Add MG_CleanUp node and improve memory management

Introduces the MG_CleanUp node for aggressive RAM/VRAM cleanup and system cache trimming. Improves safe_encode and safe_decode in mg_cade25_easy.py to better release VRAM and avoid autograd graphs. Updates MG_ControlFusion nodes to clear caches and intermediates after execution. Adjusts blend and edge parameters in mg_controlfusion.cfg for better mask blending. Updates workflow to integrate new cleanup and parameter changes.

README.md

@@ -172,6 +172,7 @@ MagicNodes/
 │  │  ├─ mg_ids.py
 │  │  └─ mg_zesmart_sampler_v1_1.py
 │  │
+│  ├─ mg_cleanup.py
 │  ├─ mg_combinode.py
 │  ├─ mg_latent_adapter.py
 │  ├─ mg_sagpu_attention.py
@@ -226,6 +227,27 @@ Depth models (Depth Anything v2)
   - Smooth start via `polish_start_after` and `polish_keep_low_ramp`
 - `eps_scale` supported for gentle exposure shaping
 
+## MG_CleanUp (final memory cleanup node)
+
+- Purpose: a tiny end-of-graph node that aggressively frees RAM/VRAM and asks the OS to return freed pages. Place it at the very end of a workflow (ideally right after SaveImage).
+- Returns: passthrough `LATENT` and a small `IMAGE` preview (32×32). Works even if you do not wire model/conditioning — cleanup still runs.
+- What it does (two passes: immediate and +150 ms):
+  - CUDA sync, `gc.collect()`, `torch.cuda.empty_cache()` + `ipc_collect()`
+  - Comfy model manager soft cache drop; when `hard_trim=true` also unloads loaded models (will reload on next run)
+  - Drops lightweight LRU/preview caches (when available)
+  - Windows: trims working set (`SetProcessWorkingSetSize` + `EmptyWorkingSet`) and best-effort system cache/standby purge
+  - Linux: `malloc_trim(0)` to release fragmented heap back to the OS
+  - Logs how much RAM/VRAM was freed in each pass
+
+- Inputs:
+  - `hard_trim` (bool): enable the strongest cleanup (unload models, OS-level trims).
+  - `sync_cuda` (bool): synchronize CUDA before cleanup (recommended).
+  - `hires_only_threshold` (int): run only when the latent longest side ≥ threshold; `0` = always.
+
+Notes:
+- Because models are unloaded in `hard_trim`, the next workflow run may take a bit longer to start (models will reload).
+- Use this node only at the end of a graph — it is intentionally aggressive.
+
 ## Depth Anything v2 (vendor)
 - Lives under `vendor/depth_anything_v2`; Apache-2.0 license
 
@@ -247,7 +269,7 @@ Depth models (Depth Anything v2)
   - For run-to-run comparability, hold your sampler seed fixed (in SuperSimple/CADE). SeedLatent itself does not expose a seed; variation is primarily controlled by the sampler seed.
 - Batch friendly: `batch_size>1` produces independent latents of the chosen size.
 
-### Magic Latent Adapter (mg_latent_adapter.py) !experimental!
+## Magic Latent Adapter (mg_latent_adapter.py) !experimental!
 - Purpose: small adapter node that generates or adapts a `LATENT` to match the target model’s latent format (channels and dimensions), including 5D layouts (`NCDHW`) when required. Two modes: `generate` (make a fresh latent aligned to VAE stride) and `adapt` (reshape/channel‑match an existing latent).
 - How it works: relies on Comfy’s `fix_empty_latent_channels` and reads the model’s `latent_format` to adjust channel count; aligns spatial size to VAE stride; handles 4D (`NCHW`) and 5D (`NCDHW`).
 - Experimental: added to ease early, experimental support for FLUX/Qwen‑like models by reducing shape/dimension friction. Still evolving; treat as opt‑in.

__init__.py

@@ -1,4 +1,4 @@
-import os, sys, importlib.util
+import os, sys, importlib.util
 
 # Normalize package name so relative imports work even if loaded by absolute path
 if __name__ != 'MagicNodes':
@@ -25,6 +25,7 @@ from .mod.hard.mg_cade25 import ComfyAdaptiveDetailEnhancer25
 from .mod.hard.mg_ids import IntelligentDetailStabilizer
 from .mod.mg_seed_latent import MagicSeedLatent
 from .mod.mg_sagpu_attention import PatchSageAttention
+from .mod.mg_cleanup import MG_CleanUp
 from .mod.mg_latent_adapter import MagicLatentAdapter
 from .mod.hard.mg_controlfusion import MG_ControlFusion
 from .mod.hard.mg_zesmart_sampler_v1_1 import MG_ZeSmartSampler
@@ -60,6 +61,7 @@ NODE_CLASS_MAPPINGS = {
     "MagicNodesCombiNode": MagicNodesCombiNode,
     "MagicSeedLatent": MagicSeedLatent,
     "PatchSageAttention": PatchSageAttention,
+    "MG_CleanUp": MG_CleanUp,
     "MagicLatentAdapter": MagicLatentAdapter, # experimental
     "MagicUpscaleModule": MagicUpscaleModule,
     "ComfyAdaptiveDetailEnhancer25": ComfyAdaptiveDetailEnhancer25,
@@ -70,12 +72,14 @@ NODE_CLASS_MAPPINGS = {
     "ComfyAdaptiveDetailEnhancer25_Easy": ComfyAdaptiveDetailEnhancer25_Easy,
     "MG_ControlFusion_Easy": MG_ControlFusion_Easy,
     "MG_SuperSimple": MG_SuperSimple,
+
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "MagicNodesCombiNode": "MG_CombiNode",
     "MagicSeedLatent": "MG_SeedLatent",
     "PatchSageAttention": "MG_AccelAttention",
+    "MG_CleanUp": "MG_CleanUp",
     "MagicLatentAdapter": "MG_LatentAdapter", # experimental
     "ComfyAdaptiveDetailEnhancer25": "MG_CADE 2.5",
     "MG_ControlFusion": "MG_ControlFusion",
@@ -97,3 +101,4 @@ __all__ = [
 
 
 
+

mod/easy/mg_cade25_easy.py

@@ -1037,12 +1037,31 @@ def _scheduler_names():
 
 
 def safe_decode(vae, lat, tile=512, ovlp=64):
-    h, w = lat["samples"].shape[-2:]
-    if min(h, w) > 1024:
-        # Increase overlap for ultra-hires to reduce seam artifacts
-        ov = 128 if max(h, w) > 2048 else ovlp
-        return vae.decode_tiled(lat["samples"], tile_x=tile, tile_y=tile, overlap=ov)
-    return vae.decode(lat["samples"])
+    # Ensure we don't build autograd graphs during final decode steps
+    with torch.inference_mode():
+        h, w = lat["samples"].shape[-2:]
+        if min(h, w) > 1024:
+            # Increase overlap for ultra-hires to reduce seam artifacts
+            ov = 128 if max(h, w) > 2048 else ovlp
+            out = vae.decode_tiled(lat["samples"], tile_x=tile, tile_y=tile, overlap=ov)
+        else:
+            out = vae.decode(lat["samples"])
+    # Move to CPU and detach to release VRAM/graphs early
+    try:
+        try:
+            out = out.detach()
+        except Exception:
+            pass
+        try:
+            out = out.to('cpu')
+        except Exception:
+            pass
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
+            torch.cuda.empty_cache()
+    except Exception:
+        pass
+    return out
 
 
 def safe_encode(vae, img, tile=512, ovlp=64):
@@ -1066,10 +1085,17 @@ def safe_encode(vae, img, tile=512, ovlp=64):
         x_nchw = img.movedim(-1, 1)
         x_nchw = F.pad(x_nchw, (0, pad_w, 0, pad_h), mode='replicate')
         x = x_nchw.movedim(1, -1)
-    if min(Ht, Wt) > 1024:
-        ov = 128 if max(Ht, Wt) > 2048 else ovlp
-        return vae.encode_tiled(x[:, :, :, :3], tile_x=tile, tile_y=tile, overlap=ov)
-    return vae.encode(x[:, :, :, :3])
+    with torch.inference_mode():
+        if min(Ht, Wt) > 1024:
+            ov = 128 if max(Ht, Wt) > 2048 else ovlp
+            out = vae.encode_tiled(x[:, :, :, :3], tile_x=tile, tile_y=tile, overlap=ov)
+        else:
+            out = vae.encode(x[:, :, :, :3])
+    try:
+        torch.cuda.synchronize() if torch.cuda.is_available() else None
+    except Exception:
+        pass
+    return out
     
 
 

mod/easy/mg_controlfusion_easy.py

@@ -7,6 +7,7 @@ import numpy as np
 
 import comfy.model_management as model_management
 from .preset_loader import get as load_preset
+from ..hard.mg_upscale_module import clear_gpu_and_ram_cache
 
 
 _DEPTH_INIT = False
@@ -568,6 +569,11 @@ class MG_ControlFusion:
         # Apply visualization brightness only for preview
         prev = (prev * float(mask_brightness)).clamp(0.0, 1.0)
         prev = prev.unsqueeze(-1).repeat(1,1,3).to(device=dev, dtype=dtype).unsqueeze(0)
+        # Best-effort cleanup of caches to avoid sticky VRAM/RAM after node finishes
+        try:
+            clear_gpu_and_ram_cache()
+        except Exception:
+            pass
         return (pos_out, neg_out, prev)
 
 

mod/hard/mg_controlfusion.py

@@ -6,6 +6,7 @@ import torch.nn.functional as F
 import numpy as np
 
 import comfy.model_management as model_management
+from .mg_upscale_module import clear_gpu_and_ram_cache
 
 
 _DEPTH_INIT = False
@@ -573,4 +574,16 @@ class MG_ControlFusion:
         # Apply visualization brightness only for preview
         prev = (prev * float(mask_brightness)).clamp(0.0, 1.0)
         prev = prev.unsqueeze(-1).repeat(1,1,3).to(device=dev, dtype=dtype).unsqueeze(0)
+        # Best-effort cleanup of heavy intermediates and caches after node finishes
+        try:
+            depth = None
+            edges = None
+            fused = None
+            hint = None
+        except Exception:
+            pass
+        try:
+            clear_gpu_and_ram_cache()
+        except Exception:
+            pass
         return (pos_out, neg_out, prev)

mod/mg_cleanup.py

@@ -0,0 +1,319 @@
+import os
+import gc
+import time
+import platform
+import ctypes
+from ctypes import wintypes
+import torch
+import torch.nn.functional as F
+import comfy.model_management as model_management
+import comfy.sample as _sample
+import comfy.samplers as _samplers
+import comfy.utils as _utils
+
+try:
+    import psutil  # type: ignore
+except Exception:  # pragma: no cover
+    psutil = None  # type: ignore
+
+
+def _get_ram_mb() -> float:
+    try:
+        if psutil is not None:
+            p = psutil.Process(os.getpid())
+            rss = float(p.memory_info().rss)
+            try:
+                private = getattr(p.memory_full_info(), "private", None)
+                if isinstance(private, (int, float)) and private > 0:
+                    rss = float(private)
+            except Exception:
+                pass
+            return rss / (1024.0 * 1024.0)
+    except Exception:
+        pass
+    return 0.0
+
+
+def _get_vram_mb_per_device() -> list[tuple[int, float, float]]:
+    out = []
+    try:
+        if torch.cuda.is_available():
+            for d in range(torch.cuda.device_count()):
+                try:
+                    reserved = float(torch.cuda.memory_reserved(d)) / (1024.0 * 1024.0)
+                    allocated = float(torch.cuda.memory_allocated(d)) / (1024.0 * 1024.0)
+                except Exception:
+                    reserved = 0.0
+                    allocated = 0.0
+                out.append((d, reserved, allocated))
+    except Exception:
+        pass
+    return out
+
+
+def _trim_working_set_windows():
+    try:
+        if platform.system().lower().startswith("win"):
+            kernel32 = ctypes.windll.kernel32  # type: ignore[attr-defined]
+            proc = kernel32.GetCurrentProcess()
+            kernel32.SetProcessWorkingSetSize(proc, ctypes.c_size_t(-1), ctypes.c_size_t(-1))
+    except Exception:
+        pass
+
+
+def _enable_win_privileges(names):
+    """Best-effort enable a set of Windows privileges for the current process."""
+    try:
+        if not platform.system().lower().startswith('win'):
+            return False
+        advapi32 = ctypes.windll.advapi32  # type: ignore[attr-defined]
+        kernel32 = ctypes.windll.kernel32  # type: ignore[attr-defined]
+        token = wintypes.HANDLE()
+        TOKEN_ADJUST_PRIVILEGES = 0x20
+        TOKEN_QUERY = 0x8
+        if not advapi32.OpenProcessToken(kernel32.GetCurrentProcess(), TOKEN_ADJUST_PRIVILEGES | TOKEN_QUERY, ctypes.byref(token)):
+            return False
+
+        class LUID(ctypes.Structure):
+            _fields_ = [("LowPart", wintypes.DWORD), ("HighPart", wintypes.LONG)]
+
+        class LUID_AND_ATTRIBUTES(ctypes.Structure):
+            _fields_ = [("Luid", LUID), ("Attributes", wintypes.DWORD)]
+
+        class TOKEN_PRIVILEGES(ctypes.Structure):
+            _fields_ = [("PrivilegeCount", wintypes.DWORD), ("Privileges", LUID_AND_ATTRIBUTES * 1)]
+
+        SE_PRIVILEGE_ENABLED = 0x2
+        success = False
+        for name in names:
+            luid = LUID()
+            if not advapi32.LookupPrivilegeValueW(None, ctypes.c_wchar_p(name), ctypes.byref(luid)):
+                continue
+            tp = TOKEN_PRIVILEGES()
+            tp.PrivilegeCount = 1
+            tp.Privileges[0].Luid = luid
+            tp.Privileges[0].Attributes = SE_PRIVILEGE_ENABLED
+            if advapi32.AdjustTokenPrivileges(token, False, ctypes.byref(tp), 0, None, None):
+                success = True
+        return success
+    except Exception:
+        return False
+
+
+def _system_cache_trim_windows():
+    """Attempt to purge standby/file caches on Windows (requires privileges)."""
+    try:
+        if not platform.system().lower().startswith('win'):
+            return False
+        _enable_win_privileges([
+            'SeIncreaseQuotaPrivilege',
+            'SeProfileSingleProcessPrivilege',
+            'SeDebugPrivilege',
+        ])
+        try:
+            kernel32 = ctypes.windll.kernel32  # type: ignore[attr-defined]
+            SIZE_T = ctypes.c_size_t
+            kernel32.SetSystemFileCacheSize(SIZE_T(-1), SIZE_T(-1), wintypes.DWORD(0))
+        except Exception:
+            pass
+        try:
+            ntdll = ctypes.windll.ntdll  # type: ignore[attr-defined]
+            SystemMemoryListInformation = 0x50
+            MemoryPurgeStandbyList = ctypes.c_ulong(4)
+            ntdll.NtSetSystemInformation(wintypes.ULONG(SystemMemoryListInformation), ctypes.byref(MemoryPurgeStandbyList), ctypes.sizeof(MemoryPurgeStandbyList))
+        except Exception:
+            pass
+        return True
+    except Exception:
+        return False
+
+
+def cleanup_memory(sync_cuda: bool = True, hard_trim: bool = True) -> dict:
+    """Run a best-effort cleanup of RAM/VRAM. Returns stats dict with before/after deltas."""
+    stats: dict = {"ram_before_mb": 0.0, "ram_after_mb": 0.0, "ram_freed_mb": 0.0, "gpu": []}
+    stats["ram_before_mb"] = _get_ram_mb()
+    gpu_before = _get_vram_mb_per_device()
+    try:
+        if sync_cuda and torch.cuda.is_available():
+            torch.cuda.synchronize()
+    except Exception:
+        pass
+    try:
+        import comfy.model_management as mm
+        if hasattr(mm, 'soft_empty_cache'):
+            mm.soft_empty_cache()
+    except Exception:
+        pass
+    try:
+        gc.collect()
+    except Exception:
+        pass
+    try:
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
+    except Exception:
+        pass
+    try:
+        time.sleep(0)
+    except Exception:
+        pass
+    if hard_trim:
+        try:
+            import comfy.model_management as mm
+            if hasattr(mm, 'unload_all_models'):
+                mm.unload_all_models()
+        except Exception:
+            pass
+        try:
+            for _ in range(2):
+                time.sleep(0)
+                gc.collect()
+        except Exception:
+            pass
+        try:
+            if hasattr(_utils, 'cleanup_lru_caches'):
+                _utils.cleanup_lru_caches()
+        except Exception:
+            pass
+        try:
+            _trim_working_set_windows()
+            psapi = ctypes.windll.psapi  # type: ignore[attr-defined]
+            kernel32 = ctypes.windll.kernel32  # type: ignore[attr-defined]
+            psapi.EmptyWorkingSet(kernel32.GetCurrentProcess())
+        except Exception:
+            pass
+        try:
+            if platform.system().lower().startswith('linux'):
+                libc = ctypes.CDLL('libc.so.6')
+                libc.malloc_trim(0)
+        except Exception:
+            pass
+        try:
+            _system_cache_trim_windows()
+        except Exception:
+            pass
+    stats["ram_after_mb"] = _get_ram_mb()
+    stats["ram_freed_mb"] = max(0.0, stats["ram_before_mb"] - stats["ram_after_mb"])
+    gpu_after = _get_vram_mb_per_device()
+    device_map = {d: (r, a) for d, r, a in gpu_before}
+    gpu_stats = []
+    for d, r_after, a_after in gpu_after:
+        r_before, a_before = device_map.get(d, (0.0, 0.0))
+        gpu_stats.append({
+            "device": d,
+            "reserved_before_mb": r_before,
+            "reserved_after_mb": r_after,
+            "reserved_freed_mb": max(0.0, r_before - r_after),
+            "allocated_before_mb": a_before,
+            "allocated_after_mb": a_after,
+            "allocated_freed_mb": max(0.0, a_before - a_after),
+        })
+    stats["gpu"] = gpu_stats
+    return stats
+
+
+class MG_CleanUp:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "samples": ("LATENT", {}),
+            },
+            "optional": {
+                "hard_trim": ("BOOLEAN", {"default": True, "tooltip": "Aggressively free RAM/VRAM and ask OS to return pages to the system."}),
+                "sync_cuda": ("BOOLEAN", {"default": True, "tooltip": "Synchronize CUDA before cleanup to flush pending kernels."}),
+                "hires_only_threshold": ("INT", {"default": 0, "min": 0, "max": 16384, "step": 64, "tooltip": "Apply only when latent longest side >= threshold (0 == always)."}),
+            }
+        }
+
+    RETURN_TYPES = ("LATENT", "IMAGE")
+    RETURN_NAMES = ("samples", "Preview")
+    FUNCTION = "apply"
+    CATEGORY = "MagicNodes"
+
+    def apply(self, samples, hard_trim=True, sync_cuda=True, hires_only_threshold=0,
+              model=None, positive=None, negative=None, vae=None):
+        img_prev = None
+        try:
+            if (model is not None) and (positive is not None) and (negative is not None) and (vae is not None):
+                lat = samples.get("samples", None)
+                if lat is not None and isinstance(lat, torch.Tensor) and lat.ndim == 4:
+                    z = lat
+                    B, C, H, W = z.shape
+                    target = 32
+                    z_ds = z if (H == target and W == target) else F.interpolate(z, size=(target, target), mode='bilinear', align_corners=False)
+                    lat_img = _sample.fix_empty_latent_channels(model, z_ds) if hasattr(_sample, 'fix_empty_latent_channels') else z_ds
+                    batch_inds = samples.get("batch_index", None)
+                    noise = _sample.prepare_noise(lat_img, int(0), batch_inds)
+                    steps = 1
+                    out = _sample.sample(
+                        model, noise, int(steps), float(1.0), "ddim", "normal",
+                        positive, negative, lat_img,
+                        denoise=float(0.10), disable_noise=False, start_step=None, last_step=None,
+                        force_full_denoise=False, noise_mask=None, callback=None,
+                        disable_pbar=not _utils.PROGRESS_BAR_ENABLED, seed=int(0)
+                    )
+                    try:
+                        img_prev = vae.decode(out)
+                        if len(img_prev.shape) == 5:
+                            img_prev = img_prev.reshape(-1, img_prev.shape[-3], img_prev.shape[-2], img_prev.shape[-1])
+                    except Exception:
+                        img_prev = None
+        except Exception:
+            img_prev = None
+
+        try:
+            do_cleanup = True
+            try:
+                if int(hires_only_threshold) > 0:
+                    z = samples.get("samples", None)
+                    if z is not None and hasattr(z, "shape") and len(z.shape) >= 4:
+                        _, _, H, W = z.shape
+                        if max(int(H), int(W)) < int(hires_only_threshold):
+                            do_cleanup = False
+            except Exception:
+                pass
+            if do_cleanup:
+                print("=== CleanUP RAM and GPU ===")
+                stats = cleanup_memory(sync_cuda=bool(sync_cuda), hard_trim=bool(hard_trim))
+                try:
+                    print(f"RAM freed: {stats['ram_freed_mb']:.1f} MB (before {stats['ram_before_mb']:.1f} -> after {stats['ram_after_mb']:.1f})")
+                except Exception:
+                    pass
+                try:
+                    for g in stats.get("gpu", []):
+                        print(
+                            f"GPU{g['device']}: reserved freed {g['reserved_freed_mb']:.1f} MB ( {g['reserved_before_mb']:.1f} -> {g['reserved_after_mb']:.1f} ), "
+                            f"allocated freed {g['allocated_freed_mb']:.1f} MB ( {g['allocated_before_mb']:.1f} -> {g['allocated_after_mb']:.1f} )"
+                        )
+                except Exception:
+                    pass
+                # Second pass after short delay to catch late releasers
+                try:
+                    time.sleep(0.150)
+                    stats2 = cleanup_memory(sync_cuda=False, hard_trim=bool(hard_trim))
+                    if stats2 and float(stats2.get('ram_freed_mb', 0.0)) > 0.0:
+                        print(f"2nd pass: RAM freed +{stats2['ram_freed_mb']:.1f} MB")
+                    try:
+                        for g in stats2.get('gpu', []):
+                            if float(g.get('reserved_freed_mb', 0.0)) > 0.0 or float(g.get('allocated_freed_mb', 0.0)) > 0.0:
+                                print(f"2nd pass GPU{g['device']}: reserved +{g['reserved_freed_mb']:.1f} MB, allocated +{g['allocated_freed_mb']:.1f} MB")
+                    except Exception:
+                        pass
+                except Exception:
+                    pass
+                print("done.")
+        except Exception:
+            pass
+
+        if img_prev is None:
+            try:
+                device = model_management.intermediate_device() if hasattr(model_management, 'intermediate_device') else 'cpu'
+                img_prev = torch.zeros((1, 32, 32, 3), dtype=torch.float32, device=device)
+            except Exception:
+                img_prev = torch.zeros((1, 32, 32, 3))
+        return (samples, img_prev)
+
+
+

pressets/mg_controlfusion.cfg

@@ -12,14 +12,14 @@ pyra_low: 109
 pyra_high: 215
 pyra_resolution: 1024
 edge_thin_iter: 0
-edge_alpha: 0.65
+edge_alpha: 0.15
 edge_boost: 0.10
 smart_tune: false
 smart_boost: 0.62
 
 # blend & strengths
 blend_mode: normal
-blend_factor: 0.35
+blend_factor: 0.600
 strength_pos: 0.45
 strength_neg: 1.10
 
@@ -65,14 +65,14 @@ pyra_low: 123
 pyra_high: 255
 pyra_resolution: 2048
 edge_thin_iter: 0
-edge_alpha: 0.20
+edge_alpha: 0.10
 edge_boost: 0.15
 smart_tune: false
 smart_boost: 0.84
 
 # blend & strengths
 blend_mode: normal
-blend_factor: 0.300
+blend_factor: 0.800
 strength_pos: 0.45
 strength_neg: 1.10
 
@@ -125,7 +125,7 @@ smart_boost: 0.56
 
 # blend & strengths
 blend_mode: normal
-blend_factor: 0.300
+blend_factor: 0.800
 strength_pos: 0.75
 strength_neg: 1.00
 

workflows/mg_Easy-Workflow.json

@@ -1 +1 @@
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