Tenshi Deepfake [repack] Guide

Most Tenshi content relies on two pillars: visual face-swapping and voice cloning. Software like Stable Diffusion, combined with tools like Roop or ReActor, allows users to graft a specific digital face onto a video frame-by-frame. Simultaneously, RVC models trained on hours of a specific character’s or voice actor's speech allow creators to make the digital avatar speak, sing, or stream with a highly accurate synthetic voice. Live-Driving and Motion Capture

| Step | Action | Resources | |------|--------|-----------| | 1 | – Tenshi is released under a non‑commercial, responsible‑use license . | Tenshi‑License.pdf (available on the official repo). | | 2 | Set Up the Environment – Docker image with GPU support; includes pretrained backbone, fine‑tuning scripts, and verification tools. | docker pull tenshi/deepfake:latest | | 3 | Collect Consent‑Based Data – Use only publicly licensed footage or obtain written consent. Store metadata (date, source, consent proof). | Consent‑Management‑Toolkit (open‑source). | | 4 | Fine‑Tune the Model – Run the tenshi_fine_tune.py script with your target data (minimum 5‑10 minutes of video). | Documentation: docs/fine_tune.md . | | 5 | Generate Content – Provide a text prompt or source video, then run tenshi_generate.py . | Example scripts in examples/ . | | 6 | Verify & Watermark – Use tenshi_verify --extract to confirm the embedded watermark. | SDK: tenshi_sdk . | | 7 | Publish with Disclosure – Add a visible caption (“Synthetic media generated using Tenshi”) and retain the provenance file. | Publishing‑Guidelines.pdf. | tenshi deepfake

: Enhancing avatars with more fluid, AI-driven movements. Most Tenshi content relies on two pillars: visual

Tenshi’s management tried everything, but the unique nature of VTubing made defense impossible. Live-Driving and Motion Capture | Step | Action