CogVideo supports text to video and image to video generation in its CogVideo and CogVideoX model projects.
Open source
24 alternativesAILab-CVC/VideoCrafter
VideoCrafter2 is a video diffusion model project focused on high-quality generation with limited data.
About AILab-CVC/VideoCrafter
VideoCrafter is a video generation and editing toolbox published by AILab-CVC as a GitHub repository. It provides diffusion models for generic text-to-video generation and image-to-video generation, with VideoCrafter2 presented as an update aimed at overcoming data limitations for high-quality video diffusion.
Key features
- Text-to-video generation from written prompts
- Image-to-video generation from a starting image
- Pretrained checkpoints distributed through Hugging Face
- Checkpoints at 320x512, 576x1024 and 640x1024 resolutions
- Local Gradio demo run with python gradio_app.py
- Inference through provided shell scripts
Best for: Developers and researchers with Python and command-line experience who want to run video diffusion models on their own machines.
Good for
- Covers both text-to-video and image-to-video generation in one repository
- Provides pretrained checkpoints and inference scripts
- Includes a local Gradio demo for interactive use
Watch out for
- Requires manual environment setup with Anaconda and Python 3.8.5
- Model checkpoints must be downloaded separately and placed in specific directories before inference
- Resolution is fixed per checkpoint rather than adjustable per generation
AILab-CVC/VideoCrafter at a glance
- Category
- AI & Machine Learning › AI Video Generators
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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AILab-CVC/VideoCrafter FAQ
What resolutions are available across the checkpoints?
Text-to-video checkpoints are listed at 320x512 (VideoCrafter2) and 576x1024 and 320x512 (VideoCrafter1). Image-to-video checkpoints are listed at 640x1024 and 320x512, both for VideoCrafter1.
What is needed to run a model locally?
Create an Anaconda environment with Python 3.8.5, install the requirements file, download the relevant pretrained checkpoint from Hugging Face into the matching checkpoints directory, then run the corresponding script or the Gradio app.
Is there a way to create videos without running the code locally?
The repository README invites users to join Discord/Floor33 to create films, alongside the local inference options.
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