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.
