How to Write Prompts for Image Generation – Part 2 – Advanced Techniques and Best Practices

Recommendation: Define the target scene in three concrete details to lock the direction before crafting prompts. Describe the subject, the setting, and the lighting, then align the goal with the overarching генерация.

Tip: Build a подборку of готовые prompts in your library to accelerate testing across разных styles. Maintain a quick checklist that shows which prompts проверили and what results were achieved; store the best performers for reuse.

Apply a disciplined testing workflow: keep множество variations, but change only one element at a time (subject, environment, lighting, or style). Track render time, fidelity, and coherence with a simple rubric, and log results in the базе. Have teammates проверили outcomes and feed insights back to the team.

When building a prompt, use a clear structure: subject, environment, lighting, style, a composition. Example: “a rugged горы range at ночная sky with светящиеся stars, in a солнечный color palette, captured from a low camera angle, photorealistic.” This demonstrates how Russian nouns interleave with English descriptors to guide the model.

Practical note for teams: keep доступны множество вариантов, and publish a central library of проверенных промптах (промптах) for разработчиков сервиса. Regularly review user feedback and performance metrics to refine prompts and expand the множество stylistic options, including sci‑fi, naturalistic, and gritty urbano settings.

Define a precise visual goal with concrete attributes: subject, style, palette, lighting, and composition

Define a single, precise visual goal for one рисунок. Lock five concrete attributes: subject, style, palette, lighting, and composition. In плане, write точные параметры and bind them with a промпт. Use бесплатные референсы and a thoughtful выбор to keep the focus. If you test botsapp workflows, попробую сохранить одинаковые элементы across генераций and compare results later.

Subject: pick a singular, readable subject with a crisp silhouette–например, a wide-eyed fox. Place it on a simple background with заросшим поверхностей to add texture. If you нарисовала a version before, reuse that reference to keep consistency in look and scale.

Style: choose a single aesthetic from cartoon, photoreal, or painterly. For clarity and approachability, start with cartoon and note it’s professional. This setup makes lines read well from thumbnail to poster and helps работать быстро.

Palette: constrain to 3-5 colors; name hues, set saturation, and control contrast. Use яркие tones for punch, or a muted mix for mood. Build a snack palette that stays compact, and keep выбор of tones tight to avoid drift between generations. If needed, tweak the параметр and retry.

Lighting: specify light direction (side, back, top), color temperature, intensity, and shadow quality. Example: dawn light, soft fill, gentle shadows on заросшим поверхностях, so color and texture stay legible. Tie lighting to palette to maintain coherence.

Composition: set framing rules before generation: rule of thirds, negative space, and leading lines toward the subject. Encode as a concise line in the промпт: “subject off-center, clean crop, lines guiding to the face.” Run несколько генераций to test stability. If you сгенерировала варианты, pick the best and adjust параметры accordingly. Принципу coherence: keep subject, style, palette, lighting constant across iterations. Например, for wide-eyed subjects, give them more space; for introspective moments, crop tighter. Остаётся цель: a clear, precise visual goal.

Layer prompts in a predictable hierarchy: base concept, style directives, and parameter modifiers

Lock the base concept first: define the subject, scene, and action. This makes the prompt доступен to a wide range of сервисе users and reduces ошибки in генераций изображений. Build from a solid базe concept, then add стиль directives, and finally adjust параметры modifiers for precision and repetition. When you keep this порядок, you can зайти into complex prompts without losing clarity.

• Base concept
  • Subject: choose a concrete character or object, e.g., девочка or кошка, not a vague silhouette.
  • Setting: place the action clearly, such as along a река or in a cozy kitchen with a visible тортa centerpiece.
  • Action or pose: define what the subject is doing (standing, looking, holding something). If you want a narrative hook, describe a simple moment that supports the mood, e.g., a girl observing clouds over a peaceful river.
  • Constraints: note any non-negotiables (composition, focal length, or negative space) and keep the запросу concise to reduce аномалии.
• Style directives
  • Medium and look: cartoon, watercolor, photorealistic, or vector; pick a visual language that matches your base concept.
  • Palette and tones: specify gede color families (pastel tones, warm hues, or high contrast) and textures (soft shading, crisp lineart).
  • Narrative feel: cheerful, dreamy, solemn–align the mood with the base concept. Especially useful when you want a consistent tonal thread across multiple images.
  • Reference prompts: leverage a lexica-like approach by naming adjectives and motifs you want to repeat across generations, e.g., “delectable palette” or “sunlit tones.”
  • Language tag: you can signal prompts in русский style or bilingual prompts if your generator supports it; this helps guide phrasing and typography – for instance, you might want letters or script to resemble handwritten cyrillic accents in the scene.
  • Examples to copy: “cartoon girl by the river, soft pastel colors, gentle shading, whimsical mood.”
• Parameter modifiers
  • Aspect ratio and framing: use –ar 16:9 for a cinematic feel or –ar 4:3 for a classic look.
  • Quality and steps: set –steps to influence detail (e.g., 50–100) and –quality or –q to balance speed and fidelity.
  • Seed and variability: assign –seed to reproduce a specific arrangement of tonality and shapes; adjust –cfg or equivalent scale to tighten or loosen alignment with the base concept.
  • Image count and iterations: if your service supports it, loop prompts with slight perturbations to collect a Множество outputs and compare variations (helpful to catch errors or anomalies).
  • Hardware or render preferences: indicate resolution or model flavor if the service offers multiple engines, for example, a dedicated “cartoon” model or a “realistic” model.

Example layered prompt (illustrative): Base concept: девочка by река, кодged mood: contemplative; Style: cartoon, pastel tones, soft shading, lexica-inspired prompt; Modifiers: –ar 16:9 –steps 60 –seed 98765 –quality 2. This structure keeps изображений predictable and reduces multiple iterations, especially when you need consistent results for a простая публикация or a серийный набор of images.

Leverage negative prompts and exclusion terms to steer away from undesired elements

Begin with a concrete recommendation: attach concise negative prompts at the start and couple them with clear exclusion terms to steer away from unwanted elements. Use -blurry to prevent blur, -noise to curb grain, and -lowres to keep details sharp. Define a small set of constraints in the parameters so that the model adheres to your intent across multiple variants. Refer to reliable sources from prompts communities and YouTube channels to see how pros phrase exclusions, then adapt those patterns to your own workflow. When you work solo, focus on one subject such as a tiger or a girl and apply exclusions consistently across all variants.

In practice, build a two-layer exclusion strategy: global exclusions that apply to every generated image and project-specific exclusions for the current task. Add negative terms to the prompt edge so they influence all iterations. Reference a source of proven phrases used by prompters, collect ideas from YouTube tutorials, and assemble a reusable exclusion list. This approach helps you generate cleaner results on your site or within a portfolio, and it minimizes the need to rework images after generation.

Common exclusions

For practical execution, assemble these terms into a concise exclusion block appended to your prompts. Keep the blocks compact and consistent across variants, then test with different sampling parameters to confirm stability. Track which exclusions most reliably suppress undesired traits and refine your source prompts accordingly. If a result still leans toward an undesired direction, double-check the image reference in the source material and adjust the promпts to tighten control over tone, lighting, and composition. This disciplined approach helps you generate cleaner images, reduce iteration cycles, and spend less time adjusting later on YouTube tutorials or within your own team’s workflow.

Build reusable templates and presets for common tasks (portraits, landscapes, product shots)

Start with three master templates and a simple preset pack you can reuse across projects. Create a prompt1 baseline for portraits, scenic scenes, and product shots, and save it in bases so you can apply tweaks quickly. This approach helps пользователей who want faster просмотр and keeps высокие качества across генераций. For пастельных palettes, prefer soft, warm tones, and if you need яркие accents you can toggle a vivid variant without rewriting prompts from scratch.

Portrait template: Build around a tight frame with an 85mm perspective, shallow DOF, and soft diffused lighting. Use a pastelных palette, a wide-eyed look when the brief calls for warmth, and a minimal background to avoid distractions. Include an optional маск layer to isolate the subject for post-processing. Save this as prompt1_portrait and keep a high-resolution variant tagged high for easier delivery to клиeнты and users.

Product shot template: Target a 1:1 aspect and macro-ready close-ups, pristine edges, and clean, neutral backgrounds. Emphasize texture with controlled highlights and a subtle color grade that preserves true tones. Use маск to separate the object from the backdrop when precision matters, and offer a простoй base and a high-contrast promepte variant for bold branding. Store under bases/product and reference prompt1_product as the quick start.

Scenic scenes template: Aim for wide vistas with 16:9 or 3:2 ratios, natural lighting, and deep color depth. Favor rich but balanced tones, a hint of atmosphere, and a restrained sharpness to keep detail without glare. Include a macro tweak only when close-up elements appear in the frame, and keep a масштабированная маск option to isolate foreground elements if needed. This preset lives in bases/scene and can be swapped with a single toggle to suit different moods.

To keep workflows simple, name presets clearly and align them with a predictable hierarchy: bases, пастельные, vivid, macro, and prompty variants like promtе or prompte. Подборку of prompts should be easy to browse on your own сайтов or within your internal репозитории, so you can quickly share (поделиться) with colleagues and clients. This approach helps users (пользователей) iterate faster and maintain consistency across собственных проектов.

Storage and reuse tips: maintain a small library that fits your team’s needs. Use a concise naming convention that encodes task, ratio, and mood (for example portrait_v1_85mm_warm, scene_v2_16x9_rich). Document each preset with a one-line note (which task it targets and the primary adjustments) and keep a simple changelog. You can quickly adapt the same base prompts for both studio and on-site shoots, which saves time on промпте iterations and ensures you’re ready for any briefing.

Example prompts:

Prompt1_portrait_high: subject in close-up, 85mm, f/1.4, soft diffused light, pastelных palette, wide-eyed expression, minimal background, mask optional, high resolution.

Prompt1_product_simple: product center, 1:1, macro detail, neutral background, crisp edges, gentle specular highlights, mask to isolate product, promtе variant for clean branding.

Prompt1_scene_warm: wide vistas, 16:9, natural light, rich but controlled color, shallow depth where foreground stands out, mask for foreground elements if needed, auxiliary variant for vivid tones.

Iterate quickly with rapid testing: batch prompts, result comparison, and thorough logging

Batch prompts into groups of 16, 32, or 64 and run them in a single session. This lets you compare outputs directly and spot patterns in how different values affect results (значения). Keep a living prompt library (библиотекой) and tag each entry with a concise note about what the prompt aims to test, including something (что-то) specific. In practice, batch selection should target high-contrast prompts and subtle, nuanced ones so you can see where the model shines on life-like details (жизни) and where it falters. Use one baseline (одному) for comparison to keep results fair.

Define batch parameters: test prompts across three dimensions: input phrasing, seed or randomness, and targeted texture or tone (тоны). For colors, experiment with yellow prompts (желтыми) and vivid (яркие) textures. Document the запросов that produced the best results and keep the prompt itself (сама) as a reference. Maintain a single baseline (одному) to measure delta across iterations.

Result comparison: use a side-by-side grid or a simple matrix in CSV to rate each image against clear criteria: alignment to the prompt, color accuracy, texture richness (текстур), and overall impact. Keep it objective; assign a score with a 0–5 scale. For the majority (большинство) of tests, outputs that match the request and keep cohesive tones win. Record deviations in the запросов column. Capture точные details (точные) such as size, aspect ratio, and any post-processing notes. If you work with других teams, agree on a shared rubric to avoid bias from different sides (сторон).

Logging: build a lightweight log structure with fields: prompt_text, batch_id, item_id, timestamp, model_name, seed, parameter (параметр), and notes. Include a reference to the original prompt (сама промпта) and the значения for key metrics (значения). Store results in a CSV or on a сайт that the компания uses; this makes it easy to audit and reproduce experiments across проектов. Also keep a copy of the code (кода) used to generate prompts to enable reproductions later (сейчас).

Cadence and automation: after each batch, review quickly; pull the top 20–30% results, extract common features, and apply them to the next set. This accelerates learning and prevents stalling on a single path; большинство improvements come from small nudges in prompts or texture cues (текстур). Use a parameter sweep but keep it focused to avoid exploding the number of запросов.

Practical tips: use an automated script to generate prompts from a base template; test both English prompts and translations (английском). For teams handling many промпты, build version control for prompts and results; when you reuse an effective phrase, note which проектов it belongs to. If you manage assets from a company (компании) or a site (сайт), log the exact запрос IDs to tie back to source datasets and textures (текстур) used in the image (картинке).