Dashboard / Repos / Liothil

Liothil

C+ 73 completed
Ai Ml
unknown / python · small
263
Files
50,559
LOC
1
Frameworks
7
Languages
OverviewFiles & MetricsGit ActivityCall GraphSecurityReports

Pipeline State

completed
Run ID
#371660
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
46.67
Framework unique
—
Isolation
—
Last stage change
2026-04-16 18:15:42
Deduplication group #48287
Member of a group with 1 similar repo(s) — canonical #6293 view group →
Top concepts (2)
Project DescriptionTesting
Repobility · MCP-ready · https://repobility.com

🧪 Code Distillation

Browse all specs →
Function bodies →
0
Real LLM specs →
0
0.0% coverage
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0
LLM insights →
1

AI Prompt

Create a command-line toolkit in Python for video LoRA training, similar to the Dimljus project. I need it to handle the entire video pipeline. Specifically, it should allow users to scan a folder of pre-cut clips to generate a validation report, ingest a long video by detecting scenes and normalizing it, and normalize clips to specific training specs like 16fps and 480p. Additionally, I need functionality to caption clips using external APIs like Gemini or Replicate, and an audit function to check existing captions. The tool should use YAML for configuration.
python cli video-processing lora machine-learning diffusion gemini replicate pytest
Generated by gemma4:latest

Catalog Information

The dimljus project is a video LORA training toolkit designed for diffusion transformer models.

Description

dimljus is a video LORA training toolkit specifically tailored for diffusion transformer models. It provides a comprehensive set of tools and features to train these models efficiently. The toolkit's primary focus is on simplifying the process of training diffusion transformers, making it more accessible to researchers and developers. With dimljus, users can easily experiment with different configurations and hyperparameters to achieve optimal results.

الوصف

dimljus هو أداة تدريب لورا للفيديو مصممة خصيصًا للmodeleس الترانسفورميشنية التفجيرية. يوفّر مجموعة من الأدوات والخصائص لتسهيل عملية تدريب هذه الموديلات بفاعلية. تتمحور مهمة أداة dimljus حول تسهيل عملية التدريب، مما يجعلها أكثر سهولة للباحثين والمطورين. باستخدام أداة dimljus، يمكن المستخدمين تجربة مختلف التكوينات والمتغيرات الحاسمة لتحقيق النتائج الأمثل.

Novelty

7/10

Tags

video-processing diffusion-transformer model-training deep-learning artificial-intelligence machine-learning computer-vision

Technologies

huggingface numpy pydantic pytorch rich

Claude Models

claude-opus-4.6

Quality Score

C+
73.0/100
Structure
83
Code Quality
73
Documentation
85
Testing
85
Practices
47
Security
56
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Good test coverage (92% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Properly licensed project

Weaknesses

  • 2165 duplicate lines detected \u2014 consider DRY refactoring
  • 7 'god files' with >500 LOC need decomposition

Recommendations

  • Address 25 TODO/FIXME items \u2014 consider tracking them as issues

Security & Health

13.8h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/
A
Risk (1)
MIT
License
8.8%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
81.5%
markdown
17.0%
yaml
1.1%
shell
0.3%
toml
0.1%
json
0.0%
text
0.0%

Frameworks

pytest

Concepts (2)

Findings produced by Repobility · scan your repo at https://repobility.com/scan/
CategoryNameDescriptionConfidence
Methodology: Repobility · https://repobility.com/research/state-of-ai-code-2026/
auto_descriptionProject DescriptionA purpose-built toolkit for video LoRA training on diffusion transformer models (Wan 2.1/2.2 T2V/I2V). Built by Alvdansen Labs.80%
auto_categoryTestingtesting70%

LLM Insights

A toolkit for training LoRA adapters on video diffusion transformer models.structured_summary
info
purpose: A toolkit for training LoRA adapters on video diffusion transformer models.
primary_domain: ml-training
reference_quality60
reuse_potential: high

Quality Timeline

1 quality score recorded.

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Methodology: Repobility · https://repobility.com/research/state-of-ai-code-2026/

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