PulpCut/Z-Image-Turbo-INT8-ConvRot-safetensors

Z-Image-Turbo, repackaged for H3ddle

The released weights rearranged into one file per subsystem so a native engine can validate and demand-page each independently. No tensor was retrained, merged, pruned, or quantized here.

file	what
transformer.safetensors	30 S3-DiT layers plus 2+2 refiners, int8 ConvRot
text_encoder.safetensors	Qwen3-4B
vae_decoder.safetensors	AutoencoderKL's decoder half
tokenizer.json	the released vocabulary, byte for byte

What was changed, and why

• One file per subsystem. Each can be checked against a reference and loaded on its own.
• The autoencoder's encoder half is gone. Text to image never encodes an image, so half of it is dead weight: 244 tensors down to 138, 167 MB to 99.
• The int8 matrices are stored input-major, transposed from the release. A GPU tile reading two weights a lane one output column apart finds them input_dim bytes apart in the original layout, so every lane of a simdgroup takes its own cache line. Transposed, both reads are adjacent — worth about 9% on Apple silicon. Values are untouched, and ConvRot rotates the activation rather than the weight, so the quantization is unaffected.
• The final layer's two small linears are back at bf16. Together they are 1.2M of 6.15B parameters — 2.5 MB against 1.2 — so the saving was never real, and final_layer.linear at [64, 3840] was the most aggressively quantized tensor in the model. This reverses quantization rather than applying it.
• recipe.json carries the constants that live only in the reference's Python: the flow-match shift, the latent scaling and shift, the DiT's hyperparameters. The engine reads them back instead of holding its own copy.

Provenance and licences

Z-Image-Turbo is by Alibaba Tongyi Lab under the Apache License 2.0. The diffusion transformer is the INT8-ConvRot quantization published by Martin Rizzo, also Apache 2.0, copied layer for layer. Both licences travel with this repository; see NOTICE.