Wan 2.7
Alibaba's open-source generative model converts text and images into high-resolution video.
Alibaba Tongyi Lab released Wan 2.7 on April 15, 2026. It is a state-of-the-art multimodal video generation AI model that transforms text and image inputs into sophisticated, high-resolution videos. Similar to how a large language model interprets prompts and infers text, this model combines a 3D Causal Video VAE architecture with a Mixture-of-Experts structure to analyze the spatiotemporal complexity within videos and consistently generate ultra-high-definition frames. Notably, it incorporates Frame Control and Multi-reference Consistency technologies, making it a next-generation video foundation model capable of seamlessly handling text-to-video, image-to-video, and reference-to-video generation within a single pipeline. Previous video generation models suffered from a critical limitation: a lack of deep understanding of the laws of physics, leading to unnatural distortions when attempting to render complex physical reactions or the intricate movements of delicate biomolecules. Wan 2.7 overcomes this limitation by integrating a unique Thinking Mode that allows it to independently infer the scene structure and construct a scenario before generating the video. This is analogous to a skilled film director meticulously designing a storyboard before filming, where the model performs a logical thought process regarding physical causality and spatial arrangement internally. As a result, it exhibits significantly improved fine-grained motion control and instruction-based editing performance compared to previous models. Furthermore, it adopts the Apache-2.0 open-source license, providing an innovative advantage by allowing anyone to fully implement a level of sophistication comparable to proprietary commercial services on their local workstation. These advanced motion visualization and physical inference capabilities make it a powerful tool for biomedical and medical researchers. For example, by providing static images from molecular docking simulations extracted from a protein structure visualization tool like PyMOL, the model can precisely extrapolate the chemical interactions and stereostructural motions of the two structures, recreating them as smooth 3D binding dynamics videos in 1080p resolution. Additionally, by reconstructing the division, apoptosis, or motility changes of cells in response to drugs based on time-lapse microscopy image datasets, it can rapidly and accurately synthesize complex cellular physiological flows for predictive and educational content without the need for large-scale imaging equipment.
💻 System Requirements
Minimum 16GB, recommended 24GB or more (24GB or more VRAM recommended when running 14B/27B MoE)
100GB or more of free space for storing model weights and caching
⚡ Installation
### 4-1. Quick Start
```bash
pip install torch diffusers transformers accelerate
pip install modelscope
modelscope download Wan-AI/Wan2.1-T2V-14B --local_dir ./Wan-Video-Weights
```
### 4-2. Detailed Installation
```bash
# 1. Clone and navigate to the GitHub repository
git clone https://github.com/Wan-Video/Wan2.1.git
cd Wan2.1
# 2. Install required dependency packages
pip install -r requirements.txt
# 3. Run a text-to-video generation test using the CLI
python generate.py \
--model_path "./Wan-Video-Weights" \
--prompt "A molecular simulation of DNA double helix replicating inside a cell nucleus, highly detailed, 3d render" \
--output_path "outputs/dna_replication.mp4" \
--resolution "1080p"
```🧬 Bio Use Cases
🔬 Visualize Receptor-Ligand Binding Dynamics for Virtual Screening and Drug Discovery
Leverage Wan 2.7's R2V (Reference-to-Video) mode to link PyMOL-based protein receptor 3D structures with ligand docking simulations, rendering molecular motion at 30fps and 1080p resolution to intuitively validate the structural suitability of drug candidates and generate presentation-ready animations in under 2 seconds.
🔬 Synthesize a 3D Time-Lapse Video of Cancer Cell Death Processes Following Drug Administration
Set the time-series images (1024x1024) of colon cancer cells captured by a fluorescence microscope as input for Wan 2.7's I2V (Image-to-Video) and specify a motion scale of 0.8 to synthesize a video of cell membrane contraction and apoptosis dynamics over 72 hours after drug administration, visualizing complex physiological changes in a 10-second clip on a single 24GB GPU.
🔬 Edit Virtual Surgery Simulation Videos for Remote Medical Education on Mobile Devices
Utilize the natural language instruction editing feature to input the prompt "Adjust the incision angle by 15 degrees" into an existing 1080p surgical demo video, modifying and regenerating the motion within 5 seconds in a consumer GPU environment, reducing the production cost of next-generation remote care guides and virtual reality content for medical personnel training by more than 90% compared to conventional methods.
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