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风格提示词——StylizationInfographic drawing, The concept character sheet 信息图表，概念字符character sheet style 人物表character sheet ilustration 人物表插画Realistic 真实感bokeh 背景散焦Ethereal 空灵 幽雅Warm tones 暖色调The lighting is soft 灯光柔和natural light 自然光ink wash 水墨Splash pigment effect 飞溅颜料cybernetic illuminations 科技光Neo-Pop 新波普艺术风格Art nouveau 新艺术Grandparentcore 复古老派风格Cleancore 简约风格red theme 红色主题sticker 贴纸Reflection 反射Backlit 逆光depth of field 景深A digital double exposure photo 双重曝光blurry foreground 模糊前景blurry background 模糊背景motion_blur 动作模糊split theme 分裂主题Paisley patterns 佩斯利图案(花纹）lineart 线条画silhouette art 剪影艺术concept art 概念艺术graffiti art 涂鸦艺术Gothic art 哥特式艺术Goblincore 地精自然风格ukiyo-e 浮世绘sumi-e 墨绘magazine cover 杂志封面commercial poster 商业海报视角提示词——ViewPerspective view 透视视角Three-quarter view 三分之一视角Thigh-level perspective 大腿水平视角close-up 特写Macro photo 微距图像Headshot 头像portrait 肖像low angle shot 低视角front and back view 前视图和后视图（正反面）various views 各种视角（多视角）Panoramic view 全景Mid-shot/Medium shot 中景cowboy_shot 牛仔镜头Waist-up view 腰部以上视图Bust shot 半身照Torso shot 躯干照foot focus 足部焦点looking at viewer 看着观众from above 俯视from below 仰视full body 全身像sideways/profile view 侧面fisheye lens 鱼眼镜头Environmental portrait 环境人像表情提示词——Facial expressionSmile 微笑grin 咧嘴笑biting lip 咬嘴唇adorable 萌tearing up/crying_tears 泪目tearful 含泪wave mouth 波浪嘴spiral_eyes 螺旋眼Cheerful 乐观nose blush 潮红running mascara 流动睫毛膏发型提示词——Hairstylesmooth 柔顺hair over one eye 刘海遮住一只眼睛twintails 双马尾ponytail 马尾辫diagonal bangs 斜刘海Dynamic hair 飘发hanging hair 垂发ahoge 呆毛braid 辫子braided bun 包子头Undercut 剃鬓发型装饰提示词——Ornamentforehead mark 额头痣mole under eye 泪痣Skindentation 勒痕eyepatch 单眼罩blindfold 眼罩hairpin 发卡hairclip 发圈headband 发箍hair holder 束发hair ribbon 发带Ribbon 缎带 蝴蝶结maid headdress 女仆头饰headveil 头纱tassel 流苏thigh strap 大腿带服装提示词——Clothingjkseifuku jk 日本女子校服miko 女巫idol clothes 偶像服competition swimsuit 竞速泳装Rococo 洛可可pelvic curtain 盆骨帘midriff 分体式halterneck 露背装enmaided 女仆装backless sweater 露背毛衣turtleneck sweater 高领毛衣French-style suspender skirt 法式吊带裙winter coat 冬大衣Trench Coat 风衣race queen 赛车女郎Highleg/Leotard 高叉紧身衣slit skirt 分衩裙Stirrup legwear 踩脚裤fishnet stockings 渔网袜thighhighs/thigh-high socks 大腿袜kneehighs 过膝袜toeless legwear 无指袜yoga pants 瑜伽裤frilled 荷叶边(花边)动作提示词——Action(crossed_legs_(sitting)/crossed legs 二郎腿坐cross-legged sitting 盘腿坐semireclining position 半卧姿势head tilt 头部倾斜leaning forward 向前俯身planted sword 种植剑heart hand duo 双人心形手double thumbs up 点赞peace sign 比耶Salute (≧ω≦)/Energetic Pose 活力姿态sitting on seiza 正坐身体提示词——Bodythick eyebrows 浓眉Abs 腹肌toned 强壮navel 露脐off-shoulder 露肩tsurime 吊梢眼cyborg 半机械人tan skin 日晒肤色cocoa skin 可可肤色fit physique 健美体态

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 QualityAnime 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 DataDatasets 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 RecognitionAnime 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 เจิมมงคลAvoiding Bias and RedundancyDatasets 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 ===================ConclusionIn 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.

FLUX Prompt Tools:https://chatgpt.com/g/g-NLx886UZW-flux-prompt-pro 👆Although I am doing my best to optimize my AI prompt generation tool, I am currently facing malicious negative reviews from competitors. If you have any suggestions for improvement, please feel free to share, and I will do my best to make the necessary optimizations. However, please refrain from giving unfair ratings, as it really discourages my creative efforts. If you find this GPT helpful, please give it a fair rating. Thank you.Halloween is the perfect time to unleash creativity with AI image generation. Whether you're looking to create spooky, eerie, or outright terrifying imagery, having the right prompts can help you get the most striking results. Below is a comprehensive list of Halloween-themed keywords and concepts that will inspire AI-generated masterpieces, from haunted scenes to mysterious creatures. Dive into the world of Halloween, and let's bring some digital spookiness to life!Key Halloween-Themed Prompt Ideas:1. Classic Halloween CharactersWitch: Imagine an old witch casting spells, her silhouette against a full moon.Wizard: Create an eerie sorcerer surrounded by mystic runes.Vampire: Depict a vampire in a dark cape, fangs showing, lurking in a misty alley.Zombie: Bring to life an undead figure rising from the grave.Mummy: Picture an ancient mummy unraveling in an Egyptian crypt.Werewolf: A werewolf howling under a full moon, its fur illuminated by moonlight.Skeleton: Visualize a dancing skeleton, clattering bones illuminated by candlelight.2. Haunted & Spooky LocationsHaunted House: An old, decaying Victorian house with broken windows and eerie shadows.Graveyard: Rows of crooked tombstones, shrouded in fog.Crypt: A dark crypt with flickering candlelight, home to mysteries.Dark Forest: A forest with towering, twisted trees, shrouded in mist.Ghost Town: An abandoned western-style town with creaky wooden doors, haunted by spirits.3. Iconic Halloween SymbolsJack-o'-Lantern: A glowing pumpkin carved with an evil grin, placed on a doorstep.Scarecrow: A scarecrow standing guard in a dark cornfield, silhouetted by the setting sun.Cauldron: A bubbling cauldron full of strange potions, surrounded by spellbooks and glowing crystals.Grim Reaper: A shadowy figure cloaked in black, holding a scythe in the midst of a graveyard.4. Spooky AnimalsBlack Cat: A black cat with piercing green eyes, walking along a haunted fence.Bat: Swarms of bats emerging from a cave, silhouetted against the twilight sky.Owl: A spooky owl with glowing eyes perched in a dark, twisted tree.Spider & Cobweb: A large, hairy spider crawling across a dusty cobweb in an old attic.5. Atmospheric EffectsFog & Mist: Thick fog rolling through a moonlit cemetery.Full Moon: A full moon casting eerie light over a spooky landscape.Shadows: Deep shadows stretching along a hallway, creating a sense of impending doom.Candlelight: Flickering candles illuminating a haunted crypt.6. Magic, Spells & PotionsSpell: A witch casting a glowing spell with intricate hand movements.Magic Book: An ancient spellbook open on a table, its pages filled with strange symbols.Curse: A dark aura surrounding a character as they invoke a powerful curse.7. Frightening Feelings & AestheticsCreepy: A narrow corridor lined with old portraits that seem to be watching.Chilling: Cold, ghostly hands reaching out from a mirror.Sinister: A sinister smile carved onto a jack-o'-lantern, surrounded by darkness.Ominous: The shadow of an unknown figure lurking at the edge of the forest.8. Monsters & BeastsGoblin: A mischievous goblin with glowing red eyes, hiding in the shadows.Demon: A fiery demon emerging from a swirling portal.Ghoul: A ghoul feasting in an eerie underground tunnel.Specter & Phantom: A translucent specter drifting through an abandoned hall.9. Elements of HorrorBlood: Drops of blood leading down a dark staircase.Mask: A creepy, cracked mask lying abandoned on the forest floor.Claws & Fangs: Close-up of sharp claws or fangs glinting in dim light.Nightmare: A scene that feels like a nightmare, filled with unsettling imagery and twisted shapes.10. Halloween ActivitiesTrick-or-Treat: Children in spooky costumes going door to door under the watchful eye of a full moon.Costume: A masquerade ball where all participants are dressed as classic Halloween monsters.Lantern Parade: A procession of jack-o'-lanterns lighting a dark, wooded path.Tips for Crafting Effective AI Image Prompts:Be Specific: The more specific your prompt, the more likely the AI will produce the result you desire. Instead of just saying "witch," say "a witch standing in front of a bubbling cauldron under a full moon."Mix and Match Themes: Combine multiple elements for unique results, such as "a vampire in a haunted graveyard surrounded by bats" or "a werewolf howling near an abandoned scarecrow."Incorporate Atmosphere: Descriptive words like "eerie," "sinister," and "chilling" can help set the mood of the image, making it more evocative.Use Action Words: Include verbs to create movement and drama, such as "howling," "casting," "emerging," or "lurking." These words make the scene more dynamic.ConclusionWith these prompts and ideas, you'll have everything you need to create an endless variety of spooky, eerie, and downright frightening AI-generated images perfect for Halloween. Whether you're aiming to create chilling ghostly landscapes or sinister creatures of the night, this list will help fuel your imagination and guide your creative process. Unleash your spooky side, and let the AI help you manifest a truly haunted Halloween experience!Happy image generating, and may your Halloween be both creative and hauntingly delightful!

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👻🎃Gathering a Dataset for a Halloween-Themed LoRA: A Comprehensive Guide for beginners🎃👻Creating a Low-Rank Adaptation (LoRA) model with a Halloween theme requires a carefully curated dataset that reflects the festive spirit, symbols, and narratives associated with Halloween. This article provides an in-depth guide on how to gather and prepare such a dataset effectively.🕯Step 1: Defining the Scope of Your DatasetBefore diving into data collection, it’s essential to define the scope of your dataset. Consider the following aspects:1. Theme and Content: Determine what specific elements of Halloween you want to focus on. Common themes include: - Traditional symbols (pumpkins, ghosts, witches) - Halloween costumes and decorations - Halloween stories, poems, and folklore - Recipes for Halloween-themed food - Activities and games related to Halloween2. Intended Use: Clarify how you plan to use the LoRA model. Will it generate creative content, classify images, or enhance existing narratives? This will influence the type of data you need.3. Target Audience: Understand who will be using your model. Tailoring your dataset to your audience (children, adults, horror enthusiasts) can help ensure relevance.👻Step 2: Identifying Data SourcesOnce you’ve defined your scope, identify potential data sources. Here are some ideas:1. Image Repositories: - Stock photo websites (e.g., Unsplash, Pexels) for high-quality Halloween-themed images. - Art platforms (e.g., DeviantArt, ArtStation) to find illustrations and artwork.2. User-Generated Content: - Social media platforms, especially Instagram and Pinterest, where users share their Halloween decorations, costumes, and celebrations. - Flickr and other photo-sharing sites where Halloween-themed albums can be found.3. Creative Commons: - Search for images under Creative Commons licenses that allow modification and use for research or training.4. YouTube: - Look for Halloween-themed videos that capture activities, recipes, or storytelling. Ensure to check usage rights.🎃Step 3: Data Collection Techniques1. Image Downloads: - Download images directly from stock photo sites or user-generated content platforms, ensuring you adhere to their usage policies.2. Batch Downloading: - Use tools like Google Images or specialized scrapers to batch download images based on specific search terms (e.g., "Halloween decorations").3. Image Generation: - Generating images using stable diffusion models is a perfectly fine method of gathering a dataset, in fact, it can be much easier and controllable. I myself have employed this method multiple times, from my very first LoRA over a year and a half ago, to my latest iteration of 🕯The Marionettist's Workshop🕯 retrained on FLUX, but was also originally a generated dataset.💀Step 4: Cleaning and Preparing the DatasetOnce you've gathered the raw data, it's time to clean and prepare it:1. Image Processing: - Resize or crop images to a consistent size, optimize for quality, and remove any non-Halloween images inadvertently collected.2. Categorization: - Organize images into folders based on sub-themes (e.g., costumes, pumpkins, haunted houses) for easier processing later.3. Dataset Structuring: - Organize the dataset into a corresponding file format (e.g., .txt files for captions/tags, .Jpeg for images) that aligns with your training requirements.🕸Step 5: Ethical Considerations and Licensing1. Copyright Compliance: Ensure that you have the right to use the content you gather. Use resources that are in the public domain or under appropriate licenses.2. Attribution: Give credit to original creators where necessary, especially for images and texts that require it.3. Sensitive Content: Be mindful of potentially sensitive or offensive material that may arise in the context of Halloween, ensuring your dataset is appropriate for your intended audience.⚰Step 6: Testing and IterationAfter gathering and preparing your dataset, and training your LoRA, test it by running preliminary training sessions. Monitor the output to assess the dataset's effectiveness in generating relevant and engaging Halloween-themed content. Based on your findings, you may need to refine the dataset further, adding new data or adjusting existing entries.🕯Conclusion🕯Gathering a Halloween-themed dataset for a LoRA model involves careful planning and execution. By defining your scope, identifying diverse data sources, and following systematic collection and preparation steps, you can create a rich and varied dataset. This will not only enhance the performance of your model but also ensure it resonates with the festive spirit of Halloween. With a well-curated dataset, your LoRA will be equipped to generate creative and engaging content that captures the essence of this beloved holiday.We all start somewhere on our digital art journey, and getting the simple things right from the start, is starting right!Have a happy Halloween, Tensorian tricksters.Love & digital kissesApolonia💋

Complete Tutorial: Scraping Image Captions from Tensor.ArtThe goal of this tutorial is to automatically grab all the captions from your image dataset on Tensor.Art and save them into individual .txt files for each image, ready to be used for LoRA training.This process is divided into two main parts:Part 1: Extracting all unique captions from the web page into a single text file using JavaScript.Part 2: Splitting that single text file into many separate .txt files using PowerShell.Part 1: Extracting All Captions from the WebsiteIn this section, we will copy all unique captions from the web page to your clipboard.Step 1: Prepare the Web PageOpen your Chrome browser and navigate to your Tensor.Art dataset page containing the images.CRUCIAL STEP: Slowly scroll down the page until ALL of the images in your dataset (e.g., all 63 images) have appeared and loaded on the screen. If you don't do this, the script will only capture captions from the visible images.Step 2: Open the Developer Tools ConsoleOnce all images are loaded, press the F12 key on your keyboard to open the Developer Tools.In the Developer Tools window that appears, click on the "Console" tab.Step 3: Run the JavaScript ScriptCopy the entire code block below:// 1. Grab ALL <p> elements inside the caption divs. const allCaptionPTags = document.querySelectorAll('.train-model-assets-image-tags p'); // 2. Create an empty array to hold the texts. let duplicatedCaptionsList = []; // 3. Loop through each element, CLEAN the text, then add it to the list. allCaptionPTags.forEach(pTag => { // Get the raw text const rawText = pTag.innerText; // CLEAN THE TEXT: Replace all sequences of whitespace with a single space, // and then remove leading/trailing spaces. const cleanedText = rawText.replace(/\s+/g, ' ').trim(); // Push the cleaned text into the list. duplicatedCaptionsList.push(cleanedText); }); // 4. Create a 'Set' from the list of cleaned text to automatically remove duplicates. const uniqueCaptions = [...new Set(duplicatedCaptionsList)]; // 5. Join the unique captions into one large text block, separated by new lines. const finalText = uniqueCaptions.join('\n'); // 6. Copy the result directly to the clipboard. copy(finalText); // 7. Display a confirmation message with the correct count. console.log(`Total cleanup successful! Exactly ${uniqueCaptions.length} unique captions have been copied to your clipboard.`);Return to the Console window in your browser, then paste the code.Press Enter.You will see a confirmation message in the console stating the number of unique captions that were successfully copied, for example: Total cleanup successful! Exactly 63 unique captions have been copied to your clipboard.Step 4: Save the Results to a Text FileCreate a new folder on your computer to store your dataset. For example: D:\LoraTraining.Open the Notepad application.Press Ctrl + V to paste all the copied captions.Click File > Save As....Navigate to the folder you just created (e.g., D:\LoraTraining).Save the file with the name e.g., caption.txt.You now have a single file containing all unique captions, each on a new line.Part 2: Splitting the caption.txt File into Individual FilesIn this section, we will use PowerShell (a built-in tool in Windows) to automatically create one .txt file for each line of text in caption.txt.Step 1: Open PowerShell in the Working FolderOpen the folder where you saved caption.txt (e.g., D:\LoraTraining).Inside the folder (not on a file), hold down the Shift key on your keyboard and right-click on an empty space.Select the "Open PowerShell window here" or "Open in Terminal" option from the context menu.Step 2: Run the PowerShell ScriptA blue (PowerShell) or black (Terminal) window will appear. Copy the entire code block below:# 1. Define the input file name and the output file format $inputFile = "caption.txt" # Customize with your file name. $outputPrefix = "image" # The result will be image_1.txt, image_2.txt, etc. # 2. Read all lines from the caption.txt file $captions = Get-Content $inputFile # 3. Create a counter $i = 1 # 4. Loop through each caption line foreach ($line in $captions) { # Make sure the line is not empty if ($line.Trim() -ne "") { # Create the new file name, e.g., image_1.txt $outputFile = "${outputPrefix}_${i}.txt" # Write the line's content to the new file Set-Content -Path $outputFile -Value $line # Increment the counter $i++ } } # 5. Display a completion message Write-Host "Done! Successfully created $($i-1) .txt files." Paste the code into the PowerShell window.Press Enter.Step 3: Verify the ResultInstantly, your D:\LoraTraining folder will be populated with many new files: image_1.txt, image_2.txt, image_3.txt, ..., all the way to image_63.txt. Each of these files contains its corresponding single-line caption.

“Replace the girl’s current outfit with the provided swimsuit, remove the previous outfit completely. Keep her exact pose, facial features, body shape, hairstyle, expression, and background unchanged. The swimsuit should fit naturally, following the body’s curves, lighting, and shadows for realism. Do not alter skin tone, proportions, or environment — only change the clothing to the swimsuit reference.”

ByteDance's Seed team has recently released the video generation model Seedance 1.0 Pro, which is causing a technological revolution in the global AI video field. This model can generate a 5-second 1080P high-definition video within 41.4 seconds. Its performance has surpassed strong competitors such as Google Veo 3 and OpenAI Sora in international authoritative evaluations, making it the champion in both text-to-video and image-to-video tracks. Technical BreakthroughSeedance employs a diffusion Transformer model that decouples the spatial and temporal layers, functioning like a well-rehearsed symphony orchestra where each module works in perfect harmony. Its composite reward system, featuring three specialized "judges": the basic reward, motion reward, and aesthetic reward models, ensures that the generated videos are stable, realistic, and artistic. Performance AdvantagesIn complex scenarios such as "a detective entering a dim room to check for clues", Seedance can precisely generate narrative videos with multiple camera cuts. Its accuracy rate for motion blur and collision detection exceeds 95%, while reducing the cost of generating a 5-second 1080P video to an industry low. Application DeploymentThe Wavespeed platform has been the first to deploy Seedance, offering APIs for direct user invocation. Short video creators can generate content in bulk at low cost, film and television professionals can quickly visualize shot-by-shot scripts, and educational institutions can dynamically demonstrate abstract concepts. This technology is reshaping the video content production chain. Hong Dingkun, vice president of technology at ByteDance, said that Seedance will work together with the DouBao series of models to build an intelligent agent ecosystem. Although the current single-generation duration is limited to 5 seconds, the team is developing a cross-shot character consistency control algorithm to pave the way for long video generation. With the opening of Seedance, a wave of democratization in video creation is coming - from professional film and television studios to street Vloggers, all can obtain production tools comparable to those of Hollywood at the price of a cup of coffee.

LoRA Bento (Beta): A calmer way to prep datasets and train LoRAs in single appAfter training LoRAs for a long time—characters, styles, tiny experiments that surprised me, and plenty that didn’t—I kept running into the same “not-hard-but-annoying” problems.Not the GPU.Not the optimizer.Not even the hyperparameters.The real pain was everything around training: organizing datasets, cleaning bad images, tracking augmentations, keeping captions consistent, and exporting something that actually works.LoRA Bento is my attempt to make that whole pipeline visible, predictable, and local-first—so you can trust what you’re training before you press Train.Beta note: this is a beta release, so expect some small, occasional bugs and rough edges.Platform support: Windows = fully supported, Linux = not supported, macOS = supports data cleaning (import + review + delete/cleanup workflows).What LoRA Bento helps you avoidTraining LoRA often looks like this:Collect images from random foldersRename or fix naming laterRealize duplicates are hurting results (too late)Notice blurry/low-quality images after hours of trainingAugment data… then overwrite something by accidentResize & pad… then re-check againCaption images… then wonder:“Did I caption everything?”“Did the augmented images get captions too?”Export a dataset… and find out it doesn’t match what your trainer expectsEach step is manageable on its own, but together they create friction, mistakes, and “silent failures” you don’t catch until the end.LoRA Bento exists to reduce that chaos.* image in demo just data from model output💗✨ What you get with LoRA Bento ✨💗💾 A dataset structure you can trustEverything is stored in a clear, predictable project layout, so you’re not guessing where files went—ever. Projects are local-first and easy to back up.🧹 Clean data before it hurts trainingLoRA Bento detects duplicate / near-duplicate images and blurry images early, so you can remove them before they poison your dataset.✂️ Crop (optional) to focus on the subjectCropping is optional, but useful when you want the model to focus on the character/subject rather than background noise. Cropped versions are preferred downstream, helping keep the training signal more consistent.Crop with a simple editorKeep original images intactCrops stay connected to the source imageAUTO CROP experimental🔁 Safer augmentation without losing trackAugmentations are handled in a way that stays repeatable and trackable. You can re-run without the “what did I overwrite?” anxiety, and variants stay connected to the originals.🖼️ Consistent training-ready images (with previews)Resize & pad your dataset in a controlled way, with previews before committing the result—so your training folder ends up clean and consistent.🏷️ Auto-tagging designed for LoRA workflowsAuto-tagging is built for LoRA caption files (comma-separated tags), with clear visibility into what the model will learn—and the ability to edit tags when needed.📦 Export datasets that actually travelExport your prepared dataset as a single ZIP so you can move it between machines, trainers, or storage without breaking the structure.Ready to use with sd_script data stucture !🚀 Train locally without “UI chaos”available for window only (experiment stage)LoRA Bento keeps the whole workflow in one place—from import to training—so you don’t need to juggle multiple apps and windows just to run a single experiment.Beta release: what to expectThis is a beta, which means:You may run into small UI bugs or edge casesSome workflows may evolve quickly based on feedbackThe goal is to get the core pipeline stable and comfortable to usePlatform support (Beta)✅ Windows: full support (recommended)❌ Linux: not supported🟡 macOS: supports data cleaning workflows (import + review + delete/cleanup)Where to downloadUse the master branch releases. Current release: v1.0.2-fix-dependency.https://github.com/GockSo/LoRA-Bento/releases/tag/v1.0.4-fix-feature-train-localor use branch masterTerms of useLoRA Bento is free to use while it’s being developed, but (for now) it’s not allowed for commercial use. This is a deliberate choice while the project is still stabilizing and community-driven.Feedback welcomeIf you’ve ever felt like LoRA training is “hard for the wrong reasons,” I’d love to hear what part of the pipeline causes you the most pain—so we can keep polishing the right things first.If you got some bug or issure can open issure on github firstComming soon featureFix some bug and hardly to use in UXMobile UI support : stay on bed find dataset , clean data and train on bedAuth module : can forward port and control app anywhere security with auth moduleTooltip Guideline for beginerTest generate image white LoRA

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Groundhog Day may be a quirky tradition, but it’s the perfect excuse to tell some pun-filled jokes! Every year, on February 2nd, we wait for the groundhog to pop out of its burrow and predict the weather for the next six weeks. But, while we wait for the shadow to appear, we can also enjoy some laughs with these hilarious Groundhog Day jokes! So, let’s put aside the weather forecasts and dive into these chuckles that are sure to bring sunshine to even the cloudiest of days.🦊 Why Are Groundhog Day Jokes So Fun?Pun Galore – Groundhog Day jokes are filled with playful wordplay that will have you giggling in no time!Relatable Tradition – Since so many people know about the groundhog’s big day, these jokes are easy to share and connect over.Whimsical & Silly – Groundhog Day is a lighthearted holiday, and these jokes capture that fun spirit perfectly.Perfect for Weather Talk – If you’re tired of the usual weather chat, these jokes will help add some extra humor to the conversation!🦊 Groundhog Day Jokes to Bring on the Giggles1. Pun-Filled Groundhog Jokes👉 Why don’t groundhogs ever get lost?💬 Because they always know how to dig their way out of any situation!👉 What’s a groundhog’s favorite type of music?💬 Burrow rock!👉 What do groundhogs do on Groundhog Day?💬 They dig into the festivities!👉 Why did the groundhog bring a shovel to the party?💬 Because he wanted to dig into the cake!👉 What did the groundhog say when it was asked to predict the weather?💬 “I’m just here to shadow you!”2. Funny Groundhog Day Predictions👉 Why do groundhogs hate winter?💬 Because they always get buried under the snow!👉 What did the groundhog say when he saw his shadow?💬 “Looks like I’ll be working from home for six more weeks!”👉 Why do groundhogs make terrible weathermen?💬 Because they’re always underground and can’t see the forecast!👉 How does a groundhog know the weather’s changing?💬 He can feel the ground moving beneath his feet!👉 Why did the groundhog wear sunglasses on Groundhog Day?💬 To avoid being blinded by his own shadow!3. Silly Groundhog-Related Questions👉 What’s a groundhog’s favorite type of exercise?💬 Burrowing squats!👉 Why don’t groundhogs like to tell jokes?💬 Because they can’t stand the shadow of a punchline!👉 What did the groundhog order at the café?💬 A muddy mocha!👉 Why did the groundhog start a blog?💬 Because he wanted to share his underground thoughts!👉 Why do groundhogs avoid social media?💬 Because they’re afraid of too many followers!4. Groundhog and Weather Jokes👉 What do you call a groundhog who loves the sunshine?💬 A shadow chaser!👉 Why was the groundhog so good at predicting the weather?💬 Because he had a six-week forecast on the horizon!👉 What do groundhogs say when they predict six more weeks of winter?💬 “Bundle up and have a good one!”👉 Why did the groundhog wear a scarf?💬 Because he wasn’t digging the cold!👉 What’s a groundhog’s least favorite season?💬 Spring – because it means he has to come out of hiding!5. Groundhog Day Humor for Kids👉 Why did the groundhog get in trouble at school?💬 Because he kept digging through everyone’s homework!👉 What did the groundhog say to his friend after predicting the weather?💬 “That’s one shadowy prediction!”👉 How do groundhogs send messages to each other?💬 Through burrow mail!👉 What did the groundhog say to the squirrel?💬 “You dig winter, I dig spring!”👉 What’s a groundhog’s favorite snack?💬 Mud pie!❓ FAQs About Groundhog Day Jokes1. Are Groundhog Day jokes only for February 2nd?Not at all! While these jokes are great for Groundhog Day, they can be fun anytime you’re talking about weather or shadows. You can tell them at any time of year for a good laugh!2. Can I tell these jokes to kids?Absolutely! These jokes are perfect for kids. They’re simple, funny, and playful, making them ideal for sharing with little ones.3. Why are these jokes so focused on shadows?That’s because on Groundhog Day, the groundhog’s shadow is key to whether there will be more winter or an early spring. It’s the perfect setup for lots of punny jokes!4. Can I use these jokes for a Groundhog Day party?Yes! These jokes are perfect for a Groundhog Day-themed party. You can sprinkle them throughout the day to keep everyone laughing while waiting for the groundhog’s big prediction.🦊 Conclusion: Shading in Some LaughterGroundhog Day is all about fun, quirky traditions—and these jokes are the perfect way to keep the good times rolling! Whether you’re watching the groundhog’s prediction or just looking for some lighthearted humor, these jokes are sure to bring some sunshine to your day. So next time you see a shadow, remember, there’s always room for a little more laughter!Now, go ahead and tell these jokes to your friends and family—they’re guaranteed to make everyone dig the humor! 🦊😄