Shruthi2606/magicbrush-lora-sd15

MagicBrush LoRA (UNet adapter) for Stable Diffusion v1.5

Model: LoRA adapter for Stable Diffusion v1.5 (UNet) Base model: runwayml/stable-diffusion-v1-5 Adapter file: unet_lora_final_5000.safetensors Trained on: MagicBrush dataset (osunlp/MagicBrush) Training steps: 5000 LoRA rank (r): 8 Device: Apple M4 Pro (MPS) Usage: Apply this adapter to SD v1.5 UNet to enable instruction-guided editing in MagicBrush style.

How to apply (Python example)

from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
import torch

pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to("mps")
lora = load_file("unet_lora_final_5000.safetensors")

# naive mapping — best-effort; adjust if attribute paths differ
def apply_lora_to_unet(unet, lora_state):
    for name, arr in lora_state.items():
        parts = name.split(".")
        target = unet
        for p in parts[:-1]:
            if hasattr(target, p):
                target = getattr(target, p)
            else:
                try:
                    idx = int(p)
                    target = target[idx]
                except Exception:
                    target = None
                    break
        if target is None:
            continue
        attr = parts[-1]
        if hasattr(target, attr):
            t = torch.from_numpy(arr) if not isinstance(arr, torch.Tensor) else arr
            getattr(target, attr).data.copy_(t.to(getattr(target, attr).device))

apply_lora_to_unet(pipe.unet, lora)
# then run pipe(...) as usual