chore(release): 1.9.1 [skip ci]

## [1.9.1](https://github.com/Artificial-Sweetener/SimpleSyrup/compare/v1.9.0...v1.9.1) (2026-09-20)

### Bug Fixes

* **contextual-diffusion:** project reference latents into views ([4cd780a](https://github.com/Artificial-Sweetener/SimpleSyrup/commit/4cd780a2451aa472ce826834e4426b65693c46e8))

CHANGELOG.md

@@ -1,3 +1,10 @@
+## [1.9.1](https://github.com/Artificial-Sweetener/SimpleSyrup/compare/v1.9.0...v1.9.1) (2026-09-20)
+
+
+### Bug Fixes
+
+* **contextual-diffusion:** project reference latents into views ([4cd780a](https://github.com/Artificial-Sweetener/SimpleSyrup/commit/4cd780a2451aa472ce826834e4426b65693c46e8))
+
 # [1.9.0](https://github.com/Artificial-Sweetener/SimpleSyrup/compare/v1.8.0...v1.9.0) (2026-09-19)
 
 

package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "simple-syrup-comfyui",
-  "version": "1.9.0",
+  "version": "1.9.1",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "simple-syrup-comfyui",
-      "version": "1.9.0",
+      "version": "1.9.1",
       "license": "AGPL-3.0-or-later",
       "devDependencies": {
         "@eslint/js": "^9.39.1",

package.json

@@ -1,6 +1,6 @@
 {
   "name": "simple-syrup-comfyui",
-  "version": "1.9.0",
+  "version": "1.9.1",
   "private": true,
   "license": "AGPL-3.0-or-later",
   "type": "module",

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "SimpleSyrup"
 description = "Workflow-focused ComfyUI extensions for image generation."
-version = "1.9.0"
+version = "1.9.1"
 license = "AGPL-3.0-or-later"
 license-files = ["LICENSE"]
 requires-python = ">=3.11"

simple_syrup/__init__.py

@@ -6,6 +6,6 @@
 
 from __future__ import annotations
 
-__version__ = "1.9.0"
+__version__ = "1.9.1"
 
 __all__: list[str] = ["__version__"]

simple_syrup/runtime/contextual_model_wrapper.py

@@ -49,6 +49,7 @@ class ContextualDiffusionModelWrapper:
         self._tile_predictions = TilePredictionAccumulator(
             plan.tile_plan,
             diffusion_mode=diffusion_mode,
+            project_canvas_reference_latents=True,
         )
 
     @property
@@ -110,6 +111,7 @@ class ContextualDiffusionModelWrapper:
         global_args = make_spatial_view_model_args(
             args=args,
             layout=global_layout,
+            project_canvas_reference_latents=True,
         )
         global_prediction = self._call_original(apply_model, global_args)
         global_view = self._plan.global_view

simple_syrup/runtime/spatial_model_arguments.py

@@ -34,8 +34,9 @@ def make_tiled_model_args(
     input_batch_size: int,
     latent_height: int,
     latent_width: int,
+    project_canvas_reference_latents: bool = False,
 ) -> dict[str, Any]:
-    """Create apply-model args for one spatial tile batch."""
+    """Create tile arguments with optional canvas-reference projection."""
 
     layout = tiled_batch_layout(
         tiles=tiles,
@@ -63,6 +64,7 @@ def make_tiled_model_args(
         conditioning=conditioning,
         layout=layout,
         view_timestep=tiled_timestep,
+        project_canvas_reference_latents=project_canvas_reference_latents,
     )
     tiled_args = args.copy()
     tiled_args["input"] = tiled_x
@@ -114,8 +116,9 @@ def make_spatial_view_model_args(
     *,
     args: dict[str, Any],
     layout: SpatialBatchLayout,
+    project_canvas_reference_latents: bool = False,
 ) -> dict[str, Any]:
-    """Create apply-model arguments for equally shaped spatial views."""
+    """Create equal-view arguments with optional canvas-reference projection."""
 
     target_shape = (layout.views[0].model_height, layout.views[0].model_width)
     if any(
@@ -149,6 +152,7 @@ def make_spatial_view_model_args(
         conditioning=conditioning,
         layout=layout,
         view_timestep=view_timestep,
+        project_canvas_reference_latents=project_canvas_reference_latents,
     )
     view_args = args.copy()
     view_args["input"] = view_x
@@ -172,14 +176,18 @@ def spatial_view_conditioning(
     conditioning: dict[str, Any],
     layout: SpatialBatchLayout,
     view_timestep: torch.Tensor,
+    project_canvas_reference_latents: bool = False,
 ) -> dict[str, Any]:
-    """Resize spatial conditioning alongside arbitrary latent views."""
+    """Project spatial conditioning and optionally canvas-aligned references."""
 
     transformed: dict[str, Any] = {}
     for key, value in conditioning.items():
         if key == "transformer_options":
             continue
-        if key in SPATIAL_INVARIANT_CONDITIONING_KEYS:
+        if (
+            key in SPATIAL_INVARIANT_CONDITIONING_KEYS
+            and not project_canvas_reference_latents
+        ):
             transformed[key] = repeat_spatial_invariant_value(
                 value,
                 view_count=layout.view_count,

simple_syrup/runtime/tile_prediction_accumulation.py

@@ -158,11 +158,18 @@ class TileBlendWeightCache:
 class TilePredictionAccumulator:
     """Evaluate tiled model views and combine them with one selected policy."""
 
-    def __init__(self, plan: TiledDiffusionPlan, *, diffusion_mode: str) -> None:
-        """Bind an immutable plan to its overlap weighting policy."""
+    def __init__(
+        self,
+        plan: TiledDiffusionPlan,
+        *,
+        diffusion_mode: str,
+        project_canvas_reference_latents: bool = False,
+    ) -> None:
+        """Bind a plan to its weighting and reference-projection policies."""
 
         self._plan = plan
         self._blend_weights = TileBlendWeightCache(plan, diffusion_mode)
+        self._project_canvas_reference_latents = project_canvas_reference_latents
 
     def predict(
         self,
@@ -183,6 +190,9 @@ class TilePredictionAccumulator:
                 input_batch_size=input_batch_size,
                 latent_height=self._plan.latent_height,
                 latent_width=self._plan.latent_width,
+                project_canvas_reference_latents=(
+                    self._project_canvas_reference_latents
+                ),
             )
             tile_output = evaluate(tiled_args)
             for index, tile in enumerate(batch):

tests/test_contextual_model_wrapper.py

@@ -182,11 +182,16 @@ def test_global_call_uses_one_full_source_reduced_model_layout(
         *,
         args: dict[str, Any],
         layout: SpatialBatchLayout,
+        project_canvas_reference_latents: bool = False,
     ) -> dict[str, Any]:
         """Capture and apply the global model-argument layout."""
 
         layouts.append(layout)
-        return transform(args=args, layout=layout)
+        return transform(
+            args=args,
+            layout=layout,
+            project_canvas_reference_latents=project_canvas_reference_latents,
+        )
 
     monkeypatch.setattr(
         wrapper_module,
@@ -389,8 +394,8 @@ def test_weighted_correction_formula_is_exact_for_both_local_fusion_modes(
     assert torch.allclose(output[:, :, 1::2], torch.full((1, 1, 8, 32), 0.5))
 
 
-def test_local_and_global_calls_receive_complete_reference_latents() -> None:
-    """Keep independent reference images intact through both spatial views."""
+def test_local_and_global_calls_project_canvas_reference_latents() -> None:
+    """Give every Contextual Diffusion view its spatially aligned reference."""
 
     reference = torch.arange(1 * 4 * 16 * 32, dtype=torch.float32).reshape(
         (1, 4, 16, 32)
@@ -419,8 +424,16 @@ def test_local_and_global_calls_receive_complete_reference_latents() -> None:
     )
 
     assert len(received) == 2
-    assert torch.equal(received[0], torch.cat((reference, reference), dim=0))
-    assert torch.equal(received[1], reference)
+    assert torch.equal(
+        received[0],
+        torch.cat((reference[..., :16], reference[..., 16:]), dim=0),
+    )
+    expected_global = torch.nn.functional.interpolate(
+        reference.reshape(-1, 1, 16, 32),
+        size=(8, 16),
+        mode="nearest-exact",
+    ).reshape(1, 4, 8, 16)
+    assert torch.equal(received[1], expected_global)
 
 
 def test_one_tile_plan_delegates_to_one_original_evaluation() -> None:

tests/test_spatial_view_model_arguments.py

@@ -144,6 +144,37 @@ def test_spatial_args_preserve_batch_metadata_references_and_source_args() -> No
     assert "spatial_batch_layout" not in existing_namespace
 
 
+def test_spatial_args_project_canvas_reference_latents_when_requested() -> None:
+    """Crop canvas-aligned references while preserving independent references."""
+
+    canvas_reference = torch.arange(1 * 2 * 4 * 8, dtype=torch.float32).reshape(
+        (1, 2, 4, 8)
+    )
+    independent_reference = torch.full((1, 2, 3, 5), 7.0)
+    layout = _layout(_left_right_views(), input_batch_size=1)
+
+    transformed = make_spatial_view_model_args(
+        args={
+            "input": torch.zeros((1, 1, 4, 8)),
+            "timestep": torch.ones((1,)),
+            "c": {"ref_latents": [canvas_reference, independent_reference]},
+        },
+        layout=layout,
+        project_canvas_reference_latents=True,
+    )
+
+    references = transformed["c"]["ref_latents"]
+    assert isinstance(references, list)
+    assert torch.equal(
+        references[0],
+        torch.cat((canvas_reference[..., :4], canvas_reference[..., 4:]), dim=0),
+    )
+    assert torch.equal(
+        references[1],
+        torch.cat((independent_reference, independent_reference), dim=0),
+    )
+
+
 @pytest.mark.parametrize(
     ("transformer_options", "message"),
     [