blip-2-vision-language

仓库: davila7/claude-code-templates
安装量: 185
排名: #4626

安装

npx skills add https://github.com/davila7/claude-code-templates --skill 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".unsqueeze(0).to(device)

Caption

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

VQA

question = txt_processors"eval" 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".unsqueeze(0).to(device) text = txt_processors"eval"

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".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)

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