Rank 128 LoRA extraction of https://civarchive.com/models/2600510/juggernaut-z againt Z-Image-Base in order to be able to use it with Z-Image-Turbo. Mainly done because my setup (sd.cpp) don't work with quantized version of ZiB and derivatives 😭

All showcases pictures are using Euler+Smoothstep (~Beta), 8 steps, CFG 1, Flow shift 3, with a Q4_K quantized version of both ZiT and Qwen3-4B and were made on a GTX 1060 (6GB VRAM) using Stable-diffusion.cpp.

Full ownership of this LoRA stays with https://civarchive.com/user/KandooAI, merge are forbidden, this is for your personal use with ZiT on-site or locally only.

The extraction was done using this script adapted to be able to handle ANY model as long as they have the same keys (which means i had to re-key Juggernaut Z to unfuse the qkv layers).

import sys, re

# Very basic args check
if len(sys.argv) < 4:
    print(f"Usage: {sys.argv[0]} origin.safetensors tuned.safetensors lora.safetensors")
    exit(1)

# Importing the libs
from safetensors.torch import load_file, save_file
import torch
from tqdm import tqdm

# No gradient needed
torch.set_grad_enabled(False)


# The most BASIC svd extraction of lora weight
def extract_lora_weight(m, r):
    u, s, vh = torch.linalg.svd(m.cuda())
    B = u[:, :r] @ torch.diag(s[:r])
    A = vh[:r, :]
    return (B.cpu().contiguous(), A.cpu().contiguous())


# Load models
print(f"Loading {sys.argv[1]}...")
orig = load_file(sys.argv[1])
print(f"Loading {sys.argv[2]}...")
tune = load_file(sys.argv[2])

wk = list(set(orig) & set(tune))

# Get ready
lora = {}
rank = 128
count = 0

# Extract LoRA
print("Extracting...")
for k in tqdm(wk):
    diff = tune[k].to(dtype=torch.float) - orig[k].to(dtype=torch.float)
    if torch.mean(torch.abs(diff)) < 0.0001 or len(diff.shape) not in [1,2]:
        # Either not the right shape or not enough difference, let's skip it
        del diff
        continue
    if len(diff.shape) == 1:
        count +=1
        if k.endswith(".bias"):
            lora[k.replace(".bias", ".diff_b")] = diff.to(dtype=torch.bfloat16)
        else:
            lora[k.replace(".weight", ".diff")] = diff.to(dtype=torch.bfloat16)
        del diff
    else:
        if diff.shape[0] <= rank or diff.shape[1] <= rank:
            # Too small, let's skip it for now
            del diff
            continue
        count +=1
        B, A = extract_lora_weight(diff, rank)
        del diff
        base_key = k.replace(".weight", "")
        lora[f"{base_key}.lora_B.weight"] = B.to(dtype=torch.bfloat16)
        lora[f"{base_key}.lora_A.weight"] = A.to(dtype=torch.bfloat16)

# Save LoRA
print(f"Stored {count}/{len(wk)} keys to LoRA.")
print(f"Saving {sys.argv[3]} ...")
save_file(lora, sys.argv[3])
print("Done!")
exit(0)