scenario-labs/z-image-lokr-repro

Z-Image LoKr — diffusers loading repro

Random weights — not a functional adapter. This file reproduces the key layout of a real Z-Image LoKr trained with an ai-toolkit/LyCORIS-style trainer (ComfyUI diffusion_model. key format), for reproducing https://github.com/huggingface/diffusers/issues/13221.

ZImagePipeline.load_lora_weights / lora_state_dict fails on it with ValueError: state_dict should be empty at this point because _convert_non_diffusers_z_image_lora_to_diffusers has no handling for lokr_w1 / lokr_w2 keys.

from diffusers import ZImagePipeline
from huggingface_hub import hf_hub_download

path = hf_hub_download("scenario-labs/z-image-lokr-repro", "zimage_lokr_dummy.safetensors")
ZImagePipeline.lora_state_dict(path)  # raises ValueError on diffusers <= 0.39 / main

Layout (612 tensors, 30 layers)

Mixed LoKr + plain LoRA:

module	format	layers	shapes
attention.to_q/k/v	LoKr	all 30	lokr_w1 [4,4], lokr_w2 [960,960], scalar alpha
adaLN_modulation.0	LoKr	all 30	lokr_w1 [4,4], lokr_w2 [3840,64], scalar alpha
attention.to_out.0	LoRA	0–17	lora_A [32,3840], lora_B [3840,32]
attention.to_out.0	LoKr	18–29	lokr_w1 [4,4], lokr_w2 [960,960], scalar alpha
feed_forward.w1/w2/w3	LoRA	all 30	rank 32

The same file loads fine in current ComfyUI (which fuses to_q/k/v into attention.qkv and supports LoKr via comfy/weight_adapter/lokr.py).