RRepoGEO

REPOGEO REPORT · LITE

thu-ml/DiT-Extrapolation

Default branch main · commit e5a25afa · scanned 5/22/2026, 11:18:17 PM

GitHub: 808 stars · 75 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 thu-ml/DiT-Extrapolation, 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
  • highreadme#1
    Strengthen README's opening to emphasize video extrapolation

    Why:

    CURRENT
    This repository provides the official implementation of RIFLEx **(ICML 2025)**, UltraViCo **(ICLR 2026)** and UltraImage , which achieve diffusion-transformer extrapolation for long video generation and high resolution image generation in a plug-and-play way.
    COPY-PASTE FIX
    This repository provides the official implementation of RIFLEx **(ICML 2025)** and UltraViCo **(ICLR 2026)**, pioneering diffusion-transformer extrapolation for **long video generation** beyond training lengths. It also includes UltraImage for high-resolution image generation, all in a plug-and-play manner.
  • mediumreadme#2
    Add a 'Comparison to Existing Methods' section

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., '## Comparison to Existing Methods'. Content could be: 'Unlike general video generation models (e.g., RunwayML Gen-1/Gen-2, Stable Video Diffusion) or frame interpolation techniques (e.g., RIFE, FILM), DiT-Extrapolation specifically focuses on enabling Diffusion Transformers to generate videos *beyond their original training length*. It also differs from general sequence models (e.g., S4, Mamba) by providing a plug-and-play solution for *extrapolation capabilities* in *pre-trained diffusion models*.'
  • lowtopics#3
    Refine topics for stronger video extrapolation signal

    Why:

    CURRENT
    cogvideox, diffusion, diffusion-models, diffusion-transformer, dit, extrapolation, generative-model, hunyuan-video, long-video-generation, position-embedding, rope, video-generation
    COPY-PASTE FIX
    cogvideox, diffusion, diffusion-models, diffusion-transformer, dit, extrapolation, generative-model, hunyuan-video, long-video-generation, position-embedding, rope, video-generation, video-extrapolation, length-extrapolation, out-of-distribution-generation

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 thu-ml/DiT-Extrapolation
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
RunwayML Gen-1 / Gen-2
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. RunwayML Gen-1 / Gen-2 · recommended 1×
  2. Stable Video Diffusion (SVD) · recommended 1×
  3. RIFE (Real-time Intermediate Flow Estimation for Video Frame Interpolation) · recommended 1×
  4. FILM (Frame Interpolation for Large Motion) · recommended 1×
  5. ControlNet · recommended 1×
  • CATEGORY QUERY
    How to generate very long videos using diffusion models beyond their original training length?
    you: not recommended
    AI recommended (in order):
    1. RunwayML Gen-1 / Gen-2
    2. Stable Video Diffusion (SVD)
    3. RIFE (Real-time Intermediate Flow Estimation for Video Frame Interpolation)
    4. FILM (Frame Interpolation for Large Motion)
    5. ControlNet
    6. AnimateDiff
    7. LoRAs (Low-Rank Adaptation)
    8. Pika Labs
    9. Krea AI
    10. ModelScope Text-to-Video Synthesis

    AI recommended 10 alternatives but never named thu-ml/DiT-Extrapolation. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a method to enhance video diffusion models for better length extrapolation capabilities.
    you: not recommended
    AI recommended (in order):
    1. S4 (Structured State Space Sequence Models) (HazyResearch/state-spaces)
    2. Mamba (state-spaces/mamba)
    3. Perceiver IO
    4. VideoGPT
    5. Google's Phenaki

    AI recommended 5 alternatives but never named thu-ml/DiT-Extrapolation. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 thu-ml/DiT-Extrapolation?
    pass
    AI named thu-ml/DiT-Extrapolation explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts thu-ml/DiT-Extrapolation in production, what risks or prerequisites should they evaluate first?
    pass
    AI named thu-ml/DiT-Extrapolation 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 thu-ml/DiT-Extrapolation solve, and who is the primary audience?
    pass
    AI did not name thu-ml/DiT-Extrapolation — 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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thu-ml/DiT-Extrapolation — 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