cl4ud1a — LoRA adapter (Z-Image Turbo, rank=16)

This folder holds the training experiment named cl4ud1a — a LoRA adapter for Tongyi-MAI/Z-Image-Turbo (Z-Image Turbo) tuned with rank 16 parameters.

Key facts

Base model: Tongyi-MAI/Z-Image-Turbo (arch: zimage:turbo)
LoRA type: UNet LoRA (linear=16, conv=16, alpha=16)
Training steps: 3000 (checkpoints saved every 250 steps)
Save format: Diffusers safetensors (dtype: bf16)
Training device: cuda
Quantization: qfloat8 applied to model and text encoder

Artifacts in this folder

cl4ud1a.safetensors — final LoRA/adapted weights (merged in training pipeline)
cl4ud1a_00000XXXXX.safetensors — saved checkpoints (step increments)
optimizer.pt — optimizer state (checkpoint)
config.yaml — original run configuration used for training
log.txt — raw training log (progress, warnings, reproducibility notes)
samples/ — generated sample images at a few checkpoints

Training & notes observed

Training used a small dataset (~14 images, augmented to 28 via flips) at mixed resolutions (768–1024). Latents and text embeddings were cached for speed.
A PIL-based EXIF parsing error appeared for one PNG during preprocessing; dataset sanitation is recommended before reproduction (see log snippet).
Assistant LoRA adapter was loaded/merged during training — see config.yaml for assistant adapter path.

How to reproduce (short)

Ensure you have the same base model (Tongyi-MAI/Z-Image-Turbo) accessible.
Recreate the environment with GPU + CUDA and BF16 support.
Use config.yaml to re-run the trainer used by the author (dataset paths will need adjustment).

Usage example (consumer)

To apply the LoRA at inference time, use your Z-Image-Turbo-compatible pipeline loader and merge or inject the safetensors file into the UNet weights (example depends on your runner/adapter).

If you plan to upload this experiment to Hugging Face: include cl4ud1a.safetensors, config.yaml, log.txt and a short model card describing license and data provenance.