Defuison - AI-Native and Core-Aligned Projects

Hyperion HyperHack Ideathon
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#Project Title: Defusion

Problem Statement

As AI technology rapidly evolves, creators still struggle to find accessible platforms that allow them to turn creativity into real, blockchain-based digital assets. Many platforms offering AI art generation lack ownership validation, fine-tuning capabilities, or Web3-integrated rewards. This creates friction for users who want to generate, own, and monetize AI-powered content seamlessly within the Web3 ecosystem.

Solution Overview

Defusion bridges the gap between AI generation and Web3 utility by empowering users to create, mint, and own AI-generated content across multiple chains. With innovative tools like Text-to-Image, Image-to-Video, and LoRA (Low-Rank Adaptation) model training, Defusion makes advanced AI tools accessible for all. On-chain check-ins reward users with credits that fuel their creative sessions, making every interaction purposeful and rewarding. By combining cutting-edge AIGC technology with Web3 ownership and gamified experiences, Defusion is building a true creative economy on-chain.

Project Description

Defusion is a Web3-native AIGC platform where creators generate stunning content and mint it as NFTs across 13 supported blockchains. With over 3.6 million images generated and more than 1.1 million transactions, Defusion is redefining how creativity and ownership intersect in the NFT world.

Core Features:
• Text-to-Image: Transform written prompts into hyper-realistic artworks using AI
• Image-to-Video: Animate still images into captivating short videos
• NFT Minting: Instantly mint creations across 13+ supported chains
• On-Chain Check-In: Daily check-ins reward users with credits for generation tasks
• Credits System: Earn credits on-chain, then spend them on image or video generation
• LoRA Training: Fine-tune Stable Diffusion models using Low-Rank Adaptation to train styles, characters, or themes. Users can export and share their trained LoRA models
• Achievement Badges: Earn unique, non-transferable badges tied to creative milestones
• Airdrops & Rewards: Unlock seasonal rewards through active participation
• Community Hub: Showcase creations, learn from peers, and join themed events

Powered by Dego Finance, Defusion stands out as a true AIGC + Web3 ecosystem. Users interact via a smooth Telegram bot and web interface that supports both beginners and pro creators. Whether you’re a meme artist, anime creator, or generative art collector, Defusion turns your passion into digital rewards—with ownership and creativity at the core.

Community Engagement Features

Defusion is built for community-powered creativity, gamifying the entire creation process with incentives and shareable achievements.

Testable Features/Tasks:
• Generate an AI image using text prompt
• Convert an image to video animation
• Mint your artwork on-chain
• Train and publish a custom LoRA model
• Complete daily on-chain check-in to earn credits
• Use credits to generate new assets
• Share creations on Twitter with event hashtags
• Refer a new user who mints an NFT
• Claim and showcase achievement badges

Points & Credits System:
• On-chain check-in = daily credits
• Generating images, minting, and using LoRA models = earn points
• Credits can be spent on Text-to-Image, Image-to-Video, or upcoming AIGC tools
• Points unlock leaderboard rankings, event prizes, and exclusive model access

Gamification Approach:
• Every task and interaction is rewarding—either with credits, badges, or visibility
• LoRA training adds creative depth and technical progression for power users
• Campaigns like seasonal meme contests, AI art duels, or theme weeks keep the platform dynamic

User Onboarding Strategy:
• No technical background needed—start with Telegram or web interface
• First-timers earn credits by simply checking in
• Advanced users can train their own models and monetize creative outputs
• Community and social features keep users engaged long after their first generation

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Nice one so, if I understand correctly with Defusion for example, could someone literally train a custom anime style using LoRA and then mint scenes from it as NFTs across different chains seamlessly ?

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nice spirit to have, would love to hear the answer about this :

  1. What makes Defusion different from other AI art platforms like Midjourney, DALL·E, or Runway, especially in terms of on-chain integration and user incentives?
  2. How does Defusion manage the compute cost of AI generation (e.g., Text-to-Image or Image-to-Video) at scale—especially with a free or credit-based model?
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What should I pay attention to when training my first LoRA model on Defusion? Can I share my trained model with other users, and will I earn points for doing so?

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Hello @Defusion , How are you?

I have few questions to ask :-

  1. How does Defusion’s Text-to-Image tool differ from existing platforms like Midjourney or Leonardo?
  2. How is ownership validated when users mint AI-generated content — is it IP-compliant, or just proof-of-creation?
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Yes, that’s absolutely correct! With Defusion, creators can train their own custom anime style using LoRA (Low-Rank Adaptation) and then generate high-quality scenes based on that style. Once generated, these scenes can be minted as NFTs across multiple supported blockchains—all from one platform.

Defusion currently supports 12 chains, making cross-chain minting seamless and efficient. It’s a powerful tool for both artists and collectors who want to bring their unique visions to life and reach audiences across ecosystems.

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Great questions—let’s break it down:

1. What makes Defusion different from platforms like Midjourney, DALL·E, or Runway?

While those platforms focus on high-quality AI generation, Defusion goes a step further by integrating Web3 at its core:

  • :link: On-Chain Integration: Users can mint AI-generated content directly as NFTs across 12+ supported blockchains. It’s not just about creating art—it’s about owning, trading, and showcasing it on-chain.
  • :wrapped_gift: User Incentives: Defusion rewards active users with credits, badges, and airdrops. These come from daily engagement, referrals, leaderboard events, and even community contests—something traditional platforms don’t offer.
  • :artist_palette: Creator Empowerment: With tools like LoRA training, Defusion lets artists fine-tune their own styles, giving creators full control over their IP and enabling monetization within the Web3 ecosystem.

2. How does Defusion manage compute cost at scale—especially with a free/credit model?

Defusion uses a credit-based system to balance accessibility and sustainability:

  • :white_check_mark: Free Onboarding: New users receive daily credits for basic generation (text-to-image, LoRA, etc.), allowing anyone to start creating at no cost.
  • :rocket: Scalable Compute Optimization: Behind the scenes, Defusion leverages a combination of cloud GPU scaling and batch optimization techniques to minimize cost per generation.
  • :coin: Incentivized Ecosystem: Users can earn or purchase credits through engagement, referrals, or holding NFTs. This gamifies the creative process and redistributes costs through community participation.

In short, Defusion isn’t just another AI art tool—it’s an on-chain creator platform where AI meets ownership, community, and rewards.

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Great question! If you’re training your first LoRA model on Defusion, here are some key tips:

:wrench: 1. Start with clean, consistent data
Use high-quality images that clearly represent the style or concept you want to train. Consistency in pose, lighting, and composition helps the model learn more effectively.

:bar_chart: 2. Limit the dataset at first
Start with a small set (e.g., 10–30 images) to test results quickly. You can scale up once you’re familiar with the process.

:gear: 3. Monitor training settings
Pay attention to learning rate, training steps, and resolution. Defusion provides default parameters, but adjusting these as you learn will improve output.

:test_tube: 4. Test generations early
Use checkpoints to preview results before finishing full training. This saves time and compute.

Yes, you can share your trained LoRA model with the community!

:globe_showing_europe_africa: Defusion allows users to publish their LoRA models for others to explore and use. When you share your model:

  • :busts_in_silhouette: It becomes available in the Discover section.
  • :trophy: You can earn points, badges, and even rewards based on usage, popularity, or events.
  • :chart_increasing: Top creators often get featured and may receive bonus airdrops.

So not only can you showcase your unique style, but you also contribute to the community and get rewarded in the process.

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  1. How does Defusion’s Text-to-Image tool differ from Midjourney or Leonardo?

While platforms like Midjourney and Leonardo focus on closed-generation ecosystems, Defusion is built with Web3 integration from the ground up:

:framed_picture: Creative Freedom: Defusion offers LoRA support, letting users fine-tune models to their own style—something not available on Midjourney.

:link: On-Chain Utility: Every generated image can be minted as an NFT across 12+ blockchains, which turns simple prompts into ownable, tradable digital assets.

:light_bulb: User Incentives: Users earn credits, badges, and airdrops through activity—creating a gamified, rewarding ecosystem for creators, unlike most AI tools that charge purely for output.

In short, Defusion isn’t just about generating images—it’s about building, owning, and monetizing your AI creativity.

  1. How is ownership validated when users mint AI-generated content?

Defusion uses proof-of-creation as the core ownership model:

:white_check_mark: When you mint your creation, the platform records your wallet as the origin—this becomes a permanent on-chain record of authorship.

:scroll: While this doesn’t replace traditional IP frameworks, it establishes verifiable provenance, which is widely accepted in Web3 as digital authorship.

:shield: Compliance tools and usage policies help avoid misuse of copyrighted material, but ultimately, creators are responsible for ensuring ethical and compliant content.

So in essence, minting on Defusion is proof that you created the content, and that you claimed ownership first—on-chain and transparent.

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A lot of ideas have come to expose creators but most have failed due to over estimation of the reach thier project solve. Can I knw what major problem defusion solve, and does it align with the major problems associated with onboarding traditional creators to web3.

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So how do you ensure fairness in rewarding the creators and those that engage.

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Defusion seems to be leading the way for a more creator-centric, future-focused AI platform. Can’t wait to see where this goes!

Thanks for answering :slight_smile: