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import argparse |
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import glob |
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import json |
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import os |
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import torch |
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from accelerate import PartialState |
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from src_inference.lora_helper import set_single_lora |
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from src_inference.pipeline import FluxPipeline |
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from PIL import Image |
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def clear_cache(transformer): |
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for _, attn_processor in transformer.attn_processors.items(): |
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attn_processor.bank_kv.clear() |
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class style_processor: |
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def __init__(self, flux_path, lora_path, omni_path, device): |
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self.device = device |
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self.base_path = flux_path |
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self.pipe = FluxPipeline.from_pretrained( |
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self.base_path, torch_dtype=torch.bfloat16 |
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).to(self.device) |
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self.style_prompt = f"{os.path.basename(lora_path).replace('_rank128_bf16.safetensors', '').replace('_', ' ').title()} style, " |
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set_single_lora( |
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self.pipe.transformer, |
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omni_path, |
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lora_weights=[1], |
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cond_size=512, |
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) |
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self.pipe.unload_lora_weights() |
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self.pipe.load_lora_weights(lora_path, weight_name="lora_name.safetensors") |
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def process(self, image_path, prompt): |
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if isinstance(image_path, str): |
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spatial_image = [Image.open(image_path).convert("RGB")] |
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elif isinstance(image_path, Image.Image): |
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spatial_image = [image_path] |
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else: |
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raise ValueError(f"Invalid image type: {type(image_path)}") |
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subject_images = [] |
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width, height = spatial_image[0].size |
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image = self.pipe( |
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prompt, |
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height=height, |
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width=width, |
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guidance_scale=3.5, |
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num_inference_steps=25, |
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max_sequence_length=512, |
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generator=torch.Generator("cpu").manual_seed(5), |
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spatial_images=spatial_image, |
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subject_images=subject_images, |
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cond_size=512, |
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).images[0] |
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clear_cache(self.pipe.transformer) |
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return image |
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def get_images_from_path(path): |
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if os.path.isdir(path): |
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return glob.glob(os.path.join(path, "*.jpg")) + glob.glob( |
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os.path.join(path, "*.png") |
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) |
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elif os.path.isfile(path) and (path.endswith(".jpg") or path.endswith(".png")): |
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return [path] |
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else: |
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return [] |
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def parse_args(): |
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parser = argparse.ArgumentParser(description="Style processor") |
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parser.add_argument("--flux_path", type=str, required=True) |
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parser.add_argument("--lora_paths", type=str, required=True, nargs="+") |
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parser.add_argument("--omni_path", type=str, required=True) |
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parser.add_argument("--output_dir", type=str, required=True) |
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parser.add_argument("--prompt_dir", type=str, required=True) |
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parser.add_argument("--images_path", type=str, required=True) |
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return parser.parse_args() |
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if __name__ == "__main__": |
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args = parse_args() |
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flux_path = args.flux_path |
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lora_paths = args.lora_paths |
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omni_path = args.omni_path |
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output_dir = args.output_dir |
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prompt_dir = args.prompt_dir |
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images_path = args.images_path |
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distributed_state = PartialState() |
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device = distributed_state.device |
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rank = int(str(device).split(":")[1]) |
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lora = lora_paths[rank] |
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output_lora_path = os.path.join(output_dir, os.path.basename(lora)) |
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os.makedirs(output_lora_path, exist_ok=True) |
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processor = style_processor(flux_path, lora, omni_path, device) |
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images_path = get_images_from_path(images_path) |
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for image_path in images_path: |
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image_output_path = os.path.join(output_lora_path, os.path.basename(image_path)) |
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if os.path.exists(image_output_path): |
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print(f"File {image_output_path} already exists, skipping.") |
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continue |
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try: |
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with open( |
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os.path.join(prompt_dir, os.path.basename(image_path) + ".json") |
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) as f: |
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prompt = json.load(f)["caption"] |
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output = processor.process(image_path, processor.style_prompt + prompt) |
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output.save(image_output_path) |
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except Exception as e: |
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print(f"Error processing {image_path}: {e}") |
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