Mineskin-Diffusion-v1.0

WiNE-iNEFF/Mineskin-Diffusion-v1.0

Mineskin-Diffusion-v1.0 is a unconditional image generation model from WiNE-iNEFF. Use it for the unconditional image generation task on the model card, and read the license before you ship it in a product. It is set up for diffusers. The card lists the license as mit.

This a new and rebuild version of old Minecraft Skin Diffusion models. Model have about 18 millions params, that make it a very fast and don't need a lot of memory. Model train in 30000 images of mans and girls skins.

Downloads · 30 days

0% of all-time downloads

All-time downloads

14.2K

Public

Parameters

18.3M

73.2 MB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors73.2 MB · 100%

At a glance

Task: Unconditional Image Generation
Library: diffusers
License: mit
Access: Public
Created: Mar 18, 2023
Updated: Sep 28, 2025
SHA: 06514e3e

Spaces

WiNE-iNEFF/Mineskin-Diffusion

Model card

From the Hugging Face model README

Mineskin Diffusion v1.0

A quick and high-quality model for creating your owns Minecraft Skins

This a new and rebuild version of old Minecraft Skin Diffusion models. Model have about 18 millions params, that make it a very fast and don't need a lot of memory. Model train in 30000 images of mans and girls skins.

Result of model

<a target="_blank" href="https://wine-ineff.github.io/SkinViewFrame/skin.html?color=c2b4ff&data=data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAAEAAAABACAYAAACqaXHeAAAToElEQVR4nO2ba4wdR3bff6equvs%2BZjjD4cxwhvMghw9pJa3IlSFZygYGgiDKA%2F4QwG8ECGD4w9r%2BsoBhB8kCsQ1jg0UQ23AS7Kf4kzeBERjIhwAJkCBebwzHu5a0kbTyaq0HXxqSQw45nOE87%2B3uqjr5UD13SJPiDEHSNhAd4OLi9q2urjqP%2Fzl1zmlhHzo8N6nGCKpK1ioQlLJXpj9F0BBweQsNHkXvvTlGVq%2BuyH7PeBQ6sjCjty9de2Jzmv0GWGsxxmCMIcsKnMsHHysGQRABjCDGYKzFugxrHVEff53Hzizcw9UnuXkAt9%2BAEDwiggD9nW0ANCqgqCrGJh6a3CHGIAAhUlc1NrOPvcCljy8NNjx58oT6EFj95MoTY8K%2BDLDWYTOXtMBaNEZi8KgqKIgRqsrzzd%2F7OsY6BFCUv%2FMzXyJz%2B07%2FSHTz4uUnKn04gAkoCpo%2BIml7Ykyyf404a%2Fjdf%2FNVOsNdWp0WRadF0cr5xr%2F7V8k0nhAdnp%2FUyVMzOnl6Ro8sTOn%2BdxyM9hVRDAEfY%2FrR7w%2BuJw2I5IXl5efnsEWHiCKArypOTh7GPEEGCEoMARGIPjyxefdlgIhgXQYoxiabVlVCiBADLz63wNqNT5hceA6DQVGsASeKmMcX1OH5GTVGESKm0TxjlCNzR1VFWF288Vhs3t8EYiSEGu9rqrKkrkrqqiLUFaqRX3j9LJQ9nHUY67DWJVNpgPJxaW3xmjhnMBbydobLhCy32Nw%2B9uYBZGx%2BUsUI6j3GuWTbMS08xogAzhkyZ5iZHgHgwuJaAjsFUAQh%2BgpjBOsM%2BJpgWqhGUFAR0EhWFMTgCSEiYoiqrC3efOAmps%2FM6%2FWPFwVg%2BvSs%2BlDSPTREVZYoQgyw%2FPHBXOL4ieO6cvmTe8aOzU7r6tXrIuMLU%2BqcpdrZYfjIKIiws9FHjBBCAE2MCHXNxLBBUFYqS2YcdVUBgssyxAhFkTEyOsTq6haC0N%2FZRozF5Tl1b5uh0VFEYGtzK3EfYeXy%2FVI8NDeuG1dWZOT4jKqvKYoM17K0hjrknRbrnyxRByEGRQwgsPopjNyPXNbqMjx2lBhLusMdUCVv9yjyAlHl2MkJXvuRL%2FCffvv3%2BMmf%2BFn%2B6H9%2Fixdf%2Fjw%2F%2FsNn%2BMO3P%2BDbf%2Fwm7UNjbN25TtHp0Op26QZL3hlnffkKArTHpuitXWF4fhbf7yGuQ1506fe3gRv3LUpjZOqZeb3x0aJMP7ugw0fGiNEzMnoY7wNm%2FhRbyzdAA6LKTr%2BXJH36hK6c33OVI3NH1d6FE8dfflmjr7ny7vfkxIvn9PKff0%2BMcRmdokWo%2Bvi6ovYVJiuQYoSr15b4zjff5MjICKt9z9LqLV75kb%2FNxW99jy%2F90tf4g2%2F8F1Zu3sK6gvahCcQ4yn6JWEeeZ40bVLqHhimGukyMDzM61ibLCw6NThBCCqmPnX1pABYL517UzWur0jo0xszzz6lgkewQqoaogg8QfKA1OUO0Dm8yVhZXZObs5zQr9jB97oXndP3Ksqwu3pDTr7yiJ176graKnO5Ql7lz5zQ0bDIaA3Xdx4ghhhrVGrEFSqAud%2Bj1d%2FjyP%2FkV1m%2Bs8C%2B%2B%2FNP8y1%2F8Ud776A127tzG%2B5qy1wOTkbeGWf%2FkCghkRQtf7RCCJ8ZAXfYQYwneE2sPok2EmTB46b13BlJzLnkaKxnGGMRkYLtceOMdeed%2FfkvW17b46E%2FflCoKYgryVpfTX3xVr733gbQ67bv16B6tioGEWyGCGETSc0zV26YuS%2Fq9HeoQWLl1k%2F%2FwKz%2FHN77yc3S6LV5%2F%2FXWee%2FlV5ucP0y4KyFpod4pf%2FMpvEEJEMfzGl36Uz70wybFnj2Io6W%2FfYXv9NsHXeF%2FR396kKitWr6%2BycbtHXfXZXL9FXVZMnjmlAGMnTirAx%2F%2F3XZk4vaAR4cr335d%2Bb4sP%2F%2Bi%2FDRh09d3ErE%2F%2Bz59I1evT395h69YK0888o5fe%2BnOZOfeKAkQNjJ%2Bc14lTx%2FX8W2%2FJ4nvvSoieD7%2FzZ3Ll3Xdk8XtvC4AJtWdjbYXe5gbry7cIOxUzUyO0M%2Bhv7%2FDH3%2Fomv%2F9vf50XpxzGZYRo%2Bad%2Fd4J%2F%2BPI5WpnQKQzD2uNXf%2Bp1zn%2F%2FMksXl9havcPO5ia%2BKvFVxc6dFXbu3OHO8gpbq2tsra2ytbZMvbOFaAqyVi9fFIDxU8e1riouvfWmAFRV%2BUDwAvB1xY2PL8iNjy9I8BUAva07Sf7qKcuSqiyZeuaEAmxt3GH6%2BWcUYHR2So%2BdPqEGFO9rQFAfMRh%2B7Mu%2FTaw20OAheHq3l%2Fn67%2F4%2BurFBXF%2Fna7%2F%2BO3Q2f4A1ht0HL1%2B7hUZJau49oU4eAhV8VaIxUpd96n4fjRFfVaCR5fOX7kHvlQufyJ3FPffmjDB1%2Bvh9AcXkqTldvWvc7jlh9cLHArD0F5dk89qyrF%2B9ITc%2BSv%2F1Nza5%2FoOPBJJnWzp%2F%2Bf5ofeHFjoozxKgQlI2bkE5%2B6f%2B1W72Hupszrw0pCKpCe6Qg1JFQetTHNE9McwmQtdLpsSoVacLoD7%2B7%2FdD5T3%2Bhq9ZA0IQXf%2FBrv8zY5DQaIhuba7zwD77ySO7wvlA4Lyx1iBgBl0kT3MoA0fcjVTCilN5QrZYIYFSxRhFhwEgxQvA0hyxAJTF9H4oCKfaAVtvRGT6EEQFnaRXt%2FW6%2Fj%2B4LhWNIUrK2QU5J0mqWve%2BEopAZIW4o58Y7zJgWz04PEypFGskbBN8XpITcCIUVDGD3DczBWkmCMTKIRo11iKQg7VHpPg3QCNYk2w0%2BqesBBDOg2Eh1JFf%2B7INtRISJHeh2DRilrMAZOD4HmYEqCKrwybI%2F0PJ9rRgDdQART132qaoSjYGq6u8%2FwV8imZ7tqjWNmBsV7Qwltdy4o8QgA7mn3AB0RjIyJ2ysVgxNCaHRFCNC2Vd%2BeNQQo%2FDtVcUK%2FGRHWTpieWupJm8ZysrTaVu2%2BobeVo4Rg8stSsQg1F6JEVBhbGIHMIhEYkw4YTND9Ol3DIpzSfYhpD0YKxgr%2BFq5%2BPbWQ9XWVUHBpw1obOw0Cu2WoSwDpmGOiBBViT6QVRGNSQ2Hhx1rqx4xkBUWa5UPl2ucEX7rl1%2FFGfjav%2F82cjOQFQ4xyXZ9VPJCqHqCGJvSayZhjYgMkil1qRjbxPzaCCkKdd0csgQ8jXE2Y0yTwpOwv065PHeopkyPEQZ5vu2%2BEjWppyF9%2B6CgBu8NIoIaiABGiCi1T6souxk%2F%2F7cO8c%2F%2B9Zu0CsvooZxKNAFOehTpsGwougawjE5tUwxlZB3DhbeFdjZF9CVlv0crA9fOqKo6gYxoiuaITX4ArCWBakzrdGLwfn8TMK3C4ayh1cpT%2Fs9lRCxiEjwkDFCiauKsgDUplBQxBI1J%2FU1aiIhiRHl%2FsWR61DF7xLB%2BrSJrG4qOIysMUZOHUQ0EXzI61eHcS0f4%2FPMtpBjh2S%2BOMHp0GOtqjBXECNZYjCSTcpml1WqSsZIwSySZYoggGDQK1u2flHVVnewtVhGRpGMC6Sgs9%2BK%2BNpngGBVpEh5VFXG5JFdEMg2bw%2FXRAvGRzb5n5OwQ%2FY1%2BwyAQk%2BwUr7gioLFkdauFcV1GOwlHVCqqepPcCVne5CIbAfiQsCAd1SFETVoa0%2Bp3Tcnl%2B3st54yS0vdx4KOj7rokgbvsERXEJM9gDCkFpoJzaUACoWRKQQTbziAEvI%2B0Oxm%2BjsS4K%2F2U57NGgIp%2BmWNDkm5WCNubVxFboQaqUGPUE2JERCgyJYa0eWMVVBoMUXyt5K3E4Lo%2BAAYYrWm2ipLs3orgEUS0cQ5pg7tAFHxN3ytFbhI21Ip1DIokrSHLxuoOrnDYdsbOWh9jlNCA7W5EZEximO%2F10dAla9v0LAHRkjzzWAPtYQMC5VYys7wAX4F3CTB3zST4QJYL4gRbCP4A%2FtttbTdIT5P9blQtoe3u77vuUGgVphkf0ZgAsV0Yev2AKtT9gJdAuVNhBTLXIHOjOTGmiCiGNJ8vPYQ4CG7qKpCHEm92o8SEeymUht62R1NkncA0pOtEJQTFOagaQNyXAVbMQPWtkzRjwxBrYpJ%2Fw4AYY%2FKzzcJCFE6PpcTH6amM71%2Fps11B1Szox18b5TsfbNGvYawrrO0EfIQ6KNv9QCwTztQ1FDslo3mk3i6JKmxteoJCboWql5QmVLFxk%2BkTG5OrmhhwNzJUn8J33V8BcEU7xeW%2BjjiT9l759KC6YaKRxh3eFX7GqDgjHB%2FL6RSWbtsye1jZ7isvLbRRhaHcMT3SYrhlaWWGlY2KGJXRrqNVWN74cA0U%2Ft4PTZIXlg%2BX16lrz%2Fz4EIdfGmbxRs3ceIYiGNHGXSe1ryMsrlT4qNSNv9%2Fe9pwcd8xOdQYa8vUPdh7OgOCFoIqIQVMETEpzJJuXqANVQ1P9t66SCptMIESGCouJgdGWwaH4hoGhjox1DD5EcjFMtJNrFRTtB16ZHSIKrN%2FeZjtEnCi5gVBWdHLLc3OG%2F%2FHdrSbvDD5GYiNpEah8QETIM4OqUtaRt7c97y2W6SkHUAGZnOqqbyLA3SKowj0ns93re7%2FTKqwRxidcsyC958zQqyIaIWgkc0LmElBxlwrvBmCdPEvFljIwNZxDA76LKzX%2F8Ss%2F05TkoHt4EsHQLyuIEWvNAKQ1cbb5Nqik0%2BXn%2F%2FFXH%2Bl4%2FBl9Rp%2FR%2F18k49PDKijGGJavbXwGGADjs0MKMHFsWMenhgbYPjU7pFMzXZ2bH9rXv5z6oenBmNnXZnTuC3NPrKnhqdPMicP7LnZ2dkQBpp%2Bbv2%2Fs1CuzenhhTA8vjCnA%2FN%2BfeeB8o8dndHR%2B%2BlOfdfj0qafOtPvSkK3Rjl67vLavKVy9up7GxL1ujc%2B9ekwB2t2MI2cnGHtulIkvHtXWUPeBc7SKjHa7uOfa9LNp03OfP61r5y88dZO8jwH9OzuP9NDrH%2B4VJz54YyklrWPF%2Bf%2F6oYQ6cuvby3L9T5ce%2FHAD1hlmXjilx144k2IYgfFTs1rVJcdeOPn0NeDk6b2Go9mFpI4zJyd1%2BsSRT3348VPpv%2BNnjj1wjK%2BSVlz%2BX6kis7n84MRkVddU%2FRKxwtL7TUXngwvi8pwYAuiT6wX6G00zZ59Nan%2FuxQS%2BZ07oseefvv3DQ3qEjp%2Ba%2BCtDbd%2Bc5vK25dRrZ1WMY%2BkHT9%2F%2BAeTEiSE1TdIjNueui5furc9NTA83B9Eml2sgcwbxgcVrj4YZD6OFV8%2FqpTfe%2ByuNRR74sPm5Q0pUFq9t%2FrUFRsdeeEaX3v9InnltSIVUDtvNerz%2FJxsy9%2FykXvnBp%2FcFLZwb1dGJgnf%2BcPmz4O5h9NS4M7cwrMYIISTzunLpr0%2BbHkZPfFHHzw5pkaUqs4%2BwvhSbpGvKJq3efHh%2FwYmzXc0ySdkoDP%2F9N79K3upAVDbXb%2FPCP3q0%2Bv9%2B9GTbuUnBDaQ8Ymb26nSIHKh32NpUk1AZ5GbJ8wJQ8p3sSS%2F3yTNgN39ojRLqvXT7QUlgkOcXIM9zRCNRI%2FoUAqMnzoDoFclSqv2eyswBmeCDIl5TUUZhZ3Nj8IJGOEi18xHpQPY0OTOkKIQQaQ8VXL2wd1ianBnW3RqiNqWuoeEIKvR6ioZUzYkqKdnZdHsbA9s7wtBRHZgNMWmLbUpthlSHtM7gbKofKClbHZux57%2F7eODqjs0d0aUrtwXg2NyIxhC5sXTvpDev3RXLL%2B8wOdXSmzf6cmz%2BsNbeI7sVGoWqDqx7Q7uA%2FnYqzVhr8E2xFWBk2BEilD3PkBpEd4ueEVUhy5rKU0x1v6pSXEsITaXKpTr%2Bk2hG%2F3QNmD01rVcvXN%2BXu2NHh3U3lR6igiqtlsO5VG%2BAJEkRwftUWcqcIUbY3KwZPy64zIAqvlJ8SMdkjUpdJgaqQLtlqKpkCnlLqOvUIXL5vYd3le1Hn4oBB9k8pN6BvWrCbtOSTf0CDZLHphPM7L5DFHVQ6ZXGOyjSjJP0IpZVCE17nstQicSYMEABl5tBW%2F%2Fj0GOD4O72B60fDdtCTECmzfXgA7LbsKDNp6kSh7hXWU6U6j%2FaVDucS0UXY9J81qWxZfkUGTB7akp3mxqXLqd4evbkERWFK5duD5ZqZG%2F7RqQ5x8e7fH7q%2FTGFbcprTRuOpB%2Bqd1WkGiBJfIlUdWiqPkqnYwlWUowQoapCMrmnwYDpuRG9euH%2BFxmuXrx937V7LuyqOorZfdtMFLMr8WZ8v1ZiNEij8raZxNjEDKxindBxlrqMFC2hXwds0XgHB7kx9PsHqH%2FvQw9kwAFaazh56rBevLAmIQYGR%2BUGmMs6kEkYFEld4%2BaCNt1b1mCIGJO0oa4ixqSbY1CIijQJoSxTbA4uh95WJAQGbXEH6Ks8GAOOzh5So5GyDqze7MmVyynhOTOba%2B5s6k5QId5l71WVOkuM7KGAAqKKFSXPhSJLxcteGjrw91lu0kaMEmptEiLJC4iAiQbT2LgIlBupJWbXLfY2tXGbT4gBy1fvLYgsHB9TEeHi5ftV%2Fi%2BTiDTtb%2Bm8vtsEVYVUAhbAZjIoaQtpIxqTd2g5Q6ttODmZ4nwDHBkuyJ2wtZM41%2BlahKZ5cpeBCldvlVy50Hvgus68OqS71ePUNEkq6QN1Xzn%2FTtrzY5%2Bsxo52da%2F%2FL0nGZQZrGXRrea%2FUdSRvbKGs09toMcLIWKRdOEJQtsuw5zYHDEtdX7vqbpqyt28A%2BvLH%2FacTBxyUmpe2MKQWdo1KiIEoe9xtmjXwzcsRhr3XCv%2FzP%2F8SwyPD6b4QGu9ocK3U%2Ba0Ifm0ZgGz4UHNtr7fg%2BR%2F72mOt%2F%2F8Bxr%2FUGC6KHu8AAAAASUVORK5CYII%3D">Example 1</a>
<a target="_blank" href="https://wine-ineff.github.io/SkinViewFrame/skin.html?color=c2b4ff&data=data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAAEAAAABACAYAAACqaXHeAAAP1ElEQVR4nO2bbaxmV1XHf2vvfc55Xu7cO%2B%2BdmXamw5QpSGNtQXmdNhKCCIIVg5Gg4YNYBAyQSIwfSCCRECVihAgxaDQRjYofVGIUqx%2BopIRKbUNLS2lpS9t5a%2Bflvj8v55y99%2FLD3s9zx7TOnfrcWD%2Fc9eHm3ueeZ5%2B11l57vf3XFjahwyd%2BUjV4RATV9FnUiCiIwG%2B%2F%2B00YUYyRjS%2BJgBGaNvCrH%2F2UPP%2FKV0aHXndCq8Kxe9durtq%2Fj%2BF4hIjw%2BBNPElU59c1vzLS%2B2ewB1chEcgFENH1JIKpiLIh5Hh5UEWbiDQBrLWIs%2B%2Ffvo9PpULqS0hXs3rkTZ%2B3M67vNHlBVVBWJMQmkijJRhsHkzxBBFEA3Fp9dfjRG2qbhoYceRlUx1qAxXvKW2WhTBaCKEZMknrxVQaxBVfOHCgoxRiCdAIywFVyKZDvS9LvGCAKCbIkSNlVAjBEjgEpmInGjMUI2cs27H1SzngSzVVt0ieKjxqkytuB0AVegAGMNxMiGDhI3qpqUIUDMPBlBVDaY2wImY8jv1vxDJvLr%2F40FpLcIYi0TeSdv1gkTWVCDAXOJ3Do7i6rJER677hht0%2BTPlJMnT%2BYjOBttGgViCATvaZsG37T4tsX7lta3hNZjjEk7ItkCjOQ%2FBJFNl9%2BUNHowsG%2FfHsqqoCwd1xzaj3UuRagZSQ699g0qYoghJIcjJoc%2B%2FpspixiMGEQg%2BICxhhhD8gvpgeQrxBC8z76CaU4wXQjS90j%2BwjiLLSzGWFxRIID3nmY8wrctGhVXlhw%2F%2FlJEDKFt6fe7fPfBh2mbhmfu%2BbYcueVWNfk9Jr%2B4aVu45JiUVUXRqRCB2LYMVleTiEdvfaOiOg0tiuKbdhrDxRqsKwCwzk3cHiEEQghYkwRGwFqHsRbVHDIFQutxZfp%2BCAFRiOTwVo945t57tsidwaHXnlABTt991xWvKQvHr1eNEQ2B3sIuls88xSc%2F8j6%2B8DdfY8%2FuBcqqpNupOHJoL4f37sCJ8K1HzxLamrnK8G93%2FgcHDx%2FlZ265mS%2F%2F%2Fb9gjcM6R9s2iDEIUHW7gFCP1kn2IkSNuLJk8fuPbJkC%2FjdkfFPTjAb4tmb1wrOoCksr67znrbfSK4RX33iMxx9%2FmnseeIzxaIRVz%2Blzq1xYbXnoiQt89mO387ofOQKafIVvagarK9TDIcZafNuydP48yxfOMx6MGQ1HjMZjmiaZ4YtNbjQcUriC0LR436C1B%2BCzf%2FwXiAhvfNNP8Lkv%2FD6%2F8q8P8%2BpjiwjKM%2FFVfPzWvdT3fYs%2F%2BdpdnH7ySbrdCvWe0bjGdbuoKmuLi8kfQPIFmhyLSDoe0TcvpuwAOIkB57pI4djV28uH3%2FM2BPj0b30AEcEWjoXC03n4XuZecR0hQv%2FOL3PTL36CB6qSX37L64nxdfgQWV6v%2BcM%2F%2FwpFWdHWNUaU4H321gI5o1QmjnH2KDErCQsLSoz0dsyjMfL5j98OCKqKdZaicuzZ22WuXyYnqErrA3XtOXNmlbaNqE%2FJihE4v7TGJ7%2F417TD9fSGjRIyC50zJxEIAUbjF9UHOBTwgc%2F85vsIIdApK0zemG6%2Fy9yeEmcNVb%2BDKxwCFCHSLg3YuWuOGJTByjDFhghVabnm6msYra%2FwzMmT5PBAypUDiOD6fULbbFlBMws9R%2Ftvv%2F2DaiTFUxH4xp1f3yhIRDj%2FvQcvu2NHf%2Fw1%2BgtvvoUfPdRLZ10Vjcq3nniWO%2B97lIM3vZpObw7vG6pOF4DoW0BYW17mG1%2F6g8uu%2F9WffauKguSQun7biexfIEblPR%2F6xAuyqOekwmVV5ECVcxjnpinwFe1YaOmXlraNdEoLInhV%2BjalsCl7tDjrMKLEqBiRXOpuXt%2FbNoAPWBRRSf0CIafFL%2Fw0PX8tIBu%2FGGPSTiLIFaSeMQR2znXpVCkzFAAnLC8tJwcYI0oSVsSAelDFirlCBStWBYmK5hLcGpOYlhd%2BqJ7jhoP3Kd9vW1KZl0yYK8y7O92SxiuFNThrsNZSFAWv%2BbEbKQrL4ZceR1WJCk3rCSHkvyNNs3lYFGuIWbEp8071RzqmL9wC5Oc%2B9GFtfSRqpFM4BJ0WMa1vUzMkNx98zvHLskQQ1kcjDg%2FOYScMqXDHmRWO3%2FAyPnLL8Y0SAMMHv%2FhVrtqzQDMa8ktHFwgh8pn7T3PtNVcjCtcePsyk5l5dXwWU06dO8%2Bnj85gspHqonlkj%2BogbN4AQrt%2BPaI1xhrbO3aqoiHMEgdv%2B6G8vqxUnKL5tCTFQGMnmNOl8CFEVkZS6AvS6HUJIZmyN5c03Hs5HBFSFu1Ye53v33s8H7n2A33jn6xEVfu%2FvvklvR4%2B5nQu885XX4kOAoHzuxAk%2B9vm%2FZNfu3Zw5e4Y9e%2Fagqpw6dZbWJyuU%2BTmKyhJCgNUm9RuMxVclomnj6M5R24IwWkdDpDQKAZq4udW6EFPPr2lanLEUhcVaQwxKVAVVQgxEVcrCoan7RYhKGzx3LXb4p%2F%2B8H1HYf%2FAQixcXuflVr%2BRV1x8kjJY5dNVOPvret%2FD5v7qDRx55jN%2F5PuxYWGD37p0sfvsOnLOILej3%2B6yurQPKaDSkKAtcWdIeOoSWDh8h7G1ZOnKExofp%2BZ%2FrVZSFQawlXt2Cgo%2BaK9jNj4Rr2jZ5UIE2egpxeD%2Fp7QliDXVbE2PEWcvaYIgC1hh8G2hz0zSSujfXXHstp06e5nsPfIc33PBy%2FuyO%2B6g6FUdfch2j8ZilxUWKqsvi0hpqChb2HECMcGF5jU5ZUFVFyiDblle8%2FOV4yU7YCDiLNUJVOHyI1OOaIj8%2FyTDFCLYqMEZSlbqZAmLO1GKIRBMJQWm9xxpJnjooIUTqpqEoCprWpzMpgnWG04OGG2%2B4IYdN4ey5iyBCUThaV%2FHGW17L3fc%2FhC1LOtayv%2BomZTc1LzmwG2st33%2FyLCuL51m3juuuO8aNN93M4uIyrabW%2B2RDYsw9BAMOwfWqfDQF0WyxuV9pRZjIdlkF1E3LcDBCNeIKR%2BNb6tEYMQbrUhvMh0gIgVHdsL62RqeqKIoC71tK67Bl7h2QvLKi1KMx6hx1iDStZ%2F3pHzIajVEUV3UQDPcvLQGpWbFj527Gw0HKPYywMD%2FHg9%2B5D3vTm7IzFUIW0IhFTGrMSDQYm5y0tWajXxhBrkQBqooxhrYNiBi8D9RtQ7%2FXz3W7TsOTs27S%2FWR9MCD4ll6%2Fj69TuGx9wFjL3I4ddHp91ppAyxivSm9%2BF66bCqP5%2BXmCTyFw8eIFIDnaZjTisR88hsZICO208ZoEE4xPijY2RSnJITRhEkrITs86izEmOc7NFLB4cZEQAs5ZhsMRUSO%2BaWmLhtZ7YlTGoxEK1HVNDIHRKBJCYvLUqVO5vBWOvfR67r%2F3XspOhSosLy4mNKfqgBGqqpwmWZP8YPeevayuroAIczsX6Pd3MG5qUDBFhXVuKpiSrMM6m5STO02ucFPABlL7TlHEbl5tOgRK5zh3%2Bil2XXUIzQVLPRpibVr49A9%2FwL7DRxEizz71g2ln2HvPibfcxj1330VRlDx79hl6vS5ra2toVI4cO4axllNPPYnJPqXT6bK4fg5bFFx14CBRI0VVoSFSD4esr6xQ9frJj4jDVVXCHVUZ1zXO2iQwyScoQuVsYshu9AQB1F7BEQitJ6qya%2B8BTj36IEBKfxFiTHF2bmE33V6f5YvPPgeUuHDxIkeOvQwxgnOO7twOuvPzaFTG9RjfeqpOleoA71OZXTiMCBcvnEeA9fW13PtPMbYqC0BYPPMUf%2FIP61lgYXEl%2BYhJzjfZc2stE4SuLArIDjBcQfbqzv7wEfbuP8jK0vkEaIpM42fUCFEZra2wvrxEMxpRj0YUZQk5ger3ewDTesFagxiDMcLShYv0OiU75%2FuA0LYNc70uvarEChTOsjIco3qAUd3Q6XQY12M6nQ5GDFft28O5M08y1%2B2AQj1BpadbnDO%2FjBjVPiDjceKHDajuciTzR69VybG2bcYYEYxJFVbbtlMNZzyIGDYWVY1cd%2FNrgEn5bBisr2GMpdvrgyq79%2B7h1KmzGSfICkoaA8C3DZ3cNPW5LL50E373vT%2BVdtwI47rO7XcDk7UueXYieOI0Keht77r9RW24bNM2bdM2bdM2bdM2bdM2bdM2bdM2bdP%2FP9ryZsE73v8hjdNWnHLP3d9M4GpGas5994HLvvMf3%2F7TGqPmwWgY3vb6DdBTlXd%2F8IXh%2F5vR5qOyL5CMNdg8IzgBS8SmAYYrGW21URGvOA0ElWl%2FMvW4tn6mZMsVYKfzBGkWYNK%2BgkvnNv9n0pjQ3aiKxLyGS2zq813MmJG2XAGqCTNICI4Q8qT5xpjcpgtgiJiMUE9GdVQvmbjdQpp5yXf82q9rzLhc4SzRB8oyIUjGCHXdYq2ZNkVjiFSdClQZ1S0faJ4g7FhII3R1gzm5mrrTziKVI%2BydS7jAfBe%2FlNBjYmqft1F4159eHv%2FfjGa2AGuEwfoIUMq5PiGGjWU1QeiIw1lLjDFBXQI%2BRkpnGc8fSOguQhChEgPOEI0Q5hwYxc53Uwu8UyIxYlHC0jq22Rz93YxmVoDPbXJjDHXdEGMCQ733VGVBCBFhwmgawAgxXcNpQstw%2F366c2larB41rB64mhAnTXilW5g8a7zRko8khxpDhK%2F880z8z6yAxoe065LxOpPOvWpSTlmU%2BBAYjMZUVYeV1RV6vV5GgS22dNlqTBqbF4ORhEqpKq50xPysEUFjOmqt9zgz%2B6TpzCtInv6s65rJLFXwEe89rQ9gEsJUN00CXkPA5KmuGCM%2BxDx1ktFl0iLWpCmzqJqB2DSlEidjMaQrPLPSzBYwHA7xbTJxHwICtG1DjEoYjeh2unjvGY8S5NW0LWuDwTQyyOTGCRse2QDOGrymaCIZcgttm%2B8jOoyR6XtnoZkVEGPC5S%2BFqcQYfFsnrx9jvmBp8%2B1TnUJukJwoqsnETbqKJ9ZkEBQ0Tu4K6hR%2FBKbX6GalmRVQj8cMB%2BuEENi1Zy9G0nFQjYgIa2trFEU5HbJYuniB%2BZ27AGjqMaimqzJZecaa6XSqZrC2yP9vRh6TlWPtlQ1AbEYzKyCEdHHqwtlTOQs0yS%2FENM4yHgwYodiiwi0ssPrs0wwungHSJcj%2Bjls3zH%2BihKyAyehsmecBNPg0VSZpDGcLDGArfMCIblUyt2Oek49%2BFyGhy5PJ4uBbdu47SK%2B%2FI98zihijiKTE%2BEt33MNwMAKE8Tih08XkCEwQZecwYhj7OB2XUUhOdkaaWQHnn36MXXv3s76ynKs%2BwRWOGCeeO1nJeLDOYHUFjTHdUDOGGCNnzy9TlSWiyrCJWIGxxA0FGME0EecsISq%2B1hRenaWuZ79xMnseMK5ZOn%2BOtqnJNS9tk66sxayQweoKg9WVHCHanBylAYZPvf%2Fn00SaGBrvk8CyYQEhpGFoI4L3flpQTaZE3vbvX5%2BJ%2F%2F8C7sNaTl8jXjQAAAAASUVORK5CYII%3D">Example 2</a>

Usage

You can run this model in <a href='https://colab.research.google.com/#fileId=https://huggingface.co/WiNE-iNEFF/Mineskin-Diffusion-v1.0/blob/main/MineskinDiffusion.ipynb' style='text-decoration: none; color: orange;'>Google Colab notebook</a>. This notebook provide all instrumens for comfortable using this model and provide 3DSkinView for looking skin in 360 view. If your want use this model on your local machine use this steps:

You should have this library numpy, transformers, diffusers, pillow, torch;

Use this pipeline for faster inference:

device = ("mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu")

class MSPipeline(DiffusionPipeline):
  def __init__(self, unet, scheduler):
     super().__init__()
     self.register_modules(unet=unet, scheduler=scheduler)

   @torch.no_grad()
   def __call__(self, batch_size = 1, num_inference_steps = 1000):
     x = torch.randn(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size).to(device)

     self.scheduler.set_timesteps(num_inference_steps)

     for t in self.progress_bar(self.scheduler.timesteps):
       model_output = self.unet(x, t).sample
       x = self.scheduler.step(model_output, t, x).prev_sample

     x = x.cpu().permute(0, 2, 3, 1).clip(0, 1).numpy() #* 255
     x = self.numpy_to_pil(x)

     return x

Download model using: pipe = MSPipeline.from_pretrained("WiNE-iNEFF/Mineskin-Diffusion-v1.0", use_safetensors=True).to(device);

Model can work with: DDIMScheduler, DDPMScheduler, DEISMultistepScheduler, LCMScheduler, PNDMScheduler, UniPCMultistepScheduler:

#Example of using DDIMScheduler
pipe.scheduler = DDIMScheduler.from_pretrained("WiNE-iNEFF/Mineskin-Diffusion-v1.0", subfolder="scheduler")

Run inference with using: images = pipe(batch_size = 1, num_inference_steps = 30);

Task: Unconditional Image Generation
Library: diffusers
License: mit
Created: Mar 18, 2023
Updated: Sep 28, 2025