+
+## Token Length-Based Model Training
+We collected approximately 1.2 million high-quality data for the training of CogVideoX-Fun. During the training, we resized the videos based on different token lengths. The entire training process is divided into three phases, with each phase corresponding to 13312 (for 512x512x49 videos), 29952 (for 768x768x49 videos), and 53248 (for 1024x1024x49 videos).
+
+Taking CogVideoX-Fun-2B as an example:
+- In the 13312 phase, the batch size is 128 with 7k training steps.
+- In the 29952 phase, the batch size is 256 with 6.5k training steps.
+- In the 53248 phase, the batch size is 128 with 5k training steps.
+
+During training, we combined high and low resolutions, enabling the model to support video generation from any resolution between 512 and 1280. For example, with a token length of 13312:
+- At a resolution of 512x512, the number of video frames is 49.
+- At a resolution of 768x768, the number of video frames is 21.
+- At a resolution of 1024x1024, the number of video frames is 9.
+
+These resolutions and corresponding lengths were mixed for training, allowing the model to generate videos at different resolutions.
+
+## Resize 3D Embedding
+In adapting CogVideoX-2B to the CogVideoX-Fun framework, it was found that the source code obtains 3D embeddings in a truncated manner. This approach only accommodates a single resolution; when the resolution changes, the embedding should also change.
+
+
+Referencing Pixart-Sigma, the above image is from the Pixart-Sigma paper. We used Positional Embeddings Interpolation (PE Interpolation) to resize 3D embeddings. PE Interpolation is more conducive to convergence than directly generating cosine and sine embeddings for different resolutions.
diff --git a/VideoX-Fun/reports/cogvideox_fun/report_v1_1.md b/VideoX-Fun/reports/cogvideox_fun/report_v1_1.md
new file mode 100644
index 0000000000000000000000000000000000000000..b716d8c72db3cc2f64bbe66b3095c6d6b8d16a34
--- /dev/null
+++ b/VideoX-Fun/reports/cogvideox_fun/report_v1_1.md
@@ -0,0 +1,32 @@
+# CogVideoX FUN v1.1 Report
+
+In CogVideoX-FUN v1.1, we performed additional filtering on the previous dataset, selecting videos with larger motion amplitudes rather than still images in motion, resulting in approximately 0.48 million videos. The model continues to support both image and video prediction, accommodating pixel values from 512x512x49, 768x768x49, 1024x1024x49, and videos with different aspect ratios. We support both image-to-video generation and video-to-video reconstruction.
+
+Additionally, we have released training and prediction code for adding control signals, along with the initial version of the Control model.
+
+Compared to version 1.0, CogVideoX-FUN V1.1 highlights the following features:
+- In the 5b model, Noise has been added to the reference images, increasing the motion amplitude of the videos.
+- Released training and prediction code for adding control signals, along with the initial version of the Control model.
+
+## Adding Noise to Reference Images
+
+Building on the original CogVideoX-FUN V1.0, we drew upon [CogVideoX](https://github.com/THUDM/CogVideo/) and [SVD](https://github.com/Stability-AI/generative-models) to add Noise upwards to the non-zero reference images to disrupt the original images, aiming for greater motion amplitude.
+
+In our 5b model, Noise has been added, while the 2b model only performed fine-tuning with new data. This is because, after attempting to add Noise in the 2b model, the generated videos exhibited excessive motion amplitude, leading to deformation and damaging the output. The 5b model, due to its stronger generative capabilities, maintains relatively stable outputs during motion.
+
+Furthermore, the prompt words significantly influence the generation results, so please describe the actions in detail to increase dynamism. If unsure how to write positive prompts, you can use phrases like "smooth motion" or "in the wind" to enhance dynamism. Additionally, it is advisable to avoid using dynamic terms like "motion" in negative prompts.
+
+## Adding Control Signals to CogVideoX-FUN
+
+On the basis of the original CogVideoX-FUN V1.0, we replaced the original mask signal with Pose control signals. The control signals are encoded using VAE and used as Guidance, along with latent data entering the patch processing flow.
+
+We filtered the 0.48 million dataset, selecting around 20,000 videos and images containing portraits for pose extraction, which served as condition control signals for training.
+
+During the training process, the videos are scaled according to different Token lengths. The entire training process is divided into two phases, with each phase comprising 13,312 (corresponding to 512x512x49 videos) and 53,248 (corresponding to 1024x1024x49 videos).
+
+Taking CogVideoX-Fun-V1.1-5b-Pose as an example:
+- In the 13312 phase, the batch size is 128, with 2.4k training steps.
+- In the 53248 phase, the batch size is 128, with 1.2k training steps.
+
+The working principle diagram is shown below:
+
diff --git a/VideoX-Fun/reports/cogvideox_fun/report_v1_1_zh-CN.md b/VideoX-Fun/reports/cogvideox_fun/report_v1_1_zh-CN.md
new file mode 100644
index 0000000000000000000000000000000000000000..81dfb7a902a71421b1baec0b3054a09a7a9b2b81
--- /dev/null
+++ b/VideoX-Fun/reports/cogvideox_fun/report_v1_1_zh-CN.md
@@ -0,0 +1,31 @@
+# CogVideoX FUN v1.1 Report
+
+在CogVideoX-FUN v1.1中，我们在之前的数据集中再次做了筛选，选出其中动作幅度较大，而不是静止画面移动的视频，数量大约为0.48m。模型依然支持图片与视频预测，支持像素值从512x512x49、768x768x49、1024x1024x49与不同纵横比的视频生成。我们支持图像到视频的生成与视频到视频的重建。
+
+另外，我们还发布了添加控制信号的训练代码与预测代码，并发布了初版的Control模型。
+
+对比V1.0版本，CogVideoX-FUN V1.1突出了以下功能：
+
+- 在5b模型中，给参考图片添加了Noise，增加了视频的运动幅度。
+- 发布了添加控制信号的训练代码与预测代码，并发布了初版的Control模型。
+
+## 参考图片添加Noise
+在原本CogVideoX-FUN V1.0的基础上，我们参考[CogVideoX](https://github.com/THUDM/CogVideo/)和[SVD](https://github.com/Stability-AI/generative-models)，在非0的参考图向上添加Noise以破环原图，追求更大的运动幅度。
+
+我们5b模型中添加了Noise，2b模型仅使用了新数据进行了finetune，因为我们在2b模型中尝试添加Noise之后，生成的视频运动幅度过大导致结果变形，破坏了生成结果，而5b模型因为更为的强大生成能力，在运动中也保持了较为稳定的输出。
+
+另外，提示词对生成结果影响较大，请尽量描写动作以增加动态性。如果不知道怎么写正向提示词，可以使用smooth motion or in the wind来增加动态性。并且尽量避免在负向提示词中出现motion等表示动态的词汇。
+
+## 添加控制信号的CogVideoX-Fun
+在原本CogVideoX-FUN V1.0的基础上，我们使用Pose控制信号替代了原本的mask信号，将控制信号使用VAE编码后作为Guidance与latent一起进入patch流程，
+
+我们在0.48m数据中进行了筛选，选择出大约20000包含人像的视频与图片进行pose提取，作为condition控制信号进行训练。
+
+在进行训练时，我们根据不同Token长度，对视频进行缩放后进行训练。整个训练过程分为两个阶段，每个阶段的13312（对应512x512x49的视频），53248（对应1024x1024x49的视频）。
+
+以CogVideoX-Fun-V1.1-5b-Pose为例子，其中：
+- 13312阶段，Batch size为128，训练步数为2.4k
+- 53248阶段，Batch size为128，训练步数为1.2k。
+
+工作原理图如下：
+
diff --git a/VideoX-Fun/reports/cogvideox_fun/report_v1_zh-CN.md b/VideoX-Fun/reports/cogvideox_fun/report_v1_zh-CN.md
new file mode 100644
index 0000000000000000000000000000000000000000..2d42f123eac46efcd8648fbee9bd4542e96924dd
--- /dev/null
+++ b/VideoX-Fun/reports/cogvideox_fun/report_v1_zh-CN.md
@@ -0,0 +1,43 @@
+# CogVideoX FUN v1 Report
+
+在CogVideoX-FUN中，我们基于CogVideoX在大约1.2m的数据上进行了训练，支持图片与视频预测，支持像素值从512x512x49、768x768x49、1024x1024x49与不同纵横比的视频生成。另外，我们支持图像到视频的生成与视频到视频的重建。
+
+对比与CogVideoX，CogVideoX FUN还突出了以下功能：
+
+- 引入InPaint模型，实现图生视频功能，可以通过首尾图指定视频生成。
+- 基于Token长度的模型训练。达成不同大小多分辨率在同一模型中的实现。
+
+## InPaint模型
+我们以[CogVideoX](https://github.com/THUDM/CogVideo/)作为基础结构，参考[EasyAnimate](https://github.com/aigc-apps/EasyAnimate)进行图生视频的模型训练。
+
+在进行视频生成的时候，将**参考视频**使用VAE进行encode，**上图黑色的部分代表需要重建的部分，白色的部分代表首图**，与噪声Latents一起堆叠后输入到Transformer中进行视频生成。
+
+我们对**被Mask的区域**进行3D Resize，直接Resize到需要重建的视频的画布大小。
+
+然后将Latent、Encode后的参考视频、被Mask的区域，concat后输入到DiT中进行噪声预测。获得最终的视频。
+
+CogVideoX FUN的Pipeline结构如下：
+
+
+## 基于Token长度的模型训练
+我们收集了大约高质量的1.2m数据进行CogVideoX-Fun的训练。
+
+在进行训练时，我们根据不同Token长度，对视频进行缩放后进行训练。整个训练过程分为三个阶段，每个阶段的13312（对应512x512x49的视频），29952（对应768x768x49的视频），53248（对应1024x1024x49的视频）。
+
+以CogVideoX-Fun-2B为例子，其中：
+- 13312阶段，Batch size为128，训练步数为7k
+- 29952阶段，Batch size为256，训练步数为6.5k。
+- 53248阶段，Batch size为128，训练步数为5k。
+
+训练时我们采用高低分辨率结合训练，因此模型支持从512到1280任意分辨率的视频生成，以13312 token长度为例：
+- 在512x512分辨率下，视频帧数为49；
+- 在768x768分辨率下，视频帧数为21；
+- 在1024x1024分辨率下，视频帧数为9；
+这些分辨率与对应长度混合训练，模型可以完成不同大小分辨率的视频生成。
+
+## Resize 3D Embedding
+在适配CogVideoX-2B到CogVideoX-Fun框架的途中，发现源码是以截断的方式去得到3D Embedding的，这样的方式只能适配单一分辨率，当分辨率发生变化时，Embedding也应当发生变化。
+
+
+
+参考Pixart-Sigma，上图来自于Pixart-Sigma论文，我们采用Positional Embeddings Interpolation（PE Interpolation）对3D embedding进行Resize，PE Interpolation相比于直接生成不同分辨率的Cos Sin Embedding更易收敛。
\ No newline at end of file
diff --git a/VideoX-Fun/reports/wan2_1_fun/report_v1_1.md b/VideoX-Fun/reports/wan2_1_fun/report_v1_1.md
new file mode 100644
index 0000000000000000000000000000000000000000..3e724f5cf4095bcb726412714f718a33371521c1
--- /dev/null
+++ b/VideoX-Fun/reports/wan2_1_fun/report_v1_1.md
@@ -0,0 +1,31 @@
+# Wan Fun v1.1 Report
+
+In Wan-Fun v1.1, we updated six models: the 14B Inpaint model, Control model, and Control-Camera model; as well as the 1.3B Inpaint model, Control model, and Control-Camera model.
+
+Compared to the previous version, the Inpaint model has been trained with a larger batch size, resulting in more stable performance. The Control model now includes a reference image model to achieve effects similar to Animate Anyone. While retaining its original functionality, it can also accept both a reference image and a control video as inputs for generation. Finally, we provide a camera control model that supports pan-and-tilt movements (left, right, up, down).
+
+Additionally, we have released training and inference code for adding reference control signals, as well as training and inference code for adding camera control signals.
+
+Compared to V1.0, Wan Fun V1.1 highlights the following features:
+
+- A more stable Inpaint model.
+- On top of the original control scheme, we’ve implemented a new control approach combining reference images and control videos.
+- Added support for a camera control model.
+
+## Implementation of Reference Image + Control Video
+In Wan-Fun V1.0, we already supported multiple control signals such as Canny, Depth, Pose, and MLSD, and implemented two control schemes: initial-image plus trajectory control, and control-video-guided generation.
+
+To further enhance the usability of the control model, we developed a new control scheme that combines reference images with control videos, akin to Animate Anyone. This feature takes a reference image as input and generates output based on control signals like Openpose (though it is not limited to Openpose—Depth signals also yield impressive results). Previously, methods like Unimate or Animate Anyone typically required the skeleton of the reference image to closely align with the control video. However, in Wan-Fun V1.1 Control, even if there is some misalignment, the system still produces acceptable results, though better alignment naturally leads to higher similarity.
+
+We encode the reference image using VAE, then tile the latent representation and concatenate the tiled features with the video features for generation. To ensure that this does not interfere with the model's original functionality, during training, we randomly initialize the reference image latent as all zeros to simulate cases where no reference image is provided. The overall workflow of the model is shown in the figure below:
+
+
+
+## Camera Control Model
+Building upon Wan-Fun V1.0, we now support additional camera information inputs for camera control.
+
+Inspired by [CameraCtrl](https://github.com/hehao13/CameraCtrl) and [EasyAnimate](https://github.com/aigc-apps/EasyAnimate), instead of directly resizing inputs like EasyAnimate does to input camera trajectories, we first use PixelUnshuffle to convert temporal information into channel information. Then, using an adapter mechanism, we transform the camera trajectory into high-level semantic information before adding it to the video features post-Conv. This allows us to achieve precise control over the camera lens movement.
+
+The overall framework of the model is shown in the figure below:
+
+
\ No newline at end of file
diff --git a/VideoX-Fun/reports/wan2_1_fun/report_v1_1_zh-CN.md b/VideoX-Fun/reports/wan2_1_fun/report_v1_1_zh-CN.md
new file mode 100644
index 0000000000000000000000000000000000000000..0c816116f5dc4c46ae2e1a8a4466c57fd6f0ccc1
--- /dev/null
+++ b/VideoX-Fun/reports/wan2_1_fun/report_v1_1_zh-CN.md
@@ -0,0 +1,31 @@
+# Wan Fun v1.1 Report
+
+在Wan-Fun v1.1中，我们更新了6个模型，分别是：14B的Inpaint模型，Control模型、Control-Camera模型；1.3B的Inpaint模型，Control模型、Control-Camera模型。
+
+相比于上一个版本，Inpaint模型经过了更大batch size的训练，模型效果的稳定性更优秀；Control模型则新增加了一个参考图模型，以实现类似于Animate Anyone的效果，在保留之前功能的基础上，我们可以同时传入参考图片和控制视频，以实现生成；最后我们提供了镜头控制模型，可以实现上下左右的镜头控制。
+
+另外，我们还发布了添加参考控制信号的训练代码与预测代码，添加镜头控制信号的训练代码和预测代码。
+
+对比V1.0版本，Wan Fun V1.1突出了以下功能：
+
+- 更为稳定的Inpaint模型。
+- 在原控制方案的基础上，实现了参考图加上控制视频的控制方案。
+- 实现了镜头控制模型。
+
+## 参考图加上控制视频的实现
+在原本Wan-Fun V1.0的中，我们已经支持了多种控制信号，如Canny、Depth、Pose、MLSD；实现了两种控制方案，如首图+轨迹控制、控制视频指导生成。
+
+为了进一步提高控制模型的可用性，我们进一步开发了参考图加上控制视频的控制方案，该功能animate anyone，输入一张参考图片，然后根据Openpose（并不局限于Openpose，Depth信号也有非常亮眼的效果）之类的控制实现生成。此前，Unimate、animate anyone之类的方案一般要求参考图和控制视频骨架基本对齐。在Wan-Fun V1.1 Control中，对齐可以有更好的相似度，不对齐也可以有一定的参考生成效果。
+
+我们将参考图使用VAE Encode之后，将latent平铺，然后将平铺后的特征与视频特征进行concat实现生成，为了不影响模型的原功能，我们在训练中随机将参考图latent初始化为全0，代表没有参考图输入，整体模型的工作框架如图所示：
+
+
+
+## 镜头控制模型
+在原本Wan-Fun V1.0的基础上，我们支持进一步输入Camera信息，以进行镜头控制。
+
+参考[CameraCtrl](https://github.com/hehao13/CameraCtrl)与[EasyAnimate](https://github.com/aigc-apps/EasyAnimate)，我们没有选择类似于EasyAnimate那种直接Resize的方式输入相机镜头的控制轨迹，而是先使用PixelUnshuffle将时序信息转换成通道信息，然后使用Adapter的方式，将相机镜头的轨迹转换成高层语义信息后再与Conv in后的视频特征相加，从而实现了视频镜头的控制。
+
+整体模型的工作框架如图所示：
+
+
\ No newline at end of file
diff --git a/VideoX-Fun/scripts/README_DEMO.md b/VideoX-Fun/scripts/README_DEMO.md
new file mode 100644
index 0000000000000000000000000000000000000000..0d0032cc84e71b0813e3415545960c3ea7d1f486
--- /dev/null
+++ b/VideoX-Fun/scripts/README_DEMO.md
@@ -0,0 +1,37 @@
+## Demo
+
+Image generation video corresponding images and prompts.
+
+If you don't know how to write positive prompts, you can use "smooth motion" or "in the wind" to add dynamism.
+
+| Image | Prompt |
+|--|--|
+|  | closeup face photo of man is smiling in black clothes, night city street, bokeh, fireworks in background |
+|  | sunset, orange sky, warm lighting, fishing boats, ocean waves, seagulls, rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, golden hour, coastal landscape, seaside scenery |
+|  | a man in an astronaut suit playing a guitar |
+|  | time-lapse of a blooming flower with leaves and a stem, blossom |
+|  | fireworks display over night city |
+|  | a beautiful woman with long hair and a dress blowing in the wind |
+|  | the dog is shaking head |
+|  | a robot is walking through a destroyed city |
+|  | a group of penguins walking on a beach |
+|  | a bonfire is lit in the middle of a field |
+|  | a boat traveling on the ocean |
+|  | pouring honey onto some slices of bread |
+|  | a sailboat sailing in rough seas with a dramatic sunset |
+|  | a boat traveling on the ocean |
+|  | a scenic view of a lake with several seagulls flying above the water. In the foreground, there is a person wearing a red garment, possibly a jacket or a shawl, observing the scenery. The lake has clear blue water, and there's a structure that appears to be a wooden pavilion or boathouse on stilts situated in the water. In the background, hills or mountains can be seen under a clear blue sky, enhancing the tranquil and picturesque setting |
+|  | A man's body shimmered with golden light in the wind |
+|  | a buried broken emerald cross glazed by the sun emitting smoke, backlit, forgotten, atmospheric AF, detailed, 8k |
+|  | A beautiful woman is smiling in the wind |
+|  | A beautiful woman is smiling in the wind |
+|  | A beautiful woman is smiling in the wind |
+|  | A beautiful woman smiles in the heavy snow |
+|  | cats smiling taking a selfie with a super wide angle lenses, opening mouth. |
+|  | The sturdy sailboat in 'Temperamental Tides', masterfully navigating the restless, pulsating waves of the deep navy sea, maintaining balance on the surging storm grey crests |
+|  | The sturdy sailboat in 'Temperamental Tides', masterfully navigating the restless, pulsating waves of the deep navy sea, maintaining balance on the surging storm grey crests |
+|  | a beach with waves crashing against it and a sunset in the background a brigantine, a sailboat in the distance, 4k |
+|  | Create an illustration that captures the essence of water. The scene should be a tranquil beach at sunrise, with the calm ocean stretching out to the horizon. The sky is painted in soft hues of pink and orange as the sun begins to rise. Gentle waves lap against the sandy shore, creating delicate ripples. The water is crystal clear, reflecting the colors of the sky, and small, glistening seashells are scattered along the shoreline. In the distance, a small sailboat with white sails drifts peacefully on the water. The overall mood of the illustration should be serene and calming, emphasizing the fluid and reflective nature of water.glowneon, glowing, sparks, lightning |
+|  | a Lighthouse battered by high winds, huge crashing waves, realistic northern lights, behind lighthouse, realistic stormy seas, high quality image, photographic, mist, and sea spray, storm clouds, angry sky, dusk, peninsula, winter, almost dark, storm, gales, elevated view point, high up perspective, night time, lighthouse light beams, position lighthouse to left of image, view from on high |
+|  | Two racing cars racing towards the camera, desert dune in the background, hyperrealistic, driver turning the wheel, more details, speed of light, a trail of intense light follows the cars, image evokes the sensation of speed, frozen movement, insane intricate detail, (masterpiece, best quality), high resolution, (ultra detailed), |
+|  | one long eared dog, beagle, making goofy faces under water, lie the wind is blowing in his open mouth bubbles wide. an annoyed goldfish swims by |
\ No newline at end of file
diff --git a/VideoX-Fun/scripts/cogvideox_fun/README_TRAIN.md b/VideoX-Fun/scripts/cogvideox_fun/README_TRAIN.md
new file mode 100644
index 0000000000000000000000000000000000000000..07f3c39e25b32cbdc4527a97426a802898436515
--- /dev/null
+++ b/VideoX-Fun/scripts/cogvideox_fun/README_TRAIN.md
@@ -0,0 +1,158 @@
+## Training Code
+
+The default training commands for the different versions are as follows:
+
+We can choose whether to use deep speed in CogVideoX-Fun, which can save a lot of video memory.
+
+Some parameters in the sh file can be confusing, and they are explained in this document:
+
+- `enable_bucket` is used to enable bucket training. When enabled, the model does not crop the images and videos at the center, but instead, it trains the entire images and videos after grouping them into buckets based on resolution.
+- `random_frame_crop` is used for random cropping on video frames to simulate videos with different frame counts.
+- `random_hw_adapt` is used to enable automatic height and width scaling for images and videos. When `random_hw_adapt` is enabled, the training images will have their height and width set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. For training videos, the height and width will be set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum.
+ - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=1024`, and `image_sample_size=1024`, the resolution of image inputs for training is `512x512` to `1024x1024`, and the resolution of video inputs for training is `512x512x49` to `1024x1024x49`.
+ - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49`.
+- `training_with_video_token_length` specifies training the model according to token length. For training images and videos, the height and width will be set to `image_sample_size` as the maximum and `video_sample_size` as the minimum.
+ - For example, when `training_with_video_token_length` is enabled, with `video_sample_n_frames=49`, `token_sample_size=1024`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49` to `1024x1024x49`.
+ - For example, when `training_with_video_token_length` is enabled, with `video_sample_n_frames=49`, `token_sample_size=512`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49` to `1024x1024x9`.
+ - The token length for a video with dimensions 512x512 and 49 frames is 13,312. We need to set the `token_sample_size = 512`.
+ - At 512x512 resolution, the number of video frames is 49 (~= 512 * 512 * 49 / 512 / 512).
+ - At 768x768 resolution, the number of video frames is 21 (~= 512 * 512 * 49 / 768 / 768).
+ - At 1024x1024 resolution, the number of video frames is 9 (~= 512 * 512 * 49 / 1024 / 1024).
+ - These resolutions combined with their corresponding lengths allow the model to generate videos of different sizes.
+- `train_mode` is used to specify the training mode, which can be either normal or i2v. Since CogVideoX-Fun uses the inpaint model to achieve image-to-video generation, the default is set to inpaint mode. If you only wish to achieve text-to-video generation, you can remove this line, and it will default to the text-to-video mode.
+- `resume_from_checkpoint` is used to set the training should be resumed from a previous checkpoint. Use a path or `"latest"` to automatically select the last available checkpoint.
+
+CogVideoX-Fun without deepspeed:
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/CogVideoX-Fun-2b-InP"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA.
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+accelerate launch --mixed_precision="bf16" scripts/cogvideox_fun/train.py \
+ --pretrained_model_name_or_path=$MODEL_NAME \
+ --train_data_dir=$DATASET_NAME \
+ --train_data_meta=$DATASET_META_NAME \
+ --image_sample_size=1024 \
+ --video_sample_size=256 \
+ --token_sample_size=512 \
+ --video_sample_stride=3 \
+ --video_sample_n_frames=49 \
+ --train_batch_size=1 \
+ --video_repeat=1 \
+ --gradient_accumulation_steps=1 \
+ --dataloader_num_workers=8 \
+ --num_train_epochs=100 \
+ --checkpointing_steps=50 \
+ --learning_rate=2e-05 \
+ --lr_scheduler="constant_with_warmup" \
+ --lr_warmup_steps=100 \
+ --seed=42 \
+ --output_dir="output_dir" \
+ --gradient_checkpointing \
+ --mixed_precision="bf16" \
+ --adam_weight_decay=3e-2 \
+ --adam_epsilon=1e-10 \
+ --vae_mini_batch=1 \
+ --max_grad_norm=0.05 \
+ --random_hw_adapt \
+ --training_with_video_token_length \
+ --enable_bucket \
+ --use_ema \
+ --train_mode="inpaint" \
+ --trainable_modules "."
+```
+
+CogVideoX-Fun with deepspeed:
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/CogVideoX-Fun-2b-InP"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA.
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+accelerate launch --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json --deepspeed_multinode_launcher standard scripts/cogvideox_fun/train.py \
+ --pretrained_model_name_or_path=$MODEL_NAME \
+ --train_data_dir=$DATASET_NAME \
+ --train_data_meta=$DATASET_META_NAME \
+ --image_sample_size=1024 \
+ --video_sample_size=256 \
+ --token_sample_size=512 \
+ --video_sample_stride=3 \
+ --video_sample_n_frames=49 \
+ --train_batch_size=4 \
+ --video_repeat=1 \
+ --gradient_accumulation_steps=1 \
+ --dataloader_num_workers=8 \
+ --num_train_epochs=100 \
+ --checkpointing_steps=50 \
+ --learning_rate=2e-05 \
+ --lr_scheduler="constant_with_warmup" \
+ --lr_warmup_steps=100 \
+ --seed=42 \
+ --output_dir="output_dir" \
+ --gradient_checkpointing \
+ --mixed_precision="bf16" \
+ --adam_weight_decay=3e-2 \
+ --adam_epsilon=1e-10 \
+ --vae_mini_batch=1 \
+ --max_grad_norm=0.05 \
+ --random_hw_adapt \
+ --training_with_video_token_length \
+ --enable_bucket \
+ --use_deepspeed \
+ --train_mode="inpaint" \
+ --trainable_modules "."
+```
+
+CogVideoX-Fun with multi machines:
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/CogVideoX-Fun-2b-InP"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA.
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+NUM_PROCESS=$((WORLD_SIZE * 8))
+
+echo "MASTER_ADDR: ${MASTER_ADDR} MASTER_PORT: ${MASTER_PORT} NUM_PROCESS: ${NUM_PROCESS}"
+
+accelerate launch --main_process_ip=$MASTER_ADDR --main_process_port=$MASTER_PORT --num_machines=$WORLD_SIZE --num_processes=$NUM_PROCESS --machine_rank=$RANK scripts/cogvideox_fun/train.py \
+ --pretrained_model_name_or_path=$MODEL_NAME \
+ --train_data_dir=$DATASET_NAME \
+ --train_data_meta=$DATASET_META_NAME \
+ --image_sample_size=1024 \
+ --video_sample_size=256 \
+ --token_sample_size=512 \
+ --video_sample_stride=3 \
+ --video_sample_n_frames=49 \
+ --train_batch_size=4 \
+ --video_repeat=1 \
+ --gradient_accumulation_steps=1 \
+ --dataloader_num_workers=8 \
+ --num_train_epochs=100 \
+ --checkpointing_steps=50 \
+ --learning_rate=2e-05 \
+ --lr_scheduler="constant_with_warmup" \
+ --lr_warmup_steps=100 \
+ --seed=42 \
+ --output_dir="output_dir" \
+ --gradient_checkpointing \
+ --mixed_precision="bf16" \
+ --adam_weight_decay=3e-2 \
+ --adam_epsilon=1e-10 \
+ --vae_mini_batch=1 \
+ --max_grad_norm=0.05 \
+ --random_hw_adapt \
+ --training_with_video_token_length \
+ --enable_bucket \
+ --train_mode="inpaint" \
+ --trainable_modules "."
+```
diff --git a/VideoX-Fun/scripts/cogvideox_fun/README_TRAIN_CONTROL.md b/VideoX-Fun/scripts/cogvideox_fun/README_TRAIN_CONTROL.md
new file mode 100644
index 0000000000000000000000000000000000000000..df66cb61f111e93924142789b65cda0404e39491
--- /dev/null
+++ b/VideoX-Fun/scripts/cogvideox_fun/README_TRAIN_CONTROL.md
@@ -0,0 +1,173 @@
+## Training Code
+
+The default training commands for the different versions are as follows:
+
+We can choose whether to use deep speed in CogVideoX-Fun, which can save a lot of video memory.
+
+The metadata_control.json is a little different from normal json in CogVideoX-Fun, you need to add a control_file_path, and [DWPose](https://github.com/IDEA-Research/DWPose) is suggested as tool to generate control file.
+
+```json
+[
+ {
+ "file_path": "train/00000001.mp4",
+ "control_file_path": "control/00000001.mp4",
+ "text": "A group of young men in suits and sunglasses are walking down a city street.",
+ "type": "video"
+ },
+ {
+ "file_path": "train/00000002.jpg",
+ "control_file_path": "control/00000002.jpg",
+ "text": "A group of young men in suits and sunglasses are walking down a city street.",
+ "type": "image"
+ },
+ .....
+]
+```
+
+Some parameters in the sh file can be confusing, and they are explained in this document:
+
+- `enable_bucket` is used to enable bucket training. When enabled, the model does not crop the images and videos at the center, but instead, it trains the entire images and videos after grouping them into buckets based on resolution.
+- `random_frame_crop` is used for random cropping on video frames to simulate videos with different frame counts.
+- `random_hw_adapt` is used to enable automatic height and width scaling for images and videos. When `random_hw_adapt` is enabled, the training images will have their height and width set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. For training videos, the height and width will be set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum.
+ - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=1024`, and `image_sample_size=1024`, the resolution of image inputs for training is `512x512` to `1024x1024`, and the resolution of video inputs for training is `512x512x49` to `1024x1024x49`.
+ - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49`.
+- `training_with_video_token_length` specifies training the model according to token length. For training images and videos, the height and width will be set to `image_sample_size` as the maximum and `video_sample_size` as the minimum.
+ - For example, when `training_with_video_token_length` is enabled, with `video_sample_n_frames=49`, `token_sample_size=1024`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49` to `1024x1024x49`.
+ - For example, when `training_with_video_token_length` is enabled, with `video_sample_n_frames=49`, `token_sample_size=512`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49` to `1024x1024x9`.
+ - The token length for a video with dimensions 512x512 and 49 frames is 13,312. We need to set the `token_sample_size = 512`.
+ - At 512x512 resolution, the number of video frames is 49 (~= 512 * 512 * 49 / 512 / 512).
+ - At 768x768 resolution, the number of video frames is 21 (~= 512 * 512 * 49 / 768 / 768).
+ - At 1024x1024 resolution, the number of video frames is 9 (~= 512 * 512 * 49 / 1024 / 1024).
+ - These resolutions combined with their corresponding lengths allow the model to generate videos of different sizes.
+- `resume_from_checkpoint` is used to set the training should be resumed from a previous checkpoint. Use a path or `"latest"` to automatically select the last available checkpoint.
+
+CogVideoX-Fun without deepspeed:
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/CogVideoX-Fun-V1.1-2b-Pose"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata_control.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA.
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+accelerate launch --mixed_precision="bf16" scripts/cogvideox_fun/train_control.py \
+ --pretrained_model_name_or_path=$MODEL_NAME \
+ --train_data_dir=$DATASET_NAME \
+ --train_data_meta=$DATASET_META_NAME \
+ --image_sample_size=1024 \
+ --video_sample_size=256 \
+ --token_sample_size=512 \
+ --video_sample_stride=3 \
+ --video_sample_n_frames=49 \
+ --train_batch_size=4 \
+ --video_repeat=1 \
+ --gradient_accumulation_steps=1 \
+ --dataloader_num_workers=8 \
+ --num_train_epochs=100 \
+ --checkpointing_steps=50 \
+ --learning_rate=2e-05 \
+ --lr_scheduler="constant_with_warmup" \
+ --lr_warmup_steps=50 \
+ --seed=43 \
+ --output_dir="output_dir" \
+ --gradient_checkpointing \
+ --mixed_precision="bf16" \
+ --adam_weight_decay=3e-2 \
+ --adam_epsilon=1e-10 \
+ --vae_mini_batch=1 \
+ --max_grad_norm=0.05 \
+ --random_hw_adapt \
+ --training_with_video_token_length \
+ --enable_bucket \
+ --trainable_modules "."
+```
+
+CogVideoX-Fun with deepspeed:
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/CogVideoX-Fun-V1.1-2b-Pose"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata_control.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA.
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+accelerate launch --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json --deepspeed_multinode_launcher standard scripts/cogvideox_fun/train.py \
+ --pretrained_model_name_or_path=$MODEL_NAME \
+ --train_data_dir=$DATASET_NAME \
+ --train_data_meta=$DATASET_META_NAME \
+ --image_sample_size=1024 \
+ --video_sample_size=256 \
+ --token_sample_size=512 \
+ --video_sample_stride=3 \
+ --video_sample_n_frames=49 \
+ --train_batch_size=4 \
+ --video_repeat=1 \
+ --gradient_accumulation_steps=1 \
+ --dataloader_num_workers=8 \
+ --num_train_epochs=100 \
+ --checkpointing_steps=50 \
+ --learning_rate=2e-05 \
+ --lr_scheduler="constant_with_warmup" \
+ --lr_warmup_steps=50 \
+ --seed=43 \
+ --output_dir="output_dir" \
+ --gradient_checkpointing \
+ --mixed_precision="bf16" \
+ --adam_weight_decay=3e-2 \
+ --adam_epsilon=1e-10 \
+ --vae_mini_batch=1 \
+ --max_grad_norm=0.05 \
+ --random_hw_adapt \
+ --training_with_video_token_length \
+ --enable_bucket \
+ --use_deepspeed \
+ --trainable_modules "."
+```
+
+CogVideoX-Fun with multi machines:
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/CogVideoX-Fun-V1.1-2b-Pose"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata_control.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA.
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+NUM_PROCESS=$((WORLD_SIZE * 8))
+
+echo "MASTER_ADDR: ${MASTER_ADDR} MASTER_PORT: ${MASTER_PORT} NUM_PROCESS: ${NUM_PROCESS}"
+
+accelerate launch --main_process_ip=$MASTER_ADDR --main_process_port=$MASTER_PORT --num_machines=$WORLD_SIZE --num_processes=$NUM_PROCESS --machine_rank=$RANK scripts/cogvideox_fun/train.py \
+ --pretrained_model_name_or_path=$MODEL_NAME \
+ --train_data_dir=$DATASET_NAME \
+ --train_data_meta=$DATASET_META_NAME \
+ --image_sample_size=1024 \
+ --video_sample_size=256 \
+ --token_sample_size=512 \
+ --video_sample_stride=3 \
+ --video_sample_n_frames=49 \
+ --train_batch_size=4 \
+ --video_repeat=1 \
+ --gradient_accumulation_steps=1 \
+ --dataloader_num_workers=8 \
+ --num_train_epochs=100 \
+ --checkpointing_steps=50 \
+ --learning_rate=2e-05 \
+ --lr_scheduler="constant_with_warmup" \
+ --lr_warmup_steps=50 \
+ --seed=43 \
+ --output_dir="output_dir" \
+ --gradient_checkpointing \
+ --mixed_precision="bf16" \
+ --adam_weight_decay=3e-2 \
+ --adam_epsilon=1e-10 \
+ --vae_mini_batch=1 \
+ --max_grad_norm=0.05 \
+ --random_hw_adapt \
+ --training_with_video_token_length \
+ --enable_bucket \
+ --trainable_modules "."
+```
\ No newline at end of file
diff --git a/VideoX-Fun/scripts/cogvideox_fun/README_TRAIN_LORA.md b/VideoX-Fun/scripts/cogvideox_fun/README_TRAIN_LORA.md
new file mode 100644
index 0000000000000000000000000000000000000000..66373e5f0111adee196c1e7ba4f75be2ac3a8aff
--- /dev/null
+++ b/VideoX-Fun/scripts/cogvideox_fun/README_TRAIN_LORA.md
@@ -0,0 +1,149 @@
+## Lora Training Code
+
+We can choose whether to use deep speed in CogVideoX-Fun, which can save a lot of video memory.
+
+Some parameters in the sh file can be confusing, and they are explained in this document:
+
+- `enable_bucket` is used to enable bucket training. When enabled, the model does not crop the images and videos at the center, but instead, it trains the entire images and videos after grouping them into buckets based on resolution.
+- `random_frame_crop` is used for random cropping on video frames to simulate videos with different frame counts.
+- `random_hw_adapt` is used to enable automatic height and width scaling for images and videos. When `random_hw_adapt` is enabled, the training images will have their height and width set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum. For training videos, the height and width will be set to `image_sample_size` as the maximum and `min(video_sample_size, 512)` as the minimum.
+ - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=1024`, and `image_sample_size=1024`, the resolution of image inputs for training is `512x512` to `1024x1024`, and the resolution of video inputs for training is `512x512x49` to `1024x1024x49`.
+ - For example, when `random_hw_adapt` is enabled, with `video_sample_n_frames=49`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49`.
+- `training_with_video_token_length` specifies training the model according to token length. For training images and videos, the height and width will be set to `image_sample_size` as the maximum and `video_sample_size` as the minimum.
+ - For example, when `training_with_video_token_length` is enabled, with `video_sample_n_frames=49`, `token_sample_size=1024`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49` to `1024x1024x49`.
+ - For example, when `training_with_video_token_length` is enabled, with `video_sample_n_frames=49`, `token_sample_size=512`, `video_sample_size=1024`, and `image_sample_size=256`, the resolution of image inputs for training is `256x256` to `1024x1024`, and the resolution of video inputs for training is `256x256x49` to `1024x1024x9`.
+ - The token length for a video with dimensions 512x512 and 49 frames is 13,312. We need to set the `token_sample_size = 512`.
+ - At 512x512 resolution, the number of video frames is 49 (~= 512 * 512 * 49 / 512 / 512).
+ - At 768x768 resolution, the number of video frames is 21 (~= 512 * 512 * 49 / 768 / 768).
+ - At 1024x1024 resolution, the number of video frames is 9 (~= 512 * 512 * 49 / 1024 / 1024).
+ - These resolutions combined with their corresponding lengths allow the model to generate videos of different sizes.
+- `train_mode` is used to specify the training mode, which can be either normal or i2v. Since CogVideoX-Fun uses the inpaint model to achieve image-to-video generation, the default is set to inpaint mode. If you only wish to achieve text-to-video generation, you can remove this line, and it will default to the text-to-video mode.
+- `resume_from_checkpoint` is used to set the training should be resumed from a previous checkpoint. Use a path or `"latest"` to automatically select the last available checkpoint.
+
+CogVideoX-Fun without deepspeed:
+
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/CogVideoX-Fun-2b-InP"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA.
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+accelerate launch --mixed_precision="bf16" scripts/cogvideox_fun/train_lora.py \
+ --pretrained_model_name_or_path=$MODEL_NAME \
+ --train_data_dir=$DATASET_NAME \
+ --train_data_meta=$DATASET_META_NAME \
+ --image_sample_size=1024 \
+ --video_sample_size=256 \
+ --token_sample_size=512 \
+ --video_sample_stride=3 \
+ --video_sample_n_frames=49 \
+ --train_batch_size=1 \
+ --video_repeat=1 \
+ --gradient_accumulation_steps=1 \
+ --dataloader_num_workers=8 \
+ --num_train_epochs=100 \
+ --checkpointing_steps=50 \
+ --learning_rate=1e-04 \
+ --seed=42 \
+ --output_dir="output_dir" \
+ --gradient_checkpointing \
+ --mixed_precision="bf16" \
+ --adam_weight_decay=3e-2 \
+ --adam_epsilon=1e-10 \
+ --vae_mini_batch=1 \
+ --max_grad_norm=0.05 \
+ --random_hw_adapt \
+ --training_with_video_token_length \
+ --enable_bucket \
+ --low_vram \
+ --train_mode="inpaint"
+```
+
+CogVideoX-Fun with deepspeed:
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/CogVideoX-Fun-2b-InP"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA.
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+accelerate launch --use_deepspeed --deepspeed_config_file config/zero_stage2_config.json --deepspeed_multinode_launcher standard scripts/cogvideox_fun/train_lora.py \
+ --pretrained_model_name_or_path=$MODEL_NAME \
+ --train_data_dir=$DATASET_NAME \
+ --train_data_meta=$DATASET_META_NAME \
+ --image_sample_size=1024 \
+ --video_sample_size=256 \
+ --token_sample_size=512 \
+ --video_sample_stride=3 \
+ --video_sample_n_frames=49 \
+ --train_batch_size=1 \
+ --video_repeat=1 \
+ --gradient_accumulation_steps=1 \
+ --dataloader_num_workers=8 \
+ --num_train_epochs=100 \
+ --checkpointing_steps=50 \
+ --learning_rate=1e-04 \
+ --seed=42 \
+ --output_dir="output_dir" \
+ --gradient_checkpointing \
+ --mixed_precision="bf16" \
+ --adam_weight_decay=3e-2 \
+ --adam_epsilon=1e-10 \
+ --vae_mini_batch=1 \
+ --max_grad_norm=0.05 \
+ --random_hw_adapt \
+ --training_with_video_token_length \
+ --enable_bucket \
+ --use_deepspeed \
+ --low_vram \
+ --train_mode="inpaint"
+```
+
+CogVideoX-Fun with multi machines:
+```sh
+export MODEL_NAME="models/Diffusion_Transformer/CogVideoX-Fun-2b-InP"
+export DATASET_NAME="datasets/internal_datasets/"
+export DATASET_META_NAME="datasets/internal_datasets/metadata.json"
+# NCCL_IB_DISABLE=1 and NCCL_P2P_DISABLE=1 are used in multi nodes without RDMA.
+# export NCCL_IB_DISABLE=1
+# export NCCL_P2P_DISABLE=1
+NCCL_DEBUG=INFO
+
+NUM_PROCESS=$((WORLD_SIZE * 8))
+
+echo "MASTER_ADDR: ${MASTER_ADDR} MASTER_PORT: ${MASTER_PORT} NUM_PROCESS: ${NUM_PROCESS}"
+
+accelerate launch --main_process_ip=$MASTER_ADDR --main_process_port=$MASTER_PORT --num_machines=$WORLD_SIZE --num_processes=$NUM_PROCESS --machine_rank=$RANK scripts/cogvideox_fun/train.py \
+ --pretrained_model_name_or_path=$MODEL_NAME \
+ --train_data_dir=$DATASET_NAME \
+ --train_data_meta=$DATASET_META_NAME \
+ --image_sample_size=1024 \
+ --video_sample_size=256 \
+ --token_sample_size=512 \
+ --video_sample_stride=3 \
+ --video_sample_n_frames=49 \
+ --train_batch_size=1 \
+ --video_repeat=1 \
+ --gradient_accumulation_steps=1 \
+ --dataloader_num_workers=8 \
+ --num_train_epochs=100 \
+ --checkpointing_steps=50 \
+ --learning_rate=1e-04 \
+ --seed=42 \
+ --output_dir="output_dir" \
+ --gradient_checkpointing \
+ --mixed_precision="bf16" \
+ --adam_weight_decay=3e-2 \
+ --adam_epsilon=1e-10 \
+ --vae_mini_batch=1 \
+ --max_grad_norm=0.05 \
+ --random_hw_adapt \
+ --training_with_video_token_length \
+ --enable_bucket \
+ --train_mode="inpaint"
+```
\ No newline at end of file
diff --git a/VideoX-Fun/scripts/cogvideox_fun/README_TRAIN_REWARD.md b/VideoX-Fun/scripts/cogvideox_fun/README_TRAIN_REWARD.md
new file mode 100644
index 0000000000000000000000000000000000000000..33bc3ef10e2f8cd7cc5ccb1dc61f56e81c5727f6
--- /dev/null
+++ b/VideoX-Fun/scripts/cogvideox_fun/README_TRAIN_REWARD.md
@@ -0,0 +1,263 @@
+# Enhance CogVideoX-Fun with Reward Backpropagation (Preference Optimization)
+We explore the Reward Backpropagation technique [1](#ref1) [2](#ref2) to optimized the generated videos by [CogVideoX-Fun-V1.1](https://github.com/aigc-apps/CogVideoX-Fun) for better alignment with human preferences.
+We provide pre-trained models (i.e. LoRAs) along with the training script. You can use these LoRAs to enhance the corresponding base model as a plug-in or train your own reward LoRA.
+
+- [Enhance CogVideoX-Fun with Reward Backpropagation (Preference Optimization)](#enhance-cogvideox-fun-with-reward-backpropagation-preference-optimization)
+ - [Demo](#demo)
+ - [CogVideoX-Fun-V1.1-5B](#cogvideox-fun-v11-5b)
+ - [CogVideoX-Fun-V1.1-2B](#cogvideox-fun-v11-2b)
+ - [Model Zoo](#model-zoo)
+ - [Inference](#inference)
+ - [Training](#training)
+ - [Setup](#setup)
+ - [Important Args](#important-args)
+ - [Limitations](#limitations)
+ - [References](#references)
+
+
+## Demo
+### CogVideoX-Fun-V1.1-5B
+
+| Prompt | +CogVideoX-Fun-V1.1-5B | +CogVideoX-Fun-V1.1-5B HPSv2.1 Reward LoRA |
+ CogVideoX-Fun-V1.1-5B MPS Reward LoRA |
+
|---|---|---|---|
| + Pig with wings flying above a diamond mountain + | ++ + | ++ + | ++ + | +
| + A dog runs through a field while a cat climbs a tree + | ++ + | ++ + | ++ + | +
| + Crystal cake shimmering beside a metal apple + | ++ + | ++ + | ++ + | +
| + Elderly artist with a white beard painting on a white canvas + | ++ + | ++ + | ++ + | +
| Prompt | +CogVideoX-Fun-V1.1-2B | +CogVideoX-Fun-V1.1-2B HPSv2.1 Reward LoRA |
+ CogVideoX-Fun-V1.1-2B MPS Reward LoRA |
+
|---|---|---|---|
| + A blue car drives past a white picket fence on a sunny day + | ++ + | ++ + | ++ + | +
| + Blue jay swooping near a red maple tree + | ++ + | ++ + | ++ + | +
| + Yellow curtains swaying near a blue sofa + | ++ + | ++ + | ++ + | +
| + White tractor plowing near a green farmhouse + | ++ + | ++ + | ++ + | +
| Prompt | +Wan2.1-Fun-1.3B-InP | +Wan2.1-Fun-1.3B-InP HPSv2.1 Reward LoRA |
+ Wan2.1-Fun-1.3B-InP MPS Reward LoRA |
+
|---|---|---|---|
|
+ A kangaroo bounds across the plain and a cow grazes
+
+
+ Expanded+In a vast, sun-drenched Australian plain, a lively kangaroo bounds with powerful leaps across the dry grass, its shadow following closely. Nearby, a serene brown and white cow grazes leisurely, its tail swishing gently in the warm breeze. The sky is a vibrant blue, dotted with fluffy clouds. + |
+ + + | ++ + | ++ + | +
|
+ A penguin waddles on the ice, a camel treks by
+
+
+ Expanded+A small penguin waddles slowly across a vast, icy surface under a clear blue sky. The penguin's short, flipper-like wings sway at its sides as it moves. Nearby, a camel treks steadily, its long legs navigating the snowy terrain with ease. The camel's fur is thick, providing warmth in the cold environment. + |
+ + + | ++ + | ++ + | +
|
+ Porcelain rabbit hopping by a golden cactus
+
+
+ Expanded+A delicate porcelain rabbit, with intricate painted details, hops gracefully across a sandy desert floor. Nearby, a golden cactus stands tall, its metallic surface glimmering in the sunlight. The backdrop features rolling sand dunes under a clear blue sky, casting gentle shadows. + |
+ + + | ++ + | ++ + | +
|
+ Pig with wings flying above a diamond mountain
+
+
+ Expanded+A whimsical pig, complete with delicate feathered wings, soars gracefully above a shimmering diamond mountain. The pig's pink skin glistens in the sunlight as it flaps its wings. The mountain below sparkles with countless facets, reflecting brilliant rays of light into the clear blue sky. + |
+ + + | ++ + | ++ + | +
| Prompt | +Wan2.1-Fun-1.3B-InP | +Wan2.1-Fun-1.3B-InP HPSv2.1 Reward LoRA |
+ Wan2.1-Fun-1.3B-InP MPS Reward LoRA |
+
|---|---|---|---|
|
+ A panda eats bamboo while a monkey swings from branch to branch
+
+
+ Expanded+In a lush green forest, a panda sits comfortably against a tree, leisurely munching on bamboo stalks. Nearby, a lively monkey swings energetically from branch to branch, its tail curling around the limbs. Sunlight filters through the canopy, casting dappled shadows on the forest floor. + |
+ + + | ++ + | ++ + | +
|
+ A dog runs through a field while a cat climbs a tree
+
+
+ Expanded+In a sunlit, expansive green field surrounded by tall trees, a playful golden retriever sprints energetically across the grass, its fur gleaming in the afternoon sun. Nearby, a nimble tabby cat gracefully climbs a sturdy tree, its claws gripping the bark effortlessly. The sky is clear blue with occasional birds flying. + |
+ + + | ++ + | ++ + | +
|
+ Elderly artist with a white beard painting on a white canvas
+
+
+ Expanded+An elderly artist with a long white beard stands in a sunlit studio surrounded by art supplies. He wears a paint-splattered apron over a casual shirt. His hand moves gracefully as he paints vibrant colors on a large white canvas positioned on an easel. The studio is filled with natural light streaming through tall windows, highlighting the textures of his work. + |
+ + + | ++ + | ++ + | +
|
+ Pig with wings flying above a diamond mountain
+
+
+ Expanded+A whimsical pig, complete with delicate feathered wings, soars gracefully above a shimmering diamond mountain. The pig's pink skin glistens in the sunlight as it flaps its wings. The mountain below sparkles with countless facets, reflecting brilliant rays of light into the clear blue sky. + |
+ + + | ++ + | ++ + | +