Merge 6d4f9e86ab into 56c74094c7

Signed-off-by: bigcat88 <bigcat88@icloud.com>

.github/workflows/stable-release.yml

@@ -145,6 +145,8 @@ jobs:
           cp -r ComfyUI/.ci/windows_${{ inputs.rel_name }}_base_files/* ./
           cp ../update_comfyui_and_python_dependencies.bat ./update/
 
+          echo 'local-portable' > ComfyUI/.comfy_environment
+
           cd ..
 
           "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable

app/frontend_management.py

@@ -27,7 +27,7 @@ def frontend_install_warning_message():
     return f"""
 {get_missing_requirements_message()}
 
-This error is happening because the ComfyUI frontend is no longer shipped as part of the main repo but as a pip package instead.
+The ComfyUI frontend is shipped in a pip package so it needs to be updated separately from the ComfyUI code.
 """.strip()
 
 def parse_version(version: str) -> tuple[int, int, int]:

app/node_replace_manager.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import logging
+
 from aiohttp import web
 
 from typing import TYPE_CHECKING, TypedDict
@@ -31,8 +33,22 @@ class NodeReplaceManager:
         self._replacements: dict[str, list[NodeReplace]] = {}
 
     def register(self, node_replace: NodeReplace):
-        """Register a node replacement mapping."""
-        self._replacements.setdefault(node_replace.old_node_id, []).append(node_replace)
+        """Register a node replacement mapping.
+
+        Idempotent: if a replacement with the same (old_node_id, new_node_id)
+        is already registered, the duplicate is ignored. This prevents stale
+        entries from accumulating when custom nodes are reloaded in the same
+        process (e.g. via ComfyUI-Manager).
+        """
+        existing = self._replacements.setdefault(node_replace.old_node_id, [])
+        for entry in existing:
+            if entry.new_node_id == node_replace.new_node_id:
+                logging.debug(
+                    "Node replacement %s -> %s already registered, ignoring duplicate.",
+                    node_replace.old_node_id, node_replace.new_node_id,
+                )
+                return
+        existing.append(node_replace)
 
     def get_replacement(self, old_node_id: str) -> list[NodeReplace] | None:
         """Get replacements for an old node ID."""

comfy/k_diffusion/sampling.py

@@ -1859,6 +1859,23 @@ def sample_ar_video(model, x, sigmas, extra_args=None, callback=None, disable=No
     output = torch.zeros_like(x)
     s_in = x.new_ones([x.shape[0]])
     current_start_frame = 0
+
+    # I2V: seed KV cache with the initial image latent before the denoising loop
+    initial_latent = transformer_options.get("ar_config", {}).get("initial_latent", None)
+    if initial_latent is not None:
+        initial_latent = inner_model.process_latent_in(initial_latent).to(device=device, dtype=model_dtype)
+        n_init = initial_latent.shape[2]
+        output[:, :, :n_init] = initial_latent
+
+        ar_state = {"start_frame": 0, "kv_caches": kv_caches, "crossattn_caches": crossattn_caches}
+        transformer_options["ar_state"] = ar_state
+        zero_sigma = sigmas.new_zeros([1])
+        _ = model(initial_latent, zero_sigma * s_in, **extra_args)
+
+        current_start_frame = n_init
+        remaining = lat_t - n_init
+        num_blocks = -(-remaining // num_frame_per_block)
+
     num_sigma_steps = len(sigmas) - 1
     total_real_steps = num_blocks * num_sigma_steps
     step_count = 0

comfy/ldm/sam3/detector.py

@@ -561,7 +561,8 @@ class SAM3Model(nn.Module):
         return high_res_masks
 
     def forward_video(self, images, initial_masks, pbar=None, text_prompts=None,
-                       new_det_thresh=0.5, max_objects=0, detect_interval=1):
+                       new_det_thresh=0.5, max_objects=0, detect_interval=1,
+                       target_device=None, target_dtype=None):
         """Track video with optional per-frame text-prompted detection."""
         bb = self.detector.backbone["vision_backbone"]
 
@@ -589,8 +590,10 @@ class SAM3Model(nn.Module):
             return self.tracker.track_video_with_detection(
                 backbone_fn, images, initial_masks, detect_fn,
                 new_det_thresh=new_det_thresh, max_objects=max_objects,
-                detect_interval=detect_interval, backbone_obj=bb, pbar=pbar)
+                detect_interval=detect_interval, backbone_obj=bb, pbar=pbar,
+                target_device=target_device, target_dtype=target_dtype)
         # SAM3 (non-multiplex) — no detection support, requires initial masks
         if initial_masks is None:
             raise ValueError("SAM3 (non-multiplex) requires initial_mask for video tracking")
-        return self.tracker.track_video(backbone_fn, images, initial_masks, pbar=pbar, backbone_obj=bb)
+        return self.tracker.track_video(backbone_fn, images, initial_masks, pbar=pbar, backbone_obj=bb,
+                                         target_device=target_device, target_dtype=target_dtype)

comfy/ldm/sam3/tracker.py

@@ -200,8 +200,13 @@ def pack_masks(masks):
 
 def unpack_masks(packed):
     """Unpack bit-packed [*, H, W//8] uint8 to bool [*, H, W*8]."""
-    shifts = torch.arange(8, device=packed.device)
-    return ((packed.unsqueeze(-1) >> shifts) & 1).view(*packed.shape[:-1], -1).bool()
+    bits = torch.tensor([1, 2, 4, 8, 16, 32, 64, 128], dtype=torch.uint8, device=packed.device)
+    return (packed.unsqueeze(-1) & bits).bool().view(*packed.shape[:-1], -1)
+
+
+def _prep_frame(images, idx, device, dt, size):
+    """Slice CPU full-res frames, transfer to GPU in target dtype, and resize to (size, size)."""
+    return comfy.utils.common_upscale(images[idx].to(device=device, dtype=dt), size, size, "bicubic", crop="disabled")
 
 
 def _compute_backbone(backbone_fn, frame, frame_idx=None):
@@ -1078,16 +1083,19 @@ class SAM3Tracker(nn.Module):
         # SAM3: drop last FPN level
         return vision_feats[:-1], vision_pos[:-1], feat_sizes[:-1]
 
-    def _track_single_object(self, backbone_fn, images, initial_mask, pbar=None):
+    def _track_single_object(self, backbone_fn, images, initial_mask, pbar=None,
+                             target_device=None, target_dtype=None):
         """Track one object, computing backbone per frame to save VRAM."""
         N = images.shape[0]
-        device, dt = images.device, images.dtype
+        device = target_device if target_device is not None else images.device
+        dt = target_dtype if target_dtype is not None else images.dtype
+        size = self.image_size
         output_dict = {"cond_frame_outputs": {}, "non_cond_frame_outputs": {}}
         all_masks = []
 
         for frame_idx in tqdm(range(N), desc="tracking"):
             vision_feats, vision_pos, feat_sizes = self._compute_backbone_frame(
-                backbone_fn, images[frame_idx:frame_idx + 1], frame_idx=frame_idx)
+                backbone_fn, _prep_frame(images, slice(frame_idx, frame_idx + 1), device, dt, size), frame_idx=frame_idx)
             mask_input = None
             if frame_idx == 0:
                 mask_input = F.interpolate(initial_mask.to(device=device, dtype=dt),
@@ -1114,12 +1122,13 @@ class SAM3Tracker(nn.Module):
 
         return torch.cat(all_masks, dim=0)  # [N, 1, H, W]
 
-    def track_video(self, backbone_fn, images, initial_masks, pbar=None, **kwargs):
+    def track_video(self, backbone_fn, images, initial_masks, pbar=None,
+                    target_device=None, target_dtype=None, **kwargs):
         """Track one or more objects across video frames.
 
         Args:
             backbone_fn: callable that returns (sam2_features, sam2_positions, trunk_out) for a frame
-            images: [N, 3, 1008, 1008] video frames
+            images: [N, 3, H, W] CPU full-res video frames (resized per-frame to self.image_size)
             initial_masks: [N_obj, 1, H, W] binary masks for first frame (one per object)
             pbar: optional progress bar
 
@@ -1130,7 +1139,8 @@ class SAM3Tracker(nn.Module):
         per_object = []
         for obj_idx in range(N_obj):
             obj_masks = self._track_single_object(
-                backbone_fn, images, initial_masks[obj_idx:obj_idx + 1], pbar=pbar)
+                backbone_fn, images, initial_masks[obj_idx:obj_idx + 1], pbar=pbar,
+                target_device=target_device, target_dtype=target_dtype)
             per_object.append(obj_masks)
 
         return torch.cat(per_object, dim=1)  # [N, N_obj, H, W]
@@ -1632,11 +1642,18 @@ class SAM31Tracker(nn.Module):
             return det_scores[new_dets].tolist() if det_scores is not None else [0.0] * new_dets.sum().item()
         return []
 
+    INTERNAL_MAX_OBJECTS = 64  # Hard ceiling on accumulated tracks; max_objects=0 or any value above this is clamped here.
+
     def track_video_with_detection(self, backbone_fn, images, initial_masks, detect_fn=None,
                                    new_det_thresh=0.5, max_objects=0, detect_interval=1,
-                                   backbone_obj=None, pbar=None):
+                                   backbone_obj=None, pbar=None, target_device=None, target_dtype=None):
         """Track with optional per-frame detection. Returns [N, max_N_obj, H, W] mask logits."""
-        N, device, dt = images.shape[0], images.device, images.dtype
+        if max_objects <= 0 or max_objects > self.INTERNAL_MAX_OBJECTS:
+            max_objects = self.INTERNAL_MAX_OBJECTS
+        N = images.shape[0]
+        device = target_device if target_device is not None else images.device
+        dt = target_dtype if target_dtype is not None else images.dtype
+        size = self.image_size
         output_dict = {"cond_frame_outputs": {}, "non_cond_frame_outputs": {}}
         all_masks = []
         idev = comfy.model_management.intermediate_device()
@@ -1656,7 +1673,7 @@ class SAM31Tracker(nn.Module):
                 prefetch = True
             except RuntimeError:
                 pass
-        cur_bb = self._compute_backbone_frame(backbone_fn, images[0:1], frame_idx=0)
+        cur_bb = self._compute_backbone_frame(backbone_fn, _prep_frame(images, slice(0, 1), device, dt, size), frame_idx=0)
 
         for frame_idx in tqdm(range(N), desc="tracking"):
             vision_feats, vision_pos, feat_sizes, high_res_prop, trunk_out = cur_bb
@@ -1666,7 +1683,7 @@ class SAM31Tracker(nn.Module):
                 backbone_stream.wait_stream(torch.cuda.current_stream(device))
                 with torch.cuda.stream(backbone_stream):
                     next_bb = self._compute_backbone_frame(
-                        backbone_fn, images[frame_idx + 1:frame_idx + 2], frame_idx=frame_idx + 1)
+                        backbone_fn, _prep_frame(images, slice(frame_idx + 1, frame_idx + 2), device, dt, size), frame_idx=frame_idx + 1)
 
             # Per-frame detection with NMS (skip if no detect_fn, or interval/max not met)
             det_masks = torch.empty(0, device=device)
@@ -1687,7 +1704,7 @@ class SAM31Tracker(nn.Module):
                 current_out = self._condition_with_masks(
                     initial_masks.to(device=device, dtype=dt), frame_idx, vision_feats, vision_pos,
                     feat_sizes, high_res_prop, output_dict, N, mux_state, backbone_obj,
-                    images[frame_idx:frame_idx + 1], trunk_out)
+                    _prep_frame(images, slice(frame_idx, frame_idx + 1), device, dt, size), trunk_out)
                 last_occluded = torch.full((mux_state.total_valid_entries,), -1, device=device, dtype=torch.long)
                 obj_scores = [1.0] * mux_state.total_valid_entries
                 if keep_alive is not None:
@@ -1702,7 +1719,7 @@ class SAM31Tracker(nn.Module):
                     current_out = self._condition_with_masks(
                         det_masks, frame_idx, vision_feats, vision_pos, feat_sizes, high_res_prop,
                         output_dict, N, mux_state, backbone_obj,
-                        images[frame_idx:frame_idx + 1], trunk_out, threshold=0.0)
+                        _prep_frame(images, slice(frame_idx, frame_idx + 1), device, dt, size), trunk_out, threshold=0.0)
                     last_occluded = torch.full((mux_state.total_valid_entries,), -1, device=device, dtype=torch.long)
                     obj_scores = det_scores[:mux_state.total_valid_entries].tolist()
                     if keep_alive is not None:
@@ -1718,7 +1735,7 @@ class SAM31Tracker(nn.Module):
                             torch.cuda.current_stream(device).wait_stream(backbone_stream)
                             cur_bb = next_bb
                         else:
-                            cur_bb = self._compute_backbone_frame(backbone_fn, images[frame_idx + 1:frame_idx + 2], frame_idx=frame_idx + 1)
+                            cur_bb = self._compute_backbone_frame(backbone_fn, _prep_frame(images, slice(frame_idx + 1, frame_idx + 2), device, dt, size), frame_idx=frame_idx + 1)
                     continue
             else:
                 N_obj = mux_state.total_valid_entries
@@ -1768,7 +1785,7 @@ class SAM31Tracker(nn.Module):
                     torch.cuda.current_stream(device).wait_stream(backbone_stream)
                     cur_bb = next_bb
                 else:
-                    cur_bb = self._compute_backbone_frame(backbone_fn, images[frame_idx + 1:frame_idx + 2], frame_idx=frame_idx + 1)
+                    cur_bb = self._compute_backbone_frame(backbone_fn, _prep_frame(images, slice(frame_idx + 1, frame_idx + 2), device, dt, size), frame_idx=frame_idx + 1)
 
         if not all_masks or all(m is None for m in all_masks):
             return {"packed_masks": None, "n_frames": N, "scores": []}

comfy/model_patcher.py

@@ -26,6 +26,7 @@ import uuid
 from typing import Callable, Optional
 
 import torch
+import tqdm
 
 import comfy.float
 import comfy.hooks
@@ -1651,7 +1652,11 @@ class ModelPatcherDynamic(ModelPatcher):
                 self.model.model_loaded_weight_memory += casted_buf.numel() * casted_buf.element_size()
 
             force_load_stat = f" Force pre-loaded {len(self.backup)} weights: {self.model.model_loaded_weight_memory // 1024} KB." if len(self.backup) > 0 else ""
-            logging.info(f"Model {self.model.__class__.__name__} prepared for dynamic VRAM loading. {allocated_size // (1024 ** 2)}MB Staged. {num_patches} patches attached.{force_load_stat}")
+            log_key = (self.patches_uuid, allocated_size, num_patches, len(self.backup), self.model.model_loaded_weight_memory)
+            in_loop = bool(getattr(tqdm.tqdm, "_instances", None))
+            level = logging.DEBUG if in_loop and getattr(self, "_last_prepare_log_key", None) == log_key else logging.INFO
+            self._last_prepare_log_key = log_key
+            logging.log(level, f"Model {self.model.__class__.__name__} prepared for dynamic VRAM loading. {allocated_size // (1024 ** 2)}MB Staged. {num_patches} patches attached.{force_load_stat}")
 
             self.model.device = device_to
             self.model.current_weight_patches_uuid = self.patches_uuid

comfy/sd.py

@@ -1122,7 +1122,17 @@ class VAE:
             else:
                 pixel_samples = pixel_samples.unsqueeze(2)
 
-        memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)  # TODO: calculate mem required for tile
+        if dims == 2:
+            default_tile_x = 512 if tile_x is None else tile_x
+            default_tile_y = 512 if tile_y is None else tile_y
+            tile_shapes = [
+                (1, pixel_samples.shape[1], min(pixel_samples.shape[2], max(1, default_tile_y)), min(pixel_samples.shape[3], max(1, default_tile_x))),
+                (1, pixel_samples.shape[1], min(pixel_samples.shape[2], max(1, default_tile_y // 2)), min(pixel_samples.shape[3], max(1, default_tile_x * 2))),
+                (1, pixel_samples.shape[1], min(pixel_samples.shape[2], max(1, default_tile_y * 2)), min(pixel_samples.shape[3], max(1, default_tile_x // 2))),
+            ]
+            memory_used = max(self.memory_used_encode(shape, self.vae_dtype) for shape in tile_shapes)
+        else:
+            memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
         model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
 
         args = {}

comfy_api_nodes/nodes_bytedance.py

@@ -1271,7 +1271,7 @@ PRICE_BADGE_VIDEO = IO.PriceBadge(
 )
 
 
-def _seedance2_text_inputs(resolutions: list[str]):
+def _seedance2_text_inputs(resolutions: list[str], default_ratio: str = "16:9"):
     return [
         IO.String.Input(
             "prompt",
@@ -1287,6 +1287,7 @@ def _seedance2_text_inputs(resolutions: list[str]):
         IO.Combo.Input(
             "ratio",
             options=["16:9", "4:3", "1:1", "3:4", "9:16", "21:9", "adaptive"],
+            default=default_ratio,
             tooltip="Aspect ratio of the output video.",
         ),
         IO.Int.Input(
@@ -1420,8 +1421,14 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
                 IO.DynamicCombo.Input(
                     "model",
                     options=[
-                        IO.DynamicCombo.Option("Seedance 2.0", _seedance2_text_inputs(["480p", "720p", "1080p"])),
-                        IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_text_inputs(["480p", "720p"])),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0",
+                            _seedance2_text_inputs(["480p", "720p", "1080p"], default_ratio="adaptive"),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0 Fast",
+                            _seedance2_text_inputs(["480p", "720p"], default_ratio="adaptive"),
+                        ),
                     ],
                     tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.",
                 ),
@@ -1588,9 +1595,9 @@ class ByteDance2FirstLastFrameNode(IO.ComfyNode):
         return IO.NodeOutput(await download_url_to_video_output(response.content.video_url))
 
 
-def _seedance2_reference_inputs(resolutions: list[str]):
+def _seedance2_reference_inputs(resolutions: list[str], default_ratio: str = "16:9"):
     return [
-        *_seedance2_text_inputs(resolutions),
+        *_seedance2_text_inputs(resolutions, default_ratio=default_ratio),
         IO.Autogrow.Input(
             "reference_images",
             template=IO.Autogrow.TemplateNames(
@@ -1668,8 +1675,14 @@ class ByteDance2ReferenceNode(IO.ComfyNode):
                 IO.DynamicCombo.Input(
                     "model",
                     options=[
-                        IO.DynamicCombo.Option("Seedance 2.0", _seedance2_reference_inputs(["480p", "720p", "1080p"])),
-                        IO.DynamicCombo.Option("Seedance 2.0 Fast", _seedance2_reference_inputs(["480p", "720p"])),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0",
+                            _seedance2_reference_inputs(["480p", "720p", "1080p"], default_ratio="adaptive"),
+                        ),
+                        IO.DynamicCombo.Option(
+                            "Seedance 2.0 Fast",
+                            _seedance2_reference_inputs(["480p", "720p"], default_ratio="adaptive"),
+                        ),
                     ],
                     tooltip="Seedance 2.0 for maximum quality; Seedance 2.0 Fast for speed optimization.",
                 ),

comfy_api_nodes/nodes_gemini.py

@@ -83,13 +83,16 @@ class GeminiImageModel(str, Enum):
 
 async def create_image_parts(
     cls: type[IO.ComfyNode],
-    images: Input.Image,
+    images: Input.Image | list[Input.Image],
     image_limit: int = 0,
 ) -> list[GeminiPart]:
     image_parts: list[GeminiPart] = []
     if image_limit < 0:
         raise ValueError("image_limit must be greater than or equal to 0 when creating Gemini image parts.")
-    total_images = get_number_of_images(images)
+
+    # Accept either a single (possibly-batched) tensor or a list of them; share URL budget across all.
+    images_list: list[Input.Image] = images if isinstance(images, list) else [images]
+    total_images = sum(get_number_of_images(img) for img in images_list)
     if total_images <= 0:
         raise ValueError("No images provided to create_image_parts; at least one image is required.")
 
@@ -98,10 +101,18 @@ async def create_image_parts(
 
     # Number of images we'll send as URLs (fileData)
     num_url_images = min(effective_max, 10)  # Vertex API max number of image links
+    upload_kwargs: dict = {"wait_label": "Uploading reference images"}
+    if effective_max > num_url_images:
+        # Split path (e.g. 11+ images): suppress per-image counter to avoid a confusing dual-fraction label.
+        upload_kwargs = {
+            "wait_label": f"Uploading reference images ({num_url_images}+)",
+            "show_batch_index": False,
+        }
     reference_images_urls = await upload_images_to_comfyapi(
         cls,
-        images,
+        images_list,
         max_images=num_url_images,
+        **upload_kwargs,
     )
     for reference_image_url in reference_images_urls:
         image_parts.append(
@@ -112,15 +123,22 @@ async def create_image_parts(
                 )
             )
         )
-    for idx in range(num_url_images, effective_max):
-        image_parts.append(
-            GeminiPart(
-                inlineData=GeminiInlineData(
-                    mimeType=GeminiMimeType.image_png,
-                    data=tensor_to_base64_string(images[idx]),
+    if effective_max > num_url_images:
+        flat: list[torch.Tensor] = []
+        for tensor in images_list:
+            if len(tensor.shape) == 4:
+                flat.extend(tensor[i] for i in range(tensor.shape[0]))
+            else:
+                flat.append(tensor)
+        for idx in range(num_url_images, effective_max):
+            image_parts.append(
+                GeminiPart(
+                    inlineData=GeminiInlineData(
+                        mimeType=GeminiMimeType.image_png,
+                        data=tensor_to_base64_string(flat[idx]),
+                    )
                 )
             )
-        )
     return image_parts
 
 
@@ -891,10 +909,6 @@ class GeminiNanoBanana2(IO.ComfyNode):
                         "9:16",
                         "16:9",
                         "21:9",
-                        # "1:4",
-                        # "4:1",
-                        # "8:1",
-                        # "1:8",
                     ],
                     default="auto",
                     tooltip="If set to 'auto', matches your input image's aspect ratio; "
@@ -902,12 +916,7 @@ class GeminiNanoBanana2(IO.ComfyNode):
                 ),
                 IO.Combo.Input(
                     "resolution",
-                    options=[
-                        # "512px",
-                        "1K",
-                        "2K",
-                        "4K",
-                    ],
+                    options=["1K", "2K", "4K"],
                     tooltip="Target output resolution. For 2K/4K the native Gemini upscaler is used.",
                 ),
                 IO.Combo.Input(
@@ -956,6 +965,7 @@ class GeminiNanoBanana2(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=GEMINI_IMAGE_2_PRICE_BADGE,
+            is_deprecated=True,
         )
 
     @classmethod
@@ -1016,6 +1026,197 @@ class GeminiNanoBanana2(IO.ComfyNode):
         )
 
 
+def _nano_banana_2_v2_model_inputs():
+    return [
+        IO.Combo.Input(
+            "aspect_ratio",
+            options=[
+                "auto",
+                "1:1",
+                "2:3",
+                "3:2",
+                "3:4",
+                "4:3",
+                "4:5",
+                "5:4",
+                "9:16",
+                "16:9",
+                "21:9",
+                "1:4",
+                "4:1",
+                "8:1",
+                "1:8",
+            ],
+            default="auto",
+            tooltip="If set to 'auto', matches your input image's aspect ratio; "
+            "if no image is provided, a 16:9 square is usually generated.",
+        ),
+        IO.Combo.Input(
+            "resolution",
+            options=["1K", "2K", "4K"],
+            tooltip="Target output resolution. For 2K/4K the native Gemini upscaler is used.",
+        ),
+        IO.Combo.Input(
+            "thinking_level",
+            options=["MINIMAL", "HIGH"],
+        ),
+        IO.Autogrow.Input(
+            "images",
+            template=IO.Autogrow.TemplateNames(
+                IO.Image.Input("image"),
+                names=[f"image_{i}" for i in range(1, 15)],
+                min=0,
+            ),
+            tooltip="Optional reference image(s). Up to 14 images total.",
+        ),
+        IO.Custom("GEMINI_INPUT_FILES").Input(
+            "files",
+            optional=True,
+            tooltip="Optional file(s) to use as context for the model. "
+                    "Accepts inputs from the Gemini Generate Content Input Files node.",
+        ),
+    ]
+
+
+class GeminiNanoBanana2V2(IO.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return IO.Schema(
+            node_id="GeminiNanoBanana2V2",
+            display_name="Nano Banana 2",
+            category="api node/image/Gemini",
+            description="Generate or edit images synchronously via Google Vertex API.",
+            inputs=[
+                IO.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="Text prompt describing the image to generate or the edits to apply. "
+                    "Include any constraints, styles, or details the model should follow.",
+                    default="",
+                ),
+                IO.DynamicCombo.Input(
+                    "model",
+                    options=[
+                        IO.DynamicCombo.Option(
+                            "Nano Banana 2 (Gemini 3.1 Flash Image)",
+                            _nano_banana_2_v2_model_inputs(),
+                        ),
+                    ],
+                ),
+                IO.Int.Input(
+                    "seed",
+                    default=42,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="When the seed is fixed to a specific value, the model makes a best effort to provide "
+                    "the same response for repeated requests. Deterministic output isn't guaranteed. "
+                    "Also, changing the model or parameter settings, such as the temperature, "
+                    "can cause variations in the response even when you use the same seed value. "
+                    "By default, a random seed value is used.",
+                ),
+                IO.Combo.Input(
+                    "response_modalities",
+                    options=["IMAGE", "IMAGE+TEXT"],
+                    advanced=True,
+                ),
+                IO.String.Input(
+                    "system_prompt",
+                    multiline=True,
+                    default=GEMINI_IMAGE_SYS_PROMPT,
+                    optional=True,
+                    tooltip="Foundational instructions that dictate an AI's behavior.",
+                    advanced=True,
+                ),
+            ],
+            outputs=[
+                IO.Image.Output(),
+                IO.String.Output(),
+                IO.Image.Output(
+                    display_name="thought_image",
+                    tooltip="First image from the model's thinking process. "
+                    "Only available with thinking_level HIGH and IMAGE+TEXT modality.",
+                ),
+            ],
+            hidden=[
+                IO.Hidden.auth_token_comfy_org,
+                IO.Hidden.api_key_comfy_org,
+                IO.Hidden.unique_id,
+            ],
+            is_api_node=True,
+            price_badge=IO.PriceBadge(
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "model.resolution"]),
+                expr="""
+                (
+                  $r := $lookup(widgets, "model.resolution");
+                  $prices := {"1k": 0.0696, "2k": 0.1014, "4k": 0.154};
+                  {"type":"usd","usd": $lookup($prices, $r), "format":{"suffix":"/Image","approximate":true}}
+                )
+                """,
+            ),
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        model: dict,
+        seed: int,
+        response_modalities: str,
+        system_prompt: str = "",
+    ) -> IO.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        model_choice = model["model"]
+        if model_choice == "Nano Banana 2 (Gemini 3.1 Flash Image)":
+            model_id = "gemini-3.1-flash-image-preview"
+        else:
+            model_id = model_choice
+
+        images = model.get("images") or {}
+        parts: list[GeminiPart] = [GeminiPart(text=prompt)]
+        if images:
+            image_tensors: list[Input.Image] = [t for t in images.values() if t is not None]
+            if image_tensors:
+                if sum(get_number_of_images(t) for t in image_tensors) > 14:
+                    raise ValueError("The current maximum number of supported images is 14.")
+                parts.extend(await create_image_parts(cls, image_tensors))
+        files = model.get("files")
+        if files is not None:
+            parts.extend(files)
+
+        image_config = GeminiImageConfig(imageSize=model["resolution"])
+        if model["aspect_ratio"] != "auto":
+            image_config.aspectRatio = model["aspect_ratio"]
+
+        gemini_system_prompt = None
+        if system_prompt:
+            gemini_system_prompt = GeminiSystemInstructionContent(parts=[GeminiTextPart(text=system_prompt)], role=None)
+
+        response = await sync_op(
+            cls,
+            ApiEndpoint(path=f"/proxy/vertexai/gemini/{model_id}", method="POST"),
+            data=GeminiImageGenerateContentRequest(
+                contents=[
+                    GeminiContent(role=GeminiRole.user, parts=parts),
+                ],
+                generationConfig=GeminiImageGenerationConfig(
+                    responseModalities=(["IMAGE"] if response_modalities == "IMAGE" else ["TEXT", "IMAGE"]),
+                    imageConfig=image_config,
+                    thinkingConfig=GeminiThinkingConfig(thinkingLevel=model["thinking_level"]),
+                ),
+                systemInstruction=gemini_system_prompt,
+            ),
+            response_model=GeminiGenerateContentResponse,
+            price_extractor=calculate_tokens_price,
+        )
+        return IO.NodeOutput(
+            await get_image_from_response(response),
+            get_text_from_response(response),
+            await get_image_from_response(response, thought=True),
+        )
+
+
 class GeminiExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@@ -1024,6 +1225,7 @@ class GeminiExtension(ComfyExtension):
             GeminiImage,
             GeminiImage2,
             GeminiNanoBanana2,
+            GeminiNanoBanana2V2,
             GeminiInputFiles,
         ]
 

comfy_api_nodes/nodes_grok.py

@@ -54,7 +54,12 @@ class GrokImageNode(IO.ComfyNode):
             inputs=[
                 IO.Combo.Input(
                     "model",
-                    options=["grok-imagine-image-pro", "grok-imagine-image", "grok-imagine-image-beta"],
+                    options=[
+                        "grok-imagine-image-quality",
+                        "grok-imagine-image-pro",
+                        "grok-imagine-image",
+                        "grok-imagine-image-beta",
+                    ],
                 ),
                 IO.String.Input(
                     "prompt",
@@ -111,10 +116,12 @@ class GrokImageNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model", "number_of_images"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "number_of_images", "resolution"]),
                 expr="""
                 (
-                  $rate := $contains(widgets.model, "pro") ? 0.07 : 0.02;
+                  $rate := widgets.model = "grok-imagine-image-quality"
+                    ? (widgets.resolution = "1k" ? 0.05 : 0.07)
+                    : ($contains(widgets.model, "pro") ? 0.07 : 0.02);
                   {"type":"usd","usd": $rate * widgets.number_of_images}
                 )
                 """,
@@ -167,7 +174,12 @@ class GrokImageEditNode(IO.ComfyNode):
             inputs=[
                 IO.Combo.Input(
                     "model",
-                    options=["grok-imagine-image-pro", "grok-imagine-image", "grok-imagine-image-beta"],
+                    options=[
+                        "grok-imagine-image-quality",
+                        "grok-imagine-image-pro",
+                        "grok-imagine-image",
+                        "grok-imagine-image-beta",
+                    ],
                 ),
                 IO.Image.Input("image", display_name="images"),
                 IO.String.Input(
@@ -228,11 +240,19 @@ class GrokImageEditNode(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["model", "number_of_images"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["model", "number_of_images", "resolution"]),
                 expr="""
                 (
-                  $rate := $contains(widgets.model, "pro") ? 0.07 : 0.02;
-                  {"type":"usd","usd": 0.002 + $rate * widgets.number_of_images}
+                  $isQualityModel := widgets.model = "grok-imagine-image-quality";
+                  $isPro := $contains(widgets.model, "pro");
+                  $rate := $isQualityModel
+                    ? (widgets.resolution = "1k" ? 0.05 : 0.07)
+                    : ($isPro ? 0.07 : 0.02);
+                  $base := $isQualityModel ? 0.01 : 0.002;
+                  $output := $rate * widgets.number_of_images;
+                  $isPro
+                    ? {"type":"usd","usd": $base + $output}
+                    : {"type":"range_usd","min_usd": $base + $output, "max_usd": 3 * $base + $output}
                 )
                 """,
             ),

comfy_api_nodes/nodes_kling.py

@@ -2787,11 +2787,15 @@ class MotionControl(IO.ComfyNode):
             ],
             is_api_node=True,
             price_badge=IO.PriceBadge(
-                depends_on=IO.PriceBadgeDepends(widgets=["mode"]),
+                depends_on=IO.PriceBadgeDepends(widgets=["mode", "model"]),
                 expr="""
                 (
-                  $prices := {"std": 0.07, "pro": 0.112};
-                  {"type":"usd","usd": $lookup($prices, widgets.mode), "format":{"suffix":"/second"}}
+                  $prices := {
+                    "kling-v3": {"std": 0.126, "pro": 0.168},
+                    "kling-v2-6": {"std": 0.07, "pro": 0.112}
+                  };
+                  $modelPrices := $lookup($prices, widgets.model);
+                  {"type":"usd","usd": $lookup($modelPrices, widgets.mode), "format":{"suffix":"/second"}}
                 )
                 """,
             ),

comfy_execution/caching.py

@@ -1,5 +1,6 @@
 import asyncio
 import bisect
+import gc
 import itertools
 import psutil
 import time
@@ -17,6 +18,7 @@ NODE_CLASS_CONTAINS_UNIQUE_ID: Dict[str, bool] = {}
 
 
 def include_unique_id_in_input(class_type: str) -> bool:
+    """Return whether a node class includes UNIQUE_ID among its hidden inputs."""
     if class_type in NODE_CLASS_CONTAINS_UNIQUE_ID:
         return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type]
     class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
@@ -24,52 +26,412 @@ def include_unique_id_in_input(class_type: str) -> bool:
     return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type]
 
 class CacheKeySet(ABC):
+    """Base helper for building and storing cache keys for prompt nodes."""
     def __init__(self, dynprompt, node_ids, is_changed_cache):
+        """Initialize cache-key storage for a dynamic prompt execution pass."""
         self.keys = {}
         self.subcache_keys = {}
 
     @abstractmethod
     async def add_keys(self, node_ids):
+        """Populate cache keys for the provided node ids."""
         raise NotImplementedError()
 
     def all_node_ids(self):
+        """Return the set of node ids currently tracked by this key set."""
         return set(self.keys.keys())
 
     def get_used_keys(self):
+        """Return the computed cache keys currently in use."""
         return self.keys.values()
 
     def get_used_subcache_keys(self):
+        """Return the computed subcache keys currently in use."""
         return self.subcache_keys.values()
 
     def get_data_key(self, node_id):
+        """Return the cache key for a node, if present."""
         return self.keys.get(node_id, None)
 
     def get_subcache_key(self, node_id):
+        """Return the subcache key for a node, if present."""
         return self.subcache_keys.get(node_id, None)
 
 class Unhashable:
-    def __init__(self):
-        self.value = float("NaN")
+    """Hashable identity sentinel for values that cannot be represented safely in cache keys."""
+    pass
 
-def to_hashable(obj):
-    # So that we don't infinitely recurse since frozenset and tuples
-    # are Sequences.
-    if isinstance(obj, (int, float, str, bool, bytes, type(None))):
-        return obj
-    elif isinstance(obj, Mapping):
-        return frozenset([(to_hashable(k), to_hashable(v)) for k, v in sorted(obj.items())])
-    elif isinstance(obj, Sequence):
-        return frozenset(zip(itertools.count(), [to_hashable(i) for i in obj]))
-    else:
-        # TODO - Support other objects like tensors?
+
+_PRIMITIVE_SIGNATURE_TYPES = (int, float, str, bool, bytes, type(None))
+_CONTAINER_SIGNATURE_TYPES = (dict, list, tuple, set, frozenset)
+_MAX_SIGNATURE_DEPTH = 32
+_MAX_SIGNATURE_CONTAINER_VISITS = 10_000
+_FAILED_SIGNATURE = object()
+
+
+def _shallow_is_changed_signature(value):
+    """Reduce execution-time `is_changed` values through a fail-closed builtin canonicalizer."""
+    value_type = type(value)
+    if value_type in _PRIMITIVE_SIGNATURE_TYPES:
+        return value
+
+    if value_type not in _CONTAINER_SIGNATURE_TYPES:
         return Unhashable()
 
+    canonical = _signature_to_hashable(value, max_nodes=64)
+    if type(canonical) is Unhashable:
+        return canonical
+    if value_type is list or value_type is tuple:
+        container_tag = "is_changed_list" if value_type is list else "is_changed_tuple"
+        return (container_tag, canonical[1])
+
+    return canonical
+
+
+def _primitive_signature_sort_key(obj):
+    """Return a deterministic ordering key for primitive signature values."""
+    obj_type = type(obj)
+    return ("primitive", obj_type.__module__, obj_type.__qualname__, repr(obj))
+
+
+def _sanitized_sort_key(obj, depth=0, max_depth=_MAX_SIGNATURE_DEPTH, active=None, memo=None):
+    """Return a deterministic ordering key for sanitized built-in container content."""
+    if depth >= max_depth:
+        return ("MAX_DEPTH",)
+
+    if active is None:
+        active = set()
+    if memo is None:
+        memo = {}
+
+    obj_type = type(obj)
+    if obj_type is Unhashable:
+        return ("UNHASHABLE",)
+    elif obj_type in _PRIMITIVE_SIGNATURE_TYPES:
+        return (obj_type.__module__, obj_type.__qualname__, repr(obj))
+    elif obj_type not in _CONTAINER_SIGNATURE_TYPES:
+        return (obj_type.__module__, obj_type.__qualname__, "OPAQUE")
+
+    obj_id = id(obj)
+    if obj_id in memo:
+        return memo[obj_id]
+    if obj_id in active:
+        return ("CYCLE",)
+
+    active.add(obj_id)
+    try:
+        if obj_type is dict:
+            items = [
+                (
+                    _sanitized_sort_key(k, depth + 1, max_depth, active, memo),
+                    _sanitized_sort_key(v, depth + 1, max_depth, active, memo),
+                )
+                for k, v in obj.items()
+            ]
+            items.sort()
+            result = ("dict", tuple(items))
+        elif obj_type is list:
+            result = ("list", tuple(_sanitized_sort_key(i, depth + 1, max_depth, active, memo) for i in obj))
+        elif obj_type is tuple:
+            result = ("tuple", tuple(_sanitized_sort_key(i, depth + 1, max_depth, active, memo) for i in obj))
+        elif obj_type is set:
+            result = ("set", tuple(sorted(_sanitized_sort_key(i, depth + 1, max_depth, active, memo) for i in obj)))
+        else:
+            result = ("frozenset", tuple(sorted(_sanitized_sort_key(i, depth + 1, max_depth, active, memo) for i in obj)))
+    finally:
+        active.discard(obj_id)
+
+    memo[obj_id] = result
+    return result
+
+
+def _signature_to_hashable_impl(obj, depth=0, max_depth=_MAX_SIGNATURE_DEPTH, active=None, memo=None, budget=None):
+    """Canonicalize signature inputs directly into their final hashable form."""
+    if depth >= max_depth:
+        return _FAILED_SIGNATURE
+
+    if active is None:
+        active = set()
+    if memo is None:
+        memo = {}
+    if budget is None:
+        budget = {"remaining": _MAX_SIGNATURE_CONTAINER_VISITS}
+
+    obj_type = type(obj)
+    if obj_type in _PRIMITIVE_SIGNATURE_TYPES:
+        return obj, _primitive_signature_sort_key(obj)
+    if obj_type is Unhashable or obj_type not in _CONTAINER_SIGNATURE_TYPES:
+        return _FAILED_SIGNATURE
+
+    obj_id = id(obj)
+    if obj_id in memo:
+        return memo[obj_id]
+    if obj_id in active:
+        return _FAILED_SIGNATURE
+
+    budget["remaining"] -= 1
+    if budget["remaining"] < 0:
+        return _FAILED_SIGNATURE
+
+    active.add(obj_id)
+    try:
+        if obj_type is dict:
+            try:
+                items = list(obj.items())
+            except RuntimeError:
+                return _FAILED_SIGNATURE
+
+            ordered_items = []
+            for key, value in items:
+                if type(key) not in _PRIMITIVE_SIGNATURE_TYPES:
+                    return _FAILED_SIGNATURE
+                key_result = (key, _primitive_signature_sort_key(key))
+                value_result = _signature_to_hashable_impl(value, depth + 1, max_depth, active, memo, budget)
+                if value_result is _FAILED_SIGNATURE:
+                    return _FAILED_SIGNATURE
+                key_value, key_sort = key_result
+                value_value, value_sort = value_result
+                ordered_items.append((key_sort, value_sort, key_value, value_value))
+
+            ordered_items.sort(key=lambda item: (item[0], item[1]))
+            for index in range(1, len(ordered_items)):
+                previous_key_sort = ordered_items[index - 1][0]
+                current_key_sort = ordered_items[index][0]
+                if previous_key_sort == current_key_sort:
+                    return _FAILED_SIGNATURE
+
+            value = ("dict", tuple((key_value, value_value) for _, _, key_value, value_value in ordered_items))
+            sort_key = ("dict", tuple((key_sort, value_sort) for key_sort, value_sort, _, _ in ordered_items))
+        elif obj_type is list or obj_type is tuple:
+            try:
+                items = list(obj)
+            except RuntimeError:
+                return _FAILED_SIGNATURE
+
+            child_results = []
+            for item in items:
+                child_result = _signature_to_hashable_impl(item, depth + 1, max_depth, active, memo, budget)
+                if child_result is _FAILED_SIGNATURE:
+                    return _FAILED_SIGNATURE
+                child_results.append(child_result)
+
+            container_tag = "list" if obj_type is list else "tuple"
+            value = (container_tag, tuple(child for child, _ in child_results))
+            sort_key = (container_tag, tuple(child_sort for _, child_sort in child_results))
+        else:
+            try:
+                items = list(obj)
+            except RuntimeError:
+                return _FAILED_SIGNATURE
+
+            ordered_items = []
+            for item in items:
+                child_result = _signature_to_hashable_impl(item, depth + 1, max_depth, active, memo, budget)
+                if child_result is _FAILED_SIGNATURE:
+                    return _FAILED_SIGNATURE
+                child_value, child_sort = child_result
+                ordered_items.append((child_sort, child_value))
+
+            ordered_items.sort(key=lambda item: item[0])
+            for index in range(1, len(ordered_items)):
+                previous_sort_key, previous_value = ordered_items[index - 1]
+                current_sort_key, current_value = ordered_items[index]
+                if previous_sort_key == current_sort_key and previous_value != current_value:
+                    return _FAILED_SIGNATURE
+
+            container_tag = "set" if obj_type is set else "frozenset"
+            value = (container_tag, tuple(child_value for _, child_value in ordered_items))
+            sort_key = (container_tag, tuple(child_sort for child_sort, _ in ordered_items))
+    finally:
+        active.discard(obj_id)
+
+    memo[obj_id] = (value, sort_key)
+    return memo[obj_id]
+
+
+def _signature_to_hashable(obj, max_nodes=_MAX_SIGNATURE_CONTAINER_VISITS):
+    """Build the final cache-signature representation in one fail-closed pass."""
+    try:
+        result = _signature_to_hashable_impl(obj, budget={"remaining": max_nodes})
+    except RuntimeError:
+        return Unhashable()
+    if result is _FAILED_SIGNATURE:
+        return Unhashable()
+    return result[0]
+
+
+def to_hashable(obj, max_nodes=_MAX_SIGNATURE_CONTAINER_VISITS):
+    """Convert sanitized prompt inputs into a stable hashable representation.
+
+    The input is expected to already be sanitized to plain built-in containers,
+    but this function still fails safe for anything unexpected. Traversal is
+    iterative and memoized so shared built-in substructures do not trigger
+    exponential re-walks during cache-key construction.
+    """
+    obj_type = type(obj)
+    if obj_type in _PRIMITIVE_SIGNATURE_TYPES or obj_type is Unhashable:
+        return obj
+    if obj_type not in _CONTAINER_SIGNATURE_TYPES:
+        return Unhashable()
+
+    memo = {}
+    active = set()
+    snapshots = {}
+    sort_memo = {}
+    processed = 0
+    # Keep traversal state separate from container snapshots/results.
+    work_stack = [(obj, False)]
+
+    def resolve_value(value):
+        """Resolve a child value from the completed memo table when available."""
+        value_type = type(value)
+        if value_type in _PRIMITIVE_SIGNATURE_TYPES or value_type is Unhashable:
+            return value
+        return memo.get(id(value), Unhashable())
+
+    def is_failed(value):
+        """Return whether a resolved child value represents failed canonicalization."""
+        return type(value) is Unhashable
+
+    def resolve_unordered_values(current_items, container_tag):
+        """Resolve a set-like container or fail closed if ordering is ambiguous."""
+        try:
+            ordered_items = [
+                (_sanitized_sort_key(item, memo=sort_memo), resolve_value(item))
+                for item in current_items
+            ]
+            if any(is_failed(value) for _, value in ordered_items):
+                return Unhashable()
+            ordered_items.sort(key=lambda item: item[0])
+        except RuntimeError:
+            return Unhashable()
+
+        for index in range(1, len(ordered_items)):
+            previous_key, previous_value = ordered_items[index - 1]
+            current_key, current_value = ordered_items[index]
+            if previous_key == current_key and previous_value != current_value:
+                return Unhashable()
+
+        return (container_tag, tuple(value for _, value in ordered_items))
+
+    while work_stack:
+        entry = work_stack.pop()
+        if len(entry) == 3:
+            _, current_id, current_type = entry
+            current = None
+            expanded = True
+        else:
+            current, expanded = entry
+            current_type = type(current)
+            current_id = id(current)
+
+        if not expanded and (current_type in _PRIMITIVE_SIGNATURE_TYPES or current_type is Unhashable):
+            continue
+        if not expanded and current_type not in _CONTAINER_SIGNATURE_TYPES:
+            memo[current_id] = Unhashable()
+            continue
+
+        if current_id in memo:
+            continue
+
+        if expanded:
+            active.discard(current_id)
+            try:
+                items = snapshots.pop(current_id, None)
+                if items is None:
+                    memo[current_id] = Unhashable()
+                    continue
+
+                if current_type is dict:
+                    ordered_items = [
+                        (_sanitized_sort_key(k, memo=sort_memo), k, resolve_value(v))
+                        for k, v in items
+                    ]
+                    if any(type(key) not in _PRIMITIVE_SIGNATURE_TYPES or is_failed(value) for _, key, value in ordered_items):
+                        memo[current_id] = Unhashable()
+                        continue
+                    ordered_items.sort(key=lambda item: item[0])
+                    for index in range(1, len(ordered_items)):
+                        if ordered_items[index - 1][0] == ordered_items[index][0]:
+                            memo[current_id] = Unhashable()
+                            break
+                    else:
+                        memo[current_id] = (
+                            "dict",
+                            tuple((key, value) for _, key, value in ordered_items),
+                        )
+                elif current_type is list:
+                    resolved_items = tuple(resolve_value(item) for item in items)
+                    if any(is_failed(item) for item in resolved_items):
+                        memo[current_id] = Unhashable()
+                    else:
+                        memo[current_id] = ("list", resolved_items)
+                elif current_type is tuple:
+                    resolved_items = tuple(resolve_value(item) for item in items)
+                    if any(is_failed(item) for item in resolved_items):
+                        memo[current_id] = Unhashable()
+                    else:
+                        memo[current_id] = ("tuple", resolved_items)
+                elif current_type is set:
+                    memo[current_id] = resolve_unordered_values(items, "set")
+                else:
+                    memo[current_id] = resolve_unordered_values(items, "frozenset")
+            except RuntimeError:
+                memo[current_id] = Unhashable()
+            continue
+
+        if current_id in active:
+            memo[current_id] = Unhashable()
+            continue
+
+        processed += 1
+        if processed > max_nodes:
+            return Unhashable()
+
+        active.add(current_id)
+        if current_type is dict:
+            try:
+                items = list(current.items())
+                snapshots[current_id] = items
+            except RuntimeError:
+                memo[current_id] = Unhashable()
+                active.discard(current_id)
+                continue
+            for key, value in items:
+                if type(key) not in _PRIMITIVE_SIGNATURE_TYPES:
+                    snapshots.pop(current_id, None)
+                    memo[current_id] = Unhashable()
+                    active.discard(current_id)
+                    break
+            else:
+                work_stack.append(("EXPANDED", current_id, current_type))
+                for _, value in reversed(items):
+                    work_stack.append((value, False))
+                continue
+            continue
+        else:
+            try:
+                items = list(current)
+                snapshots[current_id] = items
+            except RuntimeError:
+                memo[current_id] = Unhashable()
+                active.discard(current_id)
+                continue
+            work_stack.append(("EXPANDED", current_id, current_type))
+            for item in reversed(items):
+                work_stack.append((item, False))
+
+    return memo.get(id(obj), Unhashable())
+
 class CacheKeySetID(CacheKeySet):
+    """Cache-key strategy that keys nodes by node id and class type."""
     def __init__(self, dynprompt, node_ids, is_changed_cache):
+        """Initialize identity-based cache keys for the supplied dynamic prompt."""
         super().__init__(dynprompt, node_ids, is_changed_cache)
         self.dynprompt = dynprompt
 
     async def add_keys(self, node_ids):
+        """Populate identity-based keys for nodes that exist in the dynamic prompt."""
         for node_id in node_ids:
             if node_id in self.keys:
                 continue
@@ -80,15 +442,19 @@ class CacheKeySetID(CacheKeySet):
             self.subcache_keys[node_id] = (node_id, node["class_type"])
 
 class CacheKeySetInputSignature(CacheKeySet):
+    """Cache-key strategy that hashes a node's immediate inputs plus ancestor references."""
     def __init__(self, dynprompt, node_ids, is_changed_cache):
+        """Initialize input-signature-based cache keys for the supplied dynamic prompt."""
         super().__init__(dynprompt, node_ids, is_changed_cache)
         self.dynprompt = dynprompt
         self.is_changed_cache = is_changed_cache
 
     def include_node_id_in_input(self) -> bool:
+        """Return whether node ids should be included in computed input signatures."""
         return False
 
     async def add_keys(self, node_ids):
+        """Populate input-signature-based keys for nodes in the dynamic prompt."""
         for node_id in node_ids:
             if node_id in self.keys:
                 continue
@@ -99,21 +465,37 @@ class CacheKeySetInputSignature(CacheKeySet):
             self.subcache_keys[node_id] = (node_id, node["class_type"])
 
     async def get_node_signature(self, dynprompt, node_id):
+        """Build the full cache signature for a node and its ordered ancestors."""
         signature = []
         ancestors, order_mapping = self.get_ordered_ancestry(dynprompt, node_id)
-        signature.append(await self.get_immediate_node_signature(dynprompt, node_id, order_mapping))
+        immediate = await self.get_immediate_node_signature(dynprompt, node_id, order_mapping)
+        if type(immediate) is Unhashable:
+            return immediate
+        signature.append(immediate)
         for ancestor_id in ancestors:
-            signature.append(await self.get_immediate_node_signature(dynprompt, ancestor_id, order_mapping))
-        return to_hashable(signature)
+            immediate = await self.get_immediate_node_signature(dynprompt, ancestor_id, order_mapping)
+            if type(immediate) is Unhashable:
+                return immediate
+            signature.append(immediate)
+        return tuple(signature)
 
     async def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping):
+        """Build the immediate cache-signature fragment for a node.
+
+        Link inputs are reduced to ancestor references here. Non-link values
+        are canonicalized or failed closed before being appended so the final
+        node signature is assembled from already-hashable fragments.
+        """
         if not dynprompt.has_node(node_id):
             # This node doesn't exist -- we can't cache it.
-            return [float("NaN")]
+            return Unhashable()
         node = dynprompt.get_node(node_id)
         class_type = node["class_type"]
         class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
-        signature = [class_type, await self.is_changed_cache.get(node_id)]
+        is_changed_signature = _shallow_is_changed_signature(await self.is_changed_cache.get(node_id))
+        if type(is_changed_signature) is Unhashable:
+            return is_changed_signature
+        signature = [class_type, is_changed_signature]
         if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT) or include_unique_id_in_input(class_type):
             signature.append(node_id)
         inputs = node["inputs"]
@@ -123,18 +505,23 @@ class CacheKeySetInputSignature(CacheKeySet):
                 ancestor_index = ancestor_order_mapping[ancestor_id]
                 signature.append((key,("ANCESTOR", ancestor_index, ancestor_socket)))
             else:
-                signature.append((key, inputs[key]))
-        return signature
+                value_signature = to_hashable(inputs[key])
+                if type(value_signature) is Unhashable:
+                    return value_signature
+                signature.append((key, value_signature))
+        return tuple(signature)
 
     # This function returns a list of all ancestors of the given node. The order of the list is
     # deterministic based on which specific inputs the ancestor is connected by.
     def get_ordered_ancestry(self, dynprompt, node_id):
+        """Return ancestors in deterministic traversal order and their index mapping."""
         ancestors = []
         order_mapping = {}
         self.get_ordered_ancestry_internal(dynprompt, node_id, ancestors, order_mapping)
         return ancestors, order_mapping
 
     def get_ordered_ancestry_internal(self, dynprompt, node_id, ancestors, order_mapping):
+        """Recursively collect ancestors in input order without revisiting prior nodes."""
         if not dynprompt.has_node(node_id):
             return
         inputs = dynprompt.get_node(node_id)["inputs"]

comfy_execution/graph_utils.py

@@ -1,11 +1,17 @@
 def is_link(obj):
-    if not isinstance(obj, list):
+    """Return whether obj is a plain prompt link of the form [node_id, output_index]."""
+    # Prompt links produced by the frontend / GraphBuilder are plain Python
+    # lists in the form [node_id, output_index]. Some custom-node paths can
+    # inject foreign runtime objects into prompt inputs during on-prompt graph
+    # rewriting or subgraph construction. Be strict here so cache signature
+    # building never tries to treat list-like proxy objects as links.
+    if type(obj) is not list:
         return False
     if len(obj) != 2:
         return False
-    if not isinstance(obj[0], str):
+    if type(obj[0]) is not str:
         return False
-    if not isinstance(obj[1], int) and not isinstance(obj[1], float):
+    if type(obj[1]) is not int:
         return False
     return True
 

comfy_extras/nodes_advanced_samplers.py

@@ -92,7 +92,7 @@ class SamplerEulerCFGpp(io.ComfyNode):
         return io.Schema(
             node_id="SamplerEulerCFGpp",
             display_name="SamplerEulerCFG++",
-            category="_for_testing",  # "sampling/custom_sampling/samplers"
+            category="experimental",  # "sampling/custom_sampling/samplers"
             inputs=[
                 io.Combo.Input("version", options=["regular", "alternative"], advanced=True),
             ],

comfy_extras/nodes_ar_video.py

@@ -2,6 +2,7 @@
 ComfyUI nodes for autoregressive video generation (Causal Forcing, Self-Forcing, etc.).
   - EmptyARVideoLatent: create 5D [B, C, T, H, W] video latent tensors
   - SamplerARVideo: SAMPLER for the block-by-block autoregressive denoising loop
+  - ARVideoI2V: image-to-video conditioning for AR models (seeds KV cache with start image)
 """
 
 import torch
@@ -9,6 +10,7 @@ from typing_extensions import override
 
 import comfy.model_management
 import comfy.samplers
+import comfy.utils
 from comfy_api.latest import ComfyExtension, io
 
 
@@ -71,12 +73,62 @@ class SamplerARVideo(io.ComfyNode):
         return io.NodeOutput(comfy.samplers.ksampler("ar_video", extra_options))
 
 
+class ARVideoI2V(io.ComfyNode):
+    """Image-to-video setup for AR video models (Causal Forcing, Self-Forcing).
+
+    VAE-encodes the start image and stores it in the model's transformer_options
+    so that sample_ar_video can seed the KV cache before denoising.
+    Uses the same T2V model checkpoint -- no separate I2V architecture needed.
+    """
+
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ARVideoI2V",
+            category="conditioning/video_models",
+            inputs=[
+                io.Model.Input("model"),
+                io.Vae.Input("vae"),
+                io.Image.Input("start_image"),
+                io.Int.Input("width", default=832, min=16, max=8192, step=16),
+                io.Int.Input("height", default=480, min=16, max=8192, step=16),
+                io.Int.Input("length", default=81, min=1, max=1024, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=64),
+            ],
+            outputs=[
+                io.Model.Output(display_name="MODEL"),
+                io.Latent.Output(display_name="LATENT"),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, vae, start_image, width, height, length, batch_size) -> io.NodeOutput:
+        start_image = comfy.utils.common_upscale(
+            start_image[:1].movedim(-1, 1), width, height, "bilinear", "center"
+        ).movedim(1, -1)
+
+        initial_latent = vae.encode(start_image[:, :, :, :3])
+
+        m = model.clone()
+        to = m.model_options.setdefault("transformer_options", {})
+        ar_cfg = to.setdefault("ar_config", {})
+        ar_cfg["initial_latent"] = initial_latent
+
+        lat_t = ((length - 1) // 4) + 1
+        latent = torch.zeros(
+            [batch_size, 16, lat_t, height // 8, width // 8],
+            device=comfy.model_management.intermediate_device(),
+        )
+        return io.NodeOutput(m, {"samples": latent})
+
+
 class ARVideoExtension(ComfyExtension):
     @override
     async def get_node_list(self) -> list[type[io.ComfyNode]]:
         return [
             EmptyARVideoLatent,
             SamplerARVideo,
+            ARVideoI2V,
         ]
 
 

comfy_extras/nodes_attention_multiply.py

@@ -25,7 +25,7 @@ class UNetSelfAttentionMultiply(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="UNetSelfAttentionMultiply",
-            category="_for_testing/attention_experiments",
+            category="experimental/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
@@ -48,7 +48,7 @@ class UNetCrossAttentionMultiply(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="UNetCrossAttentionMultiply",
-            category="_for_testing/attention_experiments",
+            category="experimental/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
@@ -72,7 +72,7 @@ class CLIPAttentionMultiply(io.ComfyNode):
         return io.Schema(
             node_id="CLIPAttentionMultiply",
             search_aliases=["clip attention scale", "text encoder attention"],
-            category="_for_testing/attention_experiments",
+            category="experimental/attention_experiments",
             inputs=[
                 io.Clip.Input("clip"),
                 io.Float.Input("q", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),
@@ -106,7 +106,7 @@ class UNetTemporalAttentionMultiply(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="UNetTemporalAttentionMultiply",
-            category="_for_testing/attention_experiments",
+            category="experimental/attention_experiments",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("self_structural", default=1.0, min=0.0, max=10.0, step=0.01, advanced=True),

comfy_extras/nodes_audio_encoder.py

@@ -10,6 +10,7 @@ class AudioEncoderLoader(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="AudioEncoderLoader",
+            display_name="Load Audio Encoder",
             category="loaders",
             inputs=[
                 io.Combo.Input(

comfy_extras/nodes_camera_trajectory.py

@@ -153,7 +153,7 @@ class WanCameraEmbedding(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="WanCameraEmbedding",
-            category="camera",
+            category="conditioning/video_models",
             inputs=[
                 io.Combo.Input(
                     "camera_pose",

comfy_extras/nodes_cond.py

@@ -8,7 +8,7 @@ class CLIPTextEncodeControlnet(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="CLIPTextEncodeControlnet",
-            category="_for_testing/conditioning",
+            category="experimental/conditioning",
             inputs=[
                 io.Clip.Input("clip"),
                 io.Conditioning.Input("conditioning"),
@@ -35,7 +35,7 @@ class T5TokenizerOptions(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="T5TokenizerOptions",
-            category="_for_testing/conditioning",
+            category="experimental/conditioning",
             inputs=[
                 io.Clip.Input("clip"),
                 io.Int.Input("min_padding", default=0, min=0, max=10000, step=1, advanced=True),

comfy_extras/nodes_context_windows.py

@@ -10,7 +10,7 @@ class ContextWindowsManualNode(io.ComfyNode):
         return io.Schema(
             node_id="ContextWindowsManual",
             display_name="Context Windows (Manual)",
-            category="context",
+            category="model_patches",
             description="Manually set context windows.",
             inputs=[
                 io.Model.Input("model", tooltip="The model to apply context windows to during sampling."),

comfy_extras/nodes_custom_sampler.py

@@ -984,7 +984,7 @@ class AddNoise(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="AddNoise",
-            category="_for_testing/custom_sampling/noise",
+            category="experimental/custom_sampling/noise",
             is_experimental=True,
             inputs=[
                 io.Model.Input("model"),
@@ -1034,7 +1034,7 @@ class ManualSigmas(io.ComfyNode):
         return io.Schema(
             node_id="ManualSigmas",
             search_aliases=["custom noise schedule", "define sigmas"],
-            category="_for_testing/custom_sampling",
+            category="experimental/custom_sampling",
             is_experimental=True,
             inputs=[
                 io.String.Input("sigmas", default="1, 0.5", multiline=False)

comfy_extras/nodes_differential_diffusion.py

@@ -13,7 +13,7 @@ class DifferentialDiffusion(io.ComfyNode):
             node_id="DifferentialDiffusion",
             search_aliases=["inpaint gradient", "variable denoise strength"],
             display_name="Differential Diffusion",
-            category="_for_testing",
+            category="experimental",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input(

comfy_extras/nodes_fresca.py

@@ -60,7 +60,7 @@ class FreSca(io.ComfyNode):
             node_id="FreSca",
             search_aliases=["frequency guidance"],
             display_name="FreSca",
-            category="_for_testing",
+            category="experimental",
             description="Applies frequency-dependent scaling to the guidance",
             inputs=[
                 io.Model.Input("model"),

comfy_extras/nodes_hunyuan.py

@@ -131,6 +131,8 @@ class HunyuanVideo15SuperResolution(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="HunyuanVideo15SuperResolution",
+            display_name="Hunyuan Video 1.5 Super Resolution",
+            category="conditioning/video_models",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),
@@ -381,6 +383,8 @@ class HunyuanRefinerLatent(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="HunyuanRefinerLatent",
+            display_name="Hunyuan Latent Refiner",
+            category="conditioning/video_models",
             inputs=[
                 io.Conditioning.Input("positive"),
                 io.Conditioning.Input("negative"),

comfy_extras/nodes_hunyuan3d.py

@@ -40,7 +40,7 @@ class Hunyuan3Dv2Conditioning(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="Hunyuan3Dv2Conditioning",
-            category="conditioning/video_models",
+            category="conditioning/3d_models",
             inputs=[
                 IO.ClipVisionOutput.Input("clip_vision_output"),
             ],
@@ -65,7 +65,7 @@ class Hunyuan3Dv2ConditioningMultiView(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="Hunyuan3Dv2ConditioningMultiView",
-            category="conditioning/video_models",
+            category="conditioning/3d_models",
             inputs=[
                 IO.ClipVisionOutput.Input("front", optional=True),
                 IO.ClipVisionOutput.Input("left", optional=True),
@@ -424,6 +424,7 @@ class VoxelToMeshBasic(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="VoxelToMeshBasic",
+            display_name="Voxel to Mesh (Basic)",
             category="3d",
             inputs=[
                 IO.Voxel.Input("voxel"),
@@ -453,6 +454,7 @@ class VoxelToMesh(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="VoxelToMesh",
+            display_name="Voxel to Mesh",
             category="3d",
             inputs=[
                 IO.Voxel.Input("voxel"),

comfy_extras/nodes_hypernetwork.py

@@ -102,6 +102,7 @@ class HypernetworkLoader(IO.ComfyNode):
     def define_schema(cls):
         return IO.Schema(
             node_id="HypernetworkLoader",
+            display_name="Load Hypernetwork",
             category="loaders",
             inputs=[
                 IO.Model.Input("model"),

comfy_extras/nodes_lora_extract.py

@@ -91,7 +91,7 @@ class LoraSave(io.ComfyNode):
             node_id="LoraSave",
             search_aliases=["export lora"],
             display_name="Extract and Save Lora",
-            category="_for_testing",
+            category="experimental",
             inputs=[
                 io.String.Input("filename_prefix", default="loras/ComfyUI_extracted_lora"),
                 io.Int.Input("rank", default=8, min=1, max=4096, step=1, advanced=True),

comfy_extras/nodes_lt.py

@@ -594,7 +594,8 @@ class LTXVPreprocess(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LTXVPreprocess",
-            category="image",
+            display_name="LTXV Preprocess",
+            category="video/preprocessors",
             inputs=[
                 io.Image.Input("image"),
                 io.Int.Input(

comfy_extras/nodes_mahiro.py

@@ -11,7 +11,7 @@ class Mahiro(io.ComfyNode):
         return io.Schema(
             node_id="Mahiro",
             display_name="Positive-Biased Guidance",
-            category="_for_testing",
+            category="experimental",
             description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.",
             inputs=[
                 io.Model.Input("model"),

comfy_extras/nodes_math.py

@@ -70,7 +70,7 @@ class MathExpressionNode(io.ComfyNode):
         return io.Schema(
             node_id="ComfyMathExpression",
             display_name="Math Expression",
-            category="math",
+            category="logic",
             search_aliases=[
                 "expression", "formula", "calculate", "calculator",
                 "eval", "math",

comfy_extras/nodes_number_convert.py

@@ -21,7 +21,7 @@ class NumberConvertNode(io.ComfyNode):
         return io.Schema(
             node_id="ComfyNumberConvert",
             display_name="Number Convert",
-            category="math",
+            category="utils",
             search_aliases=[
                 "int to float", "float to int", "number convert",
                 "int2float", "float2int", "cast", "parse number",

comfy_extras/nodes_perpneg.py

@@ -24,8 +24,8 @@ class PerpNeg(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PerpNeg",
-            display_name="Perp-Neg (DEPRECATED by PerpNegGuider)",
-            category="_for_testing",
+            display_name="Perp-Neg (DEPRECATED by Perp-Neg Guider)",
+            category="experimental",
             inputs=[
                 io.Model.Input("model"),
                 io.Conditioning.Input("empty_conditioning"),
@@ -127,7 +127,8 @@ class PerpNegGuider(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PerpNegGuider",
-            category="_for_testing",
+            display_name="Perp-Neg Guider",
+            category="experimental",
             inputs=[
                 io.Model.Input("model"),
                 io.Conditioning.Input("positive"),

comfy_extras/nodes_photomaker.py

@@ -123,7 +123,7 @@ class PhotoMakerLoader(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PhotoMakerLoader",
-            category="_for_testing/photomaker",
+            category="experimental/photomaker",
             inputs=[
                 io.Combo.Input("photomaker_model_name", options=folder_paths.get_filename_list("photomaker")),
             ],
@@ -149,7 +149,7 @@ class PhotoMakerEncode(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="PhotoMakerEncode",
-            category="_for_testing/photomaker",
+            category="experimental/photomaker",
             inputs=[
                 io.Photomaker.Input("photomaker"),
                 io.Image.Input("image"),

comfy_extras/nodes_post_processing.py

@@ -116,6 +116,7 @@ class Quantize(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ImageQuantize",
+            display_name="Quantize Image",
             category="image/postprocessing",
             inputs=[
                 io.Image.Input("image"),
@@ -181,6 +182,7 @@ class Sharpen(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="ImageSharpen",
+            display_name="Sharpen Image",
             category="image/postprocessing",
             inputs=[
                 io.Image.Input("image"),
@@ -436,7 +438,7 @@ class ResizeImageMaskNode(io.ComfyNode):
             node_id="ResizeImageMaskNode",
             display_name="Resize Image/Mask",
             description="Resize an image or mask using various scaling methods.",
-            category="transform",
+            category="image/transform",
             search_aliases=["resize", "resize image", "resize mask", "scale", "scale image", "scale mask", "image resize", "change size", "dimensions", "shrink", "enlarge"],
             inputs=[
                 io.MatchType.Input("input", template=template),

comfy_extras/nodes_rtdetr.py

@@ -15,7 +15,7 @@ class RTDETR_detect(io.ComfyNode):
         return io.Schema(
             node_id="RTDETR_detect",
             display_name="RT-DETR Detect",
-            category="detection/",
+            category="detection",
             search_aliases=["bbox", "bounding box", "object detection", "coco"],
             inputs=[
                 io.Model.Input("model", display_name="model"),
@@ -71,7 +71,7 @@ class DrawBBoxes(io.ComfyNode):
         return io.Schema(
             node_id="DrawBBoxes",
             display_name="Draw BBoxes",
-            category="detection/",
+            category="detection",
             search_aliases=["bbox", "bounding box", "object detection", "rt_detr", "visualize detections", "coco"],
             inputs=[
                 io.Image.Input("image", optional=True),

comfy_extras/nodes_sag.py

@@ -113,7 +113,7 @@ class SelfAttentionGuidance(io.ComfyNode):
         return io.Schema(
             node_id="SelfAttentionGuidance",
             display_name="Self-Attention Guidance",
-            category="_for_testing",
+            category="experimental",
             inputs=[
                 io.Model.Input("model"),
                 io.Float.Input("scale", default=0.5, min=-2.0, max=5.0, step=0.01),

comfy_extras/nodes_sam3.py

@@ -93,7 +93,7 @@ class SAM3_Detect(io.ComfyNode):
         return io.Schema(
             node_id="SAM3_Detect",
             display_name="SAM3 Detect",
-            category="detection/",
+            category="detection",
             search_aliases=["sam3", "segment anything", "open vocabulary", "text detection", "segment"],
             inputs=[
                 io.Model.Input("model", display_name="model"),
@@ -265,15 +265,15 @@ class SAM3_VideoTrack(io.ComfyNode):
         return io.Schema(
             node_id="SAM3_VideoTrack",
             display_name="SAM3 Video Track",
-            category="detection/",
+            category="detection",
             search_aliases=["sam3", "video", "track", "propagate"],
             inputs=[
                 io.Image.Input("images", display_name="images", tooltip="Video frames as batched images"),
                 io.Model.Input("model", display_name="model"),
                 io.Mask.Input("initial_mask", display_name="initial_mask", optional=True, tooltip="Mask(s) for the first frame to track (one per object)"),
                 io.Conditioning.Input("conditioning", display_name="conditioning", optional=True, tooltip="Text conditioning for detecting new objects during tracking"),
-                io.Float.Input("detection_threshold", display_name="detection_threshold", default=0.5, min=0.0, max=1.0, step=0.01, tooltip="Score threshold for text-prompted detection"),
-                io.Int.Input("max_objects", display_name="max_objects", default=0, min=0, tooltip="Max tracked objects (0=unlimited). Initial masks count toward this limit."),
+                io.Float.Input("detection_threshold", display_name="detection_threshold", default=0.5, min=0.0, max=1.0, step=0.01, tooltip="Score threshold for text-prompted detection."),
+                io.Int.Input("max_objects", display_name="max_objects", default=4, min=0, max=64, tooltip="Max tracked objects. Initial masks count toward this limit. 0 uses the internal cap of 64."),
                 io.Int.Input("detect_interval", display_name="detect_interval", default=1, min=1, tooltip="Run detection every N frames (1=every frame). Higher values save compute."),
             ],
             outputs=[
@@ -290,8 +290,7 @@ class SAM3_VideoTrack(io.ComfyNode):
         dtype = model.model.get_dtype()
         sam3_model = model.model.diffusion_model
 
-        frames = images[..., :3].movedim(-1, 1)
-        frames_in = comfy.utils.common_upscale(frames, 1008, 1008, "bilinear", crop="disabled").to(device=device, dtype=dtype)
+        frames_in = images[..., :3].movedim(-1, 1)
 
         init_masks = None
         if initial_mask is not None:
@@ -308,7 +307,7 @@ class SAM3_VideoTrack(io.ComfyNode):
         result = sam3_model.forward_video(
             images=frames_in, initial_masks=init_masks, pbar=pbar, text_prompts=text_prompts,
             new_det_thresh=detection_threshold, max_objects=max_objects,
-            detect_interval=detect_interval)
+            detect_interval=detect_interval, target_device=device, target_dtype=dtype)
         result["orig_size"] = (H, W)
         return io.NodeOutput(result)
 
@@ -321,7 +320,7 @@ class SAM3_TrackPreview(io.ComfyNode):
         return io.Schema(
             node_id="SAM3_TrackPreview",
             display_name="SAM3 Track Preview",
-            category="detection/",
+            category="detection",
             inputs=[
                 SAM3TrackData.Input("track_data", display_name="track_data"),
                 io.Image.Input("images", display_name="images", optional=True),
@@ -449,14 +448,18 @@ class SAM3_TrackPreview(io.ComfyNode):
                     cx = (bool_masks * grid_x).sum(dim=(-1, -2)) // area
                     has = area > 1
                     scores = track_data.get("scores", [])
+                    label_scale = max(3, H // 240) # Scale font with resolutio
+                    size_caps = (area.float().sqrt() / 15).clamp_(min=1).long().tolist() #cap per-object so the number doesn't dwarf small masks
                     for obj_idx in range(N_obj):
                         if has[obj_idx]:
                             _cx, _cy = int(cx[obj_idx]), int(cy[obj_idx])
                             color = cls.COLORS[obj_idx % len(cls.COLORS)]
-                            SAM3_TrackPreview._draw_number_gpu(frame_gpu, obj_idx, _cx, _cy, color)
+                            obj_scale = min(label_scale, size_caps[obj_idx])
+                            score_scale = max(1, obj_scale * 2 // 3)
+                            SAM3_TrackPreview._draw_number_gpu(frame_gpu, obj_idx, _cx, _cy, color, scale=obj_scale)
                             if obj_idx < len(scores) and scores[obj_idx] < 1.0:
                                 SAM3_TrackPreview._draw_number_gpu(frame_gpu, int(scores[obj_idx] * 100),
-                                                                   _cx, _cy + 5 * 3 + 3, color, scale=2)
+                                                                   _cx, _cy + 5 * obj_scale + 3, color, scale=score_scale)
                     frame_cpu.copy_(frame_gpu.clamp_(0, 1).mul_(255).byte())
                 else:
                     frame_cpu.copy_(frame.clamp_(0, 1).mul_(255).byte())
@@ -475,7 +478,7 @@ class SAM3_TrackToMask(io.ComfyNode):
         return io.Schema(
             node_id="SAM3_TrackToMask",
             display_name="SAM3 Track to Mask",
-            category="detection/",
+            category="detection",
             inputs=[
                 SAM3TrackData.Input("track_data", display_name="track_data"),
                 io.String.Input("object_indices", display_name="object_indices", default="",
@@ -507,9 +510,10 @@ class SAM3_TrackToMask(io.ComfyNode):
         if not indices:
             return io.NodeOutput(torch.zeros(N, H, W, device=comfy.model_management.intermediate_device()))
 
-        selected = packed[:, indices]
-        binary = unpack_masks(selected)  # [N, len(indices), Hm, Wm] bool
-        union = binary.any(dim=1, keepdim=True).float()
+        union_packed = packed[:, indices[0]].clone()
+        for i in indices[1:]:
+            union_packed |= packed[:, i]
+        union = unpack_masks(union_packed).unsqueeze(1).float()  # [N, 1, Hm, Wm]
         mask_out = F.interpolate(union, size=(H, W), mode="bilinear", align_corners=False)[:, 0]
         return io.NodeOutput(mask_out)
 

comfy_extras/nodes_stable_cascade.py

@@ -119,7 +119,7 @@ class StableCascade_SuperResolutionControlnet(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="StableCascade_SuperResolutionControlnet",
-            category="_for_testing/stable_cascade",
+            category="experimental/stable_cascade",
             is_experimental=True,
             inputs=[
                 io.Image.Input("image"),

comfy_extras/nodes_textgen.py

@@ -26,7 +26,8 @@ class TextGenerate(io.ComfyNode):
 
         return io.Schema(
             node_id="TextGenerate",
-            category="textgen",
+            display_name="Generate Text",
+            category="text",
             search_aliases=["LLM", "gemma"],
             inputs=[
                 io.Clip.Input("clip"),
@@ -157,6 +158,7 @@ class TextGenerateLTX2Prompt(TextGenerate):
         parent_schema = super().define_schema()
         return io.Schema(
             node_id="TextGenerateLTX2Prompt",
+            display_name="Generate LTX2 Prompt",
             category=parent_schema.category,
             inputs=parent_schema.inputs,
             outputs=parent_schema.outputs,

comfy_extras/nodes_torch_compile.py

@@ -10,7 +10,7 @@ class TorchCompileModel(io.ComfyNode):
     def define_schema(cls) -> io.Schema:
         return io.Schema(
             node_id="TorchCompileModel",
-            category="_for_testing",
+            category="experimental",
             inputs=[
                 io.Model.Input("model"),
                 io.Combo.Input(

comfy_extras/nodes_train.py

@@ -1361,7 +1361,7 @@ class SaveLoRA(io.ComfyNode):
             node_id="SaveLoRA",
             search_aliases=["export lora"],
             display_name="Save LoRA Weights",
-            category="loaders",
+            category="advanced/model_merging",
             is_experimental=True,
             is_output_node=True,
             inputs=[

comfy_extras/nodes_video_model.py

@@ -15,7 +15,7 @@ class ImageOnlyCheckpointLoader:
     RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE")
     FUNCTION = "load_checkpoint"
 
-    CATEGORY = "loaders/video_models"
+    CATEGORY = "loaders"
 
     def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
         ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)

custom_nodes/websocket_image_save.py

@@ -22,7 +22,7 @@ class SaveImageWebsocket:
 
     OUTPUT_NODE = True
 
-    CATEGORY = "api/image"
+    CATEGORY = "image"
 
     def save_images(self, images):
         pbar = comfy.utils.ProgressBar(images.shape[0])
@@ -42,3 +42,7 @@ class SaveImageWebsocket:
 NODE_CLASS_MAPPINGS = {
     "SaveImageWebsocket": SaveImageWebsocket,
 }
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "SaveImageWebsocket": "Save Image (Websocket)",
+}

nodes.py

@@ -330,7 +330,7 @@ class VAEDecodeTiled:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "decode"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
 
     def decode(self, vae, samples, tile_size, overlap=64, temporal_size=64, temporal_overlap=8):
         if tile_size < overlap * 4:
@@ -377,7 +377,7 @@ class VAEEncodeTiled:
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "encode"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
 
     def encode(self, vae, pixels, tile_size, overlap, temporal_size=64, temporal_overlap=8):
         t = vae.encode_tiled(pixels, tile_x=tile_size, tile_y=tile_size, overlap=overlap, tile_t=temporal_size, overlap_t=temporal_overlap)
@@ -493,7 +493,7 @@ class SaveLatent:
 
     OUTPUT_NODE = True
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
 
     def save(self, samples, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
@@ -538,7 +538,7 @@ class LoadLatent:
         files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")]
         return {"required": {"latent": [sorted(files), ]}, }
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
 
     RETURN_TYPES = ("LATENT", )
     FUNCTION = "load"
@@ -1443,7 +1443,7 @@ class LatentBlend:
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "blend"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
 
     def blend(self, samples1, samples2, blend_factor:float, blend_mode: str="normal"):
 
@@ -2092,6 +2092,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "StyleModelLoader": "Load Style Model",
     "CLIPVisionLoader": "Load CLIP Vision",
     "UNETLoader": "Load Diffusion Model",
+    "unCLIPCheckpointLoader": "Load unCLIP Checkpoint",
+    "GLIGENLoader": "Load GLIGEN Model",
     # Conditioning
     "CLIPVisionEncode": "CLIP Vision Encode",
     "StyleModelApply": "Apply Style Model",
@@ -2140,7 +2142,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImageSharpen": "Sharpen Image",
     "ImageScaleToTotalPixels": "Scale Image to Total Pixels",
     "GetImageSize": "Get Image Size",
-    # _for_testing
+    # experimental
     "VAEDecodeTiled": "VAE Decode (Tiled)",
     "VAEEncodeTiled": "VAE Encode (Tiled)",
 }

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.43.17
-comfyui-workflow-templates==0.9.69
+comfyui-workflow-templates==0.9.72
 comfyui-embedded-docs==0.4.4
 torch
 torchsde

server.py

@@ -560,7 +560,7 @@ class PromptServer():
                             buffer.seek(0)
 
                             return web.Response(body=buffer.read(), content_type=f'image/{image_format}',
-                                                headers={"Content-Disposition": f"attachment; filename=\"{filename}\""})
+                                                headers={"Content-Disposition": f"filename=\"{filename}\""})
 
                     if 'channel' not in request.rel_url.query:
                         channel = 'rgba'
@@ -580,7 +580,7 @@ class PromptServer():
                             buffer.seek(0)
 
                             return web.Response(body=buffer.read(), content_type='image/png',
-                                                headers={"Content-Disposition": f"attachment; filename=\"{filename}\""})
+                                                headers={"Content-Disposition": f"filename=\"{filename}\""})
 
                     elif channel == 'a':
                         with Image.open(file) as img:
@@ -597,7 +597,7 @@ class PromptServer():
                             alpha_buffer.seek(0)
 
                             return web.Response(body=alpha_buffer.read(), content_type='image/png',
-                                                headers={"Content-Disposition": f"attachment; filename=\"{filename}\""})
+                                                headers={"Content-Disposition": f"filename=\"{filename}\""})
                     else:
                         # Use the content type from asset resolution if available,
                         # otherwise guess from the filename.
@@ -614,7 +614,7 @@ class PromptServer():
                         return web.FileResponse(
                             file,
                             headers={
-                                "Content-Disposition": f"attachment; filename=\"{filename}\"",
+                                "Content-Disposition": f"filename=\"{filename}\"",
                                 "Content-Type": content_type
                             }
                         )

tests-unit/app_test/node_replace_manager_test.py

@@ -0,0 +1,90 @@
+"""Tests for NodeReplaceManager registration behavior."""
+import importlib
+import sys
+import types
+
+import pytest
+
+
+@pytest.fixture
+def NodeReplaceManager(monkeypatch):
+    """Provide NodeReplaceManager with `nodes` stubbed.
+
+    `app.node_replace_manager` does `import nodes` at module level, which pulls in
+    torch + the full ComfyUI graph. register() doesn't actually need it, so we
+    stub `nodes` per-test (via monkeypatch so it's torn down) and reload the
+    module so it picks up the stub instead of any cached real import.
+    """
+    fake_nodes = types.ModuleType("nodes")
+    fake_nodes.NODE_CLASS_MAPPINGS = {}
+    monkeypatch.setitem(sys.modules, "nodes", fake_nodes)
+    monkeypatch.delitem(sys.modules, "app.node_replace_manager", raising=False)
+    module = importlib.import_module("app.node_replace_manager")
+    yield module.NodeReplaceManager
+    # Drop the freshly-imported module so the next test (or a later real import
+    # of `nodes`) starts from a clean slate.
+    sys.modules.pop("app.node_replace_manager", None)
+
+
+class FakeNodeReplace:
+    """Lightweight stand-in for comfy_api.latest._io.NodeReplace."""
+    def __init__(self, new_node_id, old_node_id, old_widget_ids=None,
+                 input_mapping=None, output_mapping=None):
+        self.new_node_id = new_node_id
+        self.old_node_id = old_node_id
+        self.old_widget_ids = old_widget_ids
+        self.input_mapping = input_mapping
+        self.output_mapping = output_mapping
+
+
+def test_register_adds_replacement(NodeReplaceManager):
+    manager = NodeReplaceManager()
+    manager.register(FakeNodeReplace(new_node_id="NewNode", old_node_id="OldNode"))
+    assert manager.has_replacement("OldNode")
+    assert len(manager.get_replacement("OldNode")) == 1
+
+
+def test_register_allows_multiple_alternatives_for_same_old_node(NodeReplaceManager):
+    """Different new_node_ids for the same old_node_id should all be kept."""
+    manager = NodeReplaceManager()
+    manager.register(FakeNodeReplace(new_node_id="AltA", old_node_id="OldNode"))
+    manager.register(FakeNodeReplace(new_node_id="AltB", old_node_id="OldNode"))
+    replacements = manager.get_replacement("OldNode")
+    assert len(replacements) == 2
+    assert {r.new_node_id for r in replacements} == {"AltA", "AltB"}
+
+
+def test_register_is_idempotent_for_duplicate_pair(NodeReplaceManager):
+    """Re-registering the same (old_node_id, new_node_id) should be a no-op."""
+    manager = NodeReplaceManager()
+    manager.register(FakeNodeReplace(new_node_id="NewNode", old_node_id="OldNode"))
+    manager.register(FakeNodeReplace(new_node_id="NewNode", old_node_id="OldNode"))
+    manager.register(FakeNodeReplace(new_node_id="NewNode", old_node_id="OldNode"))
+    assert len(manager.get_replacement("OldNode")) == 1
+
+
+def test_register_idempotent_preserves_first_registration(NodeReplaceManager):
+    """First registration wins; later duplicates with different mappings are ignored."""
+    manager = NodeReplaceManager()
+    first = FakeNodeReplace(
+        new_node_id="NewNode", old_node_id="OldNode",
+        input_mapping=[{"new_id": "a", "old_id": "x"}],
+    )
+    second = FakeNodeReplace(
+        new_node_id="NewNode", old_node_id="OldNode",
+        input_mapping=[{"new_id": "b", "old_id": "y"}],
+    )
+    manager.register(first)
+    manager.register(second)
+    replacements = manager.get_replacement("OldNode")
+    assert len(replacements) == 1
+    assert replacements[0] is first
+
+
+def test_register_dedupe_does_not_affect_other_old_nodes(NodeReplaceManager):
+    manager = NodeReplaceManager()
+    manager.register(FakeNodeReplace(new_node_id="NewA", old_node_id="OldA"))
+    manager.register(FakeNodeReplace(new_node_id="NewA", old_node_id="OldA"))
+    manager.register(FakeNodeReplace(new_node_id="NewB", old_node_id="OldB"))
+    assert len(manager.get_replacement("OldA")) == 1
+    assert len(manager.get_replacement("OldB")) == 1

tests-unit/execution_test/caching_test.py

@@ -0,0 +1,473 @@
+"""Unit tests for cache-signature canonicalization hardening."""
+
+import asyncio
+import importlib
+import sys
+import types
+
+import pytest
+
+
+class _DummyNode:
+    """Minimal node stub used to satisfy cache-signature class lookups."""
+
+    @staticmethod
+    def INPUT_TYPES():
+        """Return a minimal empty input schema for unit tests."""
+        return {"required": {}}
+
+
+class _FakeDynPrompt:
+    """Small DynamicPrompt stand-in with only the methods these tests need."""
+
+    def __init__(self, nodes_by_id):
+        """Store test nodes by id."""
+        self._nodes_by_id = nodes_by_id
+
+    def has_node(self, node_id):
+        """Return whether the fake prompt contains the requested node."""
+        return node_id in self._nodes_by_id
+
+    def get_node(self, node_id):
+        """Return the stored node payload for the requested id."""
+        return self._nodes_by_id[node_id]
+
+
+class _FakeIsChangedCache:
+    """Async stub for `is_changed` lookups used by cache-key generation."""
+
+    def __init__(self, values):
+        """Store canned `is_changed` responses keyed by node id."""
+        self._values = values
+
+    async def get(self, node_id):
+        """Return the canned `is_changed` value for a node."""
+        return self._values[node_id]
+
+
+class _OpaqueValue:
+    """Hashable opaque object used to exercise fail-closed unordered hashing paths."""
+
+
+@pytest.fixture
+def caching_module(monkeypatch):
+    """Import `comfy_execution.caching` with lightweight stub dependencies."""
+    torch_module = types.ModuleType("torch")
+    psutil_module = types.ModuleType("psutil")
+    nodes_module = types.ModuleType("nodes")
+    nodes_module.NODE_CLASS_MAPPINGS = {}
+    graph_module = types.ModuleType("comfy_execution.graph")
+
+    class DynamicPrompt:
+        """Placeholder graph type so the caching module can import cleanly."""
+
+        pass
+
+    graph_module.DynamicPrompt = DynamicPrompt
+
+    monkeypatch.setitem(sys.modules, "torch", torch_module)
+    monkeypatch.setitem(sys.modules, "psutil", psutil_module)
+    monkeypatch.setitem(sys.modules, "nodes", nodes_module)
+    monkeypatch.setitem(sys.modules, "comfy_execution.graph", graph_module)
+    monkeypatch.delitem(sys.modules, "comfy_execution.caching", raising=False)
+
+    module = importlib.import_module("comfy_execution.caching")
+    module = importlib.reload(module)
+    return module, nodes_module
+
+
+def test_signature_to_hashable_handles_shared_builtin_substructures(caching_module):
+    """Shared built-in substructures should canonicalize without collapsing to Unhashable."""
+    caching, _ = caching_module
+    shared = [{"value": 1}, {"value": 2}]
+
+    signature = caching._signature_to_hashable([shared, shared])
+
+    assert signature[0] == "list"
+    assert signature[1][0] == signature[1][1]
+    assert signature[1][0][0] == "list"
+    assert signature[1][0][1][0] == ("dict", (("value", 1),))
+    assert signature[1][0][1][1] == ("dict", (("value", 2),))
+
+
+def test_signature_to_hashable_fails_closed_on_opaque_values(caching_module):
+    """Opaque values should collapse the full signature to Unhashable immediately."""
+    caching, _ = caching_module
+
+    signature = caching._signature_to_hashable(["safe", object()])
+
+    assert isinstance(signature, caching.Unhashable)
+
+
+def test_signature_to_hashable_stops_descending_after_failure(caching_module, monkeypatch):
+    """Once canonicalization fails, later recursive descent should stop immediately."""
+    caching, _ = caching_module
+    original = caching._signature_to_hashable_impl
+    marker = object()
+    marker_seen = False
+
+    def tracking_canonicalize(obj, *args, **kwargs):
+        """Track whether recursion reaches the nested marker after failure."""
+        nonlocal marker_seen
+        if obj is marker:
+            marker_seen = True
+        return original(obj, *args, **kwargs)
+
+    monkeypatch.setattr(caching, "_signature_to_hashable_impl", tracking_canonicalize)
+
+    signature = caching._signature_to_hashable([object(), [marker]])
+
+    assert isinstance(signature, caching.Unhashable)
+    assert marker_seen is False
+
+
+def test_signature_to_hashable_snapshots_list_before_recursing(caching_module, monkeypatch):
+    """List canonicalization should read a point-in-time snapshot before recursive descent."""
+    caching, _ = caching_module
+    original = caching._signature_to_hashable_impl
+    marker = ("marker",)
+    values = [marker, 2]
+
+    def mutating_canonicalize(obj, *args, **kwargs):
+        """Mutate the live list during recursion to verify snapshot-based traversal."""
+        if obj is marker:
+            values[1] = 3
+        return original(obj, *args, **kwargs)
+
+    monkeypatch.setattr(caching, "_signature_to_hashable_impl", mutating_canonicalize)
+
+    signature = caching._signature_to_hashable(values)
+
+    assert signature == ("list", (("tuple", ("marker",)), 2))
+    assert values[1] == 3
+
+
+def test_signature_to_hashable_snapshots_dict_before_recursing(caching_module, monkeypatch):
+    """Dict canonicalization should read a point-in-time snapshot before recursive descent."""
+    caching, _ = caching_module
+    original = caching._signature_to_hashable_impl
+    marker = ("marker",)
+    values = {"first": marker, "second": 2}
+
+    def mutating_canonicalize(obj, *args, **kwargs):
+        """Mutate the live dict during recursion to verify snapshot-based traversal."""
+        if obj is marker:
+            values["second"] = 3
+        return original(obj, *args, **kwargs)
+
+    monkeypatch.setattr(caching, "_signature_to_hashable_impl", mutating_canonicalize)
+
+    signature = caching._signature_to_hashable(values)
+
+    assert signature == ("dict", (("first", ("tuple", ("marker",))), ("second", 2)))
+    assert values["second"] == 3
+
+
+@pytest.mark.parametrize(
+    "container_factory",
+    [
+        lambda marker: [marker],
+        lambda marker: (marker,),
+        lambda marker: {marker},
+        lambda marker: frozenset({marker}),
+        lambda marker: {"key": marker},
+    ],
+)
+def test_signature_to_hashable_fails_closed_on_runtimeerror(caching_module, monkeypatch, container_factory):
+    """Traversal RuntimeError should degrade canonicalization to Unhashable."""
+    caching, _ = caching_module
+    original = caching._signature_to_hashable_impl
+    marker = object()
+
+    def raising_canonicalize(obj, *args, **kwargs):
+        """Raise a traversal RuntimeError for the marker value and delegate otherwise."""
+        if obj is marker:
+            raise RuntimeError("container changed during iteration")
+        return original(obj, *args, **kwargs)
+
+    monkeypatch.setattr(caching, "_signature_to_hashable_impl", raising_canonicalize)
+
+    signature = caching._signature_to_hashable(container_factory(marker))
+
+    assert isinstance(signature, caching.Unhashable)
+
+
+def test_to_hashable_handles_shared_builtin_substructures(caching_module):
+    """The legacy helper should still hash sanitized built-ins stably when used directly."""
+    caching, _ = caching_module
+    shared = [{"value": 1}, {"value": 2}]
+
+    sanitized = [shared, shared]
+    hashable = caching.to_hashable(sanitized)
+
+    assert hashable[0] == "list"
+    assert hashable[1][0] == hashable[1][1]
+    assert hashable[1][0][0] == "list"
+
+
+def test_to_hashable_uses_parent_snapshot_during_expanded_phase(caching_module, monkeypatch):
+    """Expanded-phase assembly should not reread a live parent container after snapshotting."""
+    caching, _ = caching_module
+    original_sort_key = caching._sanitized_sort_key
+    outer = [{"marker"}, 2]
+
+    def mutating_sort_key(obj, *args, **kwargs):
+        """Mutate the live parent while a child container is being canonicalized."""
+        if obj == "marker":
+            outer[1] = 3
+        return original_sort_key(obj, *args, **kwargs)
+
+    monkeypatch.setattr(caching, "_sanitized_sort_key", mutating_sort_key)
+
+    hashable = caching.to_hashable(outer)
+
+    assert hashable == ("list", (("set", ("marker",)), 2))
+    assert outer[1] == 3
+
+
+def test_to_hashable_fails_closed_for_ordered_container_with_opaque_child(caching_module):
+    """Ordered containers should fail closed when a child cannot be canonicalized."""
+    caching, _ = caching_module
+
+    result = caching.to_hashable([object()])
+
+    assert isinstance(result, caching.Unhashable)
+
+
+def test_to_hashable_canonicalizes_dict_insertion_order(caching_module):
+    """Dicts with the same content should hash identically regardless of insertion order."""
+    caching, _ = caching_module
+
+    first = {"b": 2, "a": 1}
+    second = {"a": 1, "b": 2}
+
+    assert caching.to_hashable(first) == ("dict", (("a", 1), ("b", 2)))
+    assert caching.to_hashable(first) == caching.to_hashable(second)
+
+
+def test_to_hashable_fails_closed_for_opaque_dict_key(caching_module):
+    """Opaque dict keys should fail closed instead of being traversed during hashing."""
+    caching, _ = caching_module
+
+    hashable = caching.to_hashable({_OpaqueValue(): 1})
+
+    assert isinstance(hashable, caching.Unhashable)
+
+
+@pytest.mark.parametrize(
+    "container_factory",
+    [
+        set,
+        frozenset,
+    ],
+)
+def test_to_hashable_fails_closed_on_runtimeerror(caching_module, monkeypatch, container_factory):
+    """Traversal RuntimeError should degrade unordered hash conversion to Unhashable."""
+    caching, _ = caching_module
+
+    def raising_sort_key(obj, *args, **kwargs):
+        """Raise a traversal RuntimeError while unordered values are canonicalized."""
+        raise RuntimeError("container changed during iteration")
+
+    monkeypatch.setattr(caching, "_sanitized_sort_key", raising_sort_key)
+
+    hashable = caching.to_hashable(container_factory({"value"}))
+
+    assert isinstance(hashable, caching.Unhashable)
+
+
+def test_to_hashable_fails_closed_for_ambiguous_dict_ordering(caching_module, monkeypatch):
+    """Ambiguous dict key ordering should fail closed instead of using insertion order."""
+    caching, _ = caching_module
+    original_sort_key = caching._sanitized_sort_key
+    ambiguous = {"a": 1, "b": 1}
+
+    def colliding_sort_key(obj, *args, **kwargs):
+        """Force two distinct primitive keys to share the same ordering key."""
+        if obj == "a" or obj == "b":
+            return ("COLLIDE",)
+        return original_sort_key(obj, *args, **kwargs)
+
+    monkeypatch.setattr(caching, "_sanitized_sort_key", colliding_sort_key)
+
+    hashable = caching.to_hashable(ambiguous)
+
+    assert isinstance(hashable, caching.Unhashable)
+
+
+def test_signature_to_hashable_fails_closed_for_ambiguous_dict_ordering(caching_module, monkeypatch):
+    """Ambiguous dict sort ties should fail closed instead of depending on input order."""
+    caching, _ = caching_module
+    original_sort_key = caching._primitive_signature_sort_key
+    ambiguous = {"a": 1, "b": 1}
+
+    def colliding_sort_key(obj):
+        """Force two distinct primitive keys to share the same ordering key."""
+        if obj == "a" or obj == "b":
+            return ("COLLIDE",)
+        return original_sort_key(obj)
+
+    monkeypatch.setattr(caching, "_primitive_signature_sort_key", colliding_sort_key)
+
+    sanitized = caching._signature_to_hashable(ambiguous)
+
+    assert isinstance(sanitized, caching.Unhashable)
+
+
+def test_signature_to_hashable_fails_closed_for_opaque_dict_key(caching_module):
+    """Opaque dict keys should fail closed instead of being recursively canonicalized."""
+    caching, _ = caching_module
+
+    sanitized = caching._signature_to_hashable({_OpaqueValue(): 1})
+
+    assert isinstance(sanitized, caching.Unhashable)
+
+
+def test_signature_to_hashable_fails_closed_on_dict_key_sort_collisions_even_with_distinct_values(caching_module, monkeypatch):
+    """Different values must not mask dict key-sort collisions during canonicalization."""
+    caching, _ = caching_module
+    original_sort_key = caching._primitive_signature_sort_key
+
+    def colliding_sort_key(obj):
+        """Force two distinct primitive keys to share the same ordering key."""
+        if obj == "a" or obj == "b":
+            return ("COLLIDE",)
+        return original_sort_key(obj)
+
+    monkeypatch.setattr(caching, "_primitive_signature_sort_key", colliding_sort_key)
+
+    sanitized = caching._signature_to_hashable({"a": 1, "b": 2})
+
+    assert isinstance(sanitized, caching.Unhashable)
+
+
+@pytest.mark.parametrize(
+    "container_factory",
+    [
+        set,
+        frozenset,
+    ],
+)
+def test_to_hashable_fails_closed_for_ambiguous_unordered_values(caching_module, monkeypatch, container_factory):
+    """Ambiguous unordered values should fail closed instead of depending on iteration order."""
+    caching, _ = caching_module
+    original_sort_key = caching._sanitized_sort_key
+    container = container_factory({"a", "b"})
+
+    def colliding_sort_key(obj, *args, **kwargs):
+        """Force two distinct primitive values to share the same ordering key."""
+        if obj == "a" or obj == "b":
+            return ("COLLIDE",)
+        return original_sort_key(obj, *args, **kwargs)
+
+    monkeypatch.setattr(caching, "_sanitized_sort_key", colliding_sort_key)
+
+    hashable = caching.to_hashable(container)
+
+    assert isinstance(hashable, caching.Unhashable)
+
+
+def test_get_node_signature_returns_top_level_unhashable_for_tainted_signature(caching_module, monkeypatch):
+    """Tainted full signatures should fail closed before `to_hashable()` runs."""
+    caching, nodes_module = caching_module
+    monkeypatch.setitem(nodes_module.NODE_CLASS_MAPPINGS, "UnitTestNode", _DummyNode)
+    monkeypatch.setattr(
+        caching,
+        "to_hashable",
+        lambda *_args, **_kwargs: pytest.fail("to_hashable should not run for tainted signatures"),
+    )
+
+    is_changed_value = []
+    is_changed_value.append(is_changed_value)
+
+    dynprompt = _FakeDynPrompt(
+        {
+            "node": {
+                "class_type": "UnitTestNode",
+                "inputs": {"value": 5},
+            }
+        }
+    )
+    key_set = caching.CacheKeySetInputSignature(
+        dynprompt,
+        ["node"],
+        _FakeIsChangedCache({"node": is_changed_value}),
+    )
+
+    signature = asyncio.run(key_set.get_node_signature(dynprompt, "node"))
+
+    assert isinstance(signature, caching.Unhashable)
+
+
+def test_shallow_is_changed_signature_accepts_primitive_lists(caching_module):
+    """Primitive-only `is_changed` lists should stay hashable without deep descent."""
+    caching, _ = caching_module
+
+    sanitized = caching._shallow_is_changed_signature([1, "two", None, True])
+
+    assert sanitized == ("is_changed_list", (1, "two", None, True))
+
+
+def test_shallow_is_changed_signature_accepts_structured_builtin_fingerprint_lists(caching_module):
+    """Structured built-in `is_changed` fingerprints should remain representable."""
+    caching, _ = caching_module
+
+    sanitized = caching._shallow_is_changed_signature([("seed", 42), {"cfg": 8}])
+
+    assert sanitized == (
+        "is_changed_list",
+        (
+            ("tuple", ("seed", 42)),
+            ("dict", (("cfg", 8),)),
+        ),
+    )
+
+
+def test_shallow_is_changed_signature_fails_closed_for_opaque_payload(caching_module):
+    """Opaque `is_changed` payloads should still fail closed."""
+    caching, _ = caching_module
+
+    sanitized = caching._shallow_is_changed_signature([_OpaqueValue()])
+
+    assert isinstance(sanitized, caching.Unhashable)
+
+
+def test_get_immediate_node_signature_fails_closed_for_unhashable_is_changed(caching_module, monkeypatch):
+    """Recursive `is_changed` payloads should fail the full fragment closed."""
+    caching, nodes_module = caching_module
+    monkeypatch.setitem(nodes_module.NODE_CLASS_MAPPINGS, "UnitTestNode", _DummyNode)
+
+    is_changed_value = []
+    is_changed_value.append(is_changed_value)
+    dynprompt = _FakeDynPrompt(
+        {
+            "node": {
+                "class_type": "UnitTestNode",
+                "inputs": {"value": 5},
+            }
+        }
+    )
+    key_set = caching.CacheKeySetInputSignature(
+        dynprompt,
+        ["node"],
+        _FakeIsChangedCache({"node": is_changed_value}),
+    )
+
+    signature = asyncio.run(key_set.get_immediate_node_signature(dynprompt, "node", {}))
+
+    assert isinstance(signature, caching.Unhashable)
+
+
+def test_get_immediate_node_signature_fails_closed_for_missing_node(caching_module):
+    """Missing nodes should return the fail-closed sentinel instead of a NaN tuple."""
+    caching, _ = caching_module
+    dynprompt = _FakeDynPrompt({})
+    key_set = caching.CacheKeySetInputSignature(
+        dynprompt,
+        [],
+        _FakeIsChangedCache({}),
+    )
+
+    signature = asyncio.run(key_set.get_immediate_node_signature(dynprompt, "missing", {}))
+
+    assert isinstance(signature, caching.Unhashable)

tests/execution/test_caching.py

@@ -0,0 +1,242 @@
+import asyncio
+
+from comfy_execution import caching
+
+
+class _StubDynPrompt:
+    def __init__(self, nodes):
+        self._nodes = nodes
+
+    def has_node(self, node_id):
+        return node_id in self._nodes
+
+    def get_node(self, node_id):
+        return self._nodes[node_id]
+
+
+class _StubIsChangedCache:
+    async def get(self, node_id):
+        return None
+
+
+class _StubNode:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {"required": {}}
+
+
+def test_shallow_is_changed_signature_keeps_primitive_only_list_shallow():
+    assert caching._shallow_is_changed_signature([1, "two", None, True]) == (
+        "is_changed_list",
+        (1, "two", None, True),
+    )
+
+
+def test_shallow_is_changed_signature_keeps_primitive_only_tuple_shallow():
+    assert caching._shallow_is_changed_signature((1, "two", None, True)) == (
+        "is_changed_tuple",
+        (1, "two", None, True),
+    )
+
+
+def test_shallow_is_changed_signature_keeps_structured_builtin_fingerprint_list():
+    assert caching._shallow_is_changed_signature([("seed", 42), {"cfg": 8}]) == (
+        "is_changed_list",
+        (
+            ("tuple", ("seed", 42)),
+            ("dict", (("cfg", 8),)),
+        ),
+    )
+
+
+def test_shallow_is_changed_signature_does_not_use_to_hashable(monkeypatch):
+    monkeypatch.setattr(
+        caching,
+        "to_hashable",
+        lambda *_args, **_kwargs: (_ for _ in ()).throw(
+            AssertionError("is_changed signature must not deep-canonicalize")
+        ),
+    )
+
+    signature = caching._shallow_is_changed_signature([("seed", 42), {"cfg": 8}])
+
+    assert signature == (
+        "is_changed_list",
+        (
+            ("tuple", ("seed", 42)),
+            ("dict", (("cfg", 8),)),
+        ),
+    )
+
+
+def test_get_immediate_node_signature_canonicalizes_non_link_inputs(monkeypatch):
+    live_value = [1, {"nested": [2, 3]}]
+    dynprompt = _StubDynPrompt(
+        {
+            "1": {
+                "class_type": "TestCacheNode",
+                "inputs": {"value": live_value},
+            }
+        }
+    )
+
+    monkeypatch.setitem(caching.nodes.NODE_CLASS_MAPPINGS, "TestCacheNode", _StubNode)
+    monkeypatch.setattr(caching, "NODE_CLASS_CONTAINS_UNIQUE_ID", {})
+
+    keyset = caching.CacheKeySetInputSignature(dynprompt, [], _StubIsChangedCache())
+    signature = asyncio.run(keyset.get_immediate_node_signature(dynprompt, "1", {}))
+
+    assert signature == (
+        "TestCacheNode",
+        None,
+        ("value", ("list", (1, ("dict", (("nested", ("list", (2, 3))),))))),
+    )
+
+
+def test_to_hashable_walks_dicts_without_rebinding_traversal_stack():
+    live_value = {
+        "outer": {"nested": [2, 3]},
+        "items": [{"leaf": 4}],
+    }
+
+    assert caching.to_hashable(live_value) == (
+        "dict",
+        (
+            ("items", ("list", (("dict", (("leaf", 4),)),))),
+            ("outer", ("dict", (("nested", ("list", (2, 3))),))),
+        ),
+    )
+
+
+def test_get_immediate_node_signature_fails_closed_for_opaque_non_link_input(monkeypatch):
+    class OpaqueRuntimeValue:
+        pass
+
+    live_value = OpaqueRuntimeValue()
+    dynprompt = _StubDynPrompt(
+        {
+            "1": {
+                "class_type": "TestCacheNode",
+                "inputs": {"value": live_value},
+            }
+        }
+    )
+
+    monkeypatch.setitem(caching.nodes.NODE_CLASS_MAPPINGS, "TestCacheNode", _StubNode)
+    monkeypatch.setattr(caching, "NODE_CLASS_CONTAINS_UNIQUE_ID", {})
+
+    keyset = caching.CacheKeySetInputSignature(dynprompt, [], _StubIsChangedCache())
+    signature = asyncio.run(keyset.get_immediate_node_signature(dynprompt, "1", {}))
+
+    assert isinstance(signature, caching.Unhashable)
+
+
+def test_get_node_signature_propagates_unhashable_immediate_fragment(monkeypatch):
+    class OpaqueRuntimeValue:
+        pass
+
+    dynprompt = _StubDynPrompt(
+        {
+            "1": {
+                "class_type": "TestCacheNode",
+                "inputs": {"value": OpaqueRuntimeValue()},
+            }
+        }
+    )
+
+    monkeypatch.setitem(caching.nodes.NODE_CLASS_MAPPINGS, "TestCacheNode", _StubNode)
+    monkeypatch.setattr(caching, "NODE_CLASS_CONTAINS_UNIQUE_ID", {})
+
+    keyset = caching.CacheKeySetInputSignature(dynprompt, [], _StubIsChangedCache())
+    signature = asyncio.run(keyset.get_node_signature(dynprompt, "1"))
+
+    assert isinstance(signature, caching.Unhashable)
+
+
+def test_get_node_signature_never_visits_raw_non_link_input(monkeypatch):
+    live_value = [1, 2, 3]
+    dynprompt = _StubDynPrompt(
+        {
+            "1": {
+                "class_type": "TestCacheNode",
+                "inputs": {"value": live_value},
+            }
+        }
+    )
+
+    monkeypatch.setitem(caching.nodes.NODE_CLASS_MAPPINGS, "TestCacheNode", _StubNode)
+    monkeypatch.setattr(caching, "NODE_CLASS_CONTAINS_UNIQUE_ID", {})
+    monkeypatch.setattr(
+        caching,
+        "_signature_to_hashable",
+        lambda *_args, **_kwargs: (_ for _ in ()).throw(
+            AssertionError("outer signature canonicalizer should not run")
+        ),
+    )
+
+    keyset = caching.CacheKeySetInputSignature(dynprompt, [], _StubIsChangedCache())
+    signature = asyncio.run(keyset.get_node_signature(dynprompt, "1"))
+
+    assert isinstance(signature, tuple)
+
+
+def test_get_node_signature_keeps_deep_canonicalized_input_fragment(monkeypatch):
+    live_value = 1
+    for _ in range(8):
+        live_value = [live_value]
+    expected = caching.to_hashable(live_value)
+
+    dynprompt = _StubDynPrompt(
+        {
+            "1": {
+                "class_type": "TestCacheNode",
+                "inputs": {"value": live_value},
+            }
+        }
+    )
+
+    monkeypatch.setitem(caching.nodes.NODE_CLASS_MAPPINGS, "TestCacheNode", _StubNode)
+    monkeypatch.setattr(caching, "NODE_CLASS_CONTAINS_UNIQUE_ID", {})
+
+    keyset = caching.CacheKeySetInputSignature(dynprompt, [], _StubIsChangedCache())
+    signature = asyncio.run(keyset.get_node_signature(dynprompt, "1"))
+
+    assert isinstance(signature, tuple)
+    assert signature[0][2][0] == "value"
+    assert signature[0][2][1] == expected
+
+
+def test_get_node_signature_keeps_large_precanonicalized_fragment(monkeypatch):
+    live_value = object()
+    canonical_fragment = ("tuple", tuple(("list", (index, index + 1)) for index in range(256)))
+    dynprompt = _StubDynPrompt(
+        {
+            "1": {
+                "class_type": "TestCacheNode",
+                "inputs": {"value": live_value},
+            }
+        }
+    )
+
+    monkeypatch.setitem(caching.nodes.NODE_CLASS_MAPPINGS, "TestCacheNode", _StubNode)
+    monkeypatch.setattr(caching, "NODE_CLASS_CONTAINS_UNIQUE_ID", {})
+    monkeypatch.setattr(
+        caching,
+        "to_hashable",
+        lambda value, max_nodes=caching._MAX_SIGNATURE_CONTAINER_VISITS: (
+            canonical_fragment if value is live_value else caching.Unhashable()
+        ),
+    )
+    monkeypatch.setattr(
+        caching,
+        "_signature_to_hashable",
+        lambda *_args, **_kwargs: (_ for _ in ()).throw(
+            AssertionError("outer signature canonicalizer should not run")
+        ),
+    )
+
+    keyset = caching.CacheKeySetInputSignature(dynprompt, [], _StubIsChangedCache())
+    signature = asyncio.run(keyset.get_node_signature(dynprompt, "1"))
+
+    assert isinstance(signature, tuple)
+    assert signature[0][2] == ("value", canonical_fragment)

tests/execution/testing_nodes/testing-pack/api_test_nodes.py

@@ -21,7 +21,7 @@ class TestAsyncProgressUpdate(ComfyNodeABC):
 
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "execute"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     async def execute(self, value, sleep_seconds):
         start = time.time()
@@ -51,7 +51,7 @@ class TestSyncProgressUpdate(ComfyNodeABC):
 
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "execute"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     def execute(self, value, sleep_seconds):
         start = time.time()

tests/execution/testing_nodes/testing-pack/async_test_nodes.py

@@ -21,7 +21,7 @@ class TestAsyncValidation(ComfyNodeABC):
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "process"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     @classmethod
     async def VALIDATE_INPUTS(cls, value, threshold):
@@ -53,7 +53,7 @@ class TestAsyncError(ComfyNodeABC):
 
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "error_execution"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     async def error_execution(self, value, error_after):
         await asyncio.sleep(error_after)
@@ -74,7 +74,7 @@ class TestAsyncValidationError(ComfyNodeABC):
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "process"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     @classmethod
     async def VALIDATE_INPUTS(cls, value, max_value):
@@ -105,7 +105,7 @@ class TestAsyncTimeout(ComfyNodeABC):
 
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "timeout_execution"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     async def timeout_execution(self, value, timeout, operation_time):
         try:
@@ -129,7 +129,7 @@ class TestSyncError(ComfyNodeABC):
 
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "sync_error"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     def sync_error(self, value):
         raise RuntimeError("Intentional sync execution error for testing")
@@ -150,7 +150,7 @@ class TestAsyncLazyCheck(ComfyNodeABC):
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "process"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     async def check_lazy_status(self, condition, input1, input2):
         # Simulate async checking (e.g., querying remote service)
@@ -184,7 +184,7 @@ class TestDynamicAsyncGeneration(ComfyNodeABC):
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "generate_async_workflow"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     def generate_async_workflow(self, image1, image2, num_async_nodes, sleep_duration):
         g = GraphBuilder()
@@ -229,7 +229,7 @@ class TestAsyncResourceUser(ComfyNodeABC):
 
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "use_resource"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     async def use_resource(self, value, resource_id, duration):
         # Check if resource is already in use
@@ -265,7 +265,7 @@ class TestAsyncBatchProcessing(ComfyNodeABC):
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "process_batch"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     async def process_batch(self, images, process_time_per_item, unique_id):
         batch_size = images.shape[0]
@@ -305,7 +305,7 @@ class TestAsyncConcurrentLimit(ComfyNodeABC):
 
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "limited_execution"
-    CATEGORY = "_for_testing/async"
+    CATEGORY = "experimental/async"
 
     async def limited_execution(self, value, duration, node_id):
         async with self._semaphore:

tests/execution/testing_nodes/testing-pack/specific_tests.py

@@ -409,7 +409,7 @@ class TestSleep(ComfyNodeABC):
     RETURN_TYPES = (IO.ANY,)
     FUNCTION = "sleep"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
 
     async def sleep(self, value, seconds, unique_id):
         pbar = ProgressBar(seconds, node_id=unique_id)
@@ -440,7 +440,7 @@ class TestParallelSleep(ComfyNodeABC):
         }
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "parallel_sleep"
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
     OUTPUT_NODE = True
 
     def parallel_sleep(self, image1, image2, image3, sleep1, sleep2, sleep3, unique_id):
@@ -474,7 +474,7 @@ class TestOutputNodeWithSocketOutput:
         }
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "process"
-    CATEGORY = "_for_testing"
+    CATEGORY = "experimental"
     OUTPUT_NODE = True
 
     def process(self, image, value):