blip-2-vision-language

사용 방법

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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

bash

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

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

Basic image captioning

python

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

python

# 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

python

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

python

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

python

# 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

python

# 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

python

# 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

python

# 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

python

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

python

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

python

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

python

# 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

python

# 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

python

# 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 maxnewtokens
Poor captions	Try FlanT5 variant, use prompts
Hallucinations	Lower temperature, use beam search
Wrong answers	Rephrase question, provide context

References

[Advanced Usage](references/advanced-usage.md) - Fine-tuning, integration, deployment
[Troubleshooting](references/troubleshooting.md) - 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)

사용 방법

시작 전 확인

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

관련 작업

더 많은 Skills

brainstorming-research-ideas

ara-research-manager

audiocraft-audio-generation

autogpt-agents

blip-2-vision-language

사용 방법

시작 전 확인

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

관련 작업

finishing-a-development-branch

prototype

setup-pre-commit

ecc-guide

더 많은 Skills

brainstorming-research-ideas

ara-research-manager

audiocraft-audio-generation

autogpt-agents