{"url":"/sota/image-generation-on-places50","task":{"name":"Image Generation","url":"/task/image-generation","note":null},"dataset":{"name":"Places50","url":null},"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":["LPIPS","SIFID"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"LPIPS":null,"SIFID":null}},"counts":{"rows":5,"rows_with_code":5,"rows_with_paper_page":5,"rows_dated":5,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"SinDiffusion","metrics":{"LPIPS":"0.387","SIFID":"0.06"},"uses_additional_data":false,"paper_date":"2022-11-22","paper":"/paper/sindiffusion-learning-a-diffusion-model-from","paper_url":"https://arxiv.org/abs/2211.12445v1","paper_title":"SinDiffusion: Learning a Diffusion Model from a Single Natural Image","code":"https://github.com/weilunwang/sindiffusion","n_code_links":1,"syntology":{"n_ran":13,"n_unverified":7,"n_samples":20,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"ConSinGAN","metrics":{"LPIPS":"0.305","SIFID":"0.06"},"uses_additional_data":false,"paper_date":"2022-11-22","paper":"/paper/sindiffusion-learning-a-diffusion-model-from","paper_url":"https://arxiv.org/abs/2211.12445v1","paper_title":"SinDiffusion: Learning a Diffusion Model from a Single Natural Image","code":"https://github.com/weilunwang/sindiffusion","n_code_links":1,"syntology":{"n_ran":13,"n_unverified":7,"n_samples":20,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"SinGan","metrics":{"LPIPS":"0.266","SIFID":"0.09"},"uses_additional_data":false,"paper_date":"2022-11-22","paper":"/paper/sindiffusion-learning-a-diffusion-model-from","paper_url":"https://arxiv.org/abs/2211.12445v1","paper_title":"SinDiffusion: Learning a Diffusion Model from a Single Natural Image","code":"https://github.com/weilunwang/sindiffusion","n_code_links":1,"syntology":{"n_ran":13,"n_unverified":7,"n_samples":20,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"GPNN","metrics":{"LPIPS":"0.256","SIFID":"0.07"},"uses_additional_data":false,"paper_date":"2022-11-22","paper":"/paper/sindiffusion-learning-a-diffusion-model-from","paper_url":"https://arxiv.org/abs/2211.12445v1","paper_title":"SinDiffusion: Learning a Diffusion Model from a Single Natural Image","code":"https://github.com/weilunwang/sindiffusion","n_code_links":1,"syntology":{"n_ran":13,"n_unverified":7,"n_samples":20,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"ExSinGAN","metrics":{"LPIPS":"0.248","SIFID":"0.1"},"uses_additional_data":false,"paper_date":"2022-11-22","paper":"/paper/sindiffusion-learning-a-diffusion-model-from","paper_url":"https://arxiv.org/abs/2211.12445v1","paper_title":"SinDiffusion: Learning a Diffusion Model from a Single Natural Image","code":"https://github.com/weilunwang/sindiffusion","n_code_links":1,"syntology":{"n_ran":13,"n_unverified":7,"n_samples":20,"n_pointer_only_licence":0}}],"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":5,"rows_with_any_sample_ran":5,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":13,"n_unverified":7,"n_samples":20,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":65,"n_unverified":35,"n_samples":100,"n_pointer_only_licence":0,"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"}}}