BLIP-2: Vision-Language Pre-training

Comprehensive guide to using Salesforce's BLIP-2 for vision-language tasks with frozen image encoders and large language models.

When to use BLIP-2

Use BLIP-2 when:

• Need high-quality image captioning with natural descriptions
• Building visual question answering (VQA) systems
• Require zero-shot image-text understanding without task-specific training
• Want to leverage LLM reasoning for visual tasks
• Building multimodal conversational AI
• Need image-text retrieval or matching

Key features:

• Q-Former architecture: Lightweight query transformer bridges vision and language
• Frozen backbone efficiency: No need to fine-tune large vision/language models
• Multiple LLM backends: OPT (2.7B, 6.7B) and FlanT5 (XL, XXL)
• Zero-shot capabilities: Strong performance without task-specific training
• Efficient training: Only trains Q-Former (~188M parameters)
• State-of-the-art results: Beats larger models on VQA benchmarks

Use alternatives instead:

• LLaVA: For instruction-following multimodal chat
• InstructBLIP: For improved instruction-following (BLIP-2 successor)
• GPT-4V/Claude 3: For production multimodal chat (proprietary)
• CLIP: For simple image-text similarity without generation
• Flamingo: For few-shot visual learning

Quick start

Installation

# HuggingFace Transformers (recommended)
pip install transformers accelerate torch Pillow

# Or LAVIS library (Salesforce official)
pip install salesforce-lavis

Basic image captioning

import torch
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration

# Load model and processor
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained(
    "Salesforce/blip2-opt-2.7b",
    torch_dtype=torch.float16,
    device_map="auto"
)