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chore: remove internal planning docs from repo

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Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Ethanfel
2026-02-25 17:01:47 +01:00
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docs/plans/

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# Differential Diffusion Seam Fix
## Problem
The current seam fix pass uses binary masks (1.0/0.0) with `SetLatentNoiseMask`. This creates hard transitions at band edges that can themselves become visible artifacts. Differential diffusion allows gradient masks where the value controls per-pixel denoise intensity, producing smoother seam repairs.
## Design
### GenerateSeamMask Node Changes
Add a `mode` combo input:
- **`binary`** (default): Current behavior. Output is 1.0 inside seam bands, 0.0 outside.
- **`gradient`**: Linear falloff from 1.0 at seam center to 0.0 at band edge. Value at distance `d` from center: `max(0, 1.0 - d / half_w)`. Where horizontal and vertical bands overlap (grid intersections), take `max` of both values.
The `seam_width` parameter keeps the same meaning in both modes.
### Workflow Changes
Add one `DifferentialDiffusion` node (node 24) inside the Seam Fix group. It wraps the model before it reaches the seam fix KSampler:
- Checkpoint → DifferentialDiffusion → Seam Fix KSampler (replaces direct Checkpoint → KSampler link)
- All other wiring unchanged. `SetLatentNoiseMask` still passes the mask to the latent.
### Tests
- Existing binary tests pass with explicit `mode="binary"`
- Gradient tests: center=1.0, edge=0.0, midpoint~0.5, intersection uses max

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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
```