research-blip-2

# 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([