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README.md

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+# ComfyUI-CLIPScore 🐱‍👤📊
+
+**Research-grade CLIP Score node** for ComfyUI. Raw cosine similarity (-1 to +1) between prompts and Flux/SD images.
+
+[![CLIP Score Demo](https://i.imgur.com/coaDjiA.png)](https://github.com/emniko/ComfyUI-CLIPScore)
+
+## Features
+- ✅ **RGBA support** (VAE Decode → RGB auto)
+- ✅ **Batch-first** scoring
+- ✅ **Raw cosine scores** (0.25-0.35 = excellent)
+- ✅ **Flux/SD3/SDXL** ready
+
+## Score Scale
+| Score     | Rating      | Example              |
+| --------- | ----------- | -------------------- |
+| ≥0.30     | 🎖️ Excellent | Perfect prompt match |
+| 0.25-0.29 | 👍 Good      | Strong alignment     |
+| 0.20-0.24 | ⚠️ Fair      | Usable               |
+| <0.20     | ❌ Poor      | Mismatch             |
+
+## Install
+```bash
+cd ComfyUI/custom_nodes
+git clone https://github.com/yourusername/ComfyUI-CLIPScore.git
+cd ComfyUI-CLIPScore
+pip install -r requirements.txt

__init__.py

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+from .clip_score_node import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]

clip_score_node.py

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+"""
+ComfyUI Custom Node: CLIP Score Calculator
+"""
+
+import torch
+import numpy as np
+from PIL import Image
+from transformers import CLIPProcessor, CLIPModel
+
+class CLIPScoreNode:
+    """Calculates true CLIP cosine similarity between prompt and image."""
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "prompt": ("STRING", {"multiline": True, "default": ""}),
+                "model_name": (
+                    [
+                        "openai/clip-vit-base-patch16",
+                        "openai/clip-vit-base-patch32",
+                        "openai/clip-vit-large-patch14",
+                    ],
+                    {"default": "openai/clip-vit-base-patch16"},
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("FLOAT", "STRING")
+    RETURN_NAMES = ("clip_score", "score_text")
+    FUNCTION = "calculate_clip_score"
+    CATEGORY = "metrics"
+    OUTPUT_NODE = True
+
+    def calculate_clip_score(self, image, prompt, model_name):
+        try:
+            # Load CLIP (cached after first run)
+            model = CLIPModel.from_pretrained(model_name)
+            processor = CLIPProcessor.from_pretrained(model_name)
+            
+            # Handle ComfyUI IMAGE: [B,H,W,C] or [H,W,C]
+            if len(image.shape) == 4:
+                img = image[0]  # First from batch
+            else:
+                img = image
+            
+            # [H,W,C] float32 [0,1] → numpy uint8 → PIL RGB
+            img_np = (img.cpu().numpy() * 255).astype(np.uint8)
+            if img_np.shape[-1] == 4:  # RGBA → RGB
+                img_np = img_np[:, :, :3]
+            pil_image = Image.fromarray(img_np, mode='RGB')
+            
+            # GPU inference
+            device = next(model.parameters()).device
+            inputs = processor(text=[prompt], images=pil_image, return_tensors="pt", padding=True)
+            inputs = {k: v.to(device) for k, v in inputs.items()}
+            model.to(device)
+            
+            # Raw CLIP score: cosine similarity of normalized embeddings
+            with torch.no_grad():
+                outputs = model(**inputs)
+                image_embeds = outputs.image_embeds
+                text_embeds = outputs.text_embeds
+                
+                # L2 normalize + cosine similarity (standard CLIP score)
+                image_embeds /= image_embeds.norm(p=2, dim=-1, keepdim=True) + 1e-8
+                text_embeds /= text_embeds.norm(p=2, dim=-1, keepdim=True) + 1e-8
+                score = (image_embeds @ text_embeds.T)[0, 0].item()
+            
+            # Cleanup
+            model.cpu()
+            del model, inputs
+            
+        except Exception as e:
+            score = 0.0
+            print(f"CLIP error: {str(e)}")
+        
+        score_str = f"CLIP Score: {score:.4f}"
+        print(score_str)
+        
+        return (score, score_str)
+
+
+NODE_CLASS_MAPPINGS = {
+    "CLIPScoreNode": CLIPScoreNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "CLIPScoreNode": "CLIP Score Calculator",
+}

requirements.txt

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+torch
+transformers>=4.36.0
+pillow
+numpy