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saakshigupta/
deepfake-explainer-2

Name: deepfake-explainer-2
Availability: InStock

safetensors

llama

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Deepfake Explainer Vision Model with Multiple Image Support

This model is a fine-tuned version of Llama 3.2 Vision that analyzes multiple images for signs of deepfakes.

Important: Cross-Attention Dimension Fix

This model requires a special handling for cross-attention masks. When loading the model for inference, make sure to fix the cross-attention mask dimensions as follows:

python

# Sample code for loading and using this model
from transformers import AutoProcessor, AutoModelForCausalLM
from peft import PeftModel
import torch
from PIL import Image

# Load base model and processor
base_model_id = "unsloth/llama-3.2-11b-vision-instruct"
processor = AutoProcessor.from_pretrained(base_model_id)
model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")

# Load this adapter
adapter_id = "saakshigupta/deepfake-explainer-2"
model = PeftModel.from_pretrained(model, adapter_id)

# Function to fix cross-attention masks
def fix_processor_outputs(inputs):
    if 'cross_attention_mask' in inputs and 0 in inputs['cross_attention_mask'].shape:
        batch_size, seq_len, _, num_tiles = inputs['cross_attention_mask'].shape
        visual_features = 6404  # Critical dimension
        new_mask = torch.ones((batch_size, seq_len, visual_features, num_tiles),
                            device=inputs['cross_attention_mask'].device)
        inputs['cross_attention_mask'] = new_mask
    return inputs

# Function to process multiple images
def process_multiple_images(original_image, cam_image, cam_overlay, comparison_image, query):
    # Process with all four images
    # Note: This is a simplified approach and may need adaptation based on model capabilities
    inputs = processor(
        images=[original_image, cam_image, cam_overlay, comparison_image], 
        text=query, 
        return_tensors="pt"
    )
    
    # Fix cross-attention mask
    inputs = fix_processor_outputs(inputs)
    inputs = {k: v.to(model.device) for k, v in inputs.items() if isinstance(v, torch.Tensor)}
    
    # Generate output
    with torch.no_grad():
        output_ids = model.generate(**inputs, max_new_tokens=500)
    response = processor.decode(output_ids[0], skip_special_tokens=True)
    return response

# Example usage
original_image = Image.open("path/to/original.jpg").convert("RGB")
cam_image = Image.open("path/to/cam.jpg").convert("RGB")
cam_overlay = Image.open("path/to/overlay.jpg").convert("RGB")
comparison_image = Image.open("path/to/comparison.jpg").convert("RGB")

query = "Analyze these images and explain if they show a deepfake."
response = process_multiple_images(original_image, cam_image, cam_overlay, comparison_image, query)
print(response)

This model was fine-tuned specifically for deepfake detection and explanation using multiple image inputs.