ali-vilab/i2vgen-xl
text-to-video model by ali-vilab
Older snapshot: check the original source for current information.
From the publisher
Model card & documentation
Source previewRead the publisher’s intended use, setup instructions, evaluations and limitations. The original model card is the source of truth.
VGen is an open-source video synthesis codebase developed by the Tongyi Lab of Alibaba Group, featuring state-of-the-art video generative models. This repository includes implementations of the following methods: VGen can produce high-quality videos from the input text, images, desired motion, desired subjects, and even the feedback signals provided. It also offers a variety of commonly used video generation tools such as visualization, sampling, training, inference, join training using images and videos, acceleration, and more. [](https://youtu.be/XUi0y7dxqEQ) The main features of VGen are as follows: We have provided a demo dataset that includes images and videos, along with their lists in data. Please note that the demo images used here are for testing purposes and were not included in the training. Executing the following command to enable distributed training is as easy as that. In the t2vtrain.yaml configuration file, you can specify the data, adjust the video-to-image ratio using framelens, and validate…
Read the full model card ↗ · Preview checked 2026-10-28T20:00:42.272Z
Inside the original model card — Document outline
- VGen
- 🔥News!!!
- TODO
- Preparation
- Installation
- Datasets
- Clone codeb
- Getting Started with VGen
- (1) Train your text-to-video model
- (2) Run the I2VGen-XL model
- (3) Other methods
- Customize your own approach
Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.
Links from the model card
References supplied by the publisher, not independently verified endorsements. Check the destination before downloading files or entering credentials.
Documentation belongs to its respective authors. Reported project/model license: mit. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
ali-vilab/i2vgen-xl is a text-to-video model repository published by ali-vilab on Hugging Face. The source reports the diffusers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- text-to-video
- Library
- diffusers
- Recent downloads (30 days)
- 2,826
- Cumulative likes
- 186
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 1,420,469,224
- Architecture
- Not reported
- License
- mit
- Access
- Not gated by Hugging Face
- Created
- 2023-12-15T06:28:05.000Z
- Last modified
- 2024-02-09T13:57:25.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
Check license terms and access requirements first. Review the model card’s documented loading instructions, evaluations and safety limitations. Benchmark scores are not imported by this directory, and popularity is not an accuracy ranking.
No model weights are downloaded or executed by AltAPIs. Never enable remote model code without reviewing it.
Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-09-20T06:35:54.490Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
Related models
QuantStack/Wan2.2-TI2V-5B-GGUF
QuantStack/Wan2.2-T2V-A14B-GGUF