LTX-Video 13B on IBM POWER8

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 • Quick Start • Hardware • Results • Technical Details

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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

ltx_13b_hybrid.py ⭐ Recommended

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

256×256 Preview "A glowing crystal rotating slowly in darkness"

512×512 High Resolution "A majestic phoenix rising from flames"

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

Related Projects

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

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Made with ⚡ by Elyan Labs

Proving that legacy hardware can run cutting-edge AI

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