Add latent buffer stabilization to enhancer

Introduces a latent buffer mechanism to ComfyAdaptiveDetailEnhancer25 for improved multi-iteration stability, including EMA, anchor rebasing, and masked injection. Updates presets to enable and tune latent buffer parameters, and adjusts detail and mid-frequency settings for better results. Minor README clarification and workflow file update.

README.md

@@ -73,7 +73,7 @@ Photo Dog
  
 
 
-## Install (ComfyUI 0.3.60, tested on this version)
+## Install (ComfyUI >=0.3.60)
 Preparing:
 I recomend update pytorch version: 2.8.0+cu129.
 1. PyTorch install: `pip install torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu129`
@@ -89,7 +89,7 @@ Next:
 5. Workflows
 Folder `workflows/` contains ready-to-use graphs:
 - `mg_SuperSimple-Workflow.json` — one-node pipeline (2/3/4 steps) with presets
-- `mg_Easy-Workflow.json` — the same logic built from individual Easy nodes
+- `mg_Easy-Workflow.json` — the same logic built from individual Easy nodes.
 You can save this workflow to ComfyUI `ComfyUI\user\default\workflows`
 6. Restart ComfyUI. Nodes appear under the "MagicNodes" categories.
 

mod/easy/mg_cade25_easy.py

@@ -2145,6 +2145,15 @@ class ComfyAdaptiveDetailEnhancer25:
         clipseg_blend = str(pv("clipseg_blend", clipseg_blend))
         clipseg_ref_gate = bool(pv("clipseg_ref_gate", clipseg_ref_gate))
         clipseg_ref_threshold = float(pv("clipseg_ref_threshold", clipseg_ref_threshold))
+        # Latent buffer (internal-only; configured via presets)
+        latent_buffer = bool(pv("latent_buffer", True))
+        lb_inject = float(pv("lb_inject", 0.25))
+        lb_ema = float(pv("lb_ema", 0.75))
+        lb_every = int(pv("lb_every", 1))
+        lb_anchor_every = int(pv("lb_anchor_every", 6))
+        lb_masked = bool(pv("lb_masked", True))
+        lb_rebase_thresh = float(pv("lb_rebase_thresh", 0.10))
+        lb_rebase_rate = float(pv("lb_rebase_rate", 0.25))
         polish_enable = bool(pv("polish_enable", polish_enable))
         polish_keep_low = float(pv("polish_keep_low", polish_keep_low))
         polish_edge_lock = float(pv("polish_edge_lock", polish_edge_lock))
@@ -2258,6 +2267,8 @@ class ComfyAdaptiveDetailEnhancer25:
         try:
             with torch.inference_mode():
                 __cade_noop = 0  # ensure non-empty with-block
+                # Latent buffer runtime state
+                lb_state = {"z_ema": None, "anchor": None, "drift_last": None, "ref_dist_last": None}
 
                 # Preflight: reset sticky state and build external masks once (CPU-pinned)
                 try:
@@ -2521,6 +2532,31 @@ class ComfyAdaptiveDetailEnhancer25:
                     except Exception:
                         pass
 
+                    # Latent buffer: pre-sampling injection
+                    if bool(latent_buffer) and (iterations > 1) and (i > 0) and (i % max(1, lb_every) == 0):
+                        try:
+                            z = current_latent["samples"]
+                            if lb_state["z_ema"] is None or tuple(lb_state["z_ema"].shape) != tuple(z.shape):
+                                lb_state["z_ema"] = z.clone().detach()
+                            inj = float(max(0.0, min(0.95, lb_inject)))
+                            # optional boost when far from reference (uses last known distance)
+                            try:
+                                if (ref_embed is not None) and (lb_state.get("ref_dist_last") is not None) and (lb_state["ref_dist_last"] > float(ref_threshold)):
+                                    inj = min(0.95, inj * (1.0 + min(0.5, (lb_state["ref_dist_last"] - float(ref_threshold)) * 0.75)))
+                            except Exception:
+                                pass
+                            z_ema = lb_state["z_ema"]
+                            if bool(lb_masked) and (onnx_mask_last is not None):
+                                m = onnx_mask_last.movedim(-1, 1)
+                                m = F.interpolate(m, size=(z.shape[-2], z.shape[-1]), mode='bilinear', align_corners=False).clamp(0, 1)
+                                m = m.expand(-1, z.shape[1], -1, -1)
+                                z = z * (1.0 - inj * m) + z_ema * (inj * m)
+                            else:
+                                z = z * (1.0 - inj) + z_ema * inj
+                            current_latent["samples"] = z
+                        except Exception:
+                            pass
+
                     # Sampler model prepared once above; reused across iterations (no-op here)
                     sampler_model = sampler_model
 
@@ -2711,6 +2747,32 @@ class ComfyAdaptiveDetailEnhancer25:
                     except Exception:
                         pass
 
+                    # Latent buffer: post-sampling EMA update and drift measure
+                    try:
+                        z_now = current_latent["samples"].detach()
+                        if lb_state["z_ema"] is None or tuple(lb_state["z_ema"].shape) != tuple(z_now.shape):
+                            lb_state["z_ema"] = z_now.clone()
+                            lb_state["anchor"] = z_now.clone()
+                        else:
+                            lb = float(max(0.0, min(0.99, lb_ema)))
+                            lb_state["z_ema"] = lb * lb_state["z_ema"] + (1.0 - lb) * z_now
+                        if int(lb_anchor_every) > 0 and ((i + 1) % int(lb_anchor_every) == 0):
+                            lb_state["anchor"] = lb_state["z_ema"].clone()
+                    except Exception:
+                        pass
+                    # local RMS drift (independent of UI)
+                    try:
+                        _cur = current_latent["samples"]
+                        _prev = prev_samples
+                        if _prev.device != _cur.device:
+                            _prev = _prev.to(_cur.device)
+                        if _prev.dtype != _cur.dtype:
+                            _prev = _prev.to(dtype=_cur.dtype)
+                        _diff = _cur - _prev
+                        lb_state["drift_last"] = float(torch.sqrt(torch.mean(_diff * _diff)).item())
+                    except Exception:
+                        pass
+
                     if bool(latent_compare):
                         _cur = current_latent["samples"]
                         _prev = prev_samples
@@ -2730,6 +2792,24 @@ class ComfyAdaptiveDetailEnhancer25:
                             current_denoise = max(0.20, current_denoise * damp)
                             cfg_damp = 0.997 if damp > 0.9 else 0.99
                             current_cfg = max(1.0, current_cfg * cfg_damp)
+                    # Latent buffer: optional rebase toward anchor on overshoot
+                    if bool(latent_buffer) and (iterations > 1) and (lb_state.get("anchor") is not None):
+                        try:
+                            dval = lb_state.get("drift_last", None)
+                            if (dval is not None) and (dval > float(lb_rebase_thresh)):
+                                rb = float(max(0.0, min(1.0, lb_rebase_rate)))
+                                z = current_latent["samples"]
+                                a = lb_state["anchor"]
+                                if bool(lb_masked) and (onnx_mask_last is not None):
+                                    m = onnx_mask_last.movedim(-1, 1)
+                                    m = F.interpolate(m, size=(z.shape[-2], z.shape[-1]), mode='bilinear', align_corners=False).clamp(0, 1)
+                                    m = m.expand(-1, z.shape[1], -1, -1)
+                                    z = z * (1.0 - rb * m) + a * (rb * m)
+                                else:
+                                    z = z * (1.0 - rb) + a * rb
+                                current_latent["samples"] = z
+                        except Exception:
+                            pass
                     try:
                         del prev_samples
                     except Exception:
@@ -2909,6 +2989,11 @@ class ComfyAdaptiveDetailEnhancer25:
                             if dist > ref_threshold:
                                 current_denoise = max(0.10, current_denoise * 0.9)
                                 current_cfg = max(1.0, current_cfg * 0.99)
+                            # store for next-iter latent buffer injection boost
+                            try:
+                                lb_state["ref_dist_last"] = float(dist)
+                            except Exception:
+                                pass
                         except Exception:
                             pass
 

pressets/mg_cade25.cfg

@@ -21,9 +21,19 @@ apply_ids: true
 clip_clean: true
 latent_compare: true
 
+# latent buffer (internal)
+latent_buffer: true
+lb_inject: 0.25
+lb_ema: 0.75
+lb_every: 1
+lb_anchor_every: 6
+lb_masked: true
+lb_rebase_thresh: 0.10
+lb_rebase_rate: 0.25
+
 # detail controls
-#ids_strength: 0.35
-ids_strength: 0.85
+#ids_strength: 0.85
+ids_strength: 1.45
 upscale_method: lanczos
 scale_by: 1.0
 scale_delta: -0.15
@@ -137,15 +147,26 @@ apply_ids: true
 clip_clean: true
 latent_compare: true
 
+# latent buffer (internal)
+latent_buffer: true
+lb_inject: 0.25
+lb_ema: 0.75
+lb_every: 1
+lb_anchor_every: 6
+lb_masked: true
+lb_rebase_thresh: 0.10
+lb_rebase_rate: 0.25
+
 # detail controls
-ids_strength: 0.60
+#ids_strength: 0.60
+ids_strength: 1.25
 upscale_method: lanczos
 scale_by: 1.30
 scale_delta: 0.15
 noise_offset: 0.09
 threshold: 1.000
 Sharpnes_strenght: 0.081
-accumulation: fp32+fp32
+accumulation: fp16+fp16
 
 # reference clean
 reference_clean: true
@@ -217,9 +238,9 @@ polish_keep_low_ramp: 0.20
 # mid-frequency stabilizer (hands/objects scale)
 midfreq_enable: true
 #midfreq_gain: 0.15
-midfreq_gain: 0.65
-midfreq_sigma_lo: 0.90
-midfreq_sigma_hi: 2.10
+midfreq_gain: 0.28
+midfreq_sigma_lo: 0.85
+midfreq_sigma_hi: 2.40
 
 # QSilk-AQClip-Lite (adaptive latent clipping)
 aqclip_enable: true
@@ -254,8 +275,19 @@ apply_ids: true
 clip_clean: true
 latent_compare: true
 
+# latent buffer (internal)
+latent_buffer: true
+lb_inject: 0.25
+lb_ema: 0.75
+lb_every: 1
+lb_anchor_every: 6
+lb_masked: true
+lb_rebase_thresh: 0.10
+lb_rebase_rate: 0.25
+
 # detail controls
-ids_strength: 0.25
+#ids_strength: 0.25
+ids_strength: 0.55
 upscale_method: lanczos
 scale_by: 1.20
 scale_delta: 0.10
@@ -274,7 +306,6 @@ ref_cooldown: 2
 
 # guidance
 guidance_mode: ZeResFDG
-#rescale_multiplier: 0.75
 rescale_multiplier: 0.75
 momentum_beta: 0.15
 cfg_curve: 0.85
@@ -351,16 +382,16 @@ aq_attn: true
 # core
 seed: 0
 control_after_generate: randomize
-steps: 15
+steps: 16
 cfg: 5.1
 #0.75
-denoise: 0.54
+denoise: 0.49
 sampler_name: ddim
 scheduler: MGHybrid
 iterations: 2
 steps_delta: 2.00
-cfg_delta: 0.03
-denoise_delta: 0.0500
+cfg_delta: 1.00
+denoise_delta: 0.10
 
 # Smart seed toggle (disable for this step)
 smart_seed_enable: false
@@ -372,6 +403,16 @@ apply_ids: true
 clip_clean: true
 latent_compare: true
 
+# latent buffer (internal)
+latent_buffer: true
+lb_inject: 0.25
+lb_ema: 0.75
+lb_every: 1
+lb_anchor_every: 6
+lb_masked: true
+lb_rebase_thresh: 0.10
+lb_rebase_rate: 0.25
+
 # detail controls
 ids_strength: 0.30
 upscale_method: lanczos
@@ -391,7 +432,7 @@ ref_cooldown: 2
 
 # guidance
 guidance_mode: ZeResFDG
-rescale_multiplier: 0.90
+rescale_multiplier: 0.85
 momentum_beta: 0.15
 cfg_curve: 0.60
 perp_damp: 0.85
@@ -410,7 +451,7 @@ zero_init_steps: 0
 fdg_low: 0.35
 fdg_high: 0.7
 fdg_sigma: 1.20
-ze_res_zero_steps: 8
+ze_res_zero_steps: 10
 ze_adaptive: true
 ze_r_switch_hi: 0.85
 ze_r_switch_lo: 0.25
@@ -452,7 +493,7 @@ polish_keep_low_ramp: 0.12
 # mid-frequency stabilizer (hands/objects scale)
 midfreq_enable: true
 midfreq_gain: 0.28
-midfreq_sigma_lo: 0.95
+midfreq_sigma_lo: 0.85
 midfreq_sigma_hi: 2.4
 
 

workflows/mg_Easy-Workflow.json

@@ -1 +1 @@
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S&R":"PreviewImage"},"widgets_values":[]},{"id":7,"type":"SaveImage","pos":[2796.435012229308,813.9383242288463],"size":[645.60324992196,715.7559184038435],"flags":{},"order":31,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":48},{"localized_name":"filename_prefix","name":"filename_prefix","type":"STRING","widget":{"name":"filename_prefix"},"link":null}],"outputs":[],"properties":{},"widgets_values":["ComfyUI"]},{"id":44,"type":"MG_ControlFusion_Easy","pos":[2230.0043967143683,-89.74152781984677],"size":[270,258],"flags":{},"order":24,"mode":0,"inputs":[{"localized_name":"image","name":"image","type":"IMAGE","link":110},{"localized_name":"positive","name":"positive","type":"CONDITIONING","link":157},{"localized_name":"negative","name":"negative","type":"CONDITIONING","link":158},{"localized_name":"control_net","name":"control_net","type":"CONTROL_NET","link":146},{"localized_name":"vae","name":"vae","type":"VAE","link":134},{"localized_name":"preset_step","name":"preset_step","type":"COMBO","widget":{"name":"preset_step"},"link":null},{"localized_name":"custom","name":"custom","type":"BOOLEAN","widget":{"name":"custom"},"link":null},{"localized_name":"enable_depth","name":"enable_depth","type":"BOOLEAN","widget":{"name":"enable_depth"},"link":null},{"localized_name":"enable_pyra","name":"enable_pyra","type":"BOOLEAN","widget":{"name":"enable_pyra"},"link":null},{"localized_name":"edge_alpha","name":"edge_alpha","type":"FLOAT","widget":{"name":"edge_alpha"},"link":null},{"localized_name":"blend_factor","name":"blend_factor","type":"FLOAT","widget":{"name":"blend_factor"},"link":null}],"outputs":[{"localized_name":"positive","name":"positive","type":"CONDITIONING","links":[115,122]},{"localized_name":"negative","name":"negative","type":"CONDITIONING","links":[116,121]},{"localized_name":"Mask_Preview","name":"Mask_Preview","type":"IMAGE","links":[]}],"properties":{"Node 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