Practical overview
When it fits
Use it when you want to generate a stylized animated cinemagraph from a prompt and can work with the documented local Python, PyTorch and CUDA-oriented setup.
Who it is for
- Artists exploring animated motion in text-described, stylized scenes
- Researchers working on text-guided image animation and motion synthesis
- Developers interested in adapting a documented cinemagraph-generation implementation
Documented uses
- Generate an animated cinemagraph from a text description.
- Guide a generated scene's motion direction through a prompt.
- Inspect the generation process in stages, including image creation, segmentation, optical flow and final animation.
Requirements & limits
The implementation is a public GitHub repository under the MIT license.
- The documented setup depends on CUDA-specific packages and several pretrained model files.
- Direction guidance is supported through a prompt and a dedicated inference configuration, but the README warns that exact replication may vary.
- The README describes a multi-stage pipeline and notes that running all components can take a long time.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up the documented inference environment
Prepare the listed Python, PyTorch and model dependencies before running inference.
Before you start
- Conda
- Python 3.9
- PyTorch 1.13.1, torchvision 0.14.1 and PyTorch CUDA 11.6
- The repository's requirements, ODISE and CuPy
- Pretrained model files, including Stable Diffusion v1.4
Steps
Create and activate a Conda environment with Python 3.9.
Install the listed PyTorch, torchvision and CUDA packages, plus the CUDA library and compiler packages shown in the README.
Install ODISE, the dependencies from requirements.txt and CuPy.
Download and extract the pretrained checkpoints, then obtain the specified Stable Diffusion files and repository.
Expected outcome
The dependencies and pretrained model files described by the README are prepared for the inference stages.
Guide limitations
- The README lists CUDA-specific dependencies; it does not establish equivalent setup instructions for other hardware.
- It flags possible ninja-related errors while installing mask2former and points to an ODISE issue for reference.
Run direction-guided cinemagraph inference
Generate a text-guided example with a specified scene and motion direction after preparing the environment and models.
Before you start
- The documented dependencies and pretrained models
- A prompt describing the scene and direction of motion
Steps
Use the direction-guided configuration, configs/inference_directional.yaml.
Run inference_t2c.py with the --use_hint option and a descriptive --prompt, following the README's examples.
Expected outcome
The documented inference command requests a cinemagraph guided by the prompt's motion direction.
Guide limitations
- The README says exact replicability may not be possible because the method randomly samples an angle within the direction quadrant.
- The command is documented in the repository; this guide does not claim it was executed.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- text2cinemagraph/text2cinemagraph · Official homepage title · retained in its source language