Evoy AI Technology Versions

Evoy AI Technology Versions – Technical Overview

Evoy AI evolves across multiple generations, each unlocking new capabilities in AI-powered video generation. Below is the full technical specification and comparison of each version.

🔹 Evoy AI V1 — Lightweight Generation Engine

Target Use Case: Fast generation, preview content, meme-format clips

• AI Model: Latent Diffusion Model (LDM)

• Architecture:

  • Basic UNet diffusion backbone

  • Frame-by-frame synthesis with no inter-frame coherence

• Rendering Backend:

  • Optimized for low VRAM (4–6GB)

  • CPU fallback mode enabled (longer inference time)

• Audio Support: Not supported

• Latency: ~5–10 seconds

• Video Duration: Up to 5 seconds

• Strengths:

  • Fast generation for low-resolution previews

  • Minimal hardware requirements

• Limitations:

  • No frame consistency

  • Unrealistic lighting or transitions

🔸 Evoy AI V2 — Mid-Range Realism Engine

Target Use Case: Character rendering, expressive facial animation

• AI Model: Hybrid Transformer-Diffusion

• Architecture:

  • Temporal attention encoder

  • Frame interpolation for smoother motion

  • Enhanced text-prompt parser

• Rendering Backend:

  • Requires 10–14GB VRAM (NVIDIA RTX recommended)

  • Runs on distributed inference clusters for scale

• Audio Support: Not supported

• Latency: ~15–20 seconds

• Video Duration: Up to 8 seconds

• Strengths:

  • Better facial detail and object integrity

  • Improved prompt accuracy

• Limitations:

  • No native audio

  • Still limited in cinematic realism

🔻 Evoy AI V3 — Cinematic Engine with Voice Support

Target Use Case: Storytelling, emotional content, branded video clips

• AI Model: Multi-Modal Transformer + Speech Synthesis

• Architecture:

  • Integrated video + voice model

  • Emotion tracking and lip-sync engine

  • Context-aware gesture generation

• Rendering Backend:

  • Multi-GPU node processing

  • Powered by NVIDIA Triton Inference Server

• Audio Support: Fully supported (TTS / voice cloning)

• Latency: ~25–30 seconds

• Video Duration: Up to 8 seconds

• Strengths:

  • High-fidelity realism with synced audio

  • Emotionally expressive avatars

• Limitations:

  • High GPU requirements (16GB+ VRAM per job)

🧩 Technical Stack Summary

Component	Technology
Training Framework	PyTorch + HuggingFace Transformers
Dataset Sources	WebVid, Laion-5B, proprietary motion datasets
Inference Scheduler	Custom CUDA + token-time weight allocator
Audio Engine	Tacotron2 + WaveGlow / Bark / ElevenLabs
Deployment	Kubernetes + Auto-scaling GPU nodes
API Access	REST + GraphQL endpoints (token-gated)

🚀 Future Optimizations (Planned)

• Multi-character generation

• Dynamic background environments

• 12s–20s generation support

• Real-time text-to-video stream