Practical overview
When it fits
Use it when you want to experiment with text-to-video generation guided by a structure sequence and can work with a local Python and pretrained-weight setup.
Who it is for
- Researchers studying controllable video generation.
- Developers prototyping text-to-video workflows that use structural guidance.
Documented uses
- Generate video from a text prompt with structural guidance.
- Explore depth- or edge-guided video generation using a diffusion-model workflow.
- Configure inference parameters such as video length, dimensions, frame rate and ControlNet version.
Requirements & limits
The implementation is available as a public GitHub repository under the MIT license. The README separately links to Hugging Face and Replicate demos.
- The repository’s inference example takes a video path as well as a text prompt and condition type.
- The documented checkpoints include Stable Diffusion v1.5, ControlNet variants and RIFE weights.
- Python 3.10, Conda and repository dependencies are specified; xformers is recommended to reduce memory use and running time.
- The README documents ControlNet v1.0 and v1.1 selection and an optional long-video mode.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up the Controlvideo environment and model files
Prepare the documented Python environment, dependencies and pretrained checkpoint files for the repository’s inference workflow.
Before you start
- A Conda environment manager
- Python 3.10
- The repository’s requirements.txt file
- Pretrained weights for Stable Diffusion v1.5, ControlNet and RIFE
Steps
Create and activate a Conda environment named controlvideo with Python 3.10.
Install the repository dependencies with pip install -r requirements.txt.
Download the listed pretrained weights and place them in the checkpoints/ directory, including the RIFE weights file flownet.pkl.
Expected outcome
The documented environment and checkpoint layout are prepared for the repository’s inference stage.
Guide limitations
- The README does not specify hardware requirements.
- xformers is recommended, not listed as required.
- This setup guide does not verify successful installation or generation.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Controlvideo | Generate video from a text prompt with structural guidance. | Linux | The implementation is available as a public GitHub repository under the MIT license. The README separately links to Hugging Face and Replicate demos. |
| Firered Image Edit | Apply a requested change to an input image using a written prompt. | Linux, Self-hosted | The repository lists an Apache-2.0 license and links to model and demo pages on Hugging Face and ModelScope. |
| Catvton | Generate images showing a garment on a person. | Cloud, Self-hosted | The repository documents a local Python setup and a Hugging Face demo. It states that the code, checkpoints and demo are licensed under Creative Commons BY-NC-SA 4.0 for non-commercial reuse with attribution and share-alike terms. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 5 November 2026
- Original website title
- YBYBZhang/ControlVideo · Official homepage title · retained in its source language