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LICENSE

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,10 +1,9 @@
 # ComfyUI-RBG-SmartSeedVariance 🌱
 
-<img src="https://img.shields.io/badge/ComfyUI-Compatible-blue?style=for-the-badge" alt="ComfyUI Compatible"><img src="https://img.shields.io/badge/Nodes-1-green?style=for-the-badge" alt="1 Nodes"><img src="https://img.shields.io/badge/Categories-1-orange?style=for-the-badge" alt="1 Category">
+## Advanced Seed Diversity Enhancement for ComfyUI 🪴
 
-## Advanced Seed Diversity Enhancement for ComfyUI ✨
-
-Some generative models (like Z-Image Turbo and Qwen-Image) suffer from **limited seed variance** — changing the seed produces only subtle variations or nearly identical outputs. The **RBG Smart Seed Variance** node solves this by intelligently injecting controlled noise into text embeddings during generation, creating meaningful diversity while preserving your prompt intention.
+Some generative models (like Krea 2, Z-Image Turbo and Qwen-Image) suffer from **limited seed variance & diversity bias**, changing the seed (especially in single-step or Turbo workflows) often produces nearly identical compositions or subtle variations. Conversely, highly sensitive multi-encoder models (like Flux or Wan 2.2) can easily collapse or lose prompt alignment when raw noise is injected.
+The **RBG Smart Seed Variance** node solves this by injecting targeted, mathematically structured noise directly into text embeddings during early generation steps. It creates meaningful diversity, styling variation, and composition shifting while preserving your core prompt intention.
 
 ---
 <img width="3614" height="1600" alt="Screenshot 2025-12-09 140138" src="https://github.com/user-attachments/assets/272a0bb6-d94e-400b-9be0-40df36dc7941" />
@@ -22,18 +21,45 @@ Some generative models (like Z-Image Turbo and Qwen-Image) suffer from **limited
 
 - **Model-Specific Optimization:** Automatic adjustment for your architecture:
 
-  - **Krea2**, **Z-Image Turbo**, **Ernie-Image**, **Qwen-Image**, **Flux (Dev/Schnell)**, **Wan**, **Chroma HD**, **SDXL** and more!
+  - **Krea 2**, **Z-Image Turbo**, **Ernie-Image**, **Qwen-Image**, **Flux (Dev/Schnell)**, **Wan**, **Chroma HD**, **SDXL** and more!
 
-- **25 Direction Shift Patterns:** Apply structured artistic biases instead of pure random noise.
+- **25+ Direction Shift Patterns:** Apply structured artistic biases instead of pure random noise.
 - **7 Spatial Fade Curves:** Control how noise fades across the embedding space.
 - **Flexible Noise Injection Timing:** Control when variance is applied.
 - **Prompt Token Protection:** Preserve specific parts of your prompt from noise.
-
 ---
 <img width="3626" height="1511" alt="Screenshot 2025-12-09 143429" src="https://github.com/user-attachments/assets/9d330fa1-b7b5-48c4-91ef-853dcf9a2f06" />
 
 <img width="3612" height="1508" alt="Screenshot 2025-12-09 151729" src="https://github.com/user-attachments/assets/7c2d5b50-2d75-4d79-ab0d-802a62cb4e26" />
 
+---
+### 🖼️ Output Comparison Grid
+
+> _Same Prompt & Seed — Comparing Standard KSampler vs. Smart Seed Variance across presets._
+
+|                                               **Standard Seed (No Variance)**                                                |                                                   **Balanced Preset**                                                   |                                                   **Creative Preset**                                                   |                                                   **Bold Preset**                                                   |
+| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------: |
+| <img src="https://github.com/user-attachments/assets/00fe795f-f8ec-496e-9a38-51ce132faf1d" width="220" alt="Original Seed"/> | <img src="https://github.com/user-attachments/assets/295c7e3e-e768-48d2-9893-b5e384490dd0" width="220" alt="Balanced"/> | <img src="https://github.com/user-attachments/assets/1226b481-ab7c-4fe2-8829-e0595b1ab71f" width="220" alt="Creative"/> | <img src="https://github.com/user-attachments/assets/a2f3fbe1-91c8-4b8d-ae05-2336b26b598f" width="220" alt="Bold"/> |
+|                                                   _Identical pose & frame_                                                   |                                            _Subtle lighting & detail shifts_                                            |                                             _New angle & background shift_                                              |                                           _Complete structural overhaul_                                            |
+
+
+# 🆕 What's New
+
+- **Ideogram 4.0 support** — a dedicated model preset tuned for Ideogram 4's Qwen3-VL text encoder, paired with a **NEW 🛡️ Latent Noise Guard** direction shift (outlier smoothing + centroid stabilization) to keep noise from tripping the model's built-in safety checks.
+- **NEW 🤪 Facial Dynamics & Asymmetry (Krea2)** — a new direction shift that boosts micro facial expressions on facial-descriptive prompts.
+- **NEW🗿 Visceral Expression & Grit (Krea2)** rebalanced to behave better when stacked with LoRAs at high weight.
+- **⏱️ Variance Schedule (Composition Lock 🔒):** Beyond a flat strength, control how variance evolves across the sampling timeline with 5 modes — `constant`, `decreasing`, `step_cutoff`, `hard_lock`, and `tiered_release` — using `cutoff_step`, `total_steps`, and `cutoff_strength` to map a sampling step to a point on that timeline. Lock composition early while letting fine-detail variance keep going later.
+- **Prompt Token Protection:** Preserve specific parts of your prompt from noise, with five modes:
+  - `First Quarter` / `First Half` / `Last Quarter` / `Last Half` — quick fractional protection
+  - **⚙️ Custom Regions** — type explicit token/range syntax (e.g. `0-5,15-20`)
+  - **🎲 Random Regions** — seeds a procedural protection mask for you
+- **💾 Export / Import Settings:** Save your full widget configuration to a JSON file and reload it later, or share it with the community.
+- **🖱️ Interactive Token Inspector:** A live visualization of the protection mask sits right on the node — click any token to toggle it between protected 🔒 and varied 🎲, hover for a tooltip showing its state, and after a run the bar updates to reflect the actual tokens used (not just a static preview).
+
+https://github.com/user-attachments/assets/84d09d53-187f-4410-a86f-7b1f53da9c89
+
+---
+
 ## 📥 Installation
 
 1.  Clone this repository into your `ComfyUI/custom_nodes` directory:
@@ -104,4 +130,16 @@ Contributions are always welcome! If you have any suggestions, improvements, or
 
 ## License 📜
 
+This project is licensed under the **GNU General Public License v3.0 (GPL-3.0)** — see the [LICENSE](LICENSE) file for the full text.
+ 
+**What this means in practice:**
+- ✅ You can use, study, modify, and redistribute this code — including for commercial ComfyUI workflows.
+- ✅ You can fork it and build on it.
+- ⚠️ If you distribute a **modified version**, you must release your changes under GPL-3.0 too, and make the source available to whoever you distribute it to.
+- ⚠️ You must keep the copyright notice and this license intact, and clearly mark what you changed.
+- ❌ You cannot fold this code into a closed-source or proprietary node pack.
+This matches the license of ComfyUI itself, so it stays fully compatible with the ecosystem this suite is built for.
+ 
+> Prior to [12/11/2025], this repository was released under the MIT License. Code distributed under the old MIT license terms before that date remains available under MIT for anyone who already forked/cloned it then; all new releases and changes are GPL-3.0.
+
 This project is licensed under the MIT License.

nodes/RBG_Smart_Seed_Variance.py

@@ -1,5 +1,22 @@
+# <ONE LINE NAME OF NODE / MODULE, e.g. "RBG Smart Seed Variance">
+# Copyright (C) 2025  Ramon Guthrie
+# SPDX-License-Identifier: GPL-3.0-or-later
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
 """
-RBG Smart Seed Variance 🌱
+RBG Smart Seed Variance 🌱 2.2.2
 A user-friendly ComfyUI node for enhancing seed diversity in Z-Image Turbo and Qwen-Image.
 """
 
@@ -17,7 +34,7 @@ except ImportError:
 class RBG_Smart_Seed_Variance:
     """
     Adds diversity to outputs by injecting noise into text embeddings during early generation steps.
-    Designed for Z-Image Turbo and Qwen-Image which have low seed variance.
+    Applies synchronized noise to both cross-attention tokens and global pooled output vectors.
     """
     
     # Preset configurations: (randomize_percent, strength)
@@ -34,12 +51,13 @@ class RBG_Smart_Seed_Variance:
     # Model-specific adjustments: (strength_multiplier, randomize_multiplier)
     MODEL_ADJUSTMENTS = {
         "⚡ Z-Image Turbo": (1.0, 1.0),      # Baseline - well tested with strength 15-30
-        "📸 Krea2 (SingleStream)": (1.0, 0.95), # Custom preset added for Krea2 models
+        "📸 Krea2 (SingleStream)": (1.2, 1.1),
         "🖼️ Qwen-Image": (1.0, 0.9),         # Similar architecture to Z-Image
         "🔮 Flux (Dev/Schnell)": (0.5, 0.8), # Dual encoder (CLIP + T5) - more sensitive
         "🎨 Chroma HD": (0.05, 0.5),         # Very sensitive - needs strength < 1
-        "🧧 ERNIE-Image": (0.08, 0.45),      # Very sensitive - collapses past Creative. Trained on Baidu corpus.
-        "🖌️ SDXL": (0.6, 0.8),               # Dual CLIP encoder - moderate sensitivity
+        "🧧 ERNIE-Image": (0.08, 0.45),      # Very sensitive - collapses past Creative.
+        "🔮 Ideogram 4.0": (0.4, 0.7),       # Qwen3-VL text encoder - high sensitivity, prone to noise-induced safety triggers
+        "🖌️ SDXL": (0.6, 0.7),               # Dual CLIP encoder - moderate sensitivity
         "🎬 Wan2.2": (0.8, 0.8),             # Video model - conservative
         "⚙️ Other": (0.8, 0.8),              # Conservative default
     }
@@ -69,27 +87,29 @@ class RBG_Smart_Seed_Variance:
         "📐 Order": ("compress", 0.8),      # Reduce variance, more consistent
         "🎨 Abstract": ("wave", 1.0),       # Artistic, painterly direction
         "📸 Realistic": ("sharpen", 0.9),   # Push toward photorealism
-        "🌈 Vibrant": ("positive", 1.1),    # More colorful/saturated direction
         "🌑 Moody": ("negative", 1.0),      # Darker, moodier direction
         "💭 Dreamy": ("smooth", 1.1),       # Soft, ethereal direction
-        "🎭 Dynamic Pose": ("spatial", 1.2),  # Varied poses/actions. Block-based noise.
+        "🎭 Dynamic Pose": ("spatial", 1.2),  # Varied poses/actions. Best for high-res ERNIE-Image.
         "🖼️ Composition": ("gradient", 1.0),  # Different layouts/framing
-        "🌎 Diversity": ("diversity", 1.15),  # Simple uniform noise
-        "🧬 Face-Variance Expansion": ("facevar", 1.25),  # Advanced curvature noise
+        "🛡️ Latent Noise Guard (Ideogram 4)": ("latent_noise_guard", 0.65), # Outlier smoothing + centroid stabilization
         "🗿 Visceral Expression & Grit (Krea2)": ("visceral_grit", 1.2), # Custom engineered Krea2 emotional & texture lift
+        "🤪 Facial Dynamics & Asymmetry (Krea2)": ("wild_facial_asymmetry", 1.50), # Tanh-smoothed asymmetric facial expression
+        "🎭 Expression Variance": ("expression", 1.2), # Facial expression focus
+        "🔬 Microscopic": ("microscopic", 1.3), # Ultra-high-freq detail
+        "👥 Group Diversity": ("group_diversity", 1.25), # Multi-modal noise
+        "🧬 Face-Variance Expansion": ("facevar", 1.25),  # Advanced curvature noise
+        "🌌 Cosmic": ("cosmic", 1.2), # Fractal-based noise
+        "🌎 Diversity": ("diversity", 1.15),  # Simple uniform noise
+        "🌈 Vibrant": ("positive", 1.1),    # More colorful/saturated direction
         "🧭 Semantic Drift (Centroid-Safe)": ("semantic_drift", 1.0), # Small constant shift
+        "☠️ Bone Anatomical Coherence": ("anatomical_coherence", 0.6), # Low-frequency smoothing
         "🧱 Structural Lock": ("structural_lock", 1.0), # Decaying noise structure
         "🎞️ Cinematic Framing": ("cinematic_framing", 1.1), # Gradient + center bias
-        "🪶 Texture Lift": ("texture_lift", 1.0), # Mid-range curvature
-        "💡 Studio Portrait": ("spatial", 1.0), # Early-mid structural balance
+        "🧠 Identity Stretch": ("identity_stretch", 1.25), # Mid-range curvature
+        "🪶 Texture Lift": ("texture_lift", 1.0), # High-freq residual only
         "🌸 Natural": ("pink", 1.0), # 1/f noise (Pink Noise)
         "🏞️ Landscape Depth": ("landscape_depth", 1.1), # Depth-based gradient
-        "👥 Group Diversity": ("group_diversity", 1.25), # Multi-modal noise
-        "🎭 Expression Variance": ("expression", 1.2), # Facial expression focus
         "🌊 Motion Blur": ("motion_blur", 1.1), # Directional streak patterns
-        "🔬 Microscopic": ("microscopic", 1.3), # Ultra-high-freq detail
-        "🌌 Cosmic": ("cosmic", 1.2), # Fractal-based noise
-        "☠️ Bone Anatomical Coherence": ("anatomical_coherence", 0.6), # Low-frequency smoothing
     }
     
     # Fade curves for noise application
@@ -153,9 +173,9 @@ class RBG_Smart_Seed_Variance:
                     "display": "slider",
                     "tooltip": "Strength of the direction shift effect (0-200%). 100% = default, 0% = disabled, 200% = double strength."
                 }),
-                "variance_schedule": (["constant", "decreasing", "step_cutoff", "hard_lock", "tiered_release"], {
+                "variance_schedule": (["constant", "decreasing", "step_cutoff", "tiered_release", "hard_lock"], {
                     "default": "constant",
-                    "tooltip": "Composition Lock 🔒: Control how variance changes over time. 'constant'=standard, 'decreasing'=fade out, 'step_cutoff'=block switch, 'hard_lock'=zero variance until step, 'tiered_release'=multi-phase unlock."
+                    "tooltip": "Composition Lock 🔒: Control how variance changes over time. 'constant'=standard, 'decreasing'=fade out, 'step_cutoff'=block switch, 'tiered_release'=multi-phase unlock, 'hard_lock'=zero variance until step."
                 }),
                 "cutoff_step": ("INT", {
                     "default": 8,
@@ -230,15 +250,13 @@ class RBG_Smart_Seed_Variance:
                 "This module ships with ComfyUI — check your installation."
             )
 
-        # Handle disabled mode
         if variance_preset == "❌ Disabled":
             return {"ui": {"protection_data": []}, "result": (conditioning, torch.zeros((1, 64, 64, 3)))}
         
-        # Get preset values or calculate from fine_tune_variance
         preset_config = self.PRESETS.get(variance_preset)
         if preset_config is None:
-            randomize_percent = (fine_tune_variance / 100.0) * 5.0  # 0-5%
-            strength = (fine_tune_variance / 100.0) * 50.0  # 0-50
+            randomize_percent = (fine_tune_variance / 100.0) * 5.0  
+            strength = (fine_tune_variance / 100.0) * 50.0  
         else:
             randomize_percent, strength = preset_config
         
@@ -247,14 +265,12 @@ class RBG_Smart_Seed_Variance:
         randomize_percent *= randomize_mult
         strength *= strength_mult
         
-        # Get direction shift config and apply user strength
         direction_config = self.DIRECTION_SHIFTS.get(direction_shift, None)
         if direction_config is not None:
             pattern, preset_mult = direction_config
             user_mult = shift_strength / 100.0
             direction_config = (pattern, preset_mult * user_mult)
 
-        # Create noisy conditioning
         noisy_conditioning = []
         protection_masks_for_ui = []
         heatmap_tensor = torch.zeros((1, 64, 64, 3))
@@ -267,7 +283,6 @@ class RBG_Smart_Seed_Variance:
             cond_tensor = cond[0]
             cond_dict = cond[1].copy() if len(cond) > 1 else {}
             
-            # Determine number of tokens from tensor shape
             if len(cond_tensor.shape) == 3:
                 num_tokens = cond_tensor.shape[1]
             elif len(cond_tensor.shape) == 2:
@@ -276,7 +291,6 @@ class RBG_Smart_Seed_Variance:
                 noisy_conditioning.append(cond)
                 continue
             
-            # Generate protection mask based on mode
             if protect_mode == "⚙️ Custom Regions":
                 protection_mask = self._parse_protection_regions(protect_regions, num_tokens)
             elif protect_mode == "🎲 Random Regions":
@@ -284,15 +298,12 @@ class RBG_Smart_Seed_Variance:
             else:
                 protection_mask = self._legacy_protection_to_mask(protect_mode, num_tokens)
             
-            # Match target chunk
             current_target = self._pick_target_chunk(target_vibe, i, cond_tensor)
-
             pattern = direction_config[0] if direction_config is not None else "random"
 
-            # Tweak 4: Apply base conditioning layer rebalancing to boost expression signals in base prompt
             rebalanced_cond_tensor = self._apply_base_rebalance(cond_tensor, pattern, model_type)
 
-            # Apply noise with rebalanced tensor
+            # Perturb Cross-Attention Tensor
             modified_tensor = self._apply_noise(
                 rebalanced_cond_tensor,
                 randomize_percent,
@@ -306,7 +317,13 @@ class RBG_Smart_Seed_Variance:
                 model_type=model_type,
             )
             
-            # Generate Heatmap for first conditioning chunk
+            # Perturb Pooled Output Vector (Crucial for SDXL global composition)
+            if "pooled_output" in cond_dict and isinstance(cond_dict["pooled_output"], torch.Tensor):
+                cond_dict["pooled_output"] = self._apply_noise_to_pooled(
+                    cond_dict["pooled_output"], randomize_percent, strength, direction_config,
+                    seed + i + 777, model_type=model_type
+                )
+
             if i == 0:
                 diff = (modified_tensor - cond_tensor).norm(dim=-1)
                 max_diff = diff.max()
@@ -316,7 +333,6 @@ class RBG_Smart_Seed_Variance:
                 heatmap_row = diff.view(1, 1, viz_width, 1).expand(1, 64, viz_width, 3)
                 heatmap_tensor = torch.nn.functional.interpolate(heatmap_row.permute(0, 3, 1, 2), size=(64, 512), mode='nearest').permute(0, 2, 3, 1)
             
-            # Store protection mask for UI
             if hasattr(protection_mask, 'tolist'):
                 mask_list = (protection_mask.cpu().numpy().tolist()
                              if protection_mask.device.type != 'cpu'
@@ -326,7 +342,6 @@ class RBG_Smart_Seed_Variance:
             else:
                 mask_list = []
 
-            # Serialisable metadata
             cond_dict["rbg_variance_fade_curve"] = fade_curve
             cond_dict["rbg_variance_applied"] = True
             cond_dict["rbg_direction_shift"] = direction_shift
@@ -338,7 +353,7 @@ class RBG_Smart_Seed_Variance:
             protection_masks_for_ui.append(mask_list)
             noisy_conditioning.append((modified_tensor, cond_dict))
         
-        # --- Composition Lock Logic ---
+        # --- Composition Lock Schedules ---
         if variance_schedule != "constant":
             cutoff_percent = min(1.0, max(0.0, cutoff_step / total_steps))
             
@@ -363,7 +378,6 @@ class RBG_Smart_Seed_Variance:
             elif variance_schedule == "decreasing":
                 num_segments = 5
                 new_conditioning = []
-                
                 for i in range(num_segments):
                     seg_start = (i / num_segments) * cutoff_percent
                     seg_end = ((i + 1) / num_segments) * cutoff_percent
@@ -432,7 +446,6 @@ class RBG_Smart_Seed_Variance:
                     )
                 return {"ui": {"protection_data": protection_masks_for_ui}, "result": (new_conditioning, heatmap_tensor)}
 
-        # --- Standard Noise Injection Logic ---
         if noise_injection == "🚫 None" or noise_injection == "All Steps":
             return {"ui": {"protection_data": protection_masks_for_ui}, "result": (noisy_conditioning, heatmap_tensor)}
         
@@ -484,16 +497,21 @@ class RBG_Smart_Seed_Variance:
                 protection_mask = self._legacy_protection_to_mask(protect_mode, num_tokens)
 
             current_target = self._pick_target_chunk(target_vibe, i, cond_tensor)
-
             pattern = direction_config[0] if direction_config is not None else "random"
 
-            # Tweak 4: Apply base conditioning layer rebalancing
             rebalanced_cond_tensor = self._apply_base_rebalance(cond_tensor, pattern, model_type)
 
             modified_tensor = self._apply_noise(
                 rebalanced_cond_tensor, randomize_percent, strength, protection_mask,
                 direction_config, current_target, fade_curve, seed + i, vibe_blend, model_type=model_type
             )
+
+            if "pooled_output" in cond_dict and isinstance(cond_dict["pooled_output"], torch.Tensor):
+                cond_dict["pooled_output"] = self._apply_noise_to_pooled(
+                    cond_dict["pooled_output"], randomize_percent, strength, direction_config,
+                    seed + i + 777, model_type=model_type
+                )
+
             scaled_conditioning.append((modified_tensor, cond_dict))
         return scaled_conditioning
     
@@ -588,31 +606,58 @@ class RBG_Smart_Seed_Variance:
         if embed_dim % 12 != 0:
             return cond_tensor
             
-        # Determine base multipliers for Krea2 to boost expressions/textures
         if pattern == "visceral_grit":
-            # Composition (1-3) neutral; Emotion (4-7) heavily boosted; Detail/Grit (8-12) heavily boosted
-            base_multipliers = [1.0, 1.0, 1.0, 2.2, 2.5, 2.5, 2.2, 1.8, 2.8, 2.5, 3.2, 1.8]
+            base_multipliers = [1.0, 1.0, 1.0, 1.8, 2.2, 2.2, 1.8, 1.4, 2.5, 2.2, 2.8, 1.4]
+        elif pattern == "wild_facial_asymmetry":
+            base_multipliers = [1.05, 1.05, 1.10, 1.60, 2.00, 1.80, 2.20, 2.20, 3.20, 2.40, 1.80, 1.10]
         elif pattern == "expression":
-            # Boost facial expression / emotional layers
-            base_multipliers = [1.0, 1.0, 1.0, 2.5, 2.8, 2.8, 2.2, 1.0, 1.0, 1.0, 1.0, 1.0]
+            base_multipliers = [1.0, 1.0, 1.0, 2.0, 2.4, 2.4, 3.2, 1.0, 1.0, 1.0, 1.0, 1.0]
         else:
             return cond_tensor
             
         band_width = embed_dim // 12
         band_tensor = torch.tensor(base_multipliers, dtype=cond_tensor.dtype, device=cond_tensor.device)
-        band_tensor = band_tensor.repeat_interleave(band_width) # [embed_dim]
+        band_tensor = band_tensor.repeat_interleave(band_width)
         
         return cond_tensor * band_tensor
 
+    def _apply_noise_to_pooled(self, pooled_tensor, randomize_percent, strength, direction_config, seed, model_type="⚙️ Other"):
+        """
+        Apply proportional variance noise to SDXL global pooled_output vector.
+        """
+        if pooled_tensor is None or not isinstance(pooled_tensor, torch.Tensor):
+            return pooled_tensor
+            
+        modified = pooled_tensor.clone()
+        ref_std = torch.std(modified).detach().clamp(min=1e-5)
+        generator = torch.Generator(device=pooled_tensor.device)
+        generator.manual_seed(seed)
+        
+        pattern = direction_config[0] if direction_config is not None else "random"
+        dir_mult = direction_config[1] if direction_config is not None else 1.0
+        
+        num_values = modified.numel()
+        noise = self._generate_directional_noise(num_values, pattern, strength * dir_mult, generator, pooled_tensor.device)
+        noise = noise.view_as(modified)
+        
+        noise_std = torch.std(noise).detach().clamp(min=1e-6)
+        scale_factor = (strength / 35.0) * ref_std
+        scaled_noise = (noise / noise_std) * scale_factor
+        scaled_noise = torch.clamp(scaled_noise, -ref_std * 1.5, ref_std * 1.5)
+        
+        return modified + scaled_noise
+
     def _apply_noise(self, tensor, randomize_percent, strength, protection_mask,
                      direction_config, target_tensor, fade_curve, seed, vibe_blend=0.5, model_type="⚙️ Other"):
         """
-        Apply noise to a fraction of the tensor values, optionally with directional bias and fade curve.
+        Apply noise to cross-attention tokens with scale relative to embedding standard deviation.
         """
         modified = tensor.clone()
         generator = torch.Generator(device=tensor.device)
         generator.manual_seed(seed)
         
+        ref_std = torch.std(modified).detach().clamp(min=1e-5)
+
         if direction_config is not None:
             pattern, dir_multiplier = direction_config
             strength *= dir_multiplier
@@ -655,7 +700,6 @@ class RBG_Smart_Seed_Variance:
                         else:
                             noise = vibe_noise
 
-                        # Tweak 2: Krea 2 Band-Aware Noise scaling (3D target vibe path)
                         if model_type == "📸 Krea2 (SingleStream)" and embed_dim % 12 == 0:
                             band_width = embed_dim // 12
                             if pattern == "spatial":
@@ -675,6 +719,10 @@ class RBG_Smart_Seed_Variance:
                         full_envelope = token_envelope.repeat_interleave(embed_dim).repeat(batch_size)
                         noise = noise * full_envelope
 
+                        noise_std = torch.std(noise).detach().clamp(min=1e-6)
+                        noise = (noise / noise_std) * (strength / 35.0) * ref_std
+                        noise = torch.clamp(noise, -ref_std * 1.5, ref_std * 1.5)
+
                         modifiable_values += noise
                         modified[batch_mask] = modifiable_values
 
@@ -687,7 +735,6 @@ class RBG_Smart_Seed_Variance:
                         full_envelope   = token_envelope.repeat_interleave(embed_dim).repeat(batch_size)
                         indices         = torch.randperm(total_values, generator=generator, device=tensor.device)[:num_to_modify]
                         
-                        # Tweak 2: Krea 2 Band-Aware Noise scaling (3D random path)
                         if model_type == "📸 Krea2 (SingleStream)" and embed_dim % 12 == 0:
                             band_width = embed_dim // 12
                             if pattern == "spatial":
@@ -705,7 +752,12 @@ class RBG_Smart_Seed_Variance:
                             band_scale = band_tensor[feature_indices]
                             noise = noise * band_scale
 
-                        noise           = noise * full_envelope[indices]
+                        noise = noise * full_envelope[indices]
+
+                        noise_std = torch.std(noise).detach().clamp(min=1e-6)
+                        noise = (noise / noise_std) * (strength / 35.0) * ref_std
+                        noise = torch.clamp(noise, -ref_std * 1.5, ref_std * 1.5)
+
                         modifiable_values[indices] += noise
                         modified[batch_mask] = modifiable_values
 
@@ -745,7 +797,6 @@ class RBG_Smart_Seed_Variance:
                         else:
                             noise = vibe_noise
 
-                        # Tweak 2: Krea 2 Band-Aware Noise scaling (2D target vibe path)
                         if model_type == "📸 Krea2 (SingleStream)" and embed_dim % 12 == 0:
                             band_width = embed_dim // 12
                             if pattern == "spatial":
@@ -765,6 +816,10 @@ class RBG_Smart_Seed_Variance:
                         full_envelope  = token_envelope.repeat_interleave(embed_dim)
                         noise = noise * full_envelope
 
+                        noise_std = torch.std(noise).detach().clamp(min=1e-6)
+                        noise = (noise / noise_std) * (strength / 35.0) * ref_std
+                        noise = torch.clamp(noise, -ref_std * 1.5, ref_std * 1.5)
+
                         modifiable_values += noise
                         modified[token_mask] = modifiable_values
 
@@ -777,7 +832,6 @@ class RBG_Smart_Seed_Variance:
                         full_envelope   = token_envelope.repeat_interleave(embed_dim)
                         indices         = torch.randperm(total_values, generator=generator, device=tensor.device)[:num_to_modify]
                         
-                        # Tweak 2: Krea 2 Band-Aware Noise scaling (2D random path)
                         if model_type == "📸 Krea2 (SingleStream)" and embed_dim % 12 == 0:
                             band_width = embed_dim // 12
                             if pattern == "spatial":
@@ -795,7 +849,12 @@ class RBG_Smart_Seed_Variance:
                             band_scale = band_tensor[feature_indices]
                             noise = noise * band_scale
 
-                        noise           = noise * full_envelope[indices]
+                        noise = noise * full_envelope[indices]
+
+                        noise_std = torch.std(noise).detach().clamp(min=1e-6)
+                        noise = (noise / noise_std) * (strength / 35.0) * ref_std
+                        noise = torch.clamp(noise, -ref_std * 1.5, ref_std * 1.5)
+
                         modifiable_values[indices] += noise
                         modified[token_mask] = modifiable_values
         

pyproject.toml

@@ -4,18 +4,18 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "comfyui-rbg-smartseedvariance"
-version = "2.0.0"
+version = "2.2.1"
 description = "Advanced seed diversity enhancement for ComfyUI with intelligent noise injection and directional biasing."
 readme = "README.md"
 requires-python = ">=3.8"
-license = { text = "MIT" }
+license = "GPL-3.0-or-later"
 authors = [
   { name = "Ramon Guthrie"},
 ]
 classifiers = [
     "Development Status :: 4 - Beta",
     "Intended Audience :: Developers",
-    "License :: OSI Approved :: MIT License",
+    "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3.8",
     "Programming Language :: Python :: 3.9",