import time
import torch
from PIL import Image
from src_inference.pipeline import FluxPipeline
from src_inference.lora_helper import set_single_lora

# torch.cuda.set_device(0)

def clear_cache(transformer):
    for name, attn_processor in transformer.attn_processors.items():
        attn_processor.bank_kv.clear()

base_path = "black-forest-labs/FLUX.1-dev"
pipe = FluxPipeline.from_pretrained(base_path, torch_dtype=torch.bfloat16).to("cuda")

set_single_lora(pipe.transformer, 
                "/path/to/OmniConsistency.safetensors", 
                lora_weights=[1], cond_size=512)

pipe.unload_lora_weights()
pipe.load_lora_weights("/path/to/lora_folder", 
                       weight_name="lora_name.safetensors")

image_path1 = "figure/test.png"
prompt = "3D Chibi style, Three individuals standing together in the office."

subject_images = []
spatial_image = [Image.open(image_path1).convert("RGB")]

width, height = 1024, 1024

start_time = time.time()

image = pipe(
    prompt,
    height=height,
    width=width,
    guidance_scale=3.5,
    num_inference_steps=25,
    max_sequence_length=512,
    generator=torch.Generator("cpu").manual_seed(5),
    spatial_images=spatial_image,
    subject_images=subject_images,
    cond_size=512,
).images[0]

end_time = time.time()
elapsed_time = end_time - start_time
print(f"code running time: {elapsed_time} s")

# Clear cache after generation
clear_cache(pipe.transformer)

image.save("results/output.png")