int4-flux.1-dev-lora.py

import torch
from diffusers import FluxPipeline

from nunchaku import NunchakuFluxTransformer2dModel

transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-dev")
pipeline = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")

### LoRA Related Code ###
transformer.update_lora_params(
    "mit-han-lab/svdquant-lora-collection/svdq-int4-flux.1-dev-ghibsky.safetensors"
)  # Path to your converted LoRA safetensors, can also be a remote HuggingFace path
transformer.set_lora_strength(1)  # Your LoRA strength here
### End of LoRA Related Code ###

image = pipeline(
    "GHIBSKY style, cozy mountain cabin covered in snow, with smoke curling from the chimney and a warm, inviting light spilling through the windows",
    num_inference_steps=25,
    guidance_scale=3.5,
).images[0]
image.save("flux.1-dev-ghibsky.png")