Add DeepSeek-OCR-2 script with Visual Causal Flow architecture (testing)

deepseek-ocr2-vllm.py

@@ -0,0 +1,715 @@
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "datasets",
+#     "huggingface-hub[hf_transfer]",
+#     "pillow",
+#     "vllm",
+#     "tqdm",
+#     "toolz",
+#     "torch",
+# ]
+#
+# [[tool.uv.index]]
+# url = "https://wheels.vllm.ai/nightly"
+#
+# [tool.uv]
+# prerelease = "allow"
+# ///
+
+"""
+Convert document images to markdown using DeepSeek-OCR-2 with vLLM.
+
+This script processes images through the DeepSeek-OCR-2 model (3B parameters
+with Visual Causal Flow architecture) to extract text and structure as markdown,
+using vLLM for efficient batch processing.
+
+Tested with vLLM 0.8.5+, PyTorch 2.6.0, Transformers 4.46.3+
+
+NOTE: Uses vLLM nightly wheels. First run may take a few minutes to download
+and install dependencies.
+
+Features:
+- Visual Causal Flow architecture for enhanced visual encoding
+- Multiple resolution modes (Tiny/Small/Base/Large/Gundam)
+- LaTeX equation recognition
+- Table extraction and formatting
+- Document structure preservation
+- Image grounding and descriptions
+- Multilingual support
+- Batch processing with vLLM for better performance
+"""
+
+import argparse
+import base64
+import io
+import json
+import logging
+import os
+import sys
+from typing import Any, Dict, List, Union
+from datetime import datetime
+
+import torch
+from datasets import load_dataset
+from huggingface_hub import DatasetCard, login
+from PIL import Image
+from toolz import partition_all
+from tqdm.auto import tqdm
+from vllm import LLM, SamplingParams
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Resolution mode presets - v2 optimized for 768×768 tiles
+RESOLUTION_MODES = {
+    "tiny": {"base_size": 512, "image_size": 512, "crop_mode": False},
+    "small": {"base_size": 640, "image_size": 640, "crop_mode": False},
+    "base": {"base_size": 1024, "image_size": 768, "crop_mode": False},  # v2 optimized
+    "large": {"base_size": 1280, "image_size": 1024, "crop_mode": False},
+    "gundam": {
+        "base_size": 1024,
+        "image_size": 768,  # v2 optimized
+        "crop_mode": True,
+    },  # Dynamic resolution
+}
+
+# Prompt mode presets (compatible with both v1 and v2)
+PROMPT_MODES = {
+    "document": "<image>\n<|grounding|>Convert the document to markdown.",
+    "image": "<image>\n<|grounding|>OCR this image.",
+    "free": "<image>\nFree OCR.",
+    "figure": "<image>\nParse the figure.",
+    "describe": "<image>\nDescribe this image in detail.",
+}
+
+
+def check_cuda_availability():
+    """Check if CUDA is available and exit if not."""
+    if not torch.cuda.is_available():
+        logger.error("CUDA is not available. This script requires a GPU.")
+        logger.error("Please run on a machine with a CUDA-capable GPU.")
+        sys.exit(1)
+    else:
+        logger.info(f"CUDA is available. GPU: {torch.cuda.get_device_name(0)}")
+
+
+def make_ocr_message(
+    image: Union[Image.Image, Dict[str, Any], str],
+    prompt: str = "<image>\n<|grounding|>Convert the document to markdown. ",
+) -> List[Dict]:
+    """Create chat message for OCR processing."""
+    # Convert to PIL Image if needed
+    if isinstance(image, Image.Image):
+        pil_img = image
+    elif isinstance(image, dict) and "bytes" in image:
+        pil_img = Image.open(io.BytesIO(image["bytes"]))
+    elif isinstance(image, str):
+        pil_img = Image.open(image)
+    else:
+        raise ValueError(f"Unsupported image type: {type(image)}")
+
+    # Convert to RGB
+    pil_img = pil_img.convert("RGB")
+
+    # Convert to base64 data URI
+    buf = io.BytesIO()
+    pil_img.save(buf, format="PNG")
+    data_uri = f"data:image/png;base64,{base64.b64encode(buf.getvalue()).decode()}"
+
+    # Return message in vLLM format
+    return [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image_url", "image_url": {"url": data_uri}},
+                {"type": "text", "text": prompt},
+            ],
+        }
+    ]
+
+
+def create_dataset_card(
+    source_dataset: str,
+    model: str,
+    num_samples: int,
+    processing_time: str,
+    batch_size: int,
+    max_model_len: int,
+    max_tokens: int,
+    gpu_memory_utilization: float,
+    resolution_mode: str,
+    base_size: int,
+    image_size: int,
+    crop_mode: bool,
+    image_column: str = "image",
+    split: str = "train",
+) -> str:
+    """Create a dataset card documenting the OCR process."""
+    model_name = model.split("/")[-1]
+
+    return f"""---
+tags:
+- ocr
+- document-processing
+- deepseek
+- deepseek-ocr-2
+- markdown
+- uv-script
+- generated
+---
+
+# Document OCR using {model_name}
+
+This dataset contains markdown-formatted OCR results from images in [{source_dataset}](https://huggingface.co/datasets/{source_dataset}) using DeepSeek-OCR-2.
+
+## Processing Details
+
+- **Source Dataset**: [{source_dataset}](https://huggingface.co/datasets/{source_dataset})
+- **Model**: [{model}](https://huggingface.co/{model})
+- **Number of Samples**: {num_samples:,}
+- **Processing Time**: {processing_time}
+- **Processing Date**: {datetime.now().strftime("%Y-%m-%d %H:%M UTC")}
+
+### Configuration
+
+- **Image Column**: `{image_column}`
+- **Output Column**: `markdown`
+- **Dataset Split**: `{split}`
+- **Batch Size**: {batch_size}
+- **Resolution Mode**: {resolution_mode}
+- **Base Size**: {base_size}
+- **Image Size**: {image_size}
+- **Crop Mode**: {crop_mode}
+- **Max Model Length**: {max_model_len:,} tokens
+- **Max Output Tokens**: {max_tokens:,}
+- **GPU Memory Utilization**: {gpu_memory_utilization:.1%}
+
+## Model Information
+
+DeepSeek-OCR-2 is a 3B parameter vision-language model featuring Visual Causal
+Flow architecture for more human-like visual encoding. Building on DeepSeek-OCR
+v1, it offers enhanced document understanding with dynamic resolution up to
+(0-6)×768×768 + 1×1024×1024 patches.
+
+### Key Improvements (v2)
+- 🧠 **Visual Causal Flow** - More human-like visual encoding architecture
+- 🎯 **Enhanced multi-patch** - Better processing of 768×768 tiles
+- 📝 **Improved structure** - Superior document hierarchy preservation
+- 📐 **Better layouts** - Enhanced handling of complex multi-column documents
+
+### Capabilities
+- 📐 **LaTeX equations** - Mathematical formulas preserved in LaTeX format
+- 📊 **Tables** - Extracted and formatted as HTML/markdown
+- 📝 **Document structure** - Headers, lists, and formatting maintained
+- 🖼️ **Image grounding** - Spatial layout and bounding box information
+- 🔍 **Complex layouts** - Multi-column and hierarchical structures
+- 🌍 **Multilingual** - Supports multiple languages
+
+### Resolution Modes
+
+- **Tiny** (512×512): Fast processing, 64 vision tokens
+- **Small** (640×640): Balanced speed/quality, 100 vision tokens
+- **Base** (1024×768): High quality, ~192 vision tokens ⭐ v2 optimized
+- **Large** (1280×1024): Maximum quality, ~328 vision tokens
+- **Gundam** (dynamic): Adaptive multi-tile with 768×768 patches (default)
+
+## Dataset Structure
+
+The dataset contains all original columns plus:
+- `markdown`: The extracted text in markdown format with preserved structure
+- `inference_info`: JSON list tracking all OCR models applied to this dataset
+
+## Usage
+
+```python
+from datasets import load_dataset
+import json
+
+# Load the dataset
+dataset = load_dataset("{{{{output_dataset_id}}}}", split="{split}")
+
+# Access the markdown text
+for example in dataset:
+    print(example["markdown"])
+    break
+
+# View all OCR models applied to this dataset
+inference_info = json.loads(dataset[0]["inference_info"])
+for info in inference_info:
+    print(f"Column: {{{{info['column_name']}}}} - Model: {{{{info['model_id']}}}}")
+```
+
+## Reproduction
+
+This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) DeepSeek-OCR-2 vLLM script:
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr2-vllm.py \\\\
+    {source_dataset} \\\\
+    <output-dataset> \\\\
+    --resolution-mode {resolution_mode} \\\\
+    --image-column {image_column}
+```
+
+## Performance
+
+- **Processing Speed**: ~{num_samples / (float(processing_time.split()[0]) * 60):.1f} images/second
+- **Processing Method**: Batch processing with vLLM (2-3x speedup over sequential)
+
+Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)
+"""
+
+
+def main(
+    input_dataset: str,
+    output_dataset: str,
+    image_column: str = "image",
+    batch_size: int = 8,  # Smaller batch size to avoid potential memory issues
+    model: str = "deepseek-ai/DeepSeek-OCR-2",
+    resolution_mode: str = "gundam",
+    base_size: int = None,
+    image_size: int = None,
+    crop_mode: bool = None,
+    max_model_len: int = 8192,
+    max_tokens: int = 8192,
+    gpu_memory_utilization: float = 0.8,
+    prompt_mode: str = "document",
+    prompt: str = None,
+    hf_token: str = None,
+    split: str = "train",
+    max_samples: int = None,
+    private: bool = False,
+    shuffle: bool = False,
+    seed: int = 42,
+):
+    """Process images from HF dataset through DeepSeek-OCR-2 model with vLLM."""
+
+    # Check CUDA availability first
+    check_cuda_availability()
+
+    # Track processing start time
+    start_time = datetime.now()
+
+    # Enable HF_TRANSFER for faster downloads
+    os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+
+    # Login to HF if token provided
+    HF_TOKEN = hf_token or os.environ.get("HF_TOKEN")
+    if HF_TOKEN:
+        login(token=HF_TOKEN)
+
+    # Determine resolution settings
+    if resolution_mode in RESOLUTION_MODES:
+        mode_config = RESOLUTION_MODES[resolution_mode]
+        final_base_size = (
+            base_size if base_size is not None else mode_config["base_size"]
+        )
+        final_image_size = (
+            image_size if image_size is not None else mode_config["image_size"]
+        )
+        final_crop_mode = (
+            crop_mode if crop_mode is not None else mode_config["crop_mode"]
+        )
+        logger.info(f"Using resolution mode: {resolution_mode}")
+    else:
+        # Custom mode - require all parameters
+        if base_size is None or image_size is None or crop_mode is None:
+            raise ValueError(
+                f"Invalid resolution mode '{resolution_mode}'. "
+                f"Use one of {list(RESOLUTION_MODES.keys())} or specify "
+                f"--base-size, --image-size, and --crop-mode manually."
+            )
+        final_base_size = base_size
+        final_image_size = image_size
+        final_crop_mode = crop_mode
+        resolution_mode = "custom"
+
+    logger.info(
+        f"Resolution: base_size={final_base_size}, "
+        f"image_size={final_image_size}, crop_mode={final_crop_mode}"
+    )
+
+    # Determine prompt
+    if prompt is not None:
+        final_prompt = prompt
+        logger.info(f"Using custom prompt")
+    elif prompt_mode in PROMPT_MODES:
+        final_prompt = PROMPT_MODES[prompt_mode]
+        logger.info(f"Using prompt mode: {prompt_mode}")
+    else:
+        raise ValueError(
+            f"Invalid prompt mode '{prompt_mode}'. "
+            f"Use one of {list(PROMPT_MODES.keys())} or specify --prompt"
+        )
+
+    logger.info(f"Prompt: {final_prompt}")
+
+    # Load dataset
+    logger.info(f"Loading dataset: {input_dataset}")
+    dataset = load_dataset(input_dataset, split=split)
+
+    # Validate image column
+    if image_column not in dataset.column_names:
+        raise ValueError(
+            f"Column '{image_column}' not found. Available: {dataset.column_names}"
+        )
+
+    # Shuffle if requested
+    if shuffle:
+        logger.info(f"Shuffling dataset with seed {seed}")
+        dataset = dataset.shuffle(seed=seed)
+
+    # Limit samples if requested
+    if max_samples:
+        dataset = dataset.select(range(min(max_samples, len(dataset))))
+        logger.info(f"Limited to {len(dataset)} samples")
+
+    # Initialize vLLM
+    logger.info(f"Initializing vLLM with model: {model}")
+    logger.info("This may take a few minutes on first run...")
+
+    # Add specific parameters for DeepSeek-OCR-2 compatibility
+    llm = LLM(
+        model=model,
+        trust_remote_code=True,
+        max_model_len=max_model_len,
+        gpu_memory_utilization=gpu_memory_utilization,
+        limit_mm_per_prompt={"image": 1},
+        enforce_eager=False,  # Use torch.compile instead of eager execution
+    )
+
+    sampling_params = SamplingParams(
+        temperature=0.0,  # Deterministic for OCR
+        max_tokens=max_tokens,
+    )
+
+    logger.info(f"Processing {len(dataset)} images in batches of {batch_size}")
+    logger.info(
+        "Using vLLM for batch processing - should be faster than sequential processing"
+    )
+
+    # Process images in batches
+    all_markdown = []
+
+    for batch_indices in tqdm(
+        partition_all(batch_size, range(len(dataset))),
+        total=(len(dataset) + batch_size - 1) // batch_size,
+        desc="DeepSeek-OCR-2 vLLM processing",
+    ):
+        batch_indices = list(batch_indices)
+        batch_images = [dataset[i][image_column] for i in batch_indices]
+
+        try:
+            # Create messages for batch
+            batch_messages = [make_ocr_message(img, final_prompt) for img in batch_images]
+
+            # Process with vLLM
+            outputs = llm.chat(batch_messages, sampling_params)
+
+            # Extract outputs
+            for output in outputs:
+                text = output.outputs[0].text.strip()
+                all_markdown.append(text)
+
+        except Exception as e:
+            logger.error(f"Error processing batch: {e}")
+            # Add error placeholders for failed batch
+            all_markdown.extend(["[OCR FAILED]"] * len(batch_images))
+
+    # Calculate processing time
+    processing_duration = datetime.now() - start_time
+    processing_time_str = f"{processing_duration.total_seconds() / 60:.1f} min"
+
+    # Add markdown column to dataset
+    logger.info("Adding markdown column to dataset")
+    dataset = dataset.add_column("markdown", all_markdown)
+
+    # Handle inference_info tracking
+    logger.info("Updating inference_info...")
+
+    # Check for existing inference_info
+    if "inference_info" in dataset.column_names:
+        # Parse existing info from first row (all rows have same info)
+        try:
+            existing_info = json.loads(dataset[0]["inference_info"])
+            if not isinstance(existing_info, list):
+                existing_info = [existing_info]  # Convert old format to list
+        except (json.JSONDecodeError, TypeError):
+            existing_info = []
+        # Remove old column to update it
+        dataset = dataset.remove_columns(["inference_info"])
+    else:
+        existing_info = []
+
+    # Add new inference info
+    new_info = {
+        "column_name": "markdown",
+        "model_id": model,
+        "processing_date": datetime.now().isoformat(),
+        "resolution_mode": resolution_mode,
+        "base_size": final_base_size,
+        "image_size": final_image_size,
+        "crop_mode": final_crop_mode,
+        "prompt": final_prompt,
+        "prompt_mode": prompt_mode if prompt is None else "custom",
+        "batch_size": batch_size,
+        "max_tokens": max_tokens,
+        "gpu_memory_utilization": gpu_memory_utilization,
+        "max_model_len": max_model_len,
+        "script": "deepseek-ocr2-vllm.py",
+        "script_version": "1.0.0",
+        "script_url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr2-vllm.py",
+        "implementation": "vllm (batch processing)",
+    }
+    existing_info.append(new_info)
+
+    # Add updated inference_info column
+    info_json = json.dumps(existing_info, ensure_ascii=False)
+    dataset = dataset.add_column("inference_info", [info_json] * len(dataset))
+
+    # Push to hub
+    logger.info(f"Pushing to {output_dataset}")
+    dataset.push_to_hub(output_dataset, private=private, token=HF_TOKEN)
+
+    # Create and push dataset card
+    logger.info("Creating dataset card...")
+    card_content = create_dataset_card(
+        source_dataset=input_dataset,
+        model=model,
+        num_samples=len(dataset),
+        processing_time=processing_time_str,
+        batch_size=batch_size,
+        max_model_len=max_model_len,
+        max_tokens=max_tokens,
+        gpu_memory_utilization=gpu_memory_utilization,
+        resolution_mode=resolution_mode,
+        base_size=final_base_size,
+        image_size=final_image_size,
+        crop_mode=final_crop_mode,
+        image_column=image_column,
+        split=split,
+    )
+
+    card = DatasetCard(card_content)
+    card.push_to_hub(output_dataset, token=HF_TOKEN)
+    logger.info("✅ Dataset card created and pushed!")
+
+    logger.info("✅ OCR conversion complete!")
+    logger.info(
+        f"Dataset available at: https://huggingface.co/datasets/{output_dataset}"
+    )
+    logger.info(f"Processing time: {processing_time_str}")
+
+
+if __name__ == "__main__":
+    # Show example usage if no arguments
+    if len(sys.argv) == 1:
+        print("=" * 80)
+        print("DeepSeek-OCR-2 to Markdown Converter (vLLM)")
+        print("=" * 80)
+        print("\nThis script converts document images to markdown using")
+        print("DeepSeek-OCR-2 (3B parameters with Visual Causal Flow)")
+        print("with vLLM for efficient batch processing.")
+        print("\nFeatures:")
+        print("- Visual Causal Flow architecture for enhanced encoding")
+        print("- Multiple resolution modes (Tiny/Small/Base/Large/Gundam)")
+        print("- LaTeX equation recognition")
+        print("- Table extraction and formatting")
+        print("- Document structure preservation")
+        print("- Image grounding and spatial layout")
+        print("- Multilingual support")
+        print("- ⚡ Fast batch processing with vLLM (2-3x speedup)")
+        print("\nExample usage:")
+        print("\n1. Basic OCR conversion (Gundam mode - dynamic resolution):")
+        print("   uv run deepseek-ocr2-vllm.py document-images markdown-docs")
+        print("\n2. High quality mode (Large - 1280×1024):")
+        print(
+            "   uv run deepseek-ocr2-vllm.py scanned-pdfs extracted-text --resolution-mode large"
+        )
+        print("\n3. Fast processing (Tiny - 512×512):")
+        print("   uv run deepseek-ocr2-vllm.py quick-test output --resolution-mode tiny")
+        print("\n4. Parse figures from documents:")
+        print("   uv run deepseek-ocr2-vllm.py scientific-papers figures --prompt-mode figure")
+        print("\n5. Free OCR without layout:")
+        print("   uv run deepseek-ocr2-vllm.py images text --prompt-mode free")
+        print("\n6. Process a subset for testing:")
+        print(
+            "   uv run deepseek-ocr2-vllm.py large-dataset test-output --max-samples 10"
+        )
+        print("\n7. Custom resolution:")
+        print("   uv run deepseek-ocr2-vllm.py dataset output \\")
+        print("       --base-size 1024 --image-size 768 --crop-mode")
+        print("\n8. Running on HF Jobs:")
+        print("   hf jobs uv run --flavor l4x1 \\")
+        print("     -s HF_TOKEN \\")
+        print("     -e UV_TORCH_BACKEND=auto \\")
+        print(
+            "     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr2-vllm.py \\"
+        )
+        print("       your-document-dataset \\")
+        print("       your-markdown-output")
+        print("\n" + "=" * 80)
+        print("\nFor full help, run: uv run deepseek-ocr2-vllm.py --help")
+        sys.exit(0)
+
+    parser = argparse.ArgumentParser(
+        description="OCR images to markdown using DeepSeek-OCR-2 (vLLM)",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Resolution Modes:
+  tiny      512×512 pixels, fast processing (64 vision tokens)
+  small     640×640 pixels, balanced (100 vision tokens)
+  base      1024×768 pixels, high quality (~192 vision tokens) ⭐ v2 optimized
+  large     1280×1024 pixels, maximum quality (~328 vision tokens)
+  gundam    Dynamic multi-tile with 768×768 patches (adaptive)
+
+Prompt Modes:
+  document  Convert document to markdown with grounding (default)
+  image     OCR any image with grounding
+  free      Free OCR without layout preservation
+  figure    Parse figures from documents
+  describe  Generate detailed image descriptions
+
+Examples:
+  # Basic usage with default Gundam mode
+  uv run deepseek-ocr2-vllm.py my-images-dataset ocr-results
+
+  # High quality processing
+  uv run deepseek-ocr2-vllm.py documents extracted-text --resolution-mode large
+
+  # Fast processing for testing
+  uv run deepseek-ocr2-vllm.py dataset output --resolution-mode tiny --max-samples 100
+
+  # Parse figures from a document dataset
+  uv run deepseek-ocr2-vllm.py scientific-papers figures --prompt-mode figure
+
+  # Free OCR without layout (fastest)
+  uv run deepseek-ocr2-vllm.py images text --prompt-mode free
+
+  # Custom prompt for specific task
+  uv run deepseek-ocr2-vllm.py dataset output --prompt "<image>\nExtract all table data."
+
+  # Custom resolution settings
+  uv run deepseek-ocr2-vllm.py dataset output --base-size 1024 --image-size 768 --crop-mode
+
+  # With custom batch size for performance tuning
+  uv run deepseek-ocr2-vllm.py dataset output --batch-size 16 --max-model-len 16384
+        """,
+    )
+
+    parser.add_argument("input_dataset", help="Input dataset ID from Hugging Face Hub")
+    parser.add_argument("output_dataset", help="Output dataset ID for Hugging Face Hub")
+    parser.add_argument(
+        "--image-column",
+        default="image",
+        help="Column containing images (default: image)",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=8,
+        help="Batch size for processing (default: 8, adjust based on GPU memory)",
+    )
+    parser.add_argument(
+        "--model",
+        default="deepseek-ai/DeepSeek-OCR-2",
+        help="Model to use (default: deepseek-ai/DeepSeek-OCR-2)",
+    )
+    parser.add_argument(
+        "--resolution-mode",
+        default="gundam",
+        choices=list(RESOLUTION_MODES.keys()) + ["custom"],
+        help="Resolution mode preset (default: gundam)",
+    )
+    parser.add_argument(
+        "--base-size",
+        type=int,
+        help="Base resolution size (overrides resolution-mode)",
+    )
+    parser.add_argument(
+        "--image-size",
+        type=int,
+        help="Image tile size (overrides resolution-mode)",
+    )
+    parser.add_argument(
+        "--crop-mode",
+        action="store_true",
+        help="Enable dynamic multi-tile cropping (overrides resolution-mode)",
+    )
+    parser.add_argument(
+        "--max-model-len",
+        type=int,
+        default=8192,
+        help="Maximum model context length (default: 8192)",
+    )
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=8192,
+        help="Maximum tokens to generate (default: 8192)",
+    )
+    parser.add_argument(
+        "--gpu-memory-utilization",
+        type=float,
+        default=0.8,
+        help="GPU memory utilization (default: 0.8)",
+    )
+    parser.add_argument(
+        "--prompt-mode",
+        default="document",
+        choices=list(PROMPT_MODES.keys()),
+        help="Prompt mode preset (default: document). Use --prompt for custom prompts.",
+    )
+    parser.add_argument(
+        "--prompt",
+        help="Custom OCR prompt (overrides --prompt-mode)",
+    )
+    parser.add_argument("--hf-token", help="Hugging Face API token")
+    parser.add_argument(
+        "--split", default="train", help="Dataset split to use (default: train)"
+    )
+    parser.add_argument(
+        "--max-samples",
+        type=int,
+        help="Maximum number of samples to process (for testing)",
+    )
+    parser.add_argument(
+        "--private", action="store_true", help="Make output dataset private"
+    )
+    parser.add_argument(
+        "--shuffle",
+        action="store_true",
+        help="Shuffle the dataset before processing (useful for random sampling)",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="Random seed for shuffling (default: 42)",
+    )
+
+    args = parser.parse_args()
+
+    main(
+        input_dataset=args.input_dataset,
+        output_dataset=args.output_dataset,
+        image_column=args.image_column,
+        batch_size=args.batch_size,
+        model=args.model,
+        resolution_mode=args.resolution_mode,
+        base_size=args.base_size,
+        image_size=args.image_size,
+        crop_mode=args.crop_mode if args.crop_mode else None,
+        max_model_len=args.max_model_len,
+        max_tokens=args.max_tokens,
+        gpu_memory_utilization=args.gpu_memory_utilization,
+        prompt_mode=args.prompt_mode,
+        prompt=args.prompt,
+        hf_token=args.hf_token,
+        split=args.split,
+        max_samples=args.max_samples,
+        private=args.private,
+        shuffle=args.shuffle,
+        seed=args.seed,
+    )