From 2bf32d87a4fdb351c2ab5eaba36e2e95dd4ec6ba Mon Sep 17 00:00:00 2001
From: magicwang1111 <alimagicwang@gmail.com>
Date: Mon, 25 May 2026 11:57:19 +0800
Subject: [PATCH] Add ComfyUI HighSync nodes

---
 .gitattributes                                |     8 +
 .gitignore                                    |     7 +
 README.md                                     |    47 +
 __init__.py                                   |     6 +
 high_sync/.gitignore                          |    11 +
 high_sync/README.md                           |   138 +
 high_sync/__init__.py                         |     1 +
 high_sync/configs/inference/default.yaml      |    72 +
 high_sync/configs/train/stage1.yaml           |    60 +
 high_sync/configs/train/stage2.yaml           |   109 +
 high_sync/data_preprocess.py                  |   157 +
 high_sync/image_processor.py                  |   460 +
 high_sync/imgs/model_stage2.png               |   Bin 0 -> 412514 bytes
 high_sync/inference.py                        |   398 +
 high_sync/masks/mask_16.png                   |   Bin 0 -> 136 bytes
 high_sync/masks/mask_32.png                   |   Bin 0 -> 205 bytes
 high_sync/masks/mask_512.png                  |   Bin 0 -> 3408 bytes
 high_sync/masks/mask_64.png                   |   Bin 0 -> 345 bytes
 high_sync/masks/mask_8.png                    |   Bin 0 -> 98 bytes
 high_sync/requirements.txt                    |    42 +
 high_sync/scripts/extract_meta_info_stage1.py |   114 +
 high_sync/src/__init__.py                     |     0
 high_sync/src/datasets/image_processor.py     |   370 +
 high_sync/src/datasets/mask_image.py          |   291 +
 high_sync/src/datasets/talk_video.py          |   456 +
 high_sync/src/models/attention.py             |   338 +
 high_sync/src/models/face_locator.py          |    57 +
 high_sync/src/models/motion_module.py         |   481 +
 high_sync/src/models/mutual_self_attention.py |   336 +
 high_sync/src/models/resnet.py                |   252 +
 high_sync/src/models/transformer_2d.py        |   399 +
 high_sync/src/models/transformer_3d.py        |   175 +
 high_sync/src/models/unet_2d_blocks.py        |  1077 +
 high_sync/src/models/unet_2d_condition.py     |  1310 +
 high_sync/src/models/unet_3d.py               |   668 +
 high_sync/src/models/unet_3d_blocks.py        |   872 +
 high_sync/src/models/unet_3d_echo.py          |   679 +
 high_sync/src/models/whisper/audio2feature.py |   131 +
 .../src/models/whisper/whisper/__init__.py    |   116 +
 .../src/models/whisper/whisper/__main__.py    |     4 +
 .../whisper/whisper/assets/gpt2/merges.txt    | 50001 ++++++++++++++++
 .../assets/gpt2/special_tokens_map.json       |     1 +
 .../whisper/assets/gpt2/tokenizer_config.json |     1 +
 .../whisper/whisper/assets/gpt2/vocab.json    |     1 +
 .../whisper/whisper/assets/mel_filters.npz    |   Bin 0 -> 2048 bytes
 .../assets/multilingual/added_tokens.json     |     1 +
 .../whisper/assets/multilingual/merges.txt    | 50000 +++++++++++++++
 .../multilingual/special_tokens_map.json      |     1 +
 .../assets/multilingual/tokenizer_config.json |     1 +
 .../whisper/assets/multilingual/vocab.json    |     1 +
 high_sync/src/models/whisper/whisper/audio.py |   125 +
 .../src/models/whisper/whisper/decoding.py    |   729 +
 high_sync/src/models/whisper/whisper/model.py |   290 +
 .../whisper/whisper/normalizers/__init__.py   |     2 +
 .../whisper/whisper/normalizers/basic.py      |    71 +
 .../whisper/whisper/normalizers/english.json  |  1742 +
 .../whisper/whisper/normalizers/english.py    |   543 +
 .../src/models/whisper/whisper/tokenizer.py   |   331 +
 .../src/models/whisper/whisper/transcribe.py  |   207 +
 high_sync/src/models/whisper/whisper/utils.py |    87 +
 high_sync/src/pipelines/__init__.py           |     0
 high_sync/src/pipelines/context.py            |   101 +
 high_sync/src/pipelines/face_animate.py       |   487 +
 .../src/pipelines/face_animate_context.py     |   489 +
 .../src/pipelines/face_animate_static.py      |   476 +
 .../src/pipelines/pipeline_echo_mimic.py      |   578 +
 high_sync/src/pipelines/utils.py              |    29 +
 high_sync/src/utils/draw_utils.py             |   190 +
 high_sync/src/utils/face_landmark.py          |  3305 +
 high_sync/src/utils/img_utils.py              |    84 +
 high_sync/src/utils/motion_utils.py           |   237 +
 .../mp_models/blaze_face_short_range.tflite   |   Bin 0 -> 229746 bytes
 .../face_landmarker_v2_with_blendshapes.task  |   Bin 0 -> 3758596 bytes
 high_sync/src/utils/mp_utils.py               |    94 +
 high_sync/src/utils/step_func.py              |    36 +
 high_sync/src/utils/util.py                   |   936 +
 high_sync/train_stage1.py                     |   731 +
 high_sync/train_stage2.py                     |   855 +
 highsync_wrapper/__init__.py                  |     1 +
 highsync_wrapper/download_utils.py            |    79 +
 highsync_wrapper/ffmpeg_utils.py              |    45 +
 highsync_wrapper/file_utils.py                |    92 +
 highsync_wrapper/media_utils.py               |   224 +
 highsync_wrapper/model_utils.py               |   148 +
 highsync_wrapper/runner.py                    |   323 +
 nodes.py                                      |   169 +
 requirements.txt                              |    17 +
 tests/test_download_utils.py                  |    36 +
 88 files changed, 123539 insertions(+)
 create mode 100644 .gitattributes
 create mode 100644 .gitignore
 create mode 100644 README.md
 create mode 100644 __init__.py
 create mode 100644 high_sync/.gitignore
 create mode 100644 high_sync/README.md
 create mode 100644 high_sync/__init__.py
 create mode 100644 high_sync/configs/inference/default.yaml
 create mode 100644 high_sync/configs/train/stage1.yaml
 create mode 100644 high_sync/configs/train/stage2.yaml
 create mode 100644 high_sync/data_preprocess.py
 create mode 100644 high_sync/image_processor.py
 create mode 100644 high_sync/imgs/model_stage2.png
 create mode 100644 high_sync/inference.py
 create mode 100644 high_sync/masks/mask_16.png
 create mode 100644 high_sync/masks/mask_32.png
 create mode 100644 high_sync/masks/mask_512.png
 create mode 100644 high_sync/masks/mask_64.png
 create mode 100644 high_sync/masks/mask_8.png
 create mode 100644 high_sync/requirements.txt
 create mode 100644 high_sync/scripts/extract_meta_info_stage1.py
 create mode 100644 high_sync/src/__init__.py
 create mode 100644 high_sync/src/datasets/image_processor.py
 create mode 100644 high_sync/src/datasets/mask_image.py
 create mode 100644 high_sync/src/datasets/talk_video.py
 create mode 100644 high_sync/src/models/attention.py
 create mode 100644 high_sync/src/models/face_locator.py
 create mode 100644 high_sync/src/models/motion_module.py
 create mode 100644 high_sync/src/models/mutual_self_attention.py
 create mode 100644 high_sync/src/models/resnet.py
 create mode 100644 high_sync/src/models/transformer_2d.py
 create mode 100644 high_sync/src/models/transformer_3d.py
 create mode 100644 high_sync/src/models/unet_2d_blocks.py
 create mode 100644 high_sync/src/models/unet_2d_condition.py
 create mode 100644 high_sync/src/models/unet_3d.py
 create mode 100644 high_sync/src/models/unet_3d_blocks.py
 create mode 100644 high_sync/src/models/unet_3d_echo.py
 create mode 100644 high_sync/src/models/whisper/audio2feature.py
 create mode 100644 high_sync/src/models/whisper/whisper/__init__.py
 create mode 100644 high_sync/src/models/whisper/whisper/__main__.py
 create mode 100644 high_sync/src/models/whisper/whisper/assets/gpt2/merges.txt
 create mode 100644 high_sync/src/models/whisper/whisper/assets/gpt2/special_tokens_map.json
 create mode 100644 high_sync/src/models/whisper/whisper/assets/gpt2/tokenizer_config.json
 create mode 100644 high_sync/src/models/whisper/whisper/assets/gpt2/vocab.json
 create mode 100644 high_sync/src/models/whisper/whisper/assets/mel_filters.npz
 create mode 100644 high_sync/src/models/whisper/whisper/assets/multilingual/added_tokens.json
 create mode 100644 high_sync/src/models/whisper/whisper/assets/multilingual/merges.txt
 create mode 100644 high_sync/src/models/whisper/whisper/assets/multilingual/special_tokens_map.json
 create mode 100644 high_sync/src/models/whisper/whisper/assets/multilingual/tokenizer_config.json
 create mode 100644 high_sync/src/models/whisper/whisper/assets/multilingual/vocab.json
 create mode 100644 high_sync/src/models/whisper/whisper/audio.py
 create mode 100644 high_sync/src/models/whisper/whisper/decoding.py
 create mode 100644 high_sync/src/models/whisper/whisper/model.py
 create mode 100644 high_sync/src/models/whisper/whisper/normalizers/__init__.py
 create mode 100644 high_sync/src/models/whisper/whisper/normalizers/basic.py
 create mode 100644 high_sync/src/models/whisper/whisper/normalizers/english.json
 create mode 100644 high_sync/src/models/whisper/whisper/normalizers/english.py
 create mode 100644 high_sync/src/models/whisper/whisper/tokenizer.py
 create mode 100644 high_sync/src/models/whisper/whisper/transcribe.py
 create mode 100644 high_sync/src/models/whisper/whisper/utils.py
 create mode 100644 high_sync/src/pipelines/__init__.py
 create mode 100644 high_sync/src/pipelines/context.py
 create mode 100644 high_sync/src/pipelines/face_animate.py
 create mode 100644 high_sync/src/pipelines/face_animate_context.py
 create mode 100644 high_sync/src/pipelines/face_animate_static.py
 create mode 100644 high_sync/src/pipelines/pipeline_echo_mimic.py
 create mode 100644 high_sync/src/pipelines/utils.py
 create mode 100644 high_sync/src/utils/draw_utils.py
 create mode 100644 high_sync/src/utils/face_landmark.py
 create mode 100644 high_sync/src/utils/img_utils.py
 create mode 100644 high_sync/src/utils/motion_utils.py
 create mode 100644 high_sync/src/utils/mp_models/blaze_face_short_range.tflite
 create mode 100644 high_sync/src/utils/mp_models/face_landmarker_v2_with_blendshapes.task
 create mode 100644 high_sync/src/utils/mp_utils.py
 create mode 100644 high_sync/src/utils/step_func.py
 create mode 100644 high_sync/src/utils/util.py
 create mode 100644 high_sync/train_stage1.py
 create mode 100644 high_sync/train_stage2.py
 create mode 100644 highsync_wrapper/__init__.py
 create mode 100644 highsync_wrapper/download_utils.py
 create mode 100644 highsync_wrapper/ffmpeg_utils.py
 create mode 100644 highsync_wrapper/file_utils.py
 create mode 100644 highsync_wrapper/media_utils.py
 create mode 100644 highsync_wrapper/model_utils.py
 create mode 100644 highsync_wrapper/runner.py
 create mode 100644 nodes.py
 create mode 100644 requirements.txt
 create mode 100644 tests/test_download_utils.py

diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..1fd713a
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,8 @@
+* text=auto eol=lf
+
+*.png binary
+*.jpg binary
+*.jpeg binary
+*.npz binary
+*.tflite binary
+*.task binary
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..7d70be8
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,7 @@
+__pycache__/
+*.py[cod]
+.pytest_cache/
+.venv/
+cache/
+pretrained_weights/
+outputs/
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..fc8560a
--- /dev/null
+++ b/README.md
@@ -0,0 +1,47 @@
+# ComfyUI HighSync Wrapper
+
+HighSync lip-sync nodes for ComfyUI. This wrapper uses ComfyUI `IMAGE` frames plus native `AUDIO`, runs the HighSync pipeline in-process, and returns processed frames, audio, output fps, and an encoded mp4 path.
+
+## Nodes
+
+- `HighSync Download Models`: checks or downloads the required model files into `D:/ComfyUI/models/highsync`.
+- `HighSync Model Loader`: loads and caches the VAE, UNets, scheduler, and Whisper audio processor.
+- `HighSync LipSync`: simple node for `IMAGE + AUDIO` input.
+- `HighSync LipSync Advanced`: exposes inference steps, CFG scale, seed, chunk size, denoising, trimming, and intermediate-output options.
+
+## Model Layout
+
+The default model directory is:
+
+```text
+D:/ComfyUI/models/highsync
+```
+
+It must contain:
+
+```text
+denoising_unet-500.pth
+reference_unet-500.pth
+sd-vae-ft-mse/
+sd-image-variations-diffusers/
+audio_processor/whisper_tiny.pt
+```
+
+Use `HighSync Download Models` with `download=True`, or download `saeed-5959/high_sync` from Hugging Face into that folder.
+
+## Usage
+
+1. Install `requirements.txt` into the same Python environment that runs ComfyUI.
+2. Restart ComfyUI.
+3. Use VideoHelperSuite or another loader to provide video frames as `IMAGE` and audio as `AUDIO`.
+4. Connect `HighSync Model Loader` to `HighSync LipSync`.
+
+The wrapper converts video frames to 25fps and audio to mono 16kHz for HighSync inference. The returned frame rate is always `25` in this v1 implementation.
+
+## Notes
+
+- `max_frames=0` means process all available frames after 25fps conversion.
+- `denoise_audio=False` is the default because the official `denoiser` package pins older Hydra/OmegaConf versions than current ComfyUI environments. To enable it, install the package without pulling its old dependencies: `pip install --no-deps denoiser==0.1.5`.
+- When `denoise_audio=True`, the official denoiser is loaded lazily and cached. The final mp4 still uses the adjusted original audio track to preserve voice quality.
+- DeepFace/TensorFlow are optional. The runtime tries DeepFace if it is already installed, otherwise it falls back to OpenCV Haar face detection.
+- Outputs are written under `ComfyUI/output/highsync/<task_id>/final.mp4`.
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..89822be
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,6 @@
+try:
+    from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+except ImportError:
+    from nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
diff --git a/high_sync/.gitignore b/high_sync/.gitignore
new file mode 100644
index 0000000..7a89627
--- /dev/null
+++ b/high_sync/.gitignore
@@ -0,0 +1,11 @@
+.venv/
+caache/
+data/
+dataset/
+exp_output/
+out_ref_500/
+pretrained_weights/
+runs/
+test/
+__pycache__/
+cache/
\ No newline at end of file
diff --git a/high_sync/README.md b/high_sync/README.md
new file mode 100644
index 0000000..bfcbc7a
--- /dev/null
+++ b/high_sync/README.md
@@ -0,0 +1,138 @@
+<h1 align='center'>HighSync: High-Quality Lip Synchronization via
+Latent Diffusion Models</h1>
+
+<div align='center'>
+    <a href='https://github.com/saeed5959' target='_blank'>Saeed Firouzi</a><sup>1</sup>&emsp;
+</div>
+
+<br>
+
+<div align='center'>
+    <a href='https://huggingface.co/saeed-5959/high_sync'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Model-yellow'></a>
+    <a href='https://arxiv.org/abs/2605.16918'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
+    <a href='https://huggingface.co/datasets/saeed-5959/vfhq'><img src='https://img.shields.io/badge/Dataset-Hugging_Face-CFAFD4'></a>
+</div>
+
+
+## Abstraction
+We present HighSync, an end-to-end diffusion-based
+framework for high-fidelity lip synchronization that generates
+photorealistic talking-face videos aligned with arbitrary input
+audio. Existing approaches consistently struggle to reconcile
+image quality with synchronization accuracy, producing either
+visually degraded outputs or temporally inconsistent lip move-
+ments. HighSync addresses both challenges simultaneously and,
+to our knowledge, is the first lip sync model to operate natively
+at 512×512 resolution, positioning it as a viable solution for
+professional production environments such as the film and broad-
+cast industries. Central to our approach is the identification and
+systematic elimination of a data leakage phenomenon that has
+silently undermined temporal modeling in prior work, preventing
+models from developing a genuine dependence on the audio
+signal. Comprehensive evaluations across both perceptual quality
+and synchronization accuracy metrics confirm that HighSync
+achieves state-of-the-art performance on both fronts.
+
+
+## Model Structure
+<img src='./imgs/model_stage2.png'>
+
+
+## ⚒️ Installation
+
+### Environment
+
+    Ubuntu 20 or 22
+
+### Download the Codes
+
+```bash
+  git clone https://github.com/saeed5959/high_sync
+  cd high_sync
+```
+
+
+### Install packages with `pip`
+```bash
+  pip install -r requirements.txt
+```
+
+### Install ffmpeg
+```bash
+apt-get install ffmpeg
+```
+
+### Download pretrained weights
+
+```shell
+git lfs install
+git clone https://huggingface.co/saeed-5959/high_sync pretrained_weights
+```
+
+The **pretrained_weights** is organized as follows.
+
+```
+./pretrained_weights/
+├── denoising_unet-500.pth
+├── reference_unet-500.pth
+├── sd-vae-ft-mse
+│   └── ...
+├── sd-image-variations-diffusers
+│   └── ...
+└── audio_processor
+    └── whisper_tiny.pt
+```
+
+In which **denoising_unet.pth** / **reference_unet.pth** / are the main checkpoints of **Highsync**. Other models in this hub can be also downloaded from it's original hub, thanks to their brilliant works:
+- [sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse)
+- [sd-image-variations-diffusers](https://huggingface.co/lambdalabs/sd-image-variations-diffusers)
+- [audio_processor(whisper)](https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt)
+
+### Inference
+
+1)First convert your video to fps=25
+
+```bash
+ffmpeg -i input.mp4 -r 25 out_25.mp4
+```
+
+2)Then run the python inference script:
+
+```bash
+  python -m inference --source_video "video_path.mp4" --driving_audio "audio_path.wav" --output "save_path.mp4"
+```
+
+### Dataset
+We preprocessed 3 public datasets and put their clean videos in these links :
+
+[VFHQ](https://huggingface.co/datasets/saeed-5959/vfhq)
+
+[Celebv-HQ](https://huggingface.co/datasets/saeed-5959/celebv_hq_head_talking)
+
+[HDTF](https://huggingface.co/datasets/saeed-5959/hdtf)
+
+Notice : thses videos has been preprocessed based on the paper appraoch!
+
+## 🙏🏻 Acknowledgements
+
+This work is mainly based on [EchoMimic](https://github.com/antgroup/echomimic) work.
+
+We would like to thank the contributors to the [EchoMimic](), [AnimateDiff](https://github.com/guoyww/AnimateDiff), [Moore-AnimateAnyone](https://github.com/MooreThreads/Moore-AnimateAnyone) and [MuseTalk](https://github.com/TMElyralab/MuseTalk) repositories, for their open research and exploration.
+
+We are also grateful to [V-Express](https://github.com/tencent-ailab/V-Express) and [hallo](https://github.com/fudan-generative-vision/hallo) for their outstanding work in the area of diffusion-based talking heads.
+
+If we missed any open-source projects or related articles, we would like to complement the acknowledgement of this specific work immediately.
+
+## Citation
+```bibtex
+@article{daghigh2024highsync,
+  title={HighSync: High-Quality Lip Synchronization via Latent Diffusion Models},
+  author={Saeed Firouzi Daghigh and Majid Iranpour Mobarekeh and Mostafa Alavi and Mehdi Bagheri},
+  journal={arXiv preprint arXiv:2605.16918},
+  year={2026}
+}
+```
+
+
+## 🌟 Star History
+[![Star History Chart](https://api.star-history.com/svg?repos=saeed_5959/highsync&type=Date)](https://star-history.com/#saeed_5959/highsync&Date)
diff --git a/high_sync/__init__.py b/high_sync/__init__.py
new file mode 100644
index 0000000..30fbce9
--- /dev/null
+++ b/high_sync/__init__.py
@@ -0,0 +1 @@
+"""HighSync research code packaged for the ComfyUI wrapper."""
diff --git a/high_sync/configs/inference/default.yaml b/high_sync/configs/inference/default.yaml
new file mode 100644
index 0000000..58c57a9
--- /dev/null
+++ b/high_sync/configs/inference/default.yaml
@@ -0,0 +1,72 @@
+mask_path: "./masks/mask_64.png"
+mask_512_path: "./masks/mask_512.png"
+
+weight_dtype: fp16
+
+data:
+  n_motion_frames: 2
+  n_sample_frames: 12
+  source_image:
+    width: 512
+    height: 512
+  driving_audio:
+    sample_rate: 16000
+  export_video:
+    fps: 25
+
+inference_steps: 20
+cfg_scale: 3.5
+
+denoising_unet_path: "./pretrained_weights/denoising_unet-500.pth"
+reference_unet_path: "./pretrained_weights/reference_unet-500.pth"
+
+base_model_path: "./pretrained_weights/sd-image-variations-diffusers/"
+
+motion_module_path: ""
+
+audio_model_path: "./pretrained_weights/audio_processor/whisper_tiny.pt"
+
+vae:
+  model_path: "./pretrained_weights/sd-vae-ft-mse/"
+
+save_path: ./cache
+
+unet_additional_kwargs:
+  use_inflated_groupnorm: true
+  unet_use_cross_frame_attention: false
+  unet_use_temporal_attention: false
+  use_motion_module: true
+  cross_attention_dim: 384
+  motion_module_resolutions:
+  - 1
+  - 2
+  - 4
+  - 8
+  motion_module_mid_block: true
+  motion_module_decoder_only: false
+  motion_module_type: Vanilla
+  motion_module_kwargs:
+    num_attention_heads: 8
+    num_transformer_block: 1
+    attention_block_types:
+    - Temporal_Self
+    - Temporal_Self
+    temporal_position_encoding: true
+    temporal_position_encoding_max_len: 32
+    temporal_attention_dim_div: 1
+
+
+enable_zero_snr: true
+
+noise_scheduler_kwargs:
+  beta_start: 0.00085
+  beta_end: 0.012
+  beta_schedule: "linear"
+  clip_sample: false
+  steps_offset: 1
+  ### Zero-SNR params
+  prediction_type: "v_prediction"
+  rescale_betas_zero_snr: True
+  timestep_spacing: "trailing"
+
+sampler: DDIM
diff --git a/high_sync/configs/train/stage1.yaml b/high_sync/configs/train/stage1.yaml
new file mode 100644
index 0000000..400ac8c
--- /dev/null
+++ b/high_sync/configs/train/stage1.yaml
@@ -0,0 +1,60 @@
+data:
+  train_bs: 16
+  train_width: 512
+  train_height: 512
+  train_path: "./data/echo_train.json"
+  val_path: "./data/echo_train.json"
+  # Margin of frame indexes between ref and tgt images
+  sample_margin: 30
+  mask_path: "./masks/mask_64.png"
+  mask_512_path: "./masks/mask_512.png"
+
+solver:
+  gradient_accumulation_steps: 1
+  mixed_precision: "no"
+  enable_xformers_memory_efficient_attention: True
+  gradient_checkpointing: False
+  max_train_steps: 300000
+  max_grad_norm: 1.0
+  # lr
+  learning_rate: 1.0e-5
+  scale_lr: False
+  lr_warmup_steps: 100
+  lr_scheduler: "constant"
+
+  # optimizer
+  use_8bit_adam: False
+  adam_beta1: 0.9
+  adam_beta2: 0.999
+  adam_weight_decay: 1.0e-2
+  adam_epsilon: 1.0e-8
+
+val:
+  validation_steps: 1000
+
+noise_scheduler_kwargs:
+  num_train_timesteps: 1000
+  beta_start: 0.00085
+  beta_end: 0.012
+  beta_schedule: "scaled_linear"
+  steps_offset: 1
+  clip_sample: false
+
+base_model_path: "./pretrained_weights/sd-image-variations-diffusers/"
+vae_model_path: "./pretrained_weights/sd-vae-ft-mse"
+face_analysis_model_path: "./pretrained_models/face_analysis"
+echo_path:  "./exp_output/stage1"
+
+weight_dtype: "fp16" # [fp16, fp32]
+uncond_ratio: 0.1
+noise_offset: 0.05
+snr_gamma: 5.0
+enable_zero_snr: True
+face_locator_pretrained: False
+cross_attention_dim: 384
+
+seed: 42
+resume_from_checkpoint: "latest"
+checkpointing_steps: 10000
+exp_name: "stage1"
+output_dir: "./exp_output"
diff --git a/high_sync/configs/train/stage2.yaml b/high_sync/configs/train/stage2.yaml
new file mode 100644
index 0000000..d722588
--- /dev/null
+++ b/high_sync/configs/train/stage2.yaml
@@ -0,0 +1,109 @@
+data:
+  train_bs: 4
+  val_bs: 1
+  train_width: 512
+  train_height: 512
+  fps: 25
+  sample_rate: 16000
+  n_sample_frames: 12
+  audio_margin: 2
+  train_path: "./data/echo_train.json"
+  val_path: "./data/echo_train.json"
+  mask_path: "./masks/mask_64.png"
+  mask_512_path: "./masks/mask_512.png"
+
+wav2vec_config:
+  audio_type: "vocals" # audio vocals
+  model_scale: "base" # base large
+  features: "all" # last avg all
+  model_path: ./pretrained_models/wav2vec/wav2vec2-base-960h
+audio_separator:
+  model_path: ./pretrained_models/audio_separator/Kim_Vocal_2.onnx
+face_expand_ratio: 1.2
+
+solver:
+  gradient_accumulation_steps: 1
+  mixed_precision: "no"
+  enable_xformers_memory_efficient_attention: True
+  gradient_checkpointing: True
+  max_train_steps: 100000
+  max_grad_norm: 1.0
+  # lr
+  learning_rate: 5e-6
+  scale_lr: False
+  lr_warmup_steps: 1
+  lr_scheduler: "constant"
+
+  # optimizer
+  use_8bit_adam: True
+  adam_beta1: 0.9
+  adam_beta2: 0.999
+  adam_weight_decay: 1.0e-2
+  adam_epsilon: 1.0e-8
+
+val:
+  validation_steps: 1000
+
+noise_scheduler_kwargs:
+  num_train_timesteps: 1000
+  beta_start: 0.00085
+  beta_end: 0.012
+  beta_schedule: "linear"
+  steps_offset: 1
+  clip_sample: false
+
+unet_additional_kwargs:
+  use_inflated_groupnorm: true
+  unet_use_cross_frame_attention: false
+  unet_use_temporal_attention: false
+  use_motion_module: true
+  cross_attention_dim: 384
+  motion_module_resolutions:
+    - 1
+    - 2
+    - 4
+    - 8
+  motion_module_mid_block: true
+  motion_module_decoder_only: false
+  motion_module_type: Vanilla
+  motion_module_kwargs:
+    num_attention_heads: 8
+    num_transformer_block: 1
+    attention_block_types:
+      - Temporal_Self
+      - Temporal_Self
+    temporal_position_encoding: true
+    temporal_position_encoding_max_len: 32
+    temporal_attention_dim_div: 1
+
+trainable_para:
+  - motion_modules
+
+base_model_path: "./pretrained_weights/sd-image-variations-diffusers/"
+vae_model_path: "./pretrained_weights/sd-vae-ft-mse"
+face_analysis_model_path: "./pretrained_models/face_analysis"
+mm_path: "./pretrained_weights/motion_module.pth"
+echo_path: "./exp_output" #"./pretrained_weights"
+resume_denoise_path: "./exp_output/denoising_unet-44000.pth"
+resume_reference_path: "./exp_output/reference_unet-44000.pth"
+resume: true
+
+weight_dtype: "fp16" # [fp16, fp32]
+uncond_img_ratio: 0.1
+uncond_audio_ratio: 0.0
+uncond_target_ratio: 0.0
+start_ratio: 1.0
+noise_offset: 0.05
+snr_gamma: 5.0
+enable_zero_snr: True
+stage1_ckpt_dir: "./exp_output"
+
+single_inference_times: 10
+inference_steps: 20
+cfg_scale: 3.5
+
+seed: 42
+resume_from_checkpoint: "latest"
+checkpointing_steps: 2000
+exp_name: "stage2"
+output_dir: "./exp_output"
diff --git a/high_sync/data_preprocess.py b/high_sync/data_preprocess.py
new file mode 100644
index 0000000..0b22ae2
--- /dev/null
+++ b/high_sync/data_preprocess.py
@@ -0,0 +1,157 @@
+# pylint: disable=W1203,W0718
+"""
+This module is used to process videos to prepare data for training. It utilizes various libraries and models
+to perform tasks such as video frame extraction, audio extraction, face mask generation, and face embedding extraction.
+The script takes in command-line arguments to specify the input and output directories, GPU status, level of parallelism,
+and rank for distributed processing.
+
+Usage:
+    python -m scripts.data_preprocess --input_dir /path/to/video_dir --dataset_name dataset_name --gpu_status --parallelism 4 --rank 0
+
+Example:
+    python -m scripts.data_preprocess -i data/videos -o data/output -g -p 4 -r 0
+"""
+import argparse
+import logging
+import os
+from pathlib import Path
+from typing import List
+import soundfile as sf
+import torchaudio
+from denoiser import pretrained
+from denoiser.dsp import convert_audio
+import torch
+from tqdm import tqdm
+
+from .src.models.whisper.audio2feature import Audio2Feature
+from .src.datasets.image_processor import ImageProcessorForDataProcessing
+from .src.utils.util import convert_video_to_images, extract_audio_from_videos
+
+# Configure logging
+logging.basicConfig(level=logging.INFO,
+                    format='%(asctime)s - %(levelname)s - %(message)s')
+
+
+# model = pretrained.dns64().cuda()
+
+def process_audio(input_audio_path):
+
+    wav, sr = torchaudio.load(input_audio_path)
+    wav = convert_audio(wav.cuda(), sr, model.sample_rate, model.chin)
+
+    with torch.no_grad():
+        denoised = model(wav[None])[0]
+
+    sf.write(input_audio_path, denoised.data.cpu().numpy()[0], 16000)
+
+    return
+
+
+def process_single_video(video_path: Path,
+                         output_dir: Path,
+                         audio_processor: Audio2Feature,
+                         step: int) -> None:
+    """
+    Process a single video file.
+
+    Args:
+        video_path (Path): Path to the video file.
+        output_dir (Path): Directory to save the output.
+        audio_processor (AudioProcessor): Audio processor object.
+        gpu_status (bool): Whether to use GPU for processing.
+    """
+    assert video_path.exists(), f"Video path {video_path} does not exist"
+
+    audio_emb_dir = output_dir / "audio_emb_whisper"
+    audio_emb_dir.mkdir(parents=True, exist_ok=True)
+
+    # try:
+    if step == 1:
+        images_output_dir = output_dir / 'images' / video_path.stem
+        images_output_dir.mkdir(parents=True, exist_ok=True)
+        images_output_dir = convert_video_to_images(
+            video_path, images_output_dir)
+        logging.info(f"Images saved to: {images_output_dir}")
+
+        audio_output_dir = output_dir / 'audios'
+        audio_output_dir.mkdir(parents=True, exist_ok=True)
+        audio_output_path = audio_output_dir / f'{video_path.stem}.wav'
+        audio_output_path = extract_audio_from_videos(
+            video_path, audio_output_path)
+        logging.info(f"Audio extracted to: {audio_output_path}")
+
+    else:
+        audio_path = output_dir / "audios" / f"{video_path.stem}.wav"
+        # process_audio(str(audio_path))
+        audio_emb = audio_processor.audio2feat(str(audio_path))
+        audio_emb = audio_processor.feature2chunks(feature_array=audio_emb, fps=25)
+        torch.save(audio_emb, str(
+            audio_emb_dir / f"{video_path.stem}.pt"))
+    # except Exception as e:
+    #     logging.error(f"Failed to process video {video_path}: {e}")
+
+
+def process_all_videos(input_video_list: List[Path], output_dir: Path, step: int) -> None:
+    """
+    Process all videos in the input list.
+
+    Args:
+        input_video_list (List[Path]): List of video paths to process.
+        output_dir (Path): Directory to save the output.
+        gpu_status (bool): Whether to use GPU for processing.
+    """
+    whisper_model_path = "./pretrained_weights/audio_processor/whisper_tiny.pt"
+
+    audio_processor = Audio2Feature(model_path=whisper_model_path, device="cuda") if step==2 else None
+
+    for video_path in tqdm(input_video_list, desc="Processing videos"):
+        process_single_video(video_path, output_dir, audio_processor, step)
+
+
+def get_video_paths(source_dir: Path, parallelism: int, rank: int) -> List[Path]:
+    """
+    Get paths of videos to process, partitioned for parallel processing.
+
+    Args:
+        source_dir (Path): Source directory containing videos.
+        parallelism (int): Level of parallelism.
+        rank (int): Rank for distributed processing.
+
+    Returns:
+        List[Path]: List of video paths to process.
+    """
+    video_paths = [item for item in sorted(
+        source_dir.iterdir()) if item.is_file() and item.suffix == '.mp4']
+    return [video_paths[i] for i in range(len(video_paths)) if i % parallelism == rank]
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Process videos to prepare data for training. Run this script twice with different GPU status parameters."
+    )
+    parser.add_argument("-i", "--input_dir", type=Path,
+                        required=True, help="Directory containing videos")
+    parser.add_argument("-o", "--output_dir", type=Path,
+                        help="Directory to save results, default is parent dir of input dir")
+    parser.add_argument("-s", "--step", type=int, default=1,
+                        help="Specify data processing step 1 or 2, you should run 1 and 2 sequently")
+    parser.add_argument("-p", "--parallelism", default=1,
+                        type=int, help="Level of parallelism")
+    parser.add_argument("-r", "--rank", default=0, type=int,
+                        help="Rank for distributed processing")
+
+    args = parser.parse_args()
+
+    if args.output_dir is None:
+        args.output_dir = args.input_dir.parent
+
+    video_path_list = get_video_paths(
+        args.input_dir, args.parallelism, args.rank)
+
+    videos_out_path = os.path.join(args.output_dir, "videos")
+    # os.system(f"cp -r {args.input_dir} {videos_out_path}")
+
+    if not video_path_list:
+        logging.warning("No videos to process.")
+    else:
+        process_all_videos(video_path_list, args.output_dir, args.step)
diff --git a/high_sync/image_processor.py b/high_sync/image_processor.py
new file mode 100644
index 0000000..cbd961a
--- /dev/null
+++ b/high_sync/image_processor.py
@@ -0,0 +1,460 @@
+# pylint: disable=W0718
+"""
+This module is responsible for processing images, particularly for face-related tasks.
+It uses various libraries such as OpenCV, NumPy, and InsightFace to perform tasks like
+face detection, augmentation, and mask rendering. The ImageProcessor class encapsulates
+the functionality for these operations.
+"""
+import os
+from typing import List
+
+import cv2
+import mediapipe as mp
+import numpy as np
+import torch
+from insightface.app import FaceAnalysis
+from PIL import Image, ImageOps
+from torchvision import transforms
+
+
+MEAN = 0.5
+STD = 0.5
+
+
+def _select_largest_deepface_bbox(faces):
+    max_face = 0
+    best_bbox = (0, 0, 0, 0)
+    for face in faces:
+        facial_area = face.get("facial_area") or {}
+        face_area = facial_area.get("w", 0) * facial_area.get("h", 0)
+        if face.get("confidence", 0) > 0.5 and face_area > max_face:
+            best_bbox = (
+                int(facial_area["x"]),
+                int(facial_area["y"]),
+                int(facial_area["x"] + facial_area["w"]),
+                int(facial_area["y"] + facial_area["h"]),
+            )
+            max_face = face_area
+    return best_bbox
+
+
+def _detect_face_with_deepface(frame):
+    try:
+        from deepface import DeepFace
+    except Exception:
+        return None
+
+    try:
+        faces = DeepFace.extract_faces(
+            img_path=frame,
+            detector_backend="yolov8",
+            align=False,
+            enforce_detection=False,
+            expand_percentage=0,
+        )
+    except Exception as exc:
+        print(f"DeepFace detection failed, falling back to OpenCV Haar detection: {exc}")
+        return None
+
+    if not faces:
+        return (0, 0, 0, 0)
+    return _select_largest_deepface_bbox(faces)
+
+
+def _detect_face_with_opencv(frame):
+    cascade_path = cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
+    detector = cv2.CascadeClassifier(cascade_path)
+    if detector.empty():
+        return (0, 0, 0, 0)
+
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    faces = detector.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(40, 40))
+    if len(faces) == 0:
+        return (0, 0, 0, 0)
+
+    x, y, w, h = max(faces, key=lambda item: item[2] * item[3])
+    return int(x), int(y), int(x + w), int(y + h)
+
+
+def detect_face_bbox(frame):
+    bbox = _detect_face_with_deepface(frame)
+    if bbox is None:
+        bbox = _detect_face_with_opencv(frame)
+    if bbox == (0, 0, 0, 0):
+        print("No faces detected in the image. Using the entire image as the face region.")
+    return bbox
+
+class ImageProcessor:
+    """
+    ImageProcessor is a class responsible for processing images, particularly for face-related tasks.
+    It takes in an image and performs various operations such as augmentation, face detection,
+    face embedding extraction, and rendering a face mask. The processed images are then used for
+    further analysis or recognition purposes.
+
+    Attributes:
+        img_size (int): The size of the image to be processed.
+
+    Methods:
+        preprocess(source_image_path, cache_dir):
+            Preprocesses the input image by performing augmentation, face detection,
+            face embedding extraction, and rendering a face mask.
+
+        close():
+            Closes the ImageProcessor and releases any resources being used.
+
+        _augmentation(images, transform, state=None):
+            Applies image augmentation to the input images using the given transform and state.
+
+        __enter__():
+            Enters a runtime context and returns the ImageProcessor object.
+
+        __exit__(_exc_type, _exc_val, _exc_tb):
+            Exits a runtime context and handles any exceptions that occurred during the processing.
+    """
+    def __init__(self, img_size) -> None:
+        self.img_size = img_size
+
+        self.pixel_transform = transforms.Compose(
+            [
+                transforms.Resize(self.img_size),
+                transforms.ToTensor(),
+                transforms.Normalize([MEAN], [STD]),
+            ]
+        )
+
+    def preprocess_frames(self, frames):
+        """
+        Apply preprocessing to prepare for face analysis.
+
+        Parameters:
+            source_image_path (str): The path to the source image.
+            cache_dir (str): The directory to cache intermediate results.
+
+        Returns:
+            None
+        """
+        state = torch.get_rng_state()
+        def find_max_h(bbox_list):
+
+            h_max = 0
+            for bbox in bbox_list:
+                h = bbox[3] - bbox[1]
+                if h>h_max:
+                    h_max = h
+
+            return h_max
+
+        pixel_values_ref_img_list, bbox_list, bbox_final_list, frame_crop_list = [], [], [], []
+
+        for frame in frames:
+
+            bbox_list.append(detect_face_bbox(frame))
+
+        h_max = find_max_h(bbox_list)
+
+        for frame, bbox in zip(frames, bbox_list):
+
+            if bbox==(0,0,0,0):
+                bbox_final_list.append((0,0,0,0))
+                frame_crop = frame
+                frame_crop_list.append(cv2.resize(frame_crop, (512,512)))
+            else:
+                frame_h, frame_w, _ = frame.shape
+
+                w, h = bbox[2] - bbox[0], bbox[3] - bbox[1]
+
+                if h_max < 1.4*h:
+                    h = h_max
+
+                bbox_final = (int(max(bbox[0] - 0.1*w, 0)), int(bbox[1]),
+                                    int(min(bbox[2] + 0.1*w, frame_w)), int(min(bbox[1] + 1.1*h, frame_h)))
+                # bbox_final = (0,0,512,512)
+                bbox_final_list.append(bbox_final)
+
+                frame_face = frame[bbox_final[1]:bbox_final[3], bbox_final[0]:bbox_final[2]]
+                frame_crop = cv2.resize(frame_face, (512,512))
+                frame_crop_list.append(frame_crop)
+
+            pixel_values_ref_img_list.append(self._augmentation(Image.fromarray(cv2.cvtColor(frame_crop, cv2.COLOR_BGR2RGB)).convert("RGB"), self.pixel_transform, state).unsqueeze(0))
+
+        return pixel_values_ref_img_list, bbox_final_list, frame_crop_list
+
+
+    def preprocess_frames_jump(self, frames):
+        """
+        Apply preprocessing to prepare for face analysis.
+
+        Parameters:
+            source_image_path (str): The path to the source image.
+            cache_dir (str): The directory to cache intermediate results.
+
+        Returns:
+            None
+        """
+        frame_h, frame_w, _ = frames[0].shape
+
+        state = torch.get_rng_state()
+        def find_max_h(bbox_list):
+
+            h_max = 0
+            h_min = 100000
+            for bbox in bbox_list:
+                h = bbox[3] - bbox[1]
+                if h>h_max:
+                    h_max = h
+                if h<h_min:
+                    h_min = h
+
+            return h_max, h_min
+
+        def differ(bbox_list):
+
+            jumps = []
+            count_prev = 0
+            for count, bbox in enumerate(bbox_list):
+                bbox_prev = bbox_list[max(count-1,0)]
+                diff_x = ( np.abs(bbox[0] - bbox_prev[0]) + np.abs(bbox[2] - bbox_prev[2]) ) / frame_w
+                diff_y = ( np.abs(bbox[1] - bbox_prev[1]) + np.abs(bbox[3] - bbox_prev[3]) ) / frame_h
+
+                if ( diff_x + diff_y ) > 0.2:
+                    jumps.append((count_prev, count-1))
+                    count_prev = count
+
+            jumps.append((count_prev, count))
+
+            return jumps
+
+        bbox_list = []
+
+        for frame in frames:
+
+            bbox_list.append(detect_face_bbox(frame))
+
+        pixel_values_ref_img_all, bbox_final_all, frames_list_chunk = [], [], []
+
+        jumps = differ(bbox_list)
+        for jump in jumps:
+            print(jump)
+            pixel_values_ref_img_list, bbox_final_list = [], []
+
+            bbox_chunk = bbox_list[jump[0]:jump[1]+1]
+            frames_chunk = frames[jump[0]:jump[1]+1]
+
+            h_max, h_min = find_max_h(bbox_chunk)
+            use_h = h_max < 1.4*h_min
+            print(f"use h : {use_h}")
+
+            for frame, bbox in zip(frames_chunk, bbox_chunk):
+
+                if bbox==(0,0,0,0):
+                    bbox_final_list.append((0,0,0,0))
+                    frame_crop = frame
+
+                else:
+                    w, h = bbox[2] - bbox[0], bbox[3] - bbox[1]
+
+                    if use_h:
+                        h = h_max
+
+                    bbox_final = (int(max(bbox[0] - 0.1*w, 0)), int(bbox[1]),
+                                        int(min(bbox[2] + 0.1*w, frame_w)), int(min(bbox[1] + 1.1*h, frame_h)))
+
+                    bbox_final_list.append(bbox_final)
+
+                    frame_face = frame[bbox_final[1]:bbox_final[3], bbox_final[0]:bbox_final[2]]
+                    frame_crop = cv2.resize(frame_face, (512,512))
+
+                pixel_values_ref_img_list.append(self._augmentation(Image.fromarray(cv2.cvtColor(frame_crop, cv2.COLOR_BGR2RGB)).convert("RGB"), self.pixel_transform, state).unsqueeze(0))
+
+            pixel_values_ref_img_all.append(pixel_values_ref_img_list)
+            bbox_final_all.append(bbox_final_list)
+            frames_list_chunk.append(frames_chunk)
+
+        return pixel_values_ref_img_all, bbox_final_all, frames_list_chunk
+
+
+    def preprocess_frames_deepface_no_detect(self, frames):
+        """
+        Apply preprocessing to the 14 source images to prepare for face analysis.
+
+        Parameters:
+            source_image_path (str): The path to the source image.
+            cache_dir (str): The directory to cache intermediate results.
+
+        Returns:
+            None
+        """
+        pixel_values_ref_img_list = []
+        for frame in frames:
+            pixel_values_ref_img_list.append(self._augmentation(Image.fromarray(frame).convert("RGB"), self.pixel_transform).unsqueeze(0))
+
+        return pixel_values_ref_img_list
+
+
+
+    def close(self):
+        """
+        Closes the ImageProcessor and releases any resources held by the FaceAnalysis instance.
+
+        Args:
+            self: The ImageProcessor instance.
+
+        Returns:
+            None.
+        """
+        for _, model in self.face_analysis.models.items():
+            if hasattr(model, "Dispose"):
+                model.Dispose()
+
+    def _augmentation(self, images, transform, state=None):
+        if state is not None:
+            torch.set_rng_state(state)
+        if isinstance(images, List):
+            transformed_images = [transform(img) for img in images]
+            ret_tensor = torch.stack(transformed_images, dim=0)  # (f, c, h, w)
+        else:
+            ret_tensor = transform(images)  # (c, h, w)
+        return ret_tensor
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, _exc_type, _exc_val, _exc_tb):
+        self.close()
+
+
+class ImageProcessorForDataProcessing():
+    """
+    ImageProcessor is a class responsible for processing images, particularly for face-related tasks.
+    It takes in an image and performs various operations such as augmentation, face detection,
+    face embedding extraction, and rendering a face mask. The processed images are then used for
+    further analysis or recognition purposes.
+
+    Attributes:
+        img_size (int): The size of the image to be processed.
+        face_analysis_model_path (str): The path to the face analysis model.
+
+    Methods:
+        preprocess(source_image_path, cache_dir):
+            Preprocesses the input image by performing augmentation, face detection,
+            face embedding extraction, and rendering a face mask.
+
+        close():
+            Closes the ImageProcessor and releases any resources being used.
+
+        _augmentation(images, transform, state=None):
+            Applies image augmentation to the input images using the given transform and state.
+
+        __enter__():
+            Enters a runtime context and returns the ImageProcessor object.
+
+        __exit__(_exc_type, _exc_val, _exc_tb):
+            Exits a runtime context and handles any exceptions that occurred during the processing.
+    """
+    def __init__(self, face_analysis_model_path, landmark_model_path, step) -> None:
+        if step == 2:
+            self.face_analysis = FaceAnalysis(
+                name="",
+                root=face_analysis_model_path,
+                providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
+            )
+            self.face_analysis.prepare(ctx_id=0, det_size=(640, 640))
+            self.landmarker = None
+        else:
+            BaseOptions = mp.tasks.BaseOptions
+            FaceLandmarker = mp.tasks.vision.FaceLandmarker
+            FaceLandmarkerOptions = mp.tasks.vision.FaceLandmarkerOptions
+            VisionRunningMode = mp.tasks.vision.RunningMode
+            # Create a face landmarker instance with the video mode:
+            options = FaceLandmarkerOptions(
+                base_options=BaseOptions(model_asset_path=landmark_model_path),
+                running_mode=VisionRunningMode.IMAGE,
+            )
+            self.landmarker = None #FaceLandmarker.create_from_options(options)
+            self.face_analysis = None
+
+    def preprocess(self, source_image_path: str):
+        """
+        Apply preprocessing to the source image to prepare for face analysis.
+
+        Parameters:
+            source_image_path (str): The path to the source image.
+            cache_dir (str): The directory to cache intermediate results.
+
+        Returns:
+            None
+        """
+        # 1. get face embdeding
+        face_mask, face_emb, sep_pose_mask, sep_face_mask, sep_lip_mask = None, None, None, None, None
+        if self.face_analysis:
+            frame = sorted(os.listdir(source_image_path))[0]
+            source_image = Image.open(
+                os.path.join(source_image_path, frame))
+            ref_image_pil = source_image.convert("RGB")
+
+            padding = 250
+            ref_image_pil = ImageOps.expand(ref_image_pil, border=padding, fill=0)
+
+            # 2.1 detect face
+            faces = self.face_analysis.get(cv2.cvtColor(
+                np.array(ref_image_pil.copy()), cv2.COLOR_RGB2BGR))
+
+            # use max size face
+            face = sorted(faces, key=lambda x: (
+                x["bbox"][2] - x["bbox"][0]) * (x["bbox"][3] - x["bbox"][1]))[-1]
+            # 2.2 face embedding
+            face_emb = face["embedding"]
+            if face_emb is None:
+                print(f"error in image embedding : {source_image_path}")
+                raise
+
+        if self.landmarker:
+            # 3.1 get landmark
+            landmarks, height, width = get_landmark_overframes(
+                self.landmarker, source_image_path)
+            assert len(landmarks) == len(os.listdir(source_image_path))
+
+            # 3 render face and lip mask
+            face_mask = get_union_face_mask(landmarks, height, width)
+            lip_mask = get_union_lip_mask(landmarks, height, width)
+
+            # 4 gaussian blur
+            blur_face_mask = blur_mask(face_mask, (64, 64), (51, 51))
+            blur_lip_mask = blur_mask(lip_mask, (64, 64), (31, 31))
+
+            # 5 seperate mask
+            sep_face_mask = cv2.subtract(blur_face_mask, blur_lip_mask)
+            sep_pose_mask = 255.0 - blur_face_mask
+            sep_lip_mask = blur_lip_mask
+
+        return face_mask, face_emb, sep_pose_mask, sep_face_mask, sep_lip_mask
+
+    def close(self):
+        """
+        Closes the ImageProcessor and releases any resources held by the FaceAnalysis instance.
+
+        Args:
+            self: The ImageProcessor instance.
+
+        Returns:
+            None.
+        """
+        for _, model in self.face_analysis.models.items():
+            if hasattr(model, "Dispose"):
+                model.Dispose()
+
+    def _augmentation(self, images, transform, state=None):
+        if state is not None:
+            torch.set_rng_state(state)
+        if isinstance(images, List):
+            transformed_images = [transform(img) for img in images]
+            ret_tensor = torch.stack(transformed_images, dim=0)  # (f, c, h, w)
+        else:
+            ret_tensor = transform(images)  # (c, h, w)
+        return ret_tensor
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, _exc_type, _exc_val, _exc_tb):
+        self.close()
diff --git a/high_sync/imgs/model_stage2.png b/high_sync/imgs/model_stage2.png
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diff --git a/high_sync/inference.py b/high_sync/inference.py
new file mode 100644
index 0000000..ce5430e
--- /dev/null
+++ b/high_sync/inference.py
@@ -0,0 +1,398 @@
+import argparse
+import os
+import warnings
+import torch
+from diffusers import AutoencoderKL, DDIMScheduler
+from omegaconf import OmegaConf
+from torch import nn
+import cv2
+import numpy as np
+from typing import List
+from einops import rearrange
+import librosa
+import soundfile as sf
+import torchaudio
+import imageio
+import subprocess
+from denoiser import pretrained
+from denoiser.dsp import convert_audio
+
+from .src.pipelines.face_animate import FaceAnimatePipeline
+from .src.models.unet_2d_condition import UNet2DConditionModel
+from .src.models.unet_3d_echo import EchoUNet3DConditionModel
+from .src.models.whisper.audio2feature import load_audio_model
+from .image_processor import ImageProcessor
+from .src.utils.util import save_video, filter_non_none
+
+
+warnings.filterwarnings("ignore")
+
+class Net(nn.Module):
+    """
+    The Net class combines all the necessary modules for the inference process.
+
+    Args:
+        reference_unet (UNet2DConditionModel): The UNet2DConditionModel used as a reference for inference.
+        denoising_unet (UNet3DConditionModel): The UNet3DConditionModel used for denoising the input audio.
+    """
+    def __init__(
+        self,
+        reference_unet: UNet2DConditionModel,
+        denoising_unet: EchoUNet3DConditionModel,
+    ):
+        super().__init__()
+        self.reference_unet = reference_unet
+        self.denoising_unet = denoising_unet
+
+    def forward(self,):
+        """
+        empty function to override abstract function of nn Module
+        """
+
+    def get_modules(self):
+        """
+        Simple method to avoid too-few-public-methods pylint error
+        """
+        return {
+            "reference_unet": self.reference_unet,
+            "denoising_unet": self.denoising_unet,
+        }
+
+
+def create_mask(mask_path, mask_512_path, clip_length):
+        mask = 1 - (cv2.imread(mask_path) / 255.)
+        (h, w, c) = mask.shape
+        new_mask = np.zeros((h, w, c+1))
+        new_mask[:, :, :c] = mask
+        new_mask[:, :, c] = mask[:, :, c-1]
+
+        mask = torch.FloatTensor(new_mask).permute(2,0,1).unsqueeze(1)
+
+        return mask
+
+
+def create_mask_alpha(w, h, coordinate):
+    (left, top, right, bottom) = coordinate
+
+    mask = np.ones((h,w,3))
+    mask[top:bottom, left:right] = 0
+
+    pad = min(int((right-left)/15), int((bottom-top)/15)) # 100/15=6.6% for padding
+
+    for idx in range(left, right):
+        mask[bottom-pad:bottom, idx] = np.array([np.linspace(0,1,pad)]*3).transpose(1,0)
+        mask[top:top+pad, idx] = np.array([np.linspace(1,0,pad)]*3).transpose(1,0)
+
+    for idx in range(top, bottom):
+        mask[idx, right-pad:right] = np.array([np.linspace(0,1,pad)]*3).transpose(1,0)
+        mask[idx, left:left+pad] = np.array([np.linspace(1,0,pad)]*3).transpose(1,0)
+
+    for idx, start, end in zip(range(top, top+pad), np.linspace(1,0.7, pad), np.linspace(0.7,0, pad)):
+        mask[idx, left:left+pad] = np.array([np.linspace(start,end,pad)]*3).transpose(1,0)
+        mask[idx, right-pad:right] = np.array([np.linspace(end,start,pad)]*3).transpose(1,0)
+
+    for idx, start, end in zip(range(bottom-pad, bottom), np.linspace(0.7,1, pad), np.linspace(0,0.7, pad)):
+        mask[idx, left:left+pad] = np.array([np.linspace(start,end,pad)]*3).transpose(1,0)
+        mask[idx, right-pad:right] = np.array([np.linspace(end,start,pad)]*3).transpose(1,0)
+
+    return mask
+
+def process_audio(input_audio_path, output_audio_path):
+    wav,_ = librosa.load(input_audio_path, sr=16000)
+    tmp_path = output_audio_path[:-4]+"_16000.wav"
+    sf.write(tmp_path, wav, 16000)
+
+    model = pretrained.dns64().cuda()
+    wav, sr = torchaudio.load(input_audio_path)
+    wav = convert_audio(wav.cuda(), sr, model.sample_rate, model.chin)
+
+    with torch.no_grad():
+        denoised = model(wav[None])[0]
+
+    sf.write(output_audio_path, denoised.data.cpu().numpy()[0], 16000)
+
+    return
+
+img_size = (512,512)
+
+
+def mem_gpu():
+    # Total memory
+    total = torch.cuda.get_device_properties(0).total_memory
+    # Allocated memory
+    allocated = torch.cuda.memory_allocated(0)
+    # Cached memory (used for optimization)
+    cached = torch.cuda.memory_reserved(0)
+
+    print(f"Total: {total / 1e9:.2f} GB")
+    print(f"Allocated: {allocated / 1e9:.2f} GB")
+    print(f"Cached: {cached / 1e9:.2f} GB")
+
+
+def inference_process(args: argparse.Namespace):
+    """
+    Perform inference processing.
+
+    Args:
+        args (argparse.Namespace): Command-line arguments.
+
+    This function initializes the configuration for the inference process. It sets up the necessary
+    modules and variables to prepare for the upcoming inference steps.
+    """
+    # 1. init config
+    cli_args = filter_non_none(vars(args))
+    config = OmegaConf.load(args.config)
+    config = OmegaConf.merge(config, cli_args)
+    save_path = config.save_path
+    if not os.path.exists(save_path):
+        os.makedirs(save_path)
+
+    # 2. runtime variables
+    device = torch.device(
+        "cuda") if torch.cuda.is_available() else torch.device("cpu")
+    if config.weight_dtype == "fp16":
+        weight_dtype = torch.float16
+    elif config.weight_dtype == "bf16":
+        weight_dtype = torch.bfloat16
+    elif config.weight_dtype == "fp32":
+        weight_dtype = torch.float32
+    else:
+        weight_dtype = torch.float32
+
+    # 3. prepare inference data
+    # 3.1 prepare source image, face mask
+    img_size = (config.data.source_image.width,
+                config.data.source_image.height)
+    clip_length = config.data.n_sample_frames
+    mask_path = config.mask_path
+    mask_512_path = config.mask_512_path
+
+    temp_dir = os.path.join(save_path, "img")
+    os.makedirs(temp_dir, exist_ok=True)
+    os.system(f"rm -r {temp_dir}/*")
+
+    # 3.2 prepare audio embeddings
+    sample_rate = config.data.driving_audio.sample_rate
+    assert sample_rate == 16000, "audio sample rate must be 16000"
+    fps = config.data.export_video.fps
+
+    ### load audio processor params
+    audio_processor = load_audio_model(model_path=config.audio_model_path, device=device)
+
+    audio_temp_path = os.path.join(save_path, "audio.wav")
+    process_audio(config.driving_audio, audio_temp_path)
+
+    # 4. build modules
+    sched_kwargs = OmegaConf.to_container(config.noise_scheduler_kwargs)
+    if config.enable_zero_snr:
+        sched_kwargs.update(
+            rescale_betas_zero_snr=True,
+            timestep_spacing="trailing",
+            prediction_type="v_prediction",
+        )
+    val_noise_scheduler = DDIMScheduler(**sched_kwargs)
+    sched_kwargs.update({"beta_schedule": "scaled_linear"})
+
+    vae = AutoencoderKL.from_pretrained(config.vae.model_path)
+    reference_unet = UNet2DConditionModel.from_pretrained(
+        config.base_model_path, subfolder="unet")
+    denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d(
+        config.base_model_path,
+        "",
+        subfolder="unet",
+        unet_additional_kwargs=OmegaConf.to_container(
+            config.unet_additional_kwargs)
+    )
+
+    reference_unet.load_state_dict(
+        torch.load(config.reference_unet_path, map_location="cpu"),
+    )
+
+    denoising_unet.load_state_dict(
+        torch.load(config.denoising_unet_path, map_location="cpu"),
+    )
+
+    # Freeze
+    vae.requires_grad_(False)
+    reference_unet.requires_grad_(False)
+    denoising_unet.requires_grad_(False)
+
+    # reference_unet.enable_gradient_checkpointing()
+    # denoising_unet.enable_gradient_checkpointing()
+
+    vae.eval()
+    reference_unet.eval()
+    denoising_unet.eval()
+
+    net = Net(
+        reference_unet,
+        denoising_unet
+    )
+
+
+    # 5. inference
+    pipeline = FaceAnimatePipeline(
+        vae=vae,
+        reference_unet=net.reference_unet,
+        denoising_unet=net.denoising_unet,
+        scheduler=val_noise_scheduler
+    )
+
+
+    pipeline.to(device=device, dtype=weight_dtype)
+
+    image_processor = ImageProcessor(img_size)
+
+    generator = torch.manual_seed(42)
+
+    video_stream = cv2.VideoCapture(args.source_video)
+
+    temp_video_path = config.output[:4] + "_no_audio.mp4"
+    writer = imageio.get_writer(
+    temp_video_path,
+    fps=25,
+    codec='libx264',
+    quality=10,  # 0 (low) to 10 (high)
+    ffmpeg_params=[
+        '-pix_fmt', 'yuv420p',
+        '-crf', '18'  # Lower CRF = better quality (17–23 is visually lossless range)
+    ])
+
+    whisper_feature = audio_processor.audio2feat(audio_temp_path)
+
+    whisper_chunks = audio_processor.feature2chunks(feature_array=whisper_feature, fps=fps)
+    # torch.save(whisper_chunks, "./cache/audio.pt")
+    audio_frame_num = whisper_chunks.shape[0]
+    audio_fea_final = torch.Tensor(whisper_chunks).to(dtype=vae.dtype, device=vae.device)
+    # audio_fea_final = torch.tensor(torch.load("/workspace/preprocess/audio_emb_whisper/cM096XGOCG4_C1.pt")).to(dtype=vae.dtype, device=vae.device)
+    audio_fea_final = audio_fea_final.unsqueeze(0)
+
+    video_length = min(int(video_stream.get(cv2.CAP_PROP_FRAME_COUNT)), audio_frame_num)
+    if video_length < audio_frame_num:
+        audio_fea_final = audio_fea_final[:, :video_length, :, :]
+
+    times = video_length // config.inference_frames_num
+
+    frames_video = []
+    count_motion = 0
+    count_img = 0
+    count = 0
+    finish = False
+
+    while 1:
+
+        still_reading, frame = video_stream.read()
+
+        if (not still_reading) or (count_motion >= video_length):
+            video_stream.release()
+            finish = True
+        else:
+            frames_video.append(frame)
+            count_motion += 1
+
+        if len(frames_video)==0:
+            break
+
+        if ((count_motion % config.inference_frames_num) == 0) or finish:
+
+            print(f"processing {count}/{times}")
+            source_image_pixels_all, bbox_all, frame_list_all = image_processor.preprocess_frames_jump(frames_video)
+
+            del frames_video
+
+            for source_image_pixels_list, bbox_list, frame_list in zip(source_image_pixels_all, bbox_all, frame_list_all):
+
+                print(f"generating {count}/{times}")
+
+                if bbox_list[0]!=(0,0,0,0):
+
+                    audio_tensor = audio_fea_final[:, count_img : count_img + len(frame_list)]
+
+                    main_length = len(frame_list)
+                    diff = 12 - main_length
+
+                    if diff > 0:
+                        source_image_pixels_list.extend([source_image_pixels_list[-1]]*diff)
+                        audio_tensor = torch.cat((audio_tensor, audio_tensor[:,-1:].repeat(1,diff,1,1)), dim=1)
+
+                    source_image_pixels = torch.cat(source_image_pixels_list, dim=0)
+                    mask_main = create_mask(mask_path, mask_512_path, len(frame_list))
+                    mask_main = mask_main.unsqueeze(0).to(device="cuda", dtype=weight_dtype)
+                    torch.cuda.empty_cache()
+
+                    with torch.no_grad():
+                        pipeline_output = pipeline(
+                            gt_encode=source_image_pixels,
+                            audio_tensor=audio_tensor,
+                            mask_main=mask_main,
+                            width=img_size[0],
+                            height=img_size[1],
+                            video_length=len(source_image_pixels_list),
+                            num_inference_steps=config.inference_steps,
+                            guidance_scale=config.cfg_scale,
+                            generator=generator
+                        )
+
+                    torch.cuda.empty_cache()
+
+                    images_out = pipeline_output.videos.squeeze(0).permute(1, 2, 3, 0).cpu().numpy()[:main_length]  # convert to [f, h, w, c]
+                    images_out = np.clip(images_out * 255, 0, 255).astype(np.uint8)  # to [0, 255]
+
+                    for image_out, bbox, frame in zip(images_out, bbox_list, frame_list):
+
+                        frame_h, frame_w, _ = frame.shape
+                        path = os.path.join(temp_dir, f"image_{count_img}.png")
+
+                        if bbox==(0,0,0,0):
+                            writer.append_data(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
+                            # cv2.imwrite(path, frame)
+                        else:
+                            image_out = cv2.cvtColor(image_out, cv2.COLOR_RGB2BGR)
+                            frame_in_out = frame.copy()
+                            frame_in_out[bbox[1]:bbox[3], bbox[0]:bbox[2]] = cv2.resize(image_out, (bbox[2] - bbox[0], bbox[3] - bbox[1]))
+                            mask = create_mask_alpha(frame_w, frame_h, bbox)
+                            blurred_image = (frame * mask + frame_in_out * (1-mask)).astype(np.uint8)
+                            writer.append_data(cv2.cvtColor(blurred_image, cv2.COLOR_RGB2BGR))
+                            # cv2.imwrite(path, blurred_image)
+
+                        count_img += 1
+
+                else:
+                    for frame in frame_list:
+                        path = os.path.join(temp_dir, f"image_{count_img}.png")
+                        writer.append_data(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
+                        # cv2.imwrite(path, frame)
+                        count_img += 1
+
+            count += 1
+
+            frames_video = []
+
+        if finish:
+            break
+
+    writer.close()
+
+    command = f"ffmpeg -i {temp_video_path} -i {config.driving_audio} -map 0:v -map 1:a -c:v h264 -shortest -y {config.output} -loglevel quiet"
+    subprocess.run(command, shell=True, check=True, text=True)
+
+    # output_file = config.output
+    # frames_path = os.path.join(temp_dir, f"image_%d.png")
+    # save_video(frames_path, config.driving_audio, output_file, frame_w, frame_h)
+
+    return
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("-c", "--config", default="configs/inference/default.yaml")
+    parser.add_argument("--source_video", type=str, required=False, help="source image")
+    parser.add_argument("--driving_audio", type=str, required=False, help="driving audio")
+    parser.add_argument("--output", type=str, help="output video file name", default="./cache/output.mp4")
+    parser.add_argument("--inference_frames_num", type=int, help="inference frames number", default=1500)
+
+
+    command_line_args = parser.parse_args()
+
+    inference_process(command_line_args)
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diff --git a/high_sync/requirements.txt b/high_sync/requirements.txt
new file mode 100644
index 0000000..aef8863
--- /dev/null
+++ b/high_sync/requirements.txt
@@ -0,0 +1,42 @@
+accelerate==0.28.0
+audio-separator==0.17.2
+av==12.1.0
+torchmetrics
+torchtyping
+facenet_pytorch==2.5.0
+ffmpeg-python==0.2.0
+bitsandbytes==0.43.1
+decord==0.6.0
+diffusers==0.24.0
+einops==0.8.0
+insightface==0.7.3
+librosa==0.10.2.post1
+mediapipe[vision]==0.10.14
+mlflow==2.13.1
+moviepy==1.0.3
+numpy==1.26.4
+omegaconf==2.3.0
+onnx2torch==1.5.14
+onnx==1.16.1
+onnxruntime-gpu==1.18.0
+opencv-contrib-python==4.9.0.80
+opencv-python-headless==4.9.0.80
+opencv-python==4.9.0.80
+pillow==10.3.0
+setuptools==70.0.0
+tqdm==4.66.4
+transformers==4.39.2
+xformers==0.0.25.post1
+isort==5.13.2
+pylint==3.2.2
+pre-commit==3.7.1
+gradio==4.36.1
+huggingface-hub==0.25.2
+deepspeed==0.15.2
+tensorboard
+torchaudio==2.2.2
+denoiser==0.1.5
+soundfile==0.13.1
+deepface==0.0.93
+ultralytics==8.3.82
+tf-keras==2.18.0
\ No newline at end of file
diff --git a/high_sync/scripts/extract_meta_info_stage1.py b/high_sync/scripts/extract_meta_info_stage1.py
new file mode 100644
index 0000000..f1cc2f1
--- /dev/null
+++ b/high_sync/scripts/extract_meta_info_stage1.py
@@ -0,0 +1,114 @@
+# pylint: disable=R0801
+"""
+This module is used to extract meta information from video directories.
+
+It takes in two command-line arguments: `root_path` and `dataset_name`. The `root_path`
+specifies the path to the video directory, while the `dataset_name` specifies the name
+of the dataset. The module then collects all the video folder paths, and for each video
+folder, it checks if a mask path and a face embedding path exist. If they do, it appends
+a dictionary containing the image path, mask path, and face embedding path to a list.
+
+Finally, the module writes the list of dictionaries to a JSON file with the filename
+constructed using the `dataset_name`.
+
+Usage:
+    python tools/extract_meta_info_stage1.py --root_path /path/to/video_dir --dataset_name hdtf
+
+"""
+
+import argparse
+import json
+import os
+from pathlib import Path
+import random
+import torch
+import tqdm
+
+
+def collect_video_folder_paths(root_path: Path) -> list:
+    """
+    Collect all video folder paths from the root path.
+
+    Args:
+        root_path (Path): The root directory containing video folders.
+
+    Returns:
+        list: List of video folder paths.
+    """
+    return [frames_dir.resolve() for frames_dir in root_path.iterdir() if frames_dir.is_dir()]
+
+
+def construct_meta_info(frames_dir_path: Path) -> dict:
+    """
+    Construct meta information for a given frames directory.
+
+    Args:
+        frames_dir_path (Path): The path to the frames directory.
+
+    Returns:
+        dict: A dictionary containing the meta information for the frames directory, or None if the required files do not exist.
+    """
+    audio_emb_path = str(frames_dir_path).replace("images", "audio_emb_whisper") + ".pt"
+    video_path = str(frames_dir_path).replace("images", "videos") + ".mp4"
+
+    if not (os.path.isfile(audio_emb_path) and os.path.isfile(video_path)):
+        return None
+
+    # if torch.load(audio_emb_path) is None:
+    #     print(f"audio emb is None: {audio_emb_path}")
+    #     return None
+    # if not os.path.exists(video_path):
+    #     print("no video exists")
+    #     return None
+
+    return {
+        "image_path": str(frames_dir_path),
+        "video_path": video_path,
+        "audio_emb": audio_emb_path
+    }
+
+
+def main():
+    """
+    Main function to extract meta info for training.
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-r", "--root_path", type=str,
+                        required=True, help="Root path of the video directories")
+    parser.add_argument("-n", "--dataset_name", type=str,
+                        required=True, help="Name of the dataset")
+    parser.add_argument("--meta_info_name", type=str,
+                        help="Name of the meta information file")
+
+    args = parser.parse_args()
+
+    if args.meta_info_name is None:
+        args.meta_info_name = args.dataset_name
+
+    image_dir = Path(args.root_path) / "images"
+    output_dir = Path("./data")
+    output_dir.mkdir(exist_ok=True)
+
+    # Collect all video folder paths
+    frames_dir_paths = collect_video_folder_paths(image_dir)
+    random.shuffle(frames_dir_paths)
+
+    meta_infos = []
+    for frames_dir_path in tqdm.tqdm(frames_dir_paths):
+        meta_info = construct_meta_info(frames_dir_path)
+        if meta_info:
+            meta_infos.append(meta_info)
+
+    output_file = output_dir / f"{args.meta_info_name}_train.json"
+    with output_file.open("w", encoding="utf-8") as f:
+        json.dump(meta_infos[:-100], f, indent=4)
+
+    output_file = output_dir / f"{args.meta_info_name}_val.json"
+    with output_file.open("w", encoding="utf-8") as f:
+        json.dump(meta_infos[-100:], f, indent=4)
+
+    print(f"Final data count: {len(meta_infos)}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/high_sync/src/__init__.py b/high_sync/src/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/high_sync/src/datasets/image_processor.py b/high_sync/src/datasets/image_processor.py
new file mode 100644
index 0000000..c7dad4f
--- /dev/null
+++ b/high_sync/src/datasets/image_processor.py
@@ -0,0 +1,370 @@
+# pylint: disable=W0718
+"""
+This module is responsible for processing images, particularly for face-related tasks.
+It uses various libraries such as OpenCV, NumPy, and InsightFace to perform tasks like
+face detection, augmentation, and mask rendering. The ImageProcessor class encapsulates
+the functionality for these operations.
+"""
+import os
+from typing import List
+
+import cv2
+import mediapipe as mp
+import numpy as np
+import torch
+from insightface.app import FaceAnalysis
+from PIL import Image, ImageOps
+from torchvision import transforms
+
+from ..utils.util import (blur_mask, get_landmark_overframes, get_mask,
+                          get_union_face_mask, get_union_lip_mask)
+
+MEAN = 0.5
+STD = 0.5
+
+class ImageProcessor:
+    """
+    ImageProcessor is a class responsible for processing images, particularly for face-related tasks.
+    It takes in an image and performs various operations such as augmentation, face detection,
+    face embedding extraction, and rendering a face mask. The processed images are then used for
+    further analysis or recognition purposes.
+
+    Attributes:
+        img_size (int): The size of the image to be processed.
+        face_analysis_model_path (str): The path to the face analysis model.
+
+    Methods:
+        preprocess(source_image_path, cache_dir):
+            Preprocesses the input image by performing augmentation, face detection,
+            face embedding extraction, and rendering a face mask.
+
+        close():
+            Closes the ImageProcessor and releases any resources being used.
+
+        _augmentation(images, transform, state=None):
+            Applies image augmentation to the input images using the given transform and state.
+
+        __enter__():
+            Enters a runtime context and returns the ImageProcessor object.
+
+        __exit__(_exc_type, _exc_val, _exc_tb):
+            Exits a runtime context and handles any exceptions that occurred during the processing.
+    """
+    def __init__(self, img_size, face_analysis_model_path) -> None:
+        self.img_size = img_size
+
+        self.pixel_transform = transforms.Compose(
+            [
+                transforms.Resize(self.img_size),
+                transforms.ToTensor(),
+                transforms.Normalize([MEAN], [STD]),
+            ]
+        )
+
+        self.cond_transform = transforms.Compose(
+            [
+                transforms.Resize(self.img_size),
+                transforms.ToTensor(),
+            ]
+        )
+
+        self.attn_transform_64 = transforms.Compose(
+            [
+                transforms.Resize(
+                    (self.img_size[0] // 8, self.img_size[0] // 8)),
+                transforms.ToTensor(),
+            ]
+        )
+        self.attn_transform_32 = transforms.Compose(
+            [
+                transforms.Resize(
+                    (self.img_size[0] // 16, self.img_size[0] // 16)),
+                transforms.ToTensor(),
+            ]
+        )
+        self.attn_transform_16 = transforms.Compose(
+            [
+                transforms.Resize(
+                    (self.img_size[0] // 32, self.img_size[0] // 32)),
+                transforms.ToTensor(),
+            ]
+        )
+        self.attn_transform_8 = transforms.Compose(
+            [
+                transforms.Resize(
+                    (self.img_size[0] // 64, self.img_size[0] // 64)),
+                transforms.ToTensor(),
+            ]
+        )
+
+        self.face_analysis = FaceAnalysis(
+            name="",
+            root=face_analysis_model_path,
+            providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
+        )
+        self.face_analysis.prepare(ctx_id=0, det_size=(640, 640))
+
+    def preprocess(self, frame):
+        """
+        Apply preprocessing to the source image to prepare for face analysis.
+
+        Parameters:
+            source_image_path (str): The path to the source image.
+            cache_dir (str): The directory to cache intermediate results.
+
+        Returns:
+            None
+        """
+        frame_h, frame_w, _ = frame.shape
+        # 2.1 detect face
+        faces = self.face_analysis.get(frame)
+        if not faces:
+            print("No faces detected in the image. Using the entire image as the face region.")
+            bbox = (0,0,0,0)
+            face_emb = np.zeros(512)
+            frame_crop = frame
+        else:
+            # Sort faces by size and select the largest one
+            face = sorted(faces, key=lambda x: (x["bbox"][2] - x["bbox"][0]) * (x["bbox"][3] - x["bbox"][1]), reverse=True)[0]
+            face_emb = face["embedding"]
+            w, h = face["bbox"][2] - face["bbox"][0], face["bbox"][3] - face["bbox"][1]
+            bbox = (int(max(face["bbox"][0] - 0.1*w, 0)), int(face["bbox"][1]),
+                                 int(min(face["bbox"][2] + 0.1*w, frame_w)), int(min(face["bbox"][3] + 0.1*h, frame_h)))
+            frame_crop = frame[bbox[1]:bbox[3], bbox[0]:bbox[2]]
+
+        cv2.imwrite("./x.jpg", frame_crop)
+        frame_crop = cv2.resize(frame_crop, (512,512))
+
+        pixel_values_ref_img = self._augmentation(Image.fromarray(frame_crop).convert("RGB"), self.pixel_transform)
+
+        return pixel_values_ref_img, face_emb, bbox, frame_crop
+
+
+    def preprocess_frames(self, frames):
+        """
+        Apply preprocessing to the 14 source images to prepare for face analysis.
+
+        Parameters:
+            source_image_path (str): The path to the source image.
+            cache_dir (str): The directory to cache intermediate results.
+
+        Returns:
+            None
+        """
+        def find_max_h(bbox_list):
+
+            h_max = 0
+            for bbox in bbox_list:
+                h = bbox[3]
+                if h>h_max:
+                    h_max = h
+
+            return h_max
+
+        pixel_values_ref_img_list, bbox_list, bbox_final_list, frame_crop_list = [], [], [], []
+
+        face_emb = np.zeros(512)
+
+        for frame in frames:
+
+            # 2.1 detect face
+            faces = self.face_analysis.get(frame)
+            if not faces:
+                print("No faces detected in the image. Using the entire image as the face region.")
+                bbox_list.append((0,0,0,0))
+
+            else:
+                # Sort faces by size and select the largest one
+                face = sorted(faces, key=lambda x: (x["bbox"][2] - x["bbox"][0]) * (x["bbox"][3] - x["bbox"][1]), reverse=True)[0]
+                face_emb = face["embedding"]
+                bbox_list.append(face["bbox"])
+
+        h_max = find_max_h(bbox_list)
+
+        for frame, bbox in zip(frames, bbox_list):
+
+            frame_h, frame_w, _ = frame.shape
+
+            w, h = bbox[2] - bbox[0], bbox[3] - bbox[1]
+
+            if h_max < 1.4*h:
+                h = h_max
+
+            bbox_final = (int(max(bbox[0] - 0.1*w, 0)), int(bbox[1]),
+                                 int(min(bbox[2] + 0.1*w, frame_w)), int(min(bbox[1] + 1.1*h, frame_h)))
+            bbox_final_list.append(bbox_final)
+
+            frame_crop = frame[bbox_final[1]:bbox_final[3], bbox_final[0]:bbox_final[2]]
+            frame_crop_list.append(cv2.resize(frame_crop, (512,512)))
+
+            pixel_values_ref_img_list.append(self._augmentation(Image.fromarray(frame_crop).convert("RGB"), self.pixel_transform).unsqueeze(0))
+
+        return pixel_values_ref_img_list, face_emb, bbox_list, frame_crop_list
+
+
+    def close(self):
+        """
+        Closes the ImageProcessor and releases any resources held by the FaceAnalysis instance.
+
+        Args:
+            self: The ImageProcessor instance.
+
+        Returns:
+            None.
+        """
+        for _, model in self.face_analysis.models.items():
+            if hasattr(model, "Dispose"):
+                model.Dispose()
+
+    def _augmentation(self, images, transform, state=None):
+        if state is not None:
+            torch.set_rng_state(state)
+        if isinstance(images, List):
+            transformed_images = [transform(img) for img in images]
+            ret_tensor = torch.stack(transformed_images, dim=0)  # (f, c, h, w)
+        else:
+            ret_tensor = transform(images)  # (c, h, w)
+        return ret_tensor
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, _exc_type, _exc_val, _exc_tb):
+        self.close()
+
+
+class ImageProcessorForDataProcessing():
+    """
+    ImageProcessor is a class responsible for processing images, particularly for face-related tasks.
+    It takes in an image and performs various operations such as augmentation, face detection,
+    face embedding extraction, and rendering a face mask. The processed images are then used for
+    further analysis or recognition purposes.
+
+    Attributes:
+        img_size (int): The size of the image to be processed.
+        face_analysis_model_path (str): The path to the face analysis model.
+
+    Methods:
+        preprocess(source_image_path, cache_dir):
+            Preprocesses the input image by performing augmentation, face detection,
+            face embedding extraction, and rendering a face mask.
+
+        close():
+            Closes the ImageProcessor and releases any resources being used.
+
+        _augmentation(images, transform, state=None):
+            Applies image augmentation to the input images using the given transform and state.
+
+        __enter__():
+            Enters a runtime context and returns the ImageProcessor object.
+
+        __exit__(_exc_type, _exc_val, _exc_tb):
+            Exits a runtime context and handles any exceptions that occurred during the processing.
+    """
+    def __init__(self, face_analysis_model_path, landmark_model_path, step) -> None:
+        if step == 2:
+            self.face_analysis = FaceAnalysis(
+                name="",
+                root=face_analysis_model_path,
+                providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
+            )
+            self.face_analysis.prepare(ctx_id=0, det_size=(640, 640))
+            self.landmarker = None
+        else:
+            BaseOptions = mp.tasks.BaseOptions
+            FaceLandmarker = mp.tasks.vision.FaceLandmarker
+            FaceLandmarkerOptions = mp.tasks.vision.FaceLandmarkerOptions
+            VisionRunningMode = mp.tasks.vision.RunningMode
+            # Create a face landmarker instance with the video mode:
+            options = FaceLandmarkerOptions(
+                base_options=BaseOptions(model_asset_path=landmark_model_path),
+                running_mode=VisionRunningMode.IMAGE,
+            )
+            self.landmarker = None #FaceLandmarker.create_from_options(options)
+            self.face_analysis = None
+
+    def preprocess(self, source_image_path: str):
+        """
+        Apply preprocessing to the source image to prepare for face analysis.
+
+        Parameters:
+            source_image_path (str): The path to the source image.
+            cache_dir (str): The directory to cache intermediate results.
+
+        Returns:
+            None
+        """
+        # 1. get face embdeding
+        face_mask, face_emb, sep_pose_mask, sep_face_mask, sep_lip_mask = None, None, None, None, None
+        if self.face_analysis:
+            frame = sorted(os.listdir(source_image_path))[0]
+            source_image = Image.open(
+                os.path.join(source_image_path, frame))
+            ref_image_pil = source_image.convert("RGB")
+
+            padding = 250
+            ref_image_pil = ImageOps.expand(ref_image_pil, border=padding, fill=0)
+
+            # 2.1 detect face
+            faces = self.face_analysis.get(cv2.cvtColor(
+                np.array(ref_image_pil.copy()), cv2.COLOR_RGB2BGR))
+
+            # use max size face
+            face = sorted(faces, key=lambda x: (
+                x["bbox"][2] - x["bbox"][0]) * (x["bbox"][3] - x["bbox"][1]))[-1]
+            # 2.2 face embedding
+            face_emb = face["embedding"]
+            if face_emb is None:
+                print(f"error in image embedding : {source_image_path}")
+                raise
+
+        if self.landmarker:
+            # 3.1 get landmark
+            landmarks, height, width = get_landmark_overframes(
+                self.landmarker, source_image_path)
+            assert len(landmarks) == len(os.listdir(source_image_path))
+
+            # 3 render face and lip mask
+            face_mask = get_union_face_mask(landmarks, height, width)
+            lip_mask = get_union_lip_mask(landmarks, height, width)
+
+            # 4 gaussian blur
+            blur_face_mask = blur_mask(face_mask, (64, 64), (51, 51))
+            blur_lip_mask = blur_mask(lip_mask, (64, 64), (31, 31))
+
+            # 5 seperate mask
+            sep_face_mask = cv2.subtract(blur_face_mask, blur_lip_mask)
+            sep_pose_mask = 255.0 - blur_face_mask
+            sep_lip_mask = blur_lip_mask
+
+        return face_mask, face_emb, sep_pose_mask, sep_face_mask, sep_lip_mask
+
+    def close(self):
+        """
+        Closes the ImageProcessor and releases any resources held by the FaceAnalysis instance.
+
+        Args:
+            self: The ImageProcessor instance.
+
+        Returns:
+            None.
+        """
+        for _, model in self.face_analysis.models.items():
+            if hasattr(model, "Dispose"):
+                model.Dispose()
+
+    def _augmentation(self, images, transform, state=None):
+        if state is not None:
+            torch.set_rng_state(state)
+        if isinstance(images, List):
+            transformed_images = [transform(img) for img in images]
+            ret_tensor = torch.stack(transformed_images, dim=0)  # (f, c, h, w)
+        else:
+            ret_tensor = transform(images)  # (c, h, w)
+        return ret_tensor
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, _exc_type, _exc_val, _exc_tb):
+        self.close()
diff --git a/high_sync/src/datasets/mask_image.py b/high_sync/src/datasets/mask_image.py
new file mode 100644
index 0000000..52097af
--- /dev/null
+++ b/high_sync/src/datasets/mask_image.py
@@ -0,0 +1,291 @@
+# pylint: disable=R0801
+"""
+This module contains the code for a dataset class called FaceMaskDataset, which is used to process and
+load image data related to face masks. The dataset class inherits from the PyTorch Dataset class and
+provides methods for data augmentation, getting items from the dataset, and determining the length of the
+dataset. The module also includes imports for necessary libraries such as json, random, pathlib, torch,
+PIL, and transformers.
+"""
+
+import json
+from pathlib import Path
+import cv2
+import numpy as np
+
+import torch
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+from transformers import CLIPImageProcessor
+
+
+class FaceMaskDatasetVal(Dataset):
+    """
+    FaceMaskDataset is a custom dataset for face mask images.
+
+    Args:
+        img_size (int): The size of the input images.
+        drop_ratio (float, optional): The ratio of dropped pixels during data augmentation. Defaults to 0.1.
+        data_meta_paths (list, optional): The paths to the metadata files containing image paths and labels. Defaults to ["./data/HDTF_meta.json"].
+        sample_margin (int, optional): The margin for sampling regions in the image. Defaults to 30.
+
+    Attributes:
+        img_size (int): The size of the input images.
+        drop_ratio (float): The ratio of dropped pixels during data augmentation.
+        data_meta_paths (list): The paths to the metadata files containing image paths and labels.
+        sample_margin (int): The margin for sampling regions in the image.
+        processor (CLIPImageProcessor): The image processor for preprocessing images.
+        transform (transforms.Compose): The image augmentation transform.
+    """
+
+    def __init__(
+        self,
+        img_size,
+        mask_path,
+        drop_ratio=0.1,
+        data_meta_path=None,
+        sample_margin=30,
+    ):
+        super().__init__()
+
+        self.img_size = img_size
+        self.sample_margin = sample_margin
+
+        vid_meta = []
+        with open(data_meta_path, "r", encoding="utf-8") as f:
+            vid_meta.extend(json.load(f))
+        self.vid_meta = vid_meta
+        self.length = len(self.vid_meta)
+
+        self.mask = self.create_mask(mask_path)
+
+        self.clip_image_processor = CLIPImageProcessor()
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(self.img_size),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+
+        self.cond_transform = transforms.Compose(
+            [
+                transforms.Resize(self.img_size),
+                transforms.ToTensor(),
+            ]
+        )
+
+        self.drop_ratio = drop_ratio
+
+    def create_mask(self, mask_path):
+        mask = 1 - (cv2.imread(mask_path) / 255.)
+        (h, w, c) = mask.shape
+        new_mask = np.zeros((h, w, c+1))
+        new_mask[:, :, :c] = mask
+        new_mask[:, :, c] = mask[:, :, c-1]
+
+        mask = torch.FloatTensor(new_mask).permute(2,0,1).unsqueeze(1)
+
+        return mask
+
+    def augmentation(self, image, transform, state=None):
+        """
+        Apply data augmentation to the input image.
+
+        Args:
+            image (PIL.Image): The input image.
+            transform (torchvision.transforms.Compose): The data augmentation transforms.
+            state (dict, optional): The random state for reproducibility. Defaults to None.
+
+        Returns:
+            PIL.Image: The augmented image.
+        """
+        if state is not None:
+            torch.set_rng_state(state)
+        return transform(image)
+
+    def __getitem__(self, index):
+        video_meta = self.vid_meta[index]
+        video_path = video_meta["image_path"]
+
+        video_frames = sorted(Path(video_path).iterdir())
+        video_length = len(video_frames)
+
+        margin = min(self.sample_margin, video_length)
+
+        ref_img_idx = np.random.randint(0, video_length - 1)
+        if ref_img_idx + margin < (video_length - 1):
+            tgt_img_idx = np.random.randint(
+                ref_img_idx + margin, video_length - 1)
+        elif ref_img_idx - margin > 0:
+            tgt_img_idx = np.random.randint(0, ref_img_idx - margin)
+        else:
+            tgt_img_idx = np.random.randint(0, video_length - 1)
+
+        ref_img_pil = Image.open(video_frames[ref_img_idx])
+        tgt_img_pil = Image.open(video_frames[tgt_img_idx])
+
+        assert ref_img_pil is not None, "Fail to load reference image."
+        assert tgt_img_pil is not None, "Fail to load target image."
+
+        audio_emb_path = video_meta["audio_emb"]
+        audio_emb = torch.load(audio_emb_path)
+        audio_tensor = audio_emb[tgt_img_idx]
+
+        sample = {
+            "video_dir": video_path,
+            "img": tgt_img_pil,
+            "ref_img": ref_img_pil,
+            "audio_tensor": audio_tensor,
+            "mask":self.mask,
+        }
+
+        return sample
+
+    def __len__(self):
+        return len(self.vid_meta)
+
+
+
+class FaceMaskDataset(Dataset):
+    """
+    FaceMaskDataset is a custom dataset for face mask images.
+
+    Args:
+        img_size (int): The size of the input images.
+        drop_ratio (float, optional): The ratio of dropped pixels during data augmentation. Defaults to 0.1.
+        data_meta_paths (list, optional): The paths to the metadata files containing image paths and labels. Defaults to ["./data/HDTF_meta.json"].
+        sample_margin (int, optional): The margin for sampling regions in the image. Defaults to 30.
+
+    Attributes:
+        img_size (int): The size of the input images.
+        drop_ratio (float): The ratio of dropped pixels during data augmentation.
+        data_meta_paths (list): The paths to the metadata files containing image paths and labels.
+        sample_margin (int): The margin for sampling regions in the image.
+        processor (CLIPImageProcessor): The image processor for preprocessing images.
+        transform (transforms.Compose): The image augmentation transform.
+    """
+
+    def __init__(
+        self,
+        img_size,
+        mask_path,
+        drop_ratio=0.1,
+        data_meta_path=None,
+        sample_margin=30,
+    ):
+        super().__init__()
+
+        self.img_size = img_size
+        self.sample_margin = sample_margin
+
+        vid_meta = []
+        with open(data_meta_path, "r", encoding="utf-8") as f:
+            vid_meta.extend(json.load(f))
+        self.vid_meta = vid_meta
+        self.length = len(self.vid_meta)
+
+        self.mask = self.create_mask(mask_path)
+
+        self.clip_image_processor = CLIPImageProcessor()
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(self.img_size),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+
+        self.cond_transform = transforms.Compose(
+            [
+                transforms.Resize(self.img_size),
+                transforms.ToTensor(),
+            ]
+        )
+
+        self.drop_ratio = drop_ratio
+
+    def create_mask(self, mask_path):
+        mask = 1 - (cv2.imread(mask_path) / 255.)
+        (h, w, c) = mask.shape
+        new_mask = np.zeros((h, w, c+1))
+        new_mask[:, :, :c] = mask
+        new_mask[:, :, c] = mask[:, :, c-1]
+
+        mask = torch.FloatTensor(new_mask).permute(2,0,1).unsqueeze(1)
+
+        return mask
+
+    def augmentation(self, image, transform, state=None):
+        """
+        Apply data augmentation to the input image.
+
+        Args:
+            image (PIL.Image): The input image.
+            transform (torchvision.transforms.Compose): The data augmentation transforms.
+            state (dict, optional): The random state for reproducibility. Defaults to None.
+
+        Returns:
+            PIL.Image: The augmented image.
+        """
+        if state is not None:
+            torch.set_rng_state(state)
+        return transform(image)
+
+    def __getitem__(self, index):
+        video_meta = self.vid_meta[index]
+        video_path = video_meta["image_path"]
+
+        video_frames = sorted(Path(video_path).iterdir())
+        video_length = len(video_frames)
+
+        margin = min(self.sample_margin, video_length)
+
+        ref_img_idx = np.random.randint(0, video_length - 1)
+        if ref_img_idx + margin < (video_length -1) :
+            tgt_img_idx = np.random.randint(
+                ref_img_idx + margin, video_length - 1)
+        elif ref_img_idx - margin > 0:
+            tgt_img_idx = np.random.randint(0, ref_img_idx - margin)
+        else:
+            tgt_img_idx = np.random.randint(0, video_length - 1)
+
+        ref_img_pil = Image.open(video_frames[ref_img_idx])
+        tgt_img_pil = Image.open(video_frames[tgt_img_idx])
+
+        assert ref_img_pil is not None, "Fail to load reference image."
+        assert tgt_img_pil is not None, "Fail to load target image."
+
+        state = torch.get_rng_state()
+        tgt_img = self.augmentation(tgt_img_pil, self.transform, state)
+        ref_img_vae = self.augmentation(
+            ref_img_pil, self.transform, state)
+
+        audio_emb_path = video_meta["audio_emb"]
+        audio_emb = torch.load(audio_emb_path)
+        audio_tensor = audio_emb[tgt_img_idx]
+
+        sample = {
+            "video_dir": video_path,
+            "img": tgt_img,
+            "ref_img": ref_img_vae,
+            "audio_tensor": audio_tensor,
+            "mask":self.mask,
+        }
+
+        return sample
+
+    def __len__(self):
+        return len(self.vid_meta)
+
+
+if __name__ == "__main__":
+    data = FaceMaskDataset(img_size=(512, 512))
+    train_dataloader = torch.utils.data.DataLoader(
+        data, batch_size=4, shuffle=True, num_workers=1
+    )
+    for step, batch in enumerate(train_dataloader):
+        print(batch["tgt_mask"].shape)
+        break
diff --git a/high_sync/src/datasets/talk_video.py b/high_sync/src/datasets/talk_video.py
new file mode 100644
index 0000000..2f21a45
--- /dev/null
+++ b/high_sync/src/datasets/talk_video.py
@@ -0,0 +1,456 @@
+# pylint: disable=R0801
+"""
+talking_video_dataset.py
+
+This module defines the TalkingVideoDataset class, a custom PyTorch dataset
+for handling talking video data. The dataset uses video files, masks, and
+embeddings to prepare data for tasks such as video generation and
+speech-driven video animation.
+
+Classes:
+    TalkingVideoDataset
+
+Dependencies:
+    json
+    random
+    torch
+    decord.VideoReader, decord.cpu
+    PIL.Image
+    torch.utils.data.Dataset
+    torchvision.transforms
+
+Example:
+    from talking_video_dataset import TalkingVideoDataset
+    from torch.utils.data import DataLoader
+
+    # Example configuration for the Wav2Vec model
+    class Wav2VecConfig:
+        def __init__(self, audio_type, model_scale, features):
+            self.audio_type = audio_type
+            self.model_scale = model_scale
+            self.features = features
+
+    wav2vec_cfg = Wav2VecConfig(audio_type="wav2vec2", model_scale="base", features="feature")
+
+    # Initialize dataset
+    dataset = TalkingVideoDataset(
+        img_size=(512, 512),
+        sample_rate=16000,
+        audio_margin=2,
+        n_sample_frames=16,
+        data_meta_paths=["path/to/meta1.json", "path/to/meta2.json"],
+        wav2vec_cfg=wav2vec_cfg,
+    )
+
+    # Initialize dataloader
+    dataloader = DataLoader(dataset, batch_size=4, shuffle=True)
+
+    # Fetch one batch of data
+    batch = next(iter(dataloader))
+    print(batch["pixel_values_vid"].shape)  # Example output: (4, 16, 3, 512, 512)
+
+The TalkingVideoDataset class provides methods for loading video frames, masks,
+audio embeddings, and other relevant data, applying transformations, and preparing
+the data for training and evaluation in a deep learning pipeline.
+
+Attributes:
+    img_size (tuple): The dimensions to resize the video frames to.
+    sample_rate (int): The audio sample rate.
+    audio_margin (int): The margin for audio sampling.
+    n_sample_frames (int): The number of sample frames.
+    data_meta_paths (list): List of paths to the JSON metadata files.
+    wav2vec_cfg (object): Configuration for the Wav2Vec model.
+
+Methods:
+    augmentation(images, transform, state=None): Apply transformation to input images.
+    __getitem__(index): Get a sample from the dataset at the specified index.
+    __len__(): Return the length of the dataset.
+"""
+
+import json
+from typing import List
+import cv2
+import numpy as np
+
+import torch
+from decord import VideoReader, cpu
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision import transforms
+
+class TalkingVideoDatasetVal(Dataset):
+    """
+    A dataset class for processing talking video data.
+
+    Args:
+        img_size (tuple, optional): The size of the output images. Defaults to (512, 512).
+        sample_rate (int, optional): The sample rate of the audio data. Defaults to 16000.
+        audio_margin (int, optional): The margin for the audio data. Defaults to 2.
+        n_sample_frames (int, optional): The number of sample frames. Defaults to 16.
+        data_meta_paths (list, optional): The paths to the data metadata. Defaults to None.
+        wav2vec_cfg (dict, optional): The configuration for the wav2vec model. Defaults to None.
+
+    Attributes:
+        img_size (tuple): The size of the output images.
+        sample_rate (int): The sample rate of the audio data.
+        audio_margin (int): The margin for the audio data.
+        n_sample_frames (int): The number of sample frames.
+        data_meta_paths (list): The paths to the data metadata.
+        wav2vec_cfg (dict): The configuration for the wav2vec model.
+    """
+
+    def __init__(
+        self,
+        img_size=(512, 512),
+        mask_path=None,
+        sample_rate=16000,
+        audio_margin=2,
+        n_sample_frames=16,
+        data_meta_path=None,
+        wav2vec_cfg=None,
+    ):
+        super().__init__()
+        self.sample_rate = sample_rate
+        self.img_size = img_size
+        self.audio_margin = audio_margin
+        self.n_sample_frames = n_sample_frames
+        self.audio_type = wav2vec_cfg.audio_type
+        self.audio_model = wav2vec_cfg.model_scale
+        self.audio_features = wav2vec_cfg.features
+
+        vid_meta = []
+        with open(data_meta_path, "r", encoding="utf-8") as f:
+            vid_meta.extend(json.load(f))
+        self.vid_meta = vid_meta
+        self.length = len(self.vid_meta)
+
+        self.mask_path = mask_path
+        self.mask = self.create_mask(mask_path)
+
+        self.pixel_transform = transforms.Compose(
+            [
+                transforms.Resize(self.img_size),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+
+        self.cond_transform = transforms.Compose(
+            [
+                transforms.Resize(self.img_size),
+                transforms.ToTensor(),
+            ]
+        )
+        self.attn_transform_64 = transforms.Compose(
+            [
+                transforms.Resize(
+                    (self.img_size[0] // 8, self.img_size[0] // 8)),
+                transforms.ToTensor(),
+            ]
+        )
+        self.attn_transform_32 = transforms.Compose(
+            [
+                transforms.Resize(
+                    (self.img_size[0] // 16, self.img_size[0] // 16)),
+                transforms.ToTensor(),
+            ]
+        )
+        self.attn_transform_16 = transforms.Compose(
+            [
+                transforms.Resize(
+                    (self.img_size[0] // 32, self.img_size[0] // 32)),
+                transforms.ToTensor(),
+            ]
+        )
+        self.attn_transform_8 = transforms.Compose(
+            [
+                transforms.Resize(
+                    (self.img_size[0] // 64, self.img_size[0] // 64)),
+                transforms.ToTensor(),
+            ]
+        )
+
+    def create_mask(self, mask_path):
+        mask = 1 - (cv2.imread(mask_path) / 255.)
+        (h, w, c) = mask.shape
+        new_mask = np.zeros((h, w, c+1))
+        new_mask[:, :, :c] = mask
+        new_mask[:, :, c] = mask[:, :, c-1]
+
+        mask = torch.FloatTensor(new_mask).permute(2,0,1).unsqueeze(1)
+        # mask = mask.repeat(1,self.n_sample_frames,1,1)
+
+        return mask
+
+    def augmentation(self, images, transform, state=None):
+        """
+        Apply the given transformation to the input images.
+
+        Args:
+            images (List[PIL.Image] or PIL.Image): The input images to be transformed.
+            transform (torchvision.transforms.Compose): The transformation to be applied to the images.
+            state (torch.ByteTensor, optional): The state of the random number generator.
+            If provided, it will set the RNG state to this value before applying the transformation. Defaults to None.
+
+        Returns:
+            torch.Tensor: The transformed images as a tensor.
+            If the input was a list of images, the tensor will have shape (f, c, h, w),
+            where f is the number of images, c is the number of channels, h is the height, and w is the width.
+            If the input was a single image, the tensor will have shape (c, h, w),
+            where c is the number of channels, h is the height, and w is the width.
+        """
+        if state is not None:
+            torch.set_rng_state(state)
+        if isinstance(images, List):
+            transformed_images = [transform(img) for img in images]
+            ret_tensor = torch.stack(transformed_images, dim=0)  # (f, c, h, w)
+        else:
+            ret_tensor = transform(images)  # (c, h, w)
+        return ret_tensor
+
+    def __getitem__(self, index):
+        video_meta = self.vid_meta[index]
+        video_path = video_meta["video_path"] #"/workspace/preprocess/videos/ga3GzQWqWWg_C0.mp4"
+        same = False #random.choice((True, False))
+        if same:
+            audio_emb_path = video_meta["audio_emb"]
+            audio_path = audio_emb_path.replace("audio_emb_whisper", "audios")[:-2] + "wav"
+        else:
+            if (index+1) > (len(self.vid_meta)-1):
+                new_index = index - 1
+            else:
+                new_index =  index + 1
+            video_meta_next = self.vid_meta[new_index]
+            audio_emb_path = video_meta_next["audio_emb"]
+            audio_path = audio_emb_path.replace("audio_emb_whisper", "audios")[:-2] + "wav"
+
+        # video_frames = VideoReader(video_path, ctx=cpu(0))
+        video_frames = []
+        video_stream = cv2.VideoCapture(video_path)
+        while True:
+            still_reading, frame = video_stream.read()
+            if (not still_reading):
+                video_stream.release()
+                break
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            video_frames.append(frame)
+
+        frame_list = [
+            Image.fromarray(video).convert("RGB") for video in video_frames[:5*25]] #.asnumpy()
+
+        audio_emb = torch.tensor(torch.load(audio_emb_path))
+
+        # transform
+        state = torch.get_rng_state()
+        pixel_values_vid = self.augmentation(frame_list, self.pixel_transform, state)
+
+        pixel_values_ref_img = pixel_values_vid[0]
+        pixel_values_ref_img = pixel_values_ref_img.unsqueeze(0)
+
+        sample = {
+            "video_dir": video_path,
+            "pixel_values_vid": pixel_values_vid,
+            "mask": self.mask,
+            "audio_tensor": audio_emb,
+            "pixel_values_ref_img": pixel_values_ref_img,
+            "same" : same,
+            "audio_path" : audio_path
+        }
+
+        return sample
+
+    def __len__(self):
+        return len(self.vid_meta)
+
+
+
+class TalkingVideoDataset(Dataset):
+    """
+    A dataset class for processing talking video data.
+
+    Args:
+        img_size (tuple, optional): The size of the output images. Defaults to (512, 512).
+        sample_rate (int, optional): The sample rate of the audio data. Defaults to 16000.
+        audio_margin (int, optional): The margin for the audio data. Defaults to 2.
+        n_sample_frames (int, optional): The number of sample frames. Defaults to 16.
+        data_meta_paths (list, optional): The paths to the data metadata. Defaults to None.
+        wav2vec_cfg (dict, optional): The configuration for the wav2vec model. Defaults to None.
+
+    Attributes:
+        img_size (tuple): The size of the output images.
+        sample_rate (int): The sample rate of the audio data.
+        audio_margin (int): The margin for the audio data.
+        n_sample_frames (int): The number of sample frames.
+        data_meta_paths (list): The paths to the data metadata.
+        wav2vec_cfg (dict): The configuration for the wav2vec model.
+    """
+
+    def __init__(
+        self,
+        img_size=(512, 512),
+        mask_path=None,
+        sample_rate=16000,
+        audio_margin=2,
+        n_sample_frames=16,
+        data_meta_path=None,
+        wav2vec_cfg=None,
+    ):
+        super().__init__()
+        self.sample_rate = sample_rate
+        self.img_size = img_size
+        self.audio_margin = audio_margin
+        self.n_sample_frames = n_sample_frames
+        self.audio_type = wav2vec_cfg.audio_type
+        self.audio_model = wav2vec_cfg.model_scale
+        self.audio_features = wav2vec_cfg.features
+
+        vid_meta = []
+        with open(data_meta_path, "r", encoding="utf-8") as f:
+            vid_meta.extend(json.load(f))
+        self.vid_meta = vid_meta
+        self.length = len(self.vid_meta)
+
+        self.mask_path = mask_path
+        self.mask = self.create_mask(mask_path)
+
+        self.pixel_transform = transforms.Compose(
+            [
+                transforms.Resize(self.img_size),
+                transforms.ToTensor(),
+                transforms.Normalize([0.5], [0.5]),
+            ]
+        )
+
+        self.cond_transform = transforms.Compose(
+            [
+                transforms.Resize(self.img_size),
+                transforms.ToTensor(),
+            ]
+        )
+        self.attn_transform_64 = transforms.Compose(
+            [
+                transforms.Resize(
+                    (self.img_size[0] // 8, self.img_size[0] // 8)),
+                transforms.ToTensor(),
+            ]
+        )
+        self.attn_transform_32 = transforms.Compose(
+            [
+                transforms.Resize(
+                    (self.img_size[0] // 16, self.img_size[0] // 16)),
+                transforms.ToTensor(),
+            ]
+        )
+        self.attn_transform_16 = transforms.Compose(
+            [
+                transforms.Resize(
+                    (self.img_size[0] // 32, self.img_size[0] // 32)),
+                transforms.ToTensor(),
+            ]
+        )
+        self.attn_transform_8 = transforms.Compose(
+            [
+                transforms.Resize(
+                    (self.img_size[0] // 64, self.img_size[0] // 64)),
+                transforms.ToTensor(),
+            ]
+        )
+
+    def create_mask(self, mask_path):
+        mask = 1 - (cv2.imread(mask_path) / 255.)
+        (h, w, c) = mask.shape
+        new_mask = np.zeros((h, w, c+1))
+        new_mask[:, :, :c] = mask
+        new_mask[:, :, c] = mask[:, :, c-1]
+
+        mask = torch.FloatTensor(new_mask).permute(2,0,1).unsqueeze(1)
+        mask = mask.repeat(1,self.n_sample_frames,1,1)
+
+        return mask
+
+    def augmentation(self, images, transform, state=None):
+        """
+        Apply the given transformation to the input images.
+
+        Args:
+            images (List[PIL.Image] or PIL.Image): The input images to be transformed.
+            transform (torchvision.transforms.Compose): The transformation to be applied to the images.
+            state (torch.ByteTensor, optional): The state of the random number generator.
+            If provided, it will set the RNG state to this value before applying the transformation. Defaults to None.
+
+        Returns:
+            torch.Tensor: The transformed images as a tensor.
+            If the input was a list of images, the tensor will have shape (f, c, h, w),
+            where f is the number of images, c is the number of channels, h is the height, and w is the width.
+            If the input was a single image, the tensor will have shape (c, h, w),
+            where c is the number of channels, h is the height, and w is the width.
+        """
+        if state is not None:
+            torch.set_rng_state(state)
+        if isinstance(images, List):
+            transformed_images = [transform(img) for img in images]
+            ret_tensor = torch.stack(transformed_images, dim=0)  # (f, c, h, w)
+        else:
+            ret_tensor = transform(images)  # (c, h, w)
+        return ret_tensor
+
+    def __getitem__(self, index):
+        video_meta = self.vid_meta[index]
+        video_path = video_meta["video_path"]
+        audio_emb_path = video_meta["audio_emb"]
+
+        video_frames = VideoReader(video_path, ctx=cpu(0))
+        assert (video_frames is not None and len(video_frames) > 0), "Fail to load video frames."
+        audio_emb = torch.load(audio_emb_path)
+        video_length = min(len(video_frames), audio_emb.shape[0])
+
+        assert (
+            video_length
+            > self.n_sample_frames  + 2 * self.audio_margin
+        )
+        start_idx = np.random.randint(
+            3,
+            video_length - self.n_sample_frames - self.audio_margin - 1,
+        )
+
+        videos = video_frames[start_idx : start_idx + self.n_sample_frames]
+
+        frame_list = [
+            Image.fromarray(video).convert("RGB") for video in videos.asnumpy()
+        ]
+
+        indices = (
+            torch.arange(2 * self.audio_margin + 1) - self.audio_margin
+        )  # Generates [-2, -1, 0, 1, 2]
+        center_indices = torch.arange(
+            start_idx,
+            start_idx + self.n_sample_frames,
+        )
+        audio_tensor = audio_emb[center_indices]
+
+        ref_img_idx = np.random.randint(
+            3,
+            video_length - self.n_sample_frames - self.audio_margin - 1,
+        )
+        ref_img = video_frames[ref_img_idx].asnumpy()
+        ref_img = Image.fromarray(ref_img)
+
+        # transform
+        state = torch.get_rng_state()
+        pixel_values_vid = self.augmentation(frame_list, self.pixel_transform, state)
+
+        pixel_values_ref_img = self.augmentation(ref_img, self.pixel_transform, state)
+        pixel_values_ref_img = pixel_values_ref_img.unsqueeze(0)
+
+        sample = {
+            "video_dir": video_path,
+            "pixel_values_vid": pixel_values_vid,
+            "mask": self.mask,
+            "audio_tensor": audio_tensor,
+            "pixel_values_ref_img": pixel_values_ref_img,
+        }
+
+        return sample
+
+    def __len__(self):
+        return len(self.vid_meta)
diff --git a/high_sync/src/models/attention.py b/high_sync/src/models/attention.py
new file mode 100644
index 0000000..b38f555
--- /dev/null
+++ b/high_sync/src/models/attention.py
@@ -0,0 +1,338 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py
+
+from typing import Any, Dict, Optional
+
+import torch
+from diffusers.models.attention import AdaLayerNorm, Attention, FeedForward
+from diffusers.models.embeddings import SinusoidalPositionalEmbedding
+from einops import rearrange
+from torch import nn
+
+
+class BasicTransformerBlock(nn.Module):
+    r"""
+    A basic Transformer block.
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        num_embeds_ada_norm (:
+            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
+        attention_bias (:
+            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+        only_cross_attention (`bool`, *optional*):
+            Whether to use only cross-attention layers. In this case two cross attention layers are used.
+        double_self_attention (`bool`, *optional*):
+            Whether to use two self-attention layers. In this case no cross attention layers are used.
+        upcast_attention (`bool`, *optional*):
+            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
+        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
+            Whether to use learnable elementwise affine parameters for normalization.
+        norm_type (`str`, *optional*, defaults to `"layer_norm"`):
+            The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
+        final_dropout (`bool` *optional*, defaults to False):
+            Whether to apply a final dropout after the last feed-forward layer.
+        attention_type (`str`, *optional*, defaults to `"default"`):
+            The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
+        positional_embeddings (`str`, *optional*, defaults to `None`):
+            The type of positional embeddings to apply to.
+        num_positional_embeddings (`int`, *optional*, defaults to `None`):
+            The maximum number of positional embeddings to apply.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        attention_bias: bool = False,
+        only_cross_attention: bool = False,
+        double_self_attention: bool = False,
+        upcast_attention: bool = False,
+        norm_elementwise_affine: bool = True,
+        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single'
+        norm_eps: float = 1e-5,
+        final_dropout: bool = False,
+        attention_type: str = "default",
+        positional_embeddings: Optional[str] = None,
+        num_positional_embeddings: Optional[int] = None,
+    ):
+        super().__init__()
+        self.only_cross_attention = only_cross_attention
+
+        self.use_ada_layer_norm_zero = (
+            num_embeds_ada_norm is not None
+        ) and norm_type == "ada_norm_zero"
+        self.use_ada_layer_norm = (
+            num_embeds_ada_norm is not None
+        ) and norm_type == "ada_norm"
+        self.use_ada_layer_norm_single = norm_type == "ada_norm_single"
+        self.use_layer_norm = norm_type == "layer_norm"
+
+        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
+            raise ValueError(
+                f"`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"
+                f" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."
+            )
+
+        if positional_embeddings and (num_positional_embeddings is None):
+            raise ValueError(
+                "If `positional_embedding` type is defined, `num_positition_embeddings` must also be defined."
+            )
+
+        if positional_embeddings == "sinusoidal":
+            self.pos_embed = SinusoidalPositionalEmbedding(
+                dim, max_seq_length=num_positional_embeddings
+            )
+        else:
+            self.pos_embed = None
+
+        # Define 3 blocks. Each block has its own normalization layer.
+        # 1. Self-Attn
+        if self.use_ada_layer_norm:
+            self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm)
+        elif self.use_ada_layer_norm_zero:
+            self.norm1 = AdaLayerNormZero(dim, num_embeds_ada_norm)
+        else:
+            self.norm1 = nn.LayerNorm(
+                dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps
+            )
+
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            upcast_attention=upcast_attention,
+        )
+
+        # 3. Feed-forward
+        if not self.use_ada_layer_norm_single:
+            self.norm3 = nn.LayerNorm(
+                dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps
+            )
+
+        self.ff = FeedForward(
+            dim,
+            dropout=dropout,
+            activation_fn=activation_fn,
+            final_dropout=final_dropout,
+        )
+
+        # 4. Fuser
+        if attention_type == "gated" or attention_type == "gated-text-image":
+            self.fuser = GatedSelfAttentionDense(
+                dim, cross_attention_dim, num_attention_heads, attention_head_dim
+            )
+
+        # 5. Scale-shift for PixArt-Alpha.
+        if self.use_ada_layer_norm_single:
+            self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)
+
+        # let chunk size default to None
+        self._chunk_size = None
+        self._chunk_dim = 0
+
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
+        # Sets chunk feed-forward
+        self._chunk_size = chunk_size
+        self._chunk_dim = dim
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        class_labels: Optional[torch.LongTensor] = None,
+    ) -> torch.FloatTensor:
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 0. Self-Attention
+        batch_size = hidden_states.shape[0]
+
+        if self.use_ada_layer_norm:
+            norm_hidden_states = self.norm1(hidden_states, timestep)
+        elif self.use_ada_layer_norm_zero:
+            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+            )
+        elif self.use_layer_norm:
+            norm_hidden_states = self.norm1(hidden_states)
+        elif self.use_ada_layer_norm_single:
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
+            ).chunk(6, dim=1)
+            norm_hidden_states = self.norm1(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
+            norm_hidden_states = norm_hidden_states.squeeze(1)
+        else:
+            raise ValueError("Incorrect norm used")
+
+        if self.pos_embed is not None:
+            norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+        # 1. Retrieve lora scale.
+        lora_scale = (
+            cross_attention_kwargs.get("scale", 1.0)
+            if cross_attention_kwargs is not None
+            else 1.0
+        )
+
+        # 2. Prepare GLIGEN inputs
+        cross_attention_kwargs = (
+            cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+        )
+        gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
+
+        attn_output = self.attn1(
+            norm_hidden_states,
+            attention_mask=attention_mask,
+            **cross_attention_kwargs,
+        )
+        if self.use_ada_layer_norm_zero:
+            attn_output = gate_msa.unsqueeze(1) * attn_output
+        elif self.use_ada_layer_norm_single:
+            attn_output = gate_msa * attn_output
+
+        hidden_states = attn_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        # 2.5 GLIGEN Control
+        if gligen_kwargs is not None:
+            hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
+
+
+        # 4. Feed-forward
+        if not self.use_ada_layer_norm_single:
+            norm_hidden_states = self.norm3(hidden_states)
+
+        if self.use_ada_layer_norm_zero:
+            norm_hidden_states = (
+                norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+            )
+
+        if self.use_ada_layer_norm_single:
+            norm_hidden_states = self.norm2(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
+
+        ff_output = self.ff(norm_hidden_states, scale=lora_scale)
+
+        if self.use_ada_layer_norm_zero:
+            ff_output = gate_mlp.unsqueeze(1) * ff_output
+        elif self.use_ada_layer_norm_single:
+            ff_output = gate_mlp * ff_output
+
+        hidden_states = ff_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        return hidden_states
+
+
+class TemporalBasicTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        attention_bias: bool = False,
+        only_cross_attention: bool = False,
+        upcast_attention: bool = False,
+        unet_use_cross_frame_attention=None,
+        unet_use_temporal_attention=None,
+    ):
+        super().__init__()
+        self.only_cross_attention = only_cross_attention
+        self.use_ada_layer_norm = num_embeds_ada_norm is not None
+        self.unet_use_cross_frame_attention = unet_use_cross_frame_attention
+        self.unet_use_temporal_attention = unet_use_temporal_attention
+
+        # SC-Attn
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            upcast_attention=upcast_attention,
+        )
+        self.norm1 = (
+            AdaLayerNorm(dim, num_embeds_ada_norm)
+            if self.use_ada_layer_norm
+            else nn.LayerNorm(dim)
+        )
+
+        # Audio Cross-Attn
+        if cross_attention_dim is not None:
+            self.attn2 = Attention(
+                query_dim=dim,
+                cross_attention_dim=cross_attention_dim,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+            )
+        else:
+            self.attn2 = None
+
+        if cross_attention_dim is not None:
+            self.norm2 = (
+                AdaLayerNorm(dim, num_embeds_ada_norm)
+                if self.use_ada_layer_norm
+                else nn.LayerNorm(dim)
+            )
+        else:
+            self.norm2 = None
+
+        # Feed-forward
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
+        self.norm3 = nn.LayerNorm(dim)
+        self.use_ada_layer_norm_zero = False
+
+        # Temp-Attn
+        assert unet_use_temporal_attention is not None
+        if unet_use_temporal_attention:
+            self.attn_temp = Attention(
+                query_dim=dim,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+            )
+            nn.init.zeros_(self.attn_temp.to_out[0].weight.data)
+            self.norm_temp = (
+                AdaLayerNorm(dim, num_embeds_ada_norm)
+                if self.use_ada_layer_norm
+                else nn.LayerNorm(dim)
+            )
+
+    def forward(
+        self,
+        hidden_states,
+        encoder_hidden_states=None,
+        audio_cond_fea = None,
+        timestep=None,
+        attention_mask=None,
+        video_length=None,
+    ):
+        ## implemented in mutual_self_attention.py
+        pass
+        return hidden_states
diff --git a/high_sync/src/models/face_locator.py b/high_sync/src/models/face_locator.py
new file mode 100644
index 0000000..dba6025
--- /dev/null
+++ b/high_sync/src/models/face_locator.py
@@ -0,0 +1,57 @@
+from typing import Tuple
+
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.init as init
+from diffusers.models.modeling_utils import ModelMixin
+
+from .motion_module import zero_module
+from .resnet import InflatedConv3d
+
+
+class FaceLocator(ModelMixin):
+    def __init__(
+        self,
+        conditioning_embedding_channels: int,
+        conditioning_channels: int = 1,
+        block_out_channels: Tuple[int] = (16, 32, 64, 128),
+    ):
+        super().__init__()
+        self.conv_in = InflatedConv3d(
+            conditioning_channels, block_out_channels[0], kernel_size=3, padding=1
+        )
+
+        self.blocks = nn.ModuleList([])
+
+        for i in range(len(block_out_channels) - 1):
+            channel_in = block_out_channels[i]
+            channel_out = block_out_channels[i + 1]
+            self.blocks.append(
+                InflatedConv3d(channel_in, channel_in, kernel_size=3, padding=1)
+            )
+            self.blocks.append(
+                InflatedConv3d(
+                    channel_in, channel_out, kernel_size=3, padding=1, stride=2
+                )
+            )
+
+        self.conv_out = zero_module(
+            InflatedConv3d(
+                block_out_channels[-1],
+                conditioning_embedding_channels,
+                kernel_size=3,
+                padding=1,
+            )
+        )
+
+    def forward(self, conditioning):
+        embedding = self.conv_in(conditioning)
+        embedding = F.silu(embedding)
+
+        for block in self.blocks:
+            embedding = block(embedding)
+            embedding = F.silu(embedding)
+
+        embedding = self.conv_out(embedding)
+
+        return embedding
diff --git a/high_sync/src/models/motion_module.py b/high_sync/src/models/motion_module.py
new file mode 100644
index 0000000..51d2712
--- /dev/null
+++ b/high_sync/src/models/motion_module.py
@@ -0,0 +1,481 @@
+# Adapt from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/motion_module.py
+import math
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Callable, Optional
+
+import torch
+from diffusers.models.attention import FeedForward
+from diffusers.models.attention_processor import Attention, AttnProcessor
+from diffusers.utils import BaseOutput
+from diffusers.utils.import_utils import is_xformers_available
+from einops import rearrange, repeat
+from torch import nn
+import cv2
+import numpy as np
+
+def zero_module(module):
+    # Zero out the parameters of a module and return it.
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+@dataclass
+class TemporalTransformer3DModelOutput(BaseOutput):
+    sample: torch.FloatTensor
+
+
+if is_xformers_available():
+    import xformers
+    import xformers.ops
+else:
+    xformers = None
+
+
+def get_motion_module(in_channels, motion_module_type: str, motion_module_kwargs: dict):
+    if motion_module_type == "Vanilla":
+        return VanillaTemporalModule(
+            in_channels=in_channels,
+            **motion_module_kwargs,
+        )
+    else:
+        raise ValueError
+
+
+class VanillaTemporalModule(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        num_attention_heads=8,
+        num_transformer_block=2,
+        attention_block_types=("Temporal_Self", "Temporal_Self"),
+        cross_frame_attention_mode=None,
+        temporal_position_encoding=False,
+        temporal_position_encoding_max_len=24,
+        temporal_attention_dim_div=1,
+        zero_initialize=True,
+    ):
+        super().__init__()
+
+        b = 1
+        f = 12
+        c_64, c_32, c_16, c_8 = 320 ,640 ,1280, 1280
+        masks_dir = Path(__file__).resolve().parents[2] / "masks"
+        self.mask_64 = self.create_mask(masks_dir / "mask_64.png", b, c_64, f)
+        self.mask_32 = self.create_mask(masks_dir / "mask_32.png", b, c_32, f)
+        self.mask_16 = self.create_mask(masks_dir / "mask_16.png", b, c_16, f)
+        self.mask_8 = self.create_mask(masks_dir / "mask_8.png", b, c_8, f)
+
+        self.temporal_transformer = TemporalTransformer3DModel(
+            in_channels=in_channels,
+            num_attention_heads=num_attention_heads,
+            attention_head_dim=in_channels
+            // num_attention_heads
+            // temporal_attention_dim_div,
+            num_layers=num_transformer_block,
+            attention_block_types=attention_block_types,
+            cross_frame_attention_mode=cross_frame_attention_mode,
+            temporal_position_encoding=temporal_position_encoding,
+            temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+        )
+
+        if zero_initialize:
+            self.temporal_transformer.proj_out = zero_module(
+                self.temporal_transformer.proj_out
+            )
+
+    def create_mask(self, mask_path, b, c_new, f):
+        raw_mask = cv2.imread(str(mask_path))
+        if raw_mask is None:
+            raise FileNotFoundError(f"HighSync motion mask could not be read: {mask_path}")
+        mask = torch.FloatTensor(1 - (raw_mask / 255.))
+        (h, w, c) = mask.shape
+        mask_new = torch.zeros((h,w,c_new))
+        mask_new[:, :, :c] = mask
+        mask_new[:, :, c:] = mask[:, :, c-1:c].repeat(1,1,c_new-c)
+
+        mask_new = mask_new.permute(2,0,1).unsqueeze(1).unsqueeze(0) # 1*c*1*h*w
+
+        mask_new = mask_new.repeat(b, 1, f, 1, 1) # b*c*f*h*w
+
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        return mask_new.to(device, dtype=torch.float16)
+
+
+    def forward(
+        self,
+        input_tensor,
+        temb,
+        encoder_hidden_states,
+        attention_mask=None,
+        anchor_frame_idx=None,
+    ):
+
+        b, c, f, h, w = input_tensor.shape
+
+        if h==64:
+            mask = self.mask_64
+        elif h==32:
+            mask = self.mask_32
+        elif h==16:
+            mask = self.mask_16
+        elif h==8:
+            mask = self.mask_8
+
+        hidden_states = input_tensor * mask
+        hidden_states = self.temporal_transformer(
+            hidden_states, encoder_hidden_states, attention_mask
+        )
+
+        output = hidden_states*mask + input_tensor*(1-mask)
+        # output = input_tensor
+
+        return output
+
+
+# class VanillaTemporalModule(nn.Module):
+#     def __init__(
+#         self,
+#         in_channels,
+#         num_attention_heads=8,
+#         num_transformer_block=2,
+#         attention_block_types=("Temporal_Self", "Temporal_Self"),
+#         cross_frame_attention_mode=None,
+#         temporal_position_encoding=False,
+#         temporal_position_encoding_max_len=24,
+#         temporal_attention_dim_div=1,
+#         zero_initialize=True,
+#     ):
+#         super().__init__()
+
+#         self.temporal_transformer = TemporalTransformer3DModel(
+#             in_channels=in_channels,
+#             num_attention_heads=num_attention_heads,
+#             attention_head_dim=in_channels
+#             // num_attention_heads
+#             // temporal_attention_dim_div,
+#             num_layers=num_transformer_block,
+#             attention_block_types=attention_block_types,
+#             cross_frame_attention_mode=cross_frame_attention_mode,
+#             temporal_position_encoding=temporal_position_encoding,
+#             temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+#         )
+
+#         if zero_initialize:
+#             self.temporal_transformer.proj_out = zero_module(
+#                 self.temporal_transformer.proj_out
+#             )
+
+#     def forward(
+#         self,
+#         input_tensor,
+#         temb,
+#         encoder_hidden_states,
+#         attention_mask=None,
+#         anchor_frame_idx=None,
+#     ):
+#         hidden_states = input_tensor
+#         hidden_states = self.temporal_transformer(
+#             hidden_states, encoder_hidden_states, attention_mask
+#         )
+
+#         output = hidden_states
+#         return output
+
+
+class TemporalTransformer3DModel(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        num_attention_heads,
+        attention_head_dim,
+        num_layers,
+        attention_block_types=(
+            "Temporal_Self",
+            "Temporal_Self",
+        ),
+        dropout=0.0,
+        norm_num_groups=32,
+        cross_attention_dim=768,
+        activation_fn="geglu",
+        attention_bias=False,
+        upcast_attention=False,
+        cross_frame_attention_mode=None,
+        temporal_position_encoding=False,
+        temporal_position_encoding_max_len=24,
+    ):
+        super().__init__()
+
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm = torch.nn.GroupNorm(
+            num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True
+        )
+        self.proj_in = nn.Linear(in_channels, inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                TemporalTransformerBlock(
+                    dim=inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    attention_block_types=attention_block_types,
+                    dropout=dropout,
+                    norm_num_groups=norm_num_groups,
+                    cross_attention_dim=cross_attention_dim,
+                    activation_fn=activation_fn,
+                    attention_bias=attention_bias,
+                    upcast_attention=upcast_attention,
+                    cross_frame_attention_mode=cross_frame_attention_mode,
+                    temporal_position_encoding=temporal_position_encoding,
+                    temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+                )
+                for d in range(num_layers)
+            ]
+        )
+        self.proj_out = nn.Linear(inner_dim, in_channels)
+
+    def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
+        assert (
+            hidden_states.dim() == 5
+        ), f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
+        video_length = hidden_states.shape[2]
+        hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
+
+        batch, channel, height, weight = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
+            batch, height * weight, inner_dim
+        )
+        hidden_states = self.proj_in(hidden_states)
+
+        # Transformer Blocks
+        for block in self.transformer_blocks:
+            hidden_states = block(
+                hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                video_length=video_length,
+            )
+
+        # output
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = (
+            hidden_states.reshape(batch, height, weight, inner_dim)
+            .permute(0, 3, 1, 2)
+            .contiguous()
+        )
+
+        output = hidden_states + residual
+        output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
+
+        return output
+
+
+class TemporalTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_attention_heads,
+        attention_head_dim,
+        attention_block_types=(
+            "Temporal_Self",
+            "Temporal_Self",
+        ),
+        dropout=0.0,
+        norm_num_groups=32,
+        cross_attention_dim=768,
+        activation_fn="geglu",
+        attention_bias=False,
+        upcast_attention=False,
+        cross_frame_attention_mode=None,
+        temporal_position_encoding=False,
+        temporal_position_encoding_max_len=24,
+    ):
+        super().__init__()
+
+        attention_blocks = []
+        norms = []
+
+        for block_name in attention_block_types:
+            attention_blocks.append(
+                VersatileAttention(
+                    attention_mode=block_name.split("_")[0],
+                    cross_attention_dim=cross_attention_dim
+                    if block_name.endswith("_Cross")
+                    else None,
+                    query_dim=dim,
+                    heads=num_attention_heads,
+                    dim_head=attention_head_dim,
+                    dropout=dropout,
+                    bias=attention_bias,
+                    upcast_attention=upcast_attention,
+                    cross_frame_attention_mode=cross_frame_attention_mode,
+                    temporal_position_encoding=temporal_position_encoding,
+                    temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+                )
+            )
+            norms.append(nn.LayerNorm(dim))
+
+        self.attention_blocks = nn.ModuleList(attention_blocks)
+        self.norms = nn.ModuleList(norms)
+
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
+        self.ff_norm = nn.LayerNorm(dim)
+
+    def forward(
+        self,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        video_length=None,
+    ):
+        for attention_block, norm in zip(self.attention_blocks, self.norms):
+            norm_hidden_states = norm(hidden_states)
+            hidden_states = (
+                attention_block(
+                    norm_hidden_states,
+                    encoder_hidden_states=encoder_hidden_states
+                    if attention_block.is_cross_attention
+                    else None,
+                    video_length=video_length,
+                )
+                + hidden_states
+            )
+
+        hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states
+
+        output = hidden_states
+        return output
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, dropout=0.0, max_len=24):
+        super().__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        position = torch.arange(max_len).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
+        )
+        pe = torch.zeros(1, max_len, d_model)
+        pe[0, :, 0::2] = torch.sin(position * div_term)
+        pe[0, :, 1::2] = torch.cos(position * div_term)
+        self.register_buffer("pe", pe)
+
+    def forward(self, x):
+        x = x + self.pe[:, : x.size(1)]
+        return self.dropout(x)
+
+
+class VersatileAttention(Attention):
+    def __init__(
+        self,
+        attention_mode=None,
+        cross_frame_attention_mode=None,
+        temporal_position_encoding=False,
+        temporal_position_encoding_max_len=24,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        assert attention_mode == "Temporal"
+
+        self.attention_mode = attention_mode
+        self.is_cross_attention = kwargs["cross_attention_dim"] is not None
+
+        self.pos_encoder = (
+            PositionalEncoding(
+                kwargs["query_dim"],
+                dropout=0.0,
+                max_len=temporal_position_encoding_max_len,
+            )
+            if (temporal_position_encoding and attention_mode == "Temporal")
+            else None
+        )
+
+    def extra_repr(self):
+        return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}"
+
+    def set_use_memory_efficient_attention_xformers(
+        self,
+        use_memory_efficient_attention_xformers: bool,
+        attention_op: Optional[Callable] = None,
+    ):
+        if use_memory_efficient_attention_xformers:
+            if not is_xformers_available():
+                raise ModuleNotFoundError(
+                    (
+                        "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
+                        " xformers"
+                    ),
+                    name="xformers",
+                )
+            elif not torch.cuda.is_available():
+                raise ValueError(
+                    "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is"
+                    " only available for GPU "
+                )
+            else:
+                try:
+                    # Make sure we can run the memory efficient attention
+                    _ = xformers.ops.memory_efficient_attention(
+                        torch.randn((1, 2, 40), device="cuda"),
+                        torch.randn((1, 2, 40), device="cuda"),
+                        torch.randn((1, 2, 40), device="cuda"),
+                    )
+                except Exception as e:
+                    raise e
+
+            # XFormersAttnProcessor corrupts video generation and work with Pytorch 1.13.
+            # Pytorch 2.0.1 AttnProcessor works the same as XFormersAttnProcessor in Pytorch 1.13.
+            # You don't need XFormersAttnProcessor here.
+            # processor = XFormersAttnProcessor(
+            #     attention_op=attention_op,
+            # )
+            processor = AttnProcessor()
+        else:
+            processor = AttnProcessor()
+
+        self.set_processor(processor)
+
+    def forward(
+        self,
+        hidden_states,
+        encoder_hidden_states=None,
+        attention_mask=None,
+        video_length=None,
+        **cross_attention_kwargs,
+    ):
+        if self.attention_mode == "Temporal":
+            d = hidden_states.shape[1]  # d means HxW
+            hidden_states = rearrange(
+                hidden_states, "(b f) d c -> (b d) f c", f=video_length
+            )
+
+            if self.pos_encoder is not None:
+                hidden_states = self.pos_encoder(hidden_states)
+
+            encoder_hidden_states = (
+                repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d)
+                if encoder_hidden_states is not None
+                else encoder_hidden_states
+            )
+
+        else:
+            raise NotImplementedError
+
+        hidden_states = self.processor(
+            self,
+            hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+            attention_mask=attention_mask,
+            **cross_attention_kwargs,
+        )
+
+        if self.attention_mode == "Temporal":
+            hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
+
+        return hidden_states
diff --git a/high_sync/src/models/mutual_self_attention.py b/high_sync/src/models/mutual_self_attention.py
new file mode 100644
index 0000000..b537583
--- /dev/null
+++ b/high_sync/src/models/mutual_self_attention.py
@@ -0,0 +1,336 @@
+# Adapted from https://github.com/magic-research/magic-animate/blob/main/magicanimate/models/mutual_self_attention.py
+from typing import Any, Dict, Optional
+
+import torch
+from einops import rearrange
+
+from .attention import TemporalBasicTransformerBlock
+
+from .attention import BasicTransformerBlock
+
+
+def torch_dfs(model: torch.nn.Module):
+    result = [model]
+    for child in model.children():
+        result += torch_dfs(child)
+    return result
+
+
+class ReferenceAttentionControl:
+    def __init__(
+        self,
+        unet,
+        mode="write",
+        do_classifier_free_guidance=False,
+        attention_auto_machine_weight=float("inf"),
+        gn_auto_machine_weight=1.0,
+        style_fidelity=1.0,
+        reference_attn=True,
+        reference_adain=False,
+        fusion_blocks="midup",
+        batch_size=1,
+        audio_feature_ratio = 3.0
+    ) -> None:
+        # 10. Modify self attention and group norm
+        self.unet = unet
+        self.audio_feature_ratio = audio_feature_ratio
+        assert mode in ["read", "write"]
+        assert fusion_blocks in ["midup", "full"]
+        self.reference_attn = reference_attn
+        self.reference_adain = reference_adain
+        self.fusion_blocks = fusion_blocks
+        self.register_reference_hooks(
+            mode,
+            do_classifier_free_guidance,
+            attention_auto_machine_weight,
+            gn_auto_machine_weight,
+            style_fidelity,
+            reference_attn,
+            reference_adain,
+            fusion_blocks,
+            batch_size=batch_size,
+        )
+
+    def register_reference_hooks(
+        self,
+        mode,
+        do_classifier_free_guidance,
+        attention_auto_machine_weight,
+        gn_auto_machine_weight,
+        style_fidelity,
+        reference_attn,
+        reference_adain,
+        dtype=torch.float16,
+        batch_size=1,
+        num_images_per_prompt=1,
+        device=torch.device("cuda"),
+        fusion_blocks="midup",
+    ):
+        MODE = mode
+        audio_feature_ratio = self.audio_feature_ratio
+        do_classifier_free_guidance = do_classifier_free_guidance
+        attention_auto_machine_weight = attention_auto_machine_weight
+        gn_auto_machine_weight = gn_auto_machine_weight
+        style_fidelity = style_fidelity
+        reference_attn = reference_attn
+        reference_adain = reference_adain
+        fusion_blocks = fusion_blocks
+        num_images_per_prompt = num_images_per_prompt
+        dtype = dtype
+        if do_classifier_free_guidance:
+            uc_mask = (
+                torch.Tensor(
+                    [1] * batch_size * num_images_per_prompt * 16
+                    + [0] * batch_size * num_images_per_prompt * 16
+                )
+                .to(device)
+                .bool()
+            )
+        else:
+            uc_mask = (
+                torch.Tensor([0] * batch_size * num_images_per_prompt * 2)
+                .to(device)
+                .bool()
+            )
+
+        def hacked_basic_transformer_inner_forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            audio_cond_fea: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            encoder_attention_mask: Optional[torch.FloatTensor] = None,
+            timestep: Optional[torch.LongTensor] = None,
+            cross_attention_kwargs: Dict[str, Any] = None,
+            class_labels: Optional[torch.LongTensor] = None,
+            video_length=None,
+        ):
+            if self.use_ada_layer_norm:  # False
+                norm_hidden_states = self.norm1(hidden_states, timestep)
+            elif self.use_ada_layer_norm_zero:
+                (
+                    norm_hidden_states,
+                    gate_msa,
+                    shift_mlp,
+                    scale_mlp,
+                    gate_mlp,
+                ) = self.norm1(
+                    hidden_states,
+                    timestep,
+                    class_labels,
+                    hidden_dtype=hidden_states.dtype,
+                )
+            else:
+                norm_hidden_states = self.norm1(hidden_states)
+
+            # 1. Self-Attention
+            # self.only_cross_attention = False
+            cross_attention_kwargs = (
+                cross_attention_kwargs if cross_attention_kwargs is not None else {}
+            )
+            if self.only_cross_attention:
+                attn_output = self.attn1(
+                    norm_hidden_states,
+                    attention_mask=attention_mask,
+                    **cross_attention_kwargs,
+                )
+            else:
+                if MODE == "write":
+                    self.bank.append(norm_hidden_states.clone())
+                    attn_output = self.attn1(
+                        norm_hidden_states,
+                        attention_mask=attention_mask,
+                        **cross_attention_kwargs,
+                    )
+                if MODE == "read":
+                    # bank_feas = [
+                    #     rearrange(
+                    #         d.unsqueeze(1).repeat(1, video_length, 1, 1),
+                    #         "b t l c -> (b t) l c",
+                    #     )
+                    #     for d in self.bank
+                    # ]
+
+                    bank_feas = [d
+                        for d in self.bank
+                    ]
+
+                    modify_norm_hidden_states = torch.cat(
+                        [norm_hidden_states] + bank_feas, dim=1
+                    )
+                    # Reference Cross-Attention
+                    hidden_states_uc = (
+                        self.attn1(
+                            norm_hidden_states,
+                            encoder_hidden_states=modify_norm_hidden_states,
+                            attention_mask=attention_mask,
+                        )
+                        + hidden_states
+                    )
+                    if do_classifier_free_guidance:
+                        ## inference with cfg
+                        hidden_states_c = hidden_states_uc.clone()
+                        _uc_mask = uc_mask.clone()
+                        if hidden_states.shape[0] != _uc_mask.shape[0]:
+                            _uc_mask = (
+                                torch.Tensor(
+                                    [1] * (hidden_states.shape[0] // 2)
+                                    + [0] * (hidden_states.shape[0] // 2)
+                                )
+                                .to(device)
+                                .bool()
+                            )
+                        hidden_states_c[_uc_mask] = (
+                            self.attn1(
+                                norm_hidden_states[_uc_mask],
+                                encoder_hidden_states=norm_hidden_states[_uc_mask], # B * 4096 * 768
+                                attention_mask=attention_mask,
+                            )
+                            + hidden_states[_uc_mask]
+                        )
+                        hidden_states = hidden_states_c.clone()
+                    else:
+                        hidden_states = hidden_states_uc
+
+                    if self.attn2 is not None:
+                        # Audio Cross-Attention
+                        norm_hidden_states = (
+                            self.norm2(hidden_states, timestep)
+                            if self.use_ada_layer_norm
+                            else self.norm2(hidden_states)
+                        )
+                        hidden_states = (
+                            self.attn2(
+                                norm_hidden_states,
+                                encoder_hidden_states=audio_cond_fea,
+                                attention_mask=attention_mask,
+                            ) * audio_feature_ratio
+                            + hidden_states
+                        )
+
+                    # Feed-forward
+                    hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
+
+                    # Temporal-Attention
+                    return hidden_states
+
+            if self.use_ada_layer_norm_zero:
+                attn_output = gate_msa.unsqueeze(1) * attn_output
+            hidden_states = attn_output + hidden_states
+
+            # 3. Feed-forward
+            norm_hidden_states = self.norm3(hidden_states)
+
+            if self.use_ada_layer_norm_zero:
+                norm_hidden_states = (
+                    norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+                )
+
+            ff_output = self.ff(norm_hidden_states)
+
+            if self.use_ada_layer_norm_zero:
+                ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+            hidden_states = ff_output + hidden_states
+
+            return hidden_states
+
+        if self.reference_attn:
+            if self.fusion_blocks == "midup":
+                attn_modules = [
+                    module
+                    for module in (
+                        torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks)
+                    )
+                    if isinstance(module, BasicTransformerBlock)
+                    or isinstance(module, TemporalBasicTransformerBlock)
+                ]
+            elif self.fusion_blocks == "full":
+                attn_modules = [
+                    module
+                    for module in torch_dfs(self.unet)
+                    if isinstance(module, BasicTransformerBlock)
+                    or isinstance(module, TemporalBasicTransformerBlock)
+                ]
+            attn_modules = sorted(
+                attn_modules, key=lambda x: -x.norm1.normalized_shape[0]
+            )
+
+            for i, module in enumerate(attn_modules):
+                module._original_inner_forward = module.forward
+                if isinstance(module, BasicTransformerBlock):
+                    module.forward = hacked_basic_transformer_inner_forward.__get__(
+                        module, BasicTransformerBlock
+                    )
+                if isinstance(module, TemporalBasicTransformerBlock):
+                    module.forward = hacked_basic_transformer_inner_forward.__get__(
+                        module, TemporalBasicTransformerBlock
+                    )
+
+                module.bank = []
+                module.attn_weight = float(i) / float(len(attn_modules))
+
+    def update(self, writer, do_classifier_free_guidance=False, dtype=torch.float16):
+        if self.reference_attn:
+            if self.fusion_blocks == "midup":
+                reader_attn_modules = [
+                    module
+                    for module in (
+                        torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks)
+                    )
+                    if isinstance(module, TemporalBasicTransformerBlock)
+                ]
+                writer_attn_modules = [
+                    module
+                    for module in (
+                        torch_dfs(writer.unet.mid_block)
+                        + torch_dfs(writer.unet.up_blocks)
+                    )
+                    if isinstance(module, BasicTransformerBlock)
+                ]
+            elif self.fusion_blocks == "full":
+                reader_attn_modules = [
+                    module
+                    for module in torch_dfs(self.unet)
+                    if isinstance(module, TemporalBasicTransformerBlock)
+                ]
+                writer_attn_modules = [
+                    module
+                    for module in torch_dfs(writer.unet)
+                    if isinstance(module, BasicTransformerBlock)
+                ]
+            reader_attn_modules = sorted(
+                reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]
+            )
+            writer_attn_modules = sorted(
+                writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]
+            )
+            for r, w in zip(reader_attn_modules, writer_attn_modules):
+                if do_classifier_free_guidance:
+                    r.bank = [torch.cat([v, v]).to(dtype) for v in w.bank]
+                else:
+                    r.bank = [v.clone().to(dtype) for v in w.bank]
+
+    def clear(self):
+        if self.reference_attn:
+            if self.fusion_blocks == "midup":
+                reader_attn_modules = [
+                    module
+                    for module in (
+                        torch_dfs(self.unet.mid_block) + torch_dfs(self.unet.up_blocks)
+                    )
+                    if isinstance(module, BasicTransformerBlock)
+                    or isinstance(module, TemporalBasicTransformerBlock)
+                ]
+            elif self.fusion_blocks == "full":
+                reader_attn_modules = [
+                    module
+                    for module in torch_dfs(self.unet)
+                    if isinstance(module, BasicTransformerBlock)
+                    or isinstance(module, TemporalBasicTransformerBlock)
+                ]
+            reader_attn_modules = sorted(
+                reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0]
+            )
+            for r in reader_attn_modules:
+                r.bank.clear()
diff --git a/high_sync/src/models/resnet.py b/high_sync/src/models/resnet.py
new file mode 100644
index 0000000..b489aee
--- /dev/null
+++ b/high_sync/src/models/resnet.py
@@ -0,0 +1,252 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+
+class InflatedConv3d(nn.Conv2d):
+    def forward(self, x):
+        video_length = x.shape[2]
+
+        x = rearrange(x, "b c f h w -> (b f) c h w")
+        x = super().forward(x)
+        x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
+
+        return x
+
+
+class InflatedGroupNorm(nn.GroupNorm):
+    def forward(self, x):
+        video_length = x.shape[2]
+
+        x = rearrange(x, "b c f h w -> (b f) c h w")
+        x = super().forward(x)
+        x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
+
+        return x
+
+
+class Upsample3D(nn.Module):
+    def __init__(
+        self,
+        channels,
+        use_conv=False,
+        use_conv_transpose=False,
+        out_channels=None,
+        name="conv",
+    ):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_conv_transpose = use_conv_transpose
+        self.name = name
+
+        conv = None
+        if use_conv_transpose:
+            raise NotImplementedError
+        elif use_conv:
+            self.conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1)
+
+    def forward(self, hidden_states, output_size=None):
+        assert hidden_states.shape[1] == self.channels
+
+        if self.use_conv_transpose:
+            raise NotImplementedError
+
+        # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
+        dtype = hidden_states.dtype
+        if dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(torch.float32)
+
+        # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
+        if hidden_states.shape[0] >= 64:
+            hidden_states = hidden_states.contiguous()
+
+        # if `output_size` is passed we force the interpolation output
+        # size and do not make use of `scale_factor=2`
+        if output_size is None:
+            hidden_states = F.interpolate(
+                hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest"
+            )
+        else:
+            hidden_states = F.interpolate(
+                hidden_states, size=output_size, mode="nearest"
+            )
+
+        # If the input is bfloat16, we cast back to bfloat16
+        if dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(dtype)
+
+        # if self.use_conv:
+        #     if self.name == "conv":
+        #         hidden_states = self.conv(hidden_states)
+        #     else:
+        #         hidden_states = self.Conv2d_0(hidden_states)
+        hidden_states = self.conv(hidden_states)
+
+        return hidden_states
+
+
+class Downsample3D(nn.Module):
+    def __init__(
+        self, channels, use_conv=False, out_channels=None, padding=1, name="conv"
+    ):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.padding = padding
+        stride = 2
+        self.name = name
+
+        if use_conv:
+            self.conv = InflatedConv3d(
+                self.channels, self.out_channels, 3, stride=stride, padding=padding
+            )
+        else:
+            raise NotImplementedError
+
+    def forward(self, hidden_states):
+        assert hidden_states.shape[1] == self.channels
+        if self.use_conv and self.padding == 0:
+            raise NotImplementedError
+
+        assert hidden_states.shape[1] == self.channels
+        hidden_states = self.conv(hidden_states)
+
+        return hidden_states
+
+
+class ResnetBlock3D(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels,
+        out_channels=None,
+        conv_shortcut=False,
+        dropout=0.0,
+        temb_channels=512,
+        groups=32,
+        groups_out=None,
+        pre_norm=True,
+        eps=1e-6,
+        non_linearity="swish",
+        time_embedding_norm="default",
+        output_scale_factor=1.0,
+        use_in_shortcut=None,
+        use_inflated_groupnorm=None,
+    ):
+        super().__init__()
+        self.pre_norm = pre_norm
+        self.pre_norm = True
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+        self.use_conv_shortcut = conv_shortcut
+        self.time_embedding_norm = time_embedding_norm
+        self.output_scale_factor = output_scale_factor
+
+        if groups_out is None:
+            groups_out = groups
+
+        assert use_inflated_groupnorm != None
+        if use_inflated_groupnorm:
+            self.norm1 = InflatedGroupNorm(
+                num_groups=groups, num_channels=in_channels, eps=eps, affine=True
+            )
+        else:
+            self.norm1 = torch.nn.GroupNorm(
+                num_groups=groups, num_channels=in_channels, eps=eps, affine=True
+            )
+
+        self.conv1 = InflatedConv3d(
+            in_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+
+        if temb_channels is not None:
+            if self.time_embedding_norm == "default":
+                time_emb_proj_out_channels = out_channels
+            elif self.time_embedding_norm == "scale_shift":
+                time_emb_proj_out_channels = out_channels * 2
+            else:
+                raise ValueError(
+                    f"unknown time_embedding_norm : {self.time_embedding_norm} "
+                )
+
+            self.time_emb_proj = torch.nn.Linear(
+                temb_channels, time_emb_proj_out_channels
+            )
+        else:
+            self.time_emb_proj = None
+
+        if use_inflated_groupnorm:
+            self.norm2 = InflatedGroupNorm(
+                num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True
+            )
+        else:
+            self.norm2 = torch.nn.GroupNorm(
+                num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True
+            )
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = InflatedConv3d(
+            out_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+
+        if non_linearity == "swish":
+            self.nonlinearity = lambda x: F.silu(x)
+        elif non_linearity == "mish":
+            self.nonlinearity = Mish()
+        elif non_linearity == "silu":
+            self.nonlinearity = nn.SiLU()
+
+        self.use_in_shortcut = (
+            self.in_channels != self.out_channels
+            if use_in_shortcut is None
+            else use_in_shortcut
+        )
+
+        self.conv_shortcut = None
+        if self.use_in_shortcut:
+            self.conv_shortcut = InflatedConv3d(
+                in_channels, out_channels, kernel_size=1, stride=1, padding=0
+            )
+
+    def forward(self, input_tensor, temb):
+        hidden_states = input_tensor
+
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+
+        hidden_states = self.conv1(hidden_states)
+
+        if temb is not None:
+            temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None]
+
+        if temb is not None and self.time_embedding_norm == "default":
+            hidden_states = hidden_states + temb
+
+        hidden_states = self.norm2(hidden_states)
+
+        if temb is not None and self.time_embedding_norm == "scale_shift":
+            scale, shift = torch.chunk(temb, 2, dim=1)
+            hidden_states = hidden_states * (1 + scale) + shift
+
+        hidden_states = self.nonlinearity(hidden_states)
+
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+
+        if self.conv_shortcut is not None:
+            input_tensor = self.conv_shortcut(input_tensor)
+
+        output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
+
+        return output_tensor
+
+
+class Mish(torch.nn.Module):
+    def forward(self, hidden_states):
+        return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states))
diff --git a/high_sync/src/models/transformer_2d.py b/high_sync/src/models/transformer_2d.py
new file mode 100644
index 0000000..4c75828
--- /dev/null
+++ b/high_sync/src/models/transformer_2d.py
@@ -0,0 +1,399 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py
+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+import torch
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+try:
+    from diffusers.models.embeddings import CaptionProjection
+except ImportError:
+    from diffusers.models.embeddings import PixArtAlphaTextProjection as CaptionProjection
+from diffusers.models.lora import LoRACompatibleConv, LoRACompatibleLinear
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import AdaLayerNormSingle
+from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, is_torch_version
+from torch import nn
+
+from .attention import BasicTransformerBlock
+
+
+@dataclass
+class Transformer2DModelOutput(BaseOutput):
+    """
+    The output of [`Transformer2DModel`].
+
+    Args:
+        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
+            The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
+            distributions for the unnoised latent pixels.
+    """
+
+    sample: torch.FloatTensor
+    ref_feature: torch.FloatTensor
+
+
+class Transformer2DModel(ModelMixin, ConfigMixin):
+    """
+    A 2D Transformer model for image-like data.
+
+    Parameters:
+        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
+        in_channels (`int`, *optional*):
+            The number of channels in the input and output (specify if the input is **continuous**).
+        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
+        sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
+            This is fixed during training since it is used to learn a number of position embeddings.
+        num_vector_embeds (`int`, *optional*):
+            The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
+            Includes the class for the masked latent pixel.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
+        num_embeds_ada_norm ( `int`, *optional*):
+            The number of diffusion steps used during training. Pass if at least one of the norm_layers is
+            `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
+            added to the hidden states.
+
+            During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
+        attention_bias (`bool`, *optional*):
+            Configure if the `TransformerBlocks` attention should contain a bias parameter.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 16,
+        attention_head_dim: int = 88,
+        in_channels: Optional[int] = None,
+        out_channels: Optional[int] = None,
+        num_layers: int = 1,
+        dropout: float = 0.0,
+        norm_num_groups: int = 32,
+        cross_attention_dim: Optional[int] = None,
+        attention_bias: bool = False,
+        sample_size: Optional[int] = None,
+        num_vector_embeds: Optional[int] = None,
+        patch_size: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        use_linear_projection: bool = False,
+        only_cross_attention: bool = False,
+        double_self_attention: bool = False,
+        upcast_attention: bool = False,
+        norm_type: str = "layer_norm",
+        norm_elementwise_affine: bool = True,
+        norm_eps: float = 1e-5,
+        attention_type: str = "default",
+        caption_channels: int = None,
+    ):
+        super().__init__()
+        self.use_linear_projection = use_linear_projection
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+        inner_dim = num_attention_heads * attention_head_dim
+
+        conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
+        linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear
+
+        # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
+        # Define whether input is continuous or discrete depending on configuration
+        self.is_input_continuous = (in_channels is not None) and (patch_size is None)
+        self.is_input_vectorized = num_vector_embeds is not None
+        self.is_input_patches = in_channels is not None and patch_size is not None
+
+        if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
+            deprecation_message = (
+                f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
+                " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
+                " Please make sure to update the config accordingly as leaving `norm_type` 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 `transformer/config.json` file"
+            )
+            deprecate(
+                "norm_type!=num_embeds_ada_norm",
+                "1.0.0",
+                deprecation_message,
+                standard_warn=False,
+            )
+            norm_type = "ada_norm"
+
+        if self.is_input_continuous and self.is_input_vectorized:
+            raise ValueError(
+                f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
+                " sure that either `in_channels` or `num_vector_embeds` is None."
+            )
+        elif self.is_input_vectorized and self.is_input_patches:
+            raise ValueError(
+                f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
+                " sure that either `num_vector_embeds` or `num_patches` is None."
+            )
+        elif (
+            not self.is_input_continuous
+            and not self.is_input_vectorized
+            and not self.is_input_patches
+        ):
+            raise ValueError(
+                f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
+                f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."
+            )
+
+        # 2. Define input layers
+        self.in_channels = in_channels
+
+        self.norm = torch.nn.GroupNorm(
+            num_groups=norm_num_groups,
+            num_channels=in_channels,
+            eps=1e-6,
+            affine=True,
+        )
+        if use_linear_projection:
+            self.proj_in = linear_cls(in_channels, inner_dim)
+        else:
+            self.proj_in = conv_cls(
+                in_channels, inner_dim, kernel_size=1, stride=1, padding=0
+            )
+
+        # 3. Define transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    dropout=dropout,
+                    cross_attention_dim=cross_attention_dim,
+                    activation_fn=activation_fn,
+                    num_embeds_ada_norm=num_embeds_ada_norm,
+                    attention_bias=attention_bias,
+                    only_cross_attention=only_cross_attention,
+                    double_self_attention=double_self_attention,
+                    upcast_attention=upcast_attention,
+                    norm_type=norm_type,
+                    norm_elementwise_affine=norm_elementwise_affine,
+                    norm_eps=norm_eps,
+                    attention_type=attention_type,
+                )
+                for d in range(num_layers)
+            ]
+        )
+
+        # 4. Define output layers
+        self.out_channels = in_channels if out_channels is None else out_channels
+        # TODO: should use out_channels for continuous projections
+        if use_linear_projection:
+            self.proj_out = linear_cls(inner_dim, in_channels)
+        else:
+            self.proj_out = conv_cls(
+                inner_dim, in_channels, kernel_size=1, stride=1, padding=0
+            )
+
+        # 5. PixArt-Alpha blocks.
+        self.adaln_single = None
+        self.use_additional_conditions = False
+        if norm_type == "ada_norm_single":
+            self.use_additional_conditions = self.config.sample_size == 128
+            # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use
+            # additional conditions until we find better name
+            self.adaln_single = AdaLayerNormSingle(
+                inner_dim, use_additional_conditions=self.use_additional_conditions
+            )
+
+        self.caption_projection = None
+        if caption_channels is not None:
+            self.caption_projection = CaptionProjection(
+                in_features=caption_channels, hidden_size=inner_dim
+            )
+
+        self.gradient_checkpointing = False
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        added_cond_kwargs: Dict[str, torch.Tensor] = None,
+        class_labels: Optional[torch.LongTensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ):
+        """
+        The [`Transformer2DModel`] forward method.
+
+        Args:
+            hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
+                Input `hidden_states`.
+            encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
+                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
+                self-attention.
+            timestep ( `torch.LongTensor`, *optional*):
+                Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
+            class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
+                Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
+                `AdaLayerZeroNorm`.
+            cross_attention_kwargs ( `Dict[str, Any]`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            attention_mask ( `torch.Tensor`, *optional*):
+                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
+                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
+                negative values to the attention scores corresponding to "discard" tokens.
+            encoder_attention_mask ( `torch.Tensor`, *optional*):
+                Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
+
+                    * Mask `(batch, sequence_length)` True = keep, False = discard.
+                    * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
+
+                If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
+                above. This bias will be added to the cross-attention scores.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
+                tuple.
+
+        Returns:
+            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+            `tuple` where the first element is the sample tensor.
+        """
+        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
+        #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
+        #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
+        # expects mask of shape:
+        #   [batch, key_tokens]
+        # adds singleton query_tokens dimension:
+        #   [batch,                    1, key_tokens]
+        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
+        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
+        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
+        if attention_mask is not None and attention_mask.ndim == 2:
+            # assume that mask is expressed as:
+            #   (1 = keep,      0 = discard)
+            # convert mask into a bias that can be added to attention scores:
+            #       (keep = +0,     discard = -10000.0)
+            attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # convert encoder_attention_mask to a bias the same way we do for attention_mask
+        if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
+            encoder_attention_mask = (
+                1 - encoder_attention_mask.to(hidden_states.dtype)
+            ) * -10000.0
+            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+        # Retrieve lora scale.
+        lora_scale = (
+            cross_attention_kwargs.get("scale", 1.0)
+            if cross_attention_kwargs is not None
+            else 1.0
+        )
+
+        # 1. Input
+        batch, _, height, width = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        if not self.use_linear_projection:
+            hidden_states = (
+                self.proj_in(hidden_states, scale=lora_scale)
+                if not USE_PEFT_BACKEND
+                else self.proj_in(hidden_states)
+            )
+            inner_dim = hidden_states.shape[1]
+            hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
+                batch, height * width, inner_dim
+            )
+        else:
+            inner_dim = hidden_states.shape[1]
+            hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
+                batch, height * width, inner_dim
+            )
+            hidden_states = (
+                self.proj_in(hidden_states, scale=lora_scale)
+                if not USE_PEFT_BACKEND
+                else self.proj_in(hidden_states)
+            )
+
+        # 2. Blocks
+        if self.caption_projection is not None:
+            batch_size = hidden_states.shape[0]
+            encoder_hidden_states = self.caption_projection(encoder_hidden_states)
+            encoder_hidden_states = encoder_hidden_states.view(
+                batch_size, -1, hidden_states.shape[-1]
+            )
+
+        ref_feature = hidden_states.reshape(batch, height, width, inner_dim)
+        for block in self.transformer_blocks:
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = (
+                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                )
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    attention_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    timestep,
+                    cross_attention_kwargs,
+                    class_labels,
+                    **ckpt_kwargs,
+                )
+            else:
+                hidden_states = block(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    encoder_hidden_states=encoder_hidden_states,
+                    encoder_attention_mask=encoder_attention_mask,
+                    timestep=timestep,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    class_labels=class_labels,
+                )
+
+        # 3. Output
+        if self.is_input_continuous:
+            if not self.use_linear_projection:
+                hidden_states = (
+                    hidden_states.reshape(batch, height, width, inner_dim)
+                    .permute(0, 3, 1, 2)
+                    .contiguous()
+                )
+                hidden_states = (
+                    self.proj_out(hidden_states, scale=lora_scale)
+                    if not USE_PEFT_BACKEND
+                    else self.proj_out(hidden_states)
+                )
+            else:
+                hidden_states = (
+                    self.proj_out(hidden_states, scale=lora_scale)
+                    if not USE_PEFT_BACKEND
+                    else self.proj_out(hidden_states)
+                )
+                hidden_states = (
+                    hidden_states.reshape(batch, height, width, inner_dim)
+                    .permute(0, 3, 1, 2)
+                    .contiguous()
+                )
+
+            output = hidden_states + residual
+        if not return_dict:
+            return (output, ref_feature)
+
+        return Transformer2DModelOutput(sample=output, ref_feature=ref_feature)
diff --git a/high_sync/src/models/transformer_3d.py b/high_sync/src/models/transformer_3d.py
new file mode 100644
index 0000000..dc83bf4
--- /dev/null
+++ b/high_sync/src/models/transformer_3d.py
@@ -0,0 +1,175 @@
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models import ModelMixin
+from diffusers.utils import BaseOutput
+from diffusers.utils.import_utils import is_xformers_available
+from einops import rearrange, repeat
+from torch import nn
+
+from .attention import TemporalBasicTransformerBlock
+
+
+@dataclass
+class Transformer3DModelOutput(BaseOutput):
+    sample: torch.FloatTensor
+
+
+if is_xformers_available():
+    import xformers
+    import xformers.ops
+else:
+    xformers = None
+
+
+class Transformer3DModel(ModelMixin, ConfigMixin):
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 16,
+        attention_head_dim: int = 88,
+        in_channels: Optional[int] = None,
+        num_layers: int = 1,
+        dropout: float = 0.0,
+        norm_num_groups: int = 32,
+        cross_attention_dim: Optional[int] = None,
+        attention_bias: bool = False,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        use_linear_projection: bool = False,
+        only_cross_attention: bool = False,
+        upcast_attention: bool = False,
+        unet_use_cross_frame_attention=None,
+        unet_use_temporal_attention=None,
+    ):
+        super().__init__()
+        self.use_linear_projection = use_linear_projection
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+        inner_dim = num_attention_heads * attention_head_dim
+
+        # Define input layers
+        self.in_channels = in_channels
+
+        self.norm = torch.nn.GroupNorm(
+            num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True
+        )
+        if use_linear_projection:
+            self.proj_in = nn.Linear(in_channels, inner_dim)
+        else:
+            self.proj_in = nn.Conv2d(
+                in_channels, inner_dim, kernel_size=1, stride=1, padding=0
+            )
+
+        # Define transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                TemporalBasicTransformerBlock(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    dropout=dropout,
+                    cross_attention_dim=cross_attention_dim,
+                    activation_fn=activation_fn,
+                    num_embeds_ada_norm=num_embeds_ada_norm,
+                    attention_bias=attention_bias,
+                    only_cross_attention=only_cross_attention,
+                    upcast_attention=upcast_attention,
+                    unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                    unet_use_temporal_attention=unet_use_temporal_attention,
+                )
+                for d in range(num_layers)
+            ]
+        )
+
+        # 4. Define output layers
+        if use_linear_projection:
+            self.proj_out = nn.Linear(in_channels, inner_dim)
+        else:
+            self.proj_out = nn.Conv2d(
+                inner_dim, in_channels, kernel_size=1, stride=1, padding=0
+            )
+
+        self.gradient_checkpointing = False
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        hidden_states,
+        encoder_hidden_states=None,
+        audio_cond_fea=None,
+        timestep=None,
+        return_dict: bool = True,
+    ):
+        # Input
+        assert (
+            hidden_states.dim() == 5
+        ), f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
+        video_length = hidden_states.shape[2]
+        hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
+        if audio_cond_fea.shape[0] != hidden_states.shape[0]:
+            if len(audio_cond_fea.shape) == 3:
+                audio_cond_fea = rearrange(
+                    audio_cond_fea, "b f c -> (b f) 1 c"
+                )
+            elif len(audio_cond_fea.shape) == 4:
+                audio_cond_fea = rearrange(
+                    audio_cond_fea, "b f n c -> (b f) n c"
+                )
+        batch, channel, height, weight = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        if not self.use_linear_projection:
+            hidden_states = self.proj_in(hidden_states)
+            inner_dim = hidden_states.shape[1]
+            hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
+                batch, height * weight, inner_dim
+            )
+        else:
+            inner_dim = hidden_states.shape[1]
+            hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(
+                batch, height * weight, inner_dim
+            )
+            hidden_states = self.proj_in(hidden_states)
+
+        # Blocks
+        for i, block in enumerate(self.transformer_blocks):
+            hidden_states = block(
+                hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                audio_cond_fea=audio_cond_fea,
+                timestep=timestep,
+                video_length=video_length,
+            )
+
+        # Output
+        if not self.use_linear_projection:
+            hidden_states = (
+                hidden_states.reshape(batch, height, weight, inner_dim)
+                .permute(0, 3, 1, 2)
+                .contiguous()
+            )
+            hidden_states = self.proj_out(hidden_states)
+        else:
+            hidden_states = self.proj_out(hidden_states)
+            hidden_states = (
+                hidden_states.reshape(batch, height, weight, inner_dim)
+                .permute(0, 3, 1, 2)
+                .contiguous()
+            )
+
+        output = hidden_states + residual
+
+        output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
+        if not return_dict:
+            return (output,)
+
+        return Transformer3DModelOutput(sample=output)
diff --git a/high_sync/src/models/unet_2d_blocks.py b/high_sync/src/models/unet_2d_blocks.py
new file mode 100644
index 0000000..ee05ab3
--- /dev/null
+++ b/high_sync/src/models/unet_2d_blocks.py
@@ -0,0 +1,1077 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py
+from typing import Any, Dict, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from diffusers.models.activations import get_activation
+from diffusers.models.attention_processor import Attention
+try:
+    from diffusers.models.dual_transformer_2d import DualTransformer2DModel
+except ModuleNotFoundError:
+    from diffusers.models.transformers.dual_transformer_2d import DualTransformer2DModel
+from diffusers.models.resnet import Downsample2D, ResnetBlock2D, Upsample2D
+from diffusers.utils import is_torch_version, logging
+from diffusers.utils.torch_utils import apply_freeu
+from torch import nn
+
+from .transformer_2d import Transformer2DModel
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+def get_down_block(
+    down_block_type: str,
+    num_layers: int,
+    in_channels: int,
+    out_channels: int,
+    temb_channels: int,
+    add_downsample: bool,
+    resnet_eps: float,
+    resnet_act_fn: str,
+    transformer_layers_per_block: int = 1,
+    num_attention_heads: Optional[int] = None,
+    resnet_groups: Optional[int] = None,
+    cross_attention_dim: Optional[int] = None,
+    downsample_padding: Optional[int] = None,
+    dual_cross_attention: bool = False,
+    use_linear_projection: bool = False,
+    only_cross_attention: bool = False,
+    upcast_attention: bool = False,
+    resnet_time_scale_shift: str = "default",
+    attention_type: str = "default",
+    resnet_skip_time_act: bool = False,
+    resnet_out_scale_factor: float = 1.0,
+    cross_attention_norm: Optional[str] = None,
+    attention_head_dim: Optional[int] = None,
+    downsample_type: Optional[str] = None,
+    dropout: float = 0.0,
+):
+    # If attn head dim is not defined, we default it to the number of heads
+    if attention_head_dim is None:
+        logger.warn(
+            f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
+        )
+        attention_head_dim = num_attention_heads
+
+    down_block_type = (
+        down_block_type[7:]
+        if down_block_type.startswith("UNetRes")
+        else down_block_type
+    )
+    if down_block_type == "DownBlock2D":
+        return DownBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            dropout=dropout,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif down_block_type == "CrossAttnDownBlock2D":
+        if cross_attention_dim is None:
+            raise ValueError(
+                "cross_attention_dim must be specified for CrossAttnDownBlock2D"
+            )
+        return CrossAttnDownBlock2D(
+            num_layers=num_layers,
+            transformer_layers_per_block=transformer_layers_per_block,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            dropout=dropout,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            cross_attention_dim=cross_attention_dim,
+            num_attention_heads=num_attention_heads,
+            dual_cross_attention=dual_cross_attention,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            attention_type=attention_type,
+        )
+    raise ValueError(f"{down_block_type} does not exist.")
+
+
+def get_up_block(
+    up_block_type: str,
+    num_layers: int,
+    in_channels: int,
+    out_channels: int,
+    prev_output_channel: int,
+    temb_channels: int,
+    add_upsample: bool,
+    resnet_eps: float,
+    resnet_act_fn: str,
+    resolution_idx: Optional[int] = None,
+    transformer_layers_per_block: int = 1,
+    num_attention_heads: Optional[int] = None,
+    resnet_groups: Optional[int] = None,
+    cross_attention_dim: Optional[int] = None,
+    dual_cross_attention: bool = False,
+    use_linear_projection: bool = False,
+    only_cross_attention: bool = False,
+    upcast_attention: bool = False,
+    resnet_time_scale_shift: str = "default",
+    attention_type: str = "default",
+    resnet_skip_time_act: bool = False,
+    resnet_out_scale_factor: float = 1.0,
+    cross_attention_norm: Optional[str] = None,
+    attention_head_dim: Optional[int] = None,
+    upsample_type: Optional[str] = None,
+    dropout: float = 0.0,
+) -> nn.Module:
+    # If attn head dim is not defined, we default it to the number of heads
+    if attention_head_dim is None:
+        logger.warn(
+            f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}."
+        )
+        attention_head_dim = num_attention_heads
+
+    up_block_type = (
+        up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
+    )
+    if up_block_type == "UpBlock2D":
+        return UpBlock2D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            resolution_idx=resolution_idx,
+            dropout=dropout,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+        )
+    elif up_block_type == "CrossAttnUpBlock2D":
+        if cross_attention_dim is None:
+            raise ValueError(
+                "cross_attention_dim must be specified for CrossAttnUpBlock2D"
+            )
+        return CrossAttnUpBlock2D(
+            num_layers=num_layers,
+            transformer_layers_per_block=transformer_layers_per_block,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            resolution_idx=resolution_idx,
+            dropout=dropout,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            cross_attention_dim=cross_attention_dim,
+            num_attention_heads=num_attention_heads,
+            dual_cross_attention=dual_cross_attention,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            attention_type=attention_type,
+        )
+
+    raise ValueError(f"{up_block_type} does not exist.")
+
+
+class AutoencoderTinyBlock(nn.Module):
+    """
+    Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU
+    blocks.
+
+    Args:
+        in_channels (`int`): The number of input channels.
+        out_channels (`int`): The number of output channels.
+        act_fn (`str`):
+            ` The activation function to use. Supported values are `"swish"`, `"mish"`, `"gelu"`, and `"relu"`.
+
+    Returns:
+        `torch.FloatTensor`: A tensor with the same shape as the input tensor, but with the number of channels equal to
+        `out_channels`.
+    """
+
+    def __init__(self, in_channels: int, out_channels: int, act_fn: str):
+        super().__init__()
+        act_fn = get_activation(act_fn)
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
+            act_fn,
+            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
+            act_fn,
+            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
+        )
+        self.skip = (
+            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
+            if in_channels != out_channels
+            else nn.Identity()
+        )
+        self.fuse = nn.ReLU()
+
+    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
+        return self.fuse(self.conv(x) + self.skip(x))
+
+
+class UNetMidBlock2D(nn.Module):
+    """
+    A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks.
+
+    Args:
+        in_channels (`int`): The number of input channels.
+        temb_channels (`int`): The number of temporal embedding channels.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout rate.
+        num_layers (`int`, *optional*, defaults to 1): The number of residual blocks.
+        resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks.
+        resnet_time_scale_shift (`str`, *optional*, defaults to `default`):
+            The type of normalization to apply to the time embeddings. This can help to improve the performance of the
+            model on tasks with long-range temporal dependencies.
+        resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks.
+        resnet_groups (`int`, *optional*, defaults to 32):
+            The number of groups to use in the group normalization layers of the resnet blocks.
+        attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks.
+        resnet_pre_norm (`bool`, *optional*, defaults to `True`):
+            Whether to use pre-normalization for the resnet blocks.
+        add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks.
+        attention_head_dim (`int`, *optional*, defaults to 1):
+            Dimension of a single attention head. The number of attention heads is determined based on this value and
+            the number of input channels.
+        output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor.
+
+    Returns:
+        `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size,
+        in_channels, height, width)`.
+
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",  # default, spatial
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        attn_groups: Optional[int] = None,
+        resnet_pre_norm: bool = True,
+        add_attention: bool = True,
+        attention_head_dim: int = 1,
+        output_scale_factor: float = 1.0,
+    ):
+        super().__init__()
+        resnet_groups = (
+            resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+        )
+        self.add_attention = add_attention
+
+        if attn_groups is None:
+            attn_groups = (
+                resnet_groups if resnet_time_scale_shift == "default" else None
+            )
+
+        # there is always at least one resnet
+        resnets = [
+            ResnetBlock2D(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                temb_channels=temb_channels,
+                eps=resnet_eps,
+                groups=resnet_groups,
+                dropout=dropout,
+                time_embedding_norm=resnet_time_scale_shift,
+                non_linearity=resnet_act_fn,
+                output_scale_factor=output_scale_factor,
+                pre_norm=resnet_pre_norm,
+            )
+        ]
+        attentions = []
+
+        if attention_head_dim is None:
+            logger.warn(
+                f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}."
+            )
+            attention_head_dim = in_channels
+
+        for _ in range(num_layers):
+            if self.add_attention:
+                attentions.append(
+                    Attention(
+                        in_channels,
+                        heads=in_channels // attention_head_dim,
+                        dim_head=attention_head_dim,
+                        rescale_output_factor=output_scale_factor,
+                        eps=resnet_eps,
+                        norm_num_groups=attn_groups,
+                        spatial_norm_dim=temb_channels
+                        if resnet_time_scale_shift == "spatial"
+                        else None,
+                        residual_connection=True,
+                        bias=True,
+                        upcast_softmax=True,
+                        _from_deprecated_attn_block=True,
+                    )
+                )
+            else:
+                attentions.append(None)
+
+            resnets.append(
+                ResnetBlock2D(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+    def forward(
+        self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None
+    ) -> torch.FloatTensor:
+        hidden_states = self.resnets[0](hidden_states, temb)
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            if attn is not None:
+                hidden_states = attn(hidden_states, temb=temb)
+            hidden_states = resnet(hidden_states, temb)
+
+        return hidden_states
+
+
+class UNetMidBlock2DCrossAttn(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        num_attention_heads: int = 1,
+        output_scale_factor: float = 1.0,
+        cross_attention_dim: int = 1280,
+        dual_cross_attention: bool = False,
+        use_linear_projection: bool = False,
+        upcast_attention: bool = False,
+        attention_type: str = "default",
+    ):
+        super().__init__()
+
+        self.has_cross_attention = True
+        self.num_attention_heads = num_attention_heads
+        resnet_groups = (
+            resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+        )
+
+        # support for variable transformer layers per block
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * num_layers
+
+        # there is always at least one resnet
+        resnets = [
+            ResnetBlock2D(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                temb_channels=temb_channels,
+                eps=resnet_eps,
+                groups=resnet_groups,
+                dropout=dropout,
+                time_embedding_norm=resnet_time_scale_shift,
+                non_linearity=resnet_act_fn,
+                output_scale_factor=output_scale_factor,
+                pre_norm=resnet_pre_norm,
+            )
+        ]
+        attentions = []
+
+        for i in range(num_layers):
+            if not dual_cross_attention:
+                attentions.append(
+                    Transformer2DModel(
+                        num_attention_heads,
+                        in_channels // num_attention_heads,
+                        in_channels=in_channels,
+                        num_layers=transformer_layers_per_block[i],
+                        cross_attention_dim=cross_attention_dim,
+                        norm_num_groups=resnet_groups,
+                        use_linear_projection=use_linear_projection,
+                        upcast_attention=upcast_attention,
+                        attention_type=attention_type,
+                    )
+                )
+            else:
+                attentions.append(
+                    DualTransformer2DModel(
+                        num_attention_heads,
+                        in_channels // num_attention_heads,
+                        in_channels=in_channels,
+                        num_layers=1,
+                        cross_attention_dim=cross_attention_dim,
+                        norm_num_groups=resnet_groups,
+                    )
+                )
+            resnets.append(
+                ResnetBlock2D(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        temb: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+    ) -> torch.FloatTensor:
+        lora_scale = (
+            cross_attention_kwargs.get("scale", 1.0)
+            if cross_attention_kwargs is not None
+            else 1.0
+        )
+        hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale)
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = (
+                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                )
+                hidden_states, ref_feature = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
+            else:
+                hidden_states, ref_feature = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )
+                hidden_states = resnet(hidden_states, temb, scale=lora_scale)
+
+        return hidden_states
+
+
+class CrossAttnDownBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        num_attention_heads: int = 1,
+        cross_attention_dim: int = 1280,
+        output_scale_factor: float = 1.0,
+        downsample_padding: int = 1,
+        add_downsample: bool = True,
+        dual_cross_attention: bool = False,
+        use_linear_projection: bool = False,
+        only_cross_attention: bool = False,
+        upcast_attention: bool = False,
+        attention_type: str = "default",
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        self.has_cross_attention = True
+        self.num_attention_heads = num_attention_heads
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * num_layers
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock2D(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            if not dual_cross_attention:
+                attentions.append(
+                    Transformer2DModel(
+                        num_attention_heads,
+                        out_channels // num_attention_heads,
+                        in_channels=out_channels,
+                        num_layers=transformer_layers_per_block[i],
+                        cross_attention_dim=cross_attention_dim,
+                        norm_num_groups=resnet_groups,
+                        use_linear_projection=use_linear_projection,
+                        only_cross_attention=only_cross_attention,
+                        upcast_attention=upcast_attention,
+                        attention_type=attention_type,
+                    )
+                )
+            else:
+                attentions.append(
+                    DualTransformer2DModel(
+                        num_attention_heads,
+                        out_channels // num_attention_heads,
+                        in_channels=out_channels,
+                        num_layers=1,
+                        cross_attention_dim=cross_attention_dim,
+                        norm_num_groups=resnet_groups,
+                    )
+                )
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample2D(
+                        out_channels,
+                        use_conv=True,
+                        out_channels=out_channels,
+                        padding=downsample_padding,
+                        name="op",
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        temb: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        additional_residuals: Optional[torch.FloatTensor] = None,
+    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
+        output_states = ()
+
+        lora_scale = (
+            cross_attention_kwargs.get("scale", 1.0)
+            if cross_attention_kwargs is not None
+            else 1.0
+        )
+
+        blocks = list(zip(self.resnets, self.attentions))
+
+        for i, (resnet, attn) in enumerate(blocks):
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = (
+                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                )
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
+                hidden_states, ref_feature = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )
+            else:
+                hidden_states = resnet(hidden_states, temb, scale=lora_scale)
+                hidden_states, ref_feature = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )
+
+            # apply additional residuals to the output of the last pair of resnet and attention blocks
+            if i == len(blocks) - 1 and additional_residuals is not None:
+                hidden_states = hidden_states + additional_residuals
+
+            output_states = output_states + (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states, scale=lora_scale)
+
+            output_states = output_states + (hidden_states,)
+
+        return hidden_states, output_states
+
+
+class DownBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        output_scale_factor: float = 1.0,
+        add_downsample: bool = True,
+        downsample_padding: int = 1,
+    ):
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock2D(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample2D(
+                        out_channels,
+                        use_conv=True,
+                        out_channels=out_channels,
+                        padding=downsample_padding,
+                        name="op",
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        temb: Optional[torch.FloatTensor] = None,
+        scale: float = 1.0,
+    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
+        output_states = ()
+
+        for resnet in self.resnets:
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                if is_torch_version(">=", "1.11.0"):
+                    hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(resnet),
+                        hidden_states,
+                        temb,
+                        use_reentrant=False,
+                    )
+                else:
+                    hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(resnet), hidden_states, temb
+                    )
+            else:
+                hidden_states = resnet(hidden_states, temb, scale=scale)
+
+            output_states = output_states + (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states, scale=scale)
+
+            output_states = output_states + (hidden_states,)
+
+        return hidden_states, output_states
+
+
+class CrossAttnUpBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        prev_output_channel: int,
+        temb_channels: int,
+        resolution_idx: Optional[int] = None,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        num_attention_heads: int = 1,
+        cross_attention_dim: int = 1280,
+        output_scale_factor: float = 1.0,
+        add_upsample: bool = True,
+        dual_cross_attention: bool = False,
+        use_linear_projection: bool = False,
+        only_cross_attention: bool = False,
+        upcast_attention: bool = False,
+        attention_type: str = "default",
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+
+        self.has_cross_attention = True
+        self.num_attention_heads = num_attention_heads
+
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * num_layers
+
+        for i in range(num_layers):
+            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+            resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+            resnets.append(
+                ResnetBlock2D(
+                    in_channels=resnet_in_channels + res_skip_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            if not dual_cross_attention:
+                attentions.append(
+                    Transformer2DModel(
+                        num_attention_heads,
+                        out_channels // num_attention_heads,
+                        in_channels=out_channels,
+                        num_layers=transformer_layers_per_block[i],
+                        cross_attention_dim=cross_attention_dim,
+                        norm_num_groups=resnet_groups,
+                        use_linear_projection=use_linear_projection,
+                        only_cross_attention=only_cross_attention,
+                        upcast_attention=upcast_attention,
+                        attention_type=attention_type,
+                    )
+                )
+            else:
+                attentions.append(
+                    DualTransformer2DModel(
+                        num_attention_heads,
+                        out_channels // num_attention_heads,
+                        in_channels=out_channels,
+                        num_layers=1,
+                        cross_attention_dim=cross_attention_dim,
+                        norm_num_groups=resnet_groups,
+                    )
+                )
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList(
+                [Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]
+            )
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+        self.resolution_idx = resolution_idx
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
+        temb: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        upsample_size: Optional[int] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+    ) -> torch.FloatTensor:
+        lora_scale = (
+            cross_attention_kwargs.get("scale", 1.0)
+            if cross_attention_kwargs is not None
+            else 1.0
+        )
+        is_freeu_enabled = (
+            getattr(self, "s1", None)
+            and getattr(self, "s2", None)
+            and getattr(self, "b1", None)
+            and getattr(self, "b2", None)
+        )
+
+        for resnet, attn in zip(self.resnets, self.attentions):
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+
+            # FreeU: Only operate on the first two stages
+            if is_freeu_enabled:
+                hidden_states, res_hidden_states = apply_freeu(
+                    self.resolution_idx,
+                    hidden_states,
+                    res_hidden_states,
+                    s1=self.s1,
+                    s2=self.s2,
+                    b1=self.b1,
+                    b2=self.b2,
+                )
+
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = (
+                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                )
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
+                hidden_states, ref_feature = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )
+            else:
+                hidden_states = resnet(hidden_states, temb, scale=lora_scale)
+                hidden_states, ref_feature = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(
+                    hidden_states, upsample_size, scale=lora_scale
+                )
+
+        return hidden_states
+
+
+class UpBlock2D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        prev_output_channel: int,
+        out_channels: int,
+        temb_channels: int,
+        resolution_idx: Optional[int] = None,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        output_scale_factor: float = 1.0,
+        add_upsample: bool = True,
+    ):
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+            resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+            resnets.append(
+                ResnetBlock2D(
+                    in_channels=resnet_in_channels + res_skip_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList(
+                [Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]
+            )
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+        self.resolution_idx = resolution_idx
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
+        temb: Optional[torch.FloatTensor] = None,
+        upsample_size: Optional[int] = None,
+        scale: float = 1.0,
+    ) -> torch.FloatTensor:
+        is_freeu_enabled = (
+            getattr(self, "s1", None)
+            and getattr(self, "s2", None)
+            and getattr(self, "b1", None)
+            and getattr(self, "b2", None)
+        )
+
+        for resnet in self.resnets:
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+
+            # FreeU: Only operate on the first two stages
+            if is_freeu_enabled:
+                hidden_states, res_hidden_states = apply_freeu(
+                    self.resolution_idx,
+                    hidden_states,
+                    res_hidden_states,
+                    s1=self.s1,
+                    s2=self.s2,
+                    b1=self.b1,
+                    b2=self.b2,
+                )
+
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                if is_torch_version(">=", "1.11.0"):
+                    hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(resnet),
+                        hidden_states,
+                        temb,
+                        use_reentrant=False,
+                    )
+                else:
+                    hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(resnet), hidden_states, temb
+                    )
+            else:
+                hidden_states = resnet(hidden_states, temb, scale=scale)
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states, upsample_size, scale=scale)
+
+        return hidden_states
diff --git a/high_sync/src/models/unet_2d_condition.py b/high_sync/src/models/unet_2d_condition.py
new file mode 100644
index 0000000..6ef99af
--- /dev/null
+++ b/high_sync/src/models/unet_2d_condition.py
@@ -0,0 +1,1310 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import UNet2DConditionLoadersMixin
+from diffusers.models.activations import get_activation
+from diffusers.models.attention_processor import (
+    ADDED_KV_ATTENTION_PROCESSORS,
+    CROSS_ATTENTION_PROCESSORS,
+    AttentionProcessor,
+    AttnAddedKVProcessor,
+    AttnProcessor,
+)
+from diffusers.models.embeddings import (
+    GaussianFourierProjection,
+    GLIGENTextBoundingboxProjection,
+    ImageHintTimeEmbedding,
+    ImageProjection,
+    ImageTimeEmbedding,
+    TextImageProjection,
+    TextImageTimeEmbedding,
+    TextTimeEmbedding,
+    TimestepEmbedding,
+    Timesteps,
+)
+
+PositionNet = GLIGENTextBoundingboxProjection
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    BaseOutput,
+    deprecate,
+    logging,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+
+from .unet_2d_blocks import (
+    UNetMidBlock2D,
+    UNetMidBlock2DCrossAttn,
+    get_down_block,
+    get_up_block,
+)
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class UNet2DConditionOutput(BaseOutput):
+    """
+    The output of [`UNet2DConditionModel`].
+
+    Args:
+        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
+    """
+
+    sample: torch.FloatTensor = None
+    ref_features: Tuple[torch.FloatTensor] = None
+
+
+class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
+    r"""
+    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
+    shaped output.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+
+    Parameters:
+        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
+            Height and width of input/output sample.
+        in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
+        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
+        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
+        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
+            Whether to flip the sin to cos in the time embedding.
+        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
+        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
+            The tuple of downsample blocks to use.
+        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
+            Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or
+            `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
+        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
+            The tuple of upsample blocks to use.
+        only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
+            Whether to include self-attention in the basic transformer blocks, see
+            [`~models.attention.BasicTransformerBlock`].
+        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
+            The tuple of output channels for each block.
+        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
+        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
+        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
+        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
+            If `None`, normalization and activation layers is skipped in post-processing.
+        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
+        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
+            The dimension of the cross attention features.
+        transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1):
+            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
+            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
+            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
+       reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None):
+            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling
+            blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for
+            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
+            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
+        encoder_hid_dim (`int`, *optional*, defaults to None):
+            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
+            dimension to `cross_attention_dim`.
+        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
+            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
+            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
+        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
+        num_attention_heads (`int`, *optional*):
+            The number of attention heads. If not defined, defaults to `attention_head_dim`
+        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
+            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
+        class_embed_type (`str`, *optional*, defaults to `None`):
+            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
+            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
+        addition_embed_type (`str`, *optional*, defaults to `None`):
+            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
+            "text". "text" will use the `TextTimeEmbedding` layer.
+        addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
+            Dimension for the timestep embeddings.
+        num_class_embeds (`int`, *optional*, defaults to `None`):
+            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
+            class conditioning with `class_embed_type` equal to `None`.
+        time_embedding_type (`str`, *optional*, defaults to `positional`):
+            The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
+        time_embedding_dim (`int`, *optional*, defaults to `None`):
+            An optional override for the dimension of the projected time embedding.
+        time_embedding_act_fn (`str`, *optional*, defaults to `None`):
+            Optional activation function to use only once on the time embeddings before they are passed to the rest of
+            the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
+        timestep_post_act (`str`, *optional*, defaults to `None`):
+            The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
+        time_cond_proj_dim (`int`, *optional*, defaults to `None`):
+            The dimension of `cond_proj` layer in the timestep embedding.
+        conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`,
+        *optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`,
+        *optional*): The dimension of the `class_labels` input when
+            `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
+        class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
+            embeddings with the class embeddings.
+        mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
+            Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
+            `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
+            `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
+            otherwise.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        sample_size: Optional[int] = None,
+        in_channels: int = 4,
+        out_channels: int = 4,
+        center_input_sample: bool = False,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        down_block_types: Tuple[str] = (
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "DownBlock2D",
+        ),
+        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
+        up_block_types: Tuple[str] = (
+            "UpBlock2D",
+            "CrossAttnUpBlock2D",
+            "CrossAttnUpBlock2D",
+            "CrossAttnUpBlock2D",
+        ),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        layers_per_block: Union[int, Tuple[int]] = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        dropout: float = 0.0,
+        act_fn: str = "silu",
+        norm_num_groups: Optional[int] = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: Union[int, Tuple[int]] = 1280,
+        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
+        reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
+        encoder_hid_dim: Optional[int] = None,
+        encoder_hid_dim_type: Optional[str] = None,
+        attention_head_dim: Union[int, Tuple[int]] = 8,
+        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
+        dual_cross_attention: bool = False,
+        use_linear_projection: bool = False,
+        class_embed_type: Optional[str] = None,
+        addition_embed_type: Optional[str] = None,
+        addition_time_embed_dim: Optional[int] = None,
+        num_class_embeds: Optional[int] = None,
+        upcast_attention: bool = False,
+        resnet_time_scale_shift: str = "default",
+        resnet_skip_time_act: bool = False,
+        resnet_out_scale_factor: int = 1.0,
+        time_embedding_type: str = "positional",
+        time_embedding_dim: Optional[int] = None,
+        time_embedding_act_fn: Optional[str] = None,
+        timestep_post_act: Optional[str] = None,
+        time_cond_proj_dim: Optional[int] = None,
+        conv_in_kernel: int = 3,
+        conv_out_kernel: int = 3,
+        projection_class_embeddings_input_dim: Optional[int] = None,
+        attention_type: str = "default",
+        class_embeddings_concat: bool = False,
+        mid_block_only_cross_attention: Optional[bool] = None,
+        cross_attention_norm: Optional[str] = None,
+        addition_embed_type_num_heads=64,
+    ):
+        super().__init__()
+
+        self.sample_size = sample_size
+
+        if num_attention_heads is not None:
+            raise ValueError(
+                "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
+            )
+
+        # If `num_attention_heads` is not defined (which is the case for most models)
+        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
+        # The reason for this behavior is to correct for incorrectly named variables that were introduced
+        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
+        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
+        # which is why we correct for the naming here.
+        num_attention_heads = num_attention_heads or attention_head_dim
+
+        # Check inputs
+        if len(down_block_types) != len(up_block_types):
+            raise ValueError(
+                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
+            )
+
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(only_cross_attention, bool) and len(
+            only_cross_attention
+        ) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(
+            down_block_types
+        ):
+            raise ValueError(
+                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(
+            down_block_types
+        ):
+            raise ValueError(
+                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(
+            down_block_types
+        ):
+            raise ValueError(
+                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(
+            down_block_types
+        ):
+            raise ValueError(
+                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
+            )
+        if (
+            isinstance(transformer_layers_per_block, list)
+            and reverse_transformer_layers_per_block is None
+        ):
+            for layer_number_per_block in transformer_layers_per_block:
+                if isinstance(layer_number_per_block, list):
+                    raise ValueError(
+                        "Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet."
+                    )
+
+        # input
+        conv_in_padding = (conv_in_kernel - 1) // 2
+        self.conv_in = nn.Conv2d(
+            in_channels,
+            block_out_channels[0],
+            kernel_size=conv_in_kernel,
+            padding=conv_in_padding,
+        )
+
+        # time
+        if time_embedding_type == "fourier":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
+            if time_embed_dim % 2 != 0:
+                raise ValueError(
+                    f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}."
+                )
+            self.time_proj = GaussianFourierProjection(
+                time_embed_dim // 2,
+                set_W_to_weight=False,
+                log=False,
+                flip_sin_to_cos=flip_sin_to_cos,
+            )
+            timestep_input_dim = time_embed_dim
+        elif time_embedding_type == "positional":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
+
+            self.time_proj = Timesteps(
+                block_out_channels[0], flip_sin_to_cos, freq_shift
+            )
+            timestep_input_dim = block_out_channels[0]
+        else:
+            raise ValueError(
+                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
+            )
+
+        self.time_embedding = TimestepEmbedding(
+            timestep_input_dim,
+            time_embed_dim,
+            act_fn=act_fn,
+            post_act_fn=timestep_post_act,
+            cond_proj_dim=time_cond_proj_dim,
+        )
+
+        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
+            encoder_hid_dim_type = "text_proj"
+            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
+            logger.info(
+                "encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined."
+            )
+
+        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
+            )
+
+        if encoder_hid_dim_type == "text_proj":
+            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
+        elif encoder_hid_dim_type == "text_image_proj":
+            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
+            self.encoder_hid_proj = TextImageProjection(
+                text_embed_dim=encoder_hid_dim,
+                image_embed_dim=cross_attention_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+        elif encoder_hid_dim_type == "image_proj":
+            # Kandinsky 2.2
+            self.encoder_hid_proj = ImageProjection(
+                image_embed_dim=encoder_hid_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+        elif encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
+            )
+        else:
+            self.encoder_hid_proj = None
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(
+                timestep_input_dim, time_embed_dim, act_fn=act_fn
+            )
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        elif class_embed_type == "projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
+            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
+            # 2. it projects from an arbitrary input dimension.
+            #
+            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
+            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
+            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
+            self.class_embedding = TimestepEmbedding(
+                projection_class_embeddings_input_dim, time_embed_dim
+            )
+        elif class_embed_type == "simple_projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            self.class_embedding = nn.Linear(
+                projection_class_embeddings_input_dim, time_embed_dim
+            )
+        else:
+            self.class_embedding = None
+
+        if addition_embed_type == "text":
+            if encoder_hid_dim is not None:
+                text_time_embedding_from_dim = encoder_hid_dim
+            else:
+                text_time_embedding_from_dim = cross_attention_dim
+
+            self.add_embedding = TextTimeEmbedding(
+                text_time_embedding_from_dim,
+                time_embed_dim,
+                num_heads=addition_embed_type_num_heads,
+            )
+        elif addition_embed_type == "text_image":
+            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
+            self.add_embedding = TextImageTimeEmbedding(
+                text_embed_dim=cross_attention_dim,
+                image_embed_dim=cross_attention_dim,
+                time_embed_dim=time_embed_dim,
+            )
+        elif addition_embed_type == "text_time":
+            self.add_time_proj = Timesteps(
+                addition_time_embed_dim, flip_sin_to_cos, freq_shift
+            )
+            self.add_embedding = TimestepEmbedding(
+                projection_class_embeddings_input_dim, time_embed_dim
+            )
+        elif addition_embed_type == "image":
+            # Kandinsky 2.2
+            self.add_embedding = ImageTimeEmbedding(
+                image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim
+            )
+        elif addition_embed_type == "image_hint":
+            # Kandinsky 2.2 ControlNet
+            self.add_embedding = ImageHintTimeEmbedding(
+                image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim
+            )
+        elif addition_embed_type is not None:
+            raise ValueError(
+                f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'."
+            )
+
+        if time_embedding_act_fn is None:
+            self.time_embed_act = None
+        else:
+            self.time_embed_act = get_activation(time_embedding_act_fn)
+
+        self.down_blocks = nn.ModuleList([])
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            if mid_block_only_cross_attention is None:
+                mid_block_only_cross_attention = only_cross_attention
+
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if mid_block_only_cross_attention is None:
+            mid_block_only_cross_attention = False
+
+        if isinstance(num_attention_heads, int):
+            num_attention_heads = (num_attention_heads,) * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        if isinstance(cross_attention_dim, int):
+            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
+
+        if isinstance(layers_per_block, int):
+            layers_per_block = [layers_per_block] * len(down_block_types)
+
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * len(
+                down_block_types
+            )
+
+        if class_embeddings_concat:
+            # The time embeddings are concatenated with the class embeddings. The dimension of the
+            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
+            # regular time embeddings
+            blocks_time_embed_dim = time_embed_dim * 2
+        else:
+            blocks_time_embed_dim = time_embed_dim
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block[i],
+                transformer_layers_per_block=transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim[i],
+                num_attention_heads=num_attention_heads[i],
+                downsample_padding=downsample_padding,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                attention_type=attention_type,
+                resnet_skip_time_act=resnet_skip_time_act,
+                resnet_out_scale_factor=resnet_out_scale_factor,
+                cross_attention_norm=cross_attention_norm,
+                attention_head_dim=attention_head_dim[i]
+                if attention_head_dim[i] is not None
+                else output_channel,
+                dropout=dropout,
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        if mid_block_type == "UNetMidBlock2DCrossAttn":
+            self.mid_block = UNetMidBlock2DCrossAttn(
+                transformer_layers_per_block=transformer_layers_per_block[-1],
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                dropout=dropout,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim[-1],
+                num_attention_heads=num_attention_heads[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+                attention_type=attention_type,
+            )
+        elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
+            raise NotImplementedError(f"Unsupport mid_block_type: {mid_block_type}")
+        elif mid_block_type == "UNetMidBlock2D":
+            self.mid_block = UNetMidBlock2D(
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                dropout=dropout,
+                num_layers=0,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_groups=norm_num_groups,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                add_attention=False,
+            )
+        elif mid_block_type is None:
+            self.mid_block = None
+        else:
+            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+
+        # count how many layers upsample the images
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_num_attention_heads = list(reversed(num_attention_heads))
+        reversed_layers_per_block = list(reversed(layers_per_block))
+        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
+        reversed_transformer_layers_per_block = (
+            list(reversed(transformer_layers_per_block))
+            if reverse_transformer_layers_per_block is None
+            else reverse_transformer_layers_per_block
+        )
+        only_cross_attention = list(reversed(only_cross_attention))
+
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[
+                min(i + 1, len(block_out_channels) - 1)
+            ]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=reversed_layers_per_block[i] + 1,
+                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resolution_idx=i,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=reversed_cross_attention_dim[i],
+                num_attention_heads=reversed_num_attention_heads[i],
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                attention_type=attention_type,
+                resnet_skip_time_act=resnet_skip_time_act,
+                resnet_out_scale_factor=resnet_out_scale_factor,
+                cross_attention_norm=cross_attention_norm,
+                attention_head_dim=attention_head_dim[i]
+                if attention_head_dim[i] is not None
+                else output_channel,
+                dropout=dropout,
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        if norm_num_groups is not None:
+            self.conv_norm_out = nn.GroupNorm(
+                num_channels=block_out_channels[0],
+                num_groups=norm_num_groups,
+                eps=norm_eps,
+            )
+
+            self.conv_act = get_activation(act_fn)
+
+        else:
+            self.conv_norm_out = None
+            self.conv_act = None
+        self.conv_norm_out = None
+
+        conv_out_padding = (conv_out_kernel - 1) // 2
+        # self.conv_out = nn.Conv2d(
+        #     block_out_channels[0],
+        #     out_channels,
+        #     kernel_size=conv_out_kernel,
+        #     padding=conv_out_padding,
+        # )
+
+        if attention_type in ["gated", "gated-text-image"]:
+            positive_len = 768
+            if isinstance(cross_attention_dim, int):
+                positive_len = cross_attention_dim
+            elif isinstance(cross_attention_dim, tuple) or isinstance(
+                cross_attention_dim, list
+            ):
+                positive_len = cross_attention_dim[0]
+
+            feature_type = "text-only" if attention_type == "gated" else "text-image"
+            self.position_net = PositionNet(
+                positive_len=positive_len,
+                out_dim=cross_attention_dim,
+                feature_type=feature_type,
+            )
+
+    @property
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(
+            name: str,
+            module: torch.nn.Module,
+            processors: Dict[str, AttentionProcessor],
+        ):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor(
+                    return_deprecated_lora=True
+                )
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    def set_attn_processor(
+        self,
+        processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]],
+        _remove_lora=False,
+    ):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor, _remove_lora=_remove_lora)
+                else:
+                    module.set_processor(
+                        processor.pop(f"{name}.processor"), _remove_lora=_remove_lora
+                    )
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        if all(
+            proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS
+            for proc in self.attn_processors.values()
+        ):
+            processor = AttnAddedKVProcessor()
+        elif all(
+            proc.__class__ in CROSS_ATTENTION_PROCESSORS
+            for proc in self.attn_processors.values()
+        ):
+            processor = AttnProcessor()
+        else:
+            raise ValueError(
+                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
+            )
+
+        self.set_attn_processor(processor, _remove_lora=True)
+
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
+        several steps. This is useful for saving some memory in exchange for a small decrease in speed.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
+                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_sliceable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_sliceable_dims(module)
+
+        num_sliceable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_sliceable_layers * [1]
+
+        slice_size = (
+            num_sliceable_layers * [slice_size]
+            if not isinstance(slice_size, list)
+            else slice_size
+        )
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(
+            module: torch.nn.Module, slice_size: List[int]
+        ):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    def enable_freeu(self, s1, s2, b1, b2):
+        r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
+
+        The suffixes after the scaling factors represent the stage blocks where they are being applied.
+
+        Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of 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 the "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 the "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.
+        """
+        for i, upsample_block in enumerate(self.up_blocks):
+            setattr(upsample_block, "s1", s1)
+            setattr(upsample_block, "s2", s2)
+            setattr(upsample_block, "b1", b1)
+            setattr(upsample_block, "b2", b2)
+
+    def disable_freeu(self):
+        """Disables the FreeU mechanism."""
+        freeu_keys = {"s1", "s2", "b1", "b2"}
+        for i, upsample_block in enumerate(self.up_blocks):
+            for k in freeu_keys:
+                if (
+                    hasattr(upsample_block, k)
+                    or getattr(upsample_block, k, None) is not None
+                ):
+                    setattr(upsample_block, k, None)
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        class_labels: Optional[torch.Tensor] = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+        mid_block_additional_residual: Optional[torch.Tensor] = None,
+        down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ) -> Union[UNet2DConditionOutput, Tuple]:
+        r"""
+        The [`UNet2DConditionModel`] forward method.
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The noisy input tensor with the following shape `(batch, channel, height, width)`.
+            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
+            encoder_hidden_states (`torch.FloatTensor`):
+                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
+            class_labels (`torch.Tensor`, *optional*, defaults to `None`):
+                Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings.
+            timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`):
+                Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed
+                through the `self.time_embedding` layer to obtain the timestep embeddings.
+            attention_mask (`torch.Tensor`, *optional*, defaults to `None`):
+                An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
+                is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
+                negative values to the attention scores corresponding to "discard" tokens.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            added_cond_kwargs: (`dict`, *optional*):
+                A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that
+                are passed along to the UNet blocks.
+            down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*):
+                A tuple of tensors that if specified are added to the residuals of down unet blocks.
+            mid_block_additional_residual: (`torch.Tensor`, *optional*):
+                A tensor that if specified is added to the residual of the middle unet block.
+            encoder_attention_mask (`torch.Tensor`):
+                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
+                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
+                which adds large negative values to the attention scores corresponding to "discard" tokens.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
+                tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
+            added_cond_kwargs: (`dict`, *optional*):
+                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
+                are passed along to the UNet blocks.
+            down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
+                additional residuals to be added to UNet long skip connections from down blocks to up blocks for
+                example from ControlNet side model(s)
+            mid_block_additional_residual (`torch.Tensor`, *optional*):
+                additional residual to be added to UNet mid block output, for example from ControlNet side model
+            down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*):
+                additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s)
+
+        Returns:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+                If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
+                a `tuple` is returned where the first element is the sample tensor.
+        """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        for dim in sample.shape[-2:]:
+            if dim % default_overall_up_factor != 0:
+                # Forward upsample size to force interpolation output size.
+                forward_upsample_size = True
+                break
+
+        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension
+        # expects mask of shape:
+        #   [batch, key_tokens]
+        # adds singleton query_tokens dimension:
+        #   [batch,                    1, key_tokens]
+        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
+        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
+        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
+        if attention_mask is not None:
+            # assume that mask is expressed as:
+            #   (1 = keep,      0 = discard)
+            # convert mask into a bias that can be added to attention scores:
+            #       (keep = +0,     discard = -10000.0)
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # convert encoder_attention_mask to a bias the same way we do for attention_mask
+        if encoder_attention_mask is not None:
+            encoder_attention_mask = (
+                1 - encoder_attention_mask.to(sample.dtype)
+            ) * -10000.0
+            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+        # 0. center input if necessary
+        if self.config.center_input_sample:
+            sample = 2 * sample - 1.0
+
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # `Timesteps` does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=sample.dtype)
+
+        emb = self.time_embedding(t_emb, timestep_cond)
+        aug_emb = None
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError(
+                    "class_labels should be provided when num_class_embeds > 0"
+                )
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+                # `Timesteps` does not contain any weights and will always return f32 tensors
+                # there might be better ways to encapsulate this.
+                class_labels = class_labels.to(dtype=sample.dtype)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
+
+            if self.config.class_embeddings_concat:
+                emb = torch.cat([emb, class_emb], dim=-1)
+            else:
+                emb = emb + class_emb
+
+        if self.config.addition_embed_type == "text":
+            aug_emb = self.add_embedding(encoder_hidden_states)
+        elif self.config.addition_embed_type == "text_image":
+            # Kandinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+                )
+
+            image_embs = added_cond_kwargs.get("image_embeds")
+            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
+            aug_emb = self.add_embedding(text_embs, image_embs)
+        elif self.config.addition_embed_type == "text_time":
+            # SDXL - style
+            if "text_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
+                )
+            text_embeds = added_cond_kwargs.get("text_embeds")
+            if "time_ids" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
+                )
+            time_ids = added_cond_kwargs.get("time_ids")
+            time_embeds = self.add_time_proj(time_ids.flatten())
+            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
+            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+            add_embeds = add_embeds.to(emb.dtype)
+            aug_emb = self.add_embedding(add_embeds)
+        elif self.config.addition_embed_type == "image":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+                )
+            image_embs = added_cond_kwargs.get("image_embeds")
+            aug_emb = self.add_embedding(image_embs)
+        elif self.config.addition_embed_type == "image_hint":
+            # Kandinsky 2.2 - style
+            if (
+                "image_embeds" not in added_cond_kwargs
+                or "hint" not in added_cond_kwargs
+            ):
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
+                )
+            image_embs = added_cond_kwargs.get("image_embeds")
+            hint = added_cond_kwargs.get("hint")
+            aug_emb, hint = self.add_embedding(image_embs, hint)
+            sample = torch.cat([sample, hint], dim=1)
+
+        emb = emb + aug_emb if aug_emb is not None else emb
+
+        if self.time_embed_act is not None:
+            emb = self.time_embed_act(emb)
+
+        if (
+            self.encoder_hid_proj is not None
+            and self.config.encoder_hid_dim_type == "text_proj"
+        ):
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
+        elif (
+            self.encoder_hid_proj is not None
+            and self.config.encoder_hid_dim_type == "text_image_proj"
+        ):
+            # Kadinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(
+                encoder_hidden_states, image_embeds
+            )
+        elif (
+            self.encoder_hid_proj is not None
+            and self.config.encoder_hid_dim_type == "image_proj"
+        ):
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
+        elif (
+            self.encoder_hid_proj is not None
+            and self.config.encoder_hid_dim_type == "ip_image_proj"
+        ):
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            image_embeds = self.encoder_hid_proj(image_embeds).to(
+                encoder_hidden_states.dtype
+            )
+            encoder_hidden_states = torch.cat(
+                [encoder_hidden_states, image_embeds], dim=1
+            )
+
+        # 2. pre-process
+        sample = self.conv_in(sample)
+
+        # 2.5 GLIGEN position net
+        if (
+            cross_attention_kwargs is not None
+            and cross_attention_kwargs.get("gligen", None) is not None
+        ):
+            cross_attention_kwargs = cross_attention_kwargs.copy()
+            gligen_args = cross_attention_kwargs.pop("gligen")
+            cross_attention_kwargs["gligen"] = {
+                "objs": self.position_net(**gligen_args)
+            }
+
+        # 3. down
+        lora_scale = (
+            cross_attention_kwargs.get("scale", 1.0)
+            if cross_attention_kwargs is not None
+            else 1.0
+        )
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+
+        is_controlnet = (
+            mid_block_additional_residual is not None
+            and down_block_additional_residuals is not None
+        )
+        # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets
+        is_adapter = down_intrablock_additional_residuals is not None
+        # maintain backward compatibility for legacy usage, where
+        #       T2I-Adapter and ControlNet both use down_block_additional_residuals arg
+        #       but can only use one or the other
+        if (
+            not is_adapter
+            and mid_block_additional_residual is None
+            and down_block_additional_residuals is not None
+        ):
+            deprecate(
+                "T2I should not use down_block_additional_residuals",
+                "1.3.0",
+                "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
+                       and will be removed in diffusers 1.3.0.  `down_block_additional_residuals` should only be used \
+                       for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
+                standard_warn=False,
+            )
+            down_intrablock_additional_residuals = down_block_additional_residuals
+            is_adapter = True
+
+        down_block_res_samples = (sample,)
+        tot_referece_features = ()
+        for downsample_block in self.down_blocks:
+            if (
+                hasattr(downsample_block, "has_cross_attention")
+                and downsample_block.has_cross_attention
+            ):
+                # For t2i-adapter CrossAttnDownBlock2D
+                additional_residuals = {}
+                if is_adapter and len(down_intrablock_additional_residuals) > 0:
+                    additional_residuals[
+                        "additional_residuals"
+                    ] = down_intrablock_additional_residuals.pop(0)
+
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    encoder_attention_mask=encoder_attention_mask,
+                    **additional_residuals,
+                )
+            else:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample, temb=emb, scale=lora_scale
+                )
+                if is_adapter and len(down_intrablock_additional_residuals) > 0:
+                    sample += down_intrablock_additional_residuals.pop(0)
+
+            down_block_res_samples += res_samples
+
+        if is_controlnet:
+            new_down_block_res_samples = ()
+
+            for down_block_res_sample, down_block_additional_residual in zip(
+                down_block_res_samples, down_block_additional_residuals
+            ):
+                down_block_res_sample = (
+                    down_block_res_sample + down_block_additional_residual
+                )
+                new_down_block_res_samples = new_down_block_res_samples + (
+                    down_block_res_sample,
+                )
+
+            down_block_res_samples = new_down_block_res_samples
+
+        # 4. mid
+        if self.mid_block is not None:
+            if (
+                hasattr(self.mid_block, "has_cross_attention")
+                and self.mid_block.has_cross_attention
+            ):
+                sample = self.mid_block(
+                    sample,
+                    emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    encoder_attention_mask=encoder_attention_mask,
+                )
+            else:
+                sample = self.mid_block(sample, emb)
+
+            # To support T2I-Adapter-XL
+            if (
+                is_adapter
+                and len(down_intrablock_additional_residuals) > 0
+                and sample.shape == down_intrablock_additional_residuals[0].shape
+            ):
+                sample += down_intrablock_additional_residuals.pop(0)
+
+        if is_controlnet:
+            sample = sample + mid_block_additional_residual
+
+        # 5. up
+        for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[
+                : -len(upsample_block.resnets)
+            ]
+
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
+            if (
+                hasattr(upsample_block, "has_cross_attention")
+                and upsample_block.has_cross_attention
+            ):
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    upsample_size=upsample_size,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    upsample_size=upsample_size,
+                    scale=lora_scale,
+                )
+
+        # 6. post-process
+        # if self.conv_norm_out:
+        #     sample = self.conv_norm_out(sample)
+        #     sample = self.conv_act(sample)
+        # sample = self.conv_out(sample)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (sample,)
+
+        return UNet2DConditionOutput(sample=sample)
diff --git a/high_sync/src/models/unet_3d.py b/high_sync/src/models/unet_3d.py
new file mode 100644
index 0000000..7622bed
--- /dev/null
+++ b/high_sync/src/models/unet_3d.py
@@ -0,0 +1,668 @@
+# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/unet_blocks.py
+
+from collections import OrderedDict
+from dataclasses import dataclass
+from os import PathLike
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.attention_processor import AttentionProcessor
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils import SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, BaseOutput, logging
+from safetensors.torch import load_file
+
+from .resnet import InflatedConv3d, InflatedGroupNorm
+from .unet_3d_blocks import UNetMidBlock3DCrossAttn, get_down_block, get_up_block
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class UNet3DConditionOutput(BaseOutput):
+    sample: torch.FloatTensor
+
+
+class UNet3DConditionModel(ModelMixin, ConfigMixin):
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        sample_size: Optional[int] = None,
+        in_channels: int = 4,
+        out_channels: int = 4,
+        center_input_sample: bool = False,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        down_block_types: Tuple[str] = (
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "DownBlock3D",
+        ),
+        mid_block_type: str = "UNetMidBlock3DCrossAttn",
+        up_block_types: Tuple[str] = (
+            "UpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+        ),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        layers_per_block: int = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        act_fn: str = "silu",
+        norm_num_groups: int = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: int = 1280,
+        attention_head_dim: Union[int, Tuple[int]] = 8,
+        dual_cross_attention: bool = False,
+        use_linear_projection: bool = False,
+        class_embed_type: Optional[str] = None,
+        num_class_embeds: Optional[int] = None,
+        upcast_attention: bool = False,
+        resnet_time_scale_shift: str = "default",
+        use_inflated_groupnorm=False,
+        # Additional
+        use_motion_module=False,
+        motion_module_resolutions=(1, 2, 4, 8),
+        motion_module_mid_block=False,
+        motion_module_decoder_only=False,
+        motion_module_type=None,
+        motion_module_kwargs={},
+        unet_use_cross_frame_attention=None,
+        unet_use_temporal_attention=None,
+    ):
+        super().__init__()
+
+        self.sample_size = sample_size
+        time_embed_dim = block_out_channels[0] * 4
+
+        # input
+        self.conv_in = InflatedConv3d(
+            in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)
+        )
+
+        # time
+        self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+        timestep_input_dim = block_out_channels[0]
+
+        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        self.down_blocks = nn.ModuleList([])
+        self.mid_block = None
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            res = 2**i
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=attention_head_dim[i],
+                downsample_padding=downsample_padding,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                unet_use_temporal_attention=unet_use_temporal_attention,
+                use_inflated_groupnorm=use_inflated_groupnorm,
+                use_motion_module=use_motion_module
+                and (res in motion_module_resolutions)
+                and (not motion_module_decoder_only),
+                motion_module_type=motion_module_type,
+                motion_module_kwargs=motion_module_kwargs,
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        if mid_block_type == "UNetMidBlock3DCrossAttn":
+            self.mid_block = UNetMidBlock3DCrossAttn(
+                in_channels=block_out_channels[-1],
+                temb_channels=time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=attention_head_dim[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                unet_use_temporal_attention=unet_use_temporal_attention,
+                use_inflated_groupnorm=use_inflated_groupnorm,
+                use_motion_module=use_motion_module and motion_module_mid_block,
+                motion_module_type=motion_module_type,
+                motion_module_kwargs=motion_module_kwargs,
+            )
+        else:
+            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+
+        # count how many layers upsample the videos
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_attention_head_dim = list(reversed(attention_head_dim))
+        only_cross_attention = list(reversed(only_cross_attention))
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            res = 2 ** (3 - i)
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[
+                min(i + 1, len(block_out_channels) - 1)
+            ]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=layers_per_block + 1,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=reversed_attention_head_dim[i],
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                unet_use_temporal_attention=unet_use_temporal_attention,
+                use_inflated_groupnorm=use_inflated_groupnorm,
+                use_motion_module=use_motion_module
+                and (res in motion_module_resolutions),
+                motion_module_type=motion_module_type,
+                motion_module_kwargs=motion_module_kwargs,
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        if use_inflated_groupnorm:
+            self.conv_norm_out = InflatedGroupNorm(
+                num_channels=block_out_channels[0],
+                num_groups=norm_num_groups,
+                eps=norm_eps,
+            )
+        else:
+            self.conv_norm_out = nn.GroupNorm(
+                num_channels=block_out_channels[0],
+                num_groups=norm_num_groups,
+                eps=norm_eps,
+            )
+        self.conv_act = nn.SiLU()
+        self.conv_out = InflatedConv3d(
+            block_out_channels[0], out_channels, kernel_size=3, padding=1
+        )
+
+    @property
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(
+            name: str,
+            module: torch.nn.Module,
+            processors: Dict[str, AttentionProcessor],
+        ):
+            if hasattr(module, "set_processor"):
+                processors[f"{name}.processor"] = module.processor
+
+            for sub_name, child in module.named_children():
+                if "temporal_transformer" not in sub_name:
+                    fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            if "temporal_transformer" not in name:
+                fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_slicable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_slicable_dims(module)
+
+        num_slicable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_slicable_layers * [1]
+
+        slice_size = (
+            num_slicable_layers * [slice_size]
+            if not isinstance(slice_size, list)
+            else slice_size
+        )
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(
+            module: torch.nn.Module, slice_size: List[int]
+        ):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(
+        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]
+    ):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                if "temporal_transformer" not in sub_name:
+                    fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            if "temporal_transformer" not in name:
+                fn_recursive_attn_processor(name, module, processor)
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        class_labels: Optional[torch.Tensor] = None,
+        pose_cond_fea: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+        mid_block_additional_residual: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ) -> Union[UNet3DConditionOutput, Tuple]:
+        r"""
+        Args:
+            sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
+            timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
+            encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is the sample tensor.
+        """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+            logger.info("Forward upsample size to force interpolation output size.")
+            forward_upsample_size = True
+
+        # prepare attention_mask
+        if attention_mask is not None:
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # center input if necessary
+        if self.config.center_input_sample:
+            sample = 2 * sample - 1.0
+
+        # time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=self.dtype)
+        emb = self.time_embedding(t_emb)
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError(
+                    "class_labels should be provided when num_class_embeds > 0"
+                )
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
+            emb = emb + class_emb
+
+        # pre-process
+        sample = self.conv_in(sample)
+        if pose_cond_fea is not None:
+            sample = sample + pose_cond_fea
+
+        # down
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if (
+                hasattr(downsample_block, "has_cross_attention")
+                and downsample_block.has_cross_attention
+            ):
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                )
+            else:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                )
+
+            down_block_res_samples += res_samples
+
+        if down_block_additional_residuals is not None:
+            new_down_block_res_samples = ()
+
+            for down_block_res_sample, down_block_additional_residual in zip(
+                down_block_res_samples, down_block_additional_residuals
+            ):
+                down_block_res_sample = (
+                    down_block_res_sample + down_block_additional_residual
+                )
+                new_down_block_res_samples += (down_block_res_sample,)
+
+            down_block_res_samples = new_down_block_res_samples
+
+        # mid
+        sample = self.mid_block(
+            sample,
+            emb,
+            encoder_hidden_states=encoder_hidden_states,
+            attention_mask=attention_mask,
+        )
+
+        if mid_block_additional_residual is not None:
+            sample = sample + mid_block_additional_residual
+
+        # up
+        for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[
+                : -len(upsample_block.resnets)
+            ]
+
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
+            if (
+                hasattr(upsample_block, "has_cross_attention")
+                and upsample_block.has_cross_attention
+            ):
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    upsample_size=upsample_size,
+                    attention_mask=attention_mask,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    upsample_size=upsample_size,
+                    encoder_hidden_states=encoder_hidden_states,
+                )
+
+        # post-process
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        if not return_dict:
+            return (sample,)
+
+        return UNet3DConditionOutput(sample=sample)
+
+    @classmethod
+    def from_pretrained_2d(
+        cls,
+        pretrained_model_path: PathLike,
+        motion_module_path: PathLike,
+        subfolder=None,
+        unet_additional_kwargs=None,
+        mm_zero_proj_out=False,
+    ):
+        pretrained_model_path = Path(pretrained_model_path)
+        motion_module_path = Path(motion_module_path)
+        if subfolder is not None:
+            pretrained_model_path = pretrained_model_path.joinpath(subfolder)
+        logger.info(
+            f"loaded temporal unet's pretrained weights from {pretrained_model_path} ..."
+        )
+
+        config_file = pretrained_model_path / "config.json"
+        if not (config_file.exists() and config_file.is_file()):
+            raise RuntimeError(f"{config_file} does not exist or is not a file")
+
+        unet_config = cls.load_config(config_file)
+        unet_config["_class_name"] = cls.__name__
+        unet_config["down_block_types"] = [
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "DownBlock3D",
+        ]
+        unet_config["up_block_types"] = [
+            "UpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+        ]
+        unet_config["mid_block_type"] = "UNetMidBlock3DCrossAttn"
+
+        model = cls.from_config(unet_config, **unet_additional_kwargs)
+        # load the vanilla weights
+        if pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME).exists():
+            logger.debug(
+                f"loading safeTensors weights from {pretrained_model_path} ..."
+            )
+            state_dict = load_file(
+                pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME), device="cpu"
+            )
+
+        elif pretrained_model_path.joinpath(WEIGHTS_NAME).exists():
+            logger.debug(f"loading weights from {pretrained_model_path} ...")
+            state_dict = torch.load(
+                pretrained_model_path.joinpath(WEIGHTS_NAME),
+                map_location="cpu",
+                weights_only=True,
+            )
+        else:
+            raise FileNotFoundError(f"no weights file found in {pretrained_model_path}")
+
+        # load the motion module weights
+        if motion_module_path.exists() and motion_module_path.is_file():
+            if motion_module_path.suffix.lower() in [".pth", ".pt", ".ckpt"]:
+                logger.info(f"Load motion module params from {motion_module_path}")
+                motion_state_dict = torch.load(
+                    motion_module_path, map_location="cpu", weights_only=True
+                )
+            elif motion_module_path.suffix.lower() == ".safetensors":
+                motion_state_dict = load_file(motion_module_path, device="cpu")
+            else:
+                raise RuntimeError(
+                    f"unknown file format for motion module weights: {motion_module_path.suffix}"
+                )
+            if mm_zero_proj_out:
+                logger.info(f"Zero initialize proj_out layers in motion module...")
+                new_motion_state_dict = OrderedDict()
+                for k in motion_state_dict:
+                    if "proj_out" in k:
+                        continue
+                    new_motion_state_dict[k] = motion_state_dict[k]
+                motion_state_dict = new_motion_state_dict
+
+            # merge the state dicts
+            state_dict.update(motion_state_dict)
+
+        # load the weights into the model
+        m, u = model.load_state_dict(state_dict, strict=False)
+        logger.debug(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
+
+        params = [
+            p.numel() if "temporal" in n else 0 for n, p in model.named_parameters()
+        ]
+        logger.info(f"Loaded {sum(params) / 1e6}M-parameter motion module")
+
+        return model
diff --git a/high_sync/src/models/unet_3d_blocks.py b/high_sync/src/models/unet_3d_blocks.py
new file mode 100644
index 0000000..14a0b93
--- /dev/null
+++ b/high_sync/src/models/unet_3d_blocks.py
@@ -0,0 +1,872 @@
+# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py
+
+import pdb
+
+import torch
+from torch import nn
+
+from .motion_module import get_motion_module
+
+# from .motion_module import get_motion_module
+from .resnet import Downsample3D, ResnetBlock3D, Upsample3D
+from .transformer_3d import Transformer3DModel
+
+
+def get_down_block(
+    down_block_type,
+    num_layers,
+    in_channels,
+    out_channels,
+    temb_channels,
+    add_downsample,
+    resnet_eps,
+    resnet_act_fn,
+    attn_num_head_channels,
+    resnet_groups=None,
+    cross_attention_dim=None,
+    downsample_padding=None,
+    dual_cross_attention=False,
+    use_linear_projection=False,
+    only_cross_attention=False,
+    upcast_attention=False,
+    resnet_time_scale_shift="default",
+    unet_use_cross_frame_attention=None,
+    unet_use_temporal_attention=None,
+    use_inflated_groupnorm=None,
+    use_motion_module=None,
+    motion_module_type=None,
+    motion_module_kwargs=None,
+):
+    down_block_type = (
+        down_block_type[7:]
+        if down_block_type.startswith("UNetRes")
+        else down_block_type
+    )
+    if down_block_type == "DownBlock3D":
+        return DownBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            use_inflated_groupnorm=use_inflated_groupnorm,
+            use_motion_module=use_motion_module,
+            motion_module_type=motion_module_type,
+            motion_module_kwargs=motion_module_kwargs,
+        )
+    elif down_block_type == "CrossAttnDownBlock3D":
+        if cross_attention_dim is None:
+            raise ValueError(
+                "cross_attention_dim must be specified for CrossAttnDownBlock3D"
+            )
+        return CrossAttnDownBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            cross_attention_dim=cross_attention_dim,
+            attn_num_head_channels=attn_num_head_channels,
+            dual_cross_attention=dual_cross_attention,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+            unet_use_temporal_attention=unet_use_temporal_attention,
+            use_inflated_groupnorm=use_inflated_groupnorm,
+            use_motion_module=use_motion_module,
+            motion_module_type=motion_module_type,
+            motion_module_kwargs=motion_module_kwargs,
+        )
+    raise ValueError(f"{down_block_type} does not exist.")
+
+
+def get_up_block(
+    up_block_type,
+    num_layers,
+    in_channels,
+    out_channels,
+    prev_output_channel,
+    temb_channels,
+    add_upsample,
+    resnet_eps,
+    resnet_act_fn,
+    attn_num_head_channels,
+    resnet_groups=None,
+    cross_attention_dim=None,
+    dual_cross_attention=False,
+    use_linear_projection=False,
+    only_cross_attention=False,
+    upcast_attention=False,
+    resnet_time_scale_shift="default",
+    unet_use_cross_frame_attention=None,
+    unet_use_temporal_attention=None,
+    use_inflated_groupnorm=None,
+    use_motion_module=None,
+    motion_module_type=None,
+    motion_module_kwargs=None,
+):
+    up_block_type = (
+        up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
+    )
+    if up_block_type == "UpBlock3D":
+        return UpBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            use_inflated_groupnorm=use_inflated_groupnorm,
+            use_motion_module=use_motion_module,
+            motion_module_type=motion_module_type,
+            motion_module_kwargs=motion_module_kwargs,
+        )
+    elif up_block_type == "CrossAttnUpBlock3D":
+        if cross_attention_dim is None:
+            raise ValueError(
+                "cross_attention_dim must be specified for CrossAttnUpBlock3D"
+            )
+        return CrossAttnUpBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            cross_attention_dim=cross_attention_dim,
+            attn_num_head_channels=attn_num_head_channels,
+            dual_cross_attention=dual_cross_attention,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+            unet_use_temporal_attention=unet_use_temporal_attention,
+            use_inflated_groupnorm=use_inflated_groupnorm,
+            use_motion_module=use_motion_module,
+            motion_module_type=motion_module_type,
+            motion_module_kwargs=motion_module_kwargs,
+        )
+    raise ValueError(f"{up_block_type} does not exist.")
+
+
+class UNetMidBlock3DCrossAttn(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        output_scale_factor=1.0,
+        cross_attention_dim=1280,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        upcast_attention=False,
+        unet_use_cross_frame_attention=None,
+        unet_use_temporal_attention=None,
+        use_inflated_groupnorm=None,
+        use_motion_module=None,
+        motion_module_type=None,
+        motion_module_kwargs=None,
+    ):
+        super().__init__()
+
+        self.has_cross_attention = True
+        self.attn_num_head_channels = attn_num_head_channels
+        resnet_groups = (
+            resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+        )
+
+        # there is always at least one resnet
+        resnets = [
+            ResnetBlock3D(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                temb_channels=temb_channels,
+                eps=resnet_eps,
+                groups=resnet_groups,
+                dropout=dropout,
+                time_embedding_norm=resnet_time_scale_shift,
+                non_linearity=resnet_act_fn,
+                output_scale_factor=output_scale_factor,
+                pre_norm=resnet_pre_norm,
+                use_inflated_groupnorm=use_inflated_groupnorm,
+            )
+        ]
+        attentions = []
+        motion_modules = []
+
+        for _ in range(num_layers):
+            if dual_cross_attention:
+                raise NotImplementedError
+            attentions.append(
+                Transformer3DModel(
+                    attn_num_head_channels,
+                    in_channels // attn_num_head_channels,
+                    in_channels=in_channels,
+                    num_layers=1,
+                    cross_attention_dim=cross_attention_dim,
+                    norm_num_groups=resnet_groups,
+                    use_linear_projection=use_linear_projection,
+                    upcast_attention=upcast_attention,
+                    unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                    unet_use_temporal_attention=unet_use_temporal_attention,
+                )
+            )
+            motion_modules.append(
+                get_motion_module(
+                    in_channels=in_channels,
+                    motion_module_type=motion_module_type,
+                    motion_module_kwargs=motion_module_kwargs,
+                )
+                if use_motion_module
+                else None
+            )
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                    use_inflated_groupnorm=use_inflated_groupnorm,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+        self.motion_modules = nn.ModuleList(motion_modules)
+
+    def forward(
+        self,
+        hidden_states,
+        temb=None,
+        encoder_hidden_states=None,
+        audio_cond_fea = None,
+        attention_mask=None,
+    ):
+        hidden_states = self.resnets[0](hidden_states, temb)
+        for attn, resnet, motion_module in zip(
+            self.attentions, self.resnets[1:], self.motion_modules
+        ):
+            hidden_states = attn(
+                hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                audio_cond_fea = audio_cond_fea
+            ).sample
+            hidden_states = (
+                motion_module(
+                    hidden_states, temb, encoder_hidden_states=encoder_hidden_states
+                )
+                if motion_module is not None
+                else hidden_states
+            )
+            hidden_states = resnet(hidden_states, temb)
+
+        return hidden_states
+
+
+class CrossAttnDownBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        cross_attention_dim=1280,
+        output_scale_factor=1.0,
+        downsample_padding=1,
+        add_downsample=True,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        only_cross_attention=False,
+        upcast_attention=False,
+        unet_use_cross_frame_attention=None,
+        unet_use_temporal_attention=None,
+        use_inflated_groupnorm=None,
+        use_motion_module=None,
+        motion_module_type=None,
+        motion_module_kwargs=None,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+        motion_modules = []
+
+        self.has_cross_attention = True
+        self.attn_num_head_channels = attn_num_head_channels
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                    use_inflated_groupnorm=use_inflated_groupnorm,
+                )
+            )
+            if dual_cross_attention:
+                raise NotImplementedError
+            attentions.append(
+                Transformer3DModel(
+                    attn_num_head_channels,
+                    out_channels // attn_num_head_channels,
+                    in_channels=out_channels,
+                    num_layers=1,
+                    cross_attention_dim=cross_attention_dim,
+                    norm_num_groups=resnet_groups,
+                    use_linear_projection=use_linear_projection,
+                    only_cross_attention=only_cross_attention,
+                    upcast_attention=upcast_attention,
+                    unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                    unet_use_temporal_attention=unet_use_temporal_attention,
+                )
+            )
+            motion_modules.append(
+                get_motion_module(
+                    in_channels=out_channels,
+                    motion_module_type=motion_module_type,
+                    motion_module_kwargs=motion_module_kwargs,
+                )
+                if use_motion_module
+                else None
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+        self.motion_modules = nn.ModuleList(motion_modules)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample3D(
+                        out_channels,
+                        use_conv=True,
+                        out_channels=out_channels,
+                        padding=downsample_padding,
+                        name="op",
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states,
+        temb=None,
+        encoder_hidden_states=None,
+        audio_cond_fea=None,
+        attention_mask=None,
+    ):
+        output_states = ()
+
+        for i, (resnet, attn, motion_module) in enumerate(
+            zip(self.resnets, self.attentions, self.motion_modules)
+        ):
+            # self.gradient_checkpointing = False
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet), hidden_states, temb
+                )
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(attn, return_dict=False),
+                    hidden_states,
+                    encoder_hidden_states,
+                    audio_cond_fea,
+                )[0]
+
+                # add motion module
+                hidden_states = (
+                    motion_module(
+                        hidden_states, temb, encoder_hidden_states=encoder_hidden_states
+                    )
+                    if motion_module is not None
+                    else hidden_states
+                )
+
+            else:
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    audio_cond_fea = audio_cond_fea,
+                ).sample
+
+                # add motion module
+                hidden_states = (
+                    motion_module(
+                        hidden_states, temb, encoder_hidden_states=encoder_hidden_states
+                    )
+                    if motion_module is not None
+                    else hidden_states
+                )
+
+            output_states += (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+            output_states += (hidden_states,)
+
+        return hidden_states, output_states
+
+
+class DownBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        output_scale_factor=1.0,
+        add_downsample=True,
+        downsample_padding=1,
+        use_inflated_groupnorm=None,
+        use_motion_module=None,
+        motion_module_type=None,
+        motion_module_kwargs=None,
+    ):
+        super().__init__()
+        resnets = []
+        motion_modules = []
+
+        # use_motion_module = False
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                    use_inflated_groupnorm=use_inflated_groupnorm,
+                )
+            )
+            motion_modules.append(
+                get_motion_module(
+                    in_channels=out_channels,
+                    motion_module_type=motion_module_type,
+                    motion_module_kwargs=motion_module_kwargs,
+                )
+                if use_motion_module
+                else None
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+        self.motion_modules = nn.ModuleList(motion_modules)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample3D(
+                        out_channels,
+                        use_conv=True,
+                        out_channels=out_channels,
+                        padding=downsample_padding,
+                        name="op",
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states, temb=None, encoder_hidden_states=None):
+        output_states = ()
+
+        for resnet, motion_module in zip(self.resnets, self.motion_modules):
+            # print(f"DownBlock3D {self.gradient_checkpointing = }")
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet), hidden_states, temb
+                )
+                if motion_module is not None:
+                    hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(motion_module),
+                        hidden_states.requires_grad_(),
+                        temb,
+                        encoder_hidden_states,
+                    )
+            else:
+                hidden_states = resnet(hidden_states, temb)
+
+                # add motion module
+                hidden_states = (
+                    motion_module(
+                        hidden_states, temb, encoder_hidden_states=encoder_hidden_states
+                    )
+                    if motion_module is not None
+                    else hidden_states
+                )
+
+            output_states += (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+            output_states += (hidden_states,)
+
+        return hidden_states, output_states
+
+
+class CrossAttnUpBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        prev_output_channel: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        cross_attention_dim=1280,
+        output_scale_factor=1.0,
+        add_upsample=True,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        only_cross_attention=False,
+        upcast_attention=False,
+        unet_use_cross_frame_attention=None,
+        unet_use_temporal_attention=None,
+        use_motion_module=None,
+        use_inflated_groupnorm=None,
+        motion_module_type=None,
+        motion_module_kwargs=None,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+        motion_modules = []
+
+        self.has_cross_attention = True
+        self.attn_num_head_channels = attn_num_head_channels
+
+        for i in range(num_layers):
+            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+            resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=resnet_in_channels + res_skip_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                    use_inflated_groupnorm=use_inflated_groupnorm,
+                )
+            )
+            if dual_cross_attention:
+                raise NotImplementedError
+            attentions.append(
+                Transformer3DModel(
+                    attn_num_head_channels,
+                    out_channels // attn_num_head_channels,
+                    in_channels=out_channels,
+                    num_layers=1,
+                    cross_attention_dim=cross_attention_dim,
+                    norm_num_groups=resnet_groups,
+                    use_linear_projection=use_linear_projection,
+                    only_cross_attention=only_cross_attention,
+                    upcast_attention=upcast_attention,
+                    unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                    unet_use_temporal_attention=unet_use_temporal_attention,
+                )
+            )
+            motion_modules.append(
+                get_motion_module(
+                    in_channels=out_channels,
+                    motion_module_type=motion_module_type,
+                    motion_module_kwargs=motion_module_kwargs,
+                )
+                if use_motion_module
+                else None
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+        self.motion_modules = nn.ModuleList(motion_modules)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList(
+                [Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]
+            )
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states,
+        res_hidden_states_tuple,
+        temb=None,
+        encoder_hidden_states=None,
+        audio_cond_fea=None,
+        upsample_size=None,
+        attention_mask=None,
+    ):
+        for i, (resnet, attn, motion_module) in enumerate(
+            zip(self.resnets, self.attentions, self.motion_modules)
+        ):
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet), hidden_states, temb
+                )
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    audio_cond_fea = audio_cond_fea
+                ).sample
+                if motion_module is not None:
+
+                    hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(motion_module),
+                        hidden_states.requires_grad_(),
+                        temb,
+                        encoder_hidden_states,
+                    )
+            else:
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    audio_cond_fea = audio_cond_fea
+                ).sample
+                # print("hidden_states shape1:", hidden_states.shape)
+                # add motion module
+                hidden_states = (
+                    motion_module(
+                        hidden_states, temb, encoder_hidden_states=encoder_hidden_states
+                    )
+                    if motion_module is not None
+                    else hidden_states
+                )
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states, upsample_size)
+
+        return hidden_states
+
+
+class UpBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        prev_output_channel: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        output_scale_factor=1.0,
+        add_upsample=True,
+        use_inflated_groupnorm=None,
+        use_motion_module=None,
+        motion_module_type=None,
+        motion_module_kwargs=None,
+    ):
+        super().__init__()
+        resnets = []
+        motion_modules = []
+
+        # use_motion_module = False
+        for i in range(num_layers):
+            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+            resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=resnet_in_channels + res_skip_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                    use_inflated_groupnorm=use_inflated_groupnorm,
+                )
+            )
+            motion_modules.append(
+                get_motion_module(
+                    in_channels=out_channels,
+                    motion_module_type=motion_module_type,
+                    motion_module_kwargs=motion_module_kwargs,
+                )
+                if use_motion_module
+                else None
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+        self.motion_modules = nn.ModuleList(motion_modules)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList(
+                [Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]
+            )
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states,
+        res_hidden_states_tuple,
+        temb=None,
+        upsample_size=None,
+        encoder_hidden_states=None,
+    ):
+        for resnet, motion_module in zip(self.resnets, self.motion_modules):
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            # print(f"UpBlock3D {self.gradient_checkpointing = }")
+            if self.training and self.gradient_checkpointing:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet), hidden_states, temb
+                )
+                if motion_module is not None:
+                    hidden_states = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(motion_module),
+                        hidden_states.requires_grad_(),
+                        temb,
+                        encoder_hidden_states,
+                    )
+            else:
+                # print("hidden_states shape1:", hidden_states.shape)
+                hidden_states = resnet(hidden_states, temb)
+                # print("hidden_states shape2:", hidden_states.shape)
+                hidden_states = (
+                    motion_module(
+                        hidden_states, temb, encoder_hidden_states=encoder_hidden_states
+                    )
+                    if motion_module is not None
+                    else hidden_states
+                )
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states, upsample_size)
+
+        return hidden_states
diff --git a/high_sync/src/models/unet_3d_echo.py b/high_sync/src/models/unet_3d_echo.py
new file mode 100644
index 0000000..f246a73
--- /dev/null
+++ b/high_sync/src/models/unet_3d_echo.py
@@ -0,0 +1,679 @@
+from collections import OrderedDict
+from dataclasses import dataclass
+from os import PathLike
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.attention_processor import AttentionProcessor
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils import SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, BaseOutput, logging
+from safetensors.torch import load_file
+
+from .resnet import InflatedConv3d, InflatedGroupNorm
+from .unet_3d_blocks import UNetMidBlock3DCrossAttn, get_down_block, get_up_block
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class UNet3DConditionOutput(BaseOutput):
+    sample: torch.FloatTensor
+
+
+class EchoUNet3DConditionModel(ModelMixin, ConfigMixin):
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        sample_size: Optional[int] = None,
+        in_channels: int = 4,
+        out_channels: int = 4,
+        center_input_sample: bool = False,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        down_block_types: Tuple[str] = (
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "DownBlock3D",
+        ),
+        mid_block_type: str = "UNetMidBlock3DCrossAttn",
+        up_block_types: Tuple[str] = (
+            "UpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+        ),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        layers_per_block: int = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        act_fn: str = "silu",
+        norm_num_groups: int = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: int = 1280,
+        attention_head_dim: Union[int, Tuple[int]] = 8,
+        dual_cross_attention: bool = False,
+        use_linear_projection: bool = False,
+        class_embed_type: Optional[str] = None,
+        num_class_embeds: Optional[int] = None,
+        upcast_attention: bool = False,
+        resnet_time_scale_shift: str = "default",
+        use_inflated_groupnorm=False,
+        # Additional
+        use_motion_module=False,
+        motion_module_resolutions=(1, 2, 4, 8),
+        motion_module_mid_block=False,
+        motion_module_decoder_only=False,
+        motion_module_type=None,
+        motion_module_kwargs={},
+        unet_use_cross_frame_attention=None,
+        unet_use_temporal_attention=None,
+    ):
+        super().__init__()
+
+        self.sample_size = sample_size
+        time_embed_dim = block_out_channels[0] * 4
+
+        # input
+        self.conv_in = InflatedConv3d(
+            in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)
+        )
+
+        # time
+        self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+        timestep_input_dim = block_out_channels[0]
+
+        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        self.down_blocks = nn.ModuleList([])
+        self.mid_block = None
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            res = 2**i
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=attention_head_dim[i],
+                downsample_padding=downsample_padding,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                unet_use_temporal_attention=unet_use_temporal_attention,
+                use_inflated_groupnorm=use_inflated_groupnorm,
+                use_motion_module=use_motion_module
+                and (res in motion_module_resolutions)
+                and (not motion_module_decoder_only),
+                motion_module_type=motion_module_type,
+                motion_module_kwargs=motion_module_kwargs,
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        if mid_block_type == "UNetMidBlock3DCrossAttn":
+            self.mid_block = UNetMidBlock3DCrossAttn(
+                in_channels=block_out_channels[-1],
+                temb_channels=time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=attention_head_dim[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                unet_use_temporal_attention=unet_use_temporal_attention,
+                use_inflated_groupnorm=use_inflated_groupnorm,
+                use_motion_module=use_motion_module and motion_module_mid_block,
+                motion_module_type=motion_module_type,
+                motion_module_kwargs=motion_module_kwargs,
+            )
+        else:
+            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+        self.unet_use_temporal_attention = unet_use_temporal_attention
+        # count how many layers upsample the videos
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_attention_head_dim = list(reversed(attention_head_dim))
+        only_cross_attention = list(reversed(only_cross_attention))
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            res = 2 ** (3 - i)
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[
+                min(i + 1, len(block_out_channels) - 1)
+            ]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=layers_per_block + 1,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=reversed_attention_head_dim[i],
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                unet_use_temporal_attention=unet_use_temporal_attention,
+                use_inflated_groupnorm=use_inflated_groupnorm,
+                use_motion_module=use_motion_module
+                and (res in motion_module_resolutions),
+                motion_module_type=motion_module_type,
+                motion_module_kwargs=motion_module_kwargs,
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        if use_inflated_groupnorm:
+            self.conv_norm_out = InflatedGroupNorm(
+                num_channels=block_out_channels[0],
+                num_groups=norm_num_groups,
+                eps=norm_eps,
+            )
+        else:
+            self.conv_norm_out = nn.GroupNorm(
+                num_channels=block_out_channels[0],
+                num_groups=norm_num_groups,
+                eps=norm_eps,
+            )
+        self.conv_act = nn.SiLU()
+        self.conv_out = InflatedConv3d(
+            block_out_channels[0], out_channels, kernel_size=3, padding=1
+        )
+
+    @property
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(
+            name: str,
+            module: torch.nn.Module,
+            processors: Dict[str, AttentionProcessor],
+        ):
+            if hasattr(module, "set_processor"):
+                processors[f"{name}.processor"] = module.processor
+
+            for sub_name, child in module.named_children():
+                if "temporal_transformer" not in sub_name:
+                    fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            if "temporal_transformer" not in name:
+                fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_slicable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_slicable_dims(module)
+
+        num_slicable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_slicable_layers * [1]
+
+        slice_size = (
+            num_slicable_layers * [slice_size]
+            if not isinstance(slice_size, list)
+            else slice_size
+        )
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(
+            module: torch.nn.Module, slice_size: List[int]
+        ):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(
+        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]
+    ):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                if "temporal_transformer" not in sub_name:
+                    fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            if "temporal_transformer" not in name:
+                fn_recursive_attn_processor(name, module, processor)
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        class_labels: Optional[torch.Tensor] = None,
+        audio_cond_fea: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        face_musk_fea: Optional[torch.Tensor] = None,
+        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+        mid_block_additional_residual: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ) -> Union[UNet3DConditionOutput, Tuple]:
+        r"""
+        Args:
+            sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
+            timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
+            encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is the sample tensor.
+        """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+            logger.info("Forward upsample size to force interpolation output size.")
+            forward_upsample_size = True
+
+        # prepare attention_mask
+        if attention_mask is not None:
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # center input if necessary
+        if self.config.center_input_sample:
+            sample = 2 * sample - 1.0
+
+        # time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=self.dtype)
+        emb = self.time_embedding(t_emb)
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError(
+                    "class_labels should be provided when num_class_embeds > 0"
+                )
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
+            emb = emb + class_emb
+
+        # pre-process
+        sample = self.conv_in(sample)
+        if face_musk_fea is not None:
+            # print(sample.shape, face_musk_fea.shape)
+            sample = sample + face_musk_fea
+
+        # down
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if (
+                hasattr(downsample_block, "has_cross_attention")
+                and downsample_block.has_cross_attention
+            ):
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    audio_cond_fea=audio_cond_fea,
+                    attention_mask=attention_mask,
+                )
+            else:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                )
+
+            down_block_res_samples += res_samples
+
+        if down_block_additional_residuals is not None:
+            new_down_block_res_samples = ()
+
+            for down_block_res_sample, down_block_additional_residual in zip(
+                down_block_res_samples, down_block_additional_residuals
+            ):
+                down_block_res_sample = (
+                    down_block_res_sample + down_block_additional_residual
+                )
+                new_down_block_res_samples += (down_block_res_sample,)
+
+            down_block_res_samples = new_down_block_res_samples
+
+        # mid
+        sample = self.mid_block(
+            sample,
+            emb,
+            encoder_hidden_states=encoder_hidden_states,
+            audio_cond_fea=audio_cond_fea,
+            attention_mask=attention_mask,
+        )
+
+        if mid_block_additional_residual is not None:
+            sample = sample + mid_block_additional_residual
+
+        # up
+        for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[
+                : -len(upsample_block.resnets)
+            ]
+
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
+            if (
+                hasattr(upsample_block, "has_cross_attention")
+                and upsample_block.has_cross_attention
+            ):
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    audio_cond_fea=audio_cond_fea,
+                    upsample_size=upsample_size,
+                    attention_mask=attention_mask,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    upsample_size=upsample_size,
+                    encoder_hidden_states=encoder_hidden_states,
+                )
+
+        # post-process
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        if not return_dict:
+            return (sample,)
+
+        return UNet3DConditionOutput(sample=sample)
+
+    @classmethod
+    def from_pretrained_2d(
+        cls,
+        pretrained_model_path: PathLike,
+        motion_module_path: PathLike,
+        subfolder=None,
+        unet_additional_kwargs=None,
+        mm_zero_proj_out=False,
+    ):
+        pretrained_model_path = Path(pretrained_model_path)
+        motion_module_path = Path(motion_module_path)
+        if subfolder is not None:
+            pretrained_model_path = pretrained_model_path.joinpath(subfolder)
+        logger.info(
+            f"loaded temporal unet's pretrained weights from {pretrained_model_path} ..."
+        )
+
+        config_file = pretrained_model_path / "config.json"
+        if not (config_file.exists() and config_file.is_file()):
+            raise RuntimeError(f"{config_file} does not exist or is not a file")
+
+        unet_config = cls.load_config(config_file)
+        unet_config["_class_name"] = cls.__name__
+        unet_config["down_block_types"] = [
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "DownBlock3D",
+        ]
+        unet_config["up_block_types"] = [
+            "UpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+        ]
+        unet_config["mid_block_type"] = "UNetMidBlock3DCrossAttn"
+
+        model = cls.from_config(unet_config, **unet_additional_kwargs)
+        # load the vanilla weights
+        if pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME).exists():
+            logger.debug(
+                f"loading safeTensors weights from {pretrained_model_path} ..."
+            )
+            state_dict = load_file(
+                pretrained_model_path.joinpath(SAFETENSORS_WEIGHTS_NAME), device="cpu"
+            )
+
+        elif pretrained_model_path.joinpath(WEIGHTS_NAME).exists():
+            logger.debug(f"loading weights from {pretrained_model_path} ...")
+            state_dict = torch.load(
+                pretrained_model_path.joinpath(WEIGHTS_NAME),
+                map_location="cpu",
+                weights_only=True,
+            )
+        else:
+            raise FileNotFoundError(f"no weights file found in {pretrained_model_path}")
+
+        # load the motion module weights
+        if motion_module_path.exists() and motion_module_path.is_file():
+            if motion_module_path.suffix.lower() in [".pth", ".pt", ".ckpt"]:
+                logger.info(f"Load motion module params from {motion_module_path}")
+                motion_state_dict = torch.load(
+                    motion_module_path, map_location="cpu", weights_only=True
+                )
+            elif motion_module_path.suffix.lower() == ".safetensors":
+                motion_state_dict = load_file(motion_module_path, device="cpu")
+            else:
+                raise RuntimeError(
+                    f"unknown file format for motion module weights: {motion_module_path.suffix}"
+                )
+            if mm_zero_proj_out:
+                logger.info(f"Zero initialize proj_out layers in motion module...")
+                new_motion_state_dict = OrderedDict()
+                for k in motion_state_dict:
+                    if "proj_out" in k:
+                        continue
+                    new_motion_state_dict[k] = motion_state_dict[k]
+                motion_state_dict = new_motion_state_dict
+
+            # merge the state dicts
+            state_dict.update(motion_state_dict)
+
+        init_state_dict = model.state_dict()
+
+        for key in init_state_dict:
+            if state_dict.get(key) is not None:
+                if state_dict.get(key).shape == init_state_dict[key].shape:
+                    init_state_dict[key] = state_dict.get(key)
+                # else:
+                #     print(key, " tensor shape is not matched with origin SD.")
+        # load the weights into the model
+        m, u = model.load_state_dict(init_state_dict, strict=False)
+        logger.debug(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
+
+        params = [
+            p.numel() if "temporal" in n else 0 for n, p in model.named_parameters()
+        ]
+        logger.info(f"Loaded {sum(params) / 1e6}M-parameter motion module")
+
+        return model
diff --git a/high_sync/src/models/whisper/audio2feature.py b/high_sync/src/models/whisper/audio2feature.py
new file mode 100644
index 0000000..b1cd513
--- /dev/null
+++ b/high_sync/src/models/whisper/audio2feature.py
@@ -0,0 +1,131 @@
+import os
+from .whisper import load_model
+import numpy as np
+import time
+
+class Audio2Feature():
+    def __init__(self,
+                 whisper_model_type="tiny",
+                 model_path="./models/whisper/tiny.pt",
+                 device="cuda"):
+        self.whisper_model_type = whisper_model_type
+        self.model = load_model(model_path, device=device) #
+
+    def get_sliced_feature(self,
+                           feature_array,
+                           vid_idx,
+                           audio_feat_length=[2,2],
+                           fps=25):
+        """
+        Get sliced features based on a given index
+        :param feature_array:
+        :param start_idx: the start index of the feature
+        :param audio_feat_length:
+        :return:
+        """
+        length = len(feature_array)
+        selected_feature = []
+        selected_idx = []
+
+        center_idx = int(vid_idx*50/fps)
+        left_idx = center_idx-audio_feat_length[0]*2
+        right_idx = center_idx + (audio_feat_length[1]+1)*2
+
+        for idx in range(left_idx,right_idx):
+            idx = max(0, idx)
+            idx = min(length-1, idx)
+            x = feature_array[idx]
+            selected_feature.append(x)
+            selected_idx.append(idx)
+
+        selected_feature = np.concatenate(selected_feature, axis=0)
+        # print("before reshape:", selected_feature.shape)
+        selected_feature = selected_feature.reshape(-1, 384)# 50*384
+        return selected_feature,selected_idx
+
+    def get_sliced_feature_sparse(self,feature_array, vid_idx, audio_feat_length= [2,2],fps = 25):
+        """
+        Get sliced features based on a given index
+        :param feature_array:
+        :param start_idx: the start index of the feature
+        :param audio_feat_length:
+        :return:
+        """
+        length = len(feature_array)
+        selected_feature = []
+        selected_idx = []
+
+        for dt in range(-audio_feat_length[0],audio_feat_length[1]+1):
+            left_idx = int((vid_idx+dt)*50/fps)
+            if left_idx<1 or left_idx>length-1:
+                left_idx = max(0, left_idx)
+                left_idx = min(length-1, left_idx)
+
+                x = feature_array[left_idx]
+                x = x[np.newaxis,:,:]
+                x = np.repeat(x, 2, axis=0)
+                selected_feature.append(x)
+                selected_idx.append(left_idx)
+                selected_idx.append(left_idx)
+            else:
+                x = feature_array[left_idx-1:left_idx+1]
+                selected_feature.append(x)
+                selected_idx.append(left_idx-1)
+                selected_idx.append(left_idx)
+        selected_feature = np.concatenate(selected_feature, axis=0)
+        selected_feature = selected_feature.reshape(-1, 384)# 50*384
+        return selected_feature,selected_idx
+
+
+    def feature2chunks(self,feature_array,fps,audio_feat_length = [2,2]):
+        whisper_chunks = []
+        whisper_idx_multiplier = 50./fps
+        i = 0
+        print(f"video in {fps} FPS, audio idx in 50FPS")
+        while 1:
+            start_idx = int(i * whisper_idx_multiplier)
+            selected_feature,selected_idx = self.get_sliced_feature(feature_array= feature_array,vid_idx = i,audio_feat_length=audio_feat_length,fps=fps)
+            #print(f"i:{i},selected_idx {selected_idx}")
+            whisper_chunks.append(selected_feature)
+            i += 1
+            if start_idx>len(feature_array):
+                break
+
+        return np.array(whisper_chunks)
+
+    def audio2feat(self,audio_path):
+        # get the sample rate of the audio
+        result = self.model.transcribe(audio_path)
+        embed_list = []
+        for emb in result['segments']:
+            encoder_embeddings = emb['encoder_embeddings']
+            encoder_embeddings = encoder_embeddings.transpose(0,2,1,3)
+            encoder_embeddings = encoder_embeddings.squeeze(0)
+            start_idx = int(emb['start'])
+            end_idx = int(emb['end'])
+            emb_end_idx = int((end_idx - start_idx)/2)
+            embed_list.append(encoder_embeddings[:emb_end_idx])
+        concatenated_array = np.concatenate(embed_list, axis=0)
+        return concatenated_array
+
+def load_audio_model(model_path, device):
+    audio_processor = Audio2Feature(model_path=model_path, device=device)
+    return audio_processor
+
+if __name__ == "__main__":
+    audio_processor = Audio2Feature(model_path="../../models/whisper/whisper_tiny.pt")
+    audio_path = "./test.mp3"
+    array = audio_processor.audio2feat(audio_path)
+    print(array.shape)
+    fps = 25
+    whisper_idx_multiplier = 50./fps
+
+    i = 0
+    print(f"video in {fps} FPS, audio idx in 50FPS")
+    while 1:
+        start_idx = int(i * whisper_idx_multiplier)
+        selected_feature,selected_idx = audio_processor.get_sliced_feature(feature_array= array,vid_idx = i,audio_feat_length=[2,2],fps=fps)
+        print(f"video idx {i},\t audio idx {selected_idx},\t shape {selected_feature.shape}")
+        i += 1
+        if start_idx>len(array):
+            break
diff --git a/high_sync/src/models/whisper/whisper/__init__.py b/high_sync/src/models/whisper/whisper/__init__.py
new file mode 100644
index 0000000..961138b
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/__init__.py
@@ -0,0 +1,116 @@
+import hashlib
+import io
+import os
+import urllib
+import warnings
+from typing import List, Optional, Union
+
+import torch
+from tqdm import tqdm
+
+from .audio import load_audio, log_mel_spectrogram, pad_or_trim
+from .decoding import DecodingOptions, DecodingResult, decode, detect_language
+from .model import Whisper, ModelDimensions
+from .transcribe import transcribe
+
+
+_MODELS = {
+    "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt",
+    "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt",
+    "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt",
+    "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt",
+    "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt",
+    "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt",
+    "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt",
+    "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt",
+    "large": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt",
+    "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt",
+    "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt",
+    "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt",
+}
+
+
+def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]:
+    os.makedirs(root, exist_ok=True)
+
+    expected_sha256 = url.split("/")[-2]
+    download_target = os.path.join(root, os.path.basename(url))
+
+    if os.path.exists(download_target) and not os.path.isfile(download_target):
+        raise RuntimeError(f"{download_target} exists and is not a regular file")
+
+    if os.path.isfile(download_target):
+        model_bytes = open(download_target, "rb").read()
+        if hashlib.sha256(model_bytes).hexdigest() == expected_sha256:
+            return model_bytes if in_memory else download_target
+        else:
+            warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
+
+    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
+        with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
+            while True:
+                buffer = source.read(8192)
+                if not buffer:
+                    break
+
+                output.write(buffer)
+                loop.update(len(buffer))
+
+    model_bytes = open(download_target, "rb").read()
+    if hashlib.sha256(model_bytes).hexdigest() != expected_sha256:
+        raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.")
+
+    return model_bytes if in_memory else download_target
+
+
+def available_models() -> List[str]:
+    """Returns the names of available models"""
+    return list(_MODELS.keys())
+
+
+def load_model(name: str, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False) -> Whisper:
+    """
+    Load a Whisper ASR model
+
+    Parameters
+    ----------
+    name : str
+        one of the official model names listed by `whisper.available_models()`, or
+        path to a model checkpoint containing the model dimensions and the model state_dict.
+    device : Union[str, torch.device]
+        the PyTorch device to put the model into
+    download_root: str
+        path to download the model files; by default, it uses "~/.cache/whisper"
+    in_memory: bool
+        whether to preload the model weights into host memory
+
+    Returns
+    -------
+    model : Whisper
+        The Whisper ASR model instance
+    """
+
+    if device is None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+    if download_root is None:
+        download_root = os.getenv(
+            "XDG_CACHE_HOME",
+            os.path.join(os.path.expanduser("~"), ".cache", "whisper")
+        )
+
+    if name in _MODELS:
+        checkpoint_file = _download(_MODELS[name], download_root, in_memory)
+    elif os.path.isfile(name):
+        checkpoint_file = open(name, "rb").read() if in_memory else name
+    else:
+        raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
+
+    with (io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")) as fp:
+        checkpoint = torch.load(fp, map_location=device)
+    del checkpoint_file
+
+    dims = ModelDimensions(**checkpoint["dims"])
+    model = Whisper(dims)
+    model.load_state_dict(checkpoint["model_state_dict"])
+
+    return model.to(device)
diff --git a/high_sync/src/models/whisper/whisper/__main__.py b/high_sync/src/models/whisper/whisper/__main__.py
new file mode 100644
index 0000000..bc9b04a
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/__main__.py
@@ -0,0 +1,4 @@
+from .transcribe import cli
+
+
+cli()
diff --git a/high_sync/src/models/whisper/whisper/assets/gpt2/merges.txt b/high_sync/src/models/whisper/whisper/assets/gpt2/merges.txt
new file mode 100644
index 0000000..6636bda
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/assets/gpt2/merges.txt
@@ -0,0 +1,50001 @@
+#version: 0.2 - Trained by `huggingface/tokenizers`
+Ġ t
+Ġ a
+h e
+i n
+r e
+o n
+Ġt he
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diff --git a/high_sync/src/models/whisper/whisper/assets/multilingual/added_tokens.json b/high_sync/src/models/whisper/whisper/assets/multilingual/added_tokens.json
new file mode 100644
index 0000000..5c50999
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/assets/multilingual/added_tokens.json
@@ -0,0 +1 @@
+{"<|endoftext|>": 50257}
diff --git a/high_sync/src/models/whisper/whisper/assets/multilingual/merges.txt b/high_sync/src/models/whisper/whisper/assets/multilingual/merges.txt
new file mode 100644
index 0000000..afde9f4
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/assets/multilingual/merges.txt
@@ -0,0 +1,50000 @@
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diff --git a/high_sync/src/models/whisper/whisper/assets/multilingual/special_tokens_map.json b/high_sync/src/models/whisper/whisper/assets/multilingual/special_tokens_map.json
new file mode 100644
index 0000000..817762d
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/assets/multilingual/special_tokens_map.json
@@ -0,0 +1 @@
+{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}
\ No newline at end of file
diff --git a/high_sync/src/models/whisper/whisper/assets/multilingual/tokenizer_config.json b/high_sync/src/models/whisper/whisper/assets/multilingual/tokenizer_config.json
new file mode 100644
index 0000000..0235ccf
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/assets/multilingual/tokenizer_config.json
@@ -0,0 +1 @@
+{"unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "model_max_length": 1024, "special_tokens_map_file": null, "name_or_path": "multilingual", "errors": "replace", "tokenizer_class": "GPT2Tokenizer"}
\ No newline at end of file
diff --git a/high_sync/src/models/whisper/whisper/assets/multilingual/vocab.json b/high_sync/src/models/whisper/whisper/assets/multilingual/vocab.json
new file mode 100644
index 0000000..406c3f2
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/assets/multilingual/vocab.json
@@ -0,0 +1 @@
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7569, "Ñį": 7570, "ĠÐĿÑĥ": 7571, "music": 7572, "ìĽIJ": 7573, "Ġgan": 7574, "ìĭł": 7575, "Ġdefault": 7576, "Ġbem": 7577, "Ùī": 7578, "Ġforced": 7579, "ĠObviously": 7580, "Ġstone": 7581, "Ġtie": 7582, "Ġdrinking": 7583, "Ġserved": 7584, "Cause": 7585, "Ġconference": 7586, "ĠExactly": 7587, "ãĥĪ": 7588, "łľ": 7589, "ìĻĢ": 7590, "ĠRa": 7591, "Ġfake": 7592, "Ġdiff": 7593, "ãģ©": 7594, "Ġchallenging": 7595, "Ġì¤ij": 7596, "Ïĩ": 7597, "ä»Ģ麼": 7598, "Ġintelligence": 7599, "rete": 7600, "Ġstudying": 7601, "Ġappoint": 7602, "Ġtan": 7603, "Ġим": 7604, "Ġcurve": 7605, "ĠTeam": 7606, "ĠAz": 7607, "Ġзд": 7608, "ĠMusic": 7609, "field": 7610, "iration": 7611, "Ġfailed": 7612, "Ġnovel": 7613, "Ġdifferently": 7614, "Ġescape": 7615, "ĠYo": 7616, "ĠOctober": 7617, "ıyor": 7618, "Ġdescribed": 7619, "Ġconvert": 7620, "acement": 7621, "Ġhotel": 7622, "isation": 7623, "Ġsuis": 7624, "ãģij": 7625, "åŃIJ": 7626, "æĢİ": 7627, "Ġwalked": 7628, "200": 7629, "Ġneighborhood": 7630, "isp": 7631, "ĠLos": 7632, 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"hern": 7894, "idos": 7895, "Ġemphas": 7896, "Ġmessages": 7897, "Ġgender": 7898, "ĠWenn": 7899, "Ġво": 7900, "Ġprices": 7901, "olo": 7902, "Ġпон": 7903, "wing": 7904, "ĠFil": 7905, "аем": 7906, "ĠCur": 7907, "Ġfalse": 7908, "Ġfields": 7909, "Ġsé": 7910, "24": 7911, "Ġmac": 7912, "uÅŁ": 7913, "Ġlayers": 7914, "Ġadvoc": 7915, "wan": 7916, "Ġkar": 7917, "ĠÅŀ": 7918, "Ġdecor": 7919, "Ġwalls": 7920, "oe": 7921, "issions": 7922, "Ġresol": 7923, "×¢": 7924, "ĠCarol": 7925, "ĠVide": 7926, "leep": 7927, "ĠYOU": 7928, "Ġflip": 7929, "Ġsurgery": 7930, "Ġchop": 7931, "UR": 7932, ".,": 7933, "Ġagency": 7934, "Ġwanting": 7935, "Ġsolar": 7936, "Ġhoriz": 7937, "ĠAdam": 7938, "Ġstaying": 7939, "olic": 7940, "Ġgrateful": 7941, "Ġremark": 7942, "Ġtechnologies": 7943, "Ġprotein": 7944, "å¿ĥ": 7945, "дел": 7946, "ĠMont": 7947, "Ġshoulder": 7948, "Ġza": 7949, "rey": 7950, "ĠOoh": 7951, "Ġsty": 7952, "icar": 7953, "оÑĤÑĢ": 7954, "Ġroute": 7955, "ĠTurn": 7956, "Ġbom": 7957, "Ġdebate": 7958, "Ġpossibility": 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"igs": 17156, "ologically": 17157, "opp": 17158, "Ġdizer": 17159, "ĠAllen": 17160, "Ġgli": 17161, "Ġacceleration": 17162, "Ġvitamin": 17163, "ãĥŃ": 17164, "vä": 17165, "ĠAccess": 17166, "à®Ļ": 17167, "rás": 17168, "Ġappreciated": 17169, "Ġnah": 17170, "Ġposter": 17171, "Ġtale": 17172, "Ġhighlighted": 17173, "æĸĩ": 17174, "żeli": 17175, "Ġblockchain": 17176, "Ġmicrow": 17177, "Ġcinema": 17178, "ĠChang": 17179, "ĠSearch": 17180, "usters": 17181, "ĠZero": 17182, "ĠDivision": 17183, "ÑĢаÑģ": 17184, "Ġscare": 17185, "Ġjelly": 17186, "ĠAdministration": 17187, "SO": 17188, "Ġlined": 17189, "Ġê°Ħ": 17190, "Ġgeben": 17191, "Ġsoda": 17192, "Ġwinners": 17193, "³¼": 17194, "ÙĴ": 17195, "ĠAmb": 17196, "åķıé¡Į": 17197, "åĶ": 17198, "Ġpeg": 17199, "å·±": 17200, "43": 17201, "Ġraus": 17202, "Ġrewards": 17203, "Ġinclus": 17204, "Ġhighway": 17205, "Ġhah": 17206, "Ġmultiplied": 17207, "Ġsẽ": 17208, "Ġdisciples": 17209, "Ġning": 17210, "Ġdressing": 17211, "Ġattributes": 17212, "ĠMosc": 17213, "ĠGreece": 17214, "Ġsek": 17215, "ĠLearn": 17216, "Ġjus": 17217, "rendre": 17218, "Ġpersonne": 17219, "plete": 17220, "Ġplacing": 17221, "Ġluego": 17222, "illance": 17223, "ĠобÑī": 17224, "Ġprovision": 17225, "Ġlion": 17226, "tra": 17227, "boards": 17228, "Ġbehaviour": 17229, "hey": 17230, "Ġsubscription": 17231, "Ġprotagon": 17232, "ãĥ£": 17233, "Ġvara": 17234, "ĠÅŁu": 17235, "Ġhaha": 17236, "Ġteaspoon": 17237, "æŁ": 17238, "avoir": 17239, "Ġcrypto": 17240, "ĠÑģÑĤаÑĢ": 17241, "ĠStore": 17242, "abs": 17243, "ĠStudents": 17244, "Ġlaund": 17245, "into": 17246, "Ġapproached": 17247, "°ľ": 17248, "ÑĥÑİÑī": 17249, "ĠLabor": 17250, "otes": 17251, "iatric": 17252, "ĠgroÃŁ": 17253, "utive": 17254, "Ġид": 17255, "ĠGib": 17256, "Ġplacement": 17257, "ĠdifÃŃcil": 17258, "Ġfrog": 17259, "ĠвÑģеÑħ": 17260, "ĠJr": 17261, "azed": 17262, "ÑĥÑī": 17263, "Ġê¼": 17264, "frame": 17265, "аеÑĪÑĮ": 17266, "Ġlockdown": 17267, "åij³": 17268, "Ġmedi": 17269, "Ġ×Ķ×ŀ×": 17270, "ений": 17271, "emale": 17272, "ì¢ħ": 17273, "ateral": 17274, "Ġdistant": 17275, "Ġbears": 17276, "Ġjournalist": 17277, "解": 17278, "ĠMarshall": 17279, "ĠIhnen": 17280, "uetooth": 17281, "bag": 17282, "ĠÄijã": 17283, "ĠHighness": 17284, "Ġì°į": 17285, "ика": 17286, "ĠWu": 17287, "ĠFran": 17288, "Ġpeng": 17289, "Ġfon": 17290, "Ġhypothesis": 17291, "ĠÑĢÑĥ": 17292, "Ġly": 17293, "×ļ": 17294, "ìĽĶ": 17295, "ĠRadio": 17296, "à¸ŀ": 17297, "Dav": 17298, "Ġembarrassing": 17299, "ĠìŀĪìĸ´": 17300, "Ġcasting": 17301, "Ġcage": 17302, "ĠPsych": 17303, "ĠìĿ¼ëĭ¨": 17304, "Ġž": 17305, "imb": 17306, "Ġdirectors": 17307, "SH": 17308, "ĠÏĦην": 17309, "á»ģu": 17310, "ĠkonuÅŁ": 17311, "Ġoptional": 17312, "quarters": 17313, "iker": 17314, "ĠSant": 17315, "Ġverses": 17316, "ë¶Ģ": 17317, "Ġolar": 17318, "ĠÏĩ": 17319, "ãĥķ": 17320, "Ġγια": 17321, "ĠImm": 17322, "Ġcontroversial": 17323, "Ġersten": 17324, "Ġrecip": 17325, "ĠChristianity": 17326, "Ġê´ľ": 17327, "ordon": 17328, "×ķש": 17329, "Ġslash": 17330, "ĠPf": 17331, "ÑĥдÑĮ": 17332, "×ķ×Ŀ": 17333, "ĠPerry": 17334, "Ġmamy": 17335, "Ġbackgrounds": 17336, "Ġà®İன": 17337, "Ġpendant": 17338, "ĠColumbia": 17339, "Ġinverse": 17340, "ĠÑĩеÑĢез": 17341, "Ġsv": 17342, "Ġdigging": 17343, "41": 17344, "chem": 17345, "Ġnavigation": 17346, "ĠShin": 17347, "ĠFront": 17348, "PD": 17349, "Ġbearing": 17350, "ĠWasser": 17351, "Ġwax": 17352, "ĠCHRIS": 17353, "ching": 17354, "Ġpressed": 17355, "El": 17356, "ĠDal": 17357, "onsin": 17358, "Ġbinding": 17359, "Ñģкой": 17360, "poons": 17361, "Ġmock": 17362, "arest": 17363, "кÑĢа": 17364, "MM": 17365, "Ġcorrupt": 17366, "storm": 17367, "Ġrefres": 17368, "ĠCoach": 17369, "llä": 17370, "ĠTHIS": 17371, "Ġparag": 17372, "Ġìĵ°": 17373, "pool": 17374, "Ġbillions": 17375, "Ġê¹Ģ": 17376, "group": 17377, "Ġwelcoming": 17378, "cellence": 17379, "ĠDuke": 17380, "긴": 17381, "Ġprimera": 17382, "ìł¸": 17383, "Ġpond": 17384, "Ġstatue": 17385, "Ġ구ë": 17386, "Ġhatch": 17387, "Ġinstrumental": 17388, "Ġresidential": 17389, "커": 17390, "Ġaccepting": 17391, "oshi": 17392, "date": 17393, "ĠìĶ¨": 17394, "Ġplanted": 17395, "Ġjoking": 17396, "ĠìĦľ": 17397, "Ġhated": 17398, "ĠÑĢаÑģÑģк": 17399, "Ġslept": 17400, "Ġpackages": 17401, "Ġislands": 17402, "esen": 17403, "ģı": 17404, "Ġdiagon": 17405, "ĠOsc": 17406, "Ġmesh": 17407, "Ġscales": 17408, "arity": 17409, "ĠDefense": 17410, "ãģ¡ãĤĩ": 17411, "ĠLewis": 17412, "ĠÑģегоднÑı": 17413, "Ġflies": 17414, "uinely": 17415, "ĠConsider": 17416, "Ġstark": 17417, "hew": 17418, "ĠAsÃŃ": 17419, "³´ë": 17420, "Ġpropose": 17421, "Ġíķĺë©´": 17422, "odo": 17423, "ĠNormally": 17424, "Ġheeft": 17425, "ĠHarris": 17426, "gro": 17427, "ĠBlood": 17428, "base": 17429, "ĠiOS": 17430, "Ġtouches": 17431, "Ġinspir": 17432, "Ġ×ĵ": 17433, "Ġbinary": 17434, "Ġì¶Ķ": 17435, "Ġserial": 17436, "Ġion": 17437, "Ġunemployment": 17438, "Ġodds": 17439, "ĠFab": 17440, "ĠFBI": 17441, "BRUN": 17442, "Ġweights": 17443, "νο": 17444, "atile": 17445, "Ġnurses": 17446, "Ġinvolvement": 17447, "ĠíĶ¼": 17448, "Ġgovernance": 17449, "ĠâĤ¬": 17450, "ÑĢÑĥп": 17451, "ierra": 17452, "íĺķ": 17453, "ĠJerry": 17454, "Ġbeard": 17455, "Ġsalvation": 17456, "ĠAlong": 17457, "gentle": 17458, "ĠKi": 17459, "bol": 17460, "ĠPlat": 17461, "Ġhasht": 17462, "è¿ij": 17463, "Ġware": 17464, "Ġpartie": 17465, "ycz": 17466, "Ġintr": 17467, "Fih": 17468, "nent": 17469, "Ġcheat": 17470, "ilen": 17471, "Ġë¯": 17472, "orie": 17473, "Ġfácil": 17474, "etric": 17475, "Ġaffecting": 17476, "unciation": 17477, "Ġaffairs": 17478, "Ġbee": 17479, "Ġviewing": 17480, "Ġorang": 17481, "ĠLan": 17482, "ĠСÑĤ": 17483, "ä¸ĸ": 17484, "ĠMes": 17485, "ĥģ": 17486, "erie": 17487, "Ġespa": 17488, "Ġinterpre": 17489, "Ġpossess": 17490, "Ġpurely": 17491, "rito": 17492, "found": 17493, "asma": 17494, "ìłģìĿ¸": 17495, "Ġexamine": 17496, "ĠÑĥм": 17497, "Ġbesch": 17498, "ĠTomorrow": 17499, "ĠBlock": 17500, "Ġvariant": 17501, "Ġpreference": 17502, "Ġcoaches": 17503, "Ġmedications": 17504, "ĠíĺĦ": 17505, "Ġempire": 17506, "ëĦ¤": 17507, "ĠIllinois": 17508, "Ġcrispy": 17509, "Ġthì": 17510, "Ġbees": 17511, "77": 17512, "Ġglow": 17513, "èº": 17514, "ĠStudies": 17515, "åIJĦ": 17516, "ĠChallenge": 17517, "Ġunlikely": 17518, "Ч": 17519, "ıyorsun": 17520, "DIE": 17521, "Ġminimize": 17522, "izard": 17523, "Ġún": 17524, "Ġencontrar": 17525, "ĠKill": 17526, "å»": 17527, "Ġvanilla": 17528, "ĠGrant": 17529, "ĠGT": 17530, "sea": 17531, "Ġsought": 17532, "вод": 17533, "Ġnäm": 17534, "ĠAunt": 17535, "OWN": 17536, "Ġpumpkin": 17537, "stellen": 17538, "Ġrag": 17539, "егда": 17540, "Ġstoryt": 17541, "Ġforum": 17542, "æ©Ł": 17543, "Ġestaba": 17544, "uche": 17545, "Ġcongress": 17546, "ĠRey": 17547, "Ġdramatically": 17548, "ĠSport": 17549, "ĠYellow": 17550, "Ġê³ĦìĨį": 17551, "Ġdisgusting": 17552, "ĠRecent": 17553, "Ġacquired": 17554, "Ġcables": 17555, "çĶļ": 17556, "din": 17557, "Ġvisto": 17558, "Ġcommunicating": 17559, "ÑģÑĤавлÑı": 17560, "еÑģÑĤо": 17561, "ãĥ»ãĥ»ãĥ»": 17562, "Ġrég": 17563, "Ġsocks": 17564, "Ġproces": 17565, "because": 17566, "Ġutter": 17567, "Ġcolocar": 17568, "Ġnewest": 17569, "Ġgramm": 17570, "表": 17571, "ä¸įçŁ¥éģĵ": 17572, "Ġshifting": 17573, "Ġcarrier": 17574, "ĠÑģкоÑĢ": 17575, "ĠSchw": 17576, "Ġexecuted": 17577, "Ġmaintained": 17578, "ĠÏĨ": 17579, "ĠMoses": 17580, "Ġdisse": 17581, "Ġhorr": 17582, "ãĢľ": 17583, "Ġrally": 17584, "Ġallem": 17585, "ĠEventually": 17586, "Ġdiyor": 17587, "lvania": 17588, "Ġschnell": 17589, "Ġê³¼": 17590, "Ġ매": 17591, "Ġstruggles": 17592, "late": 17593, "Ġclarify": 17594, "ément": 17595, "Ġmultiplic": 17596, "ибо": 17597, "Ġjourn": 17598, "Ġfragr": 17599, "Ġsurprisingly": 17600, "Ġdesperate": 17601, "52": 17602, "Ġsul": 17603, "ĠRead": 17604, "ĠFried": 17605, "Ġmond": 17606, "woo": 17607, "Ġorganizing": 17608, "ãģĹãĤĩãģĨ": 17609, "ĠSoon": 17610, "ĠвопÑĢоÑģ": 17611, "ĠNur": 17612, "ĠÐĹд": 17613, "Ġspider": 17614, "еÑģÑı": 17615, "Ġtutorials": 17616, "Ġnutrients": 17617, "orer": 17618, "Ġcoefficient": 17619, "Ġarrangement": 17620, "Ġpricing": 17621, "nan": 17622, "yu": 17623, "BL": 17624, "Ġtribe": 17625, "ĠHoward": 17626, "unks": 17627, "Ġnewer": 17628, "Ġprovin": 17629, "Ġprediction": 17630, "hos": 17631, "Ġolsun": 17632, "ĠAround": 17633, "Ġvier": 17634, "ĠÑģÑĤоÑĢон": 17635, "Ġvalley": 17636, "ĠEla": 17637, "ifi": 17638, "Ġgalaxy": 17639, "Ġtranqu": 17640, "Ġadvers": 17641, "ĠTemple": 17642, "iffs": 17643, "igence": 17644, "èĩªå·±": 17645, "Ġkönnte": 17646, "ĠÄijó": 17647, "Did": 17648, "Ġphotographs": 17649, "ĠAWS": 17650, "ÑĨиÑı": 17651, "Ġguards": 17652, "Ġappointed": 17653, "ĠGil": 17654, "Ġмом": 17655, "Ġcod": 17656, "ĠUnlike": 17657, "Ġevenly": 17658, "isconsin": 17659, "Ġestou": 17660, "Ġmnie": 17661, "ĠExec": 17662, "ĠMV": 17663, "ĠEine": 17664, "ä¿¡": 17665, "ĠRoger": 17666, "ĠFac": 17667, "ĠList": 17668, "Ġfuer": 17669, "аеÑĤе": 17670, "omed": 17671, "Ġattraction": 17672, "èī²": 17673, "Ġterrain": 17674, "ĠDrop": 17675, "Ġcorporations": 17676, "Ġsciences": 17677, "Ġthrone": 17678, "ãģĦãģŁ": 17679, "Ġaj": 17680, "ĠRot": 17681, "çī¹": 17682, "Ġsupporters": 17683, "ĠBere": 17684, "Here": 17685, "Ġdiferentes": 17686, "Ġsignificance": 17687, "Ïĥη": 17688, "æĪij覺å¾Ĺ": 17689, "Ġclamp": 17690, "ĠëĮĢë": 17691, "Ġfabulous": 17692, "rez": 17693, "æĮģ": 17694, "Ġassumptions": 17695, "uther": 17696, "wid": 17697, "pot": 17698, "è¿İ": 17699, "Ġyan": 17700, "ulin": 17701, "ÑĢÑĭв": 17702, "ĠSlow": 17703, "ĠPennsy": 17704, "Ġíķ´ìĦľ": 17705, "Ġmeio": 17706, "Ġwealthy": 17707, "ĠEight": 17708, "Ġpulse": 17709, "Ġfriction": 17710, "idity": 17711, "ĠHoll": 17712, "iyorum": 17713, "Ġsounded": 17714, "ĠCarr": 17715, "Ġfork": 17716, "âĺ": 17717, "ĠPA": 17718, "Ġconspir": 17719, "Ġcoding": 17720, "rt": 17721, "ĠTyp": 17722, "Ġìĸij": 17723, "Ġпог": 17724, "Ġmiser": 17725, "ĠÑģмоÑĤÑĢ": 17726, "ĠSweden": 17727, "Ġolarak": 17728, "ĠZhang": 17729, "ĠChi": 17730, "ĠTitan": 17731, "Ġscreening": 17732, "ĠSpider": 17733, "ĠÅŀimdi": 17734, "Ġobstacles": 17735, "lara": 17736, "Ġchallenged": 17737, "pse": 17738, "TON": 17739, "ụ": 17740, "ĠPi": 17741, "Ġlagi": 17742, "ieurs": 17743, "Ġhurting": 17744, "Ġneglect": 17745, "Ġgenerating": 17746, "Ġyoungest": 17747, "Ġaudit": 17748, "ĠÑĢез": 17749, "Ïģά": 17750, "Ġdonate": 17751, "ĠPDF": 17752, "Ġvisits": 17753, "Ġcruise": 17754, "PP": 17755, "aser": 17756, "Ġwsp": 17757, "backs": 17758, "ivals": 17759, "ãģĨãĤĵ": 17760, "Ġdeve": 17761, "Ġproport": 17762, "Ġcath": 17763, "ĠEffect": 17764, "Ġwinds": 17765, "ĠìĻĶ": 17766, "Ġcharts": 17767, "Ġsama": 17768, "Ġautomation": 17769, "Ġпока": 17770, "Ġolan": 17771, "Ġboats": 17772, "Ġcafe": 17773, "Ġdenied": 17774, "ĠMama": 17775, "Ġblocking": 17776, "ĠThor": 17777, "Ġphenomenal": 17778, "Ġstakeholders": 17779, "Ġunos": 17780, "ÑĥеÑĤ": 17781, "ĠAbraham": 17782, "ãģ§ãĤĤ": 17783, "Ġdetection": 17784, "Ġjuris": 17785, "Ġpowered": 17786, "zial": 17787, "Ġwelfare": 17788, "Ġupgrad": 17789, "Ġmożna": 17790, "ĠCase": 17791, "cular": 17792, "ĶìĿ´": 17793, "ãĥģ": 17794, "ĠGuess": 17795, "Ġcycles": 17796, "ä¾ĭ": 17797, "給": 17798, "rock": 17799, "umi": 17800, "Ġelite": 17801, "Ġquè": 17802, "åł±": 17803, 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"Ġdesse": 17864, "Ġgri": 17865, "ĠChairman": 17866, "çħ": 17867, "Ġcuenta": 17868, "anim": 17869, "Ġcrab": 17870, "Ġescal": 17871, "Ġpremière": 17872, "ĠGef": 17873, "Ġdining": 17874, "Ġseventh": 17875, "Ġchasing": 17876, "ĠTower": 17877, "Ġbrutal": 17878, "Ġfundamentally": 17879, "ãģ¨ãģĨ": 17880, "лениÑı": 17881, "stage": 17882, "Ġacquis": 17883, "Ġcylinder": 17884, "Ġcommander": 17885, "mem": 17886, "ĠUV": 17887, "happy": 17888, "Ġepsilon": 17889, "Ġinvitation": 17890, "Ġfarmer": 17891, "chair": 17892, "Ġdestiny": 17893, "Ġsovere": 17894, "ĠHebrew": 17895, "Ġservant": 17896, "Ġbew": 17897, "Ġgast": 17898, "uties": 17899, "Ġadministrative": 17900, "ĠCommand": 17901, "éta": 17902, "Ġnitrogen": 17903, "ê·¼": 17904, "Ġabi": 17905, "Ġvillain": 17906, "Ġblanket": 17907, "ĠSend": 17908, "Ġbeaten": 17909, "²Ħ": 17910, "Ġvolunt": 17911, "Ġscholar": 17912, "ĠEmperor": 17913, "Ġ43": 17914, "vable": 17915, "ĠDus": 17916, "ĠGU": 17917, "Ġtargeting": 17918, "www": 17919, "Ġamendment": 17920, 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"code": 22332, "Ġhierarchy": 22333, "netes": 22334, "Ġcounterpart": 22335, "Ġtôi": 22336, "Ġted": 22337, "ĠBart": 22338, "ĠëĿ¼": 22339, "ĠGenau": 22340, "Ġincoming": 22341, "ĠABC": 22342, "rique": 22343, "ĠоÑĤп": 22344, "qual": 22345, "Ġincentive": 22346, "Ġihren": 22347, "׳×Ļ": 22348, "loe": 22349, "Ġ1930": 22350, "Ġbarg": 22351, "Ġdiction": 22352, "Ġönce": 22353, "INS": 22354, "Ġreh": 22355, "isiaj": 22356, "mouth": 22357, "Ġscoring": 22358, "lık": 22359, "ĠìķĦ주": 22360, "ORIA": 22361, "ĠEstados": 22362, "Ġcompanion": 22363, "Ġassemble": 22364, "Ġpunished": 22365, "Ġital": 22366, "Ġprevents": 22367, "istes": 22368, "ĠKentucky": 22369, "Ġlocate": 22370, "Ġfasting": 22371, "ãģ¨æĢĿ": 22372, "ĥĢ": 22373, "ĠSeb": 22374, "ĠCrown": 22375, "opia": 22376, "Ġwhip": 22377, "usz": 22378, "ками": 22379, "Ġdatabases": 22380, "åŃĹ": 22381, "Ġprosec": 22382, "Ġ1997": 22383, "ĠìĤ´ì§Ŀ": 22384, "ĠSolar": 22385, "ĠPues": 22386, "ĠZen": 22387, "ollo": 22388, "ĠGuru": 22389, "Ġsqueez": 22390, "ĠÐĹа": 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"ĠëIJ©ëĭĪëĭ¤": 23630, "Ġzeg": 23631, "åĪĩ": 23632, "ĠManhattan": 23633, "Ġsujet": 23634, "uelle": 23635, "stoff": 23636, "Ġdür": 23637, "Ġsubmar": 23638, "eses": 23639, "Ġaquele": 23640, "Ġnou": 23641, "ĠFaith": 23642, "tz": 23643, "ĠÑĤомÑĥ": 23644, "aceut": 23645, "liers": 23646, "Ġbandwidth": 23647, "Æ°á»Ŀ": 23648, "Ġrespective": 23649, "ĠAve": 23650, "Ġspreadshe": 23651, "ĠSent": 23652, "icamente": 23653, "Ġinfra": 23654, "Ġlearners": 23655, "Ġà®ī": 23656, "aiah": 23657, "renal": 23658, "Ġmustard": 23659, "Ġhabt": 23660, "çĥ": 23661, "ĠQué": 23662, "Ġanalyzing": 23663, "æ¯ı": 23664, "Ġsolic": 23665, "Ġ×Ķ×ķ×IJ": 23666, "Ġcausa": 23667, "Ġwelcomed": 23668, "ĠSuccess": 23669, "Ġfacile": 23670, "ĠÐŁÐ¾ÑĤомÑĥ": 23671, "schein": 23672, "Ġfetch": 23673, "Ġstrat": 23674, "ĠÑģÑĤоиÑĤ": 23675, "ìĹIJìĦľëĬĶ": 23676, "ĠÑģпоÑģоб": 23677, "mam": 23678, "ĠserÃŃa": 23679, "naments": 23680, "writer": 23681, "Ġconsulting": 23682, "íĺĢ": 23683, "ĠBerkeley": 23684, "eu": 23685, "asive": 23686, "UU": 23687, "ĠAnalyt": 23688, "Ġsubmission": 23689, "Ġmagnificent": 23690, "enza": 23691, "Ġecon": 23692, "Ġprofiles": 23693, "Ġincar": 23694, "Ab": 23695, "ĠNun": 23696, "Ġhic": 23697, "screaming": 23698, "Ġresilient": 23699, "åĪ©": 23700, "grund": 23701, "Ġconcur": 23702, "Ġbereits": 23703, "LD": 23704, "Ġnurt": 23705, "ìī": 23706, "Ġfeast": 23707, "Ġencuent": 23708, "ĠMichel": 23709, "Ġsuprem": 23710, "\"]": 23711, "Ġfeeds": 23712, "ĠKollegen": 23713, "isser": 23714, "ĠFeng": 23715, "ĠWen": 23716, "mun": 23717, "ĠtenÃŃa": 23718, "ĠWrest": 23719, "Ġìĺ¤ëĬĺìĿĢ": 23720, "Ġstead": 23721, "Ġrestoration": 23722, "Ġdonated": 23723, "Ġdels": 23724, "Ġcensus": 23725, "Ġdesperately": 23726, "worthy": 23727, "HE": 23728, "ĠSpa": 23729, "ĠBryan": 23730, "Ġhj": 23731, "ĠRaw": 23732, "ìķĦë": 23733, "ĠCamera": 23734, "Ġzien": 23735, "Ġstyl": 23736, "ĠTW": 23737, "ĠCheese": 23738, "borne": 23739, "Ġobl": 23740, "ĠAlready": 23741, "Ġunstable": 23742, "Ġflames": 23743, "post": 23744, "Ha": 23745, "romagn": 23746, "ĠìĹĦë§Ī": 23747, "dest": 23748, "Ġkolej": 23749, "Ġtemporarily": 23750, "Ġdetermining": 23751, "ĠGlass": 23752, "ÑĢон": 23753, "olan": 23754, "Ġdominated": 23755, "åĮĸ": 23756, "____": 23757, "ĠÙĩذا": 23758, "ĠDana": 23759, "Ġdinheiro": 23760, "aqu": 23761, "민": 23762, "ĠÃłs": 23763, "ĠJoey": 23764, "ĠGriff": 23765, "Ġattain": 23766, "Ġtransitions": 23767, "ĠLiterally": 23768, "енд": 23769, "ĠHaven": 23770, "Ġgrabbing": 23771, "Ġcrystals": 23772, "ĠFourth": 23773, "Ġcandles": 23774, "ĠÑģлÑĥÑĩа": 23775, "rico": 23776, "Ġ5000": 23777, "etto": 23778, "Ġundo": 23779, "Ġkto": 23780, "Ġdivert": 23781, "Ġchir": 23782, "Ġpersec": 23783, "Ġhiking": 23784, "Ġannouncements": 23785, "çĶ±": 23786, "зÑĭ": 23787, "Ġauc": 23788, "Ġsystemic": 23789, "ĠRM": 23790, "Ïĥα": 23791, "ĠÐĶж": 23792, "Ġyar": 23793, "ĠWard": 23794, "Ġpissed": 23795, "Ġcarn": 23796, "Ġautonomous": 23797, "ãħİãħİ": 23798, "sover": 23799, "æ²ĴéĮ¯": 23800, "å¾Ī好": 23801, "Ġreflex": 23802, "Ġgardens": 23803, "Ġdated": 23804, "ì±": 23805, "amiÄĻ": 23806, "Ġcontinuity": 23807, "Ġcitizenship": 23808, "Ġschwer": 23809, "Ġzak": 23810, "table": 23811, "ĠÑģÑĩ": 23812, "è§ģ": 23813, "ĠÏĥε": 23814, "Ġgenerates": 23815, "구ëĤĺ": 23816, "öh": 23817, "óm": 23818, "alam": 23819, "ĠJUDY": 23820, "ĠBug": 23821, "Ġãģ¦": 23822, "Ġdrones": 23823, "Ġágua": 23824, "acaks": 23825, "æļ": 23826, "ĠÐļон": 23827, "×ĸ×Ķ": 23828, "Ġstrive": 23829, "ĠAltern": 23830, "Ġnearest": 23831, "Ġproyect": 23832, "tera": 23833, "ĠASHLEY": 23834, "Ġworm": 23835, "Ġreplay": 23836, "Ġtara": 23837, "ĠIndians": 23838, "ãĤ°": 23839, "icaid": 23840, "ĠìĪľ": 23841, "Ġappealing": 23842, "ĠWes": 23843, "Ġmentions": 23844, "Ġделе": 23845, "Ġkw": 23846, "Ġfragile": 23847, "isz": 23848, "ków": 23849, "hang": 23850, "color": 23851, "Ġpresidente": 23852, "87": 23853, "еÑĦ": 23854, "çĪ¸": 23855, "Ġдобав": 23856, "ĠNelson": 23857, "áfic": 23858, "ĠMICHAEL": 23859, "Ġmechanic": 23860, "Ġmetres": 23861, "ĠoczywiÅĽcie": 23862, "ĠCind": 23863, "ĠogsÃ¥": 23864, "Ġlandsca": 23865, "ACE": 23866, "Ġheadlines": 23867, "Ġcatalyst": 23868, "ĠCatch": 23869, "inkles": 23870, "Ġpills": 23871, "ordo": 23872, "Ġimmigrant": 23873, "Ġexamination": 23874, "Ġaccidents": 23875, "zÄħd": 23876, "Ġquiere": 23877, "Ġnella": 23878, "Ġ67": 23879, "Ġpassa": 23880, "Ġsuperfic": 23881, "istor": 23882, "Ġnov": 23883, "ëĭµ": 23884, "Ġmandate": 23885, "isons": 23886, "ĠVirtual": 23887, "Ġselber": 23888, "Ġcounseling": 23889, "ĠNBA": 23890, "Ġsept": 23891, "Ġbeliever": 23892, "Ġmarvel": 23893, "ĠIntegr": 23894, "ĠмÑĸ": 23895, "Ġorph": 23896, "Ġbackward": 23897, "ĠGeneration": 23898, "ĠPict": 23899, "ĠÑĤоÑĤ": 23900, "Ġtapi": 23901, "prochen": 23902, "Ġhallway": 23903, "hte": 23904, "ĠÛģÛĴ": 23905, "ĠZum": 23906, "èĢģ師": 23907, "achment": 23908, "iquer": 23909, "folg": 23910, "ĠEddie": 23911, "ĠKil": 23912, "Ġwellness": 23913, "stock": 23914, "è¼ĥ": 23915, "Ġkaç": 23916, "Ġterrorism": 23917, "Ġpointer": 23918, "Of": 23919, "heric": 23920, "ĠUltimately": 23921, "Ġmeses": 23922, "ĠTrade": 23923, "Ġpint": 23924, "Ġtuition": 23925, "Ġdisagre": 23926, "Ġê²ĮìŀĦ": 23927, "Ġmanuscript": 23928, "Ġroomm": 23929, "Ġoutputs": 23930, "еÑĨи": 23931, "Ġries": 23932, "Ġsalud": 23933, "otzdem": 23934, "Ġmasses": 23935, "ĠbyÅĤa": 23936, "Ġclearing": 23937, "Ġdiscourse": 23938, "atson": 23939, "Ġfolded": 23940, "ĠJar": 23941, "ÙĦÙī": 23942, "900": 23943, "ĠÑĥÑģп": 23944, "Ġprophecy": 23945, "Ġinterfere": 23946, "иÑħод": 23947, "à¹Į": 23948, "Ġthri": 23949, "Ġ×ŀש": 23950, "Ġlazım": 23951, "Ġ1992": 23952, "Ġfuturo": 23953, "Ġlocking": 23954, "Ġembargo": 23955, "ĠNeither": 23956, "ivamente": 23957, "ĠmÃ¥ste": 23958, "Ġmik": 23959, "Ġcollector": 23960, "екоÑĤоÑĢ": 23961, "ĠGand": 23962, "Ġsentir": 23963, "ĠMight": 23964, "å¡Ķ": 23965, "Ġganzen": 23966, "UC": 23967, "Ġrelating": 23968, "SD": 23969, "Ġmosquito": 23970, "GR": 23971, "Ġhollow": 23972, "âĺħ": 23973, "ĠWalker": 23974, "Ġaffiliate": 23975, "Ġduplicate": 23976, "нем": 23977, "Ġgrape": 23978, "ĠOrganization": 23979, "Ġsynt": 23980, "Joe": 23981, "Ġgeg": 23982, "Ġrevealing": 23983, "ĠEthan": 23984, "outer": 23985, "Ġyay": 23986, "é«Ķ": 23987, "лаÑĢ": 23988, "Ġreportedly": 23989, "Ġihrer": 23990, "Ġrecognise": 23991, "Ġbumper": 23992, "ĠRandy": 23993, "ĠVenus": 23994, "tles": 23995, "Ġappetite": 23996, "Ġglucose": 23997, "Ġchodzi": 23998, "ĠFurthermore": 23999, "tir": 24000, "Ġconta": 24001, "Ġintuition": 24002, "Ġaltitude": 24003, "Ġchunks": 24004, "ĠJoshua": 24005, "ıģım": 24006, "rylic": 24007, "leans": 24008, "ĠíĶ¼ë": 24009, "LL": 24010, "Que": 24011, "Ġgor": 24012, "ĠзнаÑĩиÑĤ": 24013, "Ġpoems": 24014, "Ġexcel": 24015, "Ġexplored": 24016, "Ġpopul": 24017, "Ġincluso": 24018, "stä": 24019, "ĠGavin": 24020, "alling": 24021, "ĠÏĦον": 24022, "é©": 24023, "arbeit": 24024, "ĠGas": 24025, "Ġglorious": 24026, "rieben": 24027, "Ġspam": 24028, "Ġindoor": 24029, "Ġthrust": 24030, "ĠAld": 24031, "ĠPrior": 24032, "Ġonboard": 24033, "ãģłãģķãģĦ": 24034, "oca": 24035, "ASH": 24036, "£ł": 24037, "ĠChristine": 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27008, "Ġamendments": 27009, "Ġöz": 27010, "Ġdigits": 27011, "urai": 27012, "Ġchaotic": 27013, "ĠMasters": 27014, "eon": 27015, "ĠCash": 27016, "ĠCuz": 27017, "Ġbedeutet": 27018, "Ġscanning": 27019, "Ġжд": 27020, "неÑĤ": 27021, "Ġcertainty": 27022, "jek": 27023, "Ġdijo": 27024, "ĠClimate": 27025, "Ġrinse": 27026, "Ġkrij": 27027, "veland": 27028, "Ġsoundtrack": 27029, "ĠSafe": 27030, "ĠNova": 27031, "94": 27032, "Ġathe": 27033, "ĠVerb": 27034, "oler": 27035, "ìĿ´ì£ł": 27036, "Ġvin": 27037, "Ġrespiratory": 27038, "ĠStudy": 27039, "ĠCAM": 27040, "Ġavocado": 27041, "ĠZhen": 27042, "Ġlatency": 27043, "Ġfeathers": 27044, "Ġcontar": 27045, "ĠвеÑī": 27046, "Ġfark": 27047, "Ġblended": 27048, "Ġexploded": 27049, "ĠXX": 27050, "ĠBenim": 27051, "Ġalguém": 27052, "istoire": 27053, "Ġconfidential": 27054, "Ġmast": 27055, "Ġì¿": 27056, "geh": 27057, "Ġdisrespect": 27058, "ĠSystems": 27059, "Æ°a": 27060, "Ed": 27061, "Ġwys": 27062, "Ġexotic": 27063, "Ġglowing": 27064, "ùng": 27065, "ounge": 27066, 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27930, "Ġfreel": 27931, "ĠAlzheimer": 27932, "ĠÅļ": 27933, "Her": 27934, "Ġtornado": 27935, "agens": 27936, "ĠìŀĪìĸ´ìĦľ": 27937, "ĠTransform": 27938, "Ġprocesso": 27939, "Ġmillise": 27940, "Ġprofessionally": 27941, "Ġmemb": 27942, "ocation": 27943, "Ġstyling": 27944, "ĠобÑıз": 27945, "ĠOperation": 27946, "Ġwygl": 27947, "ĠRan": 27948, "ĠçļĦ": 27949, "ĠKin": 27950, "á»±c": 27951, "ĠBAR": 27952, "Ġpaperwork": 27953, "Ġtule": 27954, "Ġqueria": 27955, "Ġcomply": 27956, "ĠHair": 27957, "×Ļ׼": 27958, "ĠпÑĢоÑģÑĤ": 27959, "Ġmutation": 27960, "Ġreprés": 27961, "Ġoctopus": 27962, "Ġimportantes": 27963, "Ġdeserved": 27964, "etr": 27965, "Ġdisasters": 27966, "lında": 27967, "iqué": 27968, "ĠDeshalb": 27969, "soo": 27970, "ossip": 27971, "Ġrelieved": 27972, "ĠCollins": 27973, "Ġwaterproof": 27974, "ĠYuk": 27975, "Ġcopying": 27976, "Ġbütün": 27977, "ĠHeute": 27978, "ĠEntre": 27979, "Ġresidual": 27980, "Ġcolonies": 27981, "Ġénorm": 27982, "ĠErin": 27983, "Ġstan": 27984, "Ġtremendously": 27985, "Ġcaptures": 27986, "ĠSai": 27987, "âce": 27988, "ĠmiaÅĤ": 27989, "Ġ87": 27990, "Ġlogging": 27991, "Ġinserted": 27992, "Ġinherently": 27993, "ìĿij": 27994, "lave": 27995, "ниÑĩ": 27996, "Ġfemmes": 27997, "Ġdép": 27998, "uks": 27999, "acia": 28000, "ĠWade": 28001, "Ġjij": 28002, "ĠVincent": 28003, "ĠIceland": 28004, "hem": 28005, "Ġapology": 28006, "ĠPeg": 28007, "Ġglued": 28008, "Ġcompanions": 28009, "ĠLiver": 28010, "Ġcriticized": 28011, "leading": 28012, "Ġsäga": 28013, "æ¼Ĥ": 28014, "Ġsquid": 28015, "Ġnarratives": 28016, "Ġtaka": 28017, "nez": 28018, "weit": 28019, "Ġtripod": 28020, "Ġexplic": 28021, "Ġspinal": 28022, "Ġapproximation": 28023, "Ġpagar": 28024, "ĠCalvin": 28025, "ĠведÑĮ": 28026, "Ġlac": 28027, "Ġproactive": 28028, "ĠTrain": 28029, "orf": 28030, "Ġsten": 28031, "Ġgrapes": 28032, "Ġmeus": 28033, "Ġautomat": 28034, "Ġbiased": 28035, "Ġchaîne": 28036, "coal": 28037, "Ġrencont": 28038, "ĠKum": 28039, "Ġfestivals": 28040, "Ġstartups": 28041, "Ġaka": 28042, "ãģ¹": 28043, "Ġcylind": 28044, "sna": 28045, "CRI": 28046, "Ġresultado": 28047, "Ġmilestone": 28048, "ĠÏħ": 28049, "Ġteleport": 28050, "zych": 28051, "62": 28052, "åħ³": 28053, "ĠFear": 28054, "Ġnucleus": 28055, "Ġshines": 28056, "hov": 28057, "ĠPartners": 28058, "ĠKas": 28059, "Ġnadie": 28060, "Ġalerts": 28061, "ĠBILL": 28062, "strong": 28063, "ĠNate": 28064, "ĠDenmark": 28065, "ĠCav": 28066, "OST": 28067, "hält": 28068, "ĠìķĦëĭĮ": 28069, "anyon": 28070, "Ġencourages": 28071, "ĠпоÑģÑĤав": 28072, "ĠHuang": 28073, "ãģĬé¡ĺãģĦ": 28074, "STA": 28075, "Ġpaints": 28076, "ãģĻãģĶ": 28077, "Ġschedules": 28078, "Ġcheated": 28079, "Ġapprox": 28080, "Ġï·": 28081, "Ġ».": 28082, "Ġsmiles": 28083, "isure": 28084, "Ġnered": 28085, "arden": 28086, "Ġcurt": 28087, "ĠëĮ": 28088, "ĠRoth": 28089, "Ġpuisque": 28090, "ĠGET": 28091, "ĠVeget": 28092, "Ġproduz": 28093, "ĠBelgium": 28094, "ĠCampus": 28095, "ר×Ļ×Ŀ": 28096, "icut": 28097, "ĠÑģним": 28098, "Ġréuss": 28099, "Ġslippery": 28100, "ĠEw": 28101, "ų": 28102, "ĠLegends": 28103, "ĠTiffany": 28104, "ализ": 28105, "ĠпеÑĢев": 28106, "ĠогÑĢом": 28107, "Ġcros": 28108, "ĠCE": 28109, "Bu": 28110, "Ġensures": 28111, "Ġgrandchildren": 28112, "Ġacuerdo": 28113, "Ġprisoner": 28114, "Ġthirsty": 28115, "bane": 28116, "Ġë¹ł": 28117, "Ġúltima": 28118, "ĠLaunch": 28119, "nity": 28120, "Ġcombustion": 28121, "Ġunicorn": 28122, "Ġfamille": 28123, "Ġlowering": 28124, "ĠYing": 28125, "building": 28126, "Ġduo": 28127, "ĠMéxico": 28128, "astian": 28129, "Ġ먹ìĿĦ": 28130, "ĠRalph": 28131, "Ġrewrite": 28132, "Ġglam": 28133, "ifique": 28134, "Er": 28135, "ĠRunning": 28136, "онов": 28137, "Ġmeanings": 28138, "Ġchewy": 28139, "ĠLeslie": 28140, "Ġfinest": 28141, "Ġhahaha": 28142, "ĠSTEP": 28143, "Ġloneliness": 28144, "rians": 28145, "Ġquestioned": 28146, "Ġesque": 28147, "Ġsinking": 28148, "Ġpeso": 28149, "ĠWrong": 28150, "asmine": 28151, "Ġdefinitive": 28152, "Ġbuys": 28153, "Ġcruc": 28154, "cool": 28155, "ĠëłĪ": 28156, "Ġpó": 28157, "Ġutilized": 28158, "Ġworthwhile": 28159, "ĠDylan": 28160, "ESE": 28161, "Ġvertex": 28162, "tı": 28163, "ĠFir": 28164, "Ġzaw": 28165, "ĠGed": 28166, "ĠÐĿап": 28167, "dz": 28168, "Ġcursor": 28169, "Ġswipe": 28170, "Ġinevitably": 28171, "Ġposters": 28172, "Ġinclined": 28173, "Ġgreeting": 28174, "Ġdisappointment": 28175, "ãģ¾ãģ§": 28176, "Ġrelação": 28177, "TT": 28178, "Ġrabb": 28179, "ĠMaine": 28180, "Ġanalyzed": 28181, "FE": 28182, "ĠÐŁÐ¾Ð»": 28183, "ĠSandra": 28184, "Ġplague": 28185, "ARE": 28186, "Ġvär": 28187, "ĠViv": 28188, "umed": 28189, "hando": 28190, "houette": 28191, "ĠBailey": 28192, "ä¸įéģİ": 28193, "yson": 28194, "Ġsemua": 28195, "Ġhardcore": 28196, "âĤ¬": 28197, "Ñĸм": 28198, "éra": 28199, "OTH": 28200, "Ġforeigners": 28201, "ĠPalestinian": 28202, "Ġproprio": 28203, "аний": 28204, "Ġmyths": 28205, "WH": 28206, "Ġninth": 28207, "ĠCreator": 28208, "лом": 28209, "ĠFlip": 28210, "Ġeman": 28211, "ĠkiÅŁ": 28212, "zieh": 28213, "ĠEarnest": 28214, "system": 28215, "ĸìĹIJ": 28216, "Ġarmies": 28217, "ĠOutside": 28218, "Ġharus": 28219, "æºĸ": 28220, "одаÑĢ": 28221, "Ġvisitor": 28222, "çŃĶ": 28223, "Ġstrengthening": 28224, "Ġ92": 28225, "vio": 28226, "Ġ리": 28227, "Ġgreedy": 28228, "Ġpoquito": 28229, "uder": 28230, "ĠKopf": 28231, "Ġëĭ¤ìĿĮìĹIJ": 28232, "Ġseis": 28233, "ático": 28234, "Ġtrusting": 28235, "ÃŃp": 28236, "ĠEmm": 28237, "leen": 28238, "ĠاÙĦÙĨ": 28239, "Ġrecruitment": 28240, "ĠFilip": 28241, "ĠÙĥÙĦ": 28242, "Clint": 28243, "ĠвеÑģ": 28244, "auft": 28245, "Ġdominate": 28246, "Ġresto": 28247, "Ġkra": 28248, "ái": 28249, "ĠCait": 28250, "rows": 28251, "Ġcountryside": 28252, "Ġ1945": 28253, "аÑĨиÑİ": 28254, "Ġди": 28255, "Ġkernel": 28256, "lov": 28257, "Ġcalculating": 28258, "دا": 28259, "ĠWalt": 28260, "Ġempowering": 28261, "Ġchassis": 28262, "linear": 28263, "гÑĥ": 28264, "Ġnova": 28265, "Ġuy": 28266, "Ġ69": 28267, "Ġencompass": 28268, "trl": 28269, "Ġcomputational": 28270, "Ġworms": 28271, "Ġnhiá»ģu": 28272, "Ġastronauts": 28273, "Ġves": 28274, "Ġsytu": 28275, "Ġdemanded": 28276, "Ġcs": 28277, "ĠMol": 28278, "Ġ`": 28279, "Ġchant": 28280, "Ġthereby": 28281, "Ġpenis": 28282, "Ġemoc": 28283, "wyn": 28284, "Ñĥже": 28285, "Ġtread": 28286, "óle": 28287, "Ġdeepest": 28288, "Ġmache": 28289, "ĠVent": 28290, "ĠAmsterdam": 28291, "ãĥĽ": 28292, "Ġrebel": 28293, "Ġ61": 28294, "ĠвкÑĥÑģ": 28295, "uffs": 28296, "ĠdoÄŁru": 28297, "ĠNapole": 28298, "ήÏĥ": 28299, "Ġworkouts": 28300, "ĠGlad": 28301, "неÑģ": 28302, "Ġtensions": 28303, "ĠShift": 28304, "ĠGuer": 28305, "íĮIJ": 28306, "Ġì¹ľêµ¬": 28307, "Ðĸ": 28308, "Ġimplant": 28309, "êu": 28310, "ê¸Ģ": 28311, "Ġauthorized": 28312, "CER": 28313, "ĠRV": 28314, "Ġhil": 28315, "lev": 28316, "cimento": 28317, "ĠUFO": 28318, "ìĥĪ": 28319, "è¨Ĥ": 28320, "wor": 28321, "Ġdances": 28322, "ĠPixel": 28323, "çľĭä¸Ģä¸ĭ": 28324, "Ġtrotzdem": 28325, "Ġobten": 28326, "ĠAlfred": 28327, "Ġcostly": 28328, "ĠStanley": 28329, "Ġterrorists": 28330, "ĠWid": 28331, "ħëĭĪëĭ¤": 28332, "Ġleicht": 28333, "ìĿ´ìĬ¤": 28334, "Ġdobrze": 28335, "Ġhesit": 28336, "Ġerzäh": 28337, "Ġeinige": 28338, "Ġhebt": 28339, "Ñģе": 28340, "Ġunpredict": 28341, "Cómo": 28342, "remos": 28343, "ĠThankfully": 28344, "Ġpurse": 28345, "chs": 28346, "ancer": 28347, "ulos": 28348, "stud": 28349, "æľīæ²Ĵæľī": 28350, "Ġneurolog": 28351, "ĠAncient": 28352, "Out": 28353, "awsze": 28354, "Ġoppose": 28355, "Ġantibodies": 28356, "ĠSomehow": 28357, "ropolitan": 28358, "ktor": 28359, "ĠÑģÑĤоÑĢонÑĭ": 28360, "Ġrockets": 28361, "Ġdisable": 28362, "Ġcatastroph": 28363, "´ìŀ": 28364, "Ġcyn": 28365, "ĠдÑĢÑĥзÑĮÑı": 28366, "Ġinstructors": 28367, "emaal": 28368, "Ġetwa": 28369, "Ġyuan": 28370, "ĠGround": 28371, "Ġpremiere": 28372, "Ñĩив": 28373, "Ġsaint": 28374, "yba": 28375, "Ġkok": 28376, "Ġcontractors": 28377, "Ġê°ģ": 28378, "Ġ×IJ׾": 28379, "Ġheadline": 28380, "Ġcompletamente": 28381, "Ġinexpensive": 28382, "Ġviu": 28383, "ĠGrande": 28384, "Ġbleed": 28385, "물": 28386, "Ġ73": 28387, "ĠtodavÃŃa": 28388, "ĠRush": 28389, "ĠElder": 28390, "ê°ĢëĬĶ": 28391, "ĠRou": 28392, "ĠженÑī": 28393, "ĠMira": 28394, "Ġdeine": 28395, "Ġkarma": 28396, "Ġumm": 28397, "Ġentsche": 28398, "ĠHolocaust": 28399, "Ġdiscoveries": 28400, "aments": 28401, "Ġraison": 28402, "Ġburgers": 28403, "Back": 28404, "Ġgdy": 28405, "ĠAG": 28406, "ĠDaw": 28407, "ìķł": 28408, "headed": 28409, "ĠClar": 28410, "Inst": 28411, "ĠLieutenant": 28412, "ĠAfD": 28413, "ĠCes": 28414, "Ġpersonalized": 28415, "Ġinterfaces": 28416, "à¸Īะ": 28417, "ĠÑĢеж": 28418, "Ġsuic": 28419, "Ġstarving": 28420, "Ġoxide": 28421, "Ġdecorated": 28422, "ĠDU": 28423, "ĠìĺĪìģĺ": 28424, "Ġquo": 28425, "Ġdistortion": 28426, "段": 28427, "Ġ먹ìĸ´ë": 28428, "Ġstakes": 28429, "æĺİçĻ½": 28430, "Ġsyntax": 28431, "Ġbiết": 28432, "thy": 28433, "icie": 28434, "Ġbrasile": 28435, "isis": 28436, "RC": 28437, "Ġshook": 28438, "Ġdepths": 28439, "ĠCosta": 28440, "Ġvocals": 28441, "Ġcoaster": 28442, "Ġfalou": 28443, "ettle": 28444, "Ġkennen": 28445, "Ġderive": 28446, "Ġaids": 28447, "ĠÐĿик": 28448, "Ġentwic": 28449, "Ġvertically": 28450, "ĠÍ": 28451, "ĠSUV": 28452, "Ġfireworks": 28453, "Ġspecifics": 28454, "交": 28455, "Ġinsisted": 28456, "Ġdeshalb": 28457, "ĠGonz": 28458, "love": 28459, "ĠMilitary": 28460, "ĠPierre": 28461, "ĠâĪ": 28462, "ĠWhose": 28463, "Ġperfume": 28464, "ĠÏĢε": 28465, "Ġlowered": 28466, "Ġcrosses": 28467, "Ġtranslates": 28468, "Ġarriba": 28469, "ÃŃdo": 28470, "ĠLev": 28471, "åħ§": 28472, "ĠCiao": 28473, "Ġscholarships": 28474, "Ġgestures": 28475, "ĠÑĢезÑĥлÑĮÑĤаÑĤ": 28476, "Ġquestão": 28477, "ĠColonel": 28478, "ĠBott": 28479, "رÙģ": 28480, "NING": 28481, "ĠWatching": 28482, "ĠPurple": 28483, "ÑģÑĤÑĢан": 28484, "Ġexecutives": 28485, "ĠKris": 28486, "orneys": 28487, "еннÑĭй": 28488, "Ġcoated": 28489, "Ä©": 28490, "Ġparked": 28491, "ĠÑģвеÑĤ": 28492, "!!!!!": 28493, "ĠFloyd": 28494, "ısı": 28495, "ziÄĩ": 28496, "Ġmotivate": 28497, "ĠElon": 28498, "lean": 28499, "Ĩĵ": 28500, "Ġip": 28501, "Ġniż": 28502, "ĠExperience": 28503, "ĠTina": 28504, "ĠKollege": 28505, "ĠAmbassador": 28506, "inya": 28507, "Ġtheft": 28508, "Ġheures": 28509, "ĠMyst": 28510, "Ġmaison": 28511, "leb": 28512, "Ġbowls": 28513, "ĠBürger": 28514, "ĠRoosevelt": 28515, "RP": 28516, "ê°ĢìļĶ": 28517, "ĠDelicious": 28518, "erdings": 28519, "ĠAssociate": 28520, "ousse": 28521, "ĠCort": 28522, "ĠRepeat": 28523, "ĠGlory": 28524, "Ġcontag": 28525, "à¹Ģล": 28526, "ĠParad": 28527, "ĠKerry": 28528, "Ġê¿": 28529, "ĠWave": 28530, "å¿ħ": 28531, "Ġgateway": 28532, "çIJĥ": 28533, "!ãĢį": 28534, "Ġtranscend": 28535, "Ġdamages": 28536, "Ġtails": 28537, "Ġgravitational": 28538, "ĠShield": 28539, "Ġprimitive": 28540, "Ġcarriers": 28541, "ĠHuawei": 28542, "ÙĤد": 28543, "Ġfeliz": 28544, "ĠMia": 28545, "åĥķ": 28546, "ĠпÑĢÑıмо": 28547, "ĠпÑĢоиÑģÑħодиÑĤ": 28548, "ĠMurphy": 28549, "ĠActiv": 28550, "ãĥĥãĤ¯": 28551, "Ġdiscomfort": 28552, "×ij×Ķ": 28553, "ĠKell": 28554, "ĠCentury": 28555, "Ġspaghetti": 28556, "ĠDurch": 28557, "Ġcierto": 28558, "ĠEmpress": 28559, "Ġguts": 28560, "neg": 28561, "ĠдоÑģÑĤаÑĤоÑĩно": 28562, "Ġvoluntary": 28563, "失": 28564, "Ġsquirrel": 28565, "欢": 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29381, "Ġrainfall": 29382, "ĠMouse": 29383, "Ġrefund": 29384, "ĠZw": 29385, "assemb": 29386, "Ġ220": 29387, "ĠOrd": 29388, "è§Ĵ": 29389, "Ġveins": 29390, "ĠGiant": 29391, "Ġmãe": 29392, "Ġvap": 29393, "Ġmisses": 29394, "οÏħÏĤ": 29395, "Mo": 29396, "ĠEntwick": 29397, "INT": 29398, "ÙĨت": 29399, "Ġtheoretically": 29400, "Ġtearing": 29401, "Ġtroubled": 29402, "prem": 29403, "Ġrepetitive": 29404, "Ġâĸ": 29405, "Ġheavenly": 29406, "ĠAmber": 29407, "Ġполож": 29408, "Ġíķ´ì¤": 29409, "Ġvowel": 29410, "anking": 29411, "ĠWirtschaft": 29412, "Ġirr": 29413, "Ġcozy": 29414, "Ġunfamiliar": 29415, "ĠPors": 29416, "Ġë§ŀìķĦ": 29417, "ĠTimothy": 29418, "ÑģолÑİÑĤ": 29419, "pex": 29420, "ĠVIS": 29421, ")(": 29422, "Ġsuperst": 29423, "Ġimprov": 29424, "ĠBeng": 29425, "Ġdisconnected": 29426, "Ġapt": 29427, "ÑĢен": 29428, "ĠExtra": 29429, "Ġбел": 29430, "shop": 29431, "dings": 29432, "ĠConnecticut": 29433, "ì°¬": 29434, "ĠGC": 29435, "åıĸ": 29436, "beh": 29437, "Jeremy": 29438, "ĠBatt": 29439, "ãģ¸": 29440, 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29554, "Ġkara": 29555, "าà¸Ļ": 29556, "Ġfavorable": 29557, "ĠìķĬìķĺ": 29558, "ĠHispanic": 29559, "à¸ľ": 29560, "Ġ×Ķ׼": 29561, "Ġvalidate": 29562, "ĠResident": 29563, "Ġcomenz": 29564, "beiter": 29565, "erer": 29566, "ä¸Ģèµ·": 29567, "Ġdado": 29568, "atching": 29569, "metros": 29570, "ĠHin": 29571, "ĠDum": 29572, "Ġhazır": 29573, "ĠNatalie": 29574, "Ġencryption": 29575, "оÑĩка": 29576, "mma": 29577, "houses": 29578, "Ġanalytical": 29579, "ĠDang": 29580, "first": 29581, "æŃĮ": 29582, "çºĮ": 29583, "ĠEnc": 29584, "cando": 29585, "Ġludzi": 29586, "wart": 29587, "Ġstatistic": 29588, "ĠìĤ°": 29589, "Ġcommenting": 29590, "Ġcoordinated": 29591, "ĠHyper": 29592, "åļ": 29593, "ĠBert": 29594, "çľ¾": 29595, "ĠHip": 29596, "kem": 29597, "ünü": 29598, "Ġzal": 29599, "ĠíķĺëĬĶëį°": 29600, "ĠRobot": 29601, "éĸ±": 29602, "rawn": 29603, "Ġrhetoric": 29604, "ullah": 29605, "ĠDiet": 29606, "Ġtakich": 29607, "Ġpossessed": 29608, "ĵľëĬĶ": 29609, "Ġwakes": 29610, "ĠRaf": 29611, "Mart": 29612, "Ġecc": 29613, 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"ĠSlowly": 29674, "ĠBrig": 29675, "ĠTorah": 29676, "Ġache": 29677, "Ġ???": 29678, "ĠDob": 29679, "kannt": 29680, "Mary": 29681, "Ġstam": 29682, "ĠDemon": 29683, "pla": 29684, "ĠFreund": 29685, "ĠBenn": 29686, "Ġhighs": 29687, "Ġکر": 29688, "ĠPrepare": 29689, "Ġproxy": 29690, "Ġcampo": 29691, "ĠAugen": 29692, "£¨ë": 29693, "ĠChloe": 29694, "icularly": 29695, "young": 29696, "ĠãģĮ": 29697, "©Ķë": 29698, "Ġscratching": 29699, "Ġglac": 29700, "Ġgemeinsam": 29701, "anal": 29702, "acaksın": 29703, "ĠForum": 29704, "ennial": 29705, "ĠResources": 29706, "ãģ¨æĢĿãģĦãģ¾ãģĻ": 29707, "Ġmeisten": 29708, "ĠFell": 29709, "Ġunanim": 29710, "ĠTB": 29711, "ĠSelbst": 29712, "æĨ": 29713, "Ġintimidating": 29714, "ĠGefühl": 29715, "Ġì½Ķë¡ľ": 29716, "æĭī": 29717, "idor": 29718, "iciones": 29719, "arsa": 29720, "]..": 29721, "azo": 29722, "Ġkendi": 29723, "ĠTage": 29724, "termin": 29725, "ĠProzent": 29726, "Maybe": 29727, "lé": 29728, "Ġquesti": 29729, "Ġmemes": 29730, "Ġcorre": 29731, "ĠVIP": 29732, "ĠGallery": 29733, "Ġurgency": 29734, "Ġnoche": 29735, "Ġkindly": 29736, "ĠMeredith": 29737, "ĠváºŃy": 29738, "ĠاÙĦب": 29739, "ĠEstado": 29740, "åĩºä¾Ĩ": 29741, "zug": 29742, "oque": 29743, "Ġobesity": 29744, "Off": 29745, "ĠEuropeans": 29746, "öd": 29747, "ì¹´ë": 29748, "Ġhoop": 29749, "Ġenjoys": 29750, "ĠChip": 29751, "patient": 29752, "Ġmicroscope": 29753, "Ġlegitim": 29754, "ĠÑıвлÑıеÑĤÑģÑı": 29755, "Ïĥι": 29756, "argent": 29757, "Ġsham": 29758, "Ġlicensing": 29759, "olia": 29760, "Sorry": 29761, "rama": 29762, "Ġaccelerated": 29763, "Ġwym": 29764, "Ġfairness": 29765, "ĠReading": 29766, "Ġslack": 29767, "ĠDok": 29768, "ziÄĻkujÄĻ": 29769, "Ġrubbing": 29770, "аÑĤÑĥ": 29771, "Ġallocated": 29772, "jung": 29773, "Ġpains": 29774, "Ġwinding": 29775, "Ġgeliyor": 29776, "ĠCU": 29777, "mot": 29778, "cock": 29779, "ĠPosition": 29780, "bros": 29781, "Ġlivestream": 29782, "ĠBrain": 29783, "ì°©": 29784, "Ġprzek": 29785, "ĠEi": 29786, "ĠCoco": 29787, "ба": 29788, "Ġshovel": 29789, "ãĥıãĥı": 29790, "ea": 29791, "Ġchocol": 29792, "Ġrebellion": 29793, "Ġshowc": 29794, "ĠHalo": 29795, "Ġdividend": 29796, "mission": 29797, "Ġusando": 29798, "Ġ[\"": 29799, "Ġfalei": 29800, "æĽ¸": 29801, "Black": 29802, "ĠSurely": 29803, "ĠÅ»": 29804, "Ġphilosopher": 29805, "ä½łä»¬": 29806, "Ġoverhe": 29807, "ĠBorn": 29808, "Ġobjetivo": 29809, "Ġ128": 29810, "scheid": 29811, "ĠNazis": 29812, "Ġsolche": 29813, "lift": 29814, "cede": 29815, "adors": 29816, "Ġmarshm": 29817, "ĠLORD": 29818, "ĶìĿ´íģ¬": 29819, "Ġowning": 29820, "Cont": 29821, "Ġlandscapes": 29822, "Ġlending": 29823, "ĠAuthority": 29824, "овой": 29825, "oqu": 29826, "ĠSes": 29827, "ĠFerrari": 29828, "Ġresponsabil": 29829, "Ġvários": 29830, "Ġdelic": 29831, "Ġembark": 29832, "Ġembroider": 29833, "Ġframeworks": 29834, "Ġsimmer": 29835, "Ġnacional": 29836, "Ġremainder": 29837, "ĠVielleicht": 29838, "Ġquieres": 29839, "ìĹĶ": 29840, "Ġtestoster": 29841, "ihen": 29842, "ĠOz": 29843, "èle": 29844, "Ġportrayed": 29845, "κε": 29846, "ĠPolitik": 29847, "Ġaperture": 29848, "Ġbland": 29849, "indust": 29850, "ĠобÑĢаÑĤ": 29851, "ĠThous": 29852, "Bay": 29853, "Ġdando": 29854, "Ġsher": 29855, "Ġadmissions": 29856, "ĠCrew": 29857, "ĠÑĸн": 29858, "SINGING": 29859, "Ġounce": 29860, "Ġiy": 29861, "Ġbasil": 29862, "Ġovertime": 29863, "Ġthreaten": 29864, "Ġpartnered": 29865, "ĠCann": 29866, "avana": 29867, "ĠзнаеÑĤе": 29868, "éĢĻäºĽ": 29869, "ĠоÑĤÑģ": 29870, "ĠTudo": 29871, "ì½Ķ": 29872, "ĠëĨĢë": 29873, "fel": 29874, "Ġrearr": 29875, "Ġinward": 29876, "ĠRogers": 29877, "à¹ĥห": 29878, "Ġtweak": 29879, "Ġdryer": 29880, "cession": 29881, "Ġrigorous": 29882, "ĠDaar": 29883, "omics": 29884, "Ġfats": 29885, "vad": 29886, "Ġzipper": 29887, "acceptable": 29888, "Ġdemonstrating": 29889, "ĠYum": 29890, "Ġbeau": 29891, "Ġroster": 29892, "Ġpredominantly": 29893, "еÑĢÑĥ": 29894, "ningar": 29895, "Ġtriangles": 29896, "Ġtexting": 29897, "Ġberries": 29898, "ĠìĤ¬ì§Ħ": 29899, "éĶĻ": 29900, "adder": 29901, "Ġfaites": 29902, "ĠImage": 29903, "lere": 29904, "Ġbounds": 29905, "ĠLaur": 29906, "ĠìķĦ무ë": 29907, "Ġmio": 29908, "Ġusa": 29909, "ĠØ°": 29910, "Ġtoen": 29911, "ĠJang": 29912, "že": 29913, "chod": 29914, "anan": 29915, "ĠобÑĢазом": 29916, "Ġpersever": 29917, "ĠSwe": 29918, "Ġaugment": 29919, "ä¸ĥ": 29920, "uggling": 29921, "ièrement": 29922, "istles": 29923, "acjÄĻ": 29924, "91": 29925, "Ġmah": 29926, "ĠKIR": 29927, "Die": 29928, "Ġdownhill": 29929, "Ġ1968": 29930, "оÑĢоÑĪо": 29931, "å¹¹": 29932, "ographics": 29933, "Ġtässä": 29934, "ê²łì£ł": 29935, "ĠлиÑĩ": 29936, "AUDIO": 29937, "ĠплоÑħ": 29938, "Ġproposing": 29939, "éł»": 29940, "Ġtempted": 29941, "Ġconverting": 29942, "ĠLehr": 29943, "Ġpersone": 29944, "ĠFeeling": 29945, "ìĸ´ì£¼": 29946, "ombres": 29947, "Ġ׾×Ļ": 29948, "Ġguru": 29949, "Ġdement": 29950, "низ": 29951, "иÑĤелей": 29952, "Ġcompañ": 29953, "æľª": 29954, "å¸ĮæľĽ": 29955, "Ġredo": 29956, "Ġconductor": 29957, "mia": 29958, "Ġidols": 29959, "ĠMul": 29960, "Ġinex": 29961, "Ġtämä": 29962, "Ġimpacting": 29963, "Ġdaylight": 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"ãħĭãħĭãħĭ": 30021, "ĠOriginal": 30022, "×Ļ×Ĺ": 30023, "Ġbroadcasting": 30024, "ĠпеÑĢвÑĭй": 30025, "uchi": 30026, "Ġheure": 30027, "Ġgrabs": 30028, "ĠWOR": 30029, "ĠPlaid": 30030, "Min": 30031, "Ġpaz": 30032, "ĠPuis": 30033, "umu": 30034, "itates": 30035, "Ġcoats": 30036, "Ġbuen": 30037, "Ġheir": 30038, "Ġpneum": 30039, "שר": 30040, "enser": 30041, "ĠJUDGE": 30042, "Ġblonde": 30043, "á¹Ľ": 30044, "Ġgak": 30045, "Ġsık": 30046, "Ġquoted": 30047, "Ġequipo": 30048, "Ġwishing": 30049, "ÃŃcia": 30050, "Ġverbs": 30051, "çµĦ": 30052, "ĠCanadians": 30053, "Ġgoverning": 30054, "ĠEvans": 30055, "Euro": 30056, "Ġgenres": 30057, "Ġunterschied": 30058, "ĠBecky": 30059, "³¼ê²ĮìļĶ": 30060, "Ġeinge": 30061, "ĠRaise": 30062, "oland": 30063, "ĠStrateg": 30064, "Ġeres": 30065, "ĠVeterans": 30066, "Ġbreakout": 30067, "Ġsanté": 30068, "Ġadel": 30069, "Ġinvestigated": 30070, "Ġpeur": 30071, "Ġagile": 30072, "Ġrailroad": 30073, "anska": 30074, "Ġей": 30075, "Ġexpos": 30076, "atories": 30077, "ĠContent": 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30196, "Ġextraction": 30197, "ĠAV": 30198, "ĠGibbs": 30199, "Ġíľ": 30200, "Ġsı": 30201, "IAM": 30202, "View": 30203, "ĠGRANT": 30204, "Ġ몸": 30205, "Ġverification": 30206, "Ġdepicted": 30207, "ĠMoz": 30208, "oux": 30209, "Ġtul": 30210, "Ġscanner": 30211, "Ġcomedian": 30212, "ĠVolks": 30213, "ĠJEFF": 30214, "è¨Ĥéĸ±": 30215, "§Ħ": 30216, "Ġdistraction": 30217, "rá": 30218, "ĠINTER": 30219, "Ġsincer": 30220, "Ġ×ŀת": 30221, "Ġש׳": 30222, "Ġconstructive": 30223, "arf": 30224, "ĠëĪĦë": 30225, "Ġeco": 30226, "ramos": 30227, "Ġrenewed": 30228, "inement": 30229, "ĠUb": 30230, "ĠPepper": 30231, "ì§Ģê°Ģ": 30232, "ĠDarwin": 30233, "Ġmerchand": 30234, "Ġvárias": 30235, "èce": 30236, "NG": 30237, "ĠìľĦíķ´ìĦľ": 30238, "ĠакÑĤив": 30239, "ĠUnters": 30240, "عÙĦ": 30241, "Ġintric": 30242, "omma": 30243, "ieving": 30244, "ĠCaroline": 30245, "åĵģ": 30246, "ĠPRES": 30247, "Ġperformer": 30248, "Ġautour": 30249, "ãģ¾ãģĽãĤĵ": 30250, "Ġutterly": 30251, "Ġsynthesis": 30252, "Ġlesbian": 30253, "Ġretrieve": 30254, 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30313, "mesi": 30314, "Ġrelaxation": 30315, "ĠÑģвоиÑħ": 30316, "Ġmargins": 30317, "ĠsaÄŁ": 30318, "ĠPride": 30319, "ĠÏĦοÏħÏĤ": 30320, "иÑĨи": 30321, "enci": 30322, "Does": 30323, "Ġcorpse": 30324, "Ġendurance": 30325, "Ġíŀĺ": 30326, "ì¹´": 30327, "Ġhaircut": 30328, "Ġinterrupted": 30329, "Ġwindy": 30330, "ĠCaleb": 30331, "ÏģÏĩ": 30332, "ĠPourquoi": 30333, "Ġholistic": 30334, "uclear": 30335, "ĠWhole": 30336, "士": 30337, "Act": 30338, "Ġgallon": 30339, "cade": 30340, "ĠRegional": 30341, "roads": 30342, "ĠSchne": 30343, "áng": 30344, "Ġизмен": 30345, "ãĤĪãģŃ": 30346, "Ġmenus": 30347, "Ġsplitting": 30348, "Ġpriced": 30349, "ĠÎĵ": 30350, "Ġusername": 30351, "ĠÐŀÑĩ": 30352, "Ġcompressed": 30353, "yin": 30354, "Ġguardian": 30355, "Ġgoof": 30356, "Ġchecklist": 30357, "Ġinterchange": 30358, "Ġexpedition": 30359, "Ġextern": 30360, "Ġinfrared": 30361, "engo": 30362, "Ġdenying": 30363, "Ġpackets": 30364, "onent": 30365, "BB": 30366, "ĠIncre": 30367, "Ġsini": 30368, "ÃŁer": 30369, "èg": 30370, 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"selling": 30427, "ĠTask": 30428, "hoon": 30429, "ĠCoc": 30430, "ĠParks": 30431, "Ġrepetition": 30432, "ĠÑĤÑĥда": 30433, "Ġensl": 30434, "ĠdeÄŁiÅŁ": 30435, "ĠOrlando": 30436, "ĠMainten": 30437, "æŃ¢": 30438, "ocument": 30439, "ĠHC": 30440, "Ġscooter": 30441, "ĠнапиÑģ": 30442, "Ġtighter": 30443, "Ġtease": 30444, "Ġremoves": 30445, "Ġkijken": 30446, "ĠÑģÑĥÑīеÑģÑĤв": 30447, "Ġthé": 30448, "ĠвÑĭглÑıд": 30449, "Ġrelieve": 30450, "Ġmitä": 30451, "Ġstationary": 30452, "öff": 30453, "pable": 30454, "Ġarter": 30455, "Ġdéf": 30456, "rative": 30457, "Ġconect": 30458, "Ġsaddle": 30459, "ĠDiane": 30460, "Ġcommemor": 30461, "fendim": 30462, "SÃŃ": 30463, "Ġíģ´ë": 30464, "Ġmange": 30465, "atte": 30466, "Ġarrogant": 30467, "Ġrobotic": 30468, "ĠgiÃł": 30469, "æĺ¯çļĦ": 30470, "Ġneighbourhood": 30471, "isson": 30472, "Ġдвиж": 30473, "ĠRI": 30474, "ĠNorman": 30475, "brand": 30476, "amation": 30477, "Ġrazor": 30478, "Ġmurders": 30479, "ĠÑĤÑĥ": 30480, "Ġwszystkim": 30481, "Ġutilities": 30482, "Ġmicroscop": 30483, "ê¿": 30484, "Ġdaqui": 30485, "ollar": 30486, "ĠÐĶавайÑĤе": 30487, "Ġannée": 30488, "Ġkilometres": 30489, "Ġhomosexual": 30490, "Ġarchitects": 30491, "ãģ¡ãģ¯": 30492, "Ġniye": 30493, "LER": 30494, "Ġmicrophones": 30495, "ĠStunden": 30496, "Ġconsecutive": 30497, "ienda": 30498, "vänd": 30499, "DER": 30500, "Ġlifts": 30501, "ĠMeat": 30502, "Ġsavez": 30503, "íĸĪëįĺ": 30504, "Men": 30505, "Ġdismant": 30506, "거를": 30507, "Ġinsulation": 30508, "Ġscall": 30509, "Ġspooky": 30510, "Ġparc": 30511, "Ġballet": 30512, "ĠWhatsApp": 30513, "Ġfranc": 30514, "Ġdeliberate": 30515, "ĠíħĮ": 30516, "Ġmars": 30517, "ĠZur": 30518, "Pr": 30519, "disciplinary": 30520, "Ġobsession": 30521, "ме": 30522, "Ġmarching": 30523, "ĠEmergency": 30524, "iguous": 30525, "Ġszy": 30526, "ĠLands": 30527, "Ġboarding": 30528, "ĠпоÑĩÑĤи": 30529, "Ġenvy": 30530, "Ġcompassionate": 30531, "Ġmerci": 30532, "Ġdesirable": 30533, "dale": 30534, "Ġcanım": 30535, "ĠAntar": 30536, "temps": 30537, "Ġconfigured": 30538, "ĠCompared": 30539, "neh": 30540, "icating": 30541, "Ġnickel": 30542, "ÙĪÙĤ": 30543, "ÙĥÙĪÙĨ": 30544, "opes": 30545, "Ġformulas": 30546, "ĠÐķÑģÑĤÑĮ": 30547, "Ġpobl": 30548, "ĠPJ": 30549, "ĠLud": 30550, "ä»ĬåĽŀ": 30551, "ĠBrid": 30552, "ĠHog": 30553, "ĠBris": 30554, "Jen": 30555, "Ġshading": 30556, "ĠYas": 30557, "Ġdisturbed": 30558, "Ġrecommending": 30559, "Ġcé": 30560, "ĠHOW": 30561, "ìĹĪìĸ´": 30562, "Ġreversed": 30563, "ĠInterestingly": 30564, "ioxid": 30565, "åħŃ": 30566, "Ġìĺ¤ì¼ĢìĿ´": 30567, "ếu": 30568, "xx": 30569, "Ġouais": 30570, "ĠYouTubers": 30571, "ĠRosa": 30572, "ĠHaupt": 30573, "jadi": 30574, "Ġvlogs": 30575, "Ġcultura": 30576, "ĠLeadership": 30577, "ĠHep": 30578, "Ġillum": 30579, "´ëıĻ": 30580, "Ġcustomized": 30581, "Ġmarca": 30582, "Ġquatro": 30583, "Ġнаг": 30584, "ĠSpaceX": 30585, "ĠEigen": 30586, "asting": 30587, "ĠolduÄŁu": 30588, "Ġforts": 30589, "ãģī": 30590, "riment": 30591, "iencia": 30592, "Ġtenir": 30593, "roffen": 30594, "Ġ1979": 30595, "Ġcie": 30596, "ĠëIJĺê³ł": 30597, "Ġescri": 30598, "ÏĮÏĤ": 30599, "íı¬": 30600, "uzzy": 30601, "Cong": 30602, "ìĿ¸ìĿ´": 30603, "Great": 30604, "sil": 30605, "éch": 30606, "ãģ¨ãģĭ": 30607, "Ġmultic": 30608, "ĠDisk": 30609, "²ķ": 30610, "Ġfazla": 30611, "Ġlevant": 30612, "Ġabajo": 30613, "urry": 30614, "stru": 30615, "Ġ먹ëĬĶ": 30616, "Ġaccessory": 30617, "Ġдвиг": 30618, "ĠRid": 30619, "2019": 30620, "Ġdownstream": 30621, "æķ¸": 30622, "Ġkaz": 30623, "utan": 30624, "Ġcharcoal": 30625, "Ġafect": 30626, "wu": 30627, "Ġcontexts": 30628, "Ġfeared": 30629, "ĠìĦ¤": 30630, "Ġhistories": 30631, "Ġfas": 30632, "ensible": 30633, "Ġcocoa": 30634, "illar": 30635, "geons": 30636, "Ġspirituality": 30637, "ĠPew": 30638, "Ġpharmacy": 30639, "Ġpassions": 30640, "Ġbos": 30641, "Ġallá": 30642, "Ġthriving": 30643, "ĠReact": 30644, "Ġoccupy": 30645, "Ġwithdrawal": 30646, "Ġallowance": 30647, "ĠFraktion": 30648, "Ġbuddies": 30649, "Ġidle": 30650, "Ġdissolved": 30651, "Ġprevalent": 30652, "Ġmilitar": 30653, "Ġsensing": 30654, "Ġpojaw": 30655, "Ġancora": 30656, "Ġabundant": 30657, "Ġhairst": 30658, "ãģĤãĤĮ": 30659, "Ġtwee": 30660, "Ġnächste": 30661, "ĠMöglichkeit": 30662, "Ġhoo": 30663, "ufficient": 30664, "Ġfantast": 30665, "Ġedible": 30666, "Ġëĸ¨ìĸ´ì": 30667, "ìĽĥ": 30668, "Ġvein": 30669, "ucci": 30670, "Ġdevotion": 30671, "Ġconcealer": 30672, "income": 30673, "Ġrecycled": 30674, "ĠìĬ¤íĥĢ": 30675, "Ġpontos": 30676, "Ġdessus": 30677, "Ġvérit": 30678, "Ġreflections": 30679, "ĠAA": 30680, "Ġtakeaway": 30681, "bare": 30682, "ĠContact": 30683, "eil": 30684, "ĠHear": 30685, "Ġmirac": 30686, "ĠGerilim": 30687, "ĠÑģамÑĭй": 30688, "Ġvivo": 30689, "Ġkilograms": 30690, "ĠCrim": 30691, "ût": 30692, "78": 30693, "Ġsincerely": 30694, "raz": 30695, "Ġë³µ": 30696, "Ġarriv": 30697, "Ġconception": 30698, "ĠPersian": 30699, "Ġsjäl": 30700, "Ġstarring": 30701, "ĠìķĦ무": 30702, "ĠForever": 30703, "еÑģÑĤÑĮ": 30704, "Ġveil": 30705, "Ġsubtit": 30706, "odka": 30707, "ĠоÑĤноÑĪ": 30708, "Ġcooks": 30709, "енÑı": 30710, "Kay": 30711, "Ġniños": 30712, "ĠPhone": 30713, "Ġstitching": 30714, "Ġfingerprint": 30715, "é¢ĺ": 30716, "λά": 30717, "Ġdedicate": 30718, "ĠLob": 30719, "Ġblacks": 30720, "ĠBle": 30721, "bout": 30722, "ĠÄijang": 30723, "Ġeks": 30724, "Ġsquash": 30725, "ĠKü": 30726, "odi": 30727, "ĠnÆ°á»Ľc": 30728, "Ġvoyage": 30729, "Ġplayful": 30730, "ĠØ¥ÙĦÙī": 30731, "anic": 30732, "Ġcondemn": 30733, "ĠBöyle": 30734, "ĠPolize": 30735, "ãĤ¿ãĥ¼": 30736, "Ġayuda": 30737, "Ġpam": 30738, "à¹Ħà¸Ľ": 30739, "ĠKathy": 30740, "един": 30741, "нова": 30742, "Ġbrig": 30743, "eger": 30744, "Ġeagle": 30745, "Ġvisions": 30746, "ĠíķŃìĥģ": 30747, "Ġshitty": 30748, "Ġhott": 30749, "ĠBritt": 30750, "utors": 30751, "ENTE": 30752, "æĽ²": 30753, "Ġphon": 30754, "ĠBing": 30755, "ĠподдеÑĢж": 30756, "spring": 30757, "æĸ¯": 30758, "etten": 30759, "Ġpilgr": 30760, "Ġediyor": 30761, "енÑĤÑĭ": 30762, "aggio": 30763, "Ġjul": 30764, "Ġcomprend": 30765, "teil": 30766, "Ġز": 30767, "Ġperformers": 30768, "Ġinfamous": 30769, "ĠMK": 30770, "çª": 30771, "æ³ģ": 30772, "otle": 30773, "eff": 30774, "ĠHash": 30775, "Ġcoward": 30776, "ĠBRA": 30777, "ĠDD": 30778, "Ġcomida": 30779, "Ġplata": 30780, "Ġflap": 30781, "ĠMehr": 30782, "ribution": 30783, "ĠYemen": 30784, "Ġmysteries": 30785, "ĠÄ°yi": 30786, "Ġstell": 30787, "Ġeyeliner": 30788, "Ġdeles": 30789, "Ġnailed": 30790, "Ġillnesses": 30791, "Ġstacks": 30792, "Ġtrabajar": 30793, "flower": 30794, "ciu": 30795, "Ġcrude": 30796, "Ġsubstantially": 30797, "Ġhomem": 30798, "Ġnephew": 30799, "Ġstamps": 30800, "Ġcarbs": 30801, "ÑĮÑĤе": 30802, "mooth": 30803, "Ġtunnels": 30804, "acie": 30805, "æ³¢": 30806, "ĠSeñ": 30807, "ĠHera": 30808, "ĠìķĦëĭĪìĹIJìļĶ": 30809, "ĠWyoming": 30810, "ĠHDMI": 30811, "ĠLis": 30812, "ución": 30813, "Ġsteer": 30814, "оÑİ": 30815, "иÑĤа": 30816, "NT": 30817, "Ġìĸ¼êµ´": 30818, "Ġpalms": 30819, "Ġneon": 30820, "ованиÑı": 30821, "Ġfiltering": 30822, "Ġjouer": 30823, "ĠHö": 30824, "ĠнеÑģ": 30825, "ê²łìĸ´ìļĶ": 30826, "Ġ81": 30827, "Ġstoryline": 30828, "Ġprzep": 30829, "Ġthanking": 30830, "ĠBoeing": 30831, "Ġsoftly": 30832, "jem": 30833, "алÑĮнÑĭÑħ": 30834, "Ġflashlight": 30835, "ĠпÑĥ": 30836, "ĠWOMAN": 30837, "ắc": 30838, "ÃŃch": 30839, "Ġluxurious": 30840, "Ġwün": 30841, "Ġimpactful": 30842, "Ġconson": 30843, "reu": 30844, "irring": 30845, "ifter": 30846, "Ġconstituents": 30847, "èIJ½": 30848, "Ġ94": 30849, "ĠTou": 30850, "gom": 30851, "ĠìĥĿê°ģìĿĦ": 30852, "Ġstereotypes": 30853, "Ġmożli": 30854, "åĪĨ享": 30855, "Ĥ¨": 30856, "Ġpencils": 30857, "ĠÑģлож": 30858, "Ġihrem": 30859, "ĠBesch": 30860, "ĠKoh": 30861, "ĠEntscheid": 30862, "Ġlek": 30863, "Ġförs": 30864, "Ġtotalmente": 30865, "Ġlively": 30866, "Ġentropy": 30867, "Ġdiscern": 30868, "ĠÐĹна": 30869, "Ġdov": 30870, "Ġmythology": 30871, "è¨ĺå¾Ĺ": 30872, "apanese": 30873, "Ġapproximate": 30874, "аÑĤив": 30875, "ifiable": 30876, "ĠSeo": 30877, "åĢĴ": 30878, "´ìĭ¬íŀĪ": 30879, "Ġìĺ·": 30880, "Ġtemporal": 30881, "ĠiT": 30882, "Ġestat": 30883, "ким": 30884, "Ġsprink": 30885, "Ġgrund": 30886, "Ġinfantry": 30887, "Ġschaffen": 30888, "ç´Ħ": 30889, "Ġank": 30890, "riages": 30891, "ĠYeon": 30892, "ĠMoroc": 30893, "Ġinvasive": 30894, "ģĶ": 30895, "Ġparenting": 30896, "ĠRis": 30897, "ibile": 30898, "Ġmods": 30899, "å½¢": 30900, "ĠпÑĢовеÑĢ": 30901, "ĠThing": 30902, "ĠWherever": 30903, "Ġacknowledging": 30904, "Ġpawn": 30905, "ummer": 30906, "orb": 30907, "69": 30908, "Ġretrouve": 30909, "Ġrelies": 30910, "ĠHighway": 30911, "Ġawe": 30912, "ãģ§ãģĻãģĭ": 30913, "itaire": 30914, "Ġapplicant": 30915, "Ġaisle": 30916, "worm": 30917, "Ġpayload": 30918, "Ġcarre": 30919, "ĠBach": 30920, "æł¼": 30921, "Ġì¹ľêµ¬ë": 30922, "ние": 30923, "ĠitÃŃs": 30924, "onnaise": 30925, "sol": 30926, "èı¯": 30927, "algia": 30928, "Ġrocking": 30929, "Ġbesten": 30930, "rites": 30931, "^^": 30932, "иной": 30933, "Ġbaixo": 30934, "Ġ기ìĸµ": 30935, "оÑĤÑĢи": 30936, "sim": 30937, "Ġincarn": 30938, "ëĭ¤ìĿĮ": 30939, "Ġlick": 30940, "sided": 30941, "Ġ71": 30942, "forder": 30943, "Ġresonance": 30944, "Ġtegen": 30945, "Ġmetaph": 30946, "owser": 30947, "Ġ×IJ׳×Ĺ׳×ķ": 30948, "?ãĢį": 30949, "Ġspielen": 30950, "Ġvolley": 30951, "ĶìĿ´íģ¬ìĹħ": 30952, "looked": 30953, "Ġsentenced": 30954, "Ġmultiplying": 30955, "Ġideals": 30956, "Ġwahrscheinlich": 30957, "Ġdeposits": 30958, "bilir": 30959, "Ġeffet": 30960, "illon": 30961, "Īë§Į": 30962, "Ġtestimon": 30963, "Ġzawsze": 30964, "ĠпÑĢоÑĨеÑģÑģ": 30965, "ĠLav": 30966, "ä¸įéĮ¯": 30967, "Ġtravailler": 30968, "Ġlaisse": 30969, "ĠMountains": 30970, "ĠÑĢоб": 30971, "Ġexamined": 30972, "itus": 30973, "Was": 30974, "лÑĭ": 30975, "Ġattributed": 30976, "ĠìĬ¹": 30977, "ĠBaron": 30978, "Ġgep": 30979, "Ġattent": 30980, "ĠCollection": 30981, "Ġtheat": 30982, "ĠCai": 30983, "Ġwells": 30984, "Ġhumano": 30985, "çĹħ": 30986, "ĠHast": 30987, "ĠÑħоÑĤÑı": 30988, "czas": 30989, "Ġpermits": 30990, "Ġlegg": 30991, "Ġepo": 30992, "ĠFen": 30993, "Ġthi": 30994, "ĠFoi": 30995, "Ġélect": 30996, "Ġ83": 30997, "Ġoverth": 30998, "Ġè¬Ŀè¬Ŀ": 30999, "Ġtenant": 31000, "è²·": 31001, "Next": 31002, "Ġpraised": 31003, "security": 31004, "ĠImpact": 31005, "为ä»Ģä¹Ī": 31006, "Ġvouch": 31007, "Ġnegó": 31008, "Ġunve": 31009, "Ġcriticize": 31010, "ĠKenya": 31011, "Ġtactic": 31012, "Ġlogr": 31013, "Ġpois": 31014, "Ġpapa": 31015, "speaks": 31016, "ðŁij": 31017, "ispers": 31018, "Ġsurplus": 31019, "Ġcolder": 31020, "åįĹ": 31021, "åIJ¬": 31022, "plets": 31023, "ĠVienna": 31024, "ĠLead": 31025, "Ġaerial": 31026, "ĠTah": 31027, "енÑĤов": 31028, "ĠGreeks": 31029, "Cam": 31030, "Ġmáxim": 31031, "Ġkuin": 31032, "chio": 31033, "Ġdemonstrates": 31034, "anos": 31035, "ĠCert": 31036, "ĠÑįн": 31037, "Ġblogs": 31038, "ĠìĦľìļ¸": 31039, "Ġbeams": 31040, "иков": 31041, "Ġprompted": 31042, "Ġfrightening": 31043, "ĠPorsche": 31044, "ãģĪãģ¦": 31045, "larını": 31046, "Ġchilling": 31047, "isphere": 31048, "Ġflashing": 31049, "ĠKard": 31050, "bread": 31051, "Ġexh": 31052, "Ġtycker": 31053, "Ġecological": 31054, "ĠMae": 31055, "Ġ×ŀ×IJ×ķ×ĵ": 31056, "ĠëĤĺëıĦ": 31057, "лон": 31058, "yss": 31059, "Ġpergunt": 31060, "Ġprix": 31061, "izzard": 31062, "Ġcancers": 31063, "Ġ91": 31064, "susp": 31065, "ĠItem": 31066, "ÅŁa": 31067, "Ġpest": 31068, "ĠtakÄħ": 31069, "Ġlymph": 31070, "ĠPatri": 31071, "fill": 31072, "Ġreconna": 31073, "Ġoptimism": 31074, "Ġmimic": 31075, "Ġì²ľ": 31076, "ĠMadame": 31077, "ocy": 31078, "lining": 31079, "åijĬ訴": 31080, "erme": 31081, "Ġfolders": 31082, "ĠczÅĤ": 31083, "uchar": 31084, "Ġcurso": 31085, "Ġbreach": 31086, "ниÑĤÑĮ": 31087, "ĠpamiÄĻ": 31088, "Ġelig": 31089, "Ġautop": 31090, "Flow": 31091, "Ġprogrammed": 31092, "ĠProcess": 31093, "Ġfigur": 31094, "ĠSF": 31095, "ĠEles": 31096, "Ġprogrammes": 31097, "Ġdizzy": 31098, "ìĭľê°Ħ": 31099, "Ġлибо": 31100, "Ġsniff": 31101, "ĠSebastian": 31102, "ĠHye": 31103, "Ġ4000": 31104, "Ġpermite": 31105, "æ¢Ŀ": 31106, "ĠзаÑī": 31107, "Ġguit": 31108, "ĠDais": 31109, "Ġaccordance": 31110, "Ġmodular": 31111, "ogeneous": 31112, "æĭį": 31113, "Ġpouquinho": 31114, "Ġartillery": 31115, "Ġlubric": 31116, "Ġvolcan": 31117, "ĠNH": 31118, "ðŁ¤": 31119, "Ġdean": 31120, "Rh": 31121, "Ġministre": 31122, "åĿIJ": 31123, "ĠInv": 31124, "ĠBulgar": 31125, "ĠDaten": 31126, "èİ": 31127, "Im": 31128, "Ġoriginated": 31129, "ĠNixon": 31130, "integr": 31131, "Ġlacks": 31132, "ĠNacht": 31133, "ìĸ´ëĤĺ": 31134, "camera": 31135, "Ġradish": 31136, "kiye": 31137, "Ġanges": 31138, "Ġpréf": 31139, "juk": 31140, "ĠBee": 31141, "ĠBU": 31142, "ĠвоÑģп": 31143, "ĠBT": 31144, "êmes": 31145, "ĠStück": 31146, "ĠInk": 31147, "æĪĸèĢħ": 31148, "ĠSergeant": 31149, "ĠMultip": 31150, "Ġhiçbir": 31151, "ĠСам": 31152, "ĠDé": 31153, "olph": 31154, "ìĸ¸": 31155, "Ġimpat": 31156, "ĠìķĬê³ł": 31157, "ĠÑĤакого": 31158, "ĠнавеÑĢное": 31159, "Ġunpredictable": 31160, "Ġmend": 31161, "ĠìĹĨìĸ´ìļĶ": 31162, "ĠjakieÅĽ": 31163, "Ġanni": 31164, "Ġdonné": 31165, "ĠKirsty": 31166, "Ġrectangular": 31167, "Ġempezar": 31168, "ĠExchange": 31169, "ê°Ķ": 31170, "Ġéconom": 31171, "ãģĵãĤĵ": 31172, "elin": 31173, "reibt": 31174, "Ġ×Ķפ": 31175, "Ġcemetery": 31176, "Ġespañol": 31177, "olin": 31178, "лÑİд": 31179, "Ġgrâce": 31180, "allen": 31181, "ĠPhilos": 31182, "ĠErst": 31183, "ĠìĥĪ": 31184, "ĠVid": 31185, "Give": 31186, "OH": 31187, "μο": 31188, "ĠPare": 31189, "Ġmetabolism": 31190, "Ġmaple": 31191, "Ġaxle": 31192, "ĠDy": 31193, "Ġkomme": 31194, "Ïİν": 31195, "Ġgreatness": 31196, "Ġverified": 31197, "Ġspé": 31198, "ĠFahrenheit": 31199, "ĠBren": 31200, "ĠConfeder": 31201, "Ġhistoire": 31202, "Ġeliminating": 31203, "ĠAdding": 31204, "ĠAbi": 31205, "æĿİ": 31206, "Ġhospitality": 31207, "tim": 31208, "Ġbonito": 31209, "Ġpartes": 31210, "ĠдÑĢÑĥгиÑħ": 31211, "ĠShay": 31212, "ĠSed": 31213, "Ġregrets": 31214, "Ñıми": 31215, "Ġtenants": 31216, "éĢŁ": 31217, "ĠPTS": 31218, "Ġdevi": 31219, "ĠLate": 31220, "uez": 31221, "Ġsöyl": 31222, "ãĤ»": 31223, "Ġìŀ¬ë°Į": 31224, "Ġtoggle": 31225, "Ġmasking": 31226, "алÑĮного": 31227, "Ġpersön": 31228, "Ġamerican": 31229, "fik": 31230, "ĠRGB": 31231, "enson": 31232, "ĠKA": 31233, "wwww": 31234, "ĠÑĢег": 31235, "metics": 31236, "Ġeducator": 31237, "ãĤ·ãĥ«ãĤ¯": 31238, "park": 31239, "елÑĮзÑı": 31240, "arus": 31241, "ÑĢеÑĤ": 31242, "Ġfeito": 31243, "Ġchoir": 31244, "Ġlargo": 31245, "Ġeens": 31246, "Ġwatts": 31247, "ĠSingle": 31248, "Ġsusceptible": 31249, "icer": 31250, "ĠвклÑİÑĩ": 31251, "Ġpus": 31252, "íĻĺ": 31253, "Eng": 31254, "Ġfantas": 31255, "Ġspecification": 31256, "Ġconfronted": 31257, "ĠColumbus": 31258, "ивеÑĤ": 31259, "arım": 31260, "Ġcaffeine": 31261, "munition": 31262, "Ġmigrants": 31263, "lide": 31264, "itations": 31265, "ĠGeme": 31266, "ẫ": 31267, "Ġplanner": 31268, "Ġstimulate": 31269, "Ġaproxim": 31270, "ceu": 31271, "ĠNom": 31272, "Ġvog": 31273, "ĠÑĢаÑģÑĤ": 31274, "Ġenseñ": 31275, "Ġsellers": 31276, "Ġguten": 31277, "zd": 31278, "Cal": 31279, "Ġdescript": 31280, "Ġreconciliation": 31281, "zinho": 31282, "á¹ĩa": 31283, "ãģĺãĤĥãģĤ": 31284, "acyj": 31285, "ĠCOL": 31286, "saw": 31287, "ĠíĻķìĿ¸": 31288, "Ġvarit": 31289, "Ġpartnering": 31290, "Ġdetention": 31291, "Ġbombing": 31292, "clapping": 31293, "iencies": 31294, "ondu": 31295, "AME": 31296, "Ġê°ĻìĬµëĭĪëĭ¤": 31297, "cÃŃa": 31298, "ĠпоÑģÑĤо": 31299, "ĠASMR": 31300, "Ġhomepage": 31301, "Ġsiè": 31302, "antha": 31303, "ĠPoll": 31304, "Ġigen": 31305, "cych": 31306, "Ġê°ijìŀIJ기": 31307, "Ġconsiderably": 31308, "ä»ĸçļĦ": 31309, "ĠArist": 31310, "Ġwithstand": 31311, "Ġqualitative": 31312, "ĠKraft": 31313, "ĠÑįлекÑĤ": 31314, "ĠBead": 31315, "екÑĤив": 31316, "Ġcrushing": 31317, "ì³IJ": 31318, "Ġnavy": 31319, "ÙĪÚº": 31320, "sho": 31321, "Ġoak": 31322, "ippers": 31323, "Ġsoils": 31324, "Ġpigment": 31325, "Ġevitar": 31326, "ãĥĩ": 31327, "Ġfuse": 31328, "ĠDale": 31329, ":\"": 31330, "Ġcomplètement": 31331, "Ġkel": 31332, "à¹Ĩ": 31333, "Ġquatre": 31334, "ĠUM": 31335, "Ġë§IJë": 31336, "æł¹": 31337, "ÃŃr": 31338, "Ġleisure": 31339, "ĠHousing": 31340, "Ġfolds": 31341, "estion": 31342, "ARS": 31343, "Ġmash": 31344, "urpose": 31345, "Ġaccumulated": 31346, "ĠStuff": 31347, "èªŀ": 31348, "Ġtapes": 31349, "ĠÑģилÑĮно": 31350, "ĠLOVE": 31351, "Ġ1982": 31352, "Ġscars": 31353, "Ġcapitalist": 31354, "ĠNed": 31355, "Ġsoften": 31356, "Ġnotably": 31357, "Ġforcément": 31358, "ĠRaum": 31359, "ĠнеобÑħод": 31360, "Ġtrademark": 31361, "Ġfertig": 31362, "Ġ?!": 31363, "æĹł": 31364, "Ġreinforced": 31365, "Ġrecharge": 31366, "ĠPutting": 31367, "Ġvillains": 31368, "Ġhandic": 31369, "Ġadvertisement": 31370, "تÙĬ": 31371, "ĠÑģÑĥм": 31372, "ĠRiley": 31373, "×ķ×ij×": 31374, "京": 31375, "Os": 31376, "از": 31377, "Boy": 31378, "Ġsquish": 31379, "ocket": 31380, "Ġtestify": 31381, "æ¼Ķ": 31382, "Ġ׾×ŀ×": 31383, "ĠмаÑģÑģ": 31384, "manuel": 31385, "ĠArkansas": 31386, "iffe": 31387, "Ġanalysts": 31388, "ĠDeaf": 31389, "Ġjó": 31390, "Ġgroceries": 31391, "ĠWheel": 31392, "ĠÑĢиÑģ": 31393, "Ġcòn": 31394, "ĠCob": 31395, "Ġprisons": 31396, "ève": 31397, "ĠCabinet": 31398, "Ġposed": 31399, "Ġguerre": 31400, "ĠLloyd": 31401, "Ġclerk": 31402, "Ġcrises": 31403, "ĠSho": 31404, "ĠOre": 31405, "ĠFootball": 31406, "ĠAdvis": 31407, "ĠZheng": 31408, "èį": 31409, "ĠAMY": 31410, "Ġunfor": 31411, "Ġmonaster": 31412, "Ġcompile": 31413, "Ġimmortal": 31414, "atable": 31415, "Ġparano": 31416, "Ġtiver": 31417, "ĠSteph": 31418, "ĠFuÃŁ": 31419, "Ġdiscontin": 31420, "Ġripe": 31421, "Ġhacking": 31422, "Ġsiendo": 31423, "Ġseguro": 31424, "altres": 31425, "Ġanderes": 31426, "Ġ리ë": 31427, "Ġexports": 31428, "æŃ¥": 31429, "Ġtabii": 31430, "Ġ기ëĭ¤ë": 31431, "Ġbothering": 31432, "Ġpickle": 31433, "ĠBRIAN": 31434, "Ġaltar": 31435, "ĠпÑĢиб": 31436, "Ġtransferring": 31437, "ĠVors": 31438, "ĠÙĩÙĪ": 31439, "ĠZa": 31440, "ĠFrances": 31441, "Ġbrowse": 31442, "emit": 31443, "Ġchewing": 31444, "ĠFreddy": 31445, "Ġeditors": 31446, "älle": 31447, "ĠíĮĢ": 31448, "ĠSque": 31449, "ĠCultural": 31450, "awk": 31451, "ĠSache": 31452, "ĠCarbon": 31453, "ắt": 31454, "FL": 31455, "ĠNGO": 31456, "peÅĤ": 31457, "ĠSou": 31458, "Ġhvor": 31459, "unintelligible": 31460, "Ġë²ķ": 31461, "Ġ°": 31462, "iin": 31463, "Ġ×¢×Ŀ": 31464, "Ġderrière": 31465, "Ġczym": 31466, "ĠApost": 31467, "Ġregarder": 31468, "Ġagrade": 31469, "ĠCandy": 31470, "Ġmare": 31471, "Ġintroduces": 31472, "birds": 31473, "Ġuniquely": 31474, "Ġmuk": 31475, "Ġcooker": 31476, "Ġcrews": 31477, "Ġjeito": 31478, "ERT": 31479, "¶Ħë": 31480, "nisse": 31481, "Ġef": 31482, "Ġcarte": 31483, "ĠYak": 31484, "ĠPAT": 31485, "ино": 31486, "bokki": 31487, "Ġmates": 31488, "Ġdistint": 31489, "Ġì½Ķë¡ľëĤĺ": 31490, "Ġyıl": 31491, "Ġκάν": 31492, "Ġconfigurations": 31493, "enga": 31494, "recht": 31495, "Happy": 31496, "ãĤĦãģ£ãģ¦": 31497, "invest": 31498, "Ġreconstruct": 31499, "ĠÑįÑĤомÑĥ": 31500, "Ġmosque": 31501, "raum": 31502, "Ġvoyez": 31503, "ĠNBC": 31504, "ĠìŀIJìĭł": 31505, "Ġsturdy": 31506, "Ġкап": 31507, "Ġansch": 31508, "alid": 31509, "Ġmasih": 31510, "ĠREP": 31511, "Ġì½Ķë": 31512, "Ġdeduct": 31513, "Ġsalir": 31514, "wurf": 31515, "ilot": 31516, "ĠMutter": 31517, "olds": 31518, "ĠFEMA": 31519, "ĠBib": 31520, "Ġneighboring": 31521, "Ġbliss": 31522, "Ġíĺ¼": 31523, "лиÑģÑĮ": 31524, "ĠÑĤÑĢеб": 31525, "Ġå°±æĺ¯": 31526, "Ġgrenade": 31527, "Ġegal": 31528, "Ġfinely": 31529, "Ġpetals": 31530, "Ġkeer": 31531, "Ġchyba": 31532, "Ġskipping": 31533, "Ġthirteen": 31534, "Ġgravy": 31535, "ĠSAT": 31536, "61": 31537, "Ġног": 31538, "Ġmins": 31539, "ITE": 31540, "Ġsozial": 31541, "íķĺë©´ìĦľ": 31542, "ruktur": 31543, "Ġвозмож": 31544, "ĠопÑıÑĤÑĮ": 31545, "Ġarth": 31546, "ĠCuban": 31547, "Ġtreasures": 31548, "Ġfertilizer": 31549, "Ġawakening": 31550, "Ġë°±ìĭł": 31551, "Ġrall": 31552, "Ġdepict": 31553, "ĠPablo": 31554, "Ġnineteen": 31555, "Ġwatt": 31556, "Ġentirety": 31557, "KS": 31558, "ĠWoods": 31559, "Sch": 31560, "ĠÚ©ÙĪ": 31561, "ĠDry": 31562, "ãģŀ": 31563, "uve": 31564, "Ġreconstruction": 31565, "Ġanatomy": 31566, "Ī를": 31567, "Ġbaba": 31568, "Ġlistener": 31569, "Ġsharpen": 31570, "ĠPeru": 31571, "ĠвÑĭз": 31572, "Ġrecreation": 31573, "Ġinitiate": 31574, "Ġcalor": 31575, "ĠNaj": 31576, "gee": 31577, "ĠFeels": 31578, "ĠSnapchat": 31579, "ĠTet": 31580, "ĠNest": 31581, "ĠDaf": 31582, "ĠFinish": 31583, "ĠÑĤаким": 31584, "úc": 31585, "izens": 31586, "Ġspins": 31587, "Ġembry": 31588, "Ġpassages": 31589, "Ġcient": 31590, "Ġjustification": 31591, "ä»ĸ說": 31592, "Ġolmaz": 31593, "Ġflooded": 31594, "Ġemoji": 31595, "Ġembracing": 31596, "Ġdiscard": 31597, "ĠBasic": 31598, "agog": 31599, "ĠìľĦíķ´": 31600, "Ġasylum": 31601, "erin": 31602, "Ġfim": 31603, "Ġninja": 31604, "Ġautomate": 31605, "Ġallergic": 31606, "ÿÿÿÿ": 31607, "amam": 31608, "ĠмаÑĢ": 31609, "ĠOi": 31610, "äus": 31611, "Ġinduct": 31612, "ĠBEN": 31613, "ĠzÅĤ": 31614, "Ġkażdy": 31615, "ĠAMP": 31616, "nÄĽ": 31617, "Sure": 31618, "Ġquil": 31619, "Ġespec": 31620, "rok": 31621, "BSCRI": 31622, "Ġliebe": 31623, "pus": 31624, "achsen": 31625, "Ġcricket": 31626, "ëĬIJ": 31627, "ĠFrame": 31628, "ekkür": 31629, "arb": 31630, "ĠpÅĻ": 31631, "иÑģÑģ": 31632, "Ġzeggen": 31633, "Ġdoubles": 31634, "ĠDre": 31635, "test": 31636, "insp": 31637, "boys": 31638, "Ġmão": 31639, "ĠVerse": 31640, "Ġmuscular": 31641, "ĠMALE": 31642, "Ġdulu": 31643, "Ġoccasional": 31644, "Lo": 31645, "conomic": 31646, "Ġvak": 31647, "Ġremedy": 31648, "å¤ł": 31649, "ĠâĻªâĻªâĻª": 31650, "vem": 31651, "Ġönem": 31652, "ĠkarÅŁÄ±": 31653, "ĠSharp": 31654, "hur": 31655, "Ġë°©ë²ķ": 31656, "Ġgrandson": 31657, "Ġaktiv": 31658, "ĠThrones": 31659, "ĠìķĪìĹIJ": 31660, "Ġtots": 31661, "Ġsubd": 31662, "ĠPaula": 31663, "Ġgraves": 31664, "ĠBrent": 31665, "ĠникÑĤо": 31666, "Ġsöz": 31667, "Ġcrec": 31668, "ĠVladimir": 31669, "çĸ«": 31670, "Ġпой": 31671, "Ġ\"-": 31672, "Ġpsy": 31673, "atri": 31674, "idan": 31675, "Ġaún": 31676, "Ġstandardized": 31677, "ì¹ĺë": 31678, "ĠкÑĢов": 31679, "ĠZhu": 31680, "something": 31681, "Ġ750": 31682, "Ġmujeres": 31683, "Ġait": 31684, "éĹ´": 31685, "agu": 31686, "Ġcorrected": 31687, "ikka": 31688, "eled": 31689, "ĠCareer": 31690, "owym": 31691, "Ġroommate": 31692, "Ġdescendants": 31693, "ĠNapoleon": 31694, "ĠÐĶо": 31695, "íĸĪìĸ´ìļĶ": 31696, "Ġbunun": 31697, "ĠMicha": 31698, "ç·ļ": 31699, "Ġdescob": 31700, "PI": 31701, "Ġpalabra": 31702, "Ġtracked": 31703, "Ġdependence": 31704, "ĠBarack": 31705, "åģĩ": 31706, "Ġfertility": 31707, "ĠSouthwest": 31708, "Ġincomplete": 31709, "Ġcomunic": 31710, "Ġcompris": 31711, "ĠRestaur": 31712, "Ġacron": 31713, "κα": 31714, "Ġapprentices": 31715, "Ġmusst": 31716, "ĠAbr": 31717, "Ġpentru": 31718, "ĠConsort": 31719, "ĠAvec": 31720, "Ġdumplings": 31721, "LR": 31722, "Ġwszystkie": 31723, "Ġswamp": 31724, "нев": 31725, "uggle": 31726, "Ġwatercolor": 31727, "Ġproton": 31728, "ĠEspaña": 31729, "ocking": 31730, "овал": 31731, "Ġtakim": 31732, "Very": 31733, "Ġdementia": 31734, "ĠÅŁeyi": 31735, "Jac": 31736, "ĠMacBook": 31737, "ĠLiv": 31738, "fficients": 31739, "ĠHunt": 31740, "Ġoverlay": 31741, "æĦŁè¦º": 31742, "ĠSkype": 31743, "punkt": 31744, "Ġconfined": 31745, "ĠAdrian": 31746, "رÙĥ": 31747, "ĠJeep": 31748, "Ġenquanto": 31749, "Ġanest": 31750, "оÑĤвеÑĤ": 31751, "ĠменÑĮ": 31752, "Ġirrigation": 31753, "á»ijn": 31754, "Ġeighteen": 31755, "ĠPon": 31756, "Ġrescued": 31757, "Ġ1983": 31758, "rü": 31759, "jae": 31760, "ĠJeong": 31761, "Ġamazingly": 31762, "ĠFDP": 31763, "Ġbackstage": 31764, "cue": 31765, "ĠÏĥÏĦην": 31766, "ĠاÙĦص": 31767, "Ġlivestock": 31768, "ĠWarner": 31769, "Ġmajors": 31770, "ãĥģãĥ£": 31771, "Ġcooperative": 31772, "ĠBrady": 31773, "rained": 31774, "rieb": 31775, "Ġ×ij×ŀ×": 31776, "ĠдоволÑĮно": 31777, "ĠFE": 31778, "Ġleaked": 31779, "ĠMercury": 31780, "Ġpersuade": 31781, "Ġtransformer": 31782, "ĠNorweg": 31783, "ĠìŬ룬": 31784, "ĠzrobiÄĩ": 31785, "Ġcardiovascular": 31786, "ĠCrash": 31787, "Ġgossip": 31788, "аÑģÑĤÑĮ": 31789, "Ġ쪽": 31790, "Ġswept": 31791, "ĠHorn": 31792, "ĠAté": 31793, "Ġbukan": 31794, "ĠKaw": 31795, "KY": 31796, "ĠStories": 31797, "Gary": 31798, "Ġgardening": 31799, "ĠQuickly": 31800, "ĠFalcon": 31801, "Ġovat": 31802, "cı": 31803, "ĠComplet": 31804, "ĠDate": 31805, "ĠпÑĢим": 31806, "Ġläuft": 31807, "ĠAudrey": 31808, "ĠWent": 31809, "ĠpelÃŃcul": 31810, "Ġcarriage": 31811, "Ġunacceptable": 31812, "nymi": 31813, "ĠÑģлÑĭÑĪ": 31814, "Ġterre": 31815, "uellement": 31816, "EEEE": 31817, "Ġpharmac": 31818, "hões": 31819, "Ġzich": 31820, "Ġmigrate": 31821, "ĠFry": 31822, "ñana": 31823, "ĠMuito": 31824, "EOVER": 31825, "Ġfortress": 31826, "ĠCompan": 31827, "ĠJSON": 31828, "ordnung": 31829, "Ġwarto": 31830, "Ġungef": 31831, "ìħĶìĦľ": 31832, "ĠÑĢок": 31833, "Ġpaddle": 31834, "Jared": 31835, "Ġsubmitting": 31836, "Ġlatch": 31837, "Ġfug": 31838, "ĠкоÑģ": 31839, "ĠEf": 31840, "Ġlaunches": 31841, "Ġft": 31842, "otechn": 31843, "Ġtravelled": 31844, "اÙģ": 31845, "éģķ": 31846, "Ġproch": 31847, "Ġdedim": 31848, "83": 31849, "Ġrebound": 31850, "ĠLU": 31851, "path": 31852, "ĠÑģпÑĢав": 31853, "Ġöl": 31854, "ĠíĤ¤": 31855, "Ġprivat": 31856, "Ġtractor": 31857, "ĠAttention": 31858, "Ser": 31859, "Ġcoses": 31860, "ária": 31861, "pal": 31862, "ĠìĿĢ": 31863, "Ġsuccessor": 31864, "Ġconnectors": 31865, "ĠÑĥÑģÑĤанов": 31866, "Ġgenocide": 31867, "Ġsufficiently": 31868, "ĠAixò": 31869, "Ġstabilize": 31870, "Ġcongest": 31871, "Ġcarving": 31872, "Ġzost": 31873, "ĠбÑĭÑģÑĤÑĢо": 31874, "Ġshortest": 31875, "Ġlivel": 31876, "Ġ89": 31877, "éģĬ": 31878, "Ġerk": 31879, "Ġportraits": 31880, "à¥Ģ": 31881, "èĺ": 31882, "boat": 31883, "llah": 31884, "ANC": 31885, "Ġempirical": 31886, "ĠEcho": 31887, "ĠNederland": 31888, "è¿Ļä¹Ī": 31889, "Net": 31890, "Ġcuidado": 31891, "ĠRoma": 31892, "Ġcalf": 31893, "Ġgiants": 31894, "ĠExplorer": 31895, "ĠCollect": 31896, "alition": 31897, "ĠDestiny": 31898, "Ġausge": 31899, "ĠEdu": 31900, "ĠClo": 31901, "Ġearrings": 31902, "ĠTrack": 31903, "ĠROS": 31904, "ĠBelle": 31905, "çĻ¾": 31906, "Ġpueda": 31907, "Ġdaytime": 31908, "Ġsupplier": 31909, "ĠSV": 31910, "ĠExhale": 31911, "Ġgalera": 31912, "course": 31913, "Ġcentimeter": 31914, "ĠBast": 31915, "mud": 31916, "Ġsangat": 31917, "ĠPhysical": 31918, "Ġprivately": 31919, "Ġtrata": 31920, "lynn": 31921, "illi": 31922, "Ġë©ĶìĿ´íģ¬ìĹħ": 31923, "Ġcrystall": 31924, "Ġpods": 31925, "ản": 31926, "inator": 31927, "ĠRecords": 31928, "å®ĺ": 31929, "ÄŁimiz": 31930, "issement": 31931, "hare": 31932, "hadow": 31933, "ĠDK": 31934, "ĠìķĮê³ł": 31935, "Ġwyn": 31936, "Ġrequesting": 31937, "ĠDonna": 31938, "ĠìĹ´ìĭ¬íŀĪ": 31939, "inea": 31940, "Ġexert": 31941, "ĠDuncan": 31942, "ĠвеÑĩ": 31943, "ĠHah": 31944, "à¤Ĥ": 31945, "ĠLif": 31946, "ĠFinding": 31947, "ĠNov": 31948, "Ġзнак": 31949, "ĠоÑĦ": 31950, "ĠQuè": 31951, "Ġquarterback": 31952, "ĠÑĦак": 31953, "Ġbipartisan": 31954, "ÄŁin": 31955, "Ġnécess": 31956, "Ġreferendum": 31957, "Ġcompiler": 31958, "Ġprobabil": 31959, "еди": 31960, "Ġtrader": 31961, "æĺĵ": 31962, "ĠRum": 31963, "geme": 31964, "Ġdio": 31965, "ĠbÄĻdziemy": 31966, "ĠÏĢά": 31967, "꾸": 31968, "×ķ×ĺ": 31969, "Ġà¤ķ": 31970, "Ġблаг": 31971, "Ġscalp": 31972, "ĠPause": 31973, "Ġcaption": 31974, "Ġendanger": 31975, "Ġenlar": 31976, "Ġrotten": 31977, "ãĥĥãĥĪ": 31978, "Ġwah": 31979, "èĤī": 31980, "Ġdzi": 31981, "ĠInstall": 31982, "Ay": 31983, "Ġcrear": 31984, "енÑĤа": 31985, "Ġweighing": 31986, "Ġbutterflies": 31987, "ĠGast": 31988, "äºķ": 31989, "horn": 31990, "warz": 31991, "ICEOVER": 31992, "ĠнайÑĤи": 31993, "Ġcoefficients": 31994, "ç°¡åĸ®": 31995, "ĠSpencer": 31996, "ĠHigher": 31997, "Ġcowork": 31998, "å¨ĺ": 31999, "ĠкоÑĤоÑĢое": 32000, "Ġmonit": 32001, "Ġdysfunction": 32002, "ĠÑģÑĤанов": 32003, "Ġtournaments": 32004, "Ġoyster": 32005, "BN": 32006, "Ġtrud": 32007, "slow": 32008, "ĠPenny": 32009, "ĠOdys": 32010, "ær": 32011, "Ġfou": 32012, "Ġenjoyment": 32013, "аÑĤÑĭ": 32014, "ĠwyglÄħda": 32015, "алÑĮнаÑı": 32016, "ĠProtect": 32017, "Ġmoy": 32018, "Ġclaw": 32019, "Ġsuspicion": 32020, "Ġsacrificed": 32021, "Ġgosto": 32022, "Big": 32023, "Ġaggressively": 32024, "Ġvorne": 32025, "ãĥł": 32026, "Ġblamed": 32027, "ĠSehr": 32028, "פר": 32029, "cito": 32030, "Ġseals": 32031, "Ġmujer": 32032, "ĠWeird": 32033, "Ġforens": 32034, "Ġcontributes": 32035, "estra": 32036, "Ġpog": 32037, "LOL": 32038, "Ġhacerlo": 32039, "оÑĤÑĮ": 32040, "fiction": 32041, "79": 32042, "λο": 32043, "大æ¦Ĥ": 32044, "声": 32045, "ĠÑĤоб": 32046, "ĠGS": 32047, "ĠClara": 32048, "itez": 32049, "Ġadvocating": 32050, "ĠíĶĦë": 32051, "sung": 32052, "Ġvertices": 32053, "Ġnavigating": 32054, "Ġeuropé": 32055, "çļĨ": 32056, "Ġslowed": 32057, "Ġforeground": 32058, "ĠIndustrial": 32059, "Ġadore": 32060, "ìĭŃ": 32061, "Ġcréer": 32062, "æŀĹ": 32063, "chnitt": 32064, "Ġunaware": 32065, "Ġcurly": 32066, "entar": 32067, "Ġler": 32068, "Ġprohibited": 32069, "ĠHeroes": 32070, "ĠReed": 32071, "uca": 32072, "Ġsmok": 32073, "Ġkunna": 32074, "zeitig": 32075, "immen": 32076, "ĠLun": 32077, "ĠабÑģолÑİÑĤ": 32078, "Ġdegli": 32079, "Ġvillagers": 32080, "Ġpreset": 32081, "zept": 32082, "uds": 32083, "Ġemit": 32084, "ä½łè¦ģ": 32085, "Ġëī": 32086, "ëĬĶì§Ģ": 32087, "нако": 32088, "Ġosób": 32089, "Ġ1969": 32090, "ĠÐIJÑĢ": 32091, "Ġmanchmal": 32092, "ĠBrock": 32093, "Ġmantra": 32094, "ĠWIL": 32095, "bach": 32096, "inä": 32097, "elas": 32098, "keln": 32099, "Ġdisciple": 32100, "Ġqualc": 32101, "Ġdehyd": 32102, "ìĿ´ëĿ¼ëĬĶ": 32103, "Af": 32104, "ìĦ±ìĿ´": 32105, "Ryan": 32106, "Ġpuppet": 32107, "ĠдÑĢÑĥгие": 32108, "Ġrud": 32109, "Ġpending": 32110, "Plus": 32111, "ĠìķĬìĿĦ": 32112, "Ġbá»ĭ": 32113, "ĠSega": 32114, "çe": 32115, "Ġprogrammer": 32116, "bli": 32117, "Ġunl": 32118, "Ġenslaved": 32119, "Ġsociété": 32120, "Äģh": 32121, "Ġinheritance": 32122, "ĠBangl": 32123, "ermaid": 32124, "Ġpractitioner": 32125, "ĠStalin": 32126, "ĠUser": 32127, "cible": 32128, "Ġcardiac": 32129, "ĠKoreans": 32130, "Ġdumped": 32131, "Ġ×Ķ×Ļ×Ķ": 32132, "áis": 32133, "Ġhydraulic": 32134, "oubtedly": 32135, "ĠPit": 32136, "Ġpicnic": 32137, "Ġbehöver": 32138, "ĠÑģмог": 32139, "Ġbraking": 32140, "é»ij": 32141, "utar": 32142, "ĠìĦ¸ë": 32143, "ubl": 32144, "Ġüz": 32145, "Ġmajesty": 32146, "Ġbers": 32147, "utable": 32148, "Ġhotter": 32149, "çħ§": 32150, "ÛĮÙĨ": 32151, "Ġbiases": 32152, "Ġsubjected": 32153, "Ġnaughty": 32154, "Ġcircus": 32155, "ãģĹãģĭ": 32156, "ĠImmedi": 32157, "ĠStefan": 32158, "ĠTriple": 32159, "enk": 32160, "Ġwit": 32161, "Ġrecycle": 32162, "emie": 32163, "dated": 32164, "Ġunload": 32165, "Ġpopula": 32166, "chin": 32167, "Ġyields": 32168, "Ġenglish": 32169, "ĠBonnie": 32170, "Ġspiders": 32171, "Ãģ": 32172, "Ġerosion": 32173, "éĥ¨åĪĨ": 32174, "ĠNICK": 32175, "иÑıÑħ": 32176, "Ġimpart": 32177, "Ġкни": 32178, "Ġresolutions": 32179, "Ġlithium": 32180, "Ġconvergence": 32181, "ĠTara": 32182, "Ġдве": 32183, "ths": 32184, "ĠCindy": 32185, "æĪijè¦ģ": 32186, "幫": 32187, "ĠDIE": 32188, "Ġassurance": 32189, "ĠопиÑģ": 32190, "Ġbuckets": 32191, "Ġcues": 32192, "ĠQuiet": 32193, "Ġsimilarity": 32194, "Ġfoundational": 32195, "ĠMinist": 32196, "滿": 32197, "Ġpian": 32198, "Ġcentr": 32199, "Ġnumb": 32200, "Ġmonks": 32201, "ujourd": 32202, "enzie": 32203, "Ġskateboard": 32204, "Ġdlatego": 32205, "ĠÑģоÑĤ": 32206, "ĠAE": 32207, "Ġmasterpiece": 32208, "ĠSolomon": 32209, "ĠReddit": 32210, "Ġriot": 32211, "abl": 32212, "ĠJazz": 32213, "Ġelectromagnetic": 32214, "Ġinsecure": 32215, "ĠCompet": 32216, "geries": 32217, "обод": 32218, "ł×ķ": 32219, "ðŁĴ": 32220, "Ġsenators": 32221, "ĠBrisbane": 32222, "ĠAlb": 32223, "uttering": 32224, "ĠAllow": 32225, "zero": 32226, "Ġpai": 32227, "ĠÐIJлекÑģ": 32228, "ĠDisplay": 32229, "ĠBlade": 32230, "ĠApps": 32231, "Ġpä": 32232, "ĠдеÑģÑı": 32233, "Ġquella": 32234, "ĠGao": 32235, "еннÑĭÑħ": 32236, "Ġspoilers": 32237, "Ġgallons": 32238, "ĠÙĦÙĬ": 32239, "ĠZion": 32240, "æľīä¸Ģ": 32241, "onie": 32242, "ragt": 32243, "ĠChand": 32244, "Ġë³ij": 32245, "Ġblunt": 32246, "Ġusu": 32247, "ĠKad": 32248, "rakt": 32249, "Ġcinematic": 32250, "Ġammunition": 32251, "rene": 32252, "Ġfourteen": 32253, "ĠCarn": 32254, "crit": 32255, "Ġtenure": 32256, "vu": 32257, "Ġprincipalmente": 32258, "Ġalleen": 32259, "éĢĻä¸Ģ": 32260, "Ġkomplett": 32261, "Ġdüny": 32262, "James": 32263, "Ġreceptor": 32264, "Ġoneself": 32265, "guru": 32266, "Ġmerchant": 32267, "liness": 32268, "Ġoverlooked": 32269, "Ġharmonic": 32270, "éķ¿": 32271, "ieso": 32272, "×ķ×ŀ": 32273, "colm": 32274, "ĠпÑĢоекÑĤ": 32275, "ĠAda": 32276, "اس": 32277, "Tim": 32278, "Ġrecurring": 32279, "Ġproceeds": 32280, "ĠParticularly": 32281, "ĠDownload": 32282, "etrical": 32283, "Ġmatrices": 32284, "Ġproyecto": 32285, "ancies": 32286, "ĠUhm": 32287, "Ġcaves": 32288, "Ġìĸ´ëł¤": 32289, "ĠLeaf": 32290, "ĠобÑĭÑĩ": 32291, "ĠìĿ´ìľł": 32292, "Europe": 32293, "ĠtÄħ": 32294, "Ġpuls": 32295, "Ġtakiego": 32296, "ÐĿе": 32297, "GU": 32298, "Ġfors": 32299, "Ïģγ": 32300, "Ġfotos": 32301, "Ġ))": 32302, "Ġ멤ë": 32303, "Ġaquilo": 32304, "ĠKurd": 32305, "ï¸ı": 32306, "ptic": 32307, "ĠDort": 32308, "Ġmisery": 32309, "auso": 32310, "åĬŁ": 32311, "chuckling": 32312, "ĠRidge": 32313, "ĠíĸĪìĬµëĭĪëĭ¤": 32314, "Ġ***": 32315, "客": 32316, "ĠHmmm": 32317, "Ġgeographic": 32318, "Ġanys": 32319, "Ġtalvez": 32320, "Ġskelet": 32321, "Ġsignatures": 32322, "Ġliters": 32323, "IJë©´": 32324, "ĠÑģвоего": 32325, "Ġskiing": 32326, "ĠÐľÐ¾Ñģ": 32327, "Ġadopting": 32328, "Ġhaft": 32329, "Ġsymmetric": 32330, "ĠLiqu": 32331, "Ġthyroid": 32332, "Ġmisin": 32333, "lude": 32334, "Ġhull": 32335, "ĠXD": 32336, "ĠGust": 32337, "zeich": 32338, "Ġvibrations": 32339, "Ġesemp": 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33211, "ĠпоÑģÑĤоÑıн": 33212, "ĠHood": 33213, "Huh": 33214, "Ġë¶ģ": 33215, "Ġhyung": 33216, "ĠMeeting": 33217, "Ġimporta": 33218, "Ġì°¾ìķĦ": 33219, "ĠVern": 33220, "Ġstripped": 33221, "Ġrefuses": 33222, "Ġqualifications": 33223, "opl": 33224, "ĢëıĦ": 33225, "ixÃŃ": 33226, "Ġdiab": 33227, "itime": 33228, "flows": 33229, "Ġinac": 33230, "ĠGong": 33231, "Ġmeaningless": 33232, "Ġcourageous": 33233, "Ġmicrobi": 33234, "azy": 33235, "hist": 33236, "Ġvolunteering": 33237, "VIE": 33238, "Ġviolated": 33239, "Ġsympathy": 33240, "ĠEdit": 33241, "好åĥı": 33242, "electric": 33243, "product": 33244, "Ġpandemia": 33245, "Ġgeometric": 33246, "ĠConvers": 33247, "gre": 33248, "Ġglut": 33249, "isted": 33250, "ĠاÙĦÙĥ": 33251, "ĠChain": 33252, "ĠPresent": 33253, "ĠYin": 33254, "ĠÑģог": 33255, "ĠVlog": 33256, "Ġìĸ´ë¨¸": 33257, "Ġdonn": 33258, "Ġhitch": 33259, "ucking": 33260, "ãģĬãģĦ": 33261, "wald": 33262, "risk": 33263, "Ġhari": 33264, "ĠKens": 33265, "ĠIdol": 33266, "Ġвнимание": 33267, "Ġtodd": 33268, 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33328, "หà¸Ļ": 33329, "Ġappli": 33330, "ா?": 33331, "üyorum": 33332, "ĠинÑĤеÑĢеÑģно": 33333, "Ġdisinfect": 33334, ">-": 33335, "Ġchampagne": 33336, "Ġkla": 33337, "opers": 33338, "Trans": 33339, "ĠDesert": 33340, "Ġcultivate": 33341, "ĠFucking": 33342, "idelity": 33343, "ĠÑĤан": 33344, "Ġincub": 33345, "Ġtemu": 33346, "Ġlearner": 33347, "founder": 33348, "ĠSyl": 33349, "ãĤĢ": 33350, "Ġfato": 33351, "zier": 33352, "ĠìĹĨìĿ´": 33353, "ĠìĪ¨": 33354, "Ġpsycho": 33355, "ĠÑĤелеÑĦ": 33356, "Ġregarde": 33357, "Ġrepresentations": 33358, "Ġlitigation": 33359, "Ġspann": 33360, "ults": 33361, "bior": 33362, "è¦ĭãģ¦": 33363, "ä¸įå¤ļ": 33364, "ĠSurvey": 33365, "ĠLEDs": 33366, "Ġträ": 33367, "Ġlên": 33368, "Ġantioxid": 33369, "еÑĢом": 33370, "Ġinduction": 33371, "Ġfooled": 33372, "ätzlich": 33373, "ĠговоÑĢÑıÑĤ": 33374, "ĠFact": 33375, "umbai": 33376, "Ġwiggle": 33377, "NOUN": 33378, "Ġdévelopp": 33379, "ĠClaro": 33380, "Ġì¸": 33381, "ë¬": 33382, "ãģªãĤĵãģł": 33383, "Ġaccumulate": 33384, "Ġmaintains": 33385, "ëĦ": 33386, "ĠFighter": 33387, "íĨł": 33388, "Ġmatin": 33389, "Ġcoupon": 33390, "Ġstunt": 33391, "Ġdebuted": 33392, "å¾ħãģ£ãģ¦": 33393, "Ġprag": 33394, "иваем": 33395, "73": 33396, "Ġexpres": 33397, "Ġìĺ¤ë¹ł": 33398, "ĠпеÑĢÑģон": 33399, "Ġcalculus": 33400, "Ġabrupt": 33401, "ĠInspector": 33402, "ourt": 33403, "æĸĻ": 33404, "źniej": 33405, "intense": 33406, "Ba": 33407, "Ġlounge": 33408, "Ġasthma": 33409, "ĠHiç": 33410, "ª»": 33411, "Ġeditorial": 33412, "Ġseize": 33413, "Ġkır": 33414, "Ġmouve": 33415, "Ġtierra": 33416, "Ġtestosterone": 33417, "Ġrh": 33418, "ĠKingston": 33419, "ELLE": 33420, "ĠRepresentative": 33421, "Ġ1974": 33422, "Ġiba": 33423, "Ts": 33424, "Ġsorta": 33425, "Ġ(?)": 33426, "ĠتÙĪ": 33427, "ĠëĤ´ëł¤": 33428, "Ġbekommt": 33429, "Ġspiritually": 33430, "Ġdistorted": 33431, "Mad": 33432, "Ġreim": 33433, "ánh": 33434, "ĠOttoman": 33435, "ĠRelig": 33436, "ĠEls": 33437, "Ġretained": 33438, "ĠLaughs": 33439, "æĢ»": 33440, "ĠSAS": 33441, "ĠколиÑĩеÑģÑĤво": 33442, "×ķתר": 33443, "Ġinnovate": 33444, "Ġkork": 33445, "ĠÑĢаÑģÑģказÑĭв": 33446, "ondere": 33447, "ivi": 33448, "aye": 33449, "ounty": 33450, "ĠполÑĥÑĩаеÑĤÑģÑı": 33451, "Ġbuns": 33452, "åħ«": 33453, "Ġyüzden": 33454, "Ġsurgeries": 33455, "Ø£ÙĨ": 33456, "Ġbankruptcy": 33457, "welt": 33458, "Ġsiamo": 33459, "Ġdarkest": 33460, "ĠHann": 33461, "gga": 33462, "Ġformas": 33463, "ĠDj": 33464, "named": 33465, "Ġshields": 33466, "ueller": 33467, "ĠFew": 33468, "Ġlace": 33469, "Ġfurious": 33470, "ĠYU": 33471, "Ġsocietal": 33472, "Ġjudgement": 33473, "ĠDos": 33474, "Ġjab": 33475, "laws": 33476, "Ġreinvent": 33477, "ĠKatherine": 33478, "ĠChoi": 33479, "adows": 33480, "Ġrans": 33481, "oden": 33482, "ĠMidwest": 33483, "nın": 33484, "Ġdeport": 33485, "ĠDip": 33486, "ç´ħ": 33487, "Ġatención": 33488, "ĠCourtney": 33489, "ividad": 33490, "ĠÚ©Ûģ": 33491, "Ġefficacy": 33492, "ĠBrooks": 33493, "Ġreferral": 33494, "ĠконÑĨ": 33495, "Ġmalicious": 33496, "Ġkir": 33497, "ĠGoddess": 33498, "Ġfunky": 33499, "Ġinterim": 33500, "ĠKörper": 33501, "Ġìĸ¼ë§": 33502, "kur": 33503, "Ġкли": 33504, "Ġtrucs": 33505, "gesetz": 33506, "Ġzug": 33507, "ĠGlück": 33508, "ĠMinute": 33509, "Ġprestigious": 33510, "Ġniez": 33511, "Ġconcentrations": 33512, "лаÑģÑĤи": 33513, "ĠSis": 33514, "ĠVitamin": 33515, "kov": 33516, "ĠPBS": 33517, "Ġнее": 33518, "Ġretailers": 33519, "Ġconventions": 33520, "ĠSamantha": 33521, "Ġproudly": 33522, "Jordan": 33523, "ĠJASON": 33524, "atk": 33525, "Ġtriste": 33526, "Ġstär": 33527, "Ġreiterate": 33528, "Ġposterior": 33529, "Ġ1973": 33530, "ĠPine": 33531, "ĠJuliet": 33532, "Ġpedir": 33533, "kil": 33534, "Ġoverlapping": 33535, "Ġexclude": 33536, "Ġeconóm": 33537, "Ġaccepts": 33538, "ĠSter": 33539, "決": 33540, "Ġìļ´ëıĻ": 33541, "estab": 33542, "Ġtug": 33543, "arg": 33544, "Ġlivro": 33545, "اص": 33546, "Ġseams": 33547, "Ġburaya": 33548, "Ġello": 33549, "ĠTM": 33550, "ĠPaw": 33551, "ĠIndex": 33552, "Exc": 33553, "Ġinspirational": 33554, "Ġdunk": 33555, "è°ģ": 33556, "akter": 33557, "Ġconditioner": 33558, "ĠSalut": 33559, "ÅĤec": 33560, "Ġìī½": 33561, "ĠÑĥзна": 33562, "ĠRomeo": 33563, "fruit": 33564, "ĠYO": 33565, "Ġchá»ī": 33566, "бÑĥ": 33567, "bons": 33568, "Ġreproductive": 33569, "Ġorada": 33570, "Ġíļ¨": 33571, "Ġtentar": 33572, "Ġmañana": 33573, "ãĤ¬": 33574, "Ġsolvent": 33575, "Jessica": 33576, "ĠLegal": 33577, "Ġtua": 33578, "Ġsic": 33579, "ĠEQ": 33580, "aukee": 33581, "ìĭľëĭ¤": 33582, "ĠÅŀu": 33583, "Ġadhere": 33584, "ĠTul": 33585, "Ġà®Ĩ": 33586, "Ġtextbooks": 33587, "ĠFifth": 33588, "Ġexperi": 33589, "Ġchic": 33590, "Ġheap": 33591, "inely": 33592, "atra": 33593, "Two": 33594, "Ġhelemaal": 33595, "Ġfren": 33596, "æݨ": 33597, "Ġbisher": 33598, "اش": 33599, "ĠìĦłìĥĿ": 33600, "ĠTages": 33601, "Ġsá»±": 33602, "Ġbullied": 33603, "ؤ": 33604, "Ġbenefited": 33605, "ĠPreviously": 33606, "ĠÑįÑĦÑĦ": 33607, "Ùį": 33608, "Ġsenate": 33609, "ĠMorm": 33610, "ijke": 33611, "ĠFlu": 33612, "Ġincorporating": 33613, "jack": 33614, "ĠпиÑĤ": 33615, "Ġimply": 33616, "Ġhacks": 33617, "ĠRICH": 33618, "ĠкваÑĢ": 33619, "ĠпÑĢекÑĢаÑģ": 33620, "Ġdependency": 33621, "Ġìļ©": 33622, "Ġì±ħ": 33623, "Ġwährend": 33624, "Ġsulla": 33625, "ĠPittsburgh": 33626, "Ġesempio": 33627, "¼ë¡ľ": 33628, "prot": 33629, "ĠRosen": 33630, "ĠIndependence": 33631, "Ġparsley": 33632, "iegen": 33633, "Ġhaw": 33634, "Ġaquell": 33635, "ĠCAP": 33636, "ĠÑĢабоÑĤаÑĤÑĮ": 33637, "ĠCliff": 33638, "ionar": 33639, "Ġsecuring": 33640, "æĪijåĢijçļĦ": 33641, "νε": 33642, "Ġutilis": 33643, "Ġcoule": 33644, "ĠPing": 33645, "Ġtrek": 33646, "Ġfak": 33647, "Ġenorme": 33648, "Ġìĭ«": 33649, "让": 33650, "Ġdoubling": 33651, "ĠнÑĢавиÑĤÑģÑı": 33652, "Ġhed": 33653, "hoven": 33654, "ĠStanding": 33655, "ĠmÃŃn": 33656, "ĠJimin": 33657, "Ġmonarch": 33658, "Ġcoke": 33659, "Ġmr": 33660, "Ġclic": 33661, "Ãį": 33662, "Ġimpeachment": 33663, "Ġdurability": 33664, "Ġvarios": 33665, "Ġcommercials": 33666, "Ġgreetings": 33667, "ĠRi": 33668, "ĠAppreci": 33669, "ìŀĪëĬĶ": 33670, "Ġrésult": 33671, "ért": 33672, "Ġsalute": 33673, "Ġpoderia": 33674, "Ġsunrise": 33675, "veck": 33676, "Ġreluctant": 33677, "Ġcommissioner": 33678, "念": 33679, "âte": 33680, "ĠKenny": 33681, "ĠSiri": 33682, "ãĥĥãĥĹ": 33683, "ĠëĬĺ": 33684, "ĠEE": 33685, "Ġunch": 33686, "кон": 33687, "ĠاÙĦØ¥": 33688, "Ġbelts": 33689, "Ġhass": 33690, "ĠмоÑı": 33691, "Ġdisplaced": 33692, "Ġabra": 33693, "ÎŃλ": 33694, "Ġscratches": 33695, "Ġcomet": 33696, "Ġauthorization": 33697, "ĠLLC": 33698, "Ġproduk": 33699, "Ġrehabilitation": 33700, "åŀ": 33701, "ÑĸÑĩ": 33702, "uding": 33703, "olit": 33704, "Ġ105": 33705, "Ġexpands": 33706, "Ġaltri": 33707, "ĠKomment": 33708, "Ġanf": 33709, "Pl": 33710, "ĠMana": 33711, "fed": 33712, "Ġbri": 33713, "Ġora": 33714, "Gs": 33715, "ĠGur": 33716, "uckland": 33717, "Ġjunction": 33718, "Ġironic": 33719, "ĠFeed": 33720, "Ġprakt": 33721, "ĠHammer": 33722, "ĮëıĦ": 33723, "ĠTracy": 33724, "çµ±": 33725, "ĠAside": 33726, "него": 33727, "ĠиÑģполÑĮзоваÑĤÑĮ": 33728, "Ġzaj": 33729, "Ġequitable": 33730, "Ġcurb": 33731, "ĠãģĵãĤĮ": 33732, "Ġderivatives": 33733, "Ġpuppies": 33734, "ĠKenneth": 33735, "ĠCompl": 33736, "igram": 33737, "ĠGarcia": 33738, ")\"": 33739, "ĠHarbor": 33740, "estial": 33741, "Ġä¾Ĩ": 33742, "Ġers": 33743, "æ¹": 33744, "Ġunwanted": 33745, "Ġbelang": 33746, "аго": 33747, "emb": 33748, "dos": 33749, "ĠìĻľë": 33750, "ĠBudget": 33751, "Ġbattling": 33752, "ØŃت": 33753, "kok": 33754, "наÑĩала": 33755, "Ġplag": 33756, "Ġcantidad": 33757, "Ġgrupos": 33758, "Ġplugins": 33759, "lerini": 33760, "ĠимееÑĤ": 33761, "Ġsozusagen": 33762, "olics": 33763, "Ġpueblo": 33764, "Ġreminis": 33765, "rän": 33766, "ĠMorrison": 33767, "Ġlinha": 33768, "Ġbreaths": 33769, "ĠTaste": 33770, "Ġenfrent": 33771, "ĠDocker": 33772, "Ġден": 33773, "Ġethnicity": 33774, "Ġwob": 33775, "Ġsuffers": 33776, "Ġtransitioning": 33777, "ĠRange": 33778, "ÄĻdzy": 33779, "ĠкаÑĤ": 33780, "Ġsyner": 33781, "Ġdonut": 33782, "Ġprobabilities": 33783, "ĠOmar": 33784, "Which": 33785, "uish": 33786, "isin": 33787, "Ġdemos": 33788, "ĠìłĢ기": 33789, "Ġëĺijê°Ļ": 33790, "Ġедин": 33791, "Ġcerve": 33792, "Ġjoka": 33793, "IAN": 33794, "Ġkilometer": 33795, "Ġhorizontally": 33796, "ĠBhag": 33797, "Ġ->": 33798, "ĠMonitor": 33799, "Ġknowledgeable": 33800, "Ġfav": 33801, "Ġpinned": 33802, "ĠeBay": 33803, "icker": 33804, "Ġìŀłê¹IJë§Į": 33805, "ĠXiaomi": 33806, "Ġcapit": 33807, "Ġnp": 33808, "Ġ1965": 33809, "hoe": 33810, "Ġnok": 33811, "ĠSage": 33812, "ĠнелÑĮзÑı": 33813, "ĠTow": 33814, "gam": 33815, "Ġdicen": 33816, "ĠSUBSCRIBE": 33817, "Ġreboot": 33818, "Ġpaj": 33819, "Ġë³´ìŬë": 33820, "Ġthicken": 33821, "ĠReality": 33822, "idän": 33823, "Na": 33824, "Ġê²ĥìĿĢ": 33825, "!!)": 33826, "Ġroutines": 33827, "Ġодного": 33828, "Ġexting": 33829, "Ġì¦Ŀ": 33830, "Ġsulfur": 33831, "Ġcarve": 33832, "Ġasteroid": 33833, "ĠWarrior": 33834, "Ġphotographers": 33835, "Ġpell": 33836, "Ġcrossover": 33837, "æĪijçŁ¥éģĵ": 33838, "Ġhacemos": 33839, "ĠNej": 33840, "Ġsettling": 33841, "Ġirm": 33842, "ĠBooks": 33843, "ientôt": 33844, "Ġespacio": 33845, "ĠScholars": 33846, "Ġdoomed": 33847, "ĠIRS": 33848, "wohl": 33849, "Ġsegue": 33850, "ĠëĪĦê°Ģ": 33851, "Ġpratic": 33852, "BT": 33853, "ĠConsidering": 33854, "ĠBuffalo": 33855, "Ġtrainings": 33856, "Ġgebru": 33857, "ĠGleich": 33858, "Ġpirates": 33859, "Ġenvelop": 33860, "Ġreopen": 33861, "imat": 33862, "Ġtee": 33863, "Ġsued": 33864, "feh": 33865, "Ġ×Ķק": 33866, "Ġdiets": 33867, "Ġjuntos": 33868, "asto": 33869, "Ġmisunderstood": 33870, "Ġruim": 33871, "Ġclassify": 33872, "ĠпÑĢодÑĥк": 33873, "Ġinse": 33874, "Ġillustrated": 33875, "Ġcorrosion": 33876, "Ġaccred": 33877, "ĠAuntie": 33878, "ĠпÑĢивеÑĤ": 33879, "ĠLIVE": 33880, "Ġrek": 33881, "Ġreceipt": 33882, "åĪ°åºķ": 33883, "ĠBarbie": 33884, "ĠSnake": 33885, "turn": 33886, "Jeff": 33887, "ãģĬãģĬ": 33888, "ķĦ": 33889, "VOICEOVER": 33890, "coll": 33891, "Ġrunners": 33892, "ìłľë": 33893, "osos": 33894, "moon": 33895, "Ġkeynote": 33896, "ĠInstit": 33897, "SPEAK": 33898, "Ġplugs": 33899, "Ġcurv": 33900, "ĠYuri": 33901, "ĠTheres": 33902, "ĠPs": 33903, "ĠμÏĢο": 33904, "Ġconverter": 33905, "Ġrefine": 33906, "Ġbadass": 33907, "Ġοι": 33908, "Ġregen": 33909, "azzi": 33910, "ÙĬÙģ": 33911, "Ġseized": 33912, "Ġiçer": 33913, "ilee": 33914, "Ġupstream": 33915, "Ġbuds": 33916, "Ġpim": 33917, "Ġíķĺ루": 33918, "Ġalluded": 33919, "Ġthemed": 33920, "Ġconsisting": 33921, "Ġbons": 33922, "unuz": 33923, "ĠпÑĢовод": 33924, "ĠLovely": 33925, "à¥ĭ": 33926, "Ġparach": 33927, "ĠStaats": 33928, "éļĬ": 33929, "Ġselective": 33930, "Ġfase": 33931, "ĠGeorget": 33932, "Ġcocaine": 33933, "Ġreproduction": 33934, "ĠLara": 33935, "ĠLD": 33936, "Ġgh": 33937, "Jon": 33938, "ĠlÃ¥": 33939, "ĠëijIJë": 33940, "Ġtyped": 33941, "ĠBana": 33942, "ëĵľë": 33943, "Ġsavory": 33944, "ĠZomb": 33945, "standen": 33946, "Ġpedestrian": 33947, "Ġdifférents": 33948, "Ġìĭ¸": 33949, "èī¯": 33950, "Ġcomplained": 33951, "ç¦ı": 33952, "ĠÐļÑĤо": 33953, "Ġ׾פ": 33954, "aliÅĽmy": 33955, "Ġmortar": 33956, "Ġverdict": 33957, "Ġsuficiente": 33958, "ĠMillion": 33959, "mittel": 33960, "inals": 33961, "ĠاÙĦØ®": 33962, "аÑİÑģÑĮ": 33963, "ĠmiÄĻdzy": 33964, "ĠOle": 33965, "Ġinvert": 33966, "czyÄĩ": 33967, "озможно": 33968, "starter": 33969, "Ġauditor": 33970, "ĠScout": 33971, "chien": 33972, "ĠSverige": 33973, "uffled": 33974, "Ġzehn": 33975, "ĠAuckland": 33976, "Ġargent": 33977, "Ġ1976": 33978, "ĠHoe": 33979, "Ġbothers": 33980, "Ġsocialist": 33981, "Ġpliers": 33982, "Ġemergen": 33983, "ĠXP": 33984, "еÑĢов": 33985, "More": 33986, "ĠLevi": 33987, "ĠAnders": 33988, "ibilidad": 33989, "ĠParents": 33990, "Ġinduced": 33991, "ìĸ´ì¤": 33992, "Ġbalances": 33993, "ĠвÑĭÑĪ": 33994, "Ġsubmarine": 33995, "Start": 33996, "Ġdries": 33997, "Ġvolver": 33998, "Ġticking": 33999, "cott": 34000, "Ġfaj": 34001, "prés": 34002, "ĠSabb": 34003, "ĠзаÑĩ": 34004, "ĠпокÑĥп": 34005, "Ġbaptized": 34006, "ĠBrilliant": 34007, "ĠÐijог": 34008, "Ġmots": 34009, "bits": 34010, "Ġlattice": 34011, "æĪijè·Łä½ł": 34012, "Ġcoriander": 34013, "Ġresidency": 34014, "ync": 34015, "Ġpierwszy": 34016, "ĠKnock": 34017, "ĠZap": 34018, "ĠÐķв": 34019, "견": 34020, "å°ıå¿ĥ": 34021, "Ġuneven": 34022, "ĠJas": 34023, "odor": 34024, "ç¿Ĵ": 34025, "74": 34026, "ĠSite": 34027, "Ġaconteceu": 34028, "ympt": 34029, "Ġtrilogy": 34030, "Ġlantern": 34031, "ĠZucker": 34032, "vari": 34033, "welling": 34034, "ĠPotato": 34035, "gomery": 34036, "Ġreacted": 34037, "ĠChron": 34038, "Ġjede": 34039, "beeld": 34040, "Ġtwent": 34041, "Ġlact": 34042, "æ¨Ĥ": 34043, "Ġrése": 34044, "Ġrelent": 34045, "Ġfurnace": 34046, "Ġwidget": 34047, "Ġearthquakes": 34048, "ĠAdjust": 34049, "ilit": 34050, "ĠØ£ÙĪ": 34051, "Ġhearings": 34052, "Ġdefendant": 34053, "irsiniz": 34054, "Ġbask": 34055, "cja": 34056, "ľ¨": 34057, "Ġrifles": 34058, "Ġinstal": 34059, "ĠForgive": 34060, "pical": 34061, "ĠÐŀÑĩенÑĮ": 34062, "Ġpetites": 34063, "Ġhp": 34064, "Ġrenowned": 34065, "ĠInn": 34066, "Ġ주ìĦ¸ìļĶ": 34067, "Ġemphasized": 34068, "éĹ®é¢ĺ": 34069, "ĠìŀĪì£ł": 34070, "Ġê²ĥìľ¼ë¡ľ": 34071, "ãĤĨ": 34072, "Åĵ": 34073, "gili": 34074, "Dave": 34075, "Ġexhausting": 34076, "ÅĤug": 34077, "Ġschema": 34078, "μά": 34079, "cycl": 34080, "Ġautant": 34081, "Ġparcel": 34082, "Ġmateria": 34083, "ĠBerry": 34084, "ĠÑģами": 34085, "Ġextracted": 34086, "ĠSaying": 34087, "ismatic": 34088, "ĠпопÑĢоб": 34089, "Ġneuron": 34090, "graph": 34091, "ľë©´": 34092, "Ġenclosure": 34093, "ĠJohann": 34094, "Ġaftermath": 34095, "ÑĤоб": 34096, "Ġuży": 34097, "Ġsamp": 34098, "360": 34099, "ĠMei": 34100, "Ġtaco": 34101, "Ġreceptors": 34102, "Ġpunches": 34103, "ĠHoje": 34104, "ĠÙĩÙĨا": 34105, "=\"#": 34106, "ĠAngular": 34107, "Ġmusique": 34108, "Ġrol": 34109, "Ġñ": 34110, "sterreich": 34111, "Ġclam": 34112, "ĠTreasury": 34113, "chemical": 34114, "Ġapar": 34115, "Ġappend": 34116, "Ġforbid": 34117, "ĠHamburg": 34118, "аков": 34119, "Ġê¸Ī": 34120, "ilda": 34121, "Ġpreparations": 34122, "ĠmogÄħ": 34123, "Ġcamino": 34124, "Eric": 34125, "ĠBlind": 34126, "èĪĩ": 34127, "å¹´çļĦ": 34128, "ĠDiscovery": 34129, "ì¸ł": 34130, "çĪ¶": 34131, "Ġinterpreter": 34132, "Ġbred": 34133, "ĠPsalm": 34134, "Ġdefended": 34135, "ìī¬": 34136, "ĠErfahr": 34137, "ĠPeach": 34138, "Ġmoons": 34139, "ĠOst": 34140, "Ġspécial": 34141, "Ġarriver": 34142, "ĠWis": 34143, "uci": 34144, "Ġrobotics": 34145, "IVE": 34146, "Ġsiege": 34147, "arla": 34148, "Ġseparates": 34149, "ĠTC": 34150, "íı°": 34151, "quisite": 34152, "Ġparentheses": 34153, "ике": 34154, "ç«Ļ": 34155, "Ġtrous": 34156, "建": 34157, "ĠÑģилÑĮ": 34158, "Ġbeers": 34159, "ĠплаÑĤ": 34160, "ãģĻãģĶãģĦ": 34161, "Ġsola": 34162, "Ġdès": 34163, "mingham": 34164, "ikte": 34165, "Ġoops": 34166, "Ġtwitch": 34167, "å°ĩ": 34168, "ÏĪ": 34169, "ĠShouldn": 34170, "uvre": 34171, "Ġleer": 34172, "criptions": 34173, "Ġeyeshadow": 34174, "ĠGuo": 34175, "ĠPowell": 34176, "Ġsupuesto": 34177, "Ġana": 34178, "rals": 34179, "ĠMontreal": 34180, "Ġsurfing": 34181, "ĠÐŁÐµÑĢв": 34182, "×ŀ×ķ": 34183, "Ġmilliseconds": 34184, "Ġsuburbs": 34185, "Ġplaneta": 34186, "ÑĥÑĪка": 34187, "hrlich": 34188, "ĠHY": 34189, "ĠسÛĴ": 34190, "ĠMM": 34191, "ĠEff": 34192, "åı¯æĦĽ": 34193, "ĠHS": 34194, "anson": 34195, "Ġì§ģìłij": 34196, "Ġsuo": 34197, "Ġdeploying": 34198, "Ġkunt": 34199, "tering": 34200, "Ġerect": 34201, "ìŀ¥ìĿ´": 34202, "ĠìĿĮìĭĿ": 34203, "Ġspecimen": 34204, "!...": 34205, "æĪij說": 34206, "Ġligne": 34207, "Ġkonst": 34208, "adequ": 34209, "Ġìĥģíĥľ": 34210, "Ġaccessed": 34211, "ĠPole": 34212, "kill": 34213, "Ġë²Ħë": 34214, "Ġauthenticity": 34215, "Ġappelle": 34216, "ulle": 34217, "Ġrevision": 34218, "Ġgoats": 34219, "гли": 34220, "Ġpau": 34221, "ĠRanger": 34222, "ĠImag": 34223, "author": 34224, "Ġeve": 34225, "ĠMessenger": 34226, "Ġnay": 34227, "Ġwholes": 34228, "ätte": 34229, "Ġonwards": 34230, "ĠDepois": 34231, "ĠíijľíĺĦ": 34232, "ĠSARS": 34233, "Ġwszystkich": 34234, "Ġdestru": 34235, "umbing": 34236, "Ġcompatibility": 34237, "Ġmisinformation": 34238, "odore": 34239, "ĠFavor": 34240, "eko": 34241, "ıĮ": 34242, "waukee": 34243, "ĠTeaching": 34244, "ĠKO": 34245, "Ġbetting": 34246, "Ġquests": 34247, "Ġvivre": 34248, "ĠмÑĥзÑĭ": 34249, "Ġsaga": 34250, "Ġswell": 34251, "Ġgehe": 34252, "æĢİ麼樣": 34253, "ĠоÑĢганиз": 34254, "Ġgide": 34255, "ĠGross": 34256, "Ġdalej": 34257, "Ġclaws": 34258, "á»Ļc": 34259, "Ġprejudice": 34260, "Ġinsign": 34261, "ihood": 34262, "Ġpled": 34263, "Ġdónde": 34264, "ĠPolitical": 34265, "Ġpremises": 34266, "undert": 34267, "عت": 34268, "onnen": 34269, "Ġespaço": 34270, "Ġfé": 34271, "ĠHarrison": 34272, "ĠCensus": 34273, "Ġcardio": 34274, "Ġdiy": 34275, "Ġmilieu": 34276, "Ġjournée": 34277, "ĠRelease": 34278, "NIE": 34279, "ĠMuk": 34280, "idée": 34281, "á»įi": 34282, "Ġiçinde": 34283, "ŀĻ": 34284, "Ġresonate": 34285, "Ġmoles": 34286, "ĠFlying": 34287, "ĠGloria": 34288, "ĠPastor": 34289, "ĠArena": 34290, "好ä¸į好": 34291, "NON": 34292, "олов": 34293, "ĠallÃŃ": 34294, "omat": 34295, "ìĸ´ëıĦ": 34296, "ĠcaracterÃŃst": 34297, "Ġdeclining": 34298, "ÑĸÑı": 34299, "anco": 34300, "ĠInform": 34301, "Ġbargain": 34302, "Ġbushes": 34303, "ĠNaturally": 34304, "Ġrechts": 34305, "ĠTensor": 34306, "ĠPatricia": 34307, "Ġprincipio": 34308, "ĠMumbai": 34309, "Ġwomb": 34310, "Ġnostra": 34311, "Ġdilemma": 34312, "Ġirgendwann": 34313, "Ġ1964": 34314, "ĠenergÃŃa": 34315, "ĠнаÑĢ": 34316, "Ġsegregation": 34317, "ĠAthlet": 34318, "Ġ»,": 34319, "Ġyeni": 34320, "ĠSeit": 34321, "Ġvenom": 34322, "Ġdakika": 34323, "ĠëıĮë": 34324, "ĠÃīl": 34325, "Ġfus": 34326, "ĠMog": 34327, "¦½ëĭĪëĭ¤": 34328, "Ġremar": 34329, "ĠTeddy": 34330, "Ġbreasts": 34331, "icans": 34332, "æĶ¶çľĭ": 34333, "kap": 34334, "ĠhÆ¡n": 34335, "ĠJP": 34336, "ãĥ³ãĤ¿": 34337, "Ġresurrect": 34338, "ĠìĿ¸ë": 34339, "herical": 34340, "Ġfotograf": 34341, "ĠJosé": 34342, "Ġlivelihood": 34343, "Ġbibli": 34344, "teri": 34345, "Ġvorstellen": 34346, "ĠAAA": 34347, "Ġassessing": 34348, "YA": 34349, "Ġsplend": 34350, "Ġexcav": 34351, "Ġbaptism": 34352, "yll": 34353, "wow": 34354, "Mac": 34355, "Ġplastics": 34356, "teokbokki": 34357, "Ġintéressant": 34358, "Ġcommanded": 34359, "Ġfamously": 34360, "ĠÐĺли": 34361, "ĠManuel": 34362, "Ġsouthwest": 34363, "Ġdeformation": 34364, "ÃŃculo": 34365, "ĠнаÑħодиÑĤÑģÑı": 34366, "ĠPatter": 34367, "degree": 34368, "ĠczÄĻsto": 34369, "\"-": 34370, "Ġìħĭ": 34371, "Ġmanger": 34372, "ĠTrustee": 34373, "Ģ리": 34374, "Ġpuntos": 34375, "ivable": 34376, "Ġvolatile": 34377, "ĠëĬIJ": 34378, "Ġinstability": 34379, "Ġciel": 34380, "ciÄħ": 34381, "Ġpurity": 34382, "ноÑģÑĤ": 34383, "Sil": 34384, "edar": 34385, "åĻ¨": 34386, "NOUNCER": 34387, "Ġspelled": 34388, "GER": 34389, "Ġsanctuary": 34390, "Ġaccelerating": 34391, "Ġscout": 34392, "ĠпÑĢев": 34393, "fahren": 34394, "ãģĵãģ¡ãĤī": 34395, "ĠëĤĺìĺ¨": 34396, "ĠpoczÄħt": 34397, "ĠMeu": 34398, "kaar": 34399, "³´ê³ł": 34400, "akra": 34401, "Down": 34402, "ĠÃĦr": 34403, "ĠElite": 34404, "Ġallons": 34405, "Ġmayonnaise": 34406, "ĠSustain": 34407, "prisingly": 34408, "Ġsupervis": 34409, "Ġê·¸ëłĩì£ł": 34410, "Ġunemployed": 34411, "Ġfreshly": 34412, "Ġ×ŀ×¢": 34413, "ĠDh": 34414, "Ġtackling": 34415, "Ġogr": 34416, "Ġì´Īë": 34417, "ãĤĪãĤį": 34418, "Ġloft": 34419, "arah": 34420, "ĠAirl": 34421, "ĠDir": 34422, "ĠÐľÐ¾Ð¶Ð½Ð¾": 34423, "Ġbooking": 34424, "ĠCRA": 34425, "Ġhttps": 34426, "Ġchoke": 34427, "Ġgown": 34428, "Ġnoite": 34429, "Ġzac": 34430, "istol": 34431, "Ġsecre": 34432, "Ġresembles": 34433, "Ġcuad": 34434, "ìĤ¬ê°Ģ": 34435, "show": 34436, "Ġblanc": 34437, "Ġagu": 34438, "ĠPrint": 34439, "asted": 34440, "ĠWeather": 34441, "ipl": 34442, "Ġobscure": 34443, "Ġconte": 34444, "oughs": 34445, ");": 34446, "ĠDame": 34447, "ä¸Ģ缴": 34448, "Ġclarification": 34449, "Ġintimacy": 34450, "Ġuphold": 34451, "ĠMirror": 34452, "Ġwagon": 34453, "xide": 34454, "Ġclog": 34455, "apper": 34456, "ĠImmediately": 34457, "úde": 34458, "Ġtouchdown": 34459, "Ġrooft": 34460, "аÑĪа": 34461, "Ġçıkt": 34462, "Ġlaisser": 34463, "ĠUnreal": 34464, "ensitive": 34465, "Ġ123": 34466, "Ġplaster": 34467, "Ġducks": 34468, "Ġetme": 34469, "Ġbishop": 34470, "brevi": 34471, "Ġbic": 34472, "ä¸ĭåİ»": 34473, "Ġruntime": 34474, "Ġambitions": 34475, "маÑĤ": 34476, "ĠWein": 34477, "ĠMari": 34478, "ĠíĬ¸ë": 34479, "Ġresolver": 34480, "ĠngÃły": 34481, "ĠRise": 34482, "ãĤĪãģĨãģ«": 34483, "ĠCrus": 34484, "Ġmerchandise": 34485, "Ġeli": 34486, "Ġstatewide": 34487, "Ġowl": 34488, "éģł": 34489, "æĶ¹": 34490, "Ġtwisting": 34491, "Ġcontaminated": 34492, "ĠCommerce": 34493, "hythm": 34494, "ĠÃĪ": 34495, "Ġìĭ¤ë": 34496, "Ġmusste": 34497, "uir": 34498, "Ġsums": 34499, "ĠSomewhere": 34500, "ãĥİ": 34501, "Ġkami": 34502, "Ġaired": 34503, "ĠANDREW": 34504, "Ġêº": 34505, "Ġviendo": 34506, "Ġantibody": 34507, "Ġabsolument": 34508, "Ġprotesters": 34509, "ĠQuébec": 34510, "stadt": 34511, "Shaun": 34512, "Ġchambers": 34513, "ĠWear": 34514, "ĠEffects": 34515, "Ġhazards": 34516, "Ġnei": 34517, "Ġcorazón": 34518, "Ġá¼": 34519, "ĠSG": 34520, "Ķ©": 34521, "ĠìĹŃìĭľ": 34522, "Ġcomfy": 34523, "ĠCody": 34524, "Ġpensando": 34525, "Ġganska": 34526, "ĠAcross": 34527, "öllig": 34528, "abyte": 34529, "Ġwedge": 34530, "Ġkalian": 34531, "Ġsigue": 34532, "endes": 34533, "ĠGroÃŁ": 34534, "Ġutiliser": 34535, "Ġflown": 34536, "аниÑİ": 34537, "Ġlevar": 34538, "restrial": 34539, "Ġillustrations": 34540, "Ġaslında": 34541, "BLEEP": 34542, "ĠдоÑģÑĤ": 34543, "Ġturret": 34544, "Ġsuitcase": 34545, "ziÄĻki": 34546, "Ġsketches": 34547, "Ġacred": 34548, "ĠRei": 34549, "Ġtsun": 34550, "ĠSag": 34551, "Ġthirds": 34552, "ĠKIRBY": 34553, "rai": 34554, "Ġhumanos": 34555, "Ġrecommends": 34556, "Ġextraordinarily": 34557, "Ġcommencement": 34558, "KN": 34559, "opez": 34560, "Ġ×ijש": 34561, "Ġlethal": 34562, "ĠEstamos": 34563, "Ġinspector": 34564, "ĠSeok": 34565, "eun": 34566, "Ġoffshore": 34567, "Ġgettin": 34568, "years": 34569, "ĠSilence": 34570, "ĠNatur": 34571, "upun": 34572, "Ġtrzy": 34573, "Ġnoget": 34574, "Ġhamburger": 34575, "ĠPraise": 34576, "énd": 34577, "Ġ1971": 34578, "ylie": 34579, "krit": 34580, "ĠìĥĿê°ģìĿ´": 34581, "çļ®": 34582, "Ġmomentos": 34583, "Ġesté": 34584, "Ġdissemin": 34585, "Ġgigs": 34586, "Ġdesaf": 34587, "Ġavis": 34588, "ĠZoo": 34589, "ĠìķĬìĿĢ": 34590, "häng": 34591, "åı¥": 34592, "hake": 34593, "ĠBism": 34594, "Ġrethink": 34595, "ĠMalcolm": 34596, "Ġidentifies": 34597, "lower": 34598, "ixel": 34599, "ĠtvÃ¥": 34600, "ked": 34601, "ierz": 34602, "Ġöffentlich": 34603, "Ġproclaim": 34604, "soon": 34605, "lol": 34606, "Ġloi": 34607, "Ġbitten": 34608, "rollo": 34609, "Ġsermon": 34610, "Ġesqu": 34611, "Ġjackets": 34612, "Ġgráfic": 34613, "ĠпоказÑĭв": 34614, "Ġcabeza": 34615, "chodzi": 34616, "Ġpelvis": 34617, "Ġnostalgia": 34618, "Ġbrew": 34619, "Ġshortcuts": 34620, "ĠAdemás": 34621, "Ġsuperficial": 34622, "åħ©åĢĭ": 34623, "Ġboca": 34624, "ĠæĪijæĺ¯": 34625, "imentos": 34626, "åĽłä¸º": 34627, "Ġsprouts": 34628, "é£Ľ": 34629, "ĠJonas": 34630, "ĠFlorence": 34631, "static": 34632, "daughter": 34633, "*)": 34634, "ÅĤby": 34635, "fashion": 34636, "ĠGinger": 34637, "Ġ매ë": 34638, "Ġhustle": 34639, "utos": 34640, "ĠÑĤÑıж": 34641, "ĠLös": 34642, "ש×Ļ×Ŀ": 34643, "anych": 34644, "tuber": 34645, "Ġtidy": 34646, "Ġfrontal": 34647, "Ġwhiskey": 34648, "Ġhumid": 34649, "ĠÎŁ": 34650, "Ġridge": 34651, "Ġmarin": 34652, "Ġbientôt": 34653, "ĠCarrie": 34654, "chw": 34655, "Ġtahun": 34656, "ĠErgeb": 34657, "FR": 34658, "Ġìłķë¶Ģ": 34659, "ĠSoldier": 34660, "Ġenlightenment": 34661, "Ġexamining": 34662, "ĠNotre": 34663, "Ġeram": 34664, "ĠSunny": 34665, "Ġlayered": 34666, "ĠDazu": 34667, "rades": 34668, "好åIJĥ": 34669, "ĠнаÑĪей": 34670, "Ġtimber": 34671, "Ġmanners": 34672, "ĠBirmingham": 34673, "Ġminiature": 34674, "ometers": 34675, "Ġfiller": 34676, "ĠRip": 34677, "ĠKomb": 34678, "owner": 34679, "ì¿": 34680, "idian": 34681, "Ġdemás": 34682, "ĠÙĪت": 34683, "Ġprecautions": 34684, "Ġgoverno": 34685, "zelf": 34686, "ĠComplete": 34687, "å¸ĥ": 34688, "ĠPhantom": 34689, "ãģ¾ãģļ": 34690, "Ġнез": 34691, "ĠкаÑĢÑĤ": 34692, "ĠAntwort": 34693, "ĠPfizer": 34694, "ĠFranco": 34695, "ĠwÅĤ": 34696, "Ġfrig": 34697, "esper": 34698, "Ġkale": 34699, "Ġfilmmaker": 34700, "Ġkurt": 34701, "Ġinvalid": 34702, "å±Ģ": 34703, "arella": 34704, "Äĥng": 34705, "ramento": 34706, "Ġnutritional": 34707, "Ġdictators": 34708, "Ġafin": 34709, "Ġfuzzy": 34710, "ĠGina": 34711, "ót": 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36666, "à±": 36667, "Ġstumbled": 36668, "Ġaider": 36669, "Ġsaben": 36670, "xter": 36671, "ĠCities": 36672, "ĠTürk": 36673, "ëĭ¥": 36674, "chine": 36675, "Ġtopping": 36676, "Ġpoisoned": 36677, "ĠRomania": 36678, "×ĵ×Ļ": 36679, "Ģë¡ľ": 36680, "ĠпоÑĢÑıд": 36681, "Ġchirping": 36682, "ĠìĻĦë": 36683, "×ij×¢": 36684, "Ġcuanto": 36685, "Ġdonating": 36686, "ĠRegent": 36687, "ĠBeruf": 36688, "Ġdistracting": 36689, "Ġstamina": 36690, "ĠDarren": 36691, "Ġì¶ķ": 36692, "lists": 36693, "dal": 36694, "chuss": 36695, "Ġeconomist": 36696, "ãģĪãĥ¼": 36697, "orgt": 36698, "Ġistiyorum": 36699, "è¿Ľ": 36700, "ĠSurprise": 36701, "ĠHao": 36702, "Ġìµľê³ł": 36703, "ĠGW": 36704, "ĠInner": 36705, "Ġquieren": 36706, "Ġminded": 36707, "Ġsupercomputer": 36708, "Ġdiagrams": 36709, "íĬľë": 36710, "ê²łìĸ´": 36711, "ĠобÑĬÑıÑģ": 36712, "Ġestaban": 36713, "Ġdestroys": 36714, "ĠBreaking": 36715, "ĠkarÄ±ÅŁ": 36716, "Ġrebuilding": 36717, "ľëĮĢ": 36718, "ливо": 36719, "ĠSauce": 36720, "ĠFusion": 36721, "×ķ×ŀ×": 36722, 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36781, "òng": 36782, "çŁ³": 36783, "«": 36784, "ĠClaudia": 36785, "ĠCharacter": 36786, "ниÑĨ": 36787, "coat": 36788, "Ġprogressed": 36789, "ĠFergus": 36790, "Ġìĺ¤ëĬ": 36791, "Ġoat": 36792, "ordable": 36793, "ĠLey": 36794, "ĠHeraus": 36795, "Ġresultados": 36796, "ĠKayla": 36797, "Ġriff": 36798, "Ġchegou": 36799, "Ġxi": 36800, "Ġspacious": 36801, "Ġrecognised": 36802, "Ġech": 36803, "ĠTie": 36804, "Ġlauncher": 36805, "Jim": 36806, "Ġsuppression": 36807, "ĠImpossible": 36808, "Ġguitars": 36809, "ĠFourier": 36810, "иÑĩеÑģкий": 36811, "ĠTherap": 36812, "ĠKaf": 36813, "centered": 36814, "ĠÑģооÑĤвеÑĤ": 36815, "Ġklim": 36816, "Ġcarbohydrates": 36817, "ignant": 36818, "ĠAstron": 36819, "Ġemple": 36820, "Ġdrastic": 36821, "ĠмиÑĢе": 36822, "вин": 36823, "uw": 36824, "Ġprettier": 36825, "Ġdonuts": 36826, "ĠAthena": 36827, "Ġdissert": 36828, "Ġplante": 36829, "Ġuranium": 36830, "ìĿĮë": 36831, "aré": 36832, "Ġrzecz": 36833, "Ġdisplaying": 36834, "æĪ²": 36835, "Ġsarc": 36836, "rão": 36837, 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"Ġforgiving": 37701, "Ġconcurrent": 37702, "ĠVu": 37703, "ĠíĬ¹íŀĪ": 37704, "ĠBROWN": 37705, "ounded": 37706, "\";": 37707, "Ġtremb": 37708, "Ġtiet": 37709, "ĠÑĢежим": 37710, "Ġnutshell": 37711, "елиÑĩ": 37712, "Ġlosers": 37713, "ricting": 37714, "Ġredeem": 37715, "defined": 37716, "Nice": 37717, "Ġbroadband": 37718, "KO": 37719, "Ġteasing": 37720, "Ġpartisan": 37721, "ıma": 37722, "Ġìŀ¬ë¯¸": 37723, "ĠJourney": 37724, "Ġslopes": 37725, "uning": 37726, "grunts": 37727, "Ġtäll": 37728, "Ġuncovered": 37729, "ĠmyÅĽlÄĻ": 37730, "ĠEsther": 37731, "äºİ": 37732, "ĠHealthy": 37733, "Ġë°ij": 37734, "rée": 37735, "Ġpolarization": 37736, "Ġflav": 37737, "Ġcambiar": 37738, "Ġyr": 37739, "ĠRanch": 37740, "Ġsplits": 37741, "Ġtrouvé": 37742, "åľĭ家": 37743, "Ġrecorder": 37744, "Ġdépart": 37745, "ÙĪب": 37746, "ĠKry": 37747, "Ġinteressant": 37748, "Ġederim": 37749, "ÅĽwiad": 37750, "ilateral": 37751, "wright": 37752, "Ġpourra": 37753, "êter": 37754, "Ġcamel": 37755, "áŀ": 37756, "Ġrapidement": 37757, "Ġmej": 37758, "Ġstiffness": 37759, "ADAS": 37760, "Ġdiffers": 37761, "Ġalot": 37762, "ĠSig": 37763, "ÑıÑĤелÑĮ": 37764, "Ġabstraction": 37765, "åľĺ": 37766, "Ġkeiner": 37767, "grupp": 37768, "ĠSherlock": 37769, "íĺĶ": 37770, "Ġcite": 37771, "Ġoverflow": 37772, "Ġtại": 37773, "úcar": 37774, "bula": 37775, "Ġconjunto": 37776, "ĠCI": 37777, "Ġmoderator": 37778, "Ġindirectly": 37779, "Ġalleine": 37780, "âĤ": 37781, "ÑĪиб": 37782, "Ġбаб": 37783, "Ġdanach": 37784, "Ġ1939": 37785, "Ġpromet": 37786, "Ġdestinations": 37787, "ĠIllust": 37788, "ικÏĮ": 37789, "Ġsabes": 37790, "Ġheh": 37791, "ĠGesetzent": 37792, "ĠMiz": 37793, "енко": 37794, "ĠMys": 37795, "Ь": 37796, "ĠJudaism": 37797, "Ġmustache": 37798, "Ġstimmt": 37799, "ĠGaza": 37800, "Ġvolte": 37801, "Ġnuo": 37802, "Ġmón": 37803, "ĠComput": 37804, "ูà¹Ī": 37805, "ĠRadi": 37806, "Ġexceptionally": 37807, "Ġassumes": 37808, "éĸĭå¿ĥ": 37809, "ãģĪãģ°": 37810, "inform": 37811, "Ġshrine": 37812, "æĵĬ": 37813, "Ġimplication": 37814, "ĠFitz": 37815, "æ²ĴéĹľä¿Ĥ": 37816, "!.": 37817, "Ġlt": 37818, "Ġalloy": 37819, "Ġethic": 37820, "Ġmonastery": 37821, "ìĭľì£ł": 37822, "icação": 37823, "Ġcoordinating": 37824, "ĠMoto": 37825, "Ġoverlook": 37826, "Ġchois": 37827, "Ġantibiotic": 37828, "ĠMinne": 37829, "ĠBJ": 37830, "ĠApa": 37831, "orian": 37832, "Ġspilled": 37833, "Jam": 37834, "Ġhusbands": 37835, "Ġcreations": 37836, "Ġañ": 37837, "üssel": 37838, "ĠìĿ´ìļ©": 37839, "Ġanalyse": 37840, "rose": 37841, "Ġpunched": 37842, "Ġpresque": 37843, "Ġastronomy": 37844, "Ġschwierig": 37845, "ĠEbola": 37846, "Ġcis": 37847, "Ġacet": 37848, "ĠFX": 37849, "endre": 37850, "ĠìĿĮìķħ": 37851, "Ġwebpage": 37852, "Ġfreaked": 37853, "Ġlatte": 37854, "Ġì¿ł": 37855, "Ġ머ë": 37856, "Never": 37857, "Gra": 37858, "íĻĶ를": 37859, "eyed": 37860, "Ġë°ľëĿ¼": 37861, "Ġespera": 37862, "Ġaparece": 37863, "ração": 37864, "Ġdisruptive": 37865, "ĠJoint": 37866, "urous": 37867, "reas": 37868, "ĠquerÃŃa": 37869, "Ġdistributions": 37870, "Ġexponent": 37871, "ì¹ĺ를": 37872, "Ġdl": 37873, "zhou": 37874, "ĠHearing": 37875, "å·®ä¸įå¤ļ": 37876, "ĠCraw": 37877, "Ġfloats": 37878, "ounced": 37879, "Lab": 37880, "World": 37881, "Ġburdens": 37882, "Ġauthoritarian": 37883, "ĠBolt": 37884, "ĠоднÑĥ": 37885, "Ġpigeon": 37886, "Ġdistractions": 37887, "ĠHerausforder": 37888, "Ġzest": 37889, "esc": 37890, "Ġshakes": 37891, "atas": 37892, "ĠÙħØ´": 37893, "holes": 37894, "Ġthinkers": 37895, "alta": 37896, "Ġarche": 37897, "ĠSuk": 37898, "anha": 37899, "Ġtempting": 37900, "Ġyoutuber": 37901, "Ġvì": 37902, "ĠdziaÅĤa": 37903, "ĠVatican": 37904, "Park": 37905, "Ġsupers": 37906, "ĠNikki": 37907, "ëĬIJë": 37908, "orang": 37909, "ramient": 37910, "鬼": 37911, "Ġê°ĸê³ł": 37912, "Ġdesserts": 37913, "Ġavere": 37914, "ĠGregory": 37915, "Ġëĵ¤ìĸ´ìĺ": 37916, "Ġcosting": 37917, "ĠClinic": 37918, "Ġrebels": 37919, "ĠMob": 37920, "Ġbunlar": 37921, "ĠYours": 37922, "ertime": 37923, "Ġretali": 37924, "mara": 37925, "atus": 37926, "alles": 37927, "ĠдÑĢ": 37928, "ĠдиÑģ": 37929, "Ġdiscounts": 37930, "ĠGUY": 37931, "Ġкакое": 37932, "ĠExperiment": 37933, "rement": 37934, "ĠXiang": 37935, "Ġbate": 37936, "WE": 37937, "Ġspecialize": 37938, "Ġdeity": 37939, "ĠLoki": 37940, "mag": 37941, "ĠNit": 37942, "West": 37943, "Ġmaternal": 37944, "Ġquis": 37945, "åŁºæľ¬": 37946, "broken": 37947, "Ġlasers": 37948, "Ġhakk": 37949, "ĠAngels": 37950, "Ġmastery": 37951, "antis": 37952, "Tiffany": 37953, "eee": 37954, "çij": 37955, "orem": 37956, "Ġinacc": 37957, "Ġjurisdictions": 37958, "ĠKardash": 37959, "æľº": 37960, "Il": 37961, "ĠSinn": 37962, "åĭķçĶ»": 37963, "Ġathletics": 37964, "cÄĻ": 37965, "Ġloosely": 37966, "Ġdieta": 37967, "Ag": 37968, "Ġ??": 37969, "ĠëĮĢíijľ": 37970, "Ġsuperv": 37971, "Ġnutrit": 37972, "Ġdrifting": 37973, "ĠìĦłìĥĿëĭĺ": 37974, "ĠпонÑıл": 37975, "ĠVictory": 37976, "ÙĦØ©": 37977, "×ķ׳×Ķ": 37978, "ĠпиÑĪ": 37979, "Ġshaved": 37980, "Ġmesure": 37981, "onden": 37982, "Ùĥر": 37983, "Ġexile": 37984, "ĠDesde": 37985, "ĠPinterest": 37986, "Ġattachments": 37987, "Ġhombres": 37988, "Ġfines": 37989, "ĠìĦ¸ìĥģ": 37990, "Ġsleeps": 37991, "ĠTaco": 37992, "ĠIRA": 37993, "rios": 37994, "Ġoll": 37995, "etes": 37996, "Ġunut": 37997, "fashioned": 37998, "Ġtreball": 37999, "ĠNearly": 38000, "ĠÑĢеалÑĮно": 38001, "Ġchil": 38002, "éĢ±": 38003, "ÄŁa": 38004, "ĠMEL": 38005, "roscop": 38006, "ĠCG": 38007, "Ġvenge": 38008, "Ġdishwasher": 38009, "algic": 38010, "Ġmodifier": 38011, "Ġembassy": 38012, "timer": 38013, "emics": 38014, "Ġintricate": 38015, "Ġevet": 38016, "ĠëĮĢë°ķ": 38017, "Ġisot": 38018, "ĠнаÑĥÑĩ": 38019, "ĠQuiz": 38020, "reso": 38021, "δÏİ": 38022, "Ġyelled": 38023, "Ġfeder": 38024, "ELLER": 38025, "Ġexceeded": 38026, "onas": 38027, "icano": 38028, "ĠживоÑĤ": 38029, "ĠMao": 38030, "ĠKazuto": 38031, "Ġãħĭãħĭãħĭãħĭ": 38032, "Ġfrontline": 38033, "ĠHungarian": 38034, "Ġüberall": 38035, "awat": 38036, "Ġgrips": 38037, "ições": 38038, "arnya": 38039, "ĠÍ¡": 38040, "Ġseid": 38041, "Ġanak": 38042, "Ġacabou": 38043, "íķij": 38044, "Ġnotorious": 38045, "ĠGodzilla": 38046, "Ġovercoming": 38047, "ĠPend": 38048, "Ġolabilir": 38049, "ülme": 38050, "Ġerhalten": 38051, "ãĤīãģĦ": 38052, "ê·¹": 38053, "ĠMeter": 38054, "Ġstaan": 38055, "Ol": 38056, "Ġchats": 38057, "ĠBuenos": 38058, "ÃŃve": 38059, "aluable": 38060, "Ġstrategically": 38061, "Ġcomprised": 38062, "ĠпеÑĢÑģонаж": 38063, "Ġwann": 38064, "ĠCen": 38065, "ниÑĤе": 38066, "Łģ": 38067, "ĠÑĤобой": 38068, "iad": 38069, "ĠkardeÅŁim": 38070, "ĠCongressman": 38071, "reaming": 38072, "homme": 38073, "Ġcommunaut": 38074, "Ġalcoholic": 38075, "Ġpickled": 38076, "Ġacord": 38077, "position": 38078, "egól": 38079, "Ġtroubling": 38080, "ĠMarcheg": 38081, "Ġzumindest": 38082, "Ġseamlessly": 38083, "Ġolun": 38084, "ĠTVs": 38085, "ĠпÑĢакÑĤиÑĩеÑģки": 38086, "Ġbackend": 38087, "ãģĵãĤĵãģ«ãģ¡ãģ¯": 38088, "idable": 38089, "Ġgadget": 38090, "Ġfaço": 38091, "ĠMarchegiani": 38092, "Ġë°¤": 38093, "Ġaccidental": 38094, "ĠLP": 38095, "Ġeldest": 38096, "ĠAdmiral": 38097, "ĠnÄĥm": 38098, "lever": 38099, "Ġpastel": 38100, "Ġfondo": 38101, "Connie": 38102, "Ġtercer": 38103, "Ġpact": 38104, "ĠMonte": 38105, "Ġmeats": 38106, "ĠSMS": 38107, "ĠAustralians": 38108, "ç¼": 38109, "Rhett": 38110, "Ġexactement": 38111, "Ġë¹¼": 38112, "ĠMOD": 38113, "ç¡": 38114, "ĠRapt": 38115, "ĠNoch": 38116, "Ġabort": 38117, "ĠNaval": 38118, "ĠFuji": 38119, "INTER": 38120, "ĠновÑĭй": 38121, "Ġmiejsce": 38122, "ĠICU": 38123, "ĠGraduate": 38124, "ĠGlen": 38125, "ardi": 38126, "ĠÈĺ": 38127, "Ġsolder": 38128, "Ġprofessions": 38129, "Ġorthog": 38130, "omn": 38131, "introdu": 38132, "ĠDenise": 38133, "ìŀIJ를": 38134, "Ġcorrespondence": 38135, "AMA": 38136, "Ġinflict": 38137, "Ġfand": 38138, "ĠGü": 38139, "ĠÑĩеÑĤ": 38140, "Ġtraced": 38141, "Ġpatents": 38142, "Ġambush": 38143, "Ġlotta": 38144, "ffer": 38145, "ĠWagner": 38146, "Ġimperson": 38147, "Ġextrêmement": 38148, "ÙĤت": 38149, "conduct": 38150, "Att": 38151, "ĠMueller": 38152, "ĠAlicia": 38153, "Ġcyc": 38154, "Ġhacker": 38155, "Ġtys": 38156, "Ġhail": 38157, "ĠзаÑıв": 38158, "Ġpasso": 38159, "Ġì¶Ķê°Ģ": 38160, "ĠÎĪ": 38161, "Ġpackaged": 38162, "ĠCynthia": 38163, "heet": 38164, "ä¸ŃåĽ½": 38165, "ĠNissan": 38166, "ĠQuesto": 38167, "é¨": 38168, "did": 38169, "Ġμια": 38170, "ĠEllis": 38171, "ĠAnalysis": 38172, "cemos": 38173, "Ġaseg": 38174, "ĠMyster": 38175, "ĠCao": 38176, "Ġtuv": 38177, "ĠIndustry": 38178, "ì£¼ê³ł": 38179, "otal": 38180, "Ġpequeño": 38181, "bras": 38182, "Ġcomprehend": 38183, "ĠSimpson": 38184, "ÑģÑĤвие": 38185, "ocracy": 38186, "иÑĩеÑģки": 38187, "ĠMush": 38188, "ĠLaurie": 38189, "Ġtriangular": 38190, "ĠPresents": 38191, "ĠKunden": 38192, "ç´¹": 38193, "æѦ": 38194, "ĠIss": 38195, "ĠDeck": 38196, "á»ĥn": 38197, "ĠDarkness": 38198, "Ġinflammatory": 38199, "eremiah": 38200, "Ġwarmed": 38201, "veyard": 38202, "ĠMemory": 38203, "etty": 38204, "Ġtaxpayers": 38205, "à¸ĵ": 38206, "Ø¡": 38207, "Ġpractise": 38208, "ëĭ¬ë": 38209, "Ġdrilled": 38210, "mÃ¼ÅŁ": 38211, "logo": 38212, "ĠFach": 38213, "¤ë¡ľ": 38214, "Ġübrigens": 38215, "Ġkonnten": 38216, "Ġnormalmente": 38217, "Ġargues": 38218, "ilingual": 38219, "°ë¥¼": 38220, "egal": 38221, "Ġtravaill": 38222, "ovy": 38223, "аÑĤо": 38224, "Ġruth": 38225, "ĠLights": 38226, "Ġconsisted": 38227, "×ijר×Ļ×Ŀ": 38228, "Ġstereotype": 38229, "Ġpayer": 38230, "ĠRee": 38231, "ĠAirbnb": 38232, "Ġdrowned": 38233, "ĠZoe": 38234, "Ġcanopy": 38235, "Ġbarr": 38236, "ĠноÑĩ": 38237, "Ġpagan": 38238, "Ġjars": 38239, "Ġrê": 38240, "erver": 38241, "æĪ¿": 38242, "ieben": 38243, "Ġespect": 38244, "ĠFi": 38245, "Ġunwilling": 38246, "Ġtechnician": 38247, "ặt": 38248, "member": 38249, "ĠCanal": 38250, "سÙħ": 38251, "Ġlieber": 38252, "Ġinference": 38253, "Ġhonoring": 38254, "åijµ": 38255, "ĠCampaign": 38256, "Ġlineage": 38257, "ĠStress": 38258, "Ġvictories": 38259, "Ġdeja": 38260, "×£": 38261, "êtes": 38262, "blick": 38263, "Ġменее": 38264, "oths": 38265, "ĠCouple": 38266, "Jason": 38267, "ĠNicolas": 38268, "екÑģ": 38269, "lib": 38270, "Ġherramient": 38271, "Ġ×IJ×ķ×ŀר": 38272, "Ġвидим": 38273, "millimeter": 38274, "Ġsilhouette": 38275, "Ġdriveway": 38276, "Ġcherish": 38277, "ãħłãħł": 38278, "Ġransom": 38279, "Ġinterdisciplinary": 38280, "ĠPortal": 38281, "Ġtrag": 38282, "thood": 38283, "Ġtedious": 38284, "Ġglossy": 38285, "Ġprépar": 38286, "ĠCay": 38287, "ĠTook": 38288, "ĠBottom": 38289, "Ġzig": 38290, "å«": 38291, "åį±": 38292, "represented": 38293, "à¹Ģลย": 38294, "Ġdesarrollo": 38295, "ìĦľë": 38296, "Ġviscos": 38297, "Ġmilligram": 38298, "ĠGund": 38299, "Ġferment": 38300, "drum": 38301, "Ġdrawers": 38302, "Laugh": 38303, "Ġpelos": 38304, "Ġpavement": 38305, "Ġmemoir": 38306, "avait": 38307, "Ġ2050": 38308, "¤ë¥¼": 38309, "Ġrazón": 38310, "Ġflourish": 38311, "Ġstern": 38312, "ä¸Ī": 38313, "ĠChung": 38314, "Ġserpent": 38315, "ĠGentlemen": 38316, "羣çļĦå¾Ī": 38317, "kook": 38318, "Ġlut": 38319, "importe": 38320, "parent": 38321, "Ġwsz": 38322, "Ġscree": 38323, "ĠMitarbeiter": 38324, "å·´": 38325, "mut": 38326, "Ġìĸĺ기를": 38327, "Ġsemble": 38328, "ĠOW": 38329, "Ġinvestigator": 38330, "ĠCheryl": 38331, "ĠGerald": 38332, "Ġprere": 38333, "Ġcompares": 38334, "nyt": 38335, "Ġdiferença": 38336, "?-": 38337, "Ġquá": 38338, "ר×Ļ": 38339, "Sen": 38340, "Ġheps": 38341, "Ġgratuit": 38342, "Ġconsort": 38343, "ĠSTOP": 38344, "ĠProtestant": 38345, "Ġelectrode": 38346, "âĹ": 38347, "Ġsecurely": 38348, "иÑĩеÑģкой": 38349, "Ġtää": 38350, "Ġregisters": 38351, "ĠHeavenly": 38352, "ogly": 38353, "issä": 38354, "ĠPhysics": 38355, "ĠMerkel": 38356, "Ġrév": 38357, "éĻ¢": 38358, "Ġerased": 38359, "ĠSacramento": 38360, "Ġcoffin": 38361, "Ġexacer": 38362, "Ġlanz": 38363, "Ġpoets": 38364, "ulif": 38365, "Ġì¹ĺë": 38366, "ĠNerd": 38367, "ĠNCT": 38368, "ĠHour": 38369, "nehmer": 38370, "ŀĺëıĦ": 38371, "ĠPrinci": 38372, "Sw": 38373, "mies": 38374, "armed": 38375, "ĠBeatles": 38376, "Ġpropagation": 38377, "Ġexchanged": 38378, "Ġcumulative": 38379, "Ġì§ijìĹIJ": 38380, "Ġdefeating": 38381, "æĬ±": 38382, "bels": 38383, "Ġwes": 38384, "ĠOdyssey": 38385, "ä½łæĥ³": 38386, "avior": 38387, "ĠìľĦìĹIJ": 38388, "Ġbrit": 38389, "Ġhijo": 38390, "DAY": 38391, "ĠاÙĦتÙĬ": 38392, "ĠСеÑĢг": 38393, "Ñĥка": 38394, "edsiÄĻ": 38395, "Ġimpos": 38396, "Ġellas": 38397, "Ġfirearms": 38398, "ĠNR": 38399, "Ġ×ij×IJ": 38400, "ĠÐŁÐ¾ÐºÐ°": 38401, "awi": 38402, "ĠìĦ±ê³µ": 38403, "Ġpupils": 38404, "ĠTack": 38405, "Ġfrase": 38406, "ĠShip": 38407, "Ġstad": 38408, "举": 38409, "ĠGreater": 38410, "unun": 38411, "immung": 38412, "grown": 38413, "ĠNXT": 38414, "ĠAmericas": 38415, "fox": 38416, "Ġmanten": 38417, "éłIJåĤĻ": 38418, "ĠÑģок": 38419, "Ġrikt": 38420, "lectric": 38421, "deep": 38422, "ĠзнаеÑĪÑĮ": 38423, "Ġbenut": 38424, "ĠInfrast": 38425, "ĠEmir": 38426, "ĠоÑĤпÑĢав": 38427, "ĠKimchi": 38428, "ĠFinnish": 38429, "´ìłģ": 38430, "inaire": 38431, "Ġoike": 38432, "æ¸ħæ¥ļ": 38433, "Ġhostage": 38434, "ĠButton": 38435, "ÙĤÙĬ": 38436, "eking": 38437, "ĠKazakh": 38438, "Ġcomforting": 38439, "Ġsog": 38440, "Ġgreeted": 38441, "guitar": 38442, "payer": 38443, "Ġrelational": 38444, "Ġconstruir": 38445, "çī¹åĪ¥": 38446, "opian": 38447, "ĠVolume": 38448, "ieth": 38449, "ÑģÑĤвом": 38450, "urrection": 38451, "liÅĽmy": 38452, "Ġhemisphere": 38453, "ĠBean": 38454, "IGN": 38455, "Ġkötü": 38456, "ĠFallout": 38457, "Ġbrace": 38458, "ç¹¼çºĮ": 38459, "ÏĢά": 38460, "ĠHAS": 38461, "Ġgé": 38462, "Ġcharacterize": 38463, "ặc": 38464, "ĠMilky": 38465, "Ġtumors": 38466, "Ġnuit": 38467, "ĠGaz": 38468, "ĠìŀĪëĭ¤ëĬĶ": 38469, "ĠгаÑĢ": 38470, "essment": 38471, "ĠAbe": 38472, "Ġë½ij": 38473, "ĠEinsatz": 38474, "JIN": 38475, "jä": 38476, "Cry": 38477, "ĠPromised": 38478, "ĠÑģеÑĢд": 38479, "okus": 38480, "Ġscalable": 38481, "ĠпоÑģмоÑĤÑĢеÑĤÑĮ": 38482, "ücklich": 38483, "Ġrealism": 38484, "Ġmayo": 38485, "Ġjuvenile": 38486, "Ġheadlights": 38487, "ĠgörÃ¼ÅŁ": 38488, "ĠReform": 38489, "Ġhalves": 38490, "czne": 38491, "Ġbreakup": 38492, "żej": 38493, "Ġrätt": 38494, "Day": 38495, "ĠìĿ¼ë³¸": 38496, "Ġmuerte": 38497, "Ġtunes": 38498, "ĠSmile": 38499, "record": 38500, "Ġrecherche": 38501, "atisfied": 38502, "Ġpozi": 38503, "Ġcelebrations": 38504, "isexual": 38505, "ĠROB": 38506, "thirds": 38507, "ĠFortune": 38508, "ĠÑĤой": 38509, "Ġbranded": 38510, "loo": 38511, "Ġdud": 38512, "Ġrandomized": 38513, "Ġcombin": 38514, "ä¸ĢäºĽ": 38515, "ieran": 38516, "czenia": 38517, "įãĥ«": 38518, "Ġcurator": 38519, "Ġartery": 38520, "ĠÑĥÑĪ": 38521, "ĠÑĩиÑĤ": 38522, "Ġsubsidies": 38523, "Ġblossom": 38524, "ĠTwilight": 38525, "Ġhyvä": 38526, "ĠPompe": 38527, "ĠCisco": 38528, "ĠÐŁÑĢо": 38529, "Ġbiri": 38530, "Ġgern": 38531, "Ġrebuilt": 38532, "Ġwcze": 38533, "Ġbenefici": 38534, "Ġdrummer": 38535, "Ġsolids": 38536, "Ġdiyorsun": 38537, "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸãģĦãģ¾ãģĹãģŁ": 38538, "lated": 38539, "Ġmuddy": 38540, "Ġholog": 38541, "Ġclaps": 38542, "ĠRings": 38543, "ĠOkey": 38544, "ĠBrave": 38545, "Ġvaluation": 38546, "Ġmigrant": 38547, "Ġintermitt": 38548, "Ġeigene": 38549, "iliary": 38550, "ãĥ¼ãĥĪ": 38551, "markt": 38552, "kr": 38553, "ĠRib": 38554, "á»Ļi": 38555, "Ġaccusations": 38556, "Ġarab": 38557, "wash": 38558, "ĠBardzo": 38559, "Ġugh": 38560, "esters": 38561, "ophren": 38562, "Ġalimentos": 38563, "ĠUz": 38564, "ÖĤ": 38565, "Ġ650": 38566, "ĠпÑĢиеÑħ": 38567, "FI": 38568, "Ġsampai": 38569, "Ġparlé": 38570, "hesion": 38571, "Ġsır": 38572, "Ġapparatus": 38573, "Ġcorrelated": 38574, "ĠPrincipal": 38575, "Ġcorr": 38576, "ĠOfficial": 38577, "иÑĩеÑģкие": 38578, "Ġterminals": 38579, "Should": 38580, "Ġvacun": 38581, "Ġstellt": 38582, "Ġmooi": 38583, "etzung": 38584, "ĠкÑĢа": 38585, "Ġdai": 38586, "Ġпож": 38587, "Team": 38588, "ĠPPE": 38589, "ĠÐŀÑģ": 38590, "ĠLeah": 38591, "ĠIvy": 38592, "yst": 38593, "Ġuhhh": 38594, "Ġnighttime": 38595, "Ġtrendy": 38596, "Ġsecurities": 38597, "Ġcontinents": 38598, "Ġfirsthand": 38599, "ĠVeron": 38600, "ĠëĤ®": 38601, "Ġbrowsing": 38602, "ĠCada": 38603, "tro": 38604, "Ġtramp": 38605, "reib": 38606, "Ġerstmal": 38607, "irler": 38608, "Ġpsic": 38609, "Ġgetir": 38610, "ĠNP": 38611, "Ġdzieci": 38612, "обÑĢаз": 38613, "Ġmagician": 38614, "Ġscrutiny": 38615, "Ġslab": 38616, "ĠOT": 38617, "isty": 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"Ġappreh": 38675, "ìłķìĿ´": 38676, "Pop": 38677, "Ġjong": 38678, "ĠGiul": 38679, "ĠADHD": 38680, "Ġhören": 38681, "Ġelo": 38682, "ivent": 38683, "Ġrus": 38684, "Ġoutrageous": 38685, "Ġmastered": 38686, "Ġ커": 38687, "ÙĪÙģ": 38688, "ipes": 38689, "ĠRudy": 38690, "Jacob": 38691, "Ġbullish": 38692, "Ġtapped": 38693, "Ġfaud": 38694, "izophren": 38695, "ĠÑģоÑħ": 38696, "ĠDarling": 38697, "Ġ1963": 38698, "ĠPrevention": 38699, "²Ķ": 38700, "Ġabdominal": 38701, "stones": 38702, "Ġavaient": 38703, "á»ķi": 38704, "make": 38705, "Ġsare": 38706, "ĠInstant": 38707, "кам": 38708, "Ġkeeper": 38709, "Ġblankets": 38710, "ãģ§ãģĹãĤĩãģĨ": 38711, "Ġsweats": 38712, "ĠMinneapolis": 38713, "åħ¨éĥ¨": 38714, "Ġgenommen": 38715, "Ġfasten": 38716, "ĠBrussels": 38717, "åij¼": 38718, "Ġcafeter": 38719, "Ġabsorbing": 38720, "Ġhago": 38721, "ĠElmo": 38722, "Ġgusto": 38723, "ĠYap": 38724, "Música": 38725, "Ġtert": 38726, "Ġbanda": 38727, "Ġmily": 38728, "Ġthereafter": 38729, "ĠStockholm": 38730, "ĠCarson": 38731, "Ġcalibration": 38732, "avaÅŁ": 38733, "ansa": 38734, "ikke": 38735, "Ġforesee": 38736, "Ġqualche": 38737, "Ġdeste": 38738, "æ¤": 38739, "ünüz": 38740, "Ġforge": 38741, "Dis": 38742, "esten": 38743, "Ġδια": 38744, "Ġencaps": 38745, "ĠGespr": 38746, "Ġchercher": 38747, "ickets": 38748, "ÑĤоÑĢÑĭ": 38749, "Cr": 38750, "ĠТакже": 38751, "Ġrabbits": 38752, "ĠDot": 38753, "heiten": 38754, "Ġcausal": 38755, "ĠFoster": 38756, "ajÄħc": 38757, "Ġbereit": 38758, "Ġayudar": 38759, "é«Ļ": 38760, "ãģ³": 38761, "song": 38762, "comb": 38763, "Ġfringe": 38764, "Ġcybersecurity": 38765, "Ġ뾨": 38766, "Ġkier": 38767, "Ġbeschäft": 38768, "ĠконÑĨе": 38769, "Ġfacilit": 38770, "ĠNamen": 38771, "Ġbilateral": 38772, "tx": 38773, "ĠWissenschaft": 38774, "Ġnuances": 38775, "Ġripping": 38776, "Ġfy": 38777, "ĠSicherheit": 38778, "ĠGhana": 38779, "olon": 38780, "Ġtopped": 38781, "ĠMorocco": 38782, "Ġradial": 38783, "ĠLEE": 38784, "ĠAndreas": 38785, "edd": 38786, "ĠìĹ´ë": 38787, "ĠAirlines": 38788, "ãģĵãĤį": 38789, 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"Ġliquids": 38960, "Ġcapacit": 38961, "arken": 38962, "Ġvagina": 38963, "Ġmashed": 38964, "Ġemerges": 38965, "yscy": 38966, "Ġunrelated": 38967, "ĠGuild": 38968, "Ġinverted": 38969, "itives": 38970, "Tra": 38971, "Ġbegr": 38972, "Ġalte": 38973, "ì§ķ": 38974, "ãĤģãģ¦": 38975, "ĠÑĢазÑĢабоÑĤ": 38976, "finder": 38977, "Ġдалее": 38978, "ĠблагодаÑĢ": 38979, "walker": 38980, "Ġcrater": 38981, "assadors": 38982, "rences": 38983, "inski": 38984, "ĠKIM": 38985, "ĠElliot": 38986, "2017": 38987, "ĠSr": 38988, "inka": 38989, "anov": 38990, "Ġìŀĺ못": 38991, "Ġproprietary": 38992, "displaystyle": 38993, "ĠÑģим": 38994, "Ġизб": 38995, "ĠPanel": 38996, "Ġinstincts": 38997, "ĠCommunications": 38998, "麻": 38999, "midt": 39000, "Ġë§Įëĵ¤ìĸ´": 39001, "ĠÑģлова": 39002, "ĠGilbert": 39003, "缮åīį": 39004, "Так": 39005, "voorbeeld": 39006, "еÑİÑģÑĮ": 39007, "aryn": 39008, "quez": 39009, "Ġdart": 39010, "ÑĸÑĪ": 39011, "ĠHut": 39012, "Sal": 39013, "Ġsoutheast": 39014, "Ġpesticides": 39015, "Ġhelicopters": 39016, 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"ĠìŀĪëĬĶëį°ìļĶ": 43550, "zew": 43551, "baren": 43552, "Ġstol": 43553, "Ġbelongings": 43554, "ĠResearchers": 43555, "Ġefendim": 43556, "ÏħÏĩ": 43557, "ÅĤÄħcz": 43558, "ĠUng": 43559, "ĠJub": 43560, "Ġcerebral": 43561, "á»ĩu": 43562, "Ġצר": 43563, "ĠподаÑĢ": 43564, "Ġmarched": 43565, "Ġawaken": 43566, "Ġako": 43567, "Ġacept": 43568, "Ġinitiation": 43569, "è¯ī": 43570, "lot": 43571, "ĠwÅĤas": 43572, "ĠMongol": 43573, "utral": 43574, "Ġtentang": 43575, "Ġinversion": 43576, "ĠìĿ´íĽĦ": 43577, "Ġlok": 43578, "ÅĤbym": 43579, "RS": 43580, "Ġstos": 43581, "Ġinteracts": 43582, "ĠCalendar": 43583, "Ġvanish": 43584, "Ġphysiology": 43585, "Ġlinearly": 43586, "ĠJY": 43587, "ÄŁan": 43588, "funded": 43589, "iziert": 43590, "Ġzmian": 43591, "ĠGrill": 43592, "Ġunbelievably": 43593, "otechnology": 43594, "ĠCars": 43595, "ĠÙĨÛģ": 43596, "ĠFolge": 43597, "ĠBeverly": 43598, "äischen": 43599, "Ġaumento": 43600, "ìĽĮìĦľ": 43601, "Ġmailbox": 43602, "Ġsteeds": 43603, "ĠPeak": 43604, "å·§": 43605, "Ġwykor": 43606, "Ġprawda": 43607, "иÑĤÑĭ": 43608, "Ġdiscours": 43609, "Ġaccuse": 43610, "cesso": 43611, "uire": 43612, "Ġпопад": 43613, "Ġtha": 43614, "Ġmeasurable": 43615, "beeping": 43616, "ĠInnen": 43617, "ĠпÑıÑĤÑĮ": 43618, "Ġcompeted": 43619, "ĠItalians": 43620, "Ġencontra": 43621, "Ġniew": 43622, "Ġfiltration": 43623, "ĠпÑĢоÑĦеÑģÑģ": 43624, "Ġpajamas": 43625, "Ġcilantro": 43626, "ĠSoc": 43627, "Luc": 43628, "Ġê¹Ģë": 43629, "ĠOdd": 43630, "Ġhydration": 43631, "мов": 43632, "Ġplywood": 43633, "ĠCompetition": 43634, "изнеÑģ": 43635, "flight": 43636, "ĠBeit": 43637, "bourg": 43638, "Ġcoils": 43639, "Ġcâmera": 43640, "Ġamended": 43641, "Äģm": 43642, "Angel": 43643, "ĠStacy": 43644, "flo": 43645, "Ġnormale": 43646, "Ġconsonant": 43647, "Ġaccompanying": 43648, "кÑĸ": 43649, "Ġirritated": 43650, "ĠfÃ¥tt": 43651, "Ġcrocodile": 43652, "IJĺëĬĶ": 43653, "Ġalbeit": 43654, "ĠPhilosophy": 43655, "ç´¯": 43656, "ÅĨ": 43657, "ytic": 43658, "Ġrèg": 43659, "Ġfrança": 43660, "Ġattentive": 43661, "Ham": 43662, "Ġalrededor": 43663, "æĿ¿": 43664, "sei": 43665, "ĠÑģвид": 43666, "Ġgimbal": 43667, "Ġchina": 43668, "ĠðŁİ¶": 43669, "ĠÐĴам": 43670, "Ġstimulating": 43671, "ĠOra": 43672, "ytes": 43673, "Ġheft": 43674, "Ġhaters": 43675, "Ġcomplexes": 43676, "Ġ03": 43677, "ród": 43678, "clear": 43679, "Ġbesteht": 43680, "çķĻè¨Ģ": 43681, "wny": 43682, "moil": 43683, "Ġsloppy": 43684, "Ġinsignificant": 43685, "Ġdubbed": 43686, "Ġëĸł": 43687, "Ġconsigo": 43688, "лÑĥÑĪай": 43689, "Sn": 43690, "Ġ×Ķצ": 43691, "ĠÎĮ": 43692, "Ġnadzie": 43693, "Ġfreshmen": 43694, "taa": 43695, "ĠuwagÄĻ": 43696, "ĠFavorite": 43697, "ĠCriminal": 43698, "Ġeviden": 43699, "Ġsymb": 43700, "Les": 43701, "ĠBeau": 43702, "uned": 43703, "plement": 43704, "Ac": 43705, "Ġdermat": 43706, "ĠNolan": 43707, "Ñĭп": 43708, "Ġsitt": 43709, "Ġeverlasting": 43710, "Ġestavam": 43711, "Ġмик": 43712, "Ġkhác": 43713, "Ġinvit": 43714, "Ġtreble": 43715, "Ġjig": 43716, "mani": 43717, "Ġtuvo": 43718, "ĠRUS": 43719, "ĠErde": 43720, "ĠDziÄĻkujÄĻ": 43721, "Ġblueberries": 43722, "kell": 43723, "acions": 43724, "çĪ·": 43725, "ви": 43726, "LET": 43727, "Ġsprout": 43728, "Ġspor": 43729, "Ġbên": 43730, "ĠMona": 43731, "ĠContain": 43732, "ĠKeys": 43733, "озÑı": 43734, "Ġfunción": 43735, "Ġrappelle": 43736, "Ġevolves": 43737, "Ġscraping": 43738, "Ġcomentários": 43739, "Ġpratique": 43740, "Ġauxiliary": 43741, "ĠSponge": 43742, "Ñģким": 43743, "uvo": 43744, "ĠÑģамо": 43745, "Ġsank": 43746, "Ġhighways": 43747, "Ġinventions": 43748, "Ġиногда": 43749, "Ġcreatively": 43750, "Ġbenchmarks": 43751, "oncé": 43752, "alal": 43753, "Ġsotto": 43754, "Ġcalves": 43755, "ĠMov": 43756, "Ġlavender": 43757, "Ġeyeballs": 43758, "Ġawaiting": 43759, "ĠPaty": 43760, "ÙĦÙĩ": 43761, "Ġembroidery": 43762, "Ġduh": 43763, "Ġcamar": 43764, "ĠBOB": 43765, "Ġspaced": 43766, "ĠgÅĤos": 43767, "аемÑģÑı": 43768, "Ġescapes": 43769, "ĠRogue": 43770, "zcz": 43771, "èŀ": 43772, "¬ë¥¼": 43773, "ĠMoże": 43774, "ĠеÑģÑĤе": 43775, "ĠBurada": 43776, "éĮ²": 43777, "wd": 43778, "uuuu": 43779, "Ġsash": 43780, "ĠLub": 43781, "Ġnotebooks": 43782, "Ġmae": 43783, "Ġconflicting": 43784, "Ġsummertime": 43785, "acas": 43786, "Ġbauen": 43787, "blowing": 43788, "ạo": 43789, "Ġìĸ¸ìłľ": 43790, "ä»ĬæĹ¥ãģ¯": 43791, "ĠSenhor": 43792, "ĠiPhones": 43793, "ĠQuarter": 43794, "ĠìłľëĮĢë¡ľ": 43795, "uÃŁ": 43796, "Ġë§Ī무ë": 43797, "Ġsettlers": 43798, "Ġcrest": 43799, "Ġtransc": 43800, "æĽ¾": 43801, "Ġriots": 43802, "Ġclones": 43803, "ĠOprah": 43804, "ίζ": 43805, "Ġpals": 43806, ".......": 43807, "ãģĶãģĸãģĦãģ¾ãģĻ": 43808, "ĠÑĢоÑģÑģ": 43809, "ĠLaser": 43810, "Ġzaczy": 43811, "Ġsevi": 43812, "Ġregeneration": 43813, "ìĹ¼": 43814, "would": 43815, "Ġüzerine": 43816, "ĠStraÃŁe": 43817, "Ġvengeance": 43818, "Ġrer": 43819, "ĠSafari": 43820, "ĠHEY": 43821, "çķ«": 43822, "Ġsacar": 43823, "Ġimagem": 43824, "ĠBundest": 43825, "mesan": 43826, "ĠPaste": 43827, "Ġsizz": 43828, "ĠпоÑģÑĤÑĥп": 43829, "×Ķ×ķ": 43830, "trad": 43831, "Ġfrançaise": 43832, "ĠBou": 43833, "Ġbarre": 43834, "ĠZhi": 43835, "ĠGeez": 43836, "ihad": 43837, "Ġreconoc": 43838, "Ġpelig": 43839, "Ġindices": 43840, "Ġë°ĶëĢ": 43841, "Ġconduction": 43842, "Ġìķħ": 43843, "Ġzeker": 43844, "Ġfum": 43845, "ĠWür": 43846, "breaker": 43847, "Ġsprite": 43848, "Crowd": 43849, "Ġopener": 43850, "Ġolv": 43851, "Ġbuenas": 43852, "ĠSilk": 43853, "ĠHIM": 43854, "kop": 43855, "compl": 43856, "Ġpossono": 43857, "³Ģ": 43858, "Ġoscillator": 43859, "ĠSith": 43860, "èĥ¡": 43861, "ажи": 43862, "Ġraft": 43863, "hall": 43864, "Ġschneller": 43865, "Ġimporting": 43866, "Ġassembling": 43867, "Ġubiqu": 43868, "Ġactivates": 43869, "acci": 43870, "ĵľë¥¼": 43871, "Ġcomposers": 43872, "ĠACL": 43873, "Conf": 43874, "Ġì½ĺ": 43875, "ĠнекоÑĤоÑĢÑĭе": 43876, "Ġcandies": 43877, "åĬłåħ¥": 43878, "ĠMuss": 43879, "à¹ĥà¸Ĭ": 43880, "Ġduda": 43881, "ником": 43882, "meden": 43883, "Ġìĸ´ëķĮ": 43884, "ĠYeshua": 43885, "zag": 43886, "hodou": 43887, "Ġaloud": 43888, "ĠPalmer": 43889, "imize": 43890, "ãĤ·ãĥ§": 43891, "Ġmaritime": 43892, "Ġcommunal": 43893, "Ġbadges": 43894, "Ġrugby": 43895, "Ġmarshmallow": 43896, "Ġfiery": 43897, "Ġaccountant": 43898, "Ġabla": 43899, "ĠMonroe": 43900, "ĠFont": 43901, "ĠBoost": 43902, "ĠBarnes": 43903, "answer": 43904, "ĠBurning": 43905, "Ġä¸įæĺ¯": 43906, "Ġangef": 43907, "ĠWesley": 43908, "lls": 43909, "ìµ": 43910, "ש׾": 43911, "iliÅĽmy": 43912, "×IJף": 43913, "amura": 43914, "ĠFuj": 43915, "Ġpani": 43916, "ĠTrop": 43917, "arbeiten": 43918, "Ġrue": 43919, "ĠRare": 43920, "ängen": 43921, "ĠÑģмоÑĤÑĢеÑĤÑĮ": 43922, "ĠÐļаÑĢ": 43923, "ĠMTV": 43924, "boarding": 43925, "][": 43926, "ĠëłĪë": 43927, "stanbul": 43928, "pielt": 43929, "ĠHardy": 43930, "ĠEngagement": 43931, "ĠDienst": 43932, "Ġwären": 43933, "Ġfuego": 43934, "Ġestruct": 43935, "Ġcalam": 43936, "ĠResponse": 43937, "ĠãĤĦ": 43938, "ĠMohammad": 43939, "Ġresisting": 43940, "Ġdurant": 43941, "èģ¯": 43942, "åĨµ": 43943, "ĠOLED": 43944, "Ġverz": 43945, "män": 43946, "ĠÙĨÛĴ": 43947, "Ġparanoid": 43948, "ĠAware": 43949, "ĠEngineers": 43950, "Ġprocedural": 43951, "Ġpersonnage": 43952, "Ġfarklı": 43953, "é¡Ĩ": 43954, "flowing": 43955, "ĠмеÑģÑĤа": 43956, "ĠBare": 43957, "istem": 43958, "ĠpoczÄħtku": 43959, "Ġpersonajes": 43960, "Ġìĸ´ëłµ": 43961, "Ńī": 43962, "ĠХоÑĤÑı": 43963, "Ġunsett": 43964, "ĠAbsol": 43965, "Ġấy": 43966, "ĠMAYOR": 43967, "полне": 43968, "Ġinforming": 43969, "Ġamps": 43970, "ÐŁÑĢ": 43971, "ĠëŃĶ": 43972, "aeda": 43973, "Ġ×Ķ×ij×": 43974, "ấn": 43975, "kelijk": 43976, "Ġatheist": 43977, "Ġtrout": 43978, "Ġneues": 43979, "ĠNokia": 43980, "machen": 43981, "Ġwholesale": 43982, "ırd": 43983, "Ins": 43984, "ĠÑįп": 43985, "Ġprick": 43986, "ĠKindern": 43987, "à¸Ĺำ": 43988, "Ġclassy": 43989, "Ġînt": 43990, "ĠShopify": 43991, "ĠÑģоÑĢ": 43992, "ĠзакÑĢÑĭ": 43993, "zuk": 43994, "Ġuniversally": 43995, "Ġteaspoons": 43996, "Ġrecount": 43997, "ĠnÃ¥gonting": 43998, "ĠXue": 43999, "isième": 44000, "Ġweakest": 44001, "ĠteÅŁekkür": 44002, "Ġmathematically": 44003, "ĠHos": 44004, "Ġíķľëĭ¤": 44005, "Ġpartager": 44006, "ĠDarr": 44007, "êº": 44008, "Ġεκ": 44009, "Ġgerms": 44010, "Ġgelir": 44011, "Ġdul": 44012, ",-": 44013, "Ġìĸ¸ë": 44014, "Ġ×ŀצ": 44015, "ĠÑıÑĢ": 44016, "Ġquotid": 44017, "Ġprzysz": 44018, "Ġhardness": 44019, "Ġaquatic": 44020, "ĠJungle": 44021, "ĠPCR": 44022, "ĠEliot": 44023, "Ġostr": 44024, "Ġmapa": 44025, "essä": 44026, "ĠGIR": 44027, "ĠDriving": 44028, "ĠSami": 44029, "ĠMedien": 44030, "ĠCompanies": 44031, "ĠPharm": 44032, "seits": 44033, "ĠRim": 44034, "ĠοÏĢο": 44035, "Ġweiteren": 44036, "Ġpizzas": 44037, "ĠLydia": 44038, "ĠHeights": 44039, "Ġsincerity": 44040, "Ġnossas": 44041, "ĠdÅĤ": 44042, "Ġalarming": 44043, "ĠCauc": 44044, "ĠÑģмÑĭÑģ": 44045, "facing": 44046, "bags": 44047, "WW": 44048, "ĠØ´ÙĬ": 44049, "Ġcourtroom": 44050, "ĠPhillip": 44051, "Ġê²ĥì²ĺëŁ¼": 44052, "ĠSpieler": 44053, "ãĤıãģĭ": 44054, "Ġkant": 44055, "Ġadmitting": 44056, "ãĥģãĥ£ãĥ³ãĥįãĥ«": 44057, "Ġcontainment": 44058, "å¼ł": 44059, "Ġremovable": 44060, "Ġjumper": 44061, "focused": 44062, "ĠиÑĤоге": 44063, "ĠТем": 44064, "Ġvase": 44065, "ĠUSC": 44066, "ĠMonate": 44067, "ĠJacobs": 44068, "ĠHOL": 44069, "iked": 44070, "erweise": 44071, "Ġgoodies": 44072, "Ġhomage": 44073, "׼ש×Ļ×ķ": 44074, "Ġquais": 44075, "Ġinicial": 44076, "Ġguarding": 44077, "Ġdazz": 44078, "Ġcombos": 44079, "ĠÑĥпÑĢав": 44080, "ĠTalent": 44081, "å¥ĩæĢª": 44082, "Ġór": 44083, "Ġintermittent": 44084, "ĠMcCarthy": 44085, "Ġspans": 44086, "Ġtyre": 44087, "Ġquy": 44088, "èĪĪ": 44089, "jut": 44090, "ĠZent": 44091, "Ġgat": 44092, "大åĵ¥": 44093, "Ġscaffold": 44094, "Ġnecesario": 44095, "ĠZahlen": 44096, "ĠSAND": 44097, "ĠPU": 44098, "Everything": 44099, "----------------": 44100, "ĠвзÑıÑĤÑĮ": 44101, "Ġsparks": 44102, "Ġpendulum": 44103, "×ŀף": 44104, "Ġìĥīê¹": 44105, "Ġmultiplier": 44106, "Ġладно": 44107, "urat": 44108, "Ġupsetting": 44109, "è¡Ģ": 44110, "bak": 44111, "ĠìµľëĮĢ": 44112, "Ġanál": 44113, "ĠJOE": 44114, "Ġkosten": 44115, "ĠPatty": 44116, "ĠGuin": 44117, "cked": 44118, "ĠEgyptians": 44119, "ĠCitizens": 44120, "ר׼": 44121, "ĠÐķÑīе": 44122, "Ġйого": 44123, "Ġsnowfl": 44124, "Ġlekker": 44125, "Ġacost": 44126, "ĠBabe": 44127, "Ġgamble": 44128, "Ġadjective": 44129, "кими": 44130, "oys": 44131, "Ġmontre": 44132, "ĠHyundai": 44133, "Ġmoisturizing": 44134, "Ġmozzarella": 44135, "OOO": 44136, "Ġfacult": 44137, "Ġdoet": 44138, "Ġfearless": 44139, "Ġespresso": 44140, "Ġallora": 44141, "ĠCinc": 44142, "ãĥ¼ãĤ¸": 44143, "Ġconteúdo": 44144, "ĠPelosi": 44145, "Ġminder": 44146, "root": 44147, "Ġíķłë": 44148, "Ġпад": 44149, "ĠCalling": 44150, "ĠConfig": 44151, "ĠConsole": 44152, "insky": 44153, "énergie": 44154, "Ġsolitary": 44155, "оде": 44156, "Ġguarded": 44157, "160": 44158, "ĠпÑģиÑħ": 44159, "ĠShap": 44160, "Ġtitre": 44161, "ologne": 44162, "ĠпаÑĢÑĥ": 44163, "ĠPRE": 44164, "ãĥ¼ãĥī": 44165, "Ġln": 44166, "ĠMitgl": 44167, "ĠCarry": 44168, "Ġspind": 44169, "ĠCanton": 44170, "Ġkingdoms": 44171, "remo": 44172, "Ġraging": 44173, "Ġincapable": 44174, "ĠWR": 44175, "åĨįè§ģ": 44176, "ĠÑģобÑģÑĤвен": 44177, "ĠкакиÑħ": 44178, "ĠSHE": 44179, "ëĭ¹íŀĪ": 44180, "Ġscarcity": 44181, "Ġperde": 44182, "Ġexits": 44183, "ĠSinger": 44184, "Ġsupper": 44185, "Ġmunicipality": 44186, "ĠDiversity": 44187, "Ġtiro": 44188, "iels": 44189, "ĠlÃŃder": 44190, "Ġbluff": 44191, "Ġatra": 44192, "lys": 44193, "Ġmahd": 44194, "Ġcódigo": 44195, "ĠHarlem": 44196, "rule": 44197, "icity": 44198, "Ġsimplistic": 44199, "ĠKonst": 44200, "åģ¥": 44201, "ELLI": 44202, "Ġförsta": 44203, "Ġconstitutes": 44204, "ĠÑģÑĤоÑĢонÑĥ": 44205, "Ġurged": 44206, "ĠPanda": 44207, "ì°¨ë": 44208, "rece": 44209, "Ġpatriot": 44210, "ĠCrush": 44211, "Ġwink": 44212, "ойÑĤи": 44213, "urança": 44214, "Ġseizures": 44215, "Ġelectrod": 44216, "ĠDonkey": 44217, "ĠIU": 44218, "ĠMOS": 44219, "Ġalkal": 44220, "ì´ī": 44221, "besondere": 44222, "Ġparallels": 44223, "Ġbitterness": 44224, "ättre": 44225, "essional": 44226, "Ġsoybean": 44227, "Ġcollab": 44228, "ĠReporting": 44229, "å§Ķ": 44230, "Ġкомпании": 44231, "Ġwszyscy": 44232, "ĠCrunch": 44233, "iseen": 44234, "Ġambassadors": 44235, "ĠChev": 44236, "åįĪ": 44237, "овÑĭе": 44238, "sca": 44239, "ĠÑĢеÑĪил": 44240, "оÑĤо": 44241, "Ġgleichzeitig": 44242, "mern": 44243, "üst": 44244, "ĠHae": 44245, "³´ê²łìĬµëĭĪëĭ¤": 44246, "Ġshores": 44247, "Ġdepress": 44248, "Ġahor": 44249, "ĠSteuer": 44250, "ahh": 44251, "Ġrevise": 44252, "ĠÑģамÑĭе": 44253, "jat": 44254, "Ġherbal": 44255, "Ġcuánt": 44256, "Ġbuna": 44257, "niejsze": 44258, "Finally": 44259, "×ķ×ĸ": 44260, "cje": 44261, "ĠìŀĪê±°ëĵłìļĶ": 44262, "ĠëĤĺëĪ": 44263, "Ġprzest": 44264, "ãĥ¼ãĥł": 44265, "lica": 44266, "ĠDuch": 44267, "å°įå°į": 44268, "ÑĸйÑģÑĮ": 44269, "passen": 44270, "Ġsatisfies": 44271, "ĠAdditional": 44272, "Ġcámara": 44273, "еÑĩение": 44274, "Ġpomp": 44275, "Ġë§IJìĿ´": 44276, "ĠMills": 44277, "евид": 44278, "Ġrespectable": 44279, "Ġfilament": 44280, "Ġvender": 44281, "Ġmattered": 44282, "oure": 44283, "층": 44284, "Korean": 44285, "Ġestudio": 44286, "Ġcactus": 44287, "ĠVive": 44288, "ĠRag": 44289, "Ġcompliqué": 44290, "ĠÙĪÛģ": 44291, "Ġtao": 44292, "¦¿": 44293, "Since": 44294, "Ġjeopard": 44295, "ĠSell": 44296, "åºĶ": 44297, "ĠìĺĽ": 44298, "Ġketo": 44299, "Ġintelig": 44300, "ĠAngeb": 44301, "Ġtiden": 44302, "Ġsocio": 44303, "Ġreminiscent": 44304, "Ġcaregiver": 44305, "Space": 44306, "ĠExercise": 44307, "ĠBecome": 44308, "êts": 44309, "akk": 44310, "!..": 44311, "ĠÑģпÑĢоÑģ": 44312, "ĠαÏĢο": 44313, "Ġshootings": 44314, "Ġape": 44315, "ĠSammy": 44316, "ĠKung": 44317, "Ġcuál": 44318, "ĠLup": 44319, "æĿŁ": 44320, "ä¾ĨåĪ°": 44321, "ĠÑģÑĤÑĥд": 44322, "Ġsweeter": 44323, "Ġcomum": 44324, "ĠAds": 44325, "hyung": 44326, "ĠбÑĥдÑĥÑī": 44327, "Ġwaffle": 44328, "ĠOrb": 44329, "Ġlaut": 44330, "Ġforecasting": 44331, "åª": 44332, "Ġrapping": 44333, "Ġprefers": 44334, "Ġbenz": 44335, "Ġnik": 44336, "ĠBahn": 44337, "Ġsanding": 44338, "Ġimminent": 44339, "ĠпÑĢоблемÑĭ": 44340, "Ġdoivent": 44341, "ола": 44342, "Ġżycia": 44343, "ihu": 44344, "Ġexistem": 44345, "ĠInterior": 44346, "ĠTakes": 44347, "Ġtoddler": 44348, "Ġdictatorship": 44349, "ĠSmithson": 44350, 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"Ġsurrendered": 48802, "Ġlibrarians": 48803, "125": 48804, "ĠÑģиг": 48805, "Ġuniformly": 48806, "ĠEagles": 48807, "ìķĻ": 48808, "иÑĤан": 48809, "andid": 48810, "ĠìłĪëĮĢ": 48811, "Ġض": 48812, "Ġarrests": 48813, "ĠCSV": 48814, "ĠAzerbaijan": 48815, "ortic": 48816, "ĠDX": 48817, "ĠAdventures": 48818, "Ġabus": 48819, "ĠFau": 48820, "Ġschlimm": 48821, "Ġrattling": 48822, "Ġconsumes": 48823, "ĠTolkien": 48824, "Ġresurrected": 48825, "ĠXY": 48826, "íĬ¸ê°Ģ": 48827, "ĠвÑĭÑģÑĤÑĥп": 48828, "ĠAngie": 48829, "żenia": 48830, "Mic": 48831, "ĠSheila": 48832, "achtet": 48833, "Ġoverst": 48834, "Ġlâ": 48835, "Ġineffective": 48836, "æĿ¡": 48837, "æĢİä¹ĪäºĨ": 48838, "å¿Ļ": 48839, "Ġwichtiger": 48840, "Ġvino": 48841, "Ġpum": 48842, "Ġangled": 48843, "ĠPione": 48844, "ĠMỹ": 48845, "ãģĿãĤĮãģ¯": 48846, "woÅĽÄĩ": 48847, "draw": 48848, "ัà¹Ī": 48849, "markets": 48850, "Ġcafes": 48851, "ĠCem": 48852, "âĿ¤": 48853, "ĠSuit": 48854, "MK": 48855, "Ġemphasizes": 48856, "Ġtortilla": 48857, "Ġmejorar": 48858, "ĠSurviv": 48859, "casting": 48860, "Ġeducación": 48861, "ĠGum": 48862, "uely": 48863, "ĠìĹ¬ê¸°ëĬĶ": 48864, "Ġstretchy": 48865, "ença": 48866, "Ġwithhold": 48867, "Ġexiting": 48868, "Ġenthalpy": 48869, "ĠTransit": 48870, "ılmÄ±ÅŁ": 48871, "alies": 48872, "Ġsalvar": 48873, "Ġleaned": 48874, "ĠgroÃŁes": 48875, "Ġfitt": 48876, "аки": 48877, "Sarah": 48878, "Ġhostel": 48879, "Ġfingerna": 48880, "ĠnadziejÄĻ": 48881, "wives": 48882, "Rec": 48883, "Ġspool": 48884, "аÑĤов": 48885, "ĠEnemy": 48886, "Ġfury": 48887, "Ġdetta": 48888, "ĠFay": 48889, "éļ¨": 48890, "ÑıÑİÑĤ": 48891, "Ġaproximadamente": 48892, "Ġsilos": 48893, "Ġmagist": 48894, "Ġcree": 48895, "ĠKrank": 48896, "ĠDOWN": 48897, "Ġstartled": 48898, "Ġreborn": 48899, "ĠUmwelt": 48900, "ĠSuzanne": 48901, "ниÑĨÑĭ": 48902, "outez": 48903, "ĠJAC": 48904, "yards": 48905, "radas": 48906, "rau": 48907, "ipts": 48908, "hail": 48909, "Ġparagraphs": 48910, "Ġmeglio": 48911, "Ġisolating": 48912, "Ġaceite": 48913, "ĠHarsh": 48914, "Ġcyst": 48915, "ĠBlockchain": 48916, "ĠÑħоÑĢоÑĪий": 48917, "Ġvirtuous": 48918, "Ġinvestigación": 48919, "Ġdevoir": 48920, "Ġmasturb": 48921, "ĠSale": 48922, "ÙĬرة": 48923, "ĠΧ": 48924, "ĠStraÃŁen": 48925, "Ġdikk": 48926, "Ġafore": 48927, "ĠJungkook": 48928, "Ġchociaż": 48929, "ĠDebatte": 48930, "Ġweirdly": 48931, "Ġviaje": 48932, "regist": 48933, "Help": 48934, "Ġkinderen": 48935, "Ġformulated": 48936, "Ġenfim": 48937, "ĠTowards": 48938, "коÑĹ": 48939, "ivering": 48940, "ĠдеÑĤи": 48941, "charger": 48942, "Ġpurl": 48943, "Ġacademically": 48944, "ĠNurse": 48945, "Ġdeleting": 48946, "ayo": 48947, "Ġrefusal": 48948, "Ġdepicts": 48949, "ĠDracula": 48950, "Ġtoasted": 48951, "ĠZombie": 48952, "ĠSuperior": 48953, "ĠBold": 48954, "Ġquizzes": 48955, "Ġgle": 48956, "450": 48957, "Ġcomeço": 48958, "ynn": 48959, "Ġverst": 48960, "ĠOlaf": 48961, "Ġpomoc": 48962, "ĠSask": 48963, "ëĺ": 48964, "ĠTCP": 48965, "ĠProperty": 48966, "íķĺì£ł": 48967, "à¸ľà¸¡": 48968, "boom": 48969, "aros": 48970, "ĠÑĢоÑģÑģий": 48971, "ĠбÑĭваеÑĤ": 48972, "åĩºåİ»": 48973, "ĠìĿ´ìķ¼ê¸°ë¥¼": 48974, "Ġcombien": 48975, "vacc": 48976, "Ġebenfalls": 48977, "para": 48978, "Ġзм": 48979, "Ġdesperation": 48980, "ordre": 48981, "Ġש׾×Ļ": 48982, "Ġgenerously": 48983, "ĠÐŀк": 48984, "Ġorbiting": 48985, "></": 48986, "ĠespÃŃ": 48987, "ĠCOP": 48988, "åŃ©åŃIJ": 48989, "visible": 48990, "ĠпÑĢеÑģÑĤÑĥп": 48991, "Ġstitched": 48992, "à¯Ī.": 48993, "Ġlatent": 48994, "ĠPrab": 48995, "ĠMcN": 48996, "ĠHealing": 48997, "ĠCuriosity": 48998, "cert": 48999, "Ġ민주": 49000, "Ġpatiently": 49001, "ĠYT": 49002, "foreign": 49003, "Ġvẫn": 49004, "Ġindustri": 49005, "Ġcocktails": 49006, "Ġbrighten": 49007, "Ġconsolidated": 49008, "аÑĢд": 49009, "ltry": 49010, "Ġgrille": 49011, "Ġbona": 49012, "Ġdiligently": 49013, "ĠWrestleMania": 49014, "erkt": 49015, "energy": 49016, "999": 49017, "à®ķவ": 49018, "Ġtote": 49019, "iono": 49020, "DIO": 49021, "Ġschizophrenia": 49022, "Ġpostponed": 49023, "ĠQiu": 49024, "ĠÏĥÏħν": 49025, "ĠzdjÄĻ": 49026, "Ġspannend": 49027, "ĠDIS": 49028, "Rel": 49029, "Ġrhin": 49030, "immune": 49031, "Old": 49032, "Ġplötzlich": 49033, "Ġmound": 49034, "Ġastronomical": 49035, "ĠGuid": 49036, "ĠCul": 49037, "HI": 49038, "ĠÅł": 49039, "Ġrepo": 49040, "ĠMaurice": 49041, "ä¸ĢçĤ¹": 49042, "Ġbandits": 49043, "ĠDesktop": 49044, "äss": 49045, "fta": 49046, "Ġlicence": 49047, "Ġimaginar": 49048, "ĠEntreprene": 49049, "xo": 49050, "Ġ맼ìŀĪëĬĶ": 49051, "Ġ×Ķ×ij": 49052, "Ġpumpkins": 49053, "Ġkanssa": 49054, "ĠjÄĻzy": 49055, "Ġcommunauté": 49056, "bür": 49057, "Ġerhö": 49058, "ĠWolver": 49059, "ĠSharing": 49060, "令": 49061, "Ġpakai": 49062, "Ġinsulted": 49063, "ÐľÑĭ": 49064, "оÑĹ": 49065, "Ġconsiste": 49066, "æĮij": 49067, "Ġyoungsters": 49068, "Ġgleichen": 49069, "weder": 49070, "Ġmote": 49071, "Ġclauses": 49072, "état": 49073, "prus": 49074, "Ġwast": 49075, "ç»ĻæĪij": 49076, "ĠCrisp": 49077, "ĠçĦ¶å¾Į": 49078, "Ġoffenders": 49079, "Ġconvection": 49080, "Ġconfian": 49081, "ollow": 49082, "amet": 49083, "ĠÑĹÑħ": 49084, "第äºĮåĢĭ": 49085, "fficiency": 49086, "Ġunglaub": 49087, "igans": 49088, "Ġmarketed": 49089, "ĠVAN": 49090, "Ġproclaimed": 49091, "Ġcélulas": 49092, "Ġcollide": 49093, "ĠOculus": 49094, "adore": 49095, "Ji": 49096, "Ġsustaining": 49097, "ĠFasc": 49098, "Ġsetzt": 49099, "Ġnosaltres": 49100, "Most": 49101, "ĠвÑĩ": 49102, "Ġnauc": 49103, "ĠBhar": 49104, "çĪ¸çĪ¸": 49105, "æĪijè·Łä½łè¬Ľ": 49106, "Ġyêu": 49107, "Ġtimest": 49108, "Ġpertama": 49109, "irmi": 49110, "Ġzwr": 49111, "Ġverbess": 49112, "Ġvortex": 49113, "ĠSTACK": 49114, "ثر": 49115, "¹Ħë": 49116, "ĶĶìĺ¤": 49117, "Ġlinkage": 49118, "ĠFraser": 49119, "enario": 49120, "ĠëĿ¼ëĬĶ": 49121, "ĠìĦłë°°": 49122, "hthal": 49123, "Ġê¹Į": 49124, "ĠKhông": 49125, "Ãĥ": 49126, "Ġscrambled": 49127, "ĠEink": 49128, "Ġmicroorgan": 49129, "Ġnarcissist": 49130, "ĠKombat": 49131, "Ġ맡": 49132, "ĠAGA": 49133, "Ġperfekt": 49134, "ĠSerie": 49135, "determ": 49136, "-'": 49137, "Ġponytail": 49138, "Ġkoska": 49139, "ìĵ": 49140, "Ġobec": 49141, "Ġchests": 49142, "veer": 49143, "Ġuprising": 49144, "Ġstoked": 49145, "associ": 49146, "Ġprodução": 49147, "ĠShape": 49148, "ìłľê°Ģ": 49149, "ĠëĶ°": 49150, "Ġjon": 49151, "Ġinadvert": 49152, "antas": 49153, "ĠнаконеÑĨ": 49154, "Ġå°įåķĬ": 49155, "ĠArsenal": 49156, "Ġproteg": 49157, "Ġliberté": 49158, "Ġglare": 49159, "åĪļ": 49160, "å·²ç»ı": 49161, "Ġverein": 49162, "Ġinserts": 49163, "ĠJana": 49164, "Ġwydaje": 49165, "ÅĤum": 49166, "Ġ%.": 49167, "origine": 49168, "Ġsynagogue": 49169, "Ġfallait": 49170, "Ġdisobed": 49171, "Ġantic": 49172, "ĠCycl": 49173, "Ġasynchronous": 49174, "Ġë²Įìį¨": 49175, "Ġgesund": 49176, "Ġgagn": 49177, "Ġpea": 49178, "Ġgrin": 49179, "ést": 49180, "Ġsauc": 49181, "ĠMäd": 49182, "íķ´ëıĦ": 49183, "pps": 49184, "ĠεÏĢι": 49185, "Ġpeuple": 49186, "Ġdeben": 49187, "ĠBree": 49188, "ĠÑĢолÑĮ": 49189, "Ġкаким": 49190, "Ġútil": 49191, "Ġdistributor": 49192, "алÑĭ": 49193, "ĠswojÄħ": 49194, "Ġfolklore": 49195, "Ġreceivers": 49196, "ĠMOO": 49197, "bins": 49198, "astre": 49199, "ìķĪë": 49200, "ĠëĦ£ê³ł": 49201, "Ġmultimedia": 49202, "Ġgebaut": 49203, "овÑĭÑħ": 49204, "ãy": 49205, "Ġdane": 49206, "okol": 49207, "emitism": 49208, "ONEY": 49209, "ĠyaÄŁ": 49210, "Ġchauff": 49211, "容æĺĵ": 49212, "Ġesfuer": 49213, "Äĥn": 49214, "ertas": 49215, "Ġfonctionne": 49216, "omina": 49217, "Ġivory": 49218, "ĠYoutuber": 49219, "ĠSkywalker": 49220, "иÑĩеÑģкаÑı": 49221, "toi": 49222, "Ġveya": 49223, "Ġgelernt": 49224, "Ġchancellor": 49225, "ĠStatistics": 49226, "Ġwelded": 49227, "Ġondan": 49228, "ĠSei": 49229, "Ġmedically": 49230, "Ġenergized": 49231, "ĠVia": 49232, "Ġвик": 49233, "Ġuninter": 49234, "Ġhighness": 49235, "ĠíĮĶë": 49236, "Ġamplified": 49237, "ĠSergey": 49238, "ĠMins": 49239, "warm": 49240, "pell": 49241, "ophile": 49242, "Ġhè": 49243, "ĠBelo": 49244, "ĠSketch": 49245, "Ġcharacterization": 49246, "ansen": 49247, "ĠÑĤÑĥÑĢ": 49248, "Ġãħĭãħĭãħĭ": 49249, "Note": 49250, "ĠkoÅŁ": 49251, "Ġciert": 49252, "flu": 49253, "Ġbaht": 49254, "ĠDowntown": 49255, "ĠCRIS": 49256, "odie": 49257, "140": 49258, "Ġlitres": 49259, "Ġgriev": 49260, "æ§ĺ": 49261, "ĠìĶ¨ê°Ģ": 49262, "Ġsucceeds": 49263, "Ġ__": 49264, "enting": 49265, "Ġvimos": 49266, "Ġsì": 49267, "defense": 49268, "ĠMcD": 49269, "ĠMarion": 49270, "ĠDont": 49271, "ĠDDR": 49272, "ĠLazar": 49273, "ĠDAR": 49274, "Ġkuv": 49275, "Kn": 49276, "Ġsembla": 49277, "Ġairborne": 49278, "ĠViolence": 49279, "ëIJIJ": 49280, "Ġrestraint": 49281, "Ġwhistles": 49282, "Ġscolded": 49283, "Ġacceso": 49284, "Ġabsolutamente": 49285, "ĠTyl": 49286, "ĠSap": 49287, "¶Ģë¶Ħ": 49288, "itäten": 49289, "adem": 49290, "Ġý": 49291, "Ġprescribe": 49292, "ĠMage": 49293, "ĠHelena": 49294, "å¾Īæľī": 49295, "亲": 49296, "vt": 49297, "Ġvienen": 49298, "Ġsneez": 49299, "Ġmolé": 49300, "Æ°á»Łng": 49301, "Ġtransporting": 49302, "ĠLean": 49303, "Ġkung": 49304, "ÑĥÑĢа": 49305, "ÏĦÎŃ": 49306, "utches": 49307, "onders": 49308, "liyor": 49309, "Nat": 49310, "Ġzij": 49311, "Ġmammal": 49312, "Ġkäyt": 49313, "ĠJoanna": 49314, "sent": 49315, "Ġस": 49316, "Ġvested": 49317, "ĠErfahrung": 49318, "okee": 49319, "Ġclipping": 49320, "ĠListening": 49321, "Ġ(#": 49322, "fö": 49323, "Ġvidare": 49324, "Ġbrittle": 49325, "ĠSTART": 49326, "ĠDamas": 49327, "ĠYog": 49328, "ãĤĵãģ¨": 49329, "gart": 49330, "Ġverlier": 49331, "Ġheartfelt": 49332, "ĠdoÅĽÄĩ": 49333, "ì¹ĺê°Ģ": 49334, ".»": 49335, "Ġmaximal": 49336, "Ġdistintos": 49337, "ĠìĻľëĥIJíķĺë©´": 49338, "Ġsailed": 49339, "Ġconveyed": 49340, "ĠTinder": 49341, "ĠSUPER": 49342, "ниÑĨÑĥ": 49343, "controlled": 49344, "Ġfunz": 49345, "Ġbastards": 49346, "ĠGinsburg": 49347, "Ġnuovo": 49348, "ĠPere": 49349, "ĠJES": 49350, "ĠDingen": 49351, "ĠBets": 49352, "umba": 49353, "acción": 49354, "ĠìŀĪì§Ģë§Į": 49355, "Ġretra": 49356, "ĠLaurent": 49357, "Ġpozy": 49358, "Ġgrooves": 49359, "Ġmáquina": 49360, "Ġminion": 49361, "Ġdeinen": 49362, "ĠShaun": 49363, "×Ļ×Ļ": 49364, "Ġhonorary": 49365, "osaurus": 49366, "Ġzeit": 49367, "Ġespecie": 49368, "ĠBCE": 49369, "аÑĤе": 49370, "Justin": 49371, "ĠWheels": 49372, "ĠìĿ´íķ´": 49373, "ĠبÙĬÙĨ": 49374, "Ġpropulsion": 49375, "Ġperceber": 49376, "ĠNewman": 49377, "å´": 49378, "culosis": 49379, "Mi": 49380, "ĠаккÑĥ": 49381, "Ġmastering": 49382, "Ġläh": 49383, "Ġfists": 49384, "ä»Ķ": 49385, "Ġmarinade": 49386, "Lilly": 49387, "Ġëħ¸ëł¥": 49388, "ĠYH": 49389, "Ġurgently": 49390, "Ġinformational": 49391, "Ġacordo": 49392, "izzy": 49393, "ãģĦãģı": 49394, "ìĿ´ìĸ´": 49395, "imar": 49396, "ĠëĤĺìĺ¤ë": 49397, "Ġtwenties": 49398, "Ġrasp": 49399, "Ġbumpy": 49400, "بة": 49401, "worker": 49402, "Ġquickest": 49403, "Ġattaches": 49404, "виг": 49405, "ĠëĤĺíĥĢë": 49406, "Ġpuree": 49407, "Ġoversized": 49408, "Ġstirred": 49409, "Ġjakim": 49410, "Ġhomicide": 49411, "ãĤĤãģĹ": 49412, "iscilla": 49413, "Ġì±Ļ": 49414, "Ġspeculative": 49415, "Ġassists": 49416, "main": 49417, "jähr": 49418, "indet": 49419, "ĠÅŁur": 49420, "Ġforecasts": 49421, "Ġdiversion": 49422, "Ġtare": 49423, "Ġogl": 49424, "ĠOrganisation": 49425, "ĠChevy": 49426, "Ġbaja": 49427, "andır": 49428, "ĠÙĪÙĦا": 49429, "Ġradiant": 49430, "Ġliaison": 49431, "Ġdemokrat": 49432, "ĠMARC": 49433, "ÏĢοÏħ": 49434, "Ġrunt": 49435, "Ġprécis": 49436, "Ġgeven": 49437, "Ġvéhic": 49438, "ĠJESS": 49439, "STR": 49440, "Ġìĸĺë": 49441, "Ġvisionary": 49442, "Ġburadan": 49443, "ĠãģĤãĤĬ": 49444, "Ġrebirth": 49445, "Ġexhibited": 49446, "ĠMetall": 49447, "olie": 49448, "elyn": 49449, "Ġflavours": 49450, "Ġescrito": 49451, "ĠDelete": 49452, "ĠìķĮìķĺìĸ´": 49453, "ĠÑĥкÑĢаÑĹн": 49454, "Ġinterrupting": 49455, "Ġidentific": 49456, "ĠSuzuki": 49457, "ĠLanding": 49458, "件äºĭæĥħ": 49459, "andi": 49460, "Ġestran": 49461, "Ġcouleur": 49462, "Ġagrad": 49463, "ĠSny": 49464, "Ġà®ĩல": 49465, "Ġander": 49466, "Ġrua": 49467, "Ġprise": 49468, "Ġlaure": 49469, "ĠíĬĢ": 49470, "Ġmoderation": 49471, "Ġerfahren": 49472, "Ġdeconst": 49473, "ĠReese": 49474, "ĠPK": 49475, "etos": 49476, "ãģĵãĤĮãģ§": 49477, "ĠGravity": 49478, "ĠEren": 49479, "Ġoverboard": 49480, "Ġmüsst": 49481, "ĠEmail": 49482, "еÑĢм": 49483, "ydi": 49484, "iÄĻdzy": 49485, "ĠLOU": 49486, "ĠFuÃŁball": 49487, "ĠRD": 49488, "alts": 49489, "ĠìĬ¤íĬ¸ë": 49490, "ĠÐļÑĢаÑģ": 49491, "Ġtelev": 49492, "ĠÑĢо": 49493, "Ġresignation": 49494, "Ġjingle": 49495, "ĠStudien": 49496, "ĠIX": 49497, "ĠSentinel": 49498, "ĠPang": 49499, "éĦ": 49500, "Jake": 49501, "Ġpersonagem": 49502, "Ġmédia": 49503, "ĠChern": 49504, "antically": 49505, "Ġthá»Ŀi": 49506, "Ġparalysis": 49507, "Ġjapanese": 49508, "Ġconex": 49509, "Ġefic": 49510, "Ġunderside": 49511, "Ġneol": 49512, "Ġfian": 49513, "имоÑģÑĤÑĮ": 49514, "Ġquirky": 49515, "Ġpista": 49516, "ĠClement": 49517, "nothing": 49518, "ĠпоеÑħ": 49519, "Ġhorrend": 49520, "Ġconsolidate": 49521, "ploys": 49522, "emaker": 49523, "Jennifer": 49524, "Ġnuméro": 49525, "Ġfamoso": 49526, "ĠNeptune": 49527, "ĠíĸĪìĸ´": 49528, "ĠпÑĢезид": 49529, "Ġsitcom": 49530, "Ġserio": 49531, "Ġmue": 49532, "Ġglands": 49533, "Ġbörjar": 49534, "ĠYJ": 49535, "ĠRiot": 49536, "paragus": 49537, "Ġsegurança": 49538, "Ġimmature": 49539, "ĠMadonna": 49540, "à¸į": 49541, "Ġlingering": 49542, "Ġacesso": 49543, "ĠOrient": 49544, "ĠRecomm": 49545, "Ġcomplac": 49546, "founded": 49547, "attend": 49548, "Ġcielo": 49549, "ĠZhan": 49550, "naires": 49551, "cco": 49552, "Ġ×IJ׳": 49553, "Ġstata": 49554, "Ġcontradictory": 49555, "ĠSé": 49556, "ĠSAN": 49557, "ĠConnie": 49558, "Ġëĭ¹ìĭľ": 49559, "ĠÑģамой": 49560, "Ġmajestic": 49561, "ĠPenguin": 49562, "ĠCOME": 49563, "ÃŃcios": 49564, "pero": 49565, "Ġmg": 49566, "Ġfauc": 49567, "Ġcorrer": 49568, "ĠGottes": 49569, "ĠAnglo": 49570, "Har": 49571, "á»Ĺi": 49572, "Ġvitesse": 49573, "Ġannouncer": 49574, "ĠOmaha": 49575, "kum": 49576, "Ġspared": 49577, "ĠÑĢаза": 49578, "ĠполÑĥÑĩиÑĤÑģÑı": 49579, "Ġtähän": 49580, "Ġпонад": 49581, "Ġpertaining": 49582, "ĠRate": 49583, "iern": 49584, "Gold": 49585, "Ġteste": 49586, "ĠdeÄŁild": 49587, "Ġdamping": 49588, "ĠPartnership": 49589, "zysta": 49590, "geld": 49591, "Ġsmokes": 49592, "ĠMarriage": 49593, "쪽ìĹIJ": 49594, "èħ³": 49595, "isce": 49596, "Ġtryna": 49597, "ĠDirectory": 49598, "ĠëĤĺìĺ¬": 49599, "Ġshameful": 49600, "Ġmentre": 49601, "Ġassigning": 49602, "æĺ¯éĢĻ樣": 49603, "Ġrepertoire": 49604, "Ġobjetos": 49605, "稱": 49606, "Ġunderworld": 49607, "Ġendeavors": 49608, "Ġignite": 49609, "ĠÙĪج": 49610, "Ġexperient": 49611, "ĠÐĹап": 49612, "ĠзаклÑİÑĩ": 49613, "Ġvoltages": 49614, "Ġniego": 49615, "Ġdeficits": 49616, "Ġbuenos": 49617, "ĠSleeping": 49618, "ĠSalem": 49619, "Ġunlocking": 49620, "Ġinteracted": 49621, "Ġentendeu": 49622, "ĠSuperintendent": 49623, "Ġszczegól": 49624, "Ġquas": 49625, "Ġpaling": 49626, "Ġkho": 49627, "بØŃ": 49628, "Ġcolabor": 49629, "ĠпÑĢигоÑĤов": 49630, "Ġmauv": 49631, "ĠJudas": 49632, "ĠAssist": 49633, "ĠÑĤеÑĢÑĢи": 49634, "ĠнаÑģколÑĮко": 49635, "Ġsubsidy": 49636, "ĠEmbassy": 49637, "Ġdagen": 49638, "ĠSanto": 49639, "èĪ¬": 49640, "ש×ķ×ij": 49641, "Ġabruptly": 49642, "ĠAdapt": 49643, "Ġvaak": 49644, "Ġpostal": 49645, "Ġinvestir": 49646, "Ġfiquei": 49647, "Ġdowntime": 49648, "ĠWebb": 49649, "ĠNCAA": 49650, "ĠEstoy": 49651, "олоÑĤ": 49652, "ĠìĤ¬ê±´": 49653, "Ġnationalist": 49654, "ĠKathryn": 49655, "ĠKop": 49656, "éª": 49657, "Sean": 49658, "ONA": 49659, "ĠBj": 49660, "×¢×Ŀ": 49661, "ÃŃb": 49662, "idamente": 49663, "Ġглаза": 49664, "Ġunnie": 49665, "Ġgemaakt": 49666, "ĠINTERVIEWER": 49667, "ĠHaut": 49668, "ίο": 49669, "geois": 49670, "wydd": 49671, "Ġколи": 49672, "Ġtightened": 49673, "Ġplanners": 49674, "Ġherum": 49675, "Ġgörün": 49676, "Ġelectronically": 49677, "Ġceram": 49678, "Ġëĭ¤ìĸijíķľ": 49679, "Ġepilepsy": 49680, "ĠeÄŁ": 49681, "lins": 49682, "ĠShiny": 49683, "æł¡": 49684, "ĠÑģолн": 49685, "Ġmacaron": 49686, "Ġimpacto": 49687, "ĠVegan": 49688, "zeÅĦ": 49689, "ĠRapha": 49690, "ĠPars": 49691, "ĠLEO": 49692, "ãģĬãģ£": 49693, "cü": 49694, "Ġ׾×Ķ×Ļ×ķת": 49695, "Ġähnlich": 49696, "Ġfloss": 49697, "ĠAZ": 49698, "Ġmöchten": 49699, "Ġgrooming": 49700, "Ġgrasses": 49701, "ranch": 49702, "Ġrecibir": 49703, "Ġbouncy": 49704, "ĠHobby": 49705, "Ġviktig": 49706, "Ġbegitu": 49707, "ĠPicasso": 49708, "ĠKush": 49709, "모": 49710, "Ġobstruction": 49711, "Ġë¶ĦìľĦ": 49712, "Ġmicrob": 49713, "ĠWestminster": 49714, "rops": 49715, "dul": 49716, "Ġdevo": 49717, "ĠLehrer": 49718, "ĠAdvisor": 49719, "ucken": 49720, "ĠбÑĥм": 49721, "Ġflattering": 49722, "ĠTruman": 49723, "ĠSempre": 49724, "ĠMcCain": 49725, "ĠHindus": 49726, "Julia": 49727, "Ġwatershed": 49728, "Ġlush": 49729, "ìłĦë": 49730, "Before": 49731, "ĠÐĴÑĤоÑĢ": 49732, "ĠSaaS": 49733, "Ġsitzt": 49734, "Ġbeetle": 49735, "ĠEssential": 49736, "enko": 49737, "ĠëķĮëıĦ": 49738, "Ġrevving": 49739, "Ġpoorer": 49740, "Ġcoerc": 49741, "Ġidee": 49742, "Ġcoû": 49743, "alet": 49744, "Ġzdrow": 49745, "Ġfender": 49746, "growth": 49747, "DING": 49748, "Ġzde": 49749, "ä¸ĬéĿ¢": 49750, "ENTS": 49751, "Ġfacets": 49752, "éļª": 49753, "ushima": 49754, "ĠÅŁeh": 49755, "Ġparasite": 49756, "Ġlapse": 49757, "ĠMeer": 49758, "ĠKund": 49759, "Ġslog": 49760, "Ġbrunch": 49761, "ĠChart": 49762, "arz": 49763, "ĠMUS": 49764, "Ġoffenses": 49765, "Ġinglés": 49766, "Ġfoliage": 49767, "oplan": 49768, "Aut": 49769, 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"Ġfamiliarity": 49828, "Ġдив": 49829, "Ġprojets": 49830, "Ġaqueles": 49831, "ĠGlue": 49832, "those": 49833, "Ġinception": 49834, "Ġaquellos": 49835, "Ġillusions": 49836, "Ġattends": 49837, "rese": 49838, "Ġswarm": 49839, "Ġswab": 49840, "Ġregardez": 49841, "Ġposição": 49842, "Ġakhir": 49843, "Ġextracting": 49844, "Ġanecdote": 49845, "ĠTale": 49846, "Ġвин": 49847, "Ġabges": 49848, "ĠoluÅŁ": 49849, "Ġcomplicado": 49850, "Ġcovari": 49851, "ÑĸÑĤÑĮ": 49852, "Der": 49853, "Ġ×Ļ×Ķ": 49854, "Form": 49855, "Ġìĸ´ì¨Įëĵł": 49856, "Ġreadable": 49857, "Ġinhibit": 49858, "Ġdecipher": 49859, "ĠAngry": 49860, "pg": 49861, "வத": 49862, "ĠÑģобÑģÑĤвенно": 49863, "Ġsamh": 49864, "Ġescr": 49865, "Ġencompasses": 49866, "Ġauster": 49867, "Ġconfisc": 49868, "ĠMandal": 49869, "Ġ}": 49870, "atcher": 49871, "=#": 49872, "çļĦæŶåĢĻ": 49873, "Ġкино": 49874, "Ġstal": 49875, "lungs": 49876, "Ġvole": 49877, "Ġrequis": 49878, "ĠãĤĪ": 49879, "Ġpén": 49880, "Ġlecturer": 49881, "Ġinscription": 49882, "Ġcervical": 49883, 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\ No newline at end of file
diff --git a/high_sync/src/models/whisper/whisper/audio.py b/high_sync/src/models/whisper/whisper/audio.py
new file mode 100644
index 0000000..9434453
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/audio.py
@@ -0,0 +1,125 @@
+import os
+from functools import lru_cache
+from typing import Union
+
+import ffmpeg
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from .utils import exact_div
+
+# hard-coded audio hyperparameters
+SAMPLE_RATE = 16000
+N_FFT = 400
+N_MELS = 80
+HOP_LENGTH = 160
+CHUNK_LENGTH = 30
+N_SAMPLES = CHUNK_LENGTH * SAMPLE_RATE  # 480000: number of samples in a chunk
+N_FRAMES = exact_div(N_SAMPLES, HOP_LENGTH)  # 3000: number of frames in a mel spectrogram input
+
+
+def load_audio(file: str, sr: int = SAMPLE_RATE):
+    """
+    Open an audio file and read as mono waveform, resampling as necessary
+
+    Parameters
+    ----------
+    file: str
+        The audio file to open
+
+    sr: int
+        The sample rate to resample the audio if necessary
+
+    Returns
+    -------
+    A NumPy array containing the audio waveform, in float32 dtype.
+    """
+    try:
+        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
+        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
+        out, _ = (
+            ffmpeg.input(file, threads=0)
+            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
+            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
+        )
+    except ffmpeg.Error as e:
+        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+
+    return np.frombuffer(out, np.int16).flatten().astype(np.float32) / 32768.0
+
+
+def pad_or_trim(array, length: int = N_SAMPLES, *, axis: int = -1):
+    """
+    Pad or trim the audio array to N_SAMPLES, as expected by the encoder.
+    """
+    if torch.is_tensor(array):
+        if array.shape[axis] > length:
+            array = array.index_select(dim=axis, index=torch.arange(length))
+
+        if array.shape[axis] < length:
+            pad_widths = [(0, 0)] * array.ndim
+            pad_widths[axis] = (0, length - array.shape[axis])
+            array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes])
+    else:
+        if array.shape[axis] > length:
+            array = array.take(indices=range(length), axis=axis)
+
+        if array.shape[axis] < length:
+            pad_widths = [(0, 0)] * array.ndim
+            pad_widths[axis] = (0, length - array.shape[axis])
+            array = np.pad(array, pad_widths)
+
+    return array
+
+
+@lru_cache(maxsize=None)
+def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor:
+    """
+    load the mel filterbank matrix for projecting STFT into a Mel spectrogram.
+    Allows decoupling librosa dependency; saved using:
+
+        np.savez_compressed(
+            "mel_filters.npz",
+            mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80),
+        )
+    """
+    assert n_mels == 80, f"Unsupported n_mels: {n_mels}"
+    with np.load(os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz")) as f:
+        return torch.from_numpy(f[f"mel_{n_mels}"]).to(device)
+
+
+def log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):
+    """
+    Compute the log-Mel spectrogram of
+
+    Parameters
+    ----------
+    audio: Union[str, np.ndarray, torch.Tensor], shape = (*)
+        The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz
+
+    n_mels: int
+        The number of Mel-frequency filters, only 80 is supported
+
+    Returns
+    -------
+    torch.Tensor, shape = (80, n_frames)
+        A Tensor that contains the Mel spectrogram
+    """
+    if not torch.is_tensor(audio):
+        if isinstance(audio, str):
+            audio = load_audio(audio)
+        audio = torch.from_numpy(audio)
+
+    window = torch.hann_window(N_FFT).to(audio.device)
+    stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
+
+    magnitudes = stft[:, :-1].abs() ** 2
+
+    filters = mel_filters(audio.device, n_mels)
+    mel_spec = filters @ magnitudes
+
+    log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+    log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+    log_spec = (log_spec + 4.0) / 4.0
+    return log_spec
diff --git a/high_sync/src/models/whisper/whisper/decoding.py b/high_sync/src/models/whisper/whisper/decoding.py
new file mode 100644
index 0000000..b5e54fc
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/decoding.py
@@ -0,0 +1,729 @@
+from dataclasses import dataclass, field
+from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+from torch.distributions import Categorical
+
+from .audio import CHUNK_LENGTH
+from .tokenizer import Tokenizer, get_tokenizer
+from .utils import compression_ratio
+
+if TYPE_CHECKING:
+    from .model import Whisper
+
+
+@torch.no_grad()
+def detect_language(model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None) -> Tuple[Tensor, List[dict]]:
+    """
+    Detect the spoken language in the audio, and return them as list of strings, along with the ids
+    of the most probable language tokens and the probability distribution over all language tokens.
+    This is performed outside the main decode loop in order to not interfere with kv-caching.
+
+    Returns
+    -------
+    language_tokens : Tensor, shape = (n_audio,)
+        ids of the most probable language tokens, which appears after the startoftranscript token.
+    language_probs : List[Dict[str, float]], length = n_audio
+        list of dictionaries containing the probability distribution over all languages.
+    """
+    if tokenizer is None:
+        tokenizer = get_tokenizer(model.is_multilingual)
+    if tokenizer.language is None or tokenizer.language_token not in tokenizer.sot_sequence:
+        raise ValueError(f"This model doesn't have language tokens so it can't perform lang id")
+
+    single = mel.ndim == 2
+    if single:
+        mel = mel.unsqueeze(0)
+
+    # skip encoder forward pass if already-encoded audio features were given
+    if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state):
+        mel = model.encoder(mel)
+
+    # forward pass using a single token, startoftranscript
+    n_audio = mel.shape[0]
+    x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device)  # [n_audio, 1]
+    logits = model.logits(x, mel)[:, 0]
+
+    # collect detected languages; suppress all non-language tokens
+    mask = torch.ones(logits.shape[-1], dtype=torch.bool)
+    mask[list(tokenizer.all_language_tokens)] = False
+    logits[:, mask] = -np.inf
+    language_tokens = logits.argmax(dim=-1)
+    language_token_probs = logits.softmax(dim=-1).cpu()
+    language_probs = [
+        {
+            c: language_token_probs[i, j].item()
+            for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes)
+        }
+        for i in range(n_audio)
+    ]
+
+    if single:
+        language_tokens = language_tokens[0]
+        language_probs = language_probs[0]
+
+    return language_tokens, language_probs
+
+
+@dataclass(frozen=True)
+class DecodingOptions:
+    task: str = "transcribe"  # whether to perform X->X "transcribe" or X->English "translate"
+    language: Optional[str] = None  # language that the audio is in; uses detected language if None
+
+    # sampling-related options
+    temperature: float = 0.0
+    sample_len: Optional[int] = None  # maximum number of tokens to sample
+    best_of: Optional[int] = None     # number of independent samples to collect, when t > 0
+    beam_size: Optional[int] = None   # number of beams in beam search, when t == 0
+    patience: Optional[float] = None  # patience in beam search (https://arxiv.org/abs/2204.05424)
+
+    # options for ranking generations (either beams or best-of-N samples)
+    length_penalty: Optional[float] = None   # "alpha" in Google NMT, None defaults to length norm
+
+    # prompt, prefix, and token suppression
+    prompt: Optional[Union[str, List[int]]] = None   # text or tokens for the previous context
+    prefix: Optional[Union[str, List[int]]] = None   # text or tokens to prefix the current context
+    suppress_blank: bool = True                      # this will suppress blank outputs
+
+    # list of tokens ids (or comma-separated token ids) to suppress
+    # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()`
+    suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1"
+
+    # timestamp sampling options
+    without_timestamps: bool = False              # use <|notimestamps|> to sample text tokens only
+    max_initial_timestamp: Optional[float] = 1.0  # the initial timestamp cannot be later than this
+
+    # implementation details
+    fp16: bool = True  # use fp16 for most of the calculation
+
+
+@dataclass(frozen=True)
+class DecodingResult:
+    audio_features: Tensor
+    language: str
+    encoder_embeddings: np.ndarray
+    decoder_embeddings: np.ndarray
+    language_probs: Optional[Dict[str, float]] = None
+    tokens: List[int] = field(default_factory=list)
+    text: str = ""
+    avg_logprob: float = np.nan
+    no_speech_prob: float = np.nan
+    temperature: float = np.nan
+    compression_ratio: float = np.nan
+
+
+class Inference:
+    def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor:
+        """Perform a forward pass on the decoder and return per-token logits"""
+        raise NotImplementedError
+
+    def rearrange_kv_cache(self, source_indices) -> None:
+        """Update the key-value cache according to the updated beams"""
+        raise NotImplementedError
+
+    def cleanup_caching(self) -> None:
+        """Clean up any resources or hooks after decoding is finished"""
+        pass
+
+
+class PyTorchInference(Inference):
+    def __init__(self, model: "Whisper", initial_token_length: int):
+        self.model: "Whisper" = model
+        self.initial_token_length = initial_token_length
+        self.kv_cache = {}
+        self.hooks = []
+
+    def logits(self, tokens: Tensor, audio_features: Tensor, include_embeddings=False) -> Tensor:
+        if not self.kv_cache:
+            self.kv_cache, self.hooks = self.model.install_kv_cache_hooks()
+
+        if tokens.shape[-1] > self.initial_token_length:
+            # only need to use the last token except in the first forward pass
+            tokens = tokens[:, -1:]
+
+        return_val = self.model.decoder(tokens, audio_features,
+                                        kv_cache=self.kv_cache, include_embeddings=include_embeddings)
+        return return_val
+
+    def cleanup_caching(self):
+        for hook in self.hooks:
+            hook.remove()
+
+        self.kv_cache = {}
+        self.hooks = []
+
+    def rearrange_kv_cache(self, source_indices):
+        for module, tensor in self.kv_cache.items():
+            # update the key/value cache to contain the selected sequences
+            self.kv_cache[module] = tensor[source_indices].detach()
+
+
+class SequenceRanker:
+    def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]) -> List[int]:
+        """
+        Given a list of groups of samples and their cumulative log probabilities,
+        return the indices of the samples in each group to select as the final result
+        """
+        raise NotImplementedError
+
+
+class MaximumLikelihoodRanker(SequenceRanker):
+    """
+    Select the sample with the highest log probabilities, penalized using either
+    a simple length normalization or Google NMT paper's length penalty
+    """
+
+    def __init__(self, length_penalty: Optional[float]):
+        self.length_penalty = length_penalty
+
+    def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]):
+        def scores(logprobs, lengths):
+            result = []
+            for logprob, length in zip(logprobs, lengths):
+                if self.length_penalty is None:
+                    penalty = length
+                else:
+                    # from the Google NMT paper
+                    penalty = ((5 + length) / 6) ** self.length_penalty
+                result.append(logprob / penalty)
+            return result
+
+        # get the sequence with the highest score
+        lengths = [[len(t) for t in s] for s in tokens]
+        return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)]
+
+
+class TokenDecoder:
+    def reset(self):
+        """Initialize any stateful variables for decoding a new sequence"""
+
+    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
+        """Specify how to select the next token, based on the current trace and logits
+
+        Parameters
+        ----------
+        tokens : Tensor, shape = (n_batch, current_sequence_length)
+            all tokens in the context so far, including the prefix and sot_sequence tokens
+
+        logits : Tensor, shape = (n_batch, vocab_size)
+            per-token logits of the probability distribution at the current step
+
+        sum_logprobs : Tensor, shape = (n_batch)
+            cumulative log probabilities for each sequence
+
+        Returns
+        -------
+        tokens : Tensor, shape = (n_batch, current_sequence_length + 1)
+            the tokens, appended with the selected next token
+
+        completed : bool
+            True if all sequences has reached the end of text
+
+        """
+        raise NotImplementedError
+
+    def finalize(
+        self, tokens: Tensor, sum_logprobs: Tensor
+    ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]:
+        """Finalize search and return the final candidate sequences
+
+        Parameters
+        ----------
+        tokens : Tensor, shape = (n_audio, n_group, current_sequence_length)
+            all tokens in the context so far, including the prefix and sot_sequence
+
+        sum_logprobs : Tensor, shape = (n_audio, n_group)
+            cumulative log probabilities for each sequence
+
+        Returns
+        -------
+        tokens : Sequence[Sequence[Tensor]], length = n_audio
+            sequence of Tensors containing candidate token sequences, for each audio input
+
+        sum_logprobs : List[List[float]], length = n_audio
+            sequence of cumulative log probabilities corresponding to the above
+
+        """
+        raise NotImplementedError
+
+
+class GreedyDecoder(TokenDecoder):
+    def __init__(self, temperature: float, eot: int):
+        self.temperature = temperature
+        self.eot = eot
+
+    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
+        temperature = self.temperature
+        if temperature == 0:
+            next_tokens = logits.argmax(dim=-1)
+        else:
+            next_tokens = Categorical(logits=logits / temperature).sample()
+
+        logprobs = F.log_softmax(logits.float(), dim=-1)
+        current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens]
+        sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot)
+
+        next_tokens[tokens[:, -1] == self.eot] = self.eot
+        tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1)
+
+        completed = (tokens[:, -1] == self.eot).all()
+        return tokens, completed
+
+    def finalize(self, tokens: Tensor, sum_logprobs: Tensor):
+        # make sure each sequence has at least one EOT token at the end
+        tokens = F.pad(tokens, (0, 1), value=self.eot)
+        return tokens, sum_logprobs.tolist()
+
+
+class BeamSearchDecoder(TokenDecoder):
+    def __init__(self, beam_size: int, eot: int, inference: Inference, patience: Optional[float] = None):
+        self.beam_size = beam_size
+        self.eot = eot
+        self.inference = inference
+        self.patience = patience or 1.0
+        self.max_candidates: int = round(beam_size * self.patience)
+        self.finished_sequences = None
+
+        assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})"
+
+    def reset(self):
+        self.finished_sequences = None
+
+    def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]:
+        if tokens.shape[0] % self.beam_size != 0:
+            raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0")
+
+        n_audio = tokens.shape[0] // self.beam_size
+        if self.finished_sequences is None:  # for the first update
+            self.finished_sequences = [{} for _ in range(n_audio)]
+
+        logprobs = F.log_softmax(logits.float(), dim=-1)
+        next_tokens, source_indices, finished_sequences = [], [], []
+        for i in range(n_audio):
+            scores, sources, finished = {}, {}, {}
+
+            # STEP 1: calculate the cumulative log probabilities for possible candidates
+            for j in range(self.beam_size):
+                idx = i * self.beam_size + j
+                prefix = tokens[idx].tolist()
+                for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)):
+                    new_logprob = (sum_logprobs[idx] + logprob).item()
+                    sequence = tuple(prefix + [token.item()])
+                    scores[sequence] = new_logprob
+                    sources[sequence] = idx
+
+            # STEP 2: rank the candidates and keep the top beam_size sequences for each audio
+            saved = 0
+            for sequence in sorted(scores, key=scores.get, reverse=True):
+                if sequence[-1] == self.eot:
+                    finished[sequence] = scores[sequence]
+                else:
+                    sum_logprobs[len(next_tokens)] = scores[sequence]
+                    next_tokens.append(sequence)
+                    source_indices.append(sources[sequence])
+
+                    saved += 1
+                    if saved == self.beam_size:
+                        break
+
+            finished_sequences.append(finished)
+
+        tokens = torch.tensor(next_tokens, device=tokens.device)
+        self.inference.rearrange_kv_cache(source_indices)
+
+        # add newly finished sequences to self.finished_sequences
+        assert len(self.finished_sequences) == len(finished_sequences)
+        for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences):
+            for seq in sorted(newly_finished, key=newly_finished.get, reverse=True):
+                if len(previously_finished) >= self.max_candidates:
+                    break  # the candidate list is full
+                previously_finished[seq] = newly_finished[seq]
+
+        # mark as completed if all audio has enough number of samples
+        completed = all(
+            len(sequences) >= self.max_candidates for sequences in self.finished_sequences
+        )
+        return tokens, completed
+
+    def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor):
+        # collect all finished sequences, including patience, and add unfinished ones if not enough
+        sum_logprobs = sum_logprobs.cpu()
+        for i, sequences in enumerate(self.finished_sequences):
+            if len(sequences) < self.beam_size:  # when not enough sequences are finished
+                for j in list(np.argsort(sum_logprobs[i]))[::-1]:
+                    sequence = preceding_tokens[i, j].tolist() + [self.eot]
+                    sequences[tuple(sequence)] = sum_logprobs[i][j].item()
+                    if len(sequences) >= self.beam_size:
+                        break
+
+        tokens: List[List[Tensor]] = [
+            [torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences
+        ]
+        sum_logprobs: List[List[float]] = [
+            list(sequences.values()) for sequences in self.finished_sequences
+        ]
+        return tokens, sum_logprobs
+
+
+class LogitFilter:
+    def apply(self, logits: Tensor, tokens: Tensor) -> None:
+        """Apply any filtering or masking to logits in-place
+
+        Parameters
+        ----------
+        logits : Tensor, shape = (n_batch, vocab_size)
+            per-token logits of the probability distribution at the current step
+
+        tokens : Tensor, shape = (n_batch, current_sequence_length)
+            all tokens in the context so far, including the prefix and sot_sequence tokens
+
+        """
+        raise NotImplementedError
+
+
+class SuppressBlank(LogitFilter):
+    def __init__(self, tokenizer: Tokenizer, sample_begin: int):
+        self.tokenizer = tokenizer
+        self.sample_begin = sample_begin
+
+    def apply(self, logits: Tensor, tokens: Tensor):
+        if tokens.shape[1] == self.sample_begin:
+            logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf
+
+
+class SuppressTokens(LogitFilter):
+    def __init__(self, suppress_tokens: Sequence[int]):
+        self.suppress_tokens = list(suppress_tokens)
+
+    def apply(self, logits: Tensor, tokens: Tensor):
+        logits[:, self.suppress_tokens] = -np.inf
+
+
+class ApplyTimestampRules(LogitFilter):
+    def __init__(
+        self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int]
+    ):
+        self.tokenizer = tokenizer
+        self.sample_begin = sample_begin
+        self.max_initial_timestamp_index = max_initial_timestamp_index
+
+    def apply(self, logits: Tensor, tokens: Tensor):
+        # suppress <|notimestamps|> which is handled by without_timestamps
+        if self.tokenizer.no_timestamps is not None:
+            logits[:, self.tokenizer.no_timestamps] = -np.inf
+
+        # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
+        for k in range(tokens.shape[0]):
+            seq = [t for t in tokens[k, self.sample_begin :].tolist()]
+            last_was_timestamp = len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin
+            penultimate_was_timestamp = len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin
+
+            if last_was_timestamp:
+                if penultimate_was_timestamp:  # has to be non-timestamp
+                    logits[k, self.tokenizer.timestamp_begin :] = -np.inf
+                else:  # cannot be normal text tokens
+                    logits[k, : self.tokenizer.eot] = -np.inf
+
+        # apply the `max_initial_timestamp` option
+        if tokens.shape[1] == self.sample_begin and self.max_initial_timestamp_index is not None:
+            last_allowed = self.tokenizer.timestamp_begin + self.max_initial_timestamp_index
+            logits[:, last_allowed + 1 :] = -np.inf
+
+        # if sum of probability over timestamps is above any other token, sample timestamp
+        logprobs = F.log_softmax(logits.float(), dim=-1)
+        for k in range(tokens.shape[0]):
+            timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp(dim=-1)
+            max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max()
+            if timestamp_logprob > max_text_token_logprob:
+                logits[k, : self.tokenizer.timestamp_begin] = -np.inf
+
+
+class DecodingTask:
+    inference: Inference
+    sequence_ranker: SequenceRanker
+    decoder: TokenDecoder
+    logit_filters: List[LogitFilter]
+
+    def __init__(self, model: "Whisper", options: DecodingOptions):
+        self.model = model
+
+        language = options.language or "en"
+        tokenizer = get_tokenizer(model.is_multilingual, language=language, task=options.task)
+        self.tokenizer: Tokenizer = tokenizer
+        self.options: DecodingOptions = self._verify_options(options)
+
+        self.n_group: int = options.beam_size or options.best_of or 1
+        self.n_ctx: int = model.dims.n_text_ctx
+        self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2
+
+        self.sot_sequence: Tuple[int] = tokenizer.sot_sequence
+        if self.options.without_timestamps:
+            self.sot_sequence = tokenizer.sot_sequence_including_notimestamps
+
+        self.initial_tokens: Tuple[int] = self._get_initial_tokens()
+        self.sample_begin: int = len(self.initial_tokens)
+        self.sot_index: int = self.initial_tokens.index(tokenizer.sot)
+
+        # inference: implements the forward pass through the decoder, including kv caching
+        self.inference = PyTorchInference(model, len(self.initial_tokens))
+
+        # sequence ranker: implements how to rank a group of sampled sequences
+        self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty)
+
+        # decoder: implements how to select the next tokens, given the autoregressive distribution
+        if options.beam_size is not None:
+            self.decoder = BeamSearchDecoder(
+                options.beam_size, tokenizer.eot, self.inference, options.patience
+            )
+        else:
+            self.decoder = GreedyDecoder(options.temperature, tokenizer.eot)
+
+        # logit filters: applies various rules to suppress or penalize certain tokens
+        self.logit_filters = []
+        if self.options.suppress_blank:
+            self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin))
+        if self.options.suppress_tokens:
+            self.logit_filters.append(SuppressTokens(self._get_suppress_tokens()))
+        if not options.without_timestamps:
+            precision = CHUNK_LENGTH / model.dims.n_audio_ctx  # usually 0.02 seconds
+            max_initial_timestamp_index = None
+            if options.max_initial_timestamp:
+                max_initial_timestamp_index = round(self.options.max_initial_timestamp / precision)
+            self.logit_filters.append(
+                ApplyTimestampRules(tokenizer, self.sample_begin, max_initial_timestamp_index)
+            )
+
+    def _verify_options(self, options: DecodingOptions) -> DecodingOptions:
+        if options.beam_size is not None and options.best_of is not None:
+            raise ValueError("beam_size and best_of can't be given together")
+        if options.temperature == 0:
+            if options.best_of is not None:
+                raise ValueError("best_of with greedy sampling (T=0) is not compatible")
+        if options.patience is not None and options.beam_size is None:
+            raise ValueError("patience requires beam_size to be given")
+        if options.length_penalty is not None and not (0 <= options.length_penalty <= 1):
+            raise ValueError("length_penalty (alpha) should be a value between 0 and 1")
+
+        return options
+
+    def _get_initial_tokens(self) -> Tuple[int]:
+        tokens = list(self.sot_sequence)
+        prefix = self.options.prefix
+        prompt = self.options.prompt
+
+        if prefix:
+            prefix_tokens = (
+                self.tokenizer.encode(" " + prefix.strip()) if isinstance(prefix, str) else prefix
+            )
+            if self.sample_len is not None:
+                max_prefix_len = self.n_ctx // 2 - self.sample_len
+                prefix_tokens = prefix_tokens[-max_prefix_len:]
+            tokens = tokens + prefix_tokens
+
+        if prompt:
+            prompt_tokens = (
+                self.tokenizer.encode(" " + prompt.strip()) if isinstance(prompt, str) else prompt
+            )
+            tokens = [self.tokenizer.sot_prev] + prompt_tokens[-(self.n_ctx // 2 - 1) :] + tokens
+
+        return tuple(tokens)
+
+    def _get_suppress_tokens(self) -> Tuple[int]:
+        suppress_tokens = self.options.suppress_tokens
+
+        if isinstance(suppress_tokens, str):
+            suppress_tokens = [int(t) for t in suppress_tokens.split(",")]
+
+        if -1 in suppress_tokens:
+            suppress_tokens = [t for t in suppress_tokens if t >= 0]
+            suppress_tokens.extend(self.tokenizer.non_speech_tokens)
+        elif suppress_tokens is None or len(suppress_tokens) == 0:
+            suppress_tokens = []  # interpret empty string as an empty list
+        else:
+            assert isinstance(suppress_tokens, list), "suppress_tokens must be a list"
+
+        suppress_tokens.extend(
+            [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm]
+        )
+        if self.tokenizer.no_speech is not None:
+            # no-speech probability is collected separately
+            suppress_tokens.append(self.tokenizer.no_speech)
+
+        return tuple(sorted(set(suppress_tokens)))
+
+    def _get_audio_features(self, mel: Tensor, include_embeddings: bool = False):
+        if self.options.fp16:
+            mel = mel.half()
+
+        if mel.shape[-2:] == (self.model.dims.n_audio_ctx, self.model.dims.n_audio_state):
+            # encoded audio features are given; skip audio encoding
+            audio_features = mel
+        else:
+            result = self.model.encoder(mel, include_embeddings)
+            if include_embeddings:
+                audio_features, embeddings = result
+            else:
+                audio_features = result
+
+        if audio_features.dtype != (torch.float16 if self.options.fp16 else torch.float32):
+            return TypeError(f"audio_features has an incorrect dtype: {audio_features.dtype}")
+
+        if include_embeddings:
+            return audio_features, embeddings
+        else:
+            return audio_features
+
+    def _detect_language(self, audio_features: Tensor, tokens: Tensor):
+        languages = [self.options.language] * audio_features.shape[0]
+        lang_probs = None
+
+        if self.options.language is None or self.options.task == "lang_id":
+            lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer)
+            languages = [max(probs, key=probs.get) for probs in lang_probs]
+            if self.options.language is None:
+                tokens[:, self.sot_index + 1] = lang_tokens  # write language tokens
+
+        return languages, lang_probs
+
+    def _main_loop(self, audio_features: Tensor, tokens: Tensor):
+        assert audio_features.shape[0] == tokens.shape[0]
+        n_batch = tokens.shape[0]
+        sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device)
+        no_speech_probs = [np.nan] * n_batch
+
+        try:
+            embeddings = []
+            for i in range(self.sample_len):
+                logits, token_embeddings = self.inference.logits(tokens, audio_features, include_embeddings=True)
+
+                if i == 0 and self.tokenizer.no_speech is not None:  # save no_speech_probs
+                    probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1)
+                    no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist()
+
+                # now we need to consider the logits at the last token only
+                logits = logits[:, -1]
+                token_embeddings = token_embeddings[:, :, -1]
+
+                # Append embeddings together
+                embeddings.append(token_embeddings)
+
+                # apply the logit filters, e.g. for suppressing or applying penalty to
+                for logit_filter in self.logit_filters:
+                    logit_filter.apply(logits, tokens)
+
+                # expand the tokens tensor with the selected next tokens
+                tokens, completed = self.decoder.update(tokens, logits, sum_logprobs)
+
+                if completed or tokens.shape[-1] > self.n_ctx:
+                    break
+        finally:
+            if completed:
+                embeddings = embeddings[:-1]
+            embeddings = np.stack(embeddings, 2)
+            self.inference.cleanup_caching()
+
+        return tokens, sum_logprobs, no_speech_probs, embeddings
+
+    @torch.no_grad()
+    def run(self, mel: Tensor) -> List[DecodingResult]:
+        self.decoder.reset()
+        tokenizer: Tokenizer = self.tokenizer
+        n_audio: int = mel.shape[0]
+
+        # encoder forward pass
+        forward_pass: Tuple[Tensor, np.ndarray] = self._get_audio_features(mel, include_embeddings=True)
+        audio_features, encoder_embeddings = forward_pass
+        tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1)
+
+        # detect language if requested, overwriting the language token
+        languages, language_probs = self._detect_language(audio_features, tokens)
+        if self.options.task == "lang_id":
+            return [
+                DecodingResult(audio_features=features, language=language, language_probs=probs)
+                for features, language, probs in zip(audio_features, languages, language_probs)
+            ]
+
+        # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling
+        audio_features = audio_features.repeat_interleave(self.n_group, dim=0)
+        tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device)
+
+        # call the main sampling loop
+        tokens, sum_logprobs, no_speech_probs, decoder_embeddings = self._main_loop(audio_features, tokens)
+
+        # reshape the tensors to have (n_audio, n_group) as the first two dimensions
+        audio_features = audio_features[:: self.n_group]
+        no_speech_probs = no_speech_probs[:: self.n_group]
+        assert audio_features.shape[0] == len(no_speech_probs) == n_audio
+
+        tokens = tokens.reshape(n_audio, self.n_group, -1)
+        sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group)
+
+        # get the final candidates for each group, and slice between the first sampled token and EOT
+        tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs)
+        tokens: List[List[Tensor]] = [
+            [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens
+        ]
+
+        # select the top-ranked sample in each group
+        selected = self.sequence_ranker.rank(tokens, sum_logprobs)
+        tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)]
+        texts: List[str] = [tokenizer.decode(t).strip() for t in tokens]
+
+        sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)]
+        avg_logprobs: List[float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)]
+
+        fields = (texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs)
+        if len(set(map(len, fields))) != 1:
+            raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}")
+
+        return [
+            DecodingResult(
+                audio_features=features,
+                language=language,
+                tokens=tokens,
+                text=text,
+                avg_logprob=avg_logprob,
+                no_speech_prob=no_speech_prob,
+                temperature=self.options.temperature,
+                compression_ratio=compression_ratio(text),
+                encoder_embeddings=encoder_embeddings,
+                decoder_embeddings=decoder_embeddings
+            )
+            for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields)
+        ]
+
+
+@torch.no_grad()
+def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> Union[DecodingResult, List[DecodingResult]]:
+    """
+    Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s).
+
+    Parameters
+    ----------
+    model: Whisper
+        the Whisper model instance
+
+    mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000)
+        A tensor containing the Mel spectrogram(s)
+
+    options: DecodingOptions
+        A dataclass that contains all necessary options for decoding 30-second segments
+
+    Returns
+    -------
+    result: Union[DecodingResult, List[DecodingResult]]
+        The result(s) of decoding contained in `DecodingResult` dataclass instance(s)
+    """
+    single = mel.ndim == 2
+    if single:
+        mel = mel.unsqueeze(0)
+
+    result = DecodingTask(model, options).run(mel)
+
+    if single:
+        result = result[0]
+
+    return result
diff --git a/high_sync/src/models/whisper/whisper/model.py b/high_sync/src/models/whisper/whisper/model.py
new file mode 100644
index 0000000..acb6a0b
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/model.py
@@ -0,0 +1,290 @@
+from dataclasses import dataclass
+from typing import Dict
+from typing import Iterable, Optional
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+from torch import nn
+
+from .transcribe import transcribe as transcribe_function
+from .decoding import detect_language as detect_language_function, decode as decode_function
+
+
+@dataclass
+class ModelDimensions:
+    n_mels: int
+    n_audio_ctx: int
+    n_audio_state: int
+    n_audio_head: int
+    n_audio_layer: int
+    n_vocab: int
+    n_text_ctx: int
+    n_text_state: int
+    n_text_head: int
+    n_text_layer: int
+
+
+class LayerNorm(nn.LayerNorm):
+    def forward(self, x: Tensor) -> Tensor:
+        return super().forward(x.float()).type(x.dtype)
+
+
+class Linear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        return F.linear(
+            x, self.weight.to(x.dtype), None if self.bias is None else self.bias.to(x.dtype)
+        )
+
+
+class Conv1d(nn.Conv1d):
+    def _conv_forward(self, x: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor:
+        return super()._conv_forward(
+            x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype)
+        )
+
+
+def sinusoids(length, channels, max_timescale=10000):
+    """Returns sinusoids for positional embedding"""
+    assert channels % 2 == 0
+    log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+    inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
+    scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+    return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, n_state: int, n_head: int):
+        super().__init__()
+        self.n_head = n_head
+        self.query = Linear(n_state, n_state)
+        self.key = Linear(n_state, n_state, bias=False)
+        self.value = Linear(n_state, n_state)
+        self.out = Linear(n_state, n_state)
+
+    def forward(
+        self,
+        x: Tensor,
+        xa: Optional[Tensor] = None,
+        mask: Optional[Tensor] = None,
+        kv_cache: Optional[dict] = None,
+    ):
+        q = self.query(x)
+
+        if kv_cache is None or xa is None:
+            # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors;
+            # otherwise, perform key/value projections for self- or cross-attention as usual.
+            k = self.key(x if xa is None else xa)
+            v = self.value(x if xa is None else xa)
+        else:
+            # for cross-attention, calculate keys and values once and reuse in subsequent calls.
+            k = kv_cache.get(self.key, self.key(xa))
+            v = kv_cache.get(self.value, self.value(xa))
+
+        wv = self.qkv_attention(q, k, v, mask)
+        return self.out(wv)
+
+    def qkv_attention(self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None):
+        n_batch, n_ctx, n_state = q.shape
+        scale = (n_state // self.n_head) ** -0.25
+        q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale
+        k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale
+        v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3)
+
+        qk = q @ k
+        if mask is not None:
+            qk = qk + mask[:n_ctx, :n_ctx]
+
+        w = F.softmax(qk.float(), dim=-1).to(q.dtype)
+        return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2)
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(self, n_state: int, n_head: int, cross_attention: bool = False):
+        super().__init__()
+
+        self.attn = MultiHeadAttention(n_state, n_head)
+        self.attn_ln = LayerNorm(n_state)
+
+        self.cross_attn = MultiHeadAttention(n_state, n_head) if cross_attention else None
+        self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None
+
+        n_mlp = n_state * 4
+        self.mlp = nn.Sequential(Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state))
+        self.mlp_ln = LayerNorm(n_state)
+
+    def forward(
+        self,
+        x: Tensor,
+        xa: Optional[Tensor] = None,
+        mask: Optional[Tensor] = None,
+        kv_cache: Optional[dict] = None,
+    ):
+        x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)
+        if self.cross_attn:
+            x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)
+        x = x + self.mlp(self.mlp_ln(x))
+        return x
+
+
+class AudioEncoder(nn.Module):
+    def __init__(self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
+        super().__init__()
+        self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1)
+        self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1)
+        self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state))
+
+        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
+            [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)]
+        )
+        self.ln_post = LayerNorm(n_state)
+
+    def forward(self, x: Tensor, include_embeddings: bool = False):
+        """
+        x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
+            the mel spectrogram of the audio
+        include_embeddings: bool
+            whether to include intermediate steps in the output
+        """
+        x = F.gelu(self.conv1(x))
+        x = F.gelu(self.conv2(x))
+        x = x.permute(0, 2, 1)
+
+        assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape"
+        x = (x + self.positional_embedding).to(x.dtype)
+
+        if include_embeddings:
+            embeddings = [x.cpu().detach().numpy()]
+
+        for block in self.blocks:
+            x = block(x)
+            if include_embeddings:
+                embeddings.append(x.cpu().detach().numpy())
+
+        x = self.ln_post(x)
+
+        if include_embeddings:
+            embeddings = np.stack(embeddings, axis=1)
+            return x, embeddings
+        else:
+            return x
+
+
+class TextDecoder(nn.Module):
+    def __init__(self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int):
+        super().__init__()
+
+        self.token_embedding = nn.Embedding(n_vocab, n_state)
+        self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state))
+
+        self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList(
+            [ResidualAttentionBlock(n_state, n_head, cross_attention=True) for _ in range(n_layer)]
+        )
+        self.ln = LayerNorm(n_state)
+
+        mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1)
+        self.register_buffer("mask", mask, persistent=False)
+
+    def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None, include_embeddings: bool = False):
+        """
+        x : torch.LongTensor, shape = (batch_size, <= n_ctx)
+            the text tokens
+        xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx)
+            the encoded audio features to be attended on
+        include_embeddings : bool
+            Whether to include intermediate values in the output to this function
+        """
+        offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0
+        x = self.token_embedding(x) + self.positional_embedding[offset : offset + x.shape[-1]]
+        x = x.to(xa.dtype)
+
+        if include_embeddings:
+            embeddings = [x.cpu().detach().numpy()]
+
+        for block in self.blocks:
+            x = block(x, xa, mask=self.mask, kv_cache=kv_cache)
+            if include_embeddings:
+                embeddings.append(x.cpu().detach().numpy())
+
+        x = self.ln(x)
+        logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float()
+
+        if include_embeddings:
+            embeddings = np.stack(embeddings, axis=1)
+            return logits, embeddings
+        else:
+            return logits
+
+
+class Whisper(nn.Module):
+    def __init__(self, dims: ModelDimensions):
+        super().__init__()
+        self.dims = dims
+        self.encoder = AudioEncoder(
+            self.dims.n_mels,
+            self.dims.n_audio_ctx,
+            self.dims.n_audio_state,
+            self.dims.n_audio_head,
+            self.dims.n_audio_layer,
+        )
+        self.decoder = TextDecoder(
+            self.dims.n_vocab,
+            self.dims.n_text_ctx,
+            self.dims.n_text_state,
+            self.dims.n_text_head,
+            self.dims.n_text_layer,
+        )
+
+    def embed_audio(self, mel: torch.Tensor):
+        return self.encoder.forward(mel)
+
+    def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor):
+        return self.decoder.forward(tokens, audio_features)
+
+    def forward(self, mel: torch.Tensor, tokens: torch.Tensor) -> Dict[str, torch.Tensor]:
+        return self.decoder(tokens, self.encoder(mel))
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    @property
+    def is_multilingual(self):
+        return self.dims.n_vocab == 51865
+
+    def install_kv_cache_hooks(self, cache: Optional[dict] = None):
+        """
+        The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value
+        tensors calculated for the previous positions. This method returns a dictionary that stores
+        all caches, and the necessary hooks for the key and value projection modules that save the
+        intermediate tensors to be reused during later calculations.
+
+        Returns
+        -------
+        cache : Dict[nn.Module, torch.Tensor]
+            A dictionary object mapping the key/value projection modules to its cache
+        hooks : List[RemovableHandle]
+            List of PyTorch RemovableHandle objects to stop the hooks to be called
+        """
+        cache = {**cache} if cache is not None else {}
+        hooks = []
+
+        def save_to_cache(module, _, output):
+            if module not in cache or output.shape[1] > self.decoder.positional_embedding.shape[0]:
+                cache[module] = output  # save as-is, for the first token or cross attention
+            else:
+                cache[module] = torch.cat([cache[module], output], dim=1).detach()
+            return cache[module]
+
+        def install_hooks(layer: nn.Module):
+            if isinstance(layer, MultiHeadAttention):
+                hooks.append(layer.key.register_forward_hook(save_to_cache))
+                hooks.append(layer.value.register_forward_hook(save_to_cache))
+
+        self.decoder.apply(install_hooks)
+        return cache, hooks
+
+    detect_language = detect_language_function
+    transcribe = transcribe_function
+    decode = decode_function
diff --git a/high_sync/src/models/whisper/whisper/normalizers/__init__.py b/high_sync/src/models/whisper/whisper/normalizers/__init__.py
new file mode 100644
index 0000000..0b10d5a
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/normalizers/__init__.py
@@ -0,0 +1,2 @@
+from .basic import BasicTextNormalizer
+from .english import EnglishTextNormalizer
diff --git a/high_sync/src/models/whisper/whisper/normalizers/basic.py b/high_sync/src/models/whisper/whisper/normalizers/basic.py
new file mode 100644
index 0000000..ef8d249
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/normalizers/basic.py
@@ -0,0 +1,71 @@
+import re
+import unicodedata
+
+import regex
+
+# non-ASCII letters that are not separated by "NFKD" normalization
+ADDITIONAL_DIACRITICS = {
+    "œ": "oe",
+    "Œ": "OE",
+    "ø": "o",
+    "Ø": "O",
+    "æ": "ae",
+    "Æ": "AE",
+    "ß": "ss",
+    "ẞ": "SS",
+    "đ": "d",
+    "Đ": "D",
+    "ð": "d",
+    "Ð": "D",
+    "þ": "th",
+    "Þ": "th",
+    "ł": "l",
+    "Ł": "L",
+}
+
+
+def remove_symbols_and_diacritics(s: str, keep=""):
+    """
+    Replace any other markers, symbols, and punctuations with a space,
+    and drop any diacritics (category 'Mn' and some manual mappings)
+    """
+    return "".join(
+        c
+        if c in keep
+        else ADDITIONAL_DIACRITICS[c]
+        if c in ADDITIONAL_DIACRITICS
+        else ""
+        if unicodedata.category(c) == "Mn"
+        else " "
+        if unicodedata.category(c)[0] in "MSP"
+        else c
+        for c in unicodedata.normalize("NFKD", s)
+    )
+
+
+def remove_symbols(s: str):
+    """
+    Replace any other markers, symbols, punctuations with a space, keeping diacritics
+    """
+    return "".join(
+        " " if unicodedata.category(c)[0] in "MSP" else c for c in unicodedata.normalize("NFKC", s)
+    )
+
+
+class BasicTextNormalizer:
+    def __init__(self, remove_diacritics: bool = False, split_letters: bool = False):
+        self.clean = remove_symbols_and_diacritics if remove_diacritics else remove_symbols
+        self.split_letters = split_letters
+
+    def __call__(self, s: str):
+        s = s.lower()
+        s = re.sub(r"[<\[][^>\]]*[>\]]", "", s)  # remove words between brackets
+        s = re.sub(r"\(([^)]+?)\)", "", s)  # remove words between parenthesis
+        s = self.clean(s).lower()
+
+        if self.split_letters:
+            s = " ".join(regex.findall(r"\X", s, regex.U))
+
+        s = re.sub(r"\s+", " ", s)  # replace any successive whitespace characters with a space
+
+        return s
diff --git a/high_sync/src/models/whisper/whisper/normalizers/english.json b/high_sync/src/models/whisper/whisper/normalizers/english.json
new file mode 100644
index 0000000..bd84ae7
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/normalizers/english.json
@@ -0,0 +1,1742 @@
+{
+    "accessorise": "accessorize",
+    "accessorised": "accessorized",
+    "accessorises": "accessorizes",
+    "accessorising": "accessorizing",
+    "acclimatisation": "acclimatization",
+    "acclimatise": "acclimatize",
+    "acclimatised": "acclimatized",
+    "acclimatises": "acclimatizes",
+    "acclimatising": "acclimatizing",
+    "accoutrements": "accouterments",
+    "aeon": "eon",
+    "aeons": "eons",
+    "aerogramme": "aerogram",
+    "aerogrammes": "aerograms",
+    "aeroplane": "airplane",
+    "aeroplanes": "airplanes",
+    "aesthete": "esthete",
+    "aesthetes": "esthetes",
+    "aesthetic": "esthetic",
+    "aesthetically": "esthetically",
+    "aesthetics": "esthetics",
+    "aetiology": "etiology",
+    "ageing": "aging",
+    "aggrandisement": "aggrandizement",
+    "agonise": "agonize",
+    "agonised": "agonized",
+    "agonises": "agonizes",
+    "agonising": "agonizing",
+    "agonisingly": "agonizingly",
+    "almanack": "almanac",
+    "almanacks": "almanacs",
+    "aluminium": "aluminum",
+    "amortisable": "amortizable",
+    "amortisation": "amortization",
+    "amortisations": "amortizations",
+    "amortise": "amortize",
+    "amortised": "amortized",
+    "amortises": "amortizes",
+    "amortising": "amortizing",
+    "amphitheatre": "amphitheater",
+    "amphitheatres": "amphitheaters",
+    "anaemia": "anemia",
+    "anaemic": "anemic",
+    "anaesthesia": "anesthesia",
+    "anaesthetic": "anesthetic",
+    "anaesthetics": "anesthetics",
+    "anaesthetise": "anesthetize",
+    "anaesthetised": "anesthetized",
+    "anaesthetises": "anesthetizes",
+    "anaesthetising": "anesthetizing",
+    "anaesthetist": "anesthetist",
+    "anaesthetists": "anesthetists",
+    "anaesthetize": "anesthetize",
+    "anaesthetized": "anesthetized",
+    "anaesthetizes": "anesthetizes",
+    "anaesthetizing": "anesthetizing",
+    "analogue": "analog",
+    "analogues": "analogs",
+    "analyse": "analyze",
+    "analysed": "analyzed",
+    "analyses": "analyzes",
+    "analysing": "analyzing",
+    "anglicise": "anglicize",
+    "anglicised": "anglicized",
+    "anglicises": "anglicizes",
+    "anglicising": "anglicizing",
+    "annualised": "annualized",
+    "antagonise": "antagonize",
+    "antagonised": "antagonized",
+    "antagonises": "antagonizes",
+    "antagonising": "antagonizing",
+    "apologise": "apologize",
+    "apologised": "apologized",
+    "apologises": "apologizes",
+    "apologising": "apologizing",
+    "appal": "appall",
+    "appals": "appalls",
+    "appetiser": "appetizer",
+    "appetisers": "appetizers",
+    "appetising": "appetizing",
+    "appetisingly": "appetizingly",
+    "arbour": "arbor",
+    "arbours": "arbors",
+    "archeological": "archaeological",
+    "archaeologically": "archeologically",
+    "archaeologist": "archeologist",
+    "archaeologists": "archeologists",
+    "archaeology": "archeology</span>",
+    "ardour": "ardor",
+    "armour": "armor",
+    "armoured": "armored",
+    "armourer": "armorer",
+    "armourers": "armorers",
+    "armouries": "armories",
+    "armoury": "armory",
+    "artefact": "artifact",
+    "artefacts": "artifacts",
+    "authorise": "authorize",
+    "authorised": "authorized",
+    "authorises": "authorizes",
+    "authorising": "authorizing",
+    "axe": "ax",
+    "backpedalled": "backpedaled",
+    "backpedalling": "backpedaling",
+    "bannister": "banister",
+    "bannisters": "banisters",
+    "baptise": "baptize",
+    "baptised": "baptized",
+    "baptises": "baptizes",
+    "baptising": "baptizing",
+    "bastardise": "bastardize",
+    "bastardised": "bastardized",
+    "bastardises": "bastardizes",
+    "bastardising": "bastardizing",
+    "battleax": "battleaxe",
+    "baulk": "balk",
+    "baulked": "balked",
+    "baulking": "balking",
+    "baulks": "balks",
+    "bedevilled": "bedeviled",
+    "bedevilling": "bedeviling",
+    "behaviour": "behavior",
+    "behavioural": "behavioral",
+    "behaviourism": "behaviorism",
+    "behaviourist": "behaviorist",
+    "behaviourists": "behaviorists",
+    "behaviours": "behaviors",
+    "behove": "behoove",
+    "behoved": "behooved",
+    "behoves": "behooves",
+    "bejewelled": "bejeweled",
+    "belabour": "belabor",
+    "belaboured": "belabored",
+    "belabouring": "belaboring",
+    "belabours": "belabors",
+    "bevelled": "beveled",
+    "bevvies": "bevies",
+    "bevvy": "bevy",
+    "biassed": "biased",
+    "biassing": "biasing",
+    "bingeing": "binging",
+    "bougainvillaea": "bougainvillea",
+    "bougainvillaeas": "bougainvilleas",
+    "bowdlerise": "bowdlerize",
+    "bowdlerised": "bowdlerized",
+    "bowdlerises": "bowdlerizes",
+    "bowdlerising": "bowdlerizing",
+    "breathalyse": "breathalyze",
+    "breathalysed": "breathalyzed",
+    "breathalyser": "breathalyzer",
+    "breathalysers": "breathalyzers",
+    "breathalyses": "breathalyzes",
+    "breathalysing": "breathalyzing",
+    "brutalise": "brutalize",
+    "brutalised": "brutalized",
+    "brutalises": "brutalizes",
+    "brutalising": "brutalizing",
+    "busses": "buses",
+    "bussing": "busing",
+    "caesarean": "cesarean",
+    "caesareans": "cesareans",
+    "calibre": "caliber",
+    "calibres": "calibers",
+    "calliper": "caliper",
+    "callipers": "calipers",
+    "callisthenics": "calisthenics",
+    "canalise": "canalize",
+    "canalised": "canalized",
+    "canalises": "canalizes",
+    "canalising": "canalizing",
+    "cancelation": "cancellation",
+    "cancelations": "cancellations",
+    "cancelled": "canceled",
+    "cancelling": "canceling",
+    "candour": "candor",
+    "cannibalise": "cannibalize",
+    "cannibalised": "cannibalized",
+    "cannibalises": "cannibalizes",
+    "cannibalising": "cannibalizing",
+    "canonise": "canonize",
+    "canonised": "canonized",
+    "canonises": "canonizes",
+    "canonising": "canonizing",
+    "capitalise": "capitalize",
+    "capitalised": "capitalized",
+    "capitalises": "capitalizes",
+    "capitalising": "capitalizing",
+    "caramelise": "caramelize",
+    "caramelised": "caramelized",
+    "caramelises": "caramelizes",
+    "caramelising": "caramelizing",
+    "carbonise": "carbonize",
+    "carbonised": "carbonized",
+    "carbonises": "carbonizes",
+    "carbonising": "carbonizing",
+    "carolled": "caroled",
+    "carolling": "caroling",
+    "catalogue": "catalog",
+    "catalogued": "cataloged",
+    "catalogues": "catalogs",
+    "cataloguing": "cataloging",
+    "catalyse": "catalyze",
+    "catalysed": "catalyzed",
+    "catalyses": "catalyzes",
+    "catalysing": "catalyzing",
+    "categorise": "categorize",
+    "categorised": "categorized",
+    "categorises": "categorizes",
+    "categorising": "categorizing",
+    "cauterise": "cauterize",
+    "cauterised": "cauterized",
+    "cauterises": "cauterizes",
+    "cauterising": "cauterizing",
+    "cavilled": "caviled",
+    "cavilling": "caviling",
+    "centigramme": "centigram",
+    "centigrammes": "centigrams",
+    "centilitre": "centiliter",
+    "centilitres": "centiliters",
+    "centimetre": "centimeter",
+    "centimetres": "centimeters",
+    "centralise": "centralize",
+    "centralised": "centralized",
+    "centralises": "centralizes",
+    "centralising": "centralizing",
+    "centre": "center",
+    "centred": "centered",
+    "centrefold": "centerfold",
+    "centrefolds": "centerfolds",
+    "centrepiece": "centerpiece",
+    "centrepieces": "centerpieces",
+    "centres": "centers",
+    "channelled": "channeled",
+    "channelling": "channeling",
+    "characterise": "characterize",
+    "characterised": "characterized",
+    "characterises": "characterizes",
+    "characterising": "characterizing",
+    "cheque": "check",
+    "chequebook": "checkbook",
+    "chequebooks": "checkbooks",
+    "chequered": "checkered",
+    "cheques": "checks",
+    "chilli": "chili",
+    "chimaera": "chimera",
+    "chimaeras": "chimeras",
+    "chiselled": "chiseled",
+    "chiselling": "chiseling",
+    "circularise": "circularize",
+    "circularised": "circularized",
+    "circularises": "circularizes",
+    "circularising": "circularizing",
+    "civilise": "civilize",
+    "civilised": "civilized",
+    "civilises": "civilizes",
+    "civilising": "civilizing",
+    "clamour": "clamor",
+    "clamoured": "clamored",
+    "clamouring": "clamoring",
+    "clamours": "clamors",
+    "clangour": "clangor",
+    "clarinettist": "clarinetist",
+    "clarinettists": "clarinetists",
+    "collectivise": "collectivize",
+    "collectivised": "collectivized",
+    "collectivises": "collectivizes",
+    "collectivising": "collectivizing",
+    "colonisation": "colonization",
+    "colonise": "colonize",
+    "colonised": "colonized",
+    "coloniser": "colonizer",
+    "colonisers": "colonizers",
+    "colonises": "colonizes",
+    "colonising": "colonizing",
+    "colour": "color",
+    "colourant": "colorant",
+    "colourants": "colorants",
+    "coloured": "colored",
+    "coloureds": "coloreds",
+    "colourful": "colorful",
+    "colourfully": "colorfully",
+    "colouring": "coloring",
+    "colourize": "colorize",
+    "colourized": "colorized",
+    "colourizes": "colorizes",
+    "colourizing": "colorizing",
+    "colourless": "colorless",
+    "colours": "colors",
+    "commercialise": "commercialize",
+    "commercialised": "commercialized",
+    "commercialises": "commercializes",
+    "commercialising": "commercializing",
+    "compartmentalise": "compartmentalize",
+    "compartmentalised": "compartmentalized",
+    "compartmentalises": "compartmentalizes",
+    "compartmentalising": "compartmentalizing",
+    "computerise": "computerize",
+    "computerised": "computerized",
+    "computerises": "computerizes",
+    "computerising": "computerizing",
+    "conceptualise": "conceptualize",
+    "conceptualised": "conceptualized",
+    "conceptualises": "conceptualizes",
+    "conceptualising": "conceptualizing",
+    "connexion": "connection",
+    "connexions": "connections",
+    "contextualise": "contextualize",
+    "contextualised": "contextualized",
+    "contextualises": "contextualizes",
+    "contextualising": "contextualizing",
+    "cosier": "cozier",
+    "cosies": "cozies",
+    "cosiest": "coziest",
+    "cosily": "cozily",
+    "cosiness": "coziness",
+    "cosy": "cozy",
+    "councillor": "councilor",
+    "councillors": "councilors",
+    "counselled": "counseled",
+    "counselling": "counseling",
+    "counsellor": "counselor",
+    "counsellors": "counselors",
+    "crenelated": "crenellated",
+    "criminalise": "criminalize",
+    "criminalised": "criminalized",
+    "criminalises": "criminalizes",
+    "criminalising": "criminalizing",
+    "criticise": "criticize",
+    "criticised": "criticized",
+    "criticises": "criticizes",
+    "criticising": "criticizing",
+    "crueller": "crueler",
+    "cruellest": "cruelest",
+    "crystallisation": "crystallization",
+    "crystallise": "crystallize",
+    "crystallised": "crystallized",
+    "crystallises": "crystallizes",
+    "crystallising": "crystallizing",
+    "cudgelled": "cudgeled",
+    "cudgelling": "cudgeling",
+    "customise": "customize",
+    "customised": "customized",
+    "customises": "customizes",
+    "customising": "customizing",
+    "cypher": "cipher",
+    "cyphers": "ciphers",
+    "decentralisation": "decentralization",
+    "decentralise": "decentralize",
+    "decentralised": "decentralized",
+    "decentralises": "decentralizes",
+    "decentralising": "decentralizing",
+    "decriminalisation": "decriminalization",
+    "decriminalise": "decriminalize",
+    "decriminalised": "decriminalized",
+    "decriminalises": "decriminalizes",
+    "decriminalising": "decriminalizing",
+    "defence": "defense",
+    "defenceless": "defenseless",
+    "defences": "defenses",
+    "dehumanisation": "dehumanization",
+    "dehumanise": "dehumanize",
+    "dehumanised": "dehumanized",
+    "dehumanises": "dehumanizes",
+    "dehumanising": "dehumanizing",
+    "demeanour": "demeanor",
+    "demilitarisation": "demilitarization",
+    "demilitarise": "demilitarize",
+    "demilitarised": "demilitarized",
+    "demilitarises": "demilitarizes",
+    "demilitarising": "demilitarizing",
+    "demobilisation": "demobilization",
+    "demobilise": "demobilize",
+    "demobilised": "demobilized",
+    "demobilises": "demobilizes",
+    "demobilising": "demobilizing",
+    "democratisation": "democratization",
+    "democratise": "democratize",
+    "democratised": "democratized",
+    "democratises": "democratizes",
+    "democratising": "democratizing",
+    "demonise": "demonize",
+    "demonised": "demonized",
+    "demonises": "demonizes",
+    "demonising": "demonizing",
+    "demoralisation": "demoralization",
+    "demoralise": "demoralize",
+    "demoralised": "demoralized",
+    "demoralises": "demoralizes",
+    "demoralising": "demoralizing",
+    "denationalisation": "denationalization",
+    "denationalise": "denationalize",
+    "denationalised": "denationalized",
+    "denationalises": "denationalizes",
+    "denationalising": "denationalizing",
+    "deodorise": "deodorize",
+    "deodorised": "deodorized",
+    "deodorises": "deodorizes",
+    "deodorising": "deodorizing",
+    "depersonalise": "depersonalize",
+    "depersonalised": "depersonalized",
+    "depersonalises": "depersonalizes",
+    "depersonalising": "depersonalizing",
+    "deputise": "deputize",
+    "deputised": "deputized",
+    "deputises": "deputizes",
+    "deputising": "deputizing",
+    "desensitisation": "desensitization",
+    "desensitise": "desensitize",
+    "desensitised": "desensitized",
+    "desensitises": "desensitizes",
+    "desensitising": "desensitizing",
+    "destabilisation": "destabilization",
+    "destabilise": "destabilize",
+    "destabilised": "destabilized",
+    "destabilises": "destabilizes",
+    "destabilising": "destabilizing",
+    "dialled": "dialed",
+    "dialling": "dialing",
+    "dialogue": "dialog",
+    "dialogues": "dialogs",
+    "diarrhoea": "diarrhea",
+    "digitise": "digitize",
+    "digitised": "digitized",
+    "digitises": "digitizes",
+    "digitising": "digitizing",
+    "disc": "disk",
+    "discolour": "discolor",
+    "discoloured": "discolored",
+    "discolouring": "discoloring",
+    "discolours": "discolors",
+    "discs": "disks",
+    "disembowelled": "disemboweled",
+    "disembowelling": "disemboweling",
+    "disfavour": "disfavor",
+    "dishevelled": "disheveled",
+    "dishonour": "dishonor",
+    "dishonourable": "dishonorable",
+    "dishonourably": "dishonorably",
+    "dishonoured": "dishonored",
+    "dishonouring": "dishonoring",
+    "dishonours": "dishonors",
+    "disorganisation": "disorganization",
+    "disorganised": "disorganized",
+    "distil": "distill",
+    "distils": "distills",
+    "dramatisation": "dramatization",
+    "dramatisations": "dramatizations",
+    "dramatise": "dramatize",
+    "dramatised": "dramatized",
+    "dramatises": "dramatizes",
+    "dramatising": "dramatizing",
+    "draught": "draft",
+    "draughtboard": "draftboard",
+    "draughtboards": "draftboards",
+    "draughtier": "draftier",
+    "draughtiest": "draftiest",
+    "draughts": "drafts",
+    "draughtsman": "draftsman",
+    "draughtsmanship": "draftsmanship",
+    "draughtsmen": "draftsmen",
+    "draughtswoman": "draftswoman",
+    "draughtswomen": "draftswomen",
+    "draughty": "drafty",
+    "drivelled": "driveled",
+    "drivelling": "driveling",
+    "duelled": "dueled",
+    "duelling": "dueling",
+    "economise": "economize",
+    "economised": "economized",
+    "economises": "economizes",
+    "economising": "economizing",
+    "edoema": "edema",
+    "editorialise": "editorialize",
+    "editorialised": "editorialized",
+    "editorialises": "editorializes",
+    "editorialising": "editorializing",
+    "empathise": "empathize",
+    "empathised": "empathized",
+    "empathises": "empathizes",
+    "empathising": "empathizing",
+    "emphasise": "emphasize",
+    "emphasised": "emphasized",
+    "emphasises": "emphasizes",
+    "emphasising": "emphasizing",
+    "enamelled": "enameled",
+    "enamelling": "enameling",
+    "enamoured": "enamored",
+    "encyclopaedia": "encyclopedia",
+    "encyclopaedias": "encyclopedias",
+    "encyclopaedic": "encyclopedic",
+    "endeavour": "endeavor",
+    "endeavoured": "endeavored",
+    "endeavouring": "endeavoring",
+    "endeavours": "endeavors",
+    "energise": "energize",
+    "energised": "energized",
+    "energises": "energizes",
+    "energising": "energizing",
+    "enrol": "enroll",
+    "enrols": "enrolls",
+    "enthral": "enthrall",
+    "enthrals": "enthralls",
+    "epaulette": "epaulet",
+    "epaulettes": "epaulets",
+    "epicentre": "epicenter",
+    "epicentres": "epicenters",
+    "epilogue": "epilog",
+    "epilogues": "epilogs",
+    "epitomise": "epitomize",
+    "epitomised": "epitomized",
+    "epitomises": "epitomizes",
+    "epitomising": "epitomizing",
+    "equalisation": "equalization",
+    "equalise": "equalize",
+    "equalised": "equalized",
+    "equaliser": "equalizer",
+    "equalisers": "equalizers",
+    "equalises": "equalizes",
+    "equalising": "equalizing",
+    "eulogise": "eulogize",
+    "eulogised": "eulogized",
+    "eulogises": "eulogizes",
+    "eulogising": "eulogizing",
+    "evangelise": "evangelize",
+    "evangelised": "evangelized",
+    "evangelises": "evangelizes",
+    "evangelising": "evangelizing",
+    "exorcise": "exorcize",
+    "exorcised": "exorcized",
+    "exorcises": "exorcizes",
+    "exorcising": "exorcizing",
+    "extemporisation": "extemporization",
+    "extemporise": "extemporize",
+    "extemporised": "extemporized",
+    "extemporises": "extemporizes",
+    "extemporising": "extemporizing",
+    "externalisation": "externalization",
+    "externalisations": "externalizations",
+    "externalise": "externalize",
+    "externalised": "externalized",
+    "externalises": "externalizes",
+    "externalising": "externalizing",
+    "factorise": "factorize",
+    "factorised": "factorized",
+    "factorises": "factorizes",
+    "factorising": "factorizing",
+    "faecal": "fecal",
+    "faeces": "feces",
+    "familiarisation": "familiarization",
+    "familiarise": "familiarize",
+    "familiarised": "familiarized",
+    "familiarises": "familiarizes",
+    "familiarising": "familiarizing",
+    "fantasise": "fantasize",
+    "fantasised": "fantasized",
+    "fantasises": "fantasizes",
+    "fantasising": "fantasizing",
+    "favour": "favor",
+    "favourable": "favorable",
+    "favourably": "favorably",
+    "favoured": "favored",
+    "favouring": "favoring",
+    "favourite": "favorite",
+    "favourites": "favorites",
+    "favouritism": "favoritism",
+    "favours": "favors",
+    "feminise": "feminize",
+    "feminised": "feminized",
+    "feminises": "feminizes",
+    "feminising": "feminizing",
+    "fertilisation": "fertilization",
+    "fertilise": "fertilize",
+    "fertilised": "fertilized",
+    "fertiliser": "fertilizer",
+    "fertilisers": "fertilizers",
+    "fertilises": "fertilizes",
+    "fertilising": "fertilizing",
+    "fervour": "fervor",
+    "fibre": "fiber",
+    "fibreglass": "fiberglass",
+    "fibres": "fibers",
+    "fictionalisation": "fictionalization",
+    "fictionalisations": "fictionalizations",
+    "fictionalise": "fictionalize",
+    "fictionalised": "fictionalized",
+    "fictionalises": "fictionalizes",
+    "fictionalising": "fictionalizing",
+    "fillet": "filet",
+    "filleted": "fileted",
+    "filleting": "fileting",
+    "fillets": "filets",
+    "finalisation": "finalization",
+    "finalise": "finalize",
+    "finalised": "finalized",
+    "finalises": "finalizes",
+    "finalising": "finalizing",
+    "flautist": "flutist",
+    "flautists": "flutists",
+    "flavour": "flavor",
+    "flavoured": "flavored",
+    "flavouring": "flavoring",
+    "flavourings": "flavorings",
+    "flavourless": "flavorless",
+    "flavours": "flavors",
+    "flavoursome": "flavorsome",
+    "flyer / flier": "flier / flyer",
+    "foetal": "fetal",
+    "foetid": "fetid",
+    "foetus": "fetus",
+    "foetuses": "fetuses",
+    "formalisation": "formalization",
+    "formalise": "formalize",
+    "formalised": "formalized",
+    "formalises": "formalizes",
+    "formalising": "formalizing",
+    "fossilisation": "fossilization",
+    "fossilise": "fossilize",
+    "fossilised": "fossilized",
+    "fossilises": "fossilizes",
+    "fossilising": "fossilizing",
+    "fraternisation": "fraternization",
+    "fraternise": "fraternize",
+    "fraternised": "fraternized",
+    "fraternises": "fraternizes",
+    "fraternising": "fraternizing",
+    "fulfil": "fulfill",
+    "fulfilment": "fulfillment",
+    "fulfils": "fulfills",
+    "funnelled": "funneled",
+    "funnelling": "funneling",
+    "galvanise": "galvanize",
+    "galvanised": "galvanized",
+    "galvanises": "galvanizes",
+    "galvanising": "galvanizing",
+    "gambolled": "gamboled",
+    "gambolling": "gamboling",
+    "gaol": "jail",
+    "gaolbird": "jailbird",
+    "gaolbirds": "jailbirds",
+    "gaolbreak": "jailbreak",
+    "gaolbreaks": "jailbreaks",
+    "gaoled": "jailed",
+    "gaoler": "jailer",
+    "gaolers": "jailers",
+    "gaoling": "jailing",
+    "gaols": "jails",
+    "gasses": "gases",
+    "gage": "gauge",
+    "gaged": "gauged",
+    "gages": "gauges",
+    "gaging": "gauging",
+    "generalisation": "generalization",
+    "generalisations": "generalizations",
+    "generalise": "generalize",
+    "generalised": "generalized",
+    "generalises": "generalizes",
+    "generalising": "generalizing",
+    "ghettoise": "ghettoize",
+    "ghettoised": "ghettoized",
+    "ghettoises": "ghettoizes",
+    "ghettoising": "ghettoizing",
+    "gipsies": "gypsies",
+    "glamorise": "glamorize",
+    "glamorised": "glamorized",
+    "glamorises": "glamorizes",
+    "glamorising": "glamorizing",
+    "glamor": "glamour",
+    "globalisation": "globalization",
+    "globalise": "globalize",
+    "globalised": "globalized",
+    "globalises": "globalizes",
+    "globalising": "globalizing",
+    "glueing": "gluing",
+    "goitre": "goiter",
+    "goitres": "goiters",
+    "gonorrhoea": "gonorrhea",
+    "gramme": "gram",
+    "grammes": "grams",
+    "gravelled": "graveled",
+    "grey": "gray",
+    "greyed": "grayed",
+    "greying": "graying",
+    "greyish": "grayish",
+    "greyness": "grayness",
+    "greys": "grays",
+    "grovelled": "groveled",
+    "grovelling": "groveling",
+    "groyne": "groin",
+    "groynes": "groins",
+    "gruelling": "grueling",
+    "gruellingly": "gruelingly",
+    "gryphon": "griffin",
+    "gryphons": "griffins",
+    "gynaecological": "gynecological",
+    "gynaecologist": "gynecologist",
+    "gynaecologists": "gynecologists",
+    "gynaecology": "gynecology",
+    "haematological": "hematological",
+    "haematologist": "hematologist",
+    "haematologists": "hematologists",
+    "haematology": "hematology",
+    "haemoglobin": "hemoglobin",
+    "haemophilia": "hemophilia",
+    "haemophiliac": "hemophiliac",
+    "haemophiliacs": "hemophiliacs",
+    "haemorrhage": "hemorrhage",
+    "haemorrhaged": "hemorrhaged",
+    "haemorrhages": "hemorrhages",
+    "haemorrhaging": "hemorrhaging",
+    "haemorrhoids": "hemorrhoids",
+    "harbour": "harbor",
+    "harboured": "harbored",
+    "harbouring": "harboring",
+    "harbours": "harbors",
+    "harmonisation": "harmonization",
+    "harmonise": "harmonize",
+    "harmonised": "harmonized",
+    "harmonises": "harmonizes",
+    "harmonising": "harmonizing",
+    "homoeopath": "homeopath",
+    "homoeopathic": "homeopathic",
+    "homoeopaths": "homeopaths",
+    "homoeopathy": "homeopathy",
+    "homogenise": "homogenize",
+    "homogenised": "homogenized",
+    "homogenises": "homogenizes",
+    "homogenising": "homogenizing",
+    "honour": "honor",
+    "honourable": "honorable",
+    "honourably": "honorably",
+    "honoured": "honored",
+    "honouring": "honoring",
+    "honours": "honors",
+    "hospitalisation": "hospitalization",
+    "hospitalise": "hospitalize",
+    "hospitalised": "hospitalized",
+    "hospitalises": "hospitalizes",
+    "hospitalising": "hospitalizing",
+    "humanise": "humanize",
+    "humanised": "humanized",
+    "humanises": "humanizes",
+    "humanising": "humanizing",
+    "humour": "humor",
+    "humoured": "humored",
+    "humouring": "humoring",
+    "humourless": "humorless",
+    "humours": "humors",
+    "hybridise": "hybridize",
+    "hybridised": "hybridized",
+    "hybridises": "hybridizes",
+    "hybridising": "hybridizing",
+    "hypnotise": "hypnotize",
+    "hypnotised": "hypnotized",
+    "hypnotises": "hypnotizes",
+    "hypnotising": "hypnotizing",
+    "hypothesise": "hypothesize",
+    "hypothesised": "hypothesized",
+    "hypothesises": "hypothesizes",
+    "hypothesising": "hypothesizing",
+    "idealisation": "idealization",
+    "idealise": "idealize",
+    "idealised": "idealized",
+    "idealises": "idealizes",
+    "idealising": "idealizing",
+    "idolise": "idolize",
+    "idolised": "idolized",
+    "idolises": "idolizes",
+    "idolising": "idolizing",
+    "immobilisation": "immobilization",
+    "immobilise": "immobilize",
+    "immobilised": "immobilized",
+    "immobiliser": "immobilizer",
+    "immobilisers": "immobilizers",
+    "immobilises": "immobilizes",
+    "immobilising": "immobilizing",
+    "immortalise": "immortalize",
+    "immortalised": "immortalized",
+    "immortalises": "immortalizes",
+    "immortalising": "immortalizing",
+    "immunisation": "immunization",
+    "immunise": "immunize",
+    "immunised": "immunized",
+    "immunises": "immunizes",
+    "immunising": "immunizing",
+    "impanelled": "impaneled",
+    "impanelling": "impaneling",
+    "imperilled": "imperiled",
+    "imperilling": "imperiling",
+    "individualise": "individualize",
+    "individualised": "individualized",
+    "individualises": "individualizes",
+    "individualising": "individualizing",
+    "industrialise": "industrialize",
+    "industrialised": "industrialized",
+    "industrialises": "industrializes",
+    "industrialising": "industrializing",
+    "inflexion": "inflection",
+    "inflexions": "inflections",
+    "initialise": "initialize",
+    "initialised": "initialized",
+    "initialises": "initializes",
+    "initialising": "initializing",
+    "initialled": "initialed",
+    "initialling": "initialing",
+    "instal": "install",
+    "instalment": "installment",
+    "instalments": "installments",
+    "instals": "installs",
+    "instil": "instill",
+    "instils": "instills",
+    "institutionalisation": "institutionalization",
+    "institutionalise": "institutionalize",
+    "institutionalised": "institutionalized",
+    "institutionalises": "institutionalizes",
+    "institutionalising": "institutionalizing",
+    "intellectualise": "intellectualize",
+    "intellectualised": "intellectualized",
+    "intellectualises": "intellectualizes",
+    "intellectualising": "intellectualizing",
+    "internalisation": "internalization",
+    "internalise": "internalize",
+    "internalised": "internalized",
+    "internalises": "internalizes",
+    "internalising": "internalizing",
+    "internationalisation": "internationalization",
+    "internationalise": "internationalize",
+    "internationalised": "internationalized",
+    "internationalises": "internationalizes",
+    "internationalising": "internationalizing",
+    "ionisation": "ionization",
+    "ionise": "ionize",
+    "ionised": "ionized",
+    "ioniser": "ionizer",
+    "ionisers": "ionizers",
+    "ionises": "ionizes",
+    "ionising": "ionizing",
+    "italicise": "italicize",
+    "italicised": "italicized",
+    "italicises": "italicizes",
+    "italicising": "italicizing",
+    "itemise": "itemize",
+    "itemised": "itemized",
+    "itemises": "itemizes",
+    "itemising": "itemizing",
+    "jeopardise": "jeopardize",
+    "jeopardised": "jeopardized",
+    "jeopardises": "jeopardizes",
+    "jeopardising": "jeopardizing",
+    "jewelled": "jeweled",
+    "jeweller": "jeweler",
+    "jewellers": "jewelers",
+    "jewellery": "jewelry",
+    "judgement": "judgment",
+    "kilogramme": "kilogram",
+    "kilogrammes": "kilograms",
+    "kilometre": "kilometer",
+    "kilometres": "kilometers",
+    "labelled": "labeled",
+    "labelling": "labeling",
+    "labour": "labor",
+    "laboured": "labored",
+    "labourer": "laborer",
+    "labourers": "laborers",
+    "labouring": "laboring",
+    "labours": "labors",
+    "lacklustre": "lackluster",
+    "legalisation": "legalization",
+    "legalise": "legalize",
+    "legalised": "legalized",
+    "legalises": "legalizes",
+    "legalising": "legalizing",
+    "legitimise": "legitimize",
+    "legitimised": "legitimized",
+    "legitimises": "legitimizes",
+    "legitimising": "legitimizing",
+    "leukaemia": "leukemia",
+    "levelled": "leveled",
+    "leveller": "leveler",
+    "levellers": "levelers",
+    "levelling": "leveling",
+    "libelled": "libeled",
+    "libelling": "libeling",
+    "libellous": "libelous",
+    "liberalisation": "liberalization",
+    "liberalise": "liberalize",
+    "liberalised": "liberalized",
+    "liberalises": "liberalizes",
+    "liberalising": "liberalizing",
+    "licence": "license",
+    "licenced": "licensed",
+    "licences": "licenses",
+    "licencing": "licensing",
+    "likeable": "likable",
+    "lionisation": "lionization",
+    "lionise": "lionize",
+    "lionised": "lionized",
+    "lionises": "lionizes",
+    "lionising": "lionizing",
+    "liquidise": "liquidize",
+    "liquidised": "liquidized",
+    "liquidiser": "liquidizer",
+    "liquidisers": "liquidizers",
+    "liquidises": "liquidizes",
+    "liquidising": "liquidizing",
+    "litre": "liter",
+    "litres": "liters",
+    "localise": "localize",
+    "localised": "localized",
+    "localises": "localizes",
+    "localising": "localizing",
+    "louvre": "louver",
+    "louvred": "louvered",
+    "louvres": "louvers",
+    "lustre": "luster",
+    "magnetise": "magnetize",
+    "magnetised": "magnetized",
+    "magnetises": "magnetizes",
+    "magnetising": "magnetizing",
+    "manoeuvrability": "maneuverability",
+    "manoeuvrable": "maneuverable",
+    "manoeuvre": "maneuver",
+    "manoeuvred": "maneuvered",
+    "manoeuvres": "maneuvers",
+    "manoeuvring": "maneuvering",
+    "manoeuvrings": "maneuverings",
+    "marginalisation": "marginalization",
+    "marginalise": "marginalize",
+    "marginalised": "marginalized",
+    "marginalises": "marginalizes",
+    "marginalising": "marginalizing",
+    "marshalled": "marshaled",
+    "marshalling": "marshaling",
+    "marvelled": "marveled",
+    "marvelling": "marveling",
+    "marvellous": "marvelous",
+    "marvellously": "marvelously",
+    "materialisation": "materialization",
+    "materialise": "materialize",
+    "materialised": "materialized",
+    "materialises": "materializes",
+    "materialising": "materializing",
+    "maximisation": "maximization",
+    "maximise": "maximize",
+    "maximised": "maximized",
+    "maximises": "maximizes",
+    "maximising": "maximizing",
+    "meagre": "meager",
+    "mechanisation": "mechanization",
+    "mechanise": "mechanize",
+    "mechanised": "mechanized",
+    "mechanises": "mechanizes",
+    "mechanising": "mechanizing",
+    "mediaeval": "medieval",
+    "memorialise": "memorialize",
+    "memorialised": "memorialized",
+    "memorialises": "memorializes",
+    "memorialising": "memorializing",
+    "memorise": "memorize",
+    "memorised": "memorized",
+    "memorises": "memorizes",
+    "memorising": "memorizing",
+    "mesmerise": "mesmerize",
+    "mesmerised": "mesmerized",
+    "mesmerises": "mesmerizes",
+    "mesmerising": "mesmerizing",
+    "metabolise": "metabolize",
+    "metabolised": "metabolized",
+    "metabolises": "metabolizes",
+    "metabolising": "metabolizing",
+    "metre": "meter",
+    "metres": "meters",
+    "micrometre": "micrometer",
+    "micrometres": "micrometers",
+    "militarise": "militarize",
+    "militarised": "militarized",
+    "militarises": "militarizes",
+    "militarising": "militarizing",
+    "milligramme": "milligram",
+    "milligrammes": "milligrams",
+    "millilitre": "milliliter",
+    "millilitres": "milliliters",
+    "millimetre": "millimeter",
+    "millimetres": "millimeters",
+    "miniaturisation": "miniaturization",
+    "miniaturise": "miniaturize",
+    "miniaturised": "miniaturized",
+    "miniaturises": "miniaturizes",
+    "miniaturising": "miniaturizing",
+    "minibusses": "minibuses",
+    "minimise": "minimize",
+    "minimised": "minimized",
+    "minimises": "minimizes",
+    "minimising": "minimizing",
+    "misbehaviour": "misbehavior",
+    "misdemeanour": "misdemeanor",
+    "misdemeanours": "misdemeanors",
+    "misspelt": "misspelled",
+    "mitre": "miter",
+    "mitres": "miters",
+    "mobilisation": "mobilization",
+    "mobilise": "mobilize",
+    "mobilised": "mobilized",
+    "mobilises": "mobilizes",
+    "mobilising": "mobilizing",
+    "modelled": "modeled",
+    "modeller": "modeler",
+    "modellers": "modelers",
+    "modelling": "modeling",
+    "modernise": "modernize",
+    "modernised": "modernized",
+    "modernises": "modernizes",
+    "modernising": "modernizing",
+    "moisturise": "moisturize",
+    "moisturised": "moisturized",
+    "moisturiser": "moisturizer",
+    "moisturisers": "moisturizers",
+    "moisturises": "moisturizes",
+    "moisturising": "moisturizing",
+    "monologue": "monolog",
+    "monologues": "monologs",
+    "monopolisation": "monopolization",
+    "monopolise": "monopolize",
+    "monopolised": "monopolized",
+    "monopolises": "monopolizes",
+    "monopolising": "monopolizing",
+    "moralise": "moralize",
+    "moralised": "moralized",
+    "moralises": "moralizes",
+    "moralising": "moralizing",
+    "motorised": "motorized",
+    "mould": "mold",
+    "moulded": "molded",
+    "moulder": "molder",
+    "mouldered": "moldered",
+    "mouldering": "moldering",
+    "moulders": "molders",
+    "mouldier": "moldier",
+    "mouldiest": "moldiest",
+    "moulding": "molding",
+    "mouldings": "moldings",
+    "moulds": "molds",
+    "mouldy": "moldy",
+    "moult": "molt",
+    "moulted": "molted",
+    "moulting": "molting",
+    "moults": "molts",
+    "moustache": "mustache",
+    "moustached": "mustached",
+    "moustaches": "mustaches",
+    "moustachioed": "mustachioed",
+    "multicoloured": "multicolored",
+    "nationalisation": "nationalization",
+    "nationalisations": "nationalizations",
+    "nationalise": "nationalize",
+    "nationalised": "nationalized",
+    "nationalises": "nationalizes",
+    "nationalising": "nationalizing",
+    "naturalisation": "naturalization",
+    "naturalise": "naturalize",
+    "naturalised": "naturalized",
+    "naturalises": "naturalizes",
+    "naturalising": "naturalizing",
+    "neighbour": "neighbor",
+    "neighbourhood": "neighborhood",
+    "neighbourhoods": "neighborhoods",
+    "neighbouring": "neighboring",
+    "neighbourliness": "neighborliness",
+    "neighbourly": "neighborly",
+    "neighbours": "neighbors",
+    "neutralisation": "neutralization",
+    "neutralise": "neutralize",
+    "neutralised": "neutralized",
+    "neutralises": "neutralizes",
+    "neutralising": "neutralizing",
+    "normalisation": "normalization",
+    "normalise": "normalize",
+    "normalised": "normalized",
+    "normalises": "normalizes",
+    "normalising": "normalizing",
+    "odour": "odor",
+    "odourless": "odorless",
+    "odours": "odors",
+    "oesophagus": "esophagus",
+    "oesophaguses": "esophaguses",
+    "oestrogen": "estrogen",
+    "offence": "offense",
+    "offences": "offenses",
+    "omelette": "omelet",
+    "omelettes": "omelets",
+    "optimise": "optimize",
+    "optimised": "optimized",
+    "optimises": "optimizes",
+    "optimising": "optimizing",
+    "organisation": "organization",
+    "organisational": "organizational",
+    "organisations": "organizations",
+    "organise": "organize",
+    "organised": "organized",
+    "organiser": "organizer",
+    "organisers": "organizers",
+    "organises": "organizes",
+    "organising": "organizing",
+    "orthopaedic": "orthopedic",
+    "orthopaedics": "orthopedics",
+    "ostracise": "ostracize",
+    "ostracised": "ostracized",
+    "ostracises": "ostracizes",
+    "ostracising": "ostracizing",
+    "outmanoeuvre": "outmaneuver",
+    "outmanoeuvred": "outmaneuvered",
+    "outmanoeuvres": "outmaneuvers",
+    "outmanoeuvring": "outmaneuvering",
+    "overemphasise": "overemphasize",
+    "overemphasised": "overemphasized",
+    "overemphasises": "overemphasizes",
+    "overemphasising": "overemphasizing",
+    "oxidisation": "oxidization",
+    "oxidise": "oxidize",
+    "oxidised": "oxidized",
+    "oxidises": "oxidizes",
+    "oxidising": "oxidizing",
+    "paederast": "pederast",
+    "paederasts": "pederasts",
+    "paediatric": "pediatric",
+    "paediatrician": "pediatrician",
+    "paediatricians": "pediatricians",
+    "paediatrics": "pediatrics",
+    "paedophile": "pedophile",
+    "paedophiles": "pedophiles",
+    "paedophilia": "pedophilia",
+    "palaeolithic": "paleolithic",
+    "palaeontologist": "paleontologist",
+    "palaeontologists": "paleontologists",
+    "palaeontology": "paleontology",
+    "panelled": "paneled",
+    "panelling": "paneling",
+    "panellist": "panelist",
+    "panellists": "panelists",
+    "paralyse": "paralyze",
+    "paralysed": "paralyzed",
+    "paralyses": "paralyzes",
+    "paralysing": "paralyzing",
+    "parcelled": "parceled",
+    "parcelling": "parceling",
+    "parlour": "parlor",
+    "parlours": "parlors",
+    "particularise": "particularize",
+    "particularised": "particularized",
+    "particularises": "particularizes",
+    "particularising": "particularizing",
+    "passivisation": "passivization",
+    "passivise": "passivize",
+    "passivised": "passivized",
+    "passivises": "passivizes",
+    "passivising": "passivizing",
+    "pasteurisation": "pasteurization",
+    "pasteurise": "pasteurize",
+    "pasteurised": "pasteurized",
+    "pasteurises": "pasteurizes",
+    "pasteurising": "pasteurizing",
+    "patronise": "patronize",
+    "patronised": "patronized",
+    "patronises": "patronizes",
+    "patronising": "patronizing",
+    "patronisingly": "patronizingly",
+    "pedalled": "pedaled",
+    "pedalling": "pedaling",
+    "pedestrianisation": "pedestrianization",
+    "pedestrianise": "pedestrianize",
+    "pedestrianised": "pedestrianized",
+    "pedestrianises": "pedestrianizes",
+    "pedestrianising": "pedestrianizing",
+    "penalise": "penalize",
+    "penalised": "penalized",
+    "penalises": "penalizes",
+    "penalising": "penalizing",
+    "pencilled": "penciled",
+    "pencilling": "penciling",
+    "personalise": "personalize",
+    "personalised": "personalized",
+    "personalises": "personalizes",
+    "personalising": "personalizing",
+    "pharmacopoeia": "pharmacopeia",
+    "pharmacopoeias": "pharmacopeias",
+    "philosophise": "philosophize",
+    "philosophised": "philosophized",
+    "philosophises": "philosophizes",
+    "philosophising": "philosophizing",
+    "philtre": "filter",
+    "philtres": "filters",
+    "phoney": "phony",
+    "plagiarise": "plagiarize",
+    "plagiarised": "plagiarized",
+    "plagiarises": "plagiarizes",
+    "plagiarising": "plagiarizing",
+    "plough": "plow",
+    "ploughed": "plowed",
+    "ploughing": "plowing",
+    "ploughman": "plowman",
+    "ploughmen": "plowmen",
+    "ploughs": "plows",
+    "ploughshare": "plowshare",
+    "ploughshares": "plowshares",
+    "polarisation": "polarization",
+    "polarise": "polarize",
+    "polarised": "polarized",
+    "polarises": "polarizes",
+    "polarising": "polarizing",
+    "politicisation": "politicization",
+    "politicise": "politicize",
+    "politicised": "politicized",
+    "politicises": "politicizes",
+    "politicising": "politicizing",
+    "popularisation": "popularization",
+    "popularise": "popularize",
+    "popularised": "popularized",
+    "popularises": "popularizes",
+    "popularising": "popularizing",
+    "pouffe": "pouf",
+    "pouffes": "poufs",
+    "practise": "practice",
+    "practised": "practiced",
+    "practises": "practices",
+    "practising": "practicing",
+    "praesidium": "presidium",
+    "praesidiums": "presidiums",
+    "pressurisation": "pressurization",
+    "pressurise": "pressurize",
+    "pressurised": "pressurized",
+    "pressurises": "pressurizes",
+    "pressurising": "pressurizing",
+    "pretence": "pretense",
+    "pretences": "pretenses",
+    "primaeval": "primeval",
+    "prioritisation": "prioritization",
+    "prioritise": "prioritize",
+    "prioritised": "prioritized",
+    "prioritises": "prioritizes",
+    "prioritising": "prioritizing",
+    "privatisation": "privatization",
+    "privatisations": "privatizations",
+    "privatise": "privatize",
+    "privatised": "privatized",
+    "privatises": "privatizes",
+    "privatising": "privatizing",
+    "professionalisation": "professionalization",
+    "professionalise": "professionalize",
+    "professionalised": "professionalized",
+    "professionalises": "professionalizes",
+    "professionalising": "professionalizing",
+    "programme": "program",
+    "programmes": "programs",
+    "prologue": "prolog",
+    "prologues": "prologs",
+    "propagandise": "propagandize",
+    "propagandised": "propagandized",
+    "propagandises": "propagandizes",
+    "propagandising": "propagandizing",
+    "proselytise": "proselytize",
+    "proselytised": "proselytized",
+    "proselytiser": "proselytizer",
+    "proselytisers": "proselytizers",
+    "proselytises": "proselytizes",
+    "proselytising": "proselytizing",
+    "psychoanalyse": "psychoanalyze",
+    "psychoanalysed": "psychoanalyzed",
+    "psychoanalyses": "psychoanalyzes",
+    "psychoanalysing": "psychoanalyzing",
+    "publicise": "publicize",
+    "publicised": "publicized",
+    "publicises": "publicizes",
+    "publicising": "publicizing",
+    "pulverisation": "pulverization",
+    "pulverise": "pulverize",
+    "pulverised": "pulverized",
+    "pulverises": "pulverizes",
+    "pulverising": "pulverizing",
+    "pummelled": "pummel",
+    "pummelling": "pummeled",
+    "pyjama": "pajama",
+    "pyjamas": "pajamas",
+    "pzazz": "pizzazz",
+    "quarrelled": "quarreled",
+    "quarrelling": "quarreling",
+    "radicalise": "radicalize",
+    "radicalised": "radicalized",
+    "radicalises": "radicalizes",
+    "radicalising": "radicalizing",
+    "rancour": "rancor",
+    "randomise": "randomize",
+    "randomised": "randomized",
+    "randomises": "randomizes",
+    "randomising": "randomizing",
+    "rationalisation": "rationalization",
+    "rationalisations": "rationalizations",
+    "rationalise": "rationalize",
+    "rationalised": "rationalized",
+    "rationalises": "rationalizes",
+    "rationalising": "rationalizing",
+    "ravelled": "raveled",
+    "ravelling": "raveling",
+    "realisable": "realizable",
+    "realisation": "realization",
+    "realisations": "realizations",
+    "realise": "realize",
+    "realised": "realized",
+    "realises": "realizes",
+    "realising": "realizing",
+    "recognisable": "recognizable",
+    "recognisably": "recognizably",
+    "recognisance": "recognizance",
+    "recognise": "recognize",
+    "recognised": "recognized",
+    "recognises": "recognizes",
+    "recognising": "recognizing",
+    "reconnoitre": "reconnoiter",
+    "reconnoitred": "reconnoitered",
+    "reconnoitres": "reconnoiters",
+    "reconnoitring": "reconnoitering",
+    "refuelled": "refueled",
+    "refuelling": "refueling",
+    "regularisation": "regularization",
+    "regularise": "regularize",
+    "regularised": "regularized",
+    "regularises": "regularizes",
+    "regularising": "regularizing",
+    "remodelled": "remodeled",
+    "remodelling": "remodeling",
+    "remould": "remold",
+    "remoulded": "remolded",
+    "remoulding": "remolding",
+    "remoulds": "remolds",
+    "reorganisation": "reorganization",
+    "reorganisations": "reorganizations",
+    "reorganise": "reorganize",
+    "reorganised": "reorganized",
+    "reorganises": "reorganizes",
+    "reorganising": "reorganizing",
+    "revelled": "reveled",
+    "reveller": "reveler",
+    "revellers": "revelers",
+    "revelling": "reveling",
+    "revitalise": "revitalize",
+    "revitalised": "revitalized",
+    "revitalises": "revitalizes",
+    "revitalising": "revitalizing",
+    "revolutionise": "revolutionize",
+    "revolutionised": "revolutionized",
+    "revolutionises": "revolutionizes",
+    "revolutionising": "revolutionizing",
+    "rhapsodise": "rhapsodize",
+    "rhapsodised": "rhapsodized",
+    "rhapsodises": "rhapsodizes",
+    "rhapsodising": "rhapsodizing",
+    "rigour": "rigor",
+    "rigours": "rigors",
+    "ritualised": "ritualized",
+    "rivalled": "rivaled",
+    "rivalling": "rivaling",
+    "romanticise": "romanticize",
+    "romanticised": "romanticized",
+    "romanticises": "romanticizes",
+    "romanticising": "romanticizing",
+    "rumour": "rumor",
+    "rumoured": "rumored",
+    "rumours": "rumors",
+    "sabre": "saber",
+    "sabres": "sabers",
+    "saltpetre": "saltpeter",
+    "sanitise": "sanitize",
+    "sanitised": "sanitized",
+    "sanitises": "sanitizes",
+    "sanitising": "sanitizing",
+    "satirise": "satirize",
+    "satirised": "satirized",
+    "satirises": "satirizes",
+    "satirising": "satirizing",
+    "saviour": "savior",
+    "saviours": "saviors",
+    "savour": "savor",
+    "savoured": "savored",
+    "savouries": "savories",
+    "savouring": "savoring",
+    "savours": "savors",
+    "savoury": "savory",
+    "scandalise": "scandalize",
+    "scandalised": "scandalized",
+    "scandalises": "scandalizes",
+    "scandalising": "scandalizing",
+    "sceptic": "skeptic",
+    "sceptical": "skeptical",
+    "sceptically": "skeptically",
+    "scepticism": "skepticism",
+    "sceptics": "skeptics",
+    "sceptre": "scepter",
+    "sceptres": "scepters",
+    "scrutinise": "scrutinize",
+    "scrutinised": "scrutinized",
+    "scrutinises": "scrutinizes",
+    "scrutinising": "scrutinizing",
+    "secularisation": "secularization",
+    "secularise": "secularize",
+    "secularised": "secularized",
+    "secularises": "secularizes",
+    "secularising": "secularizing",
+    "sensationalise": "sensationalize",
+    "sensationalised": "sensationalized",
+    "sensationalises": "sensationalizes",
+    "sensationalising": "sensationalizing",
+    "sensitise": "sensitize",
+    "sensitised": "sensitized",
+    "sensitises": "sensitizes",
+    "sensitising": "sensitizing",
+    "sentimentalise": "sentimentalize",
+    "sentimentalised": "sentimentalized",
+    "sentimentalises": "sentimentalizes",
+    "sentimentalising": "sentimentalizing",
+    "sepulchre": "sepulcher",
+    "sepulchres": "sepulchers",
+    "serialisation": "serialization",
+    "serialisations": "serializations",
+    "serialise": "serialize",
+    "serialised": "serialized",
+    "serialises": "serializes",
+    "serialising": "serializing",
+    "sermonise": "sermonize",
+    "sermonised": "sermonized",
+    "sermonises": "sermonizes",
+    "sermonising": "sermonizing",
+    "sheikh": "sheik",
+    "shovelled": "shoveled",
+    "shovelling": "shoveling",
+    "shrivelled": "shriveled",
+    "shrivelling": "shriveling",
+    "signalise": "signalize",
+    "signalised": "signalized",
+    "signalises": "signalizes",
+    "signalising": "signalizing",
+    "signalled": "signaled",
+    "signalling": "signaling",
+    "smoulder": "smolder",
+    "smouldered": "smoldered",
+    "smouldering": "smoldering",
+    "smoulders": "smolders",
+    "snivelled": "sniveled",
+    "snivelling": "sniveling",
+    "snorkelled": "snorkeled",
+    "snorkelling": "snorkeling",
+    "snowplough": "snowplow",
+    "snowploughs": "snowplow",
+    "socialisation": "socialization",
+    "socialise": "socialize",
+    "socialised": "socialized",
+    "socialises": "socializes",
+    "socialising": "socializing",
+    "sodomise": "sodomize",
+    "sodomised": "sodomized",
+    "sodomises": "sodomizes",
+    "sodomising": "sodomizing",
+    "solemnise": "solemnize",
+    "solemnised": "solemnized",
+    "solemnises": "solemnizes",
+    "solemnising": "solemnizing",
+    "sombre": "somber",
+    "specialisation": "specialization",
+    "specialisations": "specializations",
+    "specialise": "specialize",
+    "specialised": "specialized",
+    "specialises": "specializes",
+    "specialising": "specializing",
+    "spectre": "specter",
+    "spectres": "specters",
+    "spiralled": "spiraled",
+    "spiralling": "spiraling",
+    "splendour": "splendor",
+    "splendours": "splendors",
+    "squirrelled": "squirreled",
+    "squirrelling": "squirreling",
+    "stabilisation": "stabilization",
+    "stabilise": "stabilize",
+    "stabilised": "stabilized",
+    "stabiliser": "stabilizer",
+    "stabilisers": "stabilizers",
+    "stabilises": "stabilizes",
+    "stabilising": "stabilizing",
+    "standardisation": "standardization",
+    "standardise": "standardize",
+    "standardised": "standardized",
+    "standardises": "standardizes",
+    "standardising": "standardizing",
+    "stencilled": "stenciled",
+    "stencilling": "stenciling",
+    "sterilisation": "sterilization",
+    "sterilisations": "sterilizations",
+    "sterilise": "sterilize",
+    "sterilised": "sterilized",
+    "steriliser": "sterilizer",
+    "sterilisers": "sterilizers",
+    "sterilises": "sterilizes",
+    "sterilising": "sterilizing",
+    "stigmatisation": "stigmatization",
+    "stigmatise": "stigmatize",
+    "stigmatised": "stigmatized",
+    "stigmatises": "stigmatizes",
+    "stigmatising": "stigmatizing",
+    "storey": "story",
+    "storeys": "stories",
+    "subsidisation": "subsidization",
+    "subsidise": "subsidize",
+    "subsidised": "subsidized",
+    "subsidiser": "subsidizer",
+    "subsidisers": "subsidizers",
+    "subsidises": "subsidizes",
+    "subsidising": "subsidizing",
+    "succour": "succor",
+    "succoured": "succored",
+    "succouring": "succoring",
+    "succours": "succors",
+    "sulphate": "sulfate",
+    "sulphates": "sulfates",
+    "sulphide": "sulfide",
+    "sulphides": "sulfides",
+    "sulphur": "sulfur",
+    "sulphurous": "sulfurous",
+    "summarise": "summarize",
+    "summarised": "summarized",
+    "summarises": "summarizes",
+    "summarising": "summarizing",
+    "swivelled": "swiveled",
+    "swivelling": "swiveling",
+    "symbolise": "symbolize",
+    "symbolised": "symbolized",
+    "symbolises": "symbolizes",
+    "symbolising": "symbolizing",
+    "sympathise": "sympathize",
+    "sympathised": "sympathized",
+    "sympathiser": "sympathizer",
+    "sympathisers": "sympathizers",
+    "sympathises": "sympathizes",
+    "sympathising": "sympathizing",
+    "synchronisation": "synchronization",
+    "synchronise": "synchronize",
+    "synchronised": "synchronized",
+    "synchronises": "synchronizes",
+    "synchronising": "synchronizing",
+    "synthesise": "synthesize",
+    "synthesised": "synthesized",
+    "synthesiser": "synthesizer",
+    "synthesisers": "synthesizers",
+    "synthesises": "synthesizes",
+    "synthesising": "synthesizing",
+    "syphon": "siphon",
+    "syphoned": "siphoned",
+    "syphoning": "siphoning",
+    "syphons": "siphons",
+    "systematisation": "systematization",
+    "systematise": "systematize",
+    "systematised": "systematized",
+    "systematises": "systematizes",
+    "systematising": "systematizing",
+    "tantalise": "tantalize",
+    "tantalised": "tantalized",
+    "tantalises": "tantalizes",
+    "tantalising": "tantalizing",
+    "tantalisingly": "tantalizingly",
+    "tasselled": "tasseled",
+    "technicolour": "technicolor",
+    "temporise": "temporize",
+    "temporised": "temporized",
+    "temporises": "temporizes",
+    "temporising": "temporizing",
+    "tenderise": "tenderize",
+    "tenderised": "tenderized",
+    "tenderises": "tenderizes",
+    "tenderising": "tenderizing",
+    "terrorise": "terrorize",
+    "terrorised": "terrorized",
+    "terrorises": "terrorizes",
+    "terrorising": "terrorizing",
+    "theatre": "theater",
+    "theatregoer": "theatergoer",
+    "theatregoers": "theatergoers",
+    "theatres": "theaters",
+    "theorise": "theorize",
+    "theorised": "theorized",
+    "theorises": "theorizes",
+    "theorising": "theorizing",
+    "tonne": "ton",
+    "tonnes": "tons",
+    "towelled": "toweled",
+    "towelling": "toweling",
+    "toxaemia": "toxemia",
+    "tranquillise": "tranquilize",
+    "tranquillised": "tranquilized",
+    "tranquilliser": "tranquilizer",
+    "tranquillisers": "tranquilizers",
+    "tranquillises": "tranquilizes",
+    "tranquillising": "tranquilizing",
+    "tranquillity": "tranquility",
+    "tranquillize": "tranquilize",
+    "tranquillized": "tranquilized",
+    "tranquillizer": "tranquilizer",
+    "tranquillizers": "tranquilizers",
+    "tranquillizes": "tranquilizes",
+    "tranquillizing": "tranquilizing",
+    "tranquilly": "tranquility",
+    "transistorised": "transistorized",
+    "traumatise": "traumatize",
+    "traumatised": "traumatized",
+    "traumatises": "traumatizes",
+    "traumatising": "traumatizing",
+    "travelled": "traveled",
+    "traveller": "traveler",
+    "travellers": "travelers",
+    "travelling": "traveling",
+    "travelog": "travelogue",
+    "travelogs": "travelogues",
+    "trialled": "trialed",
+    "trialling": "trialing",
+    "tricolour": "tricolor",
+    "tricolours": "tricolors",
+    "trivialise": "trivialize",
+    "trivialised": "trivialized",
+    "trivialises": "trivializes",
+    "trivialising": "trivializing",
+    "tumour": "tumor",
+    "tumours": "tumors",
+    "tunnelled": "tunneled",
+    "tunnelling": "tunneling",
+    "tyrannise": "tyrannize",
+    "tyrannised": "tyrannized",
+    "tyrannises": "tyrannizes",
+    "tyrannising": "tyrannizing",
+    "tyre": "tire",
+    "tyres": "tires",
+    "unauthorised": "unauthorized",
+    "uncivilised": "uncivilized",
+    "underutilised": "underutilized",
+    "unequalled": "unequaled",
+    "unfavourable": "unfavorable",
+    "unfavourably": "unfavorably",
+    "unionisation": "unionization",
+    "unionise": "unionize",
+    "unionised": "unionized",
+    "unionises": "unionizes",
+    "unionising": "unionizing",
+    "unorganised": "unorganized",
+    "unravelled": "unraveled",
+    "unravelling": "unraveling",
+    "unrecognisable": "unrecognizable",
+    "unrecognised": "unrecognized",
+    "unrivalled": "unrivaled",
+    "unsavoury": "unsavory",
+    "untrammelled": "untrammeled",
+    "urbanisation": "urbanization",
+    "urbanise": "urbanize",
+    "urbanised": "urbanized",
+    "urbanises": "urbanizes",
+    "urbanising": "urbanizing",
+    "utilisable": "utilizable",
+    "utilisation": "utilization",
+    "utilise": "utilize",
+    "utilised": "utilized",
+    "utilises": "utilizes",
+    "utilising": "utilizing",
+    "valour": "valor",
+    "vandalise": "vandalize",
+    "vandalised": "vandalized",
+    "vandalises": "vandalizes",
+    "vandalising": "vandalizing",
+    "vaporisation": "vaporization",
+    "vaporise": "vaporize",
+    "vaporised": "vaporized",
+    "vaporises": "vaporizes",
+    "vaporising": "vaporizing",
+    "vapour": "vapor",
+    "vapours": "vapors",
+    "verbalise": "verbalize",
+    "verbalised": "verbalized",
+    "verbalises": "verbalizes",
+    "verbalising": "verbalizing",
+    "victimisation": "victimization",
+    "victimise": "victimize",
+    "victimised": "victimized",
+    "victimises": "victimizes",
+    "victimising": "victimizing",
+    "videodisc": "videodisk",
+    "videodiscs": "videodisks",
+    "vigour": "vigor",
+    "visualisation": "visualization",
+    "visualisations": "visualizations",
+    "visualise": "visualize",
+    "visualised": "visualized",
+    "visualises": "visualizes",
+    "visualising": "visualizing",
+    "vocalisation": "vocalization",
+    "vocalisations": "vocalizations",
+    "vocalise": "vocalize",
+    "vocalised": "vocalized",
+    "vocalises": "vocalizes",
+    "vocalising": "vocalizing",
+    "vulcanised": "vulcanized",
+    "vulgarisation": "vulgarization",
+    "vulgarise": "vulgarize",
+    "vulgarised": "vulgarized",
+    "vulgarises": "vulgarizes",
+    "vulgarising": "vulgarizing",
+    "waggon": "wagon",
+    "waggons": "wagons",
+    "watercolour": "watercolor",
+    "watercolours": "watercolors",
+    "weaselled": "weaseled",
+    "weaselling": "weaseling",
+    "westernisation": "westernization",
+    "westernise": "westernize",
+    "westernised": "westernized",
+    "westernises": "westernizes",
+    "westernising": "westernizing",
+    "womanise": "womanize",
+    "womanised": "womanized",
+    "womaniser": "womanizer",
+    "womanisers": "womanizers",
+    "womanises": "womanizes",
+    "womanising": "womanizing",
+    "woollen": "woolen",
+    "woollens": "woolens",
+    "woollies": "woolies",
+    "woolly": "wooly",
+    "worshipped": "worshiped",
+    "worshipping": "worshiping",
+    "worshipper": "worshiper",
+    "yodelled": "yodeled",
+    "yodelling": "yodeling",
+    "yoghourt": "yogurt",
+    "yoghourts": "yogurts",
+    "yoghurt": "yogurt",
+    "yoghurts": "yogurts",
+    "mhm": "hmm",
+    "mm": "hmm",
+    "mmm": "hmm"
+}
\ No newline at end of file
diff --git a/high_sync/src/models/whisper/whisper/normalizers/english.py b/high_sync/src/models/whisper/whisper/normalizers/english.py
new file mode 100644
index 0000000..d5c2bb4
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/normalizers/english.py
@@ -0,0 +1,543 @@
+import json
+import os
+import re
+from fractions import Fraction
+from typing import Iterator, List, Match, Optional, Union
+
+from more_itertools import windowed
+
+from .basic import remove_symbols_and_diacritics
+
+
+class EnglishNumberNormalizer:
+    """
+    Convert any spelled-out numbers into arabic numbers, while handling:
+
+    - remove any commas
+    - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc.
+    - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars`
+    - spell out `one` and `ones`
+    - interpret successive single-digit numbers as nominal: `one oh one` -> `101`
+    """
+
+    def __init__(self):
+        super().__init__()
+
+        self.zeros = {"o", "oh", "zero"}
+        self.ones = {
+            name: i
+            for i, name in enumerate(
+                [
+                    "one",
+                    "two",
+                    "three",
+                    "four",
+                    "five",
+                    "six",
+                    "seven",
+                    "eight",
+                    "nine",
+                    "ten",
+                    "eleven",
+                    "twelve",
+                    "thirteen",
+                    "fourteen",
+                    "fifteen",
+                    "sixteen",
+                    "seventeen",
+                    "eighteen",
+                    "nineteen",
+                ],
+                start=1,
+            )
+        }
+        self.ones_plural = {
+            "sixes" if name == "six" else name + "s": (value, "s")
+            for name, value in self.ones.items()
+        }
+        self.ones_ordinal = {
+            "zeroth": (0, "th"),
+            "first": (1, "st"),
+            "second": (2, "nd"),
+            "third": (3, "rd"),
+            "fifth": (5, "th"),
+            "twelfth": (12, "th"),
+            **{
+                name + ("h" if name.endswith("t") else "th"): (value, "th")
+                for name, value in self.ones.items()
+                if value > 3 and value != 5 and value != 12
+            },
+        }
+        self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal}
+
+        self.tens = {
+            "twenty": 20,
+            "thirty": 30,
+            "forty": 40,
+            "fifty": 50,
+            "sixty": 60,
+            "seventy": 70,
+            "eighty": 80,
+            "ninety": 90,
+        }
+        self.tens_plural = {
+            name.replace("y", "ies"): (value, "s") for name, value in self.tens.items()
+        }
+        self.tens_ordinal = {
+            name.replace("y", "ieth"): (value, "th") for name, value in self.tens.items()
+        }
+        self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal}
+
+        self.multipliers = {
+            "hundred": 100,
+            "thousand": 1_000,
+            "million": 1_000_000,
+            "billion": 1_000_000_000,
+            "trillion": 1_000_000_000_000,
+            "quadrillion": 1_000_000_000_000_000,
+            "quintillion": 1_000_000_000_000_000_000,
+            "sextillion": 1_000_000_000_000_000_000_000,
+            "septillion": 1_000_000_000_000_000_000_000_000,
+            "octillion": 1_000_000_000_000_000_000_000_000_000,
+            "nonillion": 1_000_000_000_000_000_000_000_000_000_000,
+            "decillion": 1_000_000_000_000_000_000_000_000_000_000_000,
+        }
+        self.multipliers_plural = {
+            name + "s": (value, "s") for name, value in self.multipliers.items()
+        }
+        self.multipliers_ordinal = {
+            name + "th": (value, "th") for name, value in self.multipliers.items()
+        }
+        self.multipliers_suffixed = {**self.multipliers_plural, **self.multipliers_ordinal}
+        self.decimals = {*self.ones, *self.tens, *self.zeros}
+
+        self.preceding_prefixers = {
+            "minus": "-",
+            "negative": "-",
+            "plus": "+",
+            "positive": "+",
+        }
+        self.following_prefixers = {
+            "pound": "£",
+            "pounds": "£",
+            "euro": "€",
+            "euros": "€",
+            "dollar": "$",
+            "dollars": "$",
+            "cent": "¢",
+            "cents": "¢",
+        }
+        self.prefixes = set(
+            list(self.preceding_prefixers.values()) + list(self.following_prefixers.values())
+        )
+        self.suffixers = {
+            "per": {"cent": "%"},
+            "percent": "%",
+        }
+        self.specials = {"and", "double", "triple", "point"}
+
+        self.words = set(
+            [
+                key
+                for mapping in [
+                    self.zeros,
+                    self.ones,
+                    self.ones_suffixed,
+                    self.tens,
+                    self.tens_suffixed,
+                    self.multipliers,
+                    self.multipliers_suffixed,
+                    self.preceding_prefixers,
+                    self.following_prefixers,
+                    self.suffixers,
+                    self.specials,
+                ]
+                for key in mapping
+            ]
+        )
+        self.literal_words = {"one", "ones"}
+
+    def process_words(self, words: List[str]) -> Iterator[str]:
+        prefix: Optional[str] = None
+        value: Optional[Union[str, int]] = None
+        skip = False
+
+        def to_fraction(s: str):
+            try:
+                return Fraction(s)
+            except ValueError:
+                return None
+
+        def output(result: Union[str, int]):
+            nonlocal prefix, value
+            result = str(result)
+            if prefix is not None:
+                result = prefix + result
+            value = None
+            prefix = None
+            return result
+
+        if len(words) == 0:
+            return
+
+        for prev, current, next in windowed([None] + words + [None], 3):
+            if skip:
+                skip = False
+                continue
+
+            next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next)
+            has_prefix = current[0] in self.prefixes
+            current_without_prefix = current[1:] if has_prefix else current
+            if re.match(r"^\d+(\.\d+)?$", current_without_prefix):
+                # arabic numbers (potentially with signs and fractions)
+                f = to_fraction(current_without_prefix)
+                assert f is not None
+                if value is not None:
+                    if isinstance(value, str) and value.endswith("."):
+                        # concatenate decimals / ip address components
+                        value = str(value) + str(current)
+                        continue
+                    else:
+                        yield output(value)
+
+                prefix = current[0] if has_prefix else prefix
+                if f.denominator == 1:
+                    value = f.numerator  # store integers as int
+                else:
+                    value = current_without_prefix
+            elif current not in self.words:
+                # non-numeric words
+                if value is not None:
+                    yield output(value)
+                yield output(current)
+            elif current in self.zeros:
+                value = str(value or "") + "0"
+            elif current in self.ones:
+                ones = self.ones[current]
+
+                if value is None:
+                    value = ones
+                elif isinstance(value, str) or prev in self.ones:
+                    if prev in self.tens and ones < 10:  # replace the last zero with the digit
+                        assert value[-1] == "0"
+                        value = value[:-1] + str(ones)
+                    else:
+                        value = str(value) + str(ones)
+                elif ones < 10:
+                    if value % 10 == 0:
+                        value += ones
+                    else:
+                        value = str(value) + str(ones)
+                else:  # eleven to nineteen
+                    if value % 100 == 0:
+                        value += ones
+                    else:
+                        value = str(value) + str(ones)
+            elif current in self.ones_suffixed:
+                # ordinal or cardinal; yield the number right away
+                ones, suffix = self.ones_suffixed[current]
+                if value is None:
+                    yield output(str(ones) + suffix)
+                elif isinstance(value, str) or prev in self.ones:
+                    if prev in self.tens and ones < 10:
+                        assert value[-1] == "0"
+                        yield output(value[:-1] + str(ones) + suffix)
+                    else:
+                        yield output(str(value) + str(ones) + suffix)
+                elif ones < 10:
+                    if value % 10 == 0:
+                        yield output(str(value + ones) + suffix)
+                    else:
+                        yield output(str(value) + str(ones) + suffix)
+                else:  # eleven to nineteen
+                    if value % 100 == 0:
+                        yield output(str(value + ones) + suffix)
+                    else:
+                        yield output(str(value) + str(ones) + suffix)
+                value = None
+            elif current in self.tens:
+                tens = self.tens[current]
+                if value is None:
+                    value = tens
+                elif isinstance(value, str):
+                    value = str(value) + str(tens)
+                else:
+                    if value % 100 == 0:
+                        value += tens
+                    else:
+                        value = str(value) + str(tens)
+            elif current in self.tens_suffixed:
+                # ordinal or cardinal; yield the number right away
+                tens, suffix = self.tens_suffixed[current]
+                if value is None:
+                    yield output(str(tens) + suffix)
+                elif isinstance(value, str):
+                    yield output(str(value) + str(tens) + suffix)
+                else:
+                    if value % 100 == 0:
+                        yield output(str(value + tens) + suffix)
+                    else:
+                        yield output(str(value) + str(tens) + suffix)
+            elif current in self.multipliers:
+                multiplier = self.multipliers[current]
+                if value is None:
+                    value = multiplier
+                elif isinstance(value, str) or value == 0:
+                    f = to_fraction(value)
+                    p = f * multiplier if f is not None else None
+                    if f is not None and p.denominator == 1:
+                        value = p.numerator
+                    else:
+                        yield output(value)
+                        value = multiplier
+                else:
+                    before = value // 1000 * 1000
+                    residual = value % 1000
+                    value = before + residual * multiplier
+            elif current in self.multipliers_suffixed:
+                multiplier, suffix = self.multipliers_suffixed[current]
+                if value is None:
+                    yield output(str(multiplier) + suffix)
+                elif isinstance(value, str):
+                    f = to_fraction(value)
+                    p = f * multiplier if f is not None else None
+                    if f is not None and p.denominator == 1:
+                        yield output(str(p.numerator) + suffix)
+                    else:
+                        yield output(value)
+                        yield output(str(multiplier) + suffix)
+                else:  # int
+                    before = value // 1000 * 1000
+                    residual = value % 1000
+                    value = before + residual * multiplier
+                    yield output(str(value) + suffix)
+                value = None
+            elif current in self.preceding_prefixers:
+                # apply prefix (positive, minus, etc.) if it precedes a number
+                if value is not None:
+                    yield output(value)
+
+                if next in self.words or next_is_numeric:
+                    prefix = self.preceding_prefixers[current]
+                else:
+                    yield output(current)
+            elif current in self.following_prefixers:
+                # apply prefix (dollars, cents, etc.) only after a number
+                if value is not None:
+                    prefix = self.following_prefixers[current]
+                    yield output(value)
+                else:
+                    yield output(current)
+            elif current in self.suffixers:
+                # apply suffix symbols (percent -> '%')
+                if value is not None:
+                    suffix = self.suffixers[current]
+                    if isinstance(suffix, dict):
+                        if next in suffix:
+                            yield output(str(value) + suffix[next])
+                            skip = True
+                        else:
+                            yield output(value)
+                            yield output(current)
+                    else:
+                        yield output(str(value) + suffix)
+                else:
+                    yield output(current)
+            elif current in self.specials:
+                if next not in self.words and not next_is_numeric:
+                    # apply special handling only if the next word can be numeric
+                    if value is not None:
+                        yield output(value)
+                    yield output(current)
+                elif current == "and":
+                    # ignore "and" after hundreds, thousands, etc.
+                    if prev not in self.multipliers:
+                        if value is not None:
+                            yield output(value)
+                        yield output(current)
+                elif current == "double" or current == "triple":
+                    if next in self.ones or next in self.zeros:
+                        repeats = 2 if current == "double" else 3
+                        ones = self.ones.get(next, 0)
+                        value = str(value or "") + str(ones) * repeats
+                        skip = True
+                    else:
+                        if value is not None:
+                            yield output(value)
+                        yield output(current)
+                elif current == "point":
+                    if next in self.decimals or next_is_numeric:
+                        value = str(value or "") + "."
+                else:
+                    # should all have been covered at this point
+                    raise ValueError(f"Unexpected token: {current}")
+            else:
+                # all should have been covered at this point
+                raise ValueError(f"Unexpected token: {current}")
+
+        if value is not None:
+            yield output(value)
+
+    def preprocess(self, s: str):
+        # replace "<number> and a half" with "<number> point five"
+        results = []
+
+        segments = re.split(r"\band\s+a\s+half\b", s)
+        for i, segment in enumerate(segments):
+            if len(segment.strip()) == 0:
+                continue
+            if i == len(segments) - 1:
+                results.append(segment)
+            else:
+                results.append(segment)
+                last_word = segment.rsplit(maxsplit=2)[-1]
+                if last_word in self.decimals or last_word in self.multipliers:
+                    results.append("point five")
+                else:
+                    results.append("and a half")
+
+        s = " ".join(results)
+
+        # put a space at number/letter boundary
+        s = re.sub(r"([a-z])([0-9])", r"\1 \2", s)
+        s = re.sub(r"([0-9])([a-z])", r"\1 \2", s)
+
+        # but remove spaces which could be a suffix
+        s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s)
+
+        return s
+
+    def postprocess(self, s: str):
+        def combine_cents(m: Match):
+            try:
+                currency = m.group(1)
+                integer = m.group(2)
+                cents = int(m.group(3))
+                return f"{currency}{integer}.{cents:02d}"
+            except ValueError:
+                return m.string
+
+        def extract_cents(m: Match):
+            try:
+                return f"¢{int(m.group(1))}"
+            except ValueError:
+                return m.string
+
+        # apply currency postprocessing; "$2 and ¢7" -> "$2.07"
+        s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s)
+        s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s)
+
+        # write "one(s)" instead of "1(s)", just for the readability
+        s = re.sub(r"\b1(s?)\b", r"one\1", s)
+
+        return s
+
+    def __call__(self, s: str):
+        s = self.preprocess(s)
+        s = " ".join(word for word in self.process_words(s.split()) if word is not None)
+        s = self.postprocess(s)
+
+        return s
+
+
+class EnglishSpellingNormalizer:
+    """
+    Applies British-American spelling mappings as listed in [1].
+
+    [1] https://www.tysto.com/uk-us-spelling-list.html
+    """
+
+    def __init__(self):
+        mapping_path = os.path.join(os.path.dirname(__file__), "english.json")
+        self.mapping = json.load(open(mapping_path))
+
+    def __call__(self, s: str):
+        return " ".join(self.mapping.get(word, word) for word in s.split())
+
+
+class EnglishTextNormalizer:
+    def __init__(self):
+        self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b"
+        self.replacers = {
+            # common contractions
+            r"\bwon't\b": "will not",
+            r"\bcan't\b": "can not",
+            r"\blet's\b": "let us",
+            r"\bain't\b": "aint",
+            r"\by'all\b": "you all",
+            r"\bwanna\b": "want to",
+            r"\bgotta\b": "got to",
+            r"\bgonna\b": "going to",
+            r"\bi'ma\b": "i am going to",
+            r"\bimma\b": "i am going to",
+            r"\bwoulda\b": "would have",
+            r"\bcoulda\b": "could have",
+            r"\bshoulda\b": "should have",
+            r"\bma'am\b": "madam",
+            # contractions in titles/prefixes
+            r"\bmr\b": "mister ",
+            r"\bmrs\b": "missus ",
+            r"\bst\b": "saint ",
+            r"\bdr\b": "doctor ",
+            r"\bprof\b": "professor ",
+            r"\bcapt\b": "captain ",
+            r"\bgov\b": "governor ",
+            r"\bald\b": "alderman ",
+            r"\bgen\b": "general ",
+            r"\bsen\b": "senator ",
+            r"\brep\b": "representative ",
+            r"\bpres\b": "president ",
+            r"\brev\b": "reverend ",
+            r"\bhon\b": "honorable ",
+            r"\basst\b": "assistant ",
+            r"\bassoc\b": "associate ",
+            r"\blt\b": "lieutenant ",
+            r"\bcol\b": "colonel ",
+            r"\bjr\b": "junior ",
+            r"\bsr\b": "senior ",
+            r"\besq\b": "esquire ",
+            # prefect tenses, ideally it should be any past participles, but it's harder..
+            r"'d been\b": " had been",
+            r"'s been\b": " has been",
+            r"'d gone\b": " had gone",
+            r"'s gone\b": " has gone",
+            r"'d done\b": " had done",  # "'s done" is ambiguous
+            r"'s got\b": " has got",
+            # general contractions
+            r"n't\b": " not",
+            r"'re\b": " are",
+            r"'s\b": " is",
+            r"'d\b": " would",
+            r"'ll\b": " will",
+            r"'t\b": " not",
+            r"'ve\b": " have",
+            r"'m\b": " am",
+        }
+        self.standardize_numbers = EnglishNumberNormalizer()
+        self.standardize_spellings = EnglishSpellingNormalizer()
+
+    def __call__(self, s: str):
+        s = s.lower()
+
+        s = re.sub(r"[<\[][^>\]]*[>\]]", "", s)  # remove words between brackets
+        s = re.sub(r"\(([^)]+?)\)", "", s)  # remove words between parenthesis
+        s = re.sub(self.ignore_patterns, "", s)
+        s = re.sub(r"\s+'", "'", s)  # standardize when there's a space before an apostrophe
+
+        for pattern, replacement in self.replacers.items():
+            s = re.sub(pattern, replacement, s)
+
+        s = re.sub(r"(\d),(\d)", r"\1\2", s)  # remove commas between digits
+        s = re.sub(r"\.([^0-9]|$)", r" \1", s)  # remove periods not followed by numbers
+        s = remove_symbols_and_diacritics(s, keep=".%$¢€£")  # keep some symbols for numerics
+
+        s = self.standardize_numbers(s)
+        s = self.standardize_spellings(s)
+
+        # now remove prefix/suffix symbols that are not preceded/followed by numbers
+        s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s)
+        s = re.sub(r"([^0-9])%", r"\1 ", s)
+
+        s = re.sub(r"\s+", " ", s)  # replace any successive whitespace characters with a space
+
+        return s
diff --git a/high_sync/src/models/whisper/whisper/tokenizer.py b/high_sync/src/models/whisper/whisper/tokenizer.py
new file mode 100644
index 0000000..88142c4
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/tokenizer.py
@@ -0,0 +1,331 @@
+import os
+from dataclasses import dataclass
+from functools import lru_cache
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from transformers import GPT2TokenizerFast
+
+LANGUAGES = {
+    "en": "english",
+    "zh": "chinese",
+    "de": "german",
+    "es": "spanish",
+    "ru": "russian",
+    "ko": "korean",
+    "fr": "french",
+    "ja": "japanese",
+    "pt": "portuguese",
+    "tr": "turkish",
+    "pl": "polish",
+    "ca": "catalan",
+    "nl": "dutch",
+    "ar": "arabic",
+    "sv": "swedish",
+    "it": "italian",
+    "id": "indonesian",
+    "hi": "hindi",
+    "fi": "finnish",
+    "vi": "vietnamese",
+    "iw": "hebrew",
+    "uk": "ukrainian",
+    "el": "greek",
+    "ms": "malay",
+    "cs": "czech",
+    "ro": "romanian",
+    "da": "danish",
+    "hu": "hungarian",
+    "ta": "tamil",
+    "no": "norwegian",
+    "th": "thai",
+    "ur": "urdu",
+    "hr": "croatian",
+    "bg": "bulgarian",
+    "lt": "lithuanian",
+    "la": "latin",
+    "mi": "maori",
+    "ml": "malayalam",
+    "cy": "welsh",
+    "sk": "slovak",
+    "te": "telugu",
+    "fa": "persian",
+    "lv": "latvian",
+    "bn": "bengali",
+    "sr": "serbian",
+    "az": "azerbaijani",
+    "sl": "slovenian",
+    "kn": "kannada",
+    "et": "estonian",
+    "mk": "macedonian",
+    "br": "breton",
+    "eu": "basque",
+    "is": "icelandic",
+    "hy": "armenian",
+    "ne": "nepali",
+    "mn": "mongolian",
+    "bs": "bosnian",
+    "kk": "kazakh",
+    "sq": "albanian",
+    "sw": "swahili",
+    "gl": "galician",
+    "mr": "marathi",
+    "pa": "punjabi",
+    "si": "sinhala",
+    "km": "khmer",
+    "sn": "shona",
+    "yo": "yoruba",
+    "so": "somali",
+    "af": "afrikaans",
+    "oc": "occitan",
+    "ka": "georgian",
+    "be": "belarusian",
+    "tg": "tajik",
+    "sd": "sindhi",
+    "gu": "gujarati",
+    "am": "amharic",
+    "yi": "yiddish",
+    "lo": "lao",
+    "uz": "uzbek",
+    "fo": "faroese",
+    "ht": "haitian creole",
+    "ps": "pashto",
+    "tk": "turkmen",
+    "nn": "nynorsk",
+    "mt": "maltese",
+    "sa": "sanskrit",
+    "lb": "luxembourgish",
+    "my": "myanmar",
+    "bo": "tibetan",
+    "tl": "tagalog",
+    "mg": "malagasy",
+    "as": "assamese",
+    "tt": "tatar",
+    "haw": "hawaiian",
+    "ln": "lingala",
+    "ha": "hausa",
+    "ba": "bashkir",
+    "jw": "javanese",
+    "su": "sundanese",
+}
+
+# language code lookup by name, with a few language aliases
+TO_LANGUAGE_CODE = {
+    **{language: code for code, language in LANGUAGES.items()},
+    "burmese": "my",
+    "valencian": "ca",
+    "flemish": "nl",
+    "haitian": "ht",
+    "letzeburgesch": "lb",
+    "pushto": "ps",
+    "panjabi": "pa",
+    "moldavian": "ro",
+    "moldovan": "ro",
+    "sinhalese": "si",
+    "castilian": "es",
+}
+
+
+@dataclass(frozen=True)
+class Tokenizer:
+    """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens"""
+
+    tokenizer: "GPT2TokenizerFast"
+    language: Optional[str]
+    sot_sequence: Tuple[int]
+
+    def encode(self, text, **kwargs):
+        return self.tokenizer.encode(text, **kwargs)
+
+    def decode(self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs):
+        return self.tokenizer.decode(token_ids, **kwargs)
+
+    def decode_with_timestamps(self, tokens) -> str:
+        """
+        Timestamp tokens are above the special tokens' id range and are ignored by `decode()`.
+        This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>".
+        """
+        outputs = [[]]
+        for token in tokens:
+            if token >= self.timestamp_begin:
+                timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>"
+                outputs.append(timestamp)
+                outputs.append([])
+            else:
+                outputs[-1].append(token)
+        outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs]
+        return "".join(outputs)
+
+    @property
+    @lru_cache()
+    def eot(self) -> int:
+        return self.tokenizer.eos_token_id
+
+    @property
+    @lru_cache()
+    def sot(self) -> int:
+        return self._get_single_token_id("<|startoftranscript|>")
+
+    @property
+    @lru_cache()
+    def sot_lm(self) -> int:
+        return self._get_single_token_id("<|startoflm|>")
+
+    @property
+    @lru_cache()
+    def sot_prev(self) -> int:
+        return self._get_single_token_id("<|startofprev|>")
+
+    @property
+    @lru_cache()
+    def no_speech(self) -> int:
+        return self._get_single_token_id("<|nospeech|>")
+
+    @property
+    @lru_cache()
+    def no_timestamps(self) -> int:
+        return self._get_single_token_id("<|notimestamps|>")
+
+    @property
+    @lru_cache()
+    def timestamp_begin(self) -> int:
+        return self.tokenizer.all_special_ids[-1] + 1
+
+    @property
+    @lru_cache()
+    def language_token(self) -> int:
+        """Returns the token id corresponding to the value of the `language` field"""
+        if self.language is None:
+            raise ValueError(f"This tokenizer does not have language token configured")
+
+        additional_tokens = dict(
+            zip(
+                self.tokenizer.additional_special_tokens,
+                self.tokenizer.additional_special_tokens_ids,
+            )
+        )
+        candidate = f"<|{self.language}|>"
+        if candidate in additional_tokens:
+            return additional_tokens[candidate]
+
+        raise KeyError(f"Language {self.language} not found in tokenizer.")
+
+    @property
+    @lru_cache()
+    def all_language_tokens(self) -> Tuple[int]:
+        result = []
+        for token, token_id in zip(
+            self.tokenizer.additional_special_tokens,
+            self.tokenizer.additional_special_tokens_ids,
+        ):
+            if token.strip("<|>") in LANGUAGES:
+                result.append(token_id)
+        return tuple(result)
+
+    @property
+    @lru_cache()
+    def all_language_codes(self) -> Tuple[str]:
+        return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens)
+
+    @property
+    @lru_cache()
+    def sot_sequence_including_notimestamps(self) -> Tuple[int]:
+        return tuple(list(self.sot_sequence) + [self.no_timestamps])
+
+    @property
+    @lru_cache()
+    def non_speech_tokens(self) -> Tuple[int]:
+        """
+        Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech
+        annotations, to prevent sampling texts that are not actually spoken in the audio, e.g.
+
+        - ♪♪♪
+        - ( SPEAKING FOREIGN LANGUAGE )
+        - [DAVID] Hey there,
+
+        keeping basic punctuations like commas, periods, question marks, exclamation points, etc.
+        """
+        symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』")
+        symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split()
+
+        # symbols that may be a single token or multiple tokens depending on the tokenizer.
+        # In case they're multiple tokens, suppress the first token, which is safe because:
+        # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress
+        # in generations, and in the 3-byte UTF-8 representation they share the first two bytes.
+        miscellaneous = set("♩♪♫♬♭♮♯")
+        assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous)
+
+        # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
+        result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]}
+        for symbol in symbols + list(miscellaneous):
+            for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]:
+                if len(tokens) == 1 or symbol in miscellaneous:
+                    result.add(tokens[0])
+
+        return tuple(sorted(result))
+
+    def _get_single_token_id(self, text) -> int:
+        tokens = self.tokenizer.encode(text)
+        assert len(tokens) == 1, f"{text} is not encoded as a single token"
+        return tokens[0]
+
+
+@lru_cache(maxsize=None)
+def build_tokenizer(name: str = "gpt2"):
+    os.environ["TOKENIZERS_PARALLELISM"] = "false"
+    path = os.path.join(os.path.dirname(__file__), "assets", name)
+    tokenizer = GPT2TokenizerFast.from_pretrained(path)
+
+    specials = [
+        "<|startoftranscript|>",
+        *[f"<|{lang}|>" for lang in LANGUAGES.keys()],
+        "<|translate|>",
+        "<|transcribe|>",
+        "<|startoflm|>",
+        "<|startofprev|>",
+        "<|nospeech|>",
+        "<|notimestamps|>",
+    ]
+
+    tokenizer.add_special_tokens(dict(additional_special_tokens=specials))
+    return tokenizer
+
+
+@lru_cache(maxsize=None)
+def get_tokenizer(
+    multilingual: bool,
+    *,
+    task: Optional[str] = None,  # Literal["transcribe", "translate", None]
+    language: Optional[str] = None,
+) -> Tokenizer:
+    if language is not None:
+        language = language.lower()
+        if language not in LANGUAGES:
+            if language in TO_LANGUAGE_CODE:
+                language = TO_LANGUAGE_CODE[language]
+            else:
+                raise ValueError(f"Unsupported language: {language}")
+
+    if multilingual:
+        tokenizer_name = "multilingual"
+        task = task or "transcribe"
+        language = language or "en"
+    else:
+        tokenizer_name = "gpt2"
+        task = None
+        language = None
+
+    tokenizer = build_tokenizer(name=tokenizer_name)
+    all_special_ids: List[int] = tokenizer.all_special_ids
+    sot: int = all_special_ids[1]
+    translate: int = all_special_ids[-6]
+    transcribe: int = all_special_ids[-5]
+
+    langs = tuple(LANGUAGES.keys())
+    sot_sequence = [sot]
+    if language is not None:
+        sot_sequence.append(sot + 1 + langs.index(language))
+    if task is not None:
+        sot_sequence.append(transcribe if task == "transcribe" else translate)
+
+    return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence))
diff --git a/high_sync/src/models/whisper/whisper/transcribe.py b/high_sync/src/models/whisper/whisper/transcribe.py
new file mode 100644
index 0000000..770b54d
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/transcribe.py
@@ -0,0 +1,207 @@
+import argparse
+import os
+import warnings
+from typing import List, Optional, Tuple, Union, TYPE_CHECKING
+
+import numpy as np
+import torch
+import tqdm
+
+from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram
+from .decoding import DecodingOptions, DecodingResult
+from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer
+from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, write_txt, write_vtt, write_srt
+
+if TYPE_CHECKING:
+    from .model import Whisper
+
+
+def transcribe(
+        model: "Whisper",
+        audio: Union[str, np.ndarray, torch.Tensor],
+        *,
+        verbose: Optional[bool] = None,
+        temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
+        compression_ratio_threshold: Optional[float] = 2.4,
+        logprob_threshold: Optional[float] = -1.0,
+        no_speech_threshold: Optional[float] = 0.6,
+        condition_on_previous_text: bool = True,
+        force_extraction: bool = False,
+        **decode_options,
+):
+    """
+    Transcribe an audio file using Whisper
+
+    Parameters
+    ----------
+    model: Whisper
+        The Whisper model instance
+
+    audio: Union[str, np.ndarray, torch.Tensor]
+        The path to the audio file to open, or the audio waveform
+
+    verbose: bool
+        Whether to display the text being decoded to the console. If True, displays all the details,
+        If False, displays minimal details. If None, does not display anything
+
+    temperature: Union[float, Tuple[float, ...]]
+        Temperature for sampling. It can be a tuple of temperatures, which will be successfully used
+        upon failures according to either `compression_ratio_threshold` or `logprob_threshold`.
+
+    compression_ratio_threshold: float
+        If the gzip compression ratio is above this value, treat as failed
+
+    logprob_threshold: float
+        If the average log probability over sampled tokens is below this value, treat as failed
+
+    no_speech_threshold: float
+        If the no_speech probability is higher than this value AND the average log probability
+        over sampled tokens is below `logprob_threshold`, consider the segment as silent
+
+    condition_on_previous_text: bool
+        if True, the previous output of the model is provided as a prompt for the next window;
+        disabling may make the text inconsistent across windows, but the model becomes less prone to
+        getting stuck in a failure loop, such as repetition looping or timestamps going out of sync.
+
+    decode_options: dict
+        Keyword arguments to construct `DecodingOptions` instances
+
+    Returns
+    -------
+    A dictionary containing the resulting text ("text") and segment-level details ("segments"), and
+    the spoken language ("language"), which is detected when `decode_options["language"]` is None.
+    """
+    dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32
+    if model.device == torch.device("cpu"):
+        if torch.cuda.is_available():
+            warnings.warn("Performing inference on CPU when CUDA is available")
+        if dtype == torch.float16:
+            warnings.warn("FP16 is not supported on CPU; using FP32 instead")
+            dtype = torch.float32
+
+    if dtype == torch.float32:
+        decode_options["fp16"] = False
+
+    mel = log_mel_spectrogram(audio)
+
+    all_segments = []
+    def add_segment(
+            *, start: float, end: float, encoder_embeddings
+    ):
+
+        all_segments.append(
+            {
+                "start": start,
+                "end": end,
+                "encoder_embeddings":encoder_embeddings,
+            }
+        )
+    # show the progress bar when verbose is False (otherwise the transcribed text will be printed)
+    num_frames = mel.shape[-1]
+    seek = 0
+    previous_seek_value = seek
+    sample_skip = 3000 #
+    with tqdm.tqdm(total=num_frames, unit='frames', disable=verbose is not False) as pbar:
+        while seek < num_frames:
+            # seek是开始的帧数
+            end_seek = min(seek + sample_skip, num_frames)
+            segment = pad_or_trim(mel[:,seek:seek+sample_skip], N_FRAMES).to(model.device).to(dtype)
+
+            single = segment.ndim == 2
+            if single:
+                segment = segment.unsqueeze(0)
+            if dtype == torch.float16:
+                segment = segment.half()
+            audio_features, embeddings  = model.encoder(segment, include_embeddings = True)
+
+            encoder_embeddings = embeddings
+            #print(f"encoder_embeddings shape {encoder_embeddings.shape}")
+            add_segment(
+                start=seek,
+                end=end_seek,
+                #text_tokens=tokens,
+                #result=result,
+                encoder_embeddings=encoder_embeddings,
+            )
+            seek+=sample_skip
+
+    return dict(segments=all_segments)
+
+
+def cli():
+    from . import available_models
+
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
+    parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use")
+    parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
+    parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
+    parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
+    parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")
+
+    parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
+    parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection")
+
+    parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
+    parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")
+    parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero")
+    parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search")
+    parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default")
+
+    parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
+    parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
+    parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
+    parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
+
+    parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
+    parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
+    parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
+    parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
+    parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
+
+    args = parser.parse_args().__dict__
+    model_name: str = args.pop("model")
+    model_dir: str = args.pop("model_dir")
+    output_dir: str = args.pop("output_dir")
+    device: str = args.pop("device")
+    os.makedirs(output_dir, exist_ok=True)
+
+    if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
+        if args["language"] is not None:
+            warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.")
+        args["language"] = "en"
+
+    temperature = args.pop("temperature")
+    temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback")
+    if temperature_increment_on_fallback is not None:
+        temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
+    else:
+        temperature = [temperature]
+
+    threads = args.pop("threads")
+    if threads > 0:
+        torch.set_num_threads(threads)
+
+    from . import load_model
+    model = load_model(model_name, device=device, download_root=model_dir)
+
+    for audio_path in args.pop("audio"):
+        result = transcribe(model, audio_path, temperature=temperature, **args)
+
+        audio_basename = os.path.basename(audio_path)
+
+        # save TXT
+        with open(os.path.join(output_dir, audio_basename + ".txt"), "w", encoding="utf-8") as txt:
+            write_txt(result["segments"], file=txt)
+
+        # save VTT
+        with open(os.path.join(output_dir, audio_basename + ".vtt"), "w", encoding="utf-8") as vtt:
+            write_vtt(result["segments"], file=vtt)
+
+        # save SRT
+        with open(os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8") as srt:
+            write_srt(result["segments"], file=srt)
+
+
+if __name__ == '__main__':
+    cli()
diff --git a/high_sync/src/models/whisper/whisper/utils.py b/high_sync/src/models/whisper/whisper/utils.py
new file mode 100644
index 0000000..87c91f2
--- /dev/null
+++ b/high_sync/src/models/whisper/whisper/utils.py
@@ -0,0 +1,87 @@
+import zlib
+from typing import Iterator, TextIO
+
+
+def exact_div(x, y):
+    assert x % y == 0
+    return x // y
+
+
+def str2bool(string):
+    str2val = {"True": True, "False": False}
+    if string in str2val:
+        return str2val[string]
+    else:
+        raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
+
+
+def optional_int(string):
+    return None if string == "None" else int(string)
+
+
+def optional_float(string):
+    return None if string == "None" else float(string)
+
+
+def compression_ratio(text) -> float:
+    return len(text) / len(zlib.compress(text.encode("utf-8")))
+
+
+def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = '.'):
+    assert seconds >= 0, "non-negative timestamp expected"
+    milliseconds = round(seconds * 1000.0)
+
+    hours = milliseconds // 3_600_000
+    milliseconds -= hours * 3_600_000
+
+    minutes = milliseconds // 60_000
+    milliseconds -= minutes * 60_000
+
+    seconds = milliseconds // 1_000
+    milliseconds -= seconds * 1_000
+
+    hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
+    return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
+
+
+def write_txt(transcript: Iterator[dict], file: TextIO):
+    for segment in transcript:
+        print(segment['text'].strip(), file=file, flush=True)
+
+
+def write_vtt(transcript: Iterator[dict], file: TextIO):
+    print("WEBVTT\n", file=file)
+    for segment in transcript:
+        print(
+            f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
+            f"{segment['text'].strip().replace('-->', '->')}\n",
+            file=file,
+            flush=True,
+        )
+
+
+def write_srt(transcript: Iterator[dict], file: TextIO):
+    """
+    Write a transcript to a file in SRT format.
+
+    Example usage:
+        from pathlib import Path
+        from whisper.utils import write_srt
+
+        result = transcribe(model, audio_path, temperature=temperature, **args)
+
+        # save SRT
+        audio_basename = Path(audio_path).stem
+        with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt:
+            write_srt(result["segments"], file=srt)
+    """
+    for i, segment in enumerate(transcript, start=1):
+        # write srt lines
+        print(
+            f"{i}\n"
+            f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> "
+            f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n"
+            f"{segment['text'].strip().replace('-->', '->')}\n",
+            file=file,
+            flush=True,
+        )
diff --git a/high_sync/src/pipelines/__init__.py b/high_sync/src/pipelines/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/high_sync/src/pipelines/context.py b/high_sync/src/pipelines/context.py
new file mode 100644
index 0000000..c4e642d
--- /dev/null
+++ b/high_sync/src/pipelines/context.py
@@ -0,0 +1,101 @@
+# TODO: Adapted from cli
+from typing import Callable, List, Optional
+
+import numpy as np
+
+
+def ordered_halving(val):
+    bin_str = f"{val:064b}"
+    bin_flip = bin_str[::-1]
+    as_int = int(bin_flip, 2)
+
+    return as_int / (1 << 64)
+
+
+def uniform(
+    step: int = ...,
+    num_steps: Optional[int] = None,
+    num_frames: int = ...,
+    context_size: Optional[int] = None,
+    context_stride: int = 3,
+    context_overlap: int = 4,
+    closed_loop: bool = True,
+):
+    if num_frames <= context_size:
+        yield list(range(num_frames))
+        return
+
+    context_stride = min(
+        context_stride, int(np.ceil(np.log2(num_frames / context_size))) + 1
+    )
+
+    for context_step in 1 << np.arange(context_stride):
+        pad = int(round(num_frames * ordered_halving(step)))
+        for j in range(
+            int(ordered_halving(step) * context_step) + pad,
+            num_frames + pad + (0 if closed_loop else -context_overlap),
+            (context_size * context_step - context_overlap),
+        ):
+            yield [
+                e % num_frames
+                for e in range(j, j + context_size * context_step, context_step)
+            ]
+
+
+def get_context_scheduler(name: str) -> Callable:
+    if name == "uniform":
+        return uniform
+    else:
+        raise ValueError(f"Unknown context_overlap policy {name}")
+
+
+def get_total_steps(
+    scheduler,
+    timesteps: List[int],
+    num_steps: Optional[int] = None,
+    num_frames: int = ...,
+    context_size: Optional[int] = None,
+    context_stride: int = 3,
+    context_overlap: int = 4,
+    closed_loop: bool = True,
+):
+    return sum(
+        len(
+            list(
+                scheduler(
+                    i,
+                    num_steps,
+                    num_frames,
+                    context_size,
+                    context_stride,
+                    context_overlap,
+                )
+            )
+        )
+        for i in range(len(timesteps))
+    )
+
+def modify_context_all(context):
+    '''
+    modify this context to remove first indexes in last context and expand it to 16 frames
+    example :
+        input : [[240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 0, 1, 2, 3, 4, 5]]
+        output : s = [[234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249]]
+    '''
+    context = context[0]
+
+    if context[0]==0:
+        len_context = len(context)
+        context_new = [list(np.arange(0, 12))]
+
+    else:
+        len_context = 1
+        max_value_list = context[0]
+        for c, value in enumerate(context):
+            if value > max_value_list:
+                len_context = c + 1
+                max_value_list = value
+
+        context_new = [list(np.arange(max(0,max_value_list-12+1), max_value_list+1))]
+
+    return context_new, len_context
diff --git a/high_sync/src/pipelines/face_animate.py b/high_sync/src/pipelines/face_animate.py
new file mode 100644
index 0000000..bea48a8
--- /dev/null
+++ b/high_sync/src/pipelines/face_animate.py
@@ -0,0 +1,487 @@
+# pylint: disable=R0801
+"""
+This module is responsible for animating faces in videos using a combination of deep learning techniques.
+It provides a pipeline for generating face animations by processing video frames and extracting face features.
+The module utilizes various schedulers and utilities for efficient face animation and supports different types
+    of latents for more control over the animation process.
+
+Functions and Classes:
+- FaceAnimatePipeline: A class that extends the DiffusionPipeline class from the diffusers library to handle face animation tasks.
+  - __init__: Initializes the pipeline with the necessary components (VAE, UNets, face locator, etc.).
+  - prepare_latents: Generates or loads latents for the animation process, scaling them according to the scheduler's requirements.
+  - prepare_extra_step_kwargs: Prepares extra keyword arguments for the scheduler step, ensuring compatibility with different schedulers.
+  - decode_latents: Decodes the latents into video frames, ready for animation.
+
+Usage:
+- Import the necessary packages and classes.
+- Create a FaceAnimatePipeline instance with the required components.
+- Prepare the latents for the animation process.
+- Use the pipeline to generate the animated video.
+
+Note:
+- This module is designed to work with the diffusers library, which provides the underlying framework for face animation using deep learning.
+- The module is intended for research and development purposes, and further optimization and customization may be required for specific use cases.
+"""
+
+import inspect
+from dataclasses import dataclass
+from typing import Callable, List, Optional, Union
+import math
+import numpy as np
+import torch
+from diffusers import (DDIMScheduler, DiffusionPipeline,
+                       DPMSolverMultistepScheduler,
+                       EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
+                       LMSDiscreteScheduler, PNDMScheduler)
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.utils import BaseOutput
+from diffusers.utils.torch_utils import randn_tensor
+from einops import rearrange, repeat
+from tqdm import tqdm
+
+from .context import get_context_scheduler, modify_context_all
+from ..models.mutual_self_attention import ReferenceAttentionControl
+
+
+@dataclass
+class FaceAnimatePipelineOutput(BaseOutput):
+    """
+    FaceAnimatePipelineOutput is a custom class that inherits from BaseOutput and represents the output of the FaceAnimatePipeline.
+
+    Attributes:
+        videos (Union[torch.Tensor, np.ndarray]): A tensor or numpy array containing the generated video frames.
+
+    Methods:
+        __init__(self, videos: Union[torch.Tensor, np.ndarray]): Initializes the FaceAnimatePipelineOutput object with the generated video frames.
+    """
+    videos: Union[torch.Tensor, np.ndarray]
+
+class FaceAnimatePipeline(DiffusionPipeline):
+    """
+    FaceAnimatePipeline is a custom DiffusionPipeline for animating faces.
+
+    It inherits from the DiffusionPipeline class and is used to animate faces by
+    utilizing a variational autoencoder (VAE), a reference UNet, a denoising UNet,
+    a face locator, and an image processor. The pipeline is responsible for generating
+    and animating face latents, and decoding the latents to produce the final video output.
+
+    Attributes:
+        vae (VaeImageProcessor): Variational autoencoder for processing images.
+        reference_unet (nn.Module): Reference UNet for mutual self-attention.
+        denoising_unet (nn.Module): Denoising UNet for image denoising.
+        face_locator (nn.Module): Face locator for detecting and cropping faces.
+        image_proj (nn.Module): Image projector for processing images.
+        scheduler (Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler,
+                         EulerDiscreteScheduler, EulerAncestralDiscreteScheduler,
+                         DPMSolverMultistepScheduler]): Diffusion scheduler for
+                         controlling the noise level.
+
+    Methods:
+        __init__(self, vae, reference_unet, denoising_unet, face_locator,
+                 image_proj, scheduler): Initializes the FaceAnimatePipeline
+                 with the given components and scheduler.
+        prepare_latents(self, batch_size, num_channels_latents, width, height,
+                       video_length, dtype, device, generator=None, latents=None):
+                       Prepares the initial latents for video generation.
+        prepare_extra_step_kwargs(self, generator, eta): Prepares extra keyword
+                       arguments for the scheduler step.
+        decode_latents(self, latents): Decodes the latents to produce the final
+                       video output.
+    """
+    def __init__(
+        self,
+        vae,
+        reference_unet,
+        denoising_unet,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+        ],
+    ) -> None:
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            reference_unet=reference_unet,
+            denoising_unet=denoising_unet,
+            scheduler=scheduler,
+        )
+
+        self.vae_scale_factor: int = 2 ** (len(self.vae.config.block_out_channels) - 1)
+
+        self.ref_image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True,
+        )
+
+    @property
+    def _execution_device(self):
+        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def prepare_latents(
+        self,
+        batch_size: int,                      # Number of videos to generate in parallel
+        num_channels_latents: int,           # Number of channels in the latents
+        width: int,                           # Width of the video frame
+        height: int,                         # Height of the video frame
+        video_length: int,                   # Length of the video in frames
+        dtype: torch.dtype,                 # Data type of the latents
+        device: torch.device,               # Device to store the latents on
+        generator: Optional[torch.Generator] = None,  # Random number generator for reproducibility
+        latents: Optional[torch.Tensor] = None  # Pre-generated latents (optional)
+    ):
+        """
+        Prepares the initial latents for video generation.
+
+        Args:
+            batch_size (int): Number of videos to generate in parallel.
+            num_channels_latents (int): Number of channels in the latents.
+            width (int): Width of the video frame.
+            height (int): Height of the video frame.
+            video_length (int): Length of the video in frames.
+            dtype (torch.dtype): Data type of the latents.
+            device (torch.device): Device to store the latents on.
+            generator (Optional[torch.Generator]): Random number generator for reproducibility.
+            latents (Optional[torch.Tensor]): Pre-generated latents (optional).
+
+        Returns:
+            latents (torch.Tensor): Tensor of shape (batch_size, num_channels_latents, width, height)
+            containing the initial latents for video generation.
+        """
+        shape = (
+            batch_size,
+            num_channels_latents,
+            video_length,
+            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:
+            latents = randn_tensor(
+                shape, generator=generator, device=device, dtype=dtype
+            )
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        """
+        Prepares extra keyword arguments for the scheduler step.
+
+        Args:
+            generator (Optional[torch.Generator]): Random number generator for reproducibility.
+            eta (float): The eta (η) parameter used with the DDIMScheduler.
+            It corresponds to η in the DDIM paper (https://arxiv.org/abs/2010.02502) and should be between [0, 1].
+
+        Returns:
+            dict: A dictionary containing the extra keyword arguments for the scheduler step.
+        """
+        # 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 decode_latents(self, latents):
+        """
+        Decode the latents to produce a video.
+
+        Parameters:
+        latents (torch.Tensor): The latents to be decoded.
+
+        Returns:
+        video (torch.Tensor): The decoded video.
+        video_length (int): The length of the video in frames.
+        """
+        video_length = latents.shape[2]
+        latents = 1 / 0.18215 * latents
+        latents = rearrange(latents, "b c f h w -> (b f) c h w")
+        # video = self.vae.decode(latents).sample
+        video = []
+        for frame_idx in tqdm(range(latents.shape[0])):
+            video.append(self.vae.decode(
+                latents[frame_idx: frame_idx + 1]).sample)
+        video = torch.cat(video)
+        video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
+        video = (video / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        video = video.cpu().float().numpy()
+        return video
+
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        gt_encode,
+        audio_tensor,
+        mask_main,
+        width,
+        height,
+        video_length,
+        num_inference_steps,
+        guidance_scale,
+        num_images_per_prompt=1,
+        eta: float = 0.0,
+        motion_scale: Optional[List[torch.Tensor]] = None,
+        generator: Optional[Union[torch.Generator,
+                                  List[torch.Generator]]] = None,
+        output_type: Optional[str] = "tensor",
+        return_dict: bool = True,
+        callback: Optional[Callable[[
+            int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+
+        # 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
+
+        device = self._execution_device
+
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        batch_size = 1
+
+        reference_control_writer = ReferenceAttentionControl(
+            self.reference_unet,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            mode="write",
+            batch_size=batch_size,
+            fusion_blocks="full",
+        )
+        reference_control_reader = ReferenceAttentionControl(
+            self.denoising_unet,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            mode="read",
+            batch_size=batch_size,
+            fusion_blocks="full",
+            audio_feature_ratio=3.0,
+        )
+
+        num_channels_latents = self.denoising_unet.in_channels
+
+        gt_encode = gt_encode.to(device=device, dtype=mask_main.dtype)
+
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            width,
+            height,
+            video_length,
+            gt_encode.dtype,
+            device,
+            generator,
+        )
+        # Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        gt_encode = self.ref_image_processor.preprocess(
+            gt_encode, height=height, width=width)  # (bs, c, width, height)
+        gt_encode = gt_encode.to(dtype=self.vae.dtype, device=self.vae.device) # f*c*h*w
+
+        #do vae for gt image
+        gt_encode_chunks = []
+        f = int(np.ceil(gt_encode.shape[0] / 5))
+        for i in range(f):
+            gt_encode_chunks.append(self.vae.encode(gt_encode[i*5 : (i+1)*5]).latent_dist.mean)
+        gt_encode = torch.cat(gt_encode_chunks, dim=0)
+
+        gt_encode = gt_encode.unsqueeze(0) # b*f*c*h*w
+        gt_encode = gt_encode * 0.18215
+        gt_encode = rearrange(gt_encode, "b f c h w -> b c f h w")
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+#########################################
+        num_warmup_steps = 0
+        context_schedule="uniform"
+        context_frames=12
+        context_stride=1
+        context_overlap=3
+        context_batch_size=1
+
+        context_scheduler = get_context_scheduler(context_schedule)
+
+        #### old
+        # context_queue = list(context_scheduler(0,num_inference_steps,latents.shape[2],context_frames,context_stride,context_overlap,))
+
+
+
+
+        mask_main = mask_main.repeat(1,1,video_length,1,1)
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for t_i, t in enumerate(timesteps):
+
+                latents = latents*mask_main + gt_encode*(1-mask_main)
+
+                noise_pred = torch.zeros(
+                    (
+                        latents.shape[0] * (2 if do_classifier_free_guidance else 1),
+                        *latents.shape[1:],
+                    ),
+                    device=latents.device,
+                    dtype=latents.dtype,
+                )
+                counter = torch.zeros(
+                    (1, 1, latents.shape[2], 1, 1),
+                    device=latents.device,
+                    dtype=latents.dtype,
+                )
+                #### new
+                offset = t_i % context_frames
+                offset_context_queue = list(context_scheduler(
+                    0, num_inference_steps, latents.shape[2] + offset, context_frames, context_stride, context_overlap))
+
+                context_queue = []
+                for ctx_q in offset_context_queue:
+                    new_ctx_q = [i_p - offset for i_p in ctx_q]
+                    new_ctx_q = [i_p for i_p in new_ctx_q if i_p >= 0 and i_p < latents.shape[2]]
+                    context_queue.append(new_ctx_q)
+
+                #########
+
+                num_context_batches = math.ceil(len(context_queue) / context_batch_size)
+
+                global_context = []
+                for j in range(num_context_batches):
+                    global_context.append(
+                        context_queue[
+                            j * context_batch_size : (j + 1) * context_batch_size
+                        ]
+                    )
+
+                for count, context_main in enumerate(global_context):
+
+                    context, len_context = modify_context_all(context_main)
+
+                    mid_index = int(len(context[0])/2)
+
+                    # self.reference_unet(
+                    #     gt_encode[:,:,context[0][mid_index]].repeat(
+                    #         context_batch_size * (1), 1, 1, 1),
+                    #     torch.zeros_like(t),
+                    #     encoder_hidden_states=None,
+                    #     return_dict=False,
+                    # )
+
+                    self.reference_unet(
+                        rearrange(gt_encode[0], "c f h w -> f c h w")[context[0]],
+                        torch.zeros_like(t),
+                        encoder_hidden_states=None,
+                        return_dict=False,
+                    )
+
+
+                    reference_control_reader.update(reference_control_writer, do_classifier_free_guidance=do_classifier_free_guidance)
+
+                    latent_model_input = (
+                        torch.cat([latents[:, :, c] for c in context])
+                        .to(device)
+                        .repeat(2 if do_classifier_free_guidance else 1, 1, 1, 1, 1)
+                    )
+                    c_audio_latents = torch.cat([audio_tensor[:, c] for c in context]).to(device)
+                    audio_latents = torch.cat([torch.zeros_like(c_audio_latents), c_audio_latents], 0)
+
+                    latent_model_input = self.scheduler.scale_model_input(
+                        latent_model_input, t
+                    )
+
+                    pred = self.denoising_unet(
+                        latent_model_input,
+                        t,
+                        encoder_hidden_states=None,
+                        audio_cond_fea=audio_latents if do_classifier_free_guidance else c_audio_latents,
+                        return_dict=False,
+                    )[0]
+
+                    reference_control_writer.clear()
+                    reference_control_reader.clear()
+
+                    if do_classifier_free_guidance:
+                        pred_uc, pred_c = pred.chunk(2)
+                        pred = torch.cat([pred_uc.unsqueeze(0), pred_c.unsqueeze(0)])
+                    else:
+                        pred = pred.unsqueeze(1)
+
+                    for j, c in enumerate(context_main):
+
+                        if context_main[0][0]==0:
+                            noise_pred[:, :, c[:len_context]] = noise_pred[:, :, c[:len_context]] + pred[:, j, :, :len_context]
+                        else:
+                            noise_pred[:, :, c[:len_context]] = noise_pred[:, :, c[:len_context]] + pred[:, j, :, -len_context:]
+
+                        counter[:, :, c[:len_context]] = counter[:, :, c[:len_context]] + 1
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = (noise_pred / counter).chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (
+                        noise_pred_text - noise_pred_uncond
+                    )
+
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, **extra_step_kwargs
+                ).prev_sample
+
+                if t_i == len(timesteps) - 1 or (
+                    (t_i + 1) > num_warmup_steps and (t_i + 1) % self.scheduler.order == 0
+                ):
+                    progress_bar.update()
+
+            reference_control_reader.clear()
+            reference_control_writer.clear()
+
+################################
+
+        latents = latents*mask_main + gt_encode*(1-mask_main)
+        # Post-processing
+        latents = self.decode_latents(latents)  # (b, c, f, h, w)
+
+        # Convert to tensor
+        if output_type == "tensor":
+            latents = torch.from_numpy(latents)
+
+        if not return_dict:
+            return latents
+
+        return FaceAnimatePipelineOutput(videos=latents)
diff --git a/high_sync/src/pipelines/face_animate_context.py b/high_sync/src/pipelines/face_animate_context.py
new file mode 100644
index 0000000..1cefb3d
--- /dev/null
+++ b/high_sync/src/pipelines/face_animate_context.py
@@ -0,0 +1,489 @@
+# pylint: disable=R0801
+"""
+This module is responsible for animating faces in videos using a combination of deep learning techniques.
+It provides a pipeline for generating face animations by processing video frames and extracting face features.
+The module utilizes various schedulers and utilities for efficient face animation and supports different types
+    of latents for more control over the animation process.
+
+Functions and Classes:
+- FaceAnimatePipeline: A class that extends the DiffusionPipeline class from the diffusers library to handle face animation tasks.
+  - __init__: Initializes the pipeline with the necessary components (VAE, UNets, face locator, etc.).
+  - prepare_latents: Generates or loads latents for the animation process, scaling them according to the scheduler's requirements.
+  - prepare_extra_step_kwargs: Prepares extra keyword arguments for the scheduler step, ensuring compatibility with different schedulers.
+  - decode_latents: Decodes the latents into video frames, ready for animation.
+
+Usage:
+- Import the necessary packages and classes.
+- Create a FaceAnimatePipeline instance with the required components.
+- Prepare the latents for the animation process.
+- Use the pipeline to generate the animated video.
+
+Note:
+- This module is designed to work with the diffusers library, which provides the underlying framework for face animation using deep learning.
+- The module is intended for research and development purposes, and further optimization and customization may be required for specific use cases.
+"""
+
+import inspect
+from dataclasses import dataclass
+from typing import Callable, List, Optional, Union
+import math
+import numpy as np
+import torch
+from diffusers import (DDIMScheduler, DiffusionPipeline,
+                       DPMSolverMultistepScheduler,
+                       EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
+                       LMSDiscreteScheduler, PNDMScheduler)
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.utils import BaseOutput
+from diffusers.utils.torch_utils import randn_tensor
+from einops import rearrange, repeat
+from tqdm import tqdm
+
+from .context import get_context_scheduler, modify_context_all
+from ..models.mutual_self_attention import ReferenceAttentionControl
+
+
+@dataclass
+class FaceAnimatePipelineOutput(BaseOutput):
+    """
+    FaceAnimatePipelineOutput is a custom class that inherits from BaseOutput and represents the output of the FaceAnimatePipeline.
+
+    Attributes:
+        videos (Union[torch.Tensor, np.ndarray]): A tensor or numpy array containing the generated video frames.
+
+    Methods:
+        __init__(self, videos: Union[torch.Tensor, np.ndarray]): Initializes the FaceAnimatePipelineOutput object with the generated video frames.
+    """
+    videos: Union[torch.Tensor, np.ndarray]
+
+class FaceAnimatePipeline(DiffusionPipeline):
+    """
+    FaceAnimatePipeline is a custom DiffusionPipeline for animating faces.
+
+    It inherits from the DiffusionPipeline class and is used to animate faces by
+    utilizing a variational autoencoder (VAE), a reference UNet, a denoising UNet,
+    a face locator, and an image processor. The pipeline is responsible for generating
+    and animating face latents, and decoding the latents to produce the final video output.
+
+    Attributes:
+        vae (VaeImageProcessor): Variational autoencoder for processing images.
+        reference_unet (nn.Module): Reference UNet for mutual self-attention.
+        denoising_unet (nn.Module): Denoising UNet for image denoising.
+        face_locator (nn.Module): Face locator for detecting and cropping faces.
+        image_proj (nn.Module): Image projector for processing images.
+        scheduler (Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler,
+                         EulerDiscreteScheduler, EulerAncestralDiscreteScheduler,
+                         DPMSolverMultistepScheduler]): Diffusion scheduler for
+                         controlling the noise level.
+
+    Methods:
+        __init__(self, vae, reference_unet, denoising_unet, face_locator,
+                 image_proj, scheduler): Initializes the FaceAnimatePipeline
+                 with the given components and scheduler.
+        prepare_latents(self, batch_size, num_channels_latents, width, height,
+                       video_length, dtype, device, generator=None, latents=None):
+                       Prepares the initial latents for video generation.
+        prepare_extra_step_kwargs(self, generator, eta): Prepares extra keyword
+                       arguments for the scheduler step.
+        decode_latents(self, latents): Decodes the latents to produce the final
+                       video output.
+    """
+    def __init__(
+        self,
+        vae,
+        reference_unet,
+        denoising_unet,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+        ]
+    ) -> None:
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            reference_unet=reference_unet,
+            denoising_unet=denoising_unet,
+            scheduler=scheduler,
+        )
+
+        self.vae_scale_factor: int = 2 ** (len(self.vae.config.block_out_channels) - 1)
+
+        self.ref_image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True,
+        )
+
+        self.context_connection_frames = torch.zeros((20,4,3,64,64))
+        self.context_connection_frames_prev = None
+
+    @property
+    def _execution_device(self):
+        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def prepare_latents(
+        self,
+        batch_size: int,                      # Number of videos to generate in parallel
+        num_channels_latents: int,           # Number of channels in the latents
+        width: int,                           # Width of the video frame
+        height: int,                         # Height of the video frame
+        video_length: int,                   # Length of the video in frames
+        dtype: torch.dtype,                 # Data type of the latents
+        device: torch.device,               # Device to store the latents on
+        generator: Optional[torch.Generator] = None,  # Random number generator for reproducibility
+        latents: Optional[torch.Tensor] = None  # Pre-generated latents (optional)
+    ):
+        """
+        Prepares the initial latents for video generation.
+
+        Args:
+            batch_size (int): Number of videos to generate in parallel.
+            num_channels_latents (int): Number of channels in the latents.
+            width (int): Width of the video frame.
+            height (int): Height of the video frame.
+            video_length (int): Length of the video in frames.
+            dtype (torch.dtype): Data type of the latents.
+            device (torch.device): Device to store the latents on.
+            generator (Optional[torch.Generator]): Random number generator for reproducibility.
+            latents (Optional[torch.Tensor]): Pre-generated latents (optional).
+
+        Returns:
+            latents (torch.Tensor): Tensor of shape (batch_size, num_channels_latents, width, height)
+            containing the initial latents for video generation.
+        """
+        shape = (
+            batch_size,
+            num_channels_latents,
+            video_length,
+            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:
+            latents = randn_tensor(
+                shape, generator=generator, device=device, dtype=dtype
+            )
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        """
+        Prepares extra keyword arguments for the scheduler step.
+
+        Args:
+            generator (Optional[torch.Generator]): Random number generator for reproducibility.
+            eta (float): The eta (η) parameter used with the DDIMScheduler.
+            It corresponds to η in the DDIM paper (https://arxiv.org/abs/2010.02502) and should be between [0, 1].
+
+        Returns:
+            dict: A dictionary containing the extra keyword arguments for the scheduler step.
+        """
+        # 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 decode_latents(self, latents):
+        """
+        Decode the latents to produce a video.
+
+        Parameters:
+        latents (torch.Tensor): The latents to be decoded.
+
+        Returns:
+        video (torch.Tensor): The decoded video.
+        video_length (int): The length of the video in frames.
+        """
+        video_length = latents.shape[2]
+        latents = 1 / 0.18215 * latents
+        latents = rearrange(latents, "b c f h w -> (b f) c h w")
+        # video = self.vae.decode(latents).sample
+        video = []
+        for frame_idx in tqdm(range(latents.shape[0])):
+            video.append(self.vae.decode(
+                latents[frame_idx: frame_idx + 1]).sample)
+        video = torch.cat(video)
+        video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
+        video = (video / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        video = video.cpu().float().numpy()
+        return video
+
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        gt_encode,
+        audio_tensor,
+        connection,
+        mask_main,
+        width,
+        height,
+        video_length,
+        num_inference_steps,
+        guidance_scale,
+        num_images_per_prompt=1,
+        eta: float = 0.0,
+        motion_scale: Optional[List[torch.Tensor]] = None,
+        generator: Optional[Union[torch.Generator,
+                                  List[torch.Generator]]] = None,
+        output_type: Optional[str] = "tensor",
+        return_dict: bool = True,
+        callback: Optional[Callable[[
+            int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        # 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
+
+        device = self._execution_device
+
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        batch_size = 1
+
+        reference_control_writer = ReferenceAttentionControl(
+            self.reference_unet,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            mode="write",
+            batch_size=batch_size,
+            fusion_blocks="full",
+        )
+        reference_control_reader = ReferenceAttentionControl(
+            self.denoising_unet,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            mode="read",
+            batch_size=batch_size,
+            fusion_blocks="full",
+            audio_feature_ratio=3.0,
+        )
+
+        num_channels_latents = self.denoising_unet.in_channels
+
+        gt_encode = gt_encode.to(device=device, dtype=mask_main.dtype)
+
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            width,
+            height,
+            video_length,
+            gt_encode.dtype,
+            device,
+            generator,
+        )
+
+        self.context_connection_frames[0] = latents[0,:,-3:]
+
+        # Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        gt_encode = self.ref_image_processor.preprocess(
+            gt_encode, height=height, width=width)  # (bs, c, width, height)
+        gt_encode = gt_encode.to(dtype=self.vae.dtype, device=self.vae.device) # f*c*h*w
+        #do vae for gt image
+                #do vae for gt image
+        gt_encode_chunks = []
+        f = int(np.ceil(gt_encode.shape[0] / 5))
+        for i in range(f):
+            gt_encode_chunks.append(self.vae.encode(gt_encode[i*5 : (i+1)*5]).latent_dist.mean)
+        gt_encode = torch.cat(gt_encode_chunks, dim=0)
+
+        gt_encode = gt_encode.unsqueeze(0) # b*c*f*h*w
+        gt_encode = gt_encode * 0.18215
+        gt_encode = rearrange(gt_encode, "b f c h w -> b c f h w")
+
+#########################################
+        num_warmup_steps = 0
+        context_schedule="uniform"
+        context_frames=12
+        context_stride=1
+        context_overlap=3
+        context_batch_size=1
+
+        context_scheduler = get_context_scheduler(context_schedule)
+
+        #### old
+        # context_queue = list(context_scheduler(0,num_inference_steps,latents.shape[2],context_frames,context_stride,context_overlap,))
+
+        mask_main = mask_main.repeat(1,1,video_length,1,1)
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for t_i, t in enumerate(timesteps):
+
+                if connection:
+                    latents[0,:,:3] = self.context_connection_frames_prev[t_i]
+
+                latents = latents*mask_main + gt_encode*(1-mask_main)
+
+                noise_pred = torch.zeros(
+                    (
+                        latents.shape[0] * (2 if do_classifier_free_guidance else 1),
+                        *latents.shape[1:],
+                    ),
+                    device=latents.device,
+                    dtype=latents.dtype,
+                )
+                counter = torch.zeros(
+                    (1, 1, latents.shape[2], 1, 1),
+                    device=latents.device,
+                    dtype=latents.dtype,
+                )
+
+                #### new
+                offset = t_i % context_frames
+                offset_context_queue = list(context_scheduler(
+                    0, num_inference_steps, latents.shape[2] + offset, context_frames, context_stride, context_overlap))
+
+                context_queue = []
+                for ctx_q in offset_context_queue:
+                    new_ctx_q = [i_p - offset for i_p in ctx_q]
+                    new_ctx_q = [i_p for i_p in new_ctx_q if i_p >= 0 and i_p < latents.shape[2]]
+                    context_queue.append(new_ctx_q)
+
+                #########
+
+                num_context_batches = math.ceil(len(context_queue) / context_batch_size)
+
+                global_context = []
+                for j in range(num_context_batches):
+                    global_context.append(
+                        context_queue[
+                            j * context_batch_size : (j + 1) * context_batch_size
+                        ]
+                    )
+
+                for count, context_main in enumerate(global_context):
+
+                    context, len_context = modify_context_all(context_main)
+
+                    mid_index = int(len(context[0])/2)
+
+                    self.reference_unet(
+                        gt_encode[:,:,context[0][mid_index]].repeat(
+                            context_batch_size * (1), 1, 1, 1),
+                        torch.zeros_like(t),
+                        encoder_hidden_states=None,
+                        return_dict=False,
+                    )
+                    reference_control_reader.update(reference_control_writer, do_classifier_free_guidance=do_classifier_free_guidance)
+
+                    latent_model_input = (
+                        torch.cat([latents[:, :, c] for c in context])
+                        .to(device)
+                        .repeat(2 if do_classifier_free_guidance else 1, 1, 1, 1, 1)
+                    )
+                    c_audio_latents = torch.cat([audio_tensor[:, c] for c in context]).to(device)
+                    audio_latents = torch.cat([torch.zeros_like(c_audio_latents), c_audio_latents], 0)
+
+                    latent_model_input = self.scheduler.scale_model_input(
+                        latent_model_input, t
+                    )
+
+                    pred = self.denoising_unet(
+                        latent_model_input,
+                        t,
+                        encoder_hidden_states=None,
+                        audio_cond_fea=audio_latents if do_classifier_free_guidance else c_audio_latents,
+                        return_dict=False,
+                    )[0]
+
+                    reference_control_writer.clear()
+                    reference_control_reader.clear()
+
+                    if do_classifier_free_guidance:
+                        pred_uc, pred_c = pred.chunk(2)
+                        pred = torch.cat([pred_uc.unsqueeze(0), pred_c.unsqueeze(0)])
+                    else:
+                        pred = pred.unsqueeze(1)
+
+                    for j, c in enumerate(context_main):
+
+                        if context_main[0][0]==0:
+                            noise_pred[:, :, c[:len_context]] = noise_pred[:, :, c[:len_context]] + pred[:, j, :, :len_context]
+                        else:
+                            noise_pred[:, :, c[:len_context]] = noise_pred[:, :, c[:len_context]] + pred[:, j, :, -len_context:]
+
+                        counter[:, :, c[:len_context]] = counter[:, :, c[:len_context]] + 1
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = (noise_pred / counter).chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (
+                        noise_pred_text - noise_pred_uncond
+                    )
+
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, **extra_step_kwargs
+                ).prev_sample
+
+                if t_i == len(timesteps) - 1 or (
+                    (t_i + 1) > num_warmup_steps and (t_i + 1) % self.scheduler.order == 0
+                ):
+                    progress_bar.update()
+
+                if t_i < (len(timesteps) - 1):
+                    self.context_connection_frames[t_i+1] = latents[0,:,-3:]
+
+            reference_control_reader.clear()
+            reference_control_writer.clear()
+
+################################
+
+        self.context_connection_frames_prev = self.context_connection_frames
+        self.context_connection_frames = torch.zeros((num_inference_steps,4,3,64,64))
+
+        latents = latents*mask_main + gt_encode*(1-mask_main)
+        # Post-processing
+        latents = self.decode_latents(latents)  # (b, c, f, h, w)
+
+        # Convert to tensor
+        if output_type == "tensor":
+            latents = torch.from_numpy(latents)
+
+        if not return_dict:
+            return latents
+
+        return FaceAnimatePipelineOutput(videos=latents)
diff --git a/high_sync/src/pipelines/face_animate_static.py b/high_sync/src/pipelines/face_animate_static.py
new file mode 100644
index 0000000..d41cdfb
--- /dev/null
+++ b/high_sync/src/pipelines/face_animate_static.py
@@ -0,0 +1,476 @@
+# pylint: disable=R0801
+"""
+This module is responsible for handling the animation of faces using a combination of deep learning models and image processing techniques.
+It provides a pipeline to generate realistic face animations by incorporating user-provided conditions such as facial expressions and environments.
+The module utilizes various schedulers and utilities to optimize the animation process and ensure efficient performance.
+
+Functions and Classes:
+- StaticPipelineOutput: A class that represents the output of the animation pipeline, c
+    ontaining properties and methods related to the generated images.
+- prepare_latents: A function that prepares the initial noise for the animation process,
+    scaling it according to the scheduler's requirements.
+- prepare_condition: A function that processes the user-provided conditions
+    (e.g., facial expressions) and prepares them for use in the animation pipeline.
+- decode_latents: A function that decodes the latent representations of the face animations into
+    their corresponding image formats.
+- prepare_extra_step_kwargs: A function that prepares additional parameters for each step of
+    the animation process, such as the generator and eta values.
+
+Dependencies:
+- numpy: A library for numerical computing.
+- torch: A machine learning library based on PyTorch.
+- diffusers: A library for image-to-image diffusion models.
+- transformers: A library for pre-trained transformer models.
+
+Usage:
+- To create an instance of the animation pipeline, provide the necessary components such as
+    the VAE, reference UNET, denoising UNET, face locator, and image processor.
+- Use the pipeline's methods to prepare the latents, conditions, and extra step arguments as
+    required for the animation process.
+- Generate the face animations by decoding the latents and processing the conditions.
+
+Note:
+- The module is designed to work with the diffusers library, which is based on
+    the paper "Diffusion Models for Image-to-Image Translation" (https://arxiv.org/abs/2102.02765).
+- The face animations generated by this module should be used for entertainment purposes
+    only and should respect the rights and privacy of the individuals involved.
+"""
+import inspect
+from dataclasses import dataclass
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import torch
+from diffusers import DiffusionPipeline
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.schedulers import (DDIMScheduler, DPMSolverMultistepScheduler,
+                                  EulerAncestralDiscreteScheduler,
+                                  EulerDiscreteScheduler, LMSDiscreteScheduler,
+                                  PNDMScheduler)
+from diffusers.utils import BaseOutput, is_accelerate_available
+from diffusers.utils.torch_utils import randn_tensor
+from einops import rearrange
+from tqdm import tqdm
+from transformers import CLIPImageProcessor
+
+from ..models.mutual_self_attention import ReferenceAttentionControl
+
+if is_accelerate_available():
+    from accelerate import cpu_offload
+else:
+    raise ImportError("Please install accelerate via `pip install accelerate`")
+
+
+@dataclass
+class StaticPipelineOutput(BaseOutput):
+    """
+    StaticPipelineOutput is a class that represents the output of the static pipeline.
+    It contains the images generated by the pipeline as a union of torch.Tensor and np.ndarray.
+
+    Attributes:
+        images (Union[torch.Tensor, np.ndarray]): The generated images.
+    """
+    images: Union[torch.Tensor, np.ndarray]
+
+
+class StaticPipeline(DiffusionPipeline):
+    """
+    StaticPipelineOutput is a class that represents the output of the static pipeline.
+    It contains the images generated by the pipeline as a union of torch.Tensor and np.ndarray.
+
+    Attributes:
+        images (Union[torch.Tensor, np.ndarray]): The generated images.
+    """
+    _optional_components = []
+
+    def __init__(
+        self,
+        vae,
+        reference_unet,
+        denoising_unet,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+        ],
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            reference_unet=reference_unet,
+            denoising_unet=denoising_unet,
+            scheduler=scheduler,
+        )
+        self.vae_scale_factor = 2 ** (
+            len(self.vae.config.block_out_channels) - 1)
+        self.clip_image_processor = CLIPImageProcessor()
+        self.ref_image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True
+        )
+        self.cond_image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor,
+            do_convert_rgb=True,
+            do_normalize=False,
+        )
+
+    def enable_vae_slicing(self):
+        """
+        Enable VAE slicing.
+
+        This method enables slicing for the VAE model, which can help improve the performance of decoding latents when working with large images.
+        """
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        """
+        Disable vae slicing.
+
+        This function disables the vae slicing for the StaticPipeline object.
+        It calls the `disable_slicing()` method of the vae model.
+        This is useful when you want to use the entire vae model for decoding latents
+        instead of slicing it for better performance.
+        """
+        self.vae.disable_slicing()
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        """
+        Offloads selected models to the GPU for increased performance.
+
+        Args:
+            gpu_id (int, optional): The ID of the GPU to offload models to. Defaults to 0.
+        """
+        device = torch.device(f"cuda:{gpu_id}")
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+    @property
+    def _execution_device(self):
+        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def decode_latents(self, latents):
+        """
+        Decode the given latents to video frames.
+
+        Parameters:
+        latents (torch.Tensor): The latents to be decoded. Shape: (batch_size, num_channels_latents, video_length, height, width).
+
+        Returns:
+        video (torch.Tensor): The decoded video frames. Shape: (batch_size, num_channels_latents, video_length, height, width).
+        """
+        video_length = latents.shape[2]
+        latents = 1 / 0.18215 * latents
+        latents = rearrange(latents, "b c f h w -> (b f) c h w")
+        # video = self.vae.decode(latents).sample
+        video = []
+        for frame_idx in tqdm(range(latents.shape[0])):
+            video.append(self.vae.decode(
+                latents[frame_idx: frame_idx + 1]).sample)
+        video = torch.cat(video)
+        video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
+        video = (video / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        video = video.cpu().float().numpy()
+        return video
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        """
+        Prepare extra keyword arguments for the scheduler step.
+
+        Since not all schedulers have the same signature, this function helps to create a consistent interface for the scheduler.
+
+        Args:
+            generator (Optional[torch.Generator]): A random number generator for reproducibility.
+            eta (float): The eta parameter used with the DDIMScheduler. It should be between 0 and 1.
+
+        Returns:
+            dict: A dictionary containing the extra keyword arguments for the scheduler step.
+        """
+        # 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 prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        width,
+        height,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        """
+        Prepares the initial latents for the diffusion pipeline.
+
+        Args:
+            batch_size (int): The number of images to generate in one forward pass.
+            num_channels_latents (int): The number of channels in the latents tensor.
+            width (int): The width of the latents tensor.
+            height (int): The height of the latents tensor.
+            dtype (torch.dtype): The data type of the latents tensor.
+            device (torch.device): The device to place the latents tensor on.
+            generator (Optional[torch.Generator], optional): A random number generator
+                for reproducibility. Defaults to None.
+            latents (Optional[torch.Tensor], optional): Pre-computed latents to use as
+                initial conditions for the diffusion process. Defaults to None.
+
+        Returns:
+            torch.Tensor: The prepared latents tensor.
+        """
+        shape = (
+            batch_size,
+            num_channels_latents,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(
+                shape, generator=generator, device=device, dtype=dtype
+            )
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def prepare_condition(
+        self,
+        cond_image,
+        width,
+        height,
+        device,
+        dtype,
+        do_classififer_free_guidance=False,
+    ):
+        """
+        Prepares the condition for the face animation pipeline.
+
+        Args:
+            cond_image (torch.Tensor): The conditional image tensor.
+            width (int): The width of the output image.
+            height (int): The height of the output image.
+            device (torch.device): The device to run the pipeline on.
+            dtype (torch.dtype): The data type of the tensor.
+            do_classififer_free_guidance (bool, optional): Whether to use classifier-free guidance or not. Defaults to False.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]: A tuple of processed condition and mask tensors.
+        """
+        image = self.cond_image_processor.preprocess(
+            cond_image, height=height, width=width
+        ).to(dtype=torch.float32)
+
+        image = image.to(device=device, dtype=dtype)
+
+        if do_classififer_free_guidance:
+            image = torch.cat([image] * 2)
+
+        return image
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        ref_image,
+        gt_image,
+        audio_tensor,
+        mask,
+        width,
+        height,
+        num_inference_steps,
+        guidance_scale,
+        num_images_per_prompt=1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator,
+                                  List[torch.Generator]]] = None,
+        output_type: Optional[str] = "tensor",
+        return_dict: bool = True,
+        callback: Optional[Callable[[
+            int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        **kwargs,
+    ):
+        # 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
+
+        device = self._execution_device
+
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        batch_size = 1
+
+        reference_control_writer = ReferenceAttentionControl(
+            self.reference_unet,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            mode="write",
+            batch_size=batch_size,
+            fusion_blocks="full",
+        )
+        reference_control_reader = ReferenceAttentionControl(
+            self.denoising_unet,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            mode="read",
+            batch_size=batch_size,
+            fusion_blocks="full",
+            audio_feature_ratio=3.0,
+        )
+
+        num_channels_latents = self.denoising_unet.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            width,
+            height,
+            self.vae.dtype,
+            device,
+            generator,
+        )
+        latents = latents.unsqueeze(2)  # (bs, c, 1, h', w')
+        # latents_dtype = latents.dtype
+
+        # Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # Prepare ref image latents
+        # ref_image_tensor = ref_image
+        ref_image_tensor = self.ref_image_processor.preprocess(
+            ref_image, height=height, width=width
+        )  # (bs, c, width, height)
+        ref_image_tensor = ref_image_tensor.to(
+            dtype=self.vae.dtype, device=self.vae.device
+        )
+
+        ref_image_latents = self.vae.encode(ref_image_tensor).latent_dist.mean
+        ref_image_latents = ref_image_latents * 0.18215  # (b, 4, h, w)
+
+        gt_image_tensor = self.ref_image_processor.preprocess(
+            gt_image, height=height, width=width)  # (bs, c, width, height)
+        gt_image_tensor = gt_image_tensor.to(dtype=self.vae.dtype, device=self.vae.device) # f*c*h*w
+        #do vae for gt image
+        gt_encode = self.vae.encode(gt_image_tensor).latent_dist.mean
+        gt_encode = gt_encode.unsqueeze(2) # b*c*f*h*w
+        gt_encode = gt_encode * 0.18215
+
+        # audio_tensor = torch.zeros((gt_encode.shape[0],50,384))
+        audio_tensor = torch.tensor(audio_tensor).to(dtype=self.denoising_unet.dtype, device=self.denoising_unet.device).unsqueeze(0)
+        uncond_audio_tensor = torch.zeros_like(audio_tensor)
+        audio_tensor = torch.cat([uncond_audio_tensor, audio_tensor], dim=0)
+        audio_tensor = audio_tensor.to(dtype=self.denoising_unet.dtype, device=self.denoising_unet.device)
+
+        # denoising loop
+        num_warmup_steps = len(timesteps) - \
+            num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # 1. Forward reference image
+                if i == 0:
+                    self.reference_unet(
+                        ref_image_latents.repeat(
+                            (2 if do_classifier_free_guidance else 1), 1, 1, 1
+                        ),
+                        torch.zeros_like(t),
+                        encoder_hidden_states=None,
+                        return_dict=False,
+                    )
+
+                    # 2. Update reference unet feature into denosing net
+                    reference_control_reader.update(reference_control_writer)
+
+                latents = latents*mask + gt_encode*(1-mask)
+
+                # 3.1 expand the latents if we are doing classifier free guidance
+                latent_model_input = (
+                    torch.cat(
+                        [latents] * 2) if do_classifier_free_guidance else latents
+                )
+                latent_model_input = self.scheduler.scale_model_input(
+                    latent_model_input, t
+                )
+
+                noise_pred = self.denoising_unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=None,
+                    audio_cond_fea=audio_tensor,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if 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
+                    )
+
+                # 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]
+
+                # 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)
+            reference_control_reader.clear()
+            reference_control_writer.clear()
+
+        latents = latents*mask + gt_encode*(1-mask)
+
+        # Post-processing
+        image = self.decode_latents(latents)  # (b, c, 1, h, w)
+
+        # Convert to tensor
+        if output_type == "tensor":
+            image = torch.from_numpy(image)
+
+        if not return_dict:
+            return image
+
+        return StaticPipelineOutput(images=image)
diff --git a/high_sync/src/pipelines/pipeline_echo_mimic.py b/high_sync/src/pipelines/pipeline_echo_mimic.py
new file mode 100644
index 0000000..a4471b9
--- /dev/null
+++ b/high_sync/src/pipelines/pipeline_echo_mimic.py
@@ -0,0 +1,578 @@
+import inspect
+import math
+from dataclasses import dataclass
+from typing import Callable, List, Optional, Union
+
+import numpy as np
+import torch
+from diffusers import DiffusionPipeline
+import torch.nn.functional as F
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.schedulers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
+from diffusers.utils import BaseOutput, deprecate, is_accelerate_available, logging
+from diffusers.utils.torch_utils import randn_tensor
+from einops import rearrange
+from tqdm import tqdm
+from transformers import CLIPImageProcessor
+
+from ..models.mutual_self_attention import ReferenceAttentionControl
+from .context import get_context_scheduler
+from .utils import get_tensor_interpolation_method
+
+@dataclass
+class Audio2VideoPipelineOutput(BaseOutput):
+    videos: Union[torch.Tensor, np.ndarray]
+
+
+class Audio2VideoPipeline(DiffusionPipeline):
+    _optional_components = []
+
+    def __init__(
+        self,
+        vae,
+        reference_unet,
+        denoising_unet,
+        audio_guider,
+        face_locator,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+        ],
+        image_proj_model=None,
+        tokenizer=None,
+        text_encoder=None,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            reference_unet=reference_unet,
+            denoising_unet=denoising_unet,
+            audio_guider=audio_guider,
+            face_locator=face_locator,
+            scheduler=scheduler,
+            image_proj_model=image_proj_model,
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.clip_image_processor = CLIPImageProcessor()
+        self.ref_image_processor = VaeImageProcessor(
+            vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True
+        )
+
+    def enable_vae_slicing(self):
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        self.vae.disable_slicing()
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+    @property
+    def _execution_device(self):
+        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def decode_latents(self, latents):
+        video_length = latents.shape[2]
+        latents = 1 / 0.18215 * latents
+        latents = rearrange(latents, "b c f h w -> (b f) c h w")
+        video = []
+        for frame_idx in tqdm(range(latents.shape[0])):
+            video.append(self.vae.decode(latents[frame_idx : frame_idx + 1]).sample)
+        video = torch.cat(video)
+        video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
+        video = (video / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        video = video.cpu().float().numpy()
+        return video
+
+    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 prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        width,
+        height,
+        video_length,
+        dtype,
+        device,
+        generator
+    ):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            # context_frame_length,
+            video_length,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents_seg = randn_tensor(
+            shape, generator=generator, device=device, dtype=dtype
+        )
+        latents = latents_seg
+        latents = torch.clamp(latents, -1.5, 1.5)
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_videos_per_prompt,
+        do_classifier_free_guidance,
+        negative_prompt,
+    ):
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        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]
+            )
+
+        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
+
+        text_embeddings = self.text_encoder(
+            text_input_ids.to(device),
+            attention_mask=attention_mask,
+        )
+        text_embeddings = text_embeddings[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)
+        text_embeddings = text_embeddings.view(
+            bs_embed * num_videos_per_prompt, seq_len, -1
+        )
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.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
+
+            uncond_embeddings = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            uncond_embeddings = uncond_embeddings[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(
+                batch_size * num_videos_per_prompt, seq_len, -1
+            )
+
+            # 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
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
+
+    def interpolate_latents(
+        self, latents: torch.Tensor, interpolation_factor: int, device
+    ):
+        if interpolation_factor < 2:
+            return latents
+
+        new_latents = torch.zeros(
+            (
+                latents.shape[0],
+                latents.shape[1],
+                ((latents.shape[2] - 1) * interpolation_factor) + 1,
+                latents.shape[3],
+                latents.shape[4],
+            ),
+            device=latents.device,
+            dtype=latents.dtype,
+        )
+
+        org_video_length = latents.shape[2]
+        rate = [i / interpolation_factor for i in range(interpolation_factor)][1:]
+
+        new_index = 0
+
+        v0 = None
+        v1 = None
+
+        for i0, i1 in zip(range(org_video_length), range(org_video_length)[1:]):
+            v0 = latents[:, :, i0, :, :]
+            v1 = latents[:, :, i1, :, :]
+
+            new_latents[:, :, new_index, :, :] = v0
+            new_index += 1
+
+            for f in rate:
+                v = get_tensor_interpolation_method()(
+                    v0.to(device=device), v1.to(device=device), f
+                )
+                new_latents[:, :, new_index, :, :] = v.to(latents.device)
+                new_index += 1
+
+        new_latents[:, :, new_index, :, :] = v1
+        new_index += 1
+
+        return new_latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        ref_image,
+        audio_path,
+        face_mask_tensor,
+        width,
+        height,
+        video_length,
+        num_inference_steps,
+        guidance_scale,
+        num_images_per_prompt=1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        output_type: Optional[str] = "tensor",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        context_schedule="uniform",
+        context_frames=12,
+        context_stride=1,
+        context_overlap=0,
+        context_batch_size=1,
+        interpolation_factor=1,
+        audio_sample_rate=16000,
+        fps=25,
+        audio_margin=2,
+        **kwargs,
+    ):
+        # 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
+
+        device = self._execution_device
+
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        batch_size = 1
+
+        reference_control_writer = ReferenceAttentionControl(
+            self.reference_unet,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            mode="write",
+            batch_size=batch_size,
+            fusion_blocks="full",
+        )
+        reference_control_reader = ReferenceAttentionControl(
+            self.denoising_unet,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            mode="read",
+            batch_size=batch_size,
+            fusion_blocks="full",
+        )
+
+        whisper_feature = self.audio_guider.audio2feat(audio_path)
+
+        whisper_chunks = self.audio_guider.feature2chunks(feature_array=whisper_feature, fps=fps)
+
+        print("whisper_chunks:", whisper_chunks.shape)
+        audio_frame_num = whisper_chunks.shape[0]
+        audio_fea_final = torch.Tensor(whisper_chunks).to(dtype=self.vae.dtype, device=self.vae.device)
+        audio_fea_final = audio_fea_final.unsqueeze(0)
+        print("audio_fea_final:", audio_fea_final.shape)
+        video_length = min(video_length, audio_frame_num)
+        if video_length < audio_frame_num:
+            audio_fea_final = audio_fea_final[:, :video_length, :, :]
+
+        num_channels_latents = self.denoising_unet.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            width,
+            height,
+            video_length,
+            audio_fea_final.dtype,
+            device,
+            generator
+        )
+        # print(video_length, latents.shape)
+        c_face_locator_tensor = self.face_locator(face_mask_tensor)
+        uc_face_locator_tensor = torch.zeros_like(c_face_locator_tensor)
+        face_locator_tensor = torch.cat([uc_face_locator_tensor, c_face_locator_tensor], dim=0)
+        # Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # Prepare ref image latents
+        ref_image_tensor = self.ref_image_processor.preprocess(
+            ref_image, height=height, width=width
+        )
+        ref_image_tensor = ref_image_tensor.to(
+            dtype=self.vae.dtype, device=self.vae.device
+        )
+        ref_image_latents = self.vae.encode(ref_image_tensor).latent_dist.mean
+        ref_image_latents = ref_image_latents * 0.18215  # (b , 4, h, w)
+
+        context_scheduler = get_context_scheduler(context_schedule)
+
+        # denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        context_queue = list(
+            context_scheduler(
+                0,
+                num_inference_steps,
+                latents.shape[2],
+                context_frames,
+                context_stride,
+                context_overlap,
+            )
+        )
+        print("ref_image_latents shape:", ref_image_latents.shape)
+        print("face_mask_tensor shape:", face_mask_tensor.shape)
+        print("face_locator_tensor shape:", face_locator_tensor.shape)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for t_i, t in enumerate(timesteps):
+                noise_pred = torch.zeros(
+                    (
+                        latents.shape[0] * (2 if do_classifier_free_guidance else 1),
+                        *latents.shape[1:],
+                    ),
+                    device=latents.device,
+                    dtype=latents.dtype,
+                )
+                counter = torch.zeros(
+                    (1, 1, latents.shape[2], 1, 1),
+                    device=latents.device,
+                    dtype=latents.dtype,
+                )
+
+                # 1. Forward reference image
+                if t_i == 0:
+                    self.reference_unet(
+                        ref_image_latents,
+                        torch.zeros_like(t),
+                        encoder_hidden_states=None,
+                        return_dict=False,
+                    )
+                    reference_control_reader.update(reference_control_writer, do_classifier_free_guidance=do_classifier_free_guidance)
+
+
+                num_context_batches = math.ceil(len(context_queue) / context_batch_size)
+
+                global_context = []
+                for j in range(num_context_batches):
+                    global_context.append(
+                        context_queue[
+                            j * context_batch_size : (j + 1) * context_batch_size
+                        ]
+                    )
+
+                for context in global_context:
+                    new_context = [[0 for _ in range(len(context[c_j]))] for c_j in range(len(context))]
+                    for c_j in range(len(context)):
+                        for c_i in range(len(context[c_j])):
+                            new_context[c_j][c_i] = (context[c_j][c_i] + t_i * 2) % video_length
+
+                    latent_model_input = (
+                        torch.cat([latents[:, :, c] for c in new_context])
+                        .to(device)
+                        .repeat(2 if do_classifier_free_guidance else 1, 1, 1, 1, 1)
+                    )
+                    c_audio_latents = torch.cat([audio_fea_final[:, c] for c in new_context]).to(device)
+                    audio_latents = torch.cat([torch.zeros_like(c_audio_latents), c_audio_latents], 0)
+
+                    latent_model_input = self.scheduler.scale_model_input(
+                        latent_model_input, t
+                    )
+                    print(c_audio_latents.shape)
+                    pred = self.denoising_unet(
+                        latent_model_input,
+                        t,
+                        encoder_hidden_states=None,
+                        audio_cond_fea=audio_latents if do_classifier_free_guidance else c_audio_latents,
+                        face_musk_fea=face_locator_tensor if do_classifier_free_guidance else c_face_locator_tensor,
+                        return_dict=False,
+                    )[0]
+
+                    for j, c in enumerate(new_context):
+                        noise_pred[:, :, c] = noise_pred[:, :, c] + pred
+                        counter[:, :, c] = counter[:, :, c] + 1
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = (noise_pred / counter).chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (
+                        noise_pred_text - noise_pred_uncond
+                    )
+
+                latents = self.scheduler.step(
+                    noise_pred, t, latents, **extra_step_kwargs
+                ).prev_sample
+
+                if t_i == len(timesteps) - 1 or (
+                    (t_i + 1) > num_warmup_steps and (t_i + 1) % self.scheduler.order == 0
+                ):
+                    progress_bar.update()
+
+            reference_control_reader.clear()
+            reference_control_writer.clear()
+
+        if interpolation_factor > 0:
+            latents = self.interpolate_latents(latents, interpolation_factor, device)
+        # Post-processing
+        images = self.decode_latents(latents)  # (b, c, f, h, w)
+
+        # Convert to tensor
+        if output_type == "tensor":
+            images = torch.from_numpy(images)
+
+        if not return_dict:
+            return images
+
+        return Audio2VideoPipelineOutput(videos=images)
+
+    def smooth_f_axis(self, tensor, smoothing_coef=0.2):
+        """
+        对5维tensor的F轴进行平滑，首尾帧保持原始值，其他帧应用平滑操作。
+
+        参数:
+        tensor -- 输入的5D Tensor，形状为(B, C, F, W, H)
+        smoothing_coef -- 平滑前后帧的系数，默认为0.3
+
+        返回:
+        smoothed_tensor -- 平滑后的5D Tensor
+        """
+        # 生成平滑核
+        weight_kernel = torch.tensor([smoothing_coef, 1 - 2 * smoothing_coef, smoothing_coef],
+                                     device=tensor.device, dtype=tensor.dtype).view(1, 1, -1)
+
+        b, c, f, w, h = tensor.size()
+
+        # 对于第三轴（F轴）仅平滑内部帧 (1:-1)
+        # 首先，移动要平滑的轴到最后一个维度以便应用conv1d
+        tensor_moved = rearrange(tensor, "b c f h w -> b (c h w) f")
+
+        # 对除了两端帧外的所有帧应用conv1d
+        internal_frames = F.conv1d(tensor_moved, weight_kernel)
+        print("tensor:", tensor.shape)
+
+        # 重新整理输出形状为 (B, F, C, W, H)
+        internal_frames = rearrange(internal_frames, "b (c h w) f -> b c f h w", c=c, h=h, w=w)
+        print("internal_frames:", internal_frames.shape)
+        # 将第一帧和最后一帧保持不变，合并结果
+        # 首帧 tensor[:, :, 0:1, :, :], 中间帧 internal_frames[:, :, 1:-1, :, :], 最后帧 tensor[:, :, -1:, :, :]
+        smoothed_tensor = torch.cat(
+            [tensor[:, :, 0:1, :, :], internal_frames, tensor[:, :, -1:, :, :]], dim=2)
+
+        return smoothed_tensor
diff --git a/high_sync/src/pipelines/utils.py b/high_sync/src/pipelines/utils.py
new file mode 100644
index 0000000..3cd5076
--- /dev/null
+++ b/high_sync/src/pipelines/utils.py
@@ -0,0 +1,29 @@
+import torch
+
+tensor_interpolation = None
+
+
+def get_tensor_interpolation_method():
+    return tensor_interpolation
+
+
+def set_tensor_interpolation_method(is_slerp):
+    global tensor_interpolation
+    tensor_interpolation = slerp if is_slerp else linear
+
+
+def linear(v1, v2, t):
+    return (1.0 - t) * v1 + t * v2
+
+
+def slerp(
+    v0: torch.Tensor, v1: torch.Tensor, t: float, DOT_THRESHOLD: float = 0.9995
+) -> torch.Tensor:
+    u0 = v0 / v0.norm()
+    u1 = v1 / v1.norm()
+    dot = (u0 * u1).sum()
+    if dot.abs() > DOT_THRESHOLD:
+        # logger.info(f'warning: v0 and v1 close to parallel, using linear interpolation instead.')
+        return (1.0 - t) * v0 + t * v1
+    omega = dot.acos()
+    return (((1.0 - t) * omega).sin() * v0 + (t * omega).sin() * v1) / omega.sin()
diff --git a/high_sync/src/utils/draw_utils.py b/high_sync/src/utils/draw_utils.py
new file mode 100644
index 0000000..144fb8a
--- /dev/null
+++ b/high_sync/src/utils/draw_utils.py
@@ -0,0 +1,190 @@
+import cv2
+import mediapipe as mp
+import numpy as np
+from mediapipe.framework.formats import landmark_pb2
+
+class FaceMeshVisualizer:
+    def __init__(self,
+            forehead_edge=False,
+            upface_only=False,
+            draw_eye=True,
+            draw_head=False,
+            draw_iris=True,
+            draw_eyebrow=True,
+            draw_mouse=True,
+            draw_nose=True,
+            draw_pupil=True
+        ):
+        self.mp_drawing = mp.solutions.drawing_utils
+        mp_face_mesh = mp.solutions.face_mesh
+        self.mp_face_mesh = mp_face_mesh
+        self.forehead_edge = forehead_edge
+
+        DrawingSpec = mp.solutions.drawing_styles.DrawingSpec
+        f_thick = 2
+        f_rad = 1
+        right_iris_draw = DrawingSpec(color=(10, 200, 250), thickness=f_thick, circle_radius=f_rad)
+        right_eye_draw = DrawingSpec(color=(10, 200, 180), thickness=f_thick, circle_radius=f_rad)
+        right_eyebrow_draw = DrawingSpec(color=(10, 220, 180), thickness=f_thick, circle_radius=f_rad)
+        left_iris_draw = DrawingSpec(color=(250, 200, 10), thickness=f_thick, circle_radius=f_rad)
+        left_eye_draw = DrawingSpec(color=(180, 200, 10), thickness=f_thick, circle_radius=f_rad)
+        left_eyebrow_draw = DrawingSpec(color=(180, 220, 10), thickness=f_thick, circle_radius=f_rad)
+        head_draw = DrawingSpec(color=(10, 200, 10), thickness=f_thick, circle_radius=f_rad)
+        nose_draw = DrawingSpec(color=(200, 200, 200), thickness=f_thick, circle_radius=f_rad)
+
+        mouth_draw_obl = DrawingSpec(color=(10, 180, 20), thickness=f_thick, circle_radius=f_rad)
+        mouth_draw_obr = DrawingSpec(color=(20, 10, 180), thickness=f_thick, circle_radius=f_rad)
+
+        mouth_draw_ibl = DrawingSpec(color=(100, 100, 30), thickness=f_thick, circle_radius=f_rad)
+        mouth_draw_ibr = DrawingSpec(color=(100, 150, 50), thickness=f_thick, circle_radius=f_rad)
+
+        mouth_draw_otl = DrawingSpec(color=(20, 80, 100), thickness=f_thick, circle_radius=f_rad)
+        mouth_draw_otr = DrawingSpec(color=(80, 100, 20), thickness=f_thick, circle_radius=f_rad)
+
+        mouth_draw_itl = DrawingSpec(color=(120, 100, 200), thickness=f_thick, circle_radius=f_rad)
+        mouth_draw_itr = DrawingSpec(color=(150 ,120, 100), thickness=f_thick, circle_radius=f_rad)
+
+        FACEMESH_LIPS_OUTER_BOTTOM_LEFT = [(61,146),(146,91),(91,181),(181,84),(84,17)]
+        FACEMESH_LIPS_OUTER_BOTTOM_RIGHT = [(17,314),(314,405),(405,321),(321,375),(375,291)]
+
+        FACEMESH_LIPS_INNER_BOTTOM_LEFT = [(78,95),(95,88),(88,178),(178,87),(87,14)]
+        FACEMESH_LIPS_INNER_BOTTOM_RIGHT = [(14,317),(317,402),(402,318),(318,324),(324,308)]
+
+        FACEMESH_LIPS_OUTER_TOP_LEFT = [(61,185),(185,40),(40,39),(39,37),(37,0)]
+        FACEMESH_LIPS_OUTER_TOP_RIGHT = [(0,267),(267,269),(269,270),(270,409),(409,291)]
+
+        FACEMESH_LIPS_INNER_TOP_LEFT = [(78,191),(191,80),(80,81),(81,82),(82,13)]
+        FACEMESH_LIPS_INNER_TOP_RIGHT = [(13,312),(312,311),(311,310),(310,415),(415,308)]
+
+        FACEMESH_CUSTOM_FACE_OVAL = [(176, 149), (150, 136), (356, 454), (58, 132), (152, 148), (361, 288), (251, 389), (132, 93), (389, 356), (400, 377), (136, 172), (377, 152), (323, 361), (172, 58), (454, 323), (365, 379), (379, 378), (148, 176), (93, 234), (397, 365), (149, 150), (288, 397), (234, 127), (378, 400), (127, 162), (162, 21)]
+
+        # mp_face_mesh.FACEMESH_CONTOURS has all the items we care about.
+        face_connection_spec = {}
+
+        #from IPython import embed
+        #embed()
+        if self.forehead_edge:
+            for edge in mp_face_mesh.FACEMESH_FACE_OVAL:
+                face_connection_spec[edge] = head_draw
+        else:
+            if draw_head:
+                FACEMESH_CUSTOM_FACE_OVAL_sorted = sorted(FACEMESH_CUSTOM_FACE_OVAL)
+                if upface_only:
+                    for edge in [FACEMESH_CUSTOM_FACE_OVAL_sorted[edge_idx] for edge_idx in [1,2,9,12,13,16,22,25]]:
+                        face_connection_spec[edge] = head_draw
+                else:
+                    for edge in FACEMESH_CUSTOM_FACE_OVAL_sorted:
+                        face_connection_spec[edge] = head_draw
+
+        if draw_eye:
+            for edge in mp_face_mesh.FACEMESH_LEFT_EYE:
+                face_connection_spec[edge] = left_eye_draw
+            for edge in mp_face_mesh.FACEMESH_RIGHT_EYE:
+                face_connection_spec[edge] = right_eye_draw
+
+        if draw_eyebrow:
+            for edge in mp_face_mesh.FACEMESH_LEFT_EYEBROW:
+                face_connection_spec[edge] = left_eyebrow_draw
+
+            for edge in mp_face_mesh.FACEMESH_RIGHT_EYEBROW:
+                face_connection_spec[edge] = right_eyebrow_draw
+
+        if draw_iris:
+            for edge in mp_face_mesh.FACEMESH_LEFT_IRIS:
+                face_connection_spec[edge] = left_iris_draw
+            for edge in mp_face_mesh.FACEMESH_RIGHT_IRIS:
+                face_connection_spec[edge] = right_iris_draw
+
+            #for edge in mp_face_mesh.FACEMESH_RIGHT_EYEBROW:
+            #    face_connection_spec[edge] = right_eyebrow_draw
+            # for edge in mp_face_mesh.FACEMESH_RIGHT_IRIS:
+            #    face_connection_spec[edge] = right_iris_draw
+
+        # for edge in mp_face_mesh.FACEMESH_LIPS:
+        #     face_connection_spec[edge] = mouth_draw
+
+        if draw_mouse:
+            for edge in FACEMESH_LIPS_OUTER_BOTTOM_LEFT:
+                face_connection_spec[edge] = mouth_draw_obl
+            for edge in FACEMESH_LIPS_OUTER_BOTTOM_RIGHT:
+                face_connection_spec[edge] = mouth_draw_obr
+            for edge in FACEMESH_LIPS_INNER_BOTTOM_LEFT:
+                face_connection_spec[edge] = mouth_draw_ibl
+            for edge in FACEMESH_LIPS_INNER_BOTTOM_RIGHT:
+                face_connection_spec[edge] = mouth_draw_ibr
+            for edge in FACEMESH_LIPS_OUTER_TOP_LEFT:
+                face_connection_spec[edge] = mouth_draw_otl
+            for edge in FACEMESH_LIPS_OUTER_TOP_RIGHT:
+                face_connection_spec[edge] = mouth_draw_otr
+            for edge in FACEMESH_LIPS_INNER_TOP_LEFT:
+                face_connection_spec[edge] = mouth_draw_itl
+            for edge in FACEMESH_LIPS_INNER_TOP_RIGHT:
+                face_connection_spec[edge] = mouth_draw_itr
+
+        self.face_connection_spec = face_connection_spec
+
+        self.pupil_landmark_spec = {468: right_iris_draw, 473: left_iris_draw}
+        self.nose_landmark_spec = {4: nose_draw}
+
+        self.draw_pupil = draw_pupil
+        self.draw_nose = draw_nose
+
+    def draw_points(self, image, landmark_list, drawing_spec, halfwidth: int = 2):
+        """We have a custom function to draw the pupils because the mp.draw_landmarks method requires a parameter for all
+        landmarks.  Until our PR is merged into mediapipe, we need this separate method."""
+        if len(image.shape) != 3:
+            raise ValueError("Input image must be H,W,C.")
+        image_rows, image_cols, image_channels = image.shape
+        if image_channels != 3:  # BGR channels
+            raise ValueError('Input image must contain three channel bgr data.')
+        for idx, landmark in enumerate(landmark_list.landmark):
+            if idx not in drawing_spec:
+                continue
+
+            if (
+                    (landmark.HasField('visibility') and landmark.visibility < 0.9) or
+                    (landmark.HasField('presence') and landmark.presence < 0.5)
+            ):
+                continue
+            if landmark.x >= 1.0 or landmark.x < 0 or landmark.y >= 1.0 or landmark.y < 0:
+                continue
+
+            image_x = int(image_cols * landmark.x)
+            image_y = int(image_rows * landmark.y)
+
+            draw_color = drawing_spec[idx].color
+            image[image_y - halfwidth : image_y + halfwidth, image_x - halfwidth : image_x + halfwidth, :] = draw_color
+
+
+    def draw_landmarks(self, image_size, keypoints, normed=False):
+        ini_size = [512, 512]
+        image = np.zeros([ini_size[1], ini_size[0], 3], dtype=np.uint8)
+        if keypoints is not None:
+            new_landmarks = landmark_pb2.NormalizedLandmarkList()
+            for i in range(keypoints.shape[0]):
+                landmark = new_landmarks.landmark.add()
+                if normed:
+                    landmark.x = keypoints[i, 0]
+                    landmark.y = keypoints[i, 1]
+                else:
+                    landmark.x = keypoints[i, 0] / image_size[0]
+                    landmark.y = keypoints[i, 1] / image_size[1]
+                landmark.z = 1.0
+
+            self.mp_drawing.draw_landmarks(
+                image=image,
+                landmark_list=new_landmarks,
+                connections=self.face_connection_spec.keys(),
+                landmark_drawing_spec=None,
+                connection_drawing_spec=self.face_connection_spec
+            )
+
+            if self.draw_pupil:
+                self.draw_points(image, new_landmarks, self.pupil_landmark_spec, 3)
+
+            if self.draw_nose:
+                self.draw_points(image, new_landmarks, self.nose_landmark_spec, 3)
+
+        image = cv2.resize(image, (image_size[0], image_size[1]))
+
+        return image
diff --git a/high_sync/src/utils/face_landmark.py b/high_sync/src/utils/face_landmark.py
new file mode 100644
index 0000000..b6580cb
--- /dev/null
+++ b/high_sync/src/utils/face_landmark.py
@@ -0,0 +1,3305 @@
+# Copyright 2023 The MediaPipe Authors.
+#
+# 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.
+"""MediaPipe face landmarker task."""
+
+import dataclasses
+import enum
+from typing import Callable, Mapping, Optional, List
+
+import numpy as np
+
+from mediapipe.framework.formats import classification_pb2
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.framework.formats import matrix_data_pb2
+from mediapipe.python import packet_creator
+from mediapipe.python import packet_getter
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.python._framework_bindings import packet as packet_module
+# pylint: disable=unused-import
+from mediapipe.tasks.cc.vision.face_geometry.proto import face_geometry_pb2
+# pylint: enable=unused-import
+from mediapipe.tasks.cc.vision.face_landmarker.proto import face_landmarker_graph_options_pb2
+from mediapipe.tasks.python.components.containers import category as category_module
+from mediapipe.tasks.python.components.containers import landmark as landmark_module
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.core import task_info as task_info_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+from mediapipe.tasks.python.vision.core import base_vision_task_api
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+
+_BaseOptions = base_options_module.BaseOptions
+_FaceLandmarkerGraphOptionsProto = (
+    face_landmarker_graph_options_pb2.FaceLandmarkerGraphOptions
+)
+_LayoutEnum = matrix_data_pb2.MatrixData.Layout
+_RunningMode = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+_TaskInfo = task_info_module.TaskInfo
+
+_IMAGE_IN_STREAM_NAME = 'image_in'
+_IMAGE_OUT_STREAM_NAME = 'image_out'
+_IMAGE_TAG = 'IMAGE'
+_NORM_RECT_STREAM_NAME = 'norm_rect_in'
+_NORM_RECT_TAG = 'NORM_RECT'
+_NORM_LANDMARKS_STREAM_NAME = 'norm_landmarks'
+_NORM_LANDMARKS_TAG = 'NORM_LANDMARKS'
+_BLENDSHAPES_STREAM_NAME = 'blendshapes'
+_BLENDSHAPES_TAG = 'BLENDSHAPES'
+_FACE_GEOMETRY_STREAM_NAME = 'face_geometry'
+_FACE_GEOMETRY_TAG = 'FACE_GEOMETRY'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.face_landmarker.FaceLandmarkerGraph'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+
+
+class Blendshapes(enum.IntEnum):
+  """The 52 blendshape coefficients."""
+
+  NEUTRAL = 0
+  BROW_DOWN_LEFT = 1
+  BROW_DOWN_RIGHT = 2
+  BROW_INNER_UP = 3
+  BROW_OUTER_UP_LEFT = 4
+  BROW_OUTER_UP_RIGHT = 5
+  CHEEK_PUFF = 6
+  CHEEK_SQUINT_LEFT = 7
+  CHEEK_SQUINT_RIGHT = 8
+  EYE_BLINK_LEFT = 9
+  EYE_BLINK_RIGHT = 10
+  EYE_LOOK_DOWN_LEFT = 11
+  EYE_LOOK_DOWN_RIGHT = 12
+  EYE_LOOK_IN_LEFT = 13
+  EYE_LOOK_IN_RIGHT = 14
+  EYE_LOOK_OUT_LEFT = 15
+  EYE_LOOK_OUT_RIGHT = 16
+  EYE_LOOK_UP_LEFT = 17
+  EYE_LOOK_UP_RIGHT = 18
+  EYE_SQUINT_LEFT = 19
+  EYE_SQUINT_RIGHT = 20
+  EYE_WIDE_LEFT = 21
+  EYE_WIDE_RIGHT = 22
+  JAW_FORWARD = 23
+  JAW_LEFT = 24
+  JAW_OPEN = 25
+  JAW_RIGHT = 26
+  MOUTH_CLOSE = 27
+  MOUTH_DIMPLE_LEFT = 28
+  MOUTH_DIMPLE_RIGHT = 29
+  MOUTH_FROWN_LEFT = 30
+  MOUTH_FROWN_RIGHT = 31
+  MOUTH_FUNNEL = 32
+  MOUTH_LEFT = 33
+  MOUTH_LOWER_DOWN_LEFT = 34
+  MOUTH_LOWER_DOWN_RIGHT = 35
+  MOUTH_PRESS_LEFT = 36
+  MOUTH_PRESS_RIGHT = 37
+  MOUTH_PUCKER = 38
+  MOUTH_RIGHT = 39
+  MOUTH_ROLL_LOWER = 40
+  MOUTH_ROLL_UPPER = 41
+  MOUTH_SHRUG_LOWER = 42
+  MOUTH_SHRUG_UPPER = 43
+  MOUTH_SMILE_LEFT = 44
+  MOUTH_SMILE_RIGHT = 45
+  MOUTH_STRETCH_LEFT = 46
+  MOUTH_STRETCH_RIGHT = 47
+  MOUTH_UPPER_UP_LEFT = 48
+  MOUTH_UPPER_UP_RIGHT = 49
+  NOSE_SNEER_LEFT = 50
+  NOSE_SNEER_RIGHT = 51
+
+
+class FaceLandmarksConnections:
+  """The connections between face landmarks."""
+
+  @dataclasses.dataclass
+  class Connection:
+    """The connection class for face landmarks."""
+
+    start: int
+    end: int
+
+  FACE_LANDMARKS_LIPS: List[Connection] = [
+      Connection(61, 146),
+      Connection(146, 91),
+      Connection(91, 181),
+      Connection(181, 84),
+      Connection(84, 17),
+      Connection(17, 314),
+      Connection(314, 405),
+      Connection(405, 321),
+      Connection(321, 375),
+      Connection(375, 291),
+      Connection(61, 185),
+      Connection(185, 40),
+      Connection(40, 39),
+      Connection(39, 37),
+      Connection(37, 0),
+      Connection(0, 267),
+      Connection(267, 269),
+      Connection(269, 270),
+      Connection(270, 409),
+      Connection(409, 291),
+      Connection(78, 95),
+      Connection(95, 88),
+      Connection(88, 178),
+      Connection(178, 87),
+      Connection(87, 14),
+      Connection(14, 317),
+      Connection(317, 402),
+      Connection(402, 318),
+      Connection(318, 324),
+      Connection(324, 308),
+      Connection(78, 191),
+      Connection(191, 80),
+      Connection(80, 81),
+      Connection(81, 82),
+      Connection(82, 13),
+      Connection(13, 312),
+      Connection(312, 311),
+      Connection(311, 310),
+      Connection(310, 415),
+      Connection(415, 308),
+  ]
+
+  FACE_LANDMARKS_LEFT_EYE: List[Connection] = [
+      Connection(263, 249),
+      Connection(249, 390),
+      Connection(390, 373),
+      Connection(373, 374),
+      Connection(374, 380),
+      Connection(380, 381),
+      Connection(381, 382),
+      Connection(382, 362),
+      Connection(263, 466),
+      Connection(466, 388),
+      Connection(388, 387),
+      Connection(387, 386),
+      Connection(386, 385),
+      Connection(385, 384),
+      Connection(384, 398),
+      Connection(398, 362),
+  ]
+
+  FACE_LANDMARKS_LEFT_EYEBROW: List[Connection] = [
+      Connection(276, 283),
+      Connection(283, 282),
+      Connection(282, 295),
+      Connection(295, 285),
+      Connection(300, 293),
+      Connection(293, 334),
+      Connection(334, 296),
+      Connection(296, 336),
+  ]
+
+  FACE_LANDMARKS_LEFT_IRIS: List[Connection] = [
+      Connection(474, 475),
+      Connection(475, 476),
+      Connection(476, 477),
+      Connection(477, 474),
+  ]
+
+  FACE_LANDMARKS_RIGHT_EYE: List[Connection] = [
+      Connection(33, 7),
+      Connection(7, 163),
+      Connection(163, 144),
+      Connection(144, 145),
+      Connection(145, 153),
+      Connection(153, 154),
+      Connection(154, 155),
+      Connection(155, 133),
+      Connection(33, 246),
+      Connection(246, 161),
+      Connection(161, 160),
+      Connection(160, 159),
+      Connection(159, 158),
+      Connection(158, 157),
+      Connection(157, 173),
+      Connection(173, 133),
+  ]
+
+  FACE_LANDMARKS_RIGHT_EYEBROW: List[Connection] = [
+      Connection(46, 53),
+      Connection(53, 52),
+      Connection(52, 65),
+      Connection(65, 55),
+      Connection(70, 63),
+      Connection(63, 105),
+      Connection(105, 66),
+      Connection(66, 107),
+  ]
+
+  FACE_LANDMARKS_RIGHT_IRIS: List[Connection] = [
+      Connection(469, 470),
+      Connection(470, 471),
+      Connection(471, 472),
+      Connection(472, 469),
+  ]
+
+  FACE_LANDMARKS_FACE_OVAL: List[Connection] = [
+      Connection(10, 338),
+      Connection(338, 297),
+      Connection(297, 332),
+      Connection(332, 284),
+      Connection(284, 251),
+      Connection(251, 389),
+      Connection(389, 356),
+      Connection(356, 454),
+      Connection(454, 323),
+      Connection(323, 361),
+      Connection(361, 288),
+      Connection(288, 397),
+      Connection(397, 365),
+      Connection(365, 379),
+      Connection(379, 378),
+      Connection(378, 400),
+      Connection(400, 377),
+      Connection(377, 152),
+      Connection(152, 148),
+      Connection(148, 176),
+      Connection(176, 149),
+      Connection(149, 150),
+      Connection(150, 136),
+      Connection(136, 172),
+      Connection(172, 58),
+      Connection(58, 132),
+      Connection(132, 93),
+      Connection(93, 234),
+      Connection(234, 127),
+      Connection(127, 162),
+      Connection(162, 21),
+      Connection(21, 54),
+      Connection(54, 103),
+      Connection(103, 67),
+      Connection(67, 109),
+      Connection(109, 10),
+  ]
+
+  FACE_LANDMARKS_CONTOURS: List[Connection] = (
+      FACE_LANDMARKS_LIPS
+      + FACE_LANDMARKS_LEFT_EYE
+      + FACE_LANDMARKS_LEFT_EYEBROW
+      + FACE_LANDMARKS_RIGHT_EYE
+      + FACE_LANDMARKS_RIGHT_EYEBROW
+      + FACE_LANDMARKS_FACE_OVAL
+  )
+
+  FACE_LANDMARKS_TESSELATION: List[Connection] = [
+      Connection(127, 34),
+      Connection(34, 139),
+      Connection(139, 127),
+      Connection(11, 0),
+      Connection(0, 37),
+      Connection(37, 11),
+      Connection(232, 231),
+      Connection(231, 120),
+      Connection(120, 232),
+      Connection(72, 37),
+      Connection(37, 39),
+      Connection(39, 72),
+      Connection(128, 121),
+      Connection(121, 47),
+      Connection(47, 128),
+      Connection(232, 121),
+      Connection(121, 128),
+      Connection(128, 232),
+      Connection(104, 69),
+      Connection(69, 67),
+      Connection(67, 104),
+      Connection(175, 171),
+      Connection(171, 148),
+      Connection(148, 175),
+      Connection(118, 50),
+      Connection(50, 101),
+      Connection(101, 118),
+      Connection(73, 39),
+      Connection(39, 40),
+      Connection(40, 73),
+      Connection(9, 151),
+      Connection(151, 108),
+      Connection(108, 9),
+      Connection(48, 115),
+      Connection(115, 131),
+      Connection(131, 48),
+      Connection(194, 204),
+      Connection(204, 211),
+      Connection(211, 194),
+      Connection(74, 40),
+      Connection(40, 185),
+      Connection(185, 74),
+      Connection(80, 42),
+      Connection(42, 183),
+      Connection(183, 80),
+      Connection(40, 92),
+      Connection(92, 186),
+      Connection(186, 40),
+      Connection(230, 229),
+      Connection(229, 118),
+      Connection(118, 230),
+      Connection(202, 212),
+      Connection(212, 214),
+      Connection(214, 202),
+      Connection(83, 18),
+      Connection(18, 17),
+      Connection(17, 83),
+      Connection(76, 61),
+      Connection(61, 146),
+      Connection(146, 76),
+      Connection(160, 29),
+      Connection(29, 30),
+      Connection(30, 160),
+      Connection(56, 157),
+      Connection(157, 173),
+      Connection(173, 56),
+      Connection(106, 204),
+      Connection(204, 194),
+      Connection(194, 106),
+      Connection(135, 214),
+      Connection(214, 192),
+      Connection(192, 135),
+      Connection(203, 165),
+      Connection(165, 98),
+      Connection(98, 203),
+      Connection(21, 71),
+      Connection(71, 68),
+      Connection(68, 21),
+      Connection(51, 45),
+      Connection(45, 4),
+      Connection(4, 51),
+      Connection(144, 24),
+      Connection(24, 23),
+      Connection(23, 144),
+      Connection(77, 146),
+      Connection(146, 91),
+      Connection(91, 77),
+      Connection(205, 50),
+      Connection(50, 187),
+      Connection(187, 205),
+      Connection(201, 200),
+      Connection(200, 18),
+      Connection(18, 201),
+      Connection(91, 106),
+      Connection(106, 182),
+      Connection(182, 91),
+      Connection(90, 91),
+      Connection(91, 181),
+      Connection(181, 90),
+      Connection(85, 84),
+      Connection(84, 17),
+      Connection(17, 85),
+      Connection(206, 203),
+      Connection(203, 36),
+      Connection(36, 206),
+      Connection(148, 171),
+      Connection(171, 140),
+      Connection(140, 148),
+      Connection(92, 40),
+      Connection(40, 39),
+      Connection(39, 92),
+      Connection(193, 189),
+      Connection(189, 244),
+      Connection(244, 193),
+      Connection(159, 158),
+      Connection(158, 28),
+      Connection(28, 159),
+      Connection(247, 246),
+      Connection(246, 161),
+      Connection(161, 247),
+      Connection(236, 3),
+      Connection(3, 196),
+      Connection(196, 236),
+      Connection(54, 68),
+      Connection(68, 104),
+      Connection(104, 54),
+      Connection(193, 168),
+      Connection(168, 8),
+      Connection(8, 193),
+      Connection(117, 228),
+      Connection(228, 31),
+      Connection(31, 117),
+      Connection(189, 193),
+      Connection(193, 55),
+      Connection(55, 189),
+      Connection(98, 97),
+      Connection(97, 99),
+      Connection(99, 98),
+      Connection(126, 47),
+      Connection(47, 100),
+      Connection(100, 126),
+      Connection(166, 79),
+      Connection(79, 218),
+      Connection(218, 166),
+      Connection(155, 154),
+      Connection(154, 26),
+      Connection(26, 155),
+      Connection(209, 49),
+      Connection(49, 131),
+      Connection(131, 209),
+      Connection(135, 136),
+      Connection(136, 150),
+      Connection(150, 135),
+      Connection(47, 126),
+      Connection(126, 217),
+      Connection(217, 47),
+      Connection(223, 52),
+      Connection(52, 53),
+      Connection(53, 223),
+      Connection(45, 51),
+      Connection(51, 134),
+      Connection(134, 45),
+      Connection(211, 170),
+      Connection(170, 140),
+      Connection(140, 211),
+      Connection(67, 69),
+      Connection(69, 108),
+      Connection(108, 67),
+      Connection(43, 106),
+      Connection(106, 91),
+      Connection(91, 43),
+      Connection(230, 119),
+      Connection(119, 120),
+      Connection(120, 230),
+      Connection(226, 130),
+      Connection(130, 247),
+      Connection(247, 226),
+      Connection(63, 53),
+      Connection(53, 52),
+      Connection(52, 63),
+      Connection(238, 20),
+      Connection(20, 242),
+      Connection(242, 238),
+      Connection(46, 70),
+      Connection(70, 156),
+      Connection(156, 46),
+      Connection(78, 62),
+      Connection(62, 96),
+      Connection(96, 78),
+      Connection(46, 53),
+      Connection(53, 63),
+      Connection(63, 46),
+      Connection(143, 34),
+      Connection(34, 227),
+      Connection(227, 143),
+      Connection(123, 117),
+      Connection(117, 111),
+      Connection(111, 123),
+      Connection(44, 125),
+      Connection(125, 19),
+      Connection(19, 44),
+      Connection(236, 134),
+      Connection(134, 51),
+      Connection(51, 236),
+      Connection(216, 206),
+      Connection(206, 205),
+      Connection(205, 216),
+      Connection(154, 153),
+      Connection(153, 22),
+      Connection(22, 154),
+      Connection(39, 37),
+      Connection(37, 167),
+      Connection(167, 39),
+      Connection(200, 201),
+      Connection(201, 208),
+      Connection(208, 200),
+      Connection(36, 142),
+      Connection(142, 100),
+      Connection(100, 36),
+      Connection(57, 212),
+      Connection(212, 202),
+      Connection(202, 57),
+      Connection(20, 60),
+      Connection(60, 99),
+      Connection(99, 20),
+      Connection(28, 158),
+      Connection(158, 157),
+      Connection(157, 28),
+      Connection(35, 226),
+      Connection(226, 113),
+      Connection(113, 35),
+      Connection(160, 159),
+      Connection(159, 27),
+      Connection(27, 160),
+      Connection(204, 202),
+      Connection(202, 210),
+      Connection(210, 204),
+      Connection(113, 225),
+      Connection(225, 46),
+      Connection(46, 113),
+      Connection(43, 202),
+      Connection(202, 204),
+      Connection(204, 43),
+      Connection(62, 76),
+      Connection(76, 77),
+      Connection(77, 62),
+      Connection(137, 123),
+      Connection(123, 116),
+      Connection(116, 137),
+      Connection(41, 38),
+      Connection(38, 72),
+      Connection(72, 41),
+      Connection(203, 129),
+      Connection(129, 142),
+      Connection(142, 203),
+      Connection(64, 98),
+      Connection(98, 240),
+      Connection(240, 64),
+      Connection(49, 102),
+      Connection(102, 64),
+      Connection(64, 49),
+      Connection(41, 73),
+      Connection(73, 74),
+      Connection(74, 41),
+      Connection(212, 216),
+      Connection(216, 207),
+      Connection(207, 212),
+      Connection(42, 74),
+      Connection(74, 184),
+      Connection(184, 42),
+      Connection(169, 170),
+      Connection(170, 211),
+      Connection(211, 169),
+      Connection(170, 149),
+      Connection(149, 176),
+      Connection(176, 170),
+      Connection(105, 66),
+      Connection(66, 69),
+      Connection(69, 105),
+      Connection(122, 6),
+      Connection(6, 168),
+      Connection(168, 122),
+      Connection(123, 147),
+      Connection(147, 187),
+      Connection(187, 123),
+      Connection(96, 77),
+      Connection(77, 90),
+      Connection(90, 96),
+      Connection(65, 55),
+      Connection(55, 107),
+      Connection(107, 65),
+      Connection(89, 90),
+      Connection(90, 180),
+      Connection(180, 89),
+      Connection(101, 100),
+      Connection(100, 120),
+      Connection(120, 101),
+      Connection(63, 105),
+      Connection(105, 104),
+      Connection(104, 63),
+      Connection(93, 137),
+      Connection(137, 227),
+      Connection(227, 93),
+      Connection(15, 86),
+      Connection(86, 85),
+      Connection(85, 15),
+      Connection(129, 102),
+      Connection(102, 49),
+      Connection(49, 129),
+      Connection(14, 87),
+      Connection(87, 86),
+      Connection(86, 14),
+      Connection(55, 8),
+      Connection(8, 9),
+      Connection(9, 55),
+      Connection(100, 47),
+      Connection(47, 121),
+      Connection(121, 100),
+      Connection(145, 23),
+      Connection(23, 22),
+      Connection(22, 145),
+      Connection(88, 89),
+      Connection(89, 179),
+      Connection(179, 88),
+      Connection(6, 122),
+      Connection(122, 196),
+      Connection(196, 6),
+      Connection(88, 95),
+      Connection(95, 96),
+      Connection(96, 88),
+      Connection(138, 172),
+      Connection(172, 136),
+      Connection(136, 138),
+      Connection(215, 58),
+      Connection(58, 172),
+      Connection(172, 215),
+      Connection(115, 48),
+      Connection(48, 219),
+      Connection(219, 115),
+      Connection(42, 80),
+      Connection(80, 81),
+      Connection(81, 42),
+      Connection(195, 3),
+      Connection(3, 51),
+      Connection(51, 195),
+      Connection(43, 146),
+      Connection(146, 61),
+      Connection(61, 43),
+      Connection(171, 175),
+      Connection(175, 199),
+      Connection(199, 171),
+      Connection(81, 82),
+      Connection(82, 38),
+      Connection(38, 81),
+      Connection(53, 46),
+      Connection(46, 225),
+      Connection(225, 53),
+      Connection(144, 163),
+      Connection(163, 110),
+      Connection(110, 144),
+      Connection(52, 65),
+      Connection(65, 66),
+      Connection(66, 52),
+      Connection(229, 228),
+      Connection(228, 117),
+      Connection(117, 229),
+      Connection(34, 127),
+      Connection(127, 234),
+      Connection(234, 34),
+      Connection(107, 108),
+      Connection(108, 69),
+      Connection(69, 107),
+      Connection(109, 108),
+      Connection(108, 151),
+      Connection(151, 109),
+      Connection(48, 64),
+      Connection(64, 235),
+      Connection(235, 48),
+      Connection(62, 78),
+      Connection(78, 191),
+      Connection(191, 62),
+      Connection(129, 209),
+      Connection(209, 126),
+      Connection(126, 129),
+      Connection(111, 35),
+      Connection(35, 143),
+      Connection(143, 111),
+      Connection(117, 123),
+      Connection(123, 50),
+      Connection(50, 117),
+      Connection(222, 65),
+      Connection(65, 52),
+      Connection(52, 222),
+      Connection(19, 125),
+      Connection(125, 141),
+      Connection(141, 19),
+      Connection(221, 55),
+      Connection(55, 65),
+      Connection(65, 221),
+      Connection(3, 195),
+      Connection(195, 197),
+      Connection(197, 3),
+      Connection(25, 7),
+      Connection(7, 33),
+      Connection(33, 25),
+      Connection(220, 237),
+      Connection(237, 44),
+      Connection(44, 220),
+      Connection(70, 71),
+      Connection(71, 139),
+      Connection(139, 70),
+      Connection(122, 193),
+      Connection(193, 245),
+      Connection(245, 122),
+      Connection(247, 130),
+      Connection(130, 33),
+      Connection(33, 247),
+      Connection(71, 21),
+      Connection(21, 162),
+      Connection(162, 71),
+      Connection(170, 169),
+      Connection(169, 150),
+      Connection(150, 170),
+      Connection(188, 174),
+      Connection(174, 196),
+      Connection(196, 188),
+      Connection(216, 186),
+      Connection(186, 92),
+      Connection(92, 216),
+      Connection(2, 97),
+      Connection(97, 167),
+      Connection(167, 2),
+      Connection(141, 125),
+      Connection(125, 241),
+      Connection(241, 141),
+      Connection(164, 167),
+      Connection(167, 37),
+      Connection(37, 164),
+      Connection(72, 38),
+      Connection(38, 12),
+      Connection(12, 72),
+      Connection(38, 82),
+      Connection(82, 13),
+      Connection(13, 38),
+      Connection(63, 68),
+      Connection(68, 71),
+      Connection(71, 63),
+      Connection(226, 35),
+      Connection(35, 111),
+      Connection(111, 226),
+      Connection(101, 50),
+      Connection(50, 205),
+      Connection(205, 101),
+      Connection(206, 92),
+      Connection(92, 165),
+      Connection(165, 206),
+      Connection(209, 198),
+      Connection(198, 217),
+      Connection(217, 209),
+      Connection(165, 167),
+      Connection(167, 97),
+      Connection(97, 165),
+      Connection(220, 115),
+      Connection(115, 218),
+      Connection(218, 220),
+      Connection(133, 112),
+      Connection(112, 243),
+      Connection(243, 133),
+      Connection(239, 238),
+      Connection(238, 241),
+      Connection(241, 239),
+      Connection(214, 135),
+      Connection(135, 169),
+      Connection(169, 214),
+      Connection(190, 173),
+      Connection(173, 133),
+      Connection(133, 190),
+      Connection(171, 208),
+      Connection(208, 32),
+      Connection(32, 171),
+      Connection(125, 44),
+      Connection(44, 237),
+      Connection(237, 125),
+      Connection(86, 87),
+      Connection(87, 178),
+      Connection(178, 86),
+      Connection(85, 86),
+      Connection(86, 179),
+      Connection(179, 85),
+      Connection(84, 85),
+      Connection(85, 180),
+      Connection(180, 84),
+      Connection(83, 84),
+      Connection(84, 181),
+      Connection(181, 83),
+      Connection(201, 83),
+      Connection(83, 182),
+      Connection(182, 201),
+      Connection(137, 93),
+      Connection(93, 132),
+      Connection(132, 137),
+      Connection(76, 62),
+      Connection(62, 183),
+      Connection(183, 76),
+      Connection(61, 76),
+      Connection(76, 184),
+      Connection(184, 61),
+      Connection(57, 61),
+      Connection(61, 185),
+      Connection(185, 57),
+      Connection(212, 57),
+      Connection(57, 186),
+      Connection(186, 212),
+      Connection(214, 207),
+      Connection(207, 187),
+      Connection(187, 214),
+      Connection(34, 143),
+      Connection(143, 156),
+      Connection(156, 34),
+      Connection(79, 239),
+      Connection(239, 237),
+      Connection(237, 79),
+      Connection(123, 137),
+      Connection(137, 177),
+      Connection(177, 123),
+      Connection(44, 1),
+      Connection(1, 4),
+      Connection(4, 44),
+      Connection(201, 194),
+      Connection(194, 32),
+      Connection(32, 201),
+      Connection(64, 102),
+      Connection(102, 129),
+      Connection(129, 64),
+      Connection(213, 215),
+      Connection(215, 138),
+      Connection(138, 213),
+      Connection(59, 166),
+      Connection(166, 219),
+      Connection(219, 59),
+      Connection(242, 99),
+      Connection(99, 97),
+      Connection(97, 242),
+      Connection(2, 94),
+      Connection(94, 141),
+      Connection(141, 2),
+      Connection(75, 59),
+      Connection(59, 235),
+      Connection(235, 75),
+      Connection(24, 110),
+      Connection(110, 228),
+      Connection(228, 24),
+      Connection(25, 130),
+      Connection(130, 226),
+      Connection(226, 25),
+      Connection(23, 24),
+      Connection(24, 229),
+      Connection(229, 23),
+      Connection(22, 23),
+      Connection(23, 230),
+      Connection(230, 22),
+      Connection(26, 22),
+      Connection(22, 231),
+      Connection(231, 26),
+      Connection(112, 26),
+      Connection(26, 232),
+      Connection(232, 112),
+      Connection(189, 190),
+      Connection(190, 243),
+      Connection(243, 189),
+      Connection(221, 56),
+      Connection(56, 190),
+      Connection(190, 221),
+      Connection(28, 56),
+      Connection(56, 221),
+      Connection(221, 28),
+      Connection(27, 28),
+      Connection(28, 222),
+      Connection(222, 27),
+      Connection(29, 27),
+      Connection(27, 223),
+      Connection(223, 29),
+      Connection(30, 29),
+      Connection(29, 224),
+      Connection(224, 30),
+      Connection(247, 30),
+      Connection(30, 225),
+      Connection(225, 247),
+      Connection(238, 79),
+      Connection(79, 20),
+      Connection(20, 238),
+      Connection(166, 59),
+      Connection(59, 75),
+      Connection(75, 166),
+      Connection(60, 75),
+      Connection(75, 240),
+      Connection(240, 60),
+      Connection(147, 177),
+      Connection(177, 215),
+      Connection(215, 147),
+      Connection(20, 79),
+      Connection(79, 166),
+      Connection(166, 20),
+      Connection(187, 147),
+      Connection(147, 213),
+      Connection(213, 187),
+      Connection(112, 233),
+      Connection(233, 244),
+      Connection(244, 112),
+      Connection(233, 128),
+      Connection(128, 245),
+      Connection(245, 233),
+      Connection(128, 114),
+      Connection(114, 188),
+      Connection(188, 128),
+      Connection(114, 217),
+      Connection(217, 174),
+      Connection(174, 114),
+      Connection(131, 115),
+      Connection(115, 220),
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+      Connection(305, 290),
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+      Connection(460, 305),
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+      Connection(376, 435),
+      Connection(435, 401),
+      Connection(309, 250),
+      Connection(250, 392),
+      Connection(392, 309),
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+      Connection(411, 433),
+      Connection(433, 376),
+      Connection(453, 341),
+      Connection(341, 464),
+      Connection(464, 453),
+      Connection(357, 453),
+      Connection(453, 465),
+      Connection(465, 357),
+      Connection(343, 357),
+      Connection(357, 412),
+      Connection(412, 343),
+      Connection(437, 343),
+      Connection(343, 399),
+      Connection(399, 437),
+      Connection(344, 360),
+      Connection(360, 440),
+      Connection(440, 344),
+      Connection(420, 437),
+      Connection(437, 456),
+      Connection(456, 420),
+      Connection(360, 420),
+      Connection(420, 363),
+      Connection(363, 360),
+      Connection(361, 401),
+      Connection(401, 288),
+      Connection(288, 361),
+      Connection(265, 372),
+      Connection(372, 353),
+      Connection(353, 265),
+      Connection(390, 339),
+      Connection(339, 249),
+      Connection(249, 390),
+      Connection(339, 448),
+      Connection(448, 255),
+      Connection(255, 339),
+  ]
+
+
+@dataclasses.dataclass
+class FaceLandmarkerResult:
+  """The face landmarks detection result from FaceLandmarker, where each vector element represents a single face detected in the image.
+
+  Attributes:
+    face_landmarks: Detected face landmarks in normalized image coordinates.
+    face_blendshapes: Optional face blendshapes results.
+    facial_transformation_matrixes: Optional facial transformation matrix.
+  """
+
+  face_landmarks: List[List[landmark_module.NormalizedLandmark]]
+  face_blendshapes: List[List[category_module.Category]]
+  facial_transformation_matrixes: List[np.ndarray]
+
+
+def _build_landmarker_result(
+    output_packets: Mapping[str, packet_module.Packet]
+) -> FaceLandmarkerResult:
+  """Constructs a `FaceLandmarkerResult` from output packets."""
+  face_landmarks_proto_list = packet_getter.get_proto_list(
+      output_packets[_NORM_LANDMARKS_STREAM_NAME]
+  )
+
+  face_landmarks_results = []
+  for proto in face_landmarks_proto_list:
+    face_landmarks = landmark_pb2.NormalizedLandmarkList()
+    face_landmarks.MergeFrom(proto)
+    face_landmarks_list = []
+    for face_landmark in face_landmarks.landmark:
+      face_landmarks_list.append(
+          landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
+      )
+    face_landmarks_results.append(face_landmarks_list)
+
+  face_blendshapes_results = []
+  if _BLENDSHAPES_STREAM_NAME in output_packets:
+    face_blendshapes_proto_list = packet_getter.get_proto_list(
+        output_packets[_BLENDSHAPES_STREAM_NAME]
+    )
+    for proto in face_blendshapes_proto_list:
+      face_blendshapes_categories = []
+      face_blendshapes_classifications = classification_pb2.ClassificationList()
+      face_blendshapes_classifications.MergeFrom(proto)
+      for face_blendshapes in face_blendshapes_classifications.classification:
+        face_blendshapes_categories.append(
+            category_module.Category(
+                index=face_blendshapes.index,
+                score=face_blendshapes.score,
+                display_name=face_blendshapes.display_name,
+                category_name=face_blendshapes.label,
+            )
+        )
+      face_blendshapes_results.append(face_blendshapes_categories)
+
+  facial_transformation_matrixes_results = []
+  if _FACE_GEOMETRY_STREAM_NAME in output_packets:
+    facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
+        output_packets[_FACE_GEOMETRY_STREAM_NAME]
+    )
+    for proto in facial_transformation_matrixes_proto_list:
+      if hasattr(proto, 'pose_transform_matrix'):
+        matrix_data = matrix_data_pb2.MatrixData()
+        matrix_data.MergeFrom(proto.pose_transform_matrix)
+        matrix = np.array(matrix_data.packed_data)
+        matrix = matrix.reshape((matrix_data.rows, matrix_data.cols))
+        matrix = (
+            matrix if matrix_data.layout == _LayoutEnum.ROW_MAJOR else matrix.T
+        )
+        facial_transformation_matrixes_results.append(matrix)
+
+  return FaceLandmarkerResult(
+      face_landmarks_results,
+      face_blendshapes_results,
+      facial_transformation_matrixes_results,
+  )
+
+def _build_landmarker_result2(
+    output_packets: Mapping[str, packet_module.Packet]
+) -> FaceLandmarkerResult:
+  """Constructs a `FaceLandmarkerResult` from output packets."""
+  face_landmarks_proto_list = packet_getter.get_proto_list(
+      output_packets[_NORM_LANDMARKS_STREAM_NAME]
+  )
+
+  face_landmarks_results = []
+  for proto in face_landmarks_proto_list:
+    face_landmarks = landmark_pb2.NormalizedLandmarkList()
+    face_landmarks.MergeFrom(proto)
+    face_landmarks_list = []
+    for face_landmark in face_landmarks.landmark:
+      face_landmarks_list.append(
+          landmark_module.NormalizedLandmark.create_from_pb2(face_landmark)
+      )
+    face_landmarks_results.append(face_landmarks_list)
+
+  face_blendshapes_results = []
+  if _BLENDSHAPES_STREAM_NAME in output_packets:
+    face_blendshapes_proto_list = packet_getter.get_proto_list(
+        output_packets[_BLENDSHAPES_STREAM_NAME]
+    )
+    for proto in face_blendshapes_proto_list:
+      face_blendshapes_categories = []
+      face_blendshapes_classifications = classification_pb2.ClassificationList()
+      face_blendshapes_classifications.MergeFrom(proto)
+      for face_blendshapes in face_blendshapes_classifications.classification:
+        face_blendshapes_categories.append(
+            category_module.Category(
+                index=face_blendshapes.index,
+                score=face_blendshapes.score,
+                display_name=face_blendshapes.display_name,
+                category_name=face_blendshapes.label,
+            )
+        )
+      face_blendshapes_results.append(face_blendshapes_categories)
+
+  facial_transformation_matrixes_results = []
+  if _FACE_GEOMETRY_STREAM_NAME in output_packets:
+    facial_transformation_matrixes_proto_list = packet_getter.get_proto_list(
+        output_packets[_FACE_GEOMETRY_STREAM_NAME]
+    )
+    for proto in facial_transformation_matrixes_proto_list:
+      if hasattr(proto, 'pose_transform_matrix'):
+        matrix_data = matrix_data_pb2.MatrixData()
+        matrix_data.MergeFrom(proto.pose_transform_matrix)
+        matrix = np.array(matrix_data.packed_data)
+        matrix = matrix.reshape((matrix_data.rows, matrix_data.cols))
+        matrix = (
+            matrix if matrix_data.layout == _LayoutEnum.ROW_MAJOR else matrix.T
+        )
+        facial_transformation_matrixes_results.append(matrix)
+
+  return FaceLandmarkerResult(
+      face_landmarks_results,
+      face_blendshapes_results,
+      facial_transformation_matrixes_results,
+  ), facial_transformation_matrixes_proto_list[0].mesh
+
+@dataclasses.dataclass
+class FaceLandmarkerOptions:
+  """Options for the face landmarker task.
+
+  Attributes:
+    base_options: Base options for the face landmarker task.
+    running_mode: The running mode of the task. Default to the image mode.
+      FaceLandmarker has three running modes: 1) The image mode for detecting
+      face landmarks on single image inputs. 2) The video mode for detecting
+      face landmarks on the decoded frames of a video. 3) The live stream mode
+      for detecting face landmarks on the live stream of input data, such as
+      from camera. In this mode, the "result_callback" below must be specified
+      to receive the detection results asynchronously.
+    num_faces: The maximum number of faces that can be detected by the
+      FaceLandmarker.
+    min_face_detection_confidence: The minimum confidence score for the face
+      detection to be considered successful.
+    min_face_presence_confidence: The minimum confidence score of face presence
+      score in the face landmark detection.
+    min_tracking_confidence: The minimum confidence score for the face tracking
+      to be considered successful.
+    output_face_blendshapes: Whether FaceLandmarker outputs face blendshapes
+      classification. Face blendshapes are used for rendering the 3D face model.
+    output_facial_transformation_matrixes: Whether FaceLandmarker outputs facial
+      transformation_matrix. Facial transformation matrix is used to transform
+      the face landmarks in canonical face to the detected face, so that users
+      can apply face effects on the detected landmarks.
+    result_callback: The user-defined result callback for processing live stream
+      data. The result callback should only be specified when the running mode
+      is set to the live stream mode.
+  """
+
+  base_options: _BaseOptions
+  running_mode: _RunningMode = _RunningMode.IMAGE
+  num_faces: int = 1
+  min_face_detection_confidence: float = 0.5
+  min_face_presence_confidence: float = 0.5
+  min_tracking_confidence: float = 0.5
+  output_face_blendshapes: bool = False
+  output_facial_transformation_matrixes: bool = False
+  result_callback: Optional[
+      Callable[[FaceLandmarkerResult, image_module.Image, int], None]
+  ] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _FaceLandmarkerGraphOptionsProto:
+    """Generates an FaceLandmarkerGraphOptions protobuf object."""
+    base_options_proto = self.base_options.to_pb2()
+    base_options_proto.use_stream_mode = (
+        False if self.running_mode == _RunningMode.IMAGE else True
+    )
+
+    # Initialize the face landmarker options from base options.
+    face_landmarker_options_proto = _FaceLandmarkerGraphOptionsProto(
+        base_options=base_options_proto
+    )
+
+    # Configure face detector options.
+    face_landmarker_options_proto.face_detector_graph_options.num_faces = (
+        self.num_faces
+    )
+    face_landmarker_options_proto.face_detector_graph_options.min_detection_confidence = (
+        self.min_face_detection_confidence
+    )
+
+    # Configure face landmark detector options.
+    face_landmarker_options_proto.min_tracking_confidence = (
+        self.min_tracking_confidence
+    )
+    face_landmarker_options_proto.face_landmarks_detector_graph_options.min_detection_confidence = (
+        self.min_face_detection_confidence
+    )
+    return face_landmarker_options_proto
+
+
+class FaceLandmarker(base_vision_task_api.BaseVisionTaskApi):
+  """Class that performs face landmarks detection on images."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'FaceLandmarker':
+    """Creates an `FaceLandmarker` object from a TensorFlow Lite model and the default `FaceLandmarkerOptions`.
+
+    Note that the created `FaceLandmarker` instance is in image mode, for
+    detecting face landmarks on single image inputs.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `FaceLandmarker` object that's created from the model file and the
+      default `FaceLandmarkerOptions`.
+
+    Raises:
+      ValueError: If failed to create `FaceLandmarker` object from the
+        provided file such as invalid file path.
+      RuntimeError: If other types of error occurred.
+    """
+    base_options = _BaseOptions(model_asset_path=model_path)
+    options = FaceLandmarkerOptions(
+        base_options=base_options, running_mode=_RunningMode.IMAGE
+    )
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(
+      cls, options: FaceLandmarkerOptions
+  ) -> 'FaceLandmarker':
+    """Creates the `FaceLandmarker` object from face landmarker options.
+
+    Args:
+      options: Options for the face landmarker task.
+
+    Returns:
+      `FaceLandmarker` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `FaceLandmarker` object from
+        `FaceLandmarkerOptions` such as missing the model.
+      RuntimeError: If other types of error occurred.
+    """
+
+    def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+
+      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+
+      if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+        empty_packet = output_packets[_NORM_LANDMARKS_STREAM_NAME]
+        options.result_callback(
+            FaceLandmarkerResult([], [], []),
+            image,
+            empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
+        )
+        return
+
+      face_landmarks_result = _build_landmarker_result(output_packets)
+      timestamp = output_packets[_NORM_LANDMARKS_STREAM_NAME].timestamp
+      options.result_callback(
+          face_landmarks_result,
+          image,
+          timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
+      )
+
+    output_streams = [
+        ':'.join([_NORM_LANDMARKS_TAG, _NORM_LANDMARKS_STREAM_NAME]),
+        ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
+    ]
+
+    if options.output_face_blendshapes:
+      output_streams.append(
+          ':'.join([_BLENDSHAPES_TAG, _BLENDSHAPES_STREAM_NAME])
+      )
+    if options.output_facial_transformation_matrixes:
+      output_streams.append(
+          ':'.join([_FACE_GEOMETRY_TAG, _FACE_GEOMETRY_STREAM_NAME])
+      )
+
+    task_info = _TaskInfo(
+        task_graph=_TASK_GRAPH_NAME,
+        input_streams=[
+            ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
+            ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
+        ],
+        output_streams=output_streams,
+        task_options=options,
+    )
+    return cls(
+        task_info.generate_graph_config(
+            enable_flow_limiting=options.running_mode
+            == _RunningMode.LIVE_STREAM
+        ),
+        options.running_mode,
+        packets_callback if options.result_callback else None,
+    )
+
+  def detect(
+      self,
+      image: image_module.Image,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ) -> FaceLandmarkerResult:
+    """Performs face landmarks detection on the given image.
+
+    Only use this method when the FaceLandmarker is created with the image
+    running mode.
+
+    The image can be of any size with format RGB or RGBA.
+    TODO: Describes how the input image will be preprocessed after the yuv
+    support is implemented.
+
+    Args:
+      image: MediaPipe Image.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The face landmarks detection results.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If face landmarker detection failed to run.
+    """
+
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, image, roi_allowed=False
+    )
+    output_packets = self._process_image_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
+        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
+            normalized_rect.to_pb2()
+        ),
+    })
+
+    if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+      return FaceLandmarkerResult([], [], [])
+
+    return _build_landmarker_result2(output_packets)
+
+  def detect_for_video(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ):
+    """Performs face landmarks detection on the provided video frame.
+
+    Only use this method when the FaceLandmarker is created with the video
+    running mode.
+
+    Only use this method when the FaceLandmarker is created with the video
+    running mode. It's required to provide the video frame's timestamp (in
+    milliseconds) along with the video frame. The input timestamps should be
+    monotonically increasing for adjacent calls of this method.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input video frame in milliseconds.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The face landmarks detection results.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If face landmarker detection failed to run.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, image, roi_allowed=False
+    )
+    output_packets = self._process_video_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
+            timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
+        ),
+        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
+            normalized_rect.to_pb2()
+        ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+    })
+
+    if output_packets[_NORM_LANDMARKS_STREAM_NAME].is_empty():
+      return FaceLandmarkerResult([], [], [])
+
+    return _build_landmarker_result2(output_packets)
+
+  def detect_async(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None,
+  ) -> None:
+    """Sends live image data to perform face landmarks detection.
+
+    The results will be available via the "result_callback" provided in the
+    FaceLandmarkerOptions. Only use this method when the FaceLandmarker is
+    created with the live stream running mode.
+
+    Only use this method when the FaceLandmarker is created with the live
+    stream running mode. The input timestamps should be monotonically increasing
+    for adjacent calls of this method. This method will return immediately after
+    the input image is accepted. The results will be available via the
+    `result_callback` provided in the `FaceLandmarkerOptions`. The
+    `detect_async` method is designed to process live stream data such as
+    camera input. To lower the overall latency, face landmarker may drop the
+    input images if needed. In other words, it's not guaranteed to have output
+    per input image.
+
+    The `result_callback` provides:
+      - The face landmarks detection results.
+      - The input image that the face landmarker runs on.
+      - The input timestamp in milliseconds.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input image in milliseconds.
+      image_processing_options: Options for image processing.
+
+    Raises:
+      ValueError: If the current input timestamp is smaller than what the
+      face landmarker has already processed.
+    """
+    normalized_rect = self.convert_to_normalized_rect(
+        image_processing_options, image, roi_allowed=False
+    )
+    self._send_live_stream_data({
+        _IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
+            timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND
+        ),
+        _NORM_RECT_STREAM_NAME: packet_creator.create_proto(
+            normalized_rect.to_pb2()
+        ).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+    })
\ No newline at end of file
diff --git a/high_sync/src/utils/img_utils.py b/high_sync/src/utils/img_utils.py
new file mode 100644
index 0000000..c2bbde0
--- /dev/null
+++ b/high_sync/src/utils/img_utils.py
@@ -0,0 +1,84 @@
+from PIL import Image
+import cv2
+import numpy as np
+from imageio_ffmpeg import get_ffmpeg_exe
+import pathlib
+import os
+from IPython import embed
+
+def pil_to_cv2(pil):
+    return cv2.cvtColor(np.array(pil).astype(np.uint8), cv2.COLOR_RGB2BGR)
+
+def cv2_to_pil(cv2_img):
+    return Image.fromarray(cv2.cvtColor(cv2_img, cv2.COLOR_BGR2RGB).astype(np.uint8))
+
+def center_crop_cv2(cv2_pic):
+    h, w = cv2_pic.shape[0], cv2_pic.shape[1]
+    if h > w:
+        return cv2_pic[(h - w) // 2 : (h - w) // 2 + w, :]
+    else:
+        return cv2_pic[:, (w - h) // 2 : (w - h) // 2 + h]
+
+def pils_from_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    pils = []
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame = cv2.resize(center_crop_cv2(frame), (512, 512))
+        pils.append(cv2_to_pil(frame))
+
+    return pils
+
+def save_videos_from_pils(pils, path, fps=24):
+    width, height = pils[0].size
+    print(width, height)
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+
+    pathlib.Path(path).parent.mkdir(exist_ok=True, parents=True)
+
+    output_name = pathlib.Path(path).stem
+    temp_output_path = path.replace(output_name, output_name + '-temp')
+    videowrite = cv2.VideoWriter(temp_output_path, fourcc, fps, (height, width))
+    for pil in pils:
+        frame = pil_to_cv2(pil)
+        print(frame.shape, frame.min(), frame.max())
+        videowrite.write(frame)
+
+    videowrite.release()
+    """
+    embed()
+
+    cmd = (f'{get_ffmpeg_exe()} -i "{temp_output_path}"'
+           f'-map 0:v -map 1:a -c:v h264 -shortest -y "{path}" -loglevel quiet')
+    os.system(cmd)
+    os.remove(temp_output_path)
+    """
+def save_video_from_cv2_list(pic_cv2_list, output_path, fps=30.0):
+    pathlib.Path(output_path).parent.mkdir(exist_ok=True, parents=True)
+
+    num_frames = len(pic_cv2_list)
+    height, width = pic_cv2_list[0].shape[:2]
+
+    #video_tensor = video_tensor[0, ...]
+    #_, num_frames, height, width = video_tensor.shape
+
+    output_name = pathlib.Path(output_path).stem
+    temp_output_path = output_path.replace(output_name, output_name)
+    video_writer = cv2.VideoWriter(temp_output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
+
+    for i in range(num_frames):
+        #frame_tensor = video_tensor[:, i, ...]  # [c, h, w]
+        #frame_tensor = frame_tensor.permute(1, 2, 0)  # [h, w, c]
+
+        #frame_image = (frame_tensor * 255).numpy().astype(np.uint8)
+        #frame_image = cv2.cvtColor(frame_image, cv2.COLOR_RGB2BGR)
+        frame_image = pic_cv2_list[i].astype(np.uint8)
+        video_writer.write(frame_image)
+
+    video_writer.release()
+
+
+#ffmpeg -i input_file -c:v libx264 -crf 20 -c:a aac -strict experimental -b:a 192k output_file
\ No newline at end of file
diff --git a/high_sync/src/utils/motion_utils.py b/high_sync/src/utils/motion_utils.py
new file mode 100644
index 0000000..a90a94d
--- /dev/null
+++ b/high_sync/src/utils/motion_utils.py
@@ -0,0 +1,237 @@
+from skimage.transform import estimate_transform, AffineTransform
+import numpy as np
+from IPython import embed
+import mediapipe as mp
+import copy
+
+mp_face_mesh = mp.solutions.face_mesh
+
+
+FACEMESH_LEFT_EYE = [i for i in mp_face_mesh.FACEMESH_LEFT_EYE]
+FACEMESH_RIGHT_EYE = [i for i in mp_face_mesh.FACEMESH_RIGHT_EYE]
+FACEMESH_LEFT_EYEBROW = [i for i in mp_face_mesh.FACEMESH_LEFT_EYEBROW]
+FACEMESH_RIGHT_EYEBROW = [i for i in mp_face_mesh.FACEMESH_RIGHT_EYEBROW]
+
+# copy from draw_utils
+FACEMESH_LIPS_OUTER_BOTTOM_LEFT = [(61,146),(146,91),(91,181),(181,84),(84,17)]
+FACEMESH_LIPS_OUTER_BOTTOM_RIGHT = [(17,314),(314,405),(405,321),(321,375),(375,291)]
+FACEMESH_LIPS_INNER_BOTTOM_LEFT = [(78,95),(95,88),(88,178),(178,87),(87,14)]
+FACEMESH_LIPS_INNER_BOTTOM_RIGHT = [(14,317),(317,402),(402,318),(318,324),(324,308)]
+FACEMESH_LIPS_OUTER_TOP_LEFT = [(61,185),(185,40),(40,39),(39,37),(37,0)]
+FACEMESH_LIPS_OUTER_TOP_RIGHT = [(0,267),(267,269),(269,270),(270,409),(409,291)]
+FACEMESH_LIPS_INNER_TOP_LEFT = [(78,191),(191,80),(80,81),(81,82),(82,13)]
+FACEMESH_LIPS_INNER_TOP_RIGHT = [(13,312),(312,311),(311,310),(310,415),(415,308)]
+FACEMESH_MOUSE = \
+    FACEMESH_LIPS_OUTER_BOTTOM_LEFT + \
+    FACEMESH_LIPS_OUTER_BOTTOM_RIGHT + \
+    FACEMESH_LIPS_INNER_BOTTOM_LEFT + \
+    FACEMESH_LIPS_INNER_BOTTOM_RIGHT + \
+    FACEMESH_LIPS_OUTER_TOP_LEFT + \
+    FACEMESH_LIPS_OUTER_TOP_RIGHT + \
+    FACEMESH_LIPS_INNER_TOP_LEFT + \
+    FACEMESH_LIPS_INNER_TOP_RIGHT
+
+LANDMARK_IDXES_DICT = {
+    "left_eye" : sorted(list(set([j for i in FACEMESH_LEFT_EYE for j in i])) + [473]),
+    "right_eye" : sorted(list(set([j for i in FACEMESH_RIGHT_EYE for j in i])) + [468]),
+    "mouse" : sorted(list(set([j for i in FACEMESH_MOUSE for j in i]))),
+    "nose" : sorted(list(set([1,4,5,274,275,281,44,45,51,220,440]))),
+    "left_eyebow" : sorted(list(set([j for i in FACEMESH_LEFT_EYEBROW for j in i]))),
+    "right_eyebow" : sorted(list(set([j for i in FACEMESH_RIGHT_EYEBROW for j in i]))),
+}
+
+def create_perspective_matrix(aspect_ratio):
+    kDegreesToRadians = np.pi / 180.
+    near = 1
+    far = 10000
+    perspective_matrix = np.zeros(16, dtype=np.float32)
+
+    # Standard perspective projection matrix calculations.
+    f = 1.0 / np.tan(kDegreesToRadians * 63 / 2.)
+
+    denom = 1.0 / (near - far)
+    perspective_matrix[0] = f / aspect_ratio
+    perspective_matrix[5] = f
+    perspective_matrix[10] = (near + far) * denom
+    perspective_matrix[11] = -1.
+    perspective_matrix[14] = 1. * far * near * denom
+
+    # If the environment's origin point location is in the top left corner,
+    # then skip additional flip along Y-axis is required to render correctly.
+
+    perspective_matrix[5] *= -1.
+    return perspective_matrix
+
+
+def project_points_with_trans(points_3d, transformation_matrix, image_shape):
+    P = create_perspective_matrix(image_shape[1] / image_shape[0]).reshape(4, 4).T
+    L, N, _ = points_3d.shape
+    projected_points = np.zeros((L, N, 2))
+    #embed()
+    for i in range(L):
+        points_3d_frame = points_3d[i]
+        ones = np.ones((points_3d_frame.shape[0], 1))
+        points_3d_homogeneous = np.hstack([points_3d_frame, ones])
+        transformed_points = points_3d_homogeneous @ transformation_matrix[i].T @ P
+        projected_points_frame = transformed_points[:, :2] / transformed_points[:, 3, np.newaxis] # -1 ~ 1
+        projected_points_frame[:, 0] = (projected_points_frame[:, 0] + 1) * 0.5 * image_shape[1]
+        projected_points_frame[:, 1]  = (projected_points_frame[:, 1] + 1) * 0.5 * image_shape[0]
+        projected_points[i] = projected_points_frame
+    return projected_points
+
+def project_vertices_from_ref2tgt(ref_lmks3d, tgt_trans_mat):
+    #eye_point_idxes
+    projected_vertices = project_points_with_trans(ref_lmks3d[np.newaxis, ...], tgt_trans_mat[np.newaxis, ...], [512, 512])[0]
+    return projected_vertices
+
+
+def old_motion_sync_old(sequence_driver_det, reference_det):
+    assert type(sequence_driver_det) is list
+    assert type(sequence_driver_det[0]) is type(reference_det)
+
+    lmks3d_mean = sum([i["lmks3d"] for i in sequence_driver_det]) / len(sequence_driver_det)
+    overall_transform = estimate_transform('affine', lmks3d_mean, reference_det["lmks3d"])
+
+    eye_idxes_all = LANDMARK_IDXES_DICT["left_eye"] + LANDMARK_IDXES_DICT["right_eye"]
+    for det_id in range(len(sequence_driver_det)):
+        trans = estimate_transform('affine', sequence_driver_det[det_id]["lmks"][eye_idxes_all], sequence_driver_det[det_id]["lmks3d"][eye_idxes_all])
+        sequence_driver_det[det_id]["lmks3d"] = np.vstack([
+            sequence_driver_det[det_id]["lmks3d"],
+            trans(sequence_driver_det[det_id]["lmks"][-10:])
+        ])
+
+    trans_mats = []
+    for det in sequence_driver_det:
+        trans_mats.append(det["trans_mat"] @ np.linalg.inv(sequence_driver_det[0]["trans_mat"]))
+
+    trans_mats_smooth = []
+    smooth_margin = 2
+    for tm_itx in range(len(trans_mats)):
+        smooth_idxes = [i for i in range(tm_itx - smooth_margin, tm_itx + smooth_margin + 1) if i >= 0 and i < len(trans_mats)]
+        tm = sum([trans_mats[i] for i in smooth_idxes]) / len(smooth_idxes)
+        trans_mats_smooth.append(tm)
+
+    lmks3d_smooth = []
+    smooth_margin = 1
+    for det_itx in range(len(sequence_driver_det)):
+        smooth_idxes = [i for i in range(det_itx - smooth_margin, det_itx + smooth_margin + 1) if i >= 0 and i < len(sequence_driver_det)]
+        lmks3d_smooth.append(sum([sequence_driver_det[i]["lmks3d"] for i in smooth_idxes]) / len(smooth_idxes))
+
+    for det_itx, lmks3d in enumerate(lmks3d_smooth):
+        sequence_driver_det[det_itx]["lmks3d"] = lmks3d
+
+    projected_vertices_list = []
+    for det_itx in range(len(sequence_driver_det)):
+        aligned_3d = overall_transform(sequence_driver_det[det_itx]["lmks3d"])
+        tmat = reference_det["trans_mat"] @ trans_mats_smooth[det_itx]
+        projected_vertices = project_vertices_from_ref2tgt(aligned_3d, tmat)
+        projected_vertices_list.append(projected_vertices)
+
+    # note : use normed=False after motion_sync, when draw(ing)_landmarks
+    # kps_image = vis.draw_landmarks((512, 512), projected_vertices, normed=False)
+    return  projected_vertices_list
+
+
+def motion_sync(sequence_driver_det, reference_det, per_landmark_align=True):
+    assert type(sequence_driver_det) is list
+    assert type(sequence_driver_det[0]) is type(reference_det)
+
+    eye_idxes_all = [i for i in sorted(list(set(LANDMARK_IDXES_DICT["left_eye"] + LANDMARK_IDXES_DICT["right_eye"]))) if i < len(reference_det["lmks3d"])]
+    for det_id in range(len(sequence_driver_det)):
+        trans_iris = estimate_transform('affine', sequence_driver_det[det_id]["lmks"][eye_idxes_all], sequence_driver_det[det_id]["lmks3d"][eye_idxes_all])
+        sequence_driver_det[det_id]["lmks3d"] = np.vstack([
+            sequence_driver_det[det_id]["lmks3d"],
+            trans_iris(sequence_driver_det[det_id]["lmks"][-10:])
+        ])
+
+    trans_iris = estimate_transform('affine', reference_det["lmks"][eye_idxes_all], reference_det["lmks3d"][eye_idxes_all])
+    reference_det["lmks3d"] = np.vstack([
+        reference_det["lmks3d"],
+        trans_iris(reference_det["lmks"][-10:])
+    ])
+
+    lmks3d_mean = sum([i["lmks3d"] for i in sequence_driver_det]) / len(sequence_driver_det)
+
+    landmark_trans_dict = {}
+    for landmark_name, landmark_idxes in LANDMARK_IDXES_DICT.items():
+        rf_lm = reference_det["lmks3d"][landmark_idxes]
+        dr_lm = lmks3d_mean[landmark_idxes]
+        landmark_trans_dict[landmark_name] = estimate_transform('affine', dr_lm, rf_lm)
+
+    #embed()
+    overall_transform = estimate_transform('affine', lmks3d_mean, reference_det["lmks3d"])
+    #embed()
+    #lmks3d_mean = sum([i["lmks3d"] for i in sequence_driver_det]) / len(sequence_driver_det)
+    #overall_transform = estimate_transform('affine', lmks3d_mean, reference_det["lmks3d"])
+
+    #driver_start_center = sequence_driver_det[0]["lmks3d"].mean(axis=0)
+    #reference_center = reference_det["lmks3d"].mean(axis=0)
+    #driver_start_size = ((sequence_driver_det[0]["lmks3d"] - driver_start_center)**2).sum()**(0.5)
+    #reference_size = ((reference_det["lmks3d"] - reference_center)**2).sum()**(0.5)
+
+    #reference_det_lmks3d_rescale = (reference_det["lmks3d"] - reference_center) / reference_size * driver_start_size + driver_start_center
+    #reference_transform_back = estimate_transform('affine', reference_det_lmks3d_rescale, reference_det["lmks3d"])
+
+    #driver_lmks3d_mean = sum([i["lmks3d"] for i in sequence_driver_det]) / len(sequence_driver_det)
+    #facial_transform = estimate_transform('affine', driver_lmks3d_mean, reference_det_lmks3d_rescale)
+
+
+    #for det_id in range(len(sequence_driver_det)):
+    #    trans = estimate_transform('affine', sequence_driver_det[det_id]["lmks"][:-10], sequence_driver_det[det_id]["lmks3d"])
+    #    sequence_driver_det[det_id]["lmks3d"] = trans(sequence_driver_det[det_id]["lmks"])
+
+    trans_mats = []
+    for det in sequence_driver_det:
+        trans_mats.append(det["trans_mat"] @ np.linalg.inv(sequence_driver_det[0]["trans_mat"]))
+
+    trans_mats_smooth = []
+    smooth_margin = 2
+    for tm_itx in range(len(trans_mats)):
+        smooth_idxes = [i for i in range(tm_itx - smooth_margin, tm_itx + smooth_margin + 1) if i >= 0 and i < len(trans_mats)]
+        tm = sum([trans_mats[i] for i in smooth_idxes]) / len(smooth_idxes)
+        trans_mats_smooth.append(tm)
+
+    lmks3d_smooth = []
+    smooth_margin = 1
+    for det_itx in range(len(sequence_driver_det)):
+        smooth_idxes = [i for i in range(det_itx - smooth_margin, det_itx + smooth_margin + 1) if i >= 0 and i < len(sequence_driver_det)]
+        lmks3d_smooth.append(sum([sequence_driver_det[i]["lmks3d"] for i in smooth_idxes]) / len(smooth_idxes))
+
+    for det_itx, lmks3d in enumerate(lmks3d_smooth):
+        sequence_driver_det[det_itx]["lmks3d"] = lmks3d
+
+    projected_vertices_list = []
+    for det_itx in range(len(sequence_driver_det)):
+        #aligned_3d = overall_transform(sequence_driver_det[det_itx]["lmks3d"])
+        aligned_3d = copy.deepcopy(sequence_driver_det[det_itx]["lmks3d"])
+        if per_landmark_align:
+            for landmark_name, landmark_idxes in LANDMARK_IDXES_DICT.items():
+                dr_lm = sequence_driver_det[det_itx]["lmks3d"][landmark_idxes]
+                lm_trans = landmark_trans_dict[landmark_name]
+                aligned_3d[landmark_idxes] = lm_trans(dr_lm)
+
+        #aligned_3d = lmks3d_mean
+        tmat = trans_mats_smooth[det_itx] @ reference_det["trans_mat"]
+        projected_vertices = project_vertices_from_ref2tgt(aligned_3d, tmat)
+        projected_vertices_list.append(projected_vertices)
+
+        continue
+
+        trans_ref_aligned_to_driver = (sequence_driver_det[det_itx]["trans_mat"]) @ np.linalg.inv(reference_det["trans_mat"])
+        ref_aligned_to_driver = AffineTransform(trans_ref_aligned_to_driver)(reference_det["lmks3d"])
+        det["trans_mat"] @ np.linalg.inv(sequence_driver_det[0]["trans_mat"])
+        aligned_3d = sequence_driver_det[det_itx]["lmks3d"]
+
+
+        #facial_transform(sequence_driver_det[det_itx]["lmks3d"])
+        #tmat = reference_det["trans_mat"] @ trans_mats_smooth[det_itx]
+
+        tmat = sequence_driver_det[det_itx]["trans_mat"] @ trans_mats_smooth[det_itx] #@ reference_transform_back.params
+        projected_vertices = project_vertices_from_ref2tgt(aligned_3d, tmat)
+        #embed()
+        #projected_vertices = reference_transform_back(projected_vertices)
+        projected_vertices_list.append(projected_vertices)
+
+    # note : use normed=False after motion_sync, when draw(ing)_landmarks
+    # kps_image = vis.draw_landmarks((512, 512), projected_vertices, normed=False)
+    return  projected_vertices_list
diff --git a/high_sync/src/utils/mp_models/blaze_face_short_range.tflite b/high_sync/src/utils/mp_models/blaze_face_short_range.tflite
new file mode 100644
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diff --git a/high_sync/src/utils/mp_models/face_landmarker_v2_with_blendshapes.task b/high_sync/src/utils/mp_models/face_landmarker_v2_with_blendshapes.task
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diff --git a/high_sync/src/utils/mp_utils.py b/high_sync/src/utils/mp_utils.py
new file mode 100644
index 0000000..fe966ef
--- /dev/null
+++ b/high_sync/src/utils/mp_utils.py
@@ -0,0 +1,94 @@
+import os
+import numpy as np
+import cv2
+import time
+from tqdm import tqdm
+import multiprocessing
+import glob
+
+import mediapipe as mp
+from mediapipe import solutions
+from mediapipe.framework.formats import landmark_pb2
+from mediapipe.tasks import python
+from mediapipe.tasks.python import vision
+from . import face_landmark
+
+CUR_DIR = os.path.dirname(__file__)
+
+
+class LMKExtractor():
+    def __init__(self, FPS=25):
+        # Create an FaceLandmarker object.
+        self.mode = mp.tasks.vision.FaceDetectorOptions.running_mode.IMAGE
+        base_options = python.BaseOptions(model_asset_path=os.path.join(CUR_DIR, 'mp_models/face_landmarker_v2_with_blendshapes.task'))
+        base_options.delegate = mp.tasks.BaseOptions.Delegate.CPU
+        options = vision.FaceLandmarkerOptions(base_options=base_options,
+                                            running_mode=self.mode,
+                                            output_face_blendshapes=True,
+                                            output_facial_transformation_matrixes=True,
+                                            num_faces=1)
+        self.detector = face_landmark.FaceLandmarker.create_from_options(options)
+        self.last_ts = 0
+        self.frame_ms = int(1000 / FPS)
+
+        det_base_options = python.BaseOptions(model_asset_path=os.path.join(CUR_DIR, 'mp_models/blaze_face_short_range.tflite'))
+        det_options = vision.FaceDetectorOptions(base_options=det_base_options)
+        self.det_detector = vision.FaceDetector.create_from_options(det_options)
+
+
+    def __call__(self, img):
+        frame = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame)
+        t0 = time.time()
+        if self.mode == mp.tasks.vision.FaceDetectorOptions.running_mode.VIDEO:
+            det_result = self.det_detector.detect(image)
+            if len(det_result.detections) != 1:
+                return None
+            self.last_ts += self.frame_ms
+            try:
+                detection_result, mesh3d = self.detector.detect_for_video(image, timestamp_ms=self.last_ts)
+            except:
+                return None
+        elif self.mode == mp.tasks.vision.FaceDetectorOptions.running_mode.IMAGE:
+            # det_result = self.det_detector.detect(image)
+
+            # if len(det_result.detections) != 1:
+            #     return None
+            try:
+                detection_result, mesh3d = self.detector.detect(image)
+            except:
+                return None
+
+
+        bs_list = detection_result.face_blendshapes
+        if len(bs_list) == 1:
+            bs = bs_list[0]
+            bs_values = []
+            for index in range(len(bs)):
+                bs_values.append(bs[index].score)
+            bs_values = bs_values[1:] # remove neutral
+            trans_mat = detection_result.facial_transformation_matrixes[0]
+            face_landmarks_list = detection_result.face_landmarks
+            face_landmarks = face_landmarks_list[0]
+            lmks = []
+            for index in range(len(face_landmarks)):
+                x = face_landmarks[index].x
+                y = face_landmarks[index].y
+                z = face_landmarks[index].z
+                lmks.append([x, y, z])
+            lmks = np.array(lmks)
+
+            lmks3d = np.array(mesh3d.vertex_buffer)
+            lmks3d = lmks3d.reshape(-1, 5)[:, :3]
+            mp_tris = np.array(mesh3d.index_buffer).reshape(-1, 3) + 1
+
+            return {
+                "lmks": lmks,
+                'lmks3d': lmks3d,
+                "trans_mat": trans_mat,
+                'faces': mp_tris,
+                "bs": bs_values
+            }
+        else:
+            # print('multiple faces in the image: {}'.format(img_path))
+            return None
diff --git a/high_sync/src/utils/step_func.py b/high_sync/src/utils/step_func.py
new file mode 100644
index 0000000..b530658
--- /dev/null
+++ b/high_sync/src/utils/step_func.py
@@ -0,0 +1,36 @@
+import torch
+import numpy as np
+
+def get_alpha(alphas_cumprod, timestep):
+    timestep_lt_zero_mask = torch.lt(timestep, 0).to(alphas_cumprod.dtype)
+    normal_alpha = alphas_cumprod[torch.clip(timestep, 0)]
+    one_alpha = torch.ones_like(normal_alpha).to(normal_alpha.dtype).to(normal_alpha.dtype)
+    return normal_alpha * (1 - timestep_lt_zero_mask) + one_alpha * timestep_lt_zero_mask
+
+def psuedo_velocity_wrt_noisy_and_timestep(noisy_images, noisy_images_pre, alphas_cumprod, timestep, timestep_prev):
+    alpha_prod_t = get_alpha(alphas_cumprod, timestep).view(-1, 1, 1, 1, 1).detach()
+    beta_prod_t = 1 - alpha_prod_t
+    alpha_prod_t_prev = get_alpha(alphas_cumprod, timestep_prev).view(-1, 1, 1, 1, 1).detach()
+    beta_prod_t_prev = 1 - alpha_prod_t_prev
+
+    a_s = (alpha_prod_t_prev ** (0.5)).to(noisy_images.dtype)
+    a_t = (alpha_prod_t ** (0.5)).to(noisy_images.dtype)
+    b_s = (beta_prod_t_prev ** (0.5)).to(noisy_images.dtype)
+    b_t = (beta_prod_t ** (0.5)).to(noisy_images.dtype)
+
+    psuedo_velocity = (noisy_images_pre - (
+        a_s * a_t + b_s * b_t
+    ) * noisy_images) / (
+        b_s * a_t -  a_s * b_t
+    )
+
+    return psuedo_velocity
+
+def origin_by_velocity_and_sample(velocity, noisy_images, alphas_cumprod, timestep):
+    alpha_prod_t = get_alpha(alphas_cumprod, timestep).view(-1, 1, 1, 1, 1).detach()
+    beta_prod_t = 1 - alpha_prod_t
+    a_t = (alpha_prod_t ** (0.5)).to(noisy_images.dtype)
+    b_t = (beta_prod_t ** (0.5)).to(noisy_images.dtype)
+
+    pred_original_sample = a_t * noisy_images - b_t * velocity
+    return pred_original_sample
diff --git a/high_sync/src/utils/util.py b/high_sync/src/utils/util.py
new file mode 100644
index 0000000..d0d0469
--- /dev/null
+++ b/high_sync/src/utils/util.py
@@ -0,0 +1,936 @@
+import importlib
+import os
+import os.path as osp
+import shutil
+import sys
+from pathlib import Path
+import av
+import numpy as np
+import torch
+import torchvision
+from einops import rearrange
+from PIL import Image
+import random
+import subprocess
+import sys
+from pathlib import Path
+from typing import List
+import cv2
+import mediapipe as mp
+from moviepy.editor import AudioFileClip, VideoClip
+
+
+def filter_non_none(dict_obj):
+    """
+    Filters out key-value pairs from the given dictionary where the value is None.
+
+    Args:
+        dict_obj (Dict): The dictionary to be filtered.
+
+    Returns:
+        Dict: The dictionary with key-value pairs removed where the value was None.
+
+    This function creates a new dictionary containing only the key-value pairs from
+    the original dictionary where the value is not None. It then clears the original
+    dictionary and updates it with the filtered key-value pairs.
+    """
+    non_none_filter = { k: v for k, v in dict_obj.items() if v is not None }
+    dict_obj.clear()
+    dict_obj.update(non_none_filter)
+    return dict_obj
+
+
+def import_filename(filename):
+    spec = importlib.util.spec_from_file_location("mymodule", filename)
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+def save_videos_from_pil(pil_images, path, fps=8, audio_path=None):
+    import av
+
+    save_fmt = Path(path).suffix
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+    width, height = pil_images[0].size
+
+    if save_fmt == ".mp4":
+        codec = "libx264"
+        container = av.open(path, "w")
+        stream = container.add_stream(codec, rate=fps)
+
+        stream.width = width
+        stream.height = height
+
+        for pil_image in pil_images:
+            # pil_image = Image.fromarray(image_arr).convert("RGB")
+            av_frame = av.VideoFrame.from_image(pil_image)
+            container.mux(stream.encode(av_frame))
+        container.mux(stream.encode())
+        container.close()
+
+    elif save_fmt == ".gif":
+        pil_images[0].save(
+            fp=path,
+            format="GIF",
+            append_images=pil_images[1:],
+            save_all=True,
+            duration=(1 / fps * 1000),
+            loop=0,
+        )
+    else:
+        raise ValueError("Unsupported file type. Use .mp4 or .gif.")
+
+
+def save_videos_grid(videos: torch.Tensor, path: str, audio_path=None, rescale=False, n_rows=6, fps=8):
+    videos = rearrange(videos, "b c t h w -> t b c h w")
+    height, width = videos.shape[-2:]
+    outputs = []
+
+    for x in videos:
+        x = torchvision.utils.make_grid(x, nrow=n_rows)  # (c h w)
+        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)  # (h w c)
+        if rescale:
+            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
+        x = (x * 255).numpy().astype(np.uint8)
+        x = Image.fromarray(x)
+
+        outputs.append(x)
+
+    os.makedirs(os.path.dirname(path), exist_ok=True)
+
+    save_videos_from_pil(outputs, path, fps, audio_path=audio_path)
+
+
+def read_frames(video_path):
+    container = av.open(video_path)
+
+    video_stream = next(s for s in container.streams if s.type == "video")
+    frames = []
+    for packet in container.demux(video_stream):
+        for frame in packet.decode():
+            image = Image.frombytes(
+                "RGB",
+                (frame.width, frame.height),
+                frame.to_rgb().to_ndarray(),
+            )
+            frames.append(image)
+
+    return frames
+
+
+def get_fps(video_path):
+    container = av.open(video_path)
+    video_stream = next(s for s in container.streams if s.type == "video")
+    fps = video_stream.average_rate
+    container.close()
+    return fps
+
+
+def crop_and_pad(image, rect):
+    x0, y0, x1, y1 = rect
+    h, w = image.shape[:2]
+
+    # 确保坐标在图像范围内
+    x0, y0 = max(0, x0), max(0, y0)
+    x1, y1 = min(w, x1), min(h, y1)
+
+    # 计算原始框的宽度和高度
+    width = x1 - x0
+    height = y1 - y0
+
+    # 使用较小的边长作为裁剪正方形的边长
+    side_length = min(width, height)
+
+    # 计算正方形框中心点
+    center_x = (x0 + x1) // 2
+    center_y = (y0 + y1) // 2
+
+    # 重新计算正方形框的坐标
+    new_x0 = max(0, center_x - side_length // 2)
+    new_y0 = max(0, center_y - side_length // 2)
+    new_x1 = min(w, new_x0 + side_length)
+    new_y1 = min(h, new_y0 + side_length)
+
+    # 最终裁剪框的尺寸修正（确保是正方形）
+    if (new_x1 - new_x0) != (new_y1 - new_y0):
+        side_length = min(new_x1 - new_x0, new_y1 - new_y0)
+        new_x1 = new_x0 + side_length
+        new_y1 = new_y0 + side_length
+
+    # 裁剪图像
+    cropped_image = image[new_y0:new_y1, new_x0:new_x1]
+
+    return cropped_image, (new_x0, new_y0, new_x1, new_y1)
+
+
+
+def seed_everything(seed):
+    """
+    Seeds all random number generators to ensure reproducibility.
+
+    Args:
+        seed (int): The seed value to set for all random number generators.
+    """
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed % (2**32))
+    random.seed(seed)
+
+
+
+def delete_additional_ckpt(base_path, num_keep):
+    """
+    Deletes additional checkpoint files in the given directory.
+
+    Args:
+        base_path (str): The path to the directory containing the checkpoint files.
+        num_keep (int): The number of most recent checkpoint files to keep.
+
+    Returns:
+        None
+
+    Raises:
+        FileNotFoundError: If the base_path does not exist.
+
+    Example:
+        >>> delete_additional_ckpt('path/to/checkpoints', 1)
+        # This will delete all but the most recent checkpoint file in 'path/to/checkpoints'.
+    """
+    dirs = []
+    for d in os.listdir(base_path):
+        if d.startswith("checkpoint-"):
+            dirs.append(d)
+    num_tot = len(dirs)
+    if num_tot <= num_keep:
+        return
+    # ensure ckpt is sorted and delete the ealier!
+    del_dirs = sorted(dirs, key=lambda x: int(
+        x.split("-")[-1]))[: num_tot - num_keep]
+    for d in del_dirs:
+        path_to_dir = osp.join(base_path, d)
+        if osp.exists(path_to_dir):
+            shutil.rmtree(path_to_dir)
+
+
+
+def tensor_to_video(tensor, output_video_file, audio_source, fps=25):
+    """
+    Converts a Tensor with shape [c, f, h, w] into a video and adds an audio track from the specified audio file.
+
+    Args:
+        tensor (Tensor): The Tensor to be converted, shaped [c, f, h, w].
+        output_video_file (str): The file path where the output video will be saved.
+        audio_source (str): The path to the audio file (WAV file) that contains the audio track to be added.
+        fps (int): The frame rate of the output video. Default is 25 fps.
+    """
+    tensor = tensor.permute(1, 2, 3, 0).cpu(
+    ).numpy()  # convert to [f, h, w, c]
+    tensor = np.clip(tensor * 255, 0, 255).astype(
+        np.uint8
+    )  # to [0, 255]
+
+    def make_frame(t):
+        # get index
+        frame_index = min(int(t * fps), tensor.shape[0] - 1)
+        return tensor[frame_index]
+    new_video_clip = VideoClip(make_frame, duration=tensor.shape[0] / fps)
+    audio_clip = AudioFileClip(audio_source).subclip(0, tensor.shape[0] / fps)
+    new_video_clip = new_video_clip.set_audio(audio_clip)
+    new_video_clip.write_videofile(output_video_file, fps=fps, audio_codec='aac')
+
+
+def tensor_to_video_new(tensor, output_video_file, audio_path, fps=25):
+
+    tensor = tensor.permute(1, 2, 3, 0).cpu(
+    ).numpy()  # convert to [f, h, w, c]
+    tensor = np.clip(tensor * 255, 0, 255).astype(
+        np.uint8
+    )  # to [0, 255]
+
+    f, h, w, c = tensor.shape
+    temp_video_path = output_video_file[:4] + "_no_audio.mp4"
+    video_writer = cv2.VideoWriter(temp_video_path, cv2.VideoWriter_fourcc(*'mp4v'), 25, (w, h))
+
+    for t in tensor:
+        t = cv2.cvtColor(t, cv2.COLOR_RGB2BGR)
+        video_writer.write(t)
+    video_writer.release()
+
+    cmd = (f'ffmpeg -i "{temp_video_path}" -i "{audio_path}" '
+           f'-map 0:v -map 1:a -c:v h264 -shortest -y "{output_video_file}" -loglevel quiet')
+    os.system(cmd)
+
+
+def save_video(frames_path, audio_path, output_path, frame_w, frame_h):
+
+    temp_video_path = output_path[:4] + "_no_audio.mp4"
+
+    command = f"ffmpeg -v error -hide_banner -framerate 25 -i {frames_path} -c:v libx264 -r 25 {temp_video_path} -y"
+    subprocess.run(command, shell=True, check=True, text=True)
+
+    command = f"ffmpeg -i {temp_video_path} -i {audio_path} -map 0:v -map 1:a -c:v h264 -shortest -y {output_path} -loglevel quiet"
+    subprocess.run(command, shell=True, check=True, text=True)
+
+    return
+
+
+silhouette_ids = [
+    10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288,
+    397, 365, 379, 378, 400, 377, 152, 148, 176, 149, 150, 136,
+    172, 58, 132, 93, 234, 127, 162, 21, 54, 103, 67, 109
+]
+lip_ids = [61, 185, 40, 39, 37, 0, 267, 269, 270, 409, 291,
+           146, 91, 181, 84, 17, 314, 405, 321, 375]
+
+
+def compute_face_landmarks(detection_result, h, w):
+    """
+    Compute face landmarks from a detection result.
+
+    Args:
+        detection_result (mediapipe.solutions.face_mesh.FaceMesh): The detection result containing face landmarks.
+        h (int): The height of the video frame.
+        w (int): The width of the video frame.
+
+    Returns:
+        face_landmarks_list (list): A list of face landmarks.
+    """
+    face_landmarks_list = detection_result.face_landmarks
+    if len(face_landmarks_list) != 1:
+        print("#face is invalid:", len(face_landmarks_list))
+        return []
+    return [[p.x * w, p.y * h] for p in face_landmarks_list[0]]
+
+
+def get_landmark(file):
+    """
+    This function takes a file as input and returns the facial landmarks detected in the file.
+
+    Args:
+        file (str): The path to the file containing the video or image to be processed.
+
+    Returns:
+        Tuple[List[float], List[float]]: A tuple containing two lists of floats representing the x and y coordinates of the facial landmarks.
+    """
+    model_path = "pretrained_models/face_analysis/models/face_landmarker_v2_with_blendshapes.task"
+    BaseOptions = mp.tasks.BaseOptions
+    FaceLandmarker = mp.tasks.vision.FaceLandmarker
+    FaceLandmarkerOptions = mp.tasks.vision.FaceLandmarkerOptions
+    VisionRunningMode = mp.tasks.vision.RunningMode
+    # Create a face landmarker instance with the video mode:
+    options = FaceLandmarkerOptions(
+        base_options=BaseOptions(model_asset_path=model_path),
+        running_mode=VisionRunningMode.IMAGE,
+    )
+
+    with FaceLandmarker.create_from_options(options) as landmarker:
+        image = mp.Image.create_from_file(str(file))
+        height, width = image.height, image.width
+        face_landmarker_result = landmarker.detect(image)
+        face_landmark = compute_face_landmarks(
+            face_landmarker_result, height, width)
+
+    return np.array(face_landmark), height, width
+
+
+def get_landmark_overframes(landmark_model, frames_path):
+    """
+    This function iterate frames and returns the facial landmarks detected in each frame.
+
+    Args:
+        landmark_model: mediapipe landmark model instance
+        frames_path (str): The path to the video frames.
+
+    Returns:
+        List[List[float], float, float]: A List containing two lists of floats representing the x and y coordinates of the facial landmarks.
+    """
+
+    face_landmarks = []
+
+    for file in sorted(os.listdir(frames_path)):
+        image = mp.Image.create_from_file(os.path.join(frames_path, file))
+        height, width = image.height, image.width
+        landmarker_result = landmark_model.detect(image)
+        frame_landmark = compute_face_landmarks(
+            landmarker_result, height, width)
+        face_landmarks.append(frame_landmark)
+
+    return face_landmarks, height, width
+
+
+def get_lip_mask(landmarks, height, width, out_path=None, expand_ratio=2.0):
+    """
+    Extracts the lip region from the given landmarks and saves it as an image.
+
+    Parameters:
+        landmarks (numpy.ndarray): Array of facial landmarks.
+        height (int): Height of the output lip mask image.
+        width (int): Width of the output lip mask image.
+        out_path (pathlib.Path): Path to save the lip mask image.
+        expand_ratio (float): Expand ratio of mask.
+    """
+    lip_landmarks = np.take(landmarks, lip_ids, 0)
+    min_xy_lip = np.round(np.min(lip_landmarks, 0))
+    max_xy_lip = np.round(np.max(lip_landmarks, 0))
+    min_xy_lip[0], max_xy_lip[0], min_xy_lip[1], max_xy_lip[1] = expand_region(
+        [min_xy_lip[0], max_xy_lip[0], min_xy_lip[1], max_xy_lip[1]], width, height, expand_ratio)
+    lip_mask = np.zeros((height, width), dtype=np.uint8)
+    lip_mask[round(min_xy_lip[1]):round(max_xy_lip[1]),
+             round(min_xy_lip[0]):round(max_xy_lip[0])] = 255
+    if out_path:
+        cv2.imwrite(str(out_path), lip_mask)
+        return None
+
+    return lip_mask
+
+
+def get_union_lip_mask(landmarks, height, width, expand_ratio=1):
+    """
+    Extracts the lip region from the given landmarks and saves it as an image.
+
+    Parameters:
+        landmarks (numpy.ndarray): Array of facial landmarks.
+        height (int): Height of the output lip mask image.
+        width (int): Width of the output lip mask image.
+        expand_ratio (float): Expand ratio of mask.
+    """
+    lip_masks = []
+    for landmark in landmarks:
+        lip_masks.append(get_lip_mask(landmarks=landmark, height=height,
+                     width=width, expand_ratio=expand_ratio))
+    union_mask = get_union_mask(lip_masks)
+    return union_mask
+
+
+def get_face_mask(landmarks, height, width, out_path=None, expand_ratio=1.2):
+    """
+    Generate a face mask based on the given landmarks.
+
+    Args:
+        landmarks (numpy.ndarray): The landmarks of the face.
+        height (int): The height of the output face mask image.
+        width (int): The width of the output face mask image.
+        out_path (pathlib.Path): The path to save the face mask image.
+        expand_ratio (float): Expand ratio of mask.
+    Returns:
+        None. The face mask image is saved at the specified path.
+    """
+    face_landmarks = np.take(landmarks, silhouette_ids, 0)
+    min_xy_face = np.round(np.min(face_landmarks, 0))
+    max_xy_face = np.round(np.max(face_landmarks, 0))
+    min_xy_face[0], max_xy_face[0], min_xy_face[1], max_xy_face[1] = expand_region(
+        [min_xy_face[0], max_xy_face[0], min_xy_face[1], max_xy_face[1]], width, height, expand_ratio)
+    face_mask = np.zeros((height, width), dtype=np.uint8)
+    face_mask[round(min_xy_face[1]):round(max_xy_face[1]),
+              round(min_xy_face[0]):round(max_xy_face[0])] = 255
+    if out_path:
+        cv2.imwrite(str(out_path), face_mask)
+        return None
+
+    return face_mask
+
+
+def get_union_face_mask(landmarks, height, width, expand_ratio=1):
+    """
+    Generate a face mask based on the given landmarks.
+
+    Args:
+        landmarks (numpy.ndarray): The landmarks of the face.
+        height (int): The height of the output face mask image.
+        width (int): The width of the output face mask image.
+        expand_ratio (float): Expand ratio of mask.
+    Returns:
+        None. The face mask image is saved at the specified path.
+    """
+    face_masks = []
+    for landmark in landmarks:
+        face_masks.append(get_face_mask(landmarks=landmark,height=height,width=width,expand_ratio=expand_ratio))
+    union_mask = get_union_mask(face_masks)
+    return union_mask
+
+def get_mask(file, cache_dir, face_expand_raio):
+    """
+    Generate a face mask based on the given landmarks and save it to the specified cache directory.
+
+    Args:
+        file (str): The path to the file containing the landmarks.
+        cache_dir (str): The directory to save the generated face mask.
+
+    Returns:
+        None
+    """
+    landmarks, height, width = get_landmark(file)
+    file_name = os.path.basename(file).split(".")[0]
+    get_lip_mask(landmarks, height, width, os.path.join(
+        cache_dir, f"{file_name}_lip_mask.png"))
+    get_face_mask(landmarks, height, width, os.path.join(
+        cache_dir, f"{file_name}_face_mask.png"), face_expand_raio)
+    get_blur_mask(os.path.join(
+        cache_dir, f"{file_name}_face_mask.png"), os.path.join(
+        cache_dir, f"{file_name}_face_mask_blur.png"), kernel_size=(51, 51))
+    get_blur_mask(os.path.join(
+        cache_dir, f"{file_name}_lip_mask.png"), os.path.join(
+        cache_dir, f"{file_name}_sep_lip.png"), kernel_size=(31, 31))
+    get_background_mask(os.path.join(
+        cache_dir, f"{file_name}_face_mask_blur.png"), os.path.join(
+        cache_dir, f"{file_name}_sep_background.png"))
+    get_sep_face_mask(os.path.join(
+        cache_dir, f"{file_name}_face_mask_blur.png"), os.path.join(
+        cache_dir, f"{file_name}_sep_lip.png"), os.path.join(
+        cache_dir, f"{file_name}_sep_face.png"))
+
+
+def expand_region(region, image_w, image_h, expand_ratio=1.0):
+    """
+    Expand the given region by a specified ratio.
+    Args:
+        region (tuple): A tuple containing the coordinates (min_x, max_x, min_y, max_y) of the region.
+        image_w (int): The width of the image.
+        image_h (int): The height of the image.
+        expand_ratio (float, optional): The ratio by which the region should be expanded. Defaults to 1.0.
+
+    Returns:
+        tuple: A tuple containing the expanded coordinates (min_x, max_x, min_y, max_y) of the region.
+    """
+
+    min_x, max_x, min_y, max_y = region
+    mid_x = (max_x + min_x) // 2
+    side_len_x = (max_x - min_x) * expand_ratio
+    mid_y = (max_y + min_y) // 2
+    side_len_y = (max_y - min_y) * expand_ratio
+    min_x = mid_x - side_len_x // 2
+    max_x = mid_x + side_len_x // 2
+    min_y = mid_y - side_len_y // 2
+    max_y = mid_y + side_len_y // 2
+    if min_x < 0:
+        max_x -= min_x
+        min_x = 0
+    if max_x > image_w:
+        min_x -= max_x - image_w
+        max_x = image_w
+    if min_y < 0:
+        max_y -= min_y
+        min_y = 0
+    if max_y > image_h:
+        min_y -= max_y - image_h
+        max_y = image_h
+
+    return round(min_x), round(max_x), round(min_y), round(max_y)
+
+
+def get_blur_mask(file_path, output_file_path, resize_dim=(64, 64), kernel_size=(101, 101)):
+    """
+    Read, resize, blur, normalize, and save an image.
+
+    Parameters:
+    file_path (str): Path to the input image file.
+    output_dir (str): Path to the output directory to save blurred images.
+    resize_dim (tuple): Dimensions to resize the images to.
+    kernel_size (tuple): Size of the kernel to use for Gaussian blur.
+    """
+    # Read the mask image
+    mask = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)
+
+    # Check if the image is loaded successfully
+    if mask is not None:
+        normalized_mask = blur_mask(mask,resize_dim=resize_dim,kernel_size=kernel_size)
+        # Save the normalized mask image
+        cv2.imwrite(output_file_path, normalized_mask)
+        return f"Processed, normalized, and saved: {output_file_path}"
+    return f"Failed to load image: {file_path}"
+
+
+def blur_mask(mask, resize_dim=(64, 64), kernel_size=(51, 51)):
+    """
+    Read, resize, blur, normalize, and save an image.
+
+    Parameters:
+    file_path (str): Path to the input image file.
+    resize_dim (tuple): Dimensions to resize the images to.
+    kernel_size (tuple): Size of the kernel to use for Gaussian blur.
+    """
+    # Check if the image is loaded successfully
+    normalized_mask = None
+    if mask is not None:
+        # Resize the mask image
+        resized_mask = cv2.resize(mask, resize_dim)
+        # Apply Gaussian blur to the resized mask image
+        blurred_mask = cv2.GaussianBlur(resized_mask, kernel_size, 0)
+        # Normalize the blurred image
+        normalized_mask = cv2.normalize(
+            blurred_mask, None, 0, 255, cv2.NORM_MINMAX)
+        # Save the normalized mask image
+    return normalized_mask
+
+def get_background_mask(file_path, output_file_path):
+    """
+    Read an image, invert its values, and save the result.
+
+    Parameters:
+    file_path (str): Path to the input image file.
+    output_dir (str): Path to the output directory to save the inverted image.
+    """
+    # Read the image
+    image = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)
+
+    if image is None:
+        print(f"Failed to load image: {file_path}")
+        return
+
+    # Invert the image
+    inverted_image = 1.0 - (
+        image / 255.0
+    )  # Assuming the image values are in [0, 255] range
+    # Convert back to uint8
+    inverted_image = (inverted_image * 255).astype(np.uint8)
+
+    # Save the inverted image
+    cv2.imwrite(output_file_path, inverted_image)
+    # print(f"Processed and saved: {output_file_path}")
+
+
+def get_sep_face_mask(file_path1, file_path2, output_file_path):
+    """
+    Read two images, subtract the second one from the first, and save the result.
+
+    Parameters:
+    output_dir (str): Path to the output directory to save the subtracted image.
+    """
+
+    # Read the images
+    mask1 = cv2.imread(file_path1, cv2.IMREAD_GRAYSCALE)
+    mask2 = cv2.imread(file_path2, cv2.IMREAD_GRAYSCALE)
+
+    if mask1 is None or mask2 is None:
+        print(f"Failed to load images: {file_path1}")
+        return
+
+    # Ensure the images are the same size
+    if mask1.shape != mask2.shape:
+        print(
+            f"Image shapes do not match for {file_path1}: {mask1.shape} vs {mask2.shape}"
+        )
+        return
+
+    # Subtract the second mask from the first
+    result_mask = cv2.subtract(mask1, mask2)
+
+    # Save the result mask image
+    cv2.imwrite(output_file_path, result_mask)
+    # print(f"Processed and saved: {output_file_path}")
+
+def resample_audio(input_audio_file: str, output_audio_file: str, sample_rate: int):
+    p = subprocess.Popen([
+        "ffmpeg", "-y", "-v", "error", "-i", input_audio_file, "-ar", str(sample_rate), output_audio_file
+    ])
+    ret = p.wait()
+    assert ret == 0, "Resample audio failed!"
+    return output_audio_file
+
+def get_face_region(image_path: str, detector):
+    try:
+        image = cv2.imread(image_path)
+        if image is None:
+            print(f"Failed to open image: {image_path}. Skipping...")
+            return None, None
+
+        mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=image)
+        detection_result = detector.detect(mp_image)
+
+        # Adjust mask creation for the three-channel image
+        mask = np.zeros_like(image, dtype=np.uint8)
+
+        for detection in detection_result.detections:
+            bbox = detection.bounding_box
+            start_point = (int(bbox.origin_x), int(bbox.origin_y))
+            end_point = (int(bbox.origin_x + bbox.width),
+                         int(bbox.origin_y + bbox.height))
+            cv2.rectangle(mask, start_point, end_point,
+                          (255, 255, 255), thickness=-1)
+
+        save_path = image_path.replace("images", "face_masks")
+        os.makedirs(os.path.dirname(save_path), exist_ok=True)
+        cv2.imwrite(save_path, mask)
+        # print(f"Processed and saved {save_path}")
+        return image_path, mask
+    except Exception as e:
+        print(f"Error processing image {image_path}: {e}")
+        return None, None
+
+
+def save_checkpoint(model: torch.nn.Module, save_dir: str, prefix: str, ckpt_num: int, total_limit: int = -1) -> None:
+    """
+    Save the model's state_dict to a checkpoint file.
+
+    If `total_limit` is provided, this function will remove the oldest checkpoints
+    until the total number of checkpoints is less than the specified limit.
+
+    Args:
+        model (nn.Module): The model whose state_dict is to be saved.
+        save_dir (str): The directory where the checkpoint will be saved.
+        prefix (str): The prefix for the checkpoint file name.
+        ckpt_num (int): The checkpoint number to be saved.
+        total_limit (int, optional): The maximum number of checkpoints to keep.
+            Defaults to None, in which case no checkpoints will be removed.
+
+    Raises:
+        FileNotFoundError: If the save directory does not exist.
+        ValueError: If the checkpoint number is negative.
+        OSError: If there is an error saving the checkpoint.
+    """
+
+    if not osp.exists(save_dir):
+        raise FileNotFoundError(
+            f"The save directory {save_dir} does not exist.")
+
+    if ckpt_num < 0:
+        raise ValueError(f"Checkpoint number {ckpt_num} must be non-negative.")
+
+    save_path = osp.join(save_dir, f"{prefix}-{ckpt_num}.pth")
+
+    if total_limit > 0:
+        checkpoints = os.listdir(save_dir)
+        checkpoints = [d for d in checkpoints if d.startswith(prefix)]
+        checkpoints = sorted(
+            checkpoints, key=lambda x: int(x.split("-")[1].split(".")[0])
+        )
+
+        if len(checkpoints) >= total_limit:
+            num_to_remove = len(checkpoints) - total_limit + 1
+            removing_checkpoints = checkpoints[0:num_to_remove]
+            print(
+                f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
+            )
+            print(
+                f"Removing checkpoints: {', '.join(removing_checkpoints)}"
+            )
+
+            for removing_checkpoint in removing_checkpoints:
+                removing_checkpoint_path = osp.join(
+                    save_dir, removing_checkpoint)
+                try:
+                    os.remove(removing_checkpoint_path)
+                except OSError as e:
+                    print(
+                        f"Error removing checkpoint {removing_checkpoint_path}: {e}")
+
+    state_dict = model.state_dict()
+    try:
+        torch.save(state_dict, save_path)
+        print(f"Checkpoint saved at {save_path}")
+    except OSError as e:
+        raise OSError(f"Error saving checkpoint at {save_path}: {e}") from e
+
+
+def init_output_dir(dir_list: List[str]):
+    """
+    Initialize the output directories.
+
+    This function creates the directories specified in the `dir_list`. If a directory already exists, it does nothing.
+
+    Args:
+        dir_list (List[str]): List of directory paths to create.
+    """
+    for path in dir_list:
+        os.makedirs(path, exist_ok=True)
+
+
+def load_checkpoint(cfg, save_dir, accelerator):
+    """
+    Load the most recent checkpoint from the specified directory.
+
+    This function loads the latest checkpoint from the `save_dir` if the `resume_from_checkpoint` parameter is set to "latest".
+    If a specific checkpoint is provided in `resume_from_checkpoint`, it loads that checkpoint. If no checkpoint is found,
+    it starts training from scratch.
+
+    Args:
+        cfg: The configuration object containing training parameters.
+        save_dir (str): The directory where checkpoints are saved.
+        accelerator: The accelerator object for distributed training.
+
+    Returns:
+        int: The global step at which to resume training.
+    """
+    if cfg.resume_from_checkpoint != "latest":
+        resume_dir = cfg.resume_from_checkpoint
+    else:
+        resume_dir = save_dir
+    # Get the most recent checkpoint
+    dirs = os.listdir(resume_dir)
+
+    dirs = [d for d in dirs if d.startswith("checkpoint")]
+    if len(dirs) > 0:
+        dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+        path = dirs[-1]
+        accelerator.load_state(os.path.join(resume_dir, path))
+        accelerator.print(f"Resuming from checkpoint {path}")
+        global_step = int(path.split("-")[1])
+    else:
+        accelerator.print(
+            f"Could not find checkpoint under {resume_dir}, start training from scratch")
+        global_step = 0
+
+    return global_step
+
+
+def compute_snr(noise_scheduler, timesteps):
+    """
+    Computes SNR as per
+    https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/
+            521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L847-L849
+    """
+    alphas_cumprod = noise_scheduler.alphas_cumprod
+    sqrt_alphas_cumprod = alphas_cumprod**0.5
+    sqrt_one_minus_alphas_cumprod = (1.0 - alphas_cumprod) ** 0.5
+
+    # Expand the tensors.
+    # Adapted from https://github.com/TiankaiHang/Min-SNR-Diffusion-Training/blob/
+    #              521b624bd70c67cee4bdf49225915f5945a872e3/guided_diffusion/gaussian_diffusion.py#L1026
+    sqrt_alphas_cumprod = sqrt_alphas_cumprod.to(device=timesteps.device)[
+        timesteps
+    ].float()
+    while len(sqrt_alphas_cumprod.shape) < len(timesteps.shape):
+        sqrt_alphas_cumprod = sqrt_alphas_cumprod[..., None]
+    alpha = sqrt_alphas_cumprod.expand(timesteps.shape)
+
+    sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod.to(
+        device=timesteps.device
+    )[timesteps].float()
+    while len(sqrt_one_minus_alphas_cumprod.shape) < len(timesteps.shape):
+        sqrt_one_minus_alphas_cumprod = sqrt_one_minus_alphas_cumprod[..., None]
+    sigma = sqrt_one_minus_alphas_cumprod.expand(timesteps.shape)
+
+    # Compute SNR.
+    snr = (alpha / sigma) ** 2
+    return snr
+
+
+def extract_audio_from_videos(video_path: Path, audio_output_path: Path) -> Path:
+    """
+    Extract audio from a video file and save it as a WAV file.
+
+    This function uses ffmpeg to extract the audio stream from a given video file and saves it as a WAV file
+    in the specified output directory.
+
+    Args:
+        video_path (Path): The path to the input video file.
+        output_dir (Path): The directory where the extracted audio file will be saved.
+
+    Returns:
+        Path: The path to the extracted audio file.
+
+    Raises:
+        subprocess.CalledProcessError: If the ffmpeg command fails to execute.
+    """
+    ffmpeg_command = [
+        'ffmpeg', '-y',
+        '-i', str(video_path),
+        '-vn', '-acodec',
+        "pcm_s16le", '-ar', '16000', '-ac', '2',
+        str(audio_output_path)
+    ]
+
+    try:
+        # print(f"Running command: {' '.join(ffmpeg_command)}")
+        subprocess.run(ffmpeg_command)
+    except subprocess.CalledProcessError as e:
+        print(f"Error extracting audio from video: {e}")
+        raise
+
+    return audio_output_path
+
+
+def convert_video_to_images(video_path: Path, output_dir: Path) -> Path:
+    """
+    Convert a video file into a sequence of images.
+
+    This function uses ffmpeg to convert each frame of the given video file into an image. The images are saved
+    in a directory named after the video file stem under the specified output directory.
+
+    Args:
+        video_path (Path): The path to the input video file.
+        output_dir (Path): The directory where the extracted images will be saved.
+
+    Returns:
+        Path: The path to the directory containing the extracted images.
+
+    Raises:
+        subprocess.CalledProcessError: If the ffmpeg command fails to execute.
+    """
+    ffmpeg_command = [
+        'ffmpeg',
+        '-i', str(video_path),
+        '-vf', 'fps=25',
+        str(output_dir / '%04d.png')
+    ]
+
+    try:
+        # print(f"Running command: {' '.join(ffmpeg_command)}")
+        subprocess.run(ffmpeg_command)
+    except subprocess.CalledProcessError as e:
+        print(f"Error converting video to images: {e}")
+        raise
+
+    return output_dir
+
+
+def get_union_mask(masks):
+    """
+    Compute the union of a list of masks.
+
+    This function takes a list of masks and computes their union by taking the maximum value at each pixel location.
+    Additionally, it finds the bounding box of the non-zero regions in the mask and sets the bounding box area to white.
+
+    Args:
+        masks (list of np.ndarray): List of masks to be combined.
+
+    Returns:
+        np.ndarray: The union of the input masks.
+    """
+    union_mask = None
+    for mask in masks:
+        if union_mask is None:
+            union_mask = mask
+        else:
+            union_mask = np.maximum(union_mask, mask)
+
+    if union_mask is not None:
+        # Find the bounding box of the non-zero regions in the mask
+        rows = np.any(union_mask, axis=1)
+        cols = np.any(union_mask, axis=0)
+        try:
+            ymin, ymax = np.where(rows)[0][[0, -1]]
+            xmin, xmax = np.where(cols)[0][[0, -1]]
+        except Exception as e:
+            print(str(e))
+            return 0.0
+
+        # Set bounding box area to white
+        union_mask[ymin: ymax + 1, xmin: xmax + 1] = np.max(union_mask)
+
+    return union_mask
+
+
+def move_final_checkpoint(save_dir, module_dir, prefix):
+    """
+    Move the final checkpoint file to the save directory.
+
+    This function identifies the latest checkpoint file based on the given prefix and moves it to the specified save directory.
+
+    Args:
+        save_dir (str): The directory where the final checkpoint file should be saved.
+        module_dir (str): The directory containing the checkpoint files.
+        prefix (str): The prefix used to identify checkpoint files.
+
+    Raises:
+        ValueError: If no checkpoint files are found with the specified prefix.
+    """
+    checkpoints = os.listdir(module_dir)
+    checkpoints = [d for d in checkpoints if d.startswith(prefix)]
+    checkpoints = sorted(
+        checkpoints, key=lambda x: int(x.split("-")[1].split(".")[0])
+    )
+    shutil.copy2(os.path.join(
+        module_dir, checkpoints[-1]), os.path.join(save_dir, prefix + '.pth'))
diff --git a/high_sync/train_stage1.py b/high_sync/train_stage1.py
new file mode 100644
index 0000000..90210d6
--- /dev/null
+++ b/high_sync/train_stage1.py
@@ -0,0 +1,731 @@
+# pylint: disable=E1101,C0415,W0718,R0801
+# scripts/train_stage1.py
+"""
+This is the main training script for stage 1 of the project.
+It imports necessary packages, defines necessary classes and functions, and trains the model using the provided configuration.
+
+The script includes the following classes and functions:
+
+1. Net: A PyTorch model that takes noisy latents, timesteps, reference image latents, face embeddings,
+   and face masks as input and returns the denoised latents.
+3. log_validation: A function that logs the validation information using the given VAE, image encoder,
+   network, scheduler, accelerator, width, height, and configuration.
+4. train_stage1_process: A function that processes the training stage 1 using the given configuration.
+
+The script also includes the necessary imports and a brief description of the purpose of the file.
+"""
+
+import argparse
+import copy
+import logging
+import math
+import os
+import warnings
+from datetime import datetime
+
+import cv2
+import diffusers
+import mlflow
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import DistributedDataParallelKwargs
+from diffusers import AutoencoderKL, DDIMScheduler
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version
+from diffusers.utils.import_utils import is_xformers_available
+from omegaconf import OmegaConf
+from PIL import Image
+from torch import nn
+from tqdm.auto import tqdm
+from torch.utils.tensorboard import SummaryWriter
+
+from .src.pipelines.face_animate_static import StaticPipeline
+from .src.datasets.mask_image import FaceMaskDataset, FaceMaskDatasetVal
+from .src.models.mutual_self_attention import ReferenceAttentionControl
+from .src.models.unet_2d_condition import UNet2DConditionModel
+from .src.models.unet_3d_echo import EchoUNet3DConditionModel
+from .src.utils.util import (compute_snr, delete_additional_ckpt,
+                              import_filename, init_output_dir,
+                              load_checkpoint, move_final_checkpoint,
+                              save_checkpoint, seed_everything)
+
+warnings.filterwarnings("ignore")
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.10.0.dev0")
+
+logger = get_logger(__name__, log_level="INFO")
+
+writer = SummaryWriter()
+
+class Net(nn.Module):
+    """
+    The Net class defines a neural network model that combines a reference UNet2DConditionModel,
+    a denoising UNet3DConditionModel, a face locator, and other components to animate a face in a static image.
+
+    Args:
+        reference_unet (UNet2DConditionModel): The reference UNet2DConditionModel used for face animation.
+        denoising_unet (UNet3DConditionModel): The denoising UNet3DConditionModel used for face animation.
+        face_locator (FaceLocator): The face locator model used for face animation.
+        reference_control_writer: The reference control writer component.
+        reference_control_reader: The reference control reader component.
+        imageproj: The image projection model.
+
+    Forward method:
+        noisy_latents (torch.Tensor): The noisy latents tensor.
+        timesteps (torch.Tensor): The timesteps tensor.
+        ref_image_latents (torch.Tensor): The reference image latents tensor.
+        face_emb (torch.Tensor): The face embeddings tensor.
+        face_mask (torch.Tensor): The face mask tensor.
+        uncond_fwd (bool): A flag indicating whether to perform unconditional forward pass.
+
+    Returns:
+        torch.Tensor: The output tensor of the neural network model.
+    """
+
+    def __init__(
+        self,
+        reference_unet: UNet2DConditionModel,
+        denoising_unet: EchoUNet3DConditionModel,
+        reference_control_writer: ReferenceAttentionControl,
+        reference_control_reader: ReferenceAttentionControl,
+    ):
+        super().__init__()
+        self.reference_unet = reference_unet
+        self.denoising_unet = denoising_unet
+        self.reference_control_writer = reference_control_writer
+        self.reference_control_reader = reference_control_reader
+
+    def forward(
+        self,
+        noisy_latents,
+        timesteps,
+        ref_image_latents,
+        audio_emb,
+        uncond_fwd: bool = False,
+    ):
+        """
+        Forward pass of the model.
+        Args:
+            self (Net): The model instance.
+            noisy_latents (torch.Tensor): Noisy latents.
+            timesteps (torch.Tensor): Timesteps.
+            ref_image_latents (torch.Tensor): Reference image latents.
+            face_emb (torch.Tensor): Face embedding.
+            face_mask (torch.Tensor): Face mask.
+            uncond_fwd (bool, optional): Unconditional forward pass. Defaults to False.
+
+        Returns:
+            torch.Tensor: Model prediction.
+        """
+
+        # audio_emb = torch.zeros((noisy_latents.shape[0],50,384)).to(
+        #     device=self.reference_unet.device, dtype=self.reference_unet.dtype)
+        audio_emb = audio_emb.to(device=self.reference_unet.device, dtype=self.reference_unet.dtype)
+
+        if not uncond_fwd:
+            ref_timesteps = torch.zeros_like(timesteps)
+            self.reference_unet(
+                ref_image_latents,
+                ref_timesteps,
+                encoder_hidden_states=None,
+                return_dict=False,
+            )
+            self.reference_control_reader.update(self.reference_control_writer)
+
+        model_pred = self.denoising_unet(
+            noisy_latents,
+            timesteps,
+            encoder_hidden_states=None,
+            audio_cond_fea=audio_emb,
+        ).sample
+
+        return model_pred
+
+
+def get_noise_scheduler(cfg: argparse.Namespace):
+    """
+    Create noise scheduler for training
+
+    Args:
+        cfg (omegaconf.dictconfig.DictConfig): Configuration object.
+
+    Returns:
+        train noise scheduler and val noise scheduler
+    """
+    sched_kwargs = OmegaConf.to_container(cfg.noise_scheduler_kwargs)
+    if cfg.enable_zero_snr:
+        sched_kwargs.update(
+            rescale_betas_zero_snr=True,
+            timestep_spacing="trailing",
+            prediction_type="v_prediction",
+        )
+    val_noise_scheduler = DDIMScheduler(**sched_kwargs)
+    sched_kwargs.update({"beta_schedule": "scaled_linear"})
+    train_noise_scheduler = DDIMScheduler(**sched_kwargs)
+
+    return train_noise_scheduler, val_noise_scheduler
+
+
+def log_validation(
+    vae,
+    net,
+    scheduler,
+    accelerator,
+    width,
+    height,
+    cfg,
+    save_dir,
+    global_step,
+    val_dataset,
+    weight_dtype
+):
+
+    ori_net = accelerator.unwrap_model(net)
+    ori_net = copy.deepcopy(ori_net)
+    reference_unet = ori_net.reference_unet
+    denoising_unet = ori_net.denoising_unet
+
+    generator = torch.manual_seed(42)
+
+    pipe = StaticPipeline(
+        vae=vae,
+        reference_unet=reference_unet,
+        denoising_unet=denoising_unet,
+        scheduler=scheduler,
+    )
+    pipe = pipe.to("cuda")
+
+    pil_images = []
+
+    dataset_len = len(val_dataset)
+    sample_idx = [np.random.randint(0, dataset_len-1) for _ in range(2)]
+    for idx in sample_idx:
+        sample = val_dataset[idx]
+
+        ref_image_pil = sample['ref_img']
+        # gt_image_pil = Image.fromarray(sample['tar_gt']).convert("RGB")
+        # ref_image = sample['ref_img'] # h*w*c
+        # gt_images_no_trans = sample['tar_gt_no_trans'] # f*c*h*w
+        gt_image = sample['img'] # f*c*h*w
+        mask = sample['mask'].unsqueeze(0).to(device="cuda", dtype=weight_dtype) # b*c*f*h*w
+        # mask_512 = sample['mask_512'].unsqueeze(0) # b*c*f*h*w
+        audio_tensor = sample["audio_tensor"]
+
+        image = pipe(
+            ref_image_pil,
+            gt_image,
+            audio_tensor,
+            mask,
+            width,
+            height,
+            20,
+            3.5,
+            generator=generator,
+        ).images # b*c*f*h*w
+
+        # gt_images_no_trans = gt_images_no_trans.permute(1,0,2,3).unsqueeze(0) # b*c*f*h*w
+        # image = image * mask_512 + gt_images_no_trans * (1-mask_512)
+        image = image[0, :, 0].permute(1, 2, 0).cpu().numpy()  # (c, h, w)
+        res_image_pil = Image.fromarray((image * 255).astype(np.uint8))
+        # Save ref_image, src_image and the generated_image
+        w, h = res_image_pil.size
+        canvas = Image.new("RGB", (w * 3, h), "white")
+        ref_image_pil = sample['ref_img'].convert("RGB").resize((w, h))
+        gt_image_pil = sample['img'].convert("RGB").resize((w, h))
+
+        canvas.paste(ref_image_pil, (0, 0))
+        canvas.paste(res_image_pil, (w, 0))
+        canvas.paste(gt_image_pil, (w * 2, 0))
+
+        out_file = os.path.join(save_dir, f"{global_step:06d}-{idx}.jpg")
+        canvas.save(out_file)
+
+    del pipe
+    del ori_net
+    torch.cuda.empty_cache()
+
+    return pil_images
+
+
+def train_stage1_process(cfg: argparse.Namespace) -> None:
+    """
+    Trains the model using the given configuration (cfg).
+
+    Args:
+        cfg (dict): The configuration dictionary containing the parameters for training.
+
+    Notes:
+        - This function trains the model using the given configuration.
+        - It initializes the necessary components for training, such as the pipeline, optimizer, and scheduler.
+        - The training progress is logged and tracked using the accelerator.
+        - The trained model is saved after the training is completed.
+    """
+    kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
+    accelerator = Accelerator(
+        gradient_accumulation_steps=cfg.solver.gradient_accumulation_steps,
+        mixed_precision=cfg.solver.mixed_precision,
+        log_with="mlflow",
+        project_dir="./mlruns",
+        kwargs_handlers=[kwargs],
+    )
+
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+
+    logger.info(accelerator.state, main_process_only=False)
+    if accelerator.is_local_main_process:
+        transformers.utils.logging.set_verbosity_warning()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if cfg.seed is not None:
+        seed_everything(cfg.seed)
+
+    # create output dir for training
+    exp_name = cfg.exp_name
+    save_dir = f"{cfg.output_dir}/{exp_name}"
+    checkpoint_dir = os.path.join(save_dir, "checkpoints")
+    module_dir = os.path.join(save_dir, "modules")
+    validation_dir = os.path.join(save_dir, "validation")
+
+    if accelerator.is_main_process:
+        init_output_dir([save_dir, checkpoint_dir, module_dir, validation_dir])
+
+    accelerator.wait_for_everyone()
+
+    # create model
+    if cfg.weight_dtype == "fp16":
+        weight_dtype = torch.float16
+    elif cfg.weight_dtype == "bf16":
+        weight_dtype = torch.bfloat16
+    elif cfg.weight_dtype == "fp32":
+        weight_dtype = torch.float32
+    else:
+        raise ValueError(
+            f"Do not support weight dtype: {cfg.weight_dtype} during training"
+        )
+
+    # create model
+    vae = AutoencoderKL.from_pretrained(cfg.vae_model_path).to(
+        "cuda", dtype=weight_dtype
+    )
+    reference_unet = UNet2DConditionModel.from_pretrained(
+        cfg.base_model_path,
+        subfolder="unet",
+    ).to(device="cuda", dtype=weight_dtype)
+    denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d(
+        cfg.base_model_path,
+        "",
+        subfolder="unet",
+        unet_additional_kwargs={
+            "use_motion_module": False,
+            "unet_use_temporal_attention": False,
+            "cross_attention_dim": cfg.cross_attention_dim
+        }
+    ).to(device="cuda", dtype=weight_dtype)
+
+    denoising_unet.load_state_dict(
+        torch.load(
+            os.path.join(cfg.echo_path, "denoising_unet-220000.pth"),
+            map_location="cpu",
+        ),
+        strict=False,
+    )
+    reference_unet.load_state_dict(
+        torch.load(
+            os.path.join(cfg.echo_path, "reference_unet-220000.pth"),
+            map_location="cpu",
+        ),
+        strict=False,
+    )
+
+    # Freeze
+    vae.requires_grad_(False)
+    denoising_unet.requires_grad_(True)
+    reference_unet.requires_grad_(True)
+
+    reference_control_writer = ReferenceAttentionControl(
+        reference_unet,
+        do_classifier_free_guidance=False,
+        mode="write",
+        fusion_blocks="full",
+    )
+    reference_control_reader = ReferenceAttentionControl(
+        denoising_unet,
+        do_classifier_free_guidance=False,
+        mode="read",
+        fusion_blocks="full",
+        audio_feature_ratio=3.0,
+    )
+
+    net = Net(
+        reference_unet,
+        denoising_unet,
+        reference_control_writer,
+        reference_control_reader,
+    ).to(dtype=weight_dtype)
+
+    # get noise scheduler
+    train_noise_scheduler, val_noise_scheduler = get_noise_scheduler(cfg)
+
+    # init optimizer
+    if cfg.solver.enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            reference_unet.enable_xformers_memory_efficient_attention()
+            denoising_unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError(
+                "xformers is not available. Make sure it is installed correctly"
+            )
+
+    if cfg.solver.gradient_checkpointing:
+        reference_unet.enable_gradient_checkpointing()
+        denoising_unet.enable_gradient_checkpointing()
+
+    if cfg.solver.scale_lr:
+        learning_rate = (
+            cfg.solver.learning_rate
+            * cfg.solver.gradient_accumulation_steps
+            * cfg.data.train_bs
+            * accelerator.num_processes
+        )
+    else:
+        learning_rate = cfg.solver.learning_rate
+
+    # Initialize the optimizer
+    if cfg.solver.use_8bit_adam:
+        try:
+            import bitsandbytes as bnb
+        except ImportError as exc:
+            raise ImportError(
+                "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
+            ) from exc
+
+        optimizer_cls = bnb.optim.AdamW8bit
+    else:
+        optimizer_cls = torch.optim.AdamW
+
+    trainable_params = list(
+        filter(lambda p: p.requires_grad, net.parameters()))
+    optimizer = optimizer_cls(
+        trainable_params,
+        lr=learning_rate,
+        betas=(cfg.solver.adam_beta1, cfg.solver.adam_beta2),
+        weight_decay=cfg.solver.adam_weight_decay,
+        eps=cfg.solver.adam_epsilon,
+    )
+
+    # init scheduler
+    lr_scheduler = get_scheduler(
+        cfg.solver.lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=cfg.solver.lr_warmup_steps
+        * cfg.solver.gradient_accumulation_steps,
+        num_training_steps=cfg.solver.max_train_steps
+        * cfg.solver.gradient_accumulation_steps,
+    )
+
+    # get data loader
+    train_dataset = FaceMaskDataset(
+        img_size=(cfg.data.train_width, cfg.data.train_height),
+        mask_path=cfg.data.mask_path,
+        data_meta_path=cfg.data.train_path,
+        sample_margin=cfg.data.sample_margin,
+    )
+    val_dataset = FaceMaskDatasetVal(
+        img_size=(cfg.data.train_width, cfg.data.train_height),
+        mask_path=cfg.data.mask_path,
+        data_meta_path=cfg.data.val_path,
+        sample_margin=cfg.data.sample_margin,
+    )
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset, batch_size=cfg.data.train_bs, shuffle=True, num_workers=4
+    )
+
+    # Prepare everything with our `accelerator`.
+    (
+        net,
+        optimizer,
+        train_dataloader,
+        lr_scheduler,
+    ) = accelerator.prepare(
+        net,
+        optimizer,
+        train_dataloader,
+        lr_scheduler,
+    )
+    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+    num_update_steps_per_epoch = math.ceil(
+        len(train_dataloader) / cfg.solver.gradient_accumulation_steps
+    )
+    # Afterwards we recalculate our number of training epochs
+    num_train_epochs = math.ceil(
+        cfg.solver.max_train_steps / num_update_steps_per_epoch
+    )
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        run_time = datetime.now().strftime("%Y%m%d-%H%M")
+        accelerator.init_trackers(
+            cfg.exp_name,
+            init_kwargs={"mlflow": {"run_name": run_time}},
+        )
+        # dump config file
+        mlflow.log_dict(OmegaConf.to_container(cfg), "config.yaml")
+
+        logger.info(f"save config to {save_dir}")
+        OmegaConf.save(
+            cfg, os.path.join(save_dir, "config.yaml")
+        )
+    # Train!
+    total_batch_size = (
+        cfg.data.train_bs
+        * accelerator.num_processes
+        * cfg.solver.gradient_accumulation_steps
+    )
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Num Epochs = {num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {cfg.data.train_bs}")
+    logger.info(
+        f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
+    )
+    logger.info(
+        f"  Gradient Accumulation steps = {cfg.solver.gradient_accumulation_steps}"
+    )
+    logger.info(f"  Total optimization steps = {cfg.solver.max_train_steps}")
+    global_step = 0
+    first_epoch = 0
+
+    # load checkpoint
+    # Potentially load in the weights and states from a previous save
+    if cfg.resume_from_checkpoint:
+        logger.info(f"Loading checkpoint from {checkpoint_dir}")
+        global_step = load_checkpoint(cfg, checkpoint_dir, accelerator)
+        first_epoch = global_step // num_update_steps_per_epoch
+
+       # Only show the progress bar once on each machine.
+    progress_bar = tqdm(
+        range(global_step, cfg.solver.max_train_steps),
+        disable=not accelerator.is_main_process,
+    )
+    progress_bar.set_description("Steps")
+    net.train()
+    for _ in range(first_epoch, num_train_epochs):
+        train_loss = 0.0
+        for _, batch in enumerate(train_dataloader):
+            with accelerator.accumulate(net):
+                # Convert videos to latent space
+                pixel_values = batch["img"].to(weight_dtype)
+                with torch.no_grad():
+                    latents = vae.encode(pixel_values).latent_dist.sample()
+                    latents = latents.unsqueeze(2)  # (b, c, 1, h, w)
+                    latents = latents * 0.18215
+
+                noise = torch.randn_like(latents)
+                if cfg.noise_offset > 0.0:
+                    noise += cfg.noise_offset * torch.randn(
+                        (noise.shape[0], noise.shape[1], 1, 1, 1),
+                        device=noise.device,
+                    )
+
+                bsz = latents.shape[0]
+                # Sample a random timestep for each video
+                timesteps = torch.randint(
+                    0,
+                    train_noise_scheduler.num_train_timesteps,
+                    (bsz,),
+                    device=latents.device,
+                )
+                timesteps = timesteps.long()
+
+                uncond_fwd = np.random.random() < cfg.uncond_ratio
+
+                with torch.no_grad():
+                    ref_img = batch["ref_img"].to(
+                        dtype=vae.dtype, device=vae.device
+                    )
+                    ref_image_latents = vae.encode(
+                        ref_img
+                    ).latent_dist.sample()
+                    ref_image_latents = ref_image_latents * 0.18215
+
+                mask = batch["mask"].to(device="cuda", dtype=weight_dtype)
+                noise = noise * mask
+
+                # add noise
+                noisy_latents = train_noise_scheduler.add_noise(
+                    latents, noise, timesteps
+                )
+
+                # Get the target for loss depending on the prediction type
+                if train_noise_scheduler.prediction_type == "epsilon":
+                    target = noise
+                elif train_noise_scheduler.prediction_type == "v_prediction":
+                    target = train_noise_scheduler.get_velocity(
+                        latents, noise, timesteps
+                    )
+                else:
+                    raise ValueError(
+                        f"Unknown prediction type {train_noise_scheduler.prediction_type}"
+                    )
+
+                model_pred = net(
+                    noisy_latents,
+                    timesteps,
+                    ref_image_latents,
+                    audio_emb=batch["audio_tensor"].to(
+                        dtype=weight_dtype),
+                    uncond_fwd=uncond_fwd,
+                )
+
+                if cfg.snr_gamma == 0:
+                    loss = F.mse_loss(
+                        (model_pred*mask).float(), (target*mask).float(), reduction="mean"
+                    )
+                else:
+                    snr = compute_snr(train_noise_scheduler, timesteps)
+                    if train_noise_scheduler.config.prediction_type == "v_prediction":
+                        # Velocity objective requires that we add one to SNR values before we divide by them.
+                        snr = snr + 1
+                    mse_loss_weights = (
+                        torch.stack(
+                            [snr, cfg.snr_gamma * torch.ones_like(timesteps)], dim=1
+                        ).min(dim=1)[0]
+                        / snr
+                    )
+                    loss = F.mse_loss(
+                        (model_pred*mask).float(), (target*mask).float(), reduction="none"
+                    )
+                    loss = (
+                        loss.mean(dim=list(range(1, len(loss.shape))))
+                        * mse_loss_weights
+                    )
+                    loss = loss.mean()
+
+                # Gather the losses across all processes for logging (if we use distributed training).
+                avg_loss = accelerator.gather(
+                    loss.repeat(cfg.data.train_bs)).mean()
+                train_loss += avg_loss.item() / cfg.solver.gradient_accumulation_steps
+
+                # Backpropagate
+                accelerator.backward(loss*2)
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(
+                        trainable_params,
+                        cfg.solver.max_grad_norm,
+                    )
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+
+            if accelerator.sync_gradients:
+                reference_control_reader.clear()
+                reference_control_writer.clear()
+                progress_bar.update(1)
+                global_step += 1
+                accelerator.log({"train_loss": train_loss}, step=global_step)
+                train_loss = 0.0
+                if global_step % cfg.checkpointing_steps == 0 or global_step == cfg.solver.max_train_steps:
+                    accelerator.wait_for_everyone()
+                    save_path = os.path.join(
+                        checkpoint_dir, f"checkpoint-{global_step}")
+                    if accelerator.is_main_process:
+                        delete_additional_ckpt(checkpoint_dir, 0)
+                    accelerator.save_state(save_path)
+                    accelerator.wait_for_everyone()
+                    unwrap_net = accelerator.unwrap_model(net)
+                    if accelerator.is_main_process:
+                        save_checkpoint(
+                            unwrap_net.reference_unet,
+                            module_dir,
+                            "reference_unet",
+                            global_step,
+                            total_limit=1,
+                        )
+                        save_checkpoint(
+                            unwrap_net.denoising_unet,
+                            module_dir,
+                            "denoising_unet",
+                            global_step,
+                            total_limit=1,
+                        )
+
+                if global_step % cfg.val.validation_steps == 0:
+                    if accelerator.is_main_process:
+                        generator = torch.Generator(device=accelerator.device)
+                        generator.manual_seed(cfg.seed)
+                        log_validation(
+                            vae=vae,
+                            net=net,
+                            scheduler=val_noise_scheduler,
+                            accelerator=accelerator,
+                            width=cfg.data.train_width,
+                            height=cfg.data.train_height,
+                            cfg=cfg,
+                            save_dir=validation_dir,
+                            global_step=global_step,
+                            val_dataset=val_dataset,
+                            weight_dtype=weight_dtype
+                        )
+
+            logs = {
+                "step_loss": loss.detach().item(),
+                "lr": lr_scheduler.get_last_lr()[0],
+            }
+            progress_bar.set_postfix(**logs)
+
+            writer.add_scalar("loss", loss.detach().item(), global_step)
+
+            if global_step >= cfg.solver.max_train_steps:
+                # process final module weight for stage2
+                if accelerator.is_main_process:
+                    move_final_checkpoint(save_dir, module_dir, "reference_unet")
+                    move_final_checkpoint(save_dir, module_dir, "denoising_unet")
+                break
+
+    accelerator.wait_for_everyone()
+    accelerator.end_training()
+
+
+def load_config(config_path: str) -> dict:
+    """
+    Loads the configuration file.
+
+    Args:
+        config_path (str): Path to the configuration file.
+
+    Returns:
+        dict: The configuration dictionary.
+    """
+
+    if config_path.endswith(".yaml"):
+        return OmegaConf.load(config_path)
+    if config_path.endswith(".py"):
+        return import_filename(config_path).cfg
+    raise ValueError("Unsupported format for config file")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str,
+                        default="./configs/train/stage1.yaml")
+    args = parser.parse_args()
+
+    config = load_config(args.config)
+    train_stage1_process(config)
diff --git a/high_sync/train_stage2.py b/high_sync/train_stage2.py
new file mode 100644
index 0000000..d1877da
--- /dev/null
+++ b/high_sync/train_stage2.py
@@ -0,0 +1,855 @@
+# pylint: disable=E1101,C0415,W0718,R0801
+# scripts/train_stage2.py
+"""
+This is the main training script for stage 2 of the project.
+It imports necessary packages, defines necessary classes and functions, and trains the model using the provided configuration.
+
+The script includes the following classes and functions:
+
+1. Net: A PyTorch model that takes noisy latents, timesteps, reference image latents, face embeddings,
+   and face masks as input and returns the denoised latents.
+2. get_attention_mask: A function that rearranges the mask tensors to the required format.
+3. get_noise_scheduler: A function that creates and returns the noise schedulers for training and validation.
+4. process_audio_emb: A function that processes the audio embeddings to concatenate with other tensors.
+5. log_validation: A function that logs the validation information using the given VAE, image encoder,
+   network, scheduler, accelerator, width, height, and configuration.
+6. train_stage2_process: A function that processes the training stage 2 using the given configuration.
+7. load_config: A function that loads the configuration file from the given path.
+
+The script also includes the necessary imports and a brief description of the purpose of the file.
+"""
+
+import argparse
+import copy
+import logging
+import math
+import os
+import random
+import time
+import warnings
+from datetime import datetime
+from typing import List, Tuple
+
+import diffusers
+import mlflow
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import DistributedDataParallelKwargs
+from diffusers import AutoencoderKL, DDIMScheduler
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version
+from diffusers.utils.import_utils import is_xformers_available
+from einops import rearrange, repeat
+from omegaconf import OmegaConf
+import numpy as np
+from torch import nn
+from tqdm.auto import tqdm
+from torch.utils.tensorboard import SummaryWriter
+import torch.nn.init as init
+
+from .src.pipelines.face_animate import FaceAnimatePipeline
+from .src.datasets.talk_video import TalkingVideoDataset, TalkingVideoDatasetVal
+from .src.models.mutual_self_attention import ReferenceAttentionControl
+from .src.models.unet_2d_condition import UNet2DConditionModel
+from .src.models.unet_3d_echo import EchoUNet3DConditionModel
+from .src.utils.util import (compute_snr, delete_additional_ckpt,
+                              import_filename, init_output_dir,
+                              load_checkpoint, save_checkpoint,
+                              seed_everything, tensor_to_video_new)
+
+warnings.filterwarnings("ignore")
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.10.0.dev0")
+
+logger = get_logger(__name__, log_level="INFO")
+
+writer = SummaryWriter()
+
+class Net(nn.Module):
+    """
+    The Net class defines a neural network model that combines a reference UNet2DConditionModel,
+    a denoising UNet3DConditionModel, a face locator, and other components to animate a face in a static image.
+
+    Args:
+        reference_unet (UNet2DConditionModel): The reference UNet2DConditionModel used for face animation.
+        denoising_unet (UNet3DConditionModel): The denoising UNet3DConditionModel used for face animation.
+        face_locator (FaceLocator): The face locator model used for face animation.
+        reference_control_writer: The reference control writer component.
+        reference_control_reader: The reference control reader component.
+        imageproj: The image projection model.
+        audioproj: The audio projection model.
+
+    Forward method:
+        noisy_latents (torch.Tensor): The noisy latents tensor.
+        timesteps (torch.Tensor): The timesteps tensor.
+        ref_image_latents (torch.Tensor): The reference image latents tensor.
+        face_emb (torch.Tensor): The face embeddings tensor.
+        audio_emb (torch.Tensor): The audio embeddings tensor.
+        mask (torch.Tensor): Hard face mask for face locator.
+        full_mask (torch.Tensor): Pose Mask.
+        face_mask (torch.Tensor): Face Mask
+        lip_mask (torch.Tensor): Lip Mask
+        uncond_img_fwd (bool): A flag indicating whether to perform reference image unconditional forward pass.
+        uncond_audio_fwd (bool): A flag indicating whether to perform audio unconditional forward pass.
+
+    Returns:
+        torch.Tensor: The output tensor of the neural network model.
+    """
+    def __init__(
+        self,
+        reference_unet: UNet2DConditionModel,
+        denoising_unet: EchoUNet3DConditionModel,
+        reference_control_writer,
+        reference_control_reader,
+    ):
+        super().__init__()
+        self.reference_unet = reference_unet
+        self.denoising_unet = denoising_unet
+        self.reference_control_writer = reference_control_writer
+        self.reference_control_reader = reference_control_reader
+
+    def forward(
+        self,
+        noisy_latents: torch.Tensor,
+        timesteps: torch.Tensor,
+        ref_image_latents: torch.Tensor,
+        audio_emb: torch.Tensor,
+        uncond_img_fwd: bool = False,
+        uncond_audio_fwd: bool = False,
+    ):
+        """
+        simple docstring to prevent pylint error
+        """
+        audio_emb = audio_emb.to(
+            device=self.reference_unet.device, dtype=self.reference_unet.dtype)
+
+        # condition forward
+        if not uncond_img_fwd:
+            ref_timesteps = torch.zeros_like(timesteps)
+            ref_timesteps = repeat(
+                ref_timesteps,
+                "b -> (repeat b)",
+                repeat=ref_image_latents.size(0) // ref_timesteps.size(0),
+            )
+            self.reference_unet(
+                ref_image_latents,
+                ref_timesteps,
+                encoder_hidden_states=None,
+                return_dict=False,
+            )
+            self.reference_control_reader.update(self.reference_control_writer)
+
+        if uncond_audio_fwd:
+            audio_emb = torch.zeros_like(audio_emb).to(
+                device=audio_emb.device, dtype=audio_emb.dtype
+            )
+
+        model_pred = self.denoising_unet(
+            noisy_latents,
+            timesteps,
+            encoder_hidden_states=None,
+            audio_cond_fea=audio_emb,
+        ).sample
+
+        return model_pred
+
+
+def get_noise_scheduler(cfg: argparse.Namespace) -> Tuple[DDIMScheduler, DDIMScheduler]:
+    """
+    Create noise scheduler for training.
+
+    Args:
+        cfg (argparse.Namespace): Configuration object.
+
+    Returns:
+        Tuple[DDIMScheduler, DDIMScheduler]: Train noise scheduler and validation noise scheduler.
+    """
+
+    sched_kwargs = OmegaConf.to_container(cfg.noise_scheduler_kwargs)
+    if cfg.enable_zero_snr:
+        sched_kwargs.update(
+            rescale_betas_zero_snr=True,
+            timestep_spacing="trailing",
+            prediction_type="v_prediction",
+        )
+    val_noise_scheduler = DDIMScheduler(**sched_kwargs)
+    sched_kwargs.update({"beta_schedule": "scaled_linear"})
+    train_noise_scheduler = DDIMScheduler(**sched_kwargs)
+
+    return train_noise_scheduler, val_noise_scheduler
+
+
+def log_validation(
+    accelerator: Accelerator,
+    vae: AutoencoderKL,
+    net: Net,
+    scheduler: DDIMScheduler,
+    width: int,
+    height: int,
+    clip_length: int = 14,
+    generator: torch.Generator = None,
+    cfg: dict = None,
+    save_dir: str = None,
+    global_step: int = 0,
+    times: int = None,
+    val_dataset = None,
+    weight_dtype = None
+) -> None:
+    """
+    Log validation video during the training process.
+
+    Args:
+        accelerator (Accelerator): The accelerator for distributed training.
+        vae (AutoencoderKL): The autoencoder model.
+        net (Net): The main neural network model.
+        scheduler (DDIMScheduler): The scheduler for noise.
+        width (int): The width of the input images.
+        height (int): The height of the input images.
+        clip_length (int): The length of the video clips. Defaults to 24.
+        generator (torch.Generator): The random number generator. Defaults to None.
+        cfg (dict): The configuration dictionary. Defaults to None.
+        save_dir (str): The directory to save validation results. Defaults to None.
+        global_step (int): The current global step in training. Defaults to 0.
+        times (int): The number of inference times. Defaults to None.
+        face_analysis_model_path (str): The path to the face analysis model. Defaults to "".
+
+    Returns:
+        torch.Tensor: The tensor result of the validation.
+    """
+
+    generator = torch.manual_seed(42)
+
+    pipeline = FaceAnimatePipeline(
+        vae=vae,
+        reference_unet=accelerator.unwrap_model(net).reference_unet,
+        denoising_unet=accelerator.unwrap_model(net).denoising_unet,
+        scheduler=scheduler,
+    )
+    pipeline = pipeline.to("cuda")
+
+    dataset_len = len(val_dataset)
+    sample_idx = [np.random.randint(0, dataset_len-1) for _ in range(1)]
+
+    for idx in sample_idx:
+        sample = val_dataset[idx]
+        same = sample["same"]
+        video_path = sample["video_dir"]
+        audio_path = sample["audio_path"]
+        audio_emb = sample["audio_tensor"]
+
+        target_images = sample["pixel_values_vid"].to(device="cuda", dtype=weight_dtype)
+        mask = sample['mask'].unsqueeze(0).to(device="cuda", dtype=weight_dtype) # b*c*f*h*w
+
+        min_time = min(audio_emb.shape[0], target_images.shape[0], 5*25)
+        target_images = target_images[:min_time]
+        audio_emb = audio_emb[:min_time]
+        tensor_result = []
+        generator = torch.manual_seed(42)
+
+        audio_emb = audio_emb.unsqueeze(0).to(device="cuda", dtype=weight_dtype)
+
+        try:
+            pipeline_output = pipeline(
+                gt_encode=target_images,
+                audio_tensor=audio_emb,
+                mask_main=mask,
+                width=cfg.data.train_width,
+                height=cfg.data.train_height,
+                video_length=min_time,
+                num_inference_steps=cfg.inference_steps,
+                guidance_scale=cfg.cfg_scale,
+                generator=generator,
+            )
+        except Exception as e:
+            import traceback
+            print("Error encountered during training step:")
+            traceback.print_exc()  # Prints the full error traceback
+            exit(1)
+
+        tensor_result.append(pipeline_output.videos)
+
+        tensor_result = torch.cat(tensor_result, dim=2)
+        tensor_result = tensor_result.squeeze(0)
+        tensor_result = tensor_result[:, :]
+        video_name = os.path.basename(video_path).split('.')[-2]
+        output_file = os.path.join(save_dir,f"{global_step}_{video_name}_{idx}_{same}.mp4")
+        # save the result after all iteration
+        tensor_to_video_new(tensor_result, output_file, audio_path)
+
+
+    # clean up
+    # del tmp_denoising_unet
+    del pipeline
+    torch.cuda.empty_cache()
+
+    return tensor_result
+
+
+def train_stage2_process(cfg: argparse.Namespace) -> None:
+    """
+    Trains the model using the given configuration (cfg).
+
+    Args:
+        cfg (dict): The configuration dictionary containing the parameters for training.
+
+    Notes:
+        - This function trains the model using the given configuration.
+        - It initializes the necessary components for training, such as the pipeline, optimizer, and scheduler.
+        - The training progress is logged and tracked using the accelerator.
+        - The trained model is saved after the training is completed.
+    """
+    kwargs = DistributedDataParallelKwargs(find_unused_parameters=False)
+    accelerator = Accelerator(
+        gradient_accumulation_steps=cfg.solver.gradient_accumulation_steps,
+        mixed_precision=cfg.solver.mixed_precision,
+        log_with="mlflow",
+        project_dir="./mlruns",
+        kwargs_handlers=[kwargs],
+    )
+
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state, main_process_only=False)
+    if accelerator.is_local_main_process:
+        transformers.utils.logging.set_verbosity_warning()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if cfg.seed is not None:
+        seed_everything(cfg.seed)
+
+    # create output dir for training
+    exp_name = cfg.exp_name
+    save_dir = f"{cfg.output_dir}/{exp_name}"
+    checkpoint_dir = os.path.join(save_dir, "checkpoints")
+    module_dir = os.path.join(save_dir, "modules")
+    validation_dir = os.path.join(save_dir, "validation")
+    if accelerator.is_main_process:
+        init_output_dir([save_dir, checkpoint_dir, module_dir, validation_dir])
+
+    accelerator.wait_for_everyone()
+
+    if cfg.weight_dtype == "fp16":
+        weight_dtype = torch.float16
+    elif cfg.weight_dtype == "bf16":
+        weight_dtype = torch.bfloat16
+    elif cfg.weight_dtype == "fp32":
+        weight_dtype = torch.float32
+    else:
+        raise ValueError(
+            f"Do not support weight dtype: {cfg.weight_dtype} during training"
+        )
+
+    # Create Models
+    vae = AutoencoderKL.from_pretrained(cfg.vae_model_path).to(
+        "cuda", dtype=weight_dtype
+    )
+    reference_unet = UNet2DConditionModel.from_pretrained(
+        cfg.base_model_path,
+        subfolder="unet",
+    ).to(device="cuda", dtype=weight_dtype)
+    denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d(
+        cfg.base_model_path,
+        cfg.mm_path,
+        subfolder="unet",
+        unet_additional_kwargs=OmegaConf.to_container(
+            cfg.unet_additional_kwargs)
+    ).to(device="cuda", dtype=weight_dtype)
+
+    # load module weight from stage 1
+    stage1_ckpt_dir = cfg.stage1_ckpt_dir
+    denoising_unet.load_state_dict(
+        torch.load(
+            os.path.join(stage1_ckpt_dir, "denoising_unet-44000.pth"),
+            map_location="cpu",
+        ),
+        strict=False,
+    )
+    reference_unet.load_state_dict(
+        torch.load(
+            os.path.join(stage1_ckpt_dir, "reference_unet-44000.pth"),
+            map_location="cpu",
+        ),
+        strict=False,
+    )
+
+    # Freeze
+    vae.requires_grad_(False)
+    reference_unet.requires_grad_(False)
+    denoising_unet.requires_grad_(False)
+
+    # Set motion module learnable
+    trainable_modules = cfg.trainable_para
+    for name, module in denoising_unet.named_modules():
+        if any(trainable_mod in name for trainable_mod in trainable_modules):
+            for param in module.parameters():
+                param.requires_grad_(True)
+
+    # audio attention parames of denoising_unet should be updated
+    for name, param in denoising_unet.named_parameters():
+        if ("attn2" in name) or ("norm2" in name):
+            param.requires_grad_(True)
+
+    reference_control_writer = ReferenceAttentionControl(
+        reference_unet,
+        do_classifier_free_guidance=False,
+        mode="write",
+        fusion_blocks="full",
+    )
+    reference_control_reader = ReferenceAttentionControl(
+        denoising_unet,
+        do_classifier_free_guidance=False,
+        mode="read",
+        fusion_blocks="full",
+        audio_feature_ratio=3.0,
+    )
+
+    net = Net(
+        reference_unet,
+        denoising_unet,
+        reference_control_writer,
+        reference_control_reader,
+    ).to(dtype=weight_dtype)
+
+    # unwrap_net = accelerator.unwrap_model(net)
+    # print(f"mm denoise : {net.denoising_unet.state_dict()['down_blocks.0.motion_modules.0.temporal_transformer.transformer_blocks.0.attention_blocks.1.to_v.weight']}")
+
+    if cfg.resume:
+        denoising_unet.load_state_dict(
+            torch.load(
+                cfg.resume_denoise_path,
+                map_location="cpu",
+            ),
+            strict=False,
+        )
+        reference_unet.load_state_dict(
+            torch.load(
+                cfg.resume_reference_path,
+                map_location="cpu",
+            ),
+            strict=False,
+        )
+        print("loaded weight from resume model")
+    else:
+        #load weight of net for just audio and motion
+        pretrained_model = torch.load(os.path.join(cfg.echo_path, "denoising_unet.pth"),map_location="cpu",)
+        weight_modules = ["motion_modules"]
+
+        filtered_state_dict = {}
+        for k, v in pretrained_model.items():
+            if any(trainable_mod in k for trainable_mod in weight_modules):
+                filtered_state_dict[k] = v
+
+        m , u = net.denoising_unet.load_state_dict(filtered_state_dict, strict=False)
+
+
+        # assert len(m) == 0 and len(u) == 0, "Fail to load correct checkpoint."
+        print("loaded weight from Echo")
+
+    # get noise scheduler
+    train_noise_scheduler, val_noise_scheduler = get_noise_scheduler(cfg)
+
+    if cfg.solver.enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            reference_unet.enable_xformers_memory_efficient_attention()
+            denoising_unet.enable_xformers_memory_efficient_attention()
+
+        else:
+            raise ValueError(
+                "xformers is not available. Make sure it is installed correctly"
+            )
+
+    if cfg.solver.gradient_checkpointing:
+        reference_unet.enable_gradient_checkpointing()
+        denoising_unet.enable_gradient_checkpointing()
+
+    if cfg.solver.scale_lr:
+        learning_rate = (
+            cfg.solver.learning_rate
+            * cfg.solver.gradient_accumulation_steps
+            * cfg.data.train_bs
+            * accelerator.num_processes
+        )
+    else:
+        learning_rate = cfg.solver.learning_rate
+
+    # Initialize the optimizer
+    if cfg.solver.use_8bit_adam:
+        try:
+            import bitsandbytes as bnb
+        except ImportError as exc:
+            raise ImportError(
+                "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
+            ) from exc
+        optimizer_cls = bnb.optim.AdamW8bit
+    else:
+        optimizer_cls = torch.optim.AdamW
+
+    trainable_params = list(
+        filter(lambda p: p.requires_grad, net.parameters()))
+    logger.info(f"Total trainable params {len(trainable_params)}")
+    optimizer = optimizer_cls(
+        trainable_params,
+        lr=learning_rate,
+        betas=(cfg.solver.adam_beta1, cfg.solver.adam_beta2),
+        weight_decay=cfg.solver.adam_weight_decay,
+        eps=cfg.solver.adam_epsilon,
+    )
+
+    # Scheduler
+    lr_scheduler = get_scheduler(
+        cfg.solver.lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=cfg.solver.lr_warmup_steps
+        * cfg.solver.gradient_accumulation_steps,
+        num_training_steps=cfg.solver.max_train_steps
+        * cfg.solver.gradient_accumulation_steps,
+    )
+
+    # get data loader
+    train_dataset = TalkingVideoDataset(
+        img_size=(cfg.data.train_width, cfg.data.train_height),
+        mask_path=cfg.data.mask_path,
+        sample_rate=cfg.data.sample_rate,
+        n_sample_frames=cfg.data.n_sample_frames,
+        audio_margin=cfg.data.audio_margin,
+        data_meta_path=cfg.data.train_path,
+        wav2vec_cfg=cfg.wav2vec_config,
+    )
+
+    val_dataset = TalkingVideoDatasetVal(
+        img_size=(cfg.data.train_width, cfg.data.train_height),
+        mask_path=cfg.data.mask_path,
+        sample_rate=cfg.data.sample_rate,
+        n_sample_frames=cfg.data.n_sample_frames,
+        audio_margin=cfg.data.audio_margin,
+        data_meta_path=cfg.data.val_path,
+        wav2vec_cfg=cfg.wav2vec_config,
+    )
+    train_dataloader = torch.utils.data.DataLoader(
+        train_dataset, batch_size=cfg.data.train_bs, shuffle=True, num_workers=16
+    )
+
+    # Prepare everything with our `accelerator`.
+    (
+        net,
+        optimizer,
+        train_dataloader,
+        lr_scheduler,
+    ) = accelerator.prepare(
+        net,
+        optimizer,
+        train_dataloader,
+        lr_scheduler,
+    )
+
+    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+    num_update_steps_per_epoch = math.ceil(
+        len(train_dataloader) / cfg.solver.gradient_accumulation_steps
+    )
+    # Afterwards we recalculate our number of training epochs
+    num_train_epochs = math.ceil(
+        cfg.solver.max_train_steps / num_update_steps_per_epoch
+    )
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        run_time = datetime.now().strftime("%Y%m%d-%H%M")
+        accelerator.init_trackers(
+            exp_name,
+            init_kwargs={"mlflow": {"run_name": run_time}},
+        )
+        # dump config file
+        mlflow.log_dict(
+            OmegaConf.to_container(
+                cfg), "config.yaml"
+        )
+        logger.info(f"save config to {save_dir}")
+        OmegaConf.save(
+            cfg, os.path.join(save_dir, "config.yaml")
+        )
+
+    # Train!
+    total_batch_size = (
+        cfg.data.train_bs
+        * accelerator.num_processes
+        * cfg.solver.gradient_accumulation_steps
+    )
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Num Epochs = {num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {cfg.data.train_bs}")
+    logger.info(
+        f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}"
+    )
+    logger.info(
+        f"  Gradient Accumulation steps = {cfg.solver.gradient_accumulation_steps}"
+    )
+    logger.info(f"  Total optimization steps = {cfg.solver.max_train_steps}")
+    global_step = 8000
+    first_epoch = 0
+
+    # # Potentially load in the weights and states from a previous save
+    if cfg.resume_from_checkpoint:
+        logger.info(f"Loading checkpoint from {checkpoint_dir}")
+        global_step = load_checkpoint(cfg, checkpoint_dir, accelerator)
+        first_epoch = global_step // num_update_steps_per_epoch
+
+    # Only show the progress bar once on each machine.
+    progress_bar = tqdm(
+        range(global_step, cfg.solver.max_train_steps),
+        disable=not accelerator.is_local_main_process,
+    )
+    progress_bar.set_description("Steps")
+
+
+    for _ in range(first_epoch, num_train_epochs):
+        train_loss = 0.0
+        for _, batch in enumerate(train_dataloader):
+            with accelerator.accumulate(net):
+                # Convert videos to latent space
+                pixel_values_vid = batch["pixel_values_vid"].to(weight_dtype)
+
+                with torch.no_grad():
+                    video_length = pixel_values_vid.shape[1]
+                    pixel_values_vid = rearrange(
+                        pixel_values_vid, "b f c h w -> (b f) c h w"
+                    )
+                    latents = vae.encode(pixel_values_vid).latent_dist.sample()
+                    latents = rearrange(
+                        latents, "(b f) c h w -> b c f h w", f=video_length
+                    )
+                    latents = latents * 0.18215
+
+                noise = torch.randn_like(latents)
+                if cfg.noise_offset > 0:
+                    noise += cfg.noise_offset * torch.randn(
+                        (latents.shape[0], latents.shape[1], 1, 1, 1),
+                        device=latents.device,
+                    )
+
+                bsz = latents.shape[0]
+                # Sample a random timestep for each video
+                timesteps = torch.randint(
+                    0,
+                    train_noise_scheduler.num_train_timesteps,
+                    (bsz,),
+                    device=latents.device,
+                )
+                timesteps = timesteps.long()
+
+                uncond_img_fwd = np.random.random() < cfg.uncond_img_ratio
+                uncond_audio_fwd = np.random.random() < cfg.uncond_audio_ratio
+
+                pixel_values_ref_img = batch["pixel_values_ref_img"].to(
+                    dtype=weight_dtype
+                )
+
+                ref_img_and_motion = rearrange(
+                    pixel_values_ref_img, "b f c h w -> (b f) c h w"
+                )
+
+                with torch.no_grad():
+                    ref_image_latents = vae.encode(
+                        ref_img_and_motion
+                    ).latent_dist.sample()
+                    ref_image_latents = ref_image_latents * 0.18215
+
+                mask = batch["mask"].to(device="cuda", dtype=weight_dtype)
+                noise = noise * mask
+
+                uncond_target_fwd = np.random.random() < cfg.uncond_target_ratio
+                if uncond_target_fwd:
+                    latents = latents * mask
+
+                # add noise
+                noisy_latents = train_noise_scheduler.add_noise(
+                    latents, noise, timesteps
+                )
+
+                # Get the target for loss depending on the prediction type
+                if train_noise_scheduler.prediction_type == "epsilon":
+                    target = noise
+                elif train_noise_scheduler.prediction_type == "v_prediction":
+                    target = train_noise_scheduler.get_velocity(
+                        latents, noise, timesteps
+                    )
+                else:
+                    raise ValueError(
+                        f"Unknown prediction type {train_noise_scheduler.prediction_type}"
+                    )
+
+                # ---- Forward!!! -----
+                model_pred = net(
+                    noisy_latents=noisy_latents,
+                    timesteps=timesteps,
+                    ref_image_latents=ref_image_latents,
+                    audio_emb=batch["audio_tensor"].to(
+                        dtype=weight_dtype),
+                    uncond_img_fwd=uncond_img_fwd,
+                    uncond_audio_fwd=uncond_audio_fwd,
+                )
+
+                if cfg.snr_gamma == 0:
+                    loss = F.mse_loss(
+                        (model_pred*mask).float(),
+                        (target*mask).float(),
+                        reduction="mean",
+                    )
+                else:
+                    snr = compute_snr(train_noise_scheduler, timesteps)
+                    if train_noise_scheduler.config.prediction_type == "v_prediction":
+                        # Velocity objective requires that we add one to SNR values before we divide by them.
+                        snr = snr + 1
+                    mse_loss_weights = (
+                        torch.stack(
+                            [snr, cfg.snr_gamma * torch.ones_like(timesteps)], dim=1
+                        ).min(dim=1)[0]
+                        / snr
+                    )
+                    loss = F.mse_loss(
+                        (model_pred*mask).float(),
+                        (target*mask).float(),
+                        reduction="mean",
+                    )
+                    loss = (
+                        loss.mean(dim=list(range(1, len(loss.shape))))
+                        * mse_loss_weights
+                    ).mean()
+
+                # Gather the losses across all processes for logging (if we use distributed training).
+                avg_loss = accelerator.gather(
+                    loss.repeat(cfg.data.train_bs)).mean()
+                train_loss += avg_loss.item() / cfg.solver.gradient_accumulation_steps
+
+                # Backpropagate
+                accelerator.backward(loss)
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(
+                        trainable_params,
+                        cfg.solver.max_grad_norm,
+                    )
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+
+            if accelerator.sync_gradients:
+                reference_control_reader.clear()
+                reference_control_writer.clear()
+                progress_bar.update(1)
+                global_step += 1
+                accelerator.log({"train_loss": train_loss}, step=global_step)
+                train_loss = 0.0
+
+                if global_step % cfg.val.validation_steps == 0:
+                    if accelerator.is_main_process:
+                        generator = torch.Generator(device=accelerator.device)
+                        generator.manual_seed(cfg.seed)
+
+                        log_validation(
+                            accelerator=accelerator,
+                            vae=vae,
+                            net=net,
+                            scheduler=val_noise_scheduler,
+                            width=cfg.data.train_width,
+                            height=cfg.data.train_height,
+                            clip_length=cfg.data.n_sample_frames,
+                            cfg=cfg,
+                            save_dir=validation_dir,
+                            global_step=global_step,
+                            times=cfg.single_inference_times if cfg.single_inference_times is not None else None,
+                            val_dataset=val_dataset,
+                            weight_dtype=weight_dtype
+                        )
+
+            logs = {
+                "step_loss": loss.detach().item(),
+                "lr": lr_scheduler.get_last_lr()[0],
+            }
+
+            progress_bar.set_postfix(**logs)
+
+            writer.add_scalar("loss", loss.detach().item(), global_step)
+
+            if (
+                global_step % cfg.checkpointing_steps == 0
+                or global_step == cfg.solver.max_train_steps
+            ):
+                # save model
+                save_path = os.path.join(
+                    checkpoint_dir, f"checkpoint-{global_step}")
+                if accelerator.is_main_process:
+                    delete_additional_ckpt(checkpoint_dir, 0)
+                accelerator.wait_for_everyone()
+                accelerator.save_state(save_path)
+
+                # save model weight
+                unwrap_net = accelerator.unwrap_model(net)
+                if accelerator.is_main_process:
+                    save_checkpoint(
+                        unwrap_net.reference_unet,
+                        module_dir,
+                        "reference_unet",
+                        global_step,
+                        total_limit=1,
+                    )
+                    save_checkpoint(
+                        unwrap_net.denoising_unet,
+                        module_dir,
+                        "denoising_unet",
+                        global_step,
+                        total_limit=1,
+                    )
+
+            if global_step >= cfg.solver.max_train_steps:
+                break
+
+    # Create the pipeline using the trained modules and save it.
+    accelerator.wait_for_everyone()
+    accelerator.end_training()
+
+
+def load_config(config_path: str) -> dict:
+    """
+    Loads the configuration file.
+
+    Args:
+        config_path (str): Path to the configuration file.
+
+    Returns:
+        dict: The configuration dictionary.
+    """
+
+    if config_path.endswith(".yaml"):
+        return OmegaConf.load(config_path)
+    if config_path.endswith(".py"):
+        return import_filename(config_path).cfg
+    raise ValueError("Unsupported format for config file")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--config", type=str, default="./configs/train/stage2.yaml"
+    )
+    args = parser.parse_args()
+
+    try:
+        config = load_config(args.config)
+        train_stage2_process(config)
+    except Exception as e:
+        logging.error("Failed to execute the training process: %s", e)
diff --git a/highsync_wrapper/__init__.py b/highsync_wrapper/__init__.py
new file mode 100644
index 0000000..655d420
--- /dev/null
+++ b/highsync_wrapper/__init__.py
@@ -0,0 +1 @@
+"""ComfyUI adapter utilities for HighSync."""
diff --git a/highsync_wrapper/download_utils.py b/highsync_wrapper/download_utils.py
new file mode 100644
index 0000000..8b5d237
--- /dev/null
+++ b/highsync_wrapper/download_utils.py
@@ -0,0 +1,79 @@
+from pathlib import Path
+
+from .file_utils import normalize_path, resolve_path
+
+
+CORE_FILES = (
+    "denoising_unet-500.pth",
+    "reference_unet-500.pth",
+    "audio_processor/whisper_tiny.pt",
+)
+
+ALLOW_PATTERNS = (
+    "denoising_unet-500.pth",
+    "reference_unet-500.pth",
+    "audio_processor/whisper_tiny.pt",
+    "sd-vae-ft-mse/**",
+    "sd-image-variations-diffusers/**",
+)
+
+
+def _directory_has_model_files(path):
+    if not path.is_dir():
+        return False
+    return any(candidate.is_file() for candidate in path.rglob("*"))
+
+
+def validate_required_models(models_dir):
+    root = resolve_path(models_dir)
+    missing = []
+
+    for rel_path in CORE_FILES:
+        if not (root / rel_path).is_file():
+            missing.append(rel_path)
+
+    vae_dir = root / "sd-vae-ft-mse"
+    if not _directory_has_model_files(vae_dir) or not (vae_dir / "config.json").is_file():
+        missing.append("sd-vae-ft-mse/config.json")
+
+    base_dir = root / "sd-image-variations-diffusers"
+    if not _directory_has_model_files(base_dir) or not (base_dir / "unet" / "config.json").is_file():
+        missing.append("sd-image-variations-diffusers/unet/config.json")
+
+    if missing:
+        missing_text = ", ".join(missing)
+        raise FileNotFoundError(
+            f"HighSync model files are missing in {root}: {missing_text}. "
+            "Run the HighSync Download Models node with download=True, or download "
+            "https://huggingface.co/saeed-5959/high_sync into this folder."
+        )
+
+    return root
+
+
+def download_or_check_models(models_dir, repo_id="saeed-5959/high_sync", download=False):
+    root = Path(normalize_path(models_dir))
+
+    if download:
+        try:
+            from huggingface_hub import snapshot_download
+        except ImportError as exc:
+            raise RuntimeError(
+                "huggingface_hub is required for HighSync Download Models. "
+                "Install this wrapper's requirements.txt first."
+            ) from exc
+
+        root.mkdir(parents=True, exist_ok=True)
+        snapshot_download(
+            repo_id=repo_id,
+            local_dir=str(root),
+            allow_patterns=list(ALLOW_PATTERNS),
+            repo_type="model",
+        )
+    elif not root.exists():
+        raise FileNotFoundError(
+            f"models_dir does not exist: {root}. "
+            "Run HighSync Download Models with download=True or download the models manually."
+        )
+
+    return str(validate_required_models(root))
diff --git a/highsync_wrapper/ffmpeg_utils.py b/highsync_wrapper/ffmpeg_utils.py
new file mode 100644
index 0000000..ca3d918
--- /dev/null
+++ b/highsync_wrapper/ffmpeg_utils.py
@@ -0,0 +1,45 @@
+import shutil
+import subprocess
+
+from .file_utils import resolve_path
+
+
+def check_ffmpeg():
+    ffmpeg = shutil.which("ffmpeg") or shutil.which("ffmpeg.exe")
+    if ffmpeg is None:
+        raise RuntimeError("ffmpeg not found. Please install ffmpeg and add it to PATH.")
+    return ffmpeg
+
+
+def run_ffmpeg(args, error_label):
+    command = [check_ffmpeg()] + [str(arg) for arg in args]
+    result = subprocess.run(command, capture_output=True, text=True)
+    if result.returncode != 0:
+        stderr = result.stderr.strip()
+        raise RuntimeError(f"{error_label} failed with exit code {result.returncode}: {stderr}")
+
+
+def mux_video_audio(video_path, audio_path, output_path):
+    output_file = resolve_path(output_path)
+    output_file.parent.mkdir(parents=True, exist_ok=True)
+    run_ffmpeg(
+        [
+            "-y",
+            "-i",
+            resolve_path(video_path),
+            "-i",
+            resolve_path(audio_path),
+            "-map",
+            "0:v:0",
+            "-map",
+            "1:a:0",
+            "-c:v",
+            "copy",
+            "-c:a",
+            "aac",
+            "-shortest",
+            output_file,
+        ],
+        "audio/video mux",
+    )
+    return output_file
diff --git a/highsync_wrapper/file_utils.py b/highsync_wrapper/file_utils.py
new file mode 100644
index 0000000..b5abfef
--- /dev/null
+++ b/highsync_wrapper/file_utils.py
@@ -0,0 +1,92 @@
+import os
+import re
+import shutil
+import uuid
+from datetime import datetime
+from pathlib import Path
+
+
+WINDOWS_DRIVE_RE = re.compile(r"^([A-Za-z]):[\\/](.*)$")
+
+
+def is_windows_drive_path(path):
+    return isinstance(path, str) and WINDOWS_DRIVE_RE.match(path.strip()) is not None
+
+
+def normalize_path(path):
+    if path is None:
+        return path
+
+    raw_path = str(path).strip().strip('"')
+    if raw_path == "":
+        return raw_path
+
+    match = WINDOWS_DRIVE_RE.match(raw_path)
+    if match and os.name != "nt":
+        drive = match.group(1).lower()
+        rest = match.group(2).replace("\\", "/")
+        return f"/mnt/{drive}/{rest}"
+
+    return os.path.abspath(os.path.expanduser(raw_path))
+
+
+def resolve_path(path):
+    return Path(normalize_path(path)).expanduser().resolve()
+
+
+def ensure_exists(path, label="path", is_dir=None):
+    if path is None or str(path).strip() == "":
+        raise FileNotFoundError(f"{label} is empty.")
+
+    resolved = resolve_path(path)
+    if is_dir is True and not resolved.is_dir():
+        raise FileNotFoundError(f"{label} does not exist: {resolved}")
+    if is_dir is False and not resolved.is_file():
+        raise FileNotFoundError(f"{label} does not exist: {resolved}")
+    if is_dir is None and not resolved.exists():
+        raise FileNotFoundError(f"{label} does not exist: {resolved}")
+    return resolved
+
+
+def safe_copy(src, dst):
+    src_path = ensure_exists(src, "source file", is_dir=False)
+    dst_path = resolve_path(dst)
+    dst_path.parent.mkdir(parents=True, exist_ok=True)
+    if src_path.resolve() != dst_path.resolve():
+        shutil.copy2(src_path, dst_path)
+    return dst_path
+
+
+def get_comfy_output_dir():
+    try:
+        import folder_paths
+
+        return Path(folder_paths.get_output_directory()).resolve()
+    except Exception:
+        current = Path(__file__).resolve()
+        for parent in current.parents:
+            if (parent / "custom_nodes").exists() and (parent / "output").exists():
+                return (parent / "output").resolve()
+        return Path("/mnt/d/ComfyUI/output").resolve()
+
+
+def create_task_dir(prefix="highsync"):
+    output_root = get_comfy_output_dir() / "highsync"
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    suffix = uuid.uuid4().hex[:8]
+    task_id = f"{prefix}_{timestamp}_{suffix}"
+    task_dir = output_root / task_id
+
+    paths = {
+        "task_id": task_id,
+        "task_dir": task_dir,
+        "media_dir": task_dir / "media",
+        "logs_dir": task_dir / "logs",
+        "frames_dir": task_dir / "frames",
+    }
+
+    for key, value in paths.items():
+        if key.endswith("_dir"):
+            value.mkdir(parents=True, exist_ok=True)
+
+    return paths
diff --git a/highsync_wrapper/media_utils.py b/highsync_wrapper/media_utils.py
new file mode 100644
index 0000000..998b630
--- /dev/null
+++ b/highsync_wrapper/media_utils.py
@@ -0,0 +1,224 @@
+import subprocess
+from pathlib import Path
+
+import numpy as np
+import soundfile as sf
+import torch
+
+from .ffmpeg_utils import check_ffmpeg
+from .file_utils import resolve_path
+
+
+TARGET_FPS = 25
+TARGET_AUDIO_SR = 16000
+
+
+def _as_image_tensor(images):
+    if isinstance(images, list):
+        images = torch.stack(images)
+    if not isinstance(images, torch.Tensor):
+        images = torch.as_tensor(images)
+    if images.dim() != 4:
+        raise ValueError(f"Expected IMAGE tensor with shape [frames, height, width, channels], got {tuple(images.shape)}")
+    if images.shape[-1] < 3:
+        raise ValueError(f"Expected IMAGE tensor with at least 3 channels, got {tuple(images.shape)}")
+    return images.detach().cpu()
+
+
+def images_to_uint8_rgb(images):
+    image_tensor = _as_image_tensor(images)[..., :3]
+    if image_tensor.dtype != torch.uint8:
+        image_tensor = image_tensor.float()
+        if image_tensor.numel() > 0 and float(image_tensor.max()) <= 1.0:
+            image_tensor = image_tensor * 255.0
+        image_tensor = image_tensor.clamp(0, 255).byte()
+    return np.ascontiguousarray(image_tensor.numpy())
+
+
+def frames_uint8_rgb_to_image_tensor(frames):
+    if len(frames) == 0:
+        raise ValueError("No output frames were generated.")
+    array = np.ascontiguousarray(np.stack(frames).astype(np.float32) / 255.0)
+    return torch.from_numpy(array)
+
+
+def resample_frames_to_fps(frames_rgb, input_fps, output_fps=TARGET_FPS, max_frames=0):
+    input_fps = int(input_fps)
+    output_fps = int(output_fps)
+    if input_fps <= 0 or output_fps <= 0:
+        raise ValueError("input_fps and output_fps must be positive.")
+    if len(frames_rgb) == 0:
+        raise ValueError("No IMAGE frames were provided.")
+
+    if input_fps == output_fps:
+        sampled = frames_rgb.copy()
+    else:
+        duration = len(frames_rgb) / float(input_fps)
+        target_count = max(1, int(round(duration * output_fps)))
+        indices = np.floor(np.arange(target_count) * input_fps / output_fps).astype(np.int64)
+        indices = np.clip(indices, 0, len(frames_rgb) - 1)
+        sampled = frames_rgb[indices]
+
+    max_frames = int(max_frames or 0)
+    if max_frames > 0:
+        sampled = sampled[:max_frames]
+
+    return np.ascontiguousarray(sampled)
+
+
+def _audio_waveform_to_tensor(audio):
+    if not isinstance(audio, dict) or "waveform" not in audio or "sample_rate" not in audio:
+        raise ValueError("AUDIO input must be a dict with 'waveform' and 'sample_rate'.")
+
+    waveform = audio["waveform"]
+    if not isinstance(waveform, torch.Tensor):
+        waveform = torch.as_tensor(waveform)
+    waveform = waveform.detach().cpu().float()
+
+    if waveform.dim() == 3:
+        waveform = waveform[0]
+    elif waveform.dim() == 1:
+        waveform = waveform.unsqueeze(0)
+    elif waveform.dim() != 2:
+        raise ValueError(
+            "Expected AUDIO waveform with shape [batch, channels, samples], "
+            f"[channels, samples], or [samples], got {tuple(waveform.shape)}"
+        )
+
+    return waveform.contiguous(), int(audio["sample_rate"])
+
+
+def trim_or_pad_audio(audio, duration_sec=None, pad_to_duration=False):
+    waveform, sample_rate = _audio_waveform_to_tensor(audio)
+
+    if duration_sec is not None and duration_sec > 0:
+        target_samples = max(1, int(round(float(duration_sec) * sample_rate)))
+        if waveform.shape[-1] > target_samples:
+            waveform = waveform[:, :target_samples]
+        elif pad_to_duration and waveform.shape[-1] < target_samples:
+            pad = torch.zeros((waveform.shape[0], target_samples - waveform.shape[-1]), dtype=waveform.dtype)
+            waveform = torch.cat([waveform, pad], dim=-1)
+
+    return {"waveform": waveform.unsqueeze(0).contiguous(), "sample_rate": sample_rate}
+
+
+def _resample_waveform(waveform, sample_rate, target_sample_rate):
+    if int(sample_rate) == int(target_sample_rate):
+        return waveform
+
+    try:
+        import torchaudio
+
+        resampler = torchaudio.transforms.Resample(
+            orig_freq=int(sample_rate),
+            new_freq=int(target_sample_rate),
+        )
+        return resampler(waveform)
+    except Exception as exc:
+        raise RuntimeError(
+            "Failed to resample audio with torchaudio. Install torchaudio or provide 16kHz audio."
+        ) from exc
+
+
+def save_audio_to_wav(audio, output_path, duration_sec=None, pad_to_duration=False, mono=False, sample_rate=None):
+    adjusted = trim_or_pad_audio(audio, duration_sec=duration_sec, pad_to_duration=pad_to_duration)
+    waveform = adjusted["waveform"].squeeze(0).clamp(-1.0, 1.0)
+    source_sample_rate = int(adjusted["sample_rate"])
+
+    if mono and waveform.shape[0] > 1:
+        waveform = waveform.mean(dim=0, keepdim=True)
+
+    target_sample_rate = int(sample_rate or source_sample_rate)
+    waveform = _resample_waveform(waveform, source_sample_rate, target_sample_rate)
+
+    output_file = resolve_path(output_path)
+    output_file.parent.mkdir(parents=True, exist_ok=True)
+    audio_np = waveform.transpose(0, 1).numpy()
+    sf.write(str(output_file), audio_np, target_sample_rate, subtype="PCM_16")
+    return output_file
+
+
+def encode_rgb_frames_to_mp4(frames_rgb, output_path, fps=TARGET_FPS):
+    output_file = resolve_path(output_path)
+    output_file.parent.mkdir(parents=True, exist_ok=True)
+
+    frames = np.ascontiguousarray(frames_rgb)
+    if frames.ndim != 4 or frames.shape[-1] != 3:
+        raise ValueError(f"Expected RGB frames with shape [frames, height, width, 3], got {frames.shape}")
+
+    frame_count, height, width, _ = frames.shape
+    if frame_count <= 0:
+        raise ValueError("No frames were provided to encode.")
+
+    command = [
+        check_ffmpeg(),
+        "-y",
+        "-f",
+        "rawvideo",
+        "-vcodec",
+        "rawvideo",
+        "-s",
+        f"{width}x{height}",
+        "-pix_fmt",
+        "rgb24",
+        "-r",
+        str(int(fps)),
+        "-i",
+        "-",
+        "-an",
+        "-c:v",
+        "libx264",
+        "-pix_fmt",
+        "yuv420p",
+        "-crf",
+        "18",
+        str(output_file),
+    ]
+
+    process = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    try:
+        for frame in frames:
+            process.stdin.write(np.ascontiguousarray(frame).tobytes())
+        _, stderr = process.communicate()
+    except BrokenPipeError:
+        _, stderr = process.communicate()
+    finally:
+        if process.stdin and not process.stdin.closed:
+            process.stdin.close()
+
+    if process.returncode != 0:
+        message = stderr.decode("utf-8", errors="replace").strip()
+        raise RuntimeError(f"Failed to encode HighSync output video: {message}")
+
+    return output_file
+
+
+def denoise_audio_file(input_audio_path, output_audio_path, device, cache):
+    try:
+        from denoiser import pretrained
+        from denoiser.dsp import convert_audio
+    except ImportError as exc:
+        raise RuntimeError(
+            "denoiser and torchaudio are required when denoise_audio=True. "
+            "Install this wrapper's requirements.txt or disable denoise_audio."
+        ) from exc
+
+    model = cache.get("denoiser_model")
+    model_device = torch.device(device)
+    if model is None:
+        model = pretrained.dns64().to(model_device)
+        model.eval()
+        cache["denoiser_model"] = model
+
+    audio_np, sample_rate = sf.read(str(Path(input_audio_path)), dtype="float32", always_2d=True)
+    wav = torch.from_numpy(audio_np.T.copy())
+    wav = convert_audio(wav.to(model_device), sample_rate, model.sample_rate, model.chin)
+
+    with torch.no_grad():
+        denoised = model(wav[None])[0]
+
+    output_file = resolve_path(output_audio_path)
+    output_file.parent.mkdir(parents=True, exist_ok=True)
+    audio_np = denoised.detach().cpu().clamp(-1.0, 1.0).numpy()[0]
+    sf.write(str(output_file), audio_np, int(model.sample_rate), subtype="PCM_16")
+    return output_file
diff --git a/highsync_wrapper/model_utils.py b/highsync_wrapper/model_utils.py
new file mode 100644
index 0000000..9a406e8
--- /dev/null
+++ b/highsync_wrapper/model_utils.py
@@ -0,0 +1,148 @@
+from pathlib import Path
+
+from .download_utils import validate_required_models
+
+
+_MODEL_CACHE = {}
+
+
+def _get_repo_root():
+    return Path(__file__).resolve().parents[1]
+
+
+def _torch_load(path, torch):
+    try:
+        return torch.load(path, map_location="cpu", weights_only=True)
+    except TypeError:
+        return torch.load(path, map_location="cpu")
+
+
+def _get_torch_device(torch):
+    try:
+        import comfy.model_management as model_management
+
+        return model_management.get_torch_device()
+    except Exception:
+        return torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def _weight_dtype(torch, weight_dtype):
+    dtype_text = str(weight_dtype or "fp16").lower()
+    if dtype_text == "fp16":
+        return torch.float16
+    if dtype_text == "bf16":
+        return torch.bfloat16
+    if dtype_text == "fp32":
+        return torch.float32
+    raise ValueError(f"Unsupported HighSync weight_dtype: {weight_dtype}")
+
+
+def _load_config(models_root, weight_dtype):
+    try:
+        from omegaconf import OmegaConf
+    except ImportError as exc:
+        raise RuntimeError("omegaconf is required to load HighSync config. Install requirements.txt first.") from exc
+
+    config_path = _get_repo_root() / "high_sync" / "configs" / "inference" / "default.yaml"
+    config = OmegaConf.load(str(config_path))
+
+    config.weight_dtype = str(weight_dtype)
+    config.denoising_unet_path = str(models_root / "denoising_unet-500.pth")
+    config.reference_unet_path = str(models_root / "reference_unet-500.pth")
+    config.base_model_path = str(models_root / "sd-image-variations-diffusers")
+    config.audio_model_path = str(models_root / "audio_processor" / "whisper_tiny.pt")
+    config.vae.model_path = str(models_root / "sd-vae-ft-mse")
+    config.mask_path = str(_get_repo_root() / "high_sync" / "masks" / "mask_64.png")
+    config.mask_512_path = str(_get_repo_root() / "high_sync" / "masks" / "mask_512.png")
+
+    return config
+
+
+def _build_scheduler(config):
+    try:
+        from diffusers import DDIMScheduler
+        from omegaconf import OmegaConf
+    except ImportError as exc:
+        raise RuntimeError("diffusers and omegaconf are required for HighSync. Install requirements.txt first.") from exc
+
+    sched_kwargs = OmegaConf.to_container(config.noise_scheduler_kwargs, resolve=True)
+    if bool(config.enable_zero_snr):
+        sched_kwargs.update(
+            rescale_betas_zero_snr=True,
+            timestep_spacing="trailing",
+            prediction_type="v_prediction",
+        )
+    return DDIMScheduler(**sched_kwargs)
+
+
+def load_highsync_model(models_dir, weight_dtype="fp16"):
+    try:
+        import torch
+        from diffusers import AutoencoderKL
+    except ImportError as exc:
+        raise RuntimeError(
+            "torch and diffusers are required to load HighSync. Install this wrapper's requirements.txt first."
+        ) from exc
+
+    models_root = validate_required_models(models_dir)
+    device = _get_torch_device(torch)
+    dtype = _weight_dtype(torch, weight_dtype)
+    cache_key = (str(models_root), str(weight_dtype).lower(), str(device))
+
+    if cache_key in _MODEL_CACHE:
+        return _MODEL_CACHE[cache_key]
+
+    config = _load_config(models_root, weight_dtype)
+
+    from omegaconf import OmegaConf
+
+    from high_sync.image_processor import ImageProcessor
+    from high_sync.src.models.unet_2d_condition import UNet2DConditionModel
+    from high_sync.src.models.unet_3d_echo import EchoUNet3DConditionModel
+    from high_sync.src.models.whisper.audio2feature import load_audio_model
+    from high_sync.src.pipelines.face_animate import FaceAnimatePipeline
+
+    scheduler = _build_scheduler(config)
+
+    vae = AutoencoderKL.from_pretrained(str(config.vae.model_path))
+    reference_unet = UNet2DConditionModel.from_pretrained(str(config.base_model_path), subfolder="unet")
+    denoising_unet = EchoUNet3DConditionModel.from_pretrained_2d(
+        str(config.base_model_path),
+        "",
+        subfolder="unet",
+        unet_additional_kwargs=OmegaConf.to_container(config.unet_additional_kwargs, resolve=True),
+    )
+
+    reference_unet.load_state_dict(_torch_load(str(config.reference_unet_path), torch))
+    denoising_unet.load_state_dict(_torch_load(str(config.denoising_unet_path), torch))
+
+    vae.requires_grad_(False)
+    reference_unet.requires_grad_(False)
+    denoising_unet.requires_grad_(False)
+
+    vae.eval()
+    reference_unet.eval()
+    denoising_unet.eval()
+
+    pipeline = FaceAnimatePipeline(
+        vae=vae,
+        reference_unet=reference_unet,
+        denoising_unet=denoising_unet,
+        scheduler=scheduler,
+    )
+    pipeline.to(device=device, dtype=dtype)
+
+    audio_processor = load_audio_model(model_path=str(config.audio_model_path), device=device)
+
+    model = {
+        "models_dir": str(models_root),
+        "device": device,
+        "weight_dtype": dtype,
+        "config": config,
+        "pipeline": pipeline,
+        "audio_processor": audio_processor,
+        "image_processor_cls": ImageProcessor,
+        "denoiser_model": None,
+    }
+    _MODEL_CACHE[cache_key] = model
+    return model
diff --git a/highsync_wrapper/runner.py b/highsync_wrapper/runner.py
new file mode 100644
index 0000000..d933459
--- /dev/null
+++ b/highsync_wrapper/runner.py
@@ -0,0 +1,323 @@
+import shutil
+
+import cv2
+import numpy as np
+import torch
+
+from .ffmpeg_utils import mux_video_audio
+from .file_utils import create_task_dir
+from .media_utils import (
+    TARGET_AUDIO_SR,
+    TARGET_FPS,
+    denoise_audio_file,
+    encode_rgb_frames_to_mp4,
+    frames_uint8_rgb_to_image_tensor,
+    images_to_uint8_rgb,
+    resample_frames_to_fps,
+    save_audio_to_wav,
+    trim_or_pad_audio,
+)
+
+
+def _maybe_throw_if_interrupted():
+    try:
+        import comfy.model_management as model_management
+    except Exception:
+        return
+    model_management.throw_exception_if_processing_interrupted()
+
+
+def _empty_cache(device):
+    if isinstance(device, torch.device) and device.type == "cuda" and torch.cuda.is_available():
+        torch.cuda.empty_cache()
+    try:
+        import comfy.model_management as model_management
+
+        model_management.soft_empty_cache()
+    except Exception:
+        return
+
+
+def create_mask(mask_path):
+    raw_mask = cv2.imread(str(mask_path))
+    if raw_mask is None:
+        raise FileNotFoundError(f"HighSync mask could not be read: {mask_path}")
+    mask = 1 - (raw_mask / 255.0)
+
+    h, w, c = mask.shape
+    new_mask = np.zeros((h, w, c + 1), dtype=np.float32)
+    new_mask[:, :, :c] = mask
+    new_mask[:, :, c] = mask[:, :, c - 1]
+    return torch.FloatTensor(new_mask).permute(2, 0, 1).unsqueeze(1)
+
+
+def create_mask_alpha(width, height, coordinate):
+    left, top, right, bottom = coordinate
+    mask = np.ones((height, width, 3), dtype=np.float32)
+    mask[top:bottom, left:right] = 0
+
+    pad = min(int((right - left) / 15), int((bottom - top) / 15))
+    if pad <= 0:
+        return mask
+
+    for idx in range(left, right):
+        mask[bottom - pad:bottom, idx] = np.array([np.linspace(0, 1, pad)] * 3).transpose(1, 0)
+        mask[top:top + pad, idx] = np.array([np.linspace(1, 0, pad)] * 3).transpose(1, 0)
+
+    for idx in range(top, bottom):
+        mask[idx, right - pad:right] = np.array([np.linspace(0, 1, pad)] * 3).transpose(1, 0)
+        mask[idx, left:left + pad] = np.array([np.linspace(1, 0, pad)] * 3).transpose(1, 0)
+
+    for idx, start, end in zip(range(top, top + pad), np.linspace(1, 0.7, pad), np.linspace(0.7, 0, pad)):
+        mask[idx, left:left + pad] = np.array([np.linspace(start, end, pad)] * 3).transpose(1, 0)
+        mask[idx, right - pad:right] = np.array([np.linspace(end, start, pad)] * 3).transpose(1, 0)
+
+    for idx, start, end in zip(range(bottom - pad, bottom), np.linspace(0.7, 1, pad), np.linspace(0, 0.7, pad)):
+        mask[idx, left:left + pad] = np.array([np.linspace(start, end, pad)] * 3).transpose(1, 0)
+        mask[idx, right - pad:right] = np.array([np.linspace(end, start, pad)] * 3).transpose(1, 0)
+
+    return mask
+
+
+def _prepare_audio_files(task, audio, duration_sec, trim_audio_to_video, denoise_audio, device, model_cache):
+    audio_for_video = trim_or_pad_audio(
+        audio,
+        duration_sec=duration_sec if trim_audio_to_video else None,
+        pad_to_duration=bool(trim_audio_to_video),
+    )
+
+    mux_audio_path = save_audio_to_wav(
+        audio_for_video,
+        task["media_dir"] / "mux_audio.wav",
+    )
+    feature_audio_path = save_audio_to_wav(
+        audio_for_video,
+        task["media_dir"] / "feature_audio_16k.wav",
+        mono=True,
+        sample_rate=TARGET_AUDIO_SR,
+    )
+
+    if denoise_audio:
+        feature_audio_path = denoise_audio_file(
+            feature_audio_path,
+            task["media_dir"] / "feature_audio_16k_denoised.wav",
+            device=device,
+            cache=model_cache,
+        )
+
+    return audio_for_video, mux_audio_path, feature_audio_path
+
+
+def _ensure_highsync_model(highsync_model):
+    required = ("config", "pipeline", "audio_processor", "image_processor_cls", "device", "weight_dtype")
+    if not isinstance(highsync_model, dict) or any(key not in highsync_model for key in required):
+        raise ValueError("highsync_model must come from the HighSync Model Loader node.")
+
+
+def _append_original_frame(output_frames_rgb, frame_bgr):
+    output_frames_rgb.append(cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB))
+
+
+def _run_generation_chunk(
+    pipeline,
+    image_processor,
+    config,
+    audio_fea_final,
+    frames_video,
+    output_frames_rgb,
+    count_img,
+    generator,
+    device,
+    weight_dtype,
+    inference_steps,
+    cfg_scale,
+):
+    source_image_pixels_all, bbox_all, frame_list_all = image_processor.preprocess_frames_jump(frames_video)
+
+    img_size = (int(config.data.source_image.width), int(config.data.source_image.height))
+    sample_frames = int(config.data.n_sample_frames)
+    mask_path = str(config.mask_path)
+
+    for source_image_pixels_list, bbox_list, frame_list in zip(source_image_pixels_all, bbox_all, frame_list_all):
+        _maybe_throw_if_interrupted()
+
+        if not frame_list:
+            continue
+
+        if bbox_list[0] != (0, 0, 0, 0):
+            audio_tensor = audio_fea_final[:, count_img: count_img + len(frame_list)]
+            main_length = len(frame_list)
+            diff = sample_frames - main_length
+
+            if diff > 0:
+                source_image_pixels_list.extend([source_image_pixels_list[-1]] * diff)
+                audio_tensor = torch.cat((audio_tensor, audio_tensor[:, -1:].repeat(1, diff, 1, 1)), dim=1)
+
+            source_image_pixels = torch.cat(source_image_pixels_list, dim=0)
+            mask_main = create_mask(mask_path).unsqueeze(0).to(device=device, dtype=weight_dtype)
+            _empty_cache(device)
+
+            with torch.no_grad():
+                pipeline_output = pipeline(
+                    gt_encode=source_image_pixels,
+                    audio_tensor=audio_tensor,
+                    mask_main=mask_main,
+                    width=img_size[0],
+                    height=img_size[1],
+                    video_length=len(source_image_pixels_list),
+                    num_inference_steps=int(inference_steps),
+                    guidance_scale=float(cfg_scale),
+                    generator=generator,
+                )
+
+            _empty_cache(device)
+
+            images_out = pipeline_output.videos.squeeze(0).permute(1, 2, 3, 0).cpu().numpy()[:main_length]
+            images_out = np.clip(images_out * 255, 0, 255).astype(np.uint8)
+
+            for image_out, bbox, frame in zip(images_out, bbox_list, frame_list):
+                frame_h, frame_w, _ = frame.shape
+                if bbox == (0, 0, 0, 0):
+                    _append_original_frame(output_frames_rgb, frame)
+                else:
+                    image_out_bgr = cv2.cvtColor(image_out, cv2.COLOR_RGB2BGR)
+                    frame_in_out = frame.copy()
+                    frame_in_out[bbox[1]:bbox[3], bbox[0]:bbox[2]] = cv2.resize(
+                        image_out_bgr,
+                        (bbox[2] - bbox[0], bbox[3] - bbox[1]),
+                    )
+                    mask = create_mask_alpha(frame_w, frame_h, bbox)
+                    blended = (frame * mask + frame_in_out * (1 - mask)).astype(np.uint8)
+                    _append_original_frame(output_frames_rgb, blended)
+
+                count_img += 1
+        else:
+            for frame in frame_list:
+                _append_original_frame(output_frames_rgb, frame)
+                count_img += 1
+
+    return count_img
+
+
+def run_highsync(
+    highsync_model,
+    images,
+    audio,
+    input_fps=25,
+    max_frames=0,
+    inference_steps=None,
+    cfg_scale=None,
+    seed=42,
+    preprocess_chunk_frames=1500,
+    denoise_audio=True,
+    trim_audio_to_video=True,
+    keep_intermediate_outputs=True,
+):
+    _ensure_highsync_model(highsync_model)
+
+    config = highsync_model["config"]
+    pipeline = highsync_model["pipeline"]
+    audio_processor = highsync_model["audio_processor"]
+    image_processor = highsync_model["image_processor_cls"]((int(config.data.source_image.width), int(config.data.source_image.height)))
+    device = torch.device(highsync_model["device"])
+    weight_dtype = highsync_model["weight_dtype"]
+
+    inference_steps = int(inference_steps if inference_steps is not None else config.inference_steps)
+    cfg_scale = float(cfg_scale if cfg_scale is not None else config.cfg_scale)
+    preprocess_chunk_frames = max(12, int(preprocess_chunk_frames))
+
+    task = create_task_dir()
+
+    frames_rgb = images_to_uint8_rgb(images)
+    frames_rgb = resample_frames_to_fps(frames_rgb, input_fps=input_fps, output_fps=TARGET_FPS, max_frames=max_frames)
+    frames_bgr = [cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) for frame in frames_rgb]
+    initial_duration_sec = len(frames_bgr) / float(TARGET_FPS)
+
+    _, _, feature_audio_path = _prepare_audio_files(
+        task=task,
+        audio=audio,
+        duration_sec=initial_duration_sec,
+        trim_audio_to_video=trim_audio_to_video,
+        denoise_audio=denoise_audio,
+        device=device,
+        model_cache=highsync_model,
+    )
+
+    whisper_feature = audio_processor.audio2feat(str(feature_audio_path))
+    whisper_chunks = audio_processor.feature2chunks(feature_array=whisper_feature, fps=TARGET_FPS)
+    audio_frame_num = int(whisper_chunks.shape[0])
+    video_length = min(len(frames_bgr), audio_frame_num)
+    if video_length <= 0:
+        raise RuntimeError("HighSync could not align any video frames with the provided audio.")
+
+    frames_bgr = frames_bgr[:video_length]
+    audio_fea_final = torch.as_tensor(whisper_chunks[:video_length], dtype=pipeline.vae.dtype, device=pipeline.vae.device)
+    audio_fea_final = audio_fea_final.unsqueeze(0)
+
+    final_duration_sec = video_length / float(TARGET_FPS)
+    output_audio, mux_audio_path, _ = _prepare_audio_files(
+        task=task,
+        audio=audio,
+        duration_sec=final_duration_sec,
+        trim_audio_to_video=trim_audio_to_video,
+        denoise_audio=False,
+        device=device,
+        model_cache=highsync_model,
+    )
+
+    torch.manual_seed(int(seed))
+    generator = torch.manual_seed(int(seed))
+
+    output_frames_rgb = []
+    frames_video = []
+    count_motion = 0
+    count_img = 0
+
+    while True:
+        _maybe_throw_if_interrupted()
+
+        if count_motion >= video_length:
+            finish = True
+        else:
+            frames_video.append(frames_bgr[count_motion])
+            count_motion += 1
+            finish = False
+
+        if len(frames_video) == 0:
+            break
+
+        if (count_motion % preprocess_chunk_frames == 0) or finish:
+            print(f"[HighSync] processing frames {count_img}-{count_motion} / {video_length}")
+            count_img = _run_generation_chunk(
+                pipeline=pipeline,
+                image_processor=image_processor,
+                config=config,
+                audio_fea_final=audio_fea_final,
+                frames_video=frames_video,
+                output_frames_rgb=output_frames_rgb,
+                count_img=count_img,
+                generator=generator,
+                device=device,
+                weight_dtype=weight_dtype,
+                inference_steps=inference_steps,
+                cfg_scale=cfg_scale,
+            )
+            frames_video = []
+
+        if finish:
+            break
+
+    if len(output_frames_rgb) == 0:
+        raise RuntimeError("HighSync finished without producing output frames.")
+
+    output_frames = np.ascontiguousarray(np.stack(output_frames_rgb))
+    no_audio_path = encode_rgb_frames_to_mp4(output_frames, task["media_dir"] / "highsync_no_audio.mp4", fps=TARGET_FPS)
+    final_video_path = mux_video_audio(no_audio_path, mux_audio_path, task["task_dir"] / "final.mp4")
+
+    output_images = frames_uint8_rgb_to_image_tensor(output_frames)
+
+    if not keep_intermediate_outputs:
+        for path in (task["media_dir"], task["frames_dir"], task["logs_dir"]):
+            shutil.rmtree(path, ignore_errors=True)
+
+    return output_images, output_audio, TARGET_FPS, str(final_video_path.resolve())
diff --git a/nodes.py b/nodes.py
new file mode 100644
index 0000000..e785f5f
--- /dev/null
+++ b/nodes.py
@@ -0,0 +1,169 @@
+try:
+    from .highsync_wrapper.download_utils import download_or_check_models
+except ImportError:
+    from highsync_wrapper.download_utils import download_or_check_models
+
+
+DEFAULT_MODELS_DIR = "D:/ComfyUI/models/highsync"
+
+
+class HighSyncDownloadModels:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "download": ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("models_dir",)
+    FUNCTION = "run"
+    CATEGORY = "video/lipsync"
+
+    def run(self, download):
+        models_dir = download_or_check_models(
+            models_dir=DEFAULT_MODELS_DIR,
+            repo_id="saeed-5959/high_sync",
+            download=download,
+        )
+        return (models_dir,)
+
+
+class HighSyncModelLoader:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "models_dir": ("STRING", {"default": DEFAULT_MODELS_DIR, "multiline": False}),
+                "weight_dtype": (["fp16", "bf16", "fp32"], {"default": "fp16"}),
+            }
+        }
+
+    RETURN_TYPES = ("HIGHSYNC_MODEL",)
+    RETURN_NAMES = ("highsync_model",)
+    FUNCTION = "load"
+    CATEGORY = "video/lipsync"
+
+    def load(self, models_dir, weight_dtype):
+        try:
+            from .highsync_wrapper.model_utils import load_highsync_model
+        except ImportError:
+            from highsync_wrapper.model_utils import load_highsync_model
+
+        model = load_highsync_model(models_dir=models_dir, weight_dtype=weight_dtype)
+        return (model,)
+
+
+class HighSyncLipSync:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "highsync_model": ("HIGHSYNC_MODEL",),
+                "images": ("IMAGE",),
+                "audio": ("AUDIO",),
+                "input_fps": ("INT", {"default": 25, "min": 1, "max": 120, "step": 1}),
+                "max_frames": ("INT", {"default": 0, "min": 0, "max": 100000, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "AUDIO", "INT", "STRING")
+    RETURN_NAMES = ("images", "audio", "frame_rate", "output_video_path")
+    FUNCTION = "run"
+    CATEGORY = "video/lipsync"
+
+    def run(self, highsync_model, images, audio, input_fps, max_frames):
+        try:
+            from .highsync_wrapper.runner import run_highsync
+        except ImportError:
+            from highsync_wrapper.runner import run_highsync
+
+        return run_highsync(
+            highsync_model=highsync_model,
+            images=images,
+            audio=audio,
+            input_fps=input_fps,
+            max_frames=max_frames,
+            inference_steps=None,
+            cfg_scale=None,
+            seed=42,
+            preprocess_chunk_frames=1500,
+            denoise_audio=False,
+            trim_audio_to_video=True,
+            keep_intermediate_outputs=True,
+        )
+
+
+class HighSyncLipSyncAdvanced(HighSyncLipSync):
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "highsync_model": ("HIGHSYNC_MODEL",),
+                "images": ("IMAGE",),
+                "audio": ("AUDIO",),
+                "input_fps": ("INT", {"default": 25, "min": 1, "max": 120, "step": 1}),
+                "max_frames": ("INT", {"default": 0, "min": 0, "max": 100000, "step": 1}),
+                "inference_steps": ("INT", {"default": 20, "min": 1, "max": 100, "step": 1}),
+                "cfg_scale": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 20.0, "step": 0.1}),
+                "seed": ("INT", {"default": 42, "min": 0, "max": 0xFFFFFFFF, "step": 1}),
+                "preprocess_chunk_frames": ("INT", {"default": 1500, "min": 12, "max": 10000, "step": 12}),
+                "denoise_audio": ("BOOLEAN", {"default": False}),
+                "trim_audio_to_video": ("BOOLEAN", {"default": True}),
+                "keep_intermediate_outputs": ("BOOLEAN", {"default": True}),
+            }
+        }
+
+    FUNCTION = "run_advanced"
+    CATEGORY = "video/lipsync"
+
+    def run_advanced(
+        self,
+        highsync_model,
+        images,
+        audio,
+        input_fps,
+        max_frames,
+        inference_steps,
+        cfg_scale,
+        seed,
+        preprocess_chunk_frames,
+        denoise_audio,
+        trim_audio_to_video,
+        keep_intermediate_outputs,
+    ):
+        try:
+            from .highsync_wrapper.runner import run_highsync
+        except ImportError:
+            from highsync_wrapper.runner import run_highsync
+
+        return run_highsync(
+            highsync_model=highsync_model,
+            images=images,
+            audio=audio,
+            input_fps=input_fps,
+            max_frames=max_frames,
+            inference_steps=inference_steps,
+            cfg_scale=cfg_scale,
+            seed=seed,
+            preprocess_chunk_frames=preprocess_chunk_frames,
+            denoise_audio=denoise_audio,
+            trim_audio_to_video=trim_audio_to_video,
+            keep_intermediate_outputs=keep_intermediate_outputs,
+        )
+
+
+NODE_CLASS_MAPPINGS = {
+    "HighSyncDownloadModels": HighSyncDownloadModels,
+    "HighSyncModelLoader": HighSyncModelLoader,
+    "HighSyncLipSync": HighSyncLipSync,
+    "HighSyncLipSyncAdvanced": HighSyncLipSyncAdvanced,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "HighSyncDownloadModels": "HighSync Download Models",
+    "HighSyncModelLoader": "HighSync Model Loader",
+    "HighSyncLipSync": "HighSync LipSync",
+    "HighSyncLipSyncAdvanced": "HighSync LipSync Advanced",
+}
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..8a6f9da
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,17 @@
+accelerate>=0.28.0
+diffusers>=0.24.0
+einops>=0.8.0
+huggingface-hub>=0.25.2
+omegaconf>=2.3.0
+safetensors
+transformers>=4.39.2
+torchvision
+torchaudio
+librosa>=0.10.2
+soundfile>=0.13.1
+opencv-python>=4.9.0.80
+pillow>=10.3.0
+imageio
+insightface>=0.7.3
+mediapipe>=0.10.14
+onnxruntime-gpu>=1.18.0
diff --git a/tests/test_download_utils.py b/tests/test_download_utils.py
new file mode 100644
index 0000000..8b33b2f
--- /dev/null
+++ b/tests/test_download_utils.py
@@ -0,0 +1,36 @@
+from pathlib import Path
+
+import pytest
+
+from highsync_wrapper.download_utils import validate_required_models
+
+
+def test_validate_required_models_reports_missing_files(tmp_path):
+    with pytest.raises(FileNotFoundError) as exc_info:
+        validate_required_models(tmp_path)
+
+    message = str(exc_info.value)
+    assert "denoising_unet-500.pth" in message
+    assert "reference_unet-500.pth" in message
+    assert "audio_processor/whisper_tiny.pt" in message
+    assert "sd-vae-ft-mse/config.json" in message
+    assert "sd-image-variations-diffusers/unet/config.json" in message
+
+
+def test_validate_required_models_accepts_expected_layout(tmp_path):
+    (tmp_path / "denoising_unet-500.pth").write_bytes(b"")
+    (tmp_path / "reference_unet-500.pth").write_bytes(b"")
+
+    audio_dir = tmp_path / "audio_processor"
+    audio_dir.mkdir()
+    (audio_dir / "whisper_tiny.pt").write_bytes(b"")
+
+    vae_dir = tmp_path / "sd-vae-ft-mse"
+    vae_dir.mkdir()
+    (vae_dir / "config.json").write_text("{}", encoding="utf-8")
+
+    unet_dir = tmp_path / "sd-image-variations-diffusers" / "unet"
+    unet_dir.mkdir(parents=True)
+    (unet_dir / "config.json").write_text("{}", encoding="utf-8")
+
+    assert validate_required_models(tmp_path) == Path(tmp_path).resolve()