BUG FIX and QoL. Fix cancel support to MG nodes (#6)

Implements cooperative cancellation and user interrupt propagation in mg_cade25_easy.py, mg_supersimple_easy.py, mg_cade25.py, and related modules. Adds interruptible sampler and callback wrappers, sentinel handling, and frequent cancel checks throughout long-running loops and workflows. Also improves LoRA selection robustness in mg_combinode.py by handling missing files gracefully. Updates workflow JSON to match new node logic.

mod/easy/mg_cade25_easy.py

@@ -283,7 +283,7 @@ def _clipseg_build_mask(image_bhwc: torch.Tensor,
                 cur = _encode_clip_image(image_bhwc, clip_vision, target_res=224)
                 dist = _clip_cosine_distance(cur, ref_embed)
                 if dist > float(ref_threshold):
-                    # up to +50% gain if сильно уехали
+                    # up to +50% gain if distance exceeds the reference threshold
                     gate = 1.0 + min(0.5, (dist - float(ref_threshold)) * 4.0)
                     m = m * gate
             except Exception:
@@ -1710,7 +1710,7 @@ def _build_cf_edge_mask_from_step(image_bhwc: torch.Tensor, preset_step: str) ->
         edge_alpha = _safe_float(p.get('edge_alpha', 1.0), 1.0)
         # Treat blend_factor as extra gain for edges (depth is not mixed here)
         blend_factor = _safe_float(p.get('blend_factor', 0.02), 0.02)
-        # ControlNet multipliers section — use edge_strength_mul as an additional gain for edge mask
+        # ControlNet multipliers: use edge_strength_mul as an additional gain for the edge mask
         edge_strength_mul = _safe_float(p.get('edge_strength_mul', 1.0), 1.0)
 
         # Build edges with CF PyraCanny
@@ -1901,7 +1901,10 @@ class ComfyAdaptiveDetailEnhancer25:
                      clipseg_gain=1.0, clipseg_blend="fuse", clipseg_ref_gate=False, clipseg_ref_threshold=0.03,
                     polish_enable=False, polish_keep_low=0.4, polish_edge_lock=0.2, polish_sigma=1.0,
                    polish_start_after=1, polish_keep_low_ramp=0.2,
-                    preset_step="Step 1", custom_override=False):
+                     preset_step="Step 1", custom_override=False):
+        # Cooperative cancel before any heavy work
+        model_management.throw_exception_if_processing_interrupted()
+
         # Load base preset for the selected Step. When custom_override is True,
         # visible UI controls (top-level) are kept from UI; hidden ones still come from preset.
         try:
@@ -2016,6 +2019,8 @@ class ComfyAdaptiveDetailEnhancer25:
             pass
 
         image = safe_decode(vae, latent)
+        # allow user cancel right after initial decode
+        model_management.throw_exception_if_processing_interrupted()
 
         tuned_steps, tuned_cfg, tuned_denoise = AdaptiveSamplerHelper().tune(
             image, steps, cfg, denoise)
@@ -2046,7 +2051,10 @@ class ComfyAdaptiveDetailEnhancer25:
                     str(sampler_name), str(scheduler), float(current_cfg), float(current_denoise),
                     base_seed=0, step_tag=step_tag,
                     clip_vision=clip_vision, reference_image=reference_image, clipseg_text=str(clipseg_text))
-        except Exception:
+        except Exception as e:
+            # propagate user cancel; swallow only non-interrupt errors
+            if isinstance(e, model_management.InterruptProcessingException):
+                raise
             pass
 
         # Visual separation and start marker after seed is finalized
@@ -2189,7 +2197,11 @@ class ComfyAdaptiveDetailEnhancer25:
                 clipseg_enable = False
                 # Depth gate cache for micro-detail injection (reuse per resolution)
                 depth_gate_cache = {"size": None, "mask": None}
+                # check once more right before the loop starts
+                model_management.throw_exception_if_processing_interrupted()
                 for i in range(iterations):
+                    # cooperative cancel at the start of each iteration
+                    model_management.throw_exception_if_processing_interrupted()
                     if i % 2 == 0:
                         clear_gpu_and_ram_cache()
 
@@ -2420,7 +2432,9 @@ class ComfyAdaptiveDetailEnhancer25:
                             batch_inds = current_latent.get("batch_index", None)
                             noise = _sample.prepare_noise(lat_img, int(iter_seed), batch_inds)
                             noise_mask = current_latent.get("noise_mask", None)
-                            callback = nodes.latent_preview.prepare_callback(sampler_model, int(current_steps))
+                            callback = _wrap_interruptible_callback(sampler_model, int(current_steps))
+                            # cooperative cancel just before entering sampler
+                            model_management.throw_exception_if_processing_interrupted()
                             disable_pbar = not _utils.PROGRESS_BAR_ENABLED
                             sampler_obj = _samplers.sampler_object(str(sampler_name))
                             samples = _sample.sample_custom(
@@ -2432,16 +2446,29 @@ class ComfyAdaptiveDetailEnhancer25:
                             current_latent = {**current_latent}
                             current_latent["samples"] = samples
                         except Exception as e:
+                            # Before any fallback, propagate user cancel if set
+                            try:
+                                model_management.throw_exception_if_processing_interrupted()
+                            except Exception:
+                                globals()["_MG_CANCEL_REQUESTED"] = False
+                                raise
+                            # Do not swallow user interruption; also check sentinel just in case
+                            if isinstance(e, model_management.InterruptProcessingException) or globals().get("_MG_CANCEL_REQUESTED", False):
+                                globals()["_MG_CANCEL_REQUESTED"] = False
+                                raise
                             # Fallback to original path if anything goes wrong
                             print(f"[CADE2.5][MGHybrid] fallback to common_ksampler due to: {e}")
-                            current_latent, = nodes.common_ksampler(
+                            current_latent, = _interruptible_ksampler(
                                 sampler_model, iter_seed, int(current_steps), current_cfg, sampler_name, _scheduler_names()[0],
                                 positive, negative, current_latent, denoise=current_denoise)
                     else:
-                        current_latent, = nodes.common_ksampler(
+                        current_latent, = _interruptible_ksampler(
                             sampler_model, iter_seed, int(current_steps), current_cfg, sampler_name, scheduler,
                             positive, negative, current_latent, denoise=current_denoise)
 
+                    # cooperative cancel immediately after sampling
+                    model_management.throw_exception_if_processing_interrupted()
+
                     if bool(latent_compare):
                         latent_diff = current_latent["samples"] - prev_samples
                         rms = torch.sqrt(torch.mean(latent_diff * latent_diff))
@@ -2477,6 +2504,8 @@ class ComfyAdaptiveDetailEnhancer25:
                         pass
 
                     image = safe_decode(vae, current_latent)
+                    # and again after decode before post-processing
+                    model_management.throw_exception_if_processing_interrupted()
 
                     # Polish mode: keep global form (low frequencies) from reference while letting details refine
                     if bool(polish_enable) and (i >= int(polish_start_after)):
@@ -2668,6 +2697,16 @@ class ComfyAdaptiveDetailEnhancer25:
                 CURRENT_ONNX_MASK_BCHW = None
             except Exception:
                 pass
+            try:
+                globals()["_MG_CANCEL_REQUESTED"] = False
+                clear_gpu_and_ram_cache()
+            except Exception:
+                pass
+            # best-effort cache cleanup on cancel or error
+            try:
+                clear_gpu_and_ram_cache()
+            except Exception:
+                pass
 
         if apply_ids:
             image, = IntelligentDetailStabilizer().stabilize(image, ids_strength)
@@ -2846,6 +2885,8 @@ def _smart_seed_select(model,
                        step_tag: str | None = None) -> int:
     # Log start of SmartSeed selection
     try:
+        # cooperative cancel before any smart-seed work
+        model_management.throw_exception_if_processing_interrupted()
         try:
             # Visual separation before SmartSeed block
             print("")
@@ -2885,14 +2926,18 @@ def _smart_seed_select(model,
         best_seed = cands[0]
         best_score = -1e9
         for sd in cands:
+            # allow user to cancel between candidates
+            model_management.throw_exception_if_processing_interrupted()
             try:
                 # quick KSampler preview at low steps
                 lat_in = {"samples": latent["samples"].clone()} if isinstance(latent, dict) else latent
-                lat_out, = nodes.common_ksampler(
+                lat_out, = _interruptible_ksampler(
                     model, int(sd), int(probe_steps), float(cfg), str(sampler_name), str(scheduler),
                     positive, negative, lat_in, denoise=float(min(denoise, 0.65))
                 )
                 img = safe_decode(vae, lat_out)
+                # and again right after decode
+                model_management.throw_exception_if_processing_interrupted()
                 # Base score: edge density toward a target + low speckle + balanced exposure
                 ed = _edge_density(img)
                 speck = _speckle_fraction(img)
@@ -2937,7 +2982,11 @@ def _smart_seed_select(model,
                 if score > best_score:
                     best_score = score
                     best_seed = sd
-            except Exception:
+            except Exception as e:
+                # do not swallow user interruption; also honour sentinel
+                if isinstance(e, model_management.InterruptProcessingException) or globals().get("_MG_CANCEL_REQUESTED", False):
+                    globals()["_MG_CANCEL_REQUESTED"] = False
+                    raise
                 continue
 
         # Log end with selected seed
@@ -2949,7 +2998,11 @@ def _smart_seed_select(model,
         except Exception:
             pass
         return int(best_seed & 0xFFFFFFFFFFFFFFFF)
-    except Exception:
+    except Exception as e:
+        if isinstance(e, model_management.InterruptProcessingException) or globals().get("_MG_CANCEL_REQUESTED", False):
+            globals()["_MG_CANCEL_REQUESTED"] = False
+            # propagate cancel to stop the whole prompt cleanly
+            raise
         # Fallback to time-based random
         try:
             import time
@@ -2966,3 +3019,30 @@ def _smart_seed_select(model,
         return fallback_seed
 
 
+def _wrap_interruptible_callback(model, steps):
+    base_cb = nodes.latent_preview.prepare_callback(model, int(steps))
+    def _cb(step, x0, x, total_steps):
+        model_management.throw_exception_if_processing_interrupted()
+        return base_cb(step, x0, x, total_steps)
+    return _cb
+
+def _interruptible_ksampler(model, seed, steps, cfg, sampler_name, scheduler,
+                            positive, negative, latent, denoise=1.0):
+    lat_img = _sample.fix_empty_latent_channels(model, latent["samples"])
+    batch_inds = latent.get("batch_index", None)
+    noise = _sample.prepare_noise(lat_img, int(seed), batch_inds)
+    noise_mask = latent.get("noise_mask", None)
+    callback = _wrap_interruptible_callback(model, int(steps))
+    # cooperative cancel just before sampler entry
+    model_management.throw_exception_if_processing_interrupted()
+    disable_pbar = not _utils.PROGRESS_BAR_ENABLED
+    samples = _sample.sample(
+        model, noise, int(steps), float(cfg), str(sampler_name), str(scheduler),
+        positive, negative, lat_img,
+        denoise=float(denoise), disable_noise=False, start_step=None, last_step=None,
+        force_full_denoise=False, noise_mask=noise_mask, callback=callback,
+        disable_pbar=disable_pbar, seed=int(seed)
+    )
+    out = {**latent}
+    out["samples"] = samples
+    return (out,)

mod/easy/mg_supersimple_easy.py

@@ -1,29 +1,30 @@
 from __future__ import annotations
 
-"""MG_SuperSimple: Orchestrates a 1–4 step pipeline over CF→CADE pairs.
-
-- Step 1: CADE with Step 1 preset. Exception: forces denoise=1.0.
-- Steps 2..N: ControlFusion (CF) with Step N preset → CADE with Step N preset.
-- When custom is True: visible CADE controls (seed/steps/cfg/denoise/sampler/scheduler/clipseg_text)
-  override corresponding Step presets across all steps (except step 1 denoise is always 1.0).
-- When custom is False: all CADE values come from Step presets; node UI values are ignored.
-- CF always uses its Step presets (no extra UI here) to keep the node minimal.
-
-Inputs
-- model/vae/latent/positive/negative: standard Comfy connectors
-- control_net: ControlNet module for CF (required)
-- reference_image/clip_vision: forwarded into CADE (optional)
-
-Outputs
-- (LATENT, IMAGE) from the final executed step
+"""MG_SuperSimple: orchestrates a 1..4 step pipeline over CF -> CADE pairs.
+
+Step 1: CADE with the "Step 1" preset (denoise is forced to 1.0).
+Steps 2..N: ControlFusion (CF) with the "Step N" preset, then CADE with the same "Step N" preset.
+When custom=True: visible CADE controls (seed/steps/cfg/denoise/sampler/scheduler/clipseg_text) override the corresponding Step presets across all steps (Step 1 still uses denoise=1.0).
+When custom=False: CADE values come from Step presets; node UI values are ignored. CF always uses its Step presets (kept minimal here).
+Inputs:
+
+model/vae/latent/positive/negative: standard Comfy connectors
+control_net: ControlNet module for CF (required)
+reference_image/clip_vision: forwarded into CADE (optional)
+Outputs:
+
+(LATENT, IMAGE) from the final executed step
 """
 
+
 import torch
 
 from .mg_cade25_easy import ComfyAdaptiveDetailEnhancer25 as _CADE
 from .mg_controlfusion_easy import MG_ControlFusion as _CF
 from .mg_cade25_easy import _sampler_names as _sampler_names
 from .mg_cade25_easy import _scheduler_names as _scheduler_names
+import comfy.model_management as model_management
+from ..hard.mg_upscale_module import clear_gpu_and_ram_cache
 
 
 class MG_SuperSimple:
@@ -97,10 +98,13 @@ class MG_SuperSimple:
         return pos, neg
 
     def run(self,
-            step_count, custom,
-            model, positive, negative, vae, latent, control_net,
-            seed, steps, cfg, denoise, sampler_name, scheduler, clipseg_text,
-            reference_image=None, clip_vision=None):
+             step_count, custom,
+             model, positive, negative, vae, latent, control_net,
+             seed, steps, cfg, denoise, sampler_name, scheduler, clipseg_text,
+             reference_image=None, clip_vision=None):
+        # Cooperative cancel before any heavy work
+        model_management.throw_exception_if_processing_interrupted()
+
         # Clamp step_count to 1..4
         n = int(max(1, min(4, step_count)))
 
@@ -109,40 +113,48 @@ class MG_SuperSimple:
         cur_pos = positive
         cur_neg = negative
 
-        # Step 1: CADE with Step 1 preset, denoise forced to 1.0
-        denoise_step1 = 1.0
-        lat1, img1 = self._cade(
-            preset_step="Step 1",
-            custom_override=bool(custom),
-            model=model, vae=vae, positive=cur_pos, negative=cur_neg, latent=cur_latent,
-            seed=seed, steps=steps, cfg=cfg, denoise=denoise_step1,
-            sampler_name=sampler_name, scheduler=scheduler,
-            clipseg_text=clipseg_text,
-            reference_image=reference_image, clip_vision=clip_vision,
-        )
-        cur_latent, cur_image = lat1, img1
-
-        # Steps 2..n: CF -> CADE per step
-        for i in range(2, n + 1):
-            # ControlFusion on current image/conds
-            cur_pos, cur_neg = self._cf(
-                preset_step=f"Step {i}",
-                image=cur_image, positive=cur_pos, negative=cur_neg,
-                control_net=control_net, vae=vae,
-            )
-            # CADE with shared controls
-            # If no external reference_image is provided, use the previous step image
-            # so that reference_clean / CLIP-Vision gating can take effect.
-            ref_img = reference_image if (reference_image is not None) else cur_image
-            lat_i, img_i = self._cade(
-                preset_step=f"Step {i}",
+        try:
+            # Step 1: CADE with Step 1 preset, denoise forced to 1.0
+            denoise_step1 = 1.0
+            lat1, img1 = self._cade(
+                preset_step="Step 1",
                 custom_override=bool(custom),
                 model=model, vae=vae, positive=cur_pos, negative=cur_neg, latent=cur_latent,
-                seed=seed, steps=steps, cfg=cfg, denoise=denoise,
+                seed=seed, steps=steps, cfg=cfg, denoise=denoise_step1,
                 sampler_name=sampler_name, scheduler=scheduler,
                 clipseg_text=clipseg_text,
-                reference_image=ref_img, clip_vision=clip_vision,
+                reference_image=reference_image, clip_vision=clip_vision,
             )
-            cur_latent, cur_image = lat_i, img_i
-
-        return (cur_latent, cur_image)
+            cur_latent, cur_image = lat1, img1
+
+            # Steps 2..n: CF -> CADE per step
+            for i in range(2, n + 1):
+                # allow user cancel between steps
+                model_management.throw_exception_if_processing_interrupted()
+                # ControlFusion on current image/conds
+                cur_pos, cur_neg = self._cf(
+                    preset_step=f"Step {i}",
+                    image=cur_image, positive=cur_pos, negative=cur_neg,
+                    control_net=control_net, vae=vae,
+                )
+                # CADE with shared controls
+                # If no external reference_image is provided, use the previous step image
+                # so that reference_clean / CLIP-Vision gating can take effect.
+                ref_img = reference_image if (reference_image is not None) else cur_image
+                lat_i, img_i = self._cade(
+                    preset_step=f"Step {i}",
+                    custom_override=bool(custom),
+                    model=model, vae=vae, positive=cur_pos, negative=cur_neg, latent=cur_latent,
+                    seed=seed, steps=steps, cfg=cfg, denoise=denoise,
+                    sampler_name=sampler_name, scheduler=scheduler,
+                    clipseg_text=clipseg_text,
+                    reference_image=ref_img, clip_vision=clip_vision,
+                )
+                cur_latent, cur_image = lat_i, img_i
+            return (cur_latent, cur_image)
+        finally:
+            # try to free memory on cancel or normal exit
+            try:
+                clear_gpu_and_ram_cache()
+            except Exception:
+                pass

mod/hard/mg_cade25.py

@@ -33,6 +33,9 @@ _CLIPSEG_PROC = None
 _CLIPSEG_DEV = "cpu"
 _CLIPSEG_FORCE_CPU = True  # pin CLIPSeg to CPU to avoid device drift
 
+# Cooperative cancel sentinel: set in callbacks when user interrupts
+_MG_CANCEL_REQUESTED = False
+
 # Per-iteration spatial guidance mask (B,1,H,W) in [0,1]; used by cfg_func when enabled
 # Kept for potential future use with non-ONNX masks (e.g., CLIPSeg/ControlFusion),
 # but not set by this node since ONNX paths are removed.
@@ -1540,7 +1543,16 @@ class ComfyAdaptiveDetailEnhancer25:
                 clipseg_enable = False
                 # Depth gate cache for micro-detail injection (reuse per resolution)
                 depth_gate_cache = {"size": None, "mask": None}
+                # early interruption check before starting the loop
+                try:
+                    model_management.throw_exception_if_processing_interrupted()
+                except Exception:
+                    # ensure finally-block cleanup runs and exception propagates
+                    raise
+
                 for i in range(iterations):
+                    # cooperative cancel at the start of each iteration
+                    model_management.throw_exception_if_processing_interrupted()
                     if i % 2 == 0:
                         clear_gpu_and_ram_cache()
 
@@ -1618,7 +1630,9 @@ class ComfyAdaptiveDetailEnhancer25:
                             batch_inds = current_latent.get("batch_index", None)
                             noise = _sample.prepare_noise(lat_img, int(iter_seed), batch_inds)
                             noise_mask = current_latent.get("noise_mask", None)
-                            callback = nodes.latent_preview.prepare_callback(sampler_model, int(current_steps))
+                            callback = _wrap_interruptible_callback(sampler_model, int(current_steps))
+                            # cooperative cancel just before entering sampler
+                            model_management.throw_exception_if_processing_interrupted()
                             disable_pbar = not _utils.PROGRESS_BAR_ENABLED
                             sampler_obj = _samplers.sampler_object(str(sampler_name))
                             samples = _sample.sample_custom(
@@ -1630,16 +1644,29 @@ class ComfyAdaptiveDetailEnhancer25:
                             current_latent = {**current_latent}
                             current_latent["samples"] = samples
                         except Exception as e:
+                            # Before any fallback, propagate user cancel if set
+                            try:
+                                model_management.throw_exception_if_processing_interrupted()
+                            except Exception:
+                                globals()["_MG_CANCEL_REQUESTED"] = False
+                                raise
+                            # Do not swallow user interruption; also check sentinel just in case
+                            if isinstance(e, model_management.InterruptProcessingException) or globals().get("_MG_CANCEL_REQUESTED", False):
+                                globals()["_MG_CANCEL_REQUESTED"] = False
+                                raise
                             # Fallback to original path if anything goes wrong
                             print(f"[CADE2.5][MGHybrid] fallback to common_ksampler due to: {e}")
-                            current_latent, = nodes.common_ksampler(
+                            current_latent, = _interruptible_ksampler(
                                 sampler_model, iter_seed, int(current_steps), current_cfg, sampler_name, _scheduler_names()[0],
                                 positive, negative, current_latent, denoise=current_denoise)
                     else:
-                        current_latent, = nodes.common_ksampler(
+                        current_latent, = _interruptible_ksampler(
                             sampler_model, iter_seed, int(current_steps), current_cfg, sampler_name, scheduler,
                             positive, negative, current_latent, denoise=current_denoise)
 
+                    # cooperative cancel right after sampling, before further heavy work
+                    model_management.throw_exception_if_processing_interrupted()
+
                     if bool(latent_compare):
                         latent_diff = current_latent["samples"] - prev_samples
                         rms = torch.sqrt(torch.mean(latent_diff * latent_diff))
@@ -1675,6 +1702,8 @@ class ComfyAdaptiveDetailEnhancer25:
                         pass
 
                     image = safe_decode(vae, current_latent)
+                    # allow cancel between sampling and post-decode logic
+                    model_management.throw_exception_if_processing_interrupted()
 
                     # Polish mode: keep global form (low frequencies) from reference while letting details refine
                     if bool(polish_enable) and (i >= int(polish_start_after)):
@@ -1833,6 +1862,17 @@ class ComfyAdaptiveDetailEnhancer25:
                 CURRENT_ONNX_MASK_BCHW = None
             except Exception:
                 pass
+            # reset cancel sentinel and cleanup cache
+            try:
+                globals()["_MG_CANCEL_REQUESTED"] = False
+                clear_gpu_and_ram_cache()
+            except Exception:
+                pass
+            # best-effort cleanup of GPU/CPU caches on cancel or error
+            try:
+                clear_gpu_and_ram_cache()
+            except Exception:
+                pass
 
         if apply_ids:
             image, = IntelligentDetailStabilizer().stabilize(image, ids_strength)
@@ -1862,3 +1902,33 @@ class ComfyAdaptiveDetailEnhancer25:
 
 
 
+def _wrap_interruptible_callback(model, steps):
+    base_cb = nodes.latent_preview.prepare_callback(model, int(steps))
+    def _cb(step, x0, x, total_steps):
+        # mark sentinel so outer layers avoid fallbacks on cancel
+        if model_management.processing_interrupted():
+            globals()["_MG_CANCEL_REQUESTED"] = True
+            raise model_management.InterruptProcessingException()
+        return base_cb(step, x0, x, total_steps)
+    return _cb
+
+def _interruptible_ksampler(model, seed, steps, cfg, sampler_name, scheduler,
+                            positive, negative, latent, denoise=1.0):
+    lat_img = _sample.fix_empty_latent_channels(model, latent["samples"])
+    batch_inds = latent.get("batch_index", None)
+    noise = _sample.prepare_noise(lat_img, int(seed), batch_inds)
+    noise_mask = latent.get("noise_mask", None)
+    callback = _wrap_interruptible_callback(model, int(steps))
+    # cooperative cancel just before sampler entry
+    model_management.throw_exception_if_processing_interrupted()
+    disable_pbar = not _utils.PROGRESS_BAR_ENABLED
+    samples = _sample.sample(
+        model, noise, int(steps), float(cfg), str(sampler_name), str(scheduler),
+        positive, negative, lat_img,
+        denoise=float(denoise), disable_noise=False, start_step=None, last_step=None,
+        force_full_denoise=False, noise_mask=noise_mask, callback=callback,
+        disable_pbar=disable_pbar, seed=int(seed)
+    )
+    out = {**latent}
+    out["samples"] = samples
+    return (out,)

mod/mg_combinode.py

@@ -60,6 +60,12 @@ def _unload_old_checkpoint(current_path):
 class MagicNodesCombiNode:
     @classmethod
     def INPUT_TYPES(cls):
+        def _loras_with_none():
+            try:
+                return ["None"] + list(folder_paths.get_filename_list("loras"))
+            except Exception:
+                return ["None"]
+
         return {
             "required": {
             
@@ -72,37 +78,37 @@ class MagicNodesCombiNode:
 
             # --- LoRA 1 ---
             "use_lora_1": ("BOOLEAN", {"default": True}),
-            "lora_1": (folder_paths.get_filename_list("loras"), {}),
+            "lora_1": (_loras_with_none(), {}),
             "strength_model_1": ("FLOAT", {"default": 1.0, "min": -1.5, "max": 1.5, "step": 0.01,}),
             "strength_clip_1": ("FLOAT", {"default": 1.0, "min": -1.5, "max": 1.5, "step": 0.01,}),
 
             # --- LoRA 2 ---
             "use_lora_2": ("BOOLEAN", {"default": False}),
-            "lora_2": (folder_paths.get_filename_list("loras"), {}),
+            "lora_2": (_loras_with_none(), {}),
             "strength_model_2": ("FLOAT", {"default": 0.0, "min": -1.5, "max": 1.5, "step": 0.01,}),
             "strength_clip_2": ("FLOAT", {"default": 0.0, "min": -1.5, "max": 1.5, "step": 0.01,}),
 
             # --- LoRA 3 ---
             "use_lora_3": ("BOOLEAN", {"default": False}),
-            "lora_3": (folder_paths.get_filename_list("loras"), {}),
+            "lora_3": (_loras_with_none(), {}),
             "strength_model_3": ("FLOAT", {"default": 0.0, "min": -1.5, "max": 1.5, "step": 0.01,}),
             "strength_clip_3": ("FLOAT", {"default": 0.0, "min": -1.5, "max": 1.5, "step": 0.01,}),
 
             # --- LoRA 4 ---
             "use_lora_4": ("BOOLEAN", {"default": False}),
-            "lora_4": (folder_paths.get_filename_list("loras"), {}),
+            "lora_4": (_loras_with_none(), {}),
             "strength_model_4": ("FLOAT", {"default": 0.0, "min": -1.5, "max": 1.5, "step": 0.01,}),
             "strength_clip_4": ("FLOAT", {"default": 0.0, "min": -1.5, "max": 1.5, "step": 0.01,}),
 
             # --- LoRA 5 ---
             "use_lora_5": ("BOOLEAN", {"default": False}),
-            "lora_5": (folder_paths.get_filename_list("loras"), {}),
+            "lora_5": (_loras_with_none(), {}),
             "strength_model_5": ("FLOAT", {"default": 0.0, "min": -1.5, "max": 1.5, "step": 0.01,}),
             "strength_clip_5": ("FLOAT", {"default": 0.0, "min": -1.5, "max": 1.5, "step": 0.01,}),
 
             # --- LoRA 6 ---
             "use_lora_6": ("BOOLEAN", {"default": False}),
-            "lora_6": (folder_paths.get_filename_list("loras"), {}),
+            "lora_6": (_loras_with_none(), {}),
             "strength_model_6": ("FLOAT", {"default": 0.0, "min": -1.5, "max": 1.5, "step": 0.01,}),
             "strength_clip_6": ("FLOAT", {"default": 0.0, "min": -1.5, "max": 1.5, "step": 0.01,}),
             },
@@ -302,18 +308,30 @@ class MagicNodesCombiNode:
         lora_stack = []  # list of (lora_file, sc, sm)
         defer_clip = bool(standard_pipeline)
         for use_lora, lora_name, sm, sc in loras:
-            if use_lora and lora_name:
-                lora_path = folder_paths.get_full_path_or_raise("loras", lora_name)
-                active_lora_paths.append(lora_path)
-                # keep lora object to avoid reloading
-                if lora_path in _lora_cache:
-                    lora_file = _lora_cache[lora_path]
-                else:
-                    lora_file = comfy.utils.load_torch_file(lora_path, safe_load=True)
-                    _lora_cache[lora_path] = lora_file
-                lora_stack.append((lora_file, float(sc), float(sm)))
-                sc_apply = 0.0 if defer_clip else sc
-                model, clip = comfy.sd.load_lora_for_models(model, clip, lora_file, sm, sc_apply)
+            # Skip when disabled or name empty
+            if not use_lora or not lora_name:
+                continue
+            # Resolve path safely (do not raise if missing)
+            try:
+                lora_path = folder_paths.get_full_path("loras", lora_name)
+            except Exception:
+                lora_path = None
+            if not lora_path or not os.path.exists(lora_path):
+                try:
+                    print(f"[CombiNode] LoRA '{lora_name}' not found; skipping.")
+                except Exception:
+                    pass
+                continue
+            active_lora_paths.append(lora_path)
+            # keep lora object to avoid reloading
+            if lora_path in _lora_cache:
+                lora_file = _lora_cache[lora_path]
+            else:
+                lora_file = comfy.utils.load_torch_file(lora_path, safe_load=True)
+                _lora_cache[lora_path] = lora_file
+            lora_stack.append((lora_file, float(sc), float(sm)))
+            sc_apply = 0.0 if defer_clip else sc
+            model, clip = comfy.sd.load_lora_for_models(model, clip, lora_file, sm, sc_apply)
 
         _clear_unused_loras(active_lora_paths)
         # Warn about duplicate LoRA selections across slots

workflows/mg_Easy-Workflow.json

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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":35,"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":26,"type":"PreviewImage","pos":[2402.088511427638,-384.4257289383634],"size":[204.34144162678967,246],"flags":{},"order":32,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":41}],"outputs":[],"properties":{"Node name for S&R":"PreviewImage"},"widgets_values":[]},{"id":45,"type":"PreviewImage","pos":[2134.6382392169603,-384.3467464741332],"size":[204.34144162678967,246],"flags":{},"order":29,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":123}],"outputs":[],"properties":{"Node name for 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