--- title: "image-hijacks vs ColossalAI" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/euanong-image-hijacks-vs-hpcaitech-colossalai" tools: ["euanong-image-hijacks", "hpcaitech-colossalai"] --- # image-hijacks vs ColossalAI *GraphCanon updated Jul 11, 2026* ## Verdict Pick image-hijacks when license: image-hijacks is MIT, ColossalAI is Apache-2.0; pick ColossalAI when license: ColossalAI is Apache-2.0, image-hijacks is MIT. [image-hijacks](https://image-hijacks.github.io/) reports 56 GitHub stars, 12 forks, and 8 open issues, last pushed Sep 19, 2023. [ColossalAI](https://www.colossalai.org) has 41k stars, 4.5k forks, and 501 open issues, last pushed May 25, 2026. Figures are from public GitHub metadata via [image-hijacks's repository](https://github.com/euanong/image-hijacks) and [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI). | | [image-hijacks](/tools/euanong-image-hijacks.md) | [ColossalAI](/tools/hpcaitech-colossalai.md) | | --- | --- | --- | | Tagline | Official codebase for Image Hijacks: Adversarial Images can Control Generative Models at Runtime | Making large AI models cheaper, faster and more accessible | | Stars | 56 | 41,408 | | Forks | 12 | 4,504 | | Open issues | 8 | 501 | | Language | Python | Python | | Adopt for | - | ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Computer Vision, Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [image-hijacks](/tools/euanong-image-hijacks.md) | [ColossalAI](/tools/hpcaitech-colossalai.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 1026d | 46d | | Open issues (now) | 8 | 501 | | Owner type | User | Organization | | Full report | [trust report](/tools/euanong-image-hijacks/trust.md) | [trust report](/tools/hpcaitech-colossalai/trust.md) | ## Shared compatibility - **Python**: [image-hijacks](/tools/euanong-image-hijacks.md) - Python runtime; [ColossalAI](/tools/hpcaitech-colossalai.md) - Python runtime ## Decision facts: ColossalAI - **Adopt for:** ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. ## Choose when ### Choose image-hijacks if… - License: image-hijacks is MIT, ColossalAI is Apache-2.0. - Tags unique to image-hijacks: python. - Also covers Computer Vision. ### Choose ColossalAI if… - License: ColossalAI is Apache-2.0, image-hijacks is MIT. - Tags unique to ColossalAI: ai, big-model, data-parallelism, deep-learning. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. ## When NOT to use image-hijacks - Last GitHub push was 1026 days ago (dormant maintenance, Sep 19, 2023). Validate activity before betting a new project on image-hijacks. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use ColossalAI - You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. - Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). - You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7. ## Common questions ### What is the difference between image-hijacks and ColossalAI? image-hijacks: Official codebase for Image Hijacks: Adversarial Images can Control Generative Models at Runtime. ColossalAI: Making large AI models cheaper, faster and more accessible. See the comparison table for live GitHub stats and shared categories. ### When should I choose image-hijacks over ColossalAI? Choose image-hijacks over ColossalAI when License: image-hijacks is MIT, ColossalAI is Apache-2.0; Tags unique to image-hijacks: python; Also covers Computer Vision. ### When should I choose ColossalAI over image-hijacks? Choose ColossalAI over image-hijacks when License: ColossalAI is Apache-2.0, image-hijacks is MIT; Tags unique to ColossalAI: ai, big-model, data-parallelism, deep-learning; You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### When should I avoid image-hijacks? Last GitHub push was 1026 days ago (dormant maintenance, Sep 19, 2023). Validate activity before betting a new project on image-hijacks. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid ColossalAI? You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7. ### Is image-hijacks or ColossalAI more popular on GitHub? ColossalAI has more GitHub stars (41,408 vs 56). Stars measure visibility, not whether either tool fits your constraints. ### Are image-hijacks and ColossalAI open source? Yes - both are open-source projects on GitHub (image-hijacks: MIT, ColossalAI: Apache-2.0). ### Where can I find alternatives to image-hijacks or ColossalAI? GraphCanon lists graph-backed alternatives at [image-hijacks alternatives](/tools/euanong-image-hijacks/alternatives) and [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) ([image-hijacks markdown twin](/tools/euanong-image-hijacks/alternatives.md), [ColossalAI markdown twin](/tools/hpcaitech-colossalai/alternatives.md)), 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](/compare/euanong-image-hijacks-vs-hpcaitech-colossalai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, image-hijacks or ColossalAI? image-hijacks: Dormant. ColossalAI: Steady. 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 image-hijacks and ColossalAI? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [image-hijacks trust report](/tools/euanong-image-hijacks/trust); [ColossalAI trust report](/tools/hpcaitech-colossalai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=euanong-image-hijacks`](/api/graphcanon/graph?tool=euanong-image-hijacks) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_