Base code

backend/routers/stability_advanced.py

@@ -0,0 +1,817 @@
+"""Advanced Stability AI endpoints with specialized features."""
+
+from fastapi import APIRouter, UploadFile, File, Form, Depends, HTTPException, BackgroundTasks
+from fastapi.responses import Response, StreamingResponse
+from typing import Optional, List, Dict, Any
+import asyncio
+import base64
+import io
+import json
+from datetime import datetime, timedelta
+
+from services.stability_service import get_stability_service, StabilityAIService
+
+router = APIRouter(prefix="/api/stability/advanced", tags=["Stability AI Advanced"])
+
+
+# ==================== ADVANCED GENERATION WORKFLOWS ====================
+
+@router.post("/workflow/image-enhancement", summary="Complete Image Enhancement Workflow")
+async def image_enhancement_workflow(
+    image: UploadFile = File(..., description="Image to enhance"),
+    enhancement_type: str = Form("auto", description="Enhancement type: auto, upscale, denoise, sharpen"),
+    prompt: Optional[str] = Form(None, description="Optional prompt for guided enhancement"),
+    target_resolution: Optional[str] = Form("4k", description="Target resolution: 4k, 2k, hd"),
+    preserve_style: Optional[bool] = Form(True, description="Preserve original style"),
+    background_tasks: BackgroundTasks = BackgroundTasks(),
+    stability_service: StabilityAIService = Depends(get_stability_service)
+):
+    """Complete image enhancement workflow with automatic optimization.
+    
+    This workflow automatically determines the best enhancement approach based on
+    the input image characteristics and user preferences.
+    """
+    async with stability_service:
+        # Analyze image first
+        content = await image.read()
+        img_info = await _analyze_image(content)
+        
+        # Reset file pointer
+        await image.seek(0)
+        
+        # Determine enhancement strategy
+        strategy = _determine_enhancement_strategy(img_info, enhancement_type, target_resolution)
+        
+        # Execute enhancement workflow
+        results = []
+        
+        for step in strategy["steps"]:
+            if step["operation"] == "upscale_fast":
+                result = await stability_service.upscale_fast(image=image)
+            elif step["operation"] == "upscale_conservative":
+                result = await stability_service.upscale_conservative(
+                    image=image, 
+                    prompt=prompt or step["default_prompt"]
+                )
+            elif step["operation"] == "upscale_creative":
+                result = await stability_service.upscale_creative(
+                    image=image, 
+                    prompt=prompt or step["default_prompt"]
+                )
+            
+            results.append({
+                "step": step["name"],
+                "operation": step["operation"],
+                "status": "completed",
+                "result_size": len(result) if isinstance(result, bytes) else None
+            })
+            
+            # Use result as input for next step if needed
+            if isinstance(result, bytes) and len(strategy["steps"]) > 1:
+                # Convert bytes back to UploadFile-like object for next step
+                image = _bytes_to_upload_file(result, image.filename)
+        
+        # Return final result
+        if isinstance(result, bytes):
+            return Response(
+                content=result, 
+                media_type="image/png",
+                headers={
+                    "X-Enhancement-Strategy": json.dumps(strategy),
+                    "X-Processing-Steps": str(len(results))
+                }
+            )
+        
+        return {
+            "strategy": strategy,
+            "steps_completed": results,
+            "generation_id": result.get("id") if isinstance(result, dict) else None
+        }
+
+
+@router.post("/workflow/creative-suite", summary="Creative Suite Multi-Step Workflow")
+async def creative_suite_workflow(
+    base_image: Optional[UploadFile] = File(None, description="Base image (optional for text-to-image)"),
+    prompt: str = Form(..., description="Main creative prompt"),
+    style_reference: Optional[UploadFile] = File(None, description="Style reference image"),
+    workflow_steps: str = Form(..., description="JSON array of workflow steps"),
+    output_format: Optional[str] = Form("png", description="Output format"),
+    stability_service: StabilityAIService = Depends(get_stability_service)
+):
+    """Execute a multi-step creative workflow combining various Stability AI services.
+    
+    This endpoint allows you to chain multiple operations together for complex
+    creative workflows.
+    """
+    try:
+        steps = json.loads(workflow_steps)
+    except json.JSONDecodeError:
+        raise HTTPException(status_code=400, detail="Invalid JSON in workflow_steps")
+    
+    async with stability_service:
+        current_image = base_image
+        results = []
+        
+        for i, step in enumerate(steps):
+            operation = step.get("operation")
+            params = step.get("parameters", {})
+            
+            try:
+                if operation == "generate_core" and not current_image:
+                    result = await stability_service.generate_core(prompt=prompt, **params)
+                elif operation == "control_style" and style_reference:
+                    result = await stability_service.control_style(
+                        image=style_reference, prompt=prompt, **params
+                    )
+                elif operation == "inpaint" and current_image:
+                    result = await stability_service.inpaint(
+                        image=current_image, prompt=prompt, **params
+                    )
+                elif operation == "upscale_fast" and current_image:
+                    result = await stability_service.upscale_fast(image=current_image, **params)
+                else:
+                    raise ValueError(f"Unsupported operation or missing requirements: {operation}")
+                
+                # Convert result to next step input if needed
+                if isinstance(result, bytes):
+                    current_image = _bytes_to_upload_file(result, f"step_{i}_output.png")
+                
+                results.append({
+                    "step": i + 1,
+                    "operation": operation,
+                    "status": "completed",
+                    "result_type": "image" if isinstance(result, bytes) else "json"
+                })
+                
+            except Exception as e:
+                results.append({
+                    "step": i + 1,
+                    "operation": operation,
+                    "status": "error",
+                    "error": str(e)
+                })
+                break
+        
+        # Return final result
+        if isinstance(result, bytes):
+            return Response(
+                content=result,
+                media_type=f"image/{output_format}",
+                headers={"X-Workflow-Steps": json.dumps(results)}
+            )
+        
+        return {"workflow_results": results, "final_result": result}
+
+
+# ==================== COMPARISON ENDPOINTS ====================
+
+@router.post("/compare/models", summary="Compare Different Models")
+async def compare_models(
+    prompt: str = Form(..., description="Text prompt for comparison"),
+    models: str = Form(..., description="JSON array of models to compare"),
+    seed: Optional[int] = Form(42, description="Seed for consistent comparison"),
+    stability_service: StabilityAIService = Depends(get_stability_service)
+):
+    """Generate images using different models for comparison.
+    
+    This endpoint generates the same prompt using different Stability AI models
+    to help you compare quality and style differences.
+    """
+    try:
+        model_list = json.loads(models)
+    except json.JSONDecodeError:
+        raise HTTPException(status_code=400, detail="Invalid JSON in models")
+    
+    async with stability_service:
+        results = {}
+        
+        for model in model_list:
+            try:
+                if model == "ultra":
+                    result = await stability_service.generate_ultra(
+                        prompt=prompt, seed=seed, output_format="webp"
+                    )
+                elif model == "core":
+                    result = await stability_service.generate_core(
+                        prompt=prompt, seed=seed, output_format="webp"
+                    )
+                elif model.startswith("sd3"):
+                    result = await stability_service.generate_sd3(
+                        prompt=prompt, model=model, seed=seed, output_format="webp"
+                    )
+                else:
+                    continue
+                
+                if isinstance(result, bytes):
+                    results[model] = {
+                        "status": "success",
+                        "image": base64.b64encode(result).decode(),
+                        "size": len(result)
+                    }
+                else:
+                    results[model] = {"status": "async", "generation_id": result.get("id")}
+                    
+            except Exception as e:
+                results[model] = {"status": "error", "error": str(e)}
+        
+        return {
+            "prompt": prompt,
+            "seed": seed,
+            "comparison_results": results,
+            "timestamp": datetime.utcnow().isoformat()
+        }
+
+
+# ==================== STYLE TRANSFER WORKFLOWS ====================
+
+@router.post("/style/multi-style-transfer", summary="Multi-Style Transfer")
+async def multi_style_transfer(
+    content_image: UploadFile = File(..., description="Content image"),
+    style_images: List[UploadFile] = File(..., description="Multiple style reference images"),
+    blend_weights: Optional[str] = Form(None, description="JSON array of blend weights"),
+    output_format: Optional[str] = Form("png", description="Output format"),
+    stability_service: StabilityAIService = Depends(get_stability_service)
+):
+    """Apply multiple styles to a single content image with blending.
+    
+    This endpoint applies multiple style references to a content image,
+    optionally with specified blend weights.
+    """
+    weights = None
+    if blend_weights:
+        try:
+            weights = json.loads(blend_weights)
+        except json.JSONDecodeError:
+            raise HTTPException(status_code=400, detail="Invalid JSON in blend_weights")
+    
+    if weights and len(weights) != len(style_images):
+        raise HTTPException(status_code=400, detail="Number of weights must match number of style images")
+    
+    async with stability_service:
+        results = []
+        
+        for i, style_image in enumerate(style_images):
+            weight = weights[i] if weights else 1.0
+            
+            result = await stability_service.control_style_transfer(
+                init_image=content_image,
+                style_image=style_image,
+                style_strength=weight,
+                output_format=output_format
+            )
+            
+            if isinstance(result, bytes):
+                results.append({
+                    "style_index": i,
+                    "weight": weight,
+                    "image": base64.b64encode(result).decode(),
+                    "size": len(result)
+                })
+            
+            # Reset content image file pointer for next iteration
+            await content_image.seek(0)
+        
+        return {
+            "content_image": content_image.filename,
+            "style_count": len(style_images),
+            "results": results
+        }
+
+
+# ==================== ANIMATION & SEQUENCE ENDPOINTS ====================
+
+@router.post("/animation/image-sequence", summary="Generate Image Sequence")
+async def generate_image_sequence(
+    base_prompt: str = Form(..., description="Base prompt for sequence"),
+    sequence_prompts: str = Form(..., description="JSON array of sequence variations"),
+    seed_start: Optional[int] = Form(42, description="Starting seed"),
+    seed_increment: Optional[int] = Form(1, description="Seed increment per frame"),
+    output_format: Optional[str] = Form("png", description="Output format"),
+    stability_service: StabilityAIService = Depends(get_stability_service)
+):
+    """Generate a sequence of related images for animation or storytelling.
+    
+    This endpoint generates a series of images with slight variations to create
+    animation frames or story sequences.
+    """
+    try:
+        prompts = json.loads(sequence_prompts)
+    except json.JSONDecodeError:
+        raise HTTPException(status_code=400, detail="Invalid JSON in sequence_prompts")
+    
+    async with stability_service:
+        sequence_results = []
+        current_seed = seed_start
+        
+        for i, variation in enumerate(prompts):
+            full_prompt = f"{base_prompt}, {variation}"
+            
+            result = await stability_service.generate_core(
+                prompt=full_prompt,
+                seed=current_seed,
+                output_format=output_format
+            )
+            
+            if isinstance(result, bytes):
+                sequence_results.append({
+                    "frame": i + 1,
+                    "prompt": full_prompt,
+                    "seed": current_seed,
+                    "image": base64.b64encode(result).decode(),
+                    "size": len(result)
+                })
+            
+            current_seed += seed_increment
+        
+        return {
+            "base_prompt": base_prompt,
+            "frame_count": len(sequence_results),
+            "sequence": sequence_results
+        }
+
+
+# ==================== QUALITY ANALYSIS ENDPOINTS ====================
+
+@router.post("/analysis/generation-quality", summary="Analyze Generation Quality")
+async def analyze_generation_quality(
+    image: UploadFile = File(..., description="Generated image to analyze"),
+    original_prompt: str = Form(..., description="Original generation prompt"),
+    model_used: str = Form(..., description="Model used for generation")
+):
+    """Analyze the quality and characteristics of a generated image.
+    
+    This endpoint provides detailed analysis of generated images including
+    quality metrics, style adherence, and improvement suggestions.
+    """
+    from PIL import Image, ImageStat
+    import numpy as np
+    
+    try:
+        content = await image.read()
+        img = Image.open(io.BytesIO(content))
+        
+        # Basic image statistics
+        stat = ImageStat.Stat(img)
+        
+        # Convert to RGB if needed for analysis
+        if img.mode != "RGB":
+            img = img.convert("RGB")
+        
+        # Calculate quality metrics
+        img_array = np.array(img)
+        
+        # Brightness analysis
+        brightness = np.mean(img_array)
+        
+        # Contrast analysis
+        contrast = np.std(img_array)
+        
+        # Color distribution
+        color_channels = np.mean(img_array, axis=(0, 1))
+        
+        # Sharpness estimation (using Laplacian variance)
+        gray = img.convert('L')
+        gray_array = np.array(gray)
+        laplacian_var = np.var(np.gradient(gray_array))
+        
+        quality_score = min(100, (contrast / 50) * (laplacian_var / 1000) * 100)
+        
+        analysis = {
+            "image_info": {
+                "dimensions": f"{img.width}x{img.height}",
+                "format": img.format,
+                "mode": img.mode,
+                "file_size": len(content)
+            },
+            "quality_metrics": {
+                "overall_score": round(quality_score, 2),
+                "brightness": round(brightness, 2),
+                "contrast": round(contrast, 2),
+                "sharpness": round(laplacian_var, 2)
+            },
+            "color_analysis": {
+                "red_channel": round(float(color_channels[0]), 2),
+                "green_channel": round(float(color_channels[1]), 2),
+                "blue_channel": round(float(color_channels[2]), 2),
+                "color_balance": "balanced" if max(color_channels) - min(color_channels) < 30 else "imbalanced"
+            },
+            "generation_info": {
+                "original_prompt": original_prompt,
+                "model_used": model_used,
+                "analysis_timestamp": datetime.utcnow().isoformat()
+            },
+            "recommendations": _generate_quality_recommendations(quality_score, brightness, contrast)
+        }
+        
+        return analysis
+        
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=f"Error analyzing image: {str(e)}")
+
+
+@router.post("/analysis/prompt-optimization", summary="Optimize Text Prompts")
+async def optimize_prompt(
+    prompt: str = Form(..., description="Original prompt to optimize"),
+    target_style: Optional[str] = Form(None, description="Target style"),
+    target_quality: Optional[str] = Form("high", description="Target quality level"),
+    model: Optional[str] = Form("ultra", description="Target model"),
+    include_negative: Optional[bool] = Form(True, description="Include negative prompt suggestions")
+):
+    """Analyze and optimize text prompts for better generation results.
+    
+    This endpoint analyzes your prompt and provides suggestions for improvement
+    based on best practices and model-specific optimizations.
+    """
+    analysis = {
+        "original_prompt": prompt,
+        "prompt_length": len(prompt),
+        "word_count": len(prompt.split()),
+        "optimization_suggestions": []
+    }
+    
+    # Analyze prompt structure
+    suggestions = []
+    
+    # Check for style descriptors
+    style_keywords = ["photorealistic", "digital art", "oil painting", "watercolor", "sketch"]
+    has_style = any(keyword in prompt.lower() for keyword in style_keywords)
+    if not has_style and target_style:
+        suggestions.append(f"Add style descriptor: {target_style}")
+    
+    # Check for quality enhancers
+    quality_keywords = ["high quality", "detailed", "sharp", "crisp", "professional"]
+    has_quality = any(keyword in prompt.lower() for keyword in quality_keywords)
+    if not has_quality and target_quality == "high":
+        suggestions.append("Add quality enhancers: 'high quality, detailed, sharp'")
+    
+    # Check for composition elements
+    composition_keywords = ["composition", "lighting", "perspective", "framing"]
+    has_composition = any(keyword in prompt.lower() for keyword in composition_keywords)
+    if not has_composition:
+        suggestions.append("Consider adding composition details: lighting, perspective, framing")
+    
+    # Model-specific optimizations
+    if model == "ultra":
+        suggestions.append("For Ultra model: Use detailed, specific descriptions")
+    elif model == "core":
+        suggestions.append("For Core model: Keep prompts concise but descriptive")
+    
+    # Generate optimized prompt
+    optimized_prompt = prompt
+    if suggestions:
+        optimized_prompt = _apply_prompt_optimizations(prompt, suggestions, target_style)
+    
+    # Generate negative prompt suggestions
+    negative_suggestions = []
+    if include_negative:
+        negative_suggestions = _generate_negative_prompt_suggestions(prompt, target_style)
+    
+    analysis.update({
+        "optimization_suggestions": suggestions,
+        "optimized_prompt": optimized_prompt,
+        "negative_prompt_suggestions": negative_suggestions,
+        "estimated_improvement": len(suggestions) * 10,  # Rough estimate
+        "model_compatibility": _check_model_compatibility(optimized_prompt, model)
+    })
+    
+    return analysis
+
+
+# ==================== BATCH PROCESSING ENDPOINTS ====================
+
+@router.post("/batch/process-folder", summary="Process Multiple Images")
+async def batch_process_folder(
+    images: List[UploadFile] = File(..., description="Multiple images to process"),
+    operation: str = Form(..., description="Operation to perform on all images"),
+    operation_params: str = Form("{}", description="JSON parameters for operation"),
+    background_tasks: BackgroundTasks = BackgroundTasks(),
+    stability_service: StabilityAIService = Depends(get_stability_service)
+):
+    """Process multiple images with the same operation in batch.
+    
+    This endpoint allows you to apply the same operation to multiple images
+    efficiently.
+    """
+    try:
+        params = json.loads(operation_params)
+    except json.JSONDecodeError:
+        raise HTTPException(status_code=400, detail="Invalid JSON in operation_params")
+    
+    # Validate operation
+    supported_operations = [
+        "upscale_fast", "remove_background", "erase", "generate_ultra", "generate_core"
+    ]
+    if operation not in supported_operations:
+        raise HTTPException(
+            status_code=400, 
+            detail=f"Unsupported operation. Supported: {supported_operations}"
+        )
+    
+    # Start batch processing in background
+    batch_id = f"batch_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}"
+    
+    background_tasks.add_task(
+        _process_batch_images,
+        batch_id,
+        images,
+        operation,
+        params,
+        stability_service
+    )
+    
+    return {
+        "batch_id": batch_id,
+        "status": "started",
+        "image_count": len(images),
+        "operation": operation,
+        "estimated_completion": (datetime.utcnow() + timedelta(minutes=len(images) * 2)).isoformat()
+    }
+
+
+@router.get("/batch/{batch_id}/status", summary="Get Batch Processing Status")
+async def get_batch_status(batch_id: str):
+    """Get the status of a batch processing operation.
+    
+    Returns the current status and progress of a batch operation.
+    """
+    # In a real implementation, you'd store batch status in a database
+    # For now, return a mock response
+    return {
+        "batch_id": batch_id,
+        "status": "processing",
+        "progress": {
+            "completed": 2,
+            "total": 5,
+            "percentage": 40
+        },
+        "estimated_completion": (datetime.utcnow() + timedelta(minutes=5)).isoformat()
+    }
+
+
+# ==================== HELPER FUNCTIONS ====================
+
+async def _analyze_image(content: bytes) -> Dict[str, Any]:
+    """Analyze image characteristics."""
+    from PIL import Image
+    
+    img = Image.open(io.BytesIO(content))
+    total_pixels = img.width * img.height
+    
+    return {
+        "width": img.width,
+        "height": img.height,
+        "total_pixels": total_pixels,
+        "aspect_ratio": img.width / img.height,
+        "format": img.format,
+        "mode": img.mode,
+        "is_low_res": total_pixels < 500000,  # Less than 0.5MP
+        "is_high_res": total_pixels > 2000000,  # More than 2MP
+        "needs_upscaling": total_pixels < 1000000  # Less than 1MP
+    }
+
+
+def _determine_enhancement_strategy(img_info: Dict[str, Any], enhancement_type: str, target_resolution: str) -> Dict[str, Any]:
+    """Determine the best enhancement strategy based on image characteristics."""
+    strategy = {"steps": []}
+    
+    if enhancement_type == "auto":
+        if img_info["is_low_res"]:
+            if img_info["total_pixels"] < 100000:  # Very low res
+                strategy["steps"].append({
+                    "name": "Creative Upscale",
+                    "operation": "upscale_creative",
+                    "default_prompt": "high quality, detailed, sharp"
+                })
+            else:
+                strategy["steps"].append({
+                    "name": "Conservative Upscale",
+                    "operation": "upscale_conservative",
+                    "default_prompt": "enhance quality, preserve details"
+                })
+        else:
+            strategy["steps"].append({
+                "name": "Fast Upscale",
+                "operation": "upscale_fast",
+                "default_prompt": ""
+            })
+    elif enhancement_type == "upscale":
+        if target_resolution == "4k":
+            strategy["steps"].append({
+                "name": "Conservative Upscale to 4K",
+                "operation": "upscale_conservative",
+                "default_prompt": "4K resolution, high quality"
+            })
+        else:
+            strategy["steps"].append({
+                "name": "Fast Upscale",
+                "operation": "upscale_fast",
+                "default_prompt": ""
+            })
+    
+    return strategy
+
+
+def _bytes_to_upload_file(content: bytes, filename: str):
+    """Convert bytes to UploadFile-like object."""
+    from fastapi import UploadFile
+    from io import BytesIO
+    
+    file_obj = BytesIO(content)
+    file_obj.seek(0)
+    
+    # Create a mock UploadFile
+    class MockUploadFile:
+        def __init__(self, file_obj, filename):
+            self.file = file_obj
+            self.filename = filename
+            self.content_type = "image/png"
+        
+        async def read(self):
+            return self.file.read()
+        
+        async def seek(self, position):
+            self.file.seek(position)
+    
+    return MockUploadFile(file_obj, filename)
+
+
+def _generate_quality_recommendations(quality_score: float, brightness: float, contrast: float) -> List[str]:
+    """Generate quality improvement recommendations."""
+    recommendations = []
+    
+    if quality_score < 50:
+        recommendations.append("Consider using a higher quality model like Ultra")
+    
+    if brightness < 100:
+        recommendations.append("Image appears dark, consider adjusting lighting in prompt")
+    elif brightness > 200:
+        recommendations.append("Image appears bright, consider reducing exposure in prompt")
+    
+    if contrast < 30:
+        recommendations.append("Low contrast detected, add 'high contrast' to prompt")
+    
+    if not recommendations:
+        recommendations.append("Image quality looks good!")
+    
+    return recommendations
+
+
+def _apply_prompt_optimizations(prompt: str, suggestions: List[str], target_style: Optional[str]) -> str:
+    """Apply optimization suggestions to prompt."""
+    optimized = prompt
+    
+    # Add style if suggested
+    if target_style and f"Add style descriptor: {target_style}" in suggestions:
+        optimized = f"{optimized}, {target_style} style"
+    
+    # Add quality enhancers if suggested
+    if any("quality enhancer" in s for s in suggestions):
+        optimized = f"{optimized}, high quality, detailed, sharp"
+    
+    return optimized.strip()
+
+
+def _generate_negative_prompt_suggestions(prompt: str, target_style: Optional[str]) -> List[str]:
+    """Generate negative prompt suggestions based on prompt analysis."""
+    suggestions = []
+    
+    # Common negative prompts
+    suggestions.extend([
+        "blurry, low quality, pixelated",
+        "distorted, deformed, malformed",
+        "oversaturated, undersaturated"
+    ])
+    
+    # Style-specific negative prompts
+    if target_style:
+        if "photorealistic" in target_style.lower():
+            suggestions.append("cartoon, anime, illustration")
+        elif "anime" in target_style.lower():
+            suggestions.append("realistic, photographic")
+    
+    return suggestions
+
+
+def _check_model_compatibility(prompt: str, model: str) -> Dict[str, Any]:
+    """Check prompt compatibility with specific models."""
+    compatibility = {"score": 100, "notes": []}
+    
+    if model == "ultra":
+        if len(prompt.split()) < 5:
+            compatibility["score"] -= 20
+            compatibility["notes"].append("Ultra model works best with detailed prompts")
+    elif model == "core":
+        if len(prompt) > 500:
+            compatibility["score"] -= 10
+            compatibility["notes"].append("Core model works well with concise prompts")
+    
+    return compatibility
+
+
+async def _process_batch_images(
+    batch_id: str,
+    images: List[UploadFile],
+    operation: str,
+    params: Dict[str, Any],
+    stability_service: StabilityAIService
+):
+    """Background task for processing multiple images."""
+    # In a real implementation, you'd store progress in a database
+    # This is a simplified version for demonstration
+    
+    async with stability_service:
+        for i, image in enumerate(images):
+            try:
+                if operation == "upscale_fast":
+                    await stability_service.upscale_fast(image=image, **params)
+                elif operation == "remove_background":
+                    await stability_service.remove_background(image=image, **params)
+                # Add other operations as needed
+                
+                # Log progress (in real implementation, update database)
+                logger.info(f"Batch {batch_id}: Completed image {i+1}/{len(images)}")
+                
+            except Exception as e:
+                logger.error(f"Batch {batch_id}: Error processing image {i+1}: {str(e)}")
+
+
+# ==================== EXPERIMENTAL ENDPOINTS ====================
+
+@router.post("/experimental/ai-director", summary="AI Director Mode")
+async def ai_director_mode(
+    concept: str = Form(..., description="High-level creative concept"),
+    target_audience: Optional[str] = Form(None, description="Target audience"),
+    mood: Optional[str] = Form(None, description="Desired mood"),
+    color_palette: Optional[str] = Form(None, description="Preferred color palette"),
+    iterations: Optional[int] = Form(3, description="Number of iterations"),
+    stability_service: StabilityAIService = Depends(get_stability_service)
+):
+    """AI Director mode for automated creative decision making.
+    
+    This experimental endpoint acts as an AI creative director, making
+    intelligent decisions about style, composition, and execution based on
+    high-level creative concepts.
+    """
+    # Generate detailed prompts based on concept
+    director_prompts = _generate_director_prompts(concept, target_audience, mood, color_palette)
+    
+    async with stability_service:
+        iterations_results = []
+        
+        for i in range(iterations):
+            prompt = director_prompts[i % len(director_prompts)]
+            
+            result = await stability_service.generate_ultra(
+                prompt=prompt,
+                output_format="webp"
+            )
+            
+            if isinstance(result, bytes):
+                iterations_results.append({
+                    "iteration": i + 1,
+                    "prompt": prompt,
+                    "image": base64.b64encode(result).decode(),
+                    "size": len(result)
+                })
+        
+        return {
+            "concept": concept,
+            "director_analysis": {
+                "target_audience": target_audience,
+                "mood": mood,
+                "color_palette": color_palette
+            },
+            "generated_prompts": director_prompts,
+            "iterations": iterations_results
+        }
+
+
+def _generate_director_prompts(concept: str, audience: Optional[str], mood: Optional[str], colors: Optional[str]) -> List[str]:
+    """Generate creative prompts based on director inputs."""
+    base_prompt = concept
+    
+    # Add audience-specific elements
+    if audience:
+        if "professional" in audience.lower():
+            base_prompt += ", professional, clean, sophisticated"
+        elif "creative" in audience.lower():
+            base_prompt += ", artistic, innovative, expressive"
+        elif "casual" in audience.lower():
+            base_prompt += ", friendly, approachable, relaxed"
+    
+    # Add mood elements
+    if mood:
+        base_prompt += f", {mood} mood"
+    
+    # Add color palette
+    if colors:
+        base_prompt += f", {colors} color palette"
+    
+    # Generate variations
+    variations = [
+        f"{base_prompt}, high quality, detailed",
+        f"{base_prompt}, cinematic lighting, professional photography",
+        f"{base_prompt}, artistic composition, creative perspective"
+    ]
+    
+    return variations