From 7bdcb30a72d0faaacd7bf0158607e4d2f69bc542 Mon Sep 17 00:00:00 2001
From: smthem <2102561739@qq.com>
Date: Thu, 20 Feb 2025 14:21:42 +0800
Subject: [PATCH] init

---
 Light_A_Video_node.py                         |   158 +
 __init__.py                                   |     4 +
 animate_repo/config.json                      |    16 +
 configs/relight/bear.yaml                     |    13 +
 configs/relight/boat.yaml                     |    13 +
 configs/relight/car.yaml                      |    13 +
 configs/relight/cat.yaml                      |    13 +
 configs/relight/cow.yaml                      |    13 +
 configs/relight/flowers.yaml                  |    13 +
 configs/relight/fox.yaml                      |    13 +
 configs/relight/girl.yaml                     |    13 +
 configs/relight/girl2.yaml                    |    13 +
 configs/relight/juice.yaml                    |    13 +
 configs/relight/man2.yaml                     |    13 +
 configs/relight/man4.yaml                     |    13 +
 configs/relight/plane.yaml                    |    13 +
 configs/relight/toy.yaml                      |    13 +
 configs/relight/woman.yaml                    |    13 +
 configs/relight_inpaint/bloom.yaml            |    15 +
 configs/relight_inpaint/camera.yaml           |    15 +
 configs/relight_inpaint/car.yaml              |    15 +
 configs/relight_inpaint/car_2.yaml            |    15 +
 configs/relight_inpaint/cat2.yaml             |    15 +
 configs/relight_inpaint/coin.yaml             |    15 +
 configs/relight_inpaint/dog2.yaml             |    15 +
 configs/relight_inpaint/man3.yaml             |    15 +
 configs/relight_inpaint/man3_2.yaml           |    15 +
 configs/relight_inpaint/water.yaml            |    15 +
 configs/relight_inpaint/wolf2.yaml            |    15 +
 input_animatediff/bear.mp4                    |   Bin 0 -> 252475 bytes
 input_animatediff/bloom.mp4                   |   Bin 0 -> 110051 bytes
 input_animatediff/boat.mp4                    |   Bin 0 -> 163870 bytes
 input_animatediff/camera.mp4                  |   Bin 0 -> 74789 bytes
 input_animatediff/car.mp4                     |   Bin 0 -> 221663 bytes
 input_animatediff/cat.mp4                     |   Bin 0 -> 136264 bytes
 input_animatediff/cat2.mp4                    |   Bin 0 -> 169476 bytes
 input_animatediff/coin.mp4                    |   Bin 0 -> 102631 bytes
 input_animatediff/cow.mp4                     |   Bin 0 -> 455415 bytes
 input_animatediff/dog2.mp4                    |   Bin 0 -> 71903 bytes
 input_animatediff/flowers.mp4                 |   Bin 0 -> 163157 bytes
 input_animatediff/fox.mp4                     |   Bin 0 -> 141847 bytes
 input_animatediff/girl.mp4                    |   Bin 0 -> 74024 bytes
 input_animatediff/girl2.mp4                   |   Bin 0 -> 293061 bytes
 input_animatediff/juice.mp4                   |   Bin 0 -> 87686 bytes
 input_animatediff/man.mp4                     |   Bin 0 -> 141549 bytes
 input_animatediff/man2.mp4                    |   Bin 0 -> 51978 bytes
 input_animatediff/man3.mp4                    |   Bin 0 -> 88192 bytes
 input_animatediff/man4.mp4                    |   Bin 0 -> 71453 bytes
 input_animatediff/plane.mp4                   |   Bin 0 -> 59262 bytes
 input_animatediff/toy.mp4                     |   Bin 0 -> 49025 bytes
 input_animatediff/wolf2.mp4                   |   Bin 0 -> 72036 bytes
 input_animatediff/woman.mp4                   |   Bin 0 -> 163666 bytes
 lav_paint.py                                  |   256 +
 lav_relight.py                                |   252 +
 node_utils.py                                 |   241 +
 pyproject.toml                                |    15 +
 requirements.txt                              |    17 +
 sam2.py                                       |    32 +
 sd_repo/README.md                             |    64 +
 .../preprocessor_config.json                  |    29 +
 sd_repo/model_index.json                      |    37 +
 sd_repo/safety_checker/config.json            |    28 +
 sd_repo/scheduler/scheduler_config.json       |    15 +
 sd_repo/text_encoder/config.json              |    24 +
 sd_repo/tokenizer/merges.txt                  | 48895 +++++++++++++++
 sd_repo/tokenizer/special_tokens_map.json     |    30 +
 sd_repo/tokenizer/tokenizer_config.json       |    30 +
 sd_repo/tokenizer/vocab.json                  | 49410 ++++++++++++++++
 sd_repo/unet/config.json                      |    67 +
 sd_repo/vae/config.json                       |    31 +
 .../animatediff_eul.cpython-311.pyc           |   Bin 0 -> 3941 bytes
 .../animatediff_inpaint_pipe.cpython-311.pyc  |   Bin 0 -> 53122 bytes
 .../animatediff_pipe.cpython-311.pyc          |   Bin 0 -> 50810 bytes
 src/__pycache__/ic_light.cpython-311.pyc      |   Bin 0 -> 9967 bytes
 src/__pycache__/ic_light_pipe.cpython-311.pyc |   Bin 0 -> 58277 bytes
 src/__pycache__/tools.cpython-311.pyc         |   Bin 0 -> 9793 bytes
 src/animatediff_eul.py                        |    94 +
 src/animatediff_inpaint_pipe.py               |  1077 +
 src/animatediff_pipe.py                       |  1063 +
 src/ic_light.py                               |   152 +
 src/ic_light_pipe.py                          |  1122 +
 src/tools.py                                  |   152 +
 82 files changed, 103671 insertions(+)
 create mode 100644 Light_A_Video_node.py
 create mode 100644 __init__.py
 create mode 100644 animate_repo/config.json
 create mode 100644 configs/relight/bear.yaml
 create mode 100644 configs/relight/boat.yaml
 create mode 100644 configs/relight/car.yaml
 create mode 100644 configs/relight/cat.yaml
 create mode 100644 configs/relight/cow.yaml
 create mode 100644 configs/relight/flowers.yaml
 create mode 100644 configs/relight/fox.yaml
 create mode 100644 configs/relight/girl.yaml
 create mode 100644 configs/relight/girl2.yaml
 create mode 100644 configs/relight/juice.yaml
 create mode 100644 configs/relight/man2.yaml
 create mode 100644 configs/relight/man4.yaml
 create mode 100644 configs/relight/plane.yaml
 create mode 100644 configs/relight/toy.yaml
 create mode 100644 configs/relight/woman.yaml
 create mode 100644 configs/relight_inpaint/bloom.yaml
 create mode 100644 configs/relight_inpaint/camera.yaml
 create mode 100644 configs/relight_inpaint/car.yaml
 create mode 100644 configs/relight_inpaint/car_2.yaml
 create mode 100644 configs/relight_inpaint/cat2.yaml
 create mode 100644 configs/relight_inpaint/coin.yaml
 create mode 100644 configs/relight_inpaint/dog2.yaml
 create mode 100644 configs/relight_inpaint/man3.yaml
 create mode 100644 configs/relight_inpaint/man3_2.yaml
 create mode 100644 configs/relight_inpaint/water.yaml
 create mode 100644 configs/relight_inpaint/wolf2.yaml
 create mode 100644 input_animatediff/bear.mp4
 create mode 100644 input_animatediff/bloom.mp4
 create mode 100644 input_animatediff/boat.mp4
 create mode 100644 input_animatediff/camera.mp4
 create mode 100644 input_animatediff/car.mp4
 create mode 100644 input_animatediff/cat.mp4
 create mode 100644 input_animatediff/cat2.mp4
 create mode 100644 input_animatediff/coin.mp4
 create mode 100644 input_animatediff/cow.mp4
 create mode 100644 input_animatediff/dog2.mp4
 create mode 100644 input_animatediff/flowers.mp4
 create mode 100644 input_animatediff/fox.mp4
 create mode 100644 input_animatediff/girl.mp4
 create mode 100644 input_animatediff/girl2.mp4
 create mode 100644 input_animatediff/juice.mp4
 create mode 100644 input_animatediff/man.mp4
 create mode 100644 input_animatediff/man2.mp4
 create mode 100644 input_animatediff/man3.mp4
 create mode 100644 input_animatediff/man4.mp4
 create mode 100644 input_animatediff/plane.mp4
 create mode 100644 input_animatediff/toy.mp4
 create mode 100644 input_animatediff/wolf2.mp4
 create mode 100644 input_animatediff/woman.mp4
 create mode 100644 lav_paint.py
 create mode 100644 lav_relight.py
 create mode 100644 node_utils.py
 create mode 100644 pyproject.toml
 create mode 100644 requirements.txt
 create mode 100644 sam2.py
 create mode 100644 sd_repo/README.md
 create mode 100644 sd_repo/feature_extractor/preprocessor_config.json
 create mode 100644 sd_repo/model_index.json
 create mode 100644 sd_repo/safety_checker/config.json
 create mode 100644 sd_repo/scheduler/scheduler_config.json
 create mode 100644 sd_repo/text_encoder/config.json
 create mode 100644 sd_repo/tokenizer/merges.txt
 create mode 100644 sd_repo/tokenizer/special_tokens_map.json
 create mode 100644 sd_repo/tokenizer/tokenizer_config.json
 create mode 100644 sd_repo/tokenizer/vocab.json
 create mode 100644 sd_repo/unet/config.json
 create mode 100644 sd_repo/vae/config.json
 create mode 100644 src/__pycache__/animatediff_eul.cpython-311.pyc
 create mode 100644 src/__pycache__/animatediff_inpaint_pipe.cpython-311.pyc
 create mode 100644 src/__pycache__/animatediff_pipe.cpython-311.pyc
 create mode 100644 src/__pycache__/ic_light.cpython-311.pyc
 create mode 100644 src/__pycache__/ic_light_pipe.cpython-311.pyc
 create mode 100644 src/__pycache__/tools.cpython-311.pyc
 create mode 100644 src/animatediff_eul.py
 create mode 100644 src/animatediff_inpaint_pipe.py
 create mode 100644 src/animatediff_pipe.py
 create mode 100644 src/ic_light.py
 create mode 100644 src/ic_light_pipe.py
 create mode 100644 src/tools.py

diff --git a/Light_A_Video_node.py b/Light_A_Video_node.py
new file mode 100644
index 0000000..05ebb35
--- /dev/null
+++ b/Light_A_Video_node.py
@@ -0,0 +1,158 @@
+# !/usr/bin/env python
+# -*- coding: UTF-8 -*-
+import os
+import torch
+import gc
+import numpy as np
+from diffusers import StableDiffusionPipeline
+from .lav_relight import load_ic_light_model,infer_relight
+from .node_utils import load_images,tensor2pil_list
+import folder_paths
+from .src.ic_light import BGSource
+from .src.tools import  set_all_seed
+
+MAX_SEED = np.iinfo(np.int32).max
+current_node_path = os.path.dirname(os.path.abspath(__file__))
+device = torch.device(
+    "cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+# add checkpoints dir
+Light_A_Video_weigths_path = os.path.join(folder_paths.models_dir, "Light_A_Video")
+if not os.path.exists(Light_A_Video_weigths_path):
+    os.makedirs(Light_A_Video_weigths_path)
+folder_paths.add_model_folder_path("Light_A_Video", Light_A_Video_weigths_path)
+
+
+class Light_A_Video_Loader:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": (folder_paths.get_filename_list("checkpoints"),),
+                "motion_adapter_model": (["none"] + folder_paths.get_filename_list("controlnet"),),
+                "ic_light_model": (["none"] + folder_paths.get_filename_list("controlnet"),),
+                "mode":(["relight","inpaint"],),
+            },
+        }
+
+    RETURN_TYPES = ("MODEL_Light_A_Video",)
+    RETURN_NAMES = ("model",)
+    FUNCTION = "loader_main"
+    CATEGORY = "Light_A_Video"
+
+    def loader_main(self, model,motion_adapter_model, ic_light_model,mode):
+
+        adopted_dtype = torch.float16
+
+        original_config_file=os.path.join(folder_paths.models_dir,"configs","v1-inference.yaml")
+        sd_repo = os.path.join(current_node_path, "sd_repo")
+        motion_repo=os.path.join(current_node_path,"animate_repo")
+        if model!="none":
+            ckpt_path=folder_paths.get_full_path("checkpoints",model)
+        else:
+            raise "no checkpoint"
+        try:
+            pipeline = StableDiffusionPipeline.from_single_file(
+            ckpt_path,config=sd_repo, original_config=original_config_file)
+        except:
+            pipeline = StableDiffusionPipeline.from_single_file(
+            ckpt_path, config=sd_repo,original_config_file=original_config_file)
+
+        # load model
+        print("***********Load model ***********")
+        motion_adapter_model=folder_paths.get_full_path("controlnet",motion_adapter_model)
+        ic_light_model=folder_paths.get_full_path("controlnet",ic_light_model)
+    
+        pipe,ic_light_pipe=load_ic_light_model(pipeline,ic_light_model,ckpt_path,sd_repo,motion_repo,motion_adapter_model,device,adopted_dtype,mode)
+        print("***********Load model done ***********")
+
+        gc.collect()
+        torch.cuda.empty_cache()
+        return ({"model":pipe,"ic_light_pipe":ic_light_pipe,"mode":mode,"adopted_dtype":adopted_dtype},)
+
+
+class Light_A_Video_Sampler:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL_Light_A_Video",),
+                "images": ("IMAGE",),
+                "relight_prompt": ("STRING", {"default": "a car driving on the street, neon light", "multiline": True}),
+                "inpaint_prompt": ("STRING", {"default": "a car driving on the beach, sunset over sea", "multiline": True}),
+                "n_prompt": ("STRING", {"default": "bad quality, worse quality", "multiline": True}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": MAX_SEED}),
+                "num_step": ("INT", {"default": 25, "min": 1, "max": 1024, "step": 1, "display": "number"}),
+                "strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05}),
+                "text_guide_scale": ("INT", {"default": 2, "min": 1, "max": 20, "step": 1, "display": "number"}),
+                "width": ("INT", {"default": 512, "min": 64, "max": 2048, "step": 64, "display": "number"}),
+                "height": ("INT", {"default": 512, "min": 64, "max": 2048, "step": 64, "display": "number"}),
+                "bg_target": (["LEFT", "NONE", "RIGHT", "TOP", "BOTTOM"],),
+                "mask_repo": ("STRING", {"default": "ZhengPeng7/BiRefNet"},),},
+            "optional": {"mask_img": ("IMAGE",),
+                         "fps": ("FLOAT", {"default": 8.0, "min": 8.0, "max": 100.0, "step": 0.1}),
+                         },
+            }
+
+    RETURN_TYPES = ("IMAGE", )
+    RETURN_NAMES = ("image",)
+    FUNCTION = "sampler_main"
+    CATEGORY = "Light_A_Video"
+
+    def sampler_main(self, model,images,relight_prompt,inpaint_prompt, n_prompt,seed, num_step, strength,text_guide_scale,width, height,bg_target,mask_repo,**kwargs):
+        set_all_seed(42)
+
+        local_sam=os.path.join(Light_A_Video_weigths_path,"sam2_b.pt")
+        if os.path.exists(local_sam):
+            local_sam="sam2_b.pt"
+        ref_image_list=tensor2pil_list(images,width,height)
+
+        if bg_target=="NONE":
+            bg_source = BGSource.NONE
+        elif bg_target=="LEFT":
+            bg_source = BGSource.LEFT
+        elif bg_target=="RIGHT":
+            bg_source = BGSource.RIGHT
+        elif bg_target=="TOP":
+            bg_source = BGSource.TOP
+        else:
+            bg_source = BGSource.BOTTOM
+
+        mask_img=kwargs.get("mask_img",None)
+        fps=kwargs.get("fps",8.0)
+        ic_light_pipe=model.get("ic_light_pipe")
+        pipe=model.get("model")
+        mode=model.get("mode")
+        adopted_dtype=model.get("adopted_dtype")
+
+        if isinstance(mask_img,torch.Tensor): 
+            mask_list=tensor2pil_list(mask_img,width,height)
+            if len(mask_list)==1:
+                mask_list=mask_list*len(ref_image_list)
+                print("not enough mask, repeat mask to match the number of images")
+        else:
+            mask_list=None
+        
+        print("***********Start infer  ***********")
+        iamge = infer_relight(ic_light_pipe,pipe,strength,num_step,text_guide_scale,seed,width,height,n_prompt,relight_prompt,inpaint_prompt,ref_image_list,bg_source,mode,mask_list,device,adopted_dtype,mask_repo,fps,local_sam)
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        return (load_images(iamge),)
+
+
+NODE_CLASS_MAPPINGS = {
+    "Light_A_Video_Loader": Light_A_Video_Loader,
+    "Light_A_Video_Sampler": Light_A_Video_Sampler,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Light_A_Video_Loader": "Light_A_Video_Loader",
+    "Light_A_Video_Sampler": "Light_A_Video_Sampler",
+}
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..e822c8d
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,4 @@
+
+from .Light_A_Video_node import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
diff --git a/animate_repo/config.json b/animate_repo/config.json
new file mode 100644
index 0000000..687e6b2
--- /dev/null
+++ b/animate_repo/config.json
@@ -0,0 +1,16 @@
+{
+  "_class_name": "MotionAdapter",
+  "_diffusers_version": "0.25.0.dev0",
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "motion_layers_per_block": 2,
+  "motion_max_seq_length": 32,
+  "motion_mid_block_layers_per_block": 1,
+  "motion_norm_num_groups": 32,
+  "motion_num_attention_heads": 8,
+  "use_motion_mid_block": false
+}
diff --git a/configs/relight/bear.yaml b/configs/relight/bear.yaml
new file mode 100644
index 0000000..1d20842
--- /dev/null
+++ b/configs/relight/bear.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality"
+relight_prompt: "a bear walking on the rock, nature lighting, key light"
+video_path: "input_animatediff/bear.mp4"
+bg_source: "TOP"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 42
\ No newline at end of file
diff --git a/configs/relight/boat.yaml b/configs/relight/boat.yaml
new file mode 100644
index 0000000..01d8730
--- /dev/null
+++ b/configs/relight/boat.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality"
+relight_prompt: "a boat floating on the sea, sunset"
+video_path: "input_animatediff/boat.mp4"
+bg_source: "TOP"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 42
\ No newline at end of file
diff --git a/configs/relight/car.yaml b/configs/relight/car.yaml
new file mode 100644
index 0000000..a557aaf
--- /dev/null
+++ b/configs/relight/car.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality"
+relight_prompt: "a car driving on the street, neon light"
+video_path: "input_animatediff/car.mp4"
+bg_source: "RIGHT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 2060
\ No newline at end of file
diff --git a/configs/relight/cat.yaml b/configs/relight/cat.yaml
new file mode 100644
index 0000000..0572a99
--- /dev/null
+++ b/configs/relight/cat.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality"
+relight_prompt: "a cat, red and blue neon light"
+video_path: "input_animatediff/cat.mp4"
+bg_source: "LEFT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 42
\ No newline at end of file
diff --git a/configs/relight/cow.yaml b/configs/relight/cow.yaml
new file mode 100644
index 0000000..ffa82db
--- /dev/null
+++ b/configs/relight/cow.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality"
+relight_prompt: "a cow drinking water in the river, sunset"
+video_path: "input_animatediff/cow.mp4"
+bg_source: "RIGHT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 42
\ No newline at end of file
diff --git a/configs/relight/flowers.yaml b/configs/relight/flowers.yaml
new file mode 100644
index 0000000..1b3ca42
--- /dev/null
+++ b/configs/relight/flowers.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality, unclear, blurry"
+relight_prompt: "A basket of flowers, sunshine, hard light"
+video_path: "input_animatediff/flowers.mp4"
+bg_source: "LEFT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 42
\ No newline at end of file
diff --git a/configs/relight/fox.yaml b/configs/relight/fox.yaml
new file mode 100644
index 0000000..8604aa4
--- /dev/null
+++ b/configs/relight/fox.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality"
+relight_prompt: "a fox, sunlight filtering through trees, dappled light"
+video_path: "input_animatediff/fox.mp4"
+bg_source: "LEFT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 42
\ No newline at end of file
diff --git a/configs/relight/girl.yaml b/configs/relight/girl.yaml
new file mode 100644
index 0000000..98429ca
--- /dev/null
+++ b/configs/relight/girl.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality"
+relight_prompt: "a girl, magic lit, sci-fi RGB glowing, key lighting"
+video_path: "input_animatediff/girl.mp4"
+bg_source: "BOTTOM"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 42
\ No newline at end of file
diff --git a/configs/relight/girl2.yaml b/configs/relight/girl2.yaml
new file mode 100644
index 0000000..c4c1924
--- /dev/null
+++ b/configs/relight/girl2.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality"
+relight_prompt: "an anime girl, neon light"
+video_path: "input_animatediff/girl2.mp4"
+bg_source: "RIGHT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 42
\ No newline at end of file
diff --git a/configs/relight/juice.yaml b/configs/relight/juice.yaml
new file mode 100644
index 0000000..17b4b4e
--- /dev/null
+++ b/configs/relight/juice.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality"
+relight_prompt: "Pour juice into a glass, magic golden lit"
+video_path: "input_animatediff/juice.mp4"
+bg_source: "RIGHT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 42
\ No newline at end of file
diff --git a/configs/relight/man2.yaml b/configs/relight/man2.yaml
new file mode 100644
index 0000000..7ab13dd
--- /dev/null
+++ b/configs/relight/man2.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality"
+relight_prompt: "handsome man with glasses, shadow from window, sunshine"
+video_path: "input_animatediff/man2.mp4"
+bg_source: "RIGHT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 42
\ No newline at end of file
diff --git a/configs/relight/man4.yaml b/configs/relight/man4.yaml
new file mode 100644
index 0000000..ff95418
--- /dev/null
+++ b/configs/relight/man4.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality"
+relight_prompt: "handsome man with glasses, sunlight through the blinds"
+video_path: "input_animatediff/man4.mp4"
+bg_source: "LEFT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 42
\ No newline at end of file
diff --git a/configs/relight/plane.yaml b/configs/relight/plane.yaml
new file mode 100644
index 0000000..2e07d91
--- /dev/null
+++ b/configs/relight/plane.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality"
+relight_prompt: "a plane on the runway, bottom neon light"
+video_path: "input_animatediff/plane.mp4"
+bg_source: "BOTTOM"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 42
\ No newline at end of file
diff --git a/configs/relight/toy.yaml b/configs/relight/toy.yaml
new file mode 100644
index 0000000..94d3eb6
--- /dev/null
+++ b/configs/relight/toy.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality"
+relight_prompt: "a maneki-neko toy, cozy bedroom illumination"
+video_path: "input_animatediff/toy.mp4"
+bg_source: "RIGHT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 42
\ No newline at end of file
diff --git a/configs/relight/woman.yaml b/configs/relight/woman.yaml
new file mode 100644
index 0000000..1729738
--- /dev/null
+++ b/configs/relight/woman.yaml
@@ -0,0 +1,13 @@
+n_prompt: "bad quality, worse quality"
+relight_prompt: "a woman with curly hair, natural lighting, warm atmosphere"
+video_path: "input_animatediff/woman.mp4"
+bg_source: "LEFT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 25
+text_guide_scale: 2
+seed: 42
\ No newline at end of file
diff --git a/configs/relight_inpaint/bloom.yaml b/configs/relight_inpaint/bloom.yaml
new file mode 100644
index 0000000..d4d82ed
--- /dev/null
+++ b/configs/relight_inpaint/bloom.yaml
@@ -0,0 +1,15 @@
+n_prompt: "bad quality, worse quality"
+inpaint_prompt: "a red flower blooming in the river"
+relight_prompt: "a red flower blooming in the river, nature lighting"
+
+video_path: "input_animatediff/bloom.mp4"
+bg_source: "TOP"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.4
+gamma: 0.5
+num_step: 50
+text_guide_scale: 4
+seed: 8776
\ No newline at end of file
diff --git a/configs/relight_inpaint/camera.yaml b/configs/relight_inpaint/camera.yaml
new file mode 100644
index 0000000..ca1b430
--- /dev/null
+++ b/configs/relight_inpaint/camera.yaml
@@ -0,0 +1,15 @@
+n_prompt: "bad quality, worse quality"
+inpaint_prompt: "A tiny camera on a tray, cyberpunk"
+relight_prompt: "A tiny camera on a tray, cyberpunk, neon light"
+
+video_path: "input_animatediff/camera.mp4"
+bg_source: "LEFT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.4
+gamma: 0.5
+num_step: 50
+text_guide_scale: 3
+seed: 1333
\ No newline at end of file
diff --git a/configs/relight_inpaint/car.yaml b/configs/relight_inpaint/car.yaml
new file mode 100644
index 0000000..bae0400
--- /dev/null
+++ b/configs/relight_inpaint/car.yaml
@@ -0,0 +1,15 @@
+n_prompt: "bad quality, worse quality"
+inpaint_prompt: "a car driving on the street"
+relight_prompt: "a car driving on the street, neon light"
+
+video_path: "input_animatediff/car.mp4"
+bg_source: "RIGHT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 50
+text_guide_scale: 2
+seed: 6561
\ No newline at end of file
diff --git a/configs/relight_inpaint/car_2.yaml b/configs/relight_inpaint/car_2.yaml
new file mode 100644
index 0000000..bcd5cbc
--- /dev/null
+++ b/configs/relight_inpaint/car_2.yaml
@@ -0,0 +1,15 @@
+n_prompt: "bad quality, worse quality"
+inpaint_prompt: "a car driving on the beach, sunset over sea"
+relight_prompt: "a car driving on the beach, sunset over sea, left light, shadow"
+
+video_path: "input_animatediff/car.mp4"
+bg_source: "LEFT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 50
+text_guide_scale: 2
+seed: 2409
\ No newline at end of file
diff --git a/configs/relight_inpaint/cat2.yaml b/configs/relight_inpaint/cat2.yaml
new file mode 100644
index 0000000..b66d9b8
--- /dev/null
+++ b/configs/relight_inpaint/cat2.yaml
@@ -0,0 +1,15 @@
+n_prompt: "bad quality, worse quality"
+inpaint_prompt: "A cat walking on a runway, red and blue neon lights on both sides"
+relight_prompt: "A cat walking on a runway, red and blue neon lights on both sides, key light"
+
+video_path: "input_animatediff/cat2.mp4"
+bg_source: "LEFT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 50
+text_guide_scale: 5
+seed: 2949
\ No newline at end of file
diff --git a/configs/relight_inpaint/coin.yaml b/configs/relight_inpaint/coin.yaml
new file mode 100644
index 0000000..fbe01a9
--- /dev/null
+++ b/configs/relight_inpaint/coin.yaml
@@ -0,0 +1,15 @@
+n_prompt: "bad quality, worse quality"
+inpaint_prompt: "A coin on the desk"
+relight_prompt: "A coin on the desk, natural lighting"
+
+video_path: "input_animatediff/coin.mp4"
+bg_source: "TOP"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.4
+gamma: 0.5
+num_step: 80
+text_guide_scale: 2
+seed: 4013
\ No newline at end of file
diff --git a/configs/relight_inpaint/dog2.yaml b/configs/relight_inpaint/dog2.yaml
new file mode 100644
index 0000000..ea731d1
--- /dev/null
+++ b/configs/relight_inpaint/dog2.yaml
@@ -0,0 +1,15 @@
+n_prompt: "bad quality, worse quality"
+inpaint_prompt: "a dog in the room, sunshine from window"
+relight_prompt: "a dog in the room, sunshine from window"
+
+video_path: "input_animatediff/dog2.mp4"
+bg_source: "RIGHT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.4
+gamma: 0.5
+num_step: 50
+text_guide_scale: 2
+seed: 4550
\ No newline at end of file
diff --git a/configs/relight_inpaint/man3.yaml b/configs/relight_inpaint/man3.yaml
new file mode 100644
index 0000000..5d68540
--- /dev/null
+++ b/configs/relight_inpaint/man3.yaml
@@ -0,0 +1,15 @@
+n_prompt: "bad quality, worse quality"
+inpaint_prompt: "A man in the classroom"
+relight_prompt: "A man in the classroom, sunshine from the window"
+
+video_path: "input_animatediff/man3.mp4"
+bg_source: "RIGHT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 50
+text_guide_scale: 3
+seed: 3931
\ No newline at end of file
diff --git a/configs/relight_inpaint/man3_2.yaml b/configs/relight_inpaint/man3_2.yaml
new file mode 100644
index 0000000..043125a
--- /dev/null
+++ b/configs/relight_inpaint/man3_2.yaml
@@ -0,0 +1,15 @@
+n_prompt: "bad quality, worse quality"
+inpaint_prompt: "A man in a bar, left yellow and right purple neon lights"
+relight_prompt: "A man in a bar, left yellow and right purple neon lights, hard light"
+
+video_path: "input_animatediff/man3.mp4"
+bg_source: "RIGHT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 50
+text_guide_scale: 4
+seed: 9528
\ No newline at end of file
diff --git a/configs/relight_inpaint/water.yaml b/configs/relight_inpaint/water.yaml
new file mode 100644
index 0000000..fdfc8cf
--- /dev/null
+++ b/configs/relight_inpaint/water.yaml
@@ -0,0 +1,15 @@
+n_prompt: "bad quality, worse quality"
+inpaint_prompt: "a glass of water, in the forest, magic golden lit"
+relight_prompt: "a glass of water, in the forest, magic golden lit, key light"
+
+video_path: "input_animatediff/water.mp4"
+bg_source: "TOP"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.4
+gamma: 0.5
+num_step: 50
+text_guide_scale: 4
+seed: 796
\ No newline at end of file
diff --git a/configs/relight_inpaint/wolf2.yaml b/configs/relight_inpaint/wolf2.yaml
new file mode 100644
index 0000000..34b64b8
--- /dev/null
+++ b/configs/relight_inpaint/wolf2.yaml
@@ -0,0 +1,15 @@
+n_prompt: "bad quality, worse quality"
+inpaint_prompt: "a wolf stands in an alley, detailed face, neon, Wong Kar-wai, warm"
+relight_prompt: "a wolf stands in an alley, detailed face, neon, Wong Kar-wai, warm, right light"
+
+video_path: "input_animatediff/wolf2.mp4"
+bg_source: "RIGHT"  ## NONE, LEFT, RIGHT, BOTTOM, TOP
+save_path: "output"
+
+width: 512
+height: 512
+strength: 0.5
+gamma: 0.5
+num_step: 50
+text_guide_scale: 5
+seed: 2172
\ No newline at end of file
diff --git a/input_animatediff/bear.mp4 b/input_animatediff/bear.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..43e4e66bcecf39765bf0599c8948a804ed4a72b9
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diff --git a/input_animatediff/bloom.mp4 b/input_animatediff/bloom.mp4
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diff --git a/input_animatediff/car.mp4 b/input_animatediff/car.mp4
new file mode 100644
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diff --git a/input_animatediff/cat.mp4 b/input_animatediff/cat.mp4
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diff --git a/input_animatediff/cat2.mp4 b/input_animatediff/cat2.mp4
new file mode 100644
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diff --git a/input_animatediff/coin.mp4 b/input_animatediff/coin.mp4
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diff --git a/input_animatediff/dog2.mp4 b/input_animatediff/dog2.mp4
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diff --git a/input_animatediff/juice.mp4 b/input_animatediff/juice.mp4
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diff --git a/input_animatediff/man2.mp4 b/input_animatediff/man2.mp4
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diff --git a/input_animatediff/man3.mp4 b/input_animatediff/man3.mp4
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diff --git a/input_animatediff/wolf2.mp4 b/input_animatediff/wolf2.mp4
new file mode 100644
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diff --git a/lav_paint.py b/lav_paint.py
new file mode 100644
index 0000000..970b0c1
--- /dev/null
+++ b/lav_paint.py
@@ -0,0 +1,256 @@
+import os
+import torch
+import imageio
+import argparse
+from types import MethodType
+import safetensors.torch as sf
+import torch.nn.functional as F
+from omegaconf import OmegaConf
+from transformers import CLIPTextModel, CLIPTokenizer
+from diffusers import MotionAdapter, EulerAncestralDiscreteScheduler, AutoencoderKL
+from diffusers import AutoencoderKL, UNet2DConditionModel, DPMSolverMultistepScheduler
+from diffusers.models.attention_processor import AttnProcessor2_0
+
+from .src.ic_light import BGSource
+from .src.ic_light import Relighter
+from .src.animatediff_inpaint_pipe import AnimateDiffVideoToVideoPipeline
+from .src.ic_light_pipe import StableDiffusionImg2ImgPipeline
+from .src.tools import read_video, read_mask,set_all_seed, get_fg_video
+
+
+
+
+def main(args):
+    
+    config  = OmegaConf.load(args.config)
+    device = torch.device('cuda')
+    adopted_dtype = torch.float16
+    set_all_seed(42)
+    
+    ## vdm model
+    adapter = MotionAdapter.from_pretrained(args.motion_adapter_model)
+    #MotionAdapter.from_single_file(args.motion_adapter_model, torch_dtype=adopted_dtype)
+    ## pipeline
+    pipe = AnimateDiffVideoToVideoPipeline.from_pretrained(args.sd_model, motion_adapter=adapter)
+   
+    eul_scheduler = EulerAncestralDiscreteScheduler.from_pretrained(
+        args.sd_model,
+        subfolder="scheduler",
+        beta_schedule="linear",
+    )
+
+    pipe.scheduler = eul_scheduler
+    pipe.enable_vae_slicing()
+    pipe = pipe.to(device=device, dtype=adopted_dtype)
+    pipe.vae.requires_grad_(False)
+    pipe.unet.requires_grad_(False)
+
+    ## ic-light model
+    tokenizer = CLIPTokenizer.from_pretrained(args.sd_model, subfolder="tokenizer")
+    text_encoder = CLIPTextModel.from_pretrained(args.sd_model, subfolder="text_encoder")
+    vae = AutoencoderKL.from_pretrained(args.sd_model, subfolder="vae")
+    unet = UNet2DConditionModel.from_pretrained(args.sd_model, subfolder="unet")
+    with torch.no_grad():
+        new_conv_in = torch.nn.Conv2d(8, unet.conv_in.out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding)
+        new_conv_in.weight.zero_() #torch.Size([320, 8, 3, 3])
+        new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)
+        new_conv_in.bias = unet.conv_in.bias
+        unet.conv_in = new_conv_in
+    unet_original_forward = unet.forward
+
+    def hooked_unet_forward(sample, timestep, encoder_hidden_states, **kwargs):
+        
+        c_concat = kwargs['cross_attention_kwargs']['concat_conds'].to(sample)
+        c_concat = torch.cat([c_concat] * (sample.shape[0] // c_concat.shape[0]), dim=0)
+        new_sample = torch.cat([sample, c_concat], dim=1)
+        kwargs['cross_attention_kwargs'] = {}
+        return unet_original_forward(new_sample, timestep, encoder_hidden_states, **kwargs)
+    unet.forward = hooked_unet_forward
+
+    ## ic-light model loader
+    sd_offset = sf.load_file(args.ic_light_model)
+    sd_origin = unet.state_dict()
+    sd_merged = {k: sd_origin[k] + sd_offset[k] for k in sd_origin.keys()}
+    unet.load_state_dict(sd_merged, strict=True)
+    del sd_offset, sd_origin, sd_merged
+    text_encoder = text_encoder.to(device=device, dtype=adopted_dtype)
+    vae = vae.to(device=device, dtype=adopted_dtype)
+    unet = unet.to(device=device, dtype=adopted_dtype)
+    unet.set_attn_processor(AttnProcessor2_0())
+    vae.set_attn_processor(AttnProcessor2_0())
+
+    # Consistent light attention
+    @torch.inference_mode()
+    def custom_forward_CLA(self, 
+                        hidden_states, 
+                        gamma=config.get("gamma", 0.5),
+                        encoder_hidden_states=None,
+                        attention_mask=None, 
+                        cross_attention_kwargs=None
+                        ):
+
+        batch_size, sequence_length, channel = hidden_states.shape
+        
+        residual = hidden_states
+        input_ndim = hidden_states.ndim
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        if attention_mask is not None:
+            if attention_mask.shape[-1] != query.shape[1]:
+                target_length = query.shape[1]
+                attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
+                attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
+        if self.group_norm is not None:
+            hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+        if encoder_hidden_states is None: 
+            encoder_hidden_states = hidden_states
+
+        query = self.to_q(hidden_states) 
+        key = self.to_k(encoder_hidden_states)   
+        value = self.to_v(encoder_hidden_states) 
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // self.heads
+        query = query.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+
+        hidden_states = F.scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False)
+        shape = query.shape
+        
+        # addition key and value
+        mean_key = key.reshape(2,-1,shape[1],shape[2],shape[3]).mean(dim=1,keepdim=True)
+        mean_value = value.reshape(2,-1,shape[1],shape[2],shape[3]).mean(dim=1,keepdim=True)
+        mean_key = mean_key.expand(-1,shape[0]//2,-1,-1,-1).reshape(shape[0],shape[1],shape[2],shape[3])
+        mean_value = mean_value.expand(-1,shape[0]//2,-1,-1,-1).reshape(shape[0],shape[1],shape[2],shape[3])
+        add_hidden_state = F.scaled_dot_product_attention(query, mean_key, mean_value, attn_mask=None, dropout_p=0.0, is_causal=False)
+        
+        # mix
+        hidden_states = (1-gamma)*hidden_states + gamma*add_hidden_state
+        
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+        hidden_states = self.to_out[0](hidden_states)
+        hidden_states = self.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if self.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / self.rescale_output_factor
+        return hidden_states
+
+    ### attention
+    @torch.inference_mode()
+    def prep_unet_self_attention(unet):
+        for name, module in unet.named_modules(): 
+            module_name = type(module).__name__
+            
+            name_split_list = name.split(".")
+            cond_1 = name_split_list[0] in "up_blocks"
+            cond_2 = name_split_list[-1] in ('attn1')
+            
+            if "Attention" in module_name and cond_1 and cond_2:
+                cond_3 = name_split_list[1] 
+                if cond_3 not in "3":
+                    module.forward = MethodType(custom_forward_CLA, module)
+
+        return unet
+
+    ## consistency light attention
+    unet = prep_unet_self_attention(unet)
+
+    ## ic-light-scheduler
+    ic_light_scheduler = DPMSolverMultistepScheduler(
+        num_train_timesteps=1000,
+        beta_start=0.00085,
+        beta_end=0.012,
+        algorithm_type="sde-dpmsolver++",
+        use_karras_sigmas=True,
+        steps_offset=1
+    )
+    ic_light_pipe = StableDiffusionImg2ImgPipeline(
+        vae=vae,
+        text_encoder=text_encoder,
+        tokenizer=tokenizer,
+        unet=unet,
+        scheduler=ic_light_scheduler,
+        safety_checker=None,
+        requires_safety_checker=False,
+        feature_extractor=None,
+        image_encoder=None
+    )
+    ic_light_pipe = ic_light_pipe.to(device)
+    
+    #############################  params  ######################################
+    strength = config.get("strength", 0.5)
+    num_step = config.get("num_step", 50)
+    text_guide_scale = config.get("text_guide_scale", 4)
+    seed = config.get("seed")
+    image_width = config.get("width", 512)
+    image_height = config.get("height", 512)
+    n_prompt = config.get("n_prompt", "")
+    inpaint_prompt = config.get("inpaint_prompt", "")
+    relight_prompt = config.get("relight_prompt", "")
+    video_path = config.get("video_path", "")
+    bg_source = BGSource[config.get("bg_source")]
+    save_path = config.get("save_path")
+
+    ##############################  infer  #####################################
+    generator = torch.manual_seed(seed)
+    video_name = os.path.basename(video_path)
+    video_list, video_name = read_video(video_path, image_width, image_height)
+    mask_folder = os.path.join("masks_animatediff", video_name.split('.')[-2])
+    mask_list = read_mask(mask_folder)
+
+    print("################## begin ##################")
+    ## get foreground video
+    fg_video_tensor = get_fg_video(video_list, mask_list, device, adopted_dtype) ## torch.Size([16, 3, 512, 512])
+
+    with torch.no_grad():
+        relighter = Relighter(
+            pipeline=ic_light_pipe,
+            relight_prompt=relight_prompt,
+            bg_source=bg_source, 
+            generator=generator,
+            )
+        vdm_init_latent = relighter(fg_video_tensor)
+
+        ## infer
+        num_inference_steps = num_step
+        output = pipe(
+            ic_light_pipe=ic_light_pipe,
+            relight_prompt=relight_prompt,
+            bg_source=bg_source,
+            mask=mask_list,
+            vdm_init_latent=vdm_init_latent,
+            video=video_list,
+            prompt=inpaint_prompt,
+            strength=strength,
+            negative_prompt=n_prompt,
+            guidance_scale=text_guide_scale,
+            num_inference_steps=num_inference_steps,
+            height=image_height,
+            width=image_width,
+            generator=generator,
+        )
+
+        frames = output.frames[0]
+        results_path = f"{save_path}/inpaint_{video_name}"
+        imageio.mimwrite(results_path, frames, fps=8)
+        print(f"relight with bg generation! prompt:{relight_prompt}, light:{bg_source.value}, save in {results_path}.")
+    
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    
+    parser.add_argument("--sd_model", type=str, default="stablediffusionapi/realistic-vision-v51")
+    parser.add_argument("--motion_adapter_model", type=str, default="guoyww/animatediff-motion-adapter-v1-5-3")
+    parser.add_argument("--ic_light_model", type=str, default="./models/iclight_sd15_fc.safetensors")
+    
+    parser.add_argument("--config", type=str, default="configs/relight_inpaint/car.yaml", help="the config file for each sample.")
+    
+    args = parser.parse_args()
+    main(args)
\ No newline at end of file
diff --git a/lav_relight.py b/lav_relight.py
new file mode 100644
index 0000000..c986dfc
--- /dev/null
+++ b/lav_relight.py
@@ -0,0 +1,252 @@
+import os
+import torch
+from types import MethodType
+import safetensors.torch as sf
+import torch.nn.functional as F
+from transformers import CLIPTokenizer
+from diffusers import MotionAdapter, EulerAncestralDiscreteScheduler
+from diffusers import  UNet2DConditionModel, DPMSolverMultistepScheduler
+from diffusers.models.attention_processor import AttnProcessor2_0
+from torch.hub import download_url_to_file
+from .src.tools import get_fg_video
+from .src.animatediff_pipe import AnimateDiffVideoToVideoPipeline
+from .src.animatediff_inpaint_pipe import AnimateDiffVideoToVideoPipeline as AnimateDiffVideoToVideoPipeline_inpaint
+from .src.ic_light_pipe import StableDiffusionImg2ImgPipeline
+from .src.ic_light import Relighter
+from .node_utils import image2masks
+import logging
+logging.basicConfig(level=logging.INFO)
+
+
+def load_ic_light_model(pipeline,ic_light_model,ckpt_path,sd_repo,motion_repo,motion_adapter_model,device,adopted_dtype,mode):
+     # vdm model
+    adapter = MotionAdapter.from_single_file(motion_adapter_model,config=motion_repo)
+    Unet=UNet2DConditionModel.from_single_file(ckpt_path, config=os.path.join(sd_repo, "unet"))
+    # animate pipeline
+    Vae=pipeline.vae
+    tokenizer = CLIPTokenizer.from_pretrained(sd_repo, subfolder="tokenizer")
+    Text_encoder =pipeline.text_encoder
+    del pipeline
+
+    if mode=='relight':
+        pipe=AnimateDiffVideoToVideoPipeline.from_pretrained(sd_repo,unet=Unet,vae=Vae,text_encoder=Text_encoder,tokenizer=tokenizer,motion_adapter=adapter)
+    else:
+        pipe=AnimateDiffVideoToVideoPipeline_inpaint.from_pretrained(sd_repo,unet=Unet,vae=Vae,text_encoder=Text_encoder,tokenizer=tokenizer,motion_adapter=adapter)
+    eul_scheduler = EulerAncestralDiscreteScheduler.from_pretrained(
+        sd_repo,
+        subfolder="scheduler",
+        beta_schedule="linear",
+    )
+
+    pipe.scheduler = eul_scheduler
+    pipe.enable_vae_slicing()
+    pipe = pipe.to(device=device, dtype=adopted_dtype)
+    pipe.vae.requires_grad_(False)
+    pipe.unet.requires_grad_(False)
+
+    unet=UNet2DConditionModel.from_single_file(ckpt_path,config=os.path.join(sd_repo, "unet"))
+    tokenizer=pipe.tokenizer
+    text_encoder=pipe.text_encoder
+    vae=Vae
+
+
+    with torch.no_grad():
+        new_conv_in = torch.nn.Conv2d(8, unet.conv_in.out_channels, unet.conv_in.kernel_size, unet.conv_in.stride, unet.conv_in.padding)
+        new_conv_in.weight.zero_() #torch.Size([320, 8, 3, 3])
+        new_conv_in.weight[:, :4, :, :].copy_(unet.conv_in.weight)
+        new_conv_in.bias = unet.conv_in.bias
+        unet.conv_in = new_conv_in
+    unet_original_forward = unet.forward
+    def hooked_unet_forward(sample, timestep, encoder_hidden_states, **kwargs):
+        
+        c_concat = kwargs['cross_attention_kwargs']['concat_conds'].to(sample)
+        c_concat = torch.cat([c_concat] * (sample.shape[0] // c_concat.shape[0]), dim=0)
+        new_sample = torch.cat([sample, c_concat], dim=1)
+        kwargs['cross_attention_kwargs'] = {}
+        return unet_original_forward(new_sample, timestep, encoder_hidden_states, **kwargs)
+    
+    unet.forward = hooked_unet_forward
+
+    if not os.path.exists(ic_light_model):
+        download_url_to_file(url='https://huggingface.co/lllyasviel/ic-light/resolve/main/iclight_sd15_fc.safetensors', 
+                             dst=ic_light_model) #TO DO: change to ic-light model
+    
+    sd_offset = sf.load_file(ic_light_model)
+    sd_origin = unet.state_dict()
+    sd_merged = {k: sd_origin[k] + sd_offset[k] for k in sd_origin.keys()}
+    unet.load_state_dict(sd_merged, strict=True)
+    del sd_offset, sd_origin, sd_merged
+    text_encoder = text_encoder.to(device=device, dtype=adopted_dtype)
+    vae = vae.to(device=device, dtype=adopted_dtype)
+    unet = unet.to(device=device, dtype=adopted_dtype)
+
+    unet.set_attn_processor(AttnProcessor2_0())
+    vae.set_attn_processor(AttnProcessor2_0())
+    
+    @torch.inference_mode()
+    def custom_forward_CLA(self, 
+                        hidden_states, 
+                        gamma=0.5,
+                        encoder_hidden_states=None,
+                        attention_mask=None, 
+                        cross_attention_kwargs=None
+                        ):
+
+        batch_size, sequence_length, channel = hidden_states.shape
+        
+        residual = hidden_states
+        input_ndim = hidden_states.ndim
+        if input_ndim == 4:
+            batch_size, channel, height, width = hidden_states.shape
+            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
+
+        if attention_mask is not None:
+            if attention_mask.shape[-1] != query.shape[1]:
+                target_length = query.shape[1]
+                attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
+                attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
+        if self.group_norm is not None:
+            hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+        if encoder_hidden_states is None: 
+            encoder_hidden_states = hidden_states
+
+        query = self.to_q(hidden_states) 
+        key = self.to_k(encoder_hidden_states)   
+        value = self.to_v(encoder_hidden_states) 
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // self.heads
+        query = query.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, self.heads, head_dim).transpose(1, 2)
+
+        hidden_states = F.scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False)
+        shape = query.shape
+        
+        # addition key and value
+        mean_key = key.reshape(2,-1,shape[1],shape[2],shape[3]).mean(dim=1,keepdim=True)
+        mean_value = value.reshape(2,-1,shape[1],shape[2],shape[3]).mean(dim=1,keepdim=True)
+        mean_key = mean_key.expand(-1,shape[0]//2,-1,-1,-1).reshape(shape[0],shape[1],shape[2],shape[3])
+        mean_value = mean_value.expand(-1,shape[0]//2,-1,-1,-1).reshape(shape[0],shape[1],shape[2],shape[3])
+        add_hidden_state = F.scaled_dot_product_attention(query, mean_key, mean_value, attn_mask=None, dropout_p=0.0, is_causal=False)
+        
+        # mix
+        hidden_states = (1-gamma)*hidden_states + gamma*add_hidden_state
+        
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+        hidden_states = self.to_out[0](hidden_states)
+        hidden_states = self.to_out[1](hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
+
+        if self.residual_connection:
+            hidden_states = hidden_states + residual
+
+        hidden_states = hidden_states / self.rescale_output_factor
+        return hidden_states
+
+    ### attention
+    @torch.inference_mode()
+    def prep_unet_self_attention(unet):
+        for name, module in unet.named_modules(): 
+            module_name = type(module).__name__
+            
+            name_split_list = name.split(".")
+            cond_1 = name_split_list[0] in "up_blocks"
+            cond_2 = name_split_list[-1] in ('attn1')
+            
+            if "Attention" in module_name and cond_1 and cond_2:
+                cond_3 = name_split_list[1] 
+                if cond_3 not in "3":
+                    module.forward = MethodType(custom_forward_CLA, module)
+
+        return unet
+
+    ## consistency light attention
+    unet = prep_unet_self_attention(unet)
+     ## ic-light-scheduler
+    ic_light_scheduler = DPMSolverMultistepScheduler(
+        num_train_timesteps=1000,
+        beta_start=0.00085,
+        beta_end=0.012,
+        algorithm_type="sde-dpmsolver++",
+        use_karras_sigmas=True,
+        steps_offset=1
+    )
+    ic_light_pipe = StableDiffusionImg2ImgPipeline(
+        vae=vae,
+        text_encoder=text_encoder,
+        tokenizer=tokenizer,
+        unet=unet,
+        scheduler=ic_light_scheduler,
+        safety_checker=None,
+        requires_safety_checker=False,
+        feature_extractor=None,
+        image_encoder=None
+    )
+    ic_light_pipe = ic_light_pipe.to(device)
+    return pipe,ic_light_pipe
+
+
+def infer_relight(ic_light_pipe,pipe,strength,num_step,text_guide_scale,seed,image_width,image_height,n_prompt,relight_prompt,inpaint_prompt,video_list,bg_source,mode,mask_list,device,adopted_dtype,repo,fps,local_sam):
+
+    generator = torch.manual_seed(seed)
+    if mode == "inpaint":
+        if mask_list is None:
+            if repo:
+                print("No mask_list provided, generating mask_list from repo...")
+                mask_list = image2masks(repo,video_list)
+            else:
+                from .sam2 import get_mask
+                mask_list=get_mask(video_list,fps,local_sam)
+                print("No mask_list provided, use sam2 generating mask_list from video...")
+        fg_video_tensor = get_fg_video(video_list, mask_list, device, adopted_dtype)
+        with torch.no_grad():
+            relighter = Relighter(
+                pipeline=ic_light_pipe,
+                relight_prompt=relight_prompt,
+                bg_source=bg_source, 
+                generator=generator,
+                )
+            vdm_init_latent = relighter(fg_video_tensor)
+
+            ## infer
+            num_inference_steps = num_step
+            output = pipe(
+                ic_light_pipe=ic_light_pipe,
+                relight_prompt=relight_prompt,
+                bg_source=bg_source,
+                mask=mask_list,
+                vdm_init_latent=vdm_init_latent,
+                video=video_list,
+                prompt=inpaint_prompt,
+                strength=strength,
+                negative_prompt=n_prompt,
+                guidance_scale=text_guide_scale,
+                num_inference_steps=num_inference_steps,
+                height=image_height,
+                width=image_width,
+                generator=generator,
+            )
+    else:
+        with torch.no_grad():
+            num_inference_steps = int(round(num_step / strength))
+            
+            output = pipe(
+                ic_light_pipe=ic_light_pipe,
+                relight_prompt=relight_prompt,
+                bg_source=bg_source,
+                video=video_list,
+                prompt=relight_prompt,
+                strength=strength,
+                negative_prompt=n_prompt,
+                guidance_scale=text_guide_scale,
+                num_inference_steps=num_inference_steps,
+                height=image_height,
+                width=image_width,
+                generator=generator,
+            )
+    frames = output.frames[0]
+    return frames
+
diff --git a/node_utils.py b/node_utils.py
new file mode 100644
index 0000000..77e6ca2
--- /dev/null
+++ b/node_utils.py
@@ -0,0 +1,241 @@
+# !/usr/bin/env python
+# -*- coding: UTF-8 -*-
+import os
+import torch
+from PIL import Image
+import numpy as np
+import cv2
+import gc
+import time
+from transformers import AutoModelForImageSegmentation
+from comfy.utils import common_upscale,ProgressBar
+from huggingface_hub import hf_hub_download
+import torchvision.transforms as transforms
+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 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 image2masks(repo,video_image):
+    start_time = time.time()
+    model = AutoModelForImageSegmentation.from_pretrained(repo, trust_remote_code=True)
+    torch.set_float32_matmul_precision(['high', 'highest'][0])
+    model.to('cuda')
+    model.eval()
+    # Data settings
+    image_size = (1024, 1024)
+    transform_image = transforms.Compose([
+    transforms.Resize(image_size),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    ])
+    masks=[]
+    for img in video_image:
+        input_images = transform_image(img).unsqueeze(0).to('cuda')
+        # Prediction
+        with torch.no_grad():
+            preds = model(input_images)[-1].sigmoid().cpu()
+        pred = preds[0].squeeze()
+        pred_pil = transforms.ToPILImage()(pred)
+        mask = pred_pil.resize(img.size)
+        #img.putalpha(mask)
+        masks.append(mask.convert('RGB'))
+    end_time = time.time()  
+    load_time = end_time - start_time  
+    print(f"image2masks infer time: {load_time:.4f} s")
+    model.to('cpu')
+    gc.collect()
+    torch.cuda.empty_cache()
+    return masks
+
+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,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 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/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..4c2ef9b
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,15 @@
+[project]
+name = "light_a_video"
+description = "Light-A-Video: Training-free Video Relighting via Progressive Light Fusion,you can use it in comfyUI"
+version = "1.0.0"
+license = {file = "LICENSE"}
+dependencies = ["#diffusers==0.32.1", "diffusers", "#transformers==4.47.1", "transformers", "opencv-python", "safetensors", "pillow", "einops", "peft", "imageio", "omegaconf", "#ultralytic", "tqdm==4.67.1", "protobuf==3.20", "torch", "torchvision", "moviepy"]
+
+[project.urls]
+Repository = "https://github.com/smthemex/ComfyUI_Light_A_Video"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "smthemx"
+DisplayName = "ComfyUI_Light_A_Video"
+Icon = ""
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..a67f8e4
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,17 @@
+#diffusers==0.32.1
+diffusers
+#transformers==4.47.1
+transformers
+opencv-python
+safetensors
+pillow
+einops
+peft
+imageio
+omegaconf
+#ultralytic
+tqdm==4.67.1
+protobuf==3.20
+torch
+torchvision
+moviepy
\ No newline at end of file
diff --git a/sam2.py b/sam2.py
new file mode 100644
index 0000000..5c5d91b
--- /dev/null
+++ b/sam2.py
@@ -0,0 +1,32 @@
+import os
+import torch
+#import argparse
+import numpy as np
+from PIL import Image
+import random
+import imageio
+import folder_paths
+
+
+def get_mask(video_list,fps,local_sam, x=255, y=255):
+    from ultralytics.models.sam import SAM2VideoPredictor #ultralytics>=8.3.0
+    overrides = dict(conf=0.25, task="segment", mode="predict", imgsz=1024, model=local_sam)
+    predictor = SAM2VideoPredictor(overrides=overrides)
+    file_prefix = ''.join(random.choice("0123456789") for _ in range(6))
+    frames = [np.array(img) for img in video_list]
+    video_file = os.path.join(folder_paths.get_input_directory(), f"audio_{file_prefix}_temp.mp4")
+    imageio.mimsave(video_file, frames, fps=fps, codec='libx264')
+    results = predictor(source=video_file,points=[x, y],labels=[1])
+    mask_list=[]
+    for i in range(len(results)):
+        mask = (results[i].masks.data).squeeze().to(torch.float16)
+        mask = (mask * 255).cpu().numpy().astype(np.uint8)
+        mask_image = Image.fromarray(mask).convert('RGB')
+        mask_list.append(mask_image)
+        # mask_dir = f'masks_animatediff/{video_name}'
+        # if not os.path.exists(mask_dir):  
+        #     os.makedirs(mask_dir)        
+        mask_image.save(folder_paths.get_input_directory() + f'/{str(i).zfill(3)}.png')
+    return mask_list
+
+
diff --git a/sd_repo/README.md b/sd_repo/README.md
new file mode 100644
index 0000000..32933fd
--- /dev/null
+++ b/sd_repo/README.md
@@ -0,0 +1,64 @@
+---
+license: creativeml-openrail-m
+tags:
+  - modelslab.com
+  - stable-diffusion-api
+  - text-to-image
+  - ultra-realistic
+pinned: true
+---
+
+#  API Inference
+
+![generated from modelslab.com](https://cdn2.stablediffusionapi.com/generations/bf190b5a-fe19-437c-ba05-82f29cb1f7ad-0.png)
+## Get API Key
+
+Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. 
+
+Replace Key in below code, change **model_id**  to "realistic-vision-v51"
+
+Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
+
+Try model for free: [Generate Images](https://modelslab.com/models/realistic-vision-v51)
+
+Model link: [View model](https://modelslab.com/models/realistic-vision-v51)
+
+View all models: [View Models](https://modelslab.com/models)
+
+    import requests  
+    import json  
+      
+    url =  "https://modelslab.com/api/v6/images/text2img"  
+      
+    payload = json.dumps({  
+    "key":  "your_api_key",  
+    "model_id":  "realistic-vision-v51",  
+    "prompt":  "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",  
+    "negative_prompt":  "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",  
+    "width":  "512",  
+    "height":  "512",  
+    "samples":  "1",  
+    "num_inference_steps":  "30",  
+    "safety_checker":  "no",  
+    "enhance_prompt":  "yes",  
+    "seed":  None,  
+    "guidance_scale":  7.5,  
+    "multi_lingual":  "no",  
+    "panorama":  "no",  
+    "self_attention":  "no",  
+    "upscale":  "no",  
+    "embeddings":  "embeddings_model_id",  
+    "lora":  "lora_model_id",  
+    "webhook":  None,  
+    "track_id":  None  
+    })  
+      
+    headers =  {  
+    'Content-Type':  'application/json'  
+    }  
+      
+    response = requests.request("POST", url, headers=headers, data=payload)  
+      
+    print(response.text)
+
+> Use this coupon code to get 25% off **DMGG0RBN** 
\ No newline at end of file
diff --git a/sd_repo/feature_extractor/preprocessor_config.json b/sd_repo/feature_extractor/preprocessor_config.json
new file mode 100644
index 0000000..f114e26
--- /dev/null
+++ b/sd_repo/feature_extractor/preprocessor_config.json
@@ -0,0 +1,29 @@
+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPFeatureExtractor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  },
+  "use_square_size": false
+}
diff --git a/sd_repo/model_index.json b/sd_repo/model_index.json
new file mode 100644
index 0000000..9abf643
--- /dev/null
+++ b/sd_repo/model_index.json
@@ -0,0 +1,37 @@
+{
+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.25.0.dev0",
+  "feature_extractor": [
+    "transformers",
+    "CLIPFeatureExtractor"
+  ],
+  "image_encoder": [
+    null,
+    null
+  ],
+  "requires_safety_checker": true,
+  "safety_checker": [
+    "stable_diffusion",
+    "StableDiffusionSafetyChecker"
+  ],
+  "scheduler": [
+    "diffusers",
+    "PNDMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}
diff --git a/sd_repo/safety_checker/config.json b/sd_repo/safety_checker/config.json
new file mode 100644
index 0000000..fb7746f
--- /dev/null
+++ b/sd_repo/safety_checker/config.json
@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "CompVis/stable-diffusion-safety-checker",
+  "architectures": [
+    "StableDiffusionSafetyChecker"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
+  "projection_dim": 768,
+  "text_config": {
+    "dropout": 0.0,
+    "hidden_size": 768,
+    "intermediate_size": 3072,
+    "model_type": "clip_text_model",
+    "num_attention_heads": 12
+  },
+  "torch_dtype": "float16",
+  "transformers_version": "4.35.2",
+  "vision_config": {
+    "dropout": 0.0,
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "model_type": "clip_vision_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "patch_size": 14
+  }
+}
diff --git a/sd_repo/scheduler/scheduler_config.json b/sd_repo/scheduler/scheduler_config.json
new file mode 100644
index 0000000..836b3af
--- /dev/null
+++ b/sd_repo/scheduler/scheduler_config.json
@@ -0,0 +1,15 @@
+{
+  "_class_name": "PNDMScheduler",
+  "_diffusers_version": "0.25.0.dev0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "timestep_spacing": "leading",
+  "trained_betas": null
+}
diff --git a/sd_repo/text_encoder/config.json b/sd_repo/text_encoder/config.json
new file mode 100644
index 0000000..7c28cbf
--- /dev/null
+++ b/sd_repo/text_encoder/config.json
@@ -0,0 +1,24 @@
+{
+  "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": "4.35.2",
+  "vocab_size": 49408
+}
diff --git a/sd_repo/tokenizer/merges.txt b/sd_repo/tokenizer/merges.txt
new file mode 100644
index 0000000..76e821f
--- /dev/null
+++ b/sd_repo/tokenizer/merges.txt
@@ -0,0 +1,48895 @@
+#version: 0.2
+i n
+t h
+a n
+r e
+a r
+e r
+th e</w>
+in g</w>
+o u
+o n
+s t
+o r
+e n
+o n</w>
+a l
+a t
+e r</w>
+i t
+i n</w>
+t o</w>
+r o
+i s</w>
+l e
+i c
+a t</w>
+an d</w>
+e d</w>
+o f</w>
+c h
+o r</w>
+e s</w>
+i l
+e l
+s t</w>
+a c
+o m
+a m
+l o
+a n</w>
+a y</w>
+s h
+r i
+l i
+t i
+f or</w>
+n e
+ð Ł
+r a
+h a
+d e
+o l
+v e</w>
+s i
+u r
+a l</w>
+s e
+' s</w>
+u n
+d i
+b e
+l a
+w h
+o o
+d ay</w>
+e n</w>
+m a
+n o
+l e</w>
+t o
+ou r</w>
+i r
+g h
+w it
+i t</w>
+y o
+a s
+s p
+th is</w>
+t s</w>
+at i
+yo u</w>
+wit h</w>
+a d
+i s
+a b
+l y</w>
+w e
+th e
+t e
+a s</w>
+a g
+v i
+p p
+s u
+h o
+m y</w>
+. .
+b u
+c om
+s e</w>
+er s</w>
+m e
+m e</w>
+al l</w>
+c on
+m o
+k e</w>
+g e
+ou t</w>
+en t</w>
+c o
+f e
+v er
+a r</w>
+f ro
+a u
+p o
+c e</w>
+gh t</w>
+ar e</w>
+s s</w>
+fro m</w>
+c h</w>
+t r
+ou n
+on e</w>
+b y</w>
+d o
+t h</w>
+w or
+er e</w>
+k e
+p ro
+f or
+d s</w>
+b o
+t a
+w e</w>
+g o
+h e
+t er</w>
+in g
+d e</w>
+b e</w>
+ati on</w>
+m or
+a y
+e x
+il l</w>
+p e
+k s</w>
+s c
+l u
+f u
+q u
+v er</w>
+ðŁ ĺ
+j u
+m u
+at e</w>
+an d
+v e
+k ing</w>
+m ar
+o p
+h i
+.. .</w>
+p re
+a d</w>
+r u
+th at</w>
+j o
+o f
+c e
+ne w</w>
+a m</w>
+a p
+g re
+s s
+d u
+no w</w>
+y e
+t ing</w>
+y our</w>
+it y</w>
+n i
+c i
+p ar
+g u
+f i
+a f
+p er
+t er
+u p</w>
+s o</w>
+g i
+on s</w>
+g r
+g e</w>
+b r
+p l
+' t</w>
+m i
+in e</w>
+we e
+b i
+u s</w>
+sh o
+ha ve</w>
+to day</w>
+a v
+m an
+en t
+ac k</w>
+ur e</w>
+ou r
+â Ģ
+c u
+l d</w>
+lo o
+i m
+ic e</w>
+s om
+f in
+re d</w>
+re n
+oo d</w>
+w as</w>
+ti on</w>
+p i
+i r</w>
+th er</w>
+t y</w>
+p h
+ar d</w>
+e c
+! !</w>
+m on
+mor e</w>
+w ill</w>
+t ra
+c an</w>
+c ol
+p u
+t e</w>
+w n</w>
+m b
+s o
+it i
+ju st</w>
+n ing</w>
+h ere</w>
+t u
+p a
+p r
+bu t</w>
+wh at</w>
+al ly</w>
+f ir
+m in
+c a
+an t</w>
+s a
+t ed</w>
+e v
+m ent</w>
+f a
+ge t</w>
+am e</w>
+ab out</w>
+g ra
+no t</w>
+ha pp
+ay s</w>
+m an</w>
+h is</w>
+ti me</w>
+li ke</w>
+g h</w>
+ha s</w>
+th an
+lo ve</w>
+ar t</w>
+st e
+d ing</w>
+h e</w>
+c re
+w s</w>
+w at
+d er</w>
+it e</w>
+s er
+ac e</w>
+ag e</w>
+en d</w>
+st r
+a w
+st or
+r e</w>
+c ar
+el l</w>
+al l
+p s</w>
+f ri
+p ho
+p or
+d o</w>
+a k
+w i
+f re
+wh o</w>
+sh i
+b oo
+s on</w>
+el l
+wh en</w>
+il l
+ho w</w>
+gre at</w>
+w in
+e l</w>
+b l
+s si
+al i
+som e</w>
+ðŁ Ĵ
+t on
+d er
+le s</w>
+p la
+ï ¸
+e d
+s ch
+h u
+on g</w>
+d on</w>
+k i
+s h</w>
+an n
+c or
+. .</w>
+oun d</w>
+a z
+in e
+ar y</w>
+fu l</w>
+st u
+ou ld</w>
+st i
+g o</w>
+se e</w>
+ab le</w>
+ar s</w>
+l l</w>
+m is
+b er
+c k</w>
+w a
+en ts</w>
+n o</w>
+si g
+f e</w>
+fir st</w>
+e t</w>
+sp e
+ac k
+i f</w>
+ou s</w>
+' m</w>
+st er</w>
+a pp
+an g
+an ce</w>
+an s</w>
+g ood</w>
+b re
+e ver
+the y</w>
+t ic
+com e</w>
+of f
+b ack</w>
+as e</w>
+ing s</w>
+ol d</w>
+i ght</w>
+f o
+h er</w>
+happ y</w>
+p ic
+it s</w>
+v ing</w>
+u s
+m at
+h om
+d y</w>
+e m
+s k
+y ing</w>
+the ir</w>
+le d</w>
+r y</w>
+u l
+h ar
+c k
+t on</w>
+on al</w>
+h el
+r ic
+b ir
+vi e
+w ay</w>
+t ri
+d a
+p le
+b ro
+st o
+oo l</w>
+ni ght</w>
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new file mode 100644
index 0000000..1f467f3
--- /dev/null
+++ b/sd_repo/tokenizer/special_tokens_map.json
@@ -0,0 +1,30 @@
+{
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+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": false,
+    "normalized": true,
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+    "single_word": false
+  }
+}
diff --git a/sd_repo/tokenizer/tokenizer_config.json b/sd_repo/tokenizer/tokenizer_config.json
new file mode 100644
index 0000000..1bf819b
--- /dev/null
+++ b/sd_repo/tokenizer/tokenizer_config.json
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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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+      "normalized": true,
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+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}
diff --git a/sd_repo/tokenizer/vocab.json b/sd_repo/tokenizer/vocab.json
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new file mode 100644
index 0000000..41f47bc
--- /dev/null
+++ b/sd_repo/unet/config.json
@@ -0,0 +1,67 @@
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new file mode 100644
index 0000000..8ac8ebb
--- /dev/null
+++ b/sd_repo/vae/config.json
@@ -0,0 +1,31 @@
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HcmV?d00001

diff --git a/src/animatediff_eul.py b/src/animatediff_eul.py
new file mode 100644
index 0000000..70dadbb
--- /dev/null
+++ b/src/animatediff_eul.py
@@ -0,0 +1,94 @@
+import torch
+from typing import List, Optional, Tuple, Union
+
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    BaseOutput,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import randn_tensor
+
+class EulerAncestralDiscreteSchedulerOutput(BaseOutput):
+    prev_sample: torch.FloatTensor
+    pred_original_sample: Optional[torch.FloatTensor] = None
+
+
+def eul_step(
+    self,
+    model_output: torch.FloatTensor,
+    timestep: Union[float, torch.FloatTensor],
+    sample: torch.FloatTensor, 
+    fusion_latent,
+    pipe,
+    generator: Optional[torch.Generator] = None,
+    return_dict: bool = True,
+) -> Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:
+
+    if (
+        isinstance(timestep, int)
+        or isinstance(timestep, torch.IntTensor)
+        or isinstance(timestep, torch.LongTensor)
+    ):
+        raise ValueError(
+            (
+                "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
+                " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
+                " one of the `scheduler.timesteps` as a timestep."
+            ),
+        )
+
+    if self.step_index is None:
+        self._init_step_index(timestep)
+
+    sigma = self.sigmas[self.step_index]
+
+    # Upcast to avoid precision issues when computing prev_sample
+    sample = sample.to(torch.float32)
+
+    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
+    if self.config.prediction_type == "epsilon": ## True, 计算x_0
+        pred_original_sample = sample - sigma * model_output
+    elif self.config.prediction_type == "v_prediction":
+        # * c_out + input * c_skip
+        pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
+    elif self.config.prediction_type == "sample":
+        raise NotImplementedError("prediction_type not implemented yet: sample")
+    else:
+        raise ValueError(
+            f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"
+        )
+        
+    ## fusion latent
+    pred_original_sample = fusion_latent
+    
+    sigma_from = self.sigmas[self.step_index]
+    sigma_to = self.sigmas[self.step_index + 1]
+    sigma_up = (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
+    sigma_down = (sigma_to**2 - sigma_up**2) ** 0.5
+
+    # 2. Convert to an ODE derivative
+    derivative = (sample - pred_original_sample) / sigma
+    dt = sigma_down - sigma
+
+    prev_sample = sample + derivative * dt
+
+    device = model_output.device
+    noise = randn_tensor(model_output.shape, dtype=model_output.dtype, device=device, generator=generator)
+
+    prev_sample = prev_sample + noise * sigma_up
+
+    # Cast sample back to model compatible dtype
+    prev_sample = prev_sample.to(model_output.dtype)
+
+    # upon completion increase step index by one
+    self._step_index += 1
+
+    if not return_dict:
+        return (prev_sample,)
+
+    return EulerAncestralDiscreteSchedulerOutput(
+        prev_sample=prev_sample, pred_original_sample=pred_original_sample
+    )
\ No newline at end of file
diff --git a/src/animatediff_inpaint_pipe.py b/src/animatediff_inpaint_pipe.py
new file mode 100644
index 0000000..a724785
--- /dev/null
+++ b/src/animatediff_inpaint_pipe.py
@@ -0,0 +1,1077 @@
+# 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, Union
+
+import torch
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+
+from diffusers.image_processor import PipelineImageInput
+from diffusers.loaders import IPAdapterMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.models.unets.unet_motion_model import MotionAdapter
+from diffusers.schedulers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
+from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.video_processor import VideoProcessor
+from diffusers.pipelines.free_init_utils import FreeInitMixin
+from diffusers.pipelines.free_noise_utils import AnimateDiffFreeNoiseMixin
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
+from diffusers.pipelines.animatediff.pipeline_output import AnimateDiffPipelineOutput
+
+from .ic_light import Relighter
+from einops import rearrange
+from diffusers.utils import export_to_gif
+from .animatediff_eul import eul_step
+import torch.nn.functional as F
+import numpy as np
+from .tools import numpy2pytorch
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import imageio
+        >>> import requests
+        >>> import torch
+        >>> from diffusers import AnimateDiffVideoToVideoPipeline, DDIMScheduler, MotionAdapter
+        >>> from diffusers.utils import export_to_gif
+        >>> from io import BytesIO
+        >>> from PIL import Image
+
+        >>> adapter = MotionAdapter.from_pretrained(
+        ...     "guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16
+        ... )
+        >>> pipe = AnimateDiffVideoToVideoPipeline.from_pretrained(
+        ...     "SG161222/Realistic_Vision_V5.1_noVAE", motion_adapter=adapter
+        ... ).to("cuda")
+        >>> pipe.scheduler = DDIMScheduler(
+        ...     beta_schedule="linear", steps_offset=1, clip_sample=False, timespace_spacing="linspace"
+        ... )
+
+
+        >>> def load_video(file_path: str):
+        ...     images = []
+
+        ...     if file_path.startswith(("http://", "https://")):
+        ...         # If the file_path is a URL
+        ...         response = requests.get(file_path)
+        ...         response.raise_for_status()
+        ...         content = BytesIO(response.content)
+        ...         vid = imageio.get_reader(content)
+        ...     else:
+        ...         # Assuming it's a local file path
+        ...         vid = imageio.get_reader(file_path)
+
+        ...     for frame in vid:
+        ...         pil_image = Image.fromarray(frame)
+        ...         images.append(pil_image)
+
+        ...     return images
+
+
+        >>> video = load_video(
+        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-1.gif"
+        ... )
+        >>> output = pipe(
+        ...     video=video, prompt="panda playing a guitar, on a boat, in the ocean, high quality", strength=0.5
+        ... )
+        >>> frames = output.frames[0]
+        >>> export_to_gif(frames, "animation.gif")
+        ```
+"""
+
+
+# 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
+
+
+class AnimateDiffVideoToVideoPipeline(
+    DiffusionPipeline,
+    StableDiffusionMixin,
+    TextualInversionLoaderMixin,
+    IPAdapterMixin,
+    StableDiffusionLoraLoaderMixin,
+    FreeInitMixin,
+    AnimateDiffFreeNoiseMixin,
+):
+    r"""
+    Pipeline for video-to-video generation.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, 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.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+        - [`~loaders.StableDiffusionLoraLoaderMixin.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 ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
+        tokenizer (`CLIPTokenizer`):
+            A [`~transformers.CLIPTokenizer`] to tokenize text.
+        unet ([`UNet2DConditionModel`]):
+            A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.
+        motion_adapter ([`MotionAdapter`]):
+            A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video 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`].
+    """
+
+    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
+    _optional_components = ["feature_extractor", "image_encoder", "motion_adapter"]
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        motion_adapter: MotionAdapter,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+        ],
+        feature_extractor: CLIPImageProcessor = None,
+        image_encoder: CLIPVisionModelWithProjection = None,
+    ):
+        super().__init__()
+        if isinstance(unet, UNet2DConditionModel):
+            unet = UNetMotionModel.from_unet2d(unet, motion_adapter)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            motion_adapter=motion_adapter,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+            image_encoder=image_encoder,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
+    def encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_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
+            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`).
+            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.
+            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.
+        """
+        # 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, StableDiffusionLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            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 prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            # textual inversion: process multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
+
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = text_inputs.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            if clip_skip is None:
+                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
+                prompt_embeds = prompt_embeds[0]
+            else:
+                prompt_embeds = self.text_encoder(
+                    text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
+                )
+                # Access the `hidden_states` first, that contains a tuple of
+                # all the hidden states from the encoder layers. Then index into
+                # the tuple to access the hidden states from the desired layer.
+                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
+                # We also need to apply the final LayerNorm here to not mess with the
+                # representations. The `last_hidden_states` that we typically use for
+                # obtaining the final prompt representations passes through the LayerNorm
+                # layer.
+                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
+
+        if self.text_encoder is not None:
+            prompt_embeds_dtype = self.text_encoder.dtype
+        elif self.unet is not None:
+            prompt_embeds_dtype = self.unet.dtype
+        else:
+            prompt_embeds_dtype = prompt_embeds.dtype
+
+        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_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)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            # textual inversion: process multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_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)
+
+        if self.text_encoder is not None:
+            if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        return prompt_embeds, negative_prompt_embeds
+
+    # 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
+
+    # 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
+    ):
+        image_embeds = []
+        if do_classifier_free_guidance:
+            negative_image_embeds = []
+        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."
+                )
+
+            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
+                )
+
+                image_embeds.append(single_image_embeds[None, :])
+                if do_classifier_free_guidance:
+                    negative_image_embeds.append(single_negative_image_embeds[None, :])
+        else:
+            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)
+                    negative_image_embeds.append(single_negative_image_embeds)
+                image_embeds.append(single_image_embeds)
+
+        ip_adapter_image_embeds = []
+        for i, single_image_embeds in enumerate(image_embeds):
+            single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
+            if do_classifier_free_guidance:
+                single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * num_images_per_prompt, dim=0)
+                single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)
+
+            single_image_embeds = single_image_embeds.to(device=device)
+            ip_adapter_image_embeds.append(single_image_embeds)
+
+        return ip_adapter_image_embeds
+
+    def encode_video(self, video, generator, decode_chunk_size: int = 16) -> torch.Tensor:
+        latents = []
+        for i in range(0, len(video), decode_chunk_size):
+            batch_video = video[i : i + decode_chunk_size]
+            batch_video = retrieve_latents(self.vae.encode(batch_video), generator=generator)
+            latents.append(batch_video)
+        return torch.cat(latents)
+
+    # Copied from diffusers.pipelines.animatediff.pipeline_animatediff.AnimateDiffPipeline.decode_latents
+    def decode_latents(self, latents, decode_chunk_size: int = 16):
+        latents = 1 / self.vae.config.scaling_factor * latents
+
+        batch_size, channels, num_frames, height, width = latents.shape
+        latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
+
+        video = []
+        for i in range(0, latents.shape[0], decode_chunk_size):
+            batch_latents = latents[i : i + decode_chunk_size]
+            batch_latents = self.vae.decode(batch_latents).sample
+            video.append(batch_latents)
+
+        video = torch.cat(video)
+        video = video[None, :].reshape((batch_size, num_frames, -1) + video.shape[2:]).permute(0, 2, 1, 3, 4)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        # video = video.float()
+        return video
+
+    # 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,
+        strength,
+        height,
+        width,
+        video=None,
+        latents=None,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        ip_adapter_image=None,
+        ip_adapter_image_embeds=None,
+        callback_on_step_end_tensor_inputs=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_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 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)}")
+
+        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."
+            )
+
+        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 video is not None and latents is not None:
+            raise ValueError("Only one of `video` or `latents` should be provided")
+
+        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 get_timesteps(self, num_inference_steps, timesteps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = timesteps[t_start * self.scheduler.order :]
+
+        return timesteps, num_inference_steps - t_start
+
+    def prepare_latents(
+        self,
+        video,
+        height,
+        width,
+        num_channels_latents,
+        batch_size,
+        timestep,
+        dtype,
+        device,
+        generator,
+        latents=None,
+        decode_chunk_size: int = 16,
+    ):
+        if latents is None:
+            num_frames = video.shape[1]
+        else:
+            num_frames = latents.shape[2]
+
+        shape = (
+            batch_size,
+            num_channels_latents,
+            num_frames,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            if self.vae.config.force_upcast:
+                video = video.float()
+                self.vae.to(dtype=torch.float32)
+
+            if isinstance(generator, list):
+                if 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."
+                    )
+
+                init_latents = [
+                    self.encode_video(video[i], generator[i], decode_chunk_size).unsqueeze(0)
+                    for i in range(batch_size)
+                ]
+            else:
+                ## torch.Size([1, 16, 3, 512, 512])
+                init_latents = [self.encode_video(vid, generator, decode_chunk_size).unsqueeze(0) for vid in video]
+
+            init_latents = torch.cat(init_latents, dim=0)
+
+            # restore vae to original dtype
+            if self.vae.config.force_upcast:
+                self.vae.to(dtype)
+
+            init_latents = init_latents.to(dtype)
+            init_latents = self.vae.config.scaling_factor * init_latents
+
+            if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+                # expand init_latents for batch_size
+                error_message = (
+                    f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
+                    " images (`image`). Please make sure to update your script to pass as many initial images as text prompts"
+                )
+                raise ValueError(error_message)
+            elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
+                raise ValueError(
+                    f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+                )
+            else:
+                init_latents = torch.cat([init_latents], dim=0)
+
+            noise = randn_tensor(init_latents.shape, generator=generator, device=device, dtype=dtype)
+            latents = self.scheduler.add_noise(init_latents, noise, timestep).permute(0, 2, 1, 3, 4)
+        else:
+            if shape != latents.shape:
+                # [B, C, F, H, W]
+                raise ValueError(f"`latents` expected to have {shape=}, but found {latents.shape=}")
+            latents = latents.to(device, dtype=dtype)
+
+        return latents
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @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
+
+    @property
+    def cross_attention_kwargs(self):
+        return self._cross_attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        ic_light_pipe=None,
+        relight_prompt=None,
+        bg_source=None,
+        mask=None,
+        vdm_init_latent=None,
+        video: List[List[PipelineImageInput]] = None,
+        prompt: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        timesteps: Optional[List[int]] = None,
+        sigmas: Optional[List[float]] = None,
+        guidance_scale: float = 7.5,
+        strength: float = 0.8,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_videos_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,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        decode_chunk_size: int = 16,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            video (`List[PipelineImageInput]`):
+                The input video to condition the generation on. Must be a list of images/frames of the video.
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The height in pixels of the generated video.
+            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The width in pixels of the generated video.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality videos 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.
+            strength (`float`, *optional*, defaults to 0.8):
+                Higher strength leads to more differences between original video and generated video.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](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 video
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor is generated by sampling using the supplied random `generator`. Latents should be of shape
+                `(batch_size, num_channel, num_frames, height, width)`.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `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.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated video. Choose between `torch.Tensor`, `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`AnimateDiffPipelineOutput`] instead of a plain tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            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`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                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.
+            decode_chunk_size (`int`, defaults to `16`):
+                The number of frames to decode at a time when calling `decode_latents` method.
+
+        Examples:
+
+        Returns:
+            [`pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] is
+                returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.
+        """
+
+        # 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
+
+        num_videos_per_prompt = 1
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt=prompt,
+            strength=strength,
+            height=height,
+            width=width,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            video=video,
+            latents=latents,
+            ip_adapter_image=ip_adapter_image,
+            ip_adapter_image_embeds=ip_adapter_image_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+
+        # 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
+
+        # 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 = self.encode_prompt(
+            prompt,  ## animatediff outpaint prompt
+            device,
+            num_videos_per_prompt,
+            self.do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            lora_scale=text_encoder_lora_scale,
+            clip_skip=self.clip_skip,
+        )
+
+        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_videos_per_prompt,
+                self.do_classifier_free_guidance,
+            )
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas
+        )
+        original_timesteps = timesteps
+        org_latent_timestep = original_timesteps[:1].repeat(batch_size * num_videos_per_prompt)
+
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
+
+        # 5. Prepare latent variables
+        if latents is None: 
+            video = self.video_processor.preprocess_video(video, height=height, width=width)
+            # Move the number of frames before the number of channels.
+            video = video.permute(0, 2, 1, 3, 4)
+            video = video.to(device=device, dtype=prompt_embeds.dtype) 
+
+        video_latent = self.vae.encode(video[0]).latent_dist.mode() * self.vae.config.scaling_factor
+        num_frames = video_latent.shape[0]
+        prompt_embeds_wo_negative = prompt_embeds.repeat_interleave(repeats=num_frames, dim=0)
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+        prompt_embeds = prompt_embeds.repeat_interleave(repeats=num_frames, dim=0)
+
+        latents = randn_tensor(video_latent.permute(1, 0, 2, 3).unsqueeze(0).shape, generator=generator, device=device, dtype=prompt_embeds.dtype)
+        fg_noise = randn_tensor(latents.shape, generator=generator, device=device, dtype=prompt_embeds.dtype)
+
+        ## foreground mask
+        mask_tensor = torch.from_numpy(np.stack(mask, axis=0)).float() / 255
+        mask_tensor = mask_tensor.movedim(-1, 1).to(latents.device, dtype=latents.dtype)
+        mask_latent = F.interpolate(mask_tensor, size=(latents.shape[-2], latents.shape[-1]), mode='bilinear') # torch.Size([16, 3, 64, 64])
+        mask_latent[mask_latent!=1.0] = 0
+        mask_latent = mask_latent[:,:1]
+        mask_latent = mask_latent.repeat(1, 4, 1, 1).to(latents.dtype).permute(1, 0, 2, 3).unsqueeze(0) ## torch.Size([1, 4, 16, 64, 64])
+        mask_latent = mask_latent.to(device)
+
+        ## Init Relighter
+        relighter = Relighter(
+            pipeline=ic_light_pipe,
+            relight_prompt=relight_prompt,
+            bg_source=bg_source, 
+            generator=generator, 
+            num_frames=num_frames,
+            )
+
+        bg_latents = self.vae.encode(vdm_init_latent).latent_dist.mode() * self.vae.config.scaling_factor
+        bg_latents = self.scheduler.add_noise(bg_latents.permute(1, 0, 2, 3).unsqueeze(0), fg_noise, org_latent_timestep)
+        latents = bg_latents
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Add image embeds for IP-Adapter
+        added_cond_kwargs = (
+            {"image_embeds": image_embeds}
+            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
+            else None
+        )
+
+        self._num_timesteps = len(original_timesteps)
+        num_warmup_steps = len(original_timesteps) - num_inference_steps * self.scheduler.order
+
+        # 8. Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(original_timesteps):
+
+                if t > timesteps[0]: ## outpaint
+
+                    fg_latents = self.scheduler.add_noise(video_latent.permute(1, 0, 2, 3).unsqueeze(0), fg_noise, t[None, ...])
+                    latents = latents * (1 - mask_latent) + mask_latent * fg_latents
+                    latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                    # predict the noise residual
+                    noise_pred = self.unet( ## torch.Size([1, 4, 16, 64, 64])
+                        latent_model_input,
+                        t,
+                        encoder_hidden_states=prompt_embeds,
+                        cross_attention_kwargs=self.cross_attention_kwargs,
+                        added_cond_kwargs=added_cond_kwargs,
+                    ).sample
+                    if self.do_classifier_free_guidance:
+                        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+                    output = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)
+                else:
+                    latent_model_input = latents
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                    # predict the noise residual
+                    noise_pred = self.unet( ## torch.Size([1, 4, 16, 64, 64])
+                        latent_model_input,
+                        t,
+                        encoder_hidden_states=prompt_embeds_wo_negative,
+                        cross_attention_kwargs=self.cross_attention_kwargs,
+                        added_cond_kwargs=added_cond_kwargs,
+                    ).sample
+
+                    ## progressive light fusion
+                    lbd = t/ timesteps[0]
+                    if lbd>0.15:
+                        ## lbd
+                        print(f"relight lbd = {lbd}")
+                        
+                        ## get pred_x
+                        sigma = self.scheduler.sigmas[self.scheduler.step_index]
+                        pred_x0_latent = latents - sigma * noise_pred ## torch.Size([1, 4, 16, 64, 64])
+                        
+                        ## consistent target
+                        consist_target = self.decode_latents(pred_x0_latent)  ## torch.Size([1, 3, 16, 512, 512])
+                        consist_target = rearrange(consist_target, "1 c f h w -> f c h w")
+                        
+                        ## add diff
+                        if t == timesteps[0]:
+                            org_target = video[0].to(device=consist_target.device, dtype=consist_target.dtype)
+                            detail_diff = org_target - consist_target
+                        consist_target = consist_target + lbd * (mask_tensor * detail_diff)
+                        
+                        ## relight target
+                        relight_target = relighter(consist_target) ## torch.Size([16, 3, 512, 512])
+                        fusion_target = (1 - lbd) * consist_target + lbd * relight_target  ## torch.Size([16, 3, 512, 512])
+
+                        ## fusion_target -> pixel level
+                        fusion_latent = self.vae.encode(fusion_target).latent_dist.mode() * self.vae.config.scaling_factor
+                        fusion_latent = fusion_latent.to(consist_target.dtype)
+                        fusion_latent = rearrange(fusion_latent, "f c h w -> 1 c f h w")
+                        
+                        output = eul_step(self.scheduler, noise_pred, t, latents, fusion_latent, self, **extra_step_kwargs)
+                    else:
+                        output = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)
+
+                latents = output[0]
+
+                # 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()
+
+        # 9. Post-processing
+        if output_type == "latent":
+            video = latents
+        else:
+            video_tensor = self.decode_latents(latents, decode_chunk_size)
+            video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)
+
+        # 10. Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (video,)
+
+        return AnimateDiffPipelineOutput(frames=video)
\ No newline at end of file
diff --git a/src/animatediff_pipe.py b/src/animatediff_pipe.py
new file mode 100644
index 0000000..b1e3c1a
--- /dev/null
+++ b/src/animatediff_pipe.py
@@ -0,0 +1,1063 @@
+# 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, Union
+
+import torch
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+
+from diffusers.image_processor import PipelineImageInput
+from diffusers.loaders import IPAdapterMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.models.unets.unet_motion_model import MotionAdapter
+from diffusers.schedulers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
+from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.video_processor import VideoProcessor
+from diffusers.pipelines.free_init_utils import FreeInitMixin
+from diffusers.pipelines.free_noise_utils import AnimateDiffFreeNoiseMixin
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin
+from diffusers.pipelines.animatediff.pipeline_output import AnimateDiffPipelineOutput
+
+from .ic_light import Relighter
+from einops import rearrange
+from diffusers.utils import export_to_gif
+from .animatediff_eul import eul_step
+import math
+from .tools import vis_video
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import imageio
+        >>> import requests
+        >>> import torch
+        >>> from diffusers import AnimateDiffVideoToVideoPipeline, DDIMScheduler, MotionAdapter
+        >>> from diffusers.utils import export_to_gif
+        >>> from io import BytesIO
+        >>> from PIL import Image
+
+        >>> adapter = MotionAdapter.from_pretrained(
+        ...     "guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16
+        ... )
+        >>> pipe = AnimateDiffVideoToVideoPipeline.from_pretrained(
+        ...     "SG161222/Realistic_Vision_V5.1_noVAE", motion_adapter=adapter
+        ... ).to("cuda")
+        >>> pipe.scheduler = DDIMScheduler(
+        ...     beta_schedule="linear", steps_offset=1, clip_sample=False, timespace_spacing="linspace"
+        ... )
+
+
+        >>> def load_video(file_path: str):
+        ...     images = []
+
+        ...     if file_path.startswith(("http://", "https://")):
+        ...         # If the file_path is a URL
+        ...         response = requests.get(file_path)
+        ...         response.raise_for_status()
+        ...         content = BytesIO(response.content)
+        ...         vid = imageio.get_reader(content)
+        ...     else:
+        ...         # Assuming it's a local file path
+        ...         vid = imageio.get_reader(file_path)
+
+        ...     for frame in vid:
+        ...         pil_image = Image.fromarray(frame)
+        ...         images.append(pil_image)
+
+        ...     return images
+
+
+        >>> video = load_video(
+        ...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-1.gif"
+        ... )
+        >>> output = pipe(
+        ...     video=video, prompt="panda playing a guitar, on a boat, in the ocean, high quality", strength=0.5
+        ... )
+        >>> frames = output.frames[0]
+        >>> export_to_gif(frames, "animation.gif")
+        ```
+"""
+
+
+# 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
+
+
+class AnimateDiffVideoToVideoPipeline(
+    DiffusionPipeline,
+    StableDiffusionMixin,
+    TextualInversionLoaderMixin,
+    IPAdapterMixin,
+    StableDiffusionLoraLoaderMixin,
+    FreeInitMixin,
+    AnimateDiffFreeNoiseMixin,
+):
+    r"""
+    Pipeline for video-to-video generation.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, 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.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+        - [`~loaders.StableDiffusionLoraLoaderMixin.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 ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
+        tokenizer (`CLIPTokenizer`):
+            A [`~transformers.CLIPTokenizer`] to tokenize text.
+        unet ([`UNet2DConditionModel`]):
+            A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.
+        motion_adapter ([`MotionAdapter`]):
+            A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video 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`].
+    """
+
+    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
+    _optional_components = ["feature_extractor", "image_encoder", "motion_adapter"]
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        motion_adapter: MotionAdapter,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+        ],
+        feature_extractor: CLIPImageProcessor = None,
+        image_encoder: CLIPVisionModelWithProjection = None,
+    ):
+        super().__init__()
+        if isinstance(unet, UNet2DConditionModel):
+            unet = UNetMotionModel.from_unet2d(unet, motion_adapter)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            motion_adapter=motion_adapter,
+            scheduler=scheduler,
+            feature_extractor=feature_extractor,
+            image_encoder=image_encoder,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
+    def encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_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
+            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`).
+            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.
+            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.
+        """
+        # 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, StableDiffusionLoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            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 prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            # textual inversion: process multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
+
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = text_inputs.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            if clip_skip is None:
+                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
+                prompt_embeds = prompt_embeds[0]
+            else:
+                prompt_embeds = self.text_encoder(
+                    text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
+                )
+                # Access the `hidden_states` first, that contains a tuple of
+                # all the hidden states from the encoder layers. Then index into
+                # the tuple to access the hidden states from the desired layer.
+                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
+                # We also need to apply the final LayerNorm here to not mess with the
+                # representations. The `last_hidden_states` that we typically use for
+                # obtaining the final prompt representations passes through the LayerNorm
+                # layer.
+                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
+
+        if self.text_encoder is not None:
+            prompt_embeds_dtype = self.text_encoder.dtype
+        elif self.unet is not None:
+            prompt_embeds_dtype = self.unet.dtype
+        else:
+            prompt_embeds_dtype = prompt_embeds.dtype
+
+        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_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)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            # textual inversion: process multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_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)
+
+        if self.text_encoder is not None:
+            if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        return prompt_embeds, negative_prompt_embeds
+
+    # 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
+
+    # 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
+    ):
+        image_embeds = []
+        if do_classifier_free_guidance:
+            negative_image_embeds = []
+        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."
+                )
+
+            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
+                )
+
+                image_embeds.append(single_image_embeds[None, :])
+                if do_classifier_free_guidance:
+                    negative_image_embeds.append(single_negative_image_embeds[None, :])
+        else:
+            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)
+                    negative_image_embeds.append(single_negative_image_embeds)
+                image_embeds.append(single_image_embeds)
+
+        ip_adapter_image_embeds = []
+        for i, single_image_embeds in enumerate(image_embeds):
+            single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
+            if do_classifier_free_guidance:
+                single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * num_images_per_prompt, dim=0)
+                single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)
+
+            single_image_embeds = single_image_embeds.to(device=device)
+            ip_adapter_image_embeds.append(single_image_embeds)
+
+        return ip_adapter_image_embeds
+
+    def encode_video(self, video, generator, decode_chunk_size: int = 16) -> torch.Tensor:
+        latents = []
+        for i in range(0, len(video), decode_chunk_size):
+            batch_video = video[i : i + decode_chunk_size]
+            batch_video = retrieve_latents(self.vae.encode(batch_video), generator=generator)
+            latents.append(batch_video)
+        return torch.cat(latents)
+
+    # Copied from diffusers.pipelines.animatediff.pipeline_animatediff.AnimateDiffPipeline.decode_latents
+    def decode_latents(self, latents, decode_chunk_size: int = 16):
+        latents = 1 / self.vae.config.scaling_factor * latents
+
+        batch_size, channels, num_frames, height, width = latents.shape
+        latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
+
+        video = []
+        for i in range(0, latents.shape[0], decode_chunk_size):
+            batch_latents = latents[i : i + decode_chunk_size]
+            batch_latents = self.vae.decode(batch_latents).sample
+            video.append(batch_latents)
+
+        video = torch.cat(video)
+        video = video[None, :].reshape((batch_size, num_frames, -1) + video.shape[2:]).permute(0, 2, 1, 3, 4)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        # video = video.float()
+        return video
+
+    # 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,
+        strength,
+        height,
+        width,
+        video=None,
+        latents=None,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        ip_adapter_image=None,
+        ip_adapter_image_embeds=None,
+        callback_on_step_end_tensor_inputs=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_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 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)}")
+
+        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."
+            )
+
+        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 video is not None and latents is not None:
+            raise ValueError("Only one of `video` or `latents` should be provided")
+
+        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 get_timesteps(self, num_inference_steps, timesteps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = timesteps[t_start * self.scheduler.order :]
+
+        return timesteps, num_inference_steps - t_start
+
+    def prepare_latents(
+        self,
+        video,
+        height,
+        width,
+        num_channels_latents,
+        batch_size,
+        timestep,
+        dtype,
+        device,
+        generator,
+        latents=None,
+        decode_chunk_size: int = 16,
+    ):
+        if latents is None:
+            num_frames = video.shape[1]
+        else:
+            num_frames = latents.shape[2]
+
+        shape = (
+            batch_size,
+            num_channels_latents,
+            num_frames,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            # make sure the VAE is in float32 mode, as it overflows in float16
+            if self.vae.config.force_upcast:
+                video = video.float()
+                self.vae.to(dtype=torch.float32)
+
+            if isinstance(generator, list):
+                if 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."
+                    )
+
+                init_latents = [
+                    self.encode_video(video[i], generator[i], decode_chunk_size).unsqueeze(0)
+                    for i in range(batch_size)
+                ]
+            else:
+                ## torch.Size([1, 16, 3, 512, 512])
+                init_latents = [self.encode_video(vid, generator, decode_chunk_size).unsqueeze(0) for vid in video]
+
+            init_latents = torch.cat(init_latents, dim=0)
+
+            # restore vae to original dtype
+            if self.vae.config.force_upcast:
+                self.vae.to(dtype)
+
+            init_latents = init_latents.to(dtype)
+            init_latents = self.vae.config.scaling_factor * init_latents
+
+            if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+                # expand init_latents for batch_size
+                error_message = (
+                    f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
+                    " images (`image`). Please make sure to update your script to pass as many initial images as text prompts"
+                )
+                raise ValueError(error_message)
+            elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
+                raise ValueError(
+                    f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+                )
+            else:
+                init_latents = torch.cat([init_latents], dim=0)
+
+            noise = randn_tensor(init_latents.shape, generator=generator, device=device, dtype=dtype)
+            latents = self.scheduler.add_noise(init_latents, noise, timestep).permute(0, 2, 1, 3, 4)
+        else:
+            if shape != latents.shape:
+                # [B, C, F, H, W]
+                raise ValueError(f"`latents` expected to have {shape=}, but found {latents.shape=}")
+            latents = latents.to(device, dtype=dtype)
+
+        return latents
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @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
+
+    @property
+    def cross_attention_kwargs(self):
+        return self._cross_attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        ic_light_pipe=None,
+        relight_prompt=None,
+        bg_source=None,
+        video: List[List[PipelineImageInput]] = None,
+        prompt: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        timesteps: Optional[List[int]] = None,
+        sigmas: Optional[List[float]] = None,
+        guidance_scale: float = 7.5,
+        strength: float = 0.8,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_videos_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,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        decode_chunk_size: int = 16,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            video (`List[PipelineImageInput]`):
+                The input video to condition the generation on. Must be a list of images/frames of the video.
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The height in pixels of the generated video.
+            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The width in pixels of the generated video.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality videos 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.
+            strength (`float`, *optional*, defaults to 0.8):
+                Higher strength leads to more differences between original video and generated video.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](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 video
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor is generated by sampling using the supplied random `generator`. Latents should be of shape
+                `(batch_size, num_channel, num_frames, height, width)`.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `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.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated video. Choose between `torch.Tensor`, `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`AnimateDiffPipelineOutput`] instead of a plain tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            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`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                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.
+            decode_chunk_size (`int`, defaults to `16`):
+                The number of frames to decode at a time when calling `decode_latents` method.
+
+        Examples:
+
+        Returns:
+            [`pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] is
+                returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.
+        """
+
+        # 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
+
+        num_videos_per_prompt = 1
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt=prompt,
+            strength=strength,
+            height=height,
+            width=width,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            video=video,
+            latents=latents,
+            ip_adapter_image=ip_adapter_image,
+            ip_adapter_image_embeds=ip_adapter_image_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+
+        # 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
+
+        # 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 = self.encode_prompt(
+            prompt,
+            device,
+            num_videos_per_prompt,
+            self.do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            lora_scale=text_encoder_lora_scale,
+            clip_skip=self.clip_skip,
+        )
+
+        # For classifier free guidance, we need to do two forward passes.
+        # Here we concatenate the unconditional and text embeddings into a single batch
+        # to avoid doing two forward passes
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+        
+        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_videos_per_prompt,
+                self.do_classifier_free_guidance,
+            )
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas
+        )
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
+
+        # 5. Prepare latent variables
+        if latents is None:
+            video = self.video_processor.preprocess_video(video, height=height, width=width)
+            # Move the number of frames before the number of channels.
+            video = video.permute(0, 2, 1, 3, 4)
+            video = video.to(device=device, dtype=prompt_embeds.dtype) ## torch.Size([1, 16, 3, 512, 512])
+            org_target = rearrange(video, "1 f c h w -> 1 c f h w")
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents( ## torch.Size([1, 4, 16, 64, 64])
+            video=video,
+            height=height,
+            width=width,
+            num_channels_latents=num_channels_latents,
+            batch_size=batch_size * num_videos_per_prompt,
+            timestep=latent_timestep,
+            dtype=prompt_embeds.dtype,
+            device=device,
+            generator=generator,
+            latents=latents,
+            decode_chunk_size=decode_chunk_size,
+        )
+        
+        num_frames = video.shape[1]
+        prompt_embeds = prompt_embeds.repeat(num_frames, 1, 1)
+        
+        ## Init Relighter
+        relighter = Relighter(
+            pipeline=ic_light_pipe,
+            relight_prompt=relight_prompt,
+            bg_source=bg_source, 
+            generator=generator, 
+            num_frames=num_frames,
+            )
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7. Add image embeds for IP-Adapter
+        added_cond_kwargs = (
+            {"image_embeds": image_embeds}
+            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
+            else None
+        )
+
+        num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1
+        for free_init_iter in range(num_free_init_iters):
+            if self.free_init_enabled:
+                latents, timesteps = self._apply_free_init(
+                    latents, free_init_iter, num_inference_steps, device, latents.dtype, generator
+                )
+                num_inference_steps = len(timesteps)
+                # make sure to readjust timesteps based on strength
+                timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, timesteps, strength, device)
+
+            self._num_timesteps = len(timesteps)
+            num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+
+            # 8. Denoising loop
+            with self.progress_bar(total=self._num_timesteps) as progress_bar:
+                for i, t in enumerate(timesteps):
+                    # 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
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                    # predict the noise residual
+                    noise_pred = self.unet( 
+                        latent_model_input,
+                        t,
+                        encoder_hidden_states=prompt_embeds,
+                        cross_attention_kwargs=self.cross_attention_kwargs,
+                        added_cond_kwargs=added_cond_kwargs,
+                    ).sample
+
+                    # perform guidance
+                    if self.do_classifier_free_guidance:
+                        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                    lbd = 1 - i/(num_inference_steps-1)
+                    
+                    if lbd>0.15:
+                        ## get pred_x
+                        sigma = self.scheduler.sigmas[self.scheduler.step_index]
+                        pred_x0_latent = latents - sigma * noise_pred 
+                        
+                        ## consistent target
+                        consist_target = self.decode_latents(pred_x0_latent) 
+                        
+                        if i == 0:
+                            detail_diff = org_target - consist_target
+                            
+                        consist_target = consist_target + lbd * detail_diff
+                        consist_target = rearrange(consist_target, "1 c f h w -> f c h w") 
+                        
+                        ## relight target
+                        relight_target = relighter(consist_target) 
+                        
+                        print(f"relight lbd = {lbd}")
+                        fusion_target = (1 - lbd) * consist_target + lbd * relight_target  
+
+                        ## fusion_target -> pixel level
+                        fusion_latent = self.vae.encode(fusion_target).latent_dist.mode() * self.vae.config.scaling_factor
+                        fusion_latent = fusion_latent.to(consist_target.dtype)
+                        fusion_latent = rearrange(fusion_latent, "f c h w -> 1 c f h w")
+                        
+                        output = eul_step(self.scheduler, noise_pred, t, latents, fusion_latent, self, **extra_step_kwargs) 
+                        
+                    else:
+                        output = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)
+                    
+                    latents = output[0]    
+                    if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                        progress_bar.update()
+
+        # 9. Post-processing
+        if output_type == "latent":
+            video = latents
+        else:
+            video_tensor = self.decode_latents(latents, decode_chunk_size)
+            video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)
+
+        # 10. Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (video,)
+
+        return AnimateDiffPipelineOutput(frames=video)
\ No newline at end of file
diff --git a/src/ic_light.py b/src/ic_light.py
new file mode 100644
index 0000000..b89ae9c
--- /dev/null
+++ b/src/ic_light.py
@@ -0,0 +1,152 @@
+import torch
+import numpy as np
+from enum import Enum
+import math
+
+import torch.nn.functional as F
+from .tools import resize_and_center_crop, numpy2pytorch, pad, decode_latents, encode_video
+
+class BGSource(Enum):
+    NONE = "None"
+    LEFT = "Left Light"
+    RIGHT = "Right Light"
+    TOP = "Top Light"
+    BOTTOM = "Bottom Light"
+
+class Relighter:
+    def __init__(self, 
+                 pipeline, 
+                 relight_prompt="",
+                 num_frames=16,
+                 image_width=512,
+                 image_height=512, 
+                 num_samples=1, 
+                 steps=15, 
+                 cfg=2, 
+                 lowres_denoise=0.9, 
+                 bg_source=BGSource.RIGHT, 
+                 generator=None,
+                 ):
+        
+        self.pipeline = pipeline
+        self.image_width = image_width
+        self.image_height = image_height
+        self.num_samples = num_samples
+        self.steps = steps
+        self.cfg = cfg
+        self.lowres_denoise = lowres_denoise
+        self.bg_source = bg_source
+        self.generator = generator
+        self.device = pipeline.device
+        self.num_frames = num_frames
+        self.vae = self.pipeline.vae
+        
+        self.a_prompt = "best quality"
+        self.n_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
+        positive_prompt = relight_prompt + ', ' + self.a_prompt
+        negative_prompt = self.n_prompt
+        tokenizer = self.pipeline.tokenizer
+        device = self.pipeline.device
+        vae = self.vae
+        
+        conds, unconds = self.encode_prompt_pair(tokenizer, device, positive_prompt, negative_prompt)
+        input_bg = self.create_background()
+        bg = resize_and_center_crop(input_bg, self.image_width, self.image_height)
+        bg_latent = numpy2pytorch([bg], device, vae.dtype)
+        bg_latent = vae.encode(bg_latent).latent_dist.mode() * vae.config.scaling_factor
+        
+        self.bg_latent = bg_latent.repeat(self.num_frames, 1, 1, 1) ## 固定光源
+        self.conds = conds.repeat(self.num_frames, 1, 1)
+        self.unconds = unconds.repeat(self.num_frames, 1, 1)
+        
+    def encode_prompt_inner(self, tokenizer, txt):
+        max_length = tokenizer.model_max_length
+        chunk_length = tokenizer.model_max_length - 2
+        id_start = tokenizer.bos_token_id
+        id_end = tokenizer.eos_token_id
+        id_pad = id_end
+
+        tokens = tokenizer(txt, truncation=False, add_special_tokens=False)["input_ids"]
+        chunks = [[id_start] + tokens[i: i + chunk_length] + [id_end] for i in range(0, len(tokens), chunk_length)]
+        chunks = [pad(ck, id_pad, max_length) for ck in chunks]
+
+        token_ids = torch.tensor(chunks).to(device=self.device, dtype=torch.int64)
+        conds = self.pipeline.text_encoder(token_ids).last_hidden_state
+        return conds
+
+    def encode_prompt_pair(self, tokenizer, device, positive_prompt, negative_prompt):
+        c = self.encode_prompt_inner(tokenizer, positive_prompt)
+        uc = self.encode_prompt_inner(tokenizer, negative_prompt)
+
+        c_len = float(len(c))
+        uc_len = float(len(uc))
+        max_count = max(c_len, uc_len)
+        c_repeat = int(math.ceil(max_count / c_len))
+        uc_repeat = int(math.ceil(max_count / uc_len))
+        max_chunk = max(len(c), len(uc))
+
+        c = torch.cat([c] * c_repeat, dim=0)[:max_chunk]
+        uc = torch.cat([uc] * uc_repeat, dim=0)[:max_chunk]
+
+        c = torch.cat([p[None, ...] for p in c], dim=1)
+        uc = torch.cat([p[None, ...] for p in uc], dim=1)
+
+        return c.to(device), uc.to(device)
+
+    def create_background(self):
+        
+        max_pix = 255
+        min_pix = 0
+        
+        print(f"max light pix:{max_pix}, min light pix:{min_pix}")
+        
+        if self.bg_source == BGSource.NONE:
+            return None
+        elif self.bg_source == BGSource.LEFT:
+            gradient = np.linspace(max_pix, min_pix, self.image_width)
+            image = np.tile(gradient, (self.image_height, 1))
+            return np.stack((image,) * 3, axis=-1).astype(np.uint8)
+        elif self.bg_source == BGSource.RIGHT:
+            gradient = np.linspace(min_pix, max_pix, self.image_width)
+            image = np.tile(gradient, (self.image_height, 1))
+            return np.stack((image,) * 3, axis=-1).astype(np.uint8)
+        elif self.bg_source == BGSource.TOP:
+            gradient = np.linspace(max_pix, min_pix, self.image_height)[:, None]
+            image = np.tile(gradient, (1, self.image_width))
+            return np.stack((image,) * 3, axis=-1).astype(np.uint8)
+        elif self.bg_source == BGSource.BOTTOM:
+            gradient = np.linspace(min_pix, max_pix, self.image_height)[:, None]
+            image = np.tile(gradient, (1, self.image_width))
+            return np.stack((image,) * 3, axis=-1).astype(np.uint8)
+        else:
+            raise ValueError('Wrong initial latent!')
+    
+    @torch.no_grad()
+    def __call__(self, input_video, init_latent=None, input_strength=None):
+        input_latent = encode_video(self.vae, input_video)* self.vae.config.scaling_factor
+        
+        if input_strength:
+            light_strength = input_strength
+        else:
+            light_strength = self.lowres_denoise
+
+        if not init_latent:
+            init_latent = self.bg_latent
+
+        latents = self.pipeline(
+            image=init_latent,
+            strength=light_strength,
+            prompt_embeds=self.conds,
+            negative_prompt_embeds=self.unconds,
+            width=self.image_width,
+            height=self.image_height,
+            num_inference_steps=int(round(self.steps / self.lowres_denoise)),
+            num_images_per_prompt=self.num_samples,
+            generator=self.generator,
+            output_type='latent',
+            guidance_scale=self.cfg,
+            cross_attention_kwargs={'concat_conds': input_latent},
+        ).images.to(self.pipeline.vae.dtype)
+
+        relight_video = decode_latents(self.vae, latents)
+        return relight_video
\ No newline at end of file
diff --git a/src/ic_light_pipe.py b/src/ic_light_pipe.py
new file mode 100644
index 0000000..4530bcf
--- /dev/null
+++ b/src/ic_light_pipe.py
@@ -0,0 +1,1122 @@
+# Copyright 2023 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, Union
+
+import numpy as np
+import PIL.Image
+import torch
+from packaging import version
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
+
+from diffusers.configuration_utils import FrozenDict
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
+from diffusers.models.attention_processor import FusedAttnProcessor2_0
+from diffusers.models.lora import adjust_lora_scale_text_encoder
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import (
+    PIL_INTERPOLATION,
+    USE_PEFT_BACKEND,
+    deprecate,
+    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
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import requests
+        >>> import torch
+        >>> from PIL import Image
+        >>> from io import BytesIO
+
+        >>> from diffusers import StableDiffusionImg2ImgPipeline
+
+        >>> device = "cuda"
+        >>> model_id_or_path = "runwayml/stable-diffusion-v1-5"
+        >>> pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
+        >>> pipe = pipe.to(device)
+
+        >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
+
+        >>> response = requests.get(url)
+        >>> init_image = Image.open(BytesIO(response.content)).convert("RGB")
+        >>> init_image = init_image.resize((768, 512))
+
+        >>> prompt = "A fantasy landscape, trending on artstation"
+
+        >>> images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images
+        >>> images[0].save("fantasy_landscape.png")
+        ```
+"""
+
+
+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")
+
+
+def preprocess(image):
+    deprecation_message = "The preprocess method is deprecated and will be removed in diffusers 1.0.0. Please use VaeImageProcessor.preprocess(...) instead"
+    deprecate("preprocess", "1.0.0", deprecation_message, standard_warn=False)
+    if isinstance(image, torch.Tensor):
+        return image
+    elif isinstance(image, PIL.Image.Image):
+        image = [image]
+
+    if isinstance(image[0], PIL.Image.Image):
+        w, h = image[0].size
+        w, h = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8
+
+        image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
+        image = np.concatenate(image, axis=0)
+        image = np.array(image).astype(np.float32) / 255.0
+        image = image.transpose(0, 3, 1, 2)
+        image = 2.0 * image - 1.0
+        image = torch.from_numpy(image)
+    elif isinstance(image[0], torch.Tensor):
+        image = torch.cat(image, dim=0)
+    return image
+
+
+# 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,
+    **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 support arbitrary spacing between timesteps. If `None`, then the default
+                timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps`
+                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:
+        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)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class StableDiffusionImg2ImgPipeline(
+    DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin, FromSingleFileMixin
+):
+    r"""
+    Pipeline for text-guided image-to-image generation using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, 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.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
+        - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
+        - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
+        - [`~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 ([`~transformers.CLIPTextModel`]):
+            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
+        tokenizer ([`~transformers.CLIPTokenizer`]):
+            A `CLIPTokenizer` to tokenize text.
+        unet ([`UNet2DConditionModel`]):
+            A `UNet2DConditionModel` 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`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offensive or harmful.
+            Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
+            about a model's potential harms.
+        feature_extractor ([`~transformers.CLIPImageProcessor`]):
+            A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
+    """
+
+    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
+    _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
+    _exclude_from_cpu_offload = ["safety_checker"]
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        image_encoder: CLIPVisionModelWithProjection = None,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if safety_checker is None and requires_safety_checker:
+            logger.warning(
+                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
+                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
+                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
+                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
+                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
+                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
+            )
+
+        if safety_checker is not None and feature_extractor is None:
+            raise ValueError(
+                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
+                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
+            )
+
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
+            deprecation_message = (
+                "The configuration file of the unet has set the default `sample_size` to smaller than"
+                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
+                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
+                " in the config might lead to incorrect results in future versions. If you have downloaded this"
+                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
+                " the `unet/config.json` file"
+            )
+            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(unet.config)
+            new_config["sample_size"] = 64
+            unet._internal_dict = FrozenDict(new_config)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+            image_encoder=image_encoder,
+        )
+        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.register_to_config(requires_safety_checker=requires_safety_checker)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        lora_scale: Optional[float] = None,
+        **kwargs,
+    ):
+        deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple."
+        deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False)
+
+        prompt_embeds_tuple = self.encode_prompt(
+            prompt=prompt,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            lora_scale=lora_scale,
+            **kwargs,
+        )
+
+        # concatenate for backwards comp
+        prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]])
+
+        return prompt_embeds
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt=None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = 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
+            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`).
+            prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *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.
+            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.
+        """
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, LoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            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 prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            # textual inversion: procecss multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
+
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+                text_input_ids, untruncated_ids
+            ):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = text_inputs.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            if clip_skip is None:
+                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
+                prompt_embeds = prompt_embeds[0]
+            else:
+                prompt_embeds = self.text_encoder(
+                    text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
+                )
+                # Access the `hidden_states` first, that contains a tuple of
+                # all the hidden states from the encoder layers. Then index into
+                # the tuple to access the hidden states from the desired layer.
+                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
+                # We also need to apply the final LayerNorm here to not mess with the
+                # representations. The `last_hidden_states` that we typically use for
+                # obtaining the final prompt representations passes through the LayerNorm
+                # layer.
+                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
+
+        if self.text_encoder is not None:
+            prompt_embeds_dtype = self.text_encoder.dtype
+        elif self.unet is not None:
+            prompt_embeds_dtype = self.unet.dtype
+        else:
+            prompt_embeds_dtype = prompt_embeds.dtype
+
+        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_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)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            # textual inversion: procecss multi-vector tokens if necessary
+            if isinstance(self, TextualInversionLoaderMixin):
+                uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
+
+            max_length = prompt_embeds.shape[1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            negative_prompt_embeds = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            negative_prompt_embeds = negative_prompt_embeds[0]
+
+        if do_classifier_free_guidance:
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = negative_prompt_embeds.shape[1]
+
+            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_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)
+
+        if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND:
+            # Retrieve the original scale by scaling back the LoRA layers
+            unscale_lora_layers(self.text_encoder, lora_scale)
+
+        return prompt_embeds, negative_prompt_embeds
+
+    # 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
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is None:
+            has_nsfw_concept = None
+        else:
+            if torch.is_tensor(image):
+                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
+            else:
+                feature_extractor_input = self.image_processor.numpy_to_pil(image)
+            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        return image, has_nsfw_concept
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
+    def decode_latents(self, latents):
+        deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
+        deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
+
+        latents = 1 / self.vae.config.scaling_factor * latents
+        image = self.vae.decode(latents, return_dict=False)[0]
+        image = (image / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
+        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
+        return image
+
+    # 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,
+        strength,
+        callback_steps,
+        negative_prompt=None,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+    ):
+        if strength < 0 or strength > 1:
+            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
+
+        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 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)}")
+
+        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."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+    def get_timesteps(self, num_inference_steps, strength, device):
+        # get the original timestep using init_timestep
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep, 0)
+        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
+
+        return timesteps, num_inference_steps - t_start
+
+    
+    ## 重写了初始化图构造方式, 让每一帧的加噪均相同
+    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
+        if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
+            raise ValueError(
+                f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
+            )
+        image = image.to(device=device, dtype=dtype)
+
+        batch_size = batch_size * num_images_per_prompt
+
+        if image.shape[1] == 4:
+            init_latents = image
+
+        else:
+            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."
+                )
+
+            elif isinstance(generator, list):
+                init_latents = [
+                    retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
+                    for i in range(batch_size)
+                ]
+                init_latents = torch.cat(init_latents, dim=0)
+            else:
+                init_latents = retrieve_latents(self.vae.encode(image), generator=generator)
+
+            init_latents = self.vae.config.scaling_factor * init_latents
+
+        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
+            # expand init_latents for batch_size
+            deprecation_message = (
+                f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
+                " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
+                " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
+                " your script to pass as many initial images as text prompts to suppress this warning."
+            )
+            deprecate("len(prompt) != len(image)", "1.0.0", deprecation_message, standard_warn=False)
+            additional_image_per_prompt = batch_size // init_latents.shape[0]
+            init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
+        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
+            raise ValueError(
+                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
+            )
+        else:
+            init_latents = torch.cat([init_latents], dim=0)
+
+        shape = init_latents.shape
+    
+        # original add noise
+        # noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        
+        ## add same noise
+        frame_shape = init_latents[:1].shape
+        noise = randn_tensor(frame_shape, device=device, dtype=dtype)
+        noise = noise.repeat(shape[0],1,1,1)
+
+        # get latents
+        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+        latents = init_latents
+
+        return latents
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu
+    def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
+        r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.
+
+        The suffixes after the scaling factors represent the stages where they are being applied.
+
+        Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values
+        that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
+
+        Args:
+            s1 (`float`):
+                Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
+                mitigate "oversmoothing effect" in the enhanced denoising process.
+            s2 (`float`):
+                Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
+                mitigate "oversmoothing effect" in the enhanced denoising process.
+            b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
+            b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
+        """
+        if not hasattr(self, "unet"):
+            raise ValueError("The pipeline must have `unet` for using FreeU.")
+        self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2)
+
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu
+    def disable_freeu(self):
+        """Disables the FreeU mechanism if enabled."""
+        self.unet.disable_freeu()
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.fuse_qkv_projections
+    def fuse_qkv_projections(self, unet: bool = True, vae: bool = True):
+        """
+        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query,
+        key, value) are fused. For cross-attention modules, key and value projection matrices are fused.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+
+        Args:
+            unet (`bool`, defaults to `True`): To apply fusion on the UNet.
+            vae (`bool`, defaults to `True`): To apply fusion on the VAE.
+        """
+        self.fusing_unet = False
+        self.fusing_vae = False
+
+        if unet:
+            self.fusing_unet = True
+            self.unet.fuse_qkv_projections()
+            self.unet.set_attn_processor(FusedAttnProcessor2_0())
+
+        if vae:
+            if not isinstance(self.vae, AutoencoderKL):
+                raise ValueError("`fuse_qkv_projections()` is only supported for the VAE of type `AutoencoderKL`.")
+
+            self.fusing_vae = True
+            self.vae.fuse_qkv_projections()
+            self.vae.set_attn_processor(FusedAttnProcessor2_0())
+
+    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.unfuse_qkv_projections
+    def unfuse_qkv_projections(self, unet: bool = True, vae: bool = True):
+        """Disable QKV projection fusion if enabled.
+
+        <Tip warning={true}>
+
+        This API is 🧪 experimental.
+
+        </Tip>
+
+        Args:
+            unet (`bool`, defaults to `True`): To apply fusion on the UNet.
+            vae (`bool`, defaults to `True`): To apply fusion on the VAE.
+
+        """
+        if unet:
+            if not self.fusing_unet:
+                logger.warning("The UNet was not initially fused for QKV projections. Doing nothing.")
+            else:
+                self.unet.unfuse_qkv_projections()
+                self.fusing_unet = False
+
+        if vae:
+            if not self.fusing_vae:
+                logger.warning("The VAE was not initially fused for QKV projections. Doing nothing.")
+            else:
+                self.vae.unfuse_qkv_projections()
+                self.fusing_vae = False
+
+    # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding
+    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+        """
+        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+        Args:
+            timesteps (`torch.Tensor`):
+                generate embedding vectors at these timesteps
+            embedding_dim (`int`, *optional*, defaults to 512):
+                dimension of the embeddings to generate
+            dtype:
+                data type of the generated embeddings
+
+        Returns:
+            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), 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 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 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,
+        image: PipelineImageInput = None,
+        strength: float = 0.8,
+        num_inference_steps: Optional[int] = 50,
+        timesteps: List[int] = None,
+        guidance_scale: Optional[float] = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: Optional[float] = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        clip_skip: int = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        **kwargs,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
+                `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
+                numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
+                or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
+                list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image
+                latents as `image`, but if passing latents directly it is not encoded again.
+            strength (`float`, *optional*, defaults to 0.8):
+                Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
+                starting point and more noise is added the higher the `strength`. The number of denoising steps depends
+                on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
+                process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
+                essentially ignores `image`.
+            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. This parameter is modulated by `strength`.
+            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.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.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 in
+                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            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`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                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.StableDiffusionPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
+                otherwise a `tuple` is returned where the first element is a list with the generated images and the
+                second element is a list of `bool`s indicating whether the corresponding generated image contains
+                "not-safe-for-work" (nsfw) content.
+        """
+
+        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`",
+            )
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            strength,
+            callback_steps,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        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
+
+        # 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 = self.encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            self.do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            lora_scale=text_encoder_lora_scale,
+            clip_skip=self.clip_skip,
+        )
+        # For classifier free guidance, we need to do two forward passes.
+        # Here we concatenate the unconditional and text embeddings into a single batch
+        # to avoid doing two forward passes
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        if ip_adapter_image is not None:
+            output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
+            image_embeds, negative_image_embeds = self.encode_image(
+                ip_adapter_image, device, num_images_per_prompt, output_hidden_state
+            )
+            if self.do_classifier_free_guidance:
+                image_embeds = torch.cat([negative_image_embeds, image_embeds])
+
+        # 4. Preprocess image
+        image = self.image_processor.preprocess(image)
+
+        # 5. set timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps)
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+
+        # 6. Prepare latent variables
+        latents = self.prepare_latents(
+            image, 
+            latent_timestep, 
+            batch_size,
+            num_images_per_prompt,
+            prompt_embeds.dtype,
+            device,
+            generator,
+        )
+
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 7.1 Add image embeds for IP-Adapter
+        added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
+
+        # 7.2 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)
+
+        # 8. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+        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
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                
+                # predict the noise residual
+                noise_pred = self.unet(
+                    latent_model_input, ## torch.Size([98, 4, 64, 64]) torch.Size([32, 4, 64, 64])
+                    t,
+                    encoder_hidden_states=prompt_embeds, ## torch.Size([98, 77, 768])
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs=self.cross_attention_kwargs,# torch.Size([49, 4, 64, 64])
+                    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)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                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)
+
+                # 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 not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
+                0
+            ]
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+        else:
+            image = latents
+            has_nsfw_concept = None
+
+        if has_nsfw_concept is None:
+            do_denormalize = [True] * image.shape[0]
+        else:
+            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/src/tools.py b/src/tools.py
new file mode 100644
index 0000000..542380f
--- /dev/null
+++ b/src/tools.py
@@ -0,0 +1,152 @@
+from PIL import Image,ImageSequence
+import numpy as np
+import torch
+try:
+    from moviepy import VideoFileClip
+except:
+    from moviepy.editor import VideoFileClip
+
+import os
+import imageio
+import random
+from diffusers.utils import  export_to_video
+
+def resize_and_center_crop(image, target_width, target_height):
+    pil_image = Image.fromarray(image)
+    original_width, original_height = pil_image.size
+    scale_factor = max(target_width / original_width, target_height / original_height)
+    resized_width = int(round(original_width * scale_factor))
+    resized_height = int(round(original_height * scale_factor))
+    resized_image = pil_image.resize((resized_width, resized_height), Image.LANCZOS)
+
+    left = (resized_width - target_width) / 2
+    top = (resized_height - target_height) / 2
+    right = (resized_width + target_width) / 2
+    bottom = (resized_height + target_height) / 2
+    cropped_image = resized_image.crop((left, top, right, bottom))
+    return np.array(cropped_image)
+
+def numpy2pytorch(imgs, device, dtype):
+    h = torch.from_numpy(np.stack(imgs, axis=0)).float() / 127.0 - 1.0
+    h = h.movedim(-1, 1)
+    return h.to(device=device, dtype=dtype)
+
+def get_fg_video(video_list, mask_list, device, dtype):
+    video_np = np.stack(video_list, axis=0)
+    mask_np = np.stack(mask_list, axis=0)
+    mask_bool = mask_np == 255
+    video_fg = np.where(mask_bool, video_np, 127)
+
+    h = torch.from_numpy(video_fg).float() / 127.0 - 1.0
+    h = h.movedim(-1, 1)
+    return h.to(device=device, dtype=dtype)
+
+
+def pad(x, p, i):
+    return x[:i] if len(x) >= i else x + [p] * (i - len(x))
+
+def gif_to_mp4(gif_path, mp4_path):
+    clip = VideoFileClip(gif_path)
+    clip.write_videofile(mp4_path)
+
+def generate_light_sequence(light_tensor, num_frames=16, direction="r"):
+
+    if direction in "l":
+        target_tensor = torch.rot90(light_tensor, k=1, dims=(2, 3))
+    elif direction in "r":
+        target_tensor = torch.rot90(light_tensor, k=-1, dims=(2, 3))
+    else:
+        raise ValueError("direction must be either 'r' for right or 'l' for left")
+        
+    # Generate the sequence
+    out_list = []
+    for frame_idx in range(num_frames):
+        t = frame_idx / (num_frames - 1) 
+        interpolated_matrix = (1 - t) * light_tensor + t * target_tensor
+        out_list.append(interpolated_matrix)
+    
+    out_tensor = torch.stack(out_list, dim=0).squeeze(1)
+
+    return out_tensor
+
+def tensor2vid(video: torch.Tensor, processor, output_type="np"):
+
+    batch_size, channels, num_frames, height, width = video.shape ## [1, 4, 16, 512, 512]
+    outputs = []
+    for batch_idx in range(batch_size):
+        batch_vid = video[batch_idx].permute(1, 0, 2, 3)
+        batch_output = processor.postprocess(batch_vid, output_type)
+
+        outputs.append(batch_output)
+
+    return outputs
+
+def read_video(video_path:str, image_width, image_height):
+    extension = video_path.split('.')[-1].lower()
+    video_name = os.path.basename(video_path)
+    video_list = []
+
+    if extension in "gif":
+        ## input from gif
+        video = Image.open(video_path)
+        for i, frame in enumerate(ImageSequence.Iterator(video)):
+            frame = np.array(frame.convert("RGB"))
+            frame = resize_and_center_crop(frame, image_width, image_height)
+            video_list.append(frame)
+    elif extension in "mp4":
+        ## input from mp4
+        reader = imageio.get_reader(video_path)
+        for frame in reader:
+            frame = resize_and_center_crop(frame, image_width, image_height)
+            video_list.append(frame)
+    else:
+        raise ValueError('Wrong input type')
+    
+    video_list = [Image.fromarray(frame) for frame in video_list]
+
+    return video_list, video_name
+
+def read_mask(mask_folder:str):
+    mask_files = os.listdir(mask_folder)
+    mask_files = sorted(mask_files)
+    mask_list = []
+    for mask_file in mask_files:
+        mask_path = os.path.join(mask_folder, mask_file)
+        mask = Image.open(mask_path).convert('RGB')
+        mask_list.append(mask)
+    
+    return mask_list
+
+def decode_latents(vae, latents, decode_chunk_size: int = 16):
+    
+    latents = 1 / vae.config.scaling_factor * latents
+    video = []
+    for i in range(0, latents.shape[0], decode_chunk_size):
+        batch_latents = latents[i : i + decode_chunk_size]
+        batch_latents = vae.decode(batch_latents).sample
+        video.append(batch_latents)
+
+    video = torch.cat(video)
+
+    return video
+
+def encode_video(vae, video, decode_chunk_size: int = 16) -> torch.Tensor:
+    latents = []
+    for i in range(0, len(video), decode_chunk_size):
+        batch_video = video[i : i + decode_chunk_size]
+        batch_video = vae.encode(batch_video).latent_dist.mode()
+        latents.append(batch_video)
+    return torch.cat(latents)
+
+def vis_video(input_video, video_processor, save_path):
+    ## shape: 1, c, f, h, w
+    relight_video = video_processor.postprocess_video(video=input_video, output_type="pil")
+    export_to_video(relight_video[0], save_path)
+    
+def set_all_seed(seed):
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    torch.backends.cudnn.deterministic = True
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