Вопросы и ответы

The more the new scene differs from the original photo, the more important it is to set the alignment correctly. For a simple portrait, a single line may suffice, but for a complex scene with a different angle and pose, a more detailed description will be needed.

The first layer is prompt screening. The service analyzes the text before generation begins and may reject a request it considers disallowed.

The second layer is the generation process itself. In some systems, additional safety mechanisms can intervene during generation, restrict certain outputs, or interrupt the task.

The third layer is the finished-image check. Even if the prompt passes screening and the image is successfully generated, the service may block it from being displayed, saved, or downloaded.

Why does all this exist? Corporations worry about more than someone generating something inappropriate. They also worry about lawsuits, legal restrictions, reputational damage, and responsibility for how their products are used. One particularly serious concern is non-consensual sexual deepfakes, including generated nude images of celebrities or private individuals who never agreed to them. When minors are involved, the stakes become even higher.

Age restrictions and sexual-content filters are common, but there is no universal standard. Different services establish their own rules and enforce them with varying degrees of strictness. As a result, a model may technically be capable of generating an image that a particular service does not allow users to generate or view. And yes, harmless requests sometimes get caught in the process. Algorithms can be a little overzealous too.

The exact reason for a refusal is not always visible. Services generally do not fully disclose how their safety filters work or where their thresholds are set.

You ask it to preserve a face from one reference, take the pose from a second image, borrow the lighting from a third, and then add a long prompt containing yet more conflicting requirements. The model has to decide which instructions to prioritize, and the result may be very different from what you imagined.

Try simplifying the task, removing contradictory instructions, and clearly separating their roles: what defines the scene, what specifies the character's appearance, and what changes the visual style. Sometimes shortening a prompt works better than adding another three paragraphs of instructions.

The more clearly and comprehensively a character is shown, the less the model has to guess. For body proportions, photographs of an adult in a swimsuit or underwear from several angles — front, side, and back — can provide more information than loose clothing that conceals the body's contours.

Many services do not accept fully nude reference images or subject them to additional restrictions. For tasks where preserving body proportions matters, underwear or swimsuit references can therefore be a practical compromise, provided the selected service allows them.

But reference accuracy is not the same as guaranteed filter approval. Images may themselves be screened by safety systems, and additional angles do not override restrictions. Use only images of adults that you have appropriate permission to use.

First approach — change the visual presentation.

Photorealistic images, colored-pencil drawings, cartoon illustrations, and pixel art can be treated differently by moderation systems. When a request is otherwise permitted, switching from realistic photography to an artistic style can sometimes help avoid false positives. However, this is not a universal way to remove restrictions: changing the style does not guarantee that a request will be accepted or that the result will be allowed to appear.

By the way, the Forge already has a whole collection of style blocks for these experiments. You can change the visual language of a scene, try different artistic approaches, and see how they affect the results.

Second approach — local generation.

You can run models on your own computer instead of sending every request to a commercial cloud service. This gives you more control over settings, model selection, and the generation process. However, local generation requires suitable hardware, disk space, and time for installation and configuration.

There is no magic here either: some models have their own built-in restrictions, and software tools may add further ones. Local generation offers more independence, but it requires technical preparation and does not guarantee that every model will perform every request.

In either case, it helps to understand what is limiting the result: the prompt wording, the reference image, the service's rules, or the capabilities of the model itself.

Perhaps we will get around to it someday. In the meantime, try searching for these terms:

• ComfyUI installation Windows — installing and launching the node-based generator for the first time.
• Stable Diffusion local setup — the basics of local generation and choosing a suitable configuration.
• Stable Diffusion WebUI Forge installation — installing a graphical interface for working with models.
• FLUX local inference — running and configuring models from the FLUX family.
• ComfyUI image generation workflows — ready-made node graphs built for specific tasks.

Pay attention to publication dates and model versions. This field changes quickly, and a guide written for an older version may not work with a newer one. Check the VRAM requirements before downloading tens of gigabytes of models.

That's it. You can already generate images with AI. No three-page prompts, graphics card configuration, or ritual dances around a neural network required. Maybe you'll like the result. Or maybe the face won't quite look like you, the sea will turn out strange, and an extra arm will decide to go on an adventure of its own.

And that's where things get interesting. How do you preserve your appearance? How do you control lighting, clothing, pose, and composition? Why does one instruction work while another produces nonsense? That's what prompts, reference images, style blocks, and the other Forge tools are for. Getting started is easy, and from there you can gradually learn what works, why it works, and how to get better results.

Beauty comes from many things: composition, lighting, color, perspective, anatomy, material textures, the right moment, and a hundred other details. To get consistently good results, you need to gradually learn to notice these things. Look at photography, paintings, and films. Study other artists' work, compare different results, and figure out what you actually like and why.

AI is excellent for experimenting and getting results faster. But it can amplify a bad idea just as easily as a good one. The better you understand what you're trying to achieve, the more useful the tool becomes. Visual literacy, basic knowledge, and practical experience still don't come bundled with the model.

As long as any result will do, a few words are enough. But once you have something specific in mind, you need to clarify the details. That's where long prompts come from: not to fill up the token count, but to leave less room for the model to make random decisions.

Length alone doesn't guarantee anything, though. An overloaded prompt full of contradictions can work worse than a short, clear one. The goal isn't to write more words. It's to communicate what matters.

The chosen model, service version, resolution, generation parameters, and settings available in a particular interface can also affect the result. Even if you haven't changed anything, a service may update its model or modify how requests are processed.

This isn't always a drawback. Trying several variations can lead to unexpected and interesting results. But if you need a consistent character or a repeatable scene, you'll have to define the requirements more precisely, use suitable references, and learn to control the process. A prompt sets a direction; it doesn't guarantee that every instruction will be followed exactly.

Start simple. Learn the basics of image generation, experiment with individual details, and see how lighting, camera angles, and surroundings affect the result. Then move on to reference images, character capsules, style blocks, and more advanced workflows.

Don't be afraid of failed attempts. Sometimes they teach you more than a lucky result. Save useful discoveries, compare variations, return to approaches that work, and make your tasks more challenging as you gain experience.

You don't need to know everything from the start. Curiosity, a willingness to learn, and a desire to experiment are enough. Rome wasn't built in a day — and mastering AI image generation certainly doesn't happen in one evening.

The homepage contains prompts and style blocks. You can find ready-to-use setups for generating images and changing their visual style.

The Ancient Scrolls are a collection of articles, guides, technical prompts, and other materials that help you understand the tools and use them more effectively.

All Tags is a navigation system for prompts and styles. You can explore materials by topic, visual direction, and other relevant categories.

There aren't dozens of sections, but there's not much filler either. Pick whatever interests you and start exploring.

Unlike regular prompts, which you can copy and use right away, the scrolls explain how different approaches work, why references matter, how to preserve a character's appearance, and how to build more advanced setups.

This isn't an encyclopedia for every possible situation. It's a growing collection of knowledge and practical experience. Come here when you want to do more than generate an image — when you want to understand how to achieve the result you're after.

These prompts help refine detail, lighting, color, optics, depth, and the overall photographic look. For example, you might try to give a photograph the character of a professional camera, improve tonal transitions, or make the image more visually compelling.

But an enhancer isn't a magic button. Different images need different approaches, and excessive processing can damage the original or subtly change someone's appearance. Always check the result, especially when preserving a person's face matters.

Fidelity is particularly important when dealing with faces. Aggressive restoration may do more than sharpen an image — it can effectively redraw the eyes, skin, hair, or small identifying details. The priority should therefore be preserving the original, not producing the smoothest or most impressive-looking picture.

Generative models cannot guarantee pixel-perfect preservation. A good prompt helps define the limits of acceptable changes, but the outcome also depends on the processing tool itself.