Millions of users worldwide are creating works that look like they were painted by professional artists, in just seconds, with thousands of AI tools . An Adobe survey in 2025 showed that 86% of content creators already use AI tools in their work, and 85% of them are willing to switch to any platform that can learn their personal style.
What does this mean? When anyone can create beautiful images, it is controlled repetition—the ability to produce the same character, the same product, the same style across dozens or hundreds of frames—that distinguishes an amateur AI user from a true AI graphics expert.
That's the "Consistency" problem – the final hurdle AI technology is striving to overcome, and a skill anyone serious about digital creativity needs to master right now. Let's explore how important consistency is in AI-powered product creation and how to create consistent products using AI.
Four levels of consistency in AI graphics
Most people new to AI understand consistency in a narrow sense: how to make a character's face look the same across images. But in professional production, this concept is much more complex and multifaceted.
1. Character Consistency
This is the most common level and also the most frequently discussed challenge. The core problem lies in how most AI models are built: they process each prompt independently, without any "memory" of previous image creations. The result is the same text description, but two frames with different jaw shapes, mismatched hairstyles, and even altered eye color.
Character consistency isn't just about the face. It includes subtle details that viewers will unconsciously notice when they change: mole placement, ear shape, hairline, skin texture. The requirements for clothing are even more stringent. A vague description like "black jacket" isn't enough – it needs to be specific enough—like "charcoal black leather jacket, silver metal zipper on the left shoulder, biker-style collar, prominent gray stitching"—for the AI to have enough information to consistently recreate the character.
2. Style Consistency
This level is especially important for those building brands or creating serialized comics. Art style includes: color saturation, contrast, shading, brushstroke texture, and the overall "feel" of the image.
A consistent marketing photo series will give viewers the feeling that it all came from the same author – even if it was actually created at different times, or even by different users within the same team. Conversely, style drift – however subtle – will unconsciously undermine viewer trust.
3. Product Consistency
This is the most challenging and commercially valuable level. When a brand needs to create 20 advertising images of the same product—such as a bottle of perfume—in 20 different settings, the AI needs to accurately reproduce: the bottle's proportions, the shape of the cap, the font on the label, how light reflects off the glass surface, and the precise color of the liquid inside.
If just one of these elements changes, the entire set of images becomes unusable for commercial purposes. This is why many large agencies still combine live product photography and AI to achieve the necessary accuracy.
4. Environment Consistency
This final step is often overlooked but it determines the professionalism of an entire project. If the story takes place in Hanoi in 2045 in a cyberpunk style, then the blue and red neon lights, the Vietnamese signs blurred in the fog, the "visible" humidity in the air – all these elements need to be consistently present in every shot, regardless of camera angle or time of day.
Core techniques for achieving consistency
The good news is that since mid-2024, the technology industry has produced many truly effective solutions to this problem – no longer just temporary "tricks".
Technique 1: Seed number - The simplest technical anchor point
Each AI-generated image starts with a random number called a "seed"—a probabilistic seed. If the seed is kept the same and only the prompt content is slightly modified, the AI will produce a result with an overall composition and structure very close to the original image. This is the fastest technique for making small changes (such as adjusting expression or lighting) without losing character recognition.
Limitation: When the setting changes completely or the pose becomes complex, the seed number may no longer be consistent.
Technique 2: Character Reference and Style Reference (--cref and --sref)
These are the most important parameters of Midjourney from version V6 onwards. Instead of describing the character in text, the user uploads a sample image. The model will analyze the characteristics from that image and apply them to subsequent creations.
--cref(Character reference) focuses on the character's identity - face, body.
--sref(Style reference) focuses on the overall artistic style. These two parameters can be used simultaneously: {mô tả cảnh} --sref [url ảnh phong cách] --cref [url ảnh nhân vật].
Additionally, the parameter --cw(character weight) from 0 to 100 allows you to adjust the degree of "tightness" when holding the character – values of 40–70 are suitable when you need to change the outfit but keep the face; 80–100 is used when you want the entire appearance to remain unchanged.
An important note from Midjourney's own documentation: --crefit is not designed to replicate real human faces and may produce distorted results if using real portraits. This is both a technical limitation and a deliberate ethical hurdle.
Technique 3: IP Adapter and Instant ID
In the Stable Diffusion ecosystem, the IP-Adapter (Image Prompt Adapter) is a tool that allows extracting identifying features from a reference image and "injecting" them directly into the new image creation process. It allows for better control over text and style transformation compared to purely image-based methods.
InstantID takes it a step further: it uses three components – embedding facial recognition from a recognition model, a lightweight adapter module with a detachable cross-attention mechanism, and IdentityNet for encoding facial details along with spatial control. Key advantages: InstantID requires only a single reference image, no additional training is needed, and it's particularly effective with non-realistic styles like anime or illustrations.
Technique 4: ControlNet - Spatial Structure Control
ControlNet is an extension of Stable Diffusion, providing pixel-level control over image structure. Users provide a "condition image"—which could be a human skeleton, depth map, stroke outline, or segment mask—and the model will produce results that closely adhere to that structure.
This is an indispensable tool when you need to recreate the same pose or shot composition in multiple different settings. Midjourney lacks this equivalent feature – --crefand --srefonly affects the overall aesthetics, not controlling the structure at the pixel level like ControlNet.
Technique 5: LoRA - Training AI's own "memory"
LoRA (Low-Rank Adaptation) is a computationally efficient method for fine-tuning AI models. Instead of retraining the entire model (which is costly and complex), LoRA only adjusts a small portion of the parameters – enough to "teach" the model to consistently recognize and reproduce a specific character, style, or product.
The basic process: Prepare 15–30 high-quality images of the object to be consistent, train the LoRA file (typically 30–90 minutes on a modern GPU), and then call this LoRA file in every related image generation. The CivitAI community currently hosts over 100,000 user-trained and shared models, LoRAs, and embeddings – a huge resource for both beginners and experts.
This is the method that offers the highest consistency currently available, but it requires an initial investment of time and hardware.
Technique 6: Inpainting (Generative Fill) - Change without breaking
Instead of recreating the entire image from scratch, inpainting allows users to "redraw" only a specific area while keeping the rest intact. This technique is particularly effective when needing to change facial expressions, adjust the posture of a part of the body, or replace the background without altering the character.
Technique 7: Unique Name Tagging for Characters
A simple but surprisingly effective trick: instead of using a generic description like "a girl with dark hair," give the character a unique and unusual name (e.g., "Aria_VN_2046") right from the start, along with a highly detailed description. Then, use that exact name in every subsequent prompt. This technique helps "anchor" the characteristics to a fixed semantic token, significantly reducing drift when the context changes.
Practical Process - Case Study: Building a Perfume Brand Identity
Imagine yourself in the shoes of a designer who receives a request: create 10 advertising images for a high-end perfume brand called "Morning Dew," featuring a turquoise glass bottle with a gold cap and black label. Ten different settings—from a minimalist living room to a misty morning forest—but the product must look exactly the same in every image.
Step 1 - Create the original product image (anchor image)
First, create a highly detailed "backbone" image of the perfume bottle, set against a neutral background (white or light gray). This will serve as the reference image for the entire project. Note the seed number of this image.
Step 2 - Build a fixed prompt for the product
Separate the product description into a fixed "block" that never changes across all prompts:
"Sương Mai perfume bottle, translucent jade-green glass body, matte gold cap, minimalist black label with elegant serif font, luxury product photography, sharp detail"Step 3 - Change only the background
For each of the 10 scenes, only change the environment description, keeping the product description block completely unchanged. For example:
"[product block], placed on marble bathroom counter, morning light from frosted window, soft reflections"or
"[product block], surrounded by dewy moss in Vietnamese forest at dawn, volumetric fog"
Step 4 - Use the reference image via IP-Adapter or --cref
Upload the original image of the bottle as a product reference. This ensures that the proportions, shape, and color are preserved even when the ambient light changes significantly.
Step 5 - Checking and screening
After each creation, place the result next to the reference image and check against the checklist: Is the bottle proportion correct? Is the glass color the right tone? Is the font on the label still recognizable? Any image that fails these three checks needs to be recreated.
Evaluating tools based on real-world needs in 2026
There is no single best tool for every situation. Below is a practical assessment based on specific needs:
Midjourney V7 with --cref/--sref: The best choice for high-artistic style and overall aesthetics. Simple interface, no deep technical knowledge required. Disadvantages: no pixel-level structural control, completely public community (unless paying extra for Stealth Mode).
Stable Diffusion + ComfyUI: Maximum control, most complex workflow. Combining LoRA + ControlNet + IP-Adapter in a single pipeline for near-perfect consistency. Suitable for studios and technical users. Low cost if a dedicated GPU is available.
OpenArt: A standout in 2025 thanks to its Character Profile feature – saving and reusing characters across different work sessions. Achieved high scores in independent consistency testing across multiple styles (realism, anime, animation, oil painting).
Neolemon: Specializes in 2D illustrated characters and cartoon style. After ceasing support for realistic images from mid-2025, the platform focuses entirely on children's books, comics, and educational characters. The Action Editor tool allows for changing poses from a single reference image.
Runway Gen-4: The top choice when consistency is needed in video. Its reference system is specifically designed to keep subjects and shots consistent across multiple shots – something that still image editing tools cannot achieve.
InstantID (implemented via ComfyUI/A1111): Best for preserving facial recognition from a single reference image, particularly effective with non-realistic styles. No prior training required, instant results.
The future of creative AI - Personalization, ethics, and copyright.
As models become increasingly adept at consistency, a new set of equally important questions emerge.
Unprecedented scale of personalization
The most obvious trend in 2026 is that AI will not only create consistent content but also personalize it for each target audience. A brand could produce thousands of variations of the same advertising image – each adjusting the character, setting, and emotion to suit different customer segments – while maintaining consistent product identity throughout.
Copyright issues are reshaping the industry.
The lawsuit filed by Disney and Universal against Midjourney in June 2025 is a clear signal: major IP owners are actively scrutinizing the legal boundaries of this technology. For those building businesses on AI-generated content, understanding the commercial usage rights of each platform is a crucial step. Most platforms now strictly prohibit the use of character consistency to replicate real human faces without consent.
The line between creativity and copying
When AI can reproduce an artist's "style" with high accuracy, the question of creative ethics becomes urgent. A LoRA file trained on an artist's work without permission – is it a tool or a violation? The industry currently lacks consensus, but legal trends are increasingly leaning towards protecting the rights of the original artist.
Conclude
After all the techniques and tools mentioned above, there's one important truth that needs to be acknowledged frankly - AI is getting better at execution, but it cannot navigate on its own.
Consistency isn't the ultimate goal—it's a tool to serve a creative vision. And that vision must come from humans. The question "what does this character look like?" can be answered by AI. But the question "who should this character be, what values should they represent, what emotions should they evoke in the viewer?"—that remains a task that cannot be delegated.
In the age of hybrid workflows, a good designer isn't necessarily someone who knows the most software, but rather someone who deeply understands the meaning of the images they create and knows how to use AI to amplify that vision—not to replace it.
Consistency is a technique. But meaning is always an art.
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