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 PeftModel import gc # Constants 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("9em124t2-499968") LORA_PATH = CHECKPOINT_PATH / "text_model" CAPTION_TYPE_MAP = { ("descriptive", "formal", False, False): ["Write a descriptive caption for this image in a formal tone."], ("descriptive", "formal", False, True): ["Write a descriptive caption for this image in a formal tone within {word_count} words."], ("descriptive", "formal", True, False): ["Write a {length} descriptive caption for this image in a formal tone."], ("descriptive", "informal", False, False): ["Write a descriptive caption for this image in a casual tone."], ("descriptive", "informal", False, True): ["Write a descriptive caption for this image in a casual tone within {word_count} words."], ("descriptive", "informal", True, False): ["Write a {length} descriptive caption for this image in a casual tone."], ("training_prompt", "formal", False, False): ["Write a stable diffusion prompt for this image."], ("training_prompt", "formal", False, True): ["Write a stable diffusion prompt for this image within {word_count} words."], ("training_prompt", "formal", True, False): ["Write a {length} stable diffusion prompt for this image."], ("rng-tags", "formal", False, False): ["Write a list of Booru tags for this image."], ("rng-tags", "formal", False, True): ["Write a list of Booru tags for this image within {word_count} words."], ("rng-tags", "formal", True, False): ["Write a {length} list of Booru tags for this image."], } IMAGE_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.bmp', '.webp') # Global Variables IS_NF4 = True IS_LORA = 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, IS_LORA 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(MODEL_PATH, use_fast=False) 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 False and IS_LORA and LORA_PATH.exists(): # omitted print("Loading VLM's custom text model 🤖") text_model = PeftModel.from_pretrained(model=text_model, model_id=LORA_PATH, device_map=device, quantization_config=nf4_config) text_model = text_model.merge_and_unload(safe_merge=True) # to avoid PEFT bug https://github.com/huggingface/transformers/issues/28515 else: print("VLM's custom text model isn't loaded 🤖") 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(MODEL_PATH, use_fast=False) 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="auto", torch_dtype=torch.bfloat16).eval() # device_map="auto" may cause LoRA issue if IS_LORA and LORA_PATH.exists(): print("Loading VLM's custom text model 🤖") text_model = PeftModel.from_pretrained(model=text_model, model_id=LORA_PATH, device_map=device) text_model = text_model.merge_and_unload(safe_merge=True) # to avoid PEFT bug https://github.com/huggingface/transformers/issues/28515 else: print("VLM's custom text model isn't loaded 🤖") 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_tone: str, caption_length: Union[str, int], 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 # 'rng-tags' and 'training_prompt' don't have formal/informal tones if caption_type == "rng-tags" or caption_type == "training_prompt": caption_tone = "formal" # Build prompt prompt_key = (caption_type, caption_tone, isinstance(length, str), isinstance(length, int)) if prompt_key not in CAPTION_TYPE_MAP: raise ValueError(f"Invalid caption type: {prompt_key}") prompt_str = CAPTION_TYPE_MAP[prompt_key][0].format(length=length, word_count=length) print(f"Prompt: {prompt_str}") for i in range(0, len(input_images), batch_size): batch = input_images[i:i+batch_size] # Preprocess image for input_image in input_images: try: 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]) pixel_values = pixel_values.to(device) except ValueError as e: print(f"Error processing image: {e}") print("Skipping this image and continuing...") continue # Embed image with torch.amp.autocast_mode.autocast(device, enabled=True): vision_outputs = clip_model(pixel_values=pixel_values, output_hidden_states=True) image_features = vision_outputs.hidden_states embedded_images = image_adapter(image_features).to(device) # Tokenize the prompt prompt = tokenizer.encode(prompt_str, return_tensors='pt', padding=False, truncation=False, add_special_tokens=False) # Embed prompt prompt_embeds = text_model.model.embed_tokens(prompt.to(device)) assert prompt_embeds.shape == (1, prompt.shape[1], text_model.config.hidden_size), f"Prompt shape is {prompt_embeds.shape}, expected {(1, prompt.shape[1], text_model.config.hidden_size)}" embedded_bos = text_model.model.embed_tokens(torch.tensor([[tokenizer.bos_token_id]], device=text_model.device, dtype=torch.int64)) eot_embed = image_adapter.get_eot_embedding().unsqueeze(0).to(dtype=text_model.dtype) # Construct prompts inputs_embeds = torch.cat([ embedded_bos.expand(embedded_images.shape[0], -1, -1), embedded_images.to(dtype=embedded_bos.dtype), prompt_embeds.expand(embedded_images.shape[0], -1, -1), eot_embed.expand(embedded_images.shape[0], -1, -1), ], dim=1) input_ids = torch.cat([ torch.tensor([[tokenizer.bos_token_id]], dtype=torch.long), torch.zeros((1, embedded_images.shape[1]), dtype=torch.long), prompt, torch.tensor([[tokenizer.convert_tokens_to_ids("<|eot_id|>")]], dtype=torch.long), ], dim=1).to(device) attention_mask = torch.ones_like(input_ids) generate_ids = text_model.generate(input_ids=input_ids, inputs_embeds=inputs_embeds, attention_mask=attention_mask, do_sample=True, suppress_tokens=None, max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature) # Trim off the prompt generate_ids = generate_ids[:, input_ids.shape[1]:] if generate_ids[0][-1] == tokenizer.eos_token_id or generate_ids[0][-1] == tokenizer.convert_tokens_to_ids("<|eot_id|>"): generate_ids = generate_ids[:, :-1] caption = tokenizer.batch_decode(generate_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)[0] all_captions.append(caption.strip()) if pbar: pbar.update(len(batch)) return all_captions def process_directory(input_dir: Path, output_dir: Path, caption_type: str, caption_tone: str, caption_length: Union[str, int], 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_tone, caption_length, 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", "training_prompt", "rng-tags"], help='Caption Type (default: "descriptive")') parser.add_argument("--tone", type=str, default="formal", choices=["formal", "informal"], help='Caption Tone (default: "formal")') parser.add_argument("--len", default="any", choices=["any", "very short", "short", "medium-length", "long", "very long"] + [str(i) for i in range(20, 261, 10)], help='Caption Length (default: "any")') 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", default=False, help="Use bfloat16 (default: NF4)") parser.add_argument("--nolora", action="store_true", default=False, help="Disable VLM's custom text model (default: Enable)") 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, IS_LORA args = parse_arguments() input_paths = [Path(input_path) for input_path in args.input] batch_size = args.bs caption_type = args.type caption_tone = args.tone caption_length = args.len max_new_tokens = args.tokens top_p = args.topp temperature = args.temp IS_NF4 = False if args.bf16 else True IS_LORA = False if args.nolora else 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_tone, caption_length, 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_tone, caption_length, 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()