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Enochiatron Showcase
Generated outputs from the Enochiatron Audio+Video LoRA, trained on LTX-2.3 22B DiT.
What's Here
Batch Generation (batch/)
Standard batch-generated clips using the P2 (audio+visual) LoRA checkpoint.
- 2 clips at different seeds (42, 123)
index.html— gallery viewer (download and open locally)batch_manifest.json— generation metadata
P1 vs P2 Comparison (comparison/)
Side-by-side comparison of Phase 1 (visual-only LoRA) vs Phase 2 (audio+visual LoRA):
p1/— Visual-only clips (no audio track)p2/— Audio+visual clips (Enochian chanting audio)sidebyside/— ffmpeg hstack composites with P1 left, P2 rightindex.html— comparison gallery viewer (download and open locally)comparison_manifest.json— full generation metadata
Generation Parameters
| Parameter | Value |
|---|---|
| Steps | 30 |
| CFG Scale | 3.0 |
| STG Scale | 1.0 |
| Modality Scale | 3.0 |
| LoRA Scale | 1.0 |
| Resolution | 768×352×81 frames |
| FPS | 25 |
| GPU | A100 80GB PCIe |
| P1 gen time | ~104s/clip |
| P2 gen time | ~118s/clip (+15% for audio) |
Related Repositories
| Repo | Description |
|---|---|
| enochiatron-lora | Trained LoRA checkpoints (P1 visual + P2 audio+visual) |
| enochiatron-muxed | Muxed training dataset (1027 Tron clips + 20 Enochian WAV) |
| enochiatron-encoded | Pre-encoded latents (VAE + Gemma cached .pt files) |
| timm156/enochiatron | Source code — full training & inference pipeline |
Gallery Viewing
The HTML gallery files use inline CSS with zero external dependencies. To view:
- Download the batch or comparison directory
- Open
index.htmlin any browser - Videos play directly in the browser
About Enochiatron
Enochiatron trains a simultaneous audio+video LoRA on LTX-2.3 22B DiT, fusing:
- Video: 1027 clips from Tron (digital world / "inside the computer" aesthetic)
- Audio: 20 WAV recordings of the complete Enochian language (Angelic Keys / Calls of the Aethyrs)
The result is a generative model that produces video+audio in the aesthetic intersection: digital beings speaking Enochian in neon-lit geometric spaces.
Built with Flimmer (Alvdansen Labs) training toolkit.
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