Comparison
Awesome-Diffusion-Models vs hold
Verdict
Pick Awesome-Diffusion-Models if awesome-Diffusion-Models is a curated collection of resources and academic papers on diffusion models, useful for researchers and developers interested in generative models for tasks like image generation, audio, and NLP; pick hold if hOLD for monocular video analysis of hand-object interactions without prior object models.
Markdown twin · Awesome-Diffusion-Models alternatives · hold alternatives
GraphCanon updated Sep 24, 2026
5views this month
vs
Trust & integrity
| Signal | Awesome-Diffusion-Models | hold |
|---|---|---|
| Maintenance | Dormant (778d since push) As of Sep 18, 2026 · github_public_v1 | Slowing (197d since push) As of Sep 24, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 24, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Awesome-Diffusion-Models
- A collection of resources and papers on Diffusion Models
- hold
- Method for joint reconstruction of articulated hands and objects from monocular videos
Stars
- Awesome-Diffusion-Models
- 12k
- hold
- 493
Forks
- Awesome-Diffusion-Models
- 1.0k
- hold
- 18
Open issues
- Awesome-Diffusion-Models
- 27
- hold
- 10
Language
- Awesome-Diffusion-Models
- HTML
- hold
- Python
Adopt for
- Awesome-Diffusion-Models
- Awesome-Diffusion-Models is a curated collection of resources and academic papers on diffusion models, useful for researchers and developers interested in generative models for tasks like image generation, audio, and NLP
- hold
- HOLD for monocular video analysis of hand-object interactions without prior object models.
Persona
- Awesome-Diffusion-Models
- -
- hold
- -
Runtime
- Awesome-Diffusion-Models
- -
- hold
- -
License
- Awesome-Diffusion-Models
- MIT
- hold
- MIT
Last pushed
- Awesome-Diffusion-Models
- Aug 1, 2024
- hold
- Mar 10, 2026
Categories
- Awesome-Diffusion-Models
- Computer Vision, Model Training, Speech & Audio
- hold
- Computer Vision
Trust and health
Maintenance
- Awesome-Diffusion-Models
- Dormant (18%)
- hold
- Slowing (36%)
Days since push
- Awesome-Diffusion-Models
- 778d
- hold
- 197d
Open issues (now)
- Awesome-Diffusion-Models
- 27
- hold
- 10
Stars delta
- Awesome-Diffusion-Models
- +5 (30d)
- hold
- +4 (30d)
Open issues delta
- Awesome-Diffusion-Models
- 0 (30d)
- hold
- +1 (30d)
OSV dependency advisories
- Awesome-Diffusion-Models
- No lockfile (source not queried)
- hold
- Published findings
Full report
- Awesome-Diffusion-Models
- Trust report
- hold
- Trust report
Choose Awesome-Diffusion-Models if…
- Awesome-Diffusion-Models is primarily HTML; hold is Python.
- Tags unique to Awesome-Diffusion-Models: diffusion-models, generative-model, machine-learning, score-based.
- Also covers Model Training, Speech & Audio.
- When you need a comprehensive collection of introductory materials, including posts, papers, videos, and lectures, on diffusion models
When NOT to use Awesome-Diffusion-Models
- If you are seeking a repository that provides direct implementation code for diffusion models, as Awesome-Diffusion-Models focuses on resources and papers rather than code
- When you need real-time support or interactive forums for discussing diffusion models, as this repository is a static collection of resources without an active community component
Choose hold if…
- hold is primarily Python; Awesome-Diffusion-Models is HTML.
- Tags unique to hold: 3d-reconstruction, ai, augmented-reality, computer-vision.
- When no prior knowledge of object shapes is available but joint 3D reconstruction of hands manipulating objects from single-view videos is needed
When NOT to use hold
- If there are pre-scanned object models that could enhance accuracy beyond self-reconstruction capabilities
- In scenarios where the computational resources for preprocessing and training on custom datasets are insufficient
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (diff-usion/Awesome-Diffusion-Models) · observed Sep 20, 2026
- GitHub forks (diff-usion/Awesome-Diffusion-Models) · observed Sep 20, 2026
- Last push (diff-usion/Awesome-Diffusion-Models) · observed Aug 1, 2024
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (zc-alexfan/hold) · observed Sep 24, 2026
- GitHub forks (zc-alexfan/hold) · observed Sep 24, 2026
- Last push (zc-alexfan/hold) · observed Mar 10, 2026
- License file (MIT) · observed Sep 24, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-Diffusion-Models 12k · hold 493 (synced Sep 20, 2026).
Common questions
- What is the difference between Awesome-Diffusion-Models and hold?
- Awesome-Diffusion-Models: A collection of resources and papers on Diffusion Models. hold: Method for joint reconstruction of articulated hands and objects from monocular videos. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-Diffusion-Models over hold?
- Choose Awesome-Diffusion-Models over hold when Awesome-Diffusion-Models is primarily HTML; hold is Python; Tags unique to Awesome-Diffusion-Models: diffusion-models, generative-model, machine-learning, score-based; Also covers Model Training, Speech & Audio; When you need a comprehensive collection of introductory materials, including posts, papers, videos, and lectures, on diffusion models.
- When should I choose hold over Awesome-Diffusion-Models?
- Choose hold over Awesome-Diffusion-Models when hold is primarily Python; Awesome-Diffusion-Models is HTML; Tags unique to hold: 3d-reconstruction, ai, augmented-reality, computer-vision; When no prior knowledge of object shapes is available but joint 3D reconstruction of hands manipulating objects from single-view videos is needed.
- When should I avoid Awesome-Diffusion-Models?
- If you are seeking a repository that provides direct implementation code for diffusion models, as Awesome-Diffusion-Models focuses on resources and papers rather than code When you need real-time support or interactive forums for discussing diffusion models, as this repository is a static collection of resources without an active community component
- When should I avoid hold?
- If there are pre-scanned object models that could enhance accuracy beyond self-reconstruction capabilities In scenarios where the computational resources for preprocessing and training on custom datasets are insufficient
- Is Awesome-Diffusion-Models or hold more popular on GitHub?
- Awesome-Diffusion-Models has more GitHub stars (12,371 vs 493). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-Diffusion-Models and hold open source?
- Yes - both are open-source projects on GitHub (Awesome-Diffusion-Models: MIT, hold: MIT).
- Where can I find alternatives to Awesome-Diffusion-Models or hold?
- GraphCanon lists graph-backed alternatives at Awesome-Diffusion-Models alternatives and hold alternatives (Awesome-Diffusion-Models markdown twin, hold markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, Awesome-Diffusion-Models or hold?
- Awesome-Diffusion-Models: Dormant. hold: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for Awesome-Diffusion-Models and hold?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Diffusion-Models trust report; hold trust report.