| import argparse |
| import glob |
| import json |
| import os |
|
|
| import torch |
| from accelerate import PartialState |
| from src_inference.lora_helper import set_single_lora |
| from src_inference.pipeline import FluxPipeline |
| from PIL import Image |
|
|
|
|
| def clear_cache(transformer): |
| for _, attn_processor in transformer.attn_processors.items(): |
| attn_processor.bank_kv.clear() |
|
|
|
|
| class style_processor: |
| def __init__(self, flux_path, lora_path, omni_path, device): |
| |
| self.device = device |
| self.base_path = flux_path |
| self.pipe = FluxPipeline.from_pretrained( |
| self.base_path, torch_dtype=torch.bfloat16 |
| ).to(self.device) |
| self.style_prompt = f"{os.path.basename(lora_path).replace('_rank128_bf16.safetensors', '').replace('_', ' ').title()} style, " |
|
|
| |
| set_single_lora( |
| self.pipe.transformer, |
| omni_path, |
| lora_weights=[1], |
| cond_size=512, |
| ) |
|
|
| |
| self.pipe.unload_lora_weights() |
| self.pipe.load_lora_weights(lora_path, weight_name="lora_name.safetensors") |
|
|
| def process(self, image_path, prompt): |
| if isinstance(image_path, str): |
| spatial_image = [Image.open(image_path).convert("RGB")] |
| elif isinstance(image_path, Image.Image): |
| spatial_image = [image_path] |
| else: |
| raise ValueError(f"Invalid image type: {type(image_path)}") |
|
|
| subject_images = [] |
|
|
| width, height = spatial_image[0].size |
|
|
| image = self.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] |
|
|
| |
| clear_cache(self.pipe.transformer) |
|
|
| return image |
|
|
|
|
| def get_images_from_path(path): |
| if os.path.isdir(path): |
| return glob.glob(os.path.join(path, "*.jpg")) + glob.glob( |
| os.path.join(path, "*.png") |
| ) |
| elif os.path.isfile(path) and (path.endswith(".jpg") or path.endswith(".png")): |
| return [path] |
| else: |
| return [] |
|
|
|
|
| def parse_args(): |
| parser = argparse.ArgumentParser(description="Style processor") |
| parser.add_argument("--flux_path", type=str, required=True) |
| parser.add_argument("--lora_paths", type=str, required=True, nargs="+") |
| parser.add_argument("--omni_path", type=str, required=True) |
| parser.add_argument("--output_dir", type=str, required=True) |
| parser.add_argument("--prompt_dir", type=str, required=True) |
| parser.add_argument("--images_path", type=str, required=True) |
| return parser.parse_args() |
|
|
|
|
| if __name__ == "__main__": |
| args = parse_args() |
| flux_path = args.flux_path |
| lora_paths = args.lora_paths |
| omni_path = args.omni_path |
| output_dir = args.output_dir |
| prompt_dir = args.prompt_dir |
| images_path = args.images_path |
|
|
| distributed_state = PartialState() |
|
|
| device = distributed_state.device |
| rank = int(str(device).split(":")[1]) |
| lora = lora_paths[rank] |
|
|
| output_lora_path = os.path.join(output_dir, os.path.basename(lora)) |
| os.makedirs(output_lora_path, exist_ok=True) |
|
|
| processor = style_processor(flux_path, lora, omni_path, device) |
|
|
| images_path = get_images_from_path(images_path) |
| for image_path in images_path: |
| image_output_path = os.path.join(output_lora_path, os.path.basename(image_path)) |
| if os.path.exists(image_output_path): |
| print(f"File {image_output_path} already exists, skipping.") |
| continue |
|
|
| try: |
| with open( |
| os.path.join(prompt_dir, os.path.basename(image_path) + ".json") |
| ) as f: |
| prompt = json.load(f)["caption"] |
| output = processor.process(image_path, processor.style_prompt + prompt) |
| output.save(image_output_path) |
| except Exception as e: |
| print(f"Error processing {image_path}: {e}") |
|
|
