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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")
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