initial commit

VisualQueryTemplate.py

@@ -0,0 +1,64 @@
+import re
+from PIL import Image
+import numpy as np
+import time
+from transformers import pipeline
+import torch
+
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+class VisualQueryTemplateNode:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "model": (["Salesforce/blip-vqa-base", "Salesforce/blip-vqa-capfilt-large", "dandelin/vilt-b32-finetuned-vqa", "microsoft/git-large-vqav2"], ),
+                "question": ("STRING", {"default": "{eye color} eyes, {hair style} {hair color} hair, {ethnicity} {gender}, {age number} years old, {facialhair}", "multiline": True, "dynamicPrompts": False}),
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    OUTPUT_IS_LIST = (True,)
+
+    FUNCTION = "vqa_image"
+    CATEGORY = "image"
+
+    def vqa_image(self, images, model, question):
+        start_time = time.time()
+
+        device = 0 if torch.cuda.is_available() else -1
+        vqa = pipeline(model=model, device=device)
+
+        answers = []
+
+        for image in images:
+            pil_image = tensor2pil(image).convert("RGB")
+
+            final_answer = question
+
+            matches = re.findall(r'\{([^}]*)\}', question)
+
+            for match in matches:
+
+                match_answers = vqa(question=match, image=pil_image)
+
+                print(match, match_answers)
+
+                match_answer = match_answers[0]["answer"]
+
+                final_answer = final_answer.replace("{"+match+"}", match_answer)
+
+            
+            answers.append(final_answer)
+
+        
+        end_time = time.time()
+        execution_time = end_time - start_time
+        print(f"Execution time: {execution_time} seconds")
+
+        return (answers,)