feat: Enhance StyleGuard with VLM-based style extraction for specific style_prefix

api/routers/quality.py

@@ -254,8 +254,13 @@ async def extract_style(
             return style_schema
         
         from pixelle_video.services.quality.style_guard import StyleGuard
+        from api.dependencies import get_pixelle_video
         
-        style_guard = StyleGuard()
+        # Get LLM service for VLM-based style extraction
+        pixelle_video = await get_pixelle_video()
+        llm_service = pixelle_video.llm if pixelle_video else None
+        
+        style_guard = StyleGuard(llm_service=llm_service)
         anchor = await style_guard.extract_style_anchor(actual_path)
         
         style_schema = StyleAnchorSchema(

pixelle_video/services/quality/style_guard.py

@@ -164,28 +164,112 @@ class StyleGuard:
     async def _extract_with_vlm(self, image_path: str) -> StyleAnchor:
         """Extract style using Vision Language Model"""
         try:
-            # TODO: Implement VLM call when vision-capable LLM is integrated
-            # For now, return a placeholder
-            logger.debug("VLM style extraction: using placeholder (VLM not yet integrated)")
+            if not self.llm_service:
+                logger.warning("No LLM service available, using basic extraction")
+                return self._extract_basic(image_path)
             
-            # Placeholder extraction based on common styles
-            return StyleAnchor(
-                art_style="consistent artistic",
-                color_palette="harmonious colors",
-                lighting="balanced",
-                style_prefix="maintaining visual consistency, same artistic style as previous frames",
-                reference_image=image_path,
-            )
+            import base64
+            import os
+            
+            # Read and encode image
+            if not os.path.exists(image_path):
+                logger.warning(f"Image not found: {image_path}")
+                return self._extract_basic(image_path)
+            
+            with open(image_path, "rb") as f:
+                image_data = base64.b64encode(f.read()).decode("utf-8")
+            
+            # Determine image type
+            ext = os.path.splitext(image_path)[1].lower()
+            media_type = "image/png" if ext == ".png" else "image/jpeg"
+            
+            # Call VLM to analyze style
+            style_prompt = """Analyze this image and extract its visual style characteristics.
+
+Provide a concise style description that could be used as a prefix for image generation prompts to maintain visual consistency.
+
+Output format (JSON):
+{
+    "art_style": "specific art style (e.g., oil painting, digital illustration, anime, photorealistic, watercolor, line art)",
+    "color_palette": "dominant colors and mood (e.g., warm earth tones, vibrant neon, muted pastels)",
+    "lighting": "lighting style (e.g., soft natural light, dramatic shadows, studio lighting)",
+    "texture": "visual texture (e.g., smooth, grainy, brushstroke visible)",
+    "style_prefix": "A complete prompt prefix combining all elements (30-50 words)"
+}
+
+Focus on creating a specific, reproducible style_prefix that will generate visually consistent images."""
+
+            # Try to call LLM with vision capability
+            try:
+                response = await self.llm_service(
+                    prompt=style_prompt,
+                    images=[f"data:{media_type};base64,{image_data}"],
+                    temperature=0.3,
+                    max_tokens=500
+                )
+            except Exception as e:
+                # Fallback: try without image (text-only LLM)
+                logger.warning(f"VLM call failed, using basic extraction: {e}")
+                return self._extract_basic(image_path)
+            
+            # Parse response
+            import json
+            import re
+            
+            try:
+                # Try to extract JSON from response
+                match = re.search(r'\{[\s\S]*\}', response)
+                if match:
+                    data = json.loads(match.group())
+                else:
+                    data = json.loads(response)
+                
+                anchor = StyleAnchor(
+                    art_style=data.get("art_style", ""),
+                    color_palette=data.get("color_palette", ""),
+                    lighting=data.get("lighting", ""),
+                    texture=data.get("texture", ""),
+                    style_prefix=data.get("style_prefix", ""),
+                    reference_image=image_path,
+                )
+                
+                logger.info(f"VLM extracted style: {anchor.style_prefix[:80]}...")
+                return anchor
+                
+            except (json.JSONDecodeError, KeyError) as e:
+                logger.warning(f"Failed to parse VLM response: {e}")
+                # Use the raw response as style_prefix if it looks reasonable
+                if len(response) < 200 and len(response) > 20:
+                    return StyleAnchor(
+                        style_prefix=response.strip(),
+                        reference_image=image_path,
+                    )
+                return self._extract_basic(image_path)
             
         except Exception as e:
             logger.warning(f"VLM style extraction failed: {e}")
             return self._extract_basic(image_path)
     
     def _extract_basic(self, image_path: str) -> StyleAnchor:
-        """Basic style extraction without VLM"""
-        # Return generic style anchor
+        """Basic style extraction without VLM - analyze filename for hints"""
+        import os
+        
+        filename = os.path.basename(image_path).lower()
+        
+        # Try to infer style from filename or path
+        style_hints = []
+        
+        if "anime" in filename or "cartoon" in filename:
+            style_hints.append("anime style illustration")
+        elif "realistic" in filename or "photo" in filename:
+            style_hints.append("photorealistic style")
+        elif "sketch" in filename or "line" in filename:
+            style_hints.append("sketch style, clean lines")
+        else:
+            style_hints.append("consistent visual style, high quality")
+        
         return StyleAnchor(
-            style_prefix="consistent visual style",
+            style_prefix=", ".join(style_hints),
             reference_image=image_path,
         )