RRepoGEO

REPOGEO REPORT · LITE

bloc97/CrossAttentionControl

Default branch main · commit bcb095b1 · scanned 6/27/2026, 4:13:36 PM

GitHub: 1,338 stars · 84 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)

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

AI VISIBILITY SCORE
35 /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
3 / 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 bloc97/CrossAttentionControl, 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
  • highreadme#1
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    ### Why Choose Cross Attention Control? 
Unlike methods such as InstructPix2Pix or ControlNet that often require input masks or additional model training, Cross Attention Control offers precise, semantic image editing directly during inference. It achieves fine-grained control over generated images by manipulating internal attention maps, eliminating the need for cumbersome masks or costly retraining, and incurring minimal performance penalties compared to techniques like CLIP guidance.
  • mediumabout#2
    Refine the repository description for clarity and benefit

    Why:

    CURRENT
    Unofficial implementation of "Prompt-to-Prompt Image Editing with Cross Attention Control" with Stable Diffusion
    COPY-PASTE FIX
    Achieve precise, mask-free image editing in Stable Diffusion by manipulating cross-attention during inference, without retraining. An unofficial implementation of "Prompt-to-Prompt Image Editing with Cross Attention Control".

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 bloc97/CrossAttentionControl
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
SDEdit
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. SDEdit · recommended 2×
  2. ControlNet · recommended 2×
  3. InstructPix2Pix · recommended 1×
  4. GLIDE · recommended 1×
  5. DreamBooth · recommended 1×
  • CATEGORY QUERY
    How to precisely edit generated images using text prompts without needing a mask?
    you: not recommended
    AI recommended (in order):
    1. InstructPix2Pix
    2. GLIDE
    3. SDEdit
    4. DreamBooth
    5. ControlNet

    AI recommended 5 alternatives but never named bloc97/CrossAttentionControl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I achieve precise image editing control during inference without retraining diffusion models?
    you: not recommended
    AI recommended (in order):
    1. AUTOMATIC1111 Stable Diffusion web UI
    2. ComfyUI
    3. Adobe Photoshop
    4. Diffusion Bee
    5. InvokeAI
    6. ControlNet
    7. IP-Adapter
    8. DiffEdit
    9. SDEdit
    10. GLIGEN

    AI recommended 10 alternatives but never named bloc97/CrossAttentionControl. 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 bloc97/CrossAttentionControl?
    pass
    AI named bloc97/CrossAttentionControl explicitly

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

  • If a team adopts bloc97/CrossAttentionControl in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bloc97/CrossAttentionControl 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 bloc97/CrossAttentionControl solve, and who is the primary audience?
    pass
    AI named bloc97/CrossAttentionControl 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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MARKDOWN (README)
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bloc97/CrossAttentionControl — 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