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Image & Video AI

CogVideo & CogVideoX

CogVideo & CogVideoX is an open-source Python repository containing text-to-video and image-to-video generation models, inference examples and fine-tuning resources.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

CogVideo & CogVideoX — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when an open-source, Python-based foundation is needed for experimenting with or building around text-to-video and image-to-video models.

Who it is for

  • AI researchers exploring video-generation models.
  • Python developers building video-generation prototypes.
  • Creative technologists testing text-to-video or image-to-video workflows.
  • Teams evaluating self-hosted multimodal generation components.

Documented uses

  • Generate video from a text prompt.
  • Animate a still image with prompt-guided video generation.
  • Continue an existing video through the documented model workflows.
  • Experiment with inference or fine-tuning code for CogVideoX models.
Before you choose

Requirements & limits

The repository is publicly accessible and links to CogVideoX-5B demonstration spaces on Hugging Face and ModelScope. The excerpt does not specify pricing or account requirements for those spaces.

  • The project uses Python and documents a SAT path requiring Python 3.10 through 3.12.
  • The repository is licensed under Apache-2.0.
  • Documented generation modes include text-to-video, image-to-video and video continuation.
  • The README links to both Hugging Face and ModelScope online demonstrations.
  • The project provides separate inference and fine-tuning materials.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the SAT development path

Prepare the documented SAT inference and fine-tuning path for CogVideoX models.

Before you start

  • Python 3.10, 3.11 or 3.12.
  • Dependencies specified in the repository requirements file.
  • The SAT instructions and model resources referenced by the repository.

Steps

  1. Use the repository's SAT README for the inference and fine-tuning instructions.

  2. Use a supported Python version between 3.10 and 3.12.

  3. Install the dependencies specified by the project requirements.

  4. Continue with the inference or fine-tuning code referenced in the SAT materials.

Expected outcome

A locally prepared SAT-based development path for working with the repository's CogVideoX model resources.

Guide limitations

  • The supplied instructions do not establish a successful local run or guarantee hardware compatibility.
  • The guide does not specify an account, cost or service requirement for the linked online spaces.
Documentation 1 ↗
COMPARE OPTIONS

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.

ProjectDocumented purposePlatformAccess and requirements
CogVideo & CogVideoXGenerate video from a text prompt.Cloud, Self-hostedThe repository is publicly accessible and links to CogVideoX-5B demonstration spaces on Hugging Face and ModelScope. The excerpt does not specify pricing or account requirements for those spaces.
FASHN VTON v1.5Generate a try-on preview from a person image and a garment image.CLI, Linux, macOS, Self-hostedThe repository is public and states an Apache-2.0 license. Local setup requires downloading approximately 2 GB of model weights.
PosteromniRescale a poster to a target aspect ratio using a text prompt.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
2 October 2026
Original website title
zai-org/CogVideo · Official homepage title · retained in its source language
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