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Few-Step-Diffusion-Sampling-Quality-Risk-Model

RandomForest/GradientBoosting models predicting fast-generation quality from a slower preview alone. Forward-looking classifier: 100% accuracy.

4.50

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updated 12 Aug 2026

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COVERAGE India

LICENSE Attribution 4.0 International (CC BY- 4.0)

FORMAT Synthetic

TYPE Structured

SECTOR Science, Technology and Research

UPDATES NA

About Dataset

Three scikit-learn models, GroupShuffleSplit by config_id. baseline_comparison.py shows the single-signal baseline (raw 20-step quality, no method identity) actually scores worse than majority-class guessing (66.7% vs 68.9%) a good preview genuinely doesn't guarantee 4-step success unless you know the method.

Dataset Metadata

License

Attribution 4.0 International (CC BY- 4.0)

Sector

Science, Technology and Research

Author

Jai Mali

Source Organisation

Symbiosis Skills and Professional University

Uploaded By

Data Quality Score (Beta)

4.5

Dataset Type

Structured

Data Type

Synthetic

Task Category

N.A.

Time Granularity

NA

Frequency

NA

Geographic Granularity

N.A.

Primary Key / Indicator

N.A.

Data Collection Method

N.A.

Language

N.A.

Year Range

N.A.

Date & Time

09/08/26 17:16:04

Visibility

Open

Hosted / Redirected

Hosted

Tags

Large Language Model

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