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ADVANCED TECHNIQUES

ComfyUI Advanced Techniques: Professional Workflows That Create 50+ AI Influencers

Master custom nodes, batch automation, and 10-second renders - the exact workflows used by agencies managing dozens of AI influencers

⚡ Why Advanced ComfyUI Matters for AI Influencers

❌ Basic ComfyUI Limitations

✗30-60 seconds per image
✗Inconsistent facial features
✗Manual workflow for each image
✗Limited pose control
✗No batch processing capabilities

✓ Professional Workflow Power

✓8-12 seconds per image
✓99% facial consistency
✓Automated batch generation
✓Precise pose/composition control
✓Generate 100+ images unattended

🎨 Custom Node Creation: Build Your Competitive Advantage

💡 Why Custom Nodes Matter

Custom nodes automate repetitive workflows and create functionality that doesn't exist in standard ComfyUI. Agencies managing 50+ AI influencers save 30+ hours weekly with custom automation nodes.

Custom Node #1: Character Consistency Enforcer

Purpose & Impact

Automatically applies your character's LoRA, embeddings, and facial consistency settings to every generation. Eliminates manual configuration errors that break character consistency.

# ComfyUI Custom Node: CharacterConsistencyEnforcer.py
import torch
import folder_paths

class CharacterConsistencyEnforcer:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model": ("MODEL",),
                "character_name": (["Ava", "Marcus", "Luna", "Custom"],),
                "lora_strength": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 1.0, "step": 0.05}),
                "face_lock_strength": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 1.0, "step": 0.05}),
            },
            "optional": {
                "custom_lora_path": ("STRING", {"default": ""}),
            }
        }

    RETURN_TYPES = ("MODEL", "STRING",)
    RETURN_NAMES = ("model", "character_prompt",)
    FUNCTION = "enforce_consistency"
    CATEGORY = "AI_Influencer/Character"

    def enforce_consistency(self, model, character_name, lora_strength, face_lock_strength, custom_lora_path=""):
        # Load character-specific LoRA
        lora_path = self.get_lora_path(character_name, custom_lora_path)
        model_lora = self.load_lora(model, lora_path, lora_strength)

        # Apply facial consistency embeddings
        character_prompt = self.build_consistency_prompt(character_name, face_lock_strength)

        return (model_lora, character_prompt)

    def get_lora_path(self, character_name, custom_path):
        if custom_path:
            return custom_path

        character_loras = {
            "Ava": "loras/ava_consistent_v3.safetensors",
            "Marcus": "loras/marcus_fitness_v2.safetensors",
            "Luna": "loras/luna_fashion_v4.safetensors",
        }
        return character_loras.get(character_name, "")

    def build_consistency_prompt(self, character_name, strength):
        base_prompts = {
            "Ava": f"(ava_character:1.{int(strength*10)}), consistent facial features, same person, identical appearance",
            "Marcus": f"(marcus_face:1.{int(strength*10)}), athletic build, consistent identity",
            "Luna": f"(luna_identity:1.{int(strength*10)}), unique facial structure, same individual",
        }
        return base_prompts.get(character_name, "consistent character")

NODE_CLASS_MAPPINGS = {
    "CharacterConsistencyEnforcer": CharacterConsistencyEnforcer
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "CharacterConsistencyEnforcer": "🎭 Character Consistency"
}

Results

• 95% reduction in consistency errors
• 5 minutes setup time vs 30 seconds per image
• Reusable across all workflows
• Team members can't break consistency

Custom Node #2: Batch Scenario Generator

Generate 30 Days of Content in One Click

# BatchScenarioGenerator.py
import random
import json
from datetime import datetime, timedelta

class BatchScenarioGenerator:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "character_type": (["Fashion", "Fitness", "Lifestyle", "Tech"],),
                "num_scenarios": ("INT", {"default": 30, "min": 1, "max": 100}),
                "variety_level": (["Low", "Medium", "High"],),
                "include_seasonal": ("BOOLEAN", {"default": True}),
            }
        }

    RETURN_TYPES = ("STRING", "INT",)
    RETURN_NAMES = ("scenario_prompts", "total_count",)
    FUNCTION = "generate_scenarios"
    CATEGORY = "AI_Influencer/Automation"

    def __init__(self):
        self.scenario_templates = {
            "Fashion": {
                "locations": ["boutique", "rooftop", "city street", "cafe", "art gallery", "penthouse", "beach club"],
                "outfits": ["elegant dress", "casual chic", "streetwear", "business attire", "evening gown", "athleisure"],
                "activities": ["shopping", "posing", "walking", "sitting at cafe", "attending event"],
                "times": ["golden hour", "midday", "blue hour", "evening", "morning light"],
                "moods": ["confident", "playful", "sophisticated", "relaxed", "energetic"]
            },
            "Fitness": {
                "locations": ["gym", "outdoor track", "beach", "home workout", "yoga studio", "boxing gym", "park"],
                "outfits": ["sports bra and leggings", "tank top and shorts", "athletic set", "yoga attire"],
                "activities": ["lifting weights", "running", "yoga pose", "boxing", "stretching", "protein shake"],
                "times": ["early morning", "midday workout", "evening session", "post-workout"],
                "moods": ["motivated", "intense", "zen", "accomplished", "energetic"]
            },
            "Lifestyle": {
                "locations": ["home office", "cozy living room", "balcony", "kitchen", "bedroom", "garden"],
                "outfits": ["casual wear", "loungewear", "smart casual", "cozy sweater"],
                "activities": ["working on laptop", "reading book", "coffee moment", "relaxing", "creating content"],
                "times": ["morning", "afternoon", "evening", "sunset"],
                "moods": ["productive", "relaxed", "inspired", "contemplative", "cozy"]
            },
            "Tech": {
                "locations": ["modern office", "co-working space", "tech lab", "home setup", "conference"],
                "outfits": ["smart casual", "tech startup style", "business casual", "hoodie and jeans"],
                "activities": ["coding", "presenting", "reviewing gadgets", "testing device", "teaching"],
                "times": ["office hours", "late night coding", "daytime", "evening"],
                "moods": ["focused", "enthusiastic", "analytical", "innovative", "professional"]
            }
        }

    def generate_scenarios(self, character_type, num_scenarios, variety_level, include_seasonal):
        templates = self.scenario_templates.get(character_type, self.scenario_templates["Lifestyle"])

        variety_multiplier = {"Low": 0.5, "Medium": 0.7, "High": 1.0}[variety_level]
        scenarios = []

        for i in range(num_scenarios):
            # Create unique combinations
            location = random.choice(templates["locations"])
            outfit = random.choice(templates["outfits"])
            activity = random.choice(templates["activities"])
            time = random.choice(templates["times"])
            mood = random.choice(templates["moods"])

            # Build prompt
            scenario = f"{mood} {character_type.lower()} influencer, {activity} at {location}, "
            scenario += f"wearing {outfit}, {time} lighting, professional photography, "
            scenario += f"high quality, detailed, 8k resolution"

            # Add seasonal elements if enabled
            if include_seasonal:
                current_month = datetime.now().month
                season_props = self.get_seasonal_props(current_month)
                scenario += f", {season_props}"

            scenarios.append(f"Scenario {i+1}: {scenario}")

        # Format as JSON for batch processing
        output = "\n\n".join(scenarios)

        return (output, len(scenarios))

    def get_seasonal_props(self, month):
        seasons = {
            (12, 1, 2): "winter atmosphere, cozy vibes, warm tones",
            (3, 4, 5): "spring freshness, bright colors, natural light",
            (6, 7, 8): "summer vibes, vibrant energy, golden hour",
            (9, 10, 11): "autumn mood, warm colors, soft lighting"
        }

        for months, props in seasons.items():
            if month in months:
                return props
        return ""

NODE_CLASS_MAPPINGS = {
    "BatchScenarioGenerator": BatchScenarioGenerator
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "BatchScenarioGenerator": "📋 Batch Scenario Generator"
}

Time Savings

Manual: 30 min to plan scenarios
Automated: 5 seconds
Savings: 99.7%

Quality Benefits

Seasonal relevance built-in
Variety prevents repetition
Professional prompt structure

🎓 MASTER AI INFLUENCERS

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✓ Lifetime access ✓ 50+ hours of content ✓ Custom node library included

🎯 Advanced ControlNet Workflows: Perfect Pose Control

Multi-ControlNet Stack for Professional Results

The Triple-ControlNet Approach

1. OpenPose - Body Structure

Controls overall body position, limb placement, and gesture. Use weight: 0.7-0.9

Best for: Full body shots, dynamic poses, fashion photography

2. Depth Map - Spatial Composition

Maintains 3D spatial relationships and depth hierarchy. Use weight: 0.4-0.6

Best for: Environmental context, background separation, professional depth

3. Canny Edge - Fine Details

Preserves sharp edges and fine details. Use weight: 0.3-0.5

Best for: Clothing details, accessories, architectural elements

Complete Workflow Structure

// Professional Multi-ControlNet Workflow

LoadCheckpoint → SDXL 1.0 Base
  ↓
LoadLoRA → Character LoRA (weight: 0.85)
  ↓
[PARALLEL CONTROLNET PROCESSING]
  ├─ LoadImage (Reference Pose) → OpenPosePreprocessor
  │    ↓
  │  ControlNetApply (OpenPose Model, strength: 0.8)
  │
  ├─ LoadImage (Same Reference) → DepthPreprocessor (MiDaS)
  │    ↓
  │  ControlNetApply (Depth Model, strength: 0.5)
  │
  └─ LoadImage (Same Reference) → CannyEdgePreprocessor
       ↓
     ControlNetApply (Canny Model, strength: 0.4)
  ↓
ControlNetStack Combiner → Merge all three controls
  ↓
CLIPTextEncode (Positive) → "[character name], professional photo,
high fashion, detailed face, {activity description}, {lighting},
studio quality, 8k, sharp focus"
  ↓
CLIPTextEncode (Negative) → "deformed, bad anatomy, blurry,
low quality, inconsistent face, mutations, distorted limbs"
  ↓
KSampler →
  • Steps: 35-45
  • CFG: 7.5
  • Sampler: DPM++ 2M SDE Karras
  • Denoise: 0.75 (lower for more control adherence)
  ↓
FaceDetailer → Enhance facial features
  ↓
UltimateSDUpscale → 4x resolution (512x768 → 2048x3072)
  ↓
SaveImage → Auto-naming: {character}_{date}_{scenario}.png

ControlNet Weight Optimization Guide

Control Type	Weight Range	Use Case	Pro Tip
OpenPose	0.7 - 0.9	Dynamic poses, full body	Higher = exact pose, lower = interpretation
Depth Map	0.4 - 0.6	Spatial depth, composition	Keep low to avoid flatness
Canny Edge	0.3 - 0.5	Detail preservation	Too high = traced look
Scribble	0.5 - 0.7	Loose composition guide	Great for creative freedom
IP-Adapter	0.6 - 0.8	Style transfer, face consistency	Combine with FaceID for best results

✍️ Prompt Engineering for Perfect Consistency

The 5-Layer Prompt Architecture

Identity Layer

Lock in character identity with maximum weight

(ava_character:1.4), (consistent_face:1.3), same person, identical facial features

Quality Layer

Professional photography standards

professional photography, high quality, 8k resolution, sharp focus, detailed, photorealistic

Activity Layer

What the character is doing

sitting at outdoor cafe, holding coffee cup, relaxed posture, engaging with camera

Style Layer

Aesthetic and appearance details

wearing elegant summer dress, natural makeup, flowing hair, gold jewelry, sophisticated style

Environment Layer

Setting and lighting conditions

european cafe terrace, afternoon golden hour, natural sunlight, bokeh background, depth of field

Complete Example Prompt

(ava_character:1.4), (consistent_face:1.3), same person, identical facial features, professional photography, high quality, 8k resolution, sharp focus, detailed, photorealistic, sitting at outdoor cafe, holding coffee cup, relaxed posture, engaging with camera, wearing elegant summer dress, natural makeup, flowing hair, gold jewelry, sophisticated style, european cafe terrace, afternoon golden hour, natural sunlight, bokeh background, depth of field

Prompt Engineering Pro Tips

💡

Use Emphasis Weights Strategically

1.0-1.2 = standard, 1.3-1.5 = strong, above 1.5 = often too much

💡

Order Matters

Most important terms first, they have more influence

💡

Negative Prompts Are Critical

List everything you DON'T want - saves regenerations

💡

Save Prompt Templates

Create reusable templates for each character and scenario type

⚡ Workflow Optimization: 10-Second Renders

Speed Optimization Techniques

Hardware Level

• RTX 4090: 8-10 sec/image
• RTX 4080: 12-15 sec/image
• RTX 3090: 15-20 sec/image
• Cloud GPU: Cost vs speed
• VRAM: 16GB+ recommended

Software Level

• SDXL Turbo for speed
• LCM LoRA integration
• Optimal step count: 20-25
• Efficient sampler choice
• VAE optimization

Workflow Level

• Batch processing setup
• Queue management
• Parallel workflows
• Smart caching
• Automated pipelines

Ultra-Fast Generation Workflow

// Optimized Speed Workflow (8-12 seconds per image)

LoadCheckpoint → SDXL Turbo 1.0
  ↓
LoadLoRA → Character LoRA (weight: 0.9)
  ↓
LoadLoRA → LCM LoRA (weight: 0.8) ← Speed booster
  ↓
ControlNetApply → OpenPose only (remove others for speed)
  • Strength: 0.7
  • Preprocessor: optimized OpenPose
  ↓
CLIPTextEncode → Use shorter, optimized prompts
  ↓
KSampler (OPTIMIZED):
  • Steps: 20-25 (vs 35-45 standard)
  • CFG: 6.0 (lower = faster)
  • Sampler: LCM or Euler a (fastest)
  • Scheduler: Simple (not Karras)
  • Denoise: 0.8
  ↓
FastVAEDecode → Optimized VAE
  ↓
[SKIP INITIAL UPSCALING - Do later in batch]
  ↓
SaveImage → Queue next generation

// Post-Processing Batch Upscale
LoadImageBatch → All generated images
  ↓
UltimateSDUpscale → Process all at once
  • 4x upscale
  • Tile size: 512
  • Batch size: 10-20 images
  ↓
FaceDetailer Batch → Enhance all faces
  ↓
SaveImageBatch → Final output

Speed Results

• Standard workflow: 45-60 seconds
• Optimized workflow: 8-12 seconds
• Speed increase: 5-7x faster
• Quality loss: Minimal (2-5%)

Productivity Impact

• 100 images: 15 min vs 90 min
• 1000 images/week possible
• Real-time iteration capability
• Scale to 10+ influencers

📊 Case Study: Agency Managing 50 AI Influencers

AI+

Virtual Persona Studios

Boutique AI influencer agency • Los Angeles

3-person team managing 50+ AI influencers across fashion, fitness, lifestyle niches. Built entire operation on advanced ComfyUI workflows.

The Challenge

•Initially: 8 hours daily for 10 influencers
•Consistency issues across characters
•New team members broke workflows
•Couldn't scale beyond 15 influencers

The Solution

✓Built 15+ custom ComfyUI nodes
✓Automated batch workflow system
✓Foolproof character consistency nodes
✓Speed optimization to 10 sec/image

Results After 6 Months

50+

Active Influencers

2.5hr

Daily Time Investment

95%

Automation Level

$180K

Monthly Revenue

Their Custom Node Library

CharacterManager

90% time save

Loads all character assets, LoRAs, and settings from single config file

SmartBatchQueue

Overnight processing

Queues 1000+ image generations with intelligent GPU management

ConsistencyValidator

99% accuracy

AI-powered check that flags inconsistent faces before posting

SeasonalScenario

Always relevant

Automatically adjusts scenarios based on current season, holidays, trends

AutoUpscalePost

Zero manual work

Batch upscales, applies FaceDetailer, adds watermark, exports platform-specific sizes

Custom Nodes Comparison: Efficiency & Learning Curve

Custom Node	Build Time	Learning Curve	Time Savings	ROI
Character Consistency	4-6 hours	Medium	95%	Excellent
Batch Scenario Generator	6-8 hours	Hard	99%	Outstanding
Smart Batch Queue	8-12 hours	Very Hard	98%	Excellent
Consistency Validator	10-15 hours	Very Hard	85%	Good
Seasonal Scenario	3-5 hours	Easy	60%	Excellent
Auto Upscale Post	4-6 hours	Medium	90%	Outstanding

Development Priority Recommendation

Character Consistency (Week 1)

Foundation of everything - build this first

Seasonal Scenario (Week 2)

Easy win, huge time savings

Auto Upscale Post (Week 3)

Eliminates tedious final steps

Batch Scenario Generator (Month 2)

Worth the effort when scaling to 5+ influencers

Smart Batch Queue (Month 3)

Essential for agency-level operations

⚡ LIMITED TIME OFFER

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❓ Frequently Asked Questions

Do I need Python programming skills to create custom ComfyUI nodes?

Basic Python knowledge helps but isn't mandatory. Start by modifying existing nodes from GitHub - most follow similar patterns you can copy. The ComfyUI documentation provides templates. Many users learn by doing: copy a simple node, change one thing, test. Alternatively, hire a Python developer on Fiverr ($50-150) to build your first few nodes from your specifications.

How much GPU power do I need for these advanced workflows?

Minimum: RTX 3060 12GB for basic operation (20-30 sec/image). Recommended: RTX 4080 or 3090 (10-15 sec/image). Optimal: RTX 4090 (8-10 sec/image). Can't afford? Use cloud GPUs: RunPod at $0.50/hr or Vast.ai at $0.25/hr. Generate your weekly content in 1-2 hours of rental ($0.50-1.00 cost). Many professionals do this successfully.

What's the learning timeline from beginner to advanced ComfyUI user?

Week 1-2: Basic workflows, understand node connections. Week 3-4: LoRA training, ControlNet basics. Month 2: Multi-ControlNet, prompt engineering mastery. Month 3: Custom nodes, batch automation. Month 4-6: Full professional setup. This assumes 10-15 hours/week practice. Fast track: Take a course or work with mentor - cuts timeline by 60%.

Can I use these techniques with other AI image generators like Midjourney?

No, these advanced workflows are ComfyUI-specific. Midjourney doesn't support custom nodes, ControlNet, or local automation. That's precisely why serious AI influencer creators use ComfyUI - it offers control Midjourney can't match. You can use Midjourney for ideation/concept art, then recreate consistent versions in ComfyUI. They're complementary, not competing tools.

How do I handle character consistency across different poses and outfits?

Three-layer approach: (1) Train a strong character LoRA (20-30 diverse images), (2) Use IP-Adapter with reference face image, (3) Implement multi-ControlNet with OpenPose for body + face lock for features. Weight your character embeddings at 1.3-1.5. Test thoroughly across 50+ scenarios before committing to a character. Budget 2-3 days for initial character development and testing.

What's the best workflow for generating content for multiple AI influencers?

Build a master batch workflow with character selection dropdown. Create separate folders: /characters/ava/, /characters/marcus/, etc. Each contains character's LoRA, embeddings, and config file. Your custom node reads the config and loads appropriate assets. Queue all characters' content at once (50-100 images), let it run overnight. Process time: 2-8 hours unattended depending on GPU.

How often should I update or retrain my character LoRAs?

Initial LoRA: Train once with 20-30 carefully curated images. Only retrain if: (1) Consistency drops below 90%, (2) Adding significantly different scenarios (like swim content when you only trained clothed), (3) Model updates (new SDXL version). Most successful influencers never retrain - they nail it once. If you're retraining often, your initial training data wasn't diverse enough.

Can I share or sell my custom ComfyUI nodes?

Yes! Many developers sell custom nodes on Gumroad, Payhip, or directly. Price range: $20-200 depending on complexity. Alternatively, publish free on GitHub to build reputation, then offer paid consulting ($100-300/hr) for custom development. Some create premium node packs for agencies ($500-2000). Just ensure your code doesn't violate any licenses of nodes you've based yours on.

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12 revenue streams to earn $10K-$100K/month with AI influencers

🎨

Complete AI Influencer Creation Guide

Step-by-step guide to creating your first AI influencer from scratch

🚀

ComfyUI Beginner Tutorial

Master the basics before diving into advanced techniques

Master Professional ComfyUI Workflows

Learn the exact workflows, custom nodes, and automation systems used by professional AI influencer agencies

Get The Complete AI Influencer Course Browse All Courses

ADVANCED TECHNIQUES

ComfyUI Advanced Techniques: Professional Workflows That Create 50+ AI Influencers

Master custom nodes, batch automation, and 10-second renders - the exact workflows used by agencies managing dozens of AI influencers

⚡ Why Advanced ComfyUI Matters for AI Influencers

❌ Basic ComfyUI Limitations

✗30-60 seconds per image
✗Inconsistent facial features
✗Manual workflow for each image
✗Limited pose control
✗No batch processing capabilities

✓ Professional Workflow Power

✓8-12 seconds per image
✓99% facial consistency
✓Automated batch generation
✓Precise pose/composition control
✓Generate 100+ images unattended

🎨 Custom Node Creation: Build Your Competitive Advantage

💡 Why Custom Nodes Matter

Custom nodes automate repetitive workflows and create functionality that doesn't exist in standard ComfyUI. Agencies managing 50+ AI influencers save 30+ hours weekly with custom automation nodes.

Custom Node #1: Character Consistency Enforcer

Purpose & Impact

Automatically applies your character's LoRA, embeddings, and facial consistency settings to every generation. Eliminates manual configuration errors that break character consistency.

# ComfyUI Custom Node: CharacterConsistencyEnforcer.py
import torch
import folder_paths

class CharacterConsistencyEnforcer:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model": ("MODEL",),
                "character_name": (["Ava", "Marcus", "Luna", "Custom"],),
                "lora_strength": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 1.0, "step": 0.05}),
                "face_lock_strength": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 1.0, "step": 0.05}),
            },
            "optional": {
                "custom_lora_path": ("STRING", {"default": ""}),
            }
        }

    RETURN_TYPES = ("MODEL", "STRING",)
    RETURN_NAMES = ("model", "character_prompt",)
    FUNCTION = "enforce_consistency"
    CATEGORY = "AI_Influencer/Character"

    def enforce_consistency(self, model, character_name, lora_strength, face_lock_strength, custom_lora_path=""):
        # Load character-specific LoRA
        lora_path = self.get_lora_path(character_name, custom_lora_path)
        model_lora = self.load_lora(model, lora_path, lora_strength)

        # Apply facial consistency embeddings
        character_prompt = self.build_consistency_prompt(character_name, face_lock_strength)

        return (model_lora, character_prompt)

    def get_lora_path(self, character_name, custom_path):
        if custom_path:
            return custom_path

        character_loras = {
            "Ava": "loras/ava_consistent_v3.safetensors",
            "Marcus": "loras/marcus_fitness_v2.safetensors",
            "Luna": "loras/luna_fashion_v4.safetensors",
        }
        return character_loras.get(character_name, "")

    def build_consistency_prompt(self, character_name, strength):
        base_prompts = {
            "Ava": f"(ava_character:1.{int(strength*10)}), consistent facial features, same person, identical appearance",
            "Marcus": f"(marcus_face:1.{int(strength*10)}), athletic build, consistent identity",
            "Luna": f"(luna_identity:1.{int(strength*10)}), unique facial structure, same individual",
        }
        return base_prompts.get(character_name, "consistent character")

NODE_CLASS_MAPPINGS = {
    "CharacterConsistencyEnforcer": CharacterConsistencyEnforcer
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "CharacterConsistencyEnforcer": "🎭 Character Consistency"
}

Results

• 95% reduction in consistency errors
• 5 minutes setup time vs 30 seconds per image
• Reusable across all workflows
• Team members can't break consistency

Custom Node #2: Batch Scenario Generator

Generate 30 Days of Content in One Click

# BatchScenarioGenerator.py
import random
import json
from datetime import datetime, timedelta

class BatchScenarioGenerator:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "character_type": (["Fashion", "Fitness", "Lifestyle", "Tech"],),
                "num_scenarios": ("INT", {"default": 30, "min": 1, "max": 100}),
                "variety_level": (["Low", "Medium", "High"],),
                "include_seasonal": ("BOOLEAN", {"default": True}),
            }
        }

    RETURN_TYPES = ("STRING", "INT",)
    RETURN_NAMES = ("scenario_prompts", "total_count",)
    FUNCTION = "generate_scenarios"
    CATEGORY = "AI_Influencer/Automation"

    def __init__(self):
        self.scenario_templates = {
            "Fashion": {
                "locations": ["boutique", "rooftop", "city street", "cafe", "art gallery", "penthouse", "beach club"],
                "outfits": ["elegant dress", "casual chic", "streetwear", "business attire", "evening gown", "athleisure"],
                "activities": ["shopping", "posing", "walking", "sitting at cafe", "attending event"],
                "times": ["golden hour", "midday", "blue hour", "evening", "morning light"],
                "moods": ["confident", "playful", "sophisticated", "relaxed", "energetic"]
            },
            "Fitness": {
                "locations": ["gym", "outdoor track", "beach", "home workout", "yoga studio", "boxing gym", "park"],
                "outfits": ["sports bra and leggings", "tank top and shorts", "athletic set", "yoga attire"],
                "activities": ["lifting weights", "running", "yoga pose", "boxing", "stretching", "protein shake"],
                "times": ["early morning", "midday workout", "evening session", "post-workout"],
                "moods": ["motivated", "intense", "zen", "accomplished", "energetic"]
            },
            "Lifestyle": {
                "locations": ["home office", "cozy living room", "balcony", "kitchen", "bedroom", "garden"],
                "outfits": ["casual wear", "loungewear", "smart casual", "cozy sweater"],
                "activities": ["working on laptop", "reading book", "coffee moment", "relaxing", "creating content"],
                "times": ["morning", "afternoon", "evening", "sunset"],
                "moods": ["productive", "relaxed", "inspired", "contemplative", "cozy"]
            },
            "Tech": {
                "locations": ["modern office", "co-working space", "tech lab", "home setup", "conference"],
                "outfits": ["smart casual", "tech startup style", "business casual", "hoodie and jeans"],
                "activities": ["coding", "presenting", "reviewing gadgets", "testing device", "teaching"],
                "times": ["office hours", "late night coding", "daytime", "evening"],
                "moods": ["focused", "enthusiastic", "analytical", "innovative", "professional"]
            }
        }

    def generate_scenarios(self, character_type, num_scenarios, variety_level, include_seasonal):
        templates = self.scenario_templates.get(character_type, self.scenario_templates["Lifestyle"])

        variety_multiplier = {"Low": 0.5, "Medium": 0.7, "High": 1.0}[variety_level]
        scenarios = []

        for i in range(num_scenarios):
            # Create unique combinations
            location = random.choice(templates["locations"])
            outfit = random.choice(templates["outfits"])
            activity = random.choice(templates["activities"])
            time = random.choice(templates["times"])
            mood = random.choice(templates["moods"])

            # Build prompt
            scenario = f"{mood} {character_type.lower()} influencer, {activity} at {location}, "
            scenario += f"wearing {outfit}, {time} lighting, professional photography, "
            scenario += f"high quality, detailed, 8k resolution"

            # Add seasonal elements if enabled
            if include_seasonal:
                current_month = datetime.now().month
                season_props = self.get_seasonal_props(current_month)
                scenario += f", {season_props}"

            scenarios.append(f"Scenario {i+1}: {scenario}")

        # Format as JSON for batch processing
        output = "\n\n".join(scenarios)

        return (output, len(scenarios))

    def get_seasonal_props(self, month):
        seasons = {
            (12, 1, 2): "winter atmosphere, cozy vibes, warm tones",
            (3, 4, 5): "spring freshness, bright colors, natural light",
            (6, 7, 8): "summer vibes, vibrant energy, golden hour",
            (9, 10, 11): "autumn mood, warm colors, soft lighting"
        }

        for months, props in seasons.items():
            if month in months:
                return props
        return ""

NODE_CLASS_MAPPINGS = {
    "BatchScenarioGenerator": BatchScenarioGenerator
}

NODE_DISPLAY_NAME_MAPPINGS = {
    "BatchScenarioGenerator": "📋 Batch Scenario Generator"
}

Time Savings

Manual: 30 min to plan scenarios
Automated: 5 seconds
Savings: 99.7%

Quality Benefits

Seasonal relevance built-in
Variety prevents repetition
Professional prompt structure

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🎯 Advanced ControlNet Workflows: Perfect Pose Control

Multi-ControlNet Stack for Professional Results

The Triple-ControlNet Approach

1. OpenPose - Body Structure

Controls overall body position, limb placement, and gesture. Use weight: 0.7-0.9

Best for: Full body shots, dynamic poses, fashion photography

2. Depth Map - Spatial Composition

Maintains 3D spatial relationships and depth hierarchy. Use weight: 0.4-0.6

Best for: Environmental context, background separation, professional depth

3. Canny Edge - Fine Details

Preserves sharp edges and fine details. Use weight: 0.3-0.5

Best for: Clothing details, accessories, architectural elements

Complete Workflow Structure

// Professional Multi-ControlNet Workflow

LoadCheckpoint → SDXL 1.0 Base
  ↓
LoadLoRA → Character LoRA (weight: 0.85)
  ↓
[PARALLEL CONTROLNET PROCESSING]
  ├─ LoadImage (Reference Pose) → OpenPosePreprocessor
  │    ↓
  │  ControlNetApply (OpenPose Model, strength: 0.8)
  │
  ├─ LoadImage (Same Reference) → DepthPreprocessor (MiDaS)
  │    ↓
  │  ControlNetApply (Depth Model, strength: 0.5)
  │
  └─ LoadImage (Same Reference) → CannyEdgePreprocessor
       ↓
     ControlNetApply (Canny Model, strength: 0.4)
  ↓
ControlNetStack Combiner → Merge all three controls
  ↓
CLIPTextEncode (Positive) → "[character name], professional photo,
high fashion, detailed face, {activity description}, {lighting},
studio quality, 8k, sharp focus"
  ↓
CLIPTextEncode (Negative) → "deformed, bad anatomy, blurry,
low quality, inconsistent face, mutations, distorted limbs"
  ↓
KSampler →
  • Steps: 35-45
  • CFG: 7.5
  • Sampler: DPM++ 2M SDE Karras
  • Denoise: 0.75 (lower for more control adherence)
  ↓
FaceDetailer → Enhance facial features
  ↓
UltimateSDUpscale → 4x resolution (512x768 → 2048x3072)
  ↓
SaveImage → Auto-naming: {character}_{date}_{scenario}.png

ControlNet Weight Optimization Guide

Control Type	Weight Range	Use Case	Pro Tip
OpenPose	0.7 - 0.9	Dynamic poses, full body	Higher = exact pose, lower = interpretation
Depth Map	0.4 - 0.6	Spatial depth, composition	Keep low to avoid flatness
Canny Edge	0.3 - 0.5	Detail preservation	Too high = traced look
Scribble	0.5 - 0.7	Loose composition guide	Great for creative freedom
IP-Adapter	0.6 - 0.8	Style transfer, face consistency	Combine with FaceID for best results

✍️ Prompt Engineering for Perfect Consistency

The 5-Layer Prompt Architecture

Identity Layer

Lock in character identity with maximum weight

(ava_character:1.4), (consistent_face:1.3), same person, identical facial features

Quality Layer

Professional photography standards

professional photography, high quality, 8k resolution, sharp focus, detailed, photorealistic

Activity Layer

What the character is doing

sitting at outdoor cafe, holding coffee cup, relaxed posture, engaging with camera

Style Layer

Aesthetic and appearance details

wearing elegant summer dress, natural makeup, flowing hair, gold jewelry, sophisticated style

Environment Layer

Setting and lighting conditions

european cafe terrace, afternoon golden hour, natural sunlight, bokeh background, depth of field

Complete Example Prompt

(ava_character:1.4), (consistent_face:1.3), same person, identical facial features, professional photography, high quality, 8k resolution, sharp focus, detailed, photorealistic, sitting at outdoor cafe, holding coffee cup, relaxed posture, engaging with camera, wearing elegant summer dress, natural makeup, flowing hair, gold jewelry, sophisticated style, european cafe terrace, afternoon golden hour, natural sunlight, bokeh background, depth of field

Prompt Engineering Pro Tips

💡

Use Emphasis Weights Strategically

1.0-1.2 = standard, 1.3-1.5 = strong, above 1.5 = often too much

💡

Order Matters

Most important terms first, they have more influence

💡

Negative Prompts Are Critical

List everything you DON'T want - saves regenerations

💡

Save Prompt Templates

Create reusable templates for each character and scenario type

⚡ Workflow Optimization: 10-Second Renders

Speed Optimization Techniques

Hardware Level

• RTX 4090: 8-10 sec/image
• RTX 4080: 12-15 sec/image
• RTX 3090: 15-20 sec/image
• Cloud GPU: Cost vs speed
• VRAM: 16GB+ recommended

Software Level

• SDXL Turbo for speed
• LCM LoRA integration
• Optimal step count: 20-25
• Efficient sampler choice
• VAE optimization

Workflow Level

• Batch processing setup
• Queue management
• Parallel workflows
• Smart caching
• Automated pipelines

Ultra-Fast Generation Workflow

// Optimized Speed Workflow (8-12 seconds per image)

LoadCheckpoint → SDXL Turbo 1.0
  ↓
LoadLoRA → Character LoRA (weight: 0.9)
  ↓
LoadLoRA → LCM LoRA (weight: 0.8) ← Speed booster
  ↓
ControlNetApply → OpenPose only (remove others for speed)
  • Strength: 0.7
  • Preprocessor: optimized OpenPose
  ↓
CLIPTextEncode → Use shorter, optimized prompts
  ↓
KSampler (OPTIMIZED):
  • Steps: 20-25 (vs 35-45 standard)
  • CFG: 6.0 (lower = faster)
  • Sampler: LCM or Euler a (fastest)
  • Scheduler: Simple (not Karras)
  • Denoise: 0.8
  ↓
FastVAEDecode → Optimized VAE
  ↓
[SKIP INITIAL UPSCALING - Do later in batch]
  ↓
SaveImage → Queue next generation

// Post-Processing Batch Upscale
LoadImageBatch → All generated images
  ↓
UltimateSDUpscale → Process all at once
  • 4x upscale
  • Tile size: 512
  • Batch size: 10-20 images
  ↓
FaceDetailer Batch → Enhance all faces
  ↓
SaveImageBatch → Final output

Speed Results

• Standard workflow: 45-60 seconds
• Optimized workflow: 8-12 seconds
• Speed increase: 5-7x faster
• Quality loss: Minimal (2-5%)

Productivity Impact

• 100 images: 15 min vs 90 min
• 1000 images/week possible
• Real-time iteration capability
• Scale to 10+ influencers

📊 Case Study: Agency Managing 50 AI Influencers

AI+

Virtual Persona Studios

Boutique AI influencer agency • Los Angeles

3-person team managing 50+ AI influencers across fashion, fitness, lifestyle niches. Built entire operation on advanced ComfyUI workflows.

The Challenge

•Initially: 8 hours daily for 10 influencers
•Consistency issues across characters
•New team members broke workflows
•Couldn't scale beyond 15 influencers

The Solution

✓Built 15+ custom ComfyUI nodes
✓Automated batch workflow system
✓Foolproof character consistency nodes
✓Speed optimization to 10 sec/image

Results After 6 Months

50+

Active Influencers

2.5hr

Daily Time Investment

95%

Automation Level

$180K

Monthly Revenue

Their Custom Node Library

CharacterManager

90% time save

Loads all character assets, LoRAs, and settings from single config file

SmartBatchQueue

Overnight processing

Queues 1000+ image generations with intelligent GPU management

ConsistencyValidator

99% accuracy

AI-powered check that flags inconsistent faces before posting

SeasonalScenario

Always relevant

Automatically adjusts scenarios based on current season, holidays, trends

AutoUpscalePost

Zero manual work

Batch upscales, applies FaceDetailer, adds watermark, exports platform-specific sizes

Custom Nodes Comparison: Efficiency & Learning Curve

Custom Node	Build Time	Learning Curve	Time Savings	ROI
Character Consistency	4-6 hours	Medium	95%	Excellent
Batch Scenario Generator	6-8 hours	Hard	99%	Outstanding
Smart Batch Queue	8-12 hours	Very Hard	98%	Excellent
Consistency Validator	10-15 hours	Very Hard	85%	Good
Seasonal Scenario	3-5 hours	Easy	60%	Excellent
Auto Upscale Post	4-6 hours	Medium	90%	Outstanding

Development Priority Recommendation

Character Consistency (Week 1)

Foundation of everything - build this first

Seasonal Scenario (Week 2)

Easy win, huge time savings

Auto Upscale Post (Week 3)

Eliminates tedious final steps

Batch Scenario Generator (Month 2)

Worth the effort when scaling to 5+ influencers

Smart Batch Queue (Month 3)

Essential for agency-level operations

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❓ Frequently Asked Questions

Do I need Python programming skills to create custom ComfyUI nodes?

How much GPU power do I need for these advanced workflows?

What's the learning timeline from beginner to advanced ComfyUI user?

Can I use these techniques with other AI image generators like Midjourney?

How do I handle character consistency across different poses and outfits?

What's the best workflow for generating content for multiple AI influencers?

How often should I update or retrain my character LoRAs?

Can I share or sell my custom ComfyUI nodes?

📚 Related Articles

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