Add CWN and AGC guidance options to CADE25 nodes

Introduces Conditioning Weight Normalization (CWN) and Adaptive Guidance Clipping (AGC) to both easy and hard CADE25 modules, with configurable parameters exposed in the code and presets. Updates mg_cade25.cfg to include default values for these new options and adjusts some scheduler and denoise settings for improved stability and control.

mod/easy/mg_cade25_easy.py

@@ -1322,7 +1322,9 @@ def _wrap_model_with_guidance(model, guidance_mode: str, rescale_multiplier: flo
                                 mahiro_plus_enable: bool = False, mahiro_plus_strength: float = 0.5,
                                 eps_scale_enable: bool = False, eps_scale: float = 0.0,
                                 cfg_sched_type: str = "off", cfg_sched_min: float = 0.0, cfg_sched_max: float = 0.0,
-                                cfg_sched_gamma: float = 1.5, cfg_sched_u_pow: float = 1.0):
+                                cfg_sched_gamma: float = 1.5, cfg_sched_u_pow: float = 1.0,
+                                cwn_enable: bool = True, alpha_c: float = 1.0, alpha_u: float = 1.0,
+                                agc_enable: bool = True, agc_tau: float = 2.8):
 
     """Clone model and attach a cfg mixing function implementing RescaleCFG/FDG, CFGZero*/FD, or hybrid ZeResFDG.
     guidance_mode: 'default' | 'RescaleCFG' | 'RescaleFDG' | 'CFGZero*' | 'CFGZeroFD' | 'ZeResFDG'
@@ -1494,6 +1496,17 @@ def _wrap_model_with_guidance(model, guidance_mode: str, rescale_multiplier: flo
                         return cond * 0.0
                 except Exception:
                     pass
+            # CWN for CFGZero branches: energy align cond/uncond before projection
+            if bool(cwn_enable):
+                try:
+                    _eps = 1e-6
+                    sc = (cond.pow(2).mean(dim=(1, 2, 3), keepdim=True).sqrt() + _eps)
+                    su = (uncond.pow(2).mean(dim=(1, 2, 3), keepdim=True).sqrt() + _eps)
+                    g = 0.5 * (sc + su)
+                    cond = cond * (float(alpha_c) * g / sc)
+                    uncond = uncond * (float(alpha_u) * g / su)
+                except Exception:
+                    pass
             # Project cond onto uncond subspace (batch-wise alpha)
             bsz = cond.shape[0]
             pos_flat = cond.view(bsz, -1)
@@ -1515,6 +1528,14 @@ def _wrap_model_with_guidance(model, guidance_mode: str, rescale_multiplier: flo
             lg = _local_gain_for((cond.shape[-2], cond.shape[-1]))
             if lg is not None:
                 resid = resid * lg.expand(-1, resid.shape[1], -1, -1)
+            # --- AGC for CFGZero branches ---
+            if bool(agc_enable):
+                try:
+                    t = float(max(0.5, agc_tau))
+                    resid = t * torch.tanh(resid / t)
+                except Exception:
+                    pass
+
             noise_pred = uncond * alpha + cond_scale_eff * resid
             return noise_pred
 
@@ -1536,6 +1557,14 @@ def _wrap_model_with_guidance(model, guidance_mode: str, rescale_multiplier: flo
             cond = uncond + delta
         else:
             prev_delta["t"] = delta.detach()
+        # --- Adaptive Guidance Clipping (AGC) ---
+        if bool(agc_enable):
+            try:
+                t = float(max(0.5, agc_tau))
+                delta = t * torch.tanh(delta / t)
+            except Exception:
+                pass
+
         # After momentum: optionally apply FDG and rebuild cond
         if mode == "RescaleFDG":
             # Adaptive low gain if enabled
@@ -1595,8 +1624,22 @@ def _wrap_model_with_guidance(model, guidance_mode: str, rescale_multiplier: flo
         x = x_orig / (sigma_ * sigma_ + 1.0)
         v_cond = ((x - (x_orig - cond)) * (sigma_ ** 2 + 1.0) ** 0.5) / (sigma_)
         v_uncond = ((x - (x_orig - uncond)) * (sigma_ ** 2 + 1.0) ** 0.5) / (sigma_)
-        v_cfg = v_uncond + cond_scale_eff * (v_cond - v_uncond)
-        ro_pos = torch.std(v_cond, dim=(1, 2, 3), keepdim=True)
+
+        # CWN in v-space (more stable than eps-space)
+        if bool(cwn_enable):
+            try:
+                _eps = 1e-6
+                rc = (v_cond.pow(2).mean(dim=(1,2,3), keepdim=True).sqrt() + _eps)
+                ru = (v_uncond.pow(2).mean(dim=(1,2,3), keepdim=True).sqrt() + _eps)
+                v_cond_n = (v_cond / rc) * float(alpha_c)
+                v_uncond_n = (v_uncond / ru) * float(alpha_u)
+            except Exception:
+                v_cond_n, v_uncond_n = v_cond, v_uncond
+        else:
+            v_cond_n, v_uncond_n = v_cond, v_uncond
+
+        v_cfg = v_uncond_n + cond_scale_eff * (v_cond_n - v_uncond_n)
+        ro_pos = torch.std(v_cond_n, dim=(1, 2, 3), keepdim=True)
         ro_cfg = torch.std(v_cfg, dim=(1, 2, 3), keepdim=True).clamp_min(1e-6)
         v_rescaled = v_cfg * (ro_pos / ro_cfg)
         v_final = float(rescale_multiplier) * v_rescaled + (1.0 - float(rescale_multiplier)) * v_cfg
@@ -2198,6 +2241,13 @@ class ComfyAdaptiveDetailEnhancer25:
         cfg_sched_max = float(pv("cfg_sched_max", cfg))
         cfg_sched_gamma = float(pv("cfg_sched_gamma", 1.5))
         cfg_sched_u_pow = float(pv("cfg_sched_u_pow", 1.0))
+
+        # CWN + AGC defaults (hidden in Easy; controlled via presets)
+        cwn_enable = bool(pv("cwn_enable", True))
+        alpha_c = float(pv("alpha_c", 1.0))
+        alpha_u = float(pv("alpha_u", 1.0))
+        agc_enable = bool(pv("agc_enable", True))
+        agc_tau = float(pv("agc_tau", 2.8))
         # Latent buffer (internal-only; configured via presets)
         latent_buffer = bool(pv("latent_buffer", True))
         lb_inject = float(pv("lb_inject", 0.25))
@@ -2450,7 +2500,9 @@ class ComfyAdaptiveDetailEnhancer25:
                       mahiro_plus_enable=bool(muse_blend), mahiro_plus_strength=float(muse_blend_strength),
                       eps_scale_enable=bool(eps_scale_enable), eps_scale=float(eps_scale),
                       cfg_sched_type=str(cfg_sched), cfg_sched_min=float(cfg_sched_min), cfg_sched_max=float(cfg_sched_max),
-                      cfg_sched_gamma=float(cfg_sched_gamma), cfg_sched_u_pow=float(cfg_sched_u_pow)
+                      cfg_sched_gamma=float(cfg_sched_gamma), cfg_sched_u_pow=float(cfg_sched_u_pow),
+                      cwn_enable=bool(cwn_enable), alpha_c=float(alpha_c), alpha_u=float(alpha_u),
+                      agc_enable=bool(agc_enable), agc_tau=float(agc_tau)
                   )
                 # check once more right before the loop starts
                 model_management.throw_exception_if_processing_interrupted()

mod/hard/mg_cade25.py

@@ -968,7 +968,10 @@ def _fdg_energy_fraction(delta: torch.Tensor, sigma: float = 1.0, radius: int =
 def _wrap_model_with_guidance(model, guidance_mode: str, rescale_multiplier: float, momentum_beta: float, cfg_curve: float, perp_damp: float, use_zero_init: bool=False, zero_init_steps: int=0, fdg_low: float = 0.6, fdg_high: float = 1.3, fdg_sigma: float = 1.0, ze_zero_steps: int = 0, ze_adaptive: bool = False, ze_r_switch_hi: float = 0.6, ze_r_switch_lo: float = 0.45, fdg_low_adaptive: bool = False, fdg_low_min: float = 0.45, fdg_low_max: float = 0.7, fdg_ema_beta: float = 0.8, use_local_mask: bool = False, mask_inside: float = 1.0, mask_outside: float = 1.0,
                                 midfreq_enable: bool = False, midfreq_gain: float = 0.0, midfreq_sigma_lo: float = 0.8, midfreq_sigma_hi: float = 2.0,
                                 mahiro_plus_enable: bool = False, mahiro_plus_strength: float = 0.5,
-                                eps_scale_enable: bool = False, eps_scale: float = 0.0):
+                                eps_scale_enable: bool = False, eps_scale: float = 0.0,
+                                # NEW: CWN + AGC for Hard node too
+                                cwn_enable: bool = True, alpha_c: float = 1.0, alpha_u: float = 1.0,
+                                agc_enable: bool = True, agc_tau: float = 2.8):
 
     """Clone model and attach a cfg mixing function implementing RescaleCFG/FDG, CFGZero*/FD, or hybrid ZeResFDG.
     guidance_mode: 'default' | 'RescaleCFG' | 'RescaleFDG' | 'CFGZero*' | 'CFGZeroFD' | 'ZeResFDG'
@@ -1073,6 +1076,17 @@ def _wrap_model_with_guidance(model, guidance_mode: str, rescale_multiplier: flo
                         return cond * 0.0
                 except Exception:
                     pass
+            # CWN for CFGZero branches: align energies before projection
+            if bool(cwn_enable):
+                try:
+                    _eps = 1e-6
+                    sc = (cond.pow(2).mean(dim=(1, 2, 3), keepdim=True).sqrt() + _eps)
+                    su = (uncond.pow(2).mean(dim=(1, 2, 3), keepdim=True).sqrt() + _eps)
+                    g = 0.5 * (sc + su)
+                    cond = cond * (float(alpha_c) * g / sc)
+                    uncond = uncond * (float(alpha_u) * g / su)
+                except Exception:
+                    pass
             # Project cond onto uncond subspace (batch-wise alpha)
             bsz = cond.shape[0]
             pos_flat = cond.view(bsz, -1)
@@ -1115,6 +1129,13 @@ def _wrap_model_with_guidance(model, guidance_mode: str, rescale_multiplier: flo
             cond = uncond + delta
         else:
             prev_delta["t"] = delta.detach()
+        # Adaptive Guidance Clipping on delta (Rescale path)
+        if bool(agc_enable):
+            try:
+                t = float(max(0.5, agc_tau))
+                delta = t * torch.tanh(delta / t)
+            except Exception:
+                pass
         # After momentum: optionally apply FDG and rebuild cond
         if mode == "RescaleFDG":
             # Adaptive low gain if enabled
@@ -1197,8 +1218,20 @@ def _wrap_model_with_guidance(model, guidance_mode: str, rescale_multiplier: flo
         x = x_orig / (sigma_ * sigma_ + 1.0)
         v_cond = ((x - (x_orig - cond)) * (sigma_ ** 2 + 1.0) ** 0.5) / (sigma_)
         v_uncond = ((x - (x_orig - uncond)) * (sigma_ ** 2 + 1.0) ** 0.5) / (sigma_)
-        v_cfg = v_uncond + cond_scale_eff * (v_cond - v_uncond)
-        ro_pos = torch.std(v_cond, dim=(1, 2, 3), keepdim=True)
+        # CWN in v-space for Rescale path (safer than eps-space)
+        if bool(cwn_enable):
+            try:
+                _e = 1e-6
+                rc = (v_cond.pow(2).mean(dim=(1,2,3), keepdim=True).sqrt() + _e)
+                ru = (v_uncond.pow(2).mean(dim=(1,2,3), keepdim=True).sqrt() + _e)
+                v_cond_n = (v_cond / rc) * float(alpha_c)
+                v_uncond_n = (v_uncond / ru) * float(alpha_u)
+            except Exception:
+                v_cond_n, v_uncond_n = v_cond, v_uncond
+        else:
+            v_cond_n, v_uncond_n = v_cond, v_uncond
+        v_cfg = v_uncond_n + cond_scale_eff * (v_cond_n - v_uncond_n)
+        ro_pos = torch.std(v_cond_n, dim=(1, 2, 3), keepdim=True)
         ro_cfg = torch.std(v_cfg, dim=(1, 2, 3), keepdim=True).clamp_min(1e-6)
         v_rescaled = v_cfg * (ro_pos / ro_cfg)
         v_final = float(rescale_multiplier) * v_rescaled + (1.0 - float(rescale_multiplier)) * v_cfg
@@ -1465,6 +1498,13 @@ class ComfyAdaptiveDetailEnhancer25:
                 "cfg_curve": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "S-curve shaping of cond_scale across steps (0=flat)."}),
                 "perp_damp": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Remove a small portion of the component parallel to previous delta (0-1)."}),
 
+                # Conditioning Weight Normalization (CWN) + Adaptive Guidance Clipping (AGC)
+                "cwn_enable": ("BOOLEAN", {"default": True, "tooltip": "Normalize cond/uncond energy to steady CFG mixing."}),
+                "alpha_c": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01}),
+                "alpha_u": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01}),
+                "agc_enable": ("BOOLEAN", {"default": True, "tooltip": "Soft-clip residual guidance to prevent rare spikes."}),
+                "agc_tau": ("FLOAT", {"default": 2.8, "min": 0.5, "max": 6.0, "step": 0.1}),
+
                 # NAG (Normalized Attention Guidance) toggles
                 "use_nag": ("BOOLEAN", {"default": False, "tooltip": "Apply NAG inside CrossAttention (positive branch) during this node."}),
                 "nag_scale": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 50.0, "step": 0.1}),
@@ -1555,8 +1595,9 @@ class ComfyAdaptiveDetailEnhancer25:
                      Sharpnes_strenght=0.300, threshold=0.03, latent_compare=False, accumulation="default",
                      reference_clean=False, reference_image=None, clip_vision=None, ref_preview=224, ref_threshold=0.03, ref_cooldown=1,
                      guidance_mode="RescaleCFG", rescale_multiplier=0.7, momentum_beta=0.0, cfg_curve=0.0, perp_damp=0.0,
-                      use_nag=False, nag_scale=4.0, nag_tau=2.5, nag_alpha=0.25,
-                      aqclip_enable=False, aq_tile=32, aq_stride=16, aq_alpha=2.0, aq_ema_beta=0.8, aq_attn=False,
+                     cwn_enable=True, alpha_c=1.0, alpha_u=1.0, agc_enable=True, agc_tau=2.8,
+                     use_nag=False, nag_scale=4.0, nag_tau=2.5, nag_alpha=0.25,
+                     aqclip_enable=False, aq_tile=32, aq_stride=16, aq_alpha=2.0, aq_ema_beta=0.8, aq_attn=False,
                      use_zero_init=False, zero_init_steps=0,
                      fdg_low=0.6, fdg_high=1.3, fdg_sigma=1.0, ze_res_zero_steps=2,
                      ze_adaptive=False, ze_r_switch_hi=0.60, ze_r_switch_lo=0.45,
@@ -1719,7 +1760,9 @@ class ComfyAdaptiveDetailEnhancer25:
                       fdg_low_adaptive=bool(fdg_low_adaptive), fdg_low_min=float(fdg_low_min), fdg_low_max=float(fdg_low_max), fdg_ema_beta=float(fdg_ema_beta),
                       use_local_mask=False, mask_inside=1.0, mask_outside=1.0,
                       mahiro_plus_enable=bool(muse_blend), mahiro_plus_strength=float(muse_blend_strength),
-                      eps_scale_enable=bool(eps_scale_enable), eps_scale=float(eps_scale)
+                      eps_scale_enable=bool(eps_scale_enable), eps_scale=float(eps_scale),
+                      cwn_enable=bool(cwn_enable), alpha_c=float(alpha_c), alpha_u=float(alpha_u),
+                      agc_enable=bool(agc_enable), agc_tau=float(agc_tau)
                   )
                 # early interruption check before starting the loop
                 try:

pressets/mg_cade25.cfg

@@ -65,6 +65,13 @@ momentum_beta: 0.22
 cfg_curve: 0.85
 perp_damp: 0.80
 
+# CWN/AGC
+cwn_enable: true
+alpha_c: 1.0
+alpha_u: 1.0
+agc_enable: true
+agc_tau: 2.8
+
 # NAG
 use_nag: true
 nag_scale: 4.0
@@ -140,7 +147,7 @@ seed: 0
 control_after_generate: randomize
 steps: 10
 cfg: 6.5
-denoise: 0.56
+denoise: 0.44
 sampler_name: ddim
 scheduler: MGHybrid
 iterations: 2
@@ -199,6 +206,13 @@ momentum_beta: 0.15
 cfg_curve: 0.85
 perp_damp: 0.80
 
+# CWN/AGC
+cwn_enable: true
+alpha_c: 1.0
+alpha_u: 1.0
+agc_enable: true
+agc_tau: 2.8
+
 # NAG
 use_nag: true
 nag_scale: 4.0
@@ -335,6 +349,13 @@ momentum_beta: 0.15
 cfg_curve: 0.85
 perp_damp: 0.80
 
+# CWN/AGC
+cwn_enable: true
+alpha_c: 1.0
+alpha_u: 1.0
+agc_enable: true
+agc_tau: 2.8
+
 # NAG
 use_nag: true
 nag_scale: 4.0
@@ -456,9 +477,9 @@ ref_cooldown: 2
 
 # cfg schedule (internal)
 #cfg_sched: off | cosine | warmup | u
-cfg_sched: warmup
-cfg_sched_min: 5.9
-cfg_sched_max: 7.0
+cfg_sched: cosine
+cfg_sched_min: 3.9
+cfg_sched_max: 8.0
 cfg_sched_gamma: 1.5
 cfg_sched_u_pow: 1.2
 
@@ -470,6 +491,13 @@ momentum_beta: 0.15
 cfg_curve: 0.60
 perp_damp: 0.85
 
+# CWN/AGC
+cwn_enable: true
+alpha_c: 1.0
+alpha_u: 1.0
+agc_enable: true
+agc_tau: 2.2
+
 # NAG
 use_nag: true
 nag_scale: 3.6