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#SakuraWars2 DevLog: Dealing with Images This game contains a fair bit of text within images. So for a translation patch, the images need to be extracted, redone, and patched.

NSteam 🇵🇸

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19,529 görüntüleme • 1 yıl önce •via X (Twitter)

Oyun Bilim & Teknoloji #SakuraWars2

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NSteam 🇵🇸 profil fotoğrafı

NSteam 🇵🇸1 yıl önce

This includes font sheets, all battle UI, chapter intro screens, battle screens, boss banners, minigame images, theater room names, system menus, menu backgrounds, loading screens, splash screen, game title screen, and other misc images. Currently amounting to 1,735 images!

NSteam 🇵🇸 profil fotoğrafı

NSteam 🇵🇸1 yıl önce

For some of these, like the room names in the theater & names appearing within battle UI, there were hundreds of images. To do them by hand was tedious so I wrote a tool to auto-generate them given the translated names in a text file. Some of the battle UI was done by hand though

NSteam 🇵🇸 profil fotoğrafı

NSteam 🇵🇸1 yıl önce

We got the help of some very talented artists to help redo some of the more complicated images like these boss images, chapter screens, and minigame images.

NSteam 🇵🇸 profil fotoğrafı

NSteam 🇵🇸1 yıl önce

Most images are stored as raw vdp1 format image data. Others are tiled images displayed in vdp2. These are easy to extract using tools I wrote. Others are compressed using PRS compression which was a standard across many Sega titles. These are a bit more complicated.

NSteam 🇵🇸 profil fotoğrafı

NSteam 🇵🇸1 yıl önce

Some however are encoded using some bizarre encoding algorithm. It doesn't compress the image, just obfuscates it. The Kinematron, some minigames, and credits all use this. And these are a pain.

NSteam 🇵🇸 profil fotoğrafı

NSteam 🇵🇸1 yıl önce

To extract these, the encoded data needs to be found, the encoding algorithm needs to be figured out(which Ghidra helps with) and then it needs to be reverse engineered so that I can re-encode the translated images and this part was pretty tricky.

NSteam 🇵🇸 profil fotoğrafı

NSteam 🇵🇸1 yıl önce

Above image is full of artifacts unless it is decoded. Here is one of the images properly decoded. What makes it worse is that there are multiple encoding schemes. The kinematron & minigames use one, credits use another. I call this algorithm the "Kinematron Encoding Algorithm"

NSteam 🇵🇸 profil fotoğrafı

NSteam 🇵🇸1 yıl önce

I have yet to reverse engineer the credits algorithm. Not sure if it will make it into the final patch. Maybe! But for now, everything else we have encountered has been extracted, reworked, and patched =)

burntends | Macross Do You Remember Love? profil fotoğrafı

burntends | Macross Do You Remember Love?1 yıl önce

Always great to see more news

Norbjunior profil fotoğrafı

Norbjunior1 yıl önce

@burntends2 Super cool to learn how some of this works!! Thank you for all the hard work!!!!

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Georgia's 2020 election had 1,726,651 missing original ballot images. You can NOT certify or audit votes without these images, many were destroyed or "lost." 70 Georgia counties could not produce original ballot images for the November 2020 election, 36 counties were missing 100% of all original ballot images. These images MUST be retained up to 22 months by law. These ballot images were deleted because it proved the massive amount of fraud, and they had to get rid of the evidence. 5,026,684 total cast ballots from the Secretary of State, 3,300,033 original ballot images received by VoterGA. The amount of "anomalies," impossible occurrences, and patterns of fraud, happening all throughout the country clearly shows a stolen election.

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Collaborative Score Distillation for Consistent Visual Synthesis paper page: Generative priors of large-scale text-to-image diffusion models enable a wide range of new generation and editing applications on diverse visual modalities. However, when adapting these priors to complex visual modalities, often represented as multiple images (e.g., video), achieving consistency across a set of images is challenging. In this paper, we address this challenge with a novel method, Collaborative Score Distillation (CSD). CSD is based on the Stein Variational Gradient Descent (SVGD). Specifically, we propose to consider multiple samples as "particles" in the SVGD update and combine their score functions to distill generative priors over a set of images synchronously. Thus, CSD facilitates seamless integration of information across 2D images, leading to a consistent visual synthesis across multiple samples. We show the effectiveness of CSD in a variety of tasks, encompassing the visual editing of panorama images, videos, and 3D scenes. Our results underline the competency of CSD as a versatile method for enhancing inter-sample consistency, thereby broadening the applicability of text-to-image diffusion models.

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