Llava

Llava is a community skill for building multimodal applications with LLaVA vision-language models, covering image understanding, visual question answering, image captioning, document analysis, and multi-turn visual dialogue for combined vision and language tasks.

What Is This?

Overview

Llava provides tools for processing images and text together using LLaVA vision-language models. It covers image understanding that generates detailed descriptions of visual content including objects, scenes, text, and spatial relationships, visual question answering that responds to natural language questions about image content with grounded answers, image captioning that produces concise or detailed text descriptions suitable for accessibility and metadata tagging, document analysis that extracts information from screenshots, diagrams, charts, and scanned documents, and multi-turn visual dialogue that maintains conversation context across multiple questions about the same image. The skill enables developers to build applications that reason about visual and textual content together.

Who Should Use This

This skill serves application developers integrating visual understanding into products, data analysts extracting information from visual documents, and accessibility engineers generating image descriptions for assistive technology.

Why Use It?

Problems It Solves

Separate vision and language models require complex integration to answer questions about images. Image captioning models produce generic descriptions without the ability to answer specific questions about visual content. Document analysis from screenshots requires OCR pipelines that lose layout and context information. Multi-step visual reasoning needs a unified model that understands both modalities rather than chained single-purpose tools.

Core Highlights

Image analyzer generates detailed visual content descriptions from input images. Question answerer responds to natural language queries about image content. Caption generator produces text descriptions at configurable detail levels. Document reader extracts structured information from visual documents.

How to Use It?

Basic Usage

from transformers import (
  LlavaForConditional\
    Generation,
  AutoProcessor)
from PIL import Image
import torch

class VisionModel:
  def __init__(
    self,
    model_id: str
      = 'llava-hf/'
        'llava-v1.6-'
        'mistral-7b-hf'
  ):
    self.processor = (
      AutoProcessor
        .from_pretrained(
          model_id))
    self.model = (
      LlavaForConditional\
        Generation
        .from_pretrained(
          model_id,
          torch_dtype=(
            torch.float16),
          device_map=(
            'auto')))

  def ask(
    self,
    image_path: str,
    question: str
  ) -> str:
    image = Image.open(
      image_path)
    prompt = (
      f'<image>\n'
      f'{question}')
    inputs = (
      self.processor(
        prompt,
        image,
        return_tensors=(
          'pt')))
    output = self.model\
      .generate(
        **inputs.to(
          self.model
            .device),
        max_new_tokens=(
          512))
    return self.processor\
      .decode(
        output[0],
        skip_special_tokens
          =True)

Real-World Examples

from pathlib import Path

class BatchAnalyzer:
  def __init__(
    self,
    model: VisionModel
  ):
    self.model = model

  def caption_batch(
    self,
    image_dir: str,
    prompt: str
      = 'Describe this'
        ' image in detail.'
  ) -> list[dict]:
    results = []
    img_dir = Path(
      image_dir)
    for img_path\
        in sorted(
          img_dir.glob(
            '*.png')):
      caption = (
        self.model.ask(
          str(img_path),
          prompt))
      results.append({
        'file':
          img_path.name,
        'caption':
          caption})
    return results

  def extract_info(
    self,
    image_path: str,
    questions:
      list[str]
  ) -> dict:
    answers = {}
    for q in questions:
      answers[q] = (
        self.model.ask(
          image_path, q))
    return answers

Advanced Tips

Use specific questions rather than open-ended prompts to get targeted information from images with higher accuracy. Process document screenshots at higher resolution to preserve text clarity for better extraction results. Chain multiple questions about the same image in a single conversation to build on previous answers for complex analysis tasks.

When to Use It?

Use Cases

Generate accessibility descriptions for product images in an e-commerce catalog. Extract data from chart screenshots by asking specific questions about values and trends. Analyze UI screenshots to identify design elements and layout patterns.

Related Topics

Vision-language models, LLaVA, visual question answering, image captioning, multimodal AI, document analysis, and image understanding.

Important Notes

Requirements

GPU with sufficient VRAM for vision-language model inference. Hugging Face Transformers library with model weights downloaded. PIL or Pillow library for image preprocessing.

Usage Recommendations

Do: provide clear and specific questions to get focused answers rather than vague descriptions. Preprocess images to appropriate resolution before model input to balance quality and speed. Use batch processing for large image collections to amortize model loading overhead.

Don't: expect pixel-perfect text extraction from low-resolution document images. Send extremely high-resolution images without resizing since this increases processing time without proportional quality gains. Rely on visual models for safety-critical decisions without human verification.

Limitations

Model accuracy varies across visual domains and may produce hallucinated descriptions for unfamiliar content types. Processing time scales with image resolution and output length. Multi-image reasoning within a single prompt is limited by the model architecture and context window size.