Update README.md

README.md

@@ -1,198 +1,286 @@
 ---
-library_name: diffusers
+license: apache-2.0
+pipeline_tag: text-to-video
 ---
+# Wan2.2 + Lightx2v
 
-# Model Card for Model ID
+<p align="center">
+    💜 <a href="https://wan.video"><b>Wan</b></a> &nbsp&nbsp ｜ &nbsp&nbsp 🖥️ <a href="https://github.com/Wan-Video/Wan2.2">GitHub</a> &nbsp&nbsp  | &nbsp&nbsp🤗 <a href="https://huggingface.co/Wan-AI/">Hugging Face</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/organization/Wan-AI">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://arxiv.org/abs/2503.20314">Technical Report</a> &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://wan.video/welcome?spm=a2ty_o02.30011076.0.0.6c9ee41eCcluqg">Blog</a> &nbsp&nbsp | &nbsp&nbsp💬 <a href="https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg">WeChat Group</a>&nbsp&nbsp | &nbsp&nbsp 📖 <a href="https://discord.gg/AKNgpMK4Yj">Discord</a>&nbsp&nbsp
+<br>
 
-<!-- Provide a quick summary of what the model is/does. -->
+<p align="center">
+    🔗 <a href="https://huggingface.co/lightx2v/Wan2.2-Lightning"><b>Lightx2v</b></a> — Distilled & optimized Wan2.2 for fast, high-quality 480P / 720P image-to-video generation
+</p>
+<br>
+-----
 
+[**Wan: Open and Advanced Large-Scale Video Generative Models**](https://arxiv.org/abs/2503.20314) <be>
 
 
-## Model Details
+We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations:
 
-### Model Description
+- 👍 **Effective MoE Architecture**: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.
 
-<!-- Provide a longer summary of what this model is. -->
+- 👍 **Cinematic-level Aesthetics**: Wan2.2 incorporates meticulously curated aesthetic data, complete with detailed labels for lighting, composition, contrast, color tone, and more. This allows for more precise and controllable cinematic style generation, facilitating the creation of videos with customizable aesthetic preferences.
 
-This is the model card of a 🧨 diffusers pipeline that has been pushed on the Hub. This model card has been automatically generated.
+- 👍 **Complex Motion Generation**: Compared to Wan2.1, Wan2.2 is trained on a significantly larger data, with +65.6% more images and +83.2% more videos. This expansion notably enhances the model's generalization across multiple dimensions such as motions,  semantics, and aesthetics, achieving TOP performance among all open-sourced and closed-sourced models. 
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- 👍 **Efficient High-Definition Hybrid TI2V**:  Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of **16×16×4**. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest **720P@24fps** models currently available, capable of serving both the industrial and academic sectors simultaneously.
 
-### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+This repository contains our T2V-A14B model, which supports generating 5s videos at both 480P and 720P resolutions. Built with a Mixture-of-Experts (MoE) architecture, it delivers outstanding video generation quality. On our new benchmark Wan-Bench 2.0, the model surpasses leading commercial models across most key evaluation dimensions.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
-## Uses
+## Video Demos
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+<div align="center">
+    <video width="80%" controls>
+        <source src="https://cloud.video.taobao.com/vod/4szTT1B0LqXvJzmuEURfGRA-nllnqN_G2AT0ZWkQXoQ.mp4" type="video/mp4">
+        Your browser does not support the video tag.
+    </video>
+</div>
 
-### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## 🔥 Latest News!!
 
-[More Information Needed]
+* Jul 28, 2025: 👋 We've released the inference code and model weights of **Wan2.2**.
 
-### Downstream Use [optional]
+## Community Works
+If your research or project builds upon [**Wan2.1**](https://github.com/Wan-Video/Wan2.1) or Wan2.2, we welcome you to share it with us so we can highlight it for the broader community.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-[More Information Needed]
+## 📑 Todo List
+- Wan2.2 Text-to-Video
+    - [x] Multi-GPU Inference code of the A14B and 14B models
+    - [x] Checkpoints of the A14B and 14B models
+    - [x] ComfyUI integration
+    - [x] Diffusers integration
+- Wan2.2 Image-to-Video
+    - [x] Multi-GPU Inference code of the A14B model
+    - [x] Checkpoints of the A14B model
+    - [x] ComfyUI integration
+    - [x] Diffusers integration
+- Wan2.2 Text-Image-to-Video
+    - [x] Multi-GPU Inference code of the 5B model
+    - [x] Checkpoints of the 5B model
+    - [x] ComfyUI integration
+    - [x] Diffusers integration
 
-### Out-of-Scope Use
+## Run Wan2.2
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+#### Installation
+Clone the repo:
+```sh
+git clone https://github.com/Wan-Video/Wan2.2.git
+cd Wan2.2
+```
 
-[More Information Needed]
+Install dependencies:
+```sh
+# Ensure torch >= 2.4.0
+pip install -r requirements.txt
+```
 
-## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+#### Model Download
 
-[More Information Needed]
 
-### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+| Models              | Download Links                                                                                                                              | Description |
+|--------------------|---------------------------------------------------------------------------------------------------------------------------------------------|-------------|
+| T2V-A14B    | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B)    🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-T2V-A14B)    | Text-to-Video MoE model, supports 480P & 720P |
+| I2V-A14B    | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B)    🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-I2V-A14B)    | Image-to-Video MoE model, supports 480P & 720P |
+| TI2V-5B     | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B)     🤖 [ModelScope](https://modelscope.cn/models/Wan-AI/Wan2.2-TI2V-5B)     | High-compression VAE, T2V+I2V, supports 720P |
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
-## How to Get Started with the Model
+> 💡Note: 
+> The TI2V-5B model supports 720P video generation at **24 FPS**.
 
-Use the code below to get started with the model.
 
-[More Information Needed]
+Download models using huggingface-cli:
+``` sh
+pip install "huggingface_hub[cli]"
+huggingface-cli download Wan-AI/Wan2.2-T2V-A14B --local-dir ./Wan2.2-T2V-A14B
+```
 
-## Training Details
+Download models using modelscope-cli:
+``` sh
+pip install modelscope
+modelscope download Wan-AI/Wan2.2-T2V-A14B --local_dir ./Wan2.2-T2V-A14B
+```
 
-### Training Data
+#### Run Text-to-Video Generation
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+This repository supports the `Wan2.2-T2V-A14B` Text-to-Video model and can simultaneously support video generation at 480P and 720P resolutions.
 
-[More Information Needed]
 
-### Training Procedure
+##### (1) Without Prompt Extension
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+To facilitate implementation, we will start with a basic version of the inference process that skips the [prompt extension](#2-using-prompt-extention) step.
 
-#### Preprocessing [optional]
+- Single-GPU inference
 
-[More Information Needed]
+``` sh
+python generate.py  --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --offload_model True --convert_model_dtype --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
 
+> 💡 This command can run on a GPU with at least 80GB VRAM.
 
-#### Training Hyperparameters
+> 💡If you encounter OOM (Out-of-Memory) issues, you can use the `--offload_model True`, `--convert_model_dtype` and `--t5_cpu` options to reduce GPU memory usage.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
-#### Speeds, Sizes, Times [optional]
+- Multi-GPU inference using FSDP + DeepSpeed Ulysses
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+  We use [PyTorch FSDP](https://docs.pytorch.org/docs/stable/fsdp.html) and [DeepSpeed Ulysses](https://arxiv.org/abs/2309.14509) to accelerate inference.
 
-[More Information Needed]
 
-## Evaluation
+``` sh
+torchrun --nproc_per_node=8 generate.py --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
 
-<!-- This section describes the evaluation protocols and provides the results. -->
 
-### Testing Data, Factors & Metrics
+##### (2) Using Prompt Extension
 
-#### Testing Data
+Extending the prompts can effectively enrich the details in the generated videos, further enhancing the video quality. Therefore, we recommend enabling prompt extension. We provide the following two methods for prompt extension:
 
-<!-- This should link to a Dataset Card if possible. -->
+- Use the Dashscope API for extension.
+  - Apply for a `dashscope.api_key` in advance ([EN](https://www.alibabacloud.com/help/en/model-studio/getting-started/first-api-call-to-qwen) | [CN](https://help.aliyun.com/zh/model-studio/getting-started/first-api-call-to-qwen)).
+  - Configure the environment variable `DASH_API_KEY` to specify the Dashscope API key. For users of Alibaba Cloud's international site, you also need to set the environment variable `DASH_API_URL` to 'https://dashscope-intl.aliyuncs.com/api/v1'. For more detailed instructions, please refer to the [dashscope document](https://www.alibabacloud.com/help/en/model-studio/developer-reference/use-qwen-by-calling-api?spm=a2c63.p38356.0.i1).
+  - Use the `qwen-plus` model for text-to-video tasks and `qwen-vl-max` for image-to-video tasks.
+  - You can modify the model used for extension with the parameter `--prompt_extend_model`. For example:
+```sh
+DASH_API_KEY=your_key torchrun --nproc_per_node=8 generate.py  --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'dashscope' --prompt_extend_target_lang 'zh'
+```
 
-[More Information Needed]
+- Using a local model for extension.
 
-#### Factors
+  - By default, the Qwen model on HuggingFace is used for this extension. Users can choose Qwen models or other models based on the available GPU memory size.
+  - For text-to-video tasks, you can use models like `Qwen/Qwen2.5-14B-Instruct`, `Qwen/Qwen2.5-7B-Instruct` and `Qwen/Qwen2.5-3B-Instruct`.
+  - For image-to-video tasks, you can use models like `Qwen/Qwen2.5-VL-7B-Instruct` and `Qwen/Qwen2.5-VL-3B-Instruct`.
+  - Larger models generally provide better extension results but require more GPU memory.
+  - You can modify the model used for extension with the parameter `--prompt_extend_model` , allowing you to specify either a local model path or a Hugging Face model. For example:
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+``` sh
+torchrun --nproc_per_node=8 generate.py  --task t2v-A14B --size 1280*720 --ckpt_dir ./Wan2.2-T2V-A14B --dit_fsdp --t5_fsdp --ulysses_size 8 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'local_qwen' --prompt_extend_target_lang 'zh'
+```
 
-[More Information Needed]
+- Running with Diffusers
 
-#### Metrics
+```py
+import torch
+import numpy as np
+from diffusers import WanPipeline, AutoencoderKLWan
+from diffusers.utils import export_to_video, load_image
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+dtype = torch.bfloat16
+device = "cuda:2"
+vae = AutoencoderKLWan.from_pretrained("Wan-AI/Wan2.2-T2V-A14B-Diffusers", subfolder="vae", torch_dtype=torch.float32)
+pipe = WanPipeline.from_pretrained("Wan-AI/Wan2.2-T2V-A14B-Diffusers", vae=vae, torch_dtype=dtype)
+pipe.to(device)
 
-[More Information Needed]
+height = 720
+width = 1280
 
-### Results
+prompt = "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+output = pipe(
+    prompt=prompt,
+    negative_prompt=negative_prompt,
+    height=height,
+    width=width,
+    num_frames=81,
+    guidance_scale=4.0,
+    guidance_scale_2=3.0,
+    num_inference_steps=40,
+).frames[0]
+export_to_video(output, "t2v_out.mp4", fps=16)
+```
 
-[More Information Needed]
+> 💡**Note**:This model requires features that are currently available only in the main branch of diffusers. The latest stable release on PyPI does not yet include these updates.
+> To use this model, please install the library from source:
+> ```
+> pip install git+https://github.com/huggingface/diffusers    
+> ```
 
-#### Summary
+## Computational Efficiency on Different GPUs
 
+We test the computational efficiency of different **Wan2.2** models on different GPUs in the following table. The results are presented in the format: **Total time (s) / peak GPU memory (GB)**.
 
 
-## Model Examination [optional]
+<div align="center">
+    <img src="assets/comp_effic.png" alt="" style="width: 80%;" />
+</div>
 
-<!-- Relevant interpretability work for the model goes here -->
+> The parameter settings for the tests presented in this table are as follows:
+> (1) Multi-GPU: 14B: `--ulysses_size 4/8 --dit_fsdp --t5_fsdp`, 5B: `--ulysses_size 4/8 --offload_model True --convert_model_dtype --t5_cpu`; Single-GPU: 14B: `--offload_model True --convert_model_dtype`, 5B: `--offload_model True --convert_model_dtype --t5_cpu`
+(--convert_model_dtype converts model parameter types to config.param_dtype);
+> (2) The distributed testing utilizes the built-in FSDP and Ulysses implementations, with FlashAttention3 deployed on Hopper architecture GPUs;
+> (3) Tests were run without the `--use_prompt_extend` flag;
+> (4) Reported results are the average of multiple samples taken after the warm-up phase.
 
-[More Information Needed]
 
-## Environmental Impact
+-------
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+## Introduction of Wan2.2
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+**Wan2.2** builds on the foundation of Wan2.1 with notable improvements in generation quality and model capability. This upgrade is driven by a series of key technical innovations, mainly including the Mixture-of-Experts (MoE) architecture, upgraded training data, and high-compression video generation.
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+##### (1) Mixture-of-Experts (MoE) Architecture
 
-## Technical Specifications [optional]
+Wan2.2 introduces Mixture-of-Experts (MoE) architecture into the video generation diffusion model. MoE has been widely validated in large language models as an efficient approach to increase total model parameters while keeping inference cost nearly unchanged. In Wan2.2, the A14B model series adopts a two-expert design tailored to the denoising process of diffusion models: a high-noise expert for the early stages, focusing on overall layout; and a low-noise expert for the later stages, refining video details. Each expert model has about 14B parameters, resulting in a total of 27B parameters but only 14B active parameters per step, keeping inference computation and GPU memory nearly unchanged.
 
-### Model Architecture and Objective
+<div align="center">
+    <img src="assets/moe_arch.png" alt="" style="width: 90%;" />
+</div>
 
-[More Information Needed]
+The transition point between the two experts is determined by the signal-to-noise ratio (SNR), a metric that decreases monotonically as the denoising step $t$ increases. At the beginning of the denoising process, $t$ is large and the noise level is high, so the SNR is at its minimum, denoted as ${SNR}_{min}$. In this stage, the high-noise expert is activated. We define a threshold step ${t}_{moe}$ corresponding to half of the ${SNR}_{min}$, and switch to the low-noise expert when $t<{t}_{moe}$.
 
-### Compute Infrastructure
+<div align="center">
+    <img src="assets/moe_2.png" alt="" style="width: 90%;" />
+</div>
 
-[More Information Needed]
+To validate the effectiveness of the MoE architecture, four settings are compared based on their validation loss curves. The baseline **Wan2.1** model does not employ the MoE architecture. Among the MoE-based variants, the **Wan2.1 & High-Noise Expert** reuses the Wan2.1 model as the low-noise expert while uses the  Wan2.2's high-noise expert, while the **Wan2.1 & Low-Noise Expert** uses Wan2.1 as the high-noise expert and employ the Wan2.2's low-noise expert. The **Wan2.2 (MoE)** (our final version) achieves the lowest validation loss, indicating that its generated video distribution is closest to ground-truth and exhibits superior convergence.
 
-#### Hardware
 
-[More Information Needed]
+##### (2) Efficient High-Definition Hybrid TI2V
+To enable more efficient deployment, Wan2.2 also explores a high-compression design. In addition to the 27B MoE models, a 5B dense model, i.e., TI2V-5B, is released. It is supported by a high-compression Wan2.2-VAE, which achieves a $T\times H\times W$ compression ratio of $4\times16\times16$, increasing the overall compression rate to 64 while maintaining high-quality video reconstruction. With an additional patchification layer, the total compression ratio of TI2V-5B reaches $4\times32\times32$. Without specific optimization, TI2V-5B can generate a 5-second 720P video in under 9 minutes on a single consumer-grade GPU, ranking among the fastest 720P@24fps video generation models. This model also natively supports both text-to-video and image-to-video tasks within a single unified framework, covering both academic research and practical applications.
 
-#### Software
 
-[More Information Needed]
+<div align="center">
+    <img src="assets/vae.png" alt="" style="width: 80%;" />
+</div>
 
-## Citation [optional]
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
-**BibTeX:**
+##### Comparisons to SOTAs
+We compared Wan2.2 with leading closed-source commercial models on our new Wan-Bench 2.0, evaluating performance across multiple crucial dimensions. The results demonstrate that Wan2.2 achieves superior performance compared to these leading models.
 
-[More Information Needed]
 
-**APA:**
+<div align="center">
+    <img src="assets/performance.png" alt="" style="width: 90%;" />
+</div>
 
-[More Information Needed]
+## Citation
+If you find our work helpful, please cite us.
 
-## Glossary [optional]
+```
+@article{wan2025,
+      title={Wan: Open and Advanced Large-Scale Video Generative Models}, 
+      author={Team Wan and Ang Wang and Baole Ai and Bin Wen and Chaojie Mao and Chen-Wei Xie and Di Chen and Feiwu Yu and Haiming Zhao and Jianxiao Yang and Jianyuan Zeng and Jiayu Wang and Jingfeng Zhang and Jingren Zhou and Jinkai Wang and Jixuan Chen and Kai Zhu and Kang Zhao and Keyu Yan and Lianghua Huang and Mengyang Feng and Ningyi Zhang and Pandeng Li and Pingyu Wu and Ruihang Chu and Ruili Feng and Shiwei Zhang and Siyang Sun and Tao Fang and Tianxing Wang and Tianyi Gui and Tingyu Weng and Tong Shen and Wei Lin and Wei Wang and Wei Wang and Wenmeng Zhou and Wente Wang and Wenting Shen and Wenyuan Yu and Xianzhong Shi and Xiaoming Huang and Xin Xu and Yan Kou and Yangyu Lv and Yifei Li and Yijing Liu and Yiming Wang and Yingya Zhang and Yitong Huang and Yong Li and You Wu and Yu Liu and Yulin Pan and Yun Zheng and Yuntao Hong and Yupeng Shi and Yutong Feng and Zeyinzi Jiang and Zhen Han and Zhi-Fan Wu and Ziyu Liu},
+      journal = {arXiv preprint arXiv:2503.20314},
+      year={2025}
+}
+```
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## License Agreement
+The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt).
 
-[More Information Needed]
 
-## More Information [optional]
+## Acknowledgements
 
-[More Information Needed]
+We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
 
-## Model Card Authors [optional]
 
-[More Information Needed]
 
-## Model Card Contact
-
-[More Information Needed]
+## Contact Us
+If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/AKNgpMK4Yj) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!