--- title: "custom-diffusion vs image-hijacks" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/adobe-research-custom-diffusion-vs-euanong-image-hijacks" tools: ["adobe-research-custom-diffusion", "euanong-image-hijacks"] --- # custom-diffusion vs image-hijacks *GraphCanon updated Aug 24, 2026* ## Verdict Pick custom-diffusion if custom-Diffusion is a research-driven repository focusing on enhancing text-to-image generation tasks through multi-concept customization capabilities in diffusion models and fine-tuning techniques; pick image-hijacks if image Hijacks is an advanced adversarial AI tool for generating images that can control the output of generative models at runtime. [custom-diffusion](https://www.cs.cmu.edu/~custom-diffusion) reports 2.0k GitHub stars, 140 forks, and 52 open issues, last pushed May 24, 2026. [image-hijacks](https://image-hijacks.github.io/) has 57 stars, 13 forks, and 8 open issues, last pushed Sep 19, 2023. Figures are from public GitHub metadata via [custom-diffusion's repository](https://github.com/adobe-research/custom-diffusion) and [image-hijacks's repository](https://github.com/euanong/image-hijacks). | | [custom-diffusion](/tools/adobe-research-custom-diffusion.md) | [image-hijacks](/tools/euanong-image-hijacks.md) | | --- | --- | --- | | Tagline | Research repository for multi-concept customization in text-to-image synthesis using diffusion models. | Adversarial Images Control Generative Models at Runtime | | Stars | 1,977 | 57 | | Forks | 140 | 13 | | Open issues | 52 | 8 | | Language | Python | Python | | Adopt for | Custom-Diffusion is a research-driven repository focusing on enhancing text-to-image generation tasks through multi-concept customization capabilities in diffusion models and fine-tuning techniques. | Image Hijacks is an advanced adversarial AI tool for generating images that can control the output of generative models at runtime. | | Persona | - | - | | Runtime | - | - | | License | Other | MIT | | Categories | Computer Vision, Model Training | Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [custom-diffusion](/tools/adobe-research-custom-diffusion.md) | [image-hijacks](/tools/euanong-image-hijacks.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 91d | 1050d | | Open issues (now) | 52 | 8 | | Stars delta | +1 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/adobe-research-custom-diffusion/trust.md) | [trust report](/tools/euanong-image-hijacks/trust.md) | ## Shared compatibility - **Python**: [custom-diffusion](/tools/adobe-research-custom-diffusion.md) - Python runtime; [image-hijacks](/tools/euanong-image-hijacks.md) - Python runtime ## Decision facts: custom-diffusion - **Requirements:** Min 8 GB RAM - **Adopt for:** Custom-Diffusion is a research-driven repository focusing on enhancing text-to-image generation tasks through multi-concept customization capabilities in diffusion models and fine-tuning techniques. ## Decision facts: image-hijacks - **Requirements:** Min 8 GB RAM; System-specific adjustments might be required, such as setting the `PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring` environment variable.; Large files like cached models or data are stored in the `data/` directory. Ensure this directory is appropriately configured for storage. - **Adopt for:** Image Hijacks is an advanced adversarial AI tool for generating images that can control the output of generative models at runtime. ## Choose when ### Choose custom-diffusion if… - License: custom-diffusion is Other, image-hijacks is MIT. - Requirements: Min 8 GB RAM. - Tags unique to custom-diffusion: computer-vision, customization, diffusion-models, few-shot. - Also covers Model Training. - Use Custom-Diffusion when your project requires incorporating multiple custom concepts into text-to-image synthesis, given its emphasis on handling multi-concept scenarios. ### Choose image-hijacks if… - License: image-hijacks is MIT, custom-diffusion is Other. - Requirements: Min 8 GB RAM; System-specific adjustments might be required, such as setting the `PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring` environment variable.; Large files like cached models or data are stored in the `data/` directory. Ensure this directory is appropriately configured for storage.. - Tags unique to image-hijacks: adversarial, generative, image manipulation, runtime control. - When you need to create specific adversarial scenarios where fine-tuned images manipulate generative model outputs during real-time operation. ## When NOT to use custom-diffusion - Avoid using Custom-Diffusion for immediate production deployments or simple image generation tasks as it is a research repository without extensive documentation meant for broader usability. - Do not opt for Custom-Diffusion if your project prioritizes speed over customization quality, given its focus on high-quality outputs through complex model fine-tuning processes. ## When NOT to use image-hijacks - Avoid using Image Hijacks for standard machine learning tasks that do not involve runtime manipulation of AI-generated images through adversarial means. - Do not use this tool if you are working within a constrained or sensitive environment where introducing adversarial elements poses an additional risk to system security. ## Common questions ### What is the difference between custom-diffusion and image-hijacks? custom-diffusion: Research repository for multi-concept customization in text-to-image synthesis using diffusion models.. image-hijacks: Adversarial Images Control Generative Models at Runtime. See the comparison table for live GitHub stats and shared categories. ### When should I choose custom-diffusion over image-hijacks? Choose custom-diffusion over image-hijacks when License: custom-diffusion is Other, image-hijacks is MIT; Requirements: Min 8 GB RAM; Tags unique to custom-diffusion: computer-vision, customization, diffusion-models, few-shot; Also covers Model Training; Use Custom-Diffusion when your project requires incorporating multiple custom concepts into text-to-image synthesis, given its emphasis on handling multi-concept scenarios. ### When should I choose image-hijacks over custom-diffusion? Choose image-hijacks over custom-diffusion when License: image-hijacks is MIT, custom-diffusion is Other; Requirements: Min 8 GB RAM; System-specific adjustments might be required, such as setting the `PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring` environment variable.; Large files like cached models or data are stored in the `data/` directory. Ensure this directory is appropriately configured for storage.; Tags unique to image-hijacks: adversarial, generative, image manipulation, runtime control; When you need to create specific adversarial scenarios where fine-tuned images manipulate generative model outputs during real-time operation. ### When should I avoid custom-diffusion? Avoid using Custom-Diffusion for immediate production deployments or simple image generation tasks as it is a research repository without extensive documentation meant for broader usability. Do not opt for Custom-Diffusion if your project prioritizes speed over customization quality, given its focus on high-quality outputs through complex model fine-tuning processes. ### When should I avoid image-hijacks? Avoid using Image Hijacks for standard machine learning tasks that do not involve runtime manipulation of AI-generated images through adversarial means. Do not use this tool if you are working within a constrained or sensitive environment where introducing adversarial elements poses an additional risk to system security. ### Is custom-diffusion or image-hijacks more popular on GitHub? custom-diffusion has more GitHub stars (1,977 vs 57). Stars measure visibility, not whether either tool fits your constraints. ### Are custom-diffusion and image-hijacks open source? Yes - both are open-source projects on GitHub (custom-diffusion: Other, image-hijacks: MIT). ### Where can I find alternatives to custom-diffusion or image-hijacks? GraphCanon lists graph-backed alternatives at [custom-diffusion alternatives](/tools/adobe-research-custom-diffusion/alternatives) and [image-hijacks alternatives](/tools/euanong-image-hijacks/alternatives) ([custom-diffusion markdown twin](/tools/adobe-research-custom-diffusion/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/adobe-research-custom-diffusion-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, custom-diffusion or image-hijacks? custom-diffusion: Slowing. 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 custom-diffusion and image-hijacks? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [custom-diffusion trust report](/tools/adobe-research-custom-diffusion/trust); [image-hijacks trust report](/tools/euanong-image-hijacks/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=adobe-research-custom-diffusion`](/api/graphcanon/graph?tool=adobe-research-custom-diffusion) - 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/_