# ControlNet Character Face Swap Node - Implementation Plan ## Concept Overview Create a ComfyUI node that takes a **reference image** (for pose, clothing, background, scene) and applies a **character's face** from your AI model collection. This lets you place your consistent AI characters into any pose/scene while maintaining their identity. ## Use Case Example **Input 1:** Reference image of a person sitting at a café (pose + scene) **Input 2:** Your AI character model/LoRA or reference face image **Output:** Your character sitting at the café in the same pose ## Technical Approach ### 1. **Multi-ControlNet for Structure Transfer** Extract structure from reference image: - **OpenPose** - Body pose and skeleton (PRIMARY - most important) - **Depth** - Spatial depth and 3D structure - **Canny Edge** - Sharp outlines and composition - **Normal Map** - Surface details and lighting direction - **Softedge** - Softer structural guidance ### 2. **Character Face Application Methods** #### Option A: LoRA-Based (Best for consistent characters) - Use character-specific LoRA trained on your AI character - ControlNets guide pose/scene - LoRA ensures consistent face/identity - **Strength:** LoRA weight 0.7-1.0 #### Option B: IP-Adapter FaceID (Best for flexibility) - Use IP-Adapter with FaceID model - Input: Reference image structure + Character face reference - Maintains face identity across different scenes - **Strength:** IP-Adapter weight 0.8-1.0 #### Option C: Reactor/ReActor Face Swap - Generate image with ControlNets - Use face swap to replace generated face with character face - Most accurate face transfer - Works with any character reference ### 3. **Recommended Hybrid Approach** Combine multiple methods for best results: ``` Reference Image (pose/scene) ↓ ┌─────────────────────────────────┐ │ ControlNet Preprocessing │ │ - OpenPose skeleton │ │ - Depth map │ │ - Canny edges │ │ - Normal map │ └─────────────────────────────────┘ ↓ ┌─────────────────────────────────┐ │ Initial Generation │ │ - Base model + Character LoRA │ │ - Guided by ControlNets │ │ - Prompt: character description│ └─────────────────────────────────┘ ↓ ┌─────────────────────────────────┐ │ Face Identity Reinforcement │ │ Option 1: IP-Adapter FaceID │ │ Option 2: ReActor Face Swap │ │ - Apply character face │ │ - Blend seamlessly │ └─────────────────────────────────┘ ↓ Output: Character in reference pose/scene ``` ## Node Architecture ### Input Ports #### Required 1. **reference_image** - Source image for pose/background/scene 2. **model** - Base Stable Diffusion checkpoint 3. **character_face_image** - Your character's face reference #### Character Method (Choose One) 4. **character_lora** - LoRA file for your character (optional) 5. **lora_strength** - Float (0.0-1.5, default 0.8) #### ControlNet Settings 6. **pose_strength** - Float (0.0-1.5, default 1.0) - Most important 7. **depth_strength** - Float (0.0-1.5, default 0.7) 8. **canny_strength** - Float (0.0-1.5, default 0.5) #### Generation Settings 9. **positive_prompt** - Character description + scene details 10. **negative_prompt** - Quality negatives 11. **face_method** - Choice ["IP-Adapter FaceID", "ReActor", "Both"] 12. **face_strength** - Float (0.0-1.0, default 0.85) 13. **seed** - Random seed #### Optional 14. **preserve_clothes** - Boolean (try to keep reference clothing) 15. **preserve_background** - Boolean (keep original background) 16. **style_prompt** - Override style (anime, realistic, etc.) ### Output Ports 1. **image** - Final generated image 2. **pose_preview** - OpenPose skeleton preview 3. **depth_preview** - Depth map preview 4. **face_mask** - Face region mask (debugging) ## Processing Pipeline ```python class ControlNetCharacterSwap: @classmethod def INPUT_TYPES(cls): return { "required": { "reference_image": ("IMAGE",), "character_face": ("IMAGE",), "model": ("MODEL",), "positive_prompt": ("STRING", {"multiline": True}), "pose_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0}), "face_strength": ("FLOAT", {"default": 0.85, "min": 0.0, "max": 1.0}), }, "optional": { "character_lora": ("LORA",), "lora_strength": ("FLOAT", {"default": 0.8}), "depth_strength": ("FLOAT", {"default": 0.7}), "canny_strength": ("FLOAT", {"default": 0.5}), "face_method": (["IP-Adapter FaceID", "ReActor", "Both"],), } } RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK") RETURN_NAMES = ("image", "pose_preview", "depth_preview", "face_mask") FUNCTION = "generate_character" CATEGORY = "PMA Utils/Character" def generate_character(self, reference_image, character_face, model, positive_prompt, pose_strength, face_strength, character_lora=None, lora_strength=0.8, depth_strength=0.7, canny_strength=0.5, face_method="IP-Adapter FaceID"): # STEP 1: Extract ControlNet Maps pose_map = openpose_detector(reference_image) depth_map = depth_estimator(reference_image) canny_map = canny_detector(reference_image) # STEP 2: Prepare Model working_model = model if character_lora: working_model = apply_lora(model, character_lora, lora_strength) # STEP 3: Apply ControlNets # OpenPose is primary - controls body structure latent = apply_controlnet( model=working_model, controlnet=openpose_controlnet, image=pose_map, strength=pose_strength ) # Depth adds spatial awareness latent = apply_controlnet( latent=latent, controlnet=depth_controlnet, image=depth_map, strength=depth_strength ) # Canny for fine details (lower strength to allow character features) latent = apply_controlnet( latent=latent, controlnet=canny_controlnet, image=canny_map, strength=canny_strength ) # STEP 4: Generate Base Image generated_image = generate( latent=latent, positive=positive_prompt, negative=negative_prompt, model=working_model ) # STEP 5: Apply Character Face if face_method == "IP-Adapter FaceID": final_image = apply_ipadapter_faceid( image=generated_image, face_reference=character_face, strength=face_strength ) elif face_method == "ReActor": final_image = reactor_face_swap( target_image=generated_image, source_face=character_face, strength=face_strength ) elif face_method == "Both": # First IP-Adapter, then ReActor for maximum accuracy temp_image = apply_ipadapter_faceid( image=generated_image, face_reference=character_face, strength=face_strength * 0.7 ) final_image = reactor_face_swap( target_image=temp_image, source_face=character_face, strength=face_strength ) # STEP 6: Generate Previews face_mask = detect_face_mask(final_image) return (final_image, pose_map, depth_map, face_mask) ``` ## Workflow Example ### Basic Workflow ``` ┌─────────────────┐ │ Reference Image │ (person at café) └────────┬────────┘ │ ┌────▼─────────────────────┐ │ ControlNet Preprocessors │ │ - OpenPose │ │ - Depth │ │ - Canny │ └────┬─────────────────────┘ │ ┌────▼────────────┐ ┌──────────────────┐ │ SD Generation │◄─────┤ Character LoRA │ │ + ControlNets │ └──────────────────┘ └────┬────────────┘ │ ┌────▼────────────┐ ┌──────────────────┐ │ IP-Adapter │◄─────┤ Character Face │ │ FaceID │ │ Reference │ └────┬────────────┘ └──────────────────┘ │ ┌────▼────────────┐ │ Final Output │ (Your character at café) └─────────────────┘ ``` ## Prompt Strategy ### Positive Prompt Template ``` [CHARACTER NAME/DESCRIPTION], [CHARACTER FEATURES], [REFERENCE SCENE DESCRIPTION], [POSE DESCRIPTION], [STYLE KEYWORDS], high quality, detailed face, 8k ``` **Example:** ``` Emma, young woman with long brown hair, blue eyes, fair skin, sitting at outdoor café table, holding coffee cup, relaxed pose, photorealistic, natural lighting, bokeh background, professional photo ``` ### Negative Prompt ``` bad face, deformed, ugly, blurry, low quality, bad anatomy, extra limbs, wrong proportions, watermark ``` ## Required Models & Dependencies ### ControlNet Models (SD 1.5 or SDXL) - **control_sd15_openpose.pth** - ESSENTIAL - **control_sd15_depth.pth** - ESSENTIAL - **control_sd15_canny.pth** - Recommended - **control_sd15_normal.pth** - Optional ### Face Models - **IP-Adapter FaceID Plus v2** - For face identity transfer - Model: `ip-adapter-faceid-plusv2_sdxl.bin` - CLIP: `clip_vision_SDXL_vit-h.safetensors` - **ReActor** - For face swapping - InsightFace models (buffalo_l) ### ComfyUI Custom Nodes 1. **ComfyUI-ControlNet-Aux** - Preprocessors 2. **ComfyUI-IPAdapter-Plus** - IP-Adapter support 3. **ReActor Node** - Face swapping 4. **ComfyUI-Impact-Pack** - Face detection utilities ### Your Character Assets - **Character LoRAs** - Trained on your AI characters - **Character Face References** - Clean face shots of each character ## Character Library Management ### Suggested Structure ``` /models/loras/characters/ emma_character.safetensors john_character.safetensors sarah_character.safetensors /inputs/character_faces/ emma_face.png john_face.png sarah_face.png ``` ### Node Enhancement: Character Selector Add dropdown to select from saved characters: ```python "character_preset": (self.get_character_list(),) def get_character_list(self): # Scan character directory return ["Emma", "John", "Sarah", "Custom..."] ``` ## Advanced Features ### 1. **Outfit Preservation Mode** - Extract clothing from reference with segmentation - Apply clothing ControlNet or inpainting - Keep character face + reference outfit ### 2. **Background Swapping** - Segment background from reference - Replace with custom background - Keep character + pose ### 3. **Multi-Character Scenes** - Detect multiple people in reference - Apply different character faces to each - Maintain relative positions ### 4. **Expression Transfer** - Detect facial expression from reference - Apply expression to character face - Use expression ControlNet if available ### 5. **Batch Processing** - Process multiple references with same character - Generate character pose library - Automatic prompt generation from reference ## Testing Scenarios | Scenario | Reference | Character | Expected Result | |----------|-----------|-----------|-----------------| | Portrait | Professional headshot | Emma | Emma in professional setting | | Action | Person jumping | John | John jumping, same pose | | Group | 3 people at party | Sarah (1 person) | Sarah in same scene/pose | | Historical | Vintage photo | Emma | Emma in vintage style/setting | | Fashion | Model on runway | John | John as fashion model | ## Optimization Tips ### For Best Quality 1. **OpenPose Strength:** 0.9-1.2 (maintain pose) 2. **Character LoRA:** 0.7-1.0 (strong character features) 3. **Face Method:** "Both" (IP-Adapter + ReActor) 4. **Resolution:** Match reference or use 1024x1024 for SDXL ### For Speed 1. **Reduce ControlNets:** Use only OpenPose + Depth 2. **Lower Steps:** 20-25 steps sufficient 3. **Skip ReActor:** Use only IP-Adapter FaceID 4. **Batch Process:** Queue multiple generations ### For Consistency Across Images 1. **Use Same LoRA + Weight** for all generations 2. **Use Same Face Reference** image 3. **Lock Seed** for similar variations 4. **Save Workflow** as template ## Common Issues & Solutions | Problem | Cause | Solution | |---------|-------|----------| | Wrong face | Weak face strength | Increase face_strength to 0.9-1.0 | | Wrong pose | Weak pose ControlNet | Increase pose_strength to 1.2 | | Blurry face | ControlNet conflict | Lower canny/depth, keep pose high | | Wrong clothing | Strong clothing from ref | Lower depth_strength, use outfit prompt | | Background wrong | ControlNets too strong | Lower all except pose, focus prompt on BG | | Face doesn't match character | LoRA not strong enough | Increase lora_strength or use "Both" face method | ## Implementation Phases ### Phase 1: Core Functionality - [x] Plan architecture - [ ] Implement OpenPose ControlNet integration - [ ] Implement Depth ControlNet integration - [ ] Basic generation pipeline ### Phase 2: Face Integration - [ ] IP-Adapter FaceID integration - [ ] ReActor face swap integration - [ ] Face blending quality improvements ### Phase 3: LoRA Support - [ ] Character LoRA loading - [ ] LoRA strength controls - [ ] Character library management ### Phase 4: UI/UX - [ ] Character preset selector - [ ] Preview outputs for each stage - [ ] Workflow templates ### Phase 5: Advanced Features - [ ] Outfit preservation mode - [ ] Expression transfer - [ ] Batch processing ## Expected Results **Quality Metrics:** - **Face Similarity:** 90%+ match to character reference - **Pose Accuracy:** 95%+ match to reference pose - **Scene Coherence:** Natural integration of character into scene - **Processing Time:** 15-30 seconds per image (RTX 3090) ## Conclusion This node solves the problem of placing consistent AI characters into arbitrary poses and scenes. By combining ControlNet for structure transfer with face identity models (IP-Adapter/ReActor), you can maintain character consistency while achieving any pose or setting. **Perfect for:** - Building character portfolio/lookbooks - Storytelling with consistent characters - Virtual fashion/product modeling - Character-based content creation - AI influencer content **Estimated Development Time:** 1-2 weeks **Complexity:** Medium-High **Value:** Extremely high for character-based workflows