{"id":17596044,"url":"https://github.com/gcucurull/cond-wgan-gp","last_synced_at":"2025-10-31T00:52:59.488Z","repository":{"id":129628935,"uuid":"230946195","full_name":"gcucurull/cond-wgan-gp","owner":"gcucurull","description":"Pytorch implementation of a Conditional WGAN with Gradient Penalty","archived":false,"fork":false,"pushed_at":"2020-01-02T17:03:03.000Z","size":472,"stargazers_count":40,"open_issues_count":1,"forks_count":6,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-10-31T00:52:57.636Z","etag":null,"topics":["cwgan-gp","deep-learning","gans","pytorch","pytorch-gan","wgan-gp"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/gcucurull.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-12-30T16:25:05.000Z","updated_at":"2025-10-22T01:48:48.000Z","dependencies_parsed_at":"2023-04-11T20:16:06.304Z","dependency_job_id":null,"html_url":"https://github.com/gcucurull/cond-wgan-gp","commit_stats":{"total_commits":5,"total_committers":1,"mean_commits":5.0,"dds":0.0,"last_synced_commit":"0cc9f0a30eecffe71efa625c3fbce38d321ae7f3"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/gcucurull/cond-wgan-gp","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gcucurull%2Fcond-wgan-gp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gcucurull%2Fcond-wgan-gp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gcucurull%2Fcond-wgan-gp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gcucurull%2Fcond-wgan-gp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gcucurull","download_url":"https://codeload.github.com/gcucurull/cond-wgan-gp/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gcucurull%2Fcond-wgan-gp/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":281908616,"owners_count":26582147,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-30T02:00:06.501Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cwgan-gp","deep-learning","gans","pytorch","pytorch-gan","wgan-gp"],"created_at":"2024-10-22T08:07:15.948Z","updated_at":"2025-10-31T00:52:59.452Z","avatar_url":"https://github.com/gcucurull.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Pytorch Conditional WGAN with Gradient Penalty\nPytorch implementation of a Conditional [WGAN](https://arxiv.org/abs/1701.07875) with [Gradient Penalty (GP)](https://arxiv.org/abs/1704.00028).\n\nThis implementation is adapted from the Conditional GAN and WGAN-GP implementations in this [amazing repository](https://github.com/eriklindernoren/PyTorch-GAN) with many different GAN model.\n\n# Usage\nJust run\n\n```\npython main.py\n```\n\nIt will create an `images` directory and save generated images every few iterations.\n\nIt can be trained with MNIST (default) or Fashion-MNIST just by adding the flag `--dataset fashion`.\n\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"assets/output.png\" width=\"360\"\\\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n    Example of the images generated by the model, conditioned by class.\n\u003c/p\u003e\n\nGenerated samples evolution as training progresses:\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"assets/output.gif\" width=\"360\"\\\u003e\n\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgcucurull%2Fcond-wgan-gp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgcucurull%2Fcond-wgan-gp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgcucurull%2Fcond-wgan-gp/lists"}