallel
\ No newline at end of file
diff --git a/sd/libuntpu.so b/sd/libuntpu.so
deleted file mode 100644
index b73c9538c57a3c6a79eb512a4deb5a2970f93a15..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/sd/newtool.py b/sd/npuengine.md
similarity index 65%
rename from sd/newtool.py
rename to sd/npuengine.md
index baf41e4..b38854f 100644
--- a/sd/newtool.py
+++ b/sd/npuengine.md
@@ -7,10 +7,8 @@
 
 mylog = logging.getLogger(__name__)
 MLIR_START   = 4294967296
-# print better 
-np.set_printoptions(precision=6, suppress=True)
-def cpu(self, force=True, record=True):
-    self.npy
+
+def cpu(self, force=True):
     copy_tensor_to_host(self, force)
 
 def malloc_device_addr(self):
@@ -18,7 +16,7 @@ def malloc_device_addr(self):
         self.get_bm_handle()
     malloc_device(self)
 
-def npu(self, force=True, record=True):
+def npu(self, force=True):
     copy_tensor_to_device(self, force)
 
 def malloc_host_addr(self):
@@ -40,19 +38,6 @@ def npy(self):
             shape = list(self.shape)[:self.dims]
             data_type = type_map[self.dtype]
             self.npy__ = make_c2np(self.data, shape, data_type)
-            self.is_from_np = True
-        else:
-            if self.size == 0:
-                size = self.max_size // np.dtype(type_map[self.dtype]).itemsize
-                self.npy__ = np.zeros(size, dtype=type_map[self.dtype])
-                self.is_from_np = True
-                self.data  = make_np2c(self.npy__)
-            else:
-                shape = list(self.shape)[:self.dims]
-                data_type = type_map[self.dtype]
-                self.npy__ = np.zeros(shape, dtype=data_type)
-                self.data  = make_np2c(self.npy__)
-                self.is_from_np = True
         return self.npy__
     else:
         return self.npy__
@@ -128,47 +113,16 @@ def set_numpy(self, npy, do_necessary=True):
             assert self.shape[i] == npy.shape[i]
     self.data       = make_np2c(npy)
 
-def set_dtype_shape(self, dtype, shape, regen_npy=True):
+def set_dtype_shape(self, dtype, shape):
     self.dtype = dtype
     self.dims  = len(shape)
     for i in range(self.dims):
         self.shape[i] = shape[i]
     self.size = np.prod(shape) * np.dtype(type_map[dtype]).itemsize
-    if self.is_from_np and regen_npy is True:
-        self.npy__ = np.zeros(shape, dtype=type_map[dtype])
-        self.data  = make_np2c(self.npy__)
-
-def find_father(self) -> UntensorS:
-    if self.is_copy:
-        cur_tensor = ctypes.cast(self.copy_tensor, ctypes.POINTER(UntensorS)).contents
-        return cur_tensor.find_father()
-    else:
-        return self
 
-def set_copy_tensor(self, copy_tensor:UntensorS):
+def set_copy_tensor(self, copy_tensor):
     self.is_copy     = True
-    self.copy_tensor = ctypes.addressof(copy_tensor)
-
-def diff_set_with_flag(self, npdata, flag=0, do_necessary=True):
-    # 0: force copy, 1: simple check copy , 2: force not copy 
-    tensor = self.find_father()
-    if npdata.dtype != type_map[self.dtype] and do_necessary:
-        npdata = npdata.astype(type_map[self.dtype])
-    if flag == 0:
-        tensor.data  = make_np2c(npdata)
-        tensor.npy__ = npdata
-        tensor.is_from_np = True
-        tensor.size       = npdata.size * npdata.dtype.itemsize
-        tensor.npu()
-    else:
-        ValueError("not support")
-
-def copy_tensor_data(self, tensor:UntensorS, flag=0):
-    tensor = tensor.find_father()
-    if flag == 0:
-        copy_data_from_tensor_with_flag(self, tensor, 0)
-    else:
-        ValueError("not support")
+    self.copy_tensor = copy_tensor
 
 def __del__(self):
     pass
@@ -185,9 +139,6 @@ def __del__(self):
 UntensorS.set_copy_tensor    = set_copy_tensor
 UntensorS.set_numpy          = set_numpy
 UntensorS.set_dtype_shape    = set_dtype_shape
-UntensorS.diff_set_with_flag = diff_set_with_flag
-UntensorS.copy_tensor_data   = copy_tensor_data
-UntensorS.find_father        = find_father
 UntensorS.__del__            = __del__
 
 def convert_tensor_c_into_dict(tensor_c):
@@ -435,17 +386,13 @@ def load_lora_weight_npz(self, lora_path, alpha2=1, ready_reorder=False):
         for layer in tqdm(all_keys):
             if layer in self.match_dict:
                 weight = updates[layer+".weight"]
-                alpha  = 1/updates[layer+".alpha"]
+                alpha  = updates[layer+".alpha"]
                 self.lora_weight_save_unet_opt(layer, weight, alpha, alpha2, ready_reorder)
 
-class UntoolEngineOV:
-    def __init__(self, model_path="", device_id=0, pre_malloc=True, default_io_mem=False, output_list=None, sg=False) :
+class EngineOV:
+    def __init__(self, model_path="", device_id=0, pre_malloc=True) :
         self.model_path = model_path
-        self.sg         = sg
-        if self.sg:
-            self.runtime = load_bmodel_sg(model_path, pre_malloc,default_io_mem, device_id)
-        else:
-            self.runtime = load_bmodel(model_path, pre_malloc,default_io_mem, device_id)
+        self.runtime = load_bmodel(model_path, pre_malloc, device_id)
         self.basic_info = self.model_info
         self.input_num  = self.basic_info['input_num']
         self.output_num = self.basic_info['output_num']
@@ -453,85 +400,17 @@ def __init__(self, model_path="", device_id=0, pre_malloc=True, default_io_mem=F
         self.outputs = self.output_num * [None]
         self.cur_stage = -1
         self.init_io()
-        if pre_malloc and not self.sg:
+        if pre_malloc:
             self.check_and_move_to_device()
-        self.build_input_stage_map()
-        self.build_output_stage_map()
-        self.output_list = output_list
         
     def __str__(self):
         return self.model_path
-    
-    def fill_io_max(self):
-        if not self.sg:
-            fill_maxio(self.runtime)
-        else:
-            fill_maxio_sg(self.runtime)
-        self.init_io()
-    
-    def build_input_stage_map(self):
-        input_stage_map = {}
-        stage_num       = self.basic_info['stage_num']
-        for input_idx in range(self.input_num):
-            input_stage_map[input_idx] = {}
-        for stage_idx in range(stage_num):
-            for input_idx in range(self.input_num):
-                shape = tuple(self.basic_info['stage_info'][stage_idx]['input_tensor'][input_idx]['data_shape']) 
-                input_stage_map[input_idx][shape] = stage_idx
-        self.input_stage_map = input_stage_map
-    
-    def build_output_stage_map(self):
-        output_stage_map = {}
-        stage_num = self.basic_info['stage_num']
-        for output_idx in range(self.output_num):
-            output_stage_map[output_idx] = {}
-        for stage_idx in range(stage_num):
-            for output_idx in range(self.output_num):
-                shape = tuple(self.basic_info['stage_info'][stage_idx]['output_tensor'][output_idx]['data_shape']) 
-                output_stage_map[output_idx][stage_idx] = shape
-        self.output_stage_map = output_stage_map
-        return self
-    
-    def default_input(self, default_input_map: dict={}, default_output_map: dict={}):
-        # default_input_map: 0/1: a single value
-        # if user want to set npy, please set manually
-        for idx, each_input in enumerate(self.inputs):
-            if each_input.is_copy:
-                continue
-            size               = each_input.max_size // np.dtype(type_map[each_input.dtype]).itemsize
-            each_input.npy__   = np.zeros(size, dtype=type_map[each_input.dtype])
-            each_input.is_from_np = True
-            each_input.size    = each_input.max_size
-            if idx in default_input_map:
-                value = default_input_map[idx]
-                each_input.npy__ += value
-            each_input.data    = make_np2c(each_input.npy__)
-            each_input.npu()
         
-        for idx, each_output in enumerate(self.outputs):
-            if each_output.is_copy:
-                continue
-            each_output.size = each_output.max_size // np.dtype(type_map[each_output.dtype]).itemsize
-            each_output.npy__ = np.zeros(each_output.size, dtype=type_map[each_output.dtype])
-            each_output.is_from_np = True
-            if idx in default_output_map:
-                value = default_output_map[idx]
-                each_output.npy__ += value
-            each_output.data = make_np2c(each_output.npy__)
-            each_output.npu()
-        return self
-    
     def init_io(self):
         for i in range(self.input_num):
-            if not self.sg:
-                self.inputs[i]  = get_input_tensor(self.runtime, i).contents
-            else:
-                self.inputs[i]  = get_input_tensor_sg(self.runtime, i).contents
+            self.inputs[i]  = get_input_tensor(self.runtime, i).contents
         for i in range(self.output_num):
-            if not self.sg:
-                self.outputs[i] = get_output_tensor(self.runtime, i).contents
-            else:
-                self.outputs[i] = get_output_tensor_sg(self.runtime, i).contents
+            self.outputs[i] = get_output_tensor(self.runtime, i).contents
     
     def init_output_with_np(self):
         assert self.cur_stage >= 0
@@ -557,7 +436,6 @@ def set_input_with_np(self, np_array):
         
     def check_and_move_to_device(self):
         check_move_to_device_fill_api(self.runtime, True)
-        return self
     
     def check_and_set_stage(self, shape_lists):
         cur_info  = self.model_info
@@ -579,7 +457,7 @@ def check_and_set_stage(self, shape_lists):
                 break
         if self.cur_stage == -1:
             raise ValueError("can't find stage")
-        self.set_stage(self.cur_stage)
+        set_stage(self.runtime, self.cur_stage)
         # set input and output shape 
         for i in range(self.input_num):
             self.inputs[i].set_dtype_shape(stage[self.cur_stage]['input_tensor'][i]['data_type'], stage[self.cur_stage]['input_tensor'][i]['data_shape'])
@@ -615,30 +493,26 @@ def load_lora_file(self, lora_npz_path, match_csv, alpha=1, stage_idx=0):
     @property
     def model_info(self):
         model_info_c = ModelInfoSC()
-        if not self.sg:
-            convert_model_info_into_c(get_model_info(self.runtime), model_info_c)
-        else:
-            convert_sg_model_info_into_c(get_model_sg_info(self.runtime), model_info_c)
+        convert_model_info_into_c(get_model_info(self.runtime), model_info_c)
         res = convert_model_info_c_into_dict(model_info_c)
         return res
     
-    def run_copy(self, args,stage_diff_idx=0, output_list:list=[]):
-        if output_list is None:
-            output_list = []
-        if self.output_list is not None and len(output_list) == 0:
-            output_list = self.output_list
-
+    def run_copy(self, args, return_np_list=True, force=True, copy_force=False):
+        # args must be list or dicts
         value = []
         if isinstance(args, list):
             value = args
         elif isinstance(args, dict):
             value = list(args.values())
-        self.get_stage_by_shape(value[stage_diff_idx].shape, stage_diff_idx)
-        input_map = {}
-        for idx in range(self.input_num):
-            input_map[idx] = {"data":value[idx], "flag":0}
-        res = self.run_with_np(input_map, output_list)
-        return res
+        self.set_input_with_np(value)
+        self.move_input_into_device(force, copy_force)
+        run(self.runtime)
+        self.move_output_into_host(force)
+        if return_np_list:
+            res = []
+            for i in self.outputs:
+                res.append(i.npy)
+            return res
     
     def fake_stage_run(self, stage_idx=0):
         stage = self.basic_info['stage_info'][stage_idx]
@@ -647,7 +521,14 @@ def fake_stage_run(self, stage_idx=0):
             temp = stage['input_tensor'][i]
             temp_np = np.zeros(temp['data_shape']).astype(type_map[temp['data_type']])
             value.append(temp_np)
-        res = self.run_copy(value, stage_diff_idx=0)
+        # print(value)
+        self.set_input_with_np(value)
+        self.move_input_into_device()
+        run(self.runtime)
+        self.move_output_into_host()
+        res = []
+        for i in self.outputs:
+            res.append(i.npy)
         return res
     
     def replace_coeff(self, coeff, coeff_size, stage_idx=0):
@@ -657,78 +538,8 @@ def replace_model(self, bmodel_file):
         
         pass
     
-    def set_stage(self, stage):
-        if self.sg:
-            set_stage_sg(self.runtime, stage)
-        else:
-            set_stage(self.runtime, stage)
-        self.cur_stage = stage
-    
-    def get_stage_by_shape(self, shape, idx):
-        if isinstance(shape, list):
-            shape = tuple(shape)
-        if shape not in self.input_stage_map[idx]:
-            ValueError("can't find stage")
-        stage = self.input_stage_map[idx][shape]
-        self.set_stage(stage)
-        return self
-    
-    def run_with_np(self, input_map_dict:dict={}, output_list:list=[], check_stage=True, stage_diff_idx=0, stage=-1) -> list:
-        # input_map_dict: {idx: { "data":np, "flag":0 } }
-        # output_map_dict: 
-        # 这个函数假设所有的输入都已经在device上了
-        # 0: force copy, 1: simple check copy , 2: force not copy 
-        # get stage 
-        if output_list is None:
-            output_list = []
-        if self.output_list is not None and len(output_list) == 0:
-            output_list = self.output_list
-        
-        if not check_stage and stage >= 0:
-            self.set_stage(stage)
-        
-        for k,v in input_map_dict.items():
-            input_tensor = self.inputs[k]
-            input_tensor.diff_set_with_flag(v['data'], v['flag'], True)
-            if check_stage and k == stage_diff_idx:
-                self.get_stage_by_shape(v['data'].shape, k)
-        if not self.sg:
-            run(self.runtime)
-        else:
-            run_sg(self.runtime)
-        res = []
-        for idx in output_list:
-            self.outputs[idx].set_dtype_shape(self.basic_info['stage_info'][self.cur_stage]['output_tensor'][idx]['data_type'], self.basic_info['stage_info'][self.cur_stage]['output_tensor'][idx]['data_shape'])
-            self.outputs[idx].cpu()
-            res.append(self.outputs[idx].npy)
-        return res
-    
-    def __call__(self, 
-                 args=None,
-                 stage_diff_idx=0,
-                 input_map_dict_tensor:dict=None,
-                 input_map_dict_np:dict=None,
-                 output_list:list=None,
-                 check_stage:bool=True):
-        if args is not None:
-            return self.run_copy(args, stage_diff_idx=stage_diff_idx)
-        if input_map_dict_np is not None:
-            return self.run_with_np(input_map_dict_np, output_list=output_list, check_stage=check_stage, stage_diff_idx=stage_diff_idx)
-        if input_map_dict_tensor is not None:
-            return self.run_with_tensor(input_map_dict_tensor, output_list=output_list, check_stage=check_stage, stage_diff_idx=stage_diff_idx)
-        return None
-
-    def run_with_tensor(self, input_map_dict_tensor:dict={}, output_list:list=[], check_stage=True, stage_diff_idx=0):
-        if output_list is None:
-            output_list = []
-        if self.output_list is not None and len(output_list) == 0:
-            output_list = self.output_list
-        
-        for k,v in input_map_dict_tensor.items():
-            input_tensor = self.inputs[k]
-            # check address is same and same pass
-            # check address is not same and copy d2d
-        pass
+    def __call__(self, args, return_np_list=True, force=True, copy_force=False):
+        return self.run_copy(args, return_np_list, force, copy_force)
 
     # def __del__(self):
     #     self.active_free()
@@ -741,26 +552,20 @@ def active_free(self):
         self.outputs = None
         self.basic_info = None
 
-def link_bmodel(modelsrc: UntoolEngineOV, modeldst: UntoolEngineOV, link_map:dict):
-    """link two models with map (map is (i/o, idx) : (i/o, idx) )
-    Args:
-        modelsrc (EngineOV): source model
-        modeldst (EngineOV): dest model
-        link_map (dict): map is (i/o, idx) : (i/o, idx) i=0,o=1
-    """
-    for key, value in link_map.items():
-        src_is_input = key[0] == 0
-        src_idx  = key[1]
-        dst_is_input = value[0] == 0
-        dst_idx  = value[1]
-        if src_is_input:
-            if dst_is_input:
-                modeldst.inputs[dst_idx].set_copy_tensor(modelsrc.inputs[src_idx])
-            else:
-                modeldst.outputs[dst_idx].set_copy_tensor(modelsrc.inputs[src_idx])
-        else:
-            if dst_is_input:
-                modeldst.inputs[dst_idx].set_copy_tensor(modelsrc.outputs[src_idx])
-            else:
-                modeldst.outputs[dst_idx].set_copy_tensor(modelsrc.outputs[src_idx])
 
+if __name__=="__main__":
+    # for large unet (sdxl) will be error when double batch
+    # 观察到某一些模型推理会失败 
+    # model_path = "/workspace/aa/allmodel/basic/sd152parallel/tae1_5.bmodel"
+    # model = EngineOV(model_path, 11, True)
+    # print(model.model_info)
+    # res   = model.fake_stage_run(0)
+    # del model
+    bmodel_path = "/workspace/autocompiler/autotrace/pt_models/v1-5-pruned-test/unet_2_1684x_f16.bmodel" 
+    npz_file    = "/workspace/demos/tpukern/test/mw/coeffex/lcm_lora_reorder.npz"
+    match_file  = "/workspace/demos/tpukern/test/mw/coeffex/unet_match.csv"
+    model       = EngineOV(bmodel_path, 11, True)
+    model.load_lora_file(npz_file, match_file, 1, 0)
+
+    res = model.fake_stage_run(0)
+    # import pdb;pdb.set_trace()
\ No newline at end of file
diff --git a/sd/npuengine.py b/sd/npuengine.py
new file mode 100644
index 0000000..0bfe437
--- /dev/null
+++ b/sd/npuengine.py
@@ -0,0 +1,50 @@
+# from tpu_perf.infer import SGInfer
+import numpy as np 
+import time 
+import torch
+import os
+import sophon.sail as sail 
+
+
+class EngineOV:
+    
+    def __init__(self, model_path="",output_names="",device_id=0) :
+        # 如果环境变量中没有设置device_id,则使用默认值
+        if "DEVICE_ID" in os.environ:
+            device_id = int(os.environ["DEVICE_ID"])
+            print(">>>> device_id is in os.environ. and device_id = ",device_id)
+        self.model_path = model_path
+        self.device_id = device_id
+        try:
+            self.model = sail.Engine(model_path, device_id, sail.IOMode.SYSIO)
+        except Exception as e:
+            print("load model error; please check model path and device status;")
+            print(">>>> model_path: ",model_path)
+            print(">>>> device_id: ",device_id)
+            print(">>>> sail.Engine error: ",e)
+            raise e
+        sail.set_print_flag(True)
+        self.graph_name = self.model.get_graph_names()[0]
+        self.input_name = self.model.get_input_names(self.graph_name)
+        self.output_name= self.model.get_output_names(self.graph_name)
+
+    def __str__(self):
+        return "EngineOV: model_path={}, device_id={}".format(self.model_path,self.device_id)
+    
+    def __call__(self, args):
+        if isinstance(args, list):
+            values = args
+        elif isinstance(args, dict):
+            values = list(args.values())
+        else:
+            raise TypeError("args is not list or dict")
+        args = {}
+        for i in range(len(values)):
+            args[self.input_name[i]] = values[i]
+        # import pdb; pdb.set_trace()
+        output = self.model.process(self.graph_name, args)
+        res = []
+
+        for name in self.output_name:
+            res.append(output[name])
+        return res
diff --git a/sd/npuengine2.md b/sd/npuengine2.md
new file mode 100755
index 0000000..10fcbef
--- /dev/null
+++ b/sd/npuengine2.md
@@ -0,0 +1,63 @@
+# from tpu_perf.infer import SGInfer
+import numpy as np 
+import time 
+import torch
+import os
+import sophon.sail as sail 
+
+step = 1
+
+def input_output_dump(inputs, outputs, name):
+    global step
+    name = "./test/{}_{}.npz".format(name,step)
+    input_name = "input"
+    output_name= "output"
+    res  = {}
+    for i in range(len(inputs)):
+        input_name += "_{}".format(i)
+        res[input_name] = inputs[i]
+    for i in range(len(outputs)):
+        output_name += "_{}".format(i)
+        res[output_name] = outputs[i]
+    step += 1
+    np.savez(name,**res)
+
+
+class EngineOV:
+    
+    def __init__(self, model_path="",output_names="",device_id=0) :
+        # 如果环境变量中没有设置device_id,则使用默认值
+        if "DEVICE_ID" in os.environ:
+            device_id = int(os.environ["DEVICE_ID"])
+            print(">>>> device_id is in os.environ. and device_id = ",device_id)
+        self.model_path = model_path
+        self.device_id = device_id
+        try:
+            self.model = sail.Engine(model_path, device_id, sail.IOMode.SYSIO)
+        except Exception as e:
+            raise e
+        self.graph_name = self.model.get_graph_names()[0]
+        self.input_name = self.model.get_input_names(self.graph_name)
+        self.output_name= self.model.get_output_names(self.graph_name)
+
+    def __str__(self):
+        return "EngineOV: model_path={}, device_id={}".format(self.model_path,self.device_id)
+    
+    def __call__(self, args):
+        if isinstance(args, list):
+            values = args
+        elif isinstance(args, dict):
+            values = list(args.values())
+        else:
+            raise TypeError("args is not list or dict")
+        args = {}
+        for i in range(len(values)):
+            args[self.input_name[i]] = values[i]
+        output = self.model.process(self.graph_name, args)
+        res = []
+
+        for name in self.output_name:
+            res.append(output[name])
+            
+        input_output_dump(values, res, self.model_path.split("/")[-1])
+        return res
diff --git a/sd/pipeline152device.py b/sd/pipeline152device.py
new file mode 100644
index 0000000..61c592c
--- /dev/null
+++ b/sd/pipeline152device.py
@@ -0,0 +1,923 @@
+import inspect
+
+import cv2
+from .npuengine import EngineOV
+from .scheduler import diffusers_scheduler_config
+import numpy as np
+import torch
+from transformers import CLIPTokenizer
+from tqdm import tqdm
+import threading
+from diffusers.schedulers import EulerDiscreteScheduler
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.utils import (
+    logging
+)
+import random
+import time
+import os
+from multiprocessing import  Process,Pool
+import multiprocessing
+def seed_torch(seed=1029):
+    seed=seed%4294967296
+    random.seed(seed)
+    os.environ['PYTHONHASHSEED'] = str(seed)  # 为了禁止hash随机化,使得实验可复现
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    print("set seed to:", seed)
+
+#seed_torch()
+randn_tensor = lambda *x, **kwargs: torch.randn(*x, **kwargs)
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def parallel_manager(pipe,model_path, device_id):
+    model = EngineOV(model_path, device_id=device_id)
+    while True:
+        message = pipe.recv()
+        if message['state'] == 1:
+            data = message['data']
+            # print("inner start ",time.time())
+            res  = model(data)
+            # print("inner end ",time.time())
+            pipe.send({"from_sub":res})
+        elif message['state'] == 2:
+            break  
+    del model
+
+def parallel_maneger_prompt_embed(pipe,text_encoder_path,tokenizer_path, device_id):
+    tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
+    text_encoder = EngineOV(text_encoder_path, device_id=device_id)
+    while True:
+        message = pipe.recv()
+        if message['state'] == 1:
+            prompt = message['prompt']
+            # =======================处理============================
+            text_inputs = tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = tokenizer.batch_decode(
+                    untruncated_ids[:, tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            prompt_embeds = text_encoder({"tokens": np.array(text_input_ids)})
+            prompt_embeds = prompt_embeds[0]
+            # =======================结束============================
+            pipe.send({"from_sub":prompt_embeds})
+        elif message['state'] == 2:
+            negative_prompt = message['negative_prompt']
+            prompt = message['prompt']
+            batch_size = message['batch_size']
+            # =======================处理============================
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = tokenizer.model_max_length
+            uncond_input = tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            uncond_input_text_input_ids = uncond_input.input_ids
+            negative_prompt_embeds = text_encoder({"tokens": np.array(uncond_input_text_input_ids)})
+
+            negative_prompt_embeds = negative_prompt_embeds[0]
+            # =========================结束============================
+            pipe.send({"from_sub":negative_prompt_embeds})
+
+        elif message['state'] == 3:
+            break
+    del text_encoder
+
+            
+class SD15Parallel():
+    def __init__(
+        self,
+        vae_encoder_path,
+        vae_decoder_path,
+        text_encoder_path,
+        tokenizer_path,
+        unet_path,
+        device_id=0,
+        parallel=False,
+        is_tiny_vae = True
+    ):
+        # Tokenizer load
+        self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
+        self.device_id = device_id
+        self.parallel  = parallel
+        
+        # Unet load
+        # self.unet = EngineOV(unet_path, device_id=self.device_id)
+        if self.parallel == True:
+            parent_pipe, child_pipe = multiprocessing.Pipe()
+            self.child_pipe = child_pipe
+            self.parent_pipe= parent_pipe
+            process = multiprocessing.Process(target=parallel_manager, args=(child_pipe, unet_path, 1,))
+            process.start()
+            self.process = process
+            # warmup
+            self.parent_pipe.send({"state":10,"data":np.random.rand(50,60,70)})
+            
+            parent_pipe2, child_pipe2 = multiprocessing.Pipe()
+            self.child_pipe2 = child_pipe2
+            self.parent_pipe2= parent_pipe2
+            process2 = multiprocessing.Process(target=parallel_manager, args=(child_pipe2, unet_path,0,))
+            process2.start()
+            self.process2 = process2
+            self.parent_pipe2.send({"state":10,"data":np.random.rand(40,50,60,7)})
+            
+            # text部分多进程处理
+            self.text_parent_pipe, self.text_child_pipe = multiprocessing.Pipe()
+            text_process = multiprocessing.Process(target=parallel_maneger_prompt_embed, args=(self.text_child_pipe,text_encoder_path,tokenizer_path, 1,))
+            text_process.start()
+            self.text_process = text_process
+            self.text_parent_pipe.send({"state":10,"data":np.random.rand(40,50,60,7)})
+
+            
+            self.text_parent_pipe2, self.text_child_pipe2 = multiprocessing.Pipe()
+            text_process2 = multiprocessing.Process(target=parallel_maneger_prompt_embed, args=(self.text_child_pipe2,text_encoder_path,tokenizer_path, 0,))
+            text_process2.start()
+            self.text_process2 = text_process2
+            self.text_parent_pipe2.send({"state":10,"data":np.random.rand(40,50,60,7)})
+
+
+        # VAE load
+        self.vae_decoder = EngineOV(vae_decoder_path, device_id=self.device_id)
+        self.vae_encoder = EngineOV(vae_encoder_path, device_id=self.device_id)
+        
+        # Text_Encoder load
+        self.text_encoder_path = text_encoder_path
+        self.text_encoder = EngineOV(text_encoder_path, device_id=self.device_id)
+
+        # Scheculer
+        self.scheduler = EulerDiscreteScheduler(**(diffusers_scheduler_config['Euler D']))
+        
+        # other config
+        self.config_force_zeros_for_empty_prompt = False
+        self.prompt_embeds_dtype = torch.float16
+        self.vae_config_scaling_factor = 0.18215
+        if is_tiny_vae:
+            self.vae_config_scaling_factor = 1.0
+
+        self.unet_config_in_channels = 4
+        self.vae_scale_factor = 8
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self._execution_device = 'cpu'
+
+    def set_height_width(self, height, width):
+        pass
+
+    def _preprocess_image(self, image):
+        if image.mode != "RGB":
+            image = image.convert("RGB") # RGBA or other -> RGB
+        image = np.array(image)
+        h, w = image.shape[:-1]
+        if h != 512 or w != 512:
+            image = cv2.resize(
+                image,
+                (512,512),
+                interpolation=cv2.INTER_LANCZOS4
+            )
+        # normalize
+        image = image.astype(np.float32) / 255.0
+        image = 2.0 * image - 1.0
+        # to batch
+        image = image[None].transpose(0, 3, 1, 2)
+        return image
+    
+    def _encode_image(self, init_image):
+        moments = self.vae_encoder({
+            "input.1": self._preprocess_image(init_image)
+        })[0]
+        mean, logvar = np.split(moments, 2, axis=1)
+        logvar = np.clip(logvar, -30.0, 20.0)
+        std = np.exp(logvar * 0.5)
+        latent = (mean + std * np.random.randn(*mean.shape)) * self.vae_config_scaling_factor
+        return torch.from_numpy(latent)
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt,
+        prompt_2 = None,
+        device = None,
+        num_images_per_prompt = 1,
+        do_classifier_free_guidance = True,
+        negative_prompt = None,
+        negative_prompt_2 = None,
+        prompt_embeds = None,
+        negative_prompt_embeds = None,
+        pooled_prompt_embeds = None,
+        negative_pooled_prompt_embeds = None,
+        lora_scale = None,
+    ):
+        device = device or self._execution_device
+
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # Define tokenizers and text encoders
+        tokenizers = self.tokenizer
+        text_encoders = self.text_encoder
+        
+        if prompt_embeds is None:
+            # textual inversion: procecss multi-vector tokens if necessary
+            prompt_embeds_list = []
+            prompts = prompt
+
+            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    prompt = self.maybe_convert_prompt(prompt, tokenizer)
+                text_inputs = tokenizer(
+                    prompt,
+                    padding="max_length",
+                    max_length=tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                text_input_ids = text_inputs.input_ids
+                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                    text_input_ids, untruncated_ids
+                ):
+                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+                    logger.warning(
+                        "The following part of your input was truncated because CLIP can only handle sequences up to"
+                        f" {tokenizer.model_max_length} tokens: {removed_text}"
+                    )
+
+                prompt_embeds_data = text_encoder({"prompt_tokens":text_input_ids.numpy()})
+                
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+
+                pooled_prompt_embeds = torch.tensor(prompt_embeds_data[0])
+                prompt_embeds = prompt_embeds_data[1]
+
+                prompt_embeds_list.append(torch.tensor(prompt_embeds))
+            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+        # get unconditional embeddings for classifier free guidance
+        zero_out_negative_prompt = negative_prompt is None and self.config_force_zeros_for_empty_prompt
+        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+        elif do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+            # uncond_tokens: List[str]
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt, negative_prompt_2]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = [negative_prompt, negative_prompt_2]
+
+            negative_prompt_embeds_list = []
+
+            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
+
+                max_length = prompt_embeds.shape[1]
+                uncond_input = tokenizer(
+                    negative_prompt,
+                    padding="max_length",
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+                
+                negative_prompt_embeds = text_encoder({"prompt_tokens":uncond_input.input_ids.numpy()}) #text_encoder.run(input_node, {"prompt_tokens":uncond_input.input_ids.numpy()})
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                negative_pooled_prompt_embeds = torch.tensor(negative_prompt_embeds[0])
+                negative_prompt_embeds = negative_prompt_embeds[1]
+
+                negative_prompt_embeds_list.append(torch.tensor(negative_prompt_embeds))
+
+            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2_dtype, device=device)
+        bs_embed, seq_len, _ = prompt_embeds.shape
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2_dtype, device=device)
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+            bs_embed * num_images_per_prompt, -1
+        )
+        if do_classifier_free_guidance:
+            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+                bs_embed * num_images_per_prompt, -1
+            )
+
+        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+    
+    def _encode_prompt(
+        self,
+        prompt,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds = None,
+        negative_prompt_embeds = None,
+        lora_scale = None,
+    ):
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            # textual inversion: procecss multi-vector tokens if necessary
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            prompt_embeds = self.text_encoder({"tokens": np.array(text_input_ids)})
+            prompt_embeds = prompt_embeds[0]
+            
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            #uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            uncond_input_text_input_ids = uncond_input.input_ids
+            negative_prompt_embeds = self.text_encoder({"tokens": np.array(uncond_input_text_input_ids)})
+
+            negative_prompt_embeds = negative_prompt_embeds[0]
+            
+        if do_classifier_free_guidance:
+            prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds], axis=0)
+
+        return prompt_embeds
+    
+    def _encode_prompt_parallel(
+        self,
+        prompt,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds = None,
+        negative_prompt_embeds = None,
+        lora_scale = None,
+    ):
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+        
+
+        if prompt_embeds is None:
+            # textual inversion: procecss multi-vector tokens if necessary
+            self.text_parent_pipe.send({"state":1,"prompt":prompt})
+            
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            self.text_parent_pipe2.send({"state":2,"negative_prompt":negative_prompt,
+                                     "prompt":prompt,
+                                     "batch_size":batch_size})
+            negative_prompt_embeds = self.text_parent_pipe2.recv()
+            negative_prompt_embeds = negative_prompt_embeds["from_sub"]
+
+        if prompt_embeds is None:
+            prompt_embeds = self.text_parent_pipe.recv()
+            prompt_embeds = prompt_embeds["from_sub"]
+
+    
+        if do_classifier_free_guidance:
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            prompt_embeds = np.concatenate([negative_prompt_embeds, prompt_embeds], axis=0)
+
+        return prompt_embeds
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(
+        self,
+        prompt,
+        prompt_2,
+        strength,
+        num_inference_steps,
+        callback_steps,
+        negative_prompt=None,
+        negative_prompt_2=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+        if num_inference_steps is None:
+            raise ValueError("`num_inference_steps` cannot be None.")
+        elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0:
+            raise ValueError(
+                f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type"
+                f" {type(num_inference_steps)}."
+            )
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt_2 is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
+            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+    def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):
+        # get the original timestep using init_timestep
+        if denoising_start is None:
+            init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+            t_start = max(num_inference_steps - init_timestep, 0)
+        else:
+            t_start = 0
+
+        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+
+        # Strength is irrelevant if we directly request a timestep to start at;
+        # that is, strength is determined by the denoising_start instead.
+        if denoising_start is not None:
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (denoising_start * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            timesteps = list(filter(lambda ts: ts < discrete_timestep_cutoff, timesteps))
+            return torch.tensor(timesteps), len(timesteps)
+
+        return timesteps, num_inference_steps - t_start
+
+    def prepare_rand_latents(self, batch_size, num_channels_latents, height, width, dtype, generator):
+        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+        latents = randn_tensor(shape, generator=generator, dtype=dtype)
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+        
+    def prepare_latents(
+        self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True
+    ):
+        
+        batch_size = batch_size * num_images_per_prompt
+        init_latents = self._encode_image(image)
+        init_latents = init_latents.to(dtype)
+
+        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+            # expand init_latents for batch_size
+            additional_image_per_prompt = batch_size // init_latents.shape[0]
+            init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
+        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
+            raise ValueError(
+                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+            )
+        else:
+            init_latents = torch.cat([init_latents], dim=0)
+
+        if add_noise:
+            shape = init_latents.shape
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            # get latents
+            init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+
+        latents = init_latents
+
+        return latents
+
+    def _get_add_time_ids(
+        self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, dtype
+    ):
+        if self.config_requires_aesthetics_score:
+            add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))
+            add_neg_time_ids = list(original_size + crops_coords_top_left + (negative_aesthetic_score,))
+        else:
+            add_time_ids = list(original_size + crops_coords_top_left + target_size)
+            add_neg_time_ids = list(original_size + crops_coords_top_left + target_size)
+
+        passed_add_embed_dim = (
+            self.unet_config_addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2_config_projection_dim
+        )
+        expected_add_embed_dim = self.unet_add_embedding_linear_1_in_features
+
+        if (
+            expected_add_embed_dim > passed_add_embed_dim
+            and (expected_add_embed_dim - passed_add_embed_dim) == self.unet_config_addition_time_embed_dim
+        ):
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model."
+            )
+        elif (
+            expected_add_embed_dim < passed_add_embed_dim
+            and (passed_add_embed_dim - expected_add_embed_dim) == self.unet_config_addition_time_embed_dim
+        ):
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model."
+            )
+        elif expected_add_embed_dim != passed_add_embed_dim:
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
+            )
+
+        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
+        add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)
+
+        return add_time_ids, add_neg_time_ids
+
+    def run_unet(self, inputs):
+        inputss = []
+        for i in inputs:
+            inputss.append(torch.tensor(i))
+        inputs1 = [ inputss[0][0].unsqueeze(0), inputss[1], inputss[2][0].unsqueeze(0)  ]
+        inputs2 = [ inputss[0][1].unsqueeze(0), inputss[1], inputss[2][1].unsqueeze(0)  ]
+        self.parent_pipe.send({"state":1, "data":inputs2})
+        self.parent_pipe2.send({"state":1, "data":inputs1})
+        res1    = self.parent_pipe2.recv()
+        res2    = self.parent_pipe.recv()
+        res2    = res2["from_sub"]
+        res1    = res1["from_sub"]
+        res     = np.concatenate([res1[0], res2[0]], axis=0)
+        return [res]
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt = None,
+        prompt_2 = None,
+        init_image = None,
+        strength = 0.7,
+        height = 512,
+        width = 512,
+        num_inference_steps = 50,
+        denoising_start = None,
+        denoising_end = None,
+        guidance_scale = 5.0,
+        negative_prompt = None,
+        negative_prompt_2 = None,
+        num_images_per_prompt = 1,
+        eta = 0.0,
+        generator = None,
+        latents = None,
+        prompt_embeds = None,
+        negative_prompt_embeds = None,
+        pooled_prompt_embeds = None,
+        negative_pooled_prompt_embeds = None,
+        output_type = "pil",
+        callback = None,
+        callback_steps = 1,
+        cross_attention_kwargs = None,
+        original_size = None,
+        crops_coords_top_left = (0, 0),
+        target_size = None,
+        aesthetic_score = 6.0,
+        negative_aesthetic_score = 2.5,
+        **kwargs,
+    ):
+        # 1. Check inputs. Raise error if not correct
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # 3. Encode input prompt
+        text_encoder_lora_scale = (
+            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+        )
+        start_time = time.time()
+        prompt_embeds = self._encode_prompt(
+            prompt=prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            lora_scale=text_encoder_lora_scale,
+        )
+        # 4. Preprocess images
+        # image = self._preprocess_image(image)
+        
+        # 5. Prepare timesteps and latent variables
+        def denoising_value_valid(dnv):
+            return type(denoising_end) == float and 0 < dnv < 1
+
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        if init_image is None:
+            timesteps = self.scheduler.timesteps
+            num_channels_latents = self.unet_config_in_channels
+            latents = self.prepare_rand_latents(
+                batch_size * num_images_per_prompt,
+                num_channels_latents,
+                height,
+                width,
+                self.prompt_embeds_dtype,
+                generator,
+            )
+        else:
+            timesteps, num_inference_steps = self.get_timesteps(
+                num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None)
+
+            latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+            add_noise = True if denoising_start is None else False
+            latents = self.prepare_latents(
+                init_image,
+                latent_timestep,
+                batch_size,
+                num_images_per_prompt,
+                self.prompt_embeds_dtype,
+                device,
+                generator,
+                add_noise,
+            )
+        # 7. Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        original_size = (height, width)
+        target_size = (height, width)
+
+        # 9. Denoising loop
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+        # 9.1 Apply denoising_end
+        if (
+            denoising_end is not None
+            and denoising_start is not None
+            and denoising_value_valid(denoising_end)
+            and denoising_value_valid(denoising_start)
+            and denoising_start >= denoising_end
+        ):
+            raise ValueError(
+                f"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: "
+                + f" {denoising_end} when using type float."
+            )
+        elif denoising_end is not None and denoising_value_valid(denoising_end):
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (denoising_end * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+            timesteps = timesteps[:num_inference_steps]
+            
+        for i, t in tqdm(enumerate(timesteps)):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+            timestamp = np.array([t])
+            # predict the noise residual
+            # import time
+            # tm = time.time()
+            if self.parallel:
+                noise_pred = self.run_unet([
+                    latent_model_input,
+                    timestamp,
+                    prompt_embeds]
+                )[0]
+            else:
+                noise_pred = self.unet([
+                    latent_model_input,
+                    timestamp,
+                    prompt_embeds]
+                )[0]
+            # print(time.time() - tm, noise_pred.shape)
+
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = np.array_split(noise_pred, 2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(torch.from_numpy(noise_pred), t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+            # call the callback, if provided
+            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                if callback is not None and i % callback_steps == 0:
+                    callback(i, t, latents)
+
+
+        latents = latents.to(torch.float32)
+        if not output_type == "latent":
+            init_image = torch.from_numpy(self.vae_decoder([latents / self.vae_config_scaling_factor])[0][0])
+        else:
+            return latents
+        if len(init_image.shape) == 3:
+            init_image = init_image.unsqueeze(0)
+        
+        # if self.parallel:
+        #     self.parent_pipe.send({"state":2})
+        #     self.process.join()
+        #     self.parent_pipe2.send({"state":2})
+        #     self.process2.join()
+        #     self.text_parent_pipe.send({"state":3})
+        #     self.text_process.join()
+        #     self.text_parent_pipe2.send({"state":3})
+        #     self.text_process2.join()
+        init_image = self.image_processor.postprocess(init_image, output_type=output_type)
+        return init_image[0]
+
+
+if __name__ == "__main__":
+
+    pipe = StableDiffusionPipeline(
+            vae_encoder_path='/workspace/demos/onebatch/sdv1.5/models/vae_encoder_1684x_f16.bmodel', # vae_encoder_1684x_bf16.bmodel',
+            vae_decoder_path='/workspace/demos/onebatch/sdv1.5/models/tae1_5.bmodel',
+            text_encoder_path='/workspace/demos/onebatch/sdv1.5/models/text_encoder_1684x_f32.bmodel',
+            tokenizer_path='/workspace/demos/onebatch/sdv1.5/models/tokenizer',
+            unet_path='/workspace/demos/onebatch/sdv1.5/models/unet_1684x_1bs_f16.bmodel',
+            parallel=True
+            # unet_path="/workspace/aa/sdxl/models/unet.bmodel"
+            )
+
+    prompt = "fuzzy cat"
+    negative = "strip"
+    
+    time1 = time.time()
+    image0 = pipe(prompt,
+                 negative_prompt = negative,
+                 num_inference_steps = 20)
+    print('Totoal time: {:4f}s '.format(time.time() - time1))
+    image0.save('test_xinhong.png')
+    
+
+    # 销毁 
+    # image = pipe(prompt + ", blue hair", 
+    #             image=Image.open('my_eyes.png'),
+    #             num_inference_steps = 20)
+    # image.save('girl_img2img.png')
diff --git a/sd/pipeline2.py b/sd/pipeline2.py
index 4d4a3d6..e5246d1 100644
--- a/sd/pipeline2.py
+++ b/sd/pipeline2.py
@@ -8,6 +8,7 @@
 from .scheduler import create_random_tensors, sample
 from PIL import Image, ImageFilter, ImageOps
 import PIL
+from .npuengine import EngineOV
 import time
 from . import masking
 from .utils import PromptChunk, resize_image, flatten, apply_overlay, WrapOutput
@@ -17,7 +18,6 @@
 from . import ultimate
 import random
 import os
-from .newtool import UntoolEngineOV
 
 
 opt_C = 4
@@ -58,28 +58,30 @@ def __init__(
         self.scheduler = scheduler
         self.basemodel_name = basic_model
         st_time = time.time()
-        self.text_encoder = UntoolEngineOV("./models/basic/{}/babes_te_f32.bmodel".format( # encoder_1684x_f32.bmodel
-            basic_model), device_id=self.device_id, pre_malloc=True, output_list=[0], sg=False)
+        self.text_encoder = EngineOV("./models/basic/{}/encoder_1684x_f32.bmodel".format(
+            basic_model), device_id=self.device_id)
         print("====================== Load TE in ", time.time()-st_time)
         
         st_time = time.time()
-        # unet_multize.bmodel
-        self.unet_pure = UntoolEngineOV("./models/basic/{}/unet_1_1684x_f16_attention.bmodel".format(
-            basic_model, extra), device_id=self.device_id, pre_malloc=True, output_list=[0], sg=False)
-        self.unet_pure.default_input()
+        self.unet_pure = EngineOV("./models/basic/{}/unet_multize.bmodel".format(
+            basic_model, extra), device_id=self.device_id)
         print("====================== Load UNET in ", time.time()-st_time)
         
         self.unet_lora = None
+        if os.path.exists("./models/basic/{}/unet_multize_lora.bmodel"):
+            st_time = time.time()
+            self.unet_lora = EngineOV("./models/basic/{}/unet_multize_lora.bmodel".format(
+                basic_model, extra), device_id=self.device_id)
+            print("====================== Load UNET-lora in ", time.time()-st_time)
         
         st_time = time.time()
-        # self.vae_decoder = UntoolEngineOV("./models/basic/{}/{}vae_decoder_f16_512.bmodel".format(#vae_decoder_multize.bmodel".format(
-        self.vae_decoder = UntoolEngineOV("./models/basic/{}/{}vae_decoder_multize.bmodel".format(
-            basic_model, extra), device_id=self.device_id, pre_malloc=True, output_list=[0], sg=False)
+        self.vae_decoder = EngineOV("./models/basic/{}/{}vae_decoder_multize.bmodel".format(
+            basic_model, extra), device_id=self.device_id)
         print("====================== Load VAE DE in ", time.time()-st_time)
         
         st_time = time.time()
-        self.vae_encoder = UntoolEngineOV("./models/basic/{}/{}vae_encoder_multize.bmodel".format(
-            basic_model, extra), device_id=self.device_id, pre_malloc=True, output_list=[0], sg=False)
+        self.vae_encoder = EngineOV("./models/basic/{}/{}vae_encoder_multize.bmodel".format(
+            basic_model, extra), device_id=self.device_id)
         print("====================== Load VAE EN in ", time.time()-st_time)
         
         if controlnet_name:
@@ -695,7 +697,7 @@ def __call__(
             controlnet_weight=1.0,
             use_controlnet=True,
             init_latents=None,
-            enable_prompt_weight=False,
+            enable_prompt_weight=True,
             scheduler=None,
             generator=None
     ):  
@@ -730,10 +732,9 @@ def __call__(
                 max_length=self.tokenizer.model_max_length,
                 truncation=True
             ).input_ids  # 换tokenizer后 此处一致
-            # import pdb; pdb.set_trace()
             # text_embedding use npu engine to inference
             text_embeddings = self.text_encoder(
-                {"tokens": np.array([tokens]).astype(np.int32)})[0]
+                {"tokens": np.array([tokens]).astype(np.int32)})[0] # 一致
             
             # do classifier free guidance
             if guidance_scale > 1.0 or negative_prompt is not None:
@@ -775,14 +776,14 @@ def __call__(
         
         # handle latents
         shape = self.latent_shape
-        # 这里是torch manual seed = seeds[0]
         rand_latents = create_random_tensors(shape, seeds, subseeds=subseeds, subseed_strength=subseed_strength,
                                         seed_resize_from_h=seed_resize_from_h, seed_resize_from_w=seed_resize_from_w)
+        
         if init_image is not None and mask is not None:
             mask = self._preprocess_mask(mask)
         else:
             mask = None
-        if scheduler!="LCM" and not self.is_v2 or (self.is_v2 and scheduler in ['Euler', None]):
+        if scheduler!="LCM"  and not self.is_v2 or (self.is_v2 and scheduler in ['Euler', None]):
             # run scheduler
             if scheduler is not None:
                 self.scheduler = scheduler
@@ -819,11 +820,9 @@ def get_timesteps(scheduler, num_inference_steps, strength):
                 t_start = max(num_inference_steps - init_timestep, 0)
                 timesteps = scheduler.timesteps[t_start * scheduler.order :]
                 return timesteps, num_inference_steps - t_start
-            # import pdb;pdb.set_trace()
+
             self.scheduler.set_timesteps(num_inference_steps)
-            print("scheduler.timesteps: ", self.scheduler.timesteps)
             timesteps, num_inference_steps = get_timesteps(self.scheduler, num_inference_steps, strength)
-            # print(timesteps)
             latent_timestep = timesteps[:1]
 
             # Prepare latent variables
@@ -838,55 +837,24 @@ def get_timesteps(scheduler, num_inference_steps, strength):
             
             # Denoising loop
             # self.scheduler.set_timesteps(num_inference_steps)
-            # self.scheduler.timesteps[-2] = 399
-            # self.scheduler.timesteps[-1] = 109
             timesteps = self.scheduler.timesteps
             do_classifier_free_guidance = guidance_scale > 1.0
             # num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
             extra_step_kwargs = {}
-            start_time = time.time()
-            
-            print(timesteps)
-            print(start_time)
             for i, t in tqdm(enumerate(timesteps)):
                 # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents]*2) if do_classifier_free_guidance else latents
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                controlnet_res = self.generate_zero_controlnet_data()
+                down_block_additional_residuals = controlnet_res[:-1]
+                mid_block_additional_residual = controlnet_res[-1]
                 latent_model_input = latent_model_input.numpy()
                 timestamp = np.array([t])
-                if i == 0:
-                    default_input_map = {
-                        0:{
-                            "data": latent_model_input,
-                            "flag": 0
-                        },
-                        1:{
-                            "data": timestamp,
-                            "flag": 0
-                        },
-                        2: {
-                            "data": text_embeddings[-1], # prompt embedding, no negative
-                            "flag": 0
-                        }
-                    }
-                else:
-                    default_input_map = {
-                        0:{
-                            "data": latent_model_input,
-                            "flag": 0
-                        },
-                        1:{
-                            "data": timestamp,
-                            "flag": 0
-                        }
-                    }
-                #noise_pred = self.unet([latent_model_input,timestamp,text_embeddings,mid_block_additional_residual,*down_block_additional_residuals])[0]
+                noise_pred = self.unet([latent_model_input,timestamp,text_embeddings,mid_block_additional_residual,*down_block_additional_residuals])[0]
                 # perform guidance
-                noise_pred = self.unet.run_with_np(default_input_map)[0]
                 if do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
                     noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
                 # compute the previous noisy sample x_t -> x_t-1
                 noise_pred = torch.from_numpy(noise_pred)
                 latents = self.scheduler.step(noise_pred, 
@@ -894,11 +862,9 @@ def get_timesteps(scheduler, num_inference_steps, strength):
                                               **extra_step_kwargs, 
                                               return_dict=False, 
                                               generator=generator)[0]
-            end_time = time.time()
-            print("time cost: ", end_time - start_time)
             latents = latents.numpy()
         
-        latents = latents / 0.18215 
+        latents = 1 / 0.18215 * latents
         image = self.vae_decoder({"input.1": latents.astype(np.float32)})[0]
         image = (image / 2 + 0.5).clip(0, 1)
         image = (image[0].transpose(1, 2, 0)* 255).astype(np.uint8)  # RGB
diff --git a/sd/pipelineXL.py b/sd/pipelineXL.py
new file mode 100644
index 0000000..e059d78
--- /dev/null
+++ b/sd/pipelineXL.py
@@ -0,0 +1,706 @@
+import inspect
+
+import cv2
+from .npuengine import EngineOV
+from .scheduler import diffusers_scheduler_config
+import numpy as np
+import torch
+from transformers import CLIPTokenizer
+from tqdm import tqdm
+
+from diffusers.schedulers import EulerDiscreteScheduler
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.utils import (
+    logging
+)
+randn_tensor = lambda *x, **kwargs: torch.randn(*x, **kwargs)
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+class StableDiffusionXLPipeline():
+    def __init__(
+        self,
+        vae_encoder_path,
+        vae_decoder_path,
+        text_encoder_path,
+        text_encoder_2_path,
+        tokenizer_path,
+        tokenizer_2_path,
+        unet_path,
+        is_refine=False,
+        device_id=0
+    ):
+        # Tokenizer1, 2 load
+        self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
+        self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path)
+        self.device_id = device_id
+        # Unet load
+        self.unet = EngineOV(unet_path, device_id=self.device_id)
+        
+        # VAE load
+        self.vae_decoder = EngineOV(vae_decoder_path, device_id=self.device_id)
+        self.vae_encoder = EngineOV(vae_encoder_path, device_id=self.device_id)
+        
+        # Text_Encoder1, 2, load (no need to use encoder1 in refine net)
+        self.text_encoder = None if is_refine else EngineOV(text_encoder_path, device_id=self.device_id)
+        self.text_encoder_2 = EngineOV(text_encoder_2_path, device_id=self.device_id)
+
+        # Scheculer
+        self.scheduler = EulerDiscreteScheduler(**(diffusers_scheduler_config['Euler D']))
+        
+        # other config
+        self.is_refiner = is_refine
+        self.config_force_zeros_for_empty_prompt = False
+        self.config_requires_aesthetics_score = is_refine
+        self.unet_add_embedding_linear_1_in_features = 2560 if is_refine else 2816
+        self.text_encoder_2_dtype = torch.float16
+        self.prompt_embeds_dtype = torch.float16
+        self.vae_config_scaling_factor = 0.13025
+        self.unet_config_in_channels = 4
+        self.unet_config_addition_time_embed_dim = 256
+        self.text_encoder_2_config_projection_dim = 1280
+        self.vae_scale_factor = 8
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self._execution_device = 'cpu'
+    
+    
+    def ch_unet(self, unet_path, te1_path='./xl_models/text_encoder_1684x_f32.bmodel'):
+        if self.is_refiner == (unet_path.find('refine') != -1):
+            return
+        self.is_refiner = (unet_path.find('refine') != -1)
+        # Unet load
+        del self.unet
+        self.unet = EngineOV(unet_path, device_id=self.device_id)
+        self.config_requires_aesthetics_score = self.is_refiner
+        self.unet_add_embedding_linear_1_in_features = 2560 if self.is_refiner else 2816
+        if self.is_refiner:
+            del self.text_encoder
+            self.text_encoder = None
+        else:
+            self.text_encoder = EngineOV(te1_path, device_id=self.device_id)
+        
+
+    def _preprocess_image(self, image):
+        if image.mode != "RGB":
+            image = image.convert("RGB") # RGBA or other -> RGB
+        image = np.array(image)
+        h, w = image.shape[:-1]
+        if h != 1024 or w != 1024:
+            image = cv2.resize(
+                image,
+                (1024,1024),
+                interpolation=cv2.INTER_LANCZOS4
+            )
+        # normalize
+        image = image.astype(np.float32) / 255.0
+        image = 2.0 * image - 1.0
+        # to batch
+        image = image[None].transpose(0, 3, 1, 2)
+        return image
+    
+    def _encode_image(self, init_image):
+        moments = self.vae_encoder({
+            "input.1": self._preprocess_image(init_image)
+        })[0]
+        mean, logvar = np.split(moments, 2, axis=1)
+        logvar = np.clip(logvar, -30.0, 20.0)
+        std = np.exp(logvar * 0.5)
+        latent = (mean + std * np.random.randn(*mean.shape)) * self.vae_config_scaling_factor
+        return torch.from_numpy(latent)
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt,
+        prompt_2 = None,
+        device = None,
+        num_images_per_prompt = 1,
+        do_classifier_free_guidance = True,
+        negative_prompt = None,
+        negative_prompt_2 = None,
+        prompt_embeds = None,
+        negative_prompt_embeds = None,
+        pooled_prompt_embeds = None,
+        negative_pooled_prompt_embeds = None,
+        lora_scale = None,
+    ):
+        device = device or self._execution_device
+
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # Define tokenizers and text encoders
+        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
+        text_encoders = (
+            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+        )
+
+        if prompt_embeds is None:
+            prompt_2 = prompt_2 or prompt
+            # textual inversion: procecss multi-vector tokens if necessary
+            prompt_embeds_list = []
+            prompts = [prompt, prompt_2]
+
+            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    prompt = self.maybe_convert_prompt(prompt, tokenizer)
+                text_inputs = tokenizer(
+                    prompt,
+                    padding="max_length",
+                    max_length=tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                text_input_ids = text_inputs.input_ids
+                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                    text_input_ids, untruncated_ids
+                ):
+                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+                    logger.warning(
+                        "The following part of your input was truncated because CLIP can only handle sequences up to"
+                        f" {tokenizer.model_max_length} tokens: {removed_text}"
+                    )
+
+                prompt_embeds_data = text_encoder({"prompt_tokens":text_input_ids.numpy()})
+                
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+
+                pooled_prompt_embeds = torch.tensor(prompt_embeds_data[0])
+                prompt_embeds = prompt_embeds_data[1]
+
+                prompt_embeds_list.append(torch.tensor(prompt_embeds))
+            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+        # get unconditional embeddings for classifier free guidance
+        zero_out_negative_prompt = negative_prompt is None and self.config_force_zeros_for_empty_prompt
+        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+        elif do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+            # uncond_tokens: List[str]
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt, negative_prompt_2]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = [negative_prompt, negative_prompt_2]
+
+            negative_prompt_embeds_list = []
+
+            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
+
+                max_length = prompt_embeds.shape[1]
+                uncond_input = tokenizer(
+                    negative_prompt,
+                    padding="max_length",
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+                
+                negative_prompt_embeds = text_encoder({"prompt_tokens":uncond_input.input_ids.numpy()}) #text_encoder.run(input_node, {"prompt_tokens":uncond_input.input_ids.numpy()})
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                negative_pooled_prompt_embeds = torch.tensor(negative_prompt_embeds[0])
+                negative_prompt_embeds = negative_prompt_embeds[1]
+
+                negative_prompt_embeds_list.append(torch.tensor(negative_prompt_embeds))
+
+            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2_dtype, device=device)
+        bs_embed, seq_len, _ = prompt_embeds.shape
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2_dtype, device=device)
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+            bs_embed * num_images_per_prompt, -1
+        )
+        if do_classifier_free_guidance:
+            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+                bs_embed * num_images_per_prompt, -1
+            )
+
+        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(
+        self,
+        prompt,
+        prompt_2,
+        strength,
+        num_inference_steps,
+        callback_steps,
+        negative_prompt=None,
+        negative_prompt_2=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+        if num_inference_steps is None:
+            raise ValueError("`num_inference_steps` cannot be None.")
+        elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0:
+            raise ValueError(
+                f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type"
+                f" {type(num_inference_steps)}."
+            )
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt_2 is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
+            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+    def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):
+        # get the original timestep using init_timestep
+        if denoising_start is None:
+            init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+            t_start = max(num_inference_steps - init_timestep, 0)
+        else:
+            t_start = 0
+
+        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+
+        # Strength is irrelevant if we directly request a timestep to start at;
+        # that is, strength is determined by the denoising_start instead.
+        if denoising_start is not None:
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (denoising_start * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            timesteps = list(filter(lambda ts: ts < discrete_timestep_cutoff, timesteps))
+            return torch.tensor(timesteps), len(timesteps)
+
+        return timesteps, num_inference_steps - t_start
+
+    def prepare_rand_latents(self, batch_size, num_channels_latents, height, width, dtype, generator):
+        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+        latents = randn_tensor(shape, generator=generator, dtype=dtype)
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+        
+    def prepare_latents(
+        self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True
+    ):
+        
+        batch_size = batch_size * num_images_per_prompt
+        init_latents = self._encode_image(image)
+        init_latents = init_latents.to(dtype)
+
+        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+            # expand init_latents for batch_size
+            additional_image_per_prompt = batch_size // init_latents.shape[0]
+            init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
+        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
+            raise ValueError(
+                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+            )
+        else:
+            init_latents = torch.cat([init_latents], dim=0)
+
+        if add_noise:
+            shape = init_latents.shape
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            # get latents
+            init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+
+        latents = init_latents
+
+        return latents
+
+    def _get_add_time_ids(
+        self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, dtype
+    ):
+        if self.config_requires_aesthetics_score:
+            add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))
+            add_neg_time_ids = list(original_size + crops_coords_top_left + (negative_aesthetic_score,))
+        else:
+            add_time_ids = list(original_size + crops_coords_top_left + target_size)
+            add_neg_time_ids = list(original_size + crops_coords_top_left + target_size)
+
+        passed_add_embed_dim = (
+            self.unet_config_addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2_config_projection_dim
+        )
+        expected_add_embed_dim = self.unet_add_embedding_linear_1_in_features
+
+        if (
+            expected_add_embed_dim > passed_add_embed_dim
+            and (expected_add_embed_dim - passed_add_embed_dim) == self.unet_config_addition_time_embed_dim
+        ):
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model."
+            )
+        elif (
+            expected_add_embed_dim < passed_add_embed_dim
+            and (passed_add_embed_dim - expected_add_embed_dim) == self.unet_config_addition_time_embed_dim
+        ):
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model."
+            )
+        elif expected_add_embed_dim != passed_add_embed_dim:
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
+            )
+
+        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
+        add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)
+
+        return add_time_ids, add_neg_time_ids
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt = None,
+        prompt_2 = None,
+        init_image = None,
+        strength = 0.7,
+        height = 1024,
+        width = 1024,
+        num_inference_steps = 50,
+        denoising_start = None,
+        denoising_end = None,
+        guidance_scale = 5.0,
+        negative_prompt = None,
+        negative_prompt_2 = None,
+        num_images_per_prompt = 1,
+        eta = 0.0,
+        generator = None,
+        latents = None,
+        prompt_embeds = None,
+        negative_prompt_embeds = None,
+        pooled_prompt_embeds = None,
+        negative_pooled_prompt_embeds = None,
+        output_type = "pil",
+        callback = None,
+        callback_steps = 1,
+        cross_attention_kwargs = None,
+        original_size = None,
+        crops_coords_top_left = (0, 0),
+        target_size = None,
+        aesthetic_score = 6.0,
+        negative_aesthetic_score = 2.5,
+    ):
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            strength,
+            num_inference_steps,
+            callback_steps,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+        )
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # 3. Encode input prompt
+        text_encoder_lora_scale = (
+            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+        )
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            lora_scale=text_encoder_lora_scale,
+        )
+
+        # 4. Preprocess images
+        # image = self._preprocess_image(image)
+        
+        # 5. Prepare timesteps and latent variables
+        def denoising_value_valid(dnv):
+            return type(denoising_end) == float and 0 < dnv < 1
+
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        
+        if init_image is None:
+            timesteps = self.scheduler.timesteps
+            num_channels_latents = self.unet_config_in_channels
+            latents = self.prepare_rand_latents(
+                batch_size * num_images_per_prompt,
+                num_channels_latents,
+                height,
+                width,
+                prompt_embeds.dtype,
+                generator,
+            )
+        else:
+            timesteps, num_inference_steps = self.get_timesteps(
+                num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None)
+
+            latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+            add_noise = True if denoising_start is None else False
+            latents = self.prepare_latents(
+                init_image,
+                latent_timestep,
+                batch_size,
+                num_images_per_prompt,
+                prompt_embeds.dtype,
+                device,
+                generator,
+                add_noise,
+            )
+        # 7. Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # height, width = latents.shape[-2:]
+        # height = height * self.vae_scale_factor
+        # width = width * self.vae_scale_factor
+        # original_size = original_size or (height, width)
+        # target_size = target_size or (height, width)
+        original_size = (height, width)
+        target_size = (height, width)
+
+        # 8. Prepare added time ids & embeddings
+        add_text_embeds = pooled_prompt_embeds
+        add_time_ids, add_neg_time_ids = self._get_add_time_ids(
+            original_size,
+            crops_coords_top_left,
+            target_size,
+            aesthetic_score,
+            negative_aesthetic_score,
+            dtype=prompt_embeds.dtype,
+        )
+        add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)
+
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+            if init_image is not None:
+                add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)
+                add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)
+            else:
+                add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+        # 9. Denoising loop
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        # 9.1 Apply denoising_end
+        if (
+            denoising_end is not None
+            and denoising_start is not None
+            and denoising_value_valid(denoising_end)
+            and denoising_value_valid(denoising_start)
+            and denoising_start >= denoising_end
+        ):
+            raise ValueError(
+                f"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: "
+                + f" {denoising_end} when using type float."
+            )
+        elif denoising_end is not None and denoising_value_valid(denoising_end):
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (denoising_end * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+            timesteps = timesteps[:num_inference_steps]
+
+        for i, t in enumerate(timesteps):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+            timestamp = np.array([t])
+            # predict the noise residual
+            noise_pred = self.unet([
+                latent_model_input,
+                timestamp,
+                prompt_embeds,
+                add_text_embeds,
+                add_time_ids]
+            )[0]
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = np.array_split(noise_pred, 2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(torch.from_numpy(noise_pred), t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+            # call the callback, if provided
+            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                if callback is not None and i % callback_steps == 0:
+                    callback(i, t, latents)
+        # del self.unet
+        # del self.vae_encoder
+        
+        latents = latents.to(torch.float32)
+        if not output_type == "latent":
+            init_image = torch.from_numpy(self.vae_decoder([latents / self.vae_config_scaling_factor])[0][0])
+        else:
+            return latents
+        if len(init_image.shape) == 3:
+            init_image = init_image.unsqueeze(0)
+        init_image = self.image_processor.postprocess(init_image, output_type=output_type)
+        return init_image[0]
+
+
+if __name__ == "__main__":
+    
+    from PIL import Image
+    scheduler_dict =  {
+        "_class_name": "EulerDiscreteScheduler",
+        "_diffusers_version": "0.20.0.dev0",
+        "beta_end": 0.012,
+        "beta_schedule": "scaled_linear",
+        "beta_start": 0.00085,
+        "interpolation_type": "linear",
+        "num_train_timesteps": 1000,
+        "prediction_type": "epsilon",
+        "steps_offset": 1,
+        "timestep_spacing": "leading"
+    }
+    scheduler = EulerDiscreteScheduler(**scheduler_dict)
+
+    pipe = StableDiffusionXLPipeline(
+            vae_encoder_path='./xl_models/vae_encoder_fp32_1684x_bf16.bmodel', # vae_encoder_1684x_bf16.bmodel',
+            vae_decoder_path='./xl_models/vae_decoder_1684x_bf16_2.bmodel',
+            text_encoder_path='./xl_models/text_encoder_1684x_f32.bmodel',
+            text_encoder_2_path='./xl_models/text_encoder_2_1684x_f32.bmodel',
+            tokenizer_path='./xl_models/tokenizer',
+            tokenizer_2_path='./xl_models/tokenizer_2',
+            unet_path='./xl_models/unet.bmodel')
+
+    prompt = "happy young girl, cute, 8K, HD"
+    image0 = pipe(prompt,
+                num_inference_steps = 20)
+    image0.save('girl_txt2img.png')
+
+    image = pipe(prompt + ", blue hair", 
+                image=Image.open('my_eyes.png'),
+                num_inference_steps = 20)
+    image.save('girl_img2img.png')
diff --git a/sd/pipelinexl2device.py b/sd/pipelinexl2device.py
new file mode 100644
index 0000000..a991887
--- /dev/null
+++ b/sd/pipelinexl2device.py
@@ -0,0 +1,773 @@
+import inspect
+
+import cv2
+from .npuengine import EngineOV
+from .scheduler import diffusers_scheduler_config
+import numpy as np
+import torch
+from transformers import CLIPTokenizer
+from tqdm import tqdm
+import threading
+from diffusers.schedulers import EulerDiscreteScheduler
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.loaders import LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.utils import (
+    logging
+)
+import random
+import time
+import os
+from multiprocessing import  Process
+import multiprocessing
+def seed_torch(seed=1029):
+    seed=seed%4294967296
+    random.seed(seed)
+    os.environ['PYTHONHASHSEED'] = str(seed)  # 为了禁止hash随机化,使得实验可复现
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    print("set seed to:", seed)
+
+#seed_torch()
+randn_tensor = lambda *x, **kwargs: torch.randn(*x, **kwargs)
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def parallel_manager(pipe,model_path, device_id):
+    model = EngineOV(model_path, device_id=device_id)
+    while True:
+        message = pipe.recv()
+        if message['state'] == 1:
+            data = message['data']
+            print("inner start ",time.time())
+            res  = model(data)
+            print("inner end ",time.time())
+            pipe.send({"from_sub":res})
+        elif message['state'] == 2:
+            break  
+    del model
+
+
+class SDXL2Parallel():
+    def __init__(
+        self,
+        vae_encoder_path,
+        vae_decoder_path,
+        text_encoder_path,
+        text_encoder_2_path,
+        tokenizer_path,
+        tokenizer_2_path,
+        unet_path,
+        is_refine=False,
+        device_id=0,
+        parallel=False
+    ):
+        # Tokenizer1, 2 load
+        self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
+        self.tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_2_path)
+        self.device_id = device_id
+        self.parallel  = parallel
+        # Unet load
+        if self.parallel == True:
+            parent_pipe, child_pipe = multiprocessing.Pipe()
+            self.child_pipe = child_pipe
+            self.parent_pipe= parent_pipe
+            process = multiprocessing.Process(target=parallel_manager, args=(child_pipe, unet_path, device_id,))
+            process.start()
+            self.process = process
+            # warmup
+            self.parent_pipe.send({"state":10,"data":np.random.rand(50,60,70)})
+            
+            parent_pipe2, child_pipe2 = multiprocessing.Pipe()
+            self.child_pipe2 = child_pipe2
+            self.parent_pipe2= parent_pipe2
+            process2 = multiprocessing.Process(target=parallel_manager, args=(child_pipe2, unet_path,device_id+1,))
+            process2.start()
+            self.process2 = process2
+            self.parent_pipe2.send({"state":10,"data":np.random.rand(40,50,60,7)})
+
+        # VAE load
+        self.vae_decoder = EngineOV(vae_decoder_path, device_id=self.device_id)
+        self.vae_encoder = EngineOV(vae_encoder_path, device_id=self.device_id)
+        
+        # Text_Encoder1, 2, load (no need to use encoder1 in refine net)
+        self.text_encoder = None if is_refine else EngineOV(text_encoder_path, device_id=self.device_id)
+        self.text_encoder_2 = EngineOV(text_encoder_2_path, device_id=self.device_id) 
+
+        # Scheculer
+        self.scheduler = EulerDiscreteScheduler(**(diffusers_scheduler_config['Euler D']))
+        
+        # other config
+        self.is_refiner = is_refine
+        self.config_force_zeros_for_empty_prompt = False
+        self.config_requires_aesthetics_score = is_refine
+        self.unet_add_embedding_linear_1_in_features = 2560 if is_refine else 2816
+        self.text_encoder_2_dtype = torch.float16
+        self.prompt_embeds_dtype = torch.float16
+        self.vae_config_scaling_factor = 0.13025
+        self.unet_config_in_channels = 4
+        self.unet_config_addition_time_embed_dim = 256
+        self.text_encoder_2_config_projection_dim = 1280
+        self.vae_scale_factor = 8
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self._execution_device = 'cpu'    
+
+    def set_height_width(self, height, width):
+        pass
+
+    def _preprocess_image(self, image):
+        if image.mode != "RGB":
+            image = image.convert("RGB") # RGBA or other -> RGB
+        image = np.array(image)
+        h, w = image.shape[:-1]
+        if h != 1024 or w != 1024:
+            image = cv2.resize(
+                image,
+                (1024,1024),
+                interpolation=cv2.INTER_LANCZOS4
+            )
+        # normalize
+        image = image.astype(np.float32) / 255.0
+        image = 2.0 * image - 1.0
+        # to batch
+        image = image[None].transpose(0, 3, 1, 2)
+        return image
+    
+    def _encode_image(self, init_image):
+        moments = self.vae_encoder({
+            "input.1": self._preprocess_image(init_image)
+        })[0]
+        mean, logvar = np.split(moments, 2, axis=1)
+        logvar = np.clip(logvar, -30.0, 20.0)
+        std = np.exp(logvar * 0.5)
+        latent = (mean + std * np.random.randn(*mean.shape)) * self.vae_config_scaling_factor
+        return torch.from_numpy(latent)
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt,
+        prompt_2 = None,
+        device = None,
+        num_images_per_prompt = 1,
+        do_classifier_free_guidance = True,
+        negative_prompt = None,
+        negative_prompt_2 = None,
+        prompt_embeds = None,
+        negative_prompt_embeds = None,
+        pooled_prompt_embeds = None,
+        negative_pooled_prompt_embeds = None,
+        lora_scale = None,
+    ):
+        device = device or self._execution_device
+
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # Define tokenizers and text encoders
+        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
+        text_encoders = (
+            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+        )
+
+        if prompt_embeds is None:
+            prompt_2 = prompt_2 or prompt
+            # textual inversion: procecss multi-vector tokens if necessary
+            prompt_embeds_list = []
+            prompts = [prompt, prompt_2]
+
+            for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    prompt = self.maybe_convert_prompt(prompt, tokenizer)
+                text_inputs = tokenizer(
+                    prompt,
+                    padding="max_length",
+                    max_length=tokenizer.model_max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+
+                text_input_ids = text_inputs.input_ids
+                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                    text_input_ids, untruncated_ids
+                ):
+                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+                    logger.warning(
+                        "The following part of your input was truncated because CLIP can only handle sequences up to"
+                        f" {tokenizer.model_max_length} tokens: {removed_text}"
+                    )
+
+                prompt_embeds_data = text_encoder({"prompt_tokens":text_input_ids.numpy()})
+                
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+
+                pooled_prompt_embeds = torch.tensor(prompt_embeds_data[0])
+                prompt_embeds = prompt_embeds_data[1]
+
+                prompt_embeds_list.append(torch.tensor(prompt_embeds))
+            prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+        # get unconditional embeddings for classifier free guidance
+        zero_out_negative_prompt = negative_prompt is None and self.config_force_zeros_for_empty_prompt
+        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+            negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+            negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+        elif do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+            # uncond_tokens: List[str]
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt, negative_prompt_2]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = [negative_prompt, negative_prompt_2]
+
+            negative_prompt_embeds_list = []
+
+            for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+                if isinstance(self, TextualInversionLoaderMixin):
+                    negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
+
+                max_length = prompt_embeds.shape[1]
+                uncond_input = tokenizer(
+                    negative_prompt,
+                    padding="max_length",
+                    max_length=max_length,
+                    truncation=True,
+                    return_tensors="pt",
+                )
+                
+                negative_prompt_embeds = text_encoder({"prompt_tokens":uncond_input.input_ids.numpy()}) #text_encoder.run(input_node, {"prompt_tokens":uncond_input.input_ids.numpy()})
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                negative_pooled_prompt_embeds = torch.tensor(negative_prompt_embeds[0])
+                negative_prompt_embeds = negative_prompt_embeds[1]
+
+                negative_prompt_embeds_list.append(torch.tensor(negative_prompt_embeds))
+
+            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2_dtype, device=device)
+        bs_embed, seq_len, _ = prompt_embeds.shape
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2_dtype, device=device)
+            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+            bs_embed * num_images_per_prompt, -1
+        )
+        if do_classifier_free_guidance:
+            negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+                bs_embed * num_images_per_prompt, -1
+            )
+
+        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(
+        self,
+        prompt,
+        prompt_2,
+        strength,
+        num_inference_steps,
+        callback_steps,
+        negative_prompt=None,
+        negative_prompt_2=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+    ):
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+        if num_inference_steps is None:
+            raise ValueError("`num_inference_steps` cannot be None.")
+        elif not isinstance(num_inference_steps, int) or num_inference_steps <= 0:
+            raise ValueError(
+                f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type"
+                f" {type(num_inference_steps)}."
+            )
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt_2 is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
+            raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
+
+        if negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+        elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+    def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):
+        # get the original timestep using init_timestep
+        if denoising_start is None:
+            init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+            t_start = max(num_inference_steps - init_timestep, 0)
+        else:
+            t_start = 0
+
+        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+
+        # Strength is irrelevant if we directly request a timestep to start at;
+        # that is, strength is determined by the denoising_start instead.
+        if denoising_start is not None:
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (denoising_start * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            timesteps = list(filter(lambda ts: ts < discrete_timestep_cutoff, timesteps))
+            return torch.tensor(timesteps), len(timesteps)
+
+        return timesteps, num_inference_steps - t_start
+
+    def prepare_rand_latents(self, batch_size, num_channels_latents, height, width, dtype, generator):
+        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+        latents = randn_tensor(shape, generator=generator, dtype=dtype)
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+        
+    def prepare_latents(
+        self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None, add_noise=True
+    ):
+        
+        batch_size = batch_size * num_images_per_prompt
+        init_latents = self._encode_image(image)
+        init_latents = init_latents.to(dtype)
+
+        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+            # expand init_latents for batch_size
+            additional_image_per_prompt = batch_size // init_latents.shape[0]
+            init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
+        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
+            raise ValueError(
+                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+            )
+        else:
+            init_latents = torch.cat([init_latents], dim=0)
+
+        if add_noise:
+            shape = init_latents.shape
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            # get latents
+            init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+
+        latents = init_latents
+
+        return latents
+
+    def _get_add_time_ids(
+        self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, dtype
+    ):
+        if self.config_requires_aesthetics_score:
+            add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))
+            add_neg_time_ids = list(original_size + crops_coords_top_left + (negative_aesthetic_score,))
+        else:
+            add_time_ids = list(original_size + crops_coords_top_left + target_size)
+            add_neg_time_ids = list(original_size + crops_coords_top_left + target_size)
+
+        passed_add_embed_dim = (
+            self.unet_config_addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2_config_projection_dim
+        )
+        expected_add_embed_dim = self.unet_add_embedding_linear_1_in_features
+
+        if (
+            expected_add_embed_dim > passed_add_embed_dim
+            and (expected_add_embed_dim - passed_add_embed_dim) == self.unet_config_addition_time_embed_dim
+        ):
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to enable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=True)` to make sure `aesthetic_score` {aesthetic_score} and `negative_aesthetic_score` {negative_aesthetic_score} is correctly used by the model."
+            )
+        elif (
+            expected_add_embed_dim < passed_add_embed_dim
+            and (passed_add_embed_dim - expected_add_embed_dim) == self.unet_config_addition_time_embed_dim
+        ):
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the model."
+            )
+        elif expected_add_embed_dim != passed_add_embed_dim:
+            raise ValueError(
+                f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
+            )
+
+        add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
+        add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)
+
+        return add_time_ids, add_neg_time_ids
+
+    def run_unet1(self,inputs1,t:list):
+        # print time 
+        res = self.unet(inputs1)
+        t.append(res)
+    
+    def run_unet2(self,inputs2, t:list):
+        res = self.unet2(inputs2)
+        t.append(res)
+
+    def run_unet(self, inputs):
+        inputss = []
+        for i in inputs:
+            inputss.append(torch.tensor(i))
+        inputs1 = [ inputss[0][0].unsqueeze(0), inputss[1], inputss[2][0].unsqueeze(0), inputss[3][0].unsqueeze(0), inputss[4][0].unsqueeze(0)  ]
+        inputs2 = [ inputss[0][1].unsqueeze(0), inputss[1], inputss[2][1].unsqueeze(0), inputss[3][1].unsqueeze(0), inputss[4][1].unsqueeze(0)  ]
+        self.parent_pipe.send({"state":1, "data":inputs2})
+        self.parent_pipe2.send({"state":1, "data":inputs1})
+        res1    = self.parent_pipe2.recv()
+        res2    = self.parent_pipe.recv()
+        res2    = res2["from_sub"]
+        res1    = res1["from_sub"]
+        res     = np.concatenate([res1[0], res2[0]], axis=0)
+        return [res]
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt = None,
+        prompt_2 = None,
+        init_image = None,
+        strength = 0.7,
+        height = 1024,
+        width = 1024,
+        num_inference_steps = 50,
+        denoising_start = None,
+        denoising_end = None,
+        guidance_scale = 5.0,
+        negative_prompt = None,
+        negative_prompt_2 = None,
+        num_images_per_prompt = 1,
+        eta = 0.0,
+        generator = None,
+        latents = None,
+        prompt_embeds = None,
+        negative_prompt_embeds = None,
+        pooled_prompt_embeds = None,
+        negative_pooled_prompt_embeds = None,
+        output_type = "pil",
+        callback = None,
+        callback_steps = 1,
+        cross_attention_kwargs = None,
+        original_size = None,
+        crops_coords_top_left = (0, 0),
+        target_size = None,
+        aesthetic_score = 6.0,
+        negative_aesthetic_score = 2.5,
+        **kwargs,
+    ):
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            strength,
+            num_inference_steps,
+            callback_steps,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+        )
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        # 3. Encode input prompt
+        text_encoder_lora_scale = (
+            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+        )
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            negative_prompt_2=negative_prompt_2,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+            lora_scale=text_encoder_lora_scale,
+        )
+
+        # 4. Preprocess images
+        # image = self._preprocess_image(image)
+        
+        # 5. Prepare timesteps and latent variables
+        def denoising_value_valid(dnv):
+            return type(denoising_end) == float and 0 < dnv < 1
+
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        
+        if init_image is None:
+            timesteps = self.scheduler.timesteps
+            num_channels_latents = self.unet_config_in_channels
+            latents = self.prepare_rand_latents(
+                batch_size * num_images_per_prompt,
+                num_channels_latents,
+                height,
+                width,
+                prompt_embeds.dtype,
+                generator,
+            )
+        else:
+            timesteps, num_inference_steps = self.get_timesteps(
+                num_inference_steps, strength, device, denoising_start=denoising_start if denoising_value_valid else None)
+
+            latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+            add_noise = True if denoising_start is None else False
+            latents = self.prepare_latents(
+                init_image,
+                latent_timestep,
+                batch_size,
+                num_images_per_prompt,
+                prompt_embeds.dtype,
+                device,
+                generator,
+                add_noise,
+            )
+        # 7. Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # height, width = latents.shape[-2:]
+        # height = height * self.vae_scale_factor
+        # width = width * self.vae_scale_factor
+        # original_size = original_size or (height, width)
+        # target_size = target_size or (height, width)
+        original_size = (height, width)
+        target_size = (height, width)
+
+        # 8. Prepare added time ids & embeddings
+        add_text_embeds = pooled_prompt_embeds
+        add_time_ids, add_neg_time_ids = self._get_add_time_ids(
+            original_size,
+            crops_coords_top_left,
+            target_size,
+            aesthetic_score,
+            negative_aesthetic_score,
+            dtype=prompt_embeds.dtype,
+        )
+        add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)
+
+        if do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+            if init_image is not None:
+                add_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1)
+                add_time_ids = torch.cat([add_neg_time_ids, add_time_ids], dim=0)
+            else:
+                add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+        # 9. Denoising loop
+
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        # 9.1 Apply denoising_end
+        if (
+            denoising_end is not None
+            and denoising_start is not None
+            and denoising_value_valid(denoising_end)
+            and denoising_value_valid(denoising_start)
+            and denoising_start >= denoising_end
+        ):
+            raise ValueError(
+                f"`denoising_start`: {denoising_start} cannot be larger than or equal to `denoising_end`: "
+                + f" {denoising_end} when using type float."
+            )
+        elif denoising_end is not None and denoising_value_valid(denoising_end):
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (denoising_end * self.scheduler.config.num_train_timesteps)
+                )
+            )
+            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+            timesteps = timesteps[:num_inference_steps]
+
+        for i, t in enumerate(timesteps):
+            # expand the latents if we are doing classifier free guidance
+            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+            timestamp = np.array([t])
+            # predict the noise residual
+            # import time
+            tm = time.time()
+            if self.parallel:
+                noise_pred = self.run_unet([
+                    latent_model_input,
+                    timestamp,
+                    prompt_embeds,
+                    add_text_embeds,
+                    add_time_ids]
+                )[0]
+            else:
+                noise_pred = self.unet([
+                    latent_model_input,
+                    timestamp,
+                    prompt_embeds,
+                    add_text_embeds,
+                    add_time_ids]
+                )[0]
+            # save noise
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = np.array_split(noise_pred, 2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(torch.from_numpy(noise_pred), t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+            # call the callback, if provided
+            if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                if callback is not None and i % callback_steps == 0:
+                    callback(i, t, latents)
+        # del self.unet
+        # del self.vae_encoder
+
+        
+        latents = latents.to(torch.float32)
+        if not output_type == "latent":
+            init_image = torch.from_numpy(self.vae_decoder([latents / self.vae_config_scaling_factor])[0][0])
+        else:
+            return latents
+        if len(init_image.shape) == 3:
+            init_image = init_image.unsqueeze(0)
+        
+        # if self.parallel:
+        #     self.parent_pipe.send({"state":2})
+        #     self.process.join()
+        #     self.parent_pipe2.send({"state":2})
+        #     self.process2.join()
+        init_image = self.image_processor.postprocess(init_image, output_type=output_type)
+        return init_image[0]
+
+
+if __name__ == "__main__":
+    
+    from PIL import Image
+
+    pipe = StableDiffusionXLPipeline(
+            vae_encoder_path='/workspace/aa/sdxl/models/vae_encoder_fp32_1684x_bf16.bmodel', # vae_encoder_1684x_bf16.bmodel',
+            # vae_decoder_path='/workspace/aa/sdxl/models/vae_decoder_1684x_bf16_2.bmodel',
+            vae_decoder_path = "/workspace/zxh_workspace/xl_models/vae_decoder_taesdxl_float16_1024_1024.bmodel",
+            text_encoder_path='/workspace/aa/sdxl/models/text_encoder_1684x_f32.bmodel',
+            text_encoder_2_path='/workspace/aa/sdxl/models/text_encoder_2_1684x_f32.bmodel',
+            tokenizer_path='/workspace/aa/sdxl/models/tokenizer',
+            tokenizer_2_path='/workspace/aa/sdxl/models/tokenizer_2',
+            unet_path='/workspace/demos/onebatch/models/basic/unet.bmodel',
+            parallel=True
+            )
+
+    prompt = "happy young girl, cute, 8K, HD"
+    image0 = pipe(prompt,
+                num_inference_steps = 20)
+    image0.save('girl_txt2img2.png')
+    # 销毁 
+    # image = pipe(prompt + ", blue hair", 
+    #             image=Image.open('my_eyes.png'),
+    #             num_inference_steps = 20)
+    # image.save('girl_img2img.png')
diff --git a/sd/scheduler.py b/sd/scheduler.py
index 8e3b126..a9ff2c0 100644
--- a/sd/scheduler.py
+++ b/sd/scheduler.py
@@ -911,7 +911,7 @@ def setup_img2img_steps(steps,denoising_strength):
 
 
 
-def sample(steps, x,
+def sample(steps, x, 
            scheduler_name=None, 
            guidance_scale=7, 
            controlnet_weight=1.0 , 
diff --git a/sd/untool.py b/sd/untool.py
index 49e8ac0..711cba9 100644
--- a/sd/untool.py
+++ b/sd/untool.py
@@ -12,9 +12,8 @@
 MAX_CMD_GROUP_NUM = 64
 MAX_STAGE_NUM     = 64
 
-#so_path = ctypes.CDLL('/usr/local/untool/lib/libuntpu.so')
-so_path = os.path.abspath(os.path.join(os.path.dirname(__file__), 'libuntpu.so'))
-lib     = ctypes.CDLL(so_path)
+lib = ctypes.CDLL('/usr/local/untool/lib/libuntpu.so')
+
 int_point = ctypes.POINTER(ctypes.c_int)
 int_      = ctypes.c_int
 ulonglong = ctypes.c_ulonglong
@@ -203,16 +202,15 @@ class ModelInfoSC(ctypes.Structure):
 
 lib.find_optimal.restype  = ctypes.POINTER(UntensorS)
 lib.find_optimal.argtypes = [ctypes.POINTER(UntensorS)]
-def find_optimal(tensor) -> ctypes.POINTER(UntensorS):
+def find_optimal(tensor):
     """
     untensor find_optimal(untensor tensor);
     :param tensor: 	ctypes.POINTER(UntensorS)
     """
     return lib.find_optimal(ctypes.byref(tensor))
 
-lib.copy_tensor.restype  = None
 lib.copy_tensor.argtypes = [ctypes.POINTER(UntensorS), ctypes.POINTER(UntensorS)]
-def copy_tensor(src, dst) -> None:
+def copy_tensor(src, dst):
     """
     void copy_tensor (untensor src, untensor dst);
     :param src: 	ctypes.POINTER(UntensorS)
@@ -220,9 +218,8 @@ def copy_tensor(src, dst) -> None:
     """
     return lib.copy_tensor(ctypes.byref(src), ctypes.byref(dst))
 
-lib.copy_tensor_to_device.restype  = None
 lib.copy_tensor_to_device.argtypes = [ctypes.POINTER(UntensorS), ctypes.c_bool]
-def copy_tensor_to_device(tensor, force) -> None:
+def copy_tensor_to_device(tensor, force):
     """
     void copy_tensor_to_device(untensor tensor, bool force);
     :param tensor: 	ctypes.POINTER(UntensorS)
@@ -230,9 +227,8 @@ def copy_tensor_to_device(tensor, force) -> None:
     """
     return lib.copy_tensor_to_device(ctypes.byref(tensor), force)
 
-lib.set_data.restype  = None
 lib.set_data.argtypes = [ctypes.POINTER(UntensorS), ctypes.c_void_p, ctypes.c_size_t, ctypes.c_bool]
-def set_data(tensor, data, size, is_copy) -> None:
+def set_data(tensor, data, size, is_copy):
     """
     void set_data(untensor tensor, void* data, size_t size, bool is_copy);
     :param tensor: 	ctypes.POINTER(UntensorS)
@@ -242,9 +238,8 @@ def set_data(tensor, data, size, is_copy) -> None:
     """
     return lib.set_data(ctypes.byref(tensor), data, ctypes.c_size_t(size), is_copy)
 
-lib.copy_tensor_to_host.restype  = None
 lib.copy_tensor_to_host.argtypes = [ctypes.POINTER(UntensorS), ctypes.c_bool]
-def copy_tensor_to_host(tensor, force) -> None:
+def copy_tensor_to_host(tensor, force):
     """
     void copy_tensor_to_host(untensor tensor, bool force);
     :param tensor: 	ctypes.POINTER(UntensorS)
@@ -252,9 +247,8 @@ def copy_tensor_to_host(tensor, force) -> None:
     """
     return lib.copy_tensor_to_host(ctypes.byref(tensor), force)
 
-lib.show_device_data.restype  = None
 lib.show_device_data.argtypes = [ctypes.POINTER(UntensorS), ctypes.c_int, ctypes.c_int]
-def show_device_data(tensor, start, len) -> None:
+def show_device_data(tensor, start, len):
     """
     void show_device_data(untensor tensor, int start, int len);
     :param tensor: 	ctypes.POINTER(UntensorS)
@@ -265,7 +259,7 @@ def show_device_data(tensor, start, len) -> None:
 
 lib.tensor_shape_is_same.restype  = ctypes.c_bool
 lib.tensor_shape_is_same.argtypes = [ctypes.POINTER(UntensorS), ctypes.POINTER(UntensorS)]
-def tensor_shape_is_same(tensor1, tensor2) -> ctypes.c_bool:
+def tensor_shape_is_same(tensor1, tensor2):
     """
     bool tensor_shape_is_same(untensor tensor1, untensor tensor2);
     :param tensor1: 	ctypes.POINTER(UntensorS)
@@ -275,7 +269,7 @@ def tensor_shape_is_same(tensor1, tensor2) -> ctypes.c_bool:
 
 lib.create_tensor.restype  = ctypes.POINTER(UntensorS)
 lib.create_tensor.argtypes = [ctypes.c_int, ctypes.POINTER(ctypes.c_uint64), ctypes.c_int, ctypes.c_bool, ctypes.c_int]
-def create_tensor(dims, shape, dtype, is_malloc, device_id) -> ctypes.POINTER(UntensorS):
+def create_tensor(dims, shape, dtype, is_malloc, device_id):
     """
     untensor create_tensor(int dims, uint64_t * shape, int dtype, bool is_malloc, int device_id);
     :param dims: 	ctypes.c_int
@@ -286,9 +280,8 @@ def create_tensor(dims, shape, dtype, is_malloc, device_id) -> ctypes.POINTER(Un
     """
     return lib.create_tensor(ctypes.c_int(dims), make2_c_uint64_list(shape), ctypes.c_int(dtype), is_malloc, ctypes.c_int(device_id))
 
-lib.free_data.restype  = None
 lib.free_data.argtypes = [ctypes.POINTER(UntensorS)]
-def free_data(tensor) -> None:
+def free_data(tensor):
     """
     void free_data(untensor tensor);
     :param tensor: 	ctypes.POINTER(UntensorS)
@@ -297,25 +290,23 @@ def free_data(tensor) -> None:
 
 lib.get_tensor_device_address.restype  = ctypes.c_uint64
 lib.get_tensor_device_address.argtypes = [ctypes.POINTER(UntensorS)]
-def get_tensor_device_address(tensor) -> ctypes.c_uint64:
+def get_tensor_device_address(tensor):
     """
     u64 get_tensor_device_address(untensor tensor);
     :param tensor: 	ctypes.POINTER(UntensorS)
     """
     return lib.get_tensor_device_address(ctypes.byref(tensor))
 
-lib.malloc_host_data.restype  = None
 lib.malloc_host_data.argtypes = [ctypes.POINTER(UntensorS)]
-def malloc_host_data(tensor) -> None:
+def malloc_host_data(tensor):
     """
     void malloc_host_data(untensor tensor);
     :param tensor: 	ctypes.POINTER(UntensorS)
     """
     return lib.malloc_host_data(ctypes.byref(tensor))
 
-lib.show_device_mem_data.restype  = None
 lib.show_device_mem_data.argtypes = [ctypes.c_int, ctypes.c_uint64, ctypes.c_size_t, ctypes.c_int, ctypes.c_int, ctypes.c_int]
-def show_device_mem_data(device_id, address, size, source_dtype, start, len) -> None:
+def show_device_mem_data(device_id, address, size, source_dtype, start, len):
     """
     void show_device_mem_data(int device_id, u64 address, size_t size, int source_dtype, int start, int len);
     :param device_id: 	ctypes.c_int
@@ -327,42 +318,28 @@ def show_device_mem_data(device_id, address, size, source_dtype, start, len) ->
     """
     return lib.show_device_mem_data(ctypes.c_int(device_id), ctypes.c_uint64(address), ctypes.c_size_t(size), ctypes.c_int(source_dtype), ctypes.c_int(start), ctypes.c_int(len))
 
-lib.malloc_device.restype  = None
 lib.malloc_device.argtypes = [ctypes.POINTER(UntensorS)]
-def malloc_device(tensor) -> None:
+def malloc_device(tensor):
     """
     void malloc_device(untensor tensor);
     :param tensor: 	ctypes.POINTER(UntensorS)
     """
     return lib.malloc_device(ctypes.byref(tensor))
 
-lib.copy_data_from_tensor_with_flag.restype  = None
-lib.copy_data_from_tensor_with_flag.argtypes = [ctypes.POINTER(UntensorS), ctypes.POINTER(UntensorS), ctypes.c_int]
-def copy_data_from_tensor_with_flag(src, dst, flag) -> None:
-    """
-    void copy_data_from_tensor_with_flag(untensor src, untensor dst, int flag);
-    :param src: 	ctypes.POINTER(UntensorS)
-    :param dst: 	ctypes.POINTER(UntensorS)
-    :param flag: 	ctypes.c_int
-    """
-    return lib.copy_data_from_tensor_with_flag(ctypes.byref(src), ctypes.byref(dst), ctypes.c_int(flag))
-
 
 lib.load_bmodel.restype  = ctypes.c_void_p
-lib.load_bmodel.argtypes = [ctypes.c_char_p, ctypes.c_bool, ctypes.c_bool, ctypes.c_int]
-def load_bmodel(bmodel_path, pre_malloc, default_io_mem, device_id) -> ctypes.c_void_p:
+lib.load_bmodel.argtypes = [ctypes.c_char_p, ctypes.c_bool, ctypes.c_int]
+def load_bmodel(bmodel_path, pre_malloc, device_id):
     """
-    struct un_runtime_s * load_bmodel(const char* bmodel_path, bool pre_malloc, bool default_io_mem, int device_id);
+    struct un_runtime_s * load_bmodel(const char* bmodel_path, bool pre_malloc, int device_id);
     :param bmodel_path: 	ctypes.c_char_p
     :param pre_malloc: 	ctypes.c_bool
-    :param default_io_mem: 	ctypes.c_bool
     :param device_id: 	ctypes.c_int
     """
-    return lib.load_bmodel(str2char_point(bmodel_path), pre_malloc, default_io_mem, ctypes.c_int(device_id))
+    return lib.load_bmodel(str2char_point(bmodel_path), pre_malloc, ctypes.c_int(device_id))
 
-lib.set_input_tensor.restype  = None
 lib.set_input_tensor.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.POINTER(UntensorS)]
-def set_input_tensor(runtime, index, tensor) -> None:
+def set_input_tensor(runtime, index, tensor):
     """
     void set_input_tensor (struct un_runtime_s * runtime, int index, untensor tensor);
     :param runtime: 	ctypes.c_void_p
@@ -371,9 +348,8 @@ def set_input_tensor(runtime, index, tensor) -> None:
     """
     return lib.set_input_tensor(runtime, ctypes.c_int(index), ctypes.byref(tensor))
 
-lib.set_output_tensor.restype  = None
 lib.set_output_tensor.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.POINTER(UntensorS)]
-def set_output_tensor(runtime, index, tensor) -> None:
+def set_output_tensor(runtime, index, tensor):
     """
     void set_output_tensor(struct un_runtime_s * runtime, int index, untensor tensor);
     :param runtime: 	ctypes.c_void_p
@@ -383,9 +359,8 @@ def set_output_tensor(runtime, index, tensor) -> None:
     return lib.set_output_tensor(runtime, ctypes.c_int(index), ctypes.byref(tensor))
 
 
-lib.init_io_tensor.restype  = None
 lib.init_io_tensor.argtypes = [ctypes.c_void_p, ctypes.c_int]
-def init_io_tensor(runtime, stage_dix) -> None:
+def init_io_tensor(runtime, stage_dix):
     """
     void init_io_tensor (struct un_runtime_s * runtime, int stage_dix);
     :param runtime: 	ctypes.c_void_p
@@ -394,9 +369,8 @@ def init_io_tensor(runtime, stage_dix) -> None:
     return lib.init_io_tensor(runtime, ctypes.c_int(stage_dix))
 
 
-lib.fill_io_tensor.restype  = None
 lib.fill_io_tensor.argtypes = [ctypes.c_void_p, ctypes.c_int]
-def fill_io_tensor(runtime, stage_idx) -> None:
+def fill_io_tensor(runtime, stage_idx):
     """
     void fill_io_tensor (struct un_runtime_s * runtime, int stage_idx);
     :param runtime: 	ctypes.c_void_p
@@ -405,28 +379,17 @@ def fill_io_tensor(runtime, stage_idx) -> None:
     return lib.fill_io_tensor(runtime, ctypes.c_int(stage_idx))
 
 
-lib.fill_maxio.restype  = None
-lib.fill_maxio.argtypes = [ctypes.c_void_p]
-def fill_maxio(runtime) -> None:
-    """
-    void fill_maxio(struct un_runtime_s * runtime);
-    :param runtime: 	ctypes.c_void_p
-    """
-    return lib.fill_maxio(runtime)
-
-
 lib.check_all_data.restype  = ctypes.c_bool
 lib.check_all_data.argtypes = [ctypes.c_void_p]
-def check_all_data(runtime) -> ctypes.c_bool:
+def check_all_data(runtime):
     """
     bool check_all_data(struct un_runtime_s * runtime);
     :param runtime: 	ctypes.c_void_p
     """
     return lib.check_all_data(runtime)
 
-lib.copy_input_to_device.restype  = None
 lib.copy_input_to_device.argtypes = [ctypes.c_void_p, ctypes.c_bool]
-def copy_input_to_device(runtime, force) -> None:
+def copy_input_to_device(runtime, force):
     """
     void copy_input_to_device(struct un_runtime_s * runtime, bool force);
     :param runtime: 	ctypes.c_void_p
@@ -434,9 +397,8 @@ def copy_input_to_device(runtime, force) -> None:
     """
     return lib.copy_input_to_device(runtime, force)
 
-lib.copy_output_to_host.restype  = None
 lib.copy_output_to_host.argtypes = [ctypes.c_void_p, ctypes.c_bool]
-def copy_output_to_host(runtime, force) -> None:
+def copy_output_to_host(runtime, force):
     """
     void copy_output_to_host(struct un_runtime_s * runtime, bool force);
     :param runtime: 	ctypes.c_void_p
@@ -444,9 +406,8 @@ def copy_output_to_host(runtime, force) -> None:
     """
     return lib.copy_output_to_host(runtime, force)
 
-lib.check_move_to_device_fill_api.restype  = None
 lib.check_move_to_device_fill_api.argtypes = [ctypes.c_void_p, ctypes.c_bool]
-def check_move_to_device_fill_api(runtime, default_move_coeff) -> None:
+def check_move_to_device_fill_api(runtime, default_move_coeff):
     """
     void check_move_to_device_fill_api(struct un_runtime_s * runtime, bool default_move_coeff);
     :param runtime: 	ctypes.c_void_p
@@ -454,9 +415,8 @@ def check_move_to_device_fill_api(runtime, default_move_coeff) -> None:
     """
     return lib.check_move_to_device_fill_api(runtime, default_move_coeff)
 
-lib.run.restype  = None
 lib.run.argtypes = [ctypes.c_void_p]
-def run(runtime) -> None:
+def run(runtime):
     """
     void run(struct un_runtime_s * runtime);
     :param runtime: 	ctypes.c_void_p
@@ -465,7 +425,7 @@ def run(runtime) -> None:
 
 lib.stage_match_io.restype  = ctypes.c_bool
 lib.stage_match_io.argtypes = [ctypes.c_void_p, ctypes.c_int]
-def stage_match_io(runtime, stage_index) -> ctypes.c_bool:
+def stage_match_io(runtime, stage_index):
     """
     bool stage_match_io(struct un_runtime_s * runtime, int stage_index);
     :param runtime: 	ctypes.c_void_p
@@ -473,116 +433,25 @@ def stage_match_io(runtime, stage_index) -> ctypes.c_bool:
     """
     return lib.stage_match_io(runtime, ctypes.c_int(stage_index))
 
-lib.free_runtime.restype  = None
 lib.free_runtime.argtypes = [ctypes.c_void_p]
-def free_runtime(runtime) -> None:
+def free_runtime(runtime):
     """
     void free_runtime(struct un_runtime_s * runtime);
     :param runtime: 	ctypes.c_void_p
     """
     return lib.free_runtime(runtime)
 
-lib.delete_runtime.restype  = None
 lib.delete_runtime.argtypes = [ctypes.c_void_p]
-def delete_runtime(runtime) -> None:
+def delete_runtime(runtime):
     """
     void delete_runtime(struct un_runtime_s * runtime);
     :param runtime: 	ctypes.c_void_p
     """
     return lib.delete_runtime(runtime)
 
-lib.load_bmodel_sg.restype  = ctypes.c_void_p
-lib.load_bmodel_sg.argtypes = [ctypes.c_char_p, ctypes.c_bool, ctypes.c_bool, ctypes.c_int]
-def load_bmodel_sg(bmodel_path, pre_malloc, default_io_mem, device_id) -> ctypes.c_void_p:
-    """
-    un_runtime_sg_s* load_bmodel_sg(const char* bmodel_path, bool pre_malloc, bool default_io_mem, int device_id);
-    :param bmodel_path: 	ctypes.c_char_p
-    :param pre_malloc: 	ctypes.c_bool
-    :param default_io_mem: 	ctypes.c_bool
-    :param device_id: 	ctypes.c_int
-    """
-    return lib.load_bmodel_sg(str2char_point(bmodel_path), pre_malloc, default_io_mem, ctypes.c_int(device_id))
 
-lib.run_sg.restype  = None
-lib.run_sg.argtypes = [ctypes.c_void_p]
-def run_sg(runtime) -> None:
-    """
-    void run_sg(struct un_runtime_sg_s * runtime);
-    :param runtime: 	ctypes.c_void_p
-    """
-    return lib.run_sg(runtime)
-
-lib.fill_maxio_sg.restype  = None
-lib.fill_maxio_sg.argtypes = [ctypes.c_void_p]
-def fill_maxio_sg(runtime) -> None:
-    """
-    void fill_maxio_sg(struct un_runtime_sg_s * runtime);
-    :param runtime: 	ctypes.c_void_p
-    """
-    return lib.fill_maxio_sg(runtime)
-
-lib.set_stage_sg.restype  = None
-lib.set_stage_sg.argtypes = [ctypes.c_void_p, ctypes.c_int]
-def set_stage_sg(runtime, stage_idx) -> None:
-    """
-    void set_stage_sg(struct un_runtime_sg_s * runtime, int stage_idx);
-    :param runtime: 	ctypes.c_void_p
-    :param stage_idx: 	ctypes.c_int
-    """
-    return lib.set_stage_sg(runtime, ctypes.c_int(stage_idx))
-
-lib.free_runtime_sg.restype  = None
-lib.free_runtime_sg.argtypes = [ctypes.c_void_p]
-def free_runtime_sg(runtime) -> None:
-    """
-    void free_runtime_sg(struct un_runtime_sg_s * runtime);
-    :param runtime: 	ctypes.c_void_p
-    """
-    return lib.free_runtime_sg(runtime)
-
-lib.init_tensor_sg.restype  = None
-lib.init_tensor_sg.argtypes = [ctypes.c_void_p, ctypes.c_int]
-def init_tensor_sg(runtime, stage_idx) -> None:
-    """
-    void init_tensor_sg(struct un_runtime_sg_s * runtime, int stage_idx);
-    :param runtime: 	ctypes.c_void_p
-    :param stage_idx: 	ctypes.c_int
-    """
-    return lib.init_tensor_sg(runtime, ctypes.c_int(stage_idx))
-
-lib.get_model_sg_info.restype  = ctypes.c_void_p
-lib.get_model_sg_info.argtypes = [ctypes.c_void_p]
-def get_model_sg_info(runtime) -> ctypes.c_void_p:
-    """
-    model_info_sg* get_model_sg_info(struct un_runtime_sg_s * runtime);
-    :param runtime: 	ctypes.c_void_p
-    """
-    return lib.get_model_sg_info(runtime)
-
-lib.get_input_tensor_sg.restype  = ctypes.POINTER(UntensorS)
-lib.get_input_tensor_sg.argtypes = [ctypes.c_void_p, ctypes.c_int]
-def get_input_tensor_sg(runtime, index) -> ctypes.POINTER(UntensorS):
-    """
-    untensor get_input_tensor_sg(struct un_runtime_sg_s * runtime, int index);
-    :param runtime: 	ctypes.c_void_p
-    :param index: 	ctypes.c_int
-    """
-    return lib.get_input_tensor_sg(runtime, ctypes.c_int(index))
-
-lib.get_output_tensor_sg.restype  = ctypes.POINTER(UntensorS)
-lib.get_output_tensor_sg.argtypes = [ctypes.c_void_p, ctypes.c_int]
-def get_output_tensor_sg(runtime, index) -> ctypes.POINTER(UntensorS):
-    """
-    untensor get_output_tensor_sg(struct un_runtime_sg_s * runtime, int index);
-    :param runtime: 	ctypes.c_void_p
-    :param index: 	ctypes.c_int
-    """
-    return lib.get_output_tensor_sg(runtime, ctypes.c_int(index))
-
-
-lib.replace_model_weight.restype  = None
 lib.replace_model_weight.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_size_t, ctypes.c_int]
-def replace_model_weight(runtime, new_coeff_data, size, stage_index) -> None:
+def replace_model_weight(runtime, new_coeff_data, size, stage_index):
     """
     void replace_model_weight(struct un_runtime_s * runtime, void* new_coeff_data, size_t size, int stage_index);
     :param runtime: 	ctypes.c_void_p
@@ -594,7 +463,7 @@ def replace_model_weight(runtime, new_coeff_data, size, stage_index) -> None:
 
 lib.get_model_info.restype  = ctypes.c_void_p
 lib.get_model_info.argtypes = [ctypes.c_void_p]
-def get_model_info(runtime) -> ctypes.c_void_p:
+def get_model_info(runtime):
     """
     struct model_info_s * get_model_info(struct un_runtime_s * runtime);
     :param runtime: 	ctypes.c_void_p
@@ -603,7 +472,7 @@ def get_model_info(runtime) -> ctypes.c_void_p:
 
 lib.get_input_tensor.restype  = ctypes.POINTER(UntensorS)
 lib.get_input_tensor.argtypes = [ctypes.c_void_p, ctypes.c_int]
-def get_input_tensor(runtime, index) -> ctypes.POINTER(UntensorS):
+def get_input_tensor(runtime, index):
     """
     untensor get_input_tensor(struct un_runtime_s * runtime, int index);
     :param runtime: 	ctypes.c_void_p
@@ -613,7 +482,7 @@ def get_input_tensor(runtime, index) -> ctypes.POINTER(UntensorS):
 
 lib.get_output_tensor.restype  = ctypes.POINTER(UntensorS)
 lib.get_output_tensor.argtypes = [ctypes.c_void_p, ctypes.c_int]
-def get_output_tensor(runtime, index) -> ctypes.POINTER(UntensorS):
+def get_output_tensor(runtime, index):
     """
     untensor get_output_tensor(struct un_runtime_s * runtime, int index);
     :param runtime: 	ctypes.c_void_p
@@ -621,9 +490,8 @@ def get_output_tensor(runtime, index) -> ctypes.POINTER(UntensorS):
     """
     return lib.get_output_tensor(runtime, ctypes.c_int(index))
 
-lib.set_stage.restype  = None
 lib.set_stage.argtypes = [ctypes.c_void_p, ctypes.c_int]
-def set_stage(runtime, stage_idx) -> None:
+def set_stage(runtime, stage_idx):
     """
     void set_stage(struct un_runtime_s * runtime, int stage_idx);
     :param runtime: 	ctypes.c_void_p
@@ -631,9 +499,8 @@ def set_stage(runtime, stage_idx) -> None:
     """
     return lib.set_stage(runtime, ctypes.c_int(stage_idx))
 
-lib.free_handle.restype  = None
 lib.free_handle.argtypes = [ctypes.c_void_p]
-def free_handle(bm_handle) -> None:
+def free_handle(bm_handle):
     """
     void free_handle(bm_handle_t bm_handle)
     :param bm_handle: 	ctypes.c_void_p
@@ -642,7 +509,7 @@ def free_handle(bm_handle) -> None:
 
 lib.get_device_status.restype  = ctypes.c_int
 lib.get_device_status.argtypes = [ctypes.c_int]
-def get_device_status(device_id) -> ctypes.c_int:
+def get_device_status(device_id):
     """
     int get_device_status(int device_id)
     :param device_id: 	ctypes.c_int
@@ -651,7 +518,7 @@ def get_device_status(device_id) -> ctypes.c_int:
 
 lib.get_handle.restype  = ctypes.c_void_p
 lib.get_handle.argtypes = [ctypes.c_int]
-def get_handle(device_id) -> ctypes.c_void_p:
+def get_handle(device_id):
     """
     bm_handle_t get_handle(int device_id)
     :param device_id: 	ctypes.c_int
@@ -660,25 +527,23 @@ def get_handle(device_id) -> ctypes.c_void_p:
 
 lib.read_bmodel_ctx.restype  = ctypes.c_void_p
 lib.read_bmodel_ctx.argtypes = [ctypes.c_char_p]
-def read_bmodel_ctx(filename) -> ctypes.c_void_p:
+def read_bmodel_ctx(filename):
     """
     ModelCtx* read_bmodel_ctx(const char* filename);
     :param filename: 	ctypes.c_char_p
     """
     return lib.read_bmodel_ctx(str2char_point(filename))
 
-lib.free_bmodel_ctx.restype  = None
 lib.free_bmodel_ctx.argtypes = [ctypes.c_void_p]
-def free_bmodel_ctx(model_ctx) -> None:
+def free_bmodel_ctx(model_ctx):
     """
     void free_bmodel_ctx(ModelCtx * model_ctx);
     :param model_ctx: 	ctypes.c_void_p
     """
     return lib.free_bmodel_ctx(model_ctx)
 
-lib.read_binary_by_address.restype  = None
 lib.read_binary_by_address.argtypes = [ctypes.c_void_p, ctypes.c_uint64, ctypes.c_size_t, ctypes.c_void_p]
-def read_binary_by_address(model_ctx, address, size, data) -> None:
+def read_binary_by_address(model_ctx, address, size, data):
     """
     void read_binary_by_address(ModelCtx * model_ctx, uint64_t address, size_t size, void* data);
     :param model_ctx: 	ctypes.c_void_p
@@ -690,7 +555,7 @@ def read_binary_by_address(model_ctx, address, size, data) -> None:
 
 lib.read_coeff_binary_address.restype  = ctypes.c_uint64
 lib.read_coeff_binary_address.argtypes = [ctypes.c_void_p]
-def read_coeff_binary_address(model_ctx) -> ctypes.c_uint64:
+def read_coeff_binary_address(model_ctx):
     """
     uint64_t read_coeff_binary_address(ModelCtx * model_ctx);
     :param model_ctx: 	ctypes.c_void_p
@@ -699,7 +564,7 @@ def read_coeff_binary_address(model_ctx) -> ctypes.c_uint64:
 
 lib.get_all_coeff.restype  = ctypes.c_void_p
 lib.get_all_coeff.argtypes = [ctypes.c_void_p, ctypes.c_size_t]
-def get_all_coeff(model_ctx, stage_id) -> ctypes.c_void_p:
+def get_all_coeff(model_ctx, stage_id):
     """
     void * get_all_coeff(ModelCtx * model_ctx, size_t stage_id);
     :param model_ctx: 	ctypes.c_void_p
@@ -707,9 +572,8 @@ def get_all_coeff(model_ctx, stage_id) -> ctypes.c_void_p:
     """
     return lib.get_all_coeff(model_ctx, ctypes.c_size_t(stage_id))
 
-lib.read_binary_by_address_with_mem.restype  = None
 lib.read_binary_by_address_with_mem.argtypes = [ctypes.c_void_p, ctypes.c_uint64, ctypes.c_size_t, ctypes.c_void_p]
-def read_binary_by_address_with_mem(data, address, size, data2) -> None:
+def read_binary_by_address_with_mem(data, address, size, data2):
     """
     void read_binary_by_address_with_mem(void* data, uint64_t address, size_t size, void* data2);
     :param data: 	ctypes.c_void_p
@@ -719,9 +583,8 @@ def read_binary_by_address_with_mem(data, address, size, data2) -> None:
     """
     return lib.read_binary_by_address_with_mem(data, ctypes.c_uint64(address), ctypes.c_size_t(size), data2)
 
-lib.write_binary_by_address_with_mem.restype  = None
 lib.write_binary_by_address_with_mem.argtypes = [ctypes.c_void_p, ctypes.c_uint64, ctypes.c_size_t, ctypes.c_void_p]
-def write_binary_by_address_with_mem(dest_data, address, size, src_data) -> None:
+def write_binary_by_address_with_mem(dest_data, address, size, src_data):
     """
     void write_binary_by_address_with_mem(void* dest_data, uint64_t address, size_t size, void* src_data);
     :param dest_data: 	ctypes.c_void_p
@@ -731,9 +594,8 @@ def write_binary_by_address_with_mem(dest_data, address, size, src_data) -> None
     """
     return lib.write_binary_by_address_with_mem(dest_data, ctypes.c_uint64(address), ctypes.c_size_t(size), src_data)
 
-lib.convert_model_info_into_c.restype  = None
 lib.convert_model_info_into_c.argtypes = [ctypes.c_void_p, ctypes.POINTER(ModelInfoSC)]
-def convert_model_info_into_c(model_info, model_info_c) -> None:
+def convert_model_info_into_c(model_info, model_info_c):
     """
     void convert_model_info_into_c(struct model_info_s* model_info, struct model_info_s_c * model_info_c);
     :param model_info: 	ctypes.c_void_p
@@ -741,19 +603,8 @@ def convert_model_info_into_c(model_info, model_info_c) -> None:
     """
     return lib.convert_model_info_into_c(model_info, ctypes.byref(model_info_c))
 
-lib.convert_sg_model_info_into_c.restype  = None
-lib.convert_sg_model_info_into_c.argtypes = [ctypes.c_void_p, ctypes.POINTER(ModelInfoSC)]
-def convert_sg_model_info_into_c(model_info, model_info_c) -> None:
-    """
-    void convert_sg_model_info_into_c(model_info_sg* model_info, struct model_info_s_c * model_info_c);
-    :param model_info: 	ctypes.c_void_p
-    :param model_info_c: 	ctypes.POINTER(ModelInfoSC)
-    """
-    return lib.convert_sg_model_info_into_c(model_info, ctypes.byref(model_info_c))
-
-lib.free_tensor.restype  = None
 lib.free_tensor.argtypes = [ctypes.POINTER(UntensorS)]
-def free_tensor(tensor) -> None:
+def free_tensor(tensor):
     """
     void free_tensor(untensor tensor);
     :param tensor: 	ctypes.POINTER(UntensorS)
@@ -762,16 +613,15 @@ def free_tensor(tensor) -> None:
 
 lib.get_model_info.restype  = ctypes.c_void_p
 lib.get_model_info.argtypes = [ctypes.c_void_p]
-def get_model_info(runtime) -> ctypes.c_void_p:
+def get_model_info(runtime):
     """
     struct model_info_s * get_model_info(struct un_runtime_s * runtime);
     :param runtime: 	ctypes.c_void_p
     """
     return lib.get_model_info(runtime)
 
-lib.print_data_by_fp32.restype  = None
 lib.print_data_by_fp32.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_int, ctypes.c_int, ctypes.c_int]
-def print_data_by_fp32(data, size, dtype, start, len) -> None:
+def print_data_by_fp32(data, size, dtype, start, len):
     """
     void print_data_by_fp32(void* data, int size, int dtype, int start, int len);
     :param data: 	ctypes.c_void_p
@@ -782,9 +632,8 @@ def print_data_by_fp32(data, size, dtype, start, len) -> None:
     """
     return lib.print_data_by_fp32(data, ctypes.c_int(size), ctypes.c_int(dtype), ctypes.c_int(start), ctypes.c_int(len))
 
-lib.data_convert_to_fp32.restype  = None
 lib.data_convert_to_fp32.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_int, ctypes.c_size_t]
-def data_convert_to_fp32(src, target, dtype, size) -> None:
+def data_convert_to_fp32(src, target, dtype, size):
     """
     void data_convert_to_fp32(void* src, void* target, int dtype, size_t size);
     :param src: 	ctypes.c_void_p
@@ -796,195 +645,10 @@ def data_convert_to_fp32(src, target, dtype, size) -> None:
 
 lib.convert_to_fp32.restype  = ctypes.c_float
 lib.convert_to_fp32.argtypes = [ctypes.c_void_p, ctypes.c_int]
-def convert_to_fp32(source, dtype) -> ctypes.c_float:
+def convert_to_fp32(source, dtype):
     """
     float convert_to_fp32(void* source, int dtype);
     :param source: 	ctypes.c_void_p
     :param dtype: 	ctypes.c_int
     """
     return lib.convert_to_fp32(source, ctypes.c_int(dtype))
-
-
-lib.premalloc_input_output_addr.restype  = None
-lib.premalloc_input_output_addr.argtypes = [ctypes.c_void_p]
-def premalloc_input_output_addr(model_info_p) -> None:
-    """
-    void premalloc_input_output_addr(struct model_info_s * model_info_p);
-    :param model_info_p: 	ctypes.c_void_p
-    """
-    return lib.premalloc_input_output_addr(model_info_p)
-
-lib.set_model_info_user_map.restype  = None
-lib.set_model_info_user_map.argtypes = [ctypes.c_void_p]
-def set_model_info_user_map(model_info_p) -> None:
-    """
-    void set_model_info_user_map(struct model_info_s * model_info_p);// 设置 input output addr
-    :param model_info_p: 	ctypes.c_void_p
-    """
-    return lib.set_model_info_user_map(model_info_p)
-
-lib.set_max_input_output.restype  = None
-lib.set_max_input_output.argtypes = [ctypes.c_void_p]
-def set_max_input_output(model_info_p) -> None:
-    """
-    void set_max_input_output(struct model_info_s * model_info_p);
-    :param model_info_p: 	ctypes.c_void_p
-    """
-    return lib.set_max_input_output(model_info_p)
-
-
-lib.move_to_device.restype  = None
-lib.move_to_device.argtypes = [ctypes.c_void_p, ctypes.c_bool]
-def move_to_device(model_info_p, default_move_coeff) -> None:
-    """
-    void move_to_device(struct model_info_s * model_info_p, bool default_move_coeff);
-    :param model_info_p: 	ctypes.c_void_p
-    :param default_move_coeff: 	ctypes.c_bool
-    """
-    return lib.move_to_device(model_info_p, default_move_coeff)
-
-lib.convert_bdc_cmd.restype  = None
-lib.convert_bdc_cmd.argtypes = [ctypes.c_void_p, ctypes.POINTER(ctypes.c_uint32), ctypes.c_bool]
-def convert_bdc_cmd(model_info_p, cmd, is_last) -> None:
-    """
-    void convert_bdc_cmd(struct model_info_s * model_info_p, u32* cmd, bool is_last);// 转换 bdc cmd
-    :param model_info_p: 	ctypes.c_void_p
-    :param cmd: 	ctypes.POINTER(ctypes.c_uint32)
-    :param is_last: 	ctypes.c_bool
-    """
-    return lib.convert_bdc_cmd(model_info_p, cmd, is_last)
-
-lib.get_bdc_cmd_len.restype  = ctypes.c_uint32
-lib.get_bdc_cmd_len.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_uint64, ctypes.c_bool]
-def get_bdc_cmd_len(model_ctx, binary_cmd, start_offset, last_cmd) -> ctypes.c_uint32:
-    """
-    uint32_t get_bdc_cmd_len(ModelCtx* model_ctx, const Binary* binary_cmd, u64 start_offset, bool last_cmd);
-    :param model_ctx: 	ctypes.c_void_p
-    :param binary_cmd: 	ctypes.c_void_p
-    :param start_offset: 	ctypes.c_uint64
-    :param last_cmd: 	ctypes.c_bool
-    """
-    return lib.get_bdc_cmd_len(model_ctx, binary_cmd, ctypes.c_uint64(start_offset), last_cmd)
-
-lib.get_gdma_cmd_len.restype  = ctypes.c_uint32
-lib.get_gdma_cmd_len.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_uint64, ctypes.c_bool]
-def get_gdma_cmd_len(model_ctx, binary_cmd, start_offset, last_cmd) -> ctypes.c_uint32:
-    """
-    uint32_t get_gdma_cmd_len(ModelCtx* model_ctx, const Binary* binary_cmd, u64 start_offset, bool last_cmd);
-    :param model_ctx: 	ctypes.c_void_p
-    :param binary_cmd: 	ctypes.c_void_p
-    :param start_offset: 	ctypes.c_uint64
-    :param last_cmd: 	ctypes.c_bool
-    """
-    return lib.get_gdma_cmd_len(model_ctx, binary_cmd, ctypes.c_uint64(start_offset), last_cmd)
-
-lib.convert_gdma_cmd.restype  = None
-lib.convert_gdma_cmd.argtypes = [ctypes.c_void_p, ctypes.POINTER(ctypes.c_uint32), ctypes.c_bool, ctypes.c_size_t]
-def convert_gdma_cmd(model_info_p, cmd, is_last, stage_idx) -> None:
-    """
-    void convert_gdma_cmd(struct model_info_s * model_info_p, u32* cmd, bool is_last, size_t stage_idx);
-    :param model_info_p: 	ctypes.c_void_p
-    :param cmd: 	ctypes.POINTER(ctypes.c_uint32)
-    :param is_last: 	ctypes.c_bool
-    :param stage_idx: 	ctypes.c_size_t
-    """
-    return lib.convert_gdma_cmd(model_info_p, cmd, is_last, ctypes.c_size_t(stage_idx))
-
-lib.bdc_gdma_move_to_device.restype  = None
-lib.bdc_gdma_move_to_device.argtypes = [ctypes.c_void_p]
-def bdc_gdma_move_to_device(model_info_p) -> None:
-    """
-    void bdc_gdma_move_to_device(struct model_info_s * model_info_p);
-    :param model_info_p: 	ctypes.c_void_p
-    """
-    return lib.bdc_gdma_move_to_device(model_info_p)
-
-lib.stage_bdc_gdma_move_to_device.restype  = None
-lib.stage_bdc_gdma_move_to_device.argtypes = [ctypes.c_void_p, ctypes.c_int]
-def stage_bdc_gdma_move_to_device(model_info_p, stage_idx) -> None:
-    """
-    void stage_bdc_gdma_move_to_device(struct model_info_s * model_info_p, int stage_idx);
-    :param model_info_p: 	ctypes.c_void_p
-    :param stage_idx: 	ctypes.c_int
-    """
-    return lib.stage_bdc_gdma_move_to_device(model_info_p, ctypes.c_int(stage_idx))
-
-
-lib.malloc_coeff_neuron_on_device_set_offset.restype  = None
-lib.malloc_coeff_neuron_on_device_set_offset.argtypes = [ctypes.c_void_p]
-def malloc_coeff_neuron_on_device_set_offset(model_info_p) -> None:
-    """
-    void malloc_coeff_neuron_on_device_set_offset(struct model_info_s * model_info_p);
-    :param model_info_p: 	ctypes.c_void_p
-    """
-    return lib.malloc_coeff_neuron_on_device_set_offset(model_info_p)
-
-lib.set_coeff_neuron_map.restype  = None
-lib.set_coeff_neuron_map.argtypes = [ctypes.c_void_p]
-def set_coeff_neuron_map(model_info_p) -> None:
-    """
-    void set_coeff_neuron_map(struct model_info_s * model_info_p);
-    :param model_info_p: 	ctypes.c_void_p
-    """
-    return lib.set_coeff_neuron_map(model_info_p)
-
-lib.coeff_to_device.restype  = None
-lib.coeff_to_device.argtypes = [ctypes.c_void_p]
-def coeff_to_device(model_info_p) -> None:
-    """
-    void coeff_to_device(struct model_info_s * model_info_p);
-    :param model_info_p: 	ctypes.c_void_p
-    """
-    return lib.coeff_to_device(model_info_p)
-
-
-lib.load_kernel_module.restype  = None
-lib.load_kernel_module.argtypes = [ctypes.c_void_p]
-def load_kernel_module(model_info_p) -> None:
-    """
-    void load_kernel_module(struct model_info_s * model_info_p);
-    :param model_info_p: 	ctypes.c_void_p
-    """
-    return lib.load_kernel_module(model_info_p)
-
-
-lib.bm1684x_fill_api_info.restype  = None
-lib.bm1684x_fill_api_info.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_int]
-def bm1684x_fill_api_info(model_info_p, api_info_p, stage_idx) -> None:
-    """
-    void bm1684x_fill_api_info(struct model_info_s * model_info_p, struct api_info_t * api_info_p, int stage_idx);
-    :param model_info_p: 	ctypes.c_void_p
-    :param api_info_p: 	ctypes.c_void_p
-    :param stage_idx: 	ctypes.c_int
-    """
-    return lib.bm1684x_fill_api_info(model_info_p, api_info_p, ctypes.c_int(stage_idx))
-
-lib.fill_api_info.restype  = None
-lib.fill_api_info.argtypes = [ctypes.c_void_p]
-def fill_api_info(model_info_p) -> None:
-    """
-    void fill_api_info(struct model_info_s * model_info_p);
-    :param model_info_p: 	ctypes.c_void_p
-    """
-    return lib.fill_api_info(model_info_p)
-
-lib.run_model.restype  = None
-lib.run_model.argtypes = [ctypes.c_void_p, ctypes.c_size_t]
-def run_model(model_info_p, stage_idx) -> None:
-    """
-    void run_model(struct model_info_s * model_info_p, size_t stage_idx);
-    :param model_info_p: 	ctypes.c_void_p
-    :param stage_idx: 	ctypes.c_size_t
-    """
-    return lib.run_model(model_info_p, ctypes.c_size_t(stage_idx))
-
-lib.free_model.restype  = None
-lib.free_model.argtypes = [ctypes.c_void_p, ctypes.c_bool]
-def free_model(model_info_p, free_input_output) -> None:
-    """
-    void free_model(struct model_info_s * model_info_p, bool free_input_output);
-    :param model_info_p: 	ctypes.c_void_p
-    :param free_input_output: 	ctypes.c_bool
-    """
-    return lib.free_model(model_info_p, free_input_output)
-version='2023-12-21-14-32-11'
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..73f150e
--- /dev/null
+++ b/test.py
@@ -0,0 +1,33 @@
+from sd import StableDiffusionPipeline, StableDiffusionXLPipeline, SD2_1_MODELS, SD_XL_MODELS
+from sd import SD15Parallel, SDXL2Parallel
+from sd import UpscaleModel
+from PIL import Image
+import numpy as np
+import os
+import time
+
+DEVICE_ID = 0
+BASENAME  = "wujielcm"
+scheduler = "LCM"
+# scheduler = 'Euler a'
+
+pipe = StableDiffusionPipeline(
+    basic_model=BASENAME,
+    scheduler=scheduler
+)
+pipe.set_height_width(512,512)
+
+#npz_file = "/data/aigc/demos/tpukern/test/mw/coeffex/lcm_lora_reorder.npz"
+#match_file = "/data/aigc/demos/tpukern/test/mw/coeffex/unet_match.csv"
+#pipe.unet_pure.load_lora_file(npz_file, match_file)
+
+img_pil = pipe(
+    prompt="a chinese girl, 4k",
+    negative_prompt="low resolution",
+    num_inference_steps=4,
+    scheduler=scheduler,
+    guidance_scale=1.05
+)
+img_pil.save("test.png")
+
+# print(img_pil)
diff --git a/tokenizerV21/merges.txt b/tokenizerV21/merges.txt
new file mode 100644
index 0000000..76e821f
--- /dev/null
+++ b/tokenizerV21/merges.txt
@@ -0,0 +1,48895 @@
+#version: 0.2
+i n
+t h
+a n
+r e
+a r
+e r
+th e</w>
+in g</w>
+o u
+o n
+s t
+o r
+e n
+o n</w>
+a l
+a t
+e r</w>
+i t
+i n</w>
+t o</w>
+r o
+i s</w>
+l e
+i c
+a t</w>
+an d</w>
+e d</w>
+o f</w>
+c h
+o r</w>
+e s</w>
+i l
+e l
+s t</w>
+a c
+o m
+a m
+l o
+a n</w>
+a y</w>
+s h
+r i
+l i
+t i
+f or</w>
+n e
+ð Ł
+r a
+h a
+d e
+o l
+v e</w>
+s i
+u r
+a l</w>
+s e
+' s</w>
+u n
+d i
+b e
+l a
+w h
+o o
+d ay</w>
+e n</w>
+m a
+n o
+l e</w>
+t o
+ou r</w>
+i r
+g h
+w it
+i t</w>
+y o
+a s
+s p
+th is</w>
+t s</w>
+at i
+yo u</w>
+wit h</w>
+a d
+i s
+a b
+l y</w>
+w e
+th e
+t e
+a s</w>
+a g
+v i
+p p
+s u
+h o
+m y</w>
+. .
+b u
+c om
+s e</w>
+er s</w>
+m e
+m e</w>
+al l</w>
+c on
+m o
+k e</w>
+g e
+ou t</w>
+en t</w>
+c o
+f e
+v er
+a r</w>
+f ro
+a u
+p o
+c e</w>
+gh t</w>
+ar e</w>
+s s</w>
+fro m</w>
+c h</w>
+t r
+ou n
+on e</w>
+b y</w>
+d o
+t h</w>
+w or
+er e</w>
+k e
+p ro
+f or
+d s</w>
+b o
+t a
+w e</w>
+g o
+h e
+t er</w>
+in g
+d e</w>
+b e</w>
+ati on</w>
+m or
+a y
+e x
+il l</w>
+p e
+k s</w>
+s c
+l u
+f u
+q u
+v er</w>
+ðŁ ĺ
+j u
+m u
+at e</w>
+an d
+v e
+k ing</w>
+m ar
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diff --git a/tokenizerV21/special_tokens_map.json b/tokenizerV21/special_tokens_map.json
new file mode 100644
index 0000000..ae0c5be
--- /dev/null
+++ b/tokenizerV21/special_tokens_map.json
@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/tokenizerV21/tokenizer_config.json b/tokenizerV21/tokenizer_config.json
new file mode 100644
index 0000000..f4fe219
--- /dev/null
+++ b/tokenizerV21/tokenizer_config.json
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+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "do_lower_case": true,
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+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "model_max_length": 77,
+  "name_or_path": "hf-models/stable-diffusion-v2-768x768/tokenizer",
+  "pad_token": "<|endoftext|>",
+  "special_tokens_map_file": "./special_tokens_map.json",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/tokenizerV21/vocab.json b/tokenizerV21/vocab.json
new file mode 100644
index 0000000..469be27
--- /dev/null
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diff --git a/tokenizer_2/special_tokens_map.json b/tokenizer_2/special_tokens_map.json
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diff --git a/tokenizer_2/tokenizer_config.json b/tokenizer_2/tokenizer_config.json
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new mode 100755
diff --git a/tokenizer_2/vocab.json b/tokenizer_2/vocab.json
old mode 100644
new mode 100755
diff --git a/tpu_perf-1.2.17-py3-none-manylinux2014_aarch64.whl b/tpu_perf-1.2.17-py3-none-manylinux2014_aarch64.whl
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