kitakaze

A list of LoRA I'm currently working on or would like to work on—no particular order. It might take a year, maybe two? Anyway, it's just a reminder to myself not to forget or get too lazy. If you happen to see one you're interested in, please don't get your hopes up—truth is, I really don't have much free time... (T_T)Also, I’m calling these the doujin versions because I’ve chosen to sacrifice fidelity in order to improve usability and reduce the chance of errors. The LoRA will only include character information—there won't be original outfits, accessories, or even things like band-aids. Since anime adaptations often mess with the original designs, the general rule is: if there's a manga, it takes priority; if not, then novel illustrations. Anime is used only as a color reference.The coloring and redrawing of black and white comics requires a lot of time to select and redo, and if necessary, some additional painting styles need to be added to assist.The more steps involved, the more the similarity may drop. But through repeated refinement, I can strip out almost 100% of the unnecessary info, so that only the simplest prompt words are needed when using it, and no additional messy quality prompt words and negative prompt words are used to give full play to the performance of the checkpoint model. It'll also be easier to combine with other effect or art-style LoRA.*******， & *** – that's a secret.轟駆流 & 軍司壮太宿海仁太 & 本間聡志Mikami riku & hidaka yukioTakaki & Aston, ride & mikazuki星合の空 – arashi, maki, toma, yuta, tsubasa, shingo, shinjirou****，**、、 – another secretTenkai knights – toxsa, chooki, guren, ceylanMotomiya daisuke – da02 moviecomplete 急襲戦隊ダンジジャー – kosuke, midori, kouji刀剣乱舞 – aizen & atsushiShinra, Shou, Arthur銀河へキックオフ!! – shou, aoto, tagi, ouzou, kotashinkalion – tsuranuki, hayato, ryuji, tatsumi, gin, jou, ryota, taisei, ten****， & **** – another secret, and with only the manga, it feels super difficultVanguard – kamui & kuronoBlack☆star & soulSubaru & GarfielShirou & kogiruアライブ - 最終進化的少年 - Alive: The Final Evolution – 叶太輔 & 瀧沢勇太touma & tsuchimikadoyuta & yomogiEtc.

https://tensor.art/articles/868883505357024765 ( 1 )https://tensor.art/articles/868883998204559176 ( 2 )https://tensor.art/articles/868884792773445944 ( 3 )https://tensor.art/articles/868885754846123117 ( 4 )Extract the character from the image and place them onto a true white background. You might lose a bit of original coloring or brushstroke texture, but compared to the convenience it brings, that’s a minor issue.But don’t be too naive—things like expression, pose, clothing, and camera angle still need to be described properly. Doing so helps the AI learn accurate depth of field, which in turn helps it learn the correct body proportions. After that, even if you don’t include camera-related prompts when using the LoRA, it’ll still consistently output correct body shapes.A lot of people use cutout characters for their training data, but their tags miss things like camera info. So you might get a buff adult under the “upper body” prompt, and a toddler under “full body.”By now, you should have a solid understanding of how to prepare your training dataset. 5. Parameter Settings This part is quite abstract and highly variable. There are already many tutorials, articles, and videos online that go into detail about training parameters and their effects. Based on those resources, I arranged several parameters and ran exhaustive tests on them. Since I’m not particularly bright and tend to approach things a bit clumsily, brute-force testing has always been the most effective method for me. However, given the limits of my personal time and energy, my sample size is still too small to really compare the pros and cons of different parameter sets. That said, one thing is certain: do not use any derivative checkpoints as your base model. Stick with foundational models like Illustrious or Noobai. Using a derived checkpoint will make Lora work only in this one checkpoint. Another helpful trick for learning from others is that when you deploy LoRA locally, you can directly view the training metadata within SD or WebUI. I’ll also include the main training parameters in the descriptions of any LoRAs I upload in the future for reference. In the following section, I’ll use the long and drawn-out process of creating the LoRA for Ragun Kyoudai as an example, and give a simple explanation of what I learned through it. But before we get into that case study, let’s quickly summarize the basic LoRA training workflow:The real first is always your passion.2. Prepare your dataset.3. Tag your dataset thoroughly and accurately.4. Set your training parameters and begin training. Wait—and enjoy the surprise you’ll get at the end. As mentioned earlier, the first LoRA I made for Ragun Kyoudai was somewhat disappointing. The generated images had blurry eyes and distorted bodies. I chalked it up to poor dataset quality—after all, the anatomy and details in the original artwork weren’t realistic to begin with. I thought it was a lost cause. I searched through all kinds of LoRA training tutorials, tips, articles, and videos in hopes of salvaging it. And surprisingly, I stumbled upon something that felt like a breakthrough: it turns out you can train a LoRA using just a single image of the character. The method is pretty simple. Use one clear image of the character’s face, and then include several images of unrelated full body figures. Use the same trigger words across all of them, describing shared features like hair color, eye color, and so on. Then adjust the repeat values so that the face image and the body images get the same total weight during training. When you use this LoRA, you just need to trigger it with the facial feature tags from the original image, and it swaps in a consistent body from the other images. The resemblance outside the face isn’t great, but it dramatically reduces distortion. This inspired me—what if the “swapped-in” body could actually come from the original character, especially when working with manga? That way, I could use this method to supplement missing information. I went back through the manga and pulled references of side profiles, back views, full body shots, and various camera angles that weren’t available in the color illustrations. I tagged these grayscale images carefully using tags like greyscale, monochrome, comic, halftone, etc., to make sure the AI learned only the body shape, hairstyle, and other physical features, without picking up unwanted stylistic elements. This approach did help. But problems still lingered—blurry eyes, malformed hands, and so on. So I pushed the idea further: I used the trained LoRA to generate high-quality character portraits using detail-focused LoRAs, specific checkpoints, and adetailer. These results then became new training data. In parallel, I used other checkpoints to generate bodies alone, adjusting prompt weights like shota:0.8, toned:0.5, to guide the results closer to the target physique or my own expectations. The idea was that the AI could “fit” these new generated samples to the rest of the dataset during training. And it worked. This is how the Lagoon Engine Beta version came to be. At this point, I could completely ditch the low-resolution color and manga images from the training dataset and just use AI-generated images. I used prompts like simple background + white background to create portrait and upper body images with only the character. To avoid having blurry eyes and inconsistent facial features in full body shots, I used the faceless tag or even manually painted over the heads to prevent the AI from learning them—allowing it to focus solely on body proportions. That said, white background tends to be too bright and can wash out details, while darker backgrounds can cause excessive contrast or artifacts around the character edges. The most effective backgrounds, in my experience, are grey or pink. During this time, I also experimented with making a LoRA using just one single character portrait—again from Lagoon Engine. It was just one full color image with a clear, unobstructed view. And when I applied the same method and added new characters to create a LoRA with four characters, I hit a wall. The characters started blending together—something I’d never encountered before. With En & Jin, mixing was incredibly rare and negligible, but with four characters, it became a real problem.I adjusted parameters based on references from other multi-character LoRAs, but nothing worked. I’m still testing—trying to find out if the problem is with parameters, the need for group images, or specific prompt settings. Although the four-character LoRA was a failure, one great takeaway was this: black-and-white manga can be used to make LoRAs. With current AI redrawing tools, you can generate training data using AI itself.Example： Compared to LoRAs based on rich animation materials, using black-and-white manga is much more difficult and time-consuming. But since it’s viable, even the most obscure series have a shot at making a comeback. To summarize, creating multiple LoRAs for the same target is a process of progressive refinement, like crafting a drink from a shot of espresso. The first dataset with detailed tags is your espresso—it can be consumed as-is or mixed however you like. This method also works surprisingly well when creating LoRAs for original characters (OCs). Since OCs can have more complex features, you can start by generating a base image using a fixed seed with just hair/eye color and style. Train a first LoRA on that, then gradually add more features like dyed highlights or complex hairstyles during image generation. If the added features aren’t stable, remove some, and train another LoRA. Repeat this layering process until your OC’s full complexity is captured. This approach is far more stable than trying to generate all features in one go, even with a fixed seed, due to randomness, it’s hard to maintain complex character traits across different angles without breaking consistency. One more note: regarding character blending when using multiple LoRAs—there seems to be no foolproof way to prevent it. Even adding regularization sets during training doesn’t completely avoid it. As of now, the lowest error rate I’ve seen is when using characters from the same series, trained with the same parameters, by the same author, and ideally in the same training batch. And with that, we’ve reached the end—for now. I’ll continue to share new insights as I gain more experience. See you next time~

https://tensor.art/articles/868883505357024765 ( 1 )https://tensor.art/articles/868883998204559176 ( 2 )https://tensor.art/articles/868884792773445944 ( 3 )https://tensor.art/articles/868890182957418586 ( 5 )When it comes to training LoRAs, trying to fix all the bugs at the source is seriously exhausting. Unless you're doing LoRA training full-time, who really has the time and energy to spend so much of their free time on just one LoRA? Even if you are full-time, chances are that you'd still prioritize efficiency over perfection. And even after going through all the trouble to eliminate those bugs, the result might only be improving the “purity” from 60% to 80%—just a guess. After all, AI is still a game of randomness. The final training parameters, repeats, epochs, learning rate, optimizer, and so on will all influence the outcome. You’ll never “purify” it to 100%. And really, even 60% can already be impressive enough. So—worth it? My personal take: absolutely. If a certain character—or your OC—is someone your favorite since childhood, someone who’s part of your emotional support, someone who represents a small dream in your life, then why not? They’ll always be worth it.I’ve only made a handful of LoRAs so far, each with a bit of thought and some controlled variables. I’ve never repeated the same workflow, and each result more or less met the expectations I had at the beginning. Still, the sample size is way too small. I don’t think my experiences are close to being truly reliable yet. If you notice anything wrong, please don’t hesitate to point it out—thank you so much. And if you think there’s value in these thoughts, why not give it a try yourself?Oh, right—another disclaimer: due to the limitations of my PC setup, I have no idea what effect larger parameter values would have. All of this is based on training character LoRAs using the Illustrious model.Also, a very important note: this is not a LoRA training tutorial or a definitive guide. If you’ve never made a LoRA yourself but are interested in doing so, try searching around online and go ahead and make your first one. The quality doesn’t matter; just get familiar with the process and experience firsthand the mix of joy and frustration it brings. That said, I’ll still try to lay out the logic clearly and help you get a sense of the steps involved.0. Prepare your training set. This usually comes from anime screenshots or other material of the character you love. A lot of tutorials treat this as the most crucial step, but I won’t go into it here—you’ll understand why after reading the rest.1. Get the tools ready. You’ll need a computer, and you’ll need to download a local LoRA trainer or a tagging tool of some kind. Tools like Tensor can sometimes have unstable network connections, but they’re very convenient. If your internet is reliable, feel free to use Tensor; otherwise, I recommend doing everything on your PC.2. If you’ve never written prompts using Danbooru-style tags before, go read the tag wiki on Danbooru. Get familiar with the categories, what each one means, and look at the images they link to. This is super important—you’ll need to use those tags accurately on your training images.3. Do the auto-tagging. These tagging tools will detect the elements in your image and generate tags for them. On Tensor, just use the default model wd-v1-4-vit-tagger-v2—it’s fine, since Tensor doesn’t support many models anyway, and you can’t adjust the threshold. On PC, you can experiment with different tagger models. Try setting the threshold to 0.10 to make the tags as detailed as possible. You can adjust it based on your own needs.4. Now comes the most critical step—the one that takes up 99% of the entire training workload.After tagging is complete, fix your eyes on the first image in your dataset. Just how many different elements are in this image? Just like how the order of prompts affects output during image generation, prompts during training follow a similar rule. So don’t enable the “shuffle tokens” parameter. Put the most important tokens first—like the character’s name and “1boy.”For the character’s traits, I suggest including only two. Eye color is one of them. Avoid using obscure color names; simple ones like “red” or “blue” are more than enough. You don’t need to describe the hairstyle or hair color in detail—delete all automatically generated hair-related tags. Of course, double-check the eye color too. Sometimes it tags multiple colors like “red” and “orange” together—make sure to delete the extra ones.When it comes to hair, my experience is: if the color is complex, just write the hairstyle (e.g., “short hair”); if the hairstyle is complex, just write the color. Actually, if the training is done properly, you don’t even need to include those—just the character name is enough. But in case you use this LoRA with others that have potential for overfitting, it’s a safety measure to include them.Any tags about things like teeth, tattoos, etc., should be completely removed. If they show up in the auto-tags, delete them. The same goes for tags describing age or body type, such as “muscular,” “toned,” “young,” “child male,” “dark-skinned male,” etc. And if there are nude images in your dataset, and you think the body type looks good and you want future generations to match that body type, do not include tags like “abs” or “pectorals.”You may have realized by now—it’s precisely because those tags weren’t removed that they got explicitly flagged, and so the AI treats them as interchangeable. That’s why you might see the body shape, age, or proportions vary wildly in outputs. Sometimes the figure looks like a sheet of paper. That’s because you had “abs” and “pectorals” in your tags and didn’t realize those became part of the trigger prompts.If you don’t take the initiative to remove or add certain tags, you won’t know which ones have high enough weight to act as triggers. They’ll all blend into the chaos. If you don’t call them, they won’t appear. But if you do—even unintentionally—they’ll show up, and it might just bring total chaos.Once you’re done with all that, your character’s description should include only eye color and hair.For the character name used as a trigger word, don’t format it like Danbooru or e621. That’s because Illustrious and Noobai models already recognize a lot of characters. If your base model already knows your character, a repeated or overly formal name will only confuse it. What nickname do you usually use when referring to the character? Just go with that.See how tedious this process is, even just for tags setup? It’s far more complex than just automatically tagging everything, batch-adding names, and picking out high-frequency tags.Remember the task at the start of this section? To identify all the elements in the first image. You’ve now covered the character features. Now let’s talk about the clothing.Let’s say the boy in the image is wearing a white hoodie with blue sleeves, a tiger graphic on the front, and a chest pocket. Now you face a decision: do you want him to always wear this exact outfit, or do you want him to have a new outfit every day?Auto-tagging tools don’t always fully tag the clothing. If you want him to wear different clothes all the time, then break down this outfit and tag each part accordingly using Danbooru-style tags. But if you want him to always wear the same thing, just use a single tag like “white hoodie,” or even give the outfit a custom name.There’s more to say about clothing, but I’ll save that for the section about OCs. I already feel like this part is too long-winded, but it’s so tightly connected and info-heavy that I don’t know how to express it all clearly without rambling a bit.Next, observe the character’s expression and pose. Use Danbooru-style tags to describe them clearly. I won’t repeat this later. Just remember—tags should align with Danbooru as closely as possible. Eye direction, facial expression, hand position, arm movement, leaning forward or backward, the angle of knees and legs—is the character running, fighting, lying down, sitting, etc.? Describe every detail you can.Now, observe the background. Sky, interiors, buildings, trees—there’s a lot. Even a single wall, or objects on the wall, or the floor material indoors, or items on the floor—or what the character is holding. As mentioned earlier, if you don’t tag these things explicitly, they’re likely to show up alongside any chaotic high-weight tags you forgot to remove, suddenly appearing out of the ether.Are there other characters in the scene? If so, explain them clearly using the same process. But I recommend avoiding images like this altogether. Many LoRA datasets include them—for example, a girl standing next to the boy, or a mecha, or a robot. You need to “disassemble” these extra elements. Otherwise, they’ll linger like ghosts, randomly interfering with your generations.Also, when tagging anime screenshots, the tool often adds “white background” by default—so this becomes one of the most common carriers of chaos.At this point, you might already be feeling frustrated. The good news is that there are plenty of tools now that support automatic background removal—like the latest versions of Photoshop, some ComfyUI workflows, and various online services. These can even isolate just the clothes or other specific objects.

https://tensor.art/articles/868883505357024765 ( 1 )https://tensor.art/articles/868883998204559176 ( 2 )https://tensor.art/articles/868885754846123117 ( 4 )https://tensor.art/articles/868890182957418586 ( 5 )Alright, let’s talk about LoRA—many things in AI image generation really need to be discussed around it. But before that, I suppose it’s time for a bit of preamble again. LoRA, in my view, is the most captivating technology in AI image generation. Those styles—whether they’re imitations or memes. Those characters—one girl in a hundred different outfits, or the body of that boy you’re madly in love with. A large part of the copyright debate surrounding AI actually stems from LoRA, though people who aren’t familiar with AI might not realize this. In reality, it has hurt many people—but it has also captured many hearts. When you suddenly see an image of a boy that no one on any social media platform, in any language, is talking about—don’t you feel a sense of wonder? And when you find out that the image was created with LoRA, doesn’t your heart skip a beat? By the time you’re reading this, my first LoRA for Ragun Kyoudai has already been released. From the moment I had even the slightest thought of making a LoRA, I was determined that they had to be the first—the absolute first. But it wasn’t easy. The full-color illustrations I saved of them as a kid? Gone, thanks to broken hard drives and lost phones. The images you can find online now are barely 200x300 in resolution, and there are painfully few of them. I still remember the composition and poses of every single color illustration from 20 years ago, but in the internet of 2024, they’ve completely disappeared. All I had left were the manga and its covers, CDs, and cards. Could it be done? While searching for LoRA training tutorials and preparing the dataset for training, more and more doubts formed in my mind. Because of the art style, these images didn’t contain accurate anatomical structures. There weren’t multi-angle views—especially not from behind. Compared to datasets sourced from anime, mine felt pitifully incomplete. Still, I nervously gave it a first try. The result was surprising—AI managed to reproduce the facial features of the characters quite well. But it was basically just close-up shots. On the base model used for training, the generated images were completely unrecognizable outside the face. Switching to other derivative models, the characters no longer resembled themselves at all. So was it that AI couldn’t do it? Or was I the one who couldn’t? Or was it simply impossible to create a LoRA with such a flawed dataset? I decided to set it aside for the time being, since with my limited experience, it was hard to make a solid judgment. Later, while generating AI images, I began using LoRAs made by various creators. I wanted to know what differences existed between LoRAs—aside from the characters themselves. I didn’t discover many differences, but I did notice a lot of recurring bugs. That’s when I realized—I’d found a lead. Maybe understanding the causes of these bugs is the key to improving LoRA training. So let’s talk about it: What are these bugs? What do I think causes them? How can we minimize them during image generation? How can we reverse-engineer them to improve LoRA training? Just to clarify—as you know, these experiences are only based on LoRAs of boy characters. Not girls, and not those overly bara-styled characters either. 1. Overexposure2. Feminization3. On the base model used to train the LoRA (e.g., Pony, Illustrious), it doesn’t work properly: prompts struggle to change character poses or expressions; it’s impossible to generate multi-angle images like side or front views; eyes remain blurry even in close-ups; body shapes are deformed; figures become flat like paper; body proportions fluctuate uncontrollably.4. Because of the above, many LoRAs only work on very specific checkpoints.5. Even on various derivative checkpoints, key features like the eyes are still missing; the character doesn’t look right, appears more feminine, character traits come and go; regardless of the clothing prompt used, the original costume features are always present.6. Character blending: when using two character LoRAs, it’s hard to distinguish between them—let alone using more than two.7. Artifacts: most notably, using a white background often results in messy, chaotic backgrounds, strange character silhouettes, and even random monsters from who-knows-where.8. Sweat—and lots of sweat.9. I haven’t thought of the rest yet. I’ll add more as I write. All of these issues stem from one core cause: the training datasets used for LoRAs are almost never manually tagged. Selecting and cropping the images for your dataset may take only 1% of the time spent. Setting the training parameters and clicking “train”? Barely worth mentioning. The remaining 99% of the effort should go into manually tagging each and every image.But in reality, most people only use an auto-tagger to label the images, then bulk-edit them to add the necessary trigger words or delete unnecessary ones. Very few go in and manually fix each tag. Even fewer take the time to add detailed, specific tags to each image.AI will try to identify and learn every element in each image. When certain visual elements aren’t tagged, there’s a chance the AI will associate them with the tagged elements, blending them together.The most severe case of this kind of contamination happens with white backgrounds.You spent so much effort capturing, cropping, cleaning, and processing animation frames or generating OC images. When you finally finish training a LoRA and it works, you’re overjoyed. Those “small bugs” don’t seem to matter.But as you keep using it, they bother you more and more.So you go back and create a larger dataset. You set repeats to 20, raise epochs to 30, hoping the AI will learn the character more thoroughly.But is the result really what you wanted?After pouring in so much effort and time, you might have no choice but to tell yourself, “This is the result I was aiming for.”Yet the overexposure is worse. The feminization is worse. There are more artifacts. The characters resemble themselves even less.Why?Because the untagged elements from the training images become more deeply ingrained in the model through overfitting.So now it makes sense:Why there's always overexposure: modern anime tends to overuse highlights, and your dataset probably lacks any tag information about lighting.Why it's so hard to generate multi-angle shots, and why character sizes fluctuate wildly: because your dataset lacks tags related to camera position and angle.Why the character becomes more feminine: perhaps your tags inadvertently included terms like 1girl or ambiguous gender.Why certain actions or poses can't be generated: because tags describing body movement are missing, and the few that exist are overfitted and rigid.In short:Elements that are tagged get learned as swappable; elements that are untagged get learned as fixed.That may sound counterintuitive or even go against common sense—but it’s the truth.This also explains why, when using two character LoRAs together, they often blend: because tags for traits like eye color, hair color, hairstyle, even tiny details like streaks, bangs, short ponytails, facial scars, shark teeth—all of these are written in detail, and the more detailed the tags, the more they influence each other. Because the AI learns them as swappable—not inherent to the character.And no matter what clothing prompts you use, the same patterns from the original outfit keep showing up—because those patterns were learned under the clothes tag, which the AI considers separate and constant.LoRAs that are overfitted also tend to compete with each other over the same trigger words, fighting for influence.So, from a usage perspective, some of these bugs can be minimized.Things like overexposure, feminization, sweat—if you don’t want them, include them in your negative prompts.For elements like lighting, camera type, and viewing angle—think carefully about your composition, refer to Danbooru-style tags, describe these elements clearly and include them in your positive prompts.Also, make sure to use more effective samplers, as mentioned earlier.Use LoRAs that enhance detail but don’t interfere with style—such as NoobAI-XL Detailer. Hand-fixing LoRAs aren’t always effective, and it’s best not to stack too many together.One final reminder: you usually don’t need to add quality-related prompts. Just follow the guidance provided on the checkpoint’s official page.

https://tensor.art/articles/868883505357024765 ( 1 )https://tensor.art/articles/868884792773445944 ( 3 )https://tensor.art/articles/868885754846123117 ( 4 )https://tensor.art/articles/868890182957418586 ( 5 )Second, the prompts  are always the most critical. Many people don't realize that, and haven't read the instructions for the use of those checkpoints, that the number of prompts has an upper limit, and they are also in order, from first to last, so don't let those quality prompts occupy too much. “score_9_up,” “score_8_up,” etc., are used by the Pony model, while the Illustrious and Noobai models don't need them at all. So, regardless of which base model you're using, just follow the instructions written on the page. Whether you write a hundred perfect hands in the positive prompt or add six-finger, seven-finger hands in the negative prompt, it won’t make the hand generation stable. I used to think it would be helpful, but in the face of a lot of facts, it’s just a psychological effect. Excessive quality prompts will make the image worse, not better. The order of these quality prompts does have an effect, but it can generally be ignored. The most important factor is the order of your prompts. Although the prompts are generally random, their order and adjacency do have an impact: tokens placed earlier are more likely to produce better results than those placed later, and neighboring tokens tend to interact with each other. So if you want the image to be more in line with your imagination, it's best to conceive and write the elements of the picture in order. Here's a tool called BREAK, which recalculates the number of tokens. One of the effects it brings is that it tries to interrupt the influence between adjacent prompts. For example, writing "artist name" at the beginning and "BREAK, artist name" at the end will produce a much stronger style than writing the trigger word in the middle. Alternatively, placing it between different characters will likely make the characters more separate. Another tool is the | symbol, which strengthens the connection between two adjacent prompts and tries to merge their effects. Try experimenting with both and using them flexibly. Because of the tag-based training methods of Illustrious and Noobai, it's best to use prompts that align completely with the tags found on Danbooru. When thinking of an action or an object, it's advisable to check Danbooru for corresponding tags. You can also refer to Danbooru’s tag wiki or use many online tag-assistance websites to make your promptss more precise. Elements like lighting, camera angles, and so on can be researched for their effects and incorporated. E621 tags are only applicable to Noobai, while Danbooru tags are universal. Although natural language is not well-supported by Illustrious and Noobai, it can still be useful as a supplement. Be sure to start with a capital letter and end with a period. For example, if you want to describe a blue-eyed cat, writing "cat, blue eyes" might result in several cats with the boy's eyes, but writing "A blue eyes cat." will make sure the cat's eyes are blue. You can also use this method to add extra details after describing a character's actions using reference tags. Additionally, you can describe a scene and use AI tools like Gemini or GPT to generate natural language prompts for you. Prompt weights can also be assigned, with the most common method being using ( ) or a value like :1.5. This will make the weighted prompt appear more often or have a stronger or weaker effect. Fixing a random seed and assigning different weights to prompts is a very useful technique for fine-tuning the image. For example, if you generate an image with the right action but the character looks too muscular, you can recreate the image, find the random seed parameter, fix it, and then adjust with something like "skinny:1.2" or "skinny:0.8" to tweak the character's appearance. This method won’t usually change the original composition of the image. As for the method like (promptA:0.5, promptB:1.3, promptC:0.8), I didn’t find any pattern in it, so it can be used just as a kind of randomness. The above experiences in prompts may not be as good as good luck. Sometimes, just emptying your mind and writing randomly can lead to unexpected results. So, don’t get too caught up in it. If you can’t achieve the desired effect, just let it go and change your mindset. As for the images I’ve posted on Tensor, aside from the first few, all of the prompts have been tested using the same checkpoint on my local ComfyUI. Even though the LORA and parameters used may differ, generating a correct image doesn’t require repeating the process many times, unless it is full of bugs when it is released. There are still some things I haven’t thought of, but I’ll add them when I write the LORA section later. Third, parameter settings such as sampler, schedule, steps, CFG, etc. The principles behind these are too technical and hard to understand, but you can use simple trial and error combined with the test results of others to find the best settings.It’s really important to point out that a lot of people have never touched these settings—those options only show up when you switch Tensor to advanced mode. Free users on Civitai only get a few default choices, which are nowhere near as rich as what Tensor offers. The default sampler, “Euler normal”, generally performs quite poorly. If you haven’t tried other samplers, you might not even realize how much hidden potential your slightly underwhelming LoRA actually has.Below are the ones I use most often. The names are too long, so I’ll use abbreviations: dpm++2s_a, beta, 4, 40. If you’re using ComfyUI, switching the sampler to "res_m_a", “seeds_2”, “seeds_3” will yield unexpected surprise results. The default descriptions of these parameters on Tensor and other websites don’t fully explain their real effects, and many people haven’t tried changing them. In fact, they’re constantly evolving, and the most commonly used and recommended samplers for most checkpoints are "euler_a" and "dpm++2m", "norma" and "karras" don’t perform well in practice. Based on my experience, no matter which sampler you use, combining it with "beta" always gives the best results. If your checkpoint has bugs when using "beta", try "exponential"—these two are always the best, though they are also the slowest. Don’t mind the time; waiting an extra 10 or 20 seconds is worth it. "dpm++2s_a" is also the best in most cases, with more details and a stronger stylization. Only use something else if bugs persist regardless of how you modify the prompts. Next, "euler_dy" or "euler_smea_dy", which are supported by Tensor, offer a balance of detail between "euler_a" and "dpm++2s_a", while being more stable and having fewer bugs than "dpm++2m". Only use classic "dpm++2m" and "karras" if the checkpoint can’t handle the above parameters, and only in the most extreme cases should you resort to "euler_a" and "normal", because this combination results in images with poor details but less bugs. As for the number of steps, I personally like 30 and 40, but they aren’t crucial. More steps doesn’t always mean better results. Sometimes, for a single character image, 20 steps is more than enough, and 40 might introduce a lot of bugs. The real purpose of steps is to randomly generate a composition you’re happy with, and if there are small bugs, fixing the random seed and adjusting the steps can sometimes eliminate them. CFG has a pretty big impact on the results. The default explanation on the site doesn’t really match how it actually feels when you use it. With so many combinations of different checkpoints and LoRAs, there’s no one-size-fits-all reference — you just have to experiment. From what I’ve noticed, in general, the lower the CFG, the more conservative the composition tends to be, and the higher it is, the more exaggerated or dramatic it gets.Fourth, resolution. Each checkpoint will clearly specify the recommended resolution to use. The default resolution for Tensor is well-supported across various checkpoints with relatively fewer bugs. However, it’s quite small. You can use upscaler to increase the image resolution, but many checkpoints can generate larger resolutions directly, as long as the width and height maintain the same ratio as the recommended resolution and are multiples of 64. However, one thing to note is that compared to the default resolution, larger resolutions will result in a larger background area, while smaller resolutions will tend to have the characters occupy more of the space. Changing the resolution, even with the same parameters and seed, will still generate different images. This is also an interesting aspect, so feel free to experiment more with it.

At the beginning, please allow me to apologize—English isn't my native language, and this article was written with the help of AI translation. There may be grammatical errors or technical term inaccuracies that could cause misunderstandings.https://tensor.art/articles/868883998204559176  ( 2 )https://tensor.art/articles/868884792773445944 ( 3 )https://tensor.art/articles/868885754846123117 ( 4 )https://tensor.art/articles/868890182957418586 ( 5 )And this article is not a guide or tutorial. If you have never used AI drawing, you may not understand it. If you have made pictures or Lora, it will be easier to find possible misunderstandings and errors in the article. The variables and approaches I mention are based on personal experience, and given AI's vast randomness, they may not be universally applicable, your own experiments might yield totally different results. You can use the experience in this article as a comparison, or even as potential "wrong answers" to rule out in your own workflow. Some of my friends have just started AI painting or are preparing to start, I just hope that this article will be of some help to them. Like many people, when I first heard about AI painting, I thought it was a joke. It wasn't until more and more AI pictures of popular characters appeared on social media that I gradually changed my mind. It turned out that there was a way to make doujin like this. However, the carnival of those star boys and the grand occasion of girls who were hundreds and thousands of times more than these star boys did not make me interested in AI painting. One day, on the homepage of Twitter, I saw a post featuring an extremely obscure boy - the kind that barely anyone knows about. Why? How? At that time, my mind was full of excitement except for questions.After that, I would search for the names that lingered in my heart forever on Twitter or pixiv every day, hoping for a miracle to appear, and then, it really appeared.So I continued to wait for the results of others as if I was longing for a miracle, and still didn't think about whether I should try it. I didn't even know about websites like civitai or tensor at that time. More and more people started to make AI pictures, and then I knew about the existence of these websites from their links.These online AIs became a place for daily prayers. I never even clicked the start button, and just indulged in the joy of winning the lottery. Those pioneers shared new pictures and new things called lora every day. One day, I saw the lora of the boy that I was most fascinated with. Finally, I couldn't help it. I clicked the button, figured out how to start, copied other people's prompts, and replaced them with my favorite boy. In this way, I began to try to make pictures myself - the pictures full of bugs. The excitement gradually faded. It turned out that AI couldn't do it, or I couldn't do it. Questions replaced the excitement and occupied my brain. I continued to copy, paste, copy, and paste. Why were other people's results so good, but mine were always so disappointing? At that time, I thought that people didn't need to know the principle of fire, as long as they could use it. I just tried repeatedly without thinking.At this time, there was a phenomenon on the Internet that was becoming more and more common.That is, just hearing about AI drawing in the previous second, the next second, copying and pasting began, and the next second, sponsorship was opened to sell those error-filled picture packages - when the creators of those checkpoint and the creators of lora completely disagreed. What's worse, steal these picture packages and resell them. Not to mention those who stole the pictures that people released for free and sold them. The copyright of AI was originally controversial, and I had my own doubts, but these thieves were too utilitarian and too despicable. Why? How? In anger, I no longer have any doubts. I must think about how AI graphics came about, and I must know that fire needs air to burn and how to extinguish it. -- I have to regain the original motivation for using the Internet when I was still a chuuni boy -- sharing the unknown, sharing the joy, at least sharing it with my friends. I have no power to fight against those shameless guys, but they can never invade my heart. I'm sorry for writing so much nonsense. Let's write down my thoughts and experiences over the past year in a way that's easier to understand.These understandings are only for make character doujin and are not applicable to more creative uses. These experiences are mainly based on the use of illustrious and noobai and their derivative models. They are also based on using only prompt and basic workflows. More complex workflows, such as Controlnet, are not discussed. They can indeed produce more complete pictures, but they are still too cumbersome for spare time. Just writing prompts is enough to generate eye-catching pictures in the basic workflow. First of all, the most important conclusion is that AI drawing is still a probability game at present. All we can do is to intervene in this probability as much as possible to increase the possibility of the expected result. How to improve or change this probability to improve the quality? We need to understand a concept first. The order of AI drawing and human drawing (just digital, not traditional media) is completely opposite. When people draw, they will first conceive the complete scene of the image, then fill in the details, line draft, color, and then zoom in on a certain area to refine, such as the eyes. At this time, the resolution is very high, and even if you zoom back to the full picture, it will not affect the details. However, AI is the opposite. It first generates small details, such as eyes, and then gradually zooms in to depict the full picture. Therefore, when the resolution remains unchanged and undergoes several zooms, those initial details may be blurred. This is why AI drawing a headshot is much better than a full-body picture. So the easiest way to improve the quality is to only do the upper body, or close-up.