What is AnimeGen-T2V?
42/100
Trust Score (E)
⚠️ Use Caution
AnimeGen-T2V is a text-to-video that An AI model. It has a Nerq Trust Score of 42/100 (E). 42 GitHub stars. Published by Unknown. Last analyzed September 2026.
Why This Score
- ⚠️ Security: 0/100 — Some security concerns
- ⚠️ Maintenance: 0/100 — Maintenance activity is low
- ⚠️ Community: 42 stars, 3.3K downloads — Growing community
- ⚠️ Transparency: License: Not specified — No license specified
Trust & Safety Overview
42
TRUST SCORE
E
GRADE
42
STARS
3.3K
DOWNLOADS
What AnimeGen-T2V Does
AnimeGen-T2V is a model in the text-to-video category. An AI model. It is published by an independent developer and has no specified license. With 42 GitHub stars and 3.3K downloads, it has a small community of users and contributors.
Who Should Use AnimeGen-T2V
AnimeGen-T2V is recommended only for experimental use. Consider alternatives with higher trust scores for production systems.
Details
| Author | Unknown |
|---|---|
| Category | text-to-video |
| License | Not specified |
| Type | model |
| Source | View on GitHub |
| Security Score | 0/100 |
| Activity Score | 0/100 |
How to Get Started
Check the trust score before installing:
curl nerq.ai/v1/preflight?target=aidealab-animegen-t2v
Setup guide · Full safety report · Production review · Is it safe?
Safer Alternatives
| Tool | Trust | Stars |
|---|---|---|
| Sulphur-2-base | 48 | 1.9K |
| MiniMax-H3-Turbo-Lora | 46 | 660 |
| MiniMax-H3-Turbo-Lora-ComfyUI | 44 | 285 |
| PinkCherry_MiniMax-H3 | 44 | 270 |
| vdn-minimax-h3 | 44 | 250 |
Frequently Asked Questions
What is AnimeGen-T2V used for?
AnimeGen-T2V is a text-to-video tool. An AI model.
Is AnimeGen-T2V free?
License: Check project page. AnimeGen-T2V has 42 GitHub stars.
Is AnimeGen-T2V safe?
AnimeGen-T2V has a Nerq Trust Score of 42/100 (E). Evaluate carefully.
What are alternatives to AnimeGen-T2V?
Top alternatives: Sulphur-2-base, MiniMax-H3-Turbo-Lora, MiniMax-H3-Turbo-Lora-ComfyUI. See full comparison.
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Last updated September 2026. Trust scores based on automated analysis of public data.