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style.py

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+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):
+        # Initialize model
+        self.device = device
+        self.base_path = flux_path  # assuming 'flux' is the base 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, "
+
+        # Load OmniConsistency model
+        set_single_lora(
+            self.pipe.transformer,
+            omni_path,
+            lora_weights=[1],
+            cond_size=512,
+        )
+
+        # Load external LoRA
+        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 after generation
+        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}")