Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-24 16:00 UTC

Akegarasu / lora-scripts

SD-Trainer. LoRA & Dreambooth training scripts & GUI use kohya-ss's trainer, for diffusion model.

PythonAGPL-3.0★ 6,198 stars⑂ 705 forkssince Jan 2023View on GitHub ↗
#dreambooth#finetune#lora#stable-diffusion

Akegarasu/lora-scripts holds a health index of 25 out of 100, placing it in the At Risk band. It scores highest on Community & Adoption (72/100) and lowest on Engineering Quality (25/100). It was last updated 26 days ago. A single contributor accounts for most of its recent work.

25
overall / 100
At Risk

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

25
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 44 is calibrated to 41 on the published index scale (record calibration 2026-08-02).

Ownership

秋葉杏Personal account
3,762 followers64 public repossince Feb 2018

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIlibrarypoints to another repo — not scored4.0.02,555784 days agomedialibraryarchivedatabaseffmpegyt-dlpmpvfilesystemtorrent

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

36Weak · 21% of overall

Development activity

29At Risk
How it's scored
28.8/36Push recency — last push 26 days ago
0/36Commit cadence — 0/52 weeks with commits
0/18Commit volume — 0 commits in the last year
0/10OpenSSF Scorecard: Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Inputs used
commits_last_year0
human_commit_share1
days_since_last_push26
active_weeks_last_year0

Release discipline

47Weak
How it's scored
27/27Ships releases — 12 releases published
0/36Release recency — latest release 749 days ago
19.8/27Release cadence — a release every ~54.7 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count12
latest_release_tagv1.10.0
releases_from_tagsno
days_since_latest_release749
mean_days_between_releases54.7

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

72Good · 17% of overall

Popularity & adoption

92Excellent
How it's scored
60/60Stars — 6,198 stars
23.7/25Forks — 705 forks
8.7/15Watchers — 37 watchers
Inputs used
forks705
stars6,198
watchers37
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

50Moderate
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (AGPL-3.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges4
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

56Moderate · 23% of overall

Maintainer resilience (bus factor)

35Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
2.1/22.5Commit distribution — top contributor authored 91% of commits
13.5/13.5Contributor breadth — 29 contributors
10/10OpenSSF Scorecard: Contributors — project has 5 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled29
top_contributor_share0.906

Issue & PR responsiveness

61Moderate
How it's scored
33.7/42Issue resolution — 80% of issues closed
17.9/30PR acceptance — 46/77 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
1.5/15OpenSSF Scorecard: Code-Review — Found 3/30 approved changesets -- score normalized to 1
Inputs used
merged_prs46
open_issues131
closed_issues533
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.803
closed_unmerged_prs31
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

75Good
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
25/25Owner reach — 3,762 followers of Akegarasu
25/25Track record — 64 public repos, account ~8 yr old
Inputs used
followers3,762
owner_typeUser
is_verified—
owner_loginAkegarasu
public_repos64
account_age_days3,171
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

25At Risk · 19% of overall

Engineering practices

1Critical
How it's scored
0/24CI workflows
0/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — 0 out of 3 merged PRs checked by a CI test -- score normalized to 0
Inputs used
has_cino
has_testsno
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

60Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics — 4 topics
10/10Wiki
Inputs used
topicsdreambooth, finetune, lora, stable-diffusion
has_wikiyes
homepage—
docs_site—
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

33At Risk · 16% of overall

Security posture

16Critical
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — branch protection not enabled on development/release branches
0/2.5CI-Tests — 0 out of 3 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0.8/7.5Code-Review — Found 3/30 approved changesets -- score normalized to 1
2.5/2.5Contributors — project has 5 contributing companies or organizations
0/10Dangerous-Workflow — no data
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
0/7.5Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
0/5Packaging — no data
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — no data
0/7.5Vulnerabilities — 79 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate1.6
Excluded from scoring (no data or not applicable): Dangerous-Workflow, Packaging, Token-Permissions. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages63
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:library@4.0.0 runtime dependency closure — what installing the published package pulls in — 63 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

40Weak · 4% of overall

Agent context & guidance

16Critical
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
16/40Legible commit history — 30 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.3
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

21At Risk
How it's scored
0/18One-command bootstrap
0/22Automated tests
0/11Lint / format config
11/11Static type checking — mikazuki/tsconfig.json
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsno
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configno
typecheck_configsmikazuki/tsconfig.json
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

78Good
How it's scored
27/45Type-checkable code — Python with type-check config (mikazuki/tsconfig.json)
50.9/55Manageable file sizes — 17/229 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes288,821
source_files_sampled229
oversized_source_files17

Machine-readable interfaces

100Exceptional
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — notebooks
Inputs used
example_dirsnotebooks
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

6,198GitHub stars
29contributors
0commits, last 12 months
26days since last push
12releases
1bus factor
131open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'library' points at a different repository (https://github.com/chapmanjacobd/library/); excluded from ecosystem scoring

More detail

Star and fork history 0 ★ / 705 ⇿
0Stars
705Forks
12Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

0125250375500625750678172023-012024-122026-10
Major 0Minor 9Patch 3

Each point covers 4 days.

OpenSSF Scorecard 1.6 / 10
1.6aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-10-24 15:59 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
0CI-Tests0 out of 3 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 3/30 approved changesets -- score normalized to 1
10Contributorsproject has 5 contributing companies or organizations
n/aDangerous-Workflowno workflows found
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
n/aToken-PermissionsNo tokens found
0Vulnerabilities79 existing vulnerabilities detected
All dependencies 44

Full resolved dependency set from the GitHub dependency graph: 0 direct and 44 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIaccelerate0.30.0indirect
PyPIaccelerate0.33.0indirect
PyPIaltair4.2.2indirect
PyPIbitsandbytes0.44.0indirect
PyPIbitsandbytes0.46.0indirect
PyPIdadaptation3.1indirect
PyPIdiffusers0.25.0indirect
PyPIeasygui0.98.3indirect
PyPIeinops0.7.0indirect
PyPIfastapi0.95.1indirect
PyPIftfy6.1.1indirect
PyPIgradio3.44.2indirect
PyPIhttpx0.24.1indirect
PyPIhuggingface-hub0.24.5indirect
PyPIimagesize1.4.1indirect
PyPIlion-pytorch0.0.6indirect
PyPIlion-pytorch0.1.2indirect
PyPIlycoris-lora3.2.0.post2indirect
PyPInumpy—indirect
PyPInumpy1.26.4indirect
PyPIopen-clip-torch2.20.0indirect
PyPIopencv-python4.8.1.78indirect
PyPIpandas—indirect
PyPIpillow—indirect
PyPIprodigy-plus-schedule-free—indirect
PyPIprodigy-plus-schedule-free1.9.0indirect
PyPIprodigyopt1.0indirect
PyPIprodigyopt1.1.2indirect
PyPIpytorch-lightning1.9.0indirect
PyPIpytorch-optimizer3.7.0indirect
PyPIrequests—indirect
PyPIrich13.7.0indirect
PyPIsafetensors0.4.2indirect
PyPIsafetensors0.4.4indirect
PyPIschedulefree1.4indirect
PyPIscipy—indirect
PyPIsentencepiece0.2.0indirect
PyPItensorboard—indirect
PyPItensorboard2.10.1indirect
PyPItoml0.10.2indirect
PyPItransformers4.44.0indirect
PyPIuvicorn0.22.0indirect
PyPIvoluptuous0.13.1indirect
PyPIwandb0.16.2indirect
Dependency advisories 0

Installing pypi:library@4.0.0 pulls in 63 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Related records

Inspected repositories sharing catalogue tags or ecosystems with Akegarasu/lora-scripts. Read them side by side →

83KohakuBlueleaf/LyCORISLora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion.Excellent71lyogavin/airllmAirLLM 70B inference with single 4GB GPUGood99huggingface/diffusers🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.Exceptional98basetenlabs/trussThe simplest way to serve AI/ML models in productionExceptional98huggingface/peft🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.Exceptional96modelscope/ms-swiftUse PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.8, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM5.3, Gemma4, Llava, Phi4, ...) (AAAI 2025).Exceptional

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.