REPOGEO REPORT · LITE

ExponentialML/Text-To-Video-Finetuning

Default branch main · commit efbd149c · scanned 6/3/2026, 9:27:06 PM

GitHub: 698 stars · 111 forks

AI VISIBILITY SCORE

28 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface ExponentialML/Text-To-Video-Finetuning, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highabout#1

Update 'About' description to reflect archived status and alternative

Why:

CURRENT

Finetune ModelScope's Text To Video model using Diffusers 🧨

COPY-PASTE FIX

ARCHIVED: Finetune ModelScope's Text To Video model using Diffusers 🧨. Please use damo-vilab/i2vgen-xl for active development.

highreadme#2

Add prominent 'Archived' notice and alternative recommendation to README

Why:

COPY-PASTE FIX

Add a banner or a clear, concise paragraph at the very top of the README, before any other content, stating: '⚠️ **ARCHIVED REPOSITORY:** This project is no longer actively maintained. For active development and finetuning of video diffusion models, please refer to [damo-vilab/i2vgen-xl](https://github.com/damo-vilab/i2vgen-xl).'

mediumhomepage#3

Add a homepage URL to the repository metadata

Why:

COPY-PASTE FIX

Add a relevant URL (e.g., a project page, a blog post, or the recommended alternative's repo `https://github.com/damo-vilab/i2vgen-xl`) to the 'Homepage' field in the repository settings.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface ExponentialML/Text-To-Video-Finetuning

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

DreamBooth

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

DreamBooth · recommended 1×
LoRA · recommended 1×
Stable Diffusion XL · recommended 1×
Stable Diffusion 1.5 · recommended 1×
AnimateDiff · recommended 1×

CATEGORY QUERY
How can I adapt existing text-to-video diffusion models for custom datasets?
you: not recommended
AI recommended (in order):
1. DreamBooth
2. LoRA
3. Stable Diffusion XL
4. Stable Diffusion 1.5
5. AnimateDiff
6. ModelScopeT2V
7. SVD (Stable Video Diffusion)
8. Hugging Face Diffusers Library (huggingface/diffusers)
9. Kohya's GUI (kohya-ss/sd-scripts)
10. RunwayML Gen-1
11. RunwayML Gen-2
12. VideoCrafter
13. PyTorch (pytorch/pytorch)
14. TensorFlow (tensorflow/tensorflow)
15. Hugging Face PEFT Library (huggingface/peft)
16. Midjourney
17. DALL-E 3
18. Pika Labs
AI recommended 18 alternatives but never named ExponentialML/Text-To-Video-Finetuning. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools are available for training custom text-to-video generation models?
you: not recommended
AI recommended (in order):
1. Diffusers
2. PyTorch
3. TensorFlow
4. OpenMMLab
5. Accelerate
AI recommended 5 alternatives but never named ExponentialML/Text-To-Video-Finetuning. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of ExponentialML/Text-To-Video-Finetuning?
pass
AI named ExponentialML/Text-To-Video-Finetuning explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts ExponentialML/Text-To-Video-Finetuning in production, what risks or prerequisites should they evaluate first?
pass
AI named ExponentialML/Text-To-Video-Finetuning explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo ExponentialML/Text-To-Video-Finetuning solve, and who is the primary audience?
pass
AI did not name ExponentialML/Text-To-Video-Finetuning — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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ExponentialML/Text-To-Video-Finetuning — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite