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README.md

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+<!-- ## **HunyuanCustom** -->
+
+<p align="center">
+  <img src="assets/material/logo.png"  height=100>
+</p>
+
+# **HunyuanCustom** 🌅
+ 
+<div align="center">
+  <a href="https://github.com/Tencent/HunyuanCustom"><img src="https://img.shields.io/static/v1?label=HunyuanCustom%20Code&message=Github&color=blue"></a> &ensp;
+  <a href="https://hunyuancustom.github.io/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Web&color=green"></a> &ensp;
+  <a href="https://hunyuan.tencent.com/modelSquare/home/play?modelId=192"><img src="https://img.shields.io/static/v1?label=Playground&message=Web&color=green"></a>
+</div>
+<div align="center">
+  <a href="https://arxiv.org/pdf/2505.04512"><img src="https://img.shields.io/static/v1?label=Tech Report&message=Arxiv&color=red"></a> &ensp;
+</div>
+<div align="center">
+  <a href="https://huggingface.co/tencent/HunyuanCustom"><img src="https://img.shields.io/static/v1?label=HunyuanVideo&message=HuggingFace&color=yellow"></a> &ensp;
+</div>
+-----
+
+
+> [**HunyuanCustom: A Multimodal-Driven Architecture for Customized Video Generation**](https://arxiv.org/pdf/2505.04512) <be>
+
+
+
+## 🔥🔥🔥 News!!
+
+* May 8, 2025: 👋 We release the inference code and model weights of HunyuanCustom. [Download](models/README.md).
+
+
+## 📑 Open-source Plan
+
+- HunyuanCustom
+  - Single-Subject Video Customization
+    - [x] Inference 
+    - [x] Checkpoints
+    - [ ] ComfyUI
+  - Audio-Driven Video Customization
+  - Video-Driven Video Customization
+  - Multi-Subject Video Customization
+
+## Contents
+- [**HunyuanCustom** 🌅](#hunyuancustom-)
+  - [🔥🔥🔥 News!!](#-news)
+  - [📑 Open-source Plan](#-open-source-plan)
+  - [Contents](#contents)
+  - [**Abstract**](#abstract)
+  - [**HunyuanCustom Overall Architecture**](#hunyuancustom-overall-architecture)
+  - [🎉 **HunyuanCustom Key Features**](#-hunyuancustom-key-features)
+    - [**Multimodal Video customization**](#multimodal-video-customization)
+    - [**Various Applications**](#various-applications)
+  - [📈 Comparisons](#-comparisons)
+  - [📜 Requirements](#-requirements)
+  - [🛠️ Dependencies and Installation](#️-dependencies-and-installation)
+    - [Installation Guide for Linux](#installation-guide-for-linux)
+  - [🧱 Download Pretrained Models](#-download-pretrained-models)
+  - [🚀 Parallel Inference on Multiple GPUs](#-parallel-inference-on-multiple-gpus)
+  - [🔑 Single-gpu Inference](#-single-gpu-inference)
+    - [Run with very low VRAM](#run-with-very-low-vram)
+  - [Run a Gradio Server](#run-a-gradio-server)
+  - [🔗 BibTeX](#-bibtex)
+  - [Acknowledgements](#acknowledgements)
+---
+
+## **Abstract**
+
+Customized video generation aims to produce videos featuring specific subjects under flexible user-defined conditions, yet existing methods often struggle with identity consistency and limited input modalities. In this paper, we propose HunyuanCustom, a multi-modal customized video generation framework that emphasizes subject consistency while supporting image, audio, video, and text conditions. Built upon HunyuanVideo, our model first addresses the image-text conditioned generation task by introducing a text-image fusion module based on LLaVA for enhanced multi-modal understanding, along with an image ID enhancement module that leverages temporal concatenation to reinforce identity features across frames. To enable audio- and video-conditioned generation, we further propose modality-specific condition injection mechanisms: an AudioNet module that achieves hierarchical alignment via spatial cross-attention, and a video-driven injection module that integrates latent-compressed conditional video through a patchify-based feature-alignment network. Extensive experiments on single- and multi-subject scenarios demonstrate that HunyuanCustom significantly outperforms state-of-the-art open- and closed-source methods in terms of ID consistency, realism, and text-video alignment. Moreover, we validate its robustness across downstream tasks, including audio and video-driven customized video generation. Our results highlight the effectiveness of multi-modal conditioning and identity-preserving strategies in advancing controllable video generation.
+
+## **HunyuanCustom Overall Architecture**
+
+![image](assets/material/method.png)
+
+We propose **HunyuanCustom, a multi-modal, conditional, and controllable generation model centered on subject consistency**, built upon the Hunyuan Video generation framework. It enables the generation of subject-consistent videos conditioned on text, images, audio, and video inputs. 
+
+## 🎉 **HunyuanCustom Key Features**
+
+### **Multimodal Video customization**
+
+HunyuanCustom supports inputs in the form of **text, images, audio, and video**. 
+Specifically, it can handle single or multiple image inputs to enable customized video generation for one or more subjects. 
+Additionally, it can incorporate extra audio inputs to drive the subject to speak the corresponding audio. 
+Lastly, HunyuanCustom supports video input, allowing for the replacement of specified objects in the video with subjects from a given image.
+![image](assets/material/teaser.png)
+
+### **Various Applications**
+
+With the multi-modal capabilities of HunyuanCustom, numerous downstream tasks can be accomplished. 
+For instance, by taking multiple images as input, HunyuanCustom can facilitate **virtual human advertisements** and **virtual try-on**. Additionally, 
+with image and audio inputs, it can create **singing avatars**. Furthermore, by using an image and a video as inputs, 
+HunyuanCustom supports **video editing** by replacing subjects in the video with those in the provided image. 
+More applications await your exploration!
+![image](assets/material/application.png)
+
+
+## 📈 Comparisons
+
+To evaluate the performance of HunyuanCustom, we compared it with state-of-the-art video customization methods, 
+including VACE, Skyreels, Pika, Vidu, Keling, and Hailuo. The comparison focused on face/subject consistency, 
+video-text alignment, and overall video quality.
+
+| Models            | Face-Sim | CLIP-B-T | DINO-Sim | Temp-Consis | DD   |
+|-------------------|----------|----------|----------|-------------|------|
+| VACE-1.3B         | 0.204    | _0.308_  | 0.569    | **0.967**   | 0.53 |
+| Skyreels          | 0.402    | 0.295    | 0.579    | 0.942       | 0.72 |
+| Pika              | 0.363    | 0.305    | 0.485    | 0.928       | _0.89_ |
+| Vidu2.0           | 0.424    | 0.300    | 0.537    | _0.961_     | 0.43 |
+| Keling1.6         | 0.505    | 0.285    | _0.580_  | 0.914       | 0.78 |
+| Hailuo            | _0.526_  | **0.314**| 0.433    | 0.937       | **0.94** |
+| **HunyuanCustom (Ours)** | **0.627**| 0.306    | **0.593**| 0.958       | 0.71 |
+
+## 📜 Requirements
+
+The following table shows the requirements for running HunyuanCustom model (batch size = 1) to generate videos:
+
+|     Model    |  Setting<br/>(height/width/frame) | GPU Peak Memory  |
+|:------------:|:--------------------------------:|:----------------:|
+| HunyuanCustom   |        720px1280px129f          |       80GB        |
+| HunyuanCustom   |        512px896px129f           |       60GB        |
+
+* An NVIDIA GPU with CUDA support is required. 
+  * The model is tested on a machine with 8GPUs.
+  * **Minimum**: The minimum GPU memory required is 24GB for 720px1280px129f but very slow.
+  * **Recommended**: We recommend using a GPU with 80GB of memory for better generation quality.
+* Tested operating system: Linux
+
+
+## 🛠️ Dependencies and Installation
+
+Begin by cloning the repository:
+```shell
+git clone https://github.com/Tencent/HunyuanCustom.git
+cd HunyuanCustom
+```
+
+### Installation Guide for Linux
+
+We recommend CUDA versions 12.4 or 11.8 for the manual installation.
+
+Conda's installation instructions are available [here](https://docs.anaconda.com/free/miniconda/index.html).
+
+```shell
+# 1. Create conda environment
+conda create -n HunyuanCustom python==3.10.9
+
+# 2. Activate the environment
+conda activate HunyuanCustom
+
+# 3. Install PyTorch and other dependencies using conda
+# For CUDA 11.8
+conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=11.8 -c pytorch -c nvidia
+# For CUDA 12.4
+conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia
+
+# 4. Install pip dependencies
+python -m pip install -r requirements.txt
+python -m pip install tensorrt-cu12-bindings==10.6.0 tensorrt-cu12-libs==10.6.0
+# 5. Install flash attention v2 for acceleration (requires CUDA 11.8 or above)
+python -m pip install ninja
+python -m pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.6.3
+```
+
+In case of running into float point exception(core dump) on the specific GPU type, you may try the following solutions:
+
+```shell
+# Option 1: Making sure you have installed CUDA 12.4, CUBLAS>=12.4.5.8, and CUDNN>=9.00 (or simply using our CUDA 12 docker image).
+pip install nvidia-cublas-cu12==12.4.5.8
+export LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/nvidia/cublas/lib/
+
+# Option 2: Forcing to explictly use the CUDA 11.8 compiled version of Pytorch and all the other packages
+pip uninstall -r requirements.txt  # uninstall all packages
+pip install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu118
+pip install -r requirements.txt
+pip install ninja
+pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.6.3
+```
+
+Additionally, you can also use HunyuanVideo Docker image. Use the following command to pull and run the docker image.
+
+```shell
+# For CUDA 12.4 (updated to avoid float point exception)
+docker pull hunyuanvideo/hunyuanvideo:cuda_12
+docker run -itd --gpus all --init --net=host --uts=host --ipc=host --name hunyuanvideo --security-opt=seccomp=unconfined --ulimit=stack=67108864 --ulimit=memlock=-1 --privileged hunyuanvideo/hunyuanvideo:cuda_12
+pip install gradio==3.39.0
+
+# For CUDA 11.8
+docker pull hunyuanvideo/hunyuanvideo:cuda_11
+docker run -itd --gpus all --init --net=host --uts=host --ipc=host --name hunyuanvideo --security-opt=seccomp=unconfined --ulimit=stack=67108864 --ulimit=memlock=-1 --privileged hunyuanvideo/hunyuanvideo:cuda_11
+pip install gradio==3.39.0
+```
+
+
+## 🧱 Download Pretrained Models
+
+The details of download pretrained models are shown [here](models/README.md).
+
+## 🚀 Parallel Inference on Multiple GPUs
+
+For example, to generate a video with 8 GPUs, you can use the following command:
+
+```bash
+cd HunyuanCustom
+
+export MODEL_BASE="./models"
+export PYTHONPATH=./
+torchrun --nnodes=1 --nproc_per_node=8 --master_port 29605 hymm_sp/sample_batch.py \
+    --input './assets/images/seg_woman_01.png' \
+    --pos-prompt "Realistic, High-quality. A woman is drinking coffee at a café." \
+    --neg-prompt "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion, blurring, text, subtitles, static, picture, black border." \
+    --ckpt ${MODEL_BASE}"/hunyuancustom_720P/mp_rank_00_model_states.pt" \
+    --video-size 720 1280 \
+    --seed 1024 \
+    --sample-n-frames 129 \
+    --infer-steps 30 \
+    --flow-shift-eval-video 13.0 \
+    --save-path './results/sp_720p'
+```
+
+## 🔑 Single-gpu Inference
+
+For example, to generate a video with 1 GPU, you can use the following command:
+
+```bash
+cd HunyuanCustom
+
+export MODEL_BASE="./models"
+export CPU_OFFLOAD=1
+export PYTHONPATH=./
+python hymm_sp/sample_gpu_poor.py \
+    --input './assets/images/seg_woman_01.png' \
+    --pos-prompt "Realistic, High-quality. A woman is drinking coffee at a café." \
+    --neg-prompt "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion, blurring, text, subtitles, static, picture, black border." \
+    --ckpt ${MODEL_BASE}"/hunyuancustom_720P/mp_rank_00_model_states_fp8.pt" \
+    --video-size 512 896 \
+    --seed 1024 \
+    --sample-n-frames 129 \
+    --infer-steps 30 \
+    --flow-shift-eval-video 13.0 \
+    --save-path './results/1gpu_540p' \
+    --use-fp8
+```
+
+### Run with very low VRAM
+
+```bash
+cd HunyuanCustom
+
+export MODEL_BASE="./models"
+export CPU_OFFLOAD=1
+export PYTHONPATH=./
+python hymm_sp/sample_gpu_poor.py \
+    --input './assets/images/seg_woman_01.png' \
+    --pos-prompt "Realistic, High-quality. A woman is drinking coffee at a café." \
+    --neg-prompt "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion, blurring, text, subtitles, static, picture, black border." \
+    --ckpt ${MODEL_BASE}"/hunyuancustom_720P/mp_rank_00_model_states_fp8.pt" \
+    --video-size 720 1280 \
+    --seed 1024 \
+    --sample-n-frames 129 \
+    --infer-steps 30 \
+    --flow-shift-eval-video 13.0 \
+    --save-path './results/cpu_720p' \
+    --use-fp8 \
+    --cpu-offload 
+```
+
+
+## Run a Gradio Server
+```bash
+cd HunyuanCustom
+
+bash ./scripts/run_gradio.sh
+
+```
+
+## 🔗 BibTeX
+
+If you find [HunyuanCustom](https://arxiv.org/abs/2505.04512) useful for your research and applications, please cite using this BibTeX:
+
+```BibTeX
+@misc{hu2025hunyuancustommultimodaldrivenarchitecturecustomized,
+      title={HunyuanCustom: A Multimodal-Driven Architecture for Customized Video Generation}, 
+      author={Teng Hu and Zhentao Yu and Zhengguang Zhou and Sen Liang and Yuan Zhou and Qin Lin and Qinglin Lu},
+      year={2025},
+      eprint={2505.04512},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2505.04512}, 
+}
+```
+
+## Acknowledgements
+
+We would like to thank the contributors to the [HunyuanVideo](https://github.com/Tencent/HunyuanVideo), [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [FLUX](https://github.com/black-forest-labs/flux), [Llama](https://github.com/meta-llama/llama), [LLaVA](https://github.com/haotian-liu/LLaVA), [Xtuner](https://github.com/InternLM/xtuner), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration.