--- title: "DeepSpeed vs image-hijacks" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepspeedai-deepspeed-vs-euanong-image-hijacks" tools: ["deepspeedai-deepspeed", "euanong-image-hijacks"] --- # DeepSpeed vs image-hijacks *GraphCanon updated Jul 11, 2026* ## Verdict Pick DeepSpeed when license: DeepSpeed is Apache-2.0, image-hijacks is MIT; pick image-hijacks when license: image-hijacks is MIT, DeepSpeed is Apache-2.0. [DeepSpeed](https://www.deepspeed.ai/) reports 43k GitHub stars, 4.9k forks, and 1.3k open issues, last pushed Jul 11, 2026. [image-hijacks](https://image-hijacks.github.io/) has 56 stars, 12 forks, and 8 open issues, last pushed Sep 19, 2023. Figures are from public GitHub metadata via [DeepSpeed's repository](https://github.com/deepspeedai/DeepSpeed) and [image-hijacks's repository](https://github.com/euanong/image-hijacks). | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [image-hijacks](/tools/euanong-image-hijacks.md) | | --- | --- | --- | | Tagline | Deep learning optimization library for efficient distributed training and inference | Official codebase for Image Hijacks: Adversarial Images can Control Generative Models at Runtime | | Stars | 42,685 | 56 | | Forks | 4,883 | 12 | | Open issues | 1,302 | 8 | | Language | Python | Python | | Adopt for | Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Model Training, Inference & Serving | Model Training, Computer Vision, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [image-hijacks](/tools/euanong-image-hijacks.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1026d | | Open issues (now) | 1.3k | 8 | | Owner type | Organization | User | | Full report | [trust report](/tools/deepspeedai-deepspeed/trust.md) | [trust report](/tools/euanong-image-hijacks/trust.md) | ## Decision facts: DeepSpeed - **Adopt for:** Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression. ## Choose when ### Choose DeepSpeed if… - License: DeepSpeed is Apache-2.0, image-hijacks is MIT. - Tags unique to DeepSpeed: deep-learning, gpu, compression, machine-learning. - - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters) ### Choose image-hijacks if… - License: image-hijacks is MIT, DeepSpeed is Apache-2.0. - Tags unique to image-hijacks: python. - Also covers Computer Vision. ## When NOT to use DeepSpeed - - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs - - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively ## 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. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## Common questions ### What is the difference between DeepSpeed and image-hijacks? DeepSpeed: Deep learning optimization library for efficient distributed training and inference. image-hijacks: Official codebase for Image Hijacks: Adversarial Images can Control Generative Models at Runtime. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSpeed over image-hijacks? Choose DeepSpeed over image-hijacks when License: DeepSpeed is Apache-2.0, image-hijacks is MIT; Tags unique to DeepSpeed: deep-learning, gpu, compression, machine-learning; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters). ### When should I choose image-hijacks over DeepSpeed? Choose image-hijacks over DeepSpeed when License: image-hijacks is MIT, DeepSpeed is Apache-2.0; Tags unique to image-hijacks: python; Also covers Computer Vision. ### When should I avoid DeepSpeed? - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively ### 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. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is DeepSpeed or image-hijacks more popular on GitHub? DeepSpeed has more GitHub stars (42,685 vs 56). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSpeed and image-hijacks open source? Yes - both are open-source projects on GitHub (DeepSpeed: Apache-2.0, image-hijacks: MIT). ### Where can I find alternatives to DeepSpeed or image-hijacks? GraphCanon lists graph-backed alternatives at [DeepSpeed alternatives](/tools/deepspeedai-deepspeed/alternatives) and [image-hijacks alternatives](/tools/euanong-image-hijacks/alternatives) ([DeepSpeed markdown twin](/tools/deepspeedai-deepspeed/alternatives.md), [image-hijacks markdown twin](/tools/euanong-image-hijacks/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/deepspeedai-deepspeed-vs-euanong-image-hijacks.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSpeed or image-hijacks? DeepSpeed: Very active. image-hijacks: Dormant. 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 DeepSpeed and image-hijacks? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSpeed trust report](/tools/deepspeedai-deepspeed/trust); [image-hijacks trust report](/tools/euanong-image-hijacks/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deepspeedai-deepspeed`](/api/graphcanon/graph?tool=deepspeedai-deepspeed) - 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/_