Updated generation pages.

services/comfyui.py

@@ -6,6 +6,22 @@ from flask import current_app
 logger = logging.getLogger('gaze')
 
 
+def get_loaded_checkpoint():
+    """Return the checkpoint path currently loaded in ComfyUI, or None."""
+    try:
+        url = current_app.config.get('COMFYUI_URL', 'http://127.0.0.1:8188')
+        resp = requests.get(f'{url}/history', timeout=3)
+        if resp.ok:
+            history = resp.json()
+            if history:
+                latest = max(history.values(), key=lambda j: j.get('status', {}).get('status_str', ''))
+                nodes = latest.get('prompt', [None, None, {}])[2]
+                return nodes.get('4', {}).get('inputs', {}).get('ckpt_name')
+    except Exception:
+        pass
+    return None
+
+
 def _ensure_checkpoint_loaded(checkpoint_path):
     """Check if the desired checkpoint is loaded in ComfyUI, and force reload if not."""
     if not checkpoint_path:

services/prompts.py

@@ -1,6 +1,6 @@
 import re
 from models import db, Character
-from utils import _IDENTITY_KEYS, _WARDROBE_KEYS, parse_orientation
+from utils import _IDENTITY_KEYS, _WARDROBE_KEYS, _BODY_GROUP_KEYS, parse_orientation
 
 
 def _dedup_tags(prompt_str):
@@ -114,15 +114,21 @@ def build_prompt(data, selected_fields=None, default_fields=None, active_outfit=
     style_data = data.get('style', {})
     participants = data.get('participants', {})
 
-    # Pre-calculate Hand/Glove priority
-    # Priority: wardrobe gloves > wardrobe hands (outfit) > identity hands (character)
-    hand_val = ""
-    if wardrobe.get('gloves') and is_selected('wardrobe', 'gloves'):
-        hand_val = wardrobe.get('gloves')
-    elif wardrobe.get('hands') and is_selected('wardrobe', 'hands'):
-        hand_val = wardrobe.get('hands')
-    elif identity.get('hands') and is_selected('identity', 'hands'):
-        hand_val = identity.get('hands')
+    # Helper: collect selected values from identity + wardrobe for a given group key
+    def _group_val(key):
+        parts = []
+        id_val = identity.get(key, '')
+        wd_val = wardrobe.get(key, '')
+        if id_val and is_selected('identity', key):
+            val = id_val
+            # Filter out conflicting tags from base if participants data is present
+            if participants and key == 'base':
+                val = re.sub(r'\b(1girl|1boy|solo)\b', '', val).replace(', ,', ',').strip(', ')
+            if val:
+                parts.append(val)
+        if wd_val and is_selected('wardrobe', key):
+            parts.append(wd_val)
+        return ', '.join(parts)
 
     # 1. Main Prompt
     parts = []
@@ -131,12 +137,10 @@ def build_prompt(data, selected_fields=None, default_fields=None, active_outfit=
     if participants:
         if participants.get('solo_focus') == 'true':
             parts.append('(solo focus:1.2)')
-
         orientation = participants.get('orientation', '')
         if orientation:
             parts.extend(parse_orientation(orientation))
     else:
-        # Default behavior
         parts.append("(solo:1.2)")
 
     # Use character_id (underscores to spaces) for tags compatibility
@@ -144,13 +148,10 @@ def build_prompt(data, selected_fields=None, default_fields=None, active_outfit=
     if char_tag and is_selected('special', 'name'):
         parts.append(char_tag)
 
-    for key in ['base_specs', 'hair', 'eyes', 'extra']:
-        val = identity.get(key)
-        if val and is_selected('identity', key):
-            # Filter out conflicting tags if participants data is present
-            if participants and key == 'base_specs':
-                # Remove 1girl, 1boy, solo, etc.
-                val = re.sub(r'\b(1girl|1boy|solo)\b', '', val).replace(', ,', ',').strip(', ')
+    # Add all body groups to main prompt
+    for key in _BODY_GROUP_KEYS:
+        val = _group_val(key)
+        if val:
             parts.append(val)
 
     # Add defaults (expression, pose, scene)
@@ -159,21 +160,12 @@ def build_prompt(data, selected_fields=None, default_fields=None, active_outfit=
         if val and is_selected('defaults', key):
             parts.append(val)
 
-    # Add hand priority value to main prompt
-    if hand_val:
-        parts.append(hand_val)
-
-    for key in ['full_body', 'top', 'bottom', 'headwear', 'legwear', 'footwear', 'accessories']:
-        val = wardrobe.get(key)
-        if val and is_selected('wardrobe', key):
-            parts.append(val)
-
     # Standard character styles
     char_aesthetic = data.get('styles', {}).get('aesthetic')
     if char_aesthetic and is_selected('styles', 'aesthetic'):
         parts.append(f"{char_aesthetic} style")
 
-    # New Styles Gallery logic
+    # Styles Gallery logic
     if style_data.get('artist_name') and is_selected('style', 'artist_name'):
         parts.append(f"by {style_data['artist_name']}")
     if style_data.get('artistic_style') and is_selected('style', 'artistic_style'):
@@ -187,26 +179,98 @@ def build_prompt(data, selected_fields=None, default_fields=None, active_outfit=
     if lora.get('lora_triggers') and is_selected('lora', 'lora_triggers'):
         parts.append(lora.get('lora_triggers'))
 
-    # 2. Face Prompt: Tag, Eyes, Expression, Headwear, Action details
+    # 2. Face Prompt: head group + expression + action head
     face_parts = []
-    if char_tag and is_selected('special', 'name'): face_parts.append(char_tag)
-    if identity.get('eyes') and is_selected('identity', 'eyes'): face_parts.append(identity.get('eyes'))
-    if defaults.get('expression') and is_selected('defaults', 'expression'): face_parts.append(defaults.get('expression'))
-    if wardrobe.get('headwear') and is_selected('wardrobe', 'headwear'): face_parts.append(wardrobe.get('headwear'))
+    if char_tag and is_selected('special', 'name'):
+        face_parts.append(char_tag)
+    head_val = _group_val('head')
+    if head_val:
+        face_parts.append(head_val)
+    if defaults.get('expression') and is_selected('defaults', 'expression'):
+        face_parts.append(defaults.get('expression'))
+    if action_data.get('head') and is_selected('action', 'head'):
+        face_parts.append(action_data.get('head'))
 
-    # Add specific Action expression details if available
-    if action_data.get('head') and is_selected('action', 'head'): face_parts.append(action_data.get('head'))
-    if action_data.get('eyes') and is_selected('action', 'eyes'): face_parts.append(action_data.get('eyes'))
+    # 3. Hand Prompt: hands group + action hands
+    hand_parts = []
+    hands_val = _group_val('hands')
+    if hands_val:
+        hand_parts.append(hands_val)
+    if action_data.get('hands') and is_selected('action', 'hands'):
+        hand_parts.append(action_data.get('hands'))
 
-    # 3. Hand Prompt: Hand value (Gloves or Hands), Action details
-    hand_parts = [hand_val] if hand_val else []
-    if action_data.get('arms') and is_selected('action', 'arms'): hand_parts.append(action_data.get('arms'))
-    if action_data.get('hands') and is_selected('action', 'hands'): hand_parts.append(action_data.get('hands'))
+    # 4. Feet Prompt: feet group + action feet
+    feet_parts = []
+    feet_val = _group_val('feet')
+    if feet_val:
+        feet_parts.append(feet_val)
+    if action_data.get('feet') and is_selected('action', 'feet'):
+        feet_parts.append(action_data.get('feet'))
 
     return {
         "main": _dedup_tags(", ".join(parts)),
         "face": _dedup_tags(", ".join(face_parts)),
-        "hand": _dedup_tags(", ".join(hand_parts))
+        "hand": _dedup_tags(", ".join(hand_parts)),
+        "feet": _dedup_tags(", ".join(feet_parts)),
+    }
+
+
+def build_multi_prompt(char_a, char_b, extras_prompt=''):
+    """Build prompts for a two-character generation using BREAK separation.
+
+    Returns dict with combined prompts (main, face, hand) and per-character
+    prompts (char_a_main, char_a_face, char_b_main, char_b_face) for the
+    per-person/face ADetailer passes.
+    """
+    # Build individual prompts with all fields selected
+    prompts_a = build_prompt(char_a.data)
+    prompts_b = build_prompt(char_b.data)
+
+    # Strip solo/orientation tags from individual prompts — we'll add combined ones
+    _solo_orientation_tags = {
+        'solo', '(solo:1.2)', '(solo focus:1.2)',
+        '1girl', '1boy', '2girls', '2boys', '3girls', '3boys',
+        'hetero', 'yuri', 'yaoi',
+        'multiple girls', 'multiple boys',
+    }
+
+    def _strip_tags(prompt_str, tags_to_remove):
+        parts = [t.strip() for t in prompt_str.split(',') if t.strip()]
+        return ', '.join(p for p in parts if p.lower() not in tags_to_remove)
+
+    main_a = _strip_tags(prompts_a['main'], _solo_orientation_tags)
+    main_b = _strip_tags(prompts_b['main'], _solo_orientation_tags)
+
+    # Compute combined orientation
+    orient_a = char_a.data.get('participants', {}).get('orientation', '1F')
+    orient_b = char_b.data.get('participants', {}).get('orientation', '1F')
+
+    # Count total M and F across both characters
+    combined_m = orient_a.upper().count('M') + orient_b.upper().count('M')
+    combined_f = orient_a.upper().count('F') + orient_b.upper().count('F')
+    combined_orientation = f"{combined_m}M{combined_f}F" if combined_m else f"{combined_f}F"
+    if combined_f == 0:
+        combined_orientation = f"{combined_m}M"
+    orientation_tags = parse_orientation(combined_orientation)
+
+    # Build combined main prompt with BREAK separation
+    orientation_str = ', '.join(orientation_tags)
+    combined_main = f"{orientation_str}, {main_a} BREAK {main_b}"
+    if extras_prompt:
+        combined_main = f"{extras_prompt}, {combined_main}"
+
+    # Merge face/hand prompts for the hand detailer (shared, not per-character)
+    hand_parts = [p for p in [prompts_a['hand'], prompts_b['hand']] if p]
+
+    return {
+        "main": _dedup_tags(combined_main),
+        "face": "",  # not used — per-character face prompts go to SEGS detailers
+        "hand": _dedup_tags(', '.join(hand_parts)),
+        # Per-character prompts for SEGS-based ADetailer passes
+        "char_a_main": _dedup_tags(main_a),
+        "char_a_face": _dedup_tags(prompts_a['face']),
+        "char_b_main": _dedup_tags(main_b),
+        "char_b_face": _dedup_tags(prompts_b['face']),
     }
 
 
@@ -220,7 +284,7 @@ def build_extras_prompt(actions, outfits, scenes, styles, detailers):
         if lora.get('lora_triggers'):
             parts.append(lora['lora_triggers'])
         parts.extend(data.get('tags', []))
-        for key in ['full_body', 'additional']:
+        for key in _BODY_GROUP_KEYS:
             val = data.get('action', {}).get(key)
             if val:
                 parts.append(val)
@@ -228,7 +292,7 @@ def build_extras_prompt(actions, outfits, scenes, styles, detailers):
     for outfit in outfits:
         data = outfit.data
         wardrobe = data.get('wardrobe', {})
-        for key in ['full_body', 'headwear', 'top', 'bottom', 'legwear', 'footwear', 'hands', 'accessories']:
+        for key in _BODY_GROUP_KEYS:
             val = wardrobe.get(key)
             if val:
                 parts.append(val)

services/sync.py

@@ -153,14 +153,13 @@ def ensure_default_outfit():
             "outfit_id": "default",
             "outfit_name": "Default",
             "wardrobe": {
-                "full_body": "",
-                "headwear": "",
-                "top": "",
-                "bottom": "",
-                "legwear": "",
-                "footwear": "",
+                "base": "",
+                "head": "",
+                "upper_body": "",
+                "lower_body": "",
                 "hands": "",
-                "accessories": ""
+                "feet": "",
+                "additional": ""
             },
             "lora": {
                 "lora_name": "",
@@ -360,7 +359,8 @@ def _resolve_preset_fields(preset_data):
     char_cfg = preset_data.get('character', {})
     fields = char_cfg.get('fields', {})
 
-    for key in ['base_specs', 'hair', 'eyes', 'hands', 'arms', 'torso', 'pelvis', 'legs', 'feet', 'extra']:
+    from utils import _BODY_GROUP_KEYS
+    for key in _BODY_GROUP_KEYS:
         val = fields.get('identity', {}).get(key, True)
         if val == 'random':
             val = random.choice([True, False])
@@ -375,7 +375,7 @@ def _resolve_preset_fields(preset_data):
             selected.append(f'defaults::{key}')
 
     wardrobe_cfg = fields.get('wardrobe', {})
-    for key in ['full_body', 'headwear', 'top', 'bottom', 'legwear', 'footwear', 'hands', 'gloves', 'accessories']:
+    for key in _BODY_GROUP_KEYS:
         val = wardrobe_cfg.get('fields', {}).get(key, True)
         if val == 'random':
             val = random.choice([True, False])

services/workflow.py

@@ -9,6 +9,11 @@ from services.prompts import _cross_dedup_prompts
 
 logger = logging.getLogger('gaze')
 
+# Node IDs used by DetailerForEach in multi-char mode
+_SEGS_DETAILER_NODES = ['46', '47', '53', '54']
+# Node IDs for per-character CLIP prompts in multi-char mode
+_SEGS_PROMPT_NODES = ['44', '45', '51', '52']
+
 
 def _log_workflow_prompts(label, workflow):
     """Log the final assembled ComfyUI prompts in a consistent, readable block."""
@@ -17,7 +22,7 @@ def _log_workflow_prompts(label, workflow):
     lora_details = []
 
     # Collect detailed LoRA information
-    for node_id, label_str in [("16", "char/look"), ("17", "outfit"), ("18", "action"), ("19", "style/detail/scene")]:
+    for node_id, label_str in [("16", "char/look"), ("17", "outfit"), ("18", "action"), ("19", "style/detail/scene"), ("20", "char_b")]:
         if node_id in workflow:
             name = workflow[node_id]["inputs"].get("lora_name", "")
             if name:
@@ -41,11 +46,18 @@ def _log_workflow_prompts(label, workflow):
 
     # Extract adetailer information
     adetailer_info = []
+    # Single-char mode: FaceDetailer nodes 11 + 13
     for node_id, node_name in [("11", "Face"), ("13", "Hand")]:
         if node_id in workflow:
             adetailer_info.append(f"    {node_name} (Node {node_id}): steps={workflow[node_id]['inputs'].get('steps', '?')}, "
                                 f"cfg={workflow[node_id]['inputs'].get('cfg', '?')}, "
                                 f"denoise={workflow[node_id]['inputs'].get('denoise', '?')}")
+    # Multi-char mode: SEGS DetailerForEach nodes
+    for node_id, node_name in [("46", "Person A"), ("47", "Person B"), ("53", "Face A"), ("54", "Face B")]:
+        if node_id in workflow:
+            adetailer_info.append(f"    {node_name} (Node {node_id}): steps={workflow[node_id]['inputs'].get('steps', '?')}, "
+                                f"cfg={workflow[node_id]['inputs'].get('cfg', '?')}, "
+                                f"denoise={workflow[node_id]['inputs'].get('denoise', '?')}")
 
     face_text = workflow.get('14', {}).get('inputs', {}).get('text', '')
     hand_text = workflow.get('15', {}).get('inputs', {}).get('text', '')
@@ -95,6 +107,12 @@ def _log_workflow_prompts(label, workflow):
     if hand_text:
         lines.append(f"    [H] Hand     : {hand_text}")
 
+    # Multi-char per-character prompts
+    for node_id, lbl in [("44", "Person A"), ("45", "Person B"), ("51", "Face A"), ("52", "Face B")]:
+        txt = workflow.get(node_id, {}).get('inputs', {}).get('text', '')
+        if txt:
+            lines.append(f"    [{lbl}] : {txt}")
+
     lines.append(sep)
     logger.info("\n%s", "\n".join(lines))
 
@@ -119,8 +137,8 @@ def _apply_checkpoint_settings(workflow, ckpt_data):
     if scheduler and '3' in workflow:
         workflow['3']['inputs']['scheduler'] = scheduler
 
-    # Face/hand detailers (nodes 11, 13)
-    for node_id in ['11', '13']:
+    # Face/hand detailers (nodes 11, 13) + multi-char SEGS detailers
+    for node_id in ['11', '13'] + _SEGS_DETAILER_NODES:
         if node_id in workflow:
             if steps:
                 workflow[node_id]['inputs']['steps'] = int(steps)
@@ -131,9 +149,9 @@ def _apply_checkpoint_settings(workflow, ckpt_data):
             if scheduler:
                 workflow[node_id]['inputs']['scheduler'] = scheduler
 
-    # Prepend base_positive to positive prompts (main + face/hand detailers)
+    # Prepend base_positive to all positive prompt nodes
     if base_positive:
-        for node_id in ['6', '14', '15']:
+        for node_id in ['6', '14', '15'] + _SEGS_PROMPT_NODES:
             if node_id in workflow:
                 workflow[node_id]['inputs']['text'] = f"{base_positive}, {workflow[node_id]['inputs']['text']}"
 
@@ -149,7 +167,7 @@ def _apply_checkpoint_settings(workflow, ckpt_data):
         }
         if '8' in workflow:
             workflow['8']['inputs']['vae'] = ['21', 0]
-        for node_id in ['11', '13']:
+        for node_id in ['11', '13'] + _SEGS_DETAILER_NODES:
             if node_id in workflow:
                 workflow[node_id]['inputs']['vae'] = ['21', 0]
 
@@ -187,12 +205,246 @@ def _get_default_checkpoint():
     return ckpt.checkpoint_path, ckpt.data or {}
 
 
-def _prepare_workflow(workflow, character, prompts, checkpoint=None, custom_negative=None, outfit=None, action=None, style=None, detailer=None, scene=None, width=None, height=None, checkpoint_data=None, look=None, fixed_seed=None):
+def _inject_multi_char_detailers(workflow, prompts, model_source, clip_source):
+    """Replace single FaceDetailer (node 11) with per-character SEGS-based detailers.
+
+    Injects person detection + face detection pipelines that order detections
+    left-to-right and apply character A's prompt to the left person/face and
+    character B's prompt to the right person/face.
+
+    Nodes added:
+      40  - Person detector (UltralyticsDetectorProvider)
+      41  - Person SEGS detection (BboxDetectorSEGS)
+      42  - Filter: left person (char A)
+      43  - Filter: right person (char B)
+      44  - CLIPTextEncode: char A body prompt
+      45  - CLIPTextEncode: char B body prompt
+      46  - DetailerForEach: person A
+      47  - DetailerForEach: person B
+      48  - Face SEGS detection (BboxDetectorSEGS, reuses face detector node 10)
+      49  - Filter: left face (char A)
+      50  - Filter: right face (char B)
+      51  - CLIPTextEncode: char A face prompt
+      52  - CLIPTextEncode: char B face prompt
+      53  - DetailerForEach: face A
+      54  - DetailerForEach: face B
+
+    Image flow: VAEDecode(8) → PersonA(46) → PersonB(47) → FaceA(53) → FaceB(54) → Hand(13)
+    """
+    vae_source = ["4", 2]
+
+    # Remove old single face detailer and its prompt — we replace them
+    workflow.pop('11', None)
+    workflow.pop('14', None)
+
+    # --- Person detection ---
+    workflow['40'] = {
+        'inputs': {'model_name': 'segm/person_yolov8m-seg.pt'},
+        'class_type': 'UltralyticsDetectorProvider'
+    }
+
+    workflow['41'] = {
+        'inputs': {
+            'bbox_detector': ['40', 0],
+            'image': ['8', 0],
+            'threshold': 0.5,
+            'dilation': 10,
+            'crop_factor': 3.0,
+            'drop_size': 10,
+            'labels': 'all',
+        },
+        'class_type': 'BboxDetectorSEGS'
+    }
+
+    # Order by x1 ascending (left to right), pick index 0 = leftmost person
+    workflow['42'] = {
+        'inputs': {
+            'segs': ['41', 0],
+            'target': 'x1',
+            'order': False,
+            'take_start': 0,
+            'take_count': 1,
+        },
+        'class_type': 'ImpactSEGSOrderedFilter'
+    }
+
+    # Pick index 1 = rightmost person
+    workflow['43'] = {
+        'inputs': {
+            'segs': ['41', 0],
+            'target': 'x1',
+            'order': False,
+            'take_start': 1,
+            'take_count': 1,
+        },
+        'class_type': 'ImpactSEGSOrderedFilter'
+    }
+
+    # --- Per-character body prompts ---
+    workflow['44'] = {
+        'inputs': {'text': prompts.get('char_a_main', ''), 'clip': clip_source},
+        'class_type': 'CLIPTextEncode'
+    }
+    workflow['45'] = {
+        'inputs': {'text': prompts.get('char_b_main', ''), 'clip': clip_source},
+        'class_type': 'CLIPTextEncode'
+    }
+
+    # --- Person detailing (DetailerForEach) ---
+    _person_base = {
+        'guide_size': 512,
+        'guide_size_for': True,
+        'max_size': 1024,
+        'seed': 0,           # overwritten by seed step
+        'steps': 20,         # overwritten by checkpoint settings
+        'cfg': 3.5,          # overwritten by checkpoint settings
+        'sampler_name': 'euler_ancestral',
+        'scheduler': 'normal',
+        'denoise': 0.4,
+        'feather': 5,
+        'noise_mask': True,
+        'force_inpaint': True,
+        'wildcard': '',
+        'cycle': 1,
+        'inpaint_model': False,
+        'noise_mask_feather': 20,
+    }
+
+    workflow['46'] = {
+        'inputs': {
+            **_person_base,
+            'image': ['8', 0],
+            'segs': ['42', 0],
+            'model': model_source,
+            'clip': clip_source,
+            'vae': vae_source,
+            'positive': ['44', 0],
+            'negative': ['7', 0],
+        },
+        'class_type': 'DetailerForEach'
+    }
+
+    workflow['47'] = {
+        'inputs': {
+            **_person_base,
+            'image': ['46', 0],   # chains from person A output
+            'segs': ['43', 0],
+            'model': model_source,
+            'clip': clip_source,
+            'vae': vae_source,
+            'positive': ['45', 0],
+            'negative': ['7', 0],
+        },
+        'class_type': 'DetailerForEach'
+    }
+
+    # --- Face detection (on person-detailed image) ---
+    workflow['48'] = {
+        'inputs': {
+            'bbox_detector': ['10', 0],   # reuse existing face YOLO detector
+            'image': ['47', 0],
+            'threshold': 0.5,
+            'dilation': 10,
+            'crop_factor': 3.0,
+            'drop_size': 10,
+            'labels': 'all',
+        },
+        'class_type': 'BboxDetectorSEGS'
+    }
+
+    workflow['49'] = {
+        'inputs': {
+            'segs': ['48', 0],
+            'target': 'x1',
+            'order': False,
+            'take_start': 0,
+            'take_count': 1,
+        },
+        'class_type': 'ImpactSEGSOrderedFilter'
+    }
+
+    workflow['50'] = {
+        'inputs': {
+            'segs': ['48', 0],
+            'target': 'x1',
+            'order': False,
+            'take_start': 1,
+            'take_count': 1,
+        },
+        'class_type': 'ImpactSEGSOrderedFilter'
+    }
+
+    # --- Per-character face prompts ---
+    workflow['51'] = {
+        'inputs': {'text': prompts.get('char_a_face', ''), 'clip': clip_source},
+        'class_type': 'CLIPTextEncode'
+    }
+    workflow['52'] = {
+        'inputs': {'text': prompts.get('char_b_face', ''), 'clip': clip_source},
+        'class_type': 'CLIPTextEncode'
+    }
+
+    # --- Face detailing (DetailerForEach) ---
+    _face_base = {
+        'guide_size': 384,
+        'guide_size_for': True,
+        'max_size': 1024,
+        'seed': 0,
+        'steps': 20,
+        'cfg': 3.5,
+        'sampler_name': 'euler_ancestral',
+        'scheduler': 'normal',
+        'denoise': 0.5,
+        'feather': 5,
+        'noise_mask': True,
+        'force_inpaint': True,
+        'wildcard': '',
+        'cycle': 1,
+        'inpaint_model': False,
+        'noise_mask_feather': 20,
+    }
+
+    workflow['53'] = {
+        'inputs': {
+            **_face_base,
+            'image': ['47', 0],
+            'segs': ['49', 0],
+            'model': model_source,
+            'clip': clip_source,
+            'vae': vae_source,
+            'positive': ['51', 0],
+            'negative': ['7', 0],
+        },
+        'class_type': 'DetailerForEach'
+    }
+
+    workflow['54'] = {
+        'inputs': {
+            **_face_base,
+            'image': ['53', 0],   # chains from face A output
+            'segs': ['50', 0],
+            'model': model_source,
+            'clip': clip_source,
+            'vae': vae_source,
+            'positive': ['52', 0],
+            'negative': ['7', 0],
+        },
+        'class_type': 'DetailerForEach'
+    }
+
+    # Rewire hand detailer: image input from last face detailer instead of old node 11
+    if '13' in workflow:
+        workflow['13']['inputs']['image'] = ['54', 0]
+
+    logger.debug("Injected multi-char SEGS detailers (nodes 40-54)")
+
+
+def _prepare_workflow(workflow, character, prompts, checkpoint=None, custom_negative=None, outfit=None, action=None, style=None, detailer=None, scene=None, width=None, height=None, checkpoint_data=None, look=None, fixed_seed=None, character_b=None):
     # 1. Update prompts using replacement to preserve embeddings
     workflow["6"]["inputs"]["text"] = workflow["6"]["inputs"]["text"].replace("{{POSITIVE_PROMPT}}", prompts["main"])
 
     if custom_negative:
-        workflow["7"]["inputs"]["text"] = f"{workflow['7']['inputs']['text']}, {custom_negative}"
+        workflow["7"]["inputs"]["text"] = f"{custom_negative}, {workflow['7']['inputs']['text']}"
 
     if "14" in workflow:
         workflow["14"]["inputs"]["text"] = workflow["14"]["inputs"]["text"].replace("{{FACE_PROMPT}}", prompts["face"])
@@ -289,23 +541,39 @@ def _prepare_workflow(workflow, character, prompts, checkpoint=None, custom_nega
         clip_source = ["19", 1]
         logger.debug("Style/Detailer LoRA: %s @ %s", style_lora_name, _w19)
 
-    # Apply connections to all model/clip consumers
-    workflow["3"]["inputs"]["model"] = model_source
-    workflow["11"]["inputs"]["model"] = model_source
-    workflow["13"]["inputs"]["model"] = model_source
+    # Second character LoRA (Node 20) - for multi-character generation
+    if character_b:
+        char_b_lora_data = character_b.data.get('lora', {})
+        char_b_lora_name = char_b_lora_data.get('lora_name')
+        if char_b_lora_name and "20" in workflow:
+            _w20 = _resolve_lora_weight(char_b_lora_data)
+            workflow["20"]["inputs"]["lora_name"] = char_b_lora_name
+            workflow["20"]["inputs"]["strength_model"] = _w20
+            workflow["20"]["inputs"]["strength_clip"] = _w20
+            workflow["20"]["inputs"]["model"] = model_source
+            workflow["20"]["inputs"]["clip"] = clip_source
+            model_source = ["20", 0]
+            clip_source = ["20", 1]
+            logger.debug("Character B LoRA: %s @ %s", char_b_lora_name, _w20)
 
-    workflow["6"]["inputs"]["clip"] = clip_source
-    workflow["7"]["inputs"]["clip"] = clip_source
-    workflow["11"]["inputs"]["clip"] = clip_source
-    workflow["13"]["inputs"]["clip"] = clip_source
-    workflow["14"]["inputs"]["clip"] = clip_source
-    workflow["15"]["inputs"]["clip"] = clip_source
+    # 3b. Multi-char: inject per-character SEGS detailers (replaces node 11/14)
+    if character_b:
+        _inject_multi_char_detailers(workflow, prompts, model_source, clip_source)
+
+    # Apply connections to all model/clip consumers (conditional on node existence)
+    for nid in ["3", "11", "13"] + _SEGS_DETAILER_NODES:
+        if nid in workflow:
+            workflow[nid]["inputs"]["model"] = model_source
+
+    for nid in ["6", "7", "11", "13", "14", "15"] + _SEGS_PROMPT_NODES:
+        if nid in workflow:
+            workflow[nid]["inputs"]["clip"] = clip_source
 
     # 4. Randomize seeds (or use a fixed seed for reproducible batches like Strengths Gallery)
     gen_seed = fixed_seed if fixed_seed is not None else random.randint(1, 10**15)
-    workflow["3"]["inputs"]["seed"] = gen_seed
-    if "11" in workflow: workflow["11"]["inputs"]["seed"] = gen_seed
-    if "13" in workflow: workflow["13"]["inputs"]["seed"] = gen_seed
+    for nid in ["3", "11", "13"] + _SEGS_DETAILER_NODES:
+        if nid in workflow:
+            workflow[nid]["inputs"]["seed"] = gen_seed
 
     # 5. Set image dimensions
     if "5" in workflow:
@@ -321,7 +589,7 @@ def _prepare_workflow(workflow, character, prompts, checkpoint=None, custom_nega
     # 7. Sync sampler/scheduler from main KSampler to adetailer nodes
     sampler_name = workflow["3"]["inputs"].get("sampler_name")
     scheduler    = workflow["3"]["inputs"].get("scheduler")
-    for node_id in ["11", "13"]:
+    for node_id in ["11", "13"] + _SEGS_DETAILER_NODES:
         if node_id in workflow:
             if sampler_name:
                 workflow[node_id]["inputs"]["sampler_name"] = sampler_name