app.py

import torch
import torch.amp.autocast_mode
import os
import sys
import logging
import warnings
import argparse
from PIL import Image
from pathlib import Path
from tqdm import tqdm
from torch import nn
from transformers import AutoModel, AutoProcessor, AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast, AutoModelForCausalLM
from typing import List, Union
import torchvision.transforms.functional as TVF
from peft import PeftConfig
import gc

# Constants
IMAGE_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.bmp', '.webp')
HF_TOKEN = os.environ.get("HF_TOKEN", None)
BASE_DIR = Path(__file__).resolve().parent # Define the base directory
CLIP_PATH = "google/siglip-so400m-patch14-384"
DEFAULT_MODEL_PATH = "unsloth/Meta-Llama-3.1-8B-bnb-4bit"
#DEFAULT_MODEL_PATH = "Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2" # Works better but full weight.
CHECKPOINT_PATH = BASE_DIR / Path("cgrkzexw-599808")
LORA_PATH = CHECKPOINT_PATH / "text_model"
CAPTION_TYPE_MAP = {
    "Descriptive": [
        "Write a descriptive caption for this image in a formal tone.",
        "Write a descriptive caption for this image in a formal tone within {word_count} words.",
        "Write a {length} descriptive caption for this image in a formal tone.",
    ],
    "Descriptive (Informal)": [
        "Write a descriptive caption for this image in a casual tone.",
        "Write a descriptive caption for this image in a casual tone within {word_count} words.",
        "Write a {length} descriptive caption for this image in a casual tone.",
    ],
    "Training Prompt": [
        "Write a stable diffusion prompt for this image.",
        "Write a stable diffusion prompt for this image within {word_count} words.",
        "Write a {length} stable diffusion prompt for this image.",
    ],
    "MidJourney": [
        "Write a MidJourney prompt for this image.",
        "Write a MidJourney prompt for this image within {word_count} words.",
        "Write a {length} MidJourney prompt for this image.",
    ],
    "Booru tag list": [
        "Write a list of Booru tags for this image.",
        "Write a list of Booru tags for this image within {word_count} words.",
        "Write a {length} list of Booru tags for this image.",
    ],
    "Booru-like tag list": [
        "Write a list of Booru-like tags for this image.",
        "Write a list of Booru-like tags for this image within {word_count} words.",
        "Write a {length} list of Booru-like tags for this image.",
    ],
    "Art Critic": [
        "Analyze this image like an art critic would with information about its composition, style, symbolism, the use of color, light, any artistic movement it might belong to, etc.",
        "Analyze this image like an art critic would with information about its composition, style, symbolism, the use of color, light, any artistic movement it might belong to, etc. Keep it within {word_count} words.",
        "Analyze this image like an art critic would with information about its composition, style, symbolism, the use of color, light, any artistic movement it might belong to, etc. Keep it {length}.",
    ],
    "Product Listing": [
        "Write a caption for this image as though it were a product listing.",
        "Write a caption for this image as though it were a product listing. Keep it under {word_count} words.",
        "Write a {length} caption for this image as though it were a product listing.",
    ],
    "Social Media Post": [
        "Write a caption for this image as if it were being used for a social media post.",
        "Write a caption for this image as if it were being used for a social media post. Limit the caption to {word_count} words.",
        "Write a {length} caption for this image as if it were being used for a social media post.",
    ],
}

class ImageAdapter(nn.Module):
    def __init__(self, input_features: int, output_features: int, ln1: bool, pos_emb: bool, num_image_tokens: int, deep_extract: bool):
        super().__init__()
        self.deep_extract = deep_extract

        if self.deep_extract:
            input_features = input_features * 5

        self.linear1 = nn.Linear(input_features, output_features)
        self.activation = nn.GELU()
        self.linear2 = nn.Linear(output_features, output_features)
        self.ln1 = nn.Identity() if not ln1 else nn.LayerNorm(input_features)
        self.pos_emb = None if not pos_emb else nn.Parameter(torch.zeros(num_image_tokens, input_features))

        # Other tokens (<|image_start|>, <|image_end|>, <|eot_id|>)
        self.other_tokens = nn.Embedding(3, output_features)
        self.other_tokens.weight.data.normal_(mean=0.0, std=0.02)   # Matches HF's implementation of llama3

    def forward(self, vision_outputs: torch.Tensor):
        if self.deep_extract:
            x = torch.concat((
                vision_outputs[-2],
                vision_outputs[3],
                vision_outputs[7],
                vision_outputs[13],
                vision_outputs[20],
            ), dim=-1)
            assert len(x.shape) == 3, f"Expected 3, got {len(x.shape)}"  # batch, tokens, features
            assert x.shape[-1] == vision_outputs[-2].shape[-1] * 5, f"Expected {vision_outputs[-2].shape[-1] * 5}, got {x.shape[-1]}"
        else:
            x = vision_outputs[-2]

        x = self.ln1(x)

        if self.pos_emb is not None:
            assert x.shape[-2:] == self.pos_emb.shape, f"Expected {self.pos_emb.shape}, got {x.shape[-2:]}"
            x = x + self.pos_emb

        x = self.linear1(x)
        x = self.activation(x)
        x = self.linear2(x)

        # <|image_start|>, IMAGE, <|image_end|>
        other_tokens = self.other_tokens(torch.tensor([0, 1], device=self.other_tokens.weight.device).expand(x.shape[0], -1))
        assert other_tokens.shape == (x.shape[0], 2, x.shape[2]), f"Expected {(x.shape[0], 2, x.shape[2])}, got {other_tokens.shape}"
        x = torch.cat((other_tokens[:, 0:1], x, other_tokens[:, 1:2]), dim=1)

        return x

    def get_eot_embedding(self):
        return self.other_tokens(torch.tensor([2], device=self.other_tokens.weight.device)).squeeze(0)


# Global Variables
IS_NF4 = True
MODEL_PATH = DEFAULT_MODEL_PATH
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Running on {device}")

warnings.filterwarnings("ignore", category=UserWarning)
logging.getLogger("transformers").setLevel(logging.ERROR)

class ImageAdapter(nn.Module):
    def __init__(self, input_features: int, output_features: int, ln1: bool, pos_emb: bool, num_image_tokens: int, deep_extract: bool):
        super().__init__()
        self.deep_extract = deep_extract

        if self.deep_extract:
            input_features = input_features * 5

        self.linear1 = nn.Linear(input_features, output_features)
        self.activation = nn.GELU()
        self.linear2 = nn.Linear(output_features, output_features)
        self.ln1 = nn.Identity() if not ln1 else nn.LayerNorm(input_features)
        self.pos_emb = None if not pos_emb else nn.Parameter(torch.zeros(num_image_tokens, input_features))

        # Mode token
        #self.mode_token = nn.Embedding(n_modes, output_features)
        #self.mode_token.weight.data.normal_(mean=0.0, std=0.02)   # Matches HF's implementation of llama3

        # Other tokens (<|image_start|>, <|image_end|>, <|eot_id|>)
        self.other_tokens = nn.Embedding(3, output_features)
        self.other_tokens.weight.data.normal_(mean=0.0, std=0.02)   # Matches HF's implementation of llama3

    def forward(self, vision_outputs: torch.Tensor):
        if self.deep_extract:
            x = torch.concat((
                vision_outputs[-2],
                vision_outputs[3],
                vision_outputs[7],
                vision_outputs[13],
                vision_outputs[20],
            ), dim=-1)
            assert len(x.shape) == 3, f"Expected 3, got {len(x.shape)}"  # batch, tokens, features
            assert x.shape[-1] == vision_outputs[-2].shape[-1] * 5, f"Expected {vision_outputs[-2].shape[-1] * 5}, got {x.shape[-1]}"
        else:
            x = vision_outputs[-2]

        x = self.ln1(x)

        if self.pos_emb is not None:
            assert x.shape[-2:] == self.pos_emb.shape, f"Expected {self.pos_emb.shape}, got {x.shape[-2:]}"
            x = x + self.pos_emb

        x = self.linear1(x)
        x = self.activation(x)
        x = self.linear2(x)

        # Mode token
        #mode_token = self.mode_token(mode)
        #assert mode_token.shape == (x.shape[0], mode_token.shape[1], x.shape[2]), f"Expected {(x.shape[0], 1, x.shape[2])}, got {mode_token.shape}"
        #x = torch.cat((x, mode_token), dim=1)

        # <|image_start|>, IMAGE, <|image_end|>
        other_tokens = self.other_tokens(torch.tensor([0, 1], device=self.other_tokens.weight.device).expand(x.shape[0], -1))
        assert other_tokens.shape == (x.shape[0], 2, x.shape[2]), f"Expected {(x.shape[0], 2, x.shape[2])}, got {other_tokens.shape}"
        x = torch.cat((other_tokens[:, 0:1], x, other_tokens[:, 1:2]), dim=1)

        return x

    def get_eot_embedding(self):
        return self.other_tokens(torch.tensor([2], device=self.other_tokens.weight.device)).squeeze(0)

def load_models():
    global MODEL_PATH, IS_NF4
    try:
        if IS_NF4:
            from transformers import BitsAndBytesConfig
            nf4_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
                                            bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16)
            print("Loading in NF4")
            print("Loading CLIP 📎")
            clip_processor = AutoProcessor.from_pretrained(CLIP_PATH)
            clip_model = AutoModel.from_pretrained(CLIP_PATH).vision_model
            if (CHECKPOINT_PATH / "clip_model.pt").exists():
                print("Loading VLM's custom vision model 📎")
                checkpoint = torch.load(CHECKPOINT_PATH / "clip_model.pt", map_location='cpu', weights_only=False)
                checkpoint = {k.replace("_orig_mod.module.", ""): v for k, v in checkpoint.items()}
                clip_model.load_state_dict(checkpoint)
                del checkpoint
            clip_model.eval().requires_grad_(False).to(device)

            print("Loading tokenizer 🪙")
            tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_PATH / "text_model", use_fast=True)
            assert isinstance(tokenizer, (PreTrainedTokenizer, PreTrainedTokenizerFast)), f"Tokenizer is of type {type(tokenizer)}"

            print(f"Loading LLM: {MODEL_PATH} 🤖")
            text_model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, quantization_config=nf4_config, device_map=device, torch_dtype=torch.bfloat16).eval()

            if LORA_PATH.exists():
                print("Loading VLM's custom text model 🤖")
                peft_config = PeftConfig.from_pretrained(LORA_PATH, device_map=device, quantization_config=nf4_config)
                text_model.add_adapter(peft_config)
                text_model.enable_adapters()

            print("Loading image adapter 🖼️")
            image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size, False, False, 38, False).eval().to("cpu")
            image_adapter.load_state_dict(torch.load(CHECKPOINT_PATH / "image_adapter.pt", map_location="cpu", weights_only=False))
            image_adapter.eval().to(device)
        else:
            print("Loading in bfloat16")
            print("Loading CLIP 📎")
            clip_processor = AutoProcessor.from_pretrained(CLIP_PATH)
            clip_model = AutoModel.from_pretrained(CLIP_PATH).vision_model
            if (CHECKPOINT_PATH / "clip_model.pt").exists():
                print("Loading VLM's custom vision model 📎")
                checkpoint = torch.load(CHECKPOINT_PATH / "clip_model.pt", map_location='cpu', weights_only=False)
                checkpoint = {k.replace("_orig_mod.module.", ""): v for k, v in checkpoint.items()}
                clip_model.load_state_dict(checkpoint)
                del checkpoint
            clip_model.eval().requires_grad_(False).to(device)

            print("Loading tokenizer 🪙")
            tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_PATH / "text_model", use_fast=True)
            assert isinstance(tokenizer, (PreTrainedTokenizer, PreTrainedTokenizerFast)), f"Tokenizer is of type {type(tokenizer)}"

            print(f"Loading LLM: {MODEL_PATH} 🤖")
            text_model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map=device, torch_dtype=torch.bfloat16).eval() # device_map=auto may cause LoRA error

            if LORA_PATH.exists():
                print("Loading VLM's custom text model 🤖")
                peft_config = PeftConfig.from_pretrained(LORA_PATH, device_map=device)
                text_model.add_adapter(peft_config)
                text_model.enable_adapters()

            print("Loading image adapter 🖼️")
            image_adapter = ImageAdapter(clip_model.config.hidden_size, text_model.config.hidden_size, False, False, 38, False).eval().to("cpu")
            image_adapter.load_state_dict(torch.load(CHECKPOINT_PATH / "image_adapter.pt", map_location="cpu", weights_only=False))
    except Exception as e:
        print(f"Error loading models: {e}")
        sys.exit(1)
    finally:
        torch.cuda.empty_cache()
        gc.collect()
    return clip_processor, clip_model, tokenizer, text_model, image_adapter

@torch.inference_mode()
def stream_chat(input_images: List[Image.Image], caption_type: str, caption_length: Union[str, int], extra_options: list[str], name_input: str, custom_prompt: str,
                max_new_tokens: int, top_p: float, temperature: float, batch_size: int, pbar: tqdm, models: tuple) -> List[str]:
    global MODEL_PATH
    clip_processor, clip_model, tokenizer, text_model, image_adapter = models
    torch.cuda.empty_cache()
    all_captions = []

    # 'any' means no length specified
    length = None if caption_length == "any" else caption_length

    if isinstance(length, str):
        try:
            length = int(length)
        except ValueError:
            pass
    
    # Build prompt
    if length is None:
        map_idx = 0
    elif isinstance(length, int):
        map_idx = 1
    elif isinstance(length, str):
        map_idx = 2
    else:
        raise ValueError(f"Invalid caption length: {length}")
    
    prompt_str = CAPTION_TYPE_MAP[caption_type][map_idx]

    # Add extra options
    if len(extra_options) > 0:
        prompt_str += " " + " ".join(extra_options)
    
    # Add name, length, word_count
    prompt_str = prompt_str.format(name=name_input, length=caption_length, word_count=caption_length)

    if custom_prompt.strip() != "":
        prompt_str = custom_prompt.strip()
    
    # For debugging
    print(f"Prompt: {prompt_str}")

    for i in range(0, len(input_images), batch_size):
        batch = input_images[i:i+batch_size]
        try:
            # Preprocess image
            # NOTE: I found the default processor for so400M to have worse results than just using PIL directly
            #image = clip_processor(images=input_image, return_tensors='pt').pixel_values
            all_images = []
            for input_image in batch:
                image = input_image.resize((384, 384), Image.LANCZOS)
                pixel_values = TVF.pil_to_tensor(image).unsqueeze(0) / 255.0
                pixel_values = TVF.normalize(pixel_values, [0.5], [0.5])
                all_images.append(TVF.to_pil_image(pixel_values.squeeze()))
            batch_pixel_values = clip_processor(images=all_images, return_tensors='pt', padding=True).pixel_values.to(device)
        except ValueError as e:
            print(f"Error processing image batch: {e}")
            print("Skipping this batch and continuing...")
            continue

        # Embed image
        # This results in Batch x Image Tokens x Features
        with torch.amp.autocast_mode.autocast(device, enabled=True):
            vision_outputs = clip_model(pixel_values=batch_pixel_values, output_hidden_states=True)
            image_features = vision_outputs.hidden_states
            embedded_images = image_adapter(image_features).to(device)

        # Build the conversation
        convo = [
            {
                "role": "system",
                "content": "You are a helpful image captioner.",
            },
            {
                "role": "user",
                "content": prompt_str,
            },
        ]

        # Format the conversation
        convo_string = tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = True)
        assert isinstance(convo_string, str)

        # Tokenize the conversation
        # prompt_str is tokenized separately so we can do the calculations below
        convo_tokens = tokenizer.encode(convo_string, return_tensors="pt", add_special_tokens=False, truncation=False)
        prompt_tokens = tokenizer.encode(prompt_str, return_tensors="pt", add_special_tokens=False, truncation=False)
        assert isinstance(convo_tokens, torch.Tensor) and isinstance(prompt_tokens, torch.Tensor)
        convo_tokens = convo_tokens.squeeze(0)   # Squeeze just to make the following easier
        prompt_tokens = prompt_tokens.squeeze(0)

        # Calculate where to inject the image
        eot_id_indices = (convo_tokens == tokenizer.convert_tokens_to_ids("<|eot_id|>")).nonzero(as_tuple=True)[0].tolist()
        assert len(eot_id_indices) == 2, f"Expected 2 <|eot_id|> tokens, got {len(eot_id_indices)}"

        preamble_len = eot_id_indices[1] - prompt_tokens.shape[0]   # Number of tokens before the prompt

        # Embed the tokens
        convo_embeds = text_model.model.embed_tokens(convo_tokens.unsqueeze(0).to(device))

        # Construct the input
        input_embeds = torch.cat([
            convo_embeds[:, :preamble_len],   # Part before the prompt
            embedded_images.to(dtype=convo_embeds.dtype),   # Image
            convo_embeds[:, preamble_len:],   # The prompt and anything after it
        ], dim=1).to(device)

        input_ids = torch.cat([
            convo_tokens[:preamble_len].unsqueeze(0),
            torch.zeros((1, embedded_images.shape[1]), dtype=torch.long),   # Dummy tokens for the image (TODO: Should probably use a special token here so as not to confuse any generation algorithms that might be inspecting the input)
            convo_tokens[preamble_len:].unsqueeze(0),
        ], dim=1).to(device)
        attention_mask = torch.ones_like(input_ids)

        # Debugging
        #print(f"Input to model: {repr(tokenizer.decode(input_ids[0]))}")

        generate_ids = text_model.generate(input_ids=input_ids, inputs_embeds=input_embeds, attention_mask=attention_mask, do_sample=True, 
                                           suppress_tokens=None, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature)

        generate_ids = generate_ids[:, input_ids.shape[1]:]

        for ids in generate_ids:
            caption = tokenizer.decode(ids[:-1] if ids[-1] == tokenizer.eos_token_id else ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
            caption = caption.replace('<|end_of_text|>', '').replace('<|finetune_right_pad_id|>', '').strip()
            all_captions.append(caption)

        if pbar:
            pbar.update(len(batch))

    return all_captions

def process_directory(input_dir: Path, output_dir: Path, caption_type: str, caption_length: Union[str, int], extra_options: list[str], name_input: str, custom_prompt: str,
                      max_new_tokens: int, top_p: float, temperature: float, batch_size: int, models: tuple):
    output_dir.mkdir(parents=True, exist_ok=True)
    image_files = [f for f in input_dir.iterdir() if f.suffix.lower() in IMAGE_EXTENSIONS]
    images_to_process = [f for f in image_files if not (output_dir / f"{f.stem}.txt").exists()]

    if not images_to_process:
        print("No new images to process.")
        return

    with tqdm(total=len(images_to_process), desc="Processing images", unit="image") as pbar:
        for i in range(0, len(images_to_process), batch_size):
            batch_files = images_to_process[i:i+batch_size]
            batch_images = [Image.open(f).convert('RGB') for f in batch_files]

            captions = stream_chat(batch_images, caption_type, caption_length, extra_options, name_input, custom_prompt,
                                   max_new_tokens, top_p, temperature, batch_size, pbar, models)
            
            for file, caption in zip(batch_files, captions):
                with open(output_dir / f"{file.stem}.txt", 'w', encoding='utf-8') as f:
                    f.write(caption)

            for img in batch_images:
                img.close()

def parse_arguments():
    parser = argparse.ArgumentParser(description="Process images and generate captions.")
    parser.add_argument("input", nargs='+', help="Input image file or directory (or multiple directories)")
    parser.add_argument("--output", help="Output directory (optional)")
    parser.add_argument("--bs", type=int, default=4, help="Batch size (default: 4)")
    parser.add_argument("--type", type=str, default="Descriptive",
                        choices=["Descriptive", "Descriptive (Informal)", "Training Prompt", "MidJourney", "Booru tag list", "Booru-like tag list", "Art Critic", "Product Listing", "Social Media Post"],
                        help='Caption Type (default: "Descriptive")')
    parser.add_argument("--len", default="long",
                        choices=["any", "very short", "short", "medium-length", "long", "very long"] + [str(i) for i in range(20, 261, 10)],
                        help='Caption Length (default: "long")')
    parser.add_argument("--extra", default=[], type=list[str], help='Extra Options',
                        choices=[
                            "If there is a person/character in the image you must refer to them as {name}.",
                            "Do NOT include information about people/characters that cannot be changed (like ethnicity, gender, etc), but do still include changeable attributes (like hair style).",
                            "Include information about lighting.",
                            "Include information about camera angle.",
                            "Include information about whether there is a watermark or not.",
                            "Include information about whether there are JPEG artifacts or not.",
                            "If it is a photo you MUST include information about what camera was likely used and details such as aperture, shutter speed, ISO, etc.",
                            "Do NOT include anything sexual; keep it PG.",
                            "Do NOT mention the image's resolution.",
                            "You MUST include information about the subjective aesthetic quality of the image from low to very high.",
                            "Include information on the image's composition style, such as leading lines, rule of thirds, or symmetry.",
                            "Do NOT mention any text that is in the image.",
                            "Specify the depth of field and whether the background is in focus or blurred.",
                            "If applicable, mention the likely use of artificial or natural lighting sources.",
                            "Do NOT use any ambiguous language.",
                            "Include whether the image is sfw, suggestive, or nsfw.",
                            "ONLY describe the most important elements of the image."
                        ])
    parser.add_argument("--name", type=str, default="", help='Person/Character Name (if applicable)')
    parser.add_argument("--prompt", type=str, default="", help='Custom Prompt (optional, will override all other settings)')
    parser.add_argument("--model", type=str, default=DEFAULT_MODEL_PATH,
                        help='Huggingface LLM repo (default: "unsloth/Meta-Llama-3.1-8B-bnb-4bit")')
    parser.add_argument("--bf16", action="store_true", help="Use bfloat16 (default: NF4)")
    parser.add_argument("--tokens", type=int, default=300, help="Max tokens (default: 300)")
    parser.add_argument("--topp", type=float, default=0.9, help="Top-P (default: 0.9)")
    parser.add_argument("--temp", type=float, default=0.6, help="Temperature (default: 0.6)")
    return parser.parse_args()

def is_valid_repo(repo_id):
    from huggingface_hub import HfApi
    import re
    try:
        if not re.fullmatch(r'^[^/,\s\"\']+/[^/,\s\"\']+$', repo_id): return False
        api = HfApi()
        if api.repo_exists(repo_id=repo_id): return True
        else: return False
    except Exception as e:
        print(f"Failed to connect {repo_id}. {e}")
        return False

def main():
    global MODEL_PATH, IS_NF4
    args = parse_arguments()
    input_paths = [Path(input_path) for input_path in args.input]
    batch_size = args.bs
    caption_type = args.type
    caption_length = args.len
    extra_options = args.extra
    name_input = args.name
    custom_prompt = args.prompt
    max_new_tokens = args.tokens
    top_p = args.topp
    temperature = args.temp
    if args.bf16: IS_NF4 = False
    else: IS_NF4 = True
    if is_valid_repo(args.model): MODEL_PATH = args.model
    else: sys.exit(1)
    models = load_models()

    for input_path in input_paths:
        if input_path.is_file() and input_path.suffix.lower() in IMAGE_EXTENSIONS:
            output_path = input_path.with_suffix('.txt')
            print(f"Processing single image 🎞️: {input_path.name}")
            with tqdm(total=1, desc="Processing image", unit="image") as pbar:
                captions = stream_chat([Image.open(input_path).convert('RGB')], caption_type, caption_length, extra_options, name_input, custom_prompt,
                                       max_new_tokens, top_p, temperature, 1, pbar, models)
                with open(output_path, 'w', encoding='utf-8') as f:
                    f.write(captions[0])
            print(f"Output saved to {output_path}")
        elif input_path.is_dir():
            output_path = Path(args.output) if args.output else input_path
            print(f"Processing directory 📁: {input_path}")
            print(f"Output directory 📦: {output_path}")
            print(f"Batch size 🗄️: {batch_size}")
            process_directory(input_path, output_path, caption_type, caption_length, extra_options, name_input, custom_prompt,
                              max_new_tokens, top_p, temperature, batch_size, models)
        else:
            print(f"Invalid input: {input_path}")
            print("Skipping...")

    if not input_paths:
        print("Usage:")
        print("For single image: python app.py [image_file] [--bs batch_size]")
        print("For directory (same input/output): python app.py [directory] [--bs batch_size]")
        print("For directory (separate input/output): python app.py [directory] --output [output_directory] [--bs batch_size]")
        print("For multiple directories: python app.py [directory1] [directory2] ... [--output output_directory] [--bs batch_size]")
        sys.exit(1)

if __name__ == "__main__":
    main()