Mastering Style Replication with LoRA: How Codatta Brought the Azuki Aesthetic to AI-Generated NFTs

In the dynamic world of AI-generated art, capturing a distinctive and emotionally resonant style is the key to creating NFT collections that stand out. While foundational models like Stable Diffusion XL (SDXL) excel at producing high-quality, realistic visuals, they often fall short when tasked with replicating specific artistic aesthetics without significant fine-tuning. For projects like NFT collections, where brand identity and visual consistency are paramount, this limitation poses a challenge.

Enter LoRA (Low-Rank Adaptation), a groundbreaking fine-tuning technique that allows creators to infuse unique artistic styles into powerful base models with remarkable efficiency. In a recent research experiment conducted for one of our partners at Codatta, we set out to train a LoRA module to replicate the iconic Azuki aesthetic — known for its clean lines, vibrant color palettes, and unmistakable anime-inspired charm. The results were not only visually stunning but also a testament to the transformative potential of LoRA in AI-driven art creation.

This article dives deep into our experiment, exploring how LoRA works, the methodology behind our Azuki-inspired style transfer, the challenges we faced, and the broader implications for AI-generated NFTs and Web3 art.
I’ll also outline what’s next for this technology and how creators can leverage it to reshape digital art.

What Is LoRA and Why It Matters

LoRA, or Low-Rank Adaptation, is a lightweight fine-tuning technique designed to adapt large-scale AI models like SDXL to specific tasks or styles without the need to retrain the entire model. Traditional fine-tuning involves adjusting billions of parameters, which is computationally expensive, time-consuming, and resource-intensive. LoRA, by contrast, modifies only a small subset of parameters — often just a few million — making it faster, more efficient, and highly flexible.

At its core, LoRA works by adding low-rank updates to the weight matrices of a pre-trained model. These updates capture the stylistic or task-specific nuances while preserving the model’s general knowledge. This makes LoRA ideal for applications like:

• Style replication: Teaching a model to emulate a specific artistic aesthetic, such as Azuki’s anime-inspired look.
• Rapid experimentation: Allowing creators to test multiple styles without retraining the base model.
• Compact deployment: Enabling lightweight models that can be integrated into custom pipelines, such as NFT minting platforms.
• Resource efficiency: Reducing the computational and financial costs of fine-tuning, making it accessible to smaller teams and independent creators.

For our experiment, LoRA was the perfect tool to bridge the gap between SDXL’s general-purpose capabilities and the highly specific visual identity of Azuki NFTs. By training a LoRA module on curated Azuki-style artwork, we aimed to create a model that could generate new compositions while faithfully reproducing the aesthetic that has made Azuki a beloved brand in the Web3 space.

The Experiment: Azuki-Inspired Style Transfer

Our goal was ambitious yet clear: train a LoRA module to replicate the Azuki aesthetic — characterized by bold linework, vivid yet balanced color palettes, precise proportions, and a cohesive anime-inspired vibe — while leveraging SDXL’s ability to generate diverse compositions. Here’s how we approached it:

Step 1: Setting the Foundation with SDXL

We started with Stable Diffusion XL (SDXL), a state-of-the-art diffusion model known for its versatility and high-quality outputs. SDXL excels at generating detailed visuals across a wide range of subjects, from characters to landscapes. However, its outputs are often stylistically generic, lacking the distinct flair of a brand like Azuki. Our task was to teach SDXL to “think” like an Azuki artist without compromising its ability to create varied content.

Step 2: Curating the Training Data

The success of any LoRA model hinges on the quality and consistency of its training data. To capture the Azuki aesthetic, we curated a dataset of Azuki-style artwork, focusing on key elements such as:

• Linework: Clean, bold outlines with consistent thickness and minimal noise.
• Color Palettes: Vibrant yet harmonious colors, often with a focus on primary hues and subtle gradients.
• Proportions: Anime-inspired character designs with large, expressive eyes and balanced facial features.
• Textures and Shading: Smooth shading with minimal texture, emphasizing a polished, digital-art aesthetic.

We ensured the dataset was diverse enough to include various poses, outfits, and backgrounds while maintaining stylistic consistency. This balance was critical to avoid overfitting (where the model becomes too rigid) or underfitting (where the style is too diluted).

Step 3: Training the LoRA Module

Using the curated dataset, we trained a LoRA module to “inject” the Azuki style into SDXL. The training process involved:

• Rank: Adjusting the rank of the LoRA module, which determines its capacity to learn stylistic nuances. A higher rank allows for more complex adaptations but increases computational costs.
• Alpha: Tuning the alpha parameter, which controls how strongly the LoRA module influences the base model’s outputs. Too high, and the style dominates; too low, and the effect is barely noticeable.
• Training Iterations: Running multiple training epochs to find the sweet spot between style fidelity and output diversity.

We used a combination of automated metrics (e.g., style similarity scores) and human evaluation to assess the model’s progress, iterating until the outputs consistently reflected the Azuki aesthetic.

Step 4: Testing and Validation

Once trained, we tested the LoRA-enhanced SDXL model across a variety of prompts, from simple character portraits to complex scenes with multiple figures. The results were remarkable:

• Raw SDXL Outputs: High-quality but generic, with no clear stylistic identity.
• LoRA-Enhanced Outputs: Bold lines, saturated colors, precise proportions, and a cohesive Azuki-inspired vibe that felt authentic to the brand.

The LoRA model didn’t just mimic surface-level features — it captured the essence of Azuki’s aesthetic, from the emotional resonance of its character designs to the polished finish of its artwork. Whether generating a single character or a dynamic group composition, the model delivered consistent, brand-aligned results.

Challenges and Insights

Training a LoRA model is a delicate balancing act, and our experiment was no exception. Here are the key challenges we faced and the insights we gained:

Challenge 1: Overfitting vs. Underfitting

• Overfitting: When the model learns the training data too well, it produces repetitive outputs that lack creativity. For example, an overfitted model might generate near-identical characters instead of exploring new poses or outfits.
• Underfitting: When the model doesn’t learn enough, the style effect is weak, resulting in outputs that feel generic or only partially aligned with the target aesthetic.

Solution: We experimented with different ranks and alpha values, starting with a low rank (e.g., 16) and gradually increasing it to capture more stylistic details. We also used regularization techniques to prevent overfitting, ensuring the model could generalize to new prompts.

Challenge 2: Data Quality and Consistency

The Azuki aesthetic is highly specific, but not all Azuki-inspired artwork is equally consistent. Variations in line weight, color grading, or composition could confuse the model, leading to uneven results.

Solution: We meticulously curated the training dataset, prioritizing artworks that exemplified the core Azuki style. We also augmented the data with slight variations (e.g., adjusted brightness or contrast) to improve robustness without diluting the aesthetic.

Challenge 3: Balancing Style and Content

LoRA’s strength lies in its ability to separate style from content, but striking the right balance is tricky. If the style is too dominant, the model might ignore the input prompt; if too weak, the output lacks the desired aesthetic.

Solution: We fine-tuned the alpha parameter and used prompt engineering to guide the model’s outputs. For example, prompts like “anime character in Azuki style, wearing a kimono, standing in a futuristic city” helped ensure the model applied the Azuki style while respecting the content.

Key Insight: Training data quality is the foundation of success. A small, high-quality dataset with consistent stylistic markers outperforms a larger, noisier one. For creators looking to replicate a specific style, investing time in data curation is critical.

The Magic of Style Transfer

At its heart, LoRA-powered style transfer is about decoupling style from content, giving creators the freedom to plug new aesthetics into existing models. This flexibility unlocks a range of possibilities for AI-generated NFTs and Web3 art, including:

• Branded NFT Avatars: Projects can create NFT collections that align with their brand identity, ensuring visual consistency across thousands of unique assets.
• Community-Generated Art Styles: DAOs or communities can train LoRA models on their own artwork, enabling collaborative style creation.
• Personalized Collectibles: Collectors can commission custom NFTs by combining their preferred styles with specific themes or characters.
• Rapid A/B Testing: Creators can test multiple styles in parallel, iterating quickly to find the perfect aesthetic for their project.

By democratizing style replication, LoRA empowers artists, developers, and collectors to experiment with visual languages in ways that were previously inaccessible due to technical or financial barriers.

What’s Next for LoRA and AI-Generated NFTs

Our Azuki-inspired experiment is just the beginning. At Codatta, we’re committed to pushing the boundaries of AI-driven art and making these tools accessible to the broader Web3 community. Here’s what’s on the horizon:

1. Open-Source Release

We’re packaging our code, methodology, and training notes into an open-source release, set to launch soon on GitHub. This toolkit will enable creators to:

• Train their own LoRA models with custom styles.
• Reuse and remix styles across different collections.
• Experiment with cross-model compatibility (e.g., combining LoRA with other diffusion models).

Whether you’re a developer building an NFT platform, an artist exploring new aesthetics, or a collector commissioning custom pieces, this release will give you the tools to create with confidence.

2. Web3-Native Integrations

We’re exploring ways to integrate LoRA-trained models into Web3 ecosystems, such as:

• On-Chain Metadata Styling: Embedding style metadata in NFT smart contracts to ensure authenticity and provenance.
• Decentralized Minting Pipelines: Allowing communities to collaboratively generate and mint NFT collections using LoRA models.
• Cross-Platform Compatibility: Ensuring LoRA models work seamlessly with popular NFT platforms and marketplaces.

These integrations will bridge the gap between AI art and blockchain, creating new opportunities for creators and collectors.

3. Community Collaboration

We’re inviting artists, developers, and Web3 enthusiasts to collaborate with us on future experiments. By sharing our tools and learnings, we hope to foster a vibrant ecosystem of AI-driven art creation. Follow @codatta_io

on X and visit Website for updates on our open-source drop and sample outputs.

Implications for Web3 and Beyond

The success of our Azuki-inspired LoRA experiment highlights the broader potential of AI-driven style replication in Web3. As NFT projects increasingly prioritize brand identity and community engagement, tools like LoRA offer a way to create scalable, consistent, and emotionally resonant art. Beyond NFTs, LoRA has applications in gaming, virtual worlds, and even traditional digital art, where personalized aesthetics are becoming a key differentiator.

Moreover, LoRA’s efficiency and accessibility align with the Web3 ethos of decentralization and empowerment. By lowering the barriers to AI-driven creation, LoRA enables smaller teams, independent artists, and community-driven projects to compete with larger players, fostering a more inclusive creative landscape.

Conclusion: Reshaping Digital Art, One LoRA at a Time

The convergence of AI and Web3 is unlocking new frontiers for digital art, and LoRA is at the forefront of this revolution. Our experiment with the Azuki aesthetic demonstrates how LoRA can transform generic AI models into powerful tools for style replication, delivering results that are not only visually stunning but also deeply aligned with a brand’s identity.

As we prepare to release our methodology and tools to the community, we’re excited to see how creators will use LoRA to push the boundaries of AI-generated NFTs and beyond. From branded avatars to community-driven art styles, the possibilities are endless — and the future of digital art is brighter than ever.

Want to see the results for yourself? Visit app.codatta.io for sample outputs and stay tuned for our open-source release.

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