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Awesome-Evaluation-of-Visual-Generation

Detailed Approaches to Assess Visual Generation Quality in AI Models

Product DescriptionThe repository acts as a detailed archive of methods for evaluating visual generation models and their outputs, including images and videos. It highlights crucial areas such as model performance, generated content analysis, and alignment with user inputs. It offers resources, metrics, and methodologies concerning latent representations, condition consistency, and overall quality assessments. Community contributions via issues or pull requests are welcomed to maintain its relevance. This serves as a guide for enhancing visual generation with insights and evaluation techniques.
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