docs: add differential diffusion implementation plan

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

docs/plans/2026-02-25-differential-diffusion-seam-fix.md

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+# Differential Diffusion Seam Fix Implementation Plan
+
+> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task.
+
+**Goal:** Add gradient mask mode to GenerateSeamMask and wire DifferentialDiffusion into the seam fix workflow pass.
+
+**Architecture:** Add a `mode` combo input to GenerateSeamMask. In `gradient` mode, paint linear falloff bands instead of binary ones. In the workflow, insert a DifferentialDiffusion node wrapping the model before the seam fix KSampler.
+
+**Tech Stack:** Python, PyTorch, ComfyUI workflow JSON
+
+---
+
+### Task 1: Add gradient mode tests
+
+**Files:**
+- Modify: `tests/test_seam_mask.py`
+
+**Step 1: Write failing gradient tests**
+
+Add these tests after the existing tests in `tests/test_seam_mask.py`:
+
+```python
+def test_binary_mode_explicit():
+    """Existing behavior works when mode='binary' is passed explicitly."""
+    node = GenerateSeamMask()
+    result = node.generate(image_width=2048, image_height=2048,
+                           tile_width=1024, tile_height=1024,
+                           overlap=128, seam_width=64, mode="binary")
+    mask = result[0]
+    unique = mask.unique()
+    assert len(unique) <= 2, f"Binary mode should only have 0.0 and 1.0, got {unique}"
+    assert mask[0, 0, 960, 0].item() == 1.0, "Center should be white"
+
+
+def test_gradient_center_is_one():
+    """In gradient mode, the seam center should be 1.0."""
+    node = GenerateSeamMask()
+    result = node.generate(image_width=2048, image_height=1024,
+                           tile_width=1024, tile_height=1024,
+                           overlap=128, seam_width=64, mode="gradient")
+    mask = result[0]
+    # Seam center at x=960
+    assert mask[0, 0, 960, 0].item() == 1.0, "Gradient center should be 1.0"
+
+
+def test_gradient_edge_is_zero():
+    """In gradient mode, the band edge should be 0.0."""
+    node = GenerateSeamMask()
+    result = node.generate(image_width=2048, image_height=1024,
+                           tile_width=1024, tile_height=1024,
+                           overlap=128, seam_width=64, mode="gradient")
+    mask = result[0]
+    # Seam center=960, half_w=32, band=[928,992)
+    # Pixel 928 is at distance 32 from center -> value = 1 - 32/32 = 0.0
+    assert mask[0, 0, 928, 0].item() == 0.0, "Band edge should be 0.0"
+    assert mask[0, 0, 927, 0].item() == 0.0, "Outside band should be 0.0"
+
+
+def test_gradient_midpoint():
+    """Halfway between center and edge should be ~0.5."""
+    node = GenerateSeamMask()
+    result = node.generate(image_width=2048, image_height=1024,
+                           tile_width=1024, tile_height=1024,
+                           overlap=128, seam_width=64, mode="gradient")
+    mask = result[0]
+    # Center=960, half_w=32. Pixel at 960-16=944 -> distance=16 -> value=1-16/32=0.5
+    val = mask[0, 0, 944, 0].item()
+    assert abs(val - 0.5) < 0.01, f"Midpoint should be ~0.5, got {val}"
+
+
+def test_gradient_intersection_uses_max():
+    """Where H and V seam bands cross, the value should be the max of both."""
+    node = GenerateSeamMask()
+    result = node.generate(image_width=2048, image_height=2048,
+                           tile_width=1024, tile_height=1024,
+                           overlap=128, seam_width=64, mode="gradient")
+    mask = result[0]
+    # Both seams cross at (960, 960) — both are centers, so value should be 1.0
+    assert mask[0, 960, 960, 0].item() == 1.0, "Intersection of two centers should be 1.0"
+    # At (960, 944): vertical seam center (1.0), horizontal seam at distance 16 (0.5)
+    # max(1.0, 0.5) = 1.0
+    assert mask[0, 944, 960, 0].item() == 1.0, "On vertical center line, should be 1.0"
+
+
+def test_gradient_no_seams_single_tile():
+    """Gradient mode with single tile should also produce all zeros."""
+    node = GenerateSeamMask()
+    result = node.generate(image_width=512, image_height=512,
+                           tile_width=1024, tile_height=1024,
+                           overlap=128, seam_width=64, mode="gradient")
+    mask = result[0]
+    assert mask.sum().item() == 0.0, "Single tile should have no seams in gradient mode"
+```
+
+Also update the `__main__` block to include the new tests, and update `test_values_are_binary` to pass `mode="binary"` explicitly.
+
+**Step 2: Run tests to verify they fail**
+
+Run: `cd /media/p5/ComfyUI_UltimateSGUpscale && python -m pytest tests/test_seam_mask.py -v`
+
+Expected: New tests FAIL with `TypeError: generate() got an unexpected keyword argument 'mode'`. Existing tests still PASS (they don't pass `mode`).
+
+**Step 3: Commit**
+
+```bash
+git add tests/test_seam_mask.py
+git commit -m "test: add gradient mode tests for GenerateSeamMask"
+```
+
+---
+
+### Task 2: Add mode parameter and gradient logic to GenerateSeamMask
+
+**Files:**
+- Modify: `seam_mask_node.py:6-21` (INPUT_TYPES — add mode combo)
+- Modify: `seam_mask_node.py:44-70` (generate method — add mode parameter, gradient logic)
+
+**Step 1: Add `mode` combo to INPUT_TYPES**
+
+In `seam_mask_node.py`, add after the `seam_width` input (line 20), before the closing `}`:
+
+```python
+                "mode": (["binary", "gradient"], {"default": "binary",
+                    "tooltip": "binary: hard 0/1 mask. gradient: linear falloff for use with Differential Diffusion."}),
+```
+
+**Step 2: Update the generate method**
+
+Replace the `generate` method (lines 44-70) with:
+
+```python
+    def generate(self, image_width, image_height, tile_width, tile_height, overlap, seam_width, mode="binary"):
+        mask = torch.zeros(1, image_height, image_width, 3)
+        half_w = seam_width // 2
+
+        # Compute actual tile grids (same logic as SplitImageToTileList)
+        x_tiles = self._get_tile_positions(image_width, tile_width, overlap)
+        y_tiles = self._get_tile_positions(image_height, tile_height, overlap)
+
+        if mode == "gradient":
+            # Build 1D linear ramps for each seam, then take max across all bands
+            # Vertical seam bands
+            for i in range(len(x_tiles) - 1):
+                ovl_start = max(x_tiles[i][0], x_tiles[i + 1][0])
+                ovl_end = min(x_tiles[i][1], x_tiles[i + 1][1])
+                center = (ovl_start + ovl_end) // 2
+                x_start = max(0, center - half_w)
+                x_end = min(image_width, center + half_w)
+                for x in range(x_start, x_end):
+                    val = 1.0 - abs(x - center) / half_w
+                    mask[:, :, x, :] = torch.max(mask[:, :, x, :], torch.tensor(val))
+
+            # Horizontal seam bands
+            for i in range(len(y_tiles) - 1):
+                ovl_start = max(y_tiles[i][0], y_tiles[i + 1][0])
+                ovl_end = min(y_tiles[i][1], y_tiles[i + 1][1])
+                center = (ovl_start + ovl_end) // 2
+                y_start = max(0, center - half_w)
+                y_end = min(image_height, center + half_w)
+                for y in range(y_start, y_end):
+                    val = 1.0 - abs(y - center) / half_w
+                    mask[:, y, :, :] = torch.max(mask[:, y, :, :], torch.tensor(val))
+        else:
+            # Binary mode (original behavior)
+            for i in range(len(x_tiles) - 1):
+                ovl_start = max(x_tiles[i][0], x_tiles[i + 1][0])
+                ovl_end = min(x_tiles[i][1], x_tiles[i + 1][1])
+                center = (ovl_start + ovl_end) // 2
+                x_start = max(0, center - half_w)
+                x_end = min(image_width, center + half_w)
+                mask[:, :, x_start:x_end, :] = 1.0
+
+            for i in range(len(y_tiles) - 1):
+                ovl_start = max(y_tiles[i][0], y_tiles[i + 1][0])
+                ovl_end = min(y_tiles[i][1], y_tiles[i + 1][1])
+                center = (ovl_start + ovl_end) // 2
+                y_start = max(0, center - half_w)
+                y_end = min(image_height, center + half_w)
+                mask[:, y_start:y_end, :, :] = 1.0
+
+        return (mask,)
+```
+
+**Step 3: Run all tests**
+
+Run: `cd /media/p5/ComfyUI_UltimateSGUpscale && python -m pytest tests/test_seam_mask.py -v`
+
+Expected: ALL tests PASS (both old binary tests and new gradient tests).
+
+**Step 4: Commit**
+
+```bash
+git add seam_mask_node.py
+git commit -m "feat: add gradient mode to GenerateSeamMask for differential diffusion"
+```
+
+---
+
+### Task 3: Update workflow JSON with DifferentialDiffusion node
+
+**Files:**
+- Modify: `example_workflows/tiled-upscale-builtin-nodes.json`
+
+**Step 1: Add DifferentialDiffusion node and update wiring**
+
+Changes to the workflow JSON:
+
+1. Update `last_node_id` from 23 to 24
+2. Update `last_link_id` from 37 to 39
+3. In node 1 (CheckpointLoaderSimple), change MODEL output links from `[1, 2]` to `[1, 38]`
+4. Add new node 24 (DifferentialDiffusion) positioned at `[2560, 160]` inside the Seam Fix group:
+
+```json
+{
+    "id": 24,
+    "type": "DifferentialDiffusion",
+    "pos": [2560, 160],
+    "size": [250, 46],
+    "flags": {},
+    "order": 12,
+    "mode": 0,
+    "inputs": [
+        {"name": "model", "type": "MODEL", "link": 38}
+    ],
+    "outputs": [
+        {"name": "MODEL", "type": "MODEL", "slot_index": 0, "links": [39]}
+    ],
+    "properties": {"Node name for S&R": "DifferentialDiffusion"},
+    "widgets_values": []
+}
+```
+
+5. In node 19 (seam fix KSampler), change model input link from `2` to `39`
+6. In node 13 (GenerateSeamMask), update `widgets_values` from `[2048, 2048, 1024, 1024, 128, 64]` to `[2048, 2048, 1024, 1024, 128, 64, "gradient"]`
+7. Replace link `[2, 1, 0, 19, 0, "MODEL"]` with two new links:
+   - `[38, 1, 0, 24, 0, "MODEL"]` (Checkpoint → DD)
+   - `[39, 24, 0, 19, 0, "MODEL"]` (DD → Seam KSampler)
+8. Increment `order` by 1 for all nodes whose current order >= 12 (to make room for DD at order 12)
+
+**Step 2: Validate workflow JSON**
+
+Run: `cd /media/p5/ComfyUI_UltimateSGUpscale && python3 -c "import json; json.load(open('example_workflows/tiled-upscale-builtin-nodes.json')); print('Valid JSON')"`
+
+**Step 3: Verify no group overlap issues**
+
+Run the group membership check script from the previous session to confirm node 24 is inside Group 5 only.
+
+**Step 4: Commit**
+
+```bash
+git add example_workflows/tiled-upscale-builtin-nodes.json
+git commit -m "feat: add DifferentialDiffusion node to seam fix workflow pass"
+```
+
+---
+
+### Task 4: Update README
+
+**Files:**
+- Modify: `README.md`
+
+**Step 1: Update documentation**
+
+Add a note about the gradient mode and differential diffusion in the GenerateSeamMask section:
+
+- Add `mode` parameter to the inputs table: `mode | binary | binary: hard mask. gradient: linear falloff for Differential Diffusion.`
+- Mention that the example workflow uses gradient mode with DifferentialDiffusion for smoother seam repairs.
+
+**Step 2: Commit and push**
+
+```bash
+git add README.md
+git commit -m "docs: document gradient mode and differential diffusion"
+git push origin main
+```