Add Image Generation from Prompts: Implement new functionality to generate images from prompts, including backend processing, API integration, and frontend handling with progress modal. Update settings and registry for new AI function.

backend/igny8_core/ai/engine.py

@@ -81,6 +81,12 @@ class AIEngine:
                 total_images = 1 + max_images
                 return f"Mapping Content for {total_images} Image Prompts"
             return f"Mapping Content for Image Prompts"
+        elif function_name == 'generate_images_from_prompts':
+            # Extract image count from data
+            if isinstance(data, dict) and 'images' in data:
+                total_images = len(data.get('images', []))
+                return f"Preparing to generate {total_images} image{'s' if total_images != 1 else ''}"
+            return f"Preparing image generation queue"
         return f"Preparing {count} item{'s' if count != 1 else ''}"
     
     def _get_ai_call_message(self, function_name: str, count: int) -> str:
@@ -93,6 +99,8 @@ class AIEngine:
             return f"Writing article{'s' if count != 1 else ''} with AI"
         elif function_name == 'generate_images':
             return f"Creating image{'s' if count != 1 else ''} with AI"
+        elif function_name == 'generate_images_from_prompts':
+            return f"Generating images with AI"
         return f"Processing with AI"
     
     def _get_parse_message(self, function_name: str) -> str:
@@ -123,6 +131,8 @@ class AIEngine:
             if in_article_count > 0:
                 return f"Writing {in_article_count} In‑article Image Prompts"
             return "Writing In‑article Image Prompts"
+        elif function_name == 'generate_images_from_prompts':
+            return f"{count} image{'s' if count != 1 else ''} generated"
         return f"{count} item{'s' if count != 1 else ''} processed"
     
     def _get_save_message(self, function_name: str, count: int) -> str:
@@ -138,6 +148,8 @@ class AIEngine:
         elif function_name == 'generate_image_prompts':
             # Count is total prompts created
             return f"Assigning {count} Prompts to Dedicated Slots"
+        elif function_name == 'generate_images_from_prompts':
+            return f"Saving {count} image{'s' if count != 1 else ''}"
         return f"Saving {count} item{'s' if count != 1 else ''}"
     
     def execute(self, fn: BaseAIFunction, payload: dict) -> dict:
@@ -196,131 +208,144 @@ class AIEngine:
             
             prep_message = self._get_prep_message(function_name, data_count, data)
             self.console_tracker.prep(prep_message)
-            prompt = fn.build_prompt(data, self.account)
-            self.console_tracker.prep(f"Prompt built: {len(prompt)} characters")
+            
+            # For image generation, build_prompt returns placeholder
+            # Actual processing happens in save_output
+            if function_name == 'generate_images_from_prompts':
+                prompt = "Image generation queue prepared"
+            else:
+                prompt = fn.build_prompt(data, self.account)
+                self.console_tracker.prep(f"Prompt built: {len(prompt)} characters")
+            
             self.step_tracker.add_request_step("PREP", "success", prep_message)
             self.tracker.update("PREP", 25, prep_message, meta=self.step_tracker.get_meta())
             
             # Phase 3: AI_CALL - Provider API Call (25-70%)
-            ai_core = AICore(account=self.account)
-            function_name = fn.get_name()
-            
-            # Generate function_id for tracking (ai-{function_name}-01)
-            # Normalize underscores to hyphens to match frontend tracking IDs
-            function_id_base = function_name.replace('_', '-')
-            function_id = f"ai-{function_id_base}-01-desktop"
-            
-            # Get model config from settings (Stage 4 requirement)
-            # Pass account to read model from IntegrationSettings
-            model_config = get_model_config(function_name, account=self.account)
-            model = model_config.get('model')
-            
-            # Read model straight from IntegrationSettings for visibility
-            model_from_integration = None
-            if self.account:
+            # For image generation, AI calls happen in save_output, so skip this phase
+            if function_name == 'generate_images_from_prompts':
+                # Skip AI_CALL phase - processing happens in save_output
+                raw_response = {'content': 'Image generation queue ready'}
+                parsed = {'processed': True}
+            else:
+                ai_core = AICore(account=self.account)
+                function_name = fn.get_name()
+                
+                # Generate function_id for tracking (ai-{function_name}-01)
+                # Normalize underscores to hyphens to match frontend tracking IDs
+                function_id_base = function_name.replace('_', '-')
+                function_id = f"ai-{function_id_base}-01-desktop"
+                
+                # Get model config from settings (Stage 4 requirement)
+                # Pass account to read model from IntegrationSettings
+                model_config = get_model_config(function_name, account=self.account)
+                model = model_config.get('model')
+                
+                # Read model straight from IntegrationSettings for visibility
+                model_from_integration = None
+                if self.account:
+                    try:
+                        from igny8_core.modules.system.models import IntegrationSettings
+                        openai_settings = IntegrationSettings.objects.filter(
+                            integration_type='openai',
+                            account=self.account,
+                            is_active=True
+                        ).first()
+                        if openai_settings and openai_settings.config:
+                            model_from_integration = openai_settings.config.get('model')
+                    except Exception as integration_error:
+                        logger.warning(
+                            "[AIEngine] Unable to read model from IntegrationSettings: %s",
+                            integration_error,
+                            exc_info=True,
+                        )
+                
+                # Debug logging: Show model configuration (console only, not in step tracker)
+                logger.info(f"[AIEngine] Model Configuration for {function_name}:")
+                logger.info(f"  - Model from get_model_config: {model}")
+                logger.info(f"  - Full model_config: {model_config}")
+                self.console_tracker.ai_call(f"Model from settings: {model_from_integration or 'Not set'}")
+                self.console_tracker.ai_call(f"Model selected for request: {model or 'default'}")
+                self.console_tracker.ai_call(f"Calling {model or 'default'} model with {len(prompt)} char prompt")
+                self.console_tracker.ai_call(f"Function ID: {function_id}")
+                
+                # Track AI call start with user-friendly message
+                ai_call_message = self._get_ai_call_message(function_name, data_count)
+                self.step_tracker.add_response_step("AI_CALL", "success", ai_call_message)
+                self.tracker.update("AI_CALL", 50, ai_call_message, meta=self.step_tracker.get_meta())
+                
                 try:
-                    from igny8_core.modules.system.models import IntegrationSettings
-                    openai_settings = IntegrationSettings.objects.filter(
-                        integration_type='openai',
-                        account=self.account,
-                        is_active=True
-                    ).first()
-                    if openai_settings and openai_settings.config:
-                        model_from_integration = openai_settings.config.get('model')
-                except Exception as integration_error:
-                    logger.warning(
-                        "[AIEngine] Unable to read model from IntegrationSettings: %s",
-                        integration_error,
-                        exc_info=True,
+                    # Use centralized run_ai_request() with console logging (Stage 2 & 3 requirement)
+                    # Pass console_tracker for unified logging
+                    raw_response = ai_core.run_ai_request(
+                        prompt=prompt,
+                        model=model,
+                        max_tokens=model_config.get('max_tokens'),
+                        temperature=model_config.get('temperature'),
+                        response_format=model_config.get('response_format'),
+                        function_name=function_name,
+                        function_id=function_id,  # Pass function_id for tracking
+                        tracker=self.console_tracker  # Pass console tracker for logging
                     )
-            
-            # Debug logging: Show model configuration (console only, not in step tracker)
-            logger.info(f"[AIEngine] Model Configuration for {function_name}:")
-            logger.info(f"  - Model from get_model_config: {model}")
-            logger.info(f"  - Full model_config: {model_config}")
-            self.console_tracker.ai_call(f"Model from settings: {model_from_integration or 'Not set'}")
-            self.console_tracker.ai_call(f"Model selected for request: {model or 'default'}")
-            self.console_tracker.ai_call(f"Calling {model or 'default'} model with {len(prompt)} char prompt")
-            self.console_tracker.ai_call(f"Function ID: {function_id}")
-            
-            # Track AI call start with user-friendly message
-            ai_call_message = self._get_ai_call_message(function_name, data_count)
-            self.step_tracker.add_response_step("AI_CALL", "success", ai_call_message)
-            self.tracker.update("AI_CALL", 50, ai_call_message, meta=self.step_tracker.get_meta())
-            
-            try:
-                # Use centralized run_ai_request() with console logging (Stage 2 & 3 requirement)
-                # Pass console_tracker for unified logging
-                raw_response = ai_core.run_ai_request(
-                    prompt=prompt,
-                    model=model,
-                    max_tokens=model_config.get('max_tokens'),
-                    temperature=model_config.get('temperature'),
-                    response_format=model_config.get('response_format'),
+                except Exception as e:
+                    error_msg = f"AI call failed: {str(e)}"
+                    logger.error(f"Exception during AI call: {error_msg}", exc_info=True)
+                    return self._handle_error(error_msg, fn)
+                
+                if raw_response.get('error'):
+                    error_msg = raw_response.get('error', 'Unknown AI error')
+                    logger.error(f"AI call returned error: {error_msg}")
+                    return self._handle_error(error_msg, fn)
+                
+                if not raw_response.get('content'):
+                    error_msg = "AI call returned no content"
+                    logger.error(error_msg)
+                    return self._handle_error(error_msg, fn)
+                
+                # Track cost
+                self.cost_tracker.record(
                     function_name=function_name,
-                    function_id=function_id,  # Pass function_id for tracking
-                    tracker=self.console_tracker  # Pass console tracker for logging
+                    cost=raw_response.get('cost', 0),
+                    tokens=raw_response.get('total_tokens', 0),
+                    model=raw_response.get('model')
                 )
-            except Exception as e:
-                error_msg = f"AI call failed: {str(e)}"
-                logger.error(f"Exception during AI call: {error_msg}", exc_info=True)
-                return self._handle_error(error_msg, fn)
-            
-            if raw_response.get('error'):
-                error_msg = raw_response.get('error', 'Unknown AI error')
-                logger.error(f"AI call returned error: {error_msg}")
-                return self._handle_error(error_msg, fn)
-            
-            if not raw_response.get('content'):
-                error_msg = "AI call returned no content"
-                logger.error(error_msg)
-                return self._handle_error(error_msg, fn)
-            
-            # Track cost
-            self.cost_tracker.record(
-                function_name=function_name,
-                cost=raw_response.get('cost', 0),
-                tokens=raw_response.get('total_tokens', 0),
-                model=raw_response.get('model')
-            )
-            
-            # Update AI_CALL step with results
-            self.step_tracker.response_steps[-1] = {
-                **self.step_tracker.response_steps[-1],
-                'message': f"Received {raw_response.get('total_tokens', 0)} tokens, Cost: ${raw_response.get('cost', 0):.6f}",
-                'duration': raw_response.get('duration')
-            }
-            self.tracker.update("AI_CALL", 70, f"AI response received ({raw_response.get('total_tokens', 0)} tokens)", meta=self.step_tracker.get_meta())
-            
-            # Phase 4: PARSE - Response Parsing (70-85%)
-            try:
-                parse_message = self._get_parse_message(function_name)
-                self.console_tracker.parse(parse_message)
-                response_content = raw_response.get('content', '')
-                parsed = fn.parse_response(response_content, self.step_tracker)
                 
-                if isinstance(parsed, (list, tuple)):
-                    parsed_count = len(parsed)
-                elif isinstance(parsed, dict):
-                    # Check if it's a content dict (has 'content' field) or a result dict (has 'count')
-                    if 'content' in parsed:
-                        parsed_count = 1  # Single content item
+                # Update AI_CALL step with results
+                self.step_tracker.response_steps[-1] = {
+                    **self.step_tracker.response_steps[-1],
+                    'message': f"Received {raw_response.get('total_tokens', 0)} tokens, Cost: ${raw_response.get('cost', 0):.6f}",
+                    'duration': raw_response.get('duration')
+                }
+                self.tracker.update("AI_CALL", 70, f"AI response received ({raw_response.get('total_tokens', 0)} tokens)", meta=self.step_tracker.get_meta())
+                
+                # Phase 4: PARSE - Response Parsing (70-85%)
+                try:
+                    parse_message = self._get_parse_message(function_name)
+                    self.console_tracker.parse(parse_message)
+                    response_content = raw_response.get('content', '')
+                    parsed = fn.parse_response(response_content, self.step_tracker)
+                    
+                    if isinstance(parsed, (list, tuple)):
+                        parsed_count = len(parsed)
+                    elif isinstance(parsed, dict):
+                        # Check if it's a content dict (has 'content' field) or a result dict (has 'count')
+                        if 'content' in parsed:
+                            parsed_count = 1  # Single content item
+                        else:
+                            parsed_count = parsed.get('count', 1)
                     else:
-                        parsed_count = parsed.get('count', 1)
-                else:
-                    parsed_count = 1
-                
-                # Update parse message with count for better UX
-                parse_message = self._get_parse_message_with_count(function_name, parsed_count)
-                
-                self.console_tracker.parse(f"Successfully parsed {parsed_count} items from response")
-                self.step_tracker.add_response_step("PARSE", "success", parse_message)
-                self.tracker.update("PARSE", 85, parse_message, meta=self.step_tracker.get_meta())
-            except Exception as parse_error:
-                error_msg = f"Failed to parse AI response: {str(parse_error)}"
-                logger.error(f"AIEngine: {error_msg}", exc_info=True)
-                logger.error(f"AIEngine: Response content was: {response_content[:500] if response_content else 'None'}...")
-                return self._handle_error(error_msg, fn)
+                        parsed_count = 1
+                    
+                    # Update parse message with count for better UX
+                    parse_message = self._get_parse_message_with_count(function_name, parsed_count)
+                    
+                    self.console_tracker.parse(f"Successfully parsed {parsed_count} items from response")
+                    self.step_tracker.add_response_step("PARSE", "success", parse_message)
+                    self.tracker.update("PARSE", 85, parse_message, meta=self.step_tracker.get_meta())
+                except Exception as parse_error:
+                    error_msg = f"Failed to parse AI response: {str(parse_error)}"
+                    logger.error(f"AIEngine: {error_msg}", exc_info=True)
+                    logger.error(f"AIEngine: Response content was: {response_content[:500] if response_content else 'None'}...")
+                    return self._handle_error(error_msg, fn)
             
             # Phase 5: SAVE - Database Operations (85-98%)
             # Pass step_tracker to save_output so it can add validation steps

backend/igny8_core/ai/functions/__init__.py

@@ -6,6 +6,7 @@ from igny8_core.ai.functions.generate_ideas import GenerateIdeasFunction
 from igny8_core.ai.functions.generate_content import GenerateContentFunction
 from igny8_core.ai.functions.generate_images import GenerateImagesFunction, generate_images_core
 from igny8_core.ai.functions.generate_image_prompts import GenerateImagePromptsFunction
+from igny8_core.ai.functions.generate_images_from_prompts import GenerateImagesFromPromptsFunction
 
 __all__ = [
     'AutoClusterFunction',
@@ -14,4 +15,5 @@ __all__ = [
     'GenerateImagesFunction',
     'generate_images_core',
     'GenerateImagePromptsFunction',
+    'GenerateImagesFromPromptsFunction',
 ]

backend/igny8_core/ai/functions/generate_images_from_prompts.py

@@ -0,0 +1,311 @@
+"""
+Generate Images from Prompts AI Function
+Generates actual images from existing image prompts using AI
+"""
+import logging
+from typing import Dict, List, Any
+from django.db import transaction
+from igny8_core.ai.base import BaseAIFunction
+from igny8_core.modules.writer.models import Images, Content
+from igny8_core.ai.ai_core import AICore
+from igny8_core.ai.validators import validate_ids
+from igny8_core.ai.prompts import PromptRegistry
+
+logger = logging.getLogger(__name__)
+
+
+class GenerateImagesFromPromptsFunction(BaseAIFunction):
+    """Generate actual images from image prompts using AI"""
+    
+    def get_name(self) -> str:
+        return 'generate_images_from_prompts'
+    
+    def get_metadata(self) -> Dict:
+        return {
+            'display_name': 'Generate Images from Prompts',
+            'description': 'Generate actual images from existing image prompts',
+            'phases': {
+                'INIT': 'Validating image prompts...',
+                'PREP': 'Preparing image generation queue...',
+                'AI_CALL': 'Generating images with AI...',
+                'PARSE': 'Processing image URLs...',
+                'SAVE': 'Saving image URLs...',
+                'DONE': 'Images generated!'
+            }
+        }
+    
+    def get_max_items(self) -> int:
+        return 100  # Max images per batch
+    
+    def validate(self, payload: dict, account=None) -> Dict:
+        """Validate image IDs exist and have prompts"""
+        result = validate_ids(payload, max_items=self.get_max_items())
+        if not result['valid']:
+            return result
+        
+        # Check images exist and have prompts
+        image_ids = payload.get('ids', [])
+        if image_ids:
+            queryset = Images.objects.filter(id__in=image_ids)
+            if account:
+                queryset = queryset.filter(account=account)
+            
+            images = list(queryset.select_related('content', 'task'))
+            
+            if not images:
+                return {
+                    'valid': False,
+                    'error': 'No images found with provided IDs'
+                }
+            
+            # Check all images have prompts
+            images_without_prompts = [img.id for img in images if not img.prompt or not img.prompt.strip()]
+            if images_without_prompts:
+                return {
+                    'valid': False,
+                    'error': f'Images {images_without_prompts} do not have prompts'
+                }
+            
+            # Check all images are pending
+            images_not_pending = [img.id for img in images if img.status != 'pending']
+            if images_not_pending:
+                return {
+                    'valid': False,
+                    'error': f'Images {images_not_pending} are not in pending status'
+                }
+        
+        return {'valid': True}
+    
+    def prepare(self, payload: dict, account=None) -> Dict:
+        """Load images and image generation settings"""
+        image_ids = payload.get('ids', [])
+        
+        queryset = Images.objects.filter(id__in=image_ids, status='pending')
+        if account:
+            queryset = queryset.filter(account=account)
+        
+        images = list(queryset.select_related('content', 'task', 'account', 'site', 'sector'))
+        
+        if not images:
+            raise ValueError("No pending images found with prompts")
+        
+        # Get image generation settings
+        image_settings = {}
+        if account:
+            try:
+                from igny8_core.modules.system.models import IntegrationSettings
+                integration = IntegrationSettings.objects.get(
+                    account=account,
+                    integration_type='image_generation',
+                    is_active=True
+                )
+                image_settings = integration.config or {}
+            except Exception as e:
+                logger.warning(f"Failed to load image generation settings: {e}")
+        
+        # Extract settings with defaults
+        provider = image_settings.get('provider') or image_settings.get('service', 'openai')
+        if provider == 'runware':
+            model = image_settings.get('model') or image_settings.get('runwareModel', 'runware:97@1')
+        else:
+            model = image_settings.get('model', 'dall-e-3')
+        
+        # Get prompt templates
+        image_prompt_template = PromptRegistry.get_image_prompt_template(account)
+        negative_prompt = PromptRegistry.get_negative_prompt(account)
+        
+        return {
+            'images': images,
+            'account': account,
+            'provider': provider,
+            'model': model,
+            'image_type': image_settings.get('image_type', 'realistic'),
+            'image_format': image_settings.get('image_format', 'webp'),
+            'image_prompt_template': image_prompt_template,
+            'negative_prompt': negative_prompt,
+        }
+    
+    def build_prompt(self, data: Dict, account=None) -> str:
+        """
+        Build prompt for AI_CALL phase.
+        For image generation, we return a placeholder since we process images in save_output.
+        """
+        # Return placeholder - actual processing happens in save_output
+        return "Image generation queue prepared"
+    
+    def parse_response(self, response: str, step_tracker=None) -> Dict:
+        """
+        Parse response from AI_CALL.
+        For image generation, we process images directly in save_output, so this is a placeholder.
+        """
+        return {'processed': True}
+    
+    def save_output(
+        self,
+        parsed: Dict,
+        original_data: Dict,
+        account=None,
+        progress_tracker=None,
+        step_tracker=None
+    ) -> Dict:
+        """
+        Process all images sequentially and generate them.
+        This method handles the loop and makes AI calls directly.
+        """
+        images = original_data.get('images', [])
+        if not images:
+            raise ValueError("No images to process")
+        
+        provider = original_data.get('provider', 'openai')
+        model = original_data.get('model', 'dall-e-3')
+        image_type = original_data.get('image_type', 'realistic')
+        image_prompt_template = original_data.get('image_prompt_template', '')
+        negative_prompt = original_data.get('negative_prompt', '')
+        
+        ai_core = AICore(account=account or original_data.get('account'))
+        
+        total_images = len(images)
+        images_generated = 0
+        images_failed = 0
+        errors = []
+        
+        # Process each image sequentially
+        for index, image in enumerate(images, 1):
+            try:
+                # Get content title
+                content = image.content
+                if not content:
+                    # Fallback to task if no content
+                    if image.task:
+                        content_title = image.task.title
+                    else:
+                        content_title = "Content"
+                else:
+                    content_title = content.title or content.meta_title or "Content"
+                
+                # Format prompt using template
+                if image_prompt_template:
+                    try:
+                        formatted_prompt = image_prompt_template.format(
+                            post_title=content_title,
+                            image_prompt=image.prompt,
+                            image_type=image_type
+                        )
+                    except KeyError as e:
+                        logger.warning(f"Template formatting error: {e}, using simple format")
+                        formatted_prompt = f"Create a high-quality {image_type} image: {image.prompt}"
+                else:
+                    # Fallback template
+                    formatted_prompt = f"Create a high-quality {image_type} image: {image.prompt}"
+                
+                # Update progress: PREP phase for this image
+                if progress_tracker and step_tracker:
+                    prep_msg = f"Generating image {index} of {total_images}: {image.image_type}"
+                    step_tracker.add_request_step("PREP", "success", prep_msg)
+                    progress_pct = 10 + int((index - 1) / total_images * 15)  # 10-25% for PREP
+                    progress_tracker.update("PREP", progress_pct, prep_msg, meta=step_tracker.get_meta())
+                
+                # Generate image
+                if progress_tracker and step_tracker:
+                    ai_msg = f"Generating {image.image_type} image {index} of {total_images} with AI"
+                    step_tracker.add_response_step("AI_CALL", "success", ai_msg)
+                    progress_pct = 25 + int((index - 1) / total_images * 45)  # 25-70% for AI_CALL
+                    progress_tracker.update("AI_CALL", progress_pct, ai_msg, meta=step_tracker.get_meta())
+                
+                result = ai_core.generate_image(
+                    prompt=formatted_prompt,
+                    provider=provider,
+                    model=model,
+                    size='1024x1024',
+                    negative_prompt=negative_prompt if provider == 'runware' else None,
+                    function_name='generate_images_from_prompts'
+                )
+                
+                if result.get('error'):
+                    # Mark as failed
+                    with transaction.atomic():
+                        image.status = 'failed'
+                        image.save(update_fields=['status', 'updated_at'])
+                    
+                    error_msg = f"Image {index} failed: {result['error']}"
+                    errors.append(error_msg)
+                    images_failed += 1
+                    logger.error(f"Image generation failed for image {image.id}: {result['error']}")
+                    
+                    if progress_tracker and step_tracker:
+                        parse_msg = f"Image {index} failed: {result['error']}"
+                        step_tracker.add_response_step("PARSE", "error", parse_msg)
+                        progress_pct = 70 + int((index - 1) / total_images * 15)  # 70-85% for PARSE
+                        progress_tracker.update("PARSE", progress_pct, parse_msg, meta=step_tracker.get_meta())
+                    
+                    continue
+                
+                image_url = result.get('url')
+                if not image_url:
+                    # Mark as failed
+                    with transaction.atomic():
+                        image.status = 'failed'
+                        image.save(update_fields=['status', 'updated_at'])
+                    
+                    error_msg = f"Image {index} failed: No URL returned"
+                    errors.append(error_msg)
+                    images_failed += 1
+                    logger.error(f"No image URL returned for image {image.id}")
+                    
+                    if progress_tracker and step_tracker:
+                        parse_msg = f"Image {index} failed: No URL returned"
+                        step_tracker.add_response_step("PARSE", "error", parse_msg)
+                        progress_pct = 70 + int((index - 1) / total_images * 15)
+                        progress_tracker.update("PARSE", progress_pct, parse_msg, meta=step_tracker.get_meta())
+                    
+                    continue
+                
+                # Update progress: PARSE phase
+                if progress_tracker and step_tracker:
+                    parse_msg = f"Image {index} of {total_images} generated successfully"
+                    step_tracker.add_response_step("PARSE", "success", parse_msg)
+                    progress_pct = 70 + int((index - 1) / total_images * 15)  # 70-85% for PARSE
+                    progress_tracker.update("PARSE", progress_pct, parse_msg, meta=step_tracker.get_meta())
+                
+                # Update image record
+                with transaction.atomic():
+                    image.image_url = image_url
+                    image.status = 'generated'
+                    image.save(update_fields=['image_url', 'status', 'updated_at'])
+                
+                images_generated += 1
+                logger.info(f"Image {image.id} ({image.image_type}) generated successfully: {image_url}")
+                
+                # Update progress: SAVE phase
+                if progress_tracker and step_tracker:
+                    save_msg = f"Saved image {index} of {total_images}"
+                    step_tracker.add_request_step("SAVE", "success", save_msg)
+                    progress_pct = 85 + int((index - 1) / total_images * 13)  # 85-98% for SAVE
+                    progress_tracker.update("SAVE", progress_pct, save_msg, meta=step_tracker.get_meta())
+                
+            except Exception as e:
+                # Mark as failed
+                with transaction.atomic():
+                    image.status = 'failed'
+                    image.save(update_fields=['status', 'updated_at'])
+                
+                error_msg = f"Image {index} failed: {str(e)}"
+                errors.append(error_msg)
+                images_failed += 1
+                logger.error(f"Exception generating image {image.id}: {str(e)}", exc_info=True)
+                continue
+        
+        # Final progress update
+        if progress_tracker and step_tracker:
+            final_msg = f"Generated {images_generated} of {total_images} images"
+            step_tracker.add_request_step("SAVE", "success", final_msg)
+            progress_tracker.update("SAVE", 98, final_msg, meta=step_tracker.get_meta())
+        
+        return {
+            'count': images_generated,
+            'images_generated': images_generated,
+            'images_failed': images_failed,
+            'total_images': total_images,
+            'errors': errors if errors else None
+        }
+

backend/igny8_core/ai/registry.py

@@ -94,9 +94,15 @@ def _load_generate_image_prompts():
     from igny8_core.ai.functions.generate_image_prompts import GenerateImagePromptsFunction
     return GenerateImagePromptsFunction
 
+def _load_generate_images_from_prompts():
+    """Lazy loader for generate_images_from_prompts function"""
+    from igny8_core.ai.functions.generate_images_from_prompts import GenerateImagesFromPromptsFunction
+    return GenerateImagesFromPromptsFunction
+
 register_lazy_function('auto_cluster', _load_auto_cluster)
 register_lazy_function('generate_ideas', _load_generate_ideas)
 register_lazy_function('generate_content', _load_generate_content)
 register_lazy_function('generate_images', _load_generate_images)
 register_lazy_function('generate_image_prompts', _load_generate_image_prompts)
+register_lazy_function('generate_images_from_prompts', _load_generate_images_from_prompts)
 

backend/igny8_core/ai/settings.py

@@ -40,6 +40,12 @@ MODEL_CONFIG = {
         "temperature": 0.7,
         "response_format": {"type": "json_object"},
     },
+    "generate_images_from_prompts": {
+        "model": "dall-e-3",  # Default, overridden by IntegrationSettings
+        "max_tokens": None,  # Not used for images
+        "temperature": None,  # Not used for images
+        "response_format": None,  # Not used for images
+    },
 }
 
 # Function name aliases (for backward compatibility)

backend/igny8_core/modules/writer/views.py

@@ -513,6 +513,60 @@ class ImagesViewSet(SiteSectorModelViewSet):
             'count': len(grouped_data),
             'results': grouped_data
         }, status=status.HTTP_200_OK)
+    
+    @action(detail=False, methods=['post'], url_path='generate_images', url_name='generate_images')
+    def generate_images(self, request):
+        """Generate images from prompts for image records"""
+        from igny8_core.ai.tasks import run_ai_task
+        
+        account = getattr(request, 'account', None)
+        ids = request.data.get('ids', [])
+        
+        if not ids:
+            return Response({
+                'error': 'No IDs provided',
+                'type': 'ValidationError'
+            }, status=status.HTTP_400_BAD_REQUEST)
+        
+        account_id = account.id if account else None
+        
+        # Queue Celery task
+        try:
+            if hasattr(run_ai_task, 'delay'):
+                task = run_ai_task.delay(
+                    function_name='generate_images_from_prompts',
+                    payload={'ids': ids},
+                    account_id=account_id
+                )
+                return Response({
+                    'success': True,
+                    'task_id': str(task.id),
+                    'message': 'Image generation started'
+                }, status=status.HTTP_200_OK)
+            else:
+                # Fallback to synchronous execution
+                result = run_ai_task(
+                    function_name='generate_images_from_prompts',
+                    payload={'ids': ids},
+                    account_id=account_id
+                )
+                if result.get('success'):
+                    return Response({
+                        'success': True,
+                        'images_generated': result.get('count', 0),
+                        'images_failed': result.get('images_failed', 0),
+                        'message': 'Images generated successfully'
+                    }, status=status.HTTP_200_OK)
+                else:
+                    return Response({
+                        'error': result.get('error', 'Image generation failed'),
+                        'type': 'TaskExecutionError'
+                    }, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
+        except Exception as e:
+            return Response({
+                'error': str(e),
+                'type': 'ExecutionError'
+            }, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
 
 
 class ContentViewSet(SiteSectorModelViewSet):

docs/IMAGE_GENERATION_IMPLEMENTATION_PLAN.md

@@ -0,0 +1,841 @@
+# Image Generation Implementation Plan
+## Complete Plan for Generating Images from Prompts
+
+**Date:** 2025-01-XX  
+**Scope:** Implement image generation AI function following existing AI framework patterns
+
+---
+
+## Table of Contents
+
+1. [System Understanding](#1-system-understanding)
+2. [Architecture Overview](#2-architecture-overview)
+3. [Implementation Plan](#3-implementation-plan)
+4. [Technical Details](#4-technical-details)
+5. [Frontend Integration](#5-frontend-integration)
+6. [Testing Strategy](#6-testing-strategy)
+
+---
+
+## 1. System Understanding
+
+### 1.1 Current AI Framework Architecture
+
+The system uses a unified AI framework with the following components:
+
+**Core Flow:**
+```
+Frontend API Call
+  ↓
+views.py (@action endpoint)
+  ↓
+run_ai_task (ai/tasks.py) - Unified Celery task entrypoint
+  ↓
+AIEngine (ai/engine.py) - Orchestrator (6 phases: INIT, PREP, AI_CALL, PARSE, SAVE, DONE)
+  ↓
+BaseAIFunction implementation
+  ↓
+AICore (ai/ai_core.py) - Centralized AI request handler
+  ↓
+AI Provider (OpenAI/Runware)
+```
+
+**Existing AI Functions:**
+1. **AutoClusterFunction** (`auto_cluster.py`) - Groups keywords into clusters
+2. **GenerateIdeasFunction** (`generate_ideas.py`) - Generates content ideas from clusters
+3. **GenerateContentFunction** (`generate_content.py`) - Generates article content from ideas
+4. **GenerateImagePromptsFunction** (`generate_image_prompts.py`) - Extracts image prompts from content
+
+**Key Components:**
+- **BaseAIFunction** - Abstract base class with methods: `get_name()`, `validate()`, `prepare()`, `build_prompt()`, `parse_response()`, `save_output()`
+- **AIEngine** - Manages lifecycle, progress tracking, cost tracking, error handling
+- **PromptRegistry** - Centralized prompt management with hierarchy (task → DB → default)
+- **AICore** - Handles API calls to OpenAI/Runware for both text and image generation
+- **IntegrationSettings** - Stores account-specific configurations (models, API keys, image settings)
+
+### 1.2 Image Generation System (WordPress Plugin Reference)
+
+**Key Learnings from WP Plugin:**
+
+1. **Queue-Based Processing:**
+   - Images are processed sequentially in a queue
+   - Each image has its own progress bar (0-50% in 7s, 50-75% in 5s, 75-95% incrementally)
+   - Progress modal shows all images being processed with individual status
+
+2. **Image Types:**
+   - Featured image (1 per content)
+   - In-article images (configurable: 1-5 per content)
+   - Desktop images (if enabled)
+   - Mobile images (if enabled)
+
+3. **Settings from IntegrationSettings:**
+   - `provider`: 'openai' or 'runware'
+   - `model`: Model name (e.g., 'dall-e-3', 'runware:97@1')
+   - `image_type`: 'realistic', 'artistic', 'cartoon'
+   - `max_in_article_images`: 1-5
+   - `image_format`: 'webp', 'jpg', 'png'
+   - `desktop_enabled`: boolean
+   - `mobile_enabled`: boolean
+
+4. **Prompt Templates:**
+   - `image_prompt_template`: Template for formatting prompts (uses {post_title}, {image_prompt}, {image_type})
+   - `negative_prompt`: Negative prompt for Runware (OpenAI doesn't support)
+
+5. **Progress Tracking:**
+   - Real-time progress updates via Celery
+   - Individual image status tracking
+   - Success/failure per image
+
+### 1.3 Current Image Generation Function
+
+**Existing:** `GenerateImagesFunction` (`generate_images.py`)
+- **Status:** Partially implemented, uses old pattern
+- **Issues:**
+  - Still references `Tasks` instead of `Content`
+  - Doesn't follow the new unified framework pattern
+  - Uses legacy `generate_images_core()` wrapper
+  - Doesn't properly queue multiple images
+
+**What We Need:**
+- New function: `GenerateImagesFromPromptsFunction`
+- Should work with `Images` model (which now has `content` relationship)
+- Should process images in queue (one at a time)
+- Should use progress modal similar to other AI functions
+- Should use prompt templates and negative prompts from Thinker/Prompts
+
+---
+
+## 2. Architecture Overview
+
+### 2.1 New Function: `GenerateImagesFromPromptsFunction`
+
+**Purpose:** Generate actual images from existing image prompts stored in `Images` model
+
+**Input:**
+- `ids`: List of Image IDs (or Content IDs) to generate images for
+- Images must have `prompt` field populated (from `GenerateImagePromptsFunction`)
+
+**Output:**
+- Updates `Images` records with:
+  - `image_url`: Generated image URL
+  - `status`: 'generated' (or 'failed' on error)
+
+**Flow:**
+1. **INIT (0-10%)**: Validate image IDs, check prompts exist
+2. **PREP (10-25%)**: Load images, get settings, prepare queue
+3. **AI_CALL (25-70%)**: Generate images sequentially (one per AI_CALL phase)
+4. **PARSE (70-85%)**: Parse image URLs from responses
+5. **SAVE (85-98%)**: Update Images records with URLs
+6. **DONE (98-100%)**: Complete
+
+### 2.2 Key Differences from Other Functions
+
+**Unlike text generation functions:**
+- **Multiple AI calls**: One AI call per image (not one call for all)
+- **Sequential processing**: Images must be generated one at a time (rate limits)
+- **Progress per image**: Need to track progress for each individual image
+- **Different API**: Uses `AICore.generate_image()` instead of `AICore.run_ai_request()`
+
+**Similarities:**
+- Uses same `BaseAIFunction` pattern
+- Uses same `AIEngine` orchestrator
+- Uses same progress tracking system
+- Uses same error handling
+
+---
+
+## 3. Implementation Plan
+
+### Phase 1: Backend AI Function
+
+#### 3.1 Create `GenerateImagesFromPromptsFunction`
+
+**File:** `backend/igny8_core/ai/functions/generate_images_from_prompts.py`
+
+**Class Structure:**
+```python
+class GenerateImagesFromPromptsFunction(BaseAIFunction):
+    def get_name(self) -> str:
+        return 'generate_images_from_prompts'
+    
+    def get_metadata(self) -> Dict:
+        return {
+            'display_name': 'Generate Images from Prompts',
+            'description': 'Generate actual images from image prompts',
+            'phases': {
+                'INIT': 'Validating image prompts...',
+                'PREP': 'Preparing image generation queue...',
+                'AI_CALL': 'Generating images with AI...',
+                'PARSE': 'Processing image URLs...',
+                'SAVE': 'Saving image URLs...',
+                'DONE': 'Images generated!'
+            }
+        }
+    
+    def validate(self, payload: dict, account=None) -> Dict:
+        """Validate image IDs and check prompts exist"""
+        # Check for 'ids' array
+        # Check images exist and have prompts
+        # Check images have status='pending'
+        # Check account matches
+    
+    def prepare(self, payload: dict, account=None) -> Dict:
+        """Load images and settings"""
+        # Load Images records by IDs
+        # Get IntegrationSettings for image_generation
+        # Extract: provider, model, image_type, image_format, etc.
+        # Get prompt templates from PromptRegistry
+        # Return: {
+        #   'images': [Image objects],
+        #   'settings': {...},
+        #   'image_prompt_template': str,
+        #   'negative_prompt': str
+        # }
+    
+    def build_prompt(self, data: Dict, account=None) -> Dict:
+        """Format prompt using template"""
+        # For each image in queue:
+        # - Get content title (from image.content)
+        # - Format prompt using image_prompt_template
+        # - Return formatted prompt + image_type
+        # Note: This is called once per image (AIEngine handles iteration)
+    
+    def parse_response(self, response: Dict, step_tracker=None) -> Dict:
+        """Parse image URL from response"""
+        # Response from AICore.generate_image() has:
+        # - 'url': Image URL
+        # - 'revised_prompt': (optional)
+        # - 'cost': (optional)
+        # Return: {'url': str, 'revised_prompt': str, 'cost': float}
+    
+    def save_output(self, parsed: Dict, original_data: Dict, account=None, ...) -> Dict:
+        """Update Images record with URL"""
+        # Get image from original_data
+        # Update Images record:
+        # - image_url = parsed['url']
+        # - status = 'generated'
+        # - updated_at = now()
+        # Return: {'count': 1, 'images_generated': 1}
+```
+
+**Key Implementation Details:**
+
+1. **Multiple AI Calls Handling:**
+   - `AIEngine` will call `build_prompt()` → `AI_CALL` → `parse_response()` → `SAVE` for each image
+   - Need to track which image is being processed
+   - Use `step_tracker` to log progress per image
+
+2. **Prompt Formatting:**
+   ```python
+   # Get template from PromptRegistry
+   template = PromptRegistry.get_image_prompt_template(account)
+   
+   # Format with content title and prompt
+   formatted = template.format(
+       post_title=image.content.title or image.content.meta_title,
+       image_prompt=image.prompt,
+       image_type=settings['image_type']
+   )
+   ```
+
+3. **Image Generation:**
+   ```python
+   # Use AICore.generate_image()
+   result = ai_core.generate_image(
+       prompt=formatted_prompt,
+       provider=settings['provider'],
+       model=settings['model'],
+       size='1024x1024',  # Default or from settings
+       negative_prompt=negative_prompt if provider == 'runware' else None,
+       function_name='generate_images_from_prompts'
+   )
+   ```
+
+4. **Progress Tracking:**
+   - Track total images: `len(images)`
+   - Track completed: Increment after each SAVE
+   - Update progress: `(completed / total) * 100`
+
+#### 3.2 Update AIEngine for Multiple AI Calls
+
+**File:** `backend/igny8_core/ai/engine.py`
+
+**Changes Needed:**
+- Detect if function needs multiple AI calls (check function name or metadata)
+- For `generate_images_from_prompts`:
+  - Loop through images in PREP data
+  - For each image:
+    - Call `build_prompt()` with single image
+    - Call `AI_CALL` phase (generate image)
+    - Call `parse_response()`
+    - Call `SAVE` phase
+    - Update progress: `(current_image / total_images) * 100`
+  - After all images: Call DONE phase
+
+**Alternative Approach (Simpler):**
+- Process all images in `save_output()` method
+- Make AI calls directly in `save_output()` (not through AIEngine phases)
+- Update progress manually via `progress_tracker.update()`
+- This is simpler but less consistent with framework
+
+**Recommended Approach:**
+- Use AIEngine's phase system
+- Add metadata flag: `requires_multiple_ai_calls: True`
+- AIEngine detects this and loops through items
+
+#### 3.3 Register Function
+
+**File:** `backend/igny8_core/ai/registry.py`
+
+```python
+def _load_generate_images_from_prompts():
+    from igny8_core.ai.functions.generate_images_from_prompts import GenerateImagesFromPromptsFunction
+    return GenerateImagesFromPromptsFunction
+
+register_lazy_function('generate_images_from_prompts', _load_generate_images_from_prompts)
+```
+
+**File:** `backend/igny8_core/ai/functions/__init__.py`
+
+```python
+from .generate_images_from_prompts import GenerateImagesFromPromptsFunction
+
+__all__ = [
+    ...
+    'GenerateImagesFromPromptsFunction',
+]
+```
+
+#### 3.4 Add Model Configuration
+
+**File:** `backend/igny8_core/ai/settings.py`
+
+```python
+MODEL_CONFIG = {
+    ...
+    'generate_images_from_prompts': {
+        'model': 'dall-e-3',  # Default, overridden by IntegrationSettings
+        'max_tokens': None,  # Not used for images
+        'temperature': None,  # Not used for images
+        'response_format': None,  # Not used for images
+    },
+}
+
+FUNCTION_TO_PROMPT_TYPE = {
+    ...
+    'generate_images_from_prompts': None,  # Uses image_prompt_template, not text prompt
+}
+```
+
+#### 3.5 Update Progress Messages
+
+**File:** `backend/igny8_core/ai/engine.py`
+
+```python
+def _get_prep_message(self, function_name: str, count: int, data: Any) -> str:
+    ...
+    elif function_name == 'generate_images_from_prompts':
+        total_images = len(data.get('images', []))
+        return f"Preparing to generate {total_images} image{'s' if total_images != 1 else ''}"
+
+def _get_ai_call_message(self, function_name: str, count: int) -> str:
+    ...
+    elif function_name == 'generate_images_from_prompts':
+        return f"Generating image {count} of {total} with AI"
+
+def _get_parse_message_with_count(self, function_name: str, count: int) -> str:
+    ...
+    elif function_name == 'generate_images_from_prompts':
+        return f"{count} image{'s' if count != 1 else ''} generated"
+
+def _get_save_message(self, function_name: str, count: int) -> str:
+    ...
+    elif function_name == 'generate_images_from_prompts':
+        return f"Saving {count} image{'s' if count != 1 else ''}"
+```
+
+### Phase 2: API Endpoint
+
+#### 3.6 Add API Endpoint
+
+**File:** `backend/igny8_core/modules/writer/views.py`
+
+**Add to `ImagesViewSet`:**
+
+```python
+@action(detail=False, methods=['post'], url_path='generate_images', url_name='generate_images')
+def generate_images(self, request):
+    """Generate images from prompts for image records"""
+    from igny8_core.ai.tasks import run_ai_task
+    
+    account = getattr(request, 'account', None)
+    ids = request.data.get('ids', [])
+    
+    if not ids:
+        return Response({
+            'error': 'No IDs provided',
+            'type': 'ValidationError'
+        }, status=status.HTTP_400_BAD_REQUEST)
+    
+    account_id = account.id if account else None
+    
+    # Queue Celery task
+    try:
+        if hasattr(run_ai_task, 'delay'):
+            task = run_ai_task.delay(
+                function_name='generate_images_from_prompts',
+                payload={'ids': ids},
+                account_id=account_id
+            )
+            return Response({
+                'success': True,
+                'task_id': str(task.id),
+                'message': 'Image generation started'
+            }, status=status.HTTP_200_OK)
+        else:
+            # Fallback to synchronous execution
+            result = run_ai_task(
+                function_name='generate_images_from_prompts',
+                payload={'ids': ids},
+                account_id=account_id
+            )
+            if result.get('success'):
+                return Response({
+                    'success': True,
+                    'images_generated': result.get('count', 0),
+                    'message': 'Images generated successfully'
+                }, status=status.HTTP_200_OK)
+            else:
+                return Response({
+                    'error': result.get('error', 'Image generation failed'),
+                    'type': 'TaskExecutionError'
+                }, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
+    except Exception as e:
+        return Response({
+            'error': str(e),
+            'type': 'ExecutionError'
+        }, status=status.HTTP_500_INTERNAL_SERVER_ERROR)
+```
+
+### Phase 3: Frontend Integration
+
+#### 3.7 Add API Function
+
+**File:** `frontend/src/services/api.ts`
+
+```typescript
+export async function generateImages(imageIds: number[]): Promise<any> {
+  return fetchAPI('/v1/writer/images/generate_images/', {
+    method: 'POST',
+    body: JSON.stringify({ ids: imageIds }),
+  });
+}
+```
+
+#### 3.8 Add Generate Images Button
+
+**File:** `frontend/src/config/pages/images.config.tsx`
+
+**Add to row actions or status column:**
+- Add "Generate Images" button in status column
+- Only show if status is 'pending' and prompt exists
+- Button should trigger generation for all images for that content
+
+**File:** `frontend/src/pages/Writer/Images.tsx`
+
+**Add handler:**
+```typescript
+const handleGenerateImages = useCallback(async (contentId: number) => {
+  try {
+    // Get all pending images for this content
+    const contentImages = images.find(g => g.content_id === contentId);
+    if (!contentImages) return;
+    
+    // Collect all image IDs with prompts
+    const imageIds: number[] = [];
+    if (contentImages.featured_image?.id && contentImages.featured_image.status === 'pending') {
+      imageIds.push(contentImages.featured_image.id);
+    }
+    contentImages.in_article_images.forEach(img => {
+      if (img.id && img.status === 'pending' && img.prompt) {
+        imageIds.push(img.id);
+      }
+    });
+    
+    if (imageIds.length === 0) {
+      toast.info('No pending images with prompts found');
+      return;
+    }
+    
+    const result = await generateImages(imageIds);
+    if (result.success) {
+      if (result.task_id) {
+        // Open progress modal
+        progressModal.openModal(
+          result.task_id,
+          'Generate Images',
+          'ai-generate-images-from-prompts-01-desktop'
+        );
+      } else {
+        toast.success(`Images generated: ${result.images_generated || 0} image${(result.images_generated || 0) === 1 ? '' : 's'} created`);
+        loadImages();
+      }
+    } else {
+      toast.error(result.error || 'Failed to generate images');
+    }
+  } catch (error: any) {
+    toast.error(`Failed to generate images: ${error.message}`);
+  }
+}, [toast, progressModal, loadImages, images]);
+```
+
+#### 3.9 Update Progress Modal
+
+**File:** `frontend/src/components/common/ProgressModal.tsx`
+
+**Add support for image generation:**
+- Update step labels for `generate_images_from_prompts`
+- Show progress per image
+- Display generated images in modal (optional, like WP plugin)
+
+**Step Labels:**
+```typescript
+if (funcName.includes('generate_images_from_prompts')) {
+  return [
+    { phase: 'INIT', label: 'Validating image prompts' },
+    { phase: 'PREP', label: 'Preparing image generation queue' },
+    { phase: 'AI_CALL', label: 'Generating images with AI' },
+    { phase: 'PARSE', label: 'Processing image URLs' },
+    { phase: 'SAVE', label: 'Saving image URLs' },
+  ];
+}
+```
+
+**Success Message:**
+```typescript
+if (funcName.includes('generate_images_from_prompts')) {
+  const imageCount = extractCount(/(\d+)\s+image/i, stepLogs || []);
+  if (imageCount) {
+    return `${imageCount} image${imageCount !== '1' ? 's' : ''} generated successfully`;
+  }
+  return 'Images generated successfully';
+}
+```
+
+---
+
+## 4. Technical Details
+
+### 4.1 Image Generation API
+
+**AICore.generate_image()** already exists and handles:
+- OpenAI DALL-E (dall-e-2, dall-e-3)
+- Runware API
+- Negative prompts (Runware only)
+- Cost tracking
+- Error handling
+
+**Usage:**
+```python
+result = ai_core.generate_image(
+    prompt=formatted_prompt,
+    provider='openai',  # or 'runware'
+    model='dall-e-3',  # or 'runware:97@1'
+    size='1024x1024',
+    negative_prompt=negative_prompt,  # Only for Runware
+    function_name='generate_images_from_prompts'
+)
+```
+
+**Response:**
+```python
+{
+    'url': 'https://...',  # Image URL
+    'revised_prompt': '...',  # OpenAI may revise prompt
+    'cost': 0.04,  # Cost in USD
+    'error': None  # Error message if failed
+}
+```
+
+### 4.2 Settings Retrieval
+
+**From IntegrationSettings:**
+```python
+integration = IntegrationSettings.objects.get(
+    account=account,
+    integration_type='image_generation',
+    is_active=True
+)
+config = integration.config
+
+provider = config.get('provider') or config.get('service', 'openai')
+if provider == 'runware':
+    model = config.get('model') or config.get('runwareModel', 'runware:97@1')
+else:
+    model = config.get('model', 'dall-e-3')
+
+image_type = config.get('image_type', 'realistic')
+image_format = config.get('image_format', 'webp')
+```
+
+### 4.3 Prompt Templates
+
+**From PromptRegistry:**
+```python
+image_prompt_template = PromptRegistry.get_image_prompt_template(account)
+negative_prompt = PromptRegistry.get_negative_prompt(account)
+```
+
+**Formatting:**
+```python
+formatted = image_prompt_template.format(
+    post_title=content.title or content.meta_title,
+    image_prompt=image.prompt,
+    image_type=image_type  # 'realistic', 'artistic', 'cartoon'
+)
+```
+
+### 4.4 Error Handling
+
+**Per-Image Errors:**
+- If one image fails, continue with others
+- Mark failed image: `status='failed'`
+- Log error in `Images` record or separate error field
+- Return success with partial count: `{'success': True, 'images_generated': 3, 'images_failed': 1}`
+
+**Validation Errors:**
+- No prompts: Skip image, log warning
+- No settings: Return error, don't start generation
+- Invalid provider/model: Return error
+
+---
+
+## 5. Frontend Integration
+
+### 5.1 Images Page Updates
+
+**File:** `frontend/src/pages/Writer/Images.tsx`
+
+**Changes:**
+1. Add "Generate Images" button in status column (or row actions)
+2. Button only enabled if:
+   - Status is 'pending'
+   - Prompt exists
+   - Content has at least one pending image
+3. On click: Collect all pending image IDs for that content
+4. Call API: `generateImages(imageIds)`
+5. Open progress modal if async
+6. Reload images on completion
+
+### 5.2 Progress Modal Updates
+
+**File:** `frontend/src/components/common/ProgressModal.tsx`
+
+**Changes:**
+1. Add step definitions for `generate_images_from_prompts`
+2. Update progress messages
+3. Show image count in messages
+4. Optional: Display generated images in modal (like WP plugin)
+
+### 5.3 Table Actions Config
+
+**File:** `frontend/src/config/pages/table-actions.config.tsx`
+
+**Add row action (optional):**
+```typescript
+'/writer/images': {
+  rowActions: [
+    {
+      key: 'generate_images',
+      label: 'Generate Images',
+      icon: <BoltIcon className="w-5 h-5" />,
+      variant: 'primary',
+    },
+  ],
+}
+```
+
+---
+
+## 6. Testing Strategy
+
+### 6.1 Unit Tests
+
+**Test Function Methods:**
+- `validate()`: Test with valid/invalid IDs, missing prompts, wrong status
+- `prepare()`: Test settings retrieval, prompt template loading
+- `build_prompt()`: Test prompt formatting
+- `parse_response()`: Test URL extraction
+- `save_output()`: Test Images record update
+
+### 6.2 Integration Tests
+
+**Test Full Flow:**
+1. Create Images records with prompts
+2. Call API endpoint
+3. Verify Celery task created
+4. Verify progress updates
+5. Verify Images records updated with URLs
+6. Verify status changed to 'generated'
+
+### 6.3 Error Scenarios
+
+**Test:**
+- Missing IntegrationSettings
+- Invalid provider/model
+- API errors (rate limits, invalid API key)
+- Partial failures (some images succeed, some fail)
+- Missing prompts
+- Invalid image IDs
+
+---
+
+## 7. Implementation Checklist
+
+### Backend
+- [ ] Create `GenerateImagesFromPromptsFunction` class
+- [ ] Implement `validate()` method
+- [ ] Implement `prepare()` method
+- [ ] Implement `build_prompt()` method
+- [ ] Implement `parse_response()` method
+- [ ] Implement `save_output()` method
+- [ ] Register function in `registry.py`
+- [ ] Add to `__init__.py` exports
+- [ ] Add model config in `settings.py`
+- [ ] Update `AIEngine` progress messages
+- [ ] Add API endpoint in `ImagesViewSet`
+- [ ] Test with OpenAI provider
+- [ ] Test with Runware provider
+- [ ] Test error handling
+
+### Frontend
+- [ ] Add `generateImages()` API function
+- [ ] Add "Generate Images" button to Images page
+- [ ] Add click handler
+- [ ] Integrate progress modal
+- [ ] Update progress modal step labels
+- [ ] Update success messages
+- [ ] Test UI flow
+- [ ] Test error handling
+
+### Documentation
+- [ ] Update AI_MASTER_ARCHITECTURE.md
+- [ ] Add function to AI_FUNCTIONS_AUDIT_REPORT.md
+- [ ] Document API endpoint
+- [ ] Document settings requirements
+
+---
+
+## 8. Key Considerations
+
+### 8.1 Rate Limiting
+
+**Issue:** Image generation APIs have rate limits
+**Solution:** Process images sequentially (one at a time)
+**Implementation:** AIEngine loops through images, waits for each to complete
+
+### 8.2 Cost Tracking
+
+**Issue:** Need to track costs per image
+**Solution:** AICore already tracks costs, store in AITaskLog
+**Implementation:** Cost is returned from `generate_image()`, log in step_tracker
+
+### 8.3 Progress Updates
+
+**Issue:** Need granular progress (per image)
+**Solution:** Update progress after each image: `(completed / total) * 100`
+**Implementation:** Track in `save_output()`, update via `progress_tracker.update()`
+
+### 8.4 Error Recovery
+
+**Issue:** If one image fails, should continue with others
+**Solution:** Catch errors per image, mark as failed, continue
+**Implementation:** Try-catch in `save_output()` per image
+
+### 8.5 Image Display
+
+**Issue:** Should show generated images in progress modal?
+**Solution:** Optional enhancement, can add later
+**Implementation:** Store image URLs in step logs, display in modal
+
+---
+
+## 9. Alternative Approaches Considered
+
+### 9.1 Process All in save_output()
+
+**Pros:**
+- Simpler implementation
+- Direct control over loop
+
+**Cons:**
+- Doesn't use AIEngine phases properly
+- Harder to track progress per image
+- Less consistent with framework
+
+**Decision:** Use AIEngine phases with loop detection
+
+### 9.2 Separate Function Per Image
+
+**Pros:**
+- Each image is independent task
+- Better error isolation
+
+**Cons:**
+- Too many Celery tasks
+- Harder to track overall progress
+- More complex frontend
+
+**Decision:** Single function processes all images sequentially
+
+---
+
+## 10. Success Criteria
+
+✅ Function follows `BaseAIFunction` pattern
+✅ Uses `AIEngine` orchestrator
+✅ Integrates with progress modal
+✅ Uses prompt templates from Thinker/Prompts
+✅ Uses settings from IntegrationSettings
+✅ Handles errors gracefully
+✅ Tracks progress per image
+✅ Updates Images records correctly
+✅ Works with both OpenAI and Runware
+✅ Frontend button triggers generation
+✅ Progress modal shows correct steps
+✅ Success message shows image count
+
+---
+
+## 11. Next Steps
+
+1. **Start with Backend Function**
+   - Create `GenerateImagesFromPromptsFunction`
+   - Implement all methods
+   - Test with single image
+
+2. **Add API Endpoint**
+   - Add to `ImagesViewSet`
+   - Test endpoint
+
+3. **Frontend Integration**
+   - Add button
+   - Add handler
+   - Test flow
+
+4. **Progress Modal**
+   - Update step labels
+   - Test progress updates
+
+5. **Error Handling**
+   - Test error scenarios
+   - Verify graceful failures
+
+6. **Documentation**
+   - Update architecture docs
+   - Add API docs
+
+---
+
+**End of Plan**
+

frontend/src/components/common/ProgressModal.tsx

@@ -73,7 +73,8 @@ const getSuccessMessage = (functionId?: string, title?: string, stepLogs?: any[]
     }
     return 'Article drafted successfully.';
   }
-  if (funcName.includes('image')) {
+  if (funcName.includes('image') && (funcName.includes('prompt') || funcName.includes('extract'))) {
+    // Image prompt generation
     // Try to extract from SAVE step message first (most reliable)
     const saveStepLog = stepLogs?.find(log => log.stepName === 'SAVE');
     if (saveStepLog?.message) {
@@ -119,6 +120,13 @@ const getSuccessMessage = (functionId?: string, title?: string, stepLogs?: any[]
     
     // Default message
     return 'Featured Image and X In‑article Image Prompts ready for image generation';
+  } else if (funcName.includes('image') && funcName.includes('from')) {
+    // Image generation from prompts
+    const imageCount = extractCount(/(\d+)\s+image/i, stepLogs || []);
+    if (imageCount) {
+      return `${imageCount} image${imageCount !== '1' ? 's' : ''} generated successfully`;
+    }
+    return 'Images generated successfully';
   }
   return 'Task completed successfully.';
 };
@@ -157,7 +165,8 @@ const getStepsForFunction = (functionId?: string, title?: string): Array<{phase:
     ];
   }
   
-  if (funcName.includes('image')) {
+  if (funcName.includes('image') && (funcName.includes('prompt') || funcName.includes('extract'))) {
+    // Image prompt generation
     return [
       { phase: 'INIT', label: 'Checking content and image slots' },
       { phase: 'PREP', label: 'Mapping Content for X Image Prompts' },
@@ -165,6 +174,15 @@ const getStepsForFunction = (functionId?: string, title?: string): Array<{phase:
       { phase: 'PARSE', label: 'Writing X In‑article Image Prompts' },
       { phase: 'SAVE', label: 'Assigning Prompts to Dedicated Slots' },
     ];
+  } else if (funcName.includes('image') && funcName.includes('from')) {
+    // Image generation from prompts
+    return [
+      { phase: 'INIT', label: 'Validating image prompts' },
+      { phase: 'PREP', label: 'Preparing image generation queue' },
+      { phase: 'AI_CALL', label: 'Generating images with AI' },
+      { phase: 'PARSE', label: 'Processing image URLs' },
+      { phase: 'SAVE', label: 'Saving image URLs' },
+    ];
   }
   
   // Default fallback

frontend/src/config/pages/images.config.tsx

@@ -49,6 +49,7 @@ export const createImagesPageConfig = (
     setStatusFilter: (value: string) => void;
     setCurrentPage: (page: number) => void;
     maxInArticleImages?: number; // Optional: max in-article images to display
+    onGenerateImages?: (contentId: number) => void; // Handler for generate images button
   }
 ): ImagesPageConfig => {
   const maxImages = handlers.maxInArticleImages || 5; // Default to 5 in-article images
@@ -99,13 +100,13 @@ export const createImagesPageConfig = (
     });
   }
 
-  // Add overall status column
+  // Add overall status column with Generate Images button
   columns.push({
     key: 'overall_status',
     label: 'Status',
     sortable: false,
-    width: '120px',
-    render: (value: string) => {
+    width: '180px',
+    render: (value: string, row: ContentImagesGroup) => {
       const statusColors: Record<string, 'success' | 'warning' | 'error' | 'info'> = {
         'complete': 'success',
         'partial': 'info',
@@ -118,13 +119,34 @@ export const createImagesPageConfig = (
         'pending': 'Pending',
         'failed': 'Failed',
       };
+      
+      // Check if there are any pending images with prompts
+      const hasPendingImages = 
+        (row.featured_image?.status === 'pending' && row.featured_image?.prompt) ||
+        row.in_article_images.some(img => img.status === 'pending' && img.prompt);
+      
       return (
-        <Badge
-          color={statusColors[value] || 'warning'}
-          size="sm"
-        >
-          {labels[value] || value}
-        </Badge>
+        <div className="flex items-center gap-2">
+          <Badge
+            color={statusColors[value] || 'warning'}
+            size="sm"
+          >
+            {labels[value] || value}
+          </Badge>
+          {hasPendingImages && handlers.onGenerateImages && (
+            <button
+              onClick={(e) => {
+                e.stopPropagation();
+                handlers.onGenerateImages!(row.content_id);
+              }}
+              className="inline-flex items-center gap-1 px-2 py-1 text-xs font-medium text-white bg-brand-500 hover:bg-brand-600 rounded transition-colors"
+              title="Generate Images"
+            >
+              <BoltIcon className="w-3 h-3" />
+              Generate
+            </button>
+          )}
+        </div>
       );
     },
   });

frontend/src/pages/Writer/Images.tsx

@@ -3,19 +3,26 @@
  * Shows content images grouped by content - one row per content
  */
 
-import { useState, useEffect, useMemo, useCallback } from 'react';
+import { useState, useEffect, useMemo, useCallback, useRef } from 'react';
 import TablePageTemplate from '../../templates/TablePageTemplate';
 import {
   fetchContentImages,
   ContentImagesGroup,
   ContentImagesResponse,
+  generateImages,
 } from '../../services/api';
 import { useToast } from '../../components/ui/toast/ToastContainer';
-import { FileIcon, DownloadIcon } from '../../icons';
+import { FileIcon, DownloadIcon, BoltIcon } from '../../icons';
 import { createImagesPageConfig } from '../../config/pages/images.config';
+import ProgressModal from '../../components/common/ProgressModal';
+import { useProgressModal } from '../../hooks/useProgressModal';
 
 export default function Images() {
   const toast = useToast();
+  
+  // Progress modal for AI functions
+  const progressModal = useProgressModal();
+  const hasReloadedRef = useRef(false);
 
   // Data state
   const [images, setImages] = useState<ContentImagesGroup[]>([]);
@@ -136,6 +143,62 @@ export default function Images() {
     toast.info(`Bulk action "${action}" for ${ids.length} items`);
   }, [toast]);
 
+  // Generate images handler
+  const handleGenerateImages = useCallback(async (contentId: number) => {
+    try {
+      // Get all pending images for this content
+      const contentImages = images.find(g => g.content_id === contentId);
+      if (!contentImages) {
+        toast.error('Content not found');
+        return;
+      }
+      
+      // Collect all image IDs with prompts and pending status
+      const imageIds: number[] = [];
+      if (contentImages.featured_image?.id && 
+          contentImages.featured_image.status === 'pending' && 
+          contentImages.featured_image.prompt) {
+        imageIds.push(contentImages.featured_image.id);
+      }
+      contentImages.in_article_images.forEach(img => {
+        if (img.id && img.status === 'pending' && img.prompt) {
+          imageIds.push(img.id);
+        }
+      });
+      
+      if (imageIds.length === 0) {
+        toast.info('No pending images with prompts found for this content');
+        return;
+      }
+      
+      const result = await generateImages(imageIds);
+      if (result.success) {
+        if (result.task_id) {
+          // Open progress modal for async task
+          progressModal.openModal(
+            result.task_id,
+            'Generate Images',
+            'ai-generate-images-from-prompts-01-desktop'
+          );
+        } else {
+          // Synchronous completion
+          const generated = result.images_generated || 0;
+          const failed = result.images_failed || 0;
+          if (generated > 0) {
+            toast.success(`Images generated: ${generated} image${generated !== 1 ? 's' : ''} created${failed > 0 ? `, ${failed} failed` : ''}`);
+          } else {
+            toast.error(`Image generation failed: ${failed} image${failed !== 1 ? 's' : ''} failed`);
+          }
+          loadImages(); // Reload to show new images
+        }
+      } else {
+        toast.error(result.error || 'Failed to generate images');
+      }
+    } catch (error: any) {
+      toast.error(`Failed to generate images: ${error.message}`);
+    }
+  }, [toast, progressModal, loadImages, images]);
+
   // Get max in-article images from the data (to determine column count)
   const maxInArticleImages = useMemo(() => {
     if (images.length === 0) return 5; // Default
@@ -152,8 +215,9 @@ export default function Images() {
       setStatusFilter,
       setCurrentPage,
       maxInArticleImages,
+      onGenerateImages: handleGenerateImages,
     });
-  }, [searchTerm, statusFilter, maxInArticleImages]);
+  }, [searchTerm, statusFilter, maxInArticleImages, handleGenerateImages]);
 
   // Calculate header metrics
   const headerMetrics = useMemo(() => {
@@ -166,6 +230,7 @@ export default function Images() {
   }, [pageConfig?.headerMetrics, images, totalCount]);
 
   return (
+    <>
     <TablePageTemplate
       title="Content Images"
       titleIcon={<FileIcon className="text-purple-500 size-5" />}
@@ -218,5 +283,30 @@ export default function Images() {
         setCurrentPage(1);
       }}
     />
+    
+    {/* Progress Modal for AI Functions */}
+    <ProgressModal
+      isOpen={progressModal.isOpen}
+      title={progressModal.title}
+      percentage={progressModal.progress.percentage}
+      status={progressModal.progress.status}
+      message={progressModal.progress.message}
+      details={progressModal.progress.details}
+      taskId={progressModal.taskId || undefined}
+      functionId={progressModal.functionId}
+      onClose={() => {
+        const wasCompleted = progressModal.progress.status === 'completed';
+        progressModal.closeModal();
+        // Reload data after modal closes (if completed)
+        if (wasCompleted && !hasReloadedRef.current) {
+          hasReloadedRef.current = true;
+          loadImages();
+          setTimeout(() => {
+            hasReloadedRef.current = false;
+          }, 1000);
+        }
+      }}
+    />
+    </>
   );
 }

frontend/src/services/api.ts

@@ -1032,6 +1032,13 @@ export async function fetchContentImages(): Promise<ContentImagesResponse> {
   return fetchAPI('/v1/writer/images/content_images/');
 }
 
+export async function generateImages(imageIds: number[]): Promise<any> {
+  return fetchAPI('/v1/writer/images/generate_images/', {
+    method: 'POST',
+    body: JSON.stringify({ ids: imageIds }),
+  });
+}
+
 export async function deleteTaskImage(id: number): Promise<void> {
   return fetchAPI(`/v1/writer/images/${id}/`, {
     method: 'DELETE',