{"url":"/sota/image-generation-on-afhqv2","task":{"name":"Image Generation","url":"/task/image-generation","note":null},"dataset":{"name":"AFHQV2","url":"/dataset/afhq"},"category":"Computer Vision","categories":["Computer Vision","Medical","Miscellaneous","Natural Language Processing"],"category_note":null,"description":"**Image Generation** (synthesis) is the task of generating new images from an existing dataset.\r\n\r\n- **Unconditional generation** refers to generating samples unconditionally from the dataset, i.e. $p(y)$\r\n- **[Conditional image generation](/task/conditional-image-generation)** (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.\r\n\r\nIn this section, you can find state-of-the-art leaderboards for **unconditional generation**. For conditional  generation, and other types of image generations, refer to the subtasks.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [StyleGAN](https://github.com/NVlabs/stylegan) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["FID","EQ-T","EQ-R"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"FID":"lower","EQ-T":null,"EQ-R":null}},"counts":{"rows":7,"rows_with_code":7,"rows_with_paper_page":7,"rows_dated":7,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"Polarity-StyleGAN3","metrics":{"FID":"3.95"},"uses_additional_data":false,"paper_date":"2022-03-03","paper":"/paper/polarity-sampling-quality-and-diversity","paper_url":"https://arxiv.org/abs/2203.01993v2","paper_title":"Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values","code":"https://github.com/AhmedImtiazPrio/magnet-polarity","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"Alias-Free-T","metrics":{"EQ-R":"13.51","EQ-T":"60.15","FID":"4.04"},"uses_additional_data":false,"paper_date":"2021-06-23","paper":"/paper/alias-free-generative-adversarial-networks","paper_url":"https://arxiv.org/abs/2106.12423v4","paper_title":"Alias-Free Generative Adversarial Networks","code":"https://github.com/NVlabs/stylegan3","n_code_links":7,"syntology":{"n_ran":18,"n_unverified":13,"n_samples":31,"n_pointer_only_licence":9}},{"rank_in_archive_order":3,"model":"Alias-Free-R","metrics":{"EQ-R":"40.34","EQ-T":"64.89","FID":"4.40"},"uses_additional_data":false,"paper_date":"2021-06-23","paper":"/paper/alias-free-generative-adversarial-networks","paper_url":"https://arxiv.org/abs/2106.12423v4","paper_title":"Alias-Free Generative Adversarial Networks","code":"https://github.com/NVlabs/stylegan3","n_code_links":7,"syntology":{"n_ran":18,"n_unverified":13,"n_samples":31,"n_pointer_only_licence":9}},{"rank_in_archive_order":4,"model":"StyleGAN2","metrics":{"EQ-R":"11.50","EQ-T":"13.83","FID":"4.62"},"uses_additional_data":false,"paper_date":"2021-06-23","paper":"/paper/alias-free-generative-adversarial-networks","paper_url":"https://arxiv.org/abs/2106.12423v4","paper_title":"Alias-Free Generative Adversarial Networks","code":"https://github.com/NVlabs/stylegan3","n_code_links":7,"syntology":{"n_ran":18,"n_unverified":13,"n_samples":31,"n_pointer_only_licence":9}},{"rank_in_archive_order":5,"model":"GENIE (NFEs=15)","metrics":{"FID":"4.83"},"uses_additional_data":false,"paper_date":"2022-10-11","paper":"/paper/genie-higher-order-denoising-diffusion","paper_url":"https://arxiv.org/abs/2210.05475v1","paper_title":"GENIE: Higher-Order Denoising Diffusion Solvers","code":"https://github.com/nv-tlabs/GENIE","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"GENIE (NFEs=10)","metrics":{"FID":"4.9"},"uses_additional_data":false,"paper_date":"2022-10-11","paper":"/paper/genie-higher-order-denoising-diffusion","paper_url":"https://arxiv.org/abs/2210.05475v1","paper_title":"GENIE: Higher-Order Denoising Diffusion Solvers","code":"https://github.com/nv-tlabs/GENIE","n_code_links":1,"syntology":null},{"rank_in_archive_order":7,"model":"GENIE (NFEs=5)","metrics":{"FID":"5.53"},"uses_additional_data":false,"paper_date":"2022-10-11","paper":"/paper/genie-higher-order-denoising-diffusion","paper_url":"https://arxiv.org/abs/2210.05475v1","paper_title":"GENIE: Higher-Order Denoising Diffusion Solvers","code":"https://github.com/nv-tlabs/GENIE","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":3,"rows_with_any_sample_ran":3,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":18,"n_unverified":13,"n_samples":31,"n_pointer_only_licence":9,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":54,"n_unverified":39,"n_samples":93,"n_pointer_only_licence":27,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}