Super-squash branch 'main' using huggingface_hub

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README.md

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+---
+license: mit
+language:
+- en
+---
+# Image Captioning App
+
+This is a mod of [Wi-zz/joy-caption-pre-alpha](https://huggingface.co/Wi-zz/joy-caption-pre-alpha) and [fancyfeast/joy-caption-alpha-two](https://huggingface.co/spaces/fancyfeast/joy-caption-alpha-two). Thanks to [dominic1021](https://huggingface.co/dominic1021).
+
+# Notice: I will contribute to Wi-zz after shaping the code.
+
+## Overview
+
+This application generates descriptive captions for images using advanced ML models. It processes single images or entire directories, leveraging CLIP and LLM models for accurate and contextual captions. It has NSFW captioning support with natural language. This is just an extension of the original author's efforts to improve performance. Their repo is located here: https://huggingface.co/spaces/fancyfeast/joy-caption-alpha-two.
+
+## Features
+
+- Single image and batch processing
+- Multiple directory support
+- Custom output directory
+- Adjustable batch size
+- Progress tracking
+
+## Usage
+
+| Command | Description |
+|---------|-------------|
+| `python app.py image.jpg` | Process a single image |
+| `python app.py /path/to/directory` | Process all images in a directory |
+| `python app.py /path/to/dir1 /path/to/dir2` | Process multiple directories |
+| `python app.py /path/to/dir --output /path/to/output` | Specify output directory |
+| `python app.py /path/to/dir --bs 8` | Set batch size (default: 4) |
+
+## Technical Details
+
+- **Models**: CLIP (vision), LLM (language), custom ImageAdapter
+- **Optimization**: CUDA-enabled GPU support
+- **Error Handling**: Skips problematic images in batch processing
+
+## Requirements
+
+- Python 3.x
+- PyTorch
+- Transformers library
+- PEFT library
+- CUDA-capable GPU (recommended)
+
+## Installation
+
+Windows
+
+```bash
+git clone https://huggingface.co/John6666/joy-caption-alpha-two-cli-mod
+cd joy-caption-alpha-two-cli-mod
+python -m venv venv
+.\venv\Scripts\activate
+# Change as per https://pytorch.org/get-started/locally/
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+pip install -r requirements.txt
+```
+
+Linux
+
+```bash
+git clone https://huggingface.co/John6666/joy-caption-alpha-two-cli-mod
+cd joy-caption-alpha-two-cli-mod
+python3 -m venv venv
+source venv/bin/activate
+pip3 install torch torchvision torchaudio
+pip3 install -r requirements.txt
+```
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit a Pull Request.
+
+## License
+
+This project is licensed under the [MIT License](LICENSE).

app.py

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+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()

cgrkzexw-599808/clip_model.pt

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+size 1713067838

cgrkzexw-599808/config.yaml

@@ -0,0 +1,39 @@
+wandb_project: joy-caption-1
+device_batch_size: 2
+batch_size: 256
+learning_rate: 0.0002
+warmup_samples: 18000
+max_samples: 600000
+save_every: 50000
+test_every: 50000
+use_amp: true
+grad_scaler: true
+lr_scheduler_type: cosine
+min_lr_ratio: 0.0
+allow_tf32: true
+seed: 69
+num_workers: 8
+optimizer_type: adamw
+adam_beta1: 0.9
+adam_beta2: 0.999
+adam_eps: 1.0e-08
+adam_weight_decay: 0.0
+clip_grad_norm: 1.0
+dataset: fancyfeast/joy-captioning-20240924a
+clip_model: google/siglip-so400m-patch14-384
+text_model: ../lora-train/lora_model_vwbzycxh
+resume: null
+gradient_checkpointing: false
+test_size: 2048
+grad_scaler_init: 65536.0
+max_caption_length: 257
+num_image_tokens: 32
+adapter_type: mlp
+text_model_dtype: bfloat16
+pre_test: false
+train_image_model: true
+image_model_lr: null
+train_lora: true
+lora_r: 64
+lora_alpha: 16
+lora_dropout: 0.1

cgrkzexw-599808/image_adapter.pt

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+size 86067714

cgrkzexw-599808/text_model/README.md

@@ -0,0 +1,202 @@
+---
+base_model: unsloth/Meta-Llama-3.1-8B-Instruct
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.12.0

cgrkzexw-599808/text_model/adapter_config.json

@@ -0,0 +1,34 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "unsloth/Meta-Llama-3.1-8B-Instruct",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj",
+    "gate_proj",
+    "down_proj",
+    "o_proj",
+    "k_proj",
+    "up_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

cgrkzexw-599808/text_model/adapter_model.safetensors

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cgrkzexw-599808/text_model/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|finetune_right_pad_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

cgrkzexw-599808/text_model/tokenizer_config.json

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+{
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+      "content": "<|begin_of_text|>",
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+      "single_word": false,
+      "special": true
+    },
+    "128230": {
+      "content": "<|reserved_special_token_222|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128231": {
+      "content": "<|reserved_special_token_223|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128232": {
+      "content": "<|reserved_special_token_224|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128233": {
+      "content": "<|reserved_special_token_225|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128234": {
+      "content": "<|reserved_special_token_226|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128235": {
+      "content": "<|reserved_special_token_227|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128236": {
+      "content": "<|reserved_special_token_228|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128237": {
+      "content": "<|reserved_special_token_229|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128238": {
+      "content": "<|reserved_special_token_230|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128239": {
+      "content": "<|reserved_special_token_231|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128240": {
+      "content": "<|reserved_special_token_232|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128241": {
+      "content": "<|reserved_special_token_233|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128242": {
+      "content": "<|reserved_special_token_234|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128243": {
+      "content": "<|reserved_special_token_235|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128244": {
+      "content": "<|reserved_special_token_236|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128245": {
+      "content": "<|reserved_special_token_237|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128246": {
+      "content": "<|reserved_special_token_238|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128247": {
+      "content": "<|reserved_special_token_239|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128248": {
+      "content": "<|reserved_special_token_240|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128249": {
+      "content": "<|reserved_special_token_241|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128250": {
+      "content": "<|reserved_special_token_242|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128251": {
+      "content": "<|reserved_special_token_243|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128252": {
+      "content": "<|reserved_special_token_244|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128253": {
+      "content": "<|reserved_special_token_245|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128254": {
+      "content": "<|reserved_special_token_246|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128255": {
+      "content": "<|reserved_special_token_247|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 July 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content'] %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\n\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\n\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\n\" }}\n{{- \"Today Date: \" + date_string + \"\n\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\n\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\n\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content'] %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\n\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\n\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\n\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\n\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|finetune_right_pad_id|>",
+  "padding_side": "right",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

requirements.txt

@@ -0,0 +1,10 @@
+huggingface_hub>=0.23.4
+accelerate
+torch
+transformers==4.44.0
+sentencepiece
+bitsandbytes
+Pillow
+protobuf
+peft==0.12.0
+torchvision