blip-2-vision-language

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

Details, code examples and parameter tables: references/advanced-usage-2.md. Read it when this step applies.

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)

Agent operating procedure

1. Check the environment. Confirm GPU memory, model checkpoint and licence, framework versions and input formats.
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Run inference on a single sample at low resolution or short length first.
4. Execute the full task using the instructions and references above.
5. Validate the result. Inspect outputs qualitatively and with task metrics; check licence restrictions for generated content.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
Out-of-memory on large inputs	Reduce resolution or sequence length, use half precision, or tile the input.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (help(), --help, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Check model and dataset licences before commercial or published use.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

Related skills

• clip: OpenAI's model connecting vision and language.
• llava: Large Language and Vision Assistant.
• quantizing-models-bitsandbytes: Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss.