Text-to-video
high-fidelity short videos from prompts
// official site: github.com ↗
VIDEO GENERATION · PRO TIER
CogVideoXpro
CogVideoX is THUDM's open-source text-to-video model family (China's Tsinghua University + Zhipu AI) — generates 6-10 second videos at 720×480 with strong temporal consistency and physics. Multiple model sizes (2B, 5B params) for different VRAM budgets. Apache 2.0 license.
// What it is
A closer look.
CogVideoX is THUDM's open-source text-to-video model family (China's Tsinghua University + Zhipu AI) — generates 6-10 second videos at 720×480 with strong temporal consistency and physics. Multiple model sizes (2B, 5B params) for different VRAM budgets. Apache 2.0 license.
One of the highest-quality open video models, the answer to closed-source Runway/Pika competitors.
// Use cases
What it's for.
Concrete scenarios where teams pick CogVideoX over the SaaS alternative.
Image-to-video
extend a still image into motion
Video-to-video
re-style or extend existing video
Marketing content at scale
campaign visuals, social ads
Educational visuals
bring concepts to life
Game/film previs
high-quality storyboard videos
// Who it's for
Built for these teams.
If your team profile matches one of these, CogVideoX is a strong fit out of the box.
Studios
producing high-quality video content
AI agencies
offering video generation to clients
Researchers
studying video generation
Marketers
generating campaign visuals
Hosting providers
offering premium video gen tier
// Differentiators
Why teams pick CogVideoX.
When evaluating self-hosted options for this category, here are the dimensions on which CogVideoX consistently lands above the alternatives.
- ✓Apache 2.0 — fully open, commercial-friendly
- ✓High-quality outputs — competitive with closed Pika/Runway for many use cases
- ✓Multiple sizes — 2B (fast, 12GB), 5B (best quality, 24GB)
- ✓THUDM backing — top Chinese research institution
- ✓Active community — Diffusers integration first-class
- ✓Multi-modal — text2vid, img2vid, vid2vid
// Integrations
Connects to.
The stack you'll plug CogVideoX into — services, protocols, and adjacent apps in the BluixApps catalog.
◇
HuggingFace Diffusers
first-class pipeline
◈
ComfyUI nodes
community wrappers (very active)
◆
Gradio UI
included for web access
▣
API mode
Diffusers Python wrapper
▦
Pair with stills
Flux/SDXL → image → CogVideoX img2vid
▩
Pair with prompts
Ollama prompt-enhance → CogVideoX (better outputs)
// Adoption & deployment
Notable users & community
- 12k+ GitHub stars
- Powering production tools at multiple AI startups
- Featured prominently in HuggingFace video model leaderboards
- Active Chinese + English community
- Multiple Comm-driven LoRA / fine-tunes available
What we ship
- Cloned
THUDM/CogVideorepo - pytorch/pytorch CUDA 12.4 base
- gradio_composite_demo launcher (richest UI)
- Persistent volumes: repo, models (~20 GB), outputs (MP4)
- Port 7863 mapped, listen 0.0.0.0
- Install report at
/root/bluixapps/cogvideox.txt - Notes for switching between 2B and 5B variants
- GPU pre-flight check via
bluixapps_ensure_nvidia_runtime - Backup hook covers models + outputs
// Tips & operations
Run it properly.
Operational guidance from running this in production — what to lock down, what surprises people.
// PERFORMANCE
Model selection by VRAM
// SECURITY
Prompts
English works best; detailed descriptions improve coherence
// OPERATIONS
Length
6 seconds default, max 10 with current weights
// RELIABILITY
Resolution
720×480 default; community fine-tunes for 1280×720
// DEPLOYMENT
First boot downloads ~20 GB
for 5B model — pre-cache
// SCALING
VAE upsample option
for higher quality output
// MAINTENANCE
GPU memory tricks
enable model CPU offload for 12 GB cards
16384
// min ram (MB)
60
// min disk (GB)
7863
// access port
http
// protocol
pro
// bluixapps tier
// Alternatives in Video generation
Compare with
Project resources
Official sitegithub.com ↗