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submitted 18 hours ago* (last edited 18 hours ago) by ChaoticNeutralCzech@lemmy.one to c/morphmoe@ani.social

Artist: Onion-Oni aka TenTh from Random-tan Studio
Original post: Crabsquid on Tapas (warning: JS-heavy site)

Upscaled by waifu2x (model: upconv_7_anime_style_art_rgb). Original

See also: Seamoth and other Subnautica creatures in the comments

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Artist: Katahira Masashi | pixiv | danbooru

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D20 (Random-tan Studio) (files.catbox.moe)

Artist: Onion-Oni aka TenTh from Random-tan Studio
Original post: D20 on Tapas (warning: JS-heavy site)

Upscaled by waifu2x (model: upconv_7_anime_style_art_rgb). Original
Unlike photos, upscaling digital art with a well-trained algorithm will likely have little to no undesirable effect. Why? Well, the drawing originated as a series of brush strokes, fill areas, gradients etc. which could be represented in a vector format but are instead rendered on a pixel canvas. As long as no feature is smaller than 2 pixels, the Nyquist-Shannon sampling theorem effectively says that the original vector image can therefore be reconstructed losslessly. (This is not a fully accurate explanation, in practice algorithms need more pixels to make a good guess, especially if compression artifacts are present.) Suppose I gave you a low-res image of the flag of South Korea ๐Ÿ‡ฐ๐Ÿ‡ท and asked you to manually upscale it for printing. Knowing that the flag has no small features so there is no need to guess for detail (this assumption does not hold for photos), you could redraw it with vector shapes that use the same colors and recreate every stroke and arc in the image, and then render them at an arbitrarily high resolution. AI upscalers trained on drawings somewhat imitate this process - not adding detail, just trying to represent the original with more pixels so that it loooks sharp on an HD screen. However, the original images are so low-res that artifacts are basically inevitable, which is why a link to the original is provided.

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submitted 2 days ago* (last edited 2 days ago) by ChaoticNeutralCzech@lemmy.one to c/morphmoe@ani.social

Artist: Onion-Oni aka TenTh from Random-tan Studio
Original post: Knifehead Kaiju on Tapas (warning: JS-heavy site)

Upscaled by waifu2x (model: upconv_7_anime_style_art_rgb). Original

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Original (by Shycocoa) (files.catbox.moe)

Artist: Shycocoa | pixiv | twitter | artstation | danbooru

Full quality: .jpg 1 MB (2289โ€‰ร— 2000)

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Roomba (Random-tan Studio) (files.catbox.moe)

Artist: Onion-Oni aka TenTh from Random-tan Studio
Original post: Robot (vacuum) cleaner on Tapas (warning: JS-heavy site)

Upscaled by waifu2x (model: upconv_7_anime_style_art_rgb). Original
Unlike photos, upscaling digital art with a well-trained algorithm will likely have little to no undesirable effect. Why? Well, the drawing originated as a series of brush strokes, fill areas, gradients etc. which could be represented in a vector format but are instead rendered on a pixel canvas. As long as no feature is smaller than 2 pixels, the Nyquist-Shannon sampling theorem effectively says that the original vector image can therefore be reconstructed losslessly. (This is not a fully accurate explanation, in practice algorithms need more pixels to make a good guess, especially if compression artifacts are present.) Suppose I gave you a low-res image of the flag of South Korea ๐Ÿ‡ฐ๐Ÿ‡ท and asked you to manually upscale it for printing. Knowing that the flag has no small features so there is no need to guess for detail (this assumption does not hold for photos), you could redraw it with vector shapes that use the same colors and recreate every stroke and arc in the image, and then render them at an arbitrarily high resolution. AI upscalers trained on drawings somewhat imitate this process - not adding detail, just trying to represent the original with more pixels so that it loooks sharp on an HD screen. However, the original images are so low-res that artifacts are basically inevitable, which is why a link to the original is provided.

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Artist: Matilda Fiship | twitter | deviantart | danbooru

Full quality: .png 10 MB (5560โ€‰ร— 4298)

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Artist: Dishwasher1910 | fediverse | pixiv | twitter | artstation | patreon | danbooru

Full quality: .png 6 MB (5227โ€‰ร— 2650)

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submitted 4 days ago* (last edited 4 days ago) by ChaoticNeutralCzech@lemmy.one to c/morphmoe@ani.social

Artist: Onion-Oni aka TenTh from Random-tan Studio
Original post: The Satellite-girl on Tapas (warning: JS-heavy site)

Upscaled by waifu2x (model: upconv_7_anime_style_art_rgb). Original

This is the Horizon satellite from Random-tan Studio's cybermoe comic Sammy, page 18, prior to remastering.

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"Axolotl Chan" by Gegegek (lemmy.likes.cat)
submitted 5 days ago by sag@lemm.ee to c/morphmoe@ani.social

Source: Instagram

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Artist: Onion-Oni aka TenTh from Random-tan Studio
Original post: Watchers on Tapas (warning: JS-heavy site)

Upscaled by waifu2x (model: upconv_7_anime_style_art_rgb). Original
Unlike photos, upscaling digital art with a well-trained algorithm will likely have little to no undesirable effect. Why? Well, the drawing originated as a series of brush strokes, fill areas, gradients etc. which could be represented in a vector format but are instead rendered on a pixel canvas. As long as no feature is smaller than 2 pixels, the Nyquist-Shannon sampling theorem effectively says that the original vector image can therefore be reconstructed losslessly. (This is not a fully accurate explanation, in practice algorithms need more pixels to make a good guess, especially if compression artifacts are present.) Suppose I gave you a low-res image of the flag of South Korea ๐Ÿ‡ฐ๐Ÿ‡ท and asked you to manually upscale it for printing. Knowing that the flag has no small features so there is no need to guess for detail (this assumption does not hold for photos), you could redraw it with vector shapes that use the same colors and recreate every stroke and arc in the image, and then render them at an arbitrarily high resolution. AI upscalers trained on drawings somewhat imitate this process - not adding detail, just trying to represent the original with more pixels so that it loooks sharp on an HD screen. However, the original images are so low-res that artifacts are basically inevitable, which is why a link to the original is provided.

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Mecury (by Gia) (files.catbox.moe)

Artist: Gia | pixiv | twitter | tumblr | deviantart | danbooru

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Walkman (by Rinotuna) (files.catbox.moe)

Artist: Rinotuna | pixiv | twitter | artstation | linktree | patreon | danbooru

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Artist: Onion-Oni aka TenTh from Random-tan Studio
Original post: Blimp on Tapas (warning: JS-heavy site)

Upscaled by waifu2x (model: upconv_7_anime_style_art_rgb). Original
Unlike photos, upscaling digital art with a well-trained algorithm will likely have little to no undesirable effect. Why? Well, the drawing originated as a series of brush strokes, fill areas, gradients etc. which could be represented in a vector format but are instead rendered on a pixel canvas. As long as no feature is smaller than 2 pixels, the Nyquist-Shannon sampling theorem effectively says that the original vector image can therefore be reconstructed losslessly. (This is not a fully accurate explanation, in practice algorithms need more pixels to make a good guess, especially if compression artifacts are present.) Suppose I gave you a low-res image of the flag of South Korea ๐Ÿ‡ฐ๐Ÿ‡ท and asked you to manually upscale it for printing. Knowing that the flag has no small features so there is no need to guess for detail (this assumption does not hold for photos), you could redraw it with vector shapes that use the same colors and recreate every stroke and arc in the image, and then render them at an arbitrarily high resolution. AI upscalers trained on drawings somewhat imitate this process - not adding detail, just trying to represent the original with more pixels so that it loooks sharp on an HD screen. However, the original images are so low-res that artifacts are basically inevitable, which is why a link to the original is provided.

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submitted 1 week ago* (last edited 6 days ago) by ChaoticNeutralCzech@lemmy.one to c/morphmoe@ani.social

Artist: Onion-Oni aka TenTh from Random-tan Studio
Original post: Frostpunk steam vehicle on Tapas (warning: JS-heavy site)

Upscaled by waifu2x (model: upconv_7_anime_style_art_rgb). Original

Thanks to @BonerMan@ani.social for identifying the steam engine!

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KV-5 (Random-tan Studio) (files.catbox.moe)
submitted 1 week ago* (last edited 1 week ago) by ChaoticNeutralCzech@lemmy.one to c/morphmoe@ani.social

Artist: Onion-Oni aka TenTh from Random-tan Studio
Original post: KV-5 on Tapas (warning: JS-heavy site)

Upscaled by waifu2x (model: upconv_7_anime_style_art_rgb). Original

At this point, it's becoming pretty clear that the suggestions have been overtaken by objects with prominent spherical features.

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Earth-chan (by Nanoless) (files.catbox.moe)

Artist: Nanoless | fediverse | twitter | newgrounds | tumblr | patreon | danbooru

Full quality: .png 3 MB (2200โ€‰ร— 4000)

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Artist: Onion-Oni aka TenTh from Random-tan Studio
Original post: Interdictor class SD on Tapas (warning: JS-heavy site)

Upscaled by waifu2x (model: upconv_7_anime_style_art_rgb). Original
Unlike photos, upscaling digital art with a well-trained algorithm will likely have little to no undesirable effect. Why? Well, the drawing originated as a series of brush strokes, fill areas, gradients etc. which could be represented in a vector format but are instead rendered on a pixel canvas. As long as no feature is smaller than 2 pixels, the Nyquist-Shannon sampling theorem effectively says that the original vector image can therefore be reconstructed losslessly. (This is not a fully accurate explanation, in practice algorithms need more pixels to make a good guess, especially if compression artifacts are present.) Suppose I gave you a low-res image of the flag of South Korea ๐Ÿ‡ฐ๐Ÿ‡ท and asked you to manually upscale it for printing. Knowing that the flag has no small features so there is no need to guess for detail (this assumption does not hold for photos), you could redraw it with vector shapes that use the same colors and recreate every stroke and arc in the image, and then render them at an arbitrarily high resolution. AI upscalers trained on drawings somewhat imitate this process - not adding detail, just trying to represent the original with more pixels so that it loooks sharp on an HD screen. However, the original images are so low-res that artifacts are basically inevitable, which is why a link to the original is provided.

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Artist: Onion-Oni aka TenTh from Random-tan Studio
Original post: Milano on Tapas (warning: JS-heavy site)

Upscaled by waifu2x (model: upconv_7_anime_style_art_rgb). Original
Unlike photos, upscaling digital art with a well-trained algorithm will likely have little to no undesirable effect. Why? Well, the drawing originated as a series of brush strokes, fill areas, gradients etc. which could be represented in a vector format but are instead rendered on a pixel canvas. As long as no feature is smaller than 2 pixels, the Nyquist-Shannon sampling theorem effectively says that the original vector image can therefore be reconstructed losslessly. (This is not a fully accurate explanation, in practice algorithms need more pixels to make a good guess, especially if compression artifacts are present.) Suppose I gave you a low-res image of the flag of South Korea ๐Ÿ‡ฐ๐Ÿ‡ท and asked you to manually upscale it for printing. Knowing that the flag has no small features so there is no need to guess for detail (this assumption does not hold for photos), you could redraw it with vector shapes that use the same colors and recreate every stroke and arc in the image, and then render them at an arbitrarily high resolution. AI upscalers trained on drawings somewhat imitate this process - not adding detail, just trying to represent the original with more pixels so that it loooks sharp on an HD screen. However, the original images are so low-res that artifacts are basically inevitable, which is why a link to the original is provided.

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Artist: Onion-Oni aka TenTh from Random-tan Studio
Original post: Regina on Tapas (warning: JS-heavy site)

Upscaled by waifu2x (model: upconv_7_anime_style_art_rgb). Original
Unlike photos, upscaling digital art with a well-trained algorithm will likely have little to no undesirable effect. Why? Well, the drawing originated as a series of brush strokes, fill areas, gradients etc. which could be represented in a vector format but are instead rendered on a pixel canvas. As long as no feature is smaller than 2 pixels, the Nyquist-Shannon sampling theorem effectively says that the original vector image can therefore be reconstructed losslessly. (This is not a fully accurate explanation, in practice algorithms need more pixels to make a good guess, especially if compression artifacts are present.) Suppose I gave you a low-res image of the flag of South Korea ๐Ÿ‡ฐ๐Ÿ‡ท and asked you to manually upscale it for printing. Knowing that the flag has no small features so there is no need to guess for detail (this assumption does not hold for photos), you could redraw it with vector shapes that use the same colors and recreate every stroke and arc in the image, and then render them at an arbitrarily high resolution. AI upscalers trained on drawings somewhat imitate this process - not adding detail, just trying to represent the original with more pixels so that it loooks sharp on an HD screen. However, the original images are so low-res that artifacts are basically inevitable, which is why a link to the original is provided.

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Aloe Vera (by Rinotuna) (files.catbox.moe)

Artist: Rinotuna | pixiv | twitter | artstation | linktree | patreon | danbooru

Full quality: .jpg 2 MB (3552โ€‰ร— 3993)

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Artist: Vectorek | fediverse | pixiv | twitter | deviantart | ko-fi | patreon | danbooru

Full quality: .png 1 MB (2410โ€‰ร— 1622)

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submitted 1 week ago* (last edited 1 week ago) by ChaoticNeutralCzech@lemmy.one to c/morphmoe@ani.social

Artist: Onion-Oni aka TenTh from Random-tan Studio
Original post: The Milkshake :3 on Tapas (warning: JS-heavy site)

Upscaled by waifu2x (model: upconv_7_anime_style_art_rgb). Original

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Artist: Raviolimavioli | pixiv | twitter | artstation | deviantart | linktree | danbooru

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Original (by Rinotuna) (files.catbox.moe)

Artist: Rinotuna | pixiv | twitter | artstation | linktree | patreon | danbooru

Full quality: .jpg 1 MB (2507โ€‰ร— 3092)

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MorphMoe

299 readers
35 users here now

Anthropomorphized everyday objects etc. If it exists, someone has turned it into an anime-girl-or-guy.

  1. Posts must feature "morphmoe". Meaning non-sentient things turned into people.
  2. No nudity. Lewd art is fine, but mark it NSFW.
  3. If posting a more suggestive piece, or one with simply a lot of skin, consider still marking it NSFW.
  4. Include a link to the artist in post body, if you can.
  5. AI Generated content is not allowed.
  6. Positivity only. No shitting on the art, the artists, or the fans of the art/artist.
  7. Finally, all rules of the parent instance still apply, of course.

SauceNao can be used to effectively reverse search the creator of a piece, if you do not know it.

You may also leave the post body blanks or mention @saucechan@ani.social, in which case the bot will attempt to find and provide the source in a comment.

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founded 7 months ago
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