# Hummingbird2 **Repository Path**: freegold2010/Hummingbird2 ## Basic Information - **Project Name**: Hummingbird2 - **Description**: No description available - **Primary Language**: Python - **License**: Not specified - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-05-20 - **Last Updated**: 2026-05-20 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

Hummingbird: A Lightweight, High-Performance Video Generation Model

🔆Introduction

⚡️ Hummingbird is a series of advanced video generation models developed by the AMD AIG team and trained on AMD Instinct™ MI250 GPUs. It includes text-to-video models, image-to-video models, and image/video super-resolution models. With only 0.9B parameters, the Hummingbird model demonstrates exceptional efficiency. For text-to-video tasks, it can generate text-aligned videos in just 1.87 seconds using 4 steps on an MI250 GPU. For image-to-video tasks, it takes only 11 seconds to produce high-quality 4K videos.

Figure 1: AMD Hummingbird-0.9B Visual Performance Comparison with Stat-of-the-art T2V Models on Vbench.

| A cute happy Corgi playing in park, sunset, pixel. | A cute happy Corgi playing in park, sunset, animated style. | A cute raccoon playing guitar in the beach. | A cute raccoon playing guitar in the forest. | |------------------------|-----------------------------|-----------------------------|-----------------------------| |

| |**A quiet beach at dawn and the waves gently lapping.**|**A cute teddy bear, dressed in a red silk outfit, stands in a vibrant street, chinese new year.**|**A sandcastle being eroded by the incoming tide.**|**An astronaut flying in space, in cyberpunk style.**| |

| ## 📝 Change Log - __[2026.01.09]__: 🔥🔥Release the full code and pre-trained weight of [HummingbirdXT](https://github.com/AMD-AGI/HummingbirdXT)! - __[2026.01.08]__: 🔥🔥Our new model Hummingbird-XT has been released, this is the technical report link: : [Bridging the Last Mile: Deploying Hummingbird-XT for Efficient Video Generation on AMD Consumer-Grade Platforms](https://rocm.blogs.amd.com/artificial-intelligence/hummingbirdxt/README.html)! - __[2025.08.03]__: 🔥🔥Release [Hummingbird Image-to-Video](https://rocm.blogs.amd.com/artificial-intelligence/image-to-video/README.html) Technical Report! - __[2025.07.30]__: 🔥🔥Release pretrained Image-to-Video model and VSR model, and their training and inference code! - __[2025.03.24]__: 🔥🔥Release [AMD-Hummingbird: Towards an Efficient Text-to-Video Model](https://arxiv.org/abs/2503.18559) Paper! - __[2025.02.28]__: 🔥🔥Release [Hummingbird Text-to-Video](https://www.amd.com/en/developer/resources/technical-articles/amd-hummingbird-0-9b-text-to-video-diffusion-model-with-4-step-inferencing.html) Technical Report! - __[2025.02.26]__: 🔥🔥Release pretrained Text-to-Video models, training and inference code! ## 🚀Getting Started ### Installation #### Conda ``` conda create -n AMD_Hummingbird python=3.10 conda activate AMD_Hummingbird pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/rocm6.1 pip install -r requirements.txt ``` For rocm flash-attn, you can install it by this [link](https://github.com/ROCm/flash-attention). ``` git clone https://github.com/ROCm/flash-attention.git cd flash-attention python setup.py install ``` It will take about 1.5 hours to install. #### Docker First, you should use `docker pull` to download the image. ``` docker pull rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4 ``` Second, you can use `docker run` to run the image, for example: ``` docker run \ -v "$(pwd):/workspace" \ --device=/dev/kfd \ --device=/dev/dri \ -it \ --network=host \ --name hummingbird \ rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4 ``` When you in the container, you can use `pip` to install other dependencies: ``` pip install -r requirements.txt ``` ### Example Usage #### Text-to-Video Download the Unet pretrained checkpoint from [Hummingbird-Text-to-Video](https://huggingface.co/amd/AMD-Hummingbird-T2V/tree/main). Run below command to generate videos: ``` # for 0.7B model python inference_command_config_07B.py # for 0.9B model python inference_command_config_09B.py ``` #### Image-to-Video Download the Image-to-Video pretrained checkpoint from [Hummingbird-Image-to-Video](https://huggingface.co/amd/AMD-Hummingbird-I2V). Run below command to generate videos: ``` cd i2v sh run_hummingbird.sh ``` #### Image/Video Super-Resolution Download SR pretrained checkpoint from [Hummingbird-Image-to-Video](https://huggingface.co/amd/AMD-Hummingbird-I2V/tree/main). Run below command to generate high-resolution videos: ``` cd VSR sh inference_videos.sh ``` ## 💥Pre-training ### Data Preparation ``` # VQA cd data_pre_process/DOVER sh run.sh ``` Then you can get a score table for all video qualities, sort according to the table, and remove low-scoring videos. ``` # Remove Dolly Zoom Videos cd data_pre_process/VBench sh run.sh ``` According to the motion smoothness score csv file, you can remove low-scoring videos. ### Training #### Text-to-video ``` cd acceleration/t2v-turbo # for 0.7 B model sh train_07B.sh # for 0.9 B model sh train_09B.sh ``` #### Image/Video Super-Resolution Firstly, you should train the Realesrnet model: ``` cd VSR # for realesrnet model sh train_realesrnet.sh ``` And you will get the trained checkpoint of Realesrnet, then you can train the Realesrgan model: ``` cd VSR # for realesrgan model sh train_realesrgan.sh ``` ## 🤗Resources ### Pre-trained models - Text-to-Video: [Hummingbird-Text-to-Video](https://huggingface.co/amd/AMD-Hummingbird-T2V/tree/main) - Image-to-Video: [Hummingbird-Image-to-Video](https://huggingface.co/amd/AMD-Hummingbird-I2V/tree/main) - Image/Video Super-Resolution: [Hummingbird-SR](https://huggingface.co/amd/AMD-Hummingbird-I2V/blob/main/SR.pth) ### AMD Blogs Please refer to the following blogs to get started with using these techniques on AMD GPUs: - [PyTorch Fully Sharded Data Parallel (FSDP) on AMD GPUs with ROCm™](https://rocm.blogs.amd.com/artificial-intelligence/fsdp-training-pytorch/README.html) - [Accelerating Large Language Models with Flash Attention on AMD GPUs](https://rocm.blogs.amd.com/artificial-intelligence/flash-attention/README.html) - [Accelerate PyTorch Models using torch.compile on AMD GPUs with ROCm™](https://rocm.blogs.amd.com/artificial-intelligence/torch_compile/README.html) - [Introducing the First AMD 1B Language Models: AMD OLMo](https://www.amd.com/en/developer/resources/technical-articles/introducing-the-first-amd-1b-language-model.html) ## ❤️Acknowledgement Our codebase builds on [VideoCrafter2](https://github.com/AILab-CVC/VideoCrafter), [DynamicCrafter](https://github.com/Doubiiu/DynamiCrafter), [T2v-Turbo](https://github.com/Ji4chenLi/t2v-turbo), [Real-ESRGAN ](https://github.com/xinntao/Real-ESRGAN).Thanks the authors for sharing their awesome codebases! ## 📋Citations Feel free to cite our Hummingbird models and give us a star⭐, if you find our work helpful :) ```text @article{isobe2025amd, title={AMD-Hummingbird: Towards an Efficient Text-to-Video Model}, author={Isobe, Takashi and Cui, He and Zhou, Dong and Ge, Mengmeng and Li, Dong and Barsoum, Emad}, journal={arXiv preprint arXiv:2503.18559}, year={2025} } ```