Registro público
Informe de salud del softwareesquema 0.27.0 · métricas 1.13.0 · 2026-07-25 08:21 UTC

Akegarasu / lora-scripts

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

PythonAGPL-3.0★ 6087 estrellas⑂ 696 forksdesde ene 2023Ver en GitHub ↗

Akegarasu/lora-scripts tiene un índice de salud de 36 sobre 100, lo que lo sitúa en la banda En riesgo. Su puntuación más alta es Community & Adoption (72/100) y la más baja, Security (21/100). Se actualizó por última vez hace 319 días. Una sola persona concentra la mayor parte del trabajo reciente.

36
global / 100
En riesgo

Índice de salud del software

Las métricas se agrupan en categorías ponderadas sobre una escala de 1 a 100. El resultado global parte de su media; cuando la evidencia pública activa la Política de Jurisdicciones de Alto Riesgo, la calificación se ajusta y recibe el límite 49 (En riesgo). Preparación para IA queda fuera.

36
Excelente85-100Ejemplar; cumple prácticamente todos los criterios evaluados
Bueno70-84Saludable; carencias menores
Moderado50-69Aceptable con carencias notables; se recomienda revisión
En riesgo30-49Debilidades significativas; su adopción exige cautela
Crítico1-29Problemas graves (proyecto abandonado, un solo mantenedor, sin higiene)
VitalidadComunidad yAdopciónSostenibilidady GobernanzaCalidad deIngenieríaSeguridadPreparaciónpara IA

Perfil de puntuación

Cada eje es una categoría. La forma importa más que la media: un proyecto sano llena toda la figura, mientras que un perfil de picos y cráteres indica que la fortaleza en una dimensión enmascara el riesgo en otra.

Abandonment Policy applies a 85% multiplier to weighted overall health.

Titularidad

秋葉杏Cuenta personal
3598 seguidores62 repositorios públicosdesde feb 2018

Este repositorio pertenece a una cuenta personal. Un proyecto con un único propietario conlleva más riesgo de continuidad que uno respaldado por una organización.

Ecosistemas de paquetes

RegistroPaqueteVersiónDescargas / mesVersionesÚltima publicación
PyPIlibraryapunta a otro repositorio; no se puntúa3.1.1-70hace 11 días

Métricas por categoría

Vitalidad

¿Está vivo el proyecto: se escribe código y se publican versiones?

34En riesgo · 22% del índice global
Cómo se puntúa
3.6/36Recencia de push — último push hace 319 días
2.8/36Cadencia de commits — 4/52 semanas con commits
8.6/18Volumen de commits — 8 commits en el último año
0/10OpenSSF Scorecard: Maintained — sin datos
Datos de entrada utilizados
commits_last_year8
human_commit_share1
days_since_last_push319
active_weeks_last_year4
Cómo se puntúa
27/27Publica versiones — 12 versiones publicadas
7.2/36Recencia de las versiones — última versión hace 658 días
19.8/27Cadencia de publicación — una versión cada ~54,7 días
0/10OpenSSF Scorecard: Signed-Releases — sin datos
Datos de entrada utilizados
releases_count12
latest_release_tagv1.10.0
releases_from_tagsno
days_since_latest_release658
mean_days_between_releases54,7

Comunidad y Adopción

¿Tiene el proyecto usuarios, descargas, atención y unas condiciones acogedoras para quienes contribuyen?

72Bueno · 18% del índice global
Cómo se puntúa
60/60Estrellas — 6087 estrellas
23.7/25Forks — 696 forks
8.6/15Observadores — 36 observadores
Datos de entrada utilizados
forks696
stars6087
watchers36
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Cómo se puntúa
22.5/22.5README
22.5/22.5Licencia — licencia reconocida (AGPL-3.0)
0/18Guía CONTRIBUTING
0/13.5Código de conducta
0/7.2Plantilla de issues
0/6.3Plantilla de PR
Datos de entrada utilizados
has_readme
has_license
has_contributingno
has_issue_templateno
has_code_of_conductno
has_pull_request_templateno

Sostenibilidad y Gobernanza

¿Sobrevivirá el proyecto a sus personas: factor bus, capacidad de respuesta, quién lo respalda y mantenimiento del paquete?

56Moderado · 24% del índice global
Cómo se puntúa
9/54Factor bus — la mitad de los commits recae en 1 contribuyente(s)
2.1/22.5Distribución de commits — el principal contribuyente firma el 90% de los commits
13.5/13.5Amplitud de contribuyentes — 30 contribuyentes
0/10OpenSSF Scorecard: Contributors — sin datos
Datos de entrada utilizados
bus_factor1
contributors_sampled30
top_contributor_share0,905
Cómo se puntúa
37.5/46.8Resolución de issues — 80% de issues cerradas
23.2/38.3Aceptación de PR — 46/76 PR decididos fusionados
0/15OpenSSF Scorecard: Code-Review — sin datos
Datos de entrada utilizados
merged_prs46
open_issues132
closed_issues533
issue_closed_ratio0,802
closed_unmerged_prs30
Cómo se puntúa
10/30Respaldo de la propiedad — cuenta personal (usuario)
0/20Dominio verificado — no aplicable a cuentas de usuario
25/25Alcance del propietario — 3598 seguidores de Akegarasu
25/25Trayectoria — 62 repos públicos, cuenta de ~8 años
Datos de entrada utilizados
followers3598
owner_typeUser
is_verified
owner_loginAkegarasu
public_repos62
account_age_days3079
Excluidos de la puntuación (sin datos o no aplicable): Dominio verificado. Los pesos restantes se han renormalizado.

Calidad de Ingeniería

¿Existen unas prácticas mínimas de ingeniería y documentación?

25Crítico · 20% del índice global
Cómo se puntúa
0/24Flujos de trabajo de CI
0/24Pruebas presentes
0/16Configuración de linter
0/9.6Hooks de pre-commit
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — sin datos
Datos de entrada utilizados
has_cino
has_testsno
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentación

60Moderado
Cómo se puntúa
30/30README
0/25Directorio de documentación
0/15Sitio de documentación / página del proyecto
10/10Descripción del repositorio
10/10Topics — 4 topics
10/10Wiki
Datos de entrada utilizados
topicsdreambooth, finetune, lora, stable-diffusion
has_wiki
homepage
has_readme
has_docs_dirno
has_description

Seguridad

¿Son sólidas las prácticas visibles de seguridad y de cadena de suministro, sin exposición jurisdiccional de alto riesgo sin resolver?

21Crítico · 16% del índice global
Cómo se puntúa
0/30Política de seguridad (SECURITY.md)
0/25Configuración de Dependabot
0/25Lockfiles de dependencias
0/20Flujo de trabajo de CodeQL
Datos de entrada utilizados
sourcefile_signals
lockfiles
manifestsrequirements.txt
has_codeql_workflowno
has_security_policyno
has_dependabot_configno
Cómo se puntúa
35/35Dependencias directas libres de avisos conocidos — ninguna dependencia directa tiene un aviso conocido
25/25Dependencias indirectas libres de avisos conocidos — ninguna dependencia indirecta tiene un aviso conocido
0/40Sin avisos pendientes — ningún aviso tiene fecha de publicación
Datos de entrada utilizados
sourceosv
advisories0
affected_packages0
assessed_packages64
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluidos de la puntuación (sin datos o no aplicable): Sin avisos pendientes. Los pesos restantes se han renormalizado. Se cotejó el cierre de dependencias en tiempo de ejecución de pypi:library@3.1.1 —lo que arrastra la instalación del paquete publicado—: 64 paquetes. No se analiza la alcanzabilidad.

Preparación para IA

¿Hasta qué punto está el repositorio preparado para desarrollarse y mantenerse con agentes de codificación de IA? Es una insignia independiente y experimental — peso 0,0, de modo que se presenta por separado y no afecta a la puntuación de salud global.

32En riesgo · 0% del índice global
Cómo se puntúa
0/45Instrucciones para agentes — sin CLAUDE.md / AGENTS.md / reglas de editor
0/15Documentación legible por máquinas (llms.txt)
16/40Historial de commits legible — 30 de 100 commits humanos declaran su intención (asunto estructurado o cuerpo explicativo)
Datos de entrada utilizados
has_llms_txtno
legible_history_share0,3
agent_instruction_files
agent_instruction_max_bytes
Cómo se puntúa
0/18Arranque con un solo comando
0/22Pruebas automatizadas
0/11Configuración de lint / formato
11/11Verificación estática de tipos — mikazuki/tsconfig.json
10/10Entorno reproducible — Dockerfile
0/10Práctica demostrada con agentes — ningún commit con autoría de agente entre los últimos 100
0/8Mantenimiento automatizado — no se observan actualizaciones automáticas de dependencias
0/10OpenSSF Scorecard: Pinned-Dependencies — sin datos
Datos de entrada utilizados
has_nixno
has_testsno
lockfiles
has_dockerfile
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configno
typecheck_configsmikazuki/tsconfig.json
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
Cómo se puntúa
27/45Código verificable por tipos — Python con configuración de verificación de tipos (mikazuki/tsconfig.json)
50.9/55Tamaños de archivo manejables — 17/229 archivos fuente de más de 60 KB
Datos de entrada utilizados
primary_languagePython
largest_source_bytes288.821
source_files_sampled229
oversized_source_files17
Cómo se puntúa
0/40Esquema de API (OpenAPI/GraphQL/proto)
0/20Servidor MCP
40/40Ejemplos ejecutables — notebooks
Datos de entrada utilizados
example_dirsnotebooks
has_mcp_signalno
api_schema_files

Datos clave

6087estrellas de GitHub
30contribuidores
8commits en los últimos 12 meses
319días desde el último push
12versiones publicadas
1factor bus
132issues abiertas
PyPIecosistemas de paquetes

Advertencias de recopilación de datos

  • 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
  • OpenSSF Scorecard did not return a usable result (2026/07/25 08:21:14 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

Más detalle

Historial de estrellas y forks 0 ★ / 696 ⇿
0Estrellas
696Forks
12Versiones

Cuándo se añadió cada estrella y fork, recopilado de GitHub y agrupado por día. El crecimiento acumulado se sitúa justo encima de las adiciones diarias que lo componen, de modo que ambos se leen en conjunto: la acumulación orgánica sostenida no se parece en nada a un pico abrupto y efímero. Cuando esa diferencia es medible, se informa como autenticidad del crecimiento.

0125250375500625750677172023-012024-102026-07
Mayor 0Menor 9Parche 3

Cada punto abarca 4 días.

Todas las dependencias 44

Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 0 paquetes directos y 44 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.

RegistroPaqueteVersiónRelación
PyPIaccelerate0.30.0indirecta
PyPIaccelerate0.33.0indirecta
PyPIaltair4.2.2indirecta
PyPIbitsandbytes0.44.0indirecta
PyPIbitsandbytes0.46.0indirecta
PyPIdadaptation3.1indirecta
PyPIdiffusers0.25.0indirecta
PyPIeasygui0.98.3indirecta
PyPIeinops0.7.0indirecta
PyPIfastapi0.95.1indirecta
PyPIftfy6.1.1indirecta
PyPIgradio3.44.2indirecta
PyPIhttpx0.24.1indirecta
PyPIhuggingface-hub0.24.5indirecta
PyPIimagesize1.4.1indirecta
PyPIlion-pytorch0.0.6indirecta
PyPIlion-pytorch0.1.2indirecta
PyPIlycoris-lora3.2.0.post2indirecta
PyPInumpyindirecta
PyPInumpy1.26.4indirecta
PyPIopen-clip-torch2.20.0indirecta
PyPIopencv-python4.8.1.78indirecta
PyPIpandasindirecta
PyPIpillowindirecta
PyPIprodigy-plus-schedule-freeindirecta
PyPIprodigy-plus-schedule-free1.9.0indirecta
PyPIprodigyopt1.0indirecta
PyPIprodigyopt1.1.2indirecta
PyPIpytorch-lightning1.9.0indirecta
PyPIpytorch-optimizer3.7.0indirecta
PyPIrequestsindirecta
PyPIrich13.7.0indirecta
PyPIsafetensors0.4.2indirecta
PyPIsafetensors0.4.4indirecta
PyPIschedulefree1.4indirecta
PyPIscipyindirecta
PyPIsentencepiece0.2.0indirecta
PyPItensorboardindirecta
PyPItensorboard2.10.1indirecta
PyPItoml0.10.2indirecta
PyPItransformers4.44.0indirecta
PyPIuvicorn0.22.0indirecta
PyPIvoluptuous0.13.1indirecta
PyPIwandb0.16.2indirecta
Avisos de dependencias 0

Instalar pypi:library@3.1.1 arrastra 64 paquetes, directos y transitivos: 0 tienen avisos conocidos, de los cuales 0 son dependencias directas.

Ningún aviso conocido afecta a las dependencias evaluadas.

Un aviso significa que la versión registrada en el grafo de dependencias cae dentro del rango afectado de un aviso. No se analiza la alcanzabilidad, y el grafo incluye fijaciones de desarrollo y prueba: un hallazgo puede referirse al utillaje y no al software distribuido.

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            "value": 100,
            "inputs": {
              "source": "osv",
              "meaning": "reported as a malicious package by the OpenSSF corpus; the remedy is removal or moving off the compromised name, never an upgrade of the same artifact. Versions the registry has since pulled are listed but not scored",
              "packages": [],
              "red_flag": false,
              "assessed_packages": 64,
              "malicious_packages": 0,
              "direct_malicious_packages": 0,
              "withdrawn_malicious_packages": 0,
              "installable_malicious_packages": 0
            },
            "components": [
              {
                "key": "no_dependency_reported_as_a_malicious_package",
                "name": "No dependency reported as a malicious package",
                "detail": "no dependency is reported as a malicious package",
                "points": 100,
                "status": "met",
                "details": [
                  {
                    "code": "no_malicious_dependencies",
                    "params": {}
                  }
                ],
                "max_points": 100
              }
            ]
          },
          {
            "key": "high_risk_jurisdiction_exposure",
            "band": "excellent",
            "name": "High-Risk Jurisdiction Exposure",
            "note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
            "notes": [
              {
                "code": "jurisdiction_evidence_limits",
                "params": {}
              }
            ],
            "value": 100,
            "inputs": {
              "meaning": "self-published location evidence; not nationality or citizenship",
              "red_flag": false,
              "exposures": [],
              "policy_countries": [
                "Russia",
                "Iran",
                "North Korea"
              ],
              "review_only_matches": 0,
              "assessed_self_published_locations": 5
            },
            "components": [
              {
                "key": "policy_exposure_multiplier",
                "name": "Policy exposure multiplier",
                "detail": "no confirmed policy-scope location match",
                "points": 100,
                "status": "met",
                "details": [
                  {
                    "code": "jurisdiction_no_match",
                    "params": {}
                  }
                ],
                "max_points": 100
              }
            ]
          }
        ],
        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
      },
      {
        "key": "ai_readiness",
        "band": "at_risk",
        "name": "AI Readiness",
        "value": 32,
        "weight": 0,
        "metrics": [
          {
            "key": "ai_agent_context",
            "band": "critical",
            "name": "Agent context & guidance",
            "note": null,
            "notes": [],
            "value": 16,
            "inputs": {
              "has_llms_txt": false,
              "legible_history_share": 0.3,
              "agent_instruction_files": [],
              "agent_instruction_max_bytes": null
            },
            "components": [
              {
                "key": "agent_instructions",
                "name": "Agent instructions",
                "detail": "no CLAUDE.md / AGENTS.md / editor rules",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_agent_instructions",
                    "params": {}
                  }
                ],
                "max_points": 45
              },
              {
                "key": "machine_readable_docs_llms_txt",
                "name": "Machine-readable docs (llms.txt)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 15
              },
              {
                "key": "legible_commit_history",
                "name": "Legible commit history",
                "detail": "30 of 100 human commits state their intent (structured subject or explanatory body)",
                "points": 16,
                "status": "partial",
                "details": [
                  {
                    "code": "legible_history",
                    "params": {
                      "legible": 30,
                      "sampled": 100
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "ai_verify_loop",
            "band": "critical",
            "name": "Verify loop (build / test / typecheck)",
            "note": null,
            "notes": [],
            "value": 23,
            "inputs": {
              "has_nix": false,
              "has_tests": false,
              "lockfiles": [],
              "has_dockerfile": true,
              "typed_language": false,
              "bootstrap_files": [],
              "has_devcontainer": false,
              "has_linter_config": false,
              "typecheck_configs": [
                "mikazuki/tsconfig.json"
              ],
              "agent_commit_share": 0,
              "toolchain_manifests": [],
              "dependency_bot_commit_share": 0
            },
            "components": [
              {
                "key": "one_command_bootstrap",
                "name": "One-command bootstrap",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 18
              },
              {
                "key": "automated_tests",
                "name": "Automated tests",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 22
              },
              {
                "key": "lint_format_config",
                "name": "Lint / format config",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 11
              },
              {
                "key": "static_type_checking",
                "name": "Static type checking",
                "detail": "mikazuki/tsconfig.json",
                "points": 11,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "mikazuki/tsconfig.json"
                    }
                  }
                ],
                "max_points": 11
              },
              {
                "key": "reproducible_environment",
                "name": "Reproducible environment",
                "detail": "Dockerfile",
                "points": 10,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "Dockerfile"
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "demonstrated_agent_practice",
                "name": "Demonstrated agent practice",
                "detail": "no agent-authored commits among the last 100",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_agent_authored_commits",
                    "params": {
                      "sampled": 100
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
                "detail": "no automated dependency updates observed",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_dependency_automation",
                    "params": {}
                  }
                ],
                "max_points": 8
              },
              {
                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "OpenSSF Scorecard unavailable",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 10
              }
            ]
          },
          {
            "key": "ai_code_legibility",
            "band": "good",
            "name": "Code legibility for models",
            "note": null,
            "notes": [],
            "value": 78,
            "inputs": {
              "primary_language": "Python",
              "largest_source_bytes": 288821,
              "source_files_sampled": 229,
              "oversized_source_files": 17
            },
            "components": [
              {
                "key": "type_checkable_code",
                "name": "Type-checkable code",
                "detail": "Python with type-check config (mikazuki/tsconfig.json)",
                "points": 27,
                "status": "partial",
                "details": [
                  {
                    "code": "typecheck_config_language",
                    "params": {
                      "files": "mikazuki/tsconfig.json",
                      "language": "Python"
                    }
                  }
                ],
                "max_points": 45
              },
              {
                "key": "manageable_file_sizes",
                "name": "Manageable file sizes",
                "detail": "17/229 source files over 60KB",
                "points": 50.9,
                "status": "partial",
                "details": [
                  {
                    "code": "oversized_source_files",
                    "params": {
                      "kb": 60,
                      "sampled": 229,
                      "oversized": 17
                    }
                  }
                ],
                "max_points": 55
              }
            ]
          },
          {
            "key": "ai_interfaces",
            "band": "at_risk",
            "name": "Machine-readable interfaces",
            "note": null,
            "notes": [],
            "value": 40,
            "inputs": {
              "example_dirs": [
                "notebooks"
              ],
              "has_mcp_signal": false,
              "api_schema_files": []
            },
            "components": [
              {
                "key": "api_schema_openapi_graphql_proto",
                "name": "API schema (OpenAPI/GraphQL/proto)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 40
              },
              {
                "key": "mcp_server",
                "name": "MCP server",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 20
              },
              {
                "key": "runnable_examples",
                "name": "Runnable examples",
                "detail": "notebooks",
                "points": 40,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "notebooks"
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          }
        ],
        "description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
      }
    ],
    "metrics_version": "1.13.0"
  },
  "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",
    "OpenSSF Scorecard did not return a usable result (2026/07/25 08:21:14 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-25T08:21:53.286852Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/a/Akegarasu/lora-scripts.svg",
  "full_name": "Akegarasu/lora-scripts",
  "license_state": "standard",
  "license_spdx": "AGPL-3.0"
}

Las puntuaciones son señales, no garantías. Reflejan prácticas públicamente visibles en GitHub; no son una auditoría de código ni una garantía de seguridad.

Los datos ausentes se excluyen y los pesos se renormalizan; nunca se puntúan como cero. La metodología es versionada y abierta: métricas v1.13.0, esquema v0.27.0 — metodología completa · wiki de métricas.

Cómo se sitúa un resultado dentro del registro general: estadísticas agregadasPyPI.