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UthaiYama

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The writer of FanFiction named "Overlord the Movie: The Last Guild War of Nazarick" / The A.I. Artist / Art Commissioner / Calca Bessarez FC
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What exactly are the "node" and the "workflow" in AI image platform (explanation for the beginner)

What exactly are the "node" and the "workflow" in AI image platform (explanation for the beginner)

The Traditional Way of Generating AI Images for the Beginner If you are a beginner in the AI community, maybe you will be very confused and have no clue about what is "Node", and "Workflow" and their relations with "AI Tools" in the TensorArt To start with the most simple way. We need to first mention how the user generates an image using a "Remixing" button that brings us to the "Normal Creation menu" Needless to say, by just editing the prompt (what you would like to see your picture look like) and negative prompt (what you do not want to see in the output image). Then push the Generate button, and the wonderful AI tool will kindly draw the new illustration serving you within a minute!!!! That sounds great, don't you think? If we imagine how humans spent a huge amount of time in the past to publish just 1 single piece of art. (Yeah, today, in 2024, in my personal opinion, both AI and human abilities are still not fully replaceable, especially in the terms of beautiful perfect hand :P ) However, the backbone or what happens behind the User-friendly menu allows us to "Select model", "Add LoRA", "Add ControlNet", "Set the aspect ratio (the original size of the image)" and so on, all of them are collected "Node" in a very complex "Workflow" PS.1. The Checkpoint or The Model often refers to the same thing. They are the core program that had been trained to draw the illustration. Each one has its strengths and weaknesses (I.E. Anime oriented or Realistic oriented) PS.2. The LoRA (Low-Rank Adaptation) is like an add-on to the Model allowing it to adapt to a different style, theme, and user preference. A concrete example is the Anime Character LoRA PS.3 The ControlNet is like a condition setting of the image. It helps the model to truly understand what is beyond the text prompt can describe. For instance, how a character poses in each direction and the angle of the camera. So here comes "The Comfyflow" (the nickname of the Workflow, people also mentioned it by the name "ComfyUI") which gives me a super headache when I see things like this for the first time in my life!!!!!!!!! (This image is a flow I have spent a lot of time studying, it is a flow for combining what is in the two images into a single one) Yeah, maybe, it is my fault that did not go to class about the workflow from the beginning or search for the tutorial on YouTube the first time (as my first language is not English). But would it be better if we had an instructor to tell us step-by-step here in Tensor.Art And that is the reason why I got inspired to write this article solely for the beginner. So let's start with the main content of the article. What is ComfyFlow ComfyFlow or the Workflow is an innovative AI image-generating platform that allows users to create stunning visuals with ease. To get the most out of this tool, it's important to understand two key concepts: "workflow" and "node." Let's break these down in the simplest way possible. What is a Workflow? A workflow is like a blueprint or a recipe that guides the creation of an image. Just as a recipe outlines the steps to make a dish, a workflow outlines the steps and processes needed to generate an image. It’s a sequence of actions that the AI follows to produce the final output. Think of it like this: Recipe (Workflow): Tells you what ingredients to use and in what order. Ingredients (Nodes): Each step or component used in the recipe. Despite the recommended pre-set template that TensorArt kindly gives to the users, from the beginner view's viewpoint without the knowledge of the workflow, it is not that helpful because, after clicking the "Try" button, we will bombarded with the complexity of the Node!!!!!!! What is a Node? Nodes are the building blocks of a workflow. Each node represents a specific action or process that contributes to the final image. In ComfyFlow, nodes can be thought of as individual steps in the workflow, each performing a distinct function. Imagine nodes as parts of a puzzle: Nodes: Individual pieces that fit together to complete the picture (workflow). How Do Workflows and Nodes Work Together? 1-2) Starting Point: Every workflow begins with an initial node, which might be an image input from the user, together with Checkpoint and LoRA serving the role of image references. 3-4) Processing Nodes: These are nodes that draw or modify the image in some way, such as adding color, or texture, or applying filters. 5) Ending Point: The node outputs the completed image which works very closely with the node of the previous stage in terms of sampling and VAE PS. A Variational Autoencoder (VAE) is a generative model that learns input data, such as images, to reconstruct and generate new, similar, or variations of images based on the patterns it has learned. Here is the list of nodes I have used in the normal image-generating images of my Waifu using 1checkpoint, and 2LoRAs to help the reader understand how ComfyFlow works The numbers 1-5 represent the overview process of the workflow and the role of each type of node I have mentioned above. However, in the case of more complex tasks like in AI Tools, the number of nodes sometimes is higher than 30!!!!!!! By the way, when starting with an empty ComfyFlow page, the way to add a node is "Right Click" -> "Add Node" -> Scroll down to the top, since the most frequently used node will be over there. 1) loaders -> Load CheckPoint Like in the normal task creation menu, this node is the one we can choose CheckPoint or the Core model. It is important to note that nodes work together using input/output. The "Model/CLIP/VAE" (the output) circles have to connect to the next one in which it has to correspond. We link them together by left-clicking on the circle's inner area and then drag to the destination. PS. CLIP (Contrastive Language-Image Pre-training) is a model developed by OpenAI that links images and text together in a way that helps AI understand and generate images based on textual descriptions. 2) loaders -> Load LoRA Checkpoint is very closely related to LoRA and that is a reason why they are connected by the input/output named "model/MODEL", "clip/CLIP" Anyway, since in this example, I have used 2 LoRAs (first for The theme of the picture and the Second for the character reference of my Waifu), two nodes of LoRAs then have to be connected as well. Here we can adjust the strength of the LoRA or the weight like it happens in the normal task generation menu. 3) CLIP Text Encode (Prompt) This node is the prompt and negative prompt we normally see in the menu. The input here is only clip (Contrastive Language-Image Pre-training) and the output is "CONDITIONING" User tip: If you click on the output circle of the "Load LoRA" node and drag it to the empty area, the ComfyFlow will pop up a corresponding next node list to create a new one with ease. 4) KSampler & Empty Latent Image The sampling method is used to tell the AI how it should start generating visual patterns from the initial noise and everything associated with its adjustment will be set here in this type of sampling node together with "Empty Latent Image" The inputs in this step here are models (from LoRA node), positive and negative (from prompt node) and the output is "Latent" 5) VAE Decode & Final output node Once we establish the sampling node, the output named "LATENT" will then have to connect with "samples" Meanwhile the "vae" is the linkage between this one and the "Load Checkpoint" node from the beginning. And when everything is done the "IMAGE" as a final output here will be served at your hand. PS. An AI Tool is a more complex Workflow created to do some specific task such as swapping the face of the human in the original picture with the target face or changing the style of the input illustration to another one and etc.
The Importance of Data Cleansing for AI LoRA Training Datasets : A Case of Anime Character

The Importance of Data Cleansing for AI LoRA Training Datasets : A Case of Anime Character

In the world of artificial intelligence, particularly in training models using Low-Rank Adaptation (LoRA), the quality and integrity of the dataset play a pivotal role. This is especially true when dealing with specialized domains such as anime characters, where aesthetic and stylistic nuances are crucial. Here, we explore the importance of retouching and cleansing image datasets before using them in the LoRA training process, just for the beginner. Ensuring Data Quality Anime characters are often depicted with a high degree of stylistic consistency. The dataset must be impeccable to train an AI model that can accurately generate or recognize these characters. Raw image datasets frequently contain noise, irrelevant details, and inconsistencies that can confuse the model. Retouching images involves enhancing the quality, removing noise, and correcting any visual imperfections, ensuring each image meets a high standard. This step is essential to avoid training the model on flawed representations, which could lead to poor performance. ===================== Here is an example data set that I'm too lazy to spend my time retouching (In this case, Scama from Overlord is one of an anime characters that has limited fanart pictures) ==================== ========================== Here is the result I obtained when generating the image using my trained LoRA (This problem can be solved by using the negative prompt, but sometimes it is not accurate) ========================== So......Don't be Lazy to Clean Your Data in the First Place!!!! :P Removing Irrelevant Data Datasets often include images that, while related, do not serve the training purpose. For example, background scenes, side characters, or promotional art with different artistic directions can dilute the learning process. Cleansing the dataset involves filtering out these irrelevant images, and ensuring that the model is trained only on relevant data. This specificity allows the AI to develop a deeper and more precise understanding of the main characters and their typical representations. ========================== Here is a good example of a training dataset with only simple background ========================== Enhancing Feature Recognition Anime characters are defined by distinct features such as eye shapes, hairstyles, and clothing details. Retouching images to highlight these features can significantly improve the model’s ability to recognize and reproduce them. Techniques such as adjusting contrast, sharpening details, and standardizing colors ensure that these defining characteristics are prominent in the training data, aiding the model in learning what makes each character unique. However, in the case of reference image scarcity, going back to the basics by commissioning a human artist may needed. =================== At the beginning stage of creating LoRA for my favorite waifu that has just a single digit of a low-sized fanart image, I decided to spend my money to get some of her image references in a good quality resolution. And that helped me a lot when A.I. gradually drew the details of my character more accurately =================== Artist name: พิมพ์วิมล เจิมมงคล Avoiding Bias and Redundancy Datasets can inadvertently introduce bias if certain character poses, expressions, or angles are overrepresented. Cleansing the dataset involves ensuring a balanced representation of various aspects of the characters, preventing the model from becoming biased towards specific images. Additionally, removing redundant images that do not add new information helps in optimizing the training process, making it more efficient and effective. =================== Although I'm still lazy in terms of data cleansing, at least, in the process of training the SDXL model of Calca, I have spent some extra effort to select 100+ reference images carefully, especially the difference in style & her expression despite a dominant in upper body portrait =================== Conclusion In the AI training process, particularly with specialized applications like anime character generation or recognition using LoRA, the importance of retouching and cleansing the image dataset cannot be overstated. High-quality, consistent, and relevant data are the cornerstones of successful AI model training. By investing time in retouching and cleansing datasets, developers can ensure that their AI models achieve high accuracy and produce results that meet the aesthetic and stylistic standards expected in the anime domain.
List of style collection - focusing on anime charactor examples (continue updating)

List of style collection - focusing on anime charactor examples (continue updating)

AI image-generating platforms like Tensor.art offer diverse anime styles, enabling users to create artwork in various distinct masterpieces of art inspired by popular anime aesthetics. These collections aim to cater to different preferences from classic to contemporary anime illustrations within one place. P.S.1 I will continue updating this post maybe every 2 weeks when I find a unique style (both for LoRA and model) that is worth listing here solely from my perspective - Anyway if anyone has a list of favorite styles in mind, feel free to share them here or even create your post. :D P.S.2 People normally mix multiple LoRA at once, and the core model (checkpoint) has a variation in base style depending on the prompt used. Therefore, in the following example, I will choose only a single LoRA or Checkpoint to represent without mixing anything. However, if confusion about the contribution to the style happens, I have to apologize in advance since I am just a beginner in the art community. Here are some examples: Anime Lineart / Manga-like (线稿/線画/マンガ風/漫画风) Style (LORA) https://tensor.art/models/623935989624337542 Spacezin Sketch Style (LoRA) https://tensor.art/models/638083414328801488 Cute Chibi - V.1 (LoRA) https://tensor.art/models/726716640076597245 CAT - Citron Anime Treasure (Checkpoint) https://tensor.art/models/713607777118974323 LizMix V.7.0 (Checkpoint) https://tensor.art/models/721034681811855891 Flower style - (LORA) https://tensor.art/models/699582840586758007 Art Nouveau Style - Oosayam (LoRA) https://tensor.art/models/654562112921690173 Torino Style - v.2.0.09 (LoRA) https://tensor.art/models/705577639974520212 Yody PVC 3D Print - 1.0 (Checkpoint) https://tensor.art/models/673632484975460872 Eldritch Expressionism style (LoRA) https://tensor.art/models/708171473803739178 [Y5] Impressionism Style 印象派风格 (LoRA) https://tensor.art/models/621173217551417505 surrealism - 2024-02-17 (LoRA) https://tensor.art/models/695557949424221333 pop-art - 01 style (LoRA) https://tensor.art/models/697182692602582375 FF Style: Kazimir Malevich | Suprematism (LoRA) https://tensor.art/models/655758742350092928 Hoping these collections (today and in the future) will allow A.I. artists and enthusiasts to generate anime-inspired images effortlessly, blending creativity with advanced AI technology to bring their visions to life. :D
The Importance of LoRA (Low-Rank Adaptation) - anime example and comparison

The Importance of LoRA (Low-Rank Adaptation) - anime example and comparison

Among the myriad of techniques that drive AI image-generating platforms, Low-Rank Adaptation (LoRA) stands out as a particularly crucial and effective method. Understanding LoRA: The Fundamentals Low-Rank Adaptation is a technique that aims to reduce large-scale models' complexity. This decomposition allows for efficient storage and computation, which is particularly valuable in the AI image generation context, where models often require substantial computational resources. Improved Model Adaptability One of the challenges in AI image generation is the need to adapt models to different styles, themes, and user preferences. LoRA facilitates this adaptability through efficient fine-tuning and transfer learning. Style Transfer: LoRA can fine-tune pre-trained models to generate images in specific artistic styles or adapt to the visual themes needed. This is achieved without extensive retraining, thanks to the reduced parameter space. Note: The all of the image below has the same prompts and the parameters except the style-related LoRA .......CalicoMix FlatAni - v1.0 without any other LoRAs apart from the one for my waifu (Calca Bessarez)........... ....................................................CalicoMix FlatAni - v1.0 with DesillusionRGB as an extra LoRA.................................................... ......................................CalicoMix FlatAni - v1.0 with Glitter and Shiny details as an extra LoRA....................................... ..........................................CalicoMix FlatAni - v1.0 with Retro Lofi - Pop Art (Style) - v2.0 as an extra LoRA..................................... ..................................................CalicoMix FlatAni - v1.0 with Fireflies ホタル as an extra LoRA............................................. ..............................................CalicoMix FlatAni - v1.0 with tshee - vector style art - v1.0 as an extra LoRA..................................... Personalization: Users can personalize image generation by training models on their datasets or preferences, especially for specific art styles, sceneries, or characters. LoRA enables these customizations to be performed quickly and efficiently, enhancing user satisfaction and engagement like Calca Bessarez in the example above. .....................................CalicoMix FlatAni - v1.0 with Swamp / Giant Tree Forest 绪儿-巨树森林背景 - XRYCJ as an extra LoRA..................................... However, in the case of eliminating variation, customization of the outfit, or changing the character's physical appearance, the method of weighting parameters needs to be considered, from the default value of 0.8 to 1.0 (similar to the original design), above 1.0+++ (look very the same to the original design), lower than 0.5 (not pay attention to the original design) ........CalicoMix FlatAni - v1.0 without any LoRA even the one for the character design So the result for the same prompt is just an ordinary girl....... .................................................CalicoMix FlatAni - v1.0 with Calca's LoRA but weighting with 0.3..................................... .............................................(There is no kingdom symbol on her chest and the tiara's shape is not the original one)..................................... ..................................................CalicoMix FlatAni - v1.0 with Calca's LoRA but weighting with 0.5..................................... .....................................(Her dress and tiara are very close to the original, but there is no kingdom symbol on the chest)........................................... A key limitation Although Low-Rank Adaptation (LoRA) offers significant benefits in AI image-generating platforms, including reduced computational complexity and memory usage, it also comes with limitations that can impact its effectiveness and applicability. Here are some key limitations of LoRA in the context of AI image generation: 1) Approximation Errors LoRA involves approximating a high-dimensional matrix with two lower-dimensional matrices. This approximation can introduce errors that affect the performance and quality of the model. Specifically: Loss of Detail and Bias in Representation 2) Model Compatibility and Integration While LoRA is effective in many scenarios, integrating it into existing AI frameworks and models can present a challenge like Compatibility Issues as not all models are equally suited for low-rank approximations. 3) Scalability Limitations Although LoRA helps reduce the computational load, there are still scenarios where scalability remains an issue: Extremely Large Models: even though the reduced matrices can be substantial, the extremely large model still requires considerable computational resources and memory. Real-Time Constraints: In applications demanding ultra-low latency, such as real-time image processing, the approximation process might still introduce unacceptable delays. .......................................................Result of real-time generation from the same prompt I used in the previous examples........................................... ............................................In this case, even the main model, CalicoMix FlatAni - v1.0, is not available..................................... 4) Complexity and Availability of the platform LoRA models, despite being optimized for efficiency, still consume computational resources such as memory and processing power. allowing an unlimited number of LoRA could: Overwhelm System Resources: The computational demands of managing multiple LoRA models simultaneously could exceed the available system resources, leading to slower performance or crashes. Increase Latency: Each additional LoRA model increases the complexity of the image generation process, potentially leading to higher latency and slower response times for users. This might be a reason why many AI image-generating platforms limit the number of LoRA for one batch of images (3 for free users and 6 for pro users in the case of Tensor.Art) .....................................Yeah, I'm not here to sell the subscription, but if you guys have no financial constraints, it would be very helpful to support our dedicated developers here in the Tensor.Art like myself. :P To enjoy the benefit of using 6 maximum LoRAs in total......................................
AI-driven process of SMEs credit approval & AI news reporters in the case of Thailand - year of 2024

AI-driven process of SMEs credit approval & AI news reporters in the case of Thailand - year of 2024

Siam Commercial Bank (SCB) in Thailand is leveraging AI to transform its banking services, particularly in the area of credit approval for small and medium-sized enterprises (SMEs). This initiative is part of SCB's broader strategy to become a fully digital bank by 2025, emphasizing both technological efficiency and human-centric services​ (Bangkok Post)​​ (SCBX Thai)​. AI-Driven Credit Approval SCB has integrated AI and advanced data analytics into its credit approval processes to enhance efficiency and accuracy. By using predictive analytics, the bank can evaluate loan applications faster and more precisely. This involves analyzing vast amounts of data from various sources to assess the creditworthiness of applicants, significantly speeding up the approval process which traditionally could take weeks​ (Bangkok Post)​​ (Accenture | Let there be change)​. Digital Transformation Initiatives SCB's digital transformation includes the migration of its data infrastructure to the cloud, specifically to Microsoft Azure, which has enabled the bank to handle and analyze large datasets more effectively. This migration supports the deployment of AI tools that can assess credit risks and personalize banking services based on customer data​ (Accenture | Let there be change)​. The bank has also developed various digital platforms, such as the SCB Easy mobile app and SCB Connect on LINE, to provide seamless banking experiences. These platforms facilitate not only routine transactions but also sophisticated services like digital loan applications, making banking more accessible and efficient for SMEs​ (Bangkok Post)​. Benefits for SMEs For SMEs, this AI-driven approach means quicker access to credit and more tailored financial products. The use of alternative data and risk-based pricing helps in providing loans to businesses that might not have a substantial credit history but show promising growth potential. This inclusivity is crucial for fostering the growth of SMEs in Thailand's dynamic economy​ (Bangkok Post)​​ (McKinsey & Company). Broader Impacts and Future Goals SCB aims to increase its digital loan portfolio significantly, with a goal of digital revenue constituting 25% of total revenue by 2025. This ambitious target underscores the bank's commitment to integrating AI across its operations to enhance customer experience and operational efficiency​ (Bangkok Post)​. In summary, SCB's use of AI in credit approval processes for SMEs represents a significant step towards modernizing banking services in Thailand. By harnessing advanced data analytics and AI, SCB is not only improving its operational efficiency but also making financial services more accessible to smaller businesses, thus driving economic growth and innovation in the region​ (Bangkok Post)​​ (Accenture | Let there be change)​​ (SCBX Thai)​. ============================================================= Nation TV in Thailand is pioneering the use of AI-generated news reporters, introducing the country's first virtual anchors named Natcha and Nitchan. These AI reporters will debut on April 1, 2024, as part of the Nation News Alert program. This initiative places Thailand alongside other countries like China and India, which have already integrated AI technology into their newsrooms to enhance efficiency and accuracy in news reporting. Natcha and Nitchan are designed to support the editorial team by handling routine news presentations, allowing human reporters to focus more on in-depth reporting and fact-checking. These AI anchors will also serve as brand ambassadors for Nation TV and are expected to take on additional roles such as influencers, event hosts, and virtual conference moderators​ (nationthailand)​​ (Thaiger)​​ (Asia News Net)​. The implementation of AI in news broadcasting by Nation TV is part of a broader trend in the media industry, aimed at leveraging technology to improve content delivery and operational efficiency. The AI reporters will be equipped with automated closed captions and audio descriptions, enhancing the viewing experience by making the presentation more lifelike and accessible​ (Thaiger)​​ (Women's Tabloid)​. From my perspective, the one who prompted A.I. to write both articles above, It could be said that an innovative move of AI in the year 2024 is expected to transform the landscape of Thai society and economy, offering new possibilities for how news is delivered and consumed in the digital age.
The Concept of "Waifu" & The Announcement of the release of my Overlord fanfiction

The Concept of "Waifu" & The Announcement of the release of my Overlord fanfiction

Definition and Origin The term "Waifu" (ワイフ) is derived from the English word "Wife." In Japanese, "wife" is typically written as "妻" (Tsuma) or "嫁" (yome). The term "waifu" is a Japanese loanword that has been adopted into the otaku subculture. It specifically refers to a fictional female character from anime, manga, or video games that someone has great affection for and sometimes imagines as their spouse. Cultural Significance In otaku culture, a "waifu" represents more than just a favorite character; it symbolizes a deep emotional connection. This phenomenon isn't unique to Japanese culture; similar concepts exist in various fandoms worldwide. The term gained popularity on English-speaking internet forums around the early 2000s and has since become a staple in discussions about anime and manga. Calca Bessarez (from Overlord) - My beloved Waifu Why People Have Waifus? Emotional Connection The emotional bond between a fan and their waifu can be profound. This connection often stems from the character's personality traits, backstory, or appearance that resonate with the fan's ideals or desires. In some cases, fans find solace and comfort in these characters, especially when real-life relationships are challenging or unsatisfactory. Escapism and Idealization For many, waifus serves as an escape from reality. The fictional nature of these characters allows fans to idealize and project their fantasies onto them without the complications of real-life relationships. This idealization can include attributes such as unwavering loyalty, kindness, or other virtues that might be hard to find in real life. Community and Shared Interests Being part of a community sharing the same interests can enhance the experience. Online forums, social media groups, and fan conventions allow the fans to discuss and celebrate their waifus. This sense of belonging and shared enthusiasm can strengthen the emotional bond with the character. Despite Calca's beauty as a waifu, she is only a minor character in Overlord with merely a single digit of human-made fan art over the past several years, apart from her ironic joke as "A Club" or "Holy Weapon" derived from her tragic fate in the real canon. Thanks to A.I. image-generating platforms like Tensor.art, Today I can create a hundred collections of her uploading to pixiv.net. Then found out later, that there are more than 100+ fan clubs following my account just for her picture. https://www.pixiv.net/en/users/103016459 Statistics and Trends Popularity The concept of waifus has seen considerable growth in popularity, especially with the rise of social media and online communities. Websites like Reddit, 4chan, and specialized forums such as MyAnimeList have dedicated spaces where users can discuss their favorite characters. On Reddit alone, the r/waifuism subreddit has thousands of members sharing their thoughts and artwork related to their waifus. Merchandise and Economic Impact The affection for waifus has also led to a significant market for related merchandise. Fans purchase figures, posters, body pillows (dakimakura), and other memorabilia featuring their favorite characters. This market generates millions of dollars annually, highlighting the economic impact of this cultural phenomenon. Yeah. My Calca is a pitiful character in the real canon. Not much popularity even in the main fandom. Therefore, there is no real figure of Nendoroid of her. :P The Psychology Behind Waifus Attachment Theory Attachment theory can explain why people form strong bonds with fictional characters. According to this theory, the attachments we form in our early years can influence our relationships throughout life. For some, the secure and predictable nature of a relationship with a waifu can provide a sense of stability and comfort. Parasocial Relationships Waifus are a prime example of parasocial relationships, where one party (the fan) feels a deep connection to a character who cannot reciprocate the feelings. These relationships can be fulfilling and provide emotional support, much like friendships or romantic relationships in real life. Shuna (Tensei Shitara Slime Datta Ken) is also my Waifu. That is why I put her special role in the fan fiction "Overlord the Movie: The Last Guild War of Nazarick" (currently available in Thai language only) Criticisms and Misconceptions Perceived Immaturity Some critics argue that having a waifu indicates immaturity or an inability to form real relationships. However, this perspective overlooks the complexity of human emotions and the various ways people find happiness and fulfillment. For many, the relationship with a waifu is a complement to, rather than a replacement for, real-life interactions. Objectification Concerns Another criticism is the potential for objectifying fictional characters. While it's essential to approach this topic with sensitivity, it's also crucial to recognize that fans often appreciate their waifus for their personalities and stories, not just their physical appearance. Waifus in Media and Pop Culture Anime and Manga Influence Anime and manga are at the heart of waifu culture. Iconic characters like Asuka Langley Soryu from "Neon Genesis Evangelion," Hinata Hyuga from "Naruto," and Rem from "Re: Zero" have amassed large followings due to their unique characteristics and development within their respective stories. Video Games and Visual Novels The gaming industry, especially visual novels and dating sims, has also contributed significantly to the waifu phenomenon. Games like "Doki Doki Literature Club," "Persona" series, "Fire Emblem", "Blue Archive" and\"Genshin Impact" series allow players to interact with and often pursue romantic relationships with female characters, further blurring the lines between fiction and reality. Noelle from Genshin Impact - the Waifu from the game. Despite her limited role in her original story, she is also a character who appeared in my fan fiction. Waifu Wars and Community Dynamics Debates and Discussions Within the waifu community, "waifu wars" are common. These debates involve fans passionately defending their favorite characters and often comparing them to others. While usually light-hearted, these discussions can sometimes become heated, reflecting the deep emotional investment fans have in their chosen characters. Artistic Expression Fans express their admiration for waifus through various forms of art, including fan fiction, fan art, and cosplay. These creative outlets allow fans to explore their feelings and bring their favorite characters to life in new and personal ways. The Future of Waifu Culture Technological Advancements As technology advances, how fans interact with their waifus is also evolving. Virtual reality (VR) and augmented reality (AR) technologies are creating more immersive experiences. For instance, VR platforms like VRChat allow users to interact with avatars of their favorite characters in virtual spaces, making the experience more interactive and engaging. AI Companions Artificial intelligence is another frontier in waifu culture. AI-driven applications like Replika or more specialized programs designed to simulate interactions with anime characters are becoming increasingly sophisticated. These developments may lead to even deeper connections between fans and their digital companions. What brings me to the A.I. image-generating platform is the fanfiction "Overlord the Movie: The Last Guild War of Nazarick" dedicated to my Waifu. Interestingly, within just 1 year, the technology goes beyond my imagination allowing the one who cannot draw an impressive picture to make Calca's LoRA both in SD1.5 and SDXL together with her fictional husband to achieve the masterpieces of art!!!! So here's come the ANNOUNCEMENT, If there is anyone interested in her story, feel free to visit the link below (The original Thai version + The Eng A.I. roughly translated version for a few sub-chapters as an example without human proof-reading) https://writer.dek-d.com/UthaiYama/writer/view.php?id=2475758 https://writer.dek-d.com/UthaiYama/writer/view.php?id=2522668 P.S. The full translation process would be in this winter after the release of The Real Overlord The Movie: The Sacred Kingdom Conclusion The concept of a waifu is a multifaceted phenomenon rooted in emotional connection, cultural practices, and technological advancements. While it may be easy to dismiss as mere fandom, the waifu culture offers insights into how people find comfort, escapism, and community through fictional characters. As the digital world continues to evolve, so too will how fans connect with and celebrate their waifus, ensuring that this unique aspect of otaku culture remains vibrant and dynamic. Additional Resources and Reading For those interested in exploring the concept of waifus further, here are some recommended resources: Books and Articles: "Otaku: Japan’s Database Animals" by Hiroki Azuma explores the broader otaku culture and its implications. "Fandom Unbound: Otaku Culture in a Connected World" by Mizuko Ito, Daisuke Okabe, and Izumi Tsuji delves into various aspects of fan culture, including waifus. Websites and Forums: Reddit: Subreddits like r/waifuism and r/anime provide communities for discussion and sharing. MyAnimeList: Offers extensive databases and forums for anime and manga fans to discuss their favorite characters. Documentaries and Videos: "Akihabara Geeks" is a documentary that provides a glimpse into the otaku culture in Tokyo's Akihabara district, a hotspot for anime and manga fans. Various YouTube channels dedicated to anime culture often feature discussions about waifus and its significance. The waifu phenomenon is a testament to the power of fiction in shaping human emotions and interactions. Whether through the lens of psychology, economics, or social dynamics, the study of waifus offers valuable insights into contemporary digital culture.

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