igny8/docs/plans/implemented/IMAGE-GENERATION-GAPS.md

Image Generation System - Comprehensive Gap Analysis

Date: January 2026
Status: Audit Complete
Reviewer: System Audit

Executive Summary

This document provides a comprehensive audit of the image generation system, analyzing the flow from model configuration to image delivery, both for manual and automation workflows.

---

1. System Architecture Overview

Current Flow

User Selects Quality Tier (basic/quality/quality_option2/premium)
        ↓
AIModelConfig (database) → provider, model_name, landscape_size, square_size
        ↓
process_image_generation_queue (Celery task)
        ↓
ai_core.generate_image() → provider-specific handler
        ↓
_generate_image_openai() / _generate_image_runware()
        ↓
Downloaded to /frontend/public/images/ai-images/
        ↓
Image record updated (Images model)

Image Count Determination

User sets max_images (1-8) in Site Settings
        ↓
AISettings.get_effective_max_images(account)
        ↓
generate_image_prompts.py: 1 featured + max_images in_article
        ↓
Images created with positions: featured(0) + in_article(0,1,2,3...)

---

2. RESOLVED Issues (Previously Fixed)

✅ 2.1 Quality Tier Fallback

• Issue: get_effective_quality_tier() was returning hardcoded 'basic' instead of default model's tier
• Fixed in: ai_settings.py lines 196-232
• Solution: Now falls back to AIModelConfig.is_default=True model's quality_tier

✅ 2.2 Image Sizes from Database

• Issue: tasks.py had hardcoded MODEL_LANDSCAPE_SIZES dict
• Fixed in: tasks.py lines 242-260
• Solution: Now loads landscape_size and square_size from AIModelConfig

✅ 2.3 Settings Endpoint Default Tier

• Issue: settings_views.py returned hardcoded 'basic' as default tier
• Fixed in: settings_views.py lines 616-640
• Solution: Gets default_tier from default_image_model.quality_tier

✅ 2.4 Integration Views Dynamic Sizes

• Issue: Two endpoints in integration_views.py had hardcoded size lookup
• Fixed: Both endpoints now load from AIModelConfig

---

3. REMAINING Gaps (Action Required)

🔴 GAP-1: Hardcoded Size Constants in global_settings_models.py

Location: backend/igny8_core/modules/system/global_settings_models.py lines 180-188

Code:

# Model-specific landscape sizes (square is always 1024x1024 for all models)
MODEL_LANDSCAPE_SIZES = {
    'runware:97@1': '1280x768',   # Hi Dream Full landscape
    'bria:10@1': '1344x768',       # Bria 3.2 landscape (16:9)
    'google:4@2': '1376x768',      # Nano Banana landscape (16:9)
}

# Default square size (universal across all models)
DEFAULT_SQUARE_SIZE = '1024x1024'

Impact: These constants are UNUSED but could cause confusion. They're legacy from before the AIModelConfig migration.

Recommendation:

• Remove unused MODEL_LANDSCAPE_SIZES dict
• Remove unused DEFAULT_SQUARE_SIZE constant
• Add deprecation comment if keeping for reference

---

🔴 GAP-2: Hardcoded Size Fallbacks in Tasks

Location: backend/igny8_core/ai/tasks.py lines 254-260

Code:

else:
    # Fallback sizes if no model config (should never happen)
    model_landscape_size = '1792x1024'
    model_square_size = '1024x1024'
    logger.warning(f"[process_image_generation_queue] No model config, using fallback sizes")

Impact: LOW - Fallback only triggers if database is misconfigured. But the hardcoded sizes may not match actual model requirements.

Recommendation:

• Consider failing gracefully with clear error instead of using fallback
• Or: Load fallback from a system default in database

---

🔴 GAP-3: Frontend VALID_SIZES_BY_MODEL Hardcoded

Location: frontend/src/components/common/ImageGenerationCard.tsx lines 52-55

Code:

const VALID_SIZES_BY_MODEL: Record<string, string[]> = {
  'dall-e-3': ['1024x1024', '1024x1792', '1792x1024'],
  'dall-e-2': ['256x256', '512x512', '1024x1024'],
};

Impact: MEDIUM - Test image generation card only shows OpenAI sizes, not Runware/Bytedance sizes.

Recommendation:

• Fetch valid_sizes from AIModelConfig via API
• Or: Pass sizes from backend settings endpoint

---

🔴 GAP-4: Backend VALID_SIZES_BY_MODEL Hardcoded

Location: backend/igny8_core/ai/constants.py lines 40-43

Code:

VALID_SIZES_BY_MODEL = {
    'dall-e-3': ['1024x1024', '1024x1792', '1792x1024'],
    'dall-e-2': ['256x256', '512x512', '1024x1024'],
}

Impact: MEDIUM - Used for OpenAI validation only. Runware models bypass this validation.

Status: PARTIAL - Only affects OpenAI validation. Runware has its own validation via provider-specific code.

Recommendation:

• Move validation to AIModelConfig.valid_sizes field
• Validate against model's valid_sizes from database

---

🔴 GAP-5: Missing Runware Model Size Validation

Location: backend/igny8_core/ai/ai_core.py lines 943-1050

Code: The _generate_image_runware() method does NOT validate sizes against valid_sizes from database.

Impact: LOW - Runware API will reject invalid sizes anyway, but error message won't be clear.

Recommendation:

• Add validation: check size against AIModelConfig.valid_sizes before API call
• Return clear error: "Size X is not valid for model Y"

---

🔴 GAP-6: Seedream Minimum Pixel Validation Hardcoded

Location: backend/igny8_core/ai/ai_core.py lines 1018-1027

Code:

elif runware_model.startswith('bytedance:'):
    # Enforce minimum size for Seedream (min 3,686,400 pixels ~ 1920x1920)
    current_pixels = width * height
    if current_pixels < 3686400:
        # Use default Seedream square size
        inference_task['width'] = 2048
        inference_task['height'] = 2048

Impact: LOW - This hardcoded check works, but should come from database.

Recommendation:

• Add min_pixels field to AIModelConfig
• Check model.min_pixels instead of hardcoded 3686400

---

🔴 GAP-7: Provider-Specific Steps/CFGScale Hardcoded

Location: backend/igny8_core/ai/ai_core.py lines 995-1040

Code:

if runware_model.startswith('bria:'):
    inference_task['steps'] = 20
    # ...
elif runware_model.startswith('runware:'):
    inference_task['steps'] = 20
    inference_task['CFGScale'] = 7

Impact: LOW - Works correctly but adding new models requires code changes.

Recommendation:

• Add generation_params JSON field to AIModelConfig
• Store steps, CFGScale, etc. in database per model

---

🔴 GAP-8: Image Count Not Per-Content Configurable

Location: System-wide setting only

Current Behavior:

• max_images is a global setting (site-wide)
• All articles get the same number of in_article images

Impact: MEDIUM - Users cannot set different image counts per article/keyword.

Recommendation:

• Add images_count field to Content model (nullable, inherits from site default)
• Or: Add to Keywords model for keyword-level override

---

🟡 GAP-9: Legacy generate_images.py Function (Partially Dead Code)

Location: backend/igny8_core/ai/functions/generate_images.py

Issue: generate_images_core() function exists but appears to be legacy. Main flow uses process_image_generation_queue() in tasks.py.

Impact: LOW - Code duplication, potential maintenance burden.

Recommendation:

• Audit if generate_images.py is actually used anywhere
• If not: Add deprecation warning or remove
• If used: Ensure it uses same dynamic config loading

---

🟡 GAP-10: No Validation of quality_tier Values

Location: Multiple locations

Issue: When user selects a quality_tier, there's no validation that:

1. The tier exists in AIModelConfig
2. The tier has an active model
3. The user's plan allows that tier

Impact: MEDIUM - Could lead to runtime errors if tier doesn't exist.

Recommendation:

• Add validation in settings save endpoint
• Return error if selected tier has no active model

---

4. Image Count Flow (Working Correctly)

How image count works:

1. User configures:

   • max_images (1-8) in Site Settings → saved to AccountSettings

2. Prompt generation:

   # generate_image_prompts.py
   max_images = AISettings.get_effective_max_images(account)  # e.g., 4
   # Creates: 1 featured + 4 in_article = 5 image prompts
   

3. Image types:

   • featured - Always 1, position=0, landscape size
   • in_article - Up to max_images, positions 0,1,2,3..., alternating square/landscape

4. Size determination:

   # tasks.py
   if image.image_type == 'featured':
       image_size = featured_image_size  # landscape
   elif image.image_type == 'in_article':
       position = image.position or 0
       if position % 2 == 0:  # 0, 2
           image_size = square_size
       else:  # 1, 3
           image_size = landscape_size
   

STATUS: ✅ Working correctly. No gaps identified in image count logic.

---

5. Automation Flow (Working Correctly)

Stage 6: Image Generation

Location: automation_service.py lines 1236-1400

Flow:

1. Query Images.objects.filter(site=site, status='pending')
2. For each image: process_image_generation_queue.delay(image_ids=[image.id], ...)
3. Monitor task completion
4. Update run progress

STATUS: ✅ Uses same task as manual generation. Consistent behavior.

---

6. Model Provider Support Matrix

Provider	Models	Status	Gaps
OpenAI	dall-e-3, dall-e-2	✅ Working	Valid sizes hardcoded
Runware	runware:97@1	✅ Working	No size validation
Runware	bria:10@1	✅ Working	Steps hardcoded
Runware	google:4@2	✅ Working	Resolution param hardcoded
Runware	bytedance:seedream@4.5	✅ Working	Min pixels hardcoded

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7. Priority Action Items

High Priority

1. GAP-4/5: Implement database-driven size validation for all providers
2. GAP-10: Add quality_tier validation on save

Medium Priority

3. GAP-6/7: Move provider-specific params to AIModelConfig.generation_params
4. GAP-8: Consider per-content image count override

Low Priority

5. GAP-1: Clean up unused constants
6. GAP-9: Audit and deprecate legacy code
7. GAP-3: Fetch valid sizes from API in frontend

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8. Recommendations Summary

Short-term (Before Launch)

• Ensure all hardcoded fallbacks are clearly logged
• Test each model tier end-to-end

Medium-term (Post-Launch)

• Migrate all hardcoded params to AIModelConfig fields
• Add model validation on quality_tier save

Long-term (Future Enhancement)

• Per-content image count override
• Per-keyword image style override
• Image regeneration without deleting existing

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Appendix: Key Files Reference

File	Purpose
ai/tasks.py	Main image generation Celery task
ai/ai_core.py	Provider-specific generation methods
ai/functions/generate_image_prompts.py	Extract prompts from content
modules/system/ai_settings.py	System defaults + account overrides
modules/system/settings_views.py	Frontend settings API
business/billing/models.py	AIModelConfig model
business/automation/services/automation_service.py	Automation Stage 6