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"
)

# Load image
image = Image.open("photo.jpg").convert("RGB")

# Generate caption
inputs = processor(images=image, return_tensors="pt").to("cuda", torch.float16)
generated_ids = model.generate(**inputs, max_new_tokens=50)
caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(caption)

Visual question answering

# Ask a question about the image
question = "What color is the car in this image?"

inputs = processor(images=image, text=question, return_tensors="pt").to("cuda", torch.float16)
generated_ids = model.generate(**inputs, max_new_tokens=50)
answer = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(answer)

Using LAVIS library

import torch
from lavis.models import load_model_and_preprocess
from PIL import Image

# Load model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model, vis_processors, txt_processors = load_model_and_preprocess(
    name="blip2_opt",
    model_type="pretrain_opt2.7b",
    is_eval=True,
    device=device
)

# Process image
image = Image.open("photo.jpg").convert("RGB")
image = vis_processors["eval"](image).unsqueeze(0).to(device)

# Caption
caption = model.generate({"image": image})
print(caption)

# VQA
question = txt_processors["eval"]("What is in this image?")
answer = model.generate({"image": image, "prompt": question})
print(answer)

Core concepts

Architecture overview

BLIP-2 Architecture:
┌─────────────────────────────────────────────────────────────┐
│                        Q-Former                              │
│  ┌─────────────────────────────────────────────────────┐    │
│  │     Learned Queries (32 queries × 768 dim)          │    │
│  └────────────────────────┬────────────────────────────┘    │
│                           │                                  │
│  ┌────────────────────────▼────────────────────────────┐    │
│  │    Cross-Attention with Image Features               │    │
│  └────────────────────────┬────────────────────────────┘    │
│                           │                                  │
│  ┌────────────────────────▼────────────────────────────┐    │
│  │    Self-Attention Layers (Transformer)               │    │
│  └────────────────────────┬────────────────────────────┘    │
└───────────────────────────┼─────────────────────────────────┘
                            │
┌───────────────────────────▼─────────────────────────────────┐
│  Frozen Vision Encoder    │      Frozen LLM                  │
│  (ViT-G/14 from EVA-CLIP) │      (OPT or FlanT5)            │
└─────────────────────────────────────────────────────────────┘

Model variants

Model	LLM Backend	Size	Use Case
`blip2-opt-2.7b`	OPT-2.7B	~4GB	General captioning, VQA
`blip2-opt-6.7b`	OPT-6.7B	~8GB	Better reasoning
`blip2-flan-t5-xl`	FlanT5-XL	~5GB	Instruction following
`blip2-flan-t5-xxl`	FlanT5-XXL	~13GB	Best quality

Q-Former components

Component	Description	Parameters
Learned queries	Fixed set of learnable embeddings	32 × 768
Image transformer	Cross-attention to vision features	~108M
Text transformer	Self-attention for text	~108M
Linear projection	Maps to LLM dimension	Varies

Advanced usage

Batch processing

from PIL import Image
import torch

# Load multiple images
images = [Image.open(f"image_{i}.jpg").convert("RGB") for i in range(4)]
questions = [
    "What is shown in this image?",
    "Describe the scene.",
    "What colors are prominent?",
    "Is there a person in this image?"
]

# Process batch
inputs = processor(
    images=images,
    text=questions,
    return_tensors="pt",
    padding=True
).to("cuda", torch.float16)

# Generate
generated_ids = model.generate(**inputs, max_new_tokens=50)
answers = processor.batch_decode(generated_ids, skip_special_tokens=True)

for q, a in zip(questions, answers):
    print(f"Q: {q}\nA: {a}\n")

Controlling generation

# Control generation parameters
generated_ids = model.generate(
    **inputs,
    max_new_tokens=100,
    min_length=20,
    num_beams=5,              # Beam search
    no_repeat_ngram_size=2,   # Avoid repetition
    top_p=0.9,                # Nucleus sampling
    temperature=0.7,          # Creativity
    do_sample=True,           # Enable sampling
)

# For deterministic output
generated_ids = model.generate(
    **inputs,
    max_new_tokens=50,
    num_beams=5,
    do_sample=False,
)

Memory optimization

# 8-bit quantization
from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_8bit=True)

model = Blip2ForConditionalGeneration.from_pretrained(
    "Salesforce/blip2-opt-6.7b",
    quantization_config=quantization_config,
    device_map="auto"
)

# 4-bit quantization (more aggressive)
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)

model = Blip2ForConditionalGeneration.from_pretrained(
    "Salesforce/blip2-flan-t5-xxl",
    quantization_config=quantization_config,
    device_map="auto"
)

Image-text matching

# Using LAVIS for ITM (Image-Text Matching)
from lavis.models import load_model_and_preprocess

model, vis_processors, txt_processors = load_model_and_preprocess(
    name="blip2_image_text_matching",
    model_type="pretrain",
    is_eval=True,
    device=device
)

image = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
text = txt_processors["eval"]("a dog sitting on grass")

# Get matching score
itm_output = model({"image": image, "text_input": text}, match_head="itm")
itm_scores = torch.nn.functional.softmax(itm_output, dim=1)
print(f"Match probability: {itm_scores[:, 1].item():.3f}")

Feature extraction

# Extract image features with Q-Former
from lavis.models import load_model_and_preprocess

model, vis_processors, _ = load_model_and_preprocess(
    name="blip2_feature_extractor",
    model_type="pretrain",
    is_eval=True,
    device=device
)

image = vis_processors["eval"](raw_image).unsqueeze(0).to(device)

# Get features
features = model.extract_features({"image": image}, mode="image")
image_embeds = features.image_embeds  # Shape: [1, 32, 768]
image_features = features.image_embeds_proj  # Projected for matching

Common workflows

Workflow 1: Image captioning pipeline

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

class ImageCaptioner:
    def __init__(self, model_name="Salesforce/blip2-opt-2.7b"):
        self.processor = Blip2Processor.from_pretrained(model_name)
        self.model = Blip2ForConditionalGeneration.from_pretrained(
            model_name,
            torch_dtype=torch.float16,
            device_map="auto"
        )

    def caption(self, image_path: str, prompt: str = None) -> str:
        image = Image.open(image_path).convert("RGB")

        if prompt:
            inputs = self.processor(images=image, text=prompt, return_tensors="pt")
        else:
            inputs = self.processor(images=image, return_tensors="pt")

        inputs = inputs.to("cuda", torch.float16)

        generated_ids = self.model.generate(
            **inputs,
            max_new_tokens=50,
            num_beams=5
        )

        return self.processor.decode(generated_ids[0], skip_special_tokens=True)

    def caption_batch(self, image_paths: list, prompt: str = None) -> list:
        images = [Image.open(p).convert("RGB") for p in image_paths]

        if prompt:
            inputs = self.processor(
                images=images,
                text=[prompt] * len(images),
                return_tensors="pt",
                padding=True
            )
        else:
            inputs = self.processor(images=images, return_tensors="pt", padding=True)

        inputs = inputs.to("cuda", torch.float16)

        generated_ids = self.model.generate(**inputs, max_new_tokens=50)
        return self.processor.batch_decode(generated_ids, skip_special_tokens=True)

# Usage
captioner = ImageCaptioner()

# Single image
caption = captioner.caption("photo.jpg")
print(f"Caption: {caption}")

# With prompt for style
caption = captioner.caption("photo.jpg", "a detailed description of")
print(f"Detailed: {caption}")

# Batch processing
captions = captioner.caption_batch(["img1.jpg", "img2.jpg", "img3.jpg"])
for i, cap in enumerate(captions):
    print(f"Image {i+1}: {cap}")

Workflow 2: Visual Q&A system

class VisualQA:
    def __init__(self, model_name="Salesforce/blip2-flan-t5-xl"):
        self.processor = Blip2Processor.from_pretrained(model_name)
        self.model = Blip2ForConditionalGeneration.from_pretrained(
            model_name,
            torch_dtype=torch.float16,
            device_map="auto"
        )
        self.current_image = None
        self.current_inputs = None

    def set_image(self, image_path: str):
        """Load image for multiple questions."""
        self.current_image = Image.open(image_path).convert("RGB")

    def ask(self, question: str) -> str:
        """Ask a question about the current image."""
        if self.current_image is None:
            raise ValueError("No image set. Call set_image() first.")

        # Format question for FlanT5
        prompt = f"Question: {question} Answer:"

        inputs = self.processor(
            images=self.current_image,
            text=prompt,
            return_tensors="pt"
        ).to("cuda", torch.float16)

        generated_ids = self.model.generate(
            **inputs,
            max_new_tokens=50,
            num_beams=5
        )

        return self.processor.decode(generated_ids[0], skip_special_tokens=True)

    def ask_multiple(self, questions: list) -> dict:
        """Ask multiple questions about current image."""
        return {q: self.ask(q) for q in questions}

# Usage
vqa = VisualQA()
vqa.set_image("scene.jpg")

# Ask questions
print(vqa.ask("What objects are in this image?"))
print(vqa.ask("What is the weather like?"))
print(vqa.ask("How many people are there?"))

# Batch questions
results = vqa.ask_multiple([
    "What is the main subject?",
    "What colors are dominant?",
    "Is this indoors or outdoors?"
])

Workflow 3: Image search/retrieval

import torch
import numpy as np
from PIL import Image
from lavis.models import load_model_and_preprocess

class ImageSearchEngine:
    def __init__(self):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model, self.vis_processors, self.txt_processors = load_model_and_preprocess(
            name="blip2_feature_extractor",
            model_type="pretrain",
            is_eval=True,
            device=self.device
        )
        self.image_features = []
        self.image_paths = []

    def index_images(self, image_paths: list):
        """Build index from images."""
        self.image_paths = image_paths

        for path in image_paths:
            image = Image.open(path).convert("RGB")
            image = self.vis_processors["eval"](image).unsqueeze(0).to(self.device)

            with torch.no_grad():
                features = self.model.extract_features({"image": image}, mode="image")
                # Use projected features for matching
                self.image_features.append(
                    features.image_embeds_proj.mean(dim=1).cpu().numpy()
                )

        self.image_features = np.vstack(self.image_features)

    def search(self, query: str, top_k: int = 5) -> list:
        """Search images by text query."""
        # Get text features
        text = self.txt_processors["eval"](query)
        text_input = {"text_input": [text]}

        with torch.no_grad():
            text_features = self.model.extract_features(text_input, mode="text")
            text_embeds = text_features.text_embeds_proj[:, 0].cpu().numpy()

        # Compute similarities
        similarities = np.dot(self.image_features, text_embeds.T).squeeze()
        top_indices = np.argsort(similarities)[::-1][:top_k]

        return [(self.image_paths[i], similarities[i]) for i in top_indices]

# Usage
engine = ImageSearchEngine()
engine.index_images(["img1.jpg", "img2.jpg", "img3.jpg", ...])

# Search
results = engine.search("a sunset over the ocean", top_k=5)
for path, score in results:
    print(f"{path}: {score:.3f}")

Output format

Generation output

# Direct generation returns token IDs
generated_ids = model.generate(**inputs, max_new_tokens=50)
# Shape: [batch_size, sequence_length]

# Decode to text
text = processor.batch_decode(generated_ids, skip_special_tokens=True)
# Returns: list of strings

Feature extraction output

# Q-Former outputs
features = model.extract_features({"image": image}, mode="image")

features.image_embeds          # [B, 32, 768] - Q-Former outputs
features.image_embeds_proj     # [B, 32, 256] - Projected for matching
features.text_embeds          # [B, seq_len, 768] - Text features
features.text_embeds_proj     # [B, 256] - Projected text (CLS)

Performance optimization

GPU memory requirements

Model	FP16 VRAM	INT8 VRAM	INT4 VRAM
blip2-opt-2.7b	~8GB	~5GB	~3GB
blip2-opt-6.7b	~16GB	~9GB	~5GB
blip2-flan-t5-xl	~10GB	~6GB	~4GB
blip2-flan-t5-xxl	~26GB	~14GB	~8GB

Speed optimization

# Use Flash Attention if available
model = Blip2ForConditionalGeneration.from_pretrained(
    "Salesforce/blip2-opt-2.7b",
    torch_dtype=torch.float16,
    attn_implementation="flash_attention_2",  # Requires flash-attn
    device_map="auto"
)

# Compile model (PyTorch 2.0+)
model = torch.compile(model)

# Use smaller images (if quality allows)
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
# Default is 224x224, which is optimal

Common issues

Issue	Solution
CUDA OOM	Use INT8/INT4 quantization, smaller model
Slow generation	Use greedy decoding, reduce max_new_tokens
Poor captions	Try FlanT5 variant, use prompts
Hallucinations	Lower temperature, use beam search
Wrong answers	Rephrase question, provide context

References

Advanced Usage - Fine-tuning, integration, deployment
Troubleshooting - Common issues and solutions

Resources

Paper: https://arxiv.org/abs/2301.12597
GitHub (LAVIS): https://github.com/salesforce/LAVIS
HuggingFace: https://huggingface.co/Salesforce/blip2-opt-2.7b
Demo: https://huggingface.co/spaces/Salesforce/BLIP2
InstructBLIP: https://arxiv.org/abs/2305.06500 (successor)

blip-2-vision-language

BLIP-2: Vision-Language Pre-training

When to use BLIP-2

Quick start

Installation

Basic image captioning

Visual question answering

Using LAVIS library

Core concepts

Architecture overview

Model variants

Q-Former components

Advanced usage

Batch processing

Controlling generation

Memory optimization

Image-text matching

Feature extraction

Common workflows

Workflow 1: Image captioning pipeline

Workflow 2: Visual Q&A system

Workflow 3: Image search/retrieval

Output format

Generation output

Feature extraction output

Performance optimization

GPU memory requirements

Speed optimization

Common issues

References

Resources