From 8fe0e9cd61050c2bed3cddbdda32bd4f4cfc41bc Mon Sep 17 00:00:00 2001
From: smthem <2102561739@qq.com>
Date: Thu, 24 Jul 2025 08:49:21 +0800
Subject: [PATCH] init

---
 LICENSE                                       |    42 +
 ObjectClear_node.py                           |   228 +
 README.md                                     |    41 +-
 __init__.py                                   |     4 +
 example.png => example_workflows/example.png  |   Bin
 .../example_mult.png                          |   Bin
 example_workflows/obj.json                    |   832 +
 hugging_face/Logo.png                         |   Bin 0 -> 51952 bytes
 hugging_face/app.py                           |   657 +
 hugging_face/examples/test0.png               |   Bin 0 -> 724188 bytes
 hugging_face/examples/test1.png               |   Bin 0 -> 617064 bytes
 hugging_face/examples/test2.png               |   Bin 0 -> 466625 bytes
 hugging_face/examples/test3.png               |   Bin 0 -> 817000 bytes
 hugging_face/examples/test4.png               |   Bin 0 -> 601835 bytes
 hugging_face/examples/test5.png               |   Bin 0 -> 521952 bytes
 hugging_face/examples/test6.png               |   Bin 0 -> 547566 bytes
 hugging_face/examples/test7.png               |   Bin 0 -> 570037 bytes
 hugging_face/examples/test8.png               |   Bin 0 -> 291381 bytes
 hugging_face/examples/test9.png               |   Bin 0 -> 849215 bytes
 hugging_face/requirements.txt                 |    12 +
 hugging_face/tools/__init__.py                |     0
 hugging_face/tools/base_segmenter.py          |   129 +
 hugging_face/tools/download_util.py           |   109 +
 hugging_face/tools/interact_tools.py          |    99 +
 hugging_face/tools/mask_painter.py            |   288 +
 hugging_face/tools/misc.py                    |   131 +
 hugging_face/tools/painter.py                 |   215 +
 inference_objectclear.py                      |   104 +
 inputs/imgs/test-sample1.jpg                  |   Bin 0 -> 597768 bytes
 inputs/imgs/test-sample2.jpg                  |   Bin 0 -> 341545 bytes
 inputs/imgs/test-sample3.jpg                  |   Bin 0 -> 1729807 bytes
 inputs/imgs/test-sample4.jpg                  |   Bin 0 -> 1708971 bytes
 inputs/imgs/test-sample5.jpg                  |   Bin 0 -> 685943 bytes
 inputs/masks/test-sample1.png                 |   Bin 0 -> 9639 bytes
 inputs/masks/test-sample2.png                 |   Bin 0 -> 17646 bytes
 inputs/masks/test-sample3.png                 |   Bin 0 -> 16144 bytes
 inputs/masks/test-sample4.png                 |   Bin 0 -> 15360 bytes
 inputs/masks/test-sample5.png                 |   Bin 0 -> 24925 bytes
 node_utils.py                                 |   298 +
 objectclear/__init__.py                       |    12 +
 .../__pycache__/__init__.cpython-311.pyc      |   Bin 0 -> 513 bytes
 objectclear/models/__init__.py                |     5 +
 .../__pycache__/__init__.cpython-311.pyc      |   Bin 0 -> 375 bytes
 .../clip_image_encoder.cpython-311.pyc        |   Bin 0 -> 2694 bytes
 .../postfuse_module.cpython-311.pyc           |   Bin 0 -> 3809 bytes
 objectclear/models/clip_image_encoder.py      |    58 +
 objectclear/models/postfuse_module.py         |    56 +
 objectclear/pipelines/__init__.py             |     4 +
 .../__pycache__/__init__.cpython-311.pyc      |   Bin 0 -> 303 bytes
 .../pipeline_objectclear.cpython-311.pyc      |   Bin 0 -> 100759 bytes
 objectclear/pipelines/pipeline_objectclear.py |  2065 +
 .../pipelines/pipeline_objectclear_.py        |  2050 +
 objectclear/utils/__init__.py                 |     5 +
 .../__pycache__/__init__.cpython-311.pyc      |   Bin 0 -> 441 bytes
 .../attention_guided_fusion.cpython-311.pyc   |   Bin 0 -> 5284 bytes
 .../__pycache__/image_utils.cpython-311.pyc   |   Bin 0 -> 1983 bytes
 objectclear/utils/attention_guided_fusion.py  |    78 +
 objectclear/utils/image_utils.py              |    33 +
 pyproject.toml                                |    16 +
 requirements.txt                              |     9 +
 sdxl_repo/image_prompt_encoder/config.json    |    47 +
 sdxl_repo/model_index.json                    |    35 +
 sdxl_repo/postfuse_module/config.json         |     5 +
 sdxl_repo/scheduler/scheduler_config.json     |    18 +
 sdxl_repo/text_encoder/config.json            |    25 +
 sdxl_repo/text_encoder_2/config.json          |    25 +
 sdxl_repo/tokenizer/merges.txt                | 48895 +++++++++++++++
 sdxl_repo/tokenizer/special_tokens_map.json   |    24 +
 sdxl_repo/tokenizer/tokenizer_config.json     |    33 +
 sdxl_repo/tokenizer/vocab.json                | 49410 ++++++++++++++++
 sdxl_repo/tokenizer_2/merges.txt              | 48895 +++++++++++++++
 sdxl_repo/tokenizer_2/special_tokens_map.json |    24 +
 sdxl_repo/tokenizer_2/tokenizer_config.json   |    33 +
 sdxl_repo/tokenizer_2/vocab.json              | 49410 ++++++++++++++++
 sdxl_repo/unet/config.json                    |    78 +
 sdxl_repo/vae/config.json                     |    32 +
 76 files changed, 204566 insertions(+), 3 deletions(-)
 create mode 100644 LICENSE
 create mode 100644 ObjectClear_node.py
 create mode 100644 __init__.py
 rename example.png => example_workflows/example.png (100%)
 rename example_mult.png => example_workflows/example_mult.png (100%)
 create mode 100644 example_workflows/obj.json
 create mode 100644 hugging_face/Logo.png
 create mode 100644 hugging_face/app.py
 create mode 100644 hugging_face/examples/test0.png
 create mode 100644 hugging_face/examples/test1.png
 create mode 100644 hugging_face/examples/test2.png
 create mode 100644 hugging_face/examples/test3.png
 create mode 100644 hugging_face/examples/test4.png
 create mode 100644 hugging_face/examples/test5.png
 create mode 100644 hugging_face/examples/test6.png
 create mode 100644 hugging_face/examples/test7.png
 create mode 100644 hugging_face/examples/test8.png
 create mode 100644 hugging_face/examples/test9.png
 create mode 100644 hugging_face/requirements.txt
 create mode 100644 hugging_face/tools/__init__.py
 create mode 100644 hugging_face/tools/base_segmenter.py
 create mode 100644 hugging_face/tools/download_util.py
 create mode 100644 hugging_face/tools/interact_tools.py
 create mode 100644 hugging_face/tools/mask_painter.py
 create mode 100644 hugging_face/tools/misc.py
 create mode 100644 hugging_face/tools/painter.py
 create mode 100644 inference_objectclear.py
 create mode 100644 inputs/imgs/test-sample1.jpg
 create mode 100644 inputs/imgs/test-sample2.jpg
 create mode 100644 inputs/imgs/test-sample3.jpg
 create mode 100644 inputs/imgs/test-sample4.jpg
 create mode 100644 inputs/imgs/test-sample5.jpg
 create mode 100644 inputs/masks/test-sample1.png
 create mode 100644 inputs/masks/test-sample2.png
 create mode 100644 inputs/masks/test-sample3.png
 create mode 100644 inputs/masks/test-sample4.png
 create mode 100644 inputs/masks/test-sample5.png
 create mode 100644 node_utils.py
 create mode 100644 objectclear/__init__.py
 create mode 100644 objectclear/__pycache__/__init__.cpython-311.pyc
 create mode 100644 objectclear/models/__init__.py
 create mode 100644 objectclear/models/__pycache__/__init__.cpython-311.pyc
 create mode 100644 objectclear/models/__pycache__/clip_image_encoder.cpython-311.pyc
 create mode 100644 objectclear/models/__pycache__/postfuse_module.cpython-311.pyc
 create mode 100644 objectclear/models/clip_image_encoder.py
 create mode 100644 objectclear/models/postfuse_module.py
 create mode 100644 objectclear/pipelines/__init__.py
 create mode 100644 objectclear/pipelines/__pycache__/__init__.cpython-311.pyc
 create mode 100644 objectclear/pipelines/__pycache__/pipeline_objectclear.cpython-311.pyc
 create mode 100644 objectclear/pipelines/pipeline_objectclear.py
 create mode 100644 objectclear/pipelines/pipeline_objectclear_.py
 create mode 100644 objectclear/utils/__init__.py
 create mode 100644 objectclear/utils/__pycache__/__init__.cpython-311.pyc
 create mode 100644 objectclear/utils/__pycache__/attention_guided_fusion.cpython-311.pyc
 create mode 100644 objectclear/utils/__pycache__/image_utils.cpython-311.pyc
 create mode 100644 objectclear/utils/attention_guided_fusion.py
 create mode 100644 objectclear/utils/image_utils.py
 create mode 100644 pyproject.toml
 create mode 100644 requirements.txt
 create mode 100644 sdxl_repo/image_prompt_encoder/config.json
 create mode 100644 sdxl_repo/model_index.json
 create mode 100644 sdxl_repo/postfuse_module/config.json
 create mode 100644 sdxl_repo/scheduler/scheduler_config.json
 create mode 100644 sdxl_repo/text_encoder/config.json
 create mode 100644 sdxl_repo/text_encoder_2/config.json
 create mode 100644 sdxl_repo/tokenizer/merges.txt
 create mode 100644 sdxl_repo/tokenizer/special_tokens_map.json
 create mode 100644 sdxl_repo/tokenizer/tokenizer_config.json
 create mode 100644 sdxl_repo/tokenizer/vocab.json
 create mode 100644 sdxl_repo/tokenizer_2/merges.txt
 create mode 100644 sdxl_repo/tokenizer_2/special_tokens_map.json
 create mode 100644 sdxl_repo/tokenizer_2/tokenizer_config.json
 create mode 100644 sdxl_repo/tokenizer_2/vocab.json
 create mode 100644 sdxl_repo/unet/config.json
 create mode 100644 sdxl_repo/vae/config.json

diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..761ba99
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,42 @@
+# S-Lab License 1.0  
+
+Copyright 2025 S-Lab
+
+Redistribution and use for non-commercial purpose in source and 
+binary forms, with or without modification, are permitted provided 
+that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright 
+   notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright 
+   notice, this list of conditions and the following disclaimer in 
+   the documentation and/or other materials provided with the 
+   distribution.
+
+3. Neither the name of the copyright holder nor the names of its 
+   contributors may be used to endorse or promote products derived 
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT 
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, 
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+In the event that redistribution and/or use for commercial purpose in 
+source or binary forms, with or without modification is required, 
+please contact the contributor(s) of the work.
+
+
+---
+For inquiries permission for commercial use, please consult our team: 
+Jixin Zhao (jixinzhao0101@gmail.com),
+Dr. Shangchen Zhou (shangchenzhou@gmail.com),
+Prof. Chen Change Loy (ccloy@ntu.edu.sg)
diff --git a/ObjectClear_node.py b/ObjectClear_node.py
new file mode 100644
index 0000000..1e55b36
--- /dev/null
+++ b/ObjectClear_node.py
@@ -0,0 +1,228 @@
+# !/usr/bin/env python
+# -*- coding: UTF-8 -*-
+import os
+import torch
+import gc
+import numpy as np
+from omegaconf import OmegaConf
+from pathlib import PureWindowsPath
+import yaml
+        
+from .node_utils import gc_cleanup,tensor2pil_list,load_images,mask2pil_list,tensor_upscale
+from .inference_objectclear import loader_objectclear,inference_objectclear
+import folder_paths
+
+
+########
+MAX_SEED = np.iinfo(np.int32).max
+current_node_path = os.path.dirname(os.path.abspath(__file__))
+
+device = torch.device(
+    "cuda:0") if torch.cuda.is_available() else torch.device(
+    "mps") if torch.backends.mps.is_available() else torch.device(
+    "cpu")
+
+
+######
+
+
+class ObjectClearLoader:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "checkpoint":(["none"] + folder_paths.get_filename_list("checkpoints"),),
+                "vae":(["none"] + folder_paths.get_filename_list("vae"),),
+                "clip":(["none"] + folder_paths.get_filename_list("clip"),),
+                "use_fp16":("BOOLEAN",{"default":True}),
+            },
+        }
+
+    RETURN_TYPES = ("MODEL_ObjectClear",)
+    RETURN_NAMES = ("model",)
+    FUNCTION = "loader_main"
+    CATEGORY = "ObjectClear"
+
+    def loader_main(self,checkpoint,vae,clip,use_fp16,):
+
+
+        # load model
+        print("***********Load model ***********")
+
+        if clip == "none" :
+            raise Exception("Please select a clip model")
+
+        else:
+            weight_path=folder_paths.get_full_path("clip", clip)
+            
+        if vae == "none" :
+            raise Exception("Please select a vae model")
+
+        else:
+            vae_path=folder_paths.get_full_path("vae", vae)
+
+        
+        if checkpoint == "none" :
+            raise Exception("Please select a checkpoint model")
+
+        else:
+            model_path=folder_paths.get_full_path("checkpoints", checkpoint)
+        pipe = loader_objectclear(model_path,vae_path,current_node_path,device,None,use_fp16,weight_path)
+
+        print("***********Load model done ***********")
+
+        gc_cleanup()
+
+        return (pipe,)
+
+
+
+class ObjectClearSampler:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL_ObjectClear",),
+                "iamge": ("IMAGE",),
+                "mask": ("MASK",),
+                "positive": ("CONDITIONING", {
+                "tooltip": "The conditioning describing the attributes you want to include in the image."}),
+                "negative": ("CONDITIONING", {
+                    "tooltip": "The conditioning describing the attributes you want to exclude from the image."}),
+                "vison_emb":("CLIP_VISION_OUTPUT",),
+                "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED,
+                                 "tooltip": "The random seed used for creating the noise."}),
+                "steps": ("INT", {"default": 20, "min": 1, "max": 10000,
+                                  "tooltip": "The number of steps used in the denoising process."}),
+                "cfg": ("FLOAT", {"default": 2.5, "min": 0.1, "max": 20.0, "step": 0.1, "round": 0.01,
+                                  "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}),
+                "strength": (
+                    "FLOAT", {"default": 0.99, "min": 0.0, "max": 1.0, "step": 0.01, "round": 0.001}),
+                "short_size": (
+                    "INT", {"default": 512, "min": 512, "max": 2048, "step": 64,}),
+
+            }}
+
+    RETURN_TYPES = ("IMAGE", )
+    RETURN_NAMES = ("image",)
+    FUNCTION = "sampler_main"
+    CATEGORY = "ObjectClear"
+
+    def sampler_main(self,model,iamge,mask,positive,negative,vison_emb,seed,steps,cfg,strength,short_size):
+
+        #object_embeds=vison_emb["image_embeds"].view(1, 1, -1) # [1, 1, 768]
+        B = vison_emb["image_embeds"].shape[0]
+        obj_embeds = vison_emb["image_embeds"].view(B, 1, -1)
+        if B==1:
+            object_embeds=[obj_embeds]
+        else:
+            object_embeds=list(torch.chunk(obj_embeds, chunks=B))
+        images_list=tensor2pil_list(iamge)
+        masks_list=mask2pil_list(mask)
+        images=inference_objectclear(model,images_list,masks_list,device,positive,negative,steps,seed,strength,cfg,object_embeds,short_size)
+        gc.collect()
+        torch.cuda.empty_cache()
+        return (load_images(images), )
+
+
+class ObjectClearVision:
+    def __init__(self): 
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "iamge": ("IMAGE",),
+                "mask": ("MASK",),
+            }}
+
+    RETURN_TYPES = ("IMAGE", )
+    RETURN_NAMES = ("image",)
+    FUNCTION = "sampler_main"
+    CATEGORY = "ObjectClear"
+
+    def sampler_main(self,iamge,mask):
+        B,_,_,_=iamge.size()
+        B_,_,_=mask.size()
+        if B!=B_:
+            raise ValueError("input image and mask must have same batch size")
+        else:
+            if B==1:
+                mask=mask.unsqueeze(-1) 
+                img=iamge * (mask > 0.5)
+                return (img, )
+            else:
+                mask_list=list(torch.chunk(mask, chunks=B))
+                img_list=list(torch.chunk(iamge, chunks=B))
+                masked_img_list=[]
+                for mask_,img_ in zip(mask_list,img_list):
+                    msk=mask_.unsqueeze(-1) 
+                    image_=img_ * (msk > 0.5)
+                    masked_img_list.append(image_)
+                return (torch.cat(masked_img_list, dim=0), )
+
+class ObjectClearBatch:
+    def __init__(self): 
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "mask1": ("MASK",), # B,h,w
+                "mask2": ("MASK",),
+            }}
+
+    RETURN_TYPES = ("MASK", )
+    RETURN_NAMES = ("mask",)
+    FUNCTION = "sampler_main"
+    CATEGORY = "ObjectClear"
+
+    def sampler_main(self,mask1,mask2):
+
+        B1,height,width=mask1.size()
+        B2,_,_=mask2.size()
+        
+
+        mask1=tensor_upscale(mask1.unsqueeze(-1) ,width,height).squeeze(-1)
+        mask2=tensor_upscale(mask2.unsqueeze(-1) ,width,height).squeeze(-1)
+        
+        if B1==B2:  
+            return (torch.cat((mask1,mask2), dim=0), )    
+        else:
+            out_list = []
+            if B1==1:
+                out_list.append(mask1)
+            else:
+                for i  in list(torch.chunk(mask1, chunks=B1)):
+                    out_list.append(i)
+
+            if B2==1:
+                out_list.append(mask2)
+            else:
+                for i  in list(torch.chunk(mask2, chunks=B2)):
+                    out_list.append(i)
+            return (torch.cat(out_list, dim=0), )
+NODE_CLASS_MAPPINGS = {
+
+    "ObjectClearLoader": ObjectClearLoader,
+    "ObjectClearSampler": ObjectClearSampler,
+    "ObjectClearVision": ObjectClearVision,
+    "ObjectClearBatch": ObjectClearBatch,
+
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ObjectClearLoader": "ObjectClearLoader",
+    "ObjectClearSampler": "ObjectClearSampler",
+    "ObjectClearVision": "ObjectClearVision",
+    "ObjectClearBatch": "ObjectClearBatch"
+
+}
diff --git a/README.md b/README.md
index 433b614..0e7ef42 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,51 @@
 # ComfyUI_ObjectClear
-Complete Object Removal via Object-Effect Attention,you can try it in ComfyUI
+[ObjectClear](https://github.com/zjx0101/ObjectClear):Complete Object Removal via Object-Effect Attention,you can try it in ComfyUI
 
 
-# Coming soon
+# New
+* 此方法优势在于内绘不仅限于主体，比如去除物体，物体的影子也会去掉，表现优异。
 
+# 1 . Installation /安装
 
+In the ./ComfyUI /custom_node directory, run the following:   
+```
+git clone https://github.com/smthemex/ ComfyUI_ObjectClear.git
+```
+---
 
-# Example
+# 2 . Requirements  
+* 通常不需要安装，因为没什么特别的库
+
+```
+pip install -r requirements.txt
+```
+
+# 3 . Model/模型
+* [jixin0101/ObjectClear](https://huggingface.co/jixin0101/ObjectClear/tree/main)  download files as below /下载下方列出的模型:
+```
+├── your comfyUI path/models/checkpoints/
+|   ├──diffusion_pytorch_model.safetensors     #  unet dir/ 目录，rename or not  重命名或者随你，可以下fp16的
+├── your comfyUI path/models/vae/
+|   ├──diffusion_pytorch_model.safetensors     #  vae dir/目录，rename or not  重命名或者随你
+├── models/clip_vision/
+|   ├── diffusion_pytorch_model.safetensors    #  image_prompt_encoder 目录，其实就是这个： clip-vit-large-patch14.safetensors
+├── models/clip/
+|   ├── model.safetensors                                      # postfuse_module dir /目录 rename or not  重命名或者随你 ，可以下fp16 
+|   ├── clip_l.safetensors                                        # normal comfyUI clip 常规comfy的clip
+|   ├── clip_g.safetensors                                       # normal comfyUI clip 常规comfy的clip
+
+```
+* seg /遮罩没有内置，随便用其他的吧。
+
+# 4 . Example
 ![](https://github.com/smthemex/ComfyUI_ObjectClear/blob/main/example_mult.png)
 ![](https://github.com/smthemex/ComfyUI_ObjectClear/blob/main/example.png)
 
 
+# 5 .Tips/使用说明
+* 支持单图或多图，遮罩合并用插件带的batch节点，512是垫图最小短边尺寸（会自动裁切），模型用512*512训练的，按理此尺寸是最好的。
+* Support single or multiple images, use batch nodes with plugins for mask merging, 512 is the input image ‘s minimum short edge size, and the model is trained with 512 * 512, which is theoretically the best size.
+
 #  Citation
 ```
 @InProceedings{zhao2025ObjectClear,
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..77b27fa
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,4 @@
+
+from .ObjectClear_node import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
diff --git a/example.png b/example_workflows/example.png
similarity index 100%
rename from example.png
rename to example_workflows/example.png
diff --git a/example_mult.png b/example_workflows/example_mult.png
similarity index 100%
rename from example_mult.png
rename to example_workflows/example_mult.png
diff --git a/example_workflows/obj.json b/example_workflows/obj.json
new file mode 100644
index 0000000..29d2ce2
--- /dev/null
+++ b/example_workflows/obj.json
@@ -0,0 +1,832 @@
+{
+  "id": "d49a2aaa-7f48-41e7-b5e8-24619dec06fd",
+  "revision": 0,
+  "last_node_id": 30,
+  "last_link_id": 46,
+  "nodes": [
+    {
+      "id": 9,
+      "type": "CLIPTextEncode",
+      "pos": [
+        20464.234375,
+        -950.14892578125
+      ],
+      "size": [
+        400,
+        200
+      ],
+      "flags": {
+        "collapsed": true
+      },
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "clip",
+          "type": "CLIP",
+          "link": 19
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CONDITIONING",
+          "type": "CONDITIONING",
+          "links": [
+            25
+          ]
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CLIPTextEncode"
+      },
+      "widgets_values": [
+        ""
+      ]
+    },
+    {
+      "id": 8,
+      "type": "CLIPTextEncode",
+      "pos": [
+        20460.73046875,
+        -991.8788452148438
+      ],
+      "size": [
+        400,
+        200
+      ],
+      "flags": {
+        "collapsed": true
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diff --git a/hugging_face/app.py b/hugging_face/app.py
new file mode 100644
index 0000000..cf07fcd
--- /dev/null
+++ b/hugging_face/app.py
@@ -0,0 +1,657 @@
+import sys
+sys.path.append("../")
+
+import gradio as gr
+import os 
+from PIL import Image
+import torch
+from diffusers.utils import check_min_version
+from tools.download_util import load_file_from_url
+from tools.painter import mask_painter
+import argparse
+from objectclear.pipelines import ObjectClearPipeline
+import numpy as np
+import torchvision.transforms.functional as TF
+from scipy.ndimage import convolve, zoom
+import cv2
+import time
+
+from tools.interact_tools import SamControler
+from tools.misc import get_device
+import json
+
+check_min_version("0.30.2")
+
+
+def parse_augment():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--device', type=str, default=None)
+    parser.add_argument('--sam_model_type', type=str, default="vit_h")
+    parser.add_argument('--port', type=int, default=8000, help="only useful when running gradio applications")  
+    args = parser.parse_args()
+    
+    if not args.device:
+        args.device = str(get_device())
+
+    return args 
+
+
+def pad_to_multiple(image: np.ndarray, multiple: int = 8):
+    h, w = image.shape[:2]
+    pad_h = (multiple - h % multiple) % multiple
+    pad_w = (multiple - w % multiple) % multiple
+    if image.ndim == 3:
+        padded = np.pad(image, ((0, pad_h), (0, pad_w), (0,0)), mode='reflect')
+    else:
+        padded = np.pad(image, ((0, pad_h), (0, pad_w)), mode='reflect')
+    return padded, h, w
+
+def crop_to_original(image: np.ndarray, h: int, w: int):
+    return image[:h, :w]
+
+def wavelet_blur_np(image: np.ndarray, radius: int):
+    kernel = np.array([
+        [0.0625, 0.125, 0.0625],
+        [0.125,  0.25,  0.125],
+        [0.0625, 0.125, 0.0625]
+    ], dtype=np.float32)
+
+    blurred = np.empty_like(image)
+    for c in range(image.shape[0]):
+        blurred_c = convolve(image[c], kernel, mode='nearest')
+        if radius > 1:
+            blurred_c = zoom(zoom(blurred_c, 1 / radius, order=1), radius, order=1)
+        blurred[c] = blurred_c
+    return blurred
+
+def wavelet_decomposition_np(image: np.ndarray, levels=5):
+    high_freq = np.zeros_like(image)
+    for i in range(levels):
+        radius = 2 ** i
+        low_freq = wavelet_blur_np(image, radius)
+        high_freq += (image - low_freq)
+        image = low_freq
+    return high_freq, low_freq
+
+def wavelet_reconstruction_np(content_feat: np.ndarray, style_feat: np.ndarray):
+    content_high, _ = wavelet_decomposition_np(content_feat)
+    _, style_low = wavelet_decomposition_np(style_feat)
+    return content_high + style_low
+
+def wavelet_color_fix_np(fused: np.ndarray, mask: np.ndarray) -> np.ndarray:
+    fused_np = fused.astype(np.float32) / 255.0
+    mask_np = mask.astype(np.float32) / 255.0
+
+    fused_np = fused_np.transpose(2, 0, 1)
+    mask_np = mask_np.transpose(2, 0, 1)
+
+    result_np = wavelet_reconstruction_np(fused_np, mask_np)
+
+    result_np = result_np.transpose(1, 2, 0)
+    result_np = np.clip(result_np * 255.0, 0, 255).astype(np.uint8)
+
+    return result_np
+
+def fuse_with_wavelet(ori: np.ndarray, removed: np.ndarray, attn_map: np.ndarray, multiple: int = 8):
+    H, W = ori.shape[:2]
+    attn_map = attn_map.astype(np.float32)
+    _, attn_map = cv2.threshold(attn_map, 128, 255, cv2.THRESH_BINARY)
+    am = attn_map.astype(np.float32)
+    am = am/255.0
+    am_up = cv2.resize(am, (W, H), interpolation=cv2.INTER_NEAREST)
+
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (21,21))
+    am_d = cv2.dilate(am_up, kernel, iterations=1)
+    am_d = cv2.GaussianBlur(am_d.astype(np.float32), (9,9), sigmaX=2)
+
+    am_merged = np.maximum(am_up, am_d)
+    am_merged = np.clip(am_merged, 0, 1)
+
+    attn_up_3c = np.stack([am_merged]*3, axis=-1)
+    attn_up_ori_3c = np.stack([am_up]*3, axis=-1)
+
+    ori_out = ori * (1 - attn_up_ori_3c)
+    rem_out = removed * (1 - attn_up_ori_3c)
+
+    ori_pad, h0, w0 = pad_to_multiple(ori_out, multiple)
+    rem_pad, _, _   = pad_to_multiple(rem_out, multiple)
+
+    wave_rgb = wavelet_color_fix_np(ori_pad, rem_pad)
+    wave = crop_to_original(wave_rgb, h0, w0)
+    # fusion
+    fused = (wave * (1 - attn_up_3c) + removed * attn_up_3c).astype(np.uint8)
+    return fused
+
+
+def resize_by_short_side(image, target_short=512, resample=Image.BICUBIC):
+    w, h = image.size
+    if w < h:
+        new_w = target_short
+        new_h = int(h * target_short / w)
+        new_h = (new_h + 15) // 16 * 16 
+    else:
+        new_h = target_short
+        new_w = int(w * target_short / h)
+        new_w = (new_w + 15) // 16 * 16
+    return image.resize((new_w, new_h), resample=resample)
+
+# convert points input to prompt state
+def get_prompt(click_state, click_input):
+    inputs = json.loads(click_input)
+    points = click_state[0]
+    labels = click_state[1]
+    for input in inputs:
+        points.append(input[:2])
+        labels.append(input[2])
+    click_state[0] = points
+    click_state[1] = labels
+    prompt = {
+        "prompt_type":["click"],
+        "input_point":click_state[0],
+        "input_label":click_state[1],
+        "multimask_output":"True",
+    }
+    return prompt
+
+# use sam to get the mask
+def sam_refine(image_state, point_prompt, click_state, evt:gr.SelectData):
+    if point_prompt == "Positive":
+        coordinate = "[[{},{},1]]".format(evt.index[0], evt.index[1])
+    else:
+        coordinate = "[[{},{},0]]".format(evt.index[0], evt.index[1])
+    
+    # prompt for sam model
+    model.samcontroler.sam_controler.reset_image()
+    model.samcontroler.sam_controler.set_image(image_state["origin_image"])
+    prompt = get_prompt(click_state=click_state, click_input=coordinate)
+
+    mask, logit, painted_image = model.first_frame_click( 
+                                                      image=image_state["origin_image"], 
+                                                      points=np.array(prompt["input_point"]),
+                                                      labels=np.array(prompt["input_label"]),
+                                                      multimask=prompt["multimask_output"],
+                                                      )
+    image_state["mask"] = mask
+    image_state["logit"] = logit
+    image_state["painted_image"] = painted_image
+
+    return painted_image, image_state, click_state
+
+
+def add_multi_mask(image_state, interactive_state, mask_dropdown):
+    mask = image_state["mask"]
+    interactive_state["masks"].append(mask)
+    interactive_state["mask_names"].append("mask_{:03d}".format(len(interactive_state["masks"])))
+    mask_dropdown.append("mask_{:03d}".format(len(interactive_state["masks"])))
+    select_frame = show_mask(image_state, interactive_state, mask_dropdown)
+
+    return interactive_state, gr.update(choices=interactive_state["mask_names"], value=mask_dropdown), select_frame, [[],[]]
+
+def clear_click(image_state, click_state):
+    click_state = [[],[]]
+    input_image = image_state["origin_image"]
+    return input_image, click_state
+
+def remove_multi_mask(interactive_state, click_state, image_state):
+    interactive_state["mask_names"]= []
+    interactive_state["masks"] = []
+    click_state = [[],[]]
+    input_image = image_state["origin_image"]
+
+    return interactive_state, gr.update(choices=[],value=[]), input_image, click_state
+
+def show_mask(image_state, interactive_state, mask_dropdown):
+    mask_dropdown.sort()
+    if image_state["origin_image"] is not None:
+        select_frame = image_state["origin_image"]
+        for i in range(len(mask_dropdown)):
+            mask_number = int(mask_dropdown[i].split("_")[1]) - 1
+            mask = interactive_state["masks"][mask_number]
+            select_frame = mask_painter(select_frame, mask.astype('uint8'), mask_color=mask_number+2)
+        
+        return select_frame
+
+def upload_and_reset(image_input, interactive_state):
+    click_state = [[], []]
+
+    interactive_state["mask_names"]= []
+    interactive_state["masks"] = []
+
+    image_state, image_info, image_input = update_image_state_on_upload(image_input)
+
+    return (
+        image_state,
+        image_info,
+        image_input,
+        interactive_state,
+        click_state,
+        gr.update(choices=[], value=[]),
+    )
+
+def update_image_state_on_upload(image_input):
+    frame = image_input 
+
+    image_size = (frame.size[1], frame.size[0])
+
+    frame_np = np.array(frame)
+
+    image_state = {
+        "origin_image": frame_np,
+        "painted_image": frame_np.copy(),
+        "mask": np.zeros((image_size[0], image_size[1]), np.uint8),
+        "logit": None,
+    }
+
+    image_info = f"Image Name: uploaded.png,\nImage Size: {image_size}"
+    
+    model.samcontroler.sam_controler.reset_image()
+    model.samcontroler.sam_controler.set_image(frame_np)
+
+    return image_state, image_info, image_input
+        
+
+# SAM generator
+class MaskGenerator():
+    def __init__(self, sam_checkpoint, args):
+        self.args = args
+        self.samcontroler = SamControler(sam_checkpoint, args.sam_model_type, args.device)
+
+    def first_frame_click(self, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True):
+        mask, logit, painted_image = self.samcontroler.first_frame_click(image, points, labels, multimask)
+        return mask, logit, painted_image
+    
+
+# args, defined in track_anything.py
+args = parse_augment()
+sam_checkpoint_url_dict = {
+    'vit_h': "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth",
+    'vit_l': "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth",
+    'vit_b': "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth"
+}
+
+sam_checkpoint = load_file_from_url(sam_checkpoint_url_dict[args.sam_model_type])
+# initialize sams
+model = MaskGenerator(sam_checkpoint, args)
+
+# Build pipeline
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+pipe = ObjectClearPipeline.from_pretrained_with_custom_modules(
+    "jixin0101/ObjectClear",
+    torch_dtype=torch.float16,
+    variant='fp16',
+    apply_attention_guided_fusion=True,
+    cache_dir=None,
+)
+
+pipe.to(device)
+
+def process(image_state, interactive_state, mask_dropdown, guidance_scale, seed, num_inference_steps, strength         
+            ):
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    image_np = image_state["origin_image"]
+    image = Image.fromarray(image_np)
+    if interactive_state["masks"]:
+        if len(mask_dropdown) == 0:
+            mask_dropdown = ["mask_001"]
+        mask_dropdown.sort()
+        template_mask = interactive_state["masks"][int(mask_dropdown[0].split("_")[1]) - 1] * (int(mask_dropdown[0].split("_")[1]))
+        for i in range(1,len(mask_dropdown)):
+            mask_number = int(mask_dropdown[i].split("_")[1]) - 1 
+            template_mask = np.clip(template_mask+interactive_state["masks"][mask_number]*(mask_number+1), 0, mask_number+1)
+        image_state["mask"]= template_mask
+    else:      
+        template_mask = image_state["mask"]
+    mask = Image.fromarray((template_mask).astype(np.uint8) * 255)
+    image_or = image.copy()
+    
+    image = image.convert("RGB")
+    mask = mask.convert("RGB")
+    
+    image = resize_by_short_side(image, 512, resample=Image.BICUBIC)
+    mask = resize_by_short_side(mask, 512, resample=Image.NEAREST)
+    
+    w, h = image.size
+    
+    result = pipe(
+        prompt="remove the instance of object",
+        image=image,
+        mask_image=mask,
+        generator=generator,
+        num_inference_steps=num_inference_steps,
+        strength=strength,
+        guidance_scale=guidance_scale,
+        height=h,
+        width=w,
+    )
+    fused_img_pil = result.images[0]
+
+    return fused_img_pil.resize((image_or.size[:2])), (image.resize((image_or.size[:2])), fused_img_pil.resize((image_or.size[:2])))
+
+import base64
+with open("./Logo.png", "rb") as f:
+    img_bytes = f.read()
+img_b64 = base64.b64encode(img_bytes).decode()
+
+html_img = f'''
+<div style="display:flex; justify-content:center; align-items:center; width:100%;">
+    <img src="data:image/png;base64,{img_b64}" style="border:none; width:200px; height:auto;"/>
+</div>
+'''
+
+tutorial_url = "https://github.com/zjx0101/ObjectClear/releases/download/media/tutorial.mp4"
+assets_path = './'
+load_file_from_url(tutorial_url, assets_path)
+
+description = r"""
+<b>Official Gradio demo</b> for <a href='https://github.com/zjx0101/ObjectClear' target='_blank'><b>ObjectClear: Complete Object Removal via Object-Effect Attention</b></a>.<br>
+🔥 ObjectClear is an object removal model that can jointly eliminate the target object and its associated effects leveraging Object-Effect Attention, while preserving background consistency.<br>
+🖼️ Try to drop your image, assign the target masks with a few clicks, and get the object removal results!<br>
+
+*Note: All input images are temporarily resized (shorter side = 512 pixels) during inference to match the training resolution. Final outputs are restored to the original resolution.<br>*
+"""
+
+article = r"""<h3>
+<b>If ObjectClear is helpful, please help to star the <a href='https://github.com/zjx0101/ObjectClear' target='_blank'>Github Repo</a>. Thanks!</b></h3>
+<hr>
+
+📑 **Citation**
+<br>
+If our work is useful for your research, please consider citing:
+```bibtex
+@InProceedings{zhao2025ObjectClear,
+    title     = {{ObjectClear}: Complete Object Removal via Object-Effect Attention},
+    author    = {Zhao, Jixin and Zhou, Shangchen and Wang, Zhouxia and Yang, Peiqing and Loy, Chen Change},
+    booktitle = {arXiv preprint arXiv:2505.22636},
+    year      = {2025}
+    }
+```
+📧 **Contact**
+<br>
+If you have any questions, please feel free to reach me out at <b>jixinzhao0101@gmail.com</b>.
+<br>
+👏 **Acknowledgement**
+<br>
+This demo is adapted from [MatAnyone](https://github.com/pq-yang/MatAnyone), and leveraging segmentation capabilities from [Segment Anything](https://github.com/facebookresearch/segment-anything). Thanks for their awesome works!
+"""
+
+custom_css = """
+#input-image {
+    aspect-ratio: 1 / 1;
+    width: 100%;
+    max-width: 100%;
+    height: auto;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+
+#input-image img {
+    max-width: 100%;
+    max-height: 100%;
+    object-fit: contain;
+    display: block;
+}
+
+#main-columns {
+    gap: 60px; 
+}
+
+#main-columns > .gr-column {
+    flex: 1; 
+}
+
+#compare-image {
+    width: 100%;
+    aspect-ratio: 1 / 1; 
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    margin: 0;
+    padding: 0;
+    max-width: 100%;
+    box-sizing: border-box;
+}
+
+#compare-image svg.svelte-zyxd38 {
+    position: absolute !important; 
+    top: 50% !important;           
+    left: 50% !important;          
+    transform: translate(-50%, -50%) !important; 
+}
+
+#compare-image .icon.svelte-1oiin9d {
+    position: absolute;
+    top: 50%;
+    left: 50%;
+    transform: translate(-50%, -50%);
+}
+
+#compare-image {
+    position: relative;
+    overflow: hidden;
+}
+
+.new_button {background-color: #171717 !important; color: #ffffff !important; border: none !important;}
+.new_button:hover {background-color: #4b4b4b !important;}
+
+#start-button {
+    background: linear-gradient(135deg, #2575fc 0%, #6a11cb 100%);
+    color: white;
+    border: none;
+    padding: 12px 24px;
+    font-size: 16px;
+    font-weight: bold;
+    border-radius: 12px;
+    cursor: pointer;
+    box-shadow: 0 0 12px rgba(100, 100, 255, 0.7);
+    transition: all 0.3s ease;
+}
+#start-button:hover {
+    transform: scale(1.05);
+    box-shadow: 0 0 20px rgba(100, 100, 255, 1);
+}
+
+<style>
+.button-wrapper {
+    width: 30%;
+    text-align: center; 
+}
+.wide-button {
+    width: 83% !important;
+    background-color: black !important;
+    color: white !important;
+    border: none !important;
+    padding: 8px 0 !important;
+    font-size: 16px !important;
+    display: inline-block;
+    margin: 30px 0px 0px 50px ;
+}
+.wide-button:hover {
+    background-color: #656262 !important;
+}
+</style>
+"""
+
+
+with gr.Blocks(css=custom_css) as demo:
+    gr.HTML(html_img)
+    gr.Markdown(description)
+    with gr.Group(elem_classes="gr-monochrome-group", visible=True):
+        with gr.Row():
+            with gr.Accordion('SAM Settings (click to expand)', open=False):
+                with gr.Row():
+                    point_prompt = gr.Radio(
+                        choices=["Positive", "Negative"],
+                        value="Positive",
+                        label="Point Prompt",
+                        info="Click to add positive or negative point for target mask",
+                        interactive=True,
+                        min_width=100,
+                        scale=1)
+                    mask_dropdown = gr.Dropdown(multiselect=True, value=[], label="Mask Selection", info="Choose 1~all mask(s) added in Step 2")
+                    
+    with gr.Row(elem_id="main-columns"):
+        with gr.Column():
+            
+            click_state = gr.State([[],[]])
+
+            interactive_state = gr.State(
+                {
+                    "mask_names": [],
+                    "masks": []
+                }
+            )
+
+            image_state = gr.State(
+                {
+                "origin_image": None,
+                "painted_image": None,
+                "mask": None,
+                "logit": None
+                }
+            )
+            
+            image_info = gr.Textbox(label="Image Info", visible=False)
+            input_image = gr.Image(
+                label='Input',
+                type='pil',
+                sources=["upload"],
+                image_mode='RGB',
+                interactive=True,
+                elem_id="input-image"
+            )
+            
+            with gr.Row(equal_height=True, elem_classes="mask_button_group"):
+                clear_button_click = gr.Button(value="Clear Clicks",elem_classes="new_button", min_width=100)
+                add_mask_button = gr.Button(value="Add Mask", elem_classes="new_button", min_width=100)
+                remove_mask_button = gr.Button(value="Delete Mask", elem_classes="new_button", min_width=100)
+                
+            submit_button_component = gr.Button(
+                value='Start ObjectClear', elem_id="start-button"
+            )
+            
+            with gr.Accordion('ObjectClear Settings', open=True):
+                strength = gr.Radio(
+                    choices=[0.99, 1.0],
+                    value=0.99,
+                    label="Strength",
+                    info="0.99 better preserves the background and color; use 1.0 if object/shadow is not fully removed (default: 0.99)"
+                )
+                
+                guidance_scale = gr.Slider(
+                    minimum=1, maximum=10, step=0.5, value=2.5,
+                    label="Guidance Scale",
+                    info="Higher = stronger removal; lower = better background preservation (default: 2.5)"
+                )
+                
+                seed = gr.Slider(
+                    minimum=0, maximum=1000000, step=1, value=300000,
+                    label="Seed Value",
+                    info="Different seeds can lead to noticeably different object removal results (default: 300000)"
+                )
+                
+                num_inference_steps = gr.Slider(
+                    minimum=1, maximum=40, step=1, value=20,
+                    label="Num Inference Steps",
+                    info="Higher values may improve quality but take longer (default: 20)"
+                )
+            
+            
+        with gr.Column():
+            output_image_component = gr.Image(
+                type='pil', image_mode='RGB', label='Output', format="png", elem_id="input-image")
+            
+            output_compare_image_component = gr.ImageSlider(
+                label="Comparison", 
+                type="pil", 
+                format='png', 
+                elem_id="compare-image"
+            )
+
+        input_image.upload(
+            fn=upload_and_reset,
+            inputs=[input_image, interactive_state],
+            outputs=[
+                image_state,
+                image_info,
+                input_image,
+                interactive_state,
+                click_state,
+                mask_dropdown,
+            ]
+        )
+
+        # click select image to get mask using sam
+        input_image.select(
+            fn=sam_refine,
+            inputs=[image_state, point_prompt, click_state],
+            outputs=[input_image, image_state, click_state]
+        )
+        
+        # add different mask
+        add_mask_button.click(
+            fn=add_multi_mask,
+            inputs=[image_state, interactive_state, mask_dropdown],
+            outputs=[interactive_state, mask_dropdown, input_image, click_state]
+        )
+
+        remove_mask_button.click(
+            fn=remove_multi_mask,
+            inputs=[interactive_state, click_state, image_state],
+            outputs=[interactive_state, mask_dropdown, input_image, click_state]
+        )
+    
+        # points clear
+        clear_button_click.click(
+            fn = clear_click,
+            inputs = [image_state, click_state,],
+            outputs = [input_image, click_state],
+        )
+    
+    submit_button_component.click(
+        fn=process,
+        inputs=[
+            image_state, 
+            interactive_state,
+            mask_dropdown,
+            guidance_scale,
+            seed,
+            num_inference_steps,
+            strength
+        ],
+        outputs=[
+            output_image_component, output_compare_image_component
+        ]
+    )
+    
+    with gr.Accordion("📕 Video Tutorial (click to expand)", open=False, elem_classes="custom-bg"):
+        with gr.Row():
+            gr.Video(value="./tutorial.mp4", elem_classes="video")
+
+    gr.Markdown("---")
+    gr.Markdown("## Examples")
+
+    example_images = [
+        os.path.join(os.path.dirname(__file__), "examples", f"test{i}.png") 
+        for i in range(10)
+    ]
+    
+    examples_data = [
+        [example_images[i], None] for i in range(len(example_images))
+    ]
+
+    examples = gr.Examples(
+        examples=examples_data,
+        inputs=[input_image, interactive_state],
+        outputs=[image_state, image_info, input_image,
+                interactive_state, click_state, mask_dropdown],
+        fn=upload_and_reset,
+        run_on_click=True,
+        cache_examples=False,
+        label="Click below to load example images"
+    )
+    
+    gr.Markdown(article)
+
+
+demo.launch(debug=True, show_error=True, share=True, server_port=args.port)
\ No newline at end of file
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z{2E=?Fz~ZA;*<Y9FCWlN7TvgYzc?r^YV3@`fhM)T7bUZ%t+<4%WZRm7xnHcB6Q7O}
zDjW_r^JRI6UiWKO$}jZ8=eZ`RBwsV=9$y;gvpmLUF!D{s*;sfz$B%ervo+&6Jg`@Y
zTQ|n>4V{j19D8ft@=@P?7Hj2De2`fo>URn58Ak>ko|`My@qlYK^c}2w-pch)LCQ09
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bQ~dl3G3zxn2cA}%00000NkvXXu0mjf@+<5a

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HcmV?d00001

diff --git a/hugging_face/requirements.txt b/hugging_face/requirements.txt
new file mode 100644
index 0000000..955c26c
--- /dev/null
+++ b/hugging_face/requirements.txt
@@ -0,0 +1,12 @@
+accelerate
+torch
+torchvision
+numpy
+opencv-python
+pillow
+transformers
+scipy
+diffusers
+segment-anything
+matplotlib
+gradio==5.30.0
\ No newline at end of file
diff --git a/hugging_face/tools/__init__.py b/hugging_face/tools/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/hugging_face/tools/base_segmenter.py b/hugging_face/tools/base_segmenter.py
new file mode 100644
index 0000000..2b975bb
--- /dev/null
+++ b/hugging_face/tools/base_segmenter.py
@@ -0,0 +1,129 @@
+import time
+import torch
+import cv2
+from PIL import Image, ImageDraw, ImageOps
+import numpy as np
+from typing import Union
+from segment_anything import sam_model_registry, SamPredictor, SamAutomaticMaskGenerator
+import matplotlib.pyplot as plt
+import PIL
+from .mask_painter import mask_painter
+
+
+class BaseSegmenter:
+    def __init__(self, SAM_checkpoint, model_type, device='cuda:0'):
+        """
+        device: model device
+        SAM_checkpoint: path of SAM checkpoint
+        model_type: vit_b, vit_l, vit_h
+        """
+        print(f"Initializing BaseSegmenter to {device}")
+        assert model_type in ['vit_b', 'vit_l', 'vit_h'], 'model_type must be vit_b, vit_l, or vit_h'
+
+        self.device = device
+        self.torch_dtype = torch.float16 if 'cuda' in device else torch.float32
+        self.model = sam_model_registry[model_type](checkpoint=SAM_checkpoint)
+        self.model.to(device=self.device)
+        self.predictor = SamPredictor(self.model)
+        self.embedded = False
+
+    @torch.no_grad()
+    def set_image(self, image: np.ndarray):
+        # PIL.open(image_path) 3channel: RGB
+        # image embedding: avoid encode the same image multiple times
+        self.orignal_image = image
+        if self.embedded:
+            print('repeat embedding, please reset_image.')
+            return
+        self.predictor.set_image(image)
+        self.embedded = True
+        return
+    
+    @torch.no_grad()
+    def reset_image(self):
+        # reset image embeding
+        self.predictor.reset_image()
+        self.embedded = False
+
+    def predict(self, prompts, mode, multimask=True):
+        """
+        image: numpy array, h, w, 3
+        prompts: dictionary, 3 keys: 'point_coords', 'point_labels', 'mask_input'
+        prompts['point_coords']: numpy array [N,2]
+        prompts['point_labels']: numpy array [1,N]
+        prompts['mask_input']: numpy array [1,256,256]
+        mode: 'point' (points only), 'mask' (mask only), 'both' (consider both)
+        mask_outputs: True (return 3 masks), False (return 1 mask only)
+        whem mask_outputs=True, mask_input=logits[np.argmax(scores), :, :][None, :, :]
+        """
+        assert self.embedded, 'prediction is called before set_image (feature embedding).'
+        assert mode in ['point', 'mask', 'both'], 'mode must be point, mask, or both'
+        
+        if mode == 'point':
+            masks, scores, logits = self.predictor.predict(point_coords=prompts['point_coords'], 
+                                point_labels=prompts['point_labels'], 
+                                multimask_output=multimask)
+        elif mode == 'mask':
+            masks, scores, logits = self.predictor.predict(mask_input=prompts['mask_input'], 
+                                multimask_output=multimask)
+        elif mode == 'both':   # both
+            masks, scores, logits = self.predictor.predict(point_coords=prompts['point_coords'], 
+                                point_labels=prompts['point_labels'], 
+                                mask_input=prompts['mask_input'], 
+                                multimask_output=multimask)
+        else:
+            raise("Not implement now!")
+        # masks (n, h, w), scores (n,), logits (n, 256, 256)
+        return masks, scores, logits
+
+
+if __name__ == "__main__":
+    # load and show an image
+    image = cv2.imread('/hhd3/gaoshang/truck.jpg')
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # numpy array (h, w, 3)
+
+    # initialise BaseSegmenter
+    SAM_checkpoint= '/ssd1/gaomingqi/checkpoints/sam_vit_h_4b8939.pth'
+    model_type = 'vit_h'
+    device = "cuda:4"
+    base_segmenter = BaseSegmenter(SAM_checkpoint=SAM_checkpoint, model_type=model_type, device=device)
+    
+    # image embedding (once embedded, multiple prompts can be applied)
+    base_segmenter.set_image(image)
+    
+    # examples
+    # point only ------------------------
+    mode = 'point'
+    prompts = {
+        'point_coords': np.array([[500, 375], [1125, 625]]),
+        'point_labels': np.array([1, 1]), 
+    }
+    masks, scores, logits = base_segmenter.predict(prompts, mode, multimask=False)  # masks (n, h, w), scores (n,), logits (n, 256, 256)
+    painted_image = mask_painter(image, masks[np.argmax(scores)].astype('uint8'), background_alpha=0.8)
+    painted_image = cv2.cvtColor(painted_image, cv2.COLOR_RGB2BGR)  # numpy array (h, w, 3)
+    cv2.imwrite('/hhd3/gaoshang/truck_point.jpg', painted_image)
+
+    # both ------------------------
+    mode = 'both'
+    mask_input  = logits[np.argmax(scores), :, :]
+    prompts = {'mask_input': mask_input [None, :, :]}
+    prompts = {
+        'point_coords': np.array([[500, 375], [1125, 625]]),
+        'point_labels': np.array([1, 0]), 
+        'mask_input': mask_input[None, :, :]
+    }
+    masks, scores, logits = base_segmenter.predict(prompts, mode, multimask=True)  # masks (n, h, w), scores (n,), logits (n, 256, 256)
+    painted_image = mask_painter(image, masks[np.argmax(scores)].astype('uint8'), background_alpha=0.8)
+    painted_image = cv2.cvtColor(painted_image, cv2.COLOR_RGB2BGR)  # numpy array (h, w, 3)
+    cv2.imwrite('/hhd3/gaoshang/truck_both.jpg', painted_image)
+
+    # mask only ------------------------
+    mode = 'mask'
+    mask_input  = logits[np.argmax(scores), :, :]
+    
+    prompts = {'mask_input': mask_input[None, :, :]}
+    
+    masks, scores, logits = base_segmenter.predict(prompts, mode, multimask=True)  # masks (n, h, w), scores (n,), logits (n, 256, 256)
+    painted_image = mask_painter(image, masks[np.argmax(scores)].astype('uint8'), background_alpha=0.8)
+    painted_image = cv2.cvtColor(painted_image, cv2.COLOR_RGB2BGR)  # numpy array (h, w, 3)
+    cv2.imwrite('/hhd3/gaoshang/truck_mask.jpg', painted_image)
diff --git a/hugging_face/tools/download_util.py b/hugging_face/tools/download_util.py
new file mode 100644
index 0000000..5e8fb1b
--- /dev/null
+++ b/hugging_face/tools/download_util.py
@@ -0,0 +1,109 @@
+import math
+import os
+import requests
+from torch.hub import download_url_to_file, get_dir
+from tqdm import tqdm
+from urllib.parse import urlparse
+
+def sizeof_fmt(size, suffix='B'):
+    """Get human readable file size.
+
+    Args:
+        size (int): File size.
+        suffix (str): Suffix. Default: 'B'.
+
+    Return:
+        str: Formated file siz.
+    """
+    for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']:
+        if abs(size) < 1024.0:
+            return f'{size:3.1f} {unit}{suffix}'
+        size /= 1024.0
+    return f'{size:3.1f} Y{suffix}'
+
+
+def download_file_from_google_drive(file_id, save_path):
+    """Download files from google drive.
+    Ref:
+    https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive  # noqa E501
+    Args:
+        file_id (str): File id.
+        save_path (str): Save path.
+    """
+
+    session = requests.Session()
+    URL = 'https://docs.google.com/uc?export=download'
+    params = {'id': file_id}
+
+    response = session.get(URL, params=params, stream=True)
+    token = get_confirm_token(response)
+    if token:
+        params['confirm'] = token
+        response = session.get(URL, params=params, stream=True)
+
+    # get file size
+    response_file_size = session.get(URL, params=params, stream=True, headers={'Range': 'bytes=0-2'})
+    print(response_file_size)
+    if 'Content-Range' in response_file_size.headers:
+        file_size = int(response_file_size.headers['Content-Range'].split('/')[1])
+    else:
+        file_size = None
+
+    save_response_content(response, save_path, file_size)
+
+
+def get_confirm_token(response):
+    for key, value in response.cookies.items():
+        if key.startswith('download_warning'):
+            return value
+    return None
+
+
+def save_response_content(response, destination, file_size=None, chunk_size=32768):
+    if file_size is not None:
+        pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk')
+
+        readable_file_size = sizeof_fmt(file_size)
+    else:
+        pbar = None
+
+    with open(destination, 'wb') as f:
+        downloaded_size = 0
+        for chunk in response.iter_content(chunk_size):
+            downloaded_size += chunk_size
+            if pbar is not None:
+                pbar.update(1)
+                pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} / {readable_file_size}')
+            if chunk:  # filter out keep-alive new chunks
+                f.write(chunk)
+        if pbar is not None:
+            pbar.close()
+
+
+def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
+    """Load file form http url, will download models if necessary.
+    Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
+    Args:
+        url (str): URL to be downloaded.
+        model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir.
+            Default: None.
+        progress (bool): Whether to show the download progress. Default: True.
+        file_name (str): The downloaded file name. If None, use the file name in the url. Default: None.
+    Returns:
+        str: The path to the downloaded file.
+    """
+    if model_dir is None:  # use the pytorch hub_dir
+        hub_dir = get_dir()
+        model_dir = os.path.join(hub_dir, 'checkpoints')
+
+    os.makedirs(model_dir, exist_ok=True)
+
+    parts = urlparse(url)
+    filename = os.path.basename(parts.path)
+    if file_name is not None:
+        filename = file_name
+    cached_file = os.path.abspath(os.path.join(model_dir, filename))
+    if not os.path.exists(cached_file):
+        print(f'Downloading: "{url}" to {cached_file}\n')
+        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
+    return cached_file
\ No newline at end of file
diff --git a/hugging_face/tools/interact_tools.py b/hugging_face/tools/interact_tools.py
new file mode 100644
index 0000000..c70b8c4
--- /dev/null
+++ b/hugging_face/tools/interact_tools.py
@@ -0,0 +1,99 @@
+import time
+import torch
+import cv2
+from PIL import Image, ImageDraw, ImageOps
+import numpy as np
+from typing import Union
+from segment_anything import sam_model_registry, SamPredictor, SamAutomaticMaskGenerator
+import matplotlib.pyplot as plt
+import PIL
+from .mask_painter import mask_painter as mask_painter2
+from .base_segmenter import BaseSegmenter
+from .painter import mask_painter, point_painter
+import os
+import requests
+import sys 
+
+
+mask_color = 3
+mask_alpha = 0.7
+contour_color = 1
+contour_width = 5
+point_color_ne = 8
+point_color_ps = 50
+point_alpha = 0.9
+point_radius = 15
+contour_color = 2
+contour_width = 5
+
+
+class SamControler():
+    def __init__(self, SAM_checkpoint, model_type, device):
+        '''
+        initialize sam controler
+        '''
+        self.sam_controler = BaseSegmenter(SAM_checkpoint, model_type, device)
+        
+    
+    # def seg_again(self, image: np.ndarray):
+    #     '''
+    #     it is used when interact in video
+    #     '''
+    #     self.sam_controler.reset_image()
+    #     self.sam_controler.set_image(image)
+    #     return 
+    
+    
+    def first_frame_click(self, image: np.ndarray, points:np.ndarray, labels: np.ndarray, multimask=True,mask_color=3):
+        '''
+        it is used in first frame in video
+        return: mask, logit, painted image(mask+point)
+        '''
+        # self.sam_controler.set_image(image)
+        origal_image = self.sam_controler.orignal_image
+        neg_flag = labels[-1]
+        if neg_flag==1:
+            #find neg
+            prompts = {
+                'point_coords': points,
+                'point_labels': labels,
+            }
+            masks, scores, logits = self.sam_controler.predict(prompts, 'point', multimask)
+            mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]
+            prompts = {
+                'point_coords': points,
+                'point_labels': labels,
+                'mask_input': logit[None, :, :]
+            }
+            masks, scores, logits = self.sam_controler.predict(prompts, 'both', multimask)
+            mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]
+        else:
+           #find positive
+            prompts = {
+                'point_coords': points,
+                'point_labels': labels,
+            }
+            masks, scores, logits = self.sam_controler.predict(prompts, 'point', multimask)
+            mask, logit = masks[np.argmax(scores)], logits[np.argmax(scores), :, :]
+            
+        
+        assert len(points)==len(labels)
+        
+        painted_image = mask_painter(image, mask.astype('uint8'), mask_color, mask_alpha, contour_color, contour_width)
+        painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels>0)],axis = 1), point_color_ne, point_alpha, point_radius, contour_color, contour_width)
+        painted_image = point_painter(painted_image, np.squeeze(points[np.argwhere(labels<1)],axis = 1), point_color_ps, point_alpha, point_radius, contour_color, contour_width)
+        painted_image = Image.fromarray(painted_image)
+        
+        return mask, logit, painted_image
+    
+    
+    
+    
+    
+    
+    
+
+
+    
+    
+    
\ No newline at end of file
diff --git a/hugging_face/tools/mask_painter.py b/hugging_face/tools/mask_painter.py
new file mode 100644
index 0000000..f471ea0
--- /dev/null
+++ b/hugging_face/tools/mask_painter.py
@@ -0,0 +1,288 @@
+import cv2
+import torch
+import numpy as np
+from PIL import Image
+import copy
+import time
+
+
+def colormap(rgb=True):
+	color_list = np.array(
+		[
+			0.000, 0.000, 0.000,
+			1.000, 1.000, 1.000,
+			1.000, 0.498, 0.313,
+			0.392, 0.581, 0.929,
+			0.000, 0.447, 0.741,
+			0.850, 0.325, 0.098,
+			0.929, 0.694, 0.125,
+			0.494, 0.184, 0.556,
+			0.466, 0.674, 0.188,
+			0.301, 0.745, 0.933,
+			0.635, 0.078, 0.184,
+			0.300, 0.300, 0.300,
+			0.600, 0.600, 0.600,
+			1.000, 0.000, 0.000,
+			1.000, 0.500, 0.000,
+			0.749, 0.749, 0.000,
+			0.000, 1.000, 0.000,
+			0.000, 0.000, 1.000,
+			0.667, 0.000, 1.000,
+			0.333, 0.333, 0.000,
+			0.333, 0.667, 0.000,
+			0.333, 1.000, 0.000,
+			0.667, 0.333, 0.000,
+			0.667, 0.667, 0.000,
+			0.667, 1.000, 0.000,
+			1.000, 0.333, 0.000,
+			1.000, 0.667, 0.000,
+			1.000, 1.000, 0.000,
+			0.000, 0.333, 0.500,
+			0.000, 0.667, 0.500,
+			0.000, 1.000, 0.500,
+			0.333, 0.000, 0.500,
+			0.333, 0.333, 0.500,
+			0.333, 0.667, 0.500,
+			0.333, 1.000, 0.500,
+			0.667, 0.000, 0.500,
+			0.667, 0.333, 0.500,
+			0.667, 0.667, 0.500,
+			0.667, 1.000, 0.500,
+			1.000, 0.000, 0.500,
+			1.000, 0.333, 0.500,
+			1.000, 0.667, 0.500,
+			1.000, 1.000, 0.500,
+			0.000, 0.333, 1.000,
+			0.000, 0.667, 1.000,
+			0.000, 1.000, 1.000,
+			0.333, 0.000, 1.000,
+			0.333, 0.333, 1.000,
+			0.333, 0.667, 1.000,
+			0.333, 1.000, 1.000,
+			0.667, 0.000, 1.000,
+			0.667, 0.333, 1.000,
+			0.667, 0.667, 1.000,
+			0.667, 1.000, 1.000,
+			1.000, 0.000, 1.000,
+			1.000, 0.333, 1.000,
+			1.000, 0.667, 1.000,
+			0.167, 0.000, 0.000,
+			0.333, 0.000, 0.000,
+			0.500, 0.000, 0.000,
+			0.667, 0.000, 0.000,
+			0.833, 0.000, 0.000,
+			1.000, 0.000, 0.000,
+			0.000, 0.167, 0.000,
+			0.000, 0.333, 0.000,
+			0.000, 0.500, 0.000,
+			0.000, 0.667, 0.000,
+			0.000, 0.833, 0.000,
+			0.000, 1.000, 0.000,
+			0.000, 0.000, 0.167,
+			0.000, 0.000, 0.333,
+			0.000, 0.000, 0.500,
+			0.000, 0.000, 0.667,
+			0.000, 0.000, 0.833,
+			0.000, 0.000, 1.000,
+			0.143, 0.143, 0.143,
+			0.286, 0.286, 0.286,
+			0.429, 0.429, 0.429,
+			0.571, 0.571, 0.571,
+			0.714, 0.714, 0.714,
+			0.857, 0.857, 0.857
+		]
+	).astype(np.float32)
+	color_list = color_list.reshape((-1, 3)) * 255
+	if not rgb:
+		color_list = color_list[:, ::-1]
+	return color_list
+
+
+color_list = colormap()
+color_list = color_list.astype('uint8').tolist()
+
+
+def vis_add_mask(image, background_mask, contour_mask, background_color, contour_color, background_alpha, contour_alpha):
+	background_color = np.array(background_color)
+	contour_color = np.array(contour_color)
+
+	# background_mask = 1 - background_mask
+	# contour_mask = 1 - contour_mask
+
+	for i in range(3):
+		image[:, :, i] = image[:, :, i] * (1-background_alpha+background_mask*background_alpha) \
+			+ background_color[i] * (background_alpha-background_mask*background_alpha)
+		
+		image[:, :, i] = image[:, :, i] * (1-contour_alpha+contour_mask*contour_alpha) \
+			+ contour_color[i] * (contour_alpha-contour_mask*contour_alpha)
+
+	return image.astype('uint8')
+
+
+def mask_generator_00(mask, background_radius, contour_radius):
+	# no background width when '00'
+	# distance map
+	dist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)
+	dist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)
+	dist_map = dist_transform_fore - dist_transform_back
+	# ...:::!!!:::...
+	contour_radius += 2
+	contour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))
+	contour_mask = contour_mask / np.max(contour_mask)
+	contour_mask[contour_mask>0.5] = 1.
+
+	return mask, contour_mask
+
+
+def mask_generator_01(mask, background_radius, contour_radius):
+	# no background width when '00'
+	# distance map
+	dist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)
+	dist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)
+	dist_map = dist_transform_fore - dist_transform_back
+	# ...:::!!!:::...
+	contour_radius += 2
+	contour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))
+	contour_mask = contour_mask / np.max(contour_mask)
+	return mask, contour_mask
+
+
+def mask_generator_10(mask, background_radius, contour_radius):
+	# distance map
+	dist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)
+	dist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)
+	dist_map = dist_transform_fore - dist_transform_back
+	# .....:::::!!!!!
+	background_mask = np.clip(dist_map, -background_radius, background_radius)
+	background_mask = (background_mask - np.min(background_mask))
+	background_mask = background_mask / np.max(background_mask)
+	# ...:::!!!:::...
+	contour_radius += 2
+	contour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))
+	contour_mask = contour_mask / np.max(contour_mask)
+	contour_mask[contour_mask>0.5] = 1.
+	return background_mask, contour_mask
+
+
+def mask_generator_11(mask, background_radius, contour_radius):
+	# distance map
+	dist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)
+	dist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)
+	dist_map = dist_transform_fore - dist_transform_back
+	# .....:::::!!!!!
+	background_mask = np.clip(dist_map, -background_radius, background_radius)
+	background_mask = (background_mask - np.min(background_mask))
+	background_mask = background_mask / np.max(background_mask)
+	# ...:::!!!:::...
+	contour_radius += 2
+	contour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))
+	contour_mask = contour_mask / np.max(contour_mask)
+	return background_mask, contour_mask
+
+
+def mask_painter(input_image, input_mask, background_alpha=0.5, background_blur_radius=7, contour_width=3, contour_color=3, contour_alpha=1, mode='11'):
+	"""
+	Input:
+	input_image: numpy array
+	input_mask: numpy array
+	background_alpha: transparency of background, [0, 1], 1: all black, 0: do nothing
+	background_blur_radius: radius of background blur, must be odd number
+	contour_width: width of mask contour, must be odd number
+	contour_color: color index (in color map) of mask contour, 0: black, 1: white, >1: others
+	contour_alpha: transparency of mask contour, [0, 1], if 0: no contour highlighted
+	mode: painting mode, '00', no blur, '01' only blur contour, '10' only blur background, '11' blur both
+
+	Output:
+	painted_image: numpy array
+	"""
+	assert input_image.shape[:2] == input_mask.shape, 'different shape'
+	assert background_blur_radius % 2 * contour_width % 2 > 0, 'background_blur_radius and contour_width must be ODD'
+	assert mode in ['00', '01', '10', '11'], 'mode should be 00, 01, 10, or 11'
+
+	# downsample input image and mask
+	width, height = input_image.shape[0], input_image.shape[1]
+	res = 1024
+	ratio = min(1.0 * res / max(width, height), 1.0)  
+	input_image = cv2.resize(input_image, (int(height*ratio), int(width*ratio)))
+	input_mask = cv2.resize(input_mask, (int(height*ratio), int(width*ratio)))
+	
+	# 0: background, 1: foreground
+	msk = np.clip(input_mask, 0, 1)
+
+	# generate masks for background and contour pixels
+	background_radius = (background_blur_radius - 1) // 2
+	contour_radius = (contour_width - 1) // 2
+	generator_dict = {'00':mask_generator_00, '01':mask_generator_01, '10':mask_generator_10, '11':mask_generator_11}
+	background_mask, contour_mask = generator_dict[mode](msk, background_radius, contour_radius)
+
+	# paint
+	painted_image = vis_add_mask\
+		(input_image, background_mask, contour_mask, color_list[0], color_list[contour_color], background_alpha, contour_alpha)	# black for background
+
+	return painted_image
+
+
+if __name__ == '__main__':
+	
+	background_alpha = 0.7  	# transparency of background 1: all black, 0: do nothing
+	background_blur_radius = 31	# radius of background blur, must be odd number
+	contour_width = 11       	# contour width, must be odd number
+	contour_color = 3      		# id in color map, 0: black, 1: white, >1: others
+	contour_alpha = 1       	# transparency of background, 0: no contour highlighted
+
+	# load input image and mask
+	input_image = np.array(Image.open('./test_img/painter_input_image.jpg').convert('RGB'))
+	input_mask = np.array(Image.open('./test_img/painter_input_mask.jpg').convert('P'))
+	
+	# paint
+	overall_time_1 = 0
+	overall_time_2 = 0
+	overall_time_3 = 0
+	overall_time_4 = 0
+	overall_time_5 = 0
+	
+	for i in range(50):
+		t2 = time.time()
+		painted_image_00 = mask_painter(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha, mode='00')
+		e2 = time.time()
+
+		t3 = time.time()
+		painted_image_10 = mask_painter(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha, mode='10')
+		e3 = time.time()
+
+		t1 = time.time()
+		painted_image = mask_painter(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha)
+		e1 = time.time()
+
+		t4 = time.time()
+		painted_image_01 = mask_painter(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha, mode='01')
+		e4 = time.time()
+
+		t5 = time.time()
+		painted_image_11 = mask_painter(input_image, input_mask, background_alpha, background_blur_radius, contour_width, contour_color, contour_alpha, mode='11')
+		e5 = time.time()
+
+		overall_time_1 += (e1 - t1)
+		overall_time_2 += (e2 - t2)
+		overall_time_3 += (e3 - t3)
+		overall_time_4 += (e4 - t4)
+		overall_time_5 += (e5 - t5)
+
+	print(f'average time w gaussian: {overall_time_1/50}')
+	print(f'average time w/o gaussian00: {overall_time_2/50}')
+	print(f'average time w/o gaussian10: {overall_time_3/50}')
+	print(f'average time w/o gaussian01: {overall_time_4/50}')
+	print(f'average time w/o gaussian11: {overall_time_5/50}')
+
+	# save
+	painted_image_00 = Image.fromarray(painted_image_00)
+	painted_image_00.save('./test_img/painter_output_image_00.png')
+
+	painted_image_10 = Image.fromarray(painted_image_10)
+	painted_image_10.save('./test_img/painter_output_image_10.png')
+
+	painted_image_01 = Image.fromarray(painted_image_01)
+	painted_image_01.save('./test_img/painter_output_image_01.png')
+
+	painted_image_11 = Image.fromarray(painted_image_11)
+	painted_image_11.save('./test_img/painter_output_image_11.png')
diff --git a/hugging_face/tools/misc.py b/hugging_face/tools/misc.py
new file mode 100644
index 0000000..43b8499
--- /dev/null
+++ b/hugging_face/tools/misc.py
@@ -0,0 +1,131 @@
+import os
+import re
+import random
+import time
+import torch
+import torch.nn as nn
+import logging
+import numpy as np
+from os import path as osp
+
+def constant_init(module, val, bias=0):
+    if hasattr(module, 'weight') and module.weight is not None:
+        nn.init.constant_(module.weight, val)
+    if hasattr(module, 'bias') and module.bias is not None:
+        nn.init.constant_(module.bias, bias)
+
+initialized_logger = {}
+def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None):
+    """Get the root logger.
+    The logger will be initialized if it has not been initialized. By default a
+    StreamHandler will be added. If `log_file` is specified, a FileHandler will
+    also be added.
+    Args:
+        logger_name (str): root logger name. Default: 'basicsr'.
+        log_file (str | None): The log filename. If specified, a FileHandler
+            will be added to the root logger.
+        log_level (int): The root logger level. Note that only the process of
+            rank 0 is affected, while other processes will set the level to
+            "Error" and be silent most of the time.
+    Returns:
+        logging.Logger: The root logger.
+    """
+    logger = logging.getLogger(logger_name)
+    # if the logger has been initialized, just return it
+    if logger_name in initialized_logger:
+        return logger
+
+    format_str = '%(asctime)s %(levelname)s: %(message)s'
+    stream_handler = logging.StreamHandler()
+    stream_handler.setFormatter(logging.Formatter(format_str))
+    logger.addHandler(stream_handler)
+    logger.propagate = False
+
+    if log_file is not None:
+        logger.setLevel(log_level)
+        # add file handler
+        # file_handler = logging.FileHandler(log_file, 'w')
+        file_handler = logging.FileHandler(log_file, 'a') #Shangchen: keep the previous log
+        file_handler.setFormatter(logging.Formatter(format_str))
+        file_handler.setLevel(log_level)
+        logger.addHandler(file_handler)
+    initialized_logger[logger_name] = True
+    return logger
+
+
+IS_HIGH_VERSION = [int(m) for m in list(re.findall(r"^([0-9]+)\.([0-9]+)\.([0-9]+)([^0-9][a-zA-Z0-9]*)?(\+git.*)?$",\
+    torch.__version__)[0][:3])] >= [1, 12, 0]
+
+def gpu_is_available():
+    if IS_HIGH_VERSION:
+        if torch.backends.mps.is_available():
+            return True
+    return True if torch.cuda.is_available() and torch.backends.cudnn.is_available() else False
+
+def get_device(gpu_id=None):
+    if gpu_id is None:
+        gpu_str = ''
+    elif isinstance(gpu_id, int):
+        gpu_str = f':{gpu_id}'
+    else:
+        raise TypeError('Input should be int value.')
+
+    if IS_HIGH_VERSION:
+        if torch.backends.mps.is_available():
+            return torch.device('mps'+gpu_str)
+    return torch.device('cuda'+gpu_str if torch.cuda.is_available() and torch.backends.cudnn.is_available() else 'cpu')
+
+
+def set_random_seed(seed):
+    """Set random seeds."""
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def get_time_str():
+    return time.strftime('%Y%m%d_%H%M%S', time.localtime())
+
+
+def scandir(dir_path, suffix=None, recursive=False, full_path=False):
+    """Scan a directory to find the interested files.
+
+    Args:
+        dir_path (str): Path of the directory.
+        suffix (str | tuple(str), optional): File suffix that we are
+            interested in. Default: None.
+        recursive (bool, optional): If set to True, recursively scan the
+            directory. Default: False.
+        full_path (bool, optional): If set to True, include the dir_path.
+            Default: False.
+
+    Returns:
+        A generator for all the interested files with relative pathes.
+    """
+
+    if (suffix is not None) and not isinstance(suffix, (str, tuple)):
+        raise TypeError('"suffix" must be a string or tuple of strings')
+
+    root = dir_path
+
+    def _scandir(dir_path, suffix, recursive):
+        for entry in os.scandir(dir_path):
+            if not entry.name.startswith('.') and entry.is_file():
+                if full_path:
+                    return_path = entry.path
+                else:
+                    return_path = osp.relpath(entry.path, root)
+
+                if suffix is None:
+                    yield return_path
+                elif return_path.endswith(suffix):
+                    yield return_path
+            else:
+                if recursive:
+                    yield from _scandir(entry.path, suffix=suffix, recursive=recursive)
+                else:
+                    continue
+
+    return _scandir(dir_path, suffix=suffix, recursive=recursive)
\ No newline at end of file
diff --git a/hugging_face/tools/painter.py b/hugging_face/tools/painter.py
new file mode 100644
index 0000000..0e711d3
--- /dev/null
+++ b/hugging_face/tools/painter.py
@@ -0,0 +1,215 @@
+# paint masks, contours, or points on images, with specified colors
+import cv2
+import torch
+import numpy as np
+from PIL import Image
+import copy
+import time
+
+
+def colormap(rgb=True):
+	color_list = np.array(
+		[
+			0.000, 0.000, 0.000,
+			1.000, 1.000, 1.000,
+			1.000, 0.498, 0.313,
+			0.392, 0.581, 0.929,
+			0.000, 0.447, 0.741,
+			0.850, 0.325, 0.098,
+			0.929, 0.694, 0.125,
+			0.494, 0.184, 0.556,
+			0.466, 0.674, 0.188,
+			0.301, 0.745, 0.933,
+			0.635, 0.078, 0.184,
+			0.300, 0.300, 0.300,
+			0.600, 0.600, 0.600,
+			1.000, 0.000, 0.000,
+			1.000, 0.500, 0.000,
+			0.749, 0.749, 0.000,
+			0.000, 1.000, 0.000,
+			0.000, 0.000, 1.000,
+			0.667, 0.000, 1.000,
+			0.333, 0.333, 0.000,
+			0.333, 0.667, 0.000,
+			0.333, 1.000, 0.000,
+			0.667, 0.333, 0.000,
+			0.667, 0.667, 0.000,
+			0.667, 1.000, 0.000,
+			1.000, 0.333, 0.000,
+			1.000, 0.667, 0.000,
+			1.000, 1.000, 0.000,
+			0.000, 0.333, 0.500,
+			0.000, 0.667, 0.500,
+			0.000, 1.000, 0.500,
+			0.333, 0.000, 0.500,
+			0.333, 0.333, 0.500,
+			0.333, 0.667, 0.500,
+			0.333, 1.000, 0.500,
+			0.667, 0.000, 0.500,
+			0.667, 0.333, 0.500,
+			0.667, 0.667, 0.500,
+			0.667, 1.000, 0.500,
+			1.000, 0.000, 0.500,
+			1.000, 0.333, 0.500,
+			1.000, 0.667, 0.500,
+			1.000, 1.000, 0.500,
+			0.000, 0.333, 1.000,
+			0.000, 0.667, 1.000,
+			0.000, 1.000, 1.000,
+			0.333, 0.000, 1.000,
+			0.333, 0.333, 1.000,
+			0.333, 0.667, 1.000,
+			0.333, 1.000, 1.000,
+			0.667, 0.000, 1.000,
+			0.667, 0.333, 1.000,
+			0.667, 0.667, 1.000,
+			0.667, 1.000, 1.000,
+			1.000, 0.000, 1.000,
+			1.000, 0.333, 1.000,
+			1.000, 0.667, 1.000,
+			0.167, 0.000, 0.000,
+			0.333, 0.000, 0.000,
+			0.500, 0.000, 0.000,
+			0.667, 0.000, 0.000,
+			0.833, 0.000, 0.000,
+			1.000, 0.000, 0.000,
+			0.000, 0.167, 0.000,
+			0.000, 0.333, 0.000,
+			0.000, 0.500, 0.000,
+			0.000, 0.667, 0.000,
+			0.000, 0.833, 0.000,
+			0.000, 1.000, 0.000,
+			0.000, 0.000, 0.167,
+			0.000, 0.000, 0.333,
+			0.000, 0.000, 0.500,
+			0.000, 0.000, 0.667,
+			0.000, 0.000, 0.833,
+			0.000, 0.000, 1.000,
+			0.143, 0.143, 0.143,
+			0.286, 0.286, 0.286,
+			0.429, 0.429, 0.429,
+			0.571, 0.571, 0.571,
+			0.714, 0.714, 0.714,
+			0.857, 0.857, 0.857
+		]
+	).astype(np.float32)
+	color_list = color_list.reshape((-1, 3)) * 255
+	if not rgb:
+		color_list = color_list[:, ::-1]
+	return color_list
+
+
+color_list = colormap()
+color_list = color_list.astype('uint8').tolist()
+
+
+def vis_add_mask(image, mask, color, alpha):
+	color = np.array(color_list[color])
+	mask = mask > 0.5
+	image[mask] = image[mask] * (1-alpha) + color * alpha
+	return image.astype('uint8')
+
+def point_painter(input_image, input_points, point_color=5, point_alpha=0.9, point_radius=15, contour_color=2, contour_width=5):
+	h, w = input_image.shape[:2]
+	point_mask = np.zeros((h, w)).astype('uint8')
+	for point in input_points:
+		point_mask[point[1], point[0]] = 1
+
+	kernel = cv2.getStructuringElement(2, (point_radius, point_radius))
+	point_mask = cv2.dilate(point_mask, kernel)
+
+	contour_radius = (contour_width - 1) // 2
+	dist_transform_fore = cv2.distanceTransform(point_mask, cv2.DIST_L2, 3)
+	dist_transform_back = cv2.distanceTransform(1-point_mask, cv2.DIST_L2, 3)
+	dist_map = dist_transform_fore - dist_transform_back
+	# ...:::!!!:::...
+	contour_radius += 2
+	contour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))
+	contour_mask = contour_mask / np.max(contour_mask)
+	contour_mask[contour_mask>0.5] = 1.
+
+	# paint mask
+	painted_image = vis_add_mask(input_image.copy(), point_mask, point_color, point_alpha)
+	# paint contour
+	painted_image = vis_add_mask(painted_image.copy(), 1-contour_mask, contour_color, 1)
+	return painted_image
+
+def mask_painter(input_image, input_mask, mask_color=5, mask_alpha=0.7, contour_color=1, contour_width=3):
+	assert input_image.shape[:2] == input_mask.shape, 'different shape between image and mask'
+	# 0: background, 1: foreground
+	mask = np.clip(input_mask, 0, 1)
+	contour_radius = (contour_width - 1) // 2
+
+	dist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)
+	dist_transform_back = cv2.distanceTransform(1-mask, cv2.DIST_L2, 3)
+	dist_map = dist_transform_fore - dist_transform_back
+	# ...:::!!!:::...
+	contour_radius += 2
+	contour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))
+	contour_mask = contour_mask / np.max(contour_mask)
+	contour_mask[contour_mask>0.5] = 1.
+
+	# paint mask
+	painted_image = vis_add_mask(input_image.copy(), mask.copy(), mask_color, mask_alpha)
+	# paint contour
+	painted_image = vis_add_mask(painted_image.copy(), 1-contour_mask, contour_color, 1)
+
+	return painted_image
+
+def background_remover(input_image, input_mask):
+	"""
+	input_image: H, W, 3, np.array
+	input_mask: H, W, np.array
+
+	image_wo_background: PIL.Image	
+	"""
+	assert input_image.shape[:2] == input_mask.shape, 'different shape between image and mask'
+	# 0: background, 1: foreground
+	mask = np.expand_dims(np.clip(input_mask, 0, 1), axis=2)*255
+	image_wo_background = np.concatenate([input_image, mask], axis=2)		# H, W, 4
+	image_wo_background = Image.fromarray(image_wo_background).convert('RGBA')
+
+	return image_wo_background
+
+if __name__ == '__main__':
+	input_image = np.array(Image.open('images/painter_input_image.jpg').convert('RGB'))
+	input_mask = np.array(Image.open('images/painter_input_mask.jpg').convert('P'))
+
+	# example of mask painter
+	mask_color = 3
+	mask_alpha = 0.7
+	contour_color = 1
+	contour_width = 5
+
+	# save
+	painted_image = Image.fromarray(input_image)
+	painted_image.save('images/original.png')
+
+	painted_image = mask_painter(input_image, input_mask, mask_color, mask_alpha, contour_color, contour_width)
+	# save
+	painted_image = Image.fromarray(input_image)
+	painted_image.save('images/original1.png')
+
+	# example of point painter
+	input_image = np.array(Image.open('images/painter_input_image.jpg').convert('RGB'))
+	input_points = np.array([[500, 375], [70, 600]])	# x, y
+	point_color = 5
+	point_alpha = 0.9
+	point_radius = 15
+	contour_color = 2
+	contour_width = 5
+	painted_image_1 = point_painter(input_image, input_points, point_color, point_alpha, point_radius, contour_color, contour_width)
+	# save
+	painted_image = Image.fromarray(painted_image_1)
+	painted_image.save('images/point_painter_1.png')
+
+	input_image = np.array(Image.open('images/painter_input_image.jpg').convert('RGB'))
+	painted_image_2 = point_painter(input_image, input_points, point_color=9, point_radius=20, contour_color=29)
+	# save
+	painted_image = Image.fromarray(painted_image_2)
+	painted_image.save('images/point_painter_2.png')
+
+	# example of background remover
+	input_image = np.array(Image.open('images/original.png').convert('RGB'))
+	image_wo_background = background_remover(input_image, input_mask)	# return PIL.Image
+	image_wo_background.save('images/image_wo_background.png')
diff --git a/inference_objectclear.py b/inference_objectclear.py
new file mode 100644
index 0000000..82f0c79
--- /dev/null
+++ b/inference_objectclear.py
@@ -0,0 +1,104 @@
+import os
+import argparse
+from safetensors.torch import load_file
+import torch
+from .objectclear.pipelines import ObjectClearPipeline
+from .objectclear.utils import resize_by_short_side
+from PIL import Image
+import numpy as np
+from omegaconf import OmegaConf
+
+import gc
+
+
+def loader_objectclear(cf_model,cf_vae,cur_path,device,cache_dir,use_fp16,postfuse_path):
+     # ------------------ set up ObjectClear pipeline -------------------
+    torch_dtype = torch.float16 if use_fp16 else torch.float32
+    variant = "fp16" if use_fp16 else None
+    UNET = load_singlefile(cf_model,cur_path,device)
+    VAE = load_singlevae(cf_vae,cur_path,device)
+
+    pipe = ObjectClearPipeline.from_pretrained_with_custom_modules(
+        os.path.join(cur_path,"sdxl_repo"),
+        unet=UNET,
+        vae=VAE,
+        torch_dtype=torch_dtype,
+        apply_attention_guided_fusion=True,
+        cache_dir=cache_dir,
+        variant=variant,
+        postfuse_path=postfuse_path,
+
+    )
+    pipe.to(device)
+    #pipe.enable_model_cpu_offload()
+    return pipe
+
+
+
+def inference_objectclear(pipe,input_img_list, input_mask_list,device,positive,negative,steps=20,seed=42,strength=0.99,guidance_scale=2.5,object_embeds=None,short_size=512):
+    # -------------------- start to processing ---------------------
+    output_img_list = []
+    for i, (image, mask) in enumerate(zip(input_img_list, input_mask_list)):
+        print(f'[{i+1}/{len(input_img_list)}] Processing')
+
+        image = resize_by_short_side(image, short_size, resample=Image.BICUBIC)
+        mask = resize_by_short_side(mask, short_size, resample=Image.NEAREST)
+
+        
+        w, h = image.size
+        result = pipe(
+            prompt=None,#"remove the instance of object",
+            image=image,
+            mask_image=mask,
+            generator=torch.Generator(device=device).manual_seed(seed),
+            num_inference_steps=steps,
+            strength=strength,
+            guidance_scale=guidance_scale,
+            height=h,
+            width=w,
+            return_attn_map=False,
+            prompt_embeds= positive[0][0].to(device,dtype=torch.float16),
+            negative_prompt_embeds= negative[0][0].to(device,dtype=torch.float16),
+            pooled_prompt_embeds= positive[0][1]["pooled_output"].to(device, dtype=torch.float16),
+            negative_pooled_prompt_embeds= negative[0][1]["pooled_output"].to(device, dtype=torch.float16),
+            object_embeds=object_embeds[i].to(device, dtype=torch.float16),
+        )
+        
+        fused_img_pil = result.images[0]
+        output_img_list.append(fused_img_pil)
+
+        # save results
+        # save_path = os.path.join(result_root, f'{basename}.png')
+        # fused_img_pil = fused_img_pil.resize(image_or.size)
+        # fused_img_pil.save(save_path)
+
+    #print(f'\nAll results are saved in {result_root}')
+    return output_img_list
+
+
+
+def load_singlefile(model,cur_path,device):
+    from diffusers import UNet2DConditionModel
+    
+    config_file = os.path.join(cur_path,"sdxl_repo/unet/config.json")
+    unet_state_dict=load_file(model)
+    unet_config = UNet2DConditionModel.load_config(config_file)
+    Unet = UNet2DConditionModel.from_config(unet_config).to(device, torch.float16)
+    Unet.load_state_dict(unet_state_dict, strict=False)
+    del unet_state_dict
+    gc.collect()
+    torch.cuda.empty_cache()
+    return Unet
+
+
+def load_singlevae(VAE,cur_path,device):
+    from diffusers import AutoencoderKL
+
+    vae_config = os.path.join(cur_path, "sdxl_repo/vae/config.json")
+    vae_state_dict=load_file(VAE)
+    ae_config = AutoencoderKL.load_config(vae_config)
+    AE = AutoencoderKL.from_config(ae_config).to(device)
+    AE.load_state_dict(vae_state_dict, strict=False)
+    del vae_state_dict
+    torch.cuda.empty_cache()
+    return AE
\ No newline at end of file
diff --git a/inputs/imgs/test-sample1.jpg b/inputs/imgs/test-sample1.jpg
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diff --git a/node_utils.py b/node_utils.py
new file mode 100644
index 0000000..70b224e
--- /dev/null
+++ b/node_utils.py
@@ -0,0 +1,298 @@
+# !/usr/bin/env python
+# -*- coding: UTF-8 -*-
+import os
+import torch
+from PIL import Image
+import numpy as np
+import cv2
+import gc
+
+from comfy.utils import common_upscale,ProgressBar
+from huggingface_hub import hf_hub_download
+
+cur_path = os.path.dirname(os.path.abspath(__file__))
+device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+
+def gc_cleanup():
+    """
+    清理内存
+    """
+    gc.collect()
+    torch.cuda.empty_cache()
+
+def find_gaussian_files(base_path):
+    found_files = []
+    for root, dirs, files in os.walk(base_path):
+        for file in files:
+            if file.endswith('.pth') and 'gaussian' in file:
+                found_files.append(os.path.join(root, file))
+    return found_files
+
+def cv2pil(cv_image):
+    """
+    将OpenCV图像转换为PIL图像
+    :param cv_image: OpenCV图像
+    :return: PIL图像
+    """
+    # 将图像从BGR转换为RGB
+    rgb_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)
+    # 使用PIL的Image.fromarray方法将NumPy数组转换为PIL图像
+    pil_image = Image.fromarray(rgb_image)
+    return pil_image
+
+
+
+def tensor_to_pil(tensor):
+    image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
+    image = Image.fromarray(image_np, mode='RGB')
+    return image
+
+def tensor2pil_list(image):
+    B,_,_,_=image.size()
+    if  B==1:
+        ref_image_list=[tensor_to_pil(image)]
+    else:
+        img_list = list(torch.chunk(image, chunks=B))
+        ref_image_list = [tensor_to_pil(img) for img in img_list]
+    return ref_image_list
+
+
+def mask2pil_list(mask):
+    B,_,_=mask.size()
+    if  B==1:
+        ref_image_list = [mask2pil(mask)]  
+    else:
+        img_list = list(torch.chunk(mask, chunks=B))
+        ref_image_list = [mask2pil(img) for img in img_list]
+    return ref_image_list
+
+def tensor2pil_list_u(image,width,height):
+    B,_,_,_=image.size()
+    if  B==1:
+        ref_image_list=[tensor2pil_upscale(image,width,height)]
+    else:
+        img_list = list(torch.chunk(image, chunks=B))
+        ref_image_list = [tensor2pil_upscale(img,width,height) for img in img_list]
+    return ref_image_list
+
+
+def mask2pil_list_u(mask,width,height):
+    B,_,_=mask.size()
+    if  B==1:
+        ref_image_list = [mask2pil_u(mask,width,height)]  
+    else:
+        img_list = list(torch.chunk(mask, chunks=B))
+        ref_image_list = [mask2pil_u(img,width,height) for img in img_list]
+    return ref_image_list
+
+def mask2pil(mask):
+    upscaled_mask =mask
+    mask_np = upscaled_mask.squeeze(0).mul(255).clamp(0, 255).byte().numpy()
+    return Image.fromarray(mask_np, mode='L')
+
+def mask2pil_u(mask,width,height):
+    upscaled_mask = tensor_upscale(mask.unsqueeze(-1), width, height)
+    mask_np = upscaled_mask.squeeze(0).squeeze(-1).mul(255).clamp(0, 255).byte().numpy()
+    return Image.fromarray(mask_np, mode='L')
+
+def tensor2pil_RGBA_list(image,mask,width,height):
+    B,_,_,_=image.size()
+    # 处理mask维度
+    if len(mask.shape) == 2:
+        mask = mask.unsqueeze(0)  # 从 [H,W] 变为 [1,H,W]
+    if mask.shape[1] == 64: 
+        return tensor2pil_list(image,width,height) #[1,64,64]
+    mask = 1.0 - mask
+    if B == 1:
+        upscaled_img = tensor_upscale(image, width, height)
+        upscaled_mask = tensor_upscale(mask.unsqueeze(-1), width, height)  # 添加通道维度
+        
+        # 转换为RGBA 
+        rgb = upscaled_img.squeeze(0).mul(255).clamp(0, 255).byte().numpy()
+        alpha = upscaled_mask.squeeze(0).mul(255).clamp(0, 255).byte().numpy()
+        rgba = np.dstack((rgb, alpha))  # 合并为RGBA
+        ref_image_list = [Image.fromarray(rgba, mode='RGBA')]
+    else:
+        img_list = list(torch.chunk(image, chunks=B))
+        mask_list = list(torch.chunk(mask, chunks=B))
+        ref_image_list = []
+        for img, msk in zip(img_list, mask_list):
+            # 上采样每个图像和对应的mask
+            upscaled_img = tensor_upscale(img, width, height)
+            upscaled_mask = tensor_upscale(msk.unsqueeze(-1), width, height)
+            
+            # 转换为RGBA -
+            rgb = upscaled_img.squeeze(0).mul(255).clamp(0, 255).byte().numpy()
+            alpha = upscaled_mask.squeeze(0).mul(255).clamp(0, 255).byte().numpy()
+            rgba = np.dstack((rgb, alpha))
+            ref_image_list.append(Image.fromarray(rgba, mode='RGBA'))
+    return ref_image_list
+
+
+
+def tensor_upscale(img_tensor, width, height):
+    samples = img_tensor.movedim(-1, 1)
+    img = common_upscale(samples, width, height, "nearest-exact", "center")
+    samples = img.movedim(1, -1)
+    return samples
+
+def tensor2pil_upscale(img_tensor, width, height):
+    samples = img_tensor.movedim(-1, 1)
+    img = common_upscale(samples, width, height, "nearest-exact", "center")
+    samples = img.movedim(1, -1)
+    img_pil = tensor_to_pil(samples)
+    return img_pil
+
+
+def tensor2cv(tensor_image,RGB2BGR=True):
+    if len(tensor_image.shape)==4:#bhwc to hwc
+        tensor_image=tensor_image.squeeze(0)
+    if tensor_image.is_cuda:
+        tensor_image = tensor_image.cpu().detach()
+    tensor_image=tensor_image.numpy()
+    #反归一化
+    maxValue=tensor_image.max()
+    tensor_image=tensor_image*255/maxValue
+    img_cv2=np.uint8(tensor_image)#32 to uint8
+    if RGB2BGR:
+        img_cv2=cv2.cvtColor(img_cv2,cv2.COLOR_RGB2BGR)
+    return img_cv2
+
+def cvargb2tensor(img):
+    assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img))
+    img = torch.from_numpy(img.transpose((2, 0, 1)))
+    return img.float().div(255).unsqueeze(0)  # 255也可以改为256
+
+def cv2tensor(img):
+    assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img))
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    img = torch.from_numpy(img.transpose((2, 0, 1)))
+    return img.float().div(255).unsqueeze(0)  # 255也可以改为256
+
+def images_generator(img_list: list,):
+    #get img size
+    sizes = {}
+    for image_ in img_list:
+        if isinstance(image_,Image.Image):
+            count = sizes.get(image_.size, 0)
+            sizes[image_.size] = count + 1
+        elif isinstance(image_,np.ndarray):
+            count = sizes.get(image_.shape[:2][::-1], 0)
+            sizes[image_.shape[:2][::-1]] = count + 1
+        else:
+            raise "unsupport image list,must be pil or cv2!!!"
+    size = max(sizes.items(), key=lambda x: x[1])[0]
+    yield size[0], size[1]
+    
+    # any to tensor
+    def load_image(img_in):
+        if isinstance(img_in, Image.Image):
+            img_in=img_in.convert("RGB")
+            i = np.array(img_in, dtype=np.float32)
+            i = torch.from_numpy(i).div_(255)
+            if i.shape[0] != size[1] or i.shape[1] != size[0]:
+                i = torch.from_numpy(i).movedim(-1, 0).unsqueeze(0)
+                i = common_upscale(i, size[0], size[1], "lanczos", "center")
+                i = i.squeeze(0).movedim(0, -1).numpy()
+            return i
+        elif isinstance(img_in,np.ndarray):
+            i=cv2.cvtColor(img_in,cv2.COLOR_BGR2RGB).astype(np.float32)
+            i = torch.from_numpy(i).div_(255)
+            #print(i.shape)
+            return i
+        else:
+           raise "unsupport image list,must be pil,cv2 or tensor!!!"
+        
+    total_images = len(img_list)
+    processed_images = 0
+    pbar = ProgressBar(total_images)
+    images = map(load_image, img_list)
+    try:
+        prev_image = next(images)
+        while True:
+            next_image = next(images)
+            yield prev_image
+            processed_images += 1
+            pbar.update_absolute(processed_images, total_images)
+            prev_image = next_image
+    except StopIteration:
+        pass
+    if prev_image is not None:
+        yield prev_image
+
+def load_images(img_list: list,):
+    gen = images_generator(img_list)
+    (width, height) = next(gen)
+    images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (height, width, 3)))))
+    if len(images) == 0:
+        raise FileNotFoundError(f"No images could be loaded .")
+    return images
+
+def tensor2pil(tensor):
+    image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
+    image = Image.fromarray(image_np, mode='RGB')
+    return image
+
+def pil2narry(img):
+    narry = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0)
+    return narry
+
+def equalize_lists(list1, list2):
+    """
+    比较两个列表的长度，如果不一致，则将较短的列表复制以匹配较长列表的长度。
+    
+    参数:
+    list1 (list): 第一个列表
+    list2 (list): 第二个列表
+    
+    返回:
+    tuple: 包含两个长度相等的列表的元组
+    """
+    len1 = len(list1)
+    len2 = len(list2)
+    
+    if len1 == len2:
+        pass
+    elif len1 < len2:
+        print("list1 is shorter than list2, copying list1 to match list2's length.")
+        list1.extend(list1 * ((len2 // len1) + 1))  # 复制list1以匹配list2的长度
+        list1 = list1[:len2]  # 确保长度一致
+    else:
+        print("list2 is shorter than list1, copying list2 to match list1's length.")
+        list2.extend(list2 * ((len1 // len2) + 1))  # 复制list2以匹配list1的长度
+        list2 = list2[:len1]  # 确保长度一致
+    
+    return list1, list2
+
+def file_exists(directory, filename):
+    # 构建文件的完整路径
+    file_path = os.path.join(directory, filename)
+    # 检查文件是否存在
+    return os.path.isfile(file_path)
+
+def download_weights(file_dir,repo_id,subfolder="",pt_name=""):
+    if subfolder:
+        file_path = os.path.join(file_dir,subfolder, pt_name)
+        sub_dir=os.path.join(file_dir,subfolder)
+        if not os.path.exists(sub_dir):
+            os.makedirs(sub_dir)
+        if not os.path.exists(file_path):
+            file_path = hf_hub_download(
+                repo_id=repo_id,
+                subfolder=subfolder,
+                filename=pt_name,
+                local_dir = file_dir,
+            )
+        return file_path
+    else:
+        file_path = os.path.join(file_dir, pt_name)
+        if not os.path.exists(file_dir):
+            os.makedirs(file_dir)
+        if not os.path.exists(file_path):
+            file_path = hf_hub_download(
+                repo_id=repo_id,
+                filename=pt_name,
+                local_dir=file_dir,
+            )
+        return file_path
diff --git a/objectclear/__init__.py b/objectclear/__init__.py
new file mode 100644
index 0000000..9350284
--- /dev/null
+++ b/objectclear/__init__.py
@@ -0,0 +1,12 @@
+from .models import CLIPImageEncoder, PostfuseModule
+from .pipelines import ObjectClearPipeline
+from .utils import attention_guided_fusion, resize_by_short_side
+
+
+__all__ = [
+    "CLIPImageEncoder",
+    "PostfuseModule", 
+    "ObjectClearPipeline",
+    "attention_guided_fusion",
+    "resize_by_short_side",
+]
\ No newline at end of file
diff --git a/objectclear/__pycache__/__init__.cpython-311.pyc b/objectclear/__pycache__/__init__.cpython-311.pyc
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diff --git a/objectclear/models/__init__.py b/objectclear/models/__init__.py
new file mode 100644
index 0000000..a4726da
--- /dev/null
+++ b/objectclear/models/__init__.py
@@ -0,0 +1,5 @@
+from .clip_image_encoder import CLIPImageEncoder
+from .postfuse_module import PostfuseModule
+
+
+__all__ = ["CLIPImageEncoder", "PostfuseModule"]
\ No newline at end of file
diff --git a/objectclear/models/__pycache__/__init__.cpython-311.pyc b/objectclear/models/__pycache__/__init__.cpython-311.pyc
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diff --git a/objectclear/models/clip_image_encoder.py b/objectclear/models/clip_image_encoder.py
new file mode 100644
index 0000000..e3e8295
--- /dev/null
+++ b/objectclear/models/clip_image_encoder.py
@@ -0,0 +1,58 @@
+import torch.nn.functional as F
+import torchvision.transforms as T
+from transformers.models.clip.modeling_clip import (
+    CLIPPreTrainedModel,
+    CLIPModel,
+)
+
+
+class CLIPImageEncoder(CLIPPreTrainedModel):
+    @staticmethod
+    def from_pretrained(
+        global_model_name_or_path,
+        cache_dir
+    ):
+        model = CLIPModel.from_pretrained(
+            global_model_name_or_path,
+            subfolder="image_prompt_encoder", 
+            cache_dir=cache_dir
+        )
+        vision_model = model.vision_model
+        visual_projection = model.visual_projection
+        vision_processor = T.Normalize(
+            (0.48145466, 0.4578275, 0.40821073),
+            (0.26862954, 0.26130258, 0.27577711),
+        )
+        return CLIPImageEncoder(
+            vision_model,
+            visual_projection,
+            vision_processor,
+        )
+
+    def __init__(
+        self,
+        vision_model,
+        visual_projection,
+        vision_processor,
+    ):
+        super().__init__(vision_model.config)
+        self.vision_model = vision_model
+        self.visual_projection = visual_projection
+        self.vision_processor = vision_processor
+
+        self.image_size = vision_model.config.image_size
+
+    def forward(self, object_pixel_values):
+        b, c, h, w = object_pixel_values.shape
+
+        if h != self.image_size or w != self.image_size:
+            h, w = self.image_size, self.image_size
+            object_pixel_values = F.interpolate(
+                object_pixel_values, (h, w), mode="bilinear", antialias=True
+            )
+
+        object_pixel_values = self.vision_processor(object_pixel_values)
+        object_embeds = self.vision_model(object_pixel_values)[1]
+        object_embeds = self.visual_projection(object_embeds)
+        object_embeds = object_embeds.view(b, 1, -1)
+        return object_embeds
\ No newline at end of file
diff --git a/objectclear/models/postfuse_module.py b/objectclear/models/postfuse_module.py
new file mode 100644
index 0000000..ebccdfb
--- /dev/null
+++ b/objectclear/models/postfuse_module.py
@@ -0,0 +1,56 @@
+import torch
+import torch.nn as nn
+
+
+class PostfuseModule(nn.Module):
+    def __init__(self, embed_dim, embed_dim_img):
+        super().__init__()
+        self.mlp1 = MLP(embed_dim_img, embed_dim, embed_dim, use_residual=False)
+        self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True)
+        self.layer_norm = nn.LayerNorm(embed_dim)
+        
+    @property
+    def dtype(self):
+        try:
+            return next(self.parameters()).dtype
+        except StopIteration:
+            return torch.float32
+
+    def fuse_fn(self, object_embeds):
+        text_object_embeds = self.mlp1(object_embeds)
+        text_object_embeds = self.mlp2(text_object_embeds)
+        text_object_embeds = self.layer_norm(text_object_embeds)
+        return text_object_embeds
+
+    def forward(
+        self,
+        text_embeds,
+        object_embeds,
+        fuse_index,
+    ) -> torch.Tensor:
+        text_object_embed = self.fuse_fn(object_embeds)
+        text_embeds_new = text_embeds.clone()
+        text_embeds_new[:, fuse_index, :] = text_object_embed.squeeze(1)
+
+        return text_embeds_new
+    
+class MLP(nn.Module):
+    def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True):
+        super().__init__()
+        if use_residual:
+            assert in_dim == out_dim
+        self.layernorm = nn.LayerNorm(in_dim)
+        self.fc1 = nn.Linear(in_dim, hidden_dim)
+        self.fc2 = nn.Linear(hidden_dim, out_dim)
+        self.use_residual = use_residual
+        self.act_fn = nn.GELU()
+
+    def forward(self, x):
+        residual = x
+        x = self.layernorm(x)
+        x = self.fc1(x)
+        x = self.act_fn(x)
+        x = self.fc2(x)
+        if self.use_residual:
+            x = x + residual
+        return x
\ No newline at end of file
diff --git a/objectclear/pipelines/__init__.py b/objectclear/pipelines/__init__.py
new file mode 100644
index 0000000..d8ed4f7
--- /dev/null
+++ b/objectclear/pipelines/__init__.py
@@ -0,0 +1,4 @@
+from .pipeline_objectclear import ObjectClearPipeline
+
+
+__all__ = ["ObjectClearPipeline"]
\ No newline at end of file
diff --git a/objectclear/pipelines/__pycache__/__init__.cpython-311.pyc b/objectclear/pipelines/__pycache__/__init__.cpython-311.pyc
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HcmV?d00001

diff --git a/objectclear/pipelines/pipeline_objectclear.py b/objectclear/pipelines/pipeline_objectclear.py
new file mode 100644
index 0000000..d64ab25
--- /dev/null
+++ b/objectclear/pipelines/pipeline_objectclear.py
@@ -0,0 +1,2065 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL.Image
+import torch
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionModelWithProjection,
+)
+
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import (
+    FromSingleFileMixin,
+    IPAdapterMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    TextualInversionLoaderMixin,
+)
+from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
+    LoRAAttnProcessor2_0,
+    LoRAXFormersAttnProcessor,
+    XFormersAttnProcessor,
+)
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    is_invisible_watermark_available,
+    is_torch_xla_available,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+from dataclasses import dataclass
+
+from ..models import CLIPImageEncoder, PostfuseModule
+from ..utils import attention_guided_fusion
+import gc
+import torch.nn.functional as F
+
+
+if is_invisible_watermark_available():
+    from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import StableDiffusionXLInpaintPipeline
+        >>> from diffusers.utils import load_image
+
+        >>> pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
+        ...     "stabilityai/stable-diffusion-xl-base-1.0",
+        ...     torch_dtype=torch.float16,
+        ...     variant="fp16",
+        ...     use_safetensors=True,
+        ... )
+        >>> pipe.to("cuda")
+
+        >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+        >>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+
+        >>> init_image = load_image(img_url).convert("RGB")
+        >>> mask_image = load_image(mask_url).convert("RGB")
+
+        >>> prompt = "A majestic tiger sitting on a bench"
+        >>> image = pipe(
+        ...     prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80
+        ... ).images[0]
+        ```
+"""
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    """
+    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    """
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    # rescale the results from guidance (fixes overexposure)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg
+
+
+def mask_pil_to_torch(mask, height, width):
+    # preprocess mask
+    if isinstance(mask, (PIL.Image.Image, np.ndarray)):
+        mask = [mask]
+
+    if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
+        mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]
+        mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
+        mask = mask.astype(np.float32) / 255.0
+    elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
+        mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
+
+    mask = torch.from_numpy(mask)
+    return mask
+
+
+def prepare_mask_and_masked_image(image, mask, height, width, return_image: bool = False):
+    """
+    Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
+    converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
+    ``image`` and ``1`` for the ``mask``.
+
+    The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
+    binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
+
+    Args:
+        image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
+            It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
+            ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
+        mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
+            It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
+            ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
+
+
+    Raises:
+        ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
+        should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
+        TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
+            (ot the other way around).
+
+    Returns:
+        tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
+            dimensions: ``batch x channels x height x width``.
+    """
+
+    # checkpoint. TOD(Yiyi) - need to clean this up later
+    deprecation_message = "The prepare_mask_and_masked_image method is deprecated and will be removed in a future version. Please use VaeImageProcessor.preprocess instead"
+    deprecate(
+        "prepare_mask_and_masked_image",
+        "0.30.0",
+        deprecation_message,
+    )
+    if image is None:
+        raise ValueError("`image` input cannot be undefined.")
+
+    if mask is None:
+        raise ValueError("`mask_image` input cannot be undefined.")
+
+    if isinstance(image, torch.Tensor):
+        if not isinstance(mask, torch.Tensor):
+            mask = mask_pil_to_torch(mask, height, width)
+
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+
+        # Batch and add channel dim for single mask
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0).unsqueeze(0)
+
+        # Batch single mask or add channel dim
+        if mask.ndim == 3:
+            # Single batched mask, no channel dim or single mask not batched but channel dim
+            if mask.shape[0] == 1:
+                mask = mask.unsqueeze(0)
+
+            # Batched masks no channel dim
+            else:
+                mask = mask.unsqueeze(1)
+
+        assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
+        # assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
+        assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
+
+        # Check image is in [-1, 1]
+        # if image.min() < -1 or image.max() > 1:
+        #    raise ValueError("Image should be in [-1, 1] range")
+
+        # Check mask is in [0, 1]
+        if mask.min() < 0 or mask.max() > 1:
+            raise ValueError("Mask should be in [0, 1] range")
+
+        # Binarize mask
+        mask[mask < 0.5] = 0
+        mask[mask >= 0.5] = 1
+
+        # Image as float32
+        image = image.to(dtype=torch.float32)
+    elif isinstance(mask, torch.Tensor):
+        raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
+    else:
+        # preprocess image
+        if isinstance(image, (PIL.Image.Image, np.ndarray)):
+            image = [image]
+        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
+            # resize all images w.r.t passed height an width
+            image = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in image]
+            image = [np.array(i.convert("RGB"))[None, :] for i in image]
+            image = np.concatenate(image, axis=0)
+        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
+            image = np.concatenate([i[None, :] for i in image], axis=0)
+
+        image = image.transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+
+        mask = mask_pil_to_torch(mask, height, width)
+        mask[mask < 0.5] = 0
+        mask[mask >= 0.5] = 1
+
+    if image.shape[1] == 4:
+        # images are in latent space and thus can't
+        # be masked set masked_image to None
+        # we assume that the checkpoint is not an inpainting
+        # checkpoint. TOD(Yiyi) - need to clean this up later
+        masked_image = None
+    else:
+        masked_image = image * (mask < 0.5)
+
+    # n.b. ensure backwards compatibility as old function does not return image
+    if return_image:
+        return mask, masked_image, image
+
+    return mask, masked_image
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+@dataclass
+class ObjectClearPipelineOutput(StableDiffusionXLPipelineOutput):
+    attns: Optional[List[PIL.Image.Image]] = None
+
+
+class ObjectClearPipeline(
+    DiffusionPipeline,
+    StableDiffusionMixin,
+    TextualInversionLoaderMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    FromSingleFileMixin,
+    IPAdapterMixin,
+):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion XL.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    The pipeline also inherits the following loading methods:
+        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
+        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
+        - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+        - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
+        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion XL uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        text_encoder_2 ([` CLIPTextModelWithProjection`]):
+            Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+            specifically the
+            [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
+            variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        tokenizer_2 (`CLIPTokenizer`):
+            Second Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`):
+            Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config
+            of `stabilityai/stable-diffusion-xl-refiner-1-0`.
+        force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
+            Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
+            `stabilityai/stable-diffusion-xl-base-1-0`.
+        add_watermarker (`bool`, *optional*):
+            Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
+            watermark output images. If not defined, it will default to True if the package is installed, otherwise no
+            watermarker will be used.
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->postfuse_module->image_prompt_encoder->image_encoder->unet->vae"
+
+    _optional_components = [
+        "tokenizer",
+        "tokenizer_2",
+        "text_encoder",
+        "text_encoder_2",
+        "image_encoder",
+        "feature_extractor",
+    ]
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+        "add_text_embeds",
+        "add_time_ids",
+        "negative_pooled_prompt_embeds",
+        "add_neg_time_ids",
+        "mask",
+        "masked_image_latents",
+    ]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        #text_encoder: CLIPTextModel,
+        #text_encoder_2: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        tokenizer_2: CLIPTokenizer,
+        postfuse_module: PostfuseModule,
+        #image_prompt_encoder: CLIPImageEncoder,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        #image_encoder: CLIPVisionModelWithProjection = None,
+        feature_extractor: CLIPImageProcessor = None,
+        requires_aesthetics_score: bool = False,
+        force_zeros_for_empty_prompt: bool = True,
+        add_watermarker: Optional[bool] = None,
+        apply_attention_guided_fusion: bool = True,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            #text_encoder=text_encoder,
+            #text_encoder_2=text_encoder_2,
+            tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
+            postfuse_module=postfuse_module,
+            #image_prompt_encoder=image_prompt_encoder,
+            unet=unet,
+            #image_encoder=image_encoder,
+            feature_extractor=feature_extractor,
+            scheduler=scheduler,
+        )
+        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
+        self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)
+        self.register_to_config(apply_attention_guided_fusion=apply_attention_guided_fusion)
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.mask_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
+        )
+        self.text_encoder_2=None
+        self.text_encoder=None
+        self.image_prompt_encoder = None
+
+        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
+
+        if add_watermarker:
+            self.watermark = StableDiffusionXLWatermarker()
+        else:
+            self.watermark = None
+        
+        if self.config.apply_attention_guided_fusion:
+            self.cross_attention_scores = {}
+            self.unet = self.unet_store_cross_attention_scores(
+                self.unet, self.cross_attention_scores
+            )
+
+
+    @classmethod
+    def from_pretrained_with_custom_modules(
+        cls,
+        pretrained_model_name_or_path,
+        unet=None,
+        vae=None,
+        torch_dtype=torch.float32,
+        cache_dir=None,
+        variant=None,
+        postfuse_path=None,
+        **kwargs,
+    ):
+        from safetensors.torch import load_file
+        #from huggingface_hub import hf_hub_download
+
+        # image_prompt_encoder = CLIPImageEncoder.from_pretrained(
+        #     "F:/ComfyUI311/ComfyUI/custom_nodes/ComfyUI_ObjectClear/sdxl_repo",
+        #     cache_dir=cache_dir,
+        # )
+        #image_prompt_encoder =None
+
+        postfuse_module = PostfuseModule(embed_dim=2048, embed_dim_img=768) #2048
+        #filename = "model.safetensors"
+
+        # safetensor_path = hf_hub_download(
+        #     repo_id="jixin0101/ObjectClear",      
+        #     filename=filename,
+        #     subfolder="postfuse_module",            
+        #     cache_dir=cache_dir                     
+        # )
+        state_dict_postfuse = load_file(postfuse_path)
+        postfuse_module.load_state_dict(state_dict_postfuse)
+        del state_dict_postfuse
+
+        
+        pipe = super().from_pretrained(
+            pretrained_model_name_or_path,
+            torch_dtype=torch_dtype,
+            unet=unet,
+            vae=vae,
+            #image_prompt_encoder=image_prompt_encoder,
+            postfuse_module=postfuse_module,
+            cache_dir=cache_dir,
+            #variant=variant,
+            **kwargs,
+        )
+        
+        if torch_dtype is not None:
+            pipe.to(dtype=torch_dtype)
+
+        return pipe
+    
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
+    def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
+        dtype = next(self.image_encoder.parameters()).dtype
+
+        if not isinstance(image, torch.Tensor):
+            image = self.feature_extractor(image, return_tensors="pt").pixel_values
+
+        image = image.to(device=device, dtype=dtype)
+        if output_hidden_states:
+            image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
+            image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+            uncond_image_enc_hidden_states = self.image_encoder(
+                torch.zeros_like(image), output_hidden_states=True
+            ).hidden_states[-2]
+            uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
+                num_images_per_prompt, dim=0
+            )
+            print(image_enc_hidden_states.shape,uncond_image_enc_hidden_states.shape)
+            return image_enc_hidden_states, uncond_image_enc_hidden_states
+        else:
+            image_embeds = self.image_encoder(image).image_embeds
+            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+            uncond_image_embeds = torch.zeros_like(image_embeds)
+            print(image_embeds.shape,uncond_image_embeds.shape,123)
+            return image_embeds, uncond_image_embeds
+        
+    def unet_store_cross_attention_scores(self, unet, attention_scores):
+        from diffusers.models.attention_processor import (
+            Attention,
+            AttnProcessor,
+            AttnProcessor2_0,
+        )
+        import types
+
+        UNET_LAYER_NAMES = [
+            "down_blocks.0",
+            "down_blocks.1",
+            "down_blocks.2",
+            "mid_block",
+            "up_blocks.1",
+            "up_blocks.2",
+            "up_blocks.3",
+        ]
+
+        start_layer = 0
+        end_layer = 2
+        applicable_layers = UNET_LAYER_NAMES[start_layer:end_layer]
+
+        def make_new_get_attention_scores_fn(name):
+            def new_get_attention_scores(module, query, key, attention_mask=None):
+                attention_probs = module.old_get_attention_scores(
+                    query, key, attention_mask
+                )
+                attention_scores[name] = attention_probs
+                return attention_probs
+
+            return new_get_attention_scores
+
+        for name, module in unet.named_modules():
+            if isinstance(module, Attention) and "attn2" in name:
+                if not any(layer in name for layer in applicable_layers):
+                    continue
+                if isinstance(module.processor, AttnProcessor2_0):
+                    module.set_processor(AttnProcessor())
+                module.old_get_attention_scores = module.get_attention_scores
+                module.new_get_attention_scores = types.MethodType(
+                    make_new_get_attention_scores_fn(name), module
+                )
+                module.get_attention_scores = module.new_get_attention_scores
+
+        return unet
+    
+    def resize_attn_map_divide2(self, attn_map, mask, fuse_index):
+        bxh, num_noise_latents, num_text_tokens = attn_map.shape
+        b, max_num_objects, H, W = mask.shape
+        target_tokens = H * W
+        
+        attn_map = attn_map[:, :, fuse_index:fuse_index+1]
+        # attn_size = int(num_noise_latents**0.5)
+        num_heads = bxh // b
+        attn_map = attn_map.view(b, num_heads, H//2, W//2)
+        attn_map = attn_map.mean(dim=1, keepdim=True)
+        attn_map = F.interpolate(attn_map, size=(H, W), mode='bilinear', align_corners=False)
+        
+        min_val = attn_map.amin(dim=(2, 3), keepdim=True)
+        max_val = attn_map.amax(dim=(2, 3), keepdim=True)
+        attn_map = (attn_map - min_val) / (max_val - min_val + 1e-8)
+        
+        return attn_map
+        
+        
+
+    def clear_cross_attention_scores(self, cross_attention_scores):
+        keys = list(cross_attention_scores.keys())
+        for k in keys:
+            del cross_attention_scores[k]
+        gc.collect()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
+    def prepare_ip_adapter_image_embeds(
+        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
+    ):
+        if ip_adapter_image_embeds is None:
+            if not isinstance(ip_adapter_image, list):
+                ip_adapter_image = [ip_adapter_image]
+
+            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
+                raise ValueError(
+                    f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
+                )
+
+            image_embeds = []
+            for single_ip_adapter_image, image_proj_layer in zip(
+                ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
+            ):
+                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
+                single_image_embeds, single_negative_image_embeds = self.encode_image(
+                    single_ip_adapter_image, device, 1, output_hidden_state
+                )
+                single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
+                single_negative_image_embeds = torch.stack(
+                    [single_negative_image_embeds] * num_images_per_prompt, dim=0
+                )
+
+                if do_classifier_free_guidance:
+                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
+                    single_image_embeds = single_image_embeds.to(device)
+
+                image_embeds.append(single_image_embeds)
+        else:
+            repeat_dims = [1]
+            image_embeds = []
+            for single_image_embeds in ip_adapter_image_embeds:
+                if do_classifier_free_guidance:
+                    single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
+                    single_image_embeds = single_image_embeds.repeat(
+                        num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
+                    )
+                    single_negative_image_embeds = single_negative_image_embeds.repeat(
+                        num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))
+                    )
+                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
+                else:
+                    single_image_embeds = single_image_embeds.repeat(
+                        num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
+                    )
+                image_embeds.append(single_image_embeds)
+
+        return image_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: str,
+        prompt_2: Optional[str] = None,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[str] = None,
+        negative_prompt_2: Optional[str] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in both text-encoders
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            pooled_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            lora_scale (`float`, *optional*):
+                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+        """
+        # device = device or self._execution_device
+        device = device or self.unet.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, StableDiffusionXLLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if self.text_encoder is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder, lora_scale)
+
+            if self.text_encoder_2 is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder_2, lora_scale)
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        if prompt is not None:
+            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
+            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+            # textual inversion: process 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 = text_encoder(text_input_ids.to(device), output_hidden_states=True)
+
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                pooled_prompt_embeds = prompt_embeds[0]
+                if clip_skip is None:
+                    prompt_embeds = prompt_embeds.hidden_states[-2]
+                else:
+                    # "2" because SDXL always indexes from the penultimate layer.
+                    prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
+
+                prompt_embeds_list.append(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
+
+            # normalize str to list
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+            negative_prompt_2 = (
+                batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
+            )
+
+            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 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(
+                    uncond_input.input_ids.to(device),
+                    output_hidden_states=True,
+                )
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+                negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+        if self.text_encoder_2 is not None:
+            prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+        else:
+            prompt_embeds = prompt_embeds.to(dtype=self.unet.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]
+
+            if self.text_encoder_2 is not None:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+            else:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.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
+            )
+
+        if self.text_encoder is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        if self.text_encoder_2 is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder_2, lora_scale)
+
+        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,
+        image,
+        mask_image,
+        height,
+        width,
+        strength,
+        callback_steps,
+        output_type,
+        negative_prompt=None,
+        negative_prompt_2=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        ip_adapter_image=None,
+        ip_adapter_image_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+        padding_mask_crop=None,
+    ):
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if 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 callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        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}."
+                )
+        if padding_mask_crop is not None:
+            if not isinstance(image, PIL.Image.Image):
+                raise ValueError(
+                    f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}."
+                )
+            if not isinstance(mask_image, PIL.Image.Image):
+                raise ValueError(
+                    f"The mask image should be a PIL image when inpainting mask crop, but is of type"
+                    f" {type(mask_image)}."
+                )
+            if output_type != "pil":
+                raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.")
+
+        if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
+            raise ValueError(
+                "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
+            )
+
+        if ip_adapter_image_embeds is not None:
+            if not isinstance(ip_adapter_image_embeds, list):
+                raise ValueError(
+                    f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
+                )
+            elif ip_adapter_image_embeds[0].ndim not in [3, 4]:
+                raise ValueError(
+                    f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
+                )
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+        image=None,
+        timestep=None,
+        is_strength_max=True,
+        add_noise=True,
+        return_noise=False,
+        return_image_latents=False,
+    ):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(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."
+            )
+
+        if (image is None or timestep is None) and not is_strength_max:
+            raise ValueError(
+                "Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
+                "However, either the image or the noise timestep has not been provided."
+            )
+
+        if image.shape[1] == 4:
+            image_latents = image.to(device=device, dtype=dtype)
+            image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)
+        elif return_image_latents or (latents is None and not is_strength_max):
+            image = image.to(device=device, dtype=dtype)
+            image_latents = self._encode_vae_image(image=image, generator=generator)
+            image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)
+
+        if latents is None and add_noise:
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            # if strength is 1. then initialise the latents to noise, else initial to image + noise
+            latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
+            # if pure noise then scale the initial latents by the  Scheduler's init sigma
+            latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
+        elif add_noise:
+            noise = latents.to(device)
+            latents = noise * self.scheduler.init_noise_sigma
+        else:
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            latents = image_latents.to(device)
+
+        outputs = (latents,)
+
+        if return_noise:
+            outputs += (noise,)
+
+        if return_image_latents:
+            outputs += (image_latents,)
+
+        return outputs
+
+    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
+        dtype = image.dtype
+        if self.vae.config.force_upcast:
+            image = image.float()
+            self.vae.to(dtype=torch.float32)
+
+        if isinstance(generator, list):
+            image_latents = [
+                retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+                for i in range(image.shape[0])
+            ]
+            image_latents = torch.cat(image_latents, dim=0)
+        else:
+            #print(generator.device,self.vae.device,image.device)
+            image_latents = retrieve_latents(self.vae.encode(image), generator=generator) #TODO
+
+        if self.vae.config.force_upcast:
+            self.vae.to(dtype)
+
+        image_latents = image_latents.to(dtype)
+        image_latents = self.vae.config.scaling_factor * image_latents
+
+        return image_latents
+
+    def prepare_mask_latents(
+        self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
+    ):
+        # resize the mask to latents shape as we concatenate the mask to the latents
+        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
+        # and half precision
+        mask = torch.nn.functional.interpolate(
+            mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
+        )
+        mask = mask.to(device=device, dtype=dtype)
+
+        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
+        if mask.shape[0] < batch_size:
+            if not batch_size % mask.shape[0] == 0:
+                raise ValueError(
+                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
+                    " of masks that you pass is divisible by the total requested batch size."
+                )
+            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
+
+        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
+
+        if masked_image is not None and masked_image.shape[1] == 4:
+            masked_image_latents = masked_image
+        else:
+            masked_image_latents = None
+
+        if masked_image is not None:
+            if masked_image_latents is None:
+                masked_image = masked_image.to(device=device, dtype=dtype)
+                masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
+
+            if masked_image_latents.shape[0] < batch_size:
+                if not batch_size % masked_image_latents.shape[0] == 0:
+                    raise ValueError(
+                        "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                        f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+                        " Make sure the number of images that you pass is divisible by the total requested batch size."
+                    )
+                masked_image_latents = masked_image_latents.repeat(
+                    batch_size // masked_image_latents.shape[0], 1, 1, 1
+                )
+
+            masked_image_latents = (
+                torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
+            )
+
+            # aligning device to prevent device errors when concating it with the latent model input
+            masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
+
+        return mask, masked_image_latents
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.get_timesteps
+    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)
+                )
+            )
+
+            num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()
+            if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
+                # if the scheduler is a 2nd order scheduler we might have to do +1
+                # because `num_inference_steps` might be even given that every timestep
+                # (except the highest one) is duplicated. If `num_inference_steps` is even it would
+                # mean that we cut the timesteps in the middle of the denoising step
+                # (between 1st and 2nd derivative) which leads to incorrect results. By adding 1
+                # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler
+                num_inference_steps = num_inference_steps + 1
+
+            # because t_n+1 >= t_n, we slice the timesteps starting from the end
+            timesteps = timesteps[-num_inference_steps:]
+            return timesteps, num_inference_steps
+
+        return timesteps, num_inference_steps - t_start
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids
+    def _get_add_time_ids(
+        self,
+        original_size,
+        crops_coords_top_left,
+        target_size,
+        aesthetic_score,
+        negative_aesthetic_score,
+        negative_original_size,
+        negative_crops_coords_top_left,
+        negative_target_size,
+        dtype,
+        text_encoder_projection_dim=None,
+    ):
+        if self.config.requires_aesthetics_score:
+            add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))
+            add_neg_time_ids = list(
+                negative_original_size + negative_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(negative_original_size + crops_coords_top_left + negative_target_size)
+
+        passed_add_embed_dim = (
+            self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_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
+    def upcast_vae(self):
+        dtype = self.vae.dtype
+        self.vae.to(dtype=torch.float32)
+        use_torch_2_0_or_xformers = isinstance(
+            self.vae.decoder.mid_block.attentions[0].processor,
+            (
+                AttnProcessor2_0,
+                XFormersAttnProcessor,
+                LoRAXFormersAttnProcessor,
+                LoRAAttnProcessor2_0,
+            ),
+        )
+        # if xformers or torch_2_0 is used attention block does not need
+        # to be in float32 which can save lots of memory
+        if use_torch_2_0_or_xformers:
+            self.vae.post_quant_conv.to(dtype)
+            self.vae.decoder.conv_in.to(dtype)
+            self.vae.decoder.mid_block.to(dtype)
+
+    # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.Tensor:
+        """
+        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+        Args:
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
+            embedding_dim (`int`, *optional*, defaults to 512):
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
+
+        Returns:
+            `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
+        """
+        assert len(w.shape) == 1
+        w = w * 1000.0
+
+        half_dim = embedding_dim // 2
+        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+        emb = w.to(dtype)[:, None] * emb[None, :]
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+        if embedding_dim % 2 == 1:  # zero pad
+            emb = torch.nn.functional.pad(emb, (0, 1))
+        assert emb.shape == (w.shape[0], embedding_dim)
+        return emb
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def guidance_rescale(self):
+        return self._guidance_rescale
+
+    @property
+    def clip_skip(self):
+        return self._clip_skip
+
+    # 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.
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
+
+    @property
+    def cross_attention_kwargs(self):
+        return self._cross_attention_kwargs
+
+    @property
+    def denoising_end(self):
+        return self._denoising_end
+
+    @property
+    def denoising_start(self):
+        return self._denoising_start
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        image: PipelineImageInput = None,
+        mask_image: PipelineImageInput = None,
+        masked_image_latents: torch.Tensor = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        padding_mask_crop: Optional[int] = None,
+        strength: float = 0.9999,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        sigmas: List[float] = None,
+        denoising_start: Optional[float] = None,
+        denoising_end: Optional[float] = None,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        return_attn_map: bool = False,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        original_size: Tuple[int, int] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Tuple[int, int] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        aesthetic_score: float = 6.0,
+        negative_aesthetic_score: float = 2.5,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        object_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in both text-encoders
+            image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
+                be masked out with `mask_image` and repainted according to `prompt`.
+            mask_image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
+                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
+                instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            padding_mask_crop (`int`, *optional*, defaults to `None`):
+                The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to
+                image and mask_image. If `padding_mask_crop` is not `None`, it will first find a rectangular region
+                with the same aspect ration of the image and contains all masked area, and then expand that area based
+                on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before
+                resizing to the original image size for inpainting. This is useful when the masked area is small while
+                the image is large and contain information irrelevant for inpainting, such as background.
+            strength (`float`, *optional*, defaults to 0.9999):
+                Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be
+                between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the
+                `strength`. The number of denoising steps depends on the amount of noise initially added. When
+                `strength` is 1, added noise will be maximum and the denoising process will run for the full number of
+                iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked
+                portion of the reference `image`. Note that in the case of `denoising_start` being declared as an
+                integer, the value of `strength` will be ignored.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            denoising_start (`float`, *optional*):
+                When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be
+                bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
+                it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
+                strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
+                is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refining the Image
+                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).
+            denoising_end (`float`, *optional*):
+                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+                completed before it is intentionally prematurely terminated. As a result, the returned sample will
+                still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be
+                denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the
+                final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline
+                forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            pooled_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
+                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
+                contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.Tensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+                explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                For most cases, `target_size` should be set to the desired height and width of the generated image. If
+                not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            aesthetic_score (`float`, *optional*, defaults to 6.0):
+                Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
+                Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_aesthetic_score (`float`, *optional*, defaults to 2.5):
+                Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to
+                simulate an aesthetic score of the generated image by influencing the negative text condition.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
+                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
+                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
+                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple. `tuple. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 1. Check inputs
+        # self.check_inputs(
+        #     prompt,
+        #     prompt_2,
+        #     image,
+        #     mask_image,
+        #     height,
+        #     width,
+        #     strength,
+        #     callback_steps,
+        #     output_type,
+        #     negative_prompt,
+        #     negative_prompt_2,
+        #     prompt_embeds,
+        #     negative_prompt_embeds,
+        #     ip_adapter_image,
+        #     ip_adapter_image_embeds,
+        #     callback_on_step_end_tensor_inputs,
+        #     padding_mask_crop,
+        # )
+
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._denoising_end = denoising_end
+        self._denoising_start = denoising_start
+        self._interrupt = False
+
+        # 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
+        device = self.unet.device
+
+        # 3. Encode input prompt
+        text_encoder_lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+        )
+        if prompt is not 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=self.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,
+                clip_skip=self.clip_skip,
+            )
+
+        # 4. set timesteps
+        def denoising_value_valid(dnv):
+            return isinstance(dnv, float) and 0 < dnv < 1
+
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas
+        )
+        timesteps, num_inference_steps = self.get_timesteps(
+            num_inference_steps,
+            strength,
+            device,
+            denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,
+        )
+        # check that number of inference steps is not < 1 - as this doesn't make sense
+        if num_inference_steps < 1:
+            raise ValueError(
+                f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
+                f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
+            )
+        # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+        # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
+        is_strength_max = strength == 1.0
+
+        # 5. Preprocess mask and image
+        if padding_mask_crop is not None:
+            crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)
+            resize_mode = "fill"
+        else:
+            crops_coords = None
+            resize_mode = "default"
+
+        original_image = image
+        init_image = self.image_processor.preprocess(
+            image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode
+        )
+        init_image = init_image.to(dtype=torch.float32)
+
+        mask = self.mask_processor.preprocess(
+            mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords
+        )
+        #print(f"mask.shape: {mask.shape}",init_image.shape) #mask.shape: torch.Size([1, 1, 768, 512]) torch.Size([1, 3, 768, 512])
+        if masked_image_latents is not None:
+            masked_image = masked_image_latents
+        elif init_image.shape[1] == 4:
+            # if images are in latent space, we can't mask it
+            masked_image = None
+        else:
+            masked_image = init_image
+            # masked_image = init_image * (mask < 0.5)
+            obj_only = init_image * (mask > 0.5)
+            obj_only = obj_only.to(device=device)
+            if  self.image_prompt_encoder is not None:
+                object_embeds = self.image_prompt_encoder(obj_only)
+        #print(f"masked_image.shape: {masked_image.shape}",f"object_embeds.shape: {object_embeds.shape}") #masked_image.shape: torch.Size([1, 3, 768, 512]) object_embeds.shape: torch.Size([1, 1, 768])
+        prompt_embeds = self.postfuse_module(prompt_embeds, object_embeds, 5)
+
+        # 6. Prepare latent variables
+        num_channels_latents = self.vae.config.latent_channels
+        num_channels_unet = self.unet.config.in_channels
+        # return_image_latents = num_channels_unet == 4
+        return_image_latents = True
+
+        add_noise = True if self.denoising_start is None else False
+        latents_outputs = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+            image=init_image,
+            timestep=latent_timestep,
+            is_strength_max=is_strength_max,
+            add_noise=add_noise,
+            return_noise=True,
+            return_image_latents=return_image_latents,
+        )
+
+        if return_image_latents:
+            latents, noise, image_latents = latents_outputs
+        else:
+            latents, noise = latents_outputs
+
+        # 7. Prepare mask latent variables
+        mask, masked_image_latents = self.prepare_mask_latents(
+            mask,
+            masked_image,
+            batch_size * num_images_per_prompt,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            self.do_classifier_free_guidance,
+        )
+
+        # 8. Check that sizes of mask, masked image and latents match
+        if num_channels_unet == 9:
+            # default case for runwayml/stable-diffusion-inpainting
+            num_channels_mask = mask.shape[1]
+            num_channels_masked_image = masked_image_latents.shape[1]
+            if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
+                raise ValueError(
+                    f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
+                    f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
+                    f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
+                    f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
+                    " `pipeline.unet` or your `mask_image` or `image` input."
+                )
+        elif num_channels_unet != 4:
+            raise ValueError(
+                f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}."
+            )
+        # 8.1 Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        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)
+
+        # 10. Prepare added time ids & embeddings
+        if negative_original_size is None:
+            negative_original_size = original_size
+        if negative_target_size is None:
+            negative_target_size = target_size
+
+        add_text_embeds = pooled_prompt_embeds
+        if self.text_encoder_2 is None:
+            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+        else:
+            text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+
+        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,
+            negative_original_size,
+            negative_crops_coords_top_left,
+            negative_target_size,
+            dtype=prompt_embeds.dtype,
+            text_encoder_projection_dim=text_encoder_projection_dim,
+        )
+        add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)
+
+        if self.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)
+            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)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device)
+
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+                self.do_classifier_free_guidance,
+            )
+
+        # 11. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        if (
+            self.denoising_end is not None
+            and self.denoising_start is not None
+            and denoising_value_valid(self.denoising_end)
+            and denoising_value_valid(self.denoising_start)
+            and self.denoising_start >= self.denoising_end
+        ):
+            raise ValueError(
+                f"`denoising_start`: {self.denoising_start} cannot be larger than or equal to `denoising_end`: "
+                + f" {self.denoising_end} when using type float."
+            )
+        elif self.denoising_end is not None and denoising_value_valid(self.denoising_end):
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (self.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]
+
+        # 11.1 Optionally get Guidance Scale Embedding
+        timestep_cond = None
+        if self.unet.config.time_cond_proj_dim is not None:
+            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+            timestep_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+
+        self._num_timesteps = len(timesteps)
+        attn_map = None
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+
+                # concat latents, mask, masked_image_latents in the channel dimension
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                if num_channels_unet == 9:
+                    latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
+
+                # predict the noise residual
+                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+                if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+                    added_cond_kwargs["image_embeds"] = image_embeds
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs=self.cross_attention_kwargs,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                # progressive attention mask blending
+                fuse_index = 5
+                if self.config.apply_attention_guided_fusion:
+                    if i == len(timesteps) - 1:
+                        attn_key, attn_map = next(iter(self.cross_attention_scores.items()))
+                        attn_map = self.resize_attn_map_divide2(attn_map, mask, fuse_index)
+                        init_latents_proper = image_latents
+                        if self.do_classifier_free_guidance:
+                            _, init_mask = attn_map.chunk(2)
+                        else:
+                            init_mask = attn_map
+                        attn_map = init_mask
+                    self.clear_cross_attention_scores(self.cross_attention_scores)
+                
+                if num_channels_unet == 4:
+                    init_latents_proper = image_latents
+                    if self.do_classifier_free_guidance:
+                        init_mask, _ = mask.chunk(2)
+                    else:
+                        init_mask = mask
+
+                    if i < len(timesteps) - 1:
+                        noise_timestep = timesteps[i + 1]
+                        init_latents_proper = self.scheduler.add_noise(
+                            init_latents_proper, noise, torch.tensor([noise_timestep])
+                        )
+
+                    latents = (1 - init_mask) * init_latents_proper + init_mask * latents
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+                    negative_pooled_prompt_embeds = callback_outputs.pop(
+                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                    )
+                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    add_neg_time_ids = callback_outputs.pop("add_neg_time_ids", add_neg_time_ids)
+                    mask = callback_outputs.pop("mask", mask)
+                    masked_image_latents = callback_outputs.pop("masked_image_latents", masked_image_latents)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        if not output_type == "latent":
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+
+            if needs_upcasting:
+                self.upcast_vae()
+                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+            elif latents.dtype != self.vae.dtype:
+                if torch.backends.mps.is_available():
+                    # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                    self.vae = self.vae.to(latents.dtype)
+
+            # unscale/denormalize the latents
+            # denormalize with the mean and std if available and not None
+            has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
+            has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
+            if has_latents_mean and has_latents_std:
+                latents_mean = (
+                    torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents_std = (
+                    torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
+            else:
+                latents = latents / self.vae.config.scaling_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
+
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)
+        else:
+            return ObjectClearPipelineOutput(images=latents)
+
+        # apply watermark if available
+        if self.watermark is not None:
+            image = self.watermark.apply_watermark(image)
+
+        image = self.image_processor.postprocess(image, output_type=output_type)
+
+        if padding_mask_crop is not None:
+            image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image]
+            
+        attn_pils = []
+        if output_type == "pil" and attn_map is not None:
+            for i in range(len(attn_map)):
+                attn_np = attn_map[i].mean(dim=0).cpu().numpy() * 255.
+                attn_pil = PIL.Image.fromarray(attn_np.astype(np.uint8)).convert("L")
+                attn_pils.append(attn_pil)
+            
+            original_pils = self.image_processor.postprocess(init_image, output_type="pil")
+
+            generated_pils = image
+
+            fused_images = []
+            for i in range(len(generated_pils)):
+                ori_pil = original_pils[i]
+                gen_pil = generated_pils[i]
+                attn_pil = attn_pils[i]
+
+                fused_np = attention_guided_fusion(np.array(ori_pil), np.array(gen_pil), np.array(attn_pil))
+                fused_pil = PIL.Image.fromarray(fused_np.astype(np.uint8)).resize(ori_pil.size)
+
+                fused_images.append(fused_pil)
+
+            image = fused_images
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+    
+        if return_attn_map and len(attn_pils) > 0:
+            if not return_dict:
+                return (image, attn_pils)
+            return ObjectClearPipelineOutput(images=image, attns=attn_pils)
+        else:
+            if not return_dict:
+                return (image,)
+            return ObjectClearPipelineOutput(images=image)
diff --git a/objectclear/pipelines/pipeline_objectclear_.py b/objectclear/pipelines/pipeline_objectclear_.py
new file mode 100644
index 0000000..ff81f0e
--- /dev/null
+++ b/objectclear/pipelines/pipeline_objectclear_.py
@@ -0,0 +1,2050 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL.Image
+import torch
+from transformers import (
+    CLIPImageProcessor,
+    CLIPTextModel,
+    CLIPTextModelWithProjection,
+    CLIPTokenizer,
+    CLIPVisionModelWithProjection,
+)
+
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import (
+    FromSingleFileMixin,
+    IPAdapterMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    TextualInversionLoaderMixin,
+)
+from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
+from diffusers.models.attention_processor import (
+    AttnProcessor2_0,
+    LoRAAttnProcessor2_0,
+    LoRAXFormersAttnProcessor,
+    XFormersAttnProcessor,
+)
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    deprecate,
+    is_invisible_watermark_available,
+    is_torch_xla_available,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+from dataclasses import dataclass
+
+from ..models import CLIPImageEncoder, PostfuseModule
+from ..utils import attention_guided_fusion
+import gc
+import torch.nn.functional as F
+
+
+if is_invisible_watermark_available():
+    from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import StableDiffusionXLInpaintPipeline
+        >>> from diffusers.utils import load_image
+
+        >>> pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
+        ...     "stabilityai/stable-diffusion-xl-base-1.0",
+        ...     torch_dtype=torch.float16,
+        ...     variant="fp16",
+        ...     use_safetensors=True,
+        ... )
+        >>> pipe.to("cuda")
+
+        >>> img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+        >>> mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+
+        >>> init_image = load_image(img_url).convert("RGB")
+        >>> mask_image = load_image(mask_url).convert("RGB")
+
+        >>> prompt = "A majestic tiger sitting on a bench"
+        >>> image = pipe(
+        ...     prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=50, strength=0.80
+        ... ).images[0]
+        ```
+"""
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    """
+    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    """
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    # rescale the results from guidance (fixes overexposure)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg
+
+
+def mask_pil_to_torch(mask, height, width):
+    # preprocess mask
+    if isinstance(mask, (PIL.Image.Image, np.ndarray)):
+        mask = [mask]
+
+    if isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image):
+        mask = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in mask]
+        mask = np.concatenate([np.array(m.convert("L"))[None, None, :] for m in mask], axis=0)
+        mask = mask.astype(np.float32) / 255.0
+    elif isinstance(mask, list) and isinstance(mask[0], np.ndarray):
+        mask = np.concatenate([m[None, None, :] for m in mask], axis=0)
+
+    mask = torch.from_numpy(mask)
+    return mask
+
+
+def prepare_mask_and_masked_image(image, mask, height, width, return_image: bool = False):
+    """
+    Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
+    converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
+    ``image`` and ``1`` for the ``mask``.
+
+    The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
+    binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
+
+    Args:
+        image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
+            It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
+            ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
+        mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
+            It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
+            ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
+
+
+    Raises:
+        ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
+        should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
+        TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
+            (ot the other way around).
+
+    Returns:
+        tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
+            dimensions: ``batch x channels x height x width``.
+    """
+
+    # checkpoint. TOD(Yiyi) - need to clean this up later
+    deprecation_message = "The prepare_mask_and_masked_image method is deprecated and will be removed in a future version. Please use VaeImageProcessor.preprocess instead"
+    deprecate(
+        "prepare_mask_and_masked_image",
+        "0.30.0",
+        deprecation_message,
+    )
+    if image is None:
+        raise ValueError("`image` input cannot be undefined.")
+
+    if mask is None:
+        raise ValueError("`mask_image` input cannot be undefined.")
+
+    if isinstance(image, torch.Tensor):
+        if not isinstance(mask, torch.Tensor):
+            mask = mask_pil_to_torch(mask, height, width)
+
+        if image.ndim == 3:
+            image = image.unsqueeze(0)
+
+        # Batch and add channel dim for single mask
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0).unsqueeze(0)
+
+        # Batch single mask or add channel dim
+        if mask.ndim == 3:
+            # Single batched mask, no channel dim or single mask not batched but channel dim
+            if mask.shape[0] == 1:
+                mask = mask.unsqueeze(0)
+
+            # Batched masks no channel dim
+            else:
+                mask = mask.unsqueeze(1)
+
+        assert image.ndim == 4 and mask.ndim == 4, "Image and Mask must have 4 dimensions"
+        # assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
+        assert image.shape[0] == mask.shape[0], "Image and Mask must have the same batch size"
+
+        # Check image is in [-1, 1]
+        # if image.min() < -1 or image.max() > 1:
+        #    raise ValueError("Image should be in [-1, 1] range")
+
+        # Check mask is in [0, 1]
+        if mask.min() < 0 or mask.max() > 1:
+            raise ValueError("Mask should be in [0, 1] range")
+
+        # Binarize mask
+        mask[mask < 0.5] = 0
+        mask[mask >= 0.5] = 1
+
+        # Image as float32
+        image = image.to(dtype=torch.float32)
+    elif isinstance(mask, torch.Tensor):
+        raise TypeError(f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not")
+    else:
+        # preprocess image
+        if isinstance(image, (PIL.Image.Image, np.ndarray)):
+            image = [image]
+        if isinstance(image, list) and isinstance(image[0], PIL.Image.Image):
+            # resize all images w.r.t passed height an width
+            image = [i.resize((width, height), resample=PIL.Image.LANCZOS) for i in image]
+            image = [np.array(i.convert("RGB"))[None, :] for i in image]
+            image = np.concatenate(image, axis=0)
+        elif isinstance(image, list) and isinstance(image[0], np.ndarray):
+            image = np.concatenate([i[None, :] for i in image], axis=0)
+
+        image = image.transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+
+        mask = mask_pil_to_torch(mask, height, width)
+        mask[mask < 0.5] = 0
+        mask[mask >= 0.5] = 1
+
+    if image.shape[1] == 4:
+        # images are in latent space and thus can't
+        # be masked set masked_image to None
+        # we assume that the checkpoint is not an inpainting
+        # checkpoint. TOD(Yiyi) - need to clean this up later
+        masked_image = None
+    else:
+        masked_image = image * (mask < 0.5)
+
+    # n.b. ensure backwards compatibility as old function does not return image
+    if return_image:
+        return mask, masked_image, image
+
+    return mask, masked_image
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+@dataclass
+class ObjectClearPipelineOutput(StableDiffusionXLPipelineOutput):
+    attns: Optional[List[PIL.Image.Image]] = None
+
+
+class ObjectClearPipeline(
+    DiffusionPipeline,
+    StableDiffusionMixin,
+    TextualInversionLoaderMixin,
+    StableDiffusionXLLoraLoaderMixin,
+    FromSingleFileMixin,
+    IPAdapterMixin,
+):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion XL.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    The pipeline also inherits the following loading methods:
+        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
+        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
+        - [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+        - [`~loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
+        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion XL uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        text_encoder_2 ([` CLIPTextModelWithProjection`]):
+            Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+            specifically the
+            [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
+            variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        tokenizer_2 (`CLIPTokenizer`):
+            Second Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        requires_aesthetics_score (`bool`, *optional*, defaults to `"False"`):
+            Whether the `unet` requires a aesthetic_score condition to be passed during inference. Also see the config
+            of `stabilityai/stable-diffusion-xl-refiner-1-0`.
+        force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
+            Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
+            `stabilityai/stable-diffusion-xl-base-1-0`.
+        add_watermarker (`bool`, *optional*):
+            Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to
+            watermark output images. If not defined, it will default to True if the package is installed, otherwise no
+            watermarker will be used.
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->postfuse_module->image_prompt_encoder->image_encoder->unet->vae"
+
+    _optional_components = [
+        "tokenizer",
+        "tokenizer_2",
+        "text_encoder",
+        "text_encoder_2",
+        "image_encoder",
+        "feature_extractor",
+    ]
+    _callback_tensor_inputs = [
+        "latents",
+        "prompt_embeds",
+        "negative_prompt_embeds",
+        "add_text_embeds",
+        "add_time_ids",
+        "negative_pooled_prompt_embeds",
+        "add_neg_time_ids",
+        "mask",
+        "masked_image_latents",
+    ]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        text_encoder_2: CLIPTextModelWithProjection,
+        tokenizer: CLIPTokenizer,
+        tokenizer_2: CLIPTokenizer,
+        postfuse_module: PostfuseModule,
+        image_prompt_encoder: CLIPImageEncoder,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        image_encoder: CLIPVisionModelWithProjection = None,
+        feature_extractor: CLIPImageProcessor = None,
+        requires_aesthetics_score: bool = False,
+        force_zeros_for_empty_prompt: bool = True,
+        add_watermarker: Optional[bool] = None,
+        apply_attention_guided_fusion: bool = True,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            text_encoder_2=text_encoder_2,
+            tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
+            postfuse_module=postfuse_module,
+            image_prompt_encoder=image_prompt_encoder,
+            unet=unet,
+            image_encoder=image_encoder,
+            feature_extractor=feature_extractor,
+            scheduler=scheduler,
+        )
+        self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
+        self.register_to_config(requires_aesthetics_score=requires_aesthetics_score)
+        self.register_to_config(apply_attention_guided_fusion=apply_attention_guided_fusion)
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.mask_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True
+        )
+
+        add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
+
+        if add_watermarker:
+            self.watermark = StableDiffusionXLWatermarker()
+        else:
+            self.watermark = None
+        
+        if self.config.apply_attention_guided_fusion:
+            self.cross_attention_scores = {}
+            self.unet = self.unet_store_cross_attention_scores(
+                self.unet, self.cross_attention_scores
+            )
+
+
+    @classmethod
+    def from_pretrained_with_custom_modules(
+        cls,
+        pretrained_model_name_or_path,
+        torch_dtype=torch.float32,
+        cache_dir=None,
+        variant=None,
+        **kwargs,
+    ):
+        from safetensors.torch import load_file
+        from huggingface_hub import hf_hub_download
+
+        image_prompt_encoder = CLIPImageEncoder.from_pretrained(
+            pretrained_model_name_or_path,
+            cache_dir=cache_dir,
+        )
+
+        postfuse_module = PostfuseModule(embed_dim=2048, embed_dim_img=768)
+        filename = "model.safetensors"
+
+        safetensor_path = hf_hub_download(
+            repo_id="jixin0101/ObjectClear",      
+            filename=filename,
+            subfolder="postfuse_module",            
+            cache_dir=cache_dir                     
+        )
+        state_dict_postfuse = load_file(safetensor_path)
+        postfuse_module.load_state_dict(state_dict_postfuse)
+        
+        pipe = super().from_pretrained(
+            pretrained_model_name_or_path,
+            torch_dtype=torch_dtype,
+            image_prompt_encoder=image_prompt_encoder,
+            postfuse_module=postfuse_module,
+            cache_dir=cache_dir,
+            variant=variant,
+            **kwargs,
+        )
+        
+        if torch_dtype is not None:
+            pipe.to(dtype=torch_dtype)
+
+        return pipe
+    
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
+    def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
+        dtype = next(self.image_encoder.parameters()).dtype
+
+        if not isinstance(image, torch.Tensor):
+            image = self.feature_extractor(image, return_tensors="pt").pixel_values
+
+        image = image.to(device=device, dtype=dtype)
+        if output_hidden_states:
+            image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
+            image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+            uncond_image_enc_hidden_states = self.image_encoder(
+                torch.zeros_like(image), output_hidden_states=True
+            ).hidden_states[-2]
+            uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
+                num_images_per_prompt, dim=0
+            )
+            return image_enc_hidden_states, uncond_image_enc_hidden_states
+        else:
+            image_embeds = self.image_encoder(image).image_embeds
+            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+            uncond_image_embeds = torch.zeros_like(image_embeds)
+
+            return image_embeds, uncond_image_embeds
+        
+    def unet_store_cross_attention_scores(self, unet, attention_scores):
+        from diffusers.models.attention_processor import (
+            Attention,
+            AttnProcessor,
+            AttnProcessor2_0,
+        )
+        import types
+
+        UNET_LAYER_NAMES = [
+            "down_blocks.0",
+            "down_blocks.1",
+            "down_blocks.2",
+            "mid_block",
+            "up_blocks.1",
+            "up_blocks.2",
+            "up_blocks.3",
+        ]
+
+        start_layer = 0
+        end_layer = 2
+        applicable_layers = UNET_LAYER_NAMES[start_layer:end_layer]
+
+        def make_new_get_attention_scores_fn(name):
+            def new_get_attention_scores(module, query, key, attention_mask=None):
+                attention_probs = module.old_get_attention_scores(
+                    query, key, attention_mask
+                )
+                attention_scores[name] = attention_probs
+                return attention_probs
+
+            return new_get_attention_scores
+
+        for name, module in unet.named_modules():
+            if isinstance(module, Attention) and "attn2" in name:
+                if not any(layer in name for layer in applicable_layers):
+                    continue
+                if isinstance(module.processor, AttnProcessor2_0):
+                    module.set_processor(AttnProcessor())
+                module.old_get_attention_scores = module.get_attention_scores
+                module.new_get_attention_scores = types.MethodType(
+                    make_new_get_attention_scores_fn(name), module
+                )
+                module.get_attention_scores = module.new_get_attention_scores
+
+        return unet
+    
+    def resize_attn_map_divide2(self, attn_map, mask, fuse_index):
+        bxh, num_noise_latents, num_text_tokens = attn_map.shape
+        b, max_num_objects, H, W = mask.shape
+        target_tokens = H * W
+        
+        attn_map = attn_map[:, :, fuse_index:fuse_index+1]
+        # attn_size = int(num_noise_latents**0.5)
+        num_heads = bxh // b
+        attn_map = attn_map.view(b, num_heads, H//2, W//2)
+        attn_map = attn_map.mean(dim=1, keepdim=True)
+        attn_map = F.interpolate(attn_map, size=(H, W), mode='bilinear', align_corners=False)
+        
+        min_val = attn_map.amin(dim=(2, 3), keepdim=True)
+        max_val = attn_map.amax(dim=(2, 3), keepdim=True)
+        attn_map = (attn_map - min_val) / (max_val - min_val + 1e-8)
+        
+        return attn_map
+        
+        
+
+    def clear_cross_attention_scores(self, cross_attention_scores):
+        keys = list(cross_attention_scores.keys())
+        for k in keys:
+            del cross_attention_scores[k]
+        gc.collect()
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
+    def prepare_ip_adapter_image_embeds(
+        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
+    ):
+        if ip_adapter_image_embeds is None:
+            if not isinstance(ip_adapter_image, list):
+                ip_adapter_image = [ip_adapter_image]
+
+            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
+                raise ValueError(
+                    f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
+                )
+
+            image_embeds = []
+            for single_ip_adapter_image, image_proj_layer in zip(
+                ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
+            ):
+                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
+                single_image_embeds, single_negative_image_embeds = self.encode_image(
+                    single_ip_adapter_image, device, 1, output_hidden_state
+                )
+                single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0)
+                single_negative_image_embeds = torch.stack(
+                    [single_negative_image_embeds] * num_images_per_prompt, dim=0
+                )
+
+                if do_classifier_free_guidance:
+                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
+                    single_image_embeds = single_image_embeds.to(device)
+
+                image_embeds.append(single_image_embeds)
+        else:
+            repeat_dims = [1]
+            image_embeds = []
+            for single_image_embeds in ip_adapter_image_embeds:
+                if do_classifier_free_guidance:
+                    single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
+                    single_image_embeds = single_image_embeds.repeat(
+                        num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
+                    )
+                    single_negative_image_embeds = single_negative_image_embeds.repeat(
+                        num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:]))
+                    )
+                    single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds])
+                else:
+                    single_image_embeds = single_image_embeds.repeat(
+                        num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:]))
+                    )
+                image_embeds.append(single_image_embeds)
+
+        return image_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: str,
+        prompt_2: Optional[str] = None,
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        do_classifier_free_guidance: bool = True,
+        negative_prompt: Optional[str] = None,
+        negative_prompt_2: Optional[str] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        lora_scale: Optional[float] = None,
+        clip_skip: Optional[int] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in both text-encoders
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            do_classifier_free_guidance (`bool`):
+                whether to use classifier free guidance or not
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            pooled_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            lora_scale (`float`, *optional*):
+                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+        """
+        # device = device or self._execution_device
+        device = device or self.unet.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, StableDiffusionXLLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if self.text_encoder is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder, lora_scale)
+
+            if self.text_encoder_2 is not None:
+                if not USE_PEFT_BACKEND:
+                    adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale)
+                else:
+                    scale_lora_layers(self.text_encoder_2, lora_scale)
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+
+        if prompt is not None:
+            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
+            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+            # textual inversion: process 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 = text_encoder(text_input_ids.to(device), output_hidden_states=True)
+
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                pooled_prompt_embeds = prompt_embeds[0]
+                if clip_skip is None:
+                    prompt_embeds = prompt_embeds.hidden_states[-2]
+                else:
+                    # "2" because SDXL always indexes from the penultimate layer.
+                    prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)]
+
+                prompt_embeds_list.append(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
+
+            # normalize str to list
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+            negative_prompt_2 = (
+                batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2
+            )
+
+            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 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(
+                    uncond_input.input_ids.to(device),
+                    output_hidden_states=True,
+                )
+                # We are only ALWAYS interested in the pooled output of the final text encoder
+                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+                negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+                negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+            negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+        if self.text_encoder_2 is not None:
+            prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+        else:
+            prompt_embeds = prompt_embeds.to(dtype=self.unet.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]
+
+            if self.text_encoder_2 is not None:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+            else:
+                negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.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
+            )
+
+        if self.text_encoder is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        if self.text_encoder_2 is not None:
+            if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder_2, lora_scale)
+
+        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,
+        image,
+        mask_image,
+        height,
+        width,
+        strength,
+        callback_steps,
+        output_type,
+        negative_prompt=None,
+        negative_prompt_2=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        ip_adapter_image=None,
+        ip_adapter_image_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+        padding_mask_crop=None,
+    ):
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if 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 callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        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}."
+                )
+        if padding_mask_crop is not None:
+            if not isinstance(image, PIL.Image.Image):
+                raise ValueError(
+                    f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}."
+                )
+            if not isinstance(mask_image, PIL.Image.Image):
+                raise ValueError(
+                    f"The mask image should be a PIL image when inpainting mask crop, but is of type"
+                    f" {type(mask_image)}."
+                )
+            if output_type != "pil":
+                raise ValueError(f"The output type should be PIL when inpainting mask crop, but is" f" {output_type}.")
+
+        if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
+            raise ValueError(
+                "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
+            )
+
+        if ip_adapter_image_embeds is not None:
+            if not isinstance(ip_adapter_image_embeds, list):
+                raise ValueError(
+                    f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
+                )
+            elif ip_adapter_image_embeds[0].ndim not in [3, 4]:
+                raise ValueError(
+                    f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
+                )
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+        image=None,
+        timestep=None,
+        is_strength_max=True,
+        add_noise=True,
+        return_noise=False,
+        return_image_latents=False,
+    ):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            int(height) // self.vae_scale_factor,
+            int(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."
+            )
+
+        if (image is None or timestep is None) and not is_strength_max:
+            raise ValueError(
+                "Since strength < 1. initial latents are to be initialised as a combination of Image + Noise."
+                "However, either the image or the noise timestep has not been provided."
+            )
+
+        if image.shape[1] == 4:
+            image_latents = image.to(device=device, dtype=dtype)
+            image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)
+        elif return_image_latents or (latents is None and not is_strength_max):
+            image = image.to(device=device, dtype=dtype)
+            image_latents = self._encode_vae_image(image=image, generator=generator)
+            image_latents = image_latents.repeat(batch_size // image_latents.shape[0], 1, 1, 1)
+
+        if latents is None and add_noise:
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            # if strength is 1. then initialise the latents to noise, else initial to image + noise
+            latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
+            # if pure noise then scale the initial latents by the  Scheduler's init sigma
+            latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents
+        elif add_noise:
+            noise = latents.to(device)
+            latents = noise * self.scheduler.init_noise_sigma
+        else:
+            noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+            latents = image_latents.to(device)
+
+        outputs = (latents,)
+
+        if return_noise:
+            outputs += (noise,)
+
+        if return_image_latents:
+            outputs += (image_latents,)
+
+        return outputs
+
+    def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
+        dtype = image.dtype
+        if self.vae.config.force_upcast:
+            image = image.float()
+            self.vae.to(dtype=torch.float32)
+
+        if isinstance(generator, list):
+            image_latents = [
+                retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+                for i in range(image.shape[0])
+            ]
+            image_latents = torch.cat(image_latents, dim=0)
+        else:
+            image_latents = retrieve_latents(self.vae.encode(image), generator=generator)
+
+        if self.vae.config.force_upcast:
+            self.vae.to(dtype)
+
+        image_latents = image_latents.to(dtype)
+        image_latents = self.vae.config.scaling_factor * image_latents
+
+        return image_latents
+
+    def prepare_mask_latents(
+        self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance
+    ):
+        # resize the mask to latents shape as we concatenate the mask to the latents
+        # we do that before converting to dtype to avoid breaking in case we're using cpu_offload
+        # and half precision
+        mask = torch.nn.functional.interpolate(
+            mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor)
+        )
+        mask = mask.to(device=device, dtype=dtype)
+
+        # duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
+        if mask.shape[0] < batch_size:
+            if not batch_size % mask.shape[0] == 0:
+                raise ValueError(
+                    "The passed mask and the required batch size don't match. Masks are supposed to be duplicated to"
+                    f" a total batch size of {batch_size}, but {mask.shape[0]} masks were passed. Make sure the number"
+                    " of masks that you pass is divisible by the total requested batch size."
+                )
+            mask = mask.repeat(batch_size // mask.shape[0], 1, 1, 1)
+
+        mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask
+
+        if masked_image is not None and masked_image.shape[1] == 4:
+            masked_image_latents = masked_image
+        else:
+            masked_image_latents = None
+
+        if masked_image is not None:
+            if masked_image_latents is None:
+                masked_image = masked_image.to(device=device, dtype=dtype)
+                masked_image_latents = self._encode_vae_image(masked_image, generator=generator)
+
+            if masked_image_latents.shape[0] < batch_size:
+                if not batch_size % masked_image_latents.shape[0] == 0:
+                    raise ValueError(
+                        "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                        f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
+                        " Make sure the number of images that you pass is divisible by the total requested batch size."
+                    )
+                masked_image_latents = masked_image_latents.repeat(
+                    batch_size // masked_image_latents.shape[0], 1, 1, 1
+                )
+
+            masked_image_latents = (
+                torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents
+            )
+
+            # aligning device to prevent device errors when concating it with the latent model input
+            masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
+
+        return mask, masked_image_latents
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.get_timesteps
+    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)
+                )
+            )
+
+            num_inference_steps = (timesteps < discrete_timestep_cutoff).sum().item()
+            if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
+                # if the scheduler is a 2nd order scheduler we might have to do +1
+                # because `num_inference_steps` might be even given that every timestep
+                # (except the highest one) is duplicated. If `num_inference_steps` is even it would
+                # mean that we cut the timesteps in the middle of the denoising step
+                # (between 1st and 2nd derivative) which leads to incorrect results. By adding 1
+                # we ensure that the denoising process always ends after the 2nd derivate step of the scheduler
+                num_inference_steps = num_inference_steps + 1
+
+            # because t_n+1 >= t_n, we slice the timesteps starting from the end
+            timesteps = timesteps[-num_inference_steps:]
+            return timesteps, num_inference_steps
+
+        return timesteps, num_inference_steps - t_start
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids
+    def _get_add_time_ids(
+        self,
+        original_size,
+        crops_coords_top_left,
+        target_size,
+        aesthetic_score,
+        negative_aesthetic_score,
+        negative_original_size,
+        negative_crops_coords_top_left,
+        negative_target_size,
+        dtype,
+        text_encoder_projection_dim=None,
+    ):
+        if self.config.requires_aesthetics_score:
+            add_time_ids = list(original_size + crops_coords_top_left + (aesthetic_score,))
+            add_neg_time_ids = list(
+                negative_original_size + negative_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(negative_original_size + crops_coords_top_left + negative_target_size)
+
+        passed_add_embed_dim = (
+            self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_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
+    def upcast_vae(self):
+        dtype = self.vae.dtype
+        self.vae.to(dtype=torch.float32)
+        use_torch_2_0_or_xformers = isinstance(
+            self.vae.decoder.mid_block.attentions[0].processor,
+            (
+                AttnProcessor2_0,
+                XFormersAttnProcessor,
+                LoRAXFormersAttnProcessor,
+                LoRAAttnProcessor2_0,
+            ),
+        )
+        # if xformers or torch_2_0 is used attention block does not need
+        # to be in float32 which can save lots of memory
+        if use_torch_2_0_or_xformers:
+            self.vae.post_quant_conv.to(dtype)
+            self.vae.decoder.conv_in.to(dtype)
+            self.vae.decoder.mid_block.to(dtype)
+
+    # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
+    def get_guidance_scale_embedding(
+        self, w: torch.Tensor, embedding_dim: int = 512, dtype: torch.dtype = torch.float32
+    ) -> torch.Tensor:
+        """
+        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+        Args:
+            w (`torch.Tensor`):
+                Generate embedding vectors with a specified guidance scale to subsequently enrich timestep embeddings.
+            embedding_dim (`int`, *optional*, defaults to 512):
+                Dimension of the embeddings to generate.
+            dtype (`torch.dtype`, *optional*, defaults to `torch.float32`):
+                Data type of the generated embeddings.
+
+        Returns:
+            `torch.Tensor`: Embedding vectors with shape `(len(w), embedding_dim)`.
+        """
+        assert len(w.shape) == 1
+        w = w * 1000.0
+
+        half_dim = embedding_dim // 2
+        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+        emb = w.to(dtype)[:, None] * emb[None, :]
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+        if embedding_dim % 2 == 1:  # zero pad
+            emb = torch.nn.functional.pad(emb, (0, 1))
+        assert emb.shape == (w.shape[0], embedding_dim)
+        return emb
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def guidance_rescale(self):
+        return self._guidance_rescale
+
+    @property
+    def clip_skip(self):
+        return self._clip_skip
+
+    # 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.
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
+
+    @property
+    def cross_attention_kwargs(self):
+        return self._cross_attention_kwargs
+
+    @property
+    def denoising_end(self):
+        return self._denoising_end
+
+    @property
+    def denoising_start(self):
+        return self._denoising_start
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        image: PipelineImageInput = None,
+        mask_image: PipelineImageInput = None,
+        masked_image_latents: torch.Tensor = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        padding_mask_crop: Optional[int] = None,
+        strength: float = 0.9999,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        sigmas: List[float] = None,
+        denoising_start: Optional[float] = None,
+        denoising_end: Optional[float] = None,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        negative_prompt_2: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        return_attn_map: bool = False,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        original_size: Tuple[int, int] = None,
+        crops_coords_top_left: Tuple[int, int] = (0, 0),
+        target_size: Tuple[int, int] = None,
+        negative_original_size: Optional[Tuple[int, int]] = None,
+        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
+        negative_target_size: Optional[Tuple[int, int]] = None,
+        aesthetic_score: float = 6.0,
+        negative_aesthetic_score: float = 2.5,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        **kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                used in both text-encoders
+            image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch which will be inpainted, *i.e.* parts of the image will
+                be masked out with `mask_image` and repainted according to `prompt`.
+            mask_image (`PIL.Image.Image`):
+                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
+                repainted, while black pixels will be preserved. If `mask_image` is a PIL image, it will be converted
+                to a single channel (luminance) before use. If it's a tensor, it should contain one color channel (L)
+                instead of 3, so the expected shape would be `(B, H, W, 1)`.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image. This is set to 1024 by default for the best results.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image. This is set to 1024 by default for the best results.
+                Anything below 512 pixels won't work well for
+                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+                and checkpoints that are not specifically fine-tuned on low resolutions.
+            padding_mask_crop (`int`, *optional*, defaults to `None`):
+                The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to
+                image and mask_image. If `padding_mask_crop` is not `None`, it will first find a rectangular region
+                with the same aspect ration of the image and contains all masked area, and then expand that area based
+                on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before
+                resizing to the original image size for inpainting. This is useful when the masked area is small while
+                the image is large and contain information irrelevant for inpainting, such as background.
+            strength (`float`, *optional*, defaults to 0.9999):
+                Conceptually, indicates how much to transform the masked portion of the reference `image`. Must be
+                between 0 and 1. `image` will be used as a starting point, adding more noise to it the larger the
+                `strength`. The number of denoising steps depends on the amount of noise initially added. When
+                `strength` is 1, added noise will be maximum and the denoising process will run for the full number of
+                iterations specified in `num_inference_steps`. A value of 1, therefore, essentially ignores the masked
+                portion of the reference `image`. Note that in the case of `denoising_start` being declared as an
+                integer, the value of `strength` will be ignored.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            denoising_start (`float`, *optional*):
+                When specified, indicates the fraction (between 0.0 and 1.0) of the total denoising process to be
+                bypassed before it is initiated. Consequently, the initial part of the denoising process is skipped and
+                it is assumed that the passed `image` is a partly denoised image. Note that when this is specified,
+                strength will be ignored. The `denoising_start` parameter is particularly beneficial when this pipeline
+                is integrated into a "Mixture of Denoisers" multi-pipeline setup, as detailed in [**Refining the Image
+                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).
+            denoising_end (`float`, *optional*):
+                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+                completed before it is intentionally prematurely terminated. As a result, the returned sample will
+                still retain a substantial amount of noise (ca. final 20% of timesteps still needed) and should be
+                denoised by a successor pipeline that has `denoising_start` set to 0.8 so that it only denoises the
+                final 20% of the scheduler. The denoising_end parameter should ideally be utilized when this pipeline
+                forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output).
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            negative_prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            pooled_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+                If not provided, pooled text embeddings will be generated from `prompt` input argument.
+            negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+                input argument.
+            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
+                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
+                contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.Tensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+                `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
+                explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                For most cases, `target_size` should be set to the desired height and width of the generated image. If
+                not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in
+                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
+                micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+                To negatively condition the generation process based on a target image resolution. It should be as same
+                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
+                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
+            aesthetic_score (`float`, *optional*, defaults to 6.0):
+                Used to simulate an aesthetic score of the generated image by influencing the positive text condition.
+                Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+            negative_aesthetic_score (`float`, *optional*, defaults to 2.5):
+                Part of SDXL's micro-conditioning as explained in section 2.2 of
+                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). Can be used to
+                simulate an aesthetic score of the generated image by influencing the negative text condition.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
+                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
+                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
+                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+            `tuple. `tuple. When returning a tuple, the first element is a list with the generated images.
+        """
+
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
+            )
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 1. Check inputs
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            image,
+            mask_image,
+            height,
+            width,
+            strength,
+            callback_steps,
+            output_type,
+            negative_prompt,
+            negative_prompt_2,
+            prompt_embeds,
+            negative_prompt_embeds,
+            ip_adapter_image,
+            ip_adapter_image_embeds,
+            callback_on_step_end_tensor_inputs,
+            padding_mask_crop,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._denoising_end = denoising_end
+        self._denoising_start = denoising_start
+        self._interrupt = False
+
+        # 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
+        device = self.unet.device
+
+        # 3. Encode input prompt
+        text_encoder_lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.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=self.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,
+            clip_skip=self.clip_skip,
+        )
+
+        # 4. set timesteps
+        def denoising_value_valid(dnv):
+            return isinstance(dnv, float) and 0 < dnv < 1
+
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas
+        )
+        timesteps, num_inference_steps = self.get_timesteps(
+            num_inference_steps,
+            strength,
+            device,
+            denoising_start=self.denoising_start if denoising_value_valid(self.denoising_start) else None,
+        )
+        # check that number of inference steps is not < 1 - as this doesn't make sense
+        if num_inference_steps < 1:
+            raise ValueError(
+                f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
+                f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
+            )
+        # at which timestep to set the initial noise (n.b. 50% if strength is 0.5)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+        # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise
+        is_strength_max = strength == 1.0
+
+        # 5. Preprocess mask and image
+        if padding_mask_crop is not None:
+            crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask_crop)
+            resize_mode = "fill"
+        else:
+            crops_coords = None
+            resize_mode = "default"
+
+        original_image = image
+        init_image = self.image_processor.preprocess(
+            image, height=height, width=width, crops_coords=crops_coords, resize_mode=resize_mode
+        )
+        init_image = init_image.to(dtype=torch.float32)
+
+        mask = self.mask_processor.preprocess(
+            mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords
+        )
+
+        if masked_image_latents is not None:
+            masked_image = masked_image_latents
+        elif init_image.shape[1] == 4:
+            # if images are in latent space, we can't mask it
+            masked_image = None
+        else:
+            masked_image = init_image
+            # masked_image = init_image * (mask < 0.5)
+            obj_only = init_image * (mask > 0.5)
+            obj_only = obj_only.to(device=device)
+            object_embeds = self.image_prompt_encoder(obj_only)
+            
+        prompt_embeds = self.postfuse_module(prompt_embeds, object_embeds, 5)
+
+        # 6. Prepare latent variables
+        num_channels_latents = self.vae.config.latent_channels
+        num_channels_unet = self.unet.config.in_channels
+        # return_image_latents = num_channels_unet == 4
+        return_image_latents = True
+
+        add_noise = True if self.denoising_start is None else False
+        latents_outputs = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+            image=init_image,
+            timestep=latent_timestep,
+            is_strength_max=is_strength_max,
+            add_noise=add_noise,
+            return_noise=True,
+            return_image_latents=return_image_latents,
+        )
+
+        if return_image_latents:
+            latents, noise, image_latents = latents_outputs
+        else:
+            latents, noise = latents_outputs
+
+        # 7. Prepare mask latent variables
+        mask, masked_image_latents = self.prepare_mask_latents(
+            mask,
+            masked_image,
+            batch_size * num_images_per_prompt,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            self.do_classifier_free_guidance,
+        )
+
+        # 8. Check that sizes of mask, masked image and latents match
+        if num_channels_unet == 9:
+            # default case for runwayml/stable-diffusion-inpainting
+            num_channels_mask = mask.shape[1]
+            num_channels_masked_image = masked_image_latents.shape[1]
+            if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
+                raise ValueError(
+                    f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
+                    f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
+                    f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
+                    f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
+                    " `pipeline.unet` or your `mask_image` or `image` input."
+                )
+        elif num_channels_unet != 4:
+            raise ValueError(
+                f"The unet {self.unet.__class__} should have either 4 or 9 input channels, not {self.unet.config.in_channels}."
+            )
+        # 8.1 Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        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)
+
+        # 10. Prepare added time ids & embeddings
+        if negative_original_size is None:
+            negative_original_size = original_size
+        if negative_target_size is None:
+            negative_target_size = target_size
+
+        add_text_embeds = pooled_prompt_embeds
+        if self.text_encoder_2 is None:
+            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
+        else:
+            text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
+
+        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,
+            negative_original_size,
+            negative_crops_coords_top_left,
+            negative_target_size,
+            dtype=prompt_embeds.dtype,
+            text_encoder_projection_dim=text_encoder_projection_dim,
+        )
+        add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1)
+
+        if self.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)
+            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)
+
+        prompt_embeds = prompt_embeds.to(device)
+        add_text_embeds = add_text_embeds.to(device)
+        add_time_ids = add_time_ids.to(device)
+
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+                self.do_classifier_free_guidance,
+            )
+
+        # 11. Denoising loop
+        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+        if (
+            self.denoising_end is not None
+            and self.denoising_start is not None
+            and denoising_value_valid(self.denoising_end)
+            and denoising_value_valid(self.denoising_start)
+            and self.denoising_start >= self.denoising_end
+        ):
+            raise ValueError(
+                f"`denoising_start`: {self.denoising_start} cannot be larger than or equal to `denoising_end`: "
+                + f" {self.denoising_end} when using type float."
+            )
+        elif self.denoising_end is not None and denoising_value_valid(self.denoising_end):
+            discrete_timestep_cutoff = int(
+                round(
+                    self.scheduler.config.num_train_timesteps
+                    - (self.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]
+
+        # 11.1 Optionally get Guidance Scale Embedding
+        timestep_cond = None
+        if self.unet.config.time_cond_proj_dim is not None:
+            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+            timestep_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+
+        self._num_timesteps = len(timesteps)
+        attn_map = None
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+
+                # concat latents, mask, masked_image_latents in the channel dimension
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                if num_channels_unet == 9:
+                    latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1)
+
+                # predict the noise residual
+                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+                if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+                    added_cond_kwargs["image_embeds"] = image_embeds
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs=self.cross_attention_kwargs,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                        latents = latents.to(latents_dtype)
+
+                # progressive attention mask blending
+                fuse_index = 5
+                if self.config.apply_attention_guided_fusion:
+                    if i == len(timesteps) - 1:
+                        attn_key, attn_map = next(iter(self.cross_attention_scores.items()))
+                        attn_map = self.resize_attn_map_divide2(attn_map, mask, fuse_index)
+                        init_latents_proper = image_latents
+                        if self.do_classifier_free_guidance:
+                            _, init_mask = attn_map.chunk(2)
+                        else:
+                            init_mask = attn_map
+                        attn_map = init_mask
+                    self.clear_cross_attention_scores(self.cross_attention_scores)
+                
+                if num_channels_unet == 4:
+                    init_latents_proper = image_latents
+                    if self.do_classifier_free_guidance:
+                        init_mask, _ = mask.chunk(2)
+                    else:
+                        init_mask = mask
+
+                    if i < len(timesteps) - 1:
+                        noise_timestep = timesteps[i + 1]
+                        init_latents_proper = self.scheduler.add_noise(
+                            init_latents_proper, noise, torch.tensor([noise_timestep])
+                        )
+
+                    latents = (1 - init_mask) * init_latents_proper + init_mask * latents
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+                    add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds)
+                    negative_pooled_prompt_embeds = callback_outputs.pop(
+                        "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds
+                    )
+                    add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids)
+                    add_neg_time_ids = callback_outputs.pop("add_neg_time_ids", add_neg_time_ids)
+                    mask = callback_outputs.pop("mask", mask)
+                    masked_image_latents = callback_outputs.pop("masked_image_latents", masked_image_latents)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        if not output_type == "latent":
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+
+            if needs_upcasting:
+                self.upcast_vae()
+                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+            elif latents.dtype != self.vae.dtype:
+                if torch.backends.mps.is_available():
+                    # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+                    self.vae = self.vae.to(latents.dtype)
+
+            # unscale/denormalize the latents
+            # denormalize with the mean and std if available and not None
+            has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
+            has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
+            if has_latents_mean and has_latents_std:
+                latents_mean = (
+                    torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents_std = (
+                    torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
+                )
+                latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
+            else:
+                latents = latents / self.vae.config.scaling_factor
+
+            image = self.vae.decode(latents, return_dict=False)[0]
+
+            # cast back to fp16 if needed
+            if needs_upcasting:
+                self.vae.to(dtype=torch.float16)
+        else:
+            return ObjectClearPipelineOutput(images=latents)
+
+        # apply watermark if available
+        if self.watermark is not None:
+            image = self.watermark.apply_watermark(image)
+
+        image = self.image_processor.postprocess(image, output_type=output_type)
+
+        if padding_mask_crop is not None:
+            image = [self.image_processor.apply_overlay(mask_image, original_image, i, crops_coords) for i in image]
+            
+        attn_pils = []
+        if output_type == "pil" and attn_map is not None:
+            for i in range(len(attn_map)):
+                attn_np = attn_map[i].mean(dim=0).cpu().numpy() * 255.
+                attn_pil = PIL.Image.fromarray(attn_np.astype(np.uint8)).convert("L")
+                attn_pils.append(attn_pil)
+            
+            original_pils = self.image_processor.postprocess(init_image, output_type="pil")
+
+            generated_pils = image
+
+            fused_images = []
+            for i in range(len(generated_pils)):
+                ori_pil = original_pils[i]
+                gen_pil = generated_pils[i]
+                attn_pil = attn_pils[i]
+
+                fused_np = attention_guided_fusion(np.array(ori_pil), np.array(gen_pil), np.array(attn_pil))
+                fused_pil = PIL.Image.fromarray(fused_np.astype(np.uint8)).resize(ori_pil.size)
+
+                fused_images.append(fused_pil)
+
+            image = fused_images
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+    
+        if return_attn_map and len(attn_pils) > 0:
+            if not return_dict:
+                return (image, attn_pils)
+            return ObjectClearPipelineOutput(images=image, attns=attn_pils)
+        else:
+            if not return_dict:
+                return (image,)
+            return ObjectClearPipelineOutput(images=image)
diff --git a/objectclear/utils/__init__.py b/objectclear/utils/__init__.py
new file mode 100644
index 0000000..6ee70e2
--- /dev/null
+++ b/objectclear/utils/__init__.py
@@ -0,0 +1,5 @@
+from .attention_guided_fusion import attention_guided_fusion
+from .image_utils import pad_to_multiple, crop_to_original, resize_by_short_side
+
+
+__all__ = ["attention_guided_fusion", "pad_to_multiple", "crop_to_original", "resize_by_short_side"]
\ No newline at end of file
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diff --git a/objectclear/utils/attention_guided_fusion.py b/objectclear/utils/attention_guided_fusion.py
new file mode 100644
index 0000000..c68c34d
--- /dev/null
+++ b/objectclear/utils/attention_guided_fusion.py
@@ -0,0 +1,78 @@
+import numpy as np
+import cv2
+from scipy.ndimage import convolve, zoom
+from .image_utils import pad_to_multiple, crop_to_original
+
+
+def wavelet_blur_np(image: np.ndarray, radius: int):
+    kernel = np.array([
+        [0.0625, 0.125, 0.0625],
+        [0.125,  0.25,  0.125],
+        [0.0625, 0.125, 0.0625]
+    ], dtype=np.float32)
+
+    blurred = np.empty_like(image)
+    for c in range(image.shape[0]):
+        blurred_c = convolve(image[c], kernel, mode='nearest')
+        if radius > 1:
+            blurred_c = zoom(zoom(blurred_c, 1 / radius, order=1), radius, order=1)
+        blurred[c] = blurred_c
+    return blurred
+
+def wavelet_decomposition_np(image: np.ndarray, levels=5):
+    high_freq = np.zeros_like(image)
+    for i in range(levels):
+        radius = 2 ** i
+        low_freq = wavelet_blur_np(image, radius)
+        high_freq += (image - low_freq)
+        image = low_freq
+    return high_freq, low_freq
+
+def wavelet_reconstruction_np(content_feat: np.ndarray, style_feat: np.ndarray):
+    content_high, _ = wavelet_decomposition_np(content_feat)
+    _, style_low = wavelet_decomposition_np(style_feat)
+    return content_high + style_low
+
+def wavelet_color_fix_np(fused: np.ndarray, mask: np.ndarray) -> np.ndarray:
+    fused_np = fused.astype(np.float32) / 255.0
+    mask_np = mask.astype(np.float32) / 255.0
+
+    fused_np = fused_np.transpose(2, 0, 1)
+    mask_np = mask_np.transpose(2, 0, 1)
+
+    result_np = wavelet_reconstruction_np(fused_np, mask_np)
+
+    result_np = result_np.transpose(1, 2, 0)
+    result_np = np.clip(result_np * 255.0, 0, 255).astype(np.uint8)
+
+    return result_np
+
+def attention_guided_fusion(ori: np.ndarray, removed: np.ndarray, attn_map: np.ndarray, multiple: int = 8):
+    H, W = ori.shape[:2]
+    attn_map = attn_map.astype(np.float32)
+    _, attn_map = cv2.threshold(attn_map, 128, 255, cv2.THRESH_BINARY)
+    am = attn_map.astype(np.float32)
+    am = am/255.0
+    am_up = cv2.resize(am, (W, H), interpolation=cv2.INTER_NEAREST)
+
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (21,21))
+    am_d = cv2.dilate(am_up, kernel, iterations=1)
+    am_d = cv2.GaussianBlur(am_d.astype(np.float32), (9,9), sigmaX=2)
+
+    am_merged = np.maximum(am_up, am_d)
+    am_merged = np.clip(am_merged, 0, 1)
+
+    attn_up_3c = np.stack([am_merged]*3, axis=-1)
+    attn_up_ori_3c = np.stack([am_up]*3, axis=-1)
+
+    ori_out = ori * (1 - attn_up_ori_3c)
+    rem_out = removed * (1 - attn_up_ori_3c)
+
+    ori_pad, h0, w0 = pad_to_multiple(ori_out, multiple)
+    rem_pad, _, _   = pad_to_multiple(rem_out, multiple)
+
+    wave_rgb = wavelet_color_fix_np(ori_pad, rem_pad)
+    wave = crop_to_original(wave_rgb, h0, w0)
+
+    fused = (wave * (1 - attn_up_3c) + removed * attn_up_3c).astype(np.uint8)
+    return fused
\ No newline at end of file
diff --git a/objectclear/utils/image_utils.py b/objectclear/utils/image_utils.py
new file mode 100644
index 0000000..bd8f7eb
--- /dev/null
+++ b/objectclear/utils/image_utils.py
@@ -0,0 +1,33 @@
+import numpy as np
+from PIL import Image
+
+
+def pad_to_multiple(image: np.ndarray, multiple: int = 8):
+    h, w = image.shape[:2]
+    pad_h = (multiple - h % multiple) % multiple
+    pad_w = (multiple - w % multiple) % multiple
+    if image.ndim == 3:
+        padded = np.pad(image, ((0, pad_h), (0, pad_w), (0,0)), mode='reflect')
+    else:
+        padded = np.pad(image, ((0, pad_h), (0, pad_w)), mode='reflect')
+    return padded, h, w
+
+def crop_to_original(image: np.ndarray, h: int, w: int):
+    return image[:h, :w]
+
+def resize_by_short_side(image, target_short=512, resample=Image.BICUBIC):
+    w, h = image.size
+    if min(w, h) < target_short:
+        new_w = (w + 15) // 16 * 16
+        new_h = (h + 15) // 16 * 16
+    else:
+        if w < h:
+            new_w = target_short
+            new_h = int(h * target_short / w)
+        else:
+            new_h = target_short
+            new_w = int(w * target_short / h)
+        new_w = (new_w + 15) // 16 * 16
+        new_h = (new_h + 15) // 16 * 16
+
+    return image.resize((new_w, new_h), resample=resample)
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..e35dd34
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,16 @@
+[project]
+name = "objectclear"
+description = "ObjectClear:Complete Object Removal via Object-Effect Attention,you can try it in ComfyUI"
+version = "1.0.0"
+license = {file = "LICENSE"}
+dependencies = ["accelerate", "torch", "torchvision", "numpy", "opencv-python", "pillow", "transformers", "scipy", "diffusers"]
+
+[project.urls]
+Repository = "https://github.com/smthemex/ComfyUI_ObjectClear"
+#  Used by Comfy Registry https://registry.comfy.org
+
+[tool.comfy]
+PublisherId = "smthemex"
+DisplayName = "ComfyUI_ObjectClear"
+Icon = ""
+includes = []
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..473acb2
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,9 @@
+accelerate
+torch
+torchvision
+numpy
+opencv-python
+pillow
+transformers
+scipy
+diffusers
\ No newline at end of file
diff --git a/sdxl_repo/image_prompt_encoder/config.json b/sdxl_repo/image_prompt_encoder/config.json
new file mode 100644
index 0000000..c78d6e9
--- /dev/null
+++ b/sdxl_repo/image_prompt_encoder/config.json
@@ -0,0 +1,47 @@
+{
+  "_name_or_path": "openai/clip-vit-large-patch14",
+  "architectures": [
+    "CLIPModel"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
+  "projection_dim": 768,
+  "text_config": {
+    "attention_dropout": 0.0,
+    "dropout": 0.0,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 768,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "layer_norm_eps": 1e-05,
+    "max_position_embeddings": 77,
+    "model_type": "clip_text_model",
+    "num_attention_heads": 12,
+    "num_hidden_layers": 12,
+    "projection_dim": 768,
+    "torch_dtype": "float32",
+    "vocab_size": 49408
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.3",
+  "vision_config": {
+    "attention_dropout": 0.0,
+    "dropout": 0.0,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 1024,
+    "image_size": 224,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "layer_norm_eps": 1e-05,
+    "model_type": "clip_vision_model",
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "num_hidden_layers": 24,
+    "patch_size": 14,
+    "projection_dim": 768,
+    "torch_dtype": "float32"
+  }
+}
diff --git a/sdxl_repo/model_index.json b/sdxl_repo/model_index.json
new file mode 100644
index 0000000..03a52ff
--- /dev/null
+++ b/sdxl_repo/model_index.json
@@ -0,0 +1,35 @@
+{
+  "_class_name": "ObjectClearPipeline",
+  "_diffusers_version": "0.21.0.dev0",
+  "_name_or_path": "stabilityai/stable-diffusion-xl-base-1.0",
+  "force_zeros_for_empty_prompt": true,
+  "requires_aesthetics_score": false,
+  "scheduler": [
+    "diffusers",
+    "EulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "text_encoder_2": [
+    "transformers",
+    "CLIPTextModelWithProjection"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "tokenizer_2": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}
diff --git a/sdxl_repo/postfuse_module/config.json b/sdxl_repo/postfuse_module/config.json
new file mode 100644
index 0000000..3b721e3
--- /dev/null
+++ b/sdxl_repo/postfuse_module/config.json
@@ -0,0 +1,5 @@
+{
+    "_class_name": "PostfuseModule",
+    "embed_dim": 2048,
+    "embed_dim_img": 768
+}
\ No newline at end of file
diff --git a/sdxl_repo/scheduler/scheduler_config.json b/sdxl_repo/scheduler/scheduler_config.json
new file mode 100644
index 0000000..9e13de2
--- /dev/null
+++ b/sdxl_repo/scheduler/scheduler_config.json
@@ -0,0 +1,18 @@
+{
+  "_class_name": "EulerDiscreteScheduler",
+  "_diffusers_version": "0.21.0.dev0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "interpolation_type": "linear",
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "timestep_spacing": "leading",
+  "trained_betas": null,
+  "use_karras_sigmas": false
+}
diff --git a/sdxl_repo/text_encoder/config.json b/sdxl_repo/text_encoder/config.json
new file mode 100644
index 0000000..f1994ec
--- /dev/null
+++ b/sdxl_repo/text_encoder/config.json
@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 768,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "projection_dim": 768,
+  "torch_dtype": "float16",
+  "transformers_version": "4.29.2",
+  "vocab_size": 49408
+}
diff --git a/sdxl_repo/text_encoder_2/config.json b/sdxl_repo/text_encoder_2/config.json
new file mode 100644
index 0000000..024e3ee
--- /dev/null
+++ b/sdxl_repo/text_encoder_2/config.json
@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
+  "architectures": [
+    "CLIPTextModelWithProjection"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_size": 1280,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 5120,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 20,
+  "num_hidden_layers": 32,
+  "pad_token_id": 1,
+  "projection_dim": 1280,
+  "torch_dtype": "float16",
+  "transformers_version": "4.29.2",
+  "vocab_size": 49408
+}
diff --git a/sdxl_repo/tokenizer/merges.txt b/sdxl_repo/tokenizer/merges.txt
new file mode 100644
index 0000000..76e821f
--- /dev/null
+++ b/sdxl_repo/tokenizer/merges.txt
@@ -0,0 +1,48895 @@
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diff --git a/sdxl_repo/tokenizer/special_tokens_map.json b/sdxl_repo/tokenizer/special_tokens_map.json
new file mode 100644
index 0000000..2c2130b
--- /dev/null
+++ b/sdxl_repo/tokenizer/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
+  },
+  "pad_token": "<|endoftext|>",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/sdxl_repo/tokenizer/tokenizer_config.json b/sdxl_repo/tokenizer/tokenizer_config.json
new file mode 100644
index 0000000..2e8612a
--- /dev/null
+++ b/sdxl_repo/tokenizer/tokenizer_config.json
@@ -0,0 +1,33 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/sdxl_repo/tokenizer/vocab.json b/sdxl_repo/tokenizer/vocab.json
new file mode 100644
index 0000000..469be27
--- /dev/null
+++ b/sdxl_repo/tokenizer/vocab.json
@@ -0,0 +1,49410 @@
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+  "!!!": 11194,
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+  "!]</w>": 34466,
+  "!âĢ¦</w>": 35068,
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diff --git a/sdxl_repo/tokenizer_2/merges.txt b/sdxl_repo/tokenizer_2/merges.txt
new file mode 100644
index 0000000..76e821f
--- /dev/null
+++ b/sdxl_repo/tokenizer_2/merges.txt
@@ -0,0 +1,48895 @@
+#version: 0.2
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diff --git a/sdxl_repo/tokenizer_2/special_tokens_map.json b/sdxl_repo/tokenizer_2/special_tokens_map.json
new file mode 100644
index 0000000..ae0c5be
--- /dev/null
+++ b/sdxl_repo/tokenizer_2/special_tokens_map.json
@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "content": "<|endoftext|>",
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+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/sdxl_repo/tokenizer_2/tokenizer_config.json b/sdxl_repo/tokenizer_2/tokenizer_config.json
new file mode 100644
index 0000000..a8438e0
--- /dev/null
+++ b/sdxl_repo/tokenizer_2/tokenizer_config.json
@@ -0,0 +1,33 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "!",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/sdxl_repo/tokenizer_2/vocab.json b/sdxl_repo/tokenizer_2/vocab.json
new file mode 100644
index 0000000..469be27
--- /dev/null
+++ b/sdxl_repo/tokenizer_2/vocab.json
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diff --git a/sdxl_repo/unet/config.json b/sdxl_repo/unet/config.json
new file mode 100644
index 0000000..b26a818
--- /dev/null
+++ b/sdxl_repo/unet/config.json
@@ -0,0 +1,78 @@
+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.21.0.dev0",
+  "_name_or_path": "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
+  "act_fn": "silu",
+  "addition_embed_type": "text_time",
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": 256,
+  "attention_head_dim": [
+    5,
+    10,
+    20
+  ],
+  "attention_type": "default",
+  "block_out_channels": [
+    320,
+    640,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": 2048,
+  "cross_attention_norm": null,
+  "decay": 0.9999,
+  "down_block_types": [
+    "DownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dual_cross_attention": false,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 9,
+  "inv_gamma": 1.0,
+  "layers_per_block": 2,
+  "mid_block_only_cross_attention": null,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "min_decay": 0.0,
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "optimization_step": 37000,
+  "out_channels": 4,
+  "power": 0.6666666666666666,
+  "projection_class_embeddings_input_dim": 2816,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "sample_size": 128,
+  "time_cond_proj_dim": null,
+  "time_embedding_act_fn": null,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
+  "transformer_layers_per_block": [
+    1,
+    2,
+    10
+  ],
+  "up_block_types": [
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "UpBlock2D"
+  ],
+  "upcast_attention": null,
+  "update_after_step": 0,
+  "use_ema_warmup": false,
+  "use_linear_projection": true
+}
diff --git a/sdxl_repo/vae/config.json b/sdxl_repo/vae/config.json
new file mode 100644
index 0000000..1b23bbf
--- /dev/null
+++ b/sdxl_repo/vae/config.json
@@ -0,0 +1,32 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.21.0.dev0",
+  "_name_or_path": "diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": false,
+  "in_channels": 3,
+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 512,
+  "scaling_factor": 0.13025,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}