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Sprint projectJan 11, 2026Mumbai, India

Seed Aware Evaluation Of Semantic Stability in Text-to Image Diffusion Models

Chinmay Muralidharan · Team Chinmay

Submitted to AI Manipulation Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Seed Aware Evaluation Of Semantic Stability in Text-to Image Diffusion Models

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Text-to-image diffusion models rely on stochastic sampling, introducing variability across generations. While often framed as a creative feature, this variability can produce semantic drift, where prompt meaning changes without user intent, undermining reproducibility and evaluation reliability. We examine how random seed choice affects semantic stability in Stable Diffusion v1.5 under fixed prompts and parameters. Using three controlled seed regimes—determinism, sensitivity, and diversity—we evaluate outputs with qualitative metrics capturing structural consistency and outcome variability. Across five prompts, identical seeds yield fully reproducible images, small seed perturbations preserve core semantics with minor variation, and distant seeds frequently induce reinterpretation. We argue that seed-aware evaluation offers a lightweight tool for auditing generative models in safety-critical contexts.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. The main source of randomness in diffusion models is the initial latent noise, it would have been interesting to see how changing the seed impacts the that latent noise. As well, it would be interesting to see how directly perturbing the initial latent noise impacts generations, instead of doing that indirectly by changing the seed.

  2. 1. Clean experimental design with the three-regime framework. The cherry-picking manipulation surface is a valid concern worth exploring.

    2. "Different seeds give different outputs" is the definition of stochastic sampling, not a research contribution. The interesting question is when and why semantic drift occurs.

    3. Qualitative assessments ("High/Medium/Low") make results hard to compare or reproduce. CLIP similarity between outputs, perceptual distance, or embedding-space analysis would make findings quantitative.

    4. Testing 50–100 prompts would allow statistical claims about which prompt types are more or less stable under seed variation. 5 is too little.

    5. Can an adversary search over the seeds to find outputs supporting a desired narrative? How many seeds must be sampled? What's the success rate? Answering these would make it more fit for the hackathon.

Cite this project

@misc{muralidharan2026seed,
  title = {{Seed Aware Evaluation Of Semantic Stability in Text-to Image Diffusion Models}},
  author = {Chinmay Muralidharan},
  year = {2026},
  month = jan,
  note = {Submitted to AI Manipulation Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/seed-aware-evaluation-of-semantic-stability-in-textto-image-diffusion-models-iuq0}},
  url = {https://apartresearch.com/sprints/projects/seed-aware-evaluation-of-semantic-stability-in-textto-image-diffusion-models-iuq0}
}

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