From 0bc0db269dd1e41a14dd3271ec4c126e7512de4c Mon Sep 17 00:00:00 2001
From: chflame163 <chflame@163.com>
Date: Tue, 28 Jan 2025 14:30:04 +0800
Subject: [PATCH] first commit

---
 .gitignore                                    |   6 +
 README.md                                     |  76 +-
 __init__.py                                   |  48 ++
 image/janus_example.jpg                       | Bin 0 -> 460540 bytes
 image/janus_image_to_text_node.jpg            | Bin 0 -> 96105 bytes
 image/janus_text_to_image_node.jpg            | Bin 0 -> 112235 bytes
 image/load_janus_model.jpg                    | Bin 0 -> 59748 bytes
 py/janus/__init__.py                          |  31 +
 py/janus/janusflow/__init__.py                |  31 +
 py/janus/janusflow/models/__init__.py         |  28 +
 py/janus/janusflow/models/clip_encoder.py     | 122 +++
 .../janusflow/models/image_processing_vlm.py  | 208 +++++
 py/janus/janusflow/models/modeling_vlm.py     | 226 ++++++
 py/janus/janusflow/models/processing_vlm.py   | 455 +++++++++++
 py/janus/janusflow/models/siglip_vit.py       | 691 +++++++++++++++++
 py/janus/janusflow/models/uvit.py             | 714 ++++++++++++++++++
 py/janus/models/__init__.py                   |  28 +
 py/janus/models/clip_encoder.py               | 122 +++
 py/janus/models/image_processing_vlm.py       | 208 +++++
 py/janus/models/modeling_vlm.py               | 272 +++++++
 py/janus/models/processing_vlm.py             | 418 ++++++++++
 py/janus/models/projector.py                  | 100 +++
 py/janus/models/siglip_vit.py                 | 681 +++++++++++++++++
 py/janus/models/vq_model.py                   | 527 +++++++++++++
 py/janus/utils/__init__.py                    |  18 +
 py/janus/utils/conversation.py                | 365 +++++++++
 py/janus/utils/io.py                          |  89 +++
 py/janus_node.py                              | 292 +++++++
 pyproject.toml                                |  15 +
 requirements.txt                              |  10 +
 30 files changed, 5779 insertions(+), 2 deletions(-)
 create mode 100644 .gitignore
 create mode 100644 __init__.py
 create mode 100644 image/janus_example.jpg
 create mode 100644 image/janus_image_to_text_node.jpg
 create mode 100644 image/janus_text_to_image_node.jpg
 create mode 100644 image/load_janus_model.jpg
 create mode 100644 py/janus/__init__.py
 create mode 100644 py/janus/janusflow/__init__.py
 create mode 100644 py/janus/janusflow/models/__init__.py
 create mode 100644 py/janus/janusflow/models/clip_encoder.py
 create mode 100644 py/janus/janusflow/models/image_processing_vlm.py
 create mode 100644 py/janus/janusflow/models/modeling_vlm.py
 create mode 100644 py/janus/janusflow/models/processing_vlm.py
 create mode 100644 py/janus/janusflow/models/siglip_vit.py
 create mode 100644 py/janus/janusflow/models/uvit.py
 create mode 100644 py/janus/models/__init__.py
 create mode 100644 py/janus/models/clip_encoder.py
 create mode 100644 py/janus/models/image_processing_vlm.py
 create mode 100644 py/janus/models/modeling_vlm.py
 create mode 100644 py/janus/models/processing_vlm.py
 create mode 100644 py/janus/models/projector.py
 create mode 100644 py/janus/models/siglip_vit.py
 create mode 100644 py/janus/models/vq_model.py
 create mode 100644 py/janus/utils/__init__.py
 create mode 100644 py/janus/utils/conversation.py
 create mode 100644 py/janus/utils/io.py
 create mode 100644 py/janus_node.py
 create mode 100644 pyproject.toml
 create mode 100644 requirements.txt

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..868a782
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,6 @@
+_test_*.*
+__pycache__
+.venv
+.idea
+*.pth
+*.ini
diff --git a/README.md b/README.md
index 323b680..506fe18 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,74 @@
-# ComfyUI_Janus_Wrapper
-Unofficial implementation of deepseek/Janus in ComfyUI.
+# ComfyUI Janus Wrapper
+
+Unofficial implementation of [deepseek-ai/Janus](https://github.com/deepseek-ai/Janus) in ComfyUI.
+
+
+![image](image/janus_example.jpg)  
+
+
+## Install
+
+* Open a cmd window in the CompyUI plugin directory (e.g. "CompyUI\custom_nodes\") and type    
+```
+git clone https://github.com/chflame163/ComfyUI_Janus_Wrapper.git
+```
+
+* Install dependency packages
+Open a cmd window in the plugin directory of ```ComfyUI\custom_nodes\ComfyUI_Janus_Wrapper``` in the resource manager,
+
+&emsp;&emsp;Enter the following command for the ComfyUI Windows Official Portable:
+
+```
+..\..\..\python_embeded\python.exe -s -m pip install -r requirements.txt
+```
+&emsp;&emsp;Enter the following command for the Aki-ComfyUI:
+
+```
+..\..\python\python.exe -s -m pip install -r requirements.txt
+```
+
+## Download Models
+The model will be automatically downloaded during the first run. If the download fails, please manually download the model.
+Download all files of the [Janus-Pro-7B](https://huggingface.co/deepseek-ai/Janus-Pro-7B/tree/main ) and copy to  ```ComfyUI\models\Janus-Pro\Janus-Pro-7B``` folder.
+Download all files of the [Janus-Pro-1B](https://huggingface.co/deepseek-ai/Janus-Pro-1B/tree/main ) and copy to ```ComfyUI\models\Janus-Pro\Janus-Pro-1B``` folder.
+
+## Nodes Description
+
+### Load Janus Model
+Load the Janus model.
+![image](image/load_janus_model.jpg)  
+
+Node Options:
+* model: Select the model. There are currently two models to choose from: Janus-Pro-7B, Janus-Pro-1B
+
+### Janus Text To Image (Generation)
+Generate image nodes from text.
+![image](image/janus_text_to_image_node.jpg)  
+
+Node Options:
+* janus_model: Janus model input.
+* prompt: The prompt text.
+* size: The image size, currently only supports 384*384.
+* temperature: The temperature parameter of the model.
+* cfg: The cfg parameter of the model.。
+* token_num: The image_token_num_per_image parameter of the model.
+* batch_size: The parallel_size parameter of the model.
+* seed: The seed value.
+* control_after_gentrate: Change the seed options after generating the image.
+
+### Janus Image To Text (Understanding)
+Generate text nodes from images.
+![image](image/janus_image_to_text_node.jpg)  
+
+节点选项：
+* janus_model: Janus model input.
+* image: The input images.
+* question: Questions raised regarding visual recognition of models.
+* temperature: The temperature parameter of the model.
+* max_new_tokens: The max_new_tokens of the model.
+* seed: The seed value.
+* control_after_gentrate: Change the seed options after generating the image.
+
+
+## statement
+All nodes follows the MIT license, Some of its functional code comes from other open-source projects. Thanks to the original author. If used for commercial purposes, please refer to the original project license to authorization agreement.
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000..8bef74e
--- /dev/null
+++ b/__init__.py
@@ -0,0 +1,48 @@
+import importlib.util
+import os
+import sys
+import json
+
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+python = sys.executable
+
+def get_ext_dir(subpath=None, mkdir=False):
+    dir = os.path.dirname(__file__)
+    if subpath is not None:
+        dir = os.path.join(dir, subpath)
+
+    dir = os.path.abspath(dir)
+
+    if mkdir and not os.path.exists(dir):
+        os.makedirs(dir)
+    return dir
+
+def serialize(obj):
+    if isinstance(obj, (str, int, float, bool, list, dict, type(None))):
+        return obj
+    return str(obj)  # 转为字符串
+
+
+py = get_ext_dir("py")
+files = os.listdir(py)
+all_nodes = {}
+for file in files:
+    if not file.endswith(".py"):
+        continue
+    name = os.path.splitext(file)[0]
+    imported_module = importlib.import_module(".py.{}".format(name), __name__)
+    try:
+        NODE_CLASS_MAPPINGS = {**NODE_CLASS_MAPPINGS, **imported_module.NODE_CLASS_MAPPINGS}
+        NODE_DISPLAY_NAME_MAPPINGS = {**NODE_DISPLAY_NAME_MAPPINGS, **imported_module.NODE_DISPLAY_NAME_MAPPINGS}
+        serialized_CLASS_MAPPINGS = {k: serialize(v) for k, v in imported_module.NODE_CLASS_MAPPINGS.items()}
+        serialized_DISPLAY_NAME_MAPPINGS = {k: serialize(v) for k, v in imported_module.NODE_DISPLAY_NAME_MAPPINGS.items()}
+        all_nodes[file]={"NODE_CLASS_MAPPINGS": serialized_CLASS_MAPPINGS, "NODE_DISPLAY_NAME_MAPPINGS": serialized_DISPLAY_NAME_MAPPINGS}
+    except:
+        pass
+
+
+WEB_DIRECTORY = "./js"
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
diff --git a/image/janus_example.jpg b/image/janus_example.jpg
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HcmV?d00001

diff --git a/py/janus/__init__.py b/py/janus/__init__.py
new file mode 100644
index 0000000..09cc08c
--- /dev/null
+++ b/py/janus/__init__.py
@@ -0,0 +1,31 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+
+# check if python version is above 3.10
+import sys
+
+if sys.version_info >= (3, 10):
+    print("Python version is above 3.10, patching the collections module.")
+    # Monkey patch collections
+    import collections
+    import collections.abc
+
+    for type_name in collections.abc.__all__:
+        setattr(collections, type_name, getattr(collections.abc, type_name))
diff --git a/py/janus/janusflow/__init__.py b/py/janus/janusflow/__init__.py
new file mode 100644
index 0000000..09cc08c
--- /dev/null
+++ b/py/janus/janusflow/__init__.py
@@ -0,0 +1,31 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+
+# check if python version is above 3.10
+import sys
+
+if sys.version_info >= (3, 10):
+    print("Python version is above 3.10, patching the collections module.")
+    # Monkey patch collections
+    import collections
+    import collections.abc
+
+    for type_name in collections.abc.__all__:
+        setattr(collections, type_name, getattr(collections.abc, type_name))
diff --git a/py/janus/janusflow/models/__init__.py b/py/janus/janusflow/models/__init__.py
new file mode 100644
index 0000000..9469193
--- /dev/null
+++ b/py/janus/janusflow/models/__init__.py
@@ -0,0 +1,28 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from .image_processing_vlm import VLMImageProcessor
+from .modeling_vlm import MultiModalityCausalLM
+from .processing_vlm import VLChatProcessor
+
+__all__ = [
+    "VLMImageProcessor",
+    "VLChatProcessor",
+    "MultiModalityCausalLM",
+]
diff --git a/py/janus/janusflow/models/clip_encoder.py b/py/janus/janusflow/models/clip_encoder.py
new file mode 100644
index 0000000..ffdaba6
--- /dev/null
+++ b/py/janus/janusflow/models/clip_encoder.py
@@ -0,0 +1,122 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from typing import Dict, List, Literal, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torchvision.transforms
+from einops import rearrange
+
+from janus.janusflow.models.siglip_vit import create_siglip_vit
+
+
+class CLIPVisionTower(nn.Module):
+    def __init__(
+        self,
+        model_name: str = "siglip_large_patch16_384",
+        image_size: Union[Tuple[int, int], int] = 336,
+        select_feature: str = "patch",
+        select_layer: int = -2,
+        select_layers: list = None,
+        ckpt_path: str = "",
+        pixel_mean: Optional[List[float]] = None,
+        pixel_std: Optional[List[float]] = None,
+        **kwargs,
+    ):
+        super().__init__()
+
+        self.model_name = model_name
+        self.select_feature = select_feature
+        self.select_layer = select_layer
+        self.select_layers = select_layers
+
+        vision_tower_params = {
+            "model_name": model_name,
+            "image_size": image_size,
+            "ckpt_path": ckpt_path,
+            "select_layer": select_layer,
+        }
+        vision_tower_params.update(kwargs)
+        self.vision_tower, self.forward_kwargs = self.build_vision_tower(
+            vision_tower_params
+        )
+
+        if pixel_mean is not None and pixel_std is not None:
+            image_norm = torchvision.transforms.Normalize(
+                mean=pixel_mean, std=pixel_std
+            )
+        else:
+            image_norm = None
+
+        self.image_norm = image_norm
+
+    def build_vision_tower(self, vision_tower_params):
+        if self.model_name.startswith("siglip"):
+            self.select_feature = "same"
+            vision_tower = create_siglip_vit(**vision_tower_params)
+            forward_kwargs = dict()
+
+        elif self.model_name.startswith("sam"):
+            vision_tower = create_sam_vit(**vision_tower_params)
+            forward_kwargs = dict()
+
+        else:  # huggingface
+            from transformers import CLIPVisionModel
+
+            vision_tower = CLIPVisionModel.from_pretrained(**vision_tower_params)
+            forward_kwargs = dict(output_hidden_states=True)
+
+        return vision_tower, forward_kwargs
+
+    def feature_select(self, image_forward_outs):
+        if isinstance(image_forward_outs, torch.Tensor):
+            # the output has been the self.select_layer"s features
+            image_features = image_forward_outs
+        else:
+            image_features = image_forward_outs.hidden_states[self.select_layer]
+
+        if self.select_feature == "patch":
+            # if the output has cls_token
+            image_features = image_features[:, 1:]
+        elif self.select_feature == "cls_patch":
+            image_features = image_features
+        elif self.select_feature == "same":
+            image_features = image_features
+
+        else:
+            raise ValueError(f"Unexpected select feature: {self.select_feature}")
+        return image_features
+
+    def forward(self, images):
+        """
+
+        Args:
+            images (torch.Tensor): [b, 3, H, W]
+
+        Returns:
+            image_features (torch.Tensor): [b, n_patch, d]
+        """
+
+        if self.image_norm is not None:
+            images = self.image_norm(images)
+
+        image_forward_outs = self.vision_tower(images, **self.forward_kwargs)
+        image_features = self.feature_select(image_forward_outs)
+        return image_features
diff --git a/py/janus/janusflow/models/image_processing_vlm.py b/py/janus/janusflow/models/image_processing_vlm.py
new file mode 100644
index 0000000..367dee1
--- /dev/null
+++ b/py/janus/janusflow/models/image_processing_vlm.py
@@ -0,0 +1,208 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from typing import List, Tuple, Union
+
+import numpy as np
+import torch
+import torchvision
+import torchvision.transforms.functional
+from PIL import Image
+from transformers import AutoImageProcessor, PretrainedConfig
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
+from transformers.image_utils import to_numpy_array
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+ImageType = Union[np.ndarray, torch.Tensor, Image.Image]
+IMAGENET_MEAN = (0.48145466, 0.4578275, 0.40821073)
+IMAGENET_STD = (0.26862954, 0.26130258, 0.27577711)
+IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
+IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
+
+
+def expand2square(pil_img, background_color):
+    width, height = pil_img.size
+    if width == height:
+        return pil_img
+    elif width > height:
+        result = Image.new(pil_img.mode, (width, width), background_color)
+        result.paste(pil_img, (0, (width - height) // 2))
+        return result
+    else:
+        result = Image.new(pil_img.mode, (height, height), background_color)
+        result.paste(pil_img, ((height - width) // 2, 0))
+        return result
+
+
+class VLMImageProcessorConfig(PretrainedConfig):
+    model_type = "deepseek_vlm"
+    image_size: int
+    min_size: int
+    image_mean: Union[Tuple[float, float, float], List[float]]
+    image_std: Union[Tuple[float, float, float], List[float]]
+    rescale_factor: float
+    do_normalize: bool
+
+    def __init__(
+        self,
+        image_size: int,
+        min_size: int = 14,
+        image_mean: Union[Tuple[float, float, float], List[float]] = (
+            0.48145466,
+            0.4578275,
+            0.40821073,
+        ),
+        image_std: Union[Tuple[float, float, float], List[float]] = (
+            0.26862954,
+            0.26130258,
+            0.27577711,
+        ),
+        rescale_factor: float = 1.0 / 255.0,
+        do_normalize: bool = True,
+        **kwargs,
+    ):
+        self.image_size = image_size
+        self.min_size = min_size
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+
+        super().__init__(**kwargs)
+
+
+class VLMImageProcessor(BaseImageProcessor):
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+        self,
+        image_size: int,
+        min_size: int = 14,
+        image_mean: Union[Tuple[float, float, float], List[float]] = (
+            0.48145466,
+            0.4578275,
+            0.40821073,
+        ),
+        image_std: Union[Tuple[float, float, float], List[float]] = (
+            0.26862954,
+            0.26130258,
+            0.27577711,
+        ),
+        rescale_factor: float = 1.0 / 255.0,
+        do_normalize: bool = True,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.image_size = image_size
+        self.rescale_factor = rescale_factor
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.min_size = min_size
+        self.do_normalize = do_normalize
+
+        if image_mean is None:
+            self.background_color = (127, 127, 127)
+        else:
+            self.background_color = tuple([int(x * 255) for x in image_mean])
+
+    def resize(self, pil_img: Image) -> np.ndarray:
+        """
+
+        Args:
+            pil_img (PIL.Image): [H, W, 3] in PIL.Image in RGB
+
+        Returns:
+            x (np.ndarray): [3, self.image_size, self.image_size]
+        """
+
+        width, height = pil_img.size
+        max_size = max(width, height)
+
+        size = [
+            max(int(height / max_size * self.image_size), self.min_size),
+            max(int(width / max_size * self.image_size), self.min_size),
+        ]
+
+        if width <= 0 or height <= 0 or size[0] <= 0 or size[1] <= 0:
+            print(f"orig size = {pil_img.size}, new size = {size}")
+            raise ValueError("Invalid size!")
+
+        pil_img = torchvision.transforms.functional.resize(
+            pil_img,
+            size,
+            interpolation=torchvision.transforms.functional.InterpolationMode.BICUBIC,
+            antialias=True,
+        )
+
+        pil_img = expand2square(pil_img, self.background_color)
+        x = to_numpy_array(pil_img)
+
+        # [H, W, 3] -> [3, H, W]
+        x = np.transpose(x, (2, 0, 1))
+
+        return x
+
+    def preprocess(self, images, return_tensors: str = "pt", **kwargs) -> BatchFeature:
+        # resize and pad to [self.image_size, self.image_size]
+        # then convert from [H, W, 3] to [3, H, W]
+        images: List[np.ndarray] = [self.resize(image) for image in images]
+
+        # resacle from [0, 255] -> [0, 1]
+        images = [
+            self.rescale(
+                image=image,
+                scale=self.rescale_factor,
+                input_data_format="channels_first",
+            )
+            for image in images
+        ]
+
+        # normalize
+        if self.do_normalize:
+            images = [
+                self.normalize(
+                    image=image,
+                    mean=self.image_mean,
+                    std=self.image_std,
+                    input_data_format="channels_first",
+                )
+                for image in images
+            ]
+
+        data = {"pixel_values": images}
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+    @property
+    def default_shape(self):
+        return [3, self.image_size, self.image_size]
+
+
+AutoImageProcessor.register(VLMImageProcessorConfig, VLMImageProcessor)
+
+
+if __name__ == "__main__":
+    image_processor = VLMImageProcessor(
+        image_size=1024,
+        image_mean=IMAGENET_INCEPTION_MEAN,
+        image_std=IMAGENET_INCEPTION_STD,
+        do_normalize=True,
+    )
diff --git a/py/janus/janusflow/models/modeling_vlm.py b/py/janus/janusflow/models/modeling_vlm.py
new file mode 100644
index 0000000..3fc119b
--- /dev/null
+++ b/py/janus/janusflow/models/modeling_vlm.py
@@ -0,0 +1,226 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from attrdict import AttrDict
+from einops import rearrange
+import torch
+from transformers.configuration_utils import PretrainedConfig
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    PreTrainedModel,
+    LlamaConfig,
+    LlamaForCausalLM,
+)
+from transformers.models.llama.modeling_llama import LlamaRMSNorm
+from janus.janusflow.models.clip_encoder import CLIPVisionTower
+from janus.janusflow.models.uvit import ShallowUViTEncoder, ShallowUViTDecoder
+import torch.nn as nn
+
+
+def model_name_to_cls(cls_name):
+
+    if "CLIPVisionTower" in cls_name:
+        cls = CLIPVisionTower
+    elif "ShallowUViTEncoder" in cls_name:
+        cls = ShallowUViTEncoder
+    elif "ShallowUViTDecoder" in cls_name:
+        cls = ShallowUViTDecoder
+    else:
+        raise ValueError(f"class_name {cls_name} is invalid.")
+
+    return cls
+
+
+class VisionUnderstandEncoderConfig(PretrainedConfig):
+    model_type = "vision_und_enc"
+    cls: str = ""
+    params: AttrDict = {}
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+        self.cls = kwargs.get("cls", "")
+        if not isinstance(self.cls, str):
+            self.cls = self.cls.__name__
+
+        self.params = AttrDict(kwargs.get("params", {}))
+
+
+class VisionGenerationEncoderConfig(PretrainedConfig):
+    model_type = "vision_gen_enc"
+    cls: str = ""
+    params: AttrDict = {}
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+        self.cls = kwargs.get("cls", "")
+        if not isinstance(self.cls, str):
+            self.cls = self.cls.__name__
+
+        self.params = AttrDict(kwargs.get("params", {}))
+
+
+class VisionGenerationDecoderConfig(PretrainedConfig):
+    model_type = "vision_gen_dec"
+    cls: str = ""
+    params: AttrDict = {}
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+        self.cls = kwargs.get("cls", "")
+        if not isinstance(self.cls, str):
+            self.cls = self.cls.__name__
+
+        self.params = AttrDict(kwargs.get("params", {}))
+
+
+class MultiModalityConfig(PretrainedConfig):
+    model_type = "multi_modality"
+    vision_und_enc_config: VisionUnderstandEncoderConfig
+    language_config: LlamaConfig
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        vision_und_enc_config = kwargs.get("vision_und_enc_config", {})
+        self.vision_und_enc_config = VisionUnderstandEncoderConfig(
+            **vision_und_enc_config
+        )
+
+        vision_gen_enc_config = kwargs.get("vision_gen_enc_config", {})
+        self.vision_gen_enc_config = VisionGenerationEncoderConfig(
+            **vision_gen_enc_config
+        )
+
+        vision_gen_dec_config = kwargs.get("vision_gen_dec_config", {})
+        self.vision_gen_dec_config = VisionGenerationDecoderConfig(
+            **vision_gen_dec_config
+        )
+
+        language_config = kwargs.get("language_config", {})
+        if isinstance(language_config, LlamaConfig):
+            self.language_config = language_config
+        else:
+            self.language_config = LlamaConfig(**language_config)
+
+
+class MultiModalityPreTrainedModel(PreTrainedModel):
+    config_class = MultiModalityConfig
+    base_model_prefix = "multi_modality"
+    _no_split_modules = []
+    _skip_keys_device_placement = "past_key_values"
+
+
+class MultiModalityCausalLM(MultiModalityPreTrainedModel):
+
+    def __init__(self, config: MultiModalityConfig):
+        super().__init__(config)
+
+        # vision understanding encoder
+        vision_und_enc_config = config.vision_und_enc_config
+        vision_und_enc_cls = model_name_to_cls(vision_und_enc_config.cls)
+        self.vision_und_enc_model = vision_und_enc_cls(**vision_und_enc_config.params)
+
+        # vision understanding aligner
+        self.vision_und_enc_aligner = nn.Linear(1024, 2048, bias=True)
+
+        # begin of understanding embedding
+        self.beg_of_und_embed = nn.Parameter(torch.zeros(1, 2048))
+
+        # vision generation encoder
+        vision_gen_enc_config = config.vision_gen_enc_config
+        vision_gen_enc_cls = model_name_to_cls(vision_gen_enc_config.cls)
+        self.vision_gen_enc_model = vision_gen_enc_cls(**vision_gen_enc_config.params)
+
+        # vision generation encoder aligner
+        self.vision_gen_enc_aligner = nn.Linear(768, 2048, bias=True)
+
+        # vision generation decoder
+        vision_gen_dec_config = config.vision_gen_dec_config
+        vision_gen_dec_cls = model_name_to_cls(vision_gen_dec_config.cls)
+        self.vision_gen_dec_model = vision_gen_dec_cls(**vision_gen_dec_config.params)
+
+        # language model
+        language_config = config.language_config
+        self.language_model = LlamaForCausalLM(language_config)
+
+        # vision generation decoder aligner
+        self.vision_gen_dec_aligner_norm = LlamaRMSNorm(
+            2048, eps=language_config.rms_norm_eps
+        )
+        self.vision_gen_dec_aligner = nn.Linear(2048, 768, bias=True)
+
+    def prepare_inputs_embeds(
+        self,
+        input_ids: torch.LongTensor,
+        pixel_values: torch.FloatTensor,
+        images_seq_mask: torch.LongTensor,
+        images_emb_mask: torch.LongTensor,
+        **kwargs,
+    ):
+        """
+
+        Args:
+            input_ids (torch.LongTensor): [b, T]
+            pixel_values (torch.FloatTensor):   [b, n_images, 3, h, w]
+            images_seq_mask (torch.BoolTensor): [b, T]
+            images_emb_mask (torch.BoolTensor): [b, n_images, n_image_tokens]
+
+            assert torch.sum(images_seq_mask) == torch.sum(images_emb_mask)
+
+        Returns:
+            input_embeds (torch.Tensor): [b, T, D]
+        """
+
+        bs, n = pixel_values.shape[0:2]
+        images = rearrange(pixel_values, "b n c h w -> (b n) c h w")
+        # [b x n, T2, D]
+        images_embeds = self.vision_und_enc_model(images)
+        images_embeds = self.vision_und_enc_aligner(images_embeds)
+        # print(images_embeds.shape, self.beg_of_und_embed.shape, images_seq_mask.shape, input_ids.shape)
+        beg_of_und_embed = self.beg_of_und_embed[0].detach().clone()
+        images_embeds = torch.cat(
+            [
+                beg_of_und_embed.view(1, 1, -1).repeat(images_embeds.shape[0], 1, 1),
+                images_embeds,
+            ],
+            dim=1,
+        )
+        # [b x n, T2, D] -> [b, n x T2, D]
+        images_embeds = rearrange(images_embeds, "(b n) t d -> b (n t) d", b=bs, n=n)
+        # [b, n, T2] -> [b, n x T2]
+        images_emb_mask = rearrange(images_emb_mask, "b n t -> b (n t)")
+
+        # [b, T, D]
+        input_ids[input_ids < 0] = 0  # ignore the image embeddings
+        inputs_embeds = self.language_model.get_input_embeddings()(input_ids)
+
+        # replace with the image embeddings
+        inputs_embeds[images_seq_mask] = images_embeds[images_emb_mask]
+
+        return inputs_embeds
+
+
+AutoConfig.register("vision_und_enc", VisionUnderstandEncoderConfig)
+AutoConfig.register("vision_gen_enc", VisionGenerationEncoderConfig)
+AutoConfig.register("vision_gen_dec", VisionGenerationDecoderConfig)
+AutoConfig.register("multi_modality", MultiModalityConfig)
+AutoModelForCausalLM.register(MultiModalityConfig, MultiModalityCausalLM)
diff --git a/py/janus/janusflow/models/processing_vlm.py b/py/janus/janusflow/models/processing_vlm.py
new file mode 100644
index 0000000..a6fe494
--- /dev/null
+++ b/py/janus/janusflow/models/processing_vlm.py
@@ -0,0 +1,455 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from dataclasses import dataclass
+from typing import Dict, List
+
+import torch
+from PIL.Image import Image
+from transformers import LlamaTokenizerFast
+from transformers.processing_utils import ProcessorMixin
+
+from janus.janusflow.models.image_processing_vlm import VLMImageProcessor
+from janus.utils.conversation import get_conv_template
+
+
+class DictOutput(object):
+    def keys(self):
+        return self.__dict__.keys()
+
+    def __getitem__(self, item):
+        return self.__dict__[item]
+
+    def __setitem__(self, key, value):
+        self.__dict__[key] = value
+
+
+@dataclass
+class VLChatProcessorOutput(DictOutput):
+    sft_format: str
+    input_ids: torch.Tensor
+    pixel_values: torch.Tensor
+    num_und_image_tokens: torch.IntTensor
+
+    def __len__(self):
+        return len(self.input_ids)
+
+
+@dataclass
+class BatchedVLChatProcessorOutput(DictOutput):
+    sft_format: List[str]
+    input_ids: torch.Tensor
+    pixel_values: torch.Tensor
+    attention_mask: torch.Tensor
+    images_seq_mask: torch.BoolTensor
+    images_emb_mask: torch.BoolTensor
+
+    def to(self, device, dtype=torch.bfloat16):
+        self.input_ids = self.input_ids.to(device)
+        self.attention_mask = self.attention_mask.to(device)
+        self.images_seq_mask = self.images_seq_mask.to(device)
+        self.images_emb_mask = self.images_emb_mask.to(device)
+        self.pixel_values = self.pixel_values.to(device=device, dtype=dtype)
+        return self
+
+
+class VLChatProcessor(ProcessorMixin):
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+
+    attributes = ["image_processor", "tokenizer"]
+
+    system_prompt = (
+        "You are a helpful language and vision assistant. "
+        "You are able to understand the visual content that the user provides, "
+        "and assist the user with a variety of tasks using natural language."
+    )
+
+    def __init__(
+        self,
+        image_processor: VLMImageProcessor,
+        tokenizer: LlamaTokenizerFast,
+        image_tag: str = "<image_placeholder>",
+        image_start_tag: str = "<begin_of_image>",
+        image_end_tag: str = "<end_of_image>",
+        image_gen_tag: str = "<｜begin▁of▁generation｜>",
+        num_image_tokens: int = 576,
+        add_special_token: bool = False,
+        sft_format: str = "deepseek",
+        mask_prompt: bool = True,
+        ignore_id: int = -100,
+        **kwargs,
+    ):
+        self.image_processor = image_processor
+        self.tokenizer = tokenizer
+
+        image_id = self.tokenizer.vocab.get(image_tag)
+        if image_id is None:
+            special_tokens = [image_tag]
+            special_tokens_dict = {"additional_special_tokens": special_tokens}
+            self.tokenizer.add_special_tokens(special_tokens_dict)
+            print(f"Add image tag = {image_tag} to the tokenizer")
+
+        image_gen_id = self.tokenizer.vocab.get(image_gen_tag)
+        if image_gen_id is None:
+            special_tokens = [image_gen_tag]
+            special_tokens_dict = {"additional_special_tokens": special_tokens}
+            self.tokenizer.add_special_tokens(special_tokens_dict)
+            print(f"Add generation tag = {image_gen_tag} to the tokenizer")
+
+        assert image_start_tag is not None and image_end_tag is not None
+        boi_id = self.tokenizer.vocab.get(image_start_tag)
+        eoi_id = self.tokenizer.vocab.get(image_end_tag)
+        if boi_id is None:
+            special_tokens = [image_start_tag]
+            special_tokens_dict = {"additional_special_tokens": special_tokens}
+            self.tokenizer.add_special_tokens(special_tokens_dict)
+            print(f"Add boi tag = {image_start_tag} to the tokenizer")
+        if eoi_id is None:
+            special_tokens = [image_end_tag]
+            special_tokens_dict = {"additional_special_tokens": special_tokens}
+            self.tokenizer.add_special_tokens(special_tokens_dict)
+            print(f"Add eoi tag = {image_end_tag} to the tokenizer")
+
+        self.image_tag = image_tag
+        self.image_gen_tag = image_gen_tag
+        self.image_start_tag = image_start_tag
+        self.image_end_tag = image_end_tag
+
+        self.num_image_tokens = num_image_tokens
+        self.add_special_token = add_special_token
+        self.sft_format = sft_format
+        self.mask_prompt = mask_prompt
+        self.ignore_id = ignore_id
+        self.tokenizer.pad_token_id = self.tokenizer.vocab.get("<｜▁pad▁｜>")
+
+        super().__init__(
+            image_processor,
+            tokenizer,
+            image_tag,
+            num_image_tokens,
+            add_special_token,
+            sft_format,
+            mask_prompt,
+            ignore_id,
+            **kwargs,
+        )
+
+    def new_chat_template(self):
+        conv = get_conv_template(self.sft_format)
+        conv.set_system_message(self.system_prompt)
+        return conv
+
+    def apply_sft_template_for_multi_turn_prompts(
+        self,
+        conversations: List[Dict[str, str]],
+        sft_format: str = "deepseek",
+        system_prompt: str = "",
+    ):
+        """
+        Applies the SFT template to conversation.
+
+        An example of conversation:
+        conversation = [
+            {
+                "role": "User",
+                "content": "<image_placeholder> is Figure 1.\n<image_placeholder> is Figure 2.\nWhich image is brighter?",
+                "images": [
+                    "./multi-images/attribute_comparison_1.png",
+                    "./multi-images/attribute_comparison_2.png"
+                ]
+            },
+            {
+                "role": "Assistant",
+                "content": ""
+            }
+        ]
+
+        Args:
+            conversations (List[Dict]): A conversation with a List of Dict[str, str] text.
+            sft_format (str, optional): The format of the SFT template to use. Defaults to "deepseek".
+            system_prompt (str, optional): The system prompt to use in the SFT template. Defaults to "".
+
+        Returns:
+            sft_prompt (str): The formatted text.
+        """
+
+        conv = get_conv_template(sft_format)
+        conv.set_system_message(system_prompt)
+        for message in conversations:
+            conv.append_message(message["role"], message["content"].strip())
+        sft_prompt = conv.get_prompt().strip()
+
+        return sft_prompt
+
+    @property
+    def image_token(self):
+        return self.image_tag
+
+    @property
+    def image_id(self):
+        image_id = self.tokenizer.vocab.get(self.image_tag)
+        return image_id
+
+    @property
+    def image_start_id(self):
+        image_start_id = self.tokenizer.vocab.get(self.image_start_tag)
+        return image_start_id
+
+    @property
+    def image_end_id(self):
+        image_end_id = self.tokenizer.vocab.get(self.image_end_tag)
+        return image_end_id
+
+    @property
+    def image_start_token(self):
+        return self.image_start_tag
+
+    @property
+    def image_end_token(self):
+        return self.image_end_tag
+
+    @property
+    def pad_id(self):
+        pad_id = self.tokenizer.pad_token_id
+        if pad_id is None:
+            pad_id = self.tokenizer.eos_token_id
+
+        return pad_id
+
+    @property
+    def image_gen_id(self):
+        image_gen_id = self.tokenizer.vocab.get(self.image_gen_tag)
+        return image_gen_id
+
+    def add_image_token(
+        self,
+        image_indices: List[int],
+        input_ids: torch.LongTensor,
+    ):
+        """
+
+        Args:
+            image_indices (List[int]): [index_0, index_1, ..., index_j]
+            input_ids (torch.LongTensor): [N]
+
+        Returns:
+            input_ids (torch.LongTensor): [N + image tokens]
+            num_image_tokens (torch.IntTensor): [n_images]
+        """
+
+        input_slices = []
+
+        start = 0
+        for index in image_indices:
+            if self.add_special_token:
+                end = index + 1
+            else:
+                end = index
+
+            # original text tokens
+            input_slices.append(input_ids[start:end])
+
+            # add boi, image tokens, eoi and set the mask as False
+            input_slices.append(self.image_start_id * torch.ones((1), dtype=torch.long))
+            input_slices.append(
+                self.image_id * torch.ones((self.num_image_tokens,), dtype=torch.long)
+            )
+            input_slices.append(self.image_end_id * torch.ones((1), dtype=torch.long))
+            start = index + 1
+
+        # the left part
+        input_slices.append(input_ids[start:])
+
+        # concat all slices
+        input_ids = torch.cat(input_slices, dim=0)
+        num_image_tokens = torch.IntTensor(
+            [self.num_image_tokens + 1] * len(image_indices)
+        )
+        # we add 1 to fit generation
+
+        return input_ids, num_image_tokens
+
+    def process_one(
+        self,
+        prompt: str = None,
+        conversations: List[Dict[str, str]] = None,
+        images: List[Image] = None,
+        **kwargs,
+    ):
+        """
+
+        Args:
+            prompt (str): the formatted prompt;
+            conversations (List[Dict]): conversations with a list of messages;
+            images (List[ImageType]): the list of images;
+            **kwargs:
+
+        Returns:
+            outputs (BaseProcessorOutput): the output of the processor,
+                - input_ids (torch.LongTensor): [N + image tokens]
+                - target_ids (torch.LongTensor): [N + image tokens]
+                - images (torch.FloatTensor): [n_images, 3, H, W]
+                - image_id (int): the id of the image token
+                - num_image_tokens (List[int]): the number of image tokens
+        """
+
+        assert (
+            prompt is None or conversations is None
+        ), "prompt and conversations cannot be used at the same time."
+
+        if prompt is None:
+            # apply sft format
+            sft_format = self.apply_sft_template_for_multi_turn_prompts(
+                conversations=conversations,
+                sft_format=self.sft_format,
+                system_prompt=self.system_prompt,
+            )
+        else:
+            sft_format = prompt
+
+        # tokenize
+        input_ids = self.tokenizer.encode(sft_format)
+        input_ids = torch.LongTensor(input_ids)
+
+        # add image tokens to the input_ids
+        image_token_mask: torch.BoolTensor = input_ids == self.image_id
+        image_indices = image_token_mask.nonzero()
+
+        input_ids, num_und_image_tokens = self.add_image_token(
+            image_indices=image_indices,
+            input_ids=input_ids,
+        )
+
+        # load images
+        images_outputs = self.image_processor(images, return_tensors="pt")
+
+        prepare = VLChatProcessorOutput(
+            sft_format=sft_format,
+            input_ids=input_ids,
+            pixel_values=images_outputs.pixel_values,
+            num_und_image_tokens=num_und_image_tokens,
+        )
+
+        return prepare
+
+    def __call__(
+        self,
+        *,
+        prompt: str = None,
+        conversations: List[Dict[str, str]] = None,
+        images: List[Image] = None,
+        force_batchify: bool = True,
+        **kwargs,
+    ):
+        """
+
+        Args:
+            prompt (str): the formatted prompt;
+            conversations (List[Dict]): conversations with a list of messages;
+            images (List[ImageType]): the list of images;
+            force_batchify (bool): force batchify the inputs;
+            **kwargs:
+
+        Returns:
+            outputs (BaseProcessorOutput): the output of the processor,
+                - input_ids (torch.LongTensor): [N + image tokens]
+                - images (torch.FloatTensor): [n_images, 3, H, W]
+                - image_id (int): the id of the image token
+                - num_image_tokens (List[int]): the number of image tokens
+        """
+
+        prepare = self.process_one(
+            prompt=prompt, conversations=conversations, images=images
+        )
+
+        if force_batchify:
+            prepare = self.batchify([prepare])
+
+        return prepare
+
+    def batchify(
+        self, prepare_list: List[VLChatProcessorOutput]
+    ) -> BatchedVLChatProcessorOutput:
+        """
+        Preprocesses the inputs for multimodal inference.
+
+        Args:
+            prepare_list (List[VLChatProcessorOutput]): A list of VLChatProcessorOutput.
+
+        Returns:
+            BatchedVLChatProcessorOutput: A dictionary of the inputs to use for multimodal inference.
+        """
+
+        batch_size = len(prepare_list)
+        sft_format = []
+        n_images = []
+        seq_lens = []
+        for prepare in prepare_list:
+            # we only fill the images for understanding tasks into the mask
+            n_images.append(len(prepare.num_und_image_tokens))
+            seq_lens.append(len(prepare))
+
+        input_token_max_len = max(seq_lens)
+        max_n_images = max(1, max(n_images))
+
+        batched_input_ids = torch.full(
+            (batch_size, input_token_max_len), self.pad_id
+        ).long()  # FIXME
+        batched_attention_mask = torch.zeros((batch_size, input_token_max_len)).long()
+        batched_pixel_values = torch.zeros(
+            (batch_size, max_n_images, *self.image_processor.default_shape)
+        ).float()
+        batched_images_seq_mask = torch.zeros((batch_size, input_token_max_len)).bool()
+        batched_images_emb_mask = torch.zeros(
+            (
+                batch_size,
+                max_n_images,
+                self.num_image_tokens + 1,
+            )  # add 1 to account for <image_beg>
+        ).bool()
+
+        for i, prepare in enumerate(prepare_list):
+            input_ids = prepare.input_ids
+            seq_len = len(prepare)
+            n_image = len(prepare.num_und_image_tokens)
+            # left-padding
+            batched_attention_mask[i, -seq_len:] = 1
+            batched_input_ids[i, -seq_len:] = torch.LongTensor(input_ids)
+            batched_images_seq_mask[i, -seq_len:] = (input_ids == self.image_id) | (
+                input_ids == self.image_start_id
+            )
+
+            if n_image > 0:
+                batched_pixel_values[i, :n_image] = prepare.pixel_values
+                for j, n_image_tokens in enumerate(prepare.num_und_image_tokens):
+                    batched_images_emb_mask[i, j, :n_image_tokens] = True
+
+            sft_format.append(prepare.sft_format)
+
+        batched_prepares = BatchedVLChatProcessorOutput(
+            input_ids=batched_input_ids,
+            attention_mask=batched_attention_mask,
+            pixel_values=batched_pixel_values,
+            images_seq_mask=batched_images_seq_mask,
+            images_emb_mask=batched_images_emb_mask,
+            sft_format=sft_format,
+        )
+
+        return batched_prepares
diff --git a/py/janus/janusflow/models/siglip_vit.py b/py/janus/janusflow/models/siglip_vit.py
new file mode 100644
index 0000000..a149db3
--- /dev/null
+++ b/py/janus/janusflow/models/siglip_vit.py
@@ -0,0 +1,691 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+# https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py
+import math
+import warnings
+from dataclasses import dataclass
+from functools import partial
+from typing import (
+    Callable,
+    Dict,
+    Final,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    Union,
+)
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from timm.layers import (
+    AttentionPoolLatent,
+    DropPath,
+    LayerType,
+    Mlp,
+    PatchDropout,
+    PatchEmbed,
+    resample_abs_pos_embed,
+)
+from timm.models._manipulate import checkpoint_seq, named_apply
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+            "The distribution of values may be incorrect.",
+            stacklevel=2,
+        )
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)  # noqa: E741
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.0))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
+    # type: (torch.Tensor, float, float, float, float) -> torch.Tensor
+    r"""The original timm.models.layers.weight_init.trunc_normal_ can not handle bfloat16 yet, here we first
+    convert the tensor to float32, apply the trunc_normal_() in float32, and then convert it back to its original dtype.
+    Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn
+    from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+
+    with torch.no_grad():
+        dtype = tensor.dtype
+        tensor_fp32 = tensor.float()
+        tensor_fp32 = _no_grad_trunc_normal_(tensor_fp32, mean, std, a, b)
+        tensor_dtype = tensor_fp32.to(dtype=dtype)
+        tensor.copy_(tensor_dtype)
+
+
+def init_weights(self):
+    if self.pos_embed is not None:
+        trunc_normal_(self.pos_embed, std=self.pos_embed.shape[1] ** -0.5)
+    trunc_normal_(self.latent, std=self.latent_dim**-0.5)
+
+
+def init_weights_vit_timm(module: nn.Module, name: str = "") -> None:
+    """ViT weight initialization, original timm impl (for reproducibility)"""
+    if isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=0.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+    elif hasattr(module, "init_weights"):
+        module.init_weights()
+
+
+class Attention(nn.Module):
+    fused_attn: Final[bool]
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        qk_norm: bool = False,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+        norm_layer: nn.Module = nn.LayerNorm,
+    ) -> None:
+        super().__init__()
+        assert dim % num_heads == 0, "dim should be divisible by num_heads"
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim**-0.5
+        # self.fused_attn = use_fused_attn()
+        self.fused_attn = True
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop) if proj_drop > 0.0 else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, N, C = x.shape
+        qkv = (
+            self.qkv(x)
+            .reshape(B, N, 3, self.num_heads, self.head_dim)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q, k, v = qkv.unbind(0)
+        q, k = self.q_norm(q), self.k_norm(k)
+
+        if self.fused_attn:
+            x = F.scaled_dot_product_attention(
+                q,
+                k,
+                v,
+                dropout_p=self.attn_drop.p if self.training else 0.0,
+            )
+        else:
+            q = q * self.scale
+            attn = q @ k.transpose(-2, -1)
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = attn @ v
+
+        x = x.transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class LayerScale(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        init_values: float = 1e-5,
+        inplace: bool = False,
+    ) -> None:
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = False,
+        qk_norm: bool = False,
+        proj_drop: float = 0.0,
+        attn_drop: float = 0.0,
+        init_values: Optional[float] = None,
+        drop_path: float = 0.0,
+        act_layer: nn.Module = nn.GELU,
+        norm_layer: nn.Module = nn.LayerNorm,
+        mlp_layer: nn.Module = Mlp,
+    ) -> None:
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm,
+            attn_drop=attn_drop,
+            proj_drop=proj_drop,
+            norm_layer=norm_layer,
+        )
+        self.ls1 = (
+            LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        )
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = mlp_layer(
+            in_features=dim,
+            hidden_features=int(dim * mlp_ratio),
+            act_layer=act_layer,
+            drop=proj_drop,
+        )
+        self.ls2 = (
+            LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        )
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x))))
+        x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
+        return x
+
+
+class VisionTransformer(nn.Module):
+    """Vision Transformer
+
+    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
+        - https://arxiv.org/abs/2010.11929
+    """
+
+    dynamic_img_size: Final[bool]
+
+    def __init__(
+        self,
+        img_size: Union[int, Tuple[int, int]] = 224,
+        patch_size: Union[int, Tuple[int, int]] = 16,
+        in_chans: int = 3,
+        num_classes: int = 1000,
+        global_pool: Literal["", "avg", "token", "map"] = "token",
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        qk_norm: bool = False,
+        init_values: Optional[float] = None,
+        class_token: bool = True,
+        no_embed_class: bool = False,
+        reg_tokens: int = 0,
+        pre_norm: bool = False,
+        fc_norm: Optional[bool] = None,
+        dynamic_img_size: bool = False,
+        dynamic_img_pad: bool = False,
+        drop_rate: float = 0.0,
+        pos_drop_rate: float = 0.0,
+        patch_drop_rate: float = 0.0,
+        proj_drop_rate: float = 0.0,
+        attn_drop_rate: float = 0.0,
+        drop_path_rate: float = 0.0,
+        weight_init: Literal["skip", "jax", "jax_nlhb", "moco", ""] = "",
+        embed_layer: Callable = PatchEmbed,
+        norm_layer: Optional[LayerType] = None,
+        act_layer: Optional[LayerType] = None,
+        block_fn: Type[nn.Module] = Block,
+        mlp_layer: Type[nn.Module] = Mlp,
+        ignore_head: bool = False,
+    ) -> None:
+        """
+        Args:
+            img_size: Input image size.
+            patch_size: Patch size.
+            in_chans: Number of image input channels.
+            num_classes: Mumber of classes for classification head.
+            global_pool: Type of global pooling for final sequence (default: 'token').
+            embed_dim: Transformer embedding dimension.
+            depth: Depth of transformer.
+            num_heads: Number of attention heads.
+            mlp_ratio: Ratio of mlp hidden dim to embedding dim.
+            qkv_bias: Enable bias for qkv projections if True.
+            init_values: Layer-scale init values (layer-scale enabled if not None).
+            class_token: Use class token.
+            no_embed_class: Don't include position embeddings for class (or reg) tokens.
+            reg_tokens: Number of register tokens.
+            fc_norm: Pre head norm after pool (instead of before), if None, enabled when global_pool == 'avg'.
+            drop_rate: Head dropout rate.
+            pos_drop_rate: Position embedding dropout rate.
+            attn_drop_rate: Attention dropout rate.
+            drop_path_rate: Stochastic depth rate.
+            weight_init: Weight initialization scheme.
+            embed_layer: Patch embedding layer.
+            norm_layer: Normalization layer.
+            act_layer: MLP activation layer.
+            block_fn: Transformer block layer.
+        """
+        super().__init__()
+        assert global_pool in ("", "avg", "token", "map")
+        assert class_token or global_pool != "token"
+        use_fc_norm = global_pool == "avg" if fc_norm is None else fc_norm
+        # norm_layer = get_norm_layer(norm_layer) or partial(nn.LayerNorm, eps=1e-6)
+        # act_layer = get_act_layer(act_layer) or nn.GELU
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        act_layer = nn.GELU
+
+        self.num_classes = num_classes
+        self.global_pool = global_pool
+        self.num_features = self.embed_dim = (
+            embed_dim  # num_features for consistency with other models
+        )
+        self.num_prefix_tokens = 1 if class_token else 0
+        self.num_prefix_tokens += reg_tokens
+        self.num_reg_tokens = reg_tokens
+        self.has_class_token = class_token
+        self.no_embed_class = (
+            no_embed_class  # don't embed prefix positions (includes reg)
+        )
+        self.dynamic_img_size = dynamic_img_size
+        self.grad_checkpointing = False
+        self.ignore_head = ignore_head
+
+        embed_args = {}
+        if dynamic_img_size:
+            # flatten deferred until after pos embed
+            embed_args.update(dict(strict_img_size=False, output_fmt="NHWC"))
+        self.patch_embed = embed_layer(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            bias=not pre_norm,  # disable bias if pre-norm is used (e.g. CLIP)
+            dynamic_img_pad=dynamic_img_pad,
+            **embed_args,
+        )
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = (
+            nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
+        )
+        self.reg_token = (
+            nn.Parameter(torch.zeros(1, reg_tokens, embed_dim)) if reg_tokens else None
+        )
+        embed_len = (
+            num_patches if no_embed_class else num_patches + self.num_prefix_tokens
+        )
+        self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * 0.02)
+        self.pos_drop = nn.Dropout(p=pos_drop_rate)
+        if patch_drop_rate > 0:
+            self.patch_drop = PatchDropout(
+                patch_drop_rate,
+                num_prefix_tokens=self.num_prefix_tokens,
+            )
+        else:
+            self.patch_drop = nn.Identity()
+        self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
+
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, depth)
+        ]  # stochastic depth decay rule
+        self.blocks = nn.Sequential(
+            *[
+                block_fn(
+                    dim=embed_dim,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    qk_norm=qk_norm,
+                    init_values=init_values,
+                    proj_drop=proj_drop_rate,
+                    attn_drop=attn_drop_rate,
+                    drop_path=dpr[i],
+                    norm_layer=norm_layer,
+                    act_layer=act_layer,
+                    mlp_layer=mlp_layer,
+                )
+                for i in range(depth)
+            ]
+        )
+        self.norm = norm_layer(embed_dim) if not use_fc_norm else nn.Identity()
+
+        # Classifier Head
+        if global_pool == "map":
+            AttentionPoolLatent.init_weights = init_weights
+            self.attn_pool = AttentionPoolLatent(
+                self.embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                norm_layer=norm_layer,
+            )
+        else:
+            self.attn_pool = None
+        self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
+        self.head_drop = nn.Dropout(drop_rate)
+        self.head = (
+            nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+        )
+
+        if weight_init != "skip":
+            self.init_weights(weight_init)
+
+    def init_weights(self, mode: Literal["jax", "jax_nlhb", "moco", ""] = "") -> None:
+        assert mode in ("jax", "jax_nlhb", "moco", "")
+        # head_bias = -math.log(self.num_classes) if "nlhb" in mode else 0.0
+        trunc_normal_(self.pos_embed, std=0.02)
+        if self.cls_token is not None:
+            nn.init.normal_(self.cls_token, std=1e-6)
+        named_apply(init_weights_vit_timm, self)
+
+    @torch.jit.ignore
+    def no_weight_decay(self) -> Set:
+        return {"pos_embed", "cls_token", "dist_token"}
+
+    @torch.jit.ignore
+    def group_matcher(self, coarse: bool = False) -> Dict:
+        return dict(
+            stem=r"^cls_token|pos_embed|patch_embed",  # stem and embed
+            blocks=[(r"^blocks\.(\d+)", None), (r"^norm", (99999,))],
+        )
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable: bool = True) -> None:
+        self.grad_checkpointing = enable
+
+    @torch.jit.ignore
+    def get_classifier(self) -> nn.Module:
+        return self.head
+
+    def reset_classifier(self, num_classes: int, global_pool=None) -> None:
+        self.num_classes = num_classes
+        if global_pool is not None:
+            assert global_pool in ("", "avg", "token", "map")
+            if global_pool == "map" and self.attn_pool is None:
+                assert (
+                    False
+                ), "Cannot currently add attention pooling in reset_classifier()."
+            elif global_pool != "map " and self.attn_pool is not None:
+                self.attn_pool = None  # remove attention pooling
+            self.global_pool = global_pool
+        self.head = (
+            nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+        )
+
+    def _pos_embed(self, x: torch.Tensor) -> torch.Tensor:
+        if self.dynamic_img_size:
+            B, H, W, C = x.shape
+            pos_embed = resample_abs_pos_embed(
+                self.pos_embed,
+                (H, W),
+                num_prefix_tokens=0 if self.no_embed_class else self.num_prefix_tokens,
+            )
+            x = x.view(B, -1, C)
+        else:
+            pos_embed = self.pos_embed
+
+        to_cat = []
+        if self.cls_token is not None:
+            to_cat.append(self.cls_token.expand(x.shape[0], -1, -1))
+        if self.reg_token is not None:
+            to_cat.append(self.reg_token.expand(x.shape[0], -1, -1))
+
+        if self.no_embed_class:
+            # deit-3, updated JAX (big vision)
+            # position embedding does not overlap with class token, add then concat
+            x = x + pos_embed
+            if to_cat:
+                x = torch.cat(to_cat + [x], dim=1)
+        else:
+            # original timm, JAX, and deit vit impl
+            # pos_embed has entry for class token, concat then add
+            if to_cat:
+                x = torch.cat(to_cat + [x], dim=1)
+            x = x + pos_embed
+
+        return self.pos_drop(x)
+
+    def _intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,
+    ) -> List[torch.Tensor]:
+        outputs, num_blocks = [], len(self.blocks)
+        take_indices = set(
+            range(num_blocks - n, num_blocks) if isinstance(n, int) else n
+        )
+
+        # forward pass
+        x = self.patch_embed(x)
+        x = self._pos_embed(x)
+        x = self.patch_drop(x)
+        x = self.norm_pre(x)
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if i in take_indices:
+                outputs.append(x)
+
+        return outputs
+
+    def get_intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,
+        reshape: bool = False,
+        return_prefix_tokens: bool = False,
+        norm: bool = False,
+    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
+        """Intermediate layer accessor (NOTE: This is a WIP experiment).
+        Inspired by DINO / DINOv2 interface
+        """
+        # take last n blocks if n is an int, if in is a sequence, select by matching indices
+        outputs = self._intermediate_layers(x, n)
+        if norm:
+            outputs = [self.norm(out) for out in outputs]
+        prefix_tokens = [out[:, 0 : self.num_prefix_tokens] for out in outputs]
+        outputs = [out[:, self.num_prefix_tokens :] for out in outputs]
+
+        if reshape:
+            grid_size = self.patch_embed.grid_size
+            outputs = [
+                out.reshape(x.shape[0], grid_size[0], grid_size[1], -1)
+                .permute(0, 3, 1, 2)
+                .contiguous()
+                for out in outputs
+            ]
+
+        if return_prefix_tokens:
+            return tuple(zip(outputs, prefix_tokens))
+        return tuple(outputs)
+
+    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patch_embed(x)
+        x = self._pos_embed(x)
+        x = self.patch_drop(x)
+        x = self.norm_pre(x)
+        if self.grad_checkpointing and not torch.jit.is_scripting():
+            x = checkpoint_seq(self.blocks, x)
+        else:
+            x = self.blocks(x)
+        x = self.norm(x)
+        return x
+
+    def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
+        if self.attn_pool is not None:
+            x = self.attn_pool(x)
+        elif self.global_pool == "avg":
+            x = x[:, self.num_prefix_tokens :].mean(dim=1)
+        elif self.global_pool:
+            x = x[:, 0]  # class token
+        x = self.fc_norm(x)
+        x = self.head_drop(x)
+        return x if pre_logits else self.head(x)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.forward_features(x)
+        if not self.ignore_head:
+            x = self.forward_head(x)
+        return x
+
+
+@dataclass
+class SigLIPVisionCfg:
+    width: int = 1152
+    layers: Union[Tuple[int, int, int, int], int] = 27
+    heads: int = 16
+    patch_size: int = 14
+    image_size: Union[Tuple[int, int], int] = 336
+    global_pool: str = "map"
+    mlp_ratio: float = 3.7362
+    class_token: bool = False
+    num_classes: int = 0
+    use_checkpoint: bool = False
+
+
+SigLIP_MODEL_CONFIG = {
+    "siglip_so400m_patch14_384": {
+        "image_size": 336,
+        "patch_size": 14,
+        "width": 1152,
+        "layers": 27,
+        "heads": 16,
+        "mlp_ratio": 3.7362,
+        "global_pool": "map",
+        "use_checkpoint": False,
+    },
+    "siglip_so400m_patch14_224": {
+        "image_size": 224,
+        "patch_size": 14,
+        "width": 1152,
+        "layers": 27,
+        "heads": 16,
+        "mlp_ratio": 3.7362,
+        "global_pool": "map",
+        "use_checkpoint": False,
+    },
+    "siglip_large_patch16_384": {
+        "image_size": 384,
+        "patch_size": 16,
+        "width": 1024,
+        "layers": 24,
+        "heads": 16,
+        "mlp_ratio": 4,
+        "global_pool": "map",
+        "use_checkpoint": False,
+    },
+    "siglip_large_patch16_256": {
+        "image_size": 256,
+        "patch_size": 16,
+        "width": 1024,
+        "layers": 24,
+        "heads": 16,
+        "mlp_ratio": 4,
+        "global_pool": "map",
+        "use_checkpoint": False,
+    },
+}
+
+
+def create_siglip_vit(
+    model_name: str = "siglip_so400m_patch14_384",
+    image_size: int = 384,
+    select_layer: int = -1,
+    ckpt_path: str = "",
+    **kwargs,
+):
+    assert (
+        model_name in SigLIP_MODEL_CONFIG.keys()
+    ), f"model name should be in {SigLIP_MODEL_CONFIG.keys()}"
+
+    vision_cfg = SigLIPVisionCfg(**SigLIP_MODEL_CONFIG[model_name])
+
+    if select_layer <= 0:
+        layers = min(vision_cfg.layers, vision_cfg.layers + select_layer + 1)
+    else:
+        layers = min(vision_cfg.layers, select_layer)
+
+    model = VisionTransformer(
+        img_size=image_size,
+        patch_size=vision_cfg.patch_size,
+        embed_dim=vision_cfg.width,
+        depth=layers,
+        num_heads=vision_cfg.heads,
+        mlp_ratio=vision_cfg.mlp_ratio,
+        class_token=vision_cfg.class_token,
+        global_pool=vision_cfg.global_pool,
+        ignore_head=kwargs.get("ignore_head", True),
+        weight_init=kwargs.get("weight_init", "skip"),
+        num_classes=0,
+    )
+
+    if ckpt_path:
+        state_dict = torch.load(ckpt_path, map_location="cpu")
+
+        incompatible_keys = model.load_state_dict(state_dict, strict=False)
+        print(
+            f"SigLIP-ViT restores from {ckpt_path},\n"
+            f"\tincompatible_keys:', {incompatible_keys}."
+        )
+
+    return model
diff --git a/py/janus/janusflow/models/uvit.py b/py/janus/janusflow/models/uvit.py
new file mode 100644
index 0000000..89aeaa5
--- /dev/null
+++ b/py/janus/janusflow/models/uvit.py
@@ -0,0 +1,714 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+# modified from: https://github.com/lucidrains/denoising-diffusion-pytorch/blob/main/denoising_diffusion_pytorch/simple_diffusion.py
+import math
+import torch
+import torch.nn as nn
+import torch.distributed as dist
+import torch.nn.functional as F
+from typing import Optional, Tuple, Union
+
+import numpy as np
+import torchvision
+import torchvision.utils
+from diffusers.models.embeddings import Timesteps, TimestepEmbedding
+from transformers.models.llama.modeling_llama import LlamaRMSNorm as RMSNorm
+
+
+class ImageHead(nn.Module):
+
+    def __init__(self, decoder_cfg, gpt_cfg, layer_id=None):
+        super().__init__()
+        self.layer_id = layer_id
+        cfg = (
+            AttrDict(
+                norm_type="layernorm",
+                is_exp_norm=False,
+                sequence_parallel=False,
+                use_userbuffer=False,
+                norm_eps=1e-5,
+                norm_bias=True,
+                gradient_accumulation_fusion=True,
+                use_fp32_head_weight=False,
+            )
+            + gpt_cfg
+        )
+        group = PG.tensor_parallel_group()
+        assert cfg.norm_type in [
+            "layernorm",
+            "rmsnorm",
+        ], f"Norm type:{cfg.norm_type} not supported"
+        if cfg.norm_type == "rmsnorm":
+            self.norm = DropoutAddRMSNorm(
+                cfg.n_embed,
+                prenorm=False,
+                eps=cfg.norm_eps,
+                is_exp_norm=cfg.is_exp_norm,
+                sequence_parallel=cfg.sequence_parallel,
+            )
+        else:
+            self.norm = DropoutAddLayerNorm(
+                cfg.n_embed,
+                prenorm=False,
+                eps=cfg.norm_eps,
+                is_exp_norm=cfg.is_exp_norm,
+                sequence_parallel=cfg.sequence_parallel,
+                bias=cfg.norm_bias,
+            )
+
+        multiple_of = 256
+        if decoder_cfg.in_channels % multiple_of != 0:
+            warnings.warn(
+                f"建议把 vocab_size 设置为 {multiple_of} 的倍数, 否则会影响矩阵乘法的性能"
+            )
+
+        dtype = default_dtype = torch.get_default_dtype()
+        if cfg.use_fp32_head_weight:
+            dtype = torch.float32
+            print(
+                "使用 fp32 head weight!!!! 与原来的 bf16 head weight 不兼容\n",
+                end="",
+                flush=True,
+            )
+        torch.set_default_dtype(dtype)
+        self.head = ColumnParallelLinear(
+            cfg.n_embed,
+            decoder_cfg.in_channels,
+            bias=True,
+            group=group,
+            sequence_parallel=cfg.sequence_parallel,
+            use_userbuffer=cfg.use_userbuffer,
+            gradient_accumulation_fusion=cfg.gradient_accumulation_fusion,
+            use_fp32_output=False,
+        )
+        torch.set_default_dtype(default_dtype)
+
+        self.use_fp32_head_weight = cfg.use_fp32_head_weight
+
+    def forward(
+        self, input_args, images_split_mask: Optional[torch.BoolTensor] = None, **kwargs
+    ):
+        residual = None
+        if isinstance(input_args, tuple):
+            x, residual = input_args
+        else:
+            x = input_args
+
+        x = self.norm(x, residual)
+
+        if self.use_fp32_head_weight:
+            assert (
+                self.head.weight.dtype == torch.float32
+            ), f"head.weight is {self.head.weight.dtype}"
+            x = x.float()
+
+        if images_split_mask is None:
+            logits = self.head(x)
+        else:
+            bs, n_images = images_split_mask.shape[:2]
+            n_embed = x.shape[-1]
+
+            images_embed = torch.masked_select(
+                x.unsqueeze(1), images_split_mask.unsqueeze(-1)
+            )
+            images_embed = images_embed.view((bs * n_images, -1, n_embed))
+            logits = self.head(images_embed)
+
+        return logits
+
+
+class GlobalResponseNorm(nn.Module):
+    # Taken from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105
+    def __init__(self, dim):
+        super().__init__()
+        self.weight = nn.Parameter(torch.zeros(1, 1, 1, dim))
+        self.bias = nn.Parameter(torch.zeros(1, 1, 1, dim))
+
+    def forward(self, x):
+        gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
+        nx = gx / (gx.mean(dim=-1, keepdim=True) + 1e-6)
+
+        return torch.addcmul(self.bias, (self.weight * nx + 1), x, value=1)
+
+
+class Downsample2D(nn.Module):
+    """A 2D downsampling layer with an optional convolution.
+
+    Parameters:
+        channels (`int`):
+            number of channels in the inputs and outputs.
+        use_conv (`bool`, default `False`):
+            option to use a convolution.
+        out_channels (`int`, optional):
+            number of output channels. Defaults to `channels`.
+        padding (`int`, default `1`):
+            padding for the convolution.
+        name (`str`, default `conv`):
+            name of the downsampling 2D layer.
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        use_conv: bool = False,
+        out_channels: Optional[int] = None,
+        padding: int = 1,
+        name: str = "conv",
+        kernel_size=3,
+        stride=2,
+        norm_type=None,
+        eps=None,
+        elementwise_affine=None,
+        bias=True,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.padding = padding
+        self.name = name
+
+        if norm_type == "ln_norm":
+            self.norm = nn.LayerNorm(channels, eps, elementwise_affine)
+        elif norm_type == "rms_norm":
+            self.norm = RMSNorm(channels, eps)
+        elif norm_type is None:
+            self.norm = None
+        else:
+            raise ValueError(f"unknown norm_type: {norm_type}")
+
+        if use_conv:
+            conv = nn.Conv2d(
+                self.channels,
+                self.out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                bias=bias,
+            )
+        else:
+            assert self.channels == self.out_channels
+            conv = nn.AvgPool2d(kernel_size=stride, stride=stride)
+
+        # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
+        if name == "conv":
+            self.Conv2d_0 = conv
+            self.conv = conv
+        elif name == "Conv2d_0":
+            self.conv = conv
+        else:
+            self.conv = conv
+
+    def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:
+
+        assert hidden_states.shape[1] == self.channels
+
+        if self.norm is not None:
+            hidden_states = self.norm(hidden_states.permute(0, 2, 3, 1)).permute(
+                0, 3, 1, 2
+            )
+
+        if self.use_conv and self.padding == 0:
+            pad = (0, 1, 0, 1)
+            hidden_states = F.pad(hidden_states, pad, mode="constant", value=0)
+
+        assert hidden_states.shape[1] == self.channels
+
+        hidden_states = self.conv(hidden_states)
+
+        return hidden_states
+
+
+class Upsample2D(nn.Module):
+    """A 2D upsampling layer with an optional convolution.
+
+    Parameters:
+        channels (`int`):
+            number of channels in the inputs and outputs.
+        use_conv (`bool`, default `False`):
+            option to use a convolution.
+        use_conv_transpose (`bool`, default `False`):
+            option to use a convolution transpose.
+        out_channels (`int`, optional):
+            number of output channels. Defaults to `channels`.
+        name (`str`, default `conv`):
+            name of the upsampling 2D layer.
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        use_conv: bool = False,
+        use_conv_transpose: bool = False,
+        out_channels: Optional[int] = None,
+        name: str = "conv",
+        kernel_size: Optional[int] = None,
+        padding=1,
+        stride=2,
+        norm_type=None,
+        eps=None,
+        elementwise_affine=None,
+        bias=True,
+        interpolate=True,
+    ):
+        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
+        self.interpolate = interpolate
+        self.stride = stride
+
+        if norm_type == "ln_norm":
+            self.norm = nn.LayerNorm(channels, eps, elementwise_affine)
+        elif norm_type == "rms_norm":
+            self.norm = RMSNorm(channels, eps)
+        elif norm_type is None:
+            self.norm = None
+        else:
+            raise ValueError(f"unknown norm_type: {norm_type}")
+
+        conv = None
+        if use_conv_transpose:
+            if kernel_size is None:
+                kernel_size = 4
+            conv = nn.ConvTranspose2d(
+                channels,
+                self.out_channels,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=padding,
+                bias=bias,
+            )
+        elif use_conv:
+            if kernel_size is None:
+                kernel_size = 3
+            conv = nn.Conv2d(
+                self.channels,
+                self.out_channels,
+                kernel_size=kernel_size,
+                padding=padding,
+                bias=bias,
+            )
+
+        # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
+        if name == "conv":
+            self.conv = conv
+        else:
+            self.Conv2d_0 = conv
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        output_size: Optional[int] = None,
+        *args,
+        **kwargs,
+    ) -> torch.Tensor:
+
+        assert hidden_states.shape[1] == self.channels
+
+        if self.norm is not None:
+            hidden_states = self.norm(hidden_states.permute(0, 2, 3, 1)).permute(
+                0, 3, 1, 2
+            )
+
+        if self.use_conv_transpose:
+            return self.conv(hidden_states)
+
+        # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
+        # TODO(Suraj): Remove this cast once the issue is fixed in PyTorch
+        # https://github.com/pytorch/pytorch/issues/86679
+        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 self.interpolate:
+            if output_size is None:
+                hidden_states = F.interpolate(
+                    hidden_states, scale_factor=self.stride, 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)
+
+        # TODO(Suraj, Patrick) - clean up after weight dicts are correctly renamed
+        if self.use_conv:
+            if self.name == "conv":
+                hidden_states = self.conv(hidden_states)
+            else:
+                hidden_states = self.Conv2d_0(hidden_states)
+
+        return hidden_states
+
+
+class ConvNextBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        norm_eps,
+        elementwise_affine,
+        use_bias,
+        hidden_dropout,
+        hidden_size,
+        res_ffn_factor: int = 4,
+    ):
+        super().__init__()
+        self.depthwise = nn.Conv2d(
+            channels,
+            channels,
+            kernel_size=7,
+            padding=3,
+            groups=channels,
+            bias=use_bias,
+        )
+        self.norm = RMSNorm(channels, norm_eps)
+        self.channelwise_linear_1 = nn.Linear(
+            channels, int(channels * res_ffn_factor), bias=use_bias
+        )
+        self.channelwise_act = nn.GELU()
+        self.channelwise_norm = GlobalResponseNorm(int(channels * res_ffn_factor))
+        self.channelwise_linear_2 = nn.Linear(
+            int(channels * res_ffn_factor), channels, bias=use_bias
+        )
+        self.channelwise_dropout = nn.Dropout(hidden_dropout)
+        self.cond_embeds_mapper = nn.Linear(hidden_size, channels * 2, use_bias)
+
+    def forward(self, x, cond_embeds):
+        x_res = x
+
+        x = self.depthwise(x)
+
+        x = x.permute(0, 2, 3, 1)
+        x = self.norm(x)
+        x = self.channelwise_linear_1(x)
+        x = self.channelwise_act(x)
+        x = self.channelwise_norm(x)
+        x = self.channelwise_linear_2(x)
+        x = self.channelwise_dropout(x)
+        x = x.permute(0, 3, 1, 2)
+
+        x = x + x_res
+
+        scale, shift = self.cond_embeds_mapper(F.silu(cond_embeds)).chunk(2, dim=1)
+        # x = x * (1 + scale[:, :, None, None]) + shift[:, :, None, None]
+        x = torch.addcmul(
+            shift[:, :, None, None], x, (1 + scale)[:, :, None, None], value=1
+        )
+
+        return x
+
+
+class Patchify(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        block_out_channels,
+        patch_size,
+        bias,
+        elementwise_affine,
+        eps,
+        kernel_size=None,
+    ):
+        super().__init__()
+        if kernel_size is None:
+            kernel_size = patch_size
+        self.patch_conv = nn.Conv2d(
+            in_channels,
+            block_out_channels,
+            kernel_size=kernel_size,
+            stride=patch_size,
+            bias=bias,
+        )
+        self.norm = RMSNorm(block_out_channels, eps)
+
+    def forward(self, x):
+        embeddings = self.patch_conv(x)
+        embeddings = embeddings.permute(0, 2, 3, 1)
+        embeddings = self.norm(embeddings)
+        embeddings = embeddings.permute(0, 3, 1, 2)
+        return embeddings
+
+
+class Unpatchify(nn.Module):
+    def __init__(
+        self, in_channels, out_channels, patch_size, bias, elementwise_affine, eps
+    ):
+        super().__init__()
+        self.norm = RMSNorm(in_channels, eps)
+        self.unpatch_conv = nn.Conv2d(
+            in_channels,
+            out_channels * patch_size * patch_size,
+            kernel_size=1,
+            bias=bias,
+        )
+        self.pixel_shuffle = nn.PixelShuffle(patch_size)
+        self.patch_size = patch_size
+
+    def forward(self, x):
+        # [b, c, h, w]
+        x = x.permute(0, 2, 3, 1)
+        x = self.norm(x)
+        x = x.permute(0, 3, 1, 2)
+        x = self.unpatch_conv(x)
+        x = self.pixel_shuffle(x)
+        return x
+
+
+class UVitBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        num_res_blocks,
+        stride,
+        hidden_size,
+        hidden_dropout,
+        elementwise_affine,
+        norm_eps,
+        use_bias,
+        downsample: bool,
+        upsample: bool,
+        res_ffn_factor: int = 4,
+        seq_len=None,
+        concat_input=False,
+        original_input_channels=None,
+        use_zero=True,
+        norm_type="RMS",
+    ):
+        super().__init__()
+
+        self.res_blocks = nn.ModuleList()
+        for i in range(num_res_blocks):
+            conv_block = ConvNextBlock(
+                channels,
+                norm_eps,
+                elementwise_affine,
+                use_bias,
+                hidden_dropout,
+                hidden_size,
+                res_ffn_factor=res_ffn_factor,
+            )
+
+            self.res_blocks.append(conv_block)
+
+        if downsample:
+            self.downsample = Downsample2D(
+                channels=channels,
+                out_channels=out_channels,
+                use_conv=True,
+                name="Conv2d_0",
+                kernel_size=3,
+                padding=1,
+                stride=stride,
+                norm_type="rms_norm",
+                eps=norm_eps,
+                elementwise_affine=elementwise_affine,
+                bias=use_bias,
+            )
+        else:
+            self.downsample = None
+
+        if upsample:
+            self.upsample = Upsample2D(
+                channels=channels,
+                out_channels=out_channels,
+                use_conv_transpose=False,
+                use_conv=True,
+                kernel_size=3,
+                padding=1,
+                stride=stride,
+                name="conv",
+                norm_type="rms_norm",
+                eps=norm_eps,
+                elementwise_affine=elementwise_affine,
+                bias=use_bias,
+                interpolate=True,
+            )
+        else:
+            self.upsample = None
+
+    def forward(self, x, emb, recompute=False):
+        for res_block in self.res_blocks:
+            x = res_block(x, emb)
+
+        if self.downsample is not None:
+            x = self.downsample(x)
+
+        if self.upsample is not None:
+            x = self.upsample(x)
+
+        return x
+
+
+class ShallowUViTEncoder(nn.Module):
+    def __init__(
+        self,
+        input_channels=3,
+        stride=4,
+        kernel_size=7,
+        padding=None,
+        block_out_channels=(768,),
+        layers_in_middle=2,
+        hidden_size=2048,
+        elementwise_affine=True,
+        use_bias=True,
+        norm_eps=1e-6,
+        dropout=0.0,
+        use_mid_block=True,
+        **kwargs,
+    ):
+        super().__init__()
+
+        self.time_proj = Timesteps(
+            block_out_channels[0], flip_sin_to_cos=True, downscale_freq_shift=0
+        )
+        self.time_embed = TimestepEmbedding(
+            block_out_channels[0], hidden_size, sample_proj_bias=use_bias
+        )
+
+        if padding is None:
+            padding = math.ceil(kernel_size - stride)
+        self.in_conv = nn.Conv2d(
+            in_channels=input_channels,
+            out_channels=block_out_channels[0],
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+        )
+        if use_mid_block:
+            self.mid_block = UVitBlock(
+                block_out_channels[-1],
+                block_out_channels[-1],
+                num_res_blocks=layers_in_middle,
+                hidden_size=hidden_size,
+                hidden_dropout=dropout,
+                elementwise_affine=elementwise_affine,
+                norm_eps=norm_eps,
+                use_bias=use_bias,
+                downsample=False,
+                upsample=False,
+                stride=1,
+                res_ffn_factor=4,
+            )
+        else:
+            self.mid_block = None
+
+    def get_num_extra_tensors(self):
+        return 2
+
+    def forward(self, x, timesteps):
+
+        bs = x.shape[0]
+        dtype = x.dtype
+
+        t_emb = self.time_proj(timesteps.flatten()).view(bs, -1).to(dtype)
+        t_emb = self.time_embed(t_emb)
+        x_emb = self.in_conv(x)
+
+        if self.mid_block is not None:
+            x_emb = self.mid_block(x_emb, t_emb)
+
+        hs = [x_emb]
+        return x_emb, t_emb, hs
+
+
+class ShallowUViTDecoder(nn.Module):
+    def __init__(
+        self,
+        in_channels=768,
+        out_channels=3,
+        block_out_channels: Tuple[int] = (768,),
+        upsamples=2,
+        layers_in_middle=2,
+        hidden_size=2048,
+        elementwise_affine=True,
+        norm_eps=1e-6,
+        use_bias=True,
+        dropout=0.0,
+        use_mid_block=True,
+        **kwargs,
+    ):
+        super().__init__()
+        if use_mid_block:
+            self.mid_block = UVitBlock(
+                in_channels + block_out_channels[-1],
+                block_out_channels[
+                    -1
+                ],  # In fact, the parameter is not used because it has no effect when both downsample and upsample are set to false.
+                num_res_blocks=layers_in_middle,
+                hidden_size=hidden_size,
+                hidden_dropout=dropout,
+                elementwise_affine=elementwise_affine,
+                norm_eps=norm_eps,
+                use_bias=use_bias,
+                downsample=False,
+                upsample=False,
+                stride=1,
+                res_ffn_factor=4,
+            )
+        else:
+            self.mid_block = None
+        self.out_convs = nn.ModuleList()
+        for rank in range(upsamples):
+            if rank == upsamples - 1:
+                curr_out_channels = out_channels
+            else:
+                curr_out_channels = block_out_channels[-1]
+            if rank == 0:
+                curr_in_channels = block_out_channels[-1] + in_channels
+            else:
+                curr_in_channels = block_out_channels[-1]
+            self.out_convs.append(
+                Unpatchify(
+                    curr_in_channels,
+                    curr_out_channels,
+                    patch_size=2,
+                    bias=use_bias,
+                    elementwise_affine=elementwise_affine,
+                    eps=norm_eps,
+                )
+            )
+        self.input_norm = RMSNorm(in_channels, norm_eps)
+
+    def forward(self, x, hs, t_emb):
+
+        x = x.permute(0, 2, 3, 1)
+        x = self.input_norm(x)
+        x = x.permute(0, 3, 1, 2)
+
+        x = torch.cat([x, hs.pop()], dim=1)
+        if self.mid_block is not None:
+            x = self.mid_block(x, t_emb)
+        for out_conv in self.out_convs:
+            x = out_conv(x)
+        assert len(hs) == 0
+        return x
diff --git a/py/janus/models/__init__.py b/py/janus/models/__init__.py
new file mode 100644
index 0000000..9469193
--- /dev/null
+++ b/py/janus/models/__init__.py
@@ -0,0 +1,28 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from .image_processing_vlm import VLMImageProcessor
+from .modeling_vlm import MultiModalityCausalLM
+from .processing_vlm import VLChatProcessor
+
+__all__ = [
+    "VLMImageProcessor",
+    "VLChatProcessor",
+    "MultiModalityCausalLM",
+]
diff --git a/py/janus/models/clip_encoder.py b/py/janus/models/clip_encoder.py
new file mode 100644
index 0000000..7db8422
--- /dev/null
+++ b/py/janus/models/clip_encoder.py
@@ -0,0 +1,122 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from typing import Dict, List, Literal, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torchvision.transforms
+from einops import rearrange
+
+from .siglip_vit import create_siglip_vit
+
+
+class CLIPVisionTower(nn.Module):
+    def __init__(
+        self,
+        model_name: str = "siglip_large_patch16_384",
+        image_size: Union[Tuple[int, int], int] = 336,
+        select_feature: str = "patch",
+        select_layer: int = -2,
+        select_layers: list = None,
+        ckpt_path: str = "",
+        pixel_mean: Optional[List[float]] = None,
+        pixel_std: Optional[List[float]] = None,
+        **kwargs,
+    ):
+        super().__init__()
+
+        self.model_name = model_name
+        self.select_feature = select_feature
+        self.select_layer = select_layer
+        self.select_layers = select_layers
+
+        vision_tower_params = {
+            "model_name": model_name,
+            "image_size": image_size,
+            "ckpt_path": ckpt_path,
+            "select_layer": select_layer,
+        }
+        vision_tower_params.update(kwargs)
+        self.vision_tower, self.forward_kwargs = self.build_vision_tower(
+            vision_tower_params
+        )
+
+        if pixel_mean is not None and pixel_std is not None:
+            image_norm = torchvision.transforms.Normalize(
+                mean=pixel_mean, std=pixel_std
+            )
+        else:
+            image_norm = None
+
+        self.image_norm = image_norm
+
+    def build_vision_tower(self, vision_tower_params):
+        if self.model_name.startswith("siglip"):
+            self.select_feature = "same"
+            vision_tower = create_siglip_vit(**vision_tower_params)
+            forward_kwargs = dict()
+
+        elif self.model_name.startswith("sam"):
+            vision_tower = create_sam_vit(**vision_tower_params)
+            forward_kwargs = dict()
+
+        else:  # huggingface
+            from transformers import CLIPVisionModel
+
+            vision_tower = CLIPVisionModel.from_pretrained(**vision_tower_params)
+            forward_kwargs = dict(output_hidden_states=True)
+
+        return vision_tower, forward_kwargs
+
+    def feature_select(self, image_forward_outs):
+        if isinstance(image_forward_outs, torch.Tensor):
+            # the output has been the self.select_layer"s features
+            image_features = image_forward_outs
+        else:
+            image_features = image_forward_outs.hidden_states[self.select_layer]
+
+        if self.select_feature == "patch":
+            # if the output has cls_token
+            image_features = image_features[:, 1:]
+        elif self.select_feature == "cls_patch":
+            image_features = image_features
+        elif self.select_feature == "same":
+            image_features = image_features
+
+        else:
+            raise ValueError(f"Unexpected select feature: {self.select_feature}")
+        return image_features
+
+    def forward(self, images):
+        """
+
+        Args:
+            images (torch.Tensor): [b, 3, H, W]
+
+        Returns:
+            image_features (torch.Tensor): [b, n_patch, d]
+        """
+
+        if self.image_norm is not None:
+            images = self.image_norm(images)
+
+        image_forward_outs = self.vision_tower(images, **self.forward_kwargs)
+        image_features = self.feature_select(image_forward_outs)
+        return image_features
diff --git a/py/janus/models/image_processing_vlm.py b/py/janus/models/image_processing_vlm.py
new file mode 100644
index 0000000..367dee1
--- /dev/null
+++ b/py/janus/models/image_processing_vlm.py
@@ -0,0 +1,208 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from typing import List, Tuple, Union
+
+import numpy as np
+import torch
+import torchvision
+import torchvision.transforms.functional
+from PIL import Image
+from transformers import AutoImageProcessor, PretrainedConfig
+from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
+from transformers.image_utils import to_numpy_array
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+ImageType = Union[np.ndarray, torch.Tensor, Image.Image]
+IMAGENET_MEAN = (0.48145466, 0.4578275, 0.40821073)
+IMAGENET_STD = (0.26862954, 0.26130258, 0.27577711)
+IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5)
+IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5)
+
+
+def expand2square(pil_img, background_color):
+    width, height = pil_img.size
+    if width == height:
+        return pil_img
+    elif width > height:
+        result = Image.new(pil_img.mode, (width, width), background_color)
+        result.paste(pil_img, (0, (width - height) // 2))
+        return result
+    else:
+        result = Image.new(pil_img.mode, (height, height), background_color)
+        result.paste(pil_img, ((height - width) // 2, 0))
+        return result
+
+
+class VLMImageProcessorConfig(PretrainedConfig):
+    model_type = "deepseek_vlm"
+    image_size: int
+    min_size: int
+    image_mean: Union[Tuple[float, float, float], List[float]]
+    image_std: Union[Tuple[float, float, float], List[float]]
+    rescale_factor: float
+    do_normalize: bool
+
+    def __init__(
+        self,
+        image_size: int,
+        min_size: int = 14,
+        image_mean: Union[Tuple[float, float, float], List[float]] = (
+            0.48145466,
+            0.4578275,
+            0.40821073,
+        ),
+        image_std: Union[Tuple[float, float, float], List[float]] = (
+            0.26862954,
+            0.26130258,
+            0.27577711,
+        ),
+        rescale_factor: float = 1.0 / 255.0,
+        do_normalize: bool = True,
+        **kwargs,
+    ):
+        self.image_size = image_size
+        self.min_size = min_size
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+
+        super().__init__(**kwargs)
+
+
+class VLMImageProcessor(BaseImageProcessor):
+    model_input_names = ["pixel_values"]
+
+    def __init__(
+        self,
+        image_size: int,
+        min_size: int = 14,
+        image_mean: Union[Tuple[float, float, float], List[float]] = (
+            0.48145466,
+            0.4578275,
+            0.40821073,
+        ),
+        image_std: Union[Tuple[float, float, float], List[float]] = (
+            0.26862954,
+            0.26130258,
+            0.27577711,
+        ),
+        rescale_factor: float = 1.0 / 255.0,
+        do_normalize: bool = True,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.image_size = image_size
+        self.rescale_factor = rescale_factor
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.min_size = min_size
+        self.do_normalize = do_normalize
+
+        if image_mean is None:
+            self.background_color = (127, 127, 127)
+        else:
+            self.background_color = tuple([int(x * 255) for x in image_mean])
+
+    def resize(self, pil_img: Image) -> np.ndarray:
+        """
+
+        Args:
+            pil_img (PIL.Image): [H, W, 3] in PIL.Image in RGB
+
+        Returns:
+            x (np.ndarray): [3, self.image_size, self.image_size]
+        """
+
+        width, height = pil_img.size
+        max_size = max(width, height)
+
+        size = [
+            max(int(height / max_size * self.image_size), self.min_size),
+            max(int(width / max_size * self.image_size), self.min_size),
+        ]
+
+        if width <= 0 or height <= 0 or size[0] <= 0 or size[1] <= 0:
+            print(f"orig size = {pil_img.size}, new size = {size}")
+            raise ValueError("Invalid size!")
+
+        pil_img = torchvision.transforms.functional.resize(
+            pil_img,
+            size,
+            interpolation=torchvision.transforms.functional.InterpolationMode.BICUBIC,
+            antialias=True,
+        )
+
+        pil_img = expand2square(pil_img, self.background_color)
+        x = to_numpy_array(pil_img)
+
+        # [H, W, 3] -> [3, H, W]
+        x = np.transpose(x, (2, 0, 1))
+
+        return x
+
+    def preprocess(self, images, return_tensors: str = "pt", **kwargs) -> BatchFeature:
+        # resize and pad to [self.image_size, self.image_size]
+        # then convert from [H, W, 3] to [3, H, W]
+        images: List[np.ndarray] = [self.resize(image) for image in images]
+
+        # resacle from [0, 255] -> [0, 1]
+        images = [
+            self.rescale(
+                image=image,
+                scale=self.rescale_factor,
+                input_data_format="channels_first",
+            )
+            for image in images
+        ]
+
+        # normalize
+        if self.do_normalize:
+            images = [
+                self.normalize(
+                    image=image,
+                    mean=self.image_mean,
+                    std=self.image_std,
+                    input_data_format="channels_first",
+                )
+                for image in images
+            ]
+
+        data = {"pixel_values": images}
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+    @property
+    def default_shape(self):
+        return [3, self.image_size, self.image_size]
+
+
+AutoImageProcessor.register(VLMImageProcessorConfig, VLMImageProcessor)
+
+
+if __name__ == "__main__":
+    image_processor = VLMImageProcessor(
+        image_size=1024,
+        image_mean=IMAGENET_INCEPTION_MEAN,
+        image_std=IMAGENET_INCEPTION_STD,
+        do_normalize=True,
+    )
diff --git a/py/janus/models/modeling_vlm.py b/py/janus/models/modeling_vlm.py
new file mode 100644
index 0000000..ecc7712
--- /dev/null
+++ b/py/janus/models/modeling_vlm.py
@@ -0,0 +1,272 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+import torch
+from attrdict import AttrDict
+from einops import rearrange
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    LlamaConfig,
+    LlamaForCausalLM,
+    PreTrainedModel,
+)
+from transformers.configuration_utils import PretrainedConfig
+
+from .clip_encoder import CLIPVisionTower
+from .projector import MlpProjector
+
+
+class vision_head(torch.nn.Module):
+    def __init__(self, params):
+        super().__init__()
+        self.output_mlp_projector = torch.nn.Linear(
+            params.n_embed, params.image_token_embed
+        )
+        self.vision_activation = torch.nn.GELU()
+        self.vision_head = torch.nn.Linear(
+            params.image_token_embed, params.image_token_size
+        )
+
+    def forward(self, x):
+        x = self.output_mlp_projector(x)
+        x = self.vision_activation(x)
+        x = self.vision_head(x)
+        return x
+
+
+def model_name_to_cls(cls_name):
+    if "MlpProjector" in cls_name:
+        cls = MlpProjector
+
+    elif "CLIPVisionTower" in cls_name:
+        cls = CLIPVisionTower
+
+    elif "VQ" in cls_name:
+        from .vq_model import VQ_models
+
+        cls = VQ_models[cls_name]
+    elif "vision_head" in cls_name:
+        cls = vision_head
+    else:
+        raise ValueError(f"class_name {cls_name} is invalid.")
+
+    return cls
+
+
+class VisionConfig(PretrainedConfig):
+    model_type = "vision"
+    cls: str = ""
+    params: AttrDict = {}
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+        self.cls = kwargs.get("cls", "")
+        if not isinstance(self.cls, str):
+            self.cls = self.cls.__name__
+
+        self.params = AttrDict(kwargs.get("params", {}))
+
+
+class AlignerConfig(PretrainedConfig):
+    model_type = "aligner"
+    cls: str = ""
+    params: AttrDict = {}
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+        self.cls = kwargs.get("cls", "")
+        if not isinstance(self.cls, str):
+            self.cls = self.cls.__name__
+
+        self.params = AttrDict(kwargs.get("params", {}))
+
+
+class GenVisionConfig(PretrainedConfig):
+    model_type = "gen_vision"
+    cls: str = ""
+    params: AttrDict = {}
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+        self.cls = kwargs.get("cls", "")
+        if not isinstance(self.cls, str):
+            self.cls = self.cls.__name__
+
+        self.params = AttrDict(kwargs.get("params", {}))
+
+
+class GenAlignerConfig(PretrainedConfig):
+    model_type = "gen_aligner"
+    cls: str = ""
+    params: AttrDict = {}
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+        self.cls = kwargs.get("cls", "")
+        if not isinstance(self.cls, str):
+            self.cls = self.cls.__name__
+
+        self.params = AttrDict(kwargs.get("params", {}))
+
+
+class GenHeadConfig(PretrainedConfig):
+    model_type = "gen_head"
+    cls: str = ""
+    params: AttrDict = {}
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+        self.cls = kwargs.get("cls", "")
+        if not isinstance(self.cls, str):
+            self.cls = self.cls.__name__
+
+        self.params = AttrDict(kwargs.get("params", {}))
+
+
+class MultiModalityConfig(PretrainedConfig):
+    model_type = "multi_modality"
+    vision_config: VisionConfig
+    aligner_config: AlignerConfig
+
+    gen_vision_config: GenVisionConfig
+    gen_aligner_config: GenAlignerConfig
+    gen_head_config: GenHeadConfig
+
+    language_config: LlamaConfig
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        vision_config = kwargs.get("vision_config", {})
+        self.vision_config = VisionConfig(**vision_config)
+
+        aligner_config = kwargs.get("aligner_config", {})
+        self.aligner_config = AlignerConfig(**aligner_config)
+
+        gen_vision_config = kwargs.get("gen_vision_config", {})
+        self.gen_vision_config = GenVisionConfig(**gen_vision_config)
+
+        gen_aligner_config = kwargs.get("gen_aligner_config", {})
+        self.gen_aligner_config = GenAlignerConfig(**gen_aligner_config)
+
+        gen_head_config = kwargs.get("gen_head_config", {})
+        self.gen_head_config = GenHeadConfig(**gen_head_config)
+
+        language_config = kwargs.get("language_config", {})
+        if isinstance(language_config, LlamaConfig):
+            self.language_config = language_config
+        else:
+            self.language_config = LlamaConfig(**language_config)
+
+
+class MultiModalityPreTrainedModel(PreTrainedModel):
+    config_class = MultiModalityConfig
+    base_model_prefix = "multi_modality"
+    _no_split_modules = []
+    _skip_keys_device_placement = "past_key_values"
+
+
+class MultiModalityCausalLM(MultiModalityPreTrainedModel):
+    def __init__(self, config: MultiModalityConfig):
+        super().__init__(config)
+
+        vision_config = config.vision_config
+        vision_cls = model_name_to_cls(vision_config.cls)
+        self.vision_model = vision_cls(**vision_config.params)
+
+        aligner_config = config.aligner_config
+        aligner_cls = model_name_to_cls(aligner_config.cls)
+        self.aligner = aligner_cls(aligner_config.params)
+
+        gen_vision_config = config.gen_vision_config
+        gen_vision_cls = model_name_to_cls(gen_vision_config.cls)
+        self.gen_vision_model = gen_vision_cls()
+
+        gen_aligner_config = config.gen_aligner_config
+        gen_aligner_cls = model_name_to_cls(gen_aligner_config.cls)
+        self.gen_aligner = gen_aligner_cls(gen_aligner_config.params)
+
+        gen_head_config = config.gen_head_config
+        gen_head_cls = model_name_to_cls(gen_head_config.cls)
+        self.gen_head = gen_head_cls(gen_head_config.params)
+
+        self.gen_embed = torch.nn.Embedding(
+            gen_vision_config.params.image_token_size, gen_vision_config.params.n_embed
+        )
+
+        language_config = config.language_config
+        self.language_model = LlamaForCausalLM(language_config)
+
+    def prepare_inputs_embeds(
+        self,
+        input_ids: torch.LongTensor,
+        pixel_values: torch.FloatTensor,
+        images_seq_mask: torch.LongTensor,
+        images_emb_mask: torch.LongTensor,
+        **kwargs,
+    ):
+        """
+
+        Args:
+            input_ids (torch.LongTensor): [b, T]
+            pixel_values (torch.FloatTensor):   [b, n_images, 3, h, w]
+            images_seq_mask (torch.BoolTensor): [b, T]
+            images_emb_mask (torch.BoolTensor): [b, n_images, n_image_tokens]
+
+            assert torch.sum(images_seq_mask) == torch.sum(images_emb_mask)
+
+        Returns:
+            input_embeds (torch.Tensor): [b, T, D]
+        """
+
+        bs, n = pixel_values.shape[0:2]
+        images = rearrange(pixel_values, "b n c h w -> (b n) c h w")
+        # [b x n, T2, D]
+        images_embeds = self.aligner(self.vision_model(images))
+
+        # [b x n, T2, D] -> [b, n x T2, D]
+        images_embeds = rearrange(images_embeds, "(b n) t d -> b (n t) d", b=bs, n=n)
+        # [b, n, T2] -> [b, n x T2]
+        images_emb_mask = rearrange(images_emb_mask, "b n t -> b (n t)")
+
+        # [b, T, D]
+        input_ids[input_ids < 0] = 0  # ignore the image embeddings
+        inputs_embeds = self.language_model.get_input_embeddings()(input_ids)
+
+        # replace with the image embeddings
+        inputs_embeds[images_seq_mask] = images_embeds[images_emb_mask]
+
+        return inputs_embeds
+
+    def prepare_gen_img_embeds(self, image_ids: torch.LongTensor):
+        return self.gen_aligner(self.gen_embed(image_ids))
+
+
+AutoConfig.register("vision", VisionConfig)
+AutoConfig.register("aligner", AlignerConfig)
+AutoConfig.register("gen_vision", GenVisionConfig)
+AutoConfig.register("gen_aligner", GenAlignerConfig)
+AutoConfig.register("gen_head", GenHeadConfig)
+AutoConfig.register("multi_modality", MultiModalityConfig)
+AutoModelForCausalLM.register(MultiModalityConfig, MultiModalityCausalLM)
diff --git a/py/janus/models/processing_vlm.py b/py/janus/models/processing_vlm.py
new file mode 100644
index 0000000..469f3eb
--- /dev/null
+++ b/py/janus/models/processing_vlm.py
@@ -0,0 +1,418 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from dataclasses import dataclass
+from typing import Dict, List
+
+import torch
+from PIL.Image import Image
+from transformers import LlamaTokenizerFast
+from transformers.processing_utils import ProcessorMixin
+
+from .image_processing_vlm import VLMImageProcessor
+from ..utils.conversation import get_conv_template
+
+
+class DictOutput(object):
+    def keys(self):
+        return self.__dict__.keys()
+
+    def __getitem__(self, item):
+        return self.__dict__[item]
+
+    def __setitem__(self, key, value):
+        self.__dict__[key] = value
+
+
+@dataclass
+class VLChatProcessorOutput(DictOutput):
+    sft_format: str
+    input_ids: torch.Tensor
+    pixel_values: torch.Tensor
+    num_image_tokens: torch.IntTensor
+
+    def __len__(self):
+        return len(self.input_ids)
+
+
+@dataclass
+class BatchedVLChatProcessorOutput(DictOutput):
+    sft_format: List[str]
+    input_ids: torch.Tensor
+    pixel_values: torch.Tensor
+    attention_mask: torch.Tensor
+    images_seq_mask: torch.BoolTensor
+    images_emb_mask: torch.BoolTensor
+
+    def to(self, device, dtype=torch.bfloat16):
+        self.input_ids = self.input_ids.to(device)
+        self.attention_mask = self.attention_mask.to(device)
+        self.images_seq_mask = self.images_seq_mask.to(device)
+        self.images_emb_mask = self.images_emb_mask.to(device)
+        self.pixel_values = self.pixel_values.to(device=device, dtype=dtype)
+        return self
+
+
+class VLChatProcessor(ProcessorMixin):
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
+
+    attributes = ["image_processor", "tokenizer"]
+
+    system_prompt = (
+        "You are a helpful language and vision assistant. "
+        "You are able to understand the visual content that the user provides, "
+        "and assist the user with a variety of tasks using natural language."
+    )
+
+    def __init__(
+        self,
+        image_processor: VLMImageProcessor,
+        tokenizer: LlamaTokenizerFast,
+        image_tag: str = "<image_placeholder>",
+        image_start_tag: str = "<begin_of_image>",
+        image_end_tag: str = "<end_of_image>",
+        pad_tag: str = "<｜▁pad▁｜>",
+        num_image_tokens: int = 576,
+        add_special_token: bool = False,
+        sft_format: str = "deepseek",
+        mask_prompt: bool = True,
+        ignore_id: int = -100,
+        **kwargs,
+    ):
+        self.image_processor = image_processor
+        self.tokenizer = tokenizer
+
+        image_id = self.tokenizer.vocab.get(image_tag)
+        if image_id is None:
+            special_tokens = [image_tag]
+            special_tokens_dict = {"additional_special_tokens": special_tokens}
+            self.tokenizer.add_special_tokens(special_tokens_dict)
+            print(f"Add image tag = {image_tag} to the tokenizer")
+
+        self.image_tag = image_tag
+        self.image_start_tag = image_start_tag
+        self.image_end_tag = image_end_tag
+        self.pad_tag = pad_tag
+
+        self.num_image_tokens = num_image_tokens
+        self.add_special_token = add_special_token
+        self.sft_format = sft_format
+        self.mask_prompt = mask_prompt
+        self.ignore_id = ignore_id
+
+        super().__init__(
+            image_processor,
+            tokenizer,
+            image_tag,
+            num_image_tokens,
+            add_special_token,
+            sft_format,
+            mask_prompt,
+            ignore_id,
+            **kwargs,
+        )
+
+    def new_chat_template(self):
+        conv = get_conv_template(self.sft_format)
+        conv.set_system_message(self.system_prompt)
+        return conv
+
+    def apply_sft_template_for_multi_turn_prompts(
+        self,
+        conversations: List[Dict[str, str]],
+        sft_format: str = "deepseek",
+        system_prompt: str = "",
+    ):
+        """
+        Applies the SFT template to conversation.
+
+        An example of conversation:
+        conversation = [
+            {
+                "role": "User",
+                "content": "<image_placeholder> is Figure 1.\n<image_placeholder> is Figure 2.\nWhich image is brighter?",
+                "images": [
+                    "./multi-images/attribute_comparison_1.png",
+                    "./multi-images/attribute_comparison_2.png"
+                ]
+            },
+            {
+                "role": "Assistant",
+                "content": ""
+            }
+        ]
+
+        Args:
+            conversations (List[Dict]): A conversation with a List of Dict[str, str] text.
+            sft_format (str, optional): The format of the SFT template to use. Defaults to "deepseek".
+            system_prompt (str, optional): The system prompt to use in the SFT template. Defaults to "".
+
+        Returns:
+            sft_prompt (str): The formatted text.
+        """
+
+        conv = get_conv_template(sft_format)
+        conv.set_system_message(system_prompt)
+        for message in conversations:
+            conv.append_message(message["role"], message["content"].strip())
+        sft_prompt = conv.get_prompt().strip()
+
+        return sft_prompt
+
+    @property
+    def image_token(self):
+        return self.image_tag
+
+    @property
+    def image_id(self):
+        image_id = self.tokenizer.vocab.get(self.image_tag)
+        return image_id
+
+    @property
+    def image_start_id(self):
+        image_start_id = self.tokenizer.vocab.get(self.image_start_tag)
+        return image_start_id
+
+    @property
+    def image_end_id(self):
+        image_end_id = self.tokenizer.vocab.get(self.image_end_tag)
+        return image_end_id
+
+    @property
+    def image_start_token(self):
+        return self.image_start_tag
+
+    @property
+    def image_end_token(self):
+        return self.image_end_tag
+
+    @property
+    def pad_id(self):
+        pad_id = self.tokenizer.vocab.get(self.pad_tag)
+        # pad_id = self.tokenizer.pad_token_id
+        # if pad_id is None:
+        #     pad_id = self.tokenizer.eos_token_id
+
+        return pad_id
+
+    def add_image_token(
+        self,
+        image_indices: List[int],
+        input_ids: torch.LongTensor,
+    ):
+        """
+
+        Args:
+            image_indices (List[int]): [index_0, index_1, ..., index_j]
+            input_ids (torch.LongTensor): [N]
+
+        Returns:
+            input_ids (torch.LongTensor): [N + image tokens]
+            num_image_tokens (torch.IntTensor): [n_images]
+        """
+
+        input_slices = []
+
+        start = 0
+        for index in image_indices:
+            if self.add_special_token:
+                end = index + 1
+            else:
+                end = index
+
+            # original text tokens
+            input_slices.append(input_ids[start:end])
+
+            # add boi, image tokens, eoi and set the mask as False
+            input_slices.append(self.image_start_id * torch.ones((1), dtype=torch.long))
+            input_slices.append(
+                self.image_id * torch.ones((self.num_image_tokens,), dtype=torch.long)
+            )
+            input_slices.append(self.image_end_id * torch.ones((1), dtype=torch.long))
+            start = index + 1
+
+        # the left part
+        input_slices.append(input_ids[start:])
+
+        # concat all slices
+        input_ids = torch.cat(input_slices, dim=0)
+        num_image_tokens = torch.IntTensor([self.num_image_tokens] * len(image_indices))
+
+        return input_ids, num_image_tokens
+
+    def process_one(
+        self,
+        prompt: str = None,
+        conversations: List[Dict[str, str]] = None,
+        images: List[Image] = None,
+        **kwargs,
+    ):
+        """
+
+        Args:
+            prompt (str): the formatted prompt;
+            conversations (List[Dict]): conversations with a list of messages;
+            images (List[ImageType]): the list of images;
+            **kwargs:
+
+        Returns:
+            outputs (BaseProcessorOutput): the output of the processor,
+                - input_ids (torch.LongTensor): [N + image tokens]
+                - target_ids (torch.LongTensor): [N + image tokens]
+                - images (torch.FloatTensor): [n_images, 3, H, W]
+                - image_id (int): the id of the image token
+                - num_image_tokens (List[int]): the number of image tokens
+        """
+
+        assert (
+            prompt is None or conversations is None
+        ), "prompt and conversations cannot be used at the same time."
+
+        if prompt is None:
+            # apply sft format
+            sft_format = self.apply_sft_template_for_multi_turn_prompts(
+                conversations=conversations,
+                sft_format=self.sft_format,
+                system_prompt=self.system_prompt,
+            )
+        else:
+            sft_format = prompt
+
+        # tokenize
+        input_ids = self.tokenizer.encode(sft_format)
+        input_ids = torch.LongTensor(input_ids)
+
+        # add image tokens to the input_ids
+        image_token_mask: torch.BoolTensor = input_ids == self.image_id
+        image_indices = image_token_mask.nonzero()
+        input_ids, num_image_tokens = self.add_image_token(
+            image_indices=image_indices,
+            input_ids=input_ids,
+        )
+
+        # load images
+        images_outputs = self.image_processor(images, return_tensors="pt")
+
+        prepare = VLChatProcessorOutput(
+            sft_format=sft_format,
+            input_ids=input_ids,
+            pixel_values=images_outputs.pixel_values,
+            num_image_tokens=num_image_tokens,
+        )
+
+        return prepare
+
+    def __call__(
+        self,
+        *,
+        prompt: str = None,
+        conversations: List[Dict[str, str]] = None,
+        images: List[Image] = None,
+        force_batchify: bool = True,
+        **kwargs,
+    ):
+        """
+
+        Args:
+            prompt (str): the formatted prompt;
+            conversations (List[Dict]): conversations with a list of messages;
+            images (List[ImageType]): the list of images;
+            force_batchify (bool): force batchify the inputs;
+            **kwargs:
+
+        Returns:
+            outputs (BaseProcessorOutput): the output of the processor,
+                - input_ids (torch.LongTensor): [N + image tokens]
+                - images (torch.FloatTensor): [n_images, 3, H, W]
+                - image_id (int): the id of the image token
+                - num_image_tokens (List[int]): the number of image tokens
+        """
+
+        prepare = self.process_one(
+            prompt=prompt, conversations=conversations, images=images
+        )
+
+        if force_batchify:
+            prepare = self.batchify([prepare])
+
+        return prepare
+
+    def batchify(
+        self, prepare_list: List[VLChatProcessorOutput]
+    ) -> BatchedVLChatProcessorOutput:
+        """
+        Preprocesses the inputs for multimodal inference.
+
+        Args:
+            prepare_list (List[VLChatProcessorOutput]): A list of VLChatProcessorOutput.
+
+        Returns:
+            BatchedVLChatProcessorOutput: A dictionary of the inputs to use for multimodal inference.
+        """
+
+        batch_size = len(prepare_list)
+        sft_format = []
+        n_images = []
+        seq_lens = []
+        for prepare in prepare_list:
+            n_images.append(len(prepare.num_image_tokens))
+            seq_lens.append(len(prepare))
+
+        input_token_max_len = max(seq_lens)
+        max_n_images = max(1, max(n_images))
+
+        batched_input_ids = torch.full(
+            (batch_size, input_token_max_len), self.pad_id
+        ).long()  # FIXME
+        batched_attention_mask = torch.zeros((batch_size, input_token_max_len)).long()
+        batched_pixel_values = torch.zeros(
+            (batch_size, max_n_images, *self.image_processor.default_shape)
+        ).float()
+        batched_images_seq_mask = torch.zeros((batch_size, input_token_max_len)).bool()
+        batched_images_emb_mask = torch.zeros(
+            (batch_size, max_n_images, self.num_image_tokens)
+        ).bool()
+
+        for i, prepare in enumerate(prepare_list):
+            input_ids = prepare.input_ids
+            seq_len = len(prepare)
+            n_image = len(prepare.num_image_tokens)
+            # left-padding
+            batched_attention_mask[i, -seq_len:] = 1
+            batched_input_ids[i, -seq_len:] = torch.LongTensor(input_ids)
+            batched_images_seq_mask[i, -seq_len:] = input_ids == self.image_id
+
+            if n_image > 0:
+                batched_pixel_values[i, :n_image] = prepare.pixel_values
+                for j, n_image_tokens in enumerate(prepare.num_image_tokens):
+                    batched_images_emb_mask[i, j, :n_image_tokens] = True
+
+            sft_format.append(prepare.sft_format)
+
+        batched_prepares = BatchedVLChatProcessorOutput(
+            input_ids=batched_input_ids,
+            attention_mask=batched_attention_mask,
+            pixel_values=batched_pixel_values,
+            images_seq_mask=batched_images_seq_mask,
+            images_emb_mask=batched_images_emb_mask,
+            sft_format=sft_format,
+        )
+
+        return batched_prepares
diff --git a/py/janus/models/projector.py b/py/janus/models/projector.py
new file mode 100644
index 0000000..15f4ca3
--- /dev/null
+++ b/py/janus/models/projector.py
@@ -0,0 +1,100 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+from typing import Tuple, Union
+
+import torch
+import torch.nn as nn
+from attrdict import AttrDict
+
+
+class MlpProjector(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+
+        self.cfg = cfg
+
+        if cfg.projector_type == "identity":
+            modules = nn.Identity()
+
+        elif cfg.projector_type == "linear":
+            modules = nn.Linear(cfg.input_dim, cfg.n_embed)
+
+        elif cfg.projector_type == "mlp_gelu":
+            mlp_depth = cfg.get("depth", 1)
+            modules = [nn.Linear(cfg.input_dim, cfg.n_embed)]
+            for _ in range(1, mlp_depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
+            modules = nn.Sequential(*modules)
+
+        elif cfg.projector_type == "low_high_hybrid_split_mlp_gelu":
+            mlp_depth = cfg.get("depth", 1)
+            self.high_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
+            self.low_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2)
+
+            modules = []
+            for _ in range(1, mlp_depth):
+                modules.append(nn.GELU())
+                modules.append(nn.Linear(cfg.n_embed, cfg.n_embed))
+            modules = nn.Sequential(*modules)
+
+        else:
+            raise ValueError(f"Unknown projector type: {cfg.projector_type}")
+
+        self.layers = modules
+
+    def forward(
+        self, x_or_tuple: Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]
+    ):
+        """
+
+        Args:
+            x_or_tuple (Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]:  if it is a tuple of torch.Tensor,
+                then it comes from the hybrid vision encoder, and x = high_res_x, low_res_x);
+                otherwise it is the feature from the single vision encoder.
+
+        Returns:
+            x (torch.Tensor): [b, s, c]
+        """
+
+        if isinstance(x_or_tuple, tuple):
+            # self.cfg.projector_type == "low_high_hybrid_split_mlp_gelu":
+            high_x, low_x = x_or_tuple
+            high_x = self.high_up_proj(high_x)
+            low_x = self.low_up_proj(low_x)
+            x = torch.concat([high_x, low_x], dim=-1)
+        else:
+            x = x_or_tuple
+
+        return self.layers(x)
+
+
+if __name__ == "__main__":
+    cfg = AttrDict(
+        input_dim=1024,
+        n_embed=2048,
+        depth=2,
+        projector_type="low_high_hybrid_split_mlp_gelu",
+    )
+    inputs = (torch.rand(4, 576, 1024), torch.rand(4, 576, 1024))
+
+    m = MlpProjector(cfg)
+    out = m(inputs)
+    print(out.shape)
diff --git a/py/janus/models/siglip_vit.py b/py/janus/models/siglip_vit.py
new file mode 100644
index 0000000..ba426d6
--- /dev/null
+++ b/py/janus/models/siglip_vit.py
@@ -0,0 +1,681 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+# https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py
+import math
+import warnings
+from dataclasses import dataclass
+from functools import partial
+from typing import (
+    Callable,
+    Dict,
+    Final,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    Union,
+)
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from timm.layers import (
+    AttentionPoolLatent,
+    DropPath,
+    LayerType,
+    Mlp,
+    PatchDropout,
+    PatchEmbed,
+    resample_abs_pos_embed,
+)
+from timm.models._manipulate import checkpoint_seq, named_apply
+
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+            "The distribution of values may be incorrect.",
+            stacklevel=2,
+        )
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)  # noqa: E741
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.0))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
+    # type: (torch.Tensor, float, float, float, float) -> torch.Tensor
+    r"""The original timm.models.layers.weight_init.trunc_normal_ can not handle bfloat16 yet, here we first
+    convert the tensor to float32, apply the trunc_normal_() in float32, and then convert it back to its original dtype.
+    Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn
+    from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+
+    with torch.no_grad():
+        dtype = tensor.dtype
+        tensor_fp32 = tensor.float()
+        tensor_fp32 = _no_grad_trunc_normal_(tensor_fp32, mean, std, a, b)
+        tensor_dtype = tensor_fp32.to(dtype=dtype)
+        tensor.copy_(tensor_dtype)
+
+
+def init_weights(self):
+    if self.pos_embed is not None:
+        trunc_normal_(self.pos_embed, std=self.pos_embed.shape[1] ** -0.5)
+    trunc_normal_(self.latent, std=self.latent_dim**-0.5)
+
+
+def init_weights_vit_timm(module: nn.Module, name: str = "") -> None:
+    """ViT weight initialization, original timm impl (for reproducibility)"""
+    if isinstance(module, nn.Linear):
+        trunc_normal_(module.weight, std=0.02)
+        if module.bias is not None:
+            nn.init.zeros_(module.bias)
+    elif hasattr(module, "init_weights"):
+        module.init_weights()
+
+
+class Attention(nn.Module):
+    fused_attn: Final[bool]
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        qk_norm: bool = False,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+        norm_layer: nn.Module = nn.LayerNorm,
+    ) -> None:
+        super().__init__()
+        assert dim % num_heads == 0, "dim should be divisible by num_heads"
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim**-0.5
+        # self.fused_attn = use_fused_attn()
+        self.fused_attn = True
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop) if proj_drop > 0.0 else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, N, C = x.shape
+        qkv = (
+            self.qkv(x)
+            .reshape(B, N, 3, self.num_heads, self.head_dim)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q, k, v = qkv.unbind(0)
+        q, k = self.q_norm(q), self.k_norm(k)
+
+        if self.fused_attn:
+            x = F.scaled_dot_product_attention(
+                q,
+                k,
+                v,
+                dropout_p=self.attn_drop.p if self.training else 0.0,
+            )
+        else:
+            q = q * self.scale
+            attn = q @ k.transpose(-2, -1)
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = attn @ v
+
+        x = x.transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class LayerScale(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        init_values: float = 1e-5,
+        inplace: bool = False,
+    ) -> None:
+        super().__init__()
+        self.inplace = inplace
+        self.gamma = nn.Parameter(init_values * torch.ones(dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x.mul_(self.gamma) if self.inplace else x * self.gamma
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = False,
+        qk_norm: bool = False,
+        proj_drop: float = 0.0,
+        attn_drop: float = 0.0,
+        init_values: Optional[float] = None,
+        drop_path: float = 0.0,
+        act_layer: nn.Module = nn.GELU,
+        norm_layer: nn.Module = nn.LayerNorm,
+        mlp_layer: nn.Module = Mlp,
+    ) -> None:
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm,
+            attn_drop=attn_drop,
+            proj_drop=proj_drop,
+            norm_layer=norm_layer,
+        )
+        self.ls1 = (
+            LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        )
+        self.drop_path1 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+        self.norm2 = norm_layer(dim)
+        self.mlp = mlp_layer(
+            in_features=dim,
+            hidden_features=int(dim * mlp_ratio),
+            act_layer=act_layer,
+            drop=proj_drop,
+        )
+        self.ls2 = (
+            LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
+        )
+        self.drop_path2 = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x))))
+        x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
+        return x
+
+
+class VisionTransformer(nn.Module):
+    """Vision Transformer
+
+    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
+        - https://arxiv.org/abs/2010.11929
+    """
+
+    dynamic_img_size: Final[bool]
+
+    def __init__(
+        self,
+        img_size: Union[int, Tuple[int, int]] = 224,
+        patch_size: Union[int, Tuple[int, int]] = 16,
+        in_chans: int = 3,
+        num_classes: int = 1000,
+        global_pool: Literal["", "avg", "token", "map"] = "token",
+        embed_dim: int = 768,
+        depth: int = 12,
+        num_heads: int = 12,
+        mlp_ratio: float = 4.0,
+        qkv_bias: bool = True,
+        qk_norm: bool = False,
+        init_values: Optional[float] = None,
+        class_token: bool = True,
+        no_embed_class: bool = False,
+        reg_tokens: int = 0,
+        pre_norm: bool = False,
+        fc_norm: Optional[bool] = None,
+        dynamic_img_size: bool = False,
+        dynamic_img_pad: bool = False,
+        drop_rate: float = 0.0,
+        pos_drop_rate: float = 0.0,
+        patch_drop_rate: float = 0.0,
+        proj_drop_rate: float = 0.0,
+        attn_drop_rate: float = 0.0,
+        drop_path_rate: float = 0.0,
+        weight_init: Literal["skip", "jax", "jax_nlhb", "moco", ""] = "",
+        embed_layer: Callable = PatchEmbed,
+        norm_layer: Optional[LayerType] = None,
+        act_layer: Optional[LayerType] = None,
+        block_fn: Type[nn.Module] = Block,
+        mlp_layer: Type[nn.Module] = Mlp,
+        ignore_head: bool = False,
+    ) -> None:
+        """
+        Args:
+            img_size: Input image size.
+            patch_size: Patch size.
+            in_chans: Number of image input channels.
+            num_classes: Mumber of classes for classification head.
+            global_pool: Type of global pooling for final sequence (default: 'token').
+            embed_dim: Transformer embedding dimension.
+            depth: Depth of transformer.
+            num_heads: Number of attention heads.
+            mlp_ratio: Ratio of mlp hidden dim to embedding dim.
+            qkv_bias: Enable bias for qkv projections if True.
+            init_values: Layer-scale init values (layer-scale enabled if not None).
+            class_token: Use class token.
+            no_embed_class: Don't include position embeddings for class (or reg) tokens.
+            reg_tokens: Number of register tokens.
+            fc_norm: Pre head norm after pool (instead of before), if None, enabled when global_pool == 'avg'.
+            drop_rate: Head dropout rate.
+            pos_drop_rate: Position embedding dropout rate.
+            attn_drop_rate: Attention dropout rate.
+            drop_path_rate: Stochastic depth rate.
+            weight_init: Weight initialization scheme.
+            embed_layer: Patch embedding layer.
+            norm_layer: Normalization layer.
+            act_layer: MLP activation layer.
+            block_fn: Transformer block layer.
+        """
+        super().__init__()
+        assert global_pool in ("", "avg", "token", "map")
+        assert class_token or global_pool != "token"
+        use_fc_norm = global_pool == "avg" if fc_norm is None else fc_norm
+        # norm_layer = get_norm_layer(norm_layer) or partial(nn.LayerNorm, eps=1e-6)
+        # act_layer = get_act_layer(act_layer) or nn.GELU
+        norm_layer = partial(nn.LayerNorm, eps=1e-6)
+        act_layer = nn.GELU
+
+        self.num_classes = num_classes
+        self.global_pool = global_pool
+        self.num_features = self.embed_dim = (
+            embed_dim  # num_features for consistency with other models
+        )
+        self.num_prefix_tokens = 1 if class_token else 0
+        self.num_prefix_tokens += reg_tokens
+        self.num_reg_tokens = reg_tokens
+        self.has_class_token = class_token
+        self.no_embed_class = (
+            no_embed_class  # don't embed prefix positions (includes reg)
+        )
+        self.dynamic_img_size = dynamic_img_size
+        self.grad_checkpointing = False
+        self.ignore_head = ignore_head
+
+        embed_args = {}
+        if dynamic_img_size:
+            # flatten deferred until after pos embed
+            embed_args.update(dict(strict_img_size=False, output_fmt="NHWC"))
+        self.patch_embed = embed_layer(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            bias=not pre_norm,  # disable bias if pre-norm is used (e.g. CLIP)
+            dynamic_img_pad=dynamic_img_pad,
+            **embed_args,
+        )
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = (
+            nn.Parameter(torch.zeros(1, 1, embed_dim)) if class_token else None
+        )
+        self.reg_token = (
+            nn.Parameter(torch.zeros(1, reg_tokens, embed_dim)) if reg_tokens else None
+        )
+        embed_len = (
+            num_patches if no_embed_class else num_patches + self.num_prefix_tokens
+        )
+        self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * 0.02)
+        self.pos_drop = nn.Dropout(p=pos_drop_rate)
+        if patch_drop_rate > 0:
+            self.patch_drop = PatchDropout(
+                patch_drop_rate,
+                num_prefix_tokens=self.num_prefix_tokens,
+            )
+        else:
+            self.patch_drop = nn.Identity()
+        self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
+
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, depth)
+        ]  # stochastic depth decay rule
+        self.blocks = nn.Sequential(
+            *[
+                block_fn(
+                    dim=embed_dim,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    qk_norm=qk_norm,
+                    init_values=init_values,
+                    proj_drop=proj_drop_rate,
+                    attn_drop=attn_drop_rate,
+                    drop_path=dpr[i],
+                    norm_layer=norm_layer,
+                    act_layer=act_layer,
+                    mlp_layer=mlp_layer,
+                )
+                for i in range(depth)
+            ]
+        )
+        self.norm = norm_layer(embed_dim) if not use_fc_norm else nn.Identity()
+
+        # Classifier Head
+        if global_pool == "map":
+            AttentionPoolLatent.init_weights = init_weights
+            self.attn_pool = AttentionPoolLatent(
+                self.embed_dim,
+                num_heads=num_heads,
+                mlp_ratio=mlp_ratio,
+                norm_layer=norm_layer,
+            )
+        else:
+            self.attn_pool = None
+        self.fc_norm = norm_layer(embed_dim) if use_fc_norm else nn.Identity()
+        self.head_drop = nn.Dropout(drop_rate)
+        self.head = (
+            nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+        )
+
+        if weight_init != "skip":
+            self.init_weights(weight_init)
+
+    def init_weights(self, mode: Literal["jax", "jax_nlhb", "moco", ""] = "") -> None:
+        assert mode in ("jax", "jax_nlhb", "moco", "")
+        # head_bias = -math.log(self.num_classes) if "nlhb" in mode else 0.0
+        trunc_normal_(self.pos_embed, std=0.02)
+        if self.cls_token is not None:
+            nn.init.normal_(self.cls_token, std=1e-6)
+        named_apply(init_weights_vit_timm, self)
+
+    @torch.jit.ignore
+    def no_weight_decay(self) -> Set:
+        return {"pos_embed", "cls_token", "dist_token"}
+
+    @torch.jit.ignore
+    def group_matcher(self, coarse: bool = False) -> Dict:
+        return dict(
+            stem=r"^cls_token|pos_embed|patch_embed",  # stem and embed
+            blocks=[(r"^blocks\.(\d+)", None), (r"^norm", (99999,))],
+        )
+
+    @torch.jit.ignore
+    def set_grad_checkpointing(self, enable: bool = True) -> None:
+        self.grad_checkpointing = enable
+
+    @torch.jit.ignore
+    def get_classifier(self) -> nn.Module:
+        return self.head
+
+    def reset_classifier(self, num_classes: int, global_pool=None) -> None:
+        self.num_classes = num_classes
+        if global_pool is not None:
+            assert global_pool in ("", "avg", "token", "map")
+            if global_pool == "map" and self.attn_pool is None:
+                assert (
+                    False
+                ), "Cannot currently add attention pooling in reset_classifier()."
+            elif global_pool != "map " and self.attn_pool is not None:
+                self.attn_pool = None  # remove attention pooling
+            self.global_pool = global_pool
+        self.head = (
+            nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+        )
+
+    def _pos_embed(self, x: torch.Tensor) -> torch.Tensor:
+        if self.dynamic_img_size:
+            B, H, W, C = x.shape
+            pos_embed = resample_abs_pos_embed(
+                self.pos_embed,
+                (H, W),
+                num_prefix_tokens=0 if self.no_embed_class else self.num_prefix_tokens,
+            )
+            x = x.view(B, -1, C)
+        else:
+            pos_embed = self.pos_embed
+
+        to_cat = []
+        if self.cls_token is not None:
+            to_cat.append(self.cls_token.expand(x.shape[0], -1, -1))
+        if self.reg_token is not None:
+            to_cat.append(self.reg_token.expand(x.shape[0], -1, -1))
+
+        if self.no_embed_class:
+            # deit-3, updated JAX (big vision)
+            # position embedding does not overlap with class token, add then concat
+            x = x + pos_embed
+            if to_cat:
+                x = torch.cat(to_cat + [x], dim=1)
+        else:
+            # original timm, JAX, and deit vit impl
+            # pos_embed has entry for class token, concat then add
+            if to_cat:
+                x = torch.cat(to_cat + [x], dim=1)
+            x = x + pos_embed
+
+        return self.pos_drop(x)
+
+    def _intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,
+    ) -> List[torch.Tensor]:
+        outputs, num_blocks = [], len(self.blocks)
+        take_indices = set(
+            range(num_blocks - n, num_blocks) if isinstance(n, int) else n
+        )
+
+        # forward pass
+        x = self.patch_embed(x)
+        x = self._pos_embed(x)
+        x = self.patch_drop(x)
+        x = self.norm_pre(x)
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+            if i in take_indices:
+                outputs.append(x)
+
+        return outputs
+
+    def get_intermediate_layers(
+        self,
+        x: torch.Tensor,
+        n: Union[int, Sequence] = 1,
+        reshape: bool = False,
+        return_prefix_tokens: bool = False,
+        norm: bool = False,
+    ) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]]]:
+        """Intermediate layer accessor (NOTE: This is a WIP experiment).
+        Inspired by DINO / DINOv2 interface
+        """
+        # take last n blocks if n is an int, if in is a sequence, select by matching indices
+        outputs = self._intermediate_layers(x, n)
+        if norm:
+            outputs = [self.norm(out) for out in outputs]
+        prefix_tokens = [out[:, 0 : self.num_prefix_tokens] for out in outputs]
+        outputs = [out[:, self.num_prefix_tokens :] for out in outputs]
+
+        if reshape:
+            grid_size = self.patch_embed.grid_size
+            outputs = [
+                out.reshape(x.shape[0], grid_size[0], grid_size[1], -1)
+                .permute(0, 3, 1, 2)
+                .contiguous()
+                for out in outputs
+            ]
+
+        if return_prefix_tokens:
+            return tuple(zip(outputs, prefix_tokens))
+        return tuple(outputs)
+
+    def forward_features(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.patch_embed(x)
+        x = self._pos_embed(x)
+        x = self.patch_drop(x)
+        x = self.norm_pre(x)
+        if self.grad_checkpointing and not torch.jit.is_scripting():
+            x = checkpoint_seq(self.blocks, x)
+        else:
+            x = self.blocks(x)
+        x = self.norm(x)
+        return x
+
+    def forward_head(self, x: torch.Tensor, pre_logits: bool = False) -> torch.Tensor:
+        if self.attn_pool is not None:
+            x = self.attn_pool(x)
+        elif self.global_pool == "avg":
+            x = x[:, self.num_prefix_tokens :].mean(dim=1)
+        elif self.global_pool:
+            x = x[:, 0]  # class token
+        x = self.fc_norm(x)
+        x = self.head_drop(x)
+        return x if pre_logits else self.head(x)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.forward_features(x)
+        if not self.ignore_head:
+            x = self.forward_head(x)
+        return x
+
+
+@dataclass
+class SigLIPVisionCfg:
+    width: int = 1152
+    layers: Union[Tuple[int, int, int, int], int] = 27
+    heads: int = 16
+    patch_size: int = 14
+    image_size: Union[Tuple[int, int], int] = 336
+    global_pool: str = "map"
+    mlp_ratio: float = 3.7362
+    class_token: bool = False
+    num_classes: int = 0
+    use_checkpoint: bool = False
+
+
+SigLIP_MODEL_CONFIG = {
+    "siglip_so400m_patch14_384": {
+        "image_size": 336,
+        "patch_size": 14,
+        "width": 1152,
+        "layers": 27,
+        "heads": 16,
+        "mlp_ratio": 3.7362,
+        "global_pool": "map",
+        "use_checkpoint": False,
+    },
+    "siglip_so400m_patch14_224": {
+        "image_size": 224,
+        "patch_size": 14,
+        "width": 1152,
+        "layers": 27,
+        "heads": 16,
+        "mlp_ratio": 3.7362,
+        "global_pool": "map",
+        "use_checkpoint": False,
+    },
+    "siglip_large_patch16_384": {
+        "image_size": 384,
+        "patch_size": 16,
+        "width": 1024,
+        "layers": 24,
+        "heads": 16,
+        "mlp_ratio": 4,
+        "global_pool": "map",
+        "use_checkpoint": False,
+    },
+}
+
+
+def create_siglip_vit(
+    model_name: str = "siglip_so400m_patch14_384",
+    image_size: int = 384,
+    select_layer: int = -1,
+    ckpt_path: str = "",
+    **kwargs,
+):
+    assert (
+        model_name in SigLIP_MODEL_CONFIG.keys()
+    ), f"model name should be in {SigLIP_MODEL_CONFIG.keys()}"
+
+    vision_cfg = SigLIPVisionCfg(**SigLIP_MODEL_CONFIG[model_name])
+
+    if select_layer <= 0:
+        layers = min(vision_cfg.layers, vision_cfg.layers + select_layer + 1)
+    else:
+        layers = min(vision_cfg.layers, select_layer)
+
+    model = VisionTransformer(
+        img_size=image_size,
+        patch_size=vision_cfg.patch_size,
+        embed_dim=vision_cfg.width,
+        depth=layers,
+        num_heads=vision_cfg.heads,
+        mlp_ratio=vision_cfg.mlp_ratio,
+        class_token=vision_cfg.class_token,
+        global_pool=vision_cfg.global_pool,
+        ignore_head=kwargs.get("ignore_head", True),
+        weight_init=kwargs.get("weight_init", "skip"),
+        num_classes=0,
+    )
+
+    if ckpt_path:
+        state_dict = torch.load(ckpt_path, map_location="cpu")
+
+        incompatible_keys = model.load_state_dict(state_dict, strict=False)
+        print(
+            f"SigLIP-ViT restores from {ckpt_path},\n"
+            f"\tincompatible_keys:', {incompatible_keys}."
+        )
+
+    return model
diff --git a/py/janus/models/vq_model.py b/py/janus/models/vq_model.py
new file mode 100644
index 0000000..887b721
--- /dev/null
+++ b/py/janus/models/vq_model.py
@@ -0,0 +1,527 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+
+from dataclasses import dataclass, field
+from typing import List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from functools import partial
+
+
+@dataclass
+class ModelArgs:
+    codebook_size: int = 16384
+    codebook_embed_dim: int = 8
+    codebook_l2_norm: bool = True
+    codebook_show_usage: bool = True
+    commit_loss_beta: float = 0.25
+    entropy_loss_ratio: float = 0.0
+
+    encoder_ch_mult: List[int] = field(default_factory=lambda: [1, 1, 2, 2, 4])
+    decoder_ch_mult: List[int] = field(default_factory=lambda: [1, 1, 2, 2, 4])
+    z_channels: int = 256
+    dropout_p: float = 0.0
+
+
+class Encoder(nn.Module):
+    def __init__(
+        self,
+        in_channels=3,
+        ch=128,
+        ch_mult=(1, 1, 2, 2, 4),
+        num_res_blocks=2,
+        norm_type="group",
+        dropout=0.0,
+        resamp_with_conv=True,
+        z_channels=256,
+    ):
+        super().__init__()
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.conv_in = nn.Conv2d(in_channels, ch, kernel_size=3, stride=1, padding=1)
+
+        # downsampling
+        in_ch_mult = (1,) + tuple(ch_mult)
+        self.conv_blocks = nn.ModuleList()
+        for i_level in range(self.num_resolutions):
+            conv_block = nn.Module()
+            # res & attn
+            res_block = nn.ModuleList()
+            attn_block = nn.ModuleList()
+            block_in = ch * in_ch_mult[i_level]
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks):
+                res_block.append(
+                    ResnetBlock(
+                        block_in, block_out, dropout=dropout, norm_type=norm_type
+                    )
+                )
+                block_in = block_out
+                if i_level == self.num_resolutions - 1:
+                    attn_block.append(AttnBlock(block_in, norm_type))
+            conv_block.res = res_block
+            conv_block.attn = attn_block
+            # downsample
+            if i_level != self.num_resolutions - 1:
+                conv_block.downsample = Downsample(block_in, resamp_with_conv)
+            self.conv_blocks.append(conv_block)
+
+        # middle
+        self.mid = nn.ModuleList()
+        self.mid.append(
+            ResnetBlock(block_in, block_in, dropout=dropout, norm_type=norm_type)
+        )
+        self.mid.append(AttnBlock(block_in, norm_type=norm_type))
+        self.mid.append(
+            ResnetBlock(block_in, block_in, dropout=dropout, norm_type=norm_type)
+        )
+
+        # end
+        self.norm_out = Normalize(block_in, norm_type)
+        self.conv_out = nn.Conv2d(
+            block_in, z_channels, kernel_size=3, stride=1, padding=1
+        )
+
+    def forward(self, x):
+        h = self.conv_in(x)
+        # downsampling
+        for i_level, block in enumerate(self.conv_blocks):
+            for i_block in range(self.num_res_blocks):
+                h = block.res[i_block](h)
+                if len(block.attn) > 0:
+                    h = block.attn[i_block](h)
+            if i_level != self.num_resolutions - 1:
+                h = block.downsample(h)
+
+        # middle
+        for mid_block in self.mid:
+            h = mid_block(h)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self,
+        z_channels=256,
+        ch=128,
+        ch_mult=(1, 1, 2, 2, 4),
+        num_res_blocks=2,
+        norm_type="group",
+        dropout=0.0,
+        resamp_with_conv=True,
+        out_channels=3,
+    ):
+        super().__init__()
+        self.num_resolutions = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+
+        block_in = ch * ch_mult[self.num_resolutions - 1]
+        # z to block_in
+        self.conv_in = nn.Conv2d(
+            z_channels, block_in, kernel_size=3, stride=1, padding=1
+        )
+
+        # middle
+        self.mid = nn.ModuleList()
+        self.mid.append(
+            ResnetBlock(block_in, block_in, dropout=dropout, norm_type=norm_type)
+        )
+        self.mid.append(AttnBlock(block_in, norm_type=norm_type))
+        self.mid.append(
+            ResnetBlock(block_in, block_in, dropout=dropout, norm_type=norm_type)
+        )
+
+        # upsampling
+        self.conv_blocks = nn.ModuleList()
+        for i_level in reversed(range(self.num_resolutions)):
+            conv_block = nn.Module()
+            # res & attn
+            res_block = nn.ModuleList()
+            attn_block = nn.ModuleList()
+            block_out = ch * ch_mult[i_level]
+            for _ in range(self.num_res_blocks + 1):
+                res_block.append(
+                    ResnetBlock(
+                        block_in, block_out, dropout=dropout, norm_type=norm_type
+                    )
+                )
+                block_in = block_out
+                if i_level == self.num_resolutions - 1:
+                    attn_block.append(AttnBlock(block_in, norm_type))
+            conv_block.res = res_block
+            conv_block.attn = attn_block
+            # downsample
+            if i_level != 0:
+                conv_block.upsample = Upsample(block_in, resamp_with_conv)
+            self.conv_blocks.append(conv_block)
+
+        # end
+        self.norm_out = Normalize(block_in, norm_type)
+        self.conv_out = nn.Conv2d(
+            block_in, out_channels, kernel_size=3, stride=1, padding=1
+        )
+
+    @property
+    def last_layer(self):
+        return self.conv_out.weight
+
+    def forward(self, z):
+        # z to block_in
+        h = self.conv_in(z)
+
+        # middle
+        for mid_block in self.mid:
+            h = mid_block(h)
+
+        # upsampling
+        for i_level, block in enumerate(self.conv_blocks):
+            for i_block in range(self.num_res_blocks + 1):
+                h = block.res[i_block](h)
+                if len(block.attn) > 0:
+                    h = block.attn[i_block](h)
+            if i_level != self.num_resolutions - 1:
+                h = block.upsample(h)
+
+        # end
+        h = self.norm_out(h)
+        h = nonlinearity(h)
+        h = self.conv_out(h)
+        return h
+
+
+class VectorQuantizer(nn.Module):
+    def __init__(self, n_e, e_dim, beta, entropy_loss_ratio, l2_norm, show_usage):
+        super().__init__()
+        self.n_e = n_e
+        self.e_dim = e_dim
+        self.beta = beta
+        self.entropy_loss_ratio = entropy_loss_ratio
+        self.l2_norm = l2_norm
+        self.show_usage = show_usage
+
+        self.embedding = nn.Embedding(self.n_e, self.e_dim)
+        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
+        if self.l2_norm:
+            self.embedding.weight.data = F.normalize(
+                self.embedding.weight.data, p=2, dim=-1
+            )
+        if self.show_usage:
+            self.register_buffer("codebook_used", nn.Parameter(torch.zeros(65536)))
+
+    def forward(self, z):
+        # reshape z -> (batch, height, width, channel) and flatten
+        z = torch.einsum("b c h w -> b h w c", z).contiguous()
+        z_flattened = z.view(-1, self.e_dim)
+        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+
+        if self.l2_norm:
+            z = F.normalize(z, p=2, dim=-1)
+            z_flattened = F.normalize(z_flattened, p=2, dim=-1)
+            embedding = F.normalize(self.embedding.weight, p=2, dim=-1)
+        else:
+            embedding = self.embedding.weight
+
+        d = (
+            torch.sum(z_flattened**2, dim=1, keepdim=True)
+            + torch.sum(embedding**2, dim=1)
+            - 2
+            * torch.einsum(
+                "bd,dn->bn", z_flattened, torch.einsum("n d -> d n", embedding)
+            )
+        )
+
+        min_encoding_indices = torch.argmin(d, dim=1)
+        z_q = embedding[min_encoding_indices].view(z.shape)
+        perplexity = None
+        min_encodings = None
+        vq_loss = None
+        commit_loss = None
+        entropy_loss = None
+
+        # compute loss for embedding
+        if self.training:
+            vq_loss = torch.mean((z_q - z.detach()) ** 2)
+            commit_loss = self.beta * torch.mean((z_q.detach() - z) ** 2)
+            entropy_loss = self.entropy_loss_ratio * compute_entropy_loss(-d)
+
+        # preserve gradients
+        z_q = z + (z_q - z).detach()
+
+        # reshape back to match original input shape
+        z_q = torch.einsum("b h w c -> b c h w", z_q)
+
+        return (
+            z_q,
+            (vq_loss, commit_loss, entropy_loss),
+            (perplexity, min_encodings, min_encoding_indices),
+        )
+
+    def get_codebook_entry(self, indices, shape=None, channel_first=True):
+        # shape = (batch, channel, height, width) if channel_first else (batch, height, width, channel)
+        if self.l2_norm:
+            embedding = F.normalize(self.embedding.weight, p=2, dim=-1)
+        else:
+            embedding = self.embedding.weight
+        z_q = embedding[indices]  # (b*h*w, c)
+
+        if shape is not None:
+            if channel_first:
+                z_q = z_q.reshape(shape[0], shape[2], shape[3], shape[1])
+                # reshape back to match original input shape
+                z_q = z_q.permute(0, 3, 1, 2).contiguous()
+            else:
+                z_q = z_q.view(shape)
+        return z_q
+
+
+class ResnetBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels=None,
+        conv_shortcut=False,
+        dropout=0.0,
+        norm_type="group",
+    ):
+        super().__init__()
+        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.norm1 = Normalize(in_channels, norm_type)
+        self.conv1 = nn.Conv2d(
+            in_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+        self.norm2 = Normalize(out_channels, norm_type)
+        self.dropout = nn.Dropout(dropout)
+        self.conv2 = nn.Conv2d(
+            out_channels, out_channels, kernel_size=3, stride=1, padding=1
+        )
+
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = nn.Conv2d(
+                    in_channels, out_channels, kernel_size=3, stride=1, padding=1
+                )
+            else:
+                self.nin_shortcut = nn.Conv2d(
+                    in_channels, out_channels, kernel_size=1, stride=1, padding=0
+                )
+
+    def forward(self, x):
+        h = x
+        h = self.norm1(h)
+        h = nonlinearity(h)
+        h = self.conv1(h)
+        h = self.norm2(h)
+        h = nonlinearity(h)
+        h = self.dropout(h)
+        h = self.conv2(h)
+
+        if self.in_channels != self.out_channels:
+            if self.use_conv_shortcut:
+                x = self.conv_shortcut(x)
+            else:
+                x = self.nin_shortcut(x)
+        return x + h
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, in_channels, norm_type="group"):
+        super().__init__()
+        self.norm = Normalize(in_channels, norm_type)
+        self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
+        self.proj_out = nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, h, w = q.shape
+        q = q.reshape(b, c, h * w)
+        q = q.permute(0, 2, 1)  # b,hw,c
+        k = k.reshape(b, c, h * w)  # b,c,hw
+        w_ = torch.bmm(q, k)  # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+        w_ = w_ * (int(c) ** (-0.5))
+        w_ = F.softmax(w_, dim=2)
+
+        # attend to values
+        v = v.reshape(b, c, h * w)
+        w_ = w_.permute(0, 2, 1)  # b,hw,hw (first hw of k, second of q)
+        h_ = torch.bmm(v, w_)  # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+        h_ = h_.reshape(b, c, h, w)
+
+        h_ = self.proj_out(h_)
+
+        return x + h_
+
+
+def nonlinearity(x):
+    # swish
+    return x * torch.sigmoid(x)
+
+
+def Normalize(in_channels, norm_type="group"):
+    assert norm_type in ["group", "batch"]
+    if norm_type == "group":
+        return nn.GroupNorm(
+            num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
+        )
+    elif norm_type == "batch":
+        return nn.SyncBatchNorm(in_channels)
+
+
+class Upsample(nn.Module):
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            self.conv = nn.Conv2d(
+                in_channels, in_channels, kernel_size=3, stride=1, padding=1
+            )
+
+    def forward(self, x):
+        if x.dtype != torch.float32:
+            x = F.interpolate(x.to(torch.float), scale_factor=2.0, mode="nearest").to(
+                torch.bfloat16
+            )
+        else:
+            x = F.interpolate(x, scale_factor=2.0, mode="nearest")
+
+        if self.with_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample(nn.Module):
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            # no asymmetric padding in torch conv, must do it ourselves
+            self.conv = nn.Conv2d(
+                in_channels, in_channels, kernel_size=3, stride=2, padding=0
+            )
+
+    def forward(self, x):
+        if self.with_conv:
+            pad = (0, 1, 0, 1)
+            x = F.pad(x, pad, mode="constant", value=0)
+            x = self.conv(x)
+        else:
+            x = F.avg_pool2d(x, kernel_size=2, stride=2)
+        return x
+
+
+def compute_entropy_loss(affinity, loss_type="softmax", temperature=0.01):
+    flat_affinity = affinity.reshape(-1, affinity.shape[-1])
+    flat_affinity /= temperature
+    probs = F.softmax(flat_affinity, dim=-1)
+    log_probs = F.log_softmax(flat_affinity + 1e-5, dim=-1)
+    if loss_type == "softmax":
+        target_probs = probs
+    else:
+        raise ValueError("Entropy loss {} not supported".format(loss_type))
+    avg_probs = torch.mean(target_probs, dim=0)
+    avg_entropy = -torch.sum(avg_probs * torch.log(avg_probs + 1e-5))
+    sample_entropy = -torch.mean(torch.sum(target_probs * log_probs, dim=-1))
+    loss = sample_entropy - avg_entropy
+    return loss
+
+
+class VQModel(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.config = config
+        self.encoder = Encoder(
+            ch_mult=config.encoder_ch_mult,
+            z_channels=config.z_channels,
+            dropout=config.dropout_p,
+        )
+        self.decoder = Decoder(
+            ch_mult=config.decoder_ch_mult,
+            z_channels=config.z_channels,
+            dropout=config.dropout_p,
+        )
+
+        self.quantize = VectorQuantizer(
+            config.codebook_size,
+            config.codebook_embed_dim,
+            config.commit_loss_beta,
+            config.entropy_loss_ratio,
+            config.codebook_l2_norm,
+            config.codebook_show_usage,
+        )
+        self.quant_conv = nn.Conv2d(config.z_channels, config.codebook_embed_dim, 1)
+        self.post_quant_conv = nn.Conv2d(
+            config.codebook_embed_dim, config.z_channels, 1
+        )
+
+    def encode(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        quant, emb_loss, info = self.quantize(h)
+        return quant, emb_loss, info
+
+    def decode(self, quant):
+        quant = self.post_quant_conv(quant)
+        dec = self.decoder(quant)
+        return dec
+
+    def decode_code(self, code_b, shape=None, channel_first=True):
+        quant_b = self.quantize.get_codebook_entry(code_b, shape, channel_first)
+        dec = self.decode(quant_b)
+        return dec
+
+    def forward(self, input):
+        quant, diff, _ = self.encode(input)
+        dec = self.decode(quant)
+        return dec, diff
+
+
+#################################################################################
+#                              VQ Model Configs                                 #
+#################################################################################
+def VQ_16(**kwargs):
+    return VQModel(
+        ModelArgs(
+            encoder_ch_mult=[1, 1, 2, 2, 4], decoder_ch_mult=[1, 1, 2, 2, 4], **kwargs
+        )
+    )
+
+
+VQ_models = {"VQ-16": VQ_16}
diff --git a/py/janus/utils/__init__.py b/py/janus/utils/__init__.py
new file mode 100644
index 0000000..8cb7640
--- /dev/null
+++ b/py/janus/utils/__init__.py
@@ -0,0 +1,18 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/py/janus/utils/conversation.py b/py/janus/utils/conversation.py
new file mode 100644
index 0000000..98609d0
--- /dev/null
+++ b/py/janus/utils/conversation.py
@@ -0,0 +1,365 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+"""
+From https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+"""
+
+import dataclasses
+from enum import IntEnum, auto
+from typing import Dict, List
+
+
+class SeparatorStyle(IntEnum):
+    """Separator styles."""
+
+    ADD_COLON_SINGLE = auto()
+    ADD_COLON_TWO = auto()
+    ADD_COLON_SPACE_SINGLE = auto()
+    NO_COLON_SINGLE = auto()
+    NO_COLON_TWO = auto()
+    ADD_NEW_LINE_SINGLE = auto()
+    LLAMA2 = auto()
+    CHATGLM = auto()
+    CHATML = auto()
+    CHATINTERN = auto()
+    DOLLY = auto()
+    RWKV = auto()
+    PHOENIX = auto()
+    ROBIN = auto()
+    DeepSeek = auto()
+    PLAIN = auto()
+    ALIGNMENT = auto()
+
+
+@dataclasses.dataclass
+class Conversation:
+    """A class that manages prompt templates and keeps all conversation history."""
+
+    # The name of this template
+    name: str
+    # The template of the system prompt
+    system_template: str = "{system_message}"
+    # The system message
+    system_message: str = ""
+    # The names of two roles
+    roles: List[str] = (("USER", "ASSISTANT"),)
+    # All messages. Each item is (role, message).
+    messages: List[List[str]] = ()
+    # The number of few shot examples
+    offset: int = 0
+    # The separator style and configurations
+    sep_style: SeparatorStyle = SeparatorStyle.ADD_COLON_SINGLE
+    sep: str = "\n"
+    sep2: str = None
+    # Stop criteria (the default one is EOS token)
+    stop_str: str = None
+    # Stops generation if meeting any token in this list
+    stop_token_ids: List[int] = None
+
+    def get_prompt(self) -> str:
+        """Get the prompt for generation."""
+        system_prompt = self.system_template.format(system_message=self.system_message)
+
+        if self.sep_style == SeparatorStyle.DeepSeek:
+            seps = [self.sep, self.sep2]
+            if system_prompt == "" or system_prompt is None:
+                ret = ""
+            else:
+                ret = system_prompt + seps[0]
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + ": " + message + seps[i % 2]
+                else:
+                    ret += role + ":"
+            return ret
+        elif self.sep_style == SeparatorStyle.LLAMA2:
+            seps = [self.sep, self.sep2]
+            if self.system_message:
+                ret = system_prompt
+            else:
+                ret = "[INST] "
+            for i, (role, message) in enumerate(self.messages):
+                tag = self.roles[i % 2]
+                if message:
+                    if type(message) is tuple:  # multimodal message
+                        message, _ = message
+                    if i == 0:
+                        ret += message + " "
+                    else:
+                        ret += tag + " " + message + seps[i % 2]
+                else:
+                    ret += tag
+            return ret
+        elif self.sep_style == SeparatorStyle.PLAIN:
+            seps = [self.sep, self.sep2]
+            ret = ""
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    if i % 2 == 0:
+                        ret += message + seps[i % 2]
+                    else:
+                        ret += message + seps[i % 2]
+                else:
+                    ret += ""
+            return ret
+        elif self.sep_style == SeparatorStyle.ALIGNMENT:
+            seps = [self.sep, self.sep2]
+            ret = ""
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    if i % 2 == 0:
+                        ret += "<image>\n" + seps[i % 2]
+                    else:
+                        ret += message + seps[i % 2]
+                else:
+                    ret += ""
+            return ret
+        else:
+            raise ValueError(f"Invalid style: {self.sep_style}")
+
+    def get_prompt_for_current_round(self, content=None):
+        """Get current round formatted question prompt during sft training"""
+        if self.sep_style == SeparatorStyle.PLAIN:
+            formatted_question = "<image>\n"
+        elif self.sep_style == SeparatorStyle.DeepSeek:
+            formatted_question = (
+                f"{self.roles[0]}: " + content.strip() + self.sep + f"{self.roles[1]}:"
+            )
+        else:
+            raise ValueError(f"Unsupported sep_style: {self.sep_style}")
+        return formatted_question
+
+    def set_system_message(self, system_message: str):
+        """Set the system message."""
+        self.system_message = system_message
+
+    def append_message(self, role: str, message: str):
+        """Append a new message."""
+        self.messages.append([role, message])
+
+    def reset_message(self):
+        """Reset a new message."""
+        self.messages = []
+
+    def update_last_message(self, message: str):
+        """Update the last output.
+
+        The last message is typically set to be None when constructing the prompt,
+        so we need to update it in-place after getting the response from a model.
+        """
+        self.messages[-1][1] = message
+
+    def to_gradio_chatbot(self):
+        """Convert the conversation to gradio chatbot format."""
+        ret = []
+        for i, (role, msg) in enumerate(self.messages[self.offset :]):
+            if i % 2 == 0:
+                ret.append([msg, None])
+            else:
+                ret[-1][-1] = msg
+        return ret
+
+    def to_openai_api_messages(self):
+        """Convert the conversation to OpenAI chat completion format."""
+        system_prompt = self.system_template.format(system_message=self.system_message)
+        ret = [{"role": "system", "content": system_prompt}]
+
+        for i, (_, msg) in enumerate(self.messages[self.offset :]):
+            if i % 2 == 0:
+                ret.append({"role": "user", "content": msg})
+            else:
+                if msg is not None:
+                    ret.append({"role": "assistant", "content": msg})
+        return ret
+
+    def copy(self):
+        return Conversation(
+            name=self.name,
+            system_template=self.system_template,
+            system_message=self.system_message,
+            roles=self.roles,
+            messages=[[x, y] for x, y in self.messages],
+            offset=self.offset,
+            sep_style=self.sep_style,
+            sep=self.sep,
+            sep2=self.sep2,
+            stop_str=self.stop_str,
+            stop_token_ids=self.stop_token_ids,
+        )
+
+    def dict(self):
+        return {
+            "template_name": self.name,
+            "system_message": self.system_message,
+            "roles": self.roles,
+            "messages": self.messages,
+            "offset": self.offset,
+        }
+
+
+# A global registry for all conversation templates
+conv_templates: Dict[str, Conversation] = {}
+
+
+def register_conv_template(template: Conversation, override: bool = False):
+    """Register a new conversation template."""
+    if not override:
+        assert (
+            template.name not in conv_templates
+        ), f"{template.name} has been registered."
+
+    conv_templates[template.name] = template
+
+
+def get_conv_template(name: str) -> Conversation:
+    """Get a conversation template."""
+    return conv_templates[name].copy()
+
+
+# llava_llama2 template
+register_conv_template(
+    Conversation(
+        name="llava_llama2",
+        system_message="You are a helpful language and vision assistant. "
+        "You are able to understand the visual content that the user provides, "
+        "and assist the user with a variety of tasks using natural language.",
+        system_template="[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n",
+        roles=("[INST]", "[/INST]"),
+        messages=(),
+        offset=0,
+        sep_style=SeparatorStyle.LLAMA2,
+        sep=" ",
+        sep2=" </s><s>",
+        stop_token_ids=[2],
+    )
+)
+
+# llama2 template
+# reference: https://github.com/facebookresearch/llama/blob/cfc3fc8c1968d390eb830e65c63865e980873a06/llama/generation.py#L212
+register_conv_template(
+    Conversation(
+        name="llama-2",
+        system_template="[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n",
+        roles=("[INST]", "[/INST]"),
+        messages=(),
+        offset=0,
+        sep_style=SeparatorStyle.LLAMA2,
+        sep=" ",
+        sep2=" </s><s>",
+        stop_token_ids=[2],
+    )
+)
+
+
+# deepseek template
+register_conv_template(
+    Conversation(
+        name="deepseek_old",
+        system_template="{system_message}",
+        # system_message="You are a helpful assistant. Please answer truthfully and write out your "
+        # "thinking step by step to be sure you get the right answer.",
+        system_message="",
+        roles=("User", "Assistant"),
+        messages=(),
+        offset=0,
+        sep_style=SeparatorStyle.DeepSeek,
+        sep="\n\n",
+        sep2="<｜end▁of▁sentence｜>",
+        stop_token_ids=[100001],
+        stop_str=["User:", "<｜end▁of▁sentence｜>"],
+    )
+)
+register_conv_template(
+    Conversation(
+        name="deepseek",
+        system_template="{system_message}",
+        # system_message="You are a helpful assistant. Please answer truthfully and write out your "
+        # "thinking step by step to be sure you get the right answer.",
+        system_message="",
+        roles=("<|User|>", "<|Assistant|>"),
+        messages=(),
+        offset=0,
+        sep_style=SeparatorStyle.DeepSeek,
+        sep="\n\n",
+        sep2="<｜end▁of▁sentence｜>",
+        stop_token_ids=[100001],
+        stop_str=["<|User|>", "<｜end▁of▁sentence｜>"]
+    )
+)
+
+register_conv_template(
+    Conversation(
+        name="plain",
+        system_template="",
+        system_message="",
+        roles=("", ""),
+        messages=(),
+        offset=0,
+        sep_style=SeparatorStyle.PLAIN,
+        sep="",
+        sep2="",
+        stop_token_ids=[2],
+        stop_str=["</s>"],
+    )
+)
+
+
+register_conv_template(
+    Conversation(
+        name="alignment",
+        system_template="",
+        system_message="",
+        roles=("", ""),
+        messages=(),
+        offset=0,
+        sep_style=SeparatorStyle.ALIGNMENT,
+        sep="",
+        sep2="",
+        stop_token_ids=[2],
+        stop_str=["</s>"],
+    )
+)
+
+
+if __name__ == "__main__":
+    # print("Llama-2 template:")
+    # conv = get_conv_template("llama-2")
+    # conv.set_system_message("You are a helpful, respectful and honest assistant.")
+    # conv.append_message(conv.roles[0], "Hello!")
+    # conv.append_message(conv.roles[1], "Hi!")
+    # conv.append_message(conv.roles[0], "How are you?")
+    # conv.append_message(conv.roles[1], None)
+    # print(conv.get_prompt())
+
+    # print("\n")
+
+    print("deepseek template:")
+    conv = get_conv_template("deepseek")
+    conv.append_message(conv.roles[0], "Hello!")
+    conv.append_message(conv.roles[1], "Hi! This is Tony.")
+    conv.append_message(conv.roles[0], "Who are you?")
+    conv.append_message(conv.roles[1], "I am a helpful assistant.")
+    conv.append_message(conv.roles[0], "How are you?")
+    conv.append_message(conv.roles[1], None)
+    print(conv.get_prompt())
diff --git a/py/janus/utils/io.py b/py/janus/utils/io.py
new file mode 100644
index 0000000..497271f
--- /dev/null
+++ b/py/janus/utils/io.py
@@ -0,0 +1,89 @@
+# Copyright (c) 2023-2024 DeepSeek.
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of
+# this software and associated documentation files (the "Software"), to deal in
+# the Software without restriction, including without limitation the rights to
+# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+# the Software, and to permit persons to whom the Software is furnished to do so,
+# subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+import json
+from typing import Dict, List
+
+import PIL.Image
+import torch
+import base64
+import io
+from transformers import AutoModelForCausalLM
+
+from ..models import MultiModalityCausalLM, VLChatProcessor
+
+
+def load_pretrained_model(model_path: str):
+    vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
+    tokenizer = vl_chat_processor.tokenizer
+
+    vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(
+        model_path, trust_remote_code=True
+    )
+    vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()
+
+    return tokenizer, vl_chat_processor, vl_gpt
+
+
+def load_pil_images(conversations: List[Dict[str, str]]) -> List[PIL.Image.Image]:
+    """
+
+    Support file path or base64 images.
+
+    Args:
+        conversations (List[Dict[str, str]]): the conversations with a list of messages. An example is :
+            [
+                {
+                    "role": "User",
+                    "content": "<image_placeholder>\nExtract all information from this image and convert them into markdown format.",
+                    "images": ["./examples/table_datasets.png"]
+                },
+                {"role": "Assistant", "content": ""},
+            ]
+
+    Returns:
+        pil_images (List[PIL.Image.Image]): the list of PIL images.
+
+    """
+
+    pil_images = []
+
+    for message in conversations:
+        if "images" not in message:
+            continue
+
+        for image_data in message["images"]:
+            if image_data.startswith("data:image"):
+                # Image data is in base64 format
+                _, image_data = image_data.split(",", 1)
+                image_bytes = base64.b64decode(image_data)
+                pil_img = PIL.Image.open(io.BytesIO(image_bytes))
+            else:
+                # Image data is a file path
+                pil_img = PIL.Image.open(image_data)
+            pil_img = pil_img.convert("RGB")
+            pil_images.append(pil_img)
+
+    return pil_images
+
+
+def load_json(filepath):
+    with open(filepath, "r") as f:
+        data = json.load(f)
+        return data
diff --git a/py/janus_node.py b/py/janus_node.py
new file mode 100644
index 0000000..6c38aca
--- /dev/null
+++ b/py/janus_node.py
@@ -0,0 +1,292 @@
+import os
+import sys
+current_dir = os.path.dirname(os.path.realpath(__file__))
+sys.path.append(os.path.join(current_dir, 'Janus'))
+from PIL import Image
+import torch
+from docutils.nodes import target
+from transformers import AutoModelForCausalLM
+import random
+import numpy as np
+from .janus.models import MultiModalityCausalLM, VLChatProcessor
+from .janus.models import MultiModalityCausalLM, VLChatProcessor
+from .janus.utils.io import load_pil_images
+import folder_paths
+from comfy.utils import ProgressBar
+from tqdm import tqdm
+
+def pil2tensor(image:Image) -> torch.Tensor:
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+def tensor2pil(t_image: torch.Tensor)  -> Image:
+    return Image.fromarray(np.clip(255.0 * t_image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+def check_and_download_model(model_path, repo_id):
+    model_path = os.path.join(folder_paths.models_dir, model_path)
+
+    if not os.path.exists(model_path):
+        print(f"Downloading {repo_id} model...")
+        from huggingface_hub import snapshot_download
+        snapshot_download(repo_id=repo_id, local_dir=model_path, ignore_patterns=["*.md", "*.txt", ".git"])
+    return model_path
+
+class DZ_LoadJanusModel:
+    def __init__(self):
+        self.NODE_NAME = 'DZ_LoadJanusModel'
+    @classmethod
+    def INPUT_TYPES(self):
+        model_list = ["Janus-Pro-7B", "Janus-Pro-1B"]
+        return {
+            "required": {
+                "model": (model_list,),
+            },
+            "optional": {
+            }
+        }
+
+    RETURN_TYPES = ("JanusModel",)
+    RETURN_NAMES = ("janus_model",)
+    FUNCTION = 'load_janus_model'
+    CATEGORY = '😺dzNodes/Janus'
+
+    def load_janus_model(self, model):
+        model_path = os.path.join(folder_paths.models_dir, "Janus-Pro", model)
+        check_and_download_model(model_path, f"deepseek-ai/{model}")
+        vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
+        tokenizer = vl_chat_processor.tokenizer
+
+        vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(
+            model_path, trust_remote_code=True
+        )
+        vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()
+
+        return ({"vl_gpt": vl_gpt, "vl_chat_processor": vl_chat_processor},)
+
+
+class DZ_JanusT2I:
+
+    def __init__(self):
+        self.NODE_NAME = 'JanusT2I'
+
+
+    @classmethod
+    def INPUT_TYPES(self):
+        default_prompt = ("A massive blue whale soaring like a bird above a deep blue ocean, slicing through silky waves. "
+                          "The sky is illed with golden and purple auroras, and thewhale's body glimmers with iridescent ights. "
+                          "its tail fin skims the ocean surace,leaving a tral of glowing streams, "
+                          "The scene exudes a futuristic and surrealvibe, with floating islands and glowing crystals in the background. "
+                          "The overall composition is breathtaking and fantastical.")
+        return {
+            "required": {
+                "janus_model": ("JanusModel",),
+                "prompt": ("STRING",{"default": default_prompt, "multiline": True},),
+                "size": ("INT", {"default": 384, "min": 384, "max": 384, "step": 16}),
+                "temperature": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 2, "step": 0.1}),
+                "cfg": ("FLOAT", {"default": 5, "min": 0, "max": 32, "step": 0.1}),
+                "token_num": ("INT", {"default": 576, "min": 576, "max": 576, "step": 16}),
+                "batch_size": ("INT", {"default": 1, "min": 1, "max": 1024, "step": 1}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 1e18, "step": 1}),
+            },
+            "optional": {
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = 'janus_t2i'
+    CATEGORY = '😺dzNodes/Janus'
+
+    def janus_t2i(self, janus_model, prompt, size, temperature, cfg, token_num, batch_size, seed):
+        ret_images = []
+
+        vl_chat_processor = janus_model["vl_chat_processor"]
+        tokenizer = vl_chat_processor.tokenizer
+        vl_gpt = janus_model["vl_gpt"]
+
+        conversation = [
+            {
+                "role": "<|User|>",
+                "content": prompt,
+            },
+            {"role": "<|Assistant|>", "content": ""},
+        ]
+
+        sft_format = vl_chat_processor.apply_sft_template_for_multi_turn_prompts(
+            conversations=conversation,
+            sft_format=vl_chat_processor.sft_format,
+            system_prompt="",
+        )
+        prompt = sft_format + vl_chat_processor.image_start_tag
+
+        @torch.inference_mode()
+        def generate(
+                mmgpt: MultiModalityCausalLM,
+                vl_chat_processor: VLChatProcessor,
+                prompt: str,
+                temperature: float = 1,
+                parallel_size: int = 1,
+                cfg_weight: float = 5,
+                image_token_num_per_image: int = 576,
+                img_size: int = 384,
+                patch_size: int = 16,
+                seed: int = 0,
+        ):
+
+            torch.manual_seed(seed)
+            torch.cuda.manual_seed_all(seed)
+
+            input_ids = vl_chat_processor.tokenizer.encode(prompt)
+            input_ids = torch.LongTensor(input_ids)
+
+            tokens = torch.zeros((parallel_size * 2, len(input_ids)), dtype=torch.int).cuda()
+
+            for i in range(parallel_size * 2):
+                tokens[i, :] = input_ids
+                if i % 2 != 0:
+                    tokens[i, 1:-1] = vl_chat_processor.pad_id
+
+            inputs_embeds = mmgpt.language_model.get_input_embeddings()(tokens)
+
+            generated_tokens = torch.zeros((parallel_size, image_token_num_per_image), dtype=torch.int).cuda()
+
+            comfy_pbar = ProgressBar(image_token_num_per_image)
+            tqdm_pbar = tqdm(total=image_token_num_per_image, desc="Generating Images")
+            for i in range(image_token_num_per_image):
+                outputs = mmgpt.language_model.model(inputs_embeds=inputs_embeds, use_cache=True,
+                                                     past_key_values=outputs.past_key_values if i != 0 else None)
+                hidden_states = outputs.last_hidden_state
+
+                logits = mmgpt.gen_head(hidden_states[:, -1, :])
+                logit_cond = logits[0::2, :]
+                logit_uncond = logits[1::2, :]
+
+                logits = logit_uncond + cfg_weight * (logit_cond - logit_uncond)
+                probs = torch.softmax(logits / temperature, dim=-1)
+
+                next_token = torch.multinomial(probs, num_samples=1)
+                generated_tokens[:, i] = next_token.squeeze(dim=-1)
+
+                next_token = torch.cat([next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1)], dim=1).view(-1)
+                img_embeds = mmgpt.prepare_gen_img_embeds(next_token)
+                inputs_embeds = img_embeds.unsqueeze(dim=1)
+                comfy_pbar.update(1)
+                tqdm_pbar.update(1)
+
+            dec = mmgpt.gen_vision_model.decode_code(generated_tokens.to(dtype=torch.int),
+                                                     shape=[parallel_size, 8, img_size // patch_size,
+                                                            img_size // patch_size])
+            dec = dec.to(torch.float32).cpu().numpy().transpose(0, 2, 3, 1)
+
+            dec = np.clip((dec + 1) / 2 * 255, 0, 255)
+
+            visual_img = np.zeros((parallel_size, img_size, img_size, 3), dtype=np.uint8)
+            visual_img[:, :, :] = dec
+
+            return visual_img
+
+        visual_img = generate(
+            mmgpt=vl_gpt,
+            vl_chat_processor=vl_chat_processor,
+            prompt=prompt,
+            temperature=temperature,
+            parallel_size=batch_size,
+            cfg_weight=cfg,
+            image_token_num_per_image=token_num,
+            img_size=size,
+            seed=seed
+        )
+
+        for i in visual_img:
+            img = Image.fromarray(i)
+            ret_images.append(pil2tensor(img))
+
+        return (torch.cat(ret_images, dim=0),)
+
+class DZ_JanusI2T:
+
+    def __init__(self):
+        self.NODE_NAME = 'JanusI2T'
+
+
+    @classmethod
+    def INPUT_TYPES(self):
+
+        return {
+            "required": {
+                "janus_model": ("JanusModel",),
+                "image": ("IMAGE",),
+                "question": ("STRING",{"default": "describe this image", "multiline": True},),
+                "temperature": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 2, "step": 0.1}),
+                "max_new_tokens": ("INT", {"default": 512, "min": 8, "max": 4096, "step": 8}),
+                "seed": ("INT", {"default": 0, "min": 0, "max": 1e18, "step": 1}),
+            },
+            "optional": {
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("text",)
+    FUNCTION = 'janus_i2t'
+    CATEGORY = '😺dzNodes/Janus'
+    OUTPUT_IS_LIST = (True,)
+
+    def janus_i2t(self, janus_model, image, question, temperature, max_new_tokens, seed):
+
+        ret_texts = []
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+
+        vl_chat_processor = janus_model["vl_chat_processor"]
+        tokenizer = vl_chat_processor.tokenizer
+        vl_gpt = janus_model["vl_gpt"]
+
+        ret_text = []
+
+        for i in image:
+            img = tensor2pil(i).convert("RGB")
+            conversation = [
+                {
+                    "role": "<|User|>",
+                    "content": f"<image_placeholder>\n{question}",
+                    "images": [img],
+                },
+                {"role": "<|Assistant|>", "content": ""},
+            ]
+
+            prepare_inputs = vl_chat_processor(
+                conversations=conversation, images=[img], force_batchify=True
+            ).to(vl_gpt.device)
+
+            # # run image encoder to get the image embeddings
+            inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
+
+            # # run the model to get the response
+            outputs = vl_gpt.language_model.generate(
+                inputs_embeds=inputs_embeds,
+                attention_mask=prepare_inputs.attention_mask,
+                pad_token_id=tokenizer.eos_token_id,
+                bos_token_id=tokenizer.bos_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+                max_new_tokens=max_new_tokens,
+                do_sample=False,
+                use_cache=True,
+            )
+
+            answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
+            # print(f"{prepare_inputs['sft_format'][0]}", answer)
+            ret_text.append(answer)
+
+        return (ret_text,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "JanusTextToImage": DZ_JanusT2I,
+    "JanusImage2Text": DZ_JanusI2T,
+    "LoadJanusModel": DZ_LoadJanusModel,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "JanusTextToImage": "Janus Text To Image (Generation)",
+    "JanusImage2Text": "Janus Image To Text (Understanding)",
+    "LoadJanusModel": "Load Janus Model",
+}
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..afb54d3
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,15 @@
+[project]
+name = "comfyui_janus_wrapper"
+description = ""
+version = "0.0.1"
+license = "MIT"
+dependencies = ["numpy", "pillow", "transformers", "timm", "accelerate", "sentencepiece", "attrdict", "einops", "huggingface_hub", "tqdm"]
+
+[project.urls]
+Repository = "https://github.com/chflame163/ComfyUI_Janus_Wrapper"
+#  Used by Comfy Registry https://comfyregistry.org
+
+[tool.comfy]
+PublisherId = "chflame163"
+DisplayName = "ComfyUI_Janus_Wrapper"
+Icon = ""
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..39df7e4
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,10 @@
+numpy<2.0
+pillow
+transformers
+timm
+accelerate
+sentencepiece
+attrdict
+einops
+huggingface_hub
+tqdm
\ No newline at end of file