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Wan22_I2V_LoRA_newIVIбезчанков.json

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+          "link": 11
+        },
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 12
+        }
+      ],
+      "outputs": [
+        {
+          "name": "CLIP_VISION_OUTPUT",
+          "type": "CLIP_VISION_OUTPUT",
+          "links": [
+            14
+          ]
+        }
+      ],
+      "title": "CLIP Vision Encode",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.7.0",
+        "Node name for S&R": "CLIPVisionEncode",
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "version": "7.5.2",
+          "input_ue_unconnectable": {}
+        }
+      },
+      "widgets_values": [
+        "center"
+      ]
+    },
+    {
+      "id": 6,
+      "type": "LoadImage",
+      "pos": [
+        0,
+        570
+      ],
+      "size": [
+        340,
+        314
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            12,
+            13
+          ]
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": []
+        }
+      ],
+      "title": "Reference image (первый кадр / локация)",
+      "properties": {
+        "cnr_id": "comfy-core",
+        "ver": "0.7.0",
+        "Node name for S&R": "LoadImage",
+        "ue_properties": {
+          "widget_ue_connectable": {},
+          "version": "7.5.2",
+          "input_ue_unconnectable": {}
+        }
+      },
+      "widgets_values": [
+        "66.png",
+        "image"
+      ]
+    }
+  ],
+  "links": [
+    [
+      1,
+      1,
+      0,
+      7,
+      0,
+      "MODEL"
+    ],
+    [
+      2,
+      7,
+      0,
+      9,
+      0,
+      "MODEL"
+    ],
+    [
+      3,
+      9,
+      0,
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+      0,
+      "MODEL"
+    ],
+    [
+      4,
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+      "MODEL"
+    ],
+    [
+      5,
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+      0,
+      "MODEL"
+    ],
+    [
+      6,
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+      0,
+      "MODEL"
+    ],
+    [
+      7,
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+      2,
+      "VAE"
+    ],
+    [
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+      1,
+      "VAE"
+    ],
+    [
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+    [
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+      0,
+      "CLIP"
+    ],
+    [
+      11,
+      5,
+      0,
+      11,
+      0,
+      "CLIP_VISION"
+    ],
+    [
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+      6,
+      0,
+      11,
+      1,
+      "IMAGE"
+    ],
+    [
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+      6,
+      0,
+      14,
+      4,
+      "IMAGE"
+    ],
+    [
+      14,
+      11,
+      0,
+      14,
+      3,
+      "CLIP_VISION_OUTPUT"
+    ],
+    [
+      15,
+      12,
+      0,
+      14,
+      0,
+      "CONDITIONING"
+    ],
+    [
+      16,
+      13,
+      0,
+      14,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      17,
+      14,
+      0,
+      15,
+      1,
+      "CONDITIONING"
+    ],
+    [
+      18,
+      14,
+      1,
+      15,
+      2,
+      "CONDITIONING"
+    ],
+    [
+      19,
+      14,
+      2,
+      15,
+      3,
+      "LATENT"
+    ],
+    [
+      20,
+      14,
+      0,
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+      1,
+      "CONDITIONING"
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+    [
+      21,
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+      16,
+      2,
+      "CONDITIONING"
+    ],
+    [
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+      0,
+      16,
+      3,
+      "LATENT"
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+      0,
+      "LATENT"
+    ],
+    [
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+      0,
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+      0,
+      "IMAGE"
+    ],
+    [
+      25,
+      18,
+      0,
+      19,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [
+    {
+      "id": 1,
+      "title": "Step 1 - Model Loaders",
+      "bounding": [
+        -10,
+        -10,
+        360,
+        330
+      ],
+      "color": "#3f789e",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 2,
+      "title": "Step 2 - Speed + Character LoRAs",
+      "bounding": [
+        370,
+        -10,
+        740,
+        240
+      ],
+      "color": "#8AA",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 3,
+      "title": "Step 3 - Conditioning",
+      "bounding": [
+        370,
+        460,
+        430,
+        470
+      ],
+      "color": "#A88",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 4,
+      "title": "Step 4 - Wan Image→Video",
+      "bounding": [
+        1130,
+        490,
+        350,
+        200
+      ],
+      "color": "#A8A",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 5,
+      "title": "Step 5 - Sampling",
+      "bounding": [
+        1490,
+        -10,
+        340,
+        580
+      ],
+      "color": "#AA8",
+      "font_size": 24,
+      "flags": {}
+    },
+    {
+      "id": 6,
+      "title": "Step 6 - Decode + 32fps + Save",
+      "bounding": [
+        1850,
+        -10,
+        800,
+        720
+      ],
+      "color": "#8A8",
+      "font_size": 24,
+      "flags": {}
+    }
+  ],
+  "config": {},
+  "extra": {
+    "workflowRendererVersion": "LG",
+    "ue_links": [],
+    "links_added_by_ue": [],
+    "ds": {
+      "scale": 1.1712800000000003,
+      "offset": [
+        -1092.3755677549368,
+        -95.64397283313947
+      ]
+    },
+    "frontendVersion": "1.35.9",
+    "VHS_latentpreview": true,
+    "VHS_latentpreviewrate": 0,
+    "VHS_MetadataImage": true,
+    "VHS_KeepIntermediate": true
+  },
+  "version": 0.4
+}

newIVIгадмод.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7a15ab91d5ee7990c2980f697b9be14b4e971be5abb8f7c87afaadea2230e40
+size 613516400

wanv2v_iter_control_group.py

@@ -0,0 +1,177 @@
+from typing import Tuple
+import torch
+
+"""
+WanV2VIterControlGroup (finalized + masks)
+
+MODIFIED: This version implements a sequential, overlapping chunking logic similar to WanVaceToVideoCopy.
+- Iter 1: A simple slice from the start of the control images.
+- Iter > 1: The start of the chunk is built from the TAIL of the prev_output, ensuring temporal consistency.
+
+The mask output is aligned with this logic:
+- If `mask_master` is NOT provided (default behavior):
+  - Iter 1: All masks are WHITE (1.0 -> editable).
+  - Iter > 1: First `overlap_size` masks are BLACK (0.0 -> non-editable), remaining are WHITE (1.0 -> editable).
+- If `mask_master` IS provided:
+  - The output mask is sliced from `mask_master` to match the frame logic.
+  - Iter 1: A direct slice from `mask_master`.
+  - Iter > 1: `overlap_size` black frames are prepended, followed by a sequential slice from `mask_master`.
+"""
+
+def _sec_to_hms_ms(sec: float) -> str:
+    ms = int(round((sec - int(sec)) * 1000.0))
+    s_total = int(sec)
+    h = s_total // 3600
+    m = (s_total % 3600) // 60
+    s = s_total % 60
+    return f"{h:02d}:{m:02d}:{s:02d}.{ms:03d}"
+
+class WanV2VIterControlGroup:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "controlnet_images": ("IMAGE",),  # [T,H,W,C]
+                "chunk_size": ("INT", {"default": 81, "min": 1, "max": 4096, "step": 1}),
+                "overlap_size": ("INT", {"default": 8, "min": 0, "max": 2048, "step": 1}),
+                "iter": ("INT", {"default": 1, "min": 1, "max": 99999, "step": 1}),  # 1-based
+                "fps": ("FLOAT", {"default": 24.0, "min": 1.0, "max": 240.0, "step": 0.1}),
+            },
+            "optional": {
+                "prev_output": ("IMAGE",),        # previous sampler output [*,H,W,C]
+                "mask_master": ("MASK",),         # <<< NEW: Optional master mask input
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "STRING", "MASK")
+    RETURN_NAMES = ("control_group", "info", "edit_mask_batch")
+    FUNCTION = "build"
+    CATEGORY = "Wanv2vControlnetManager/WAN"
+
+    # ---- helpers ----
+    def _ensure_batched(self, t: torch.Tensor) -> torch.Tensor:
+        assert torch.is_tensor(t), "Expected torch.Tensor"
+        assert t.ndim == 4, f"Expected [B,H,W,C], got {tuple(t.shape)}"
+        return t
+
+    def _slice_safe(self, t: torch.Tensor, start: int, end: int) -> torch.Tensor:
+        start = max(0, int(start))
+        end = max(start, min(int(end), t.shape[0]))
+        return t[start:end]
+
+    def _last_n(self, t: torch.Tensor, n: int) -> torch.Tensor:
+        n = max(0, int(n))
+        if n == 0:
+            return t[:0]
+        n = min(n, t.shape[0])
+        return t[-n:]
+
+    def _empty_like(self, ref: torch.Tensor, B: int = 0) -> torch.Tensor:
+        _, H, W, C = ref.shape
+        return torch.empty((B, H, W, C), dtype=ref.dtype, device=ref.device)
+
+    def _pad_repeat_last(self, t: torch.Tensor, target_len: int) -> torch.Tensor:
+        """Pad batch t by repeating its last frame until target_len."""
+        if t.shape[0] == 0:
+            return t
+        need = target_len - t.shape[0]
+        if need <= 0:
+            return t[:target_len]
+        last = t[-1:].repeat(need, 1, 1, 1)
+        return torch.cat([t, last], dim=0)
+    
+    # <<< NEW: Helper for padding 3D mask tensors
+    def _pad_repeat_last_mask(self, t: torch.Tensor, target_len: int) -> torch.Tensor:
+        """Pad a 3D mask batch t by repeating its last mask until target_len."""
+        if t.shape[0] == 0:
+            return t
+        need = target_len - t.shape[0]
+        if need <= 0:
+            return t[:target_len]
+        last = t[-1:].repeat(need, 1, 1)
+        return torch.cat([t, last], dim=0)
+
+    def _build_masks(self, H: int, W: int, chunk_size: int, locked_frames: int, device, dtype=torch.float32) -> torch.Tensor:
+        B = max(1, int(chunk_size))
+        x = max(0, int(locked_frames))
+        x = min(x, B)
+        black = torch.zeros((x, H, W), dtype=dtype, device=device)
+        white = torch.ones((B - x, H, W), dtype=dtype, device=device)
+        return torch.cat([black, white], dim=0)
+
+    # ---- core ----
+    def build(
+        self,
+        controlnet_images: torch.Tensor,
+        chunk_size: int,
+        overlap_size: int,
+        iter: int,
+        fps: float,
+        prev_output: torch.Tensor = None,
+        mask_master: torch.Tensor = None, # <<< NEW: Accepting the new parameter
+    ) -> Tuple[torch.Tensor, str, torch.Tensor]:
+
+        frames = self._ensure_batched(controlnet_images)
+        T, H, W, C = frames.shape
+        device = frames.device
+        
+        # --- Frame calculation logic (This remains unchanged) ---
+        step_size = chunk_size - overlap_size
+        start_frame = (iter - 1) * step_size
+        end_frame = start_frame + chunk_size
+
+        total_seconds = float(T) / float(fps) if fps > 0 else 0.0
+        
+        if start_frame >= T and (iter > 1 and prev_output is None):
+            empty_frames = self._empty_like(frames, 0)
+            empty_masks = self._build_masks(H, W, chunk_size, 0, device)
+            info = f"iter={iter}: Processing complete. Total frames={T}."
+            return empty_frames, info, empty_masks
+
+        # --- Frame building logic (This remains unchanged) ---
+        if iter == 1 or prev_output is None:
+            built_chunk = self._slice_safe(frames, start_frame, end_frame)
+            built_chunk = self._pad_repeat_last(built_chunk, chunk_size)
+            locked_frames = 0
+            info = (f"iter={iter} (Start): Built chunk of {built_chunk.shape[0]} frames from master video [{start_frame}:{end_frame}]. "
+                    f"Locked frames: {locked_frames}.")
+        else:
+            prev_out = self._ensure_batched(prev_output)
+            overlap_frames = self._last_n(prev_out, overlap_size)
+            num_new_frames = chunk_size - overlap_frames.shape[0]
+            new_frames_start = start_frame + overlap_size
+            new_frames_end = new_frames_start + num_new_frames
+            new_frames_chunk = self._slice_safe(frames, new_frames_start, new_frames_end)
+            built_chunk = torch.cat([overlap_frames, new_frames_chunk], dim=0)
+            built_chunk = self._pad_repeat_last(built_chunk, chunk_size)
+            locked_frames = overlap_size
+            info = (f"iter={iter} (Overlap): Built chunk of {built_chunk.shape[0]} frames. "
+                    f"Using {overlap_frames.shape[0]} from prev_output and {new_frames_chunk.shape[0]} new frames. "
+                    f"Locked frames: {locked_frames}.")
+        
+        # --- Generate Mask and Final Return ---
+        # <<< NEW: Conditional logic for mask generation
+        if mask_master is None:
+            # DEFAULT BEHAVIOR: Generate mask procedurally (this is the original, working logic)
+            final_masks = self._build_masks(H, W, chunk_size, locked_frames, device)
+        else:
+            # CUSTOM BEHAVIOR: Splice the mask from the provided mask_master
+            if iter == 1:
+                # For iter 1, just slice the master mask from the beginning
+                final_masks = self._slice_safe(mask_master, start_frame, end_frame)
+                final_masks = self._pad_repeat_last_mask(final_masks, chunk_size)
+            else:
+                # For iter > 1, prepend black masks for the overlap, then slice the rest
+                black_prepend = torch.zeros((overlap_size, H, W), dtype=mask_master.dtype, device=device)
+                
+                num_new_masks = chunk_size - overlap_size
+                new_masks_start = start_frame + overlap_size
+                new_masks_end = new_masks_start + num_new_masks
+                new_mask_chunk = self._slice_safe(mask_master, new_masks_start, new_masks_end)
+
+                final_masks = torch.cat([black_prepend, new_mask_chunk], dim=0)
+                final_masks = self._pad_repeat_last_mask(final_masks, chunk_size)
+
+        final_info = f"{info} Total source frames={T} ({_sec_to_hms_ms(total_seconds)} @ {fps:.2f}fps)."
+        
+        return built_chunk, final_info, final_masks