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ltx-video-power8

LTX-Video 13B inference on IBM POWER8 S824 - First 13B video diffusion model on PowerPC

Scottcjn
Repository
Component
Minimum
CPU
CPU
RAM
RAM
Storage
Storage

Specs

  • ComponentMinimum
  • CPUCPU
  • RAMRAM
  • StorageStorage
  • OSOS
  • Checkpoint KeyDiffusers Key
  • patchify_proj.*proj_in.*
  • attn*.q_norm.*attn*.norm_q.*
  • attn*.k_norm.*attn*.norm_k.*
  • adaln_single.*time_embed.*
  • ProjectDescription
  • llama-cpp-power8AltiVec/VSX optimized llama.cpp for POWER8
  • nvidia-power8-patchesModern NVIDIA drivers for POWER8 via OCuLink
  • power8-projectsUbuntu 22.04 build, PSE LLM, Darwin cross-compile

README

# LTX-Video 13B on IBM POWER8 [![PowerPC](https://img.shields.io/badge/PowerPC-POWER8-blue)](https://github.com/Scottcjn/ltx-video-power8) [![Python](https://img.shields.io/badge/Python-3.11+-green)](https://python.org) [![License](https://img.shields.io/badge/License-MIT-yellow)](LICENSE) [![Diffusers](https://img.shields.io/badge/Diffusers-0.32+-orange)](https://huggingface.co/docs/diffusers) [![BCOS Certified](https://img.shields.io/badge/BCOS-Certified-brightgreen?style=flat&logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCIgZmlsbD0id2hpdGUiPjxwYXRoIGQ9Ik0xMiAxTDMgNXY2YzAgNS41NSAzLjg0IDEwLjc0IDkgMTIgNS4xNi0xLjI2IDktNi40NSA5LTEyVjVsLTktNHptLTIgMTZsLTQtNCA1LjQxLTUuNDEgMS40MSAxLjQxTDEwIDE0bDYtNiAxLjQxIDEuNDFMMTAgMTd6Ii8+PC9zdmc+)](BCOS.md) **World's First 13B Video Diffusion Model on PowerPC Architecture** *Run state-of-the-art AI video generation on IBM POWER8 servers with 320GB RAM* [Features](#features) • [Quick Start](#quick-start) • [Hardware](#hardware-requirements) • [Results](#results) • [Technical Details](#technical-details)

Why This Matters

  • First Ever: No one has run a 13B parameter video diffusion model on PowerPC before
  • Legacy Hardware Revival: Your IBM POWER8 server can now generate AI videos
  • 320GB RAM Advantage: Fits entire model in memory - no GPU required
  • Open Source: Full pipeline, not just inference scripts

Features

  • QK-Norm Key Mapping for distilled 13B model architecture
  • Latent Packing/Unpacking for transformer input format
  • POWER8 Stack Workaround for multi-threading stability
  • Hybrid Threading - 5.7× speedup with smart thread switching

Quick Start

# Clone the repo
git clone https://github.com/Scottcjn/ltx-video-power8.git
cd ltx-video-power8

# Download models (you need both)
# 1. 13B distilled model → ~/models/ltx-video-13b/ltxv-13b-0.9.8-distilled.safetensors
# 2. Full LTX-Video model → ~/models/ltx-video-full/

# Run inference
cd scripts
python3 ltx_13b_hybrid.py

Hardware Requirements

Component Minimum Recommended
CPU IBM POWER8 POWER8 S824 (dual 8-core)
RAM 64GB 320GB
Storage 50GB 100GB SSD
OS Ubuntu 20.04 Ubuntu 20.04 LTS

Model Architecture

The 13B distilled LTX-Video model uses different key names than standard diffusers:

Checkpoint Key Diffusers Key
patchify_proj.* proj_in.*
attn*.q_norm.* attn*.norm_q.*
attn*.k_norm.* attn*.norm_k.*
adaln_single.* time_embed.*

Our scripts handle this mapping automatically.

Scripts

ltx_13b_full.py

Complete single-threaded pipeline. Safe but slow.

  • Resolution: 256×256 | Frames: 9 | Steps: 4
  • Time: ~30 seconds

Hybrid multi-threaded pipeline with 5.7× speedup.

  • Multi-threading for transformer (safe on POWER8)
  • Single-thread for VAE decode (required)
  • Time: ~65 seconds vs 370 seconds

ltx_13b_hires.py

High-resolution generation.

  • Resolution: 512×512 | Frames: 17 | Steps: 8
  • Time: ~54 minutes

Results

Configuration Resolution Frames Time Output
Single-thread 256×256 9 30s 511KB
Hybrid 256×256 9 65s 531KB
High-res 512×512 17 54min 3.9MB

Example Outputs

<table>
<tr>
<td align="center">

<br>
<sub><b>256×256 Preview</b><br>"A glowing crystal rotating slowly in darkness"</sub>
</td>
<td align="center">

<br>
<sub><b>512×512 High Resolution</b><br>"A majestic phoenix rising from flames"</sub>
</td>
</tr>
</table>

Technical Details

POWER8 Stack Corruption Workaround

POWER8 exhibits stack smashing errors with multi-threaded PyTorch in certain code paths (particularly VAE decode):

# Force single-threaded for VAE
os.environ["OMP_NUM_THREADS"] = "1"
torch.set_num_threads(1)

The hybrid script dynamically switches between multi-threaded (transformer) and single-threaded (VAE) modes.

Latent Packing

The diffusers transformer expects pre-patchified input:

def pack_latents(latents, patch_size=1, patch_size_t=1):
    # [B, C, F, H, W] → [B, F*H*W, C]
    ...

RoPE Dimensions

Rotary position embeddings expect latent-space dimensions, not video dimensions:

# Correct: pass latent dimensions
num_frames = (FRAMES - 1) // 8 + 1  # latent frames
height = RESOLUTION // 32           # latent height  
width = RESOLUTION // 32            # latent width

Dependencies

torch>=2.1.0
diffusers>=0.32.0
transformers>=4.39.0
safetensors
pillow
numpy
Project Description
llama-cpp-power8 AltiVec/VSX optimized llama.cpp for POWER8
nvidia-power8-patches Modern NVIDIA drivers for POWER8 via OCuLink
power8-projects Ubuntu 22.04 build, PSE LLM, Darwin cross-compile

Acknowledgments

  • Lightricks for the LTX-Video model
  • Hugging Face for diffusers library
  • IBM for POWER8 architecture documentation

License

MIT License

**Made with ⚡ by [Elyan Labs](https://elyanlabs.ai)** *Proving that legacy hardware can run cutting-edge AI*

Part of the Elyan Labs Ecosystem

  • BoTTube — AI video platform where 119+ agents create content
  • RustChain — Proof-of-Antiquity blockchain with hardware attestation
  • GitHub