RRepoGEO

REPOGEO REPORT · LITE

deep-floyd/IF

Default branch develop · commit ffc81638 · scanned 5/25/2026, 10:32:07 AM

GitHub: 7,816 stars · 527 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 deep-floyd/IF, 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise description to the About section

    Why:

    COPY-PASTE FIX
    DeepFloyd IF is a novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding, featuring a cascaded pixel diffusion architecture for high-resolution image generation up to 1024x1024px.
  • highreadme#2
    Reposition README intro to highlight pixel-space differentiator

    Why:

    CURRENT
    We introduce DeepFloyd IF, a novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding. DeepFloyd IF is a modular composed of a frozen text encoder and three cascaded pixel diffusion modules: a base model that generates 64x64 px image based on text prompt and two super-resolution models, each designed to generate images of increasing resolution: 256x256 px and 1024x1024 px.
    COPY-PASTE FIX
    We introduce DeepFloyd IF, a novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding. Unlike latent diffusion models, DeepFloyd IF operates directly in pixel space, utilizing a modular cascaded pixel diffusion architecture composed of a frozen text encoder and three cascaded pixel diffusion modules: a base model that generates 64x64 px image based on text prompt and two super-resolution models, each designed to generate images of increasing resolution: 256x256 px and 1024x1024 px.

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 deep-floyd/IF
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stable Diffusion XL (SDXL)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Diffusion XL (SDXL) · recommended 1×
  2. Stable Diffusion 1.5 / 2.1 · recommended 1×
  3. Realistic Vision · recommended 1×
  4. Photon · recommended 1×
  5. Juggernaut XL · recommended 1×
  • CATEGORY QUERY
    What are the best open-source text-to-image models for generating photorealistic high-resolution images?
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion XL (SDXL)
    2. Stable Diffusion 1.5 / 2.1
    3. Realistic Vision
    4. Photon
    5. Juggernaut XL
    6. DreamShaper
    7. Deliberate
    8. DeepFloyd IF
    9. Kandinsky 2.2
    10. PixArt-α

    AI recommended 10 alternatives but never named deep-floyd/IF. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an AI model that can generate and upscale images from text to 1024x1024 resolution.
    you: not recommended
    AI recommended (in order):
    1. Midjourney
    2. Stable Diffusion XL
    3. DALL-E 3
    4. Adobe Firefly
    5. Leonardo.Ai
    6. Fooocus

    AI recommended 6 alternatives but never named deep-floyd/IF. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 deep-floyd/IF?
    pass
    AI did not name deep-floyd/IF — 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?

  • If a team adopts deep-floyd/IF in production, what risks or prerequisites should they evaluate first?
    pass
    AI named deep-floyd/IF 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 deep-floyd/IF solve, and who is the primary audience?
    pass
    AI named deep-floyd/IF explicitly

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

Embed your GEO score

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deep-floyd/IF — 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