From ddd6464d7388133c4e60cdd9dd5cf383d2f52ee8 Mon Sep 17 00:00:00 2001 From: Gin3601 Date: Thu, 21 May 2026 17:20:51 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E5=B1=80=E9=83=A8=E9=87=8D?= =?UTF-8?q?=E7=BB=98=E7=BB=93=E6=9E=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 53 + __init__.py | 1 + .../2026-05-21-pro-image-edit-inpaint.md | 3168 +++++++++++++++++ ...026-05-21-pro-image-edit-inpaint-design.md | 482 +++ image.png | Bin 0 -> 1878201 bytes model_loader/nodes.py | 19 + pro_edit/__init__.py | 38 + pro_edit/backends/__init__.py | 3 + pro_edit/backends/base.py | 33 + pro_edit/backends/flux.py | 50 + pro_edit/backends/registry.py | 89 + pro_edit/backends/sdxl.py | 45 + pro_edit/pro_edit_bridge.py | 153 + pro_edit/pro_edit_finish.py | 110 + pro_edit/pro_edit_prepare.py | 130 + pro_edit/pro_edit_utils.py | 405 +++ tests/test_pro_edit_bridge.py | 138 + tests/test_pro_edit_capabilities.py | 208 ++ tests/test_pro_edit_docs.py | 26 + tests/test_pro_edit_finish.py | 114 + tests/test_pro_edit_helpers.py | 344 ++ tests/test_pro_edit_prepare_flux.py | 124 + tests/test_pro_edit_prepare_sdxl.py | 129 + tests/test_pro_edit_registration.py | 156 + tests/test_pro_edit_registry.py | 87 + utils/model_info.py | 138 +- 测试工作流.json | 802 +++-- 27 files changed, 6656 insertions(+), 389 deletions(-) create mode 100644 docs/superpowers/plans/2026-05-21-pro-image-edit-inpaint.md create mode 100644 docs/superpowers/specs/2026-05-21-pro-image-edit-inpaint-design.md create mode 100644 image.png create mode 100644 pro_edit/__init__.py create mode 100644 pro_edit/backends/__init__.py create mode 100644 pro_edit/backends/base.py create mode 100644 pro_edit/backends/flux.py create mode 100644 pro_edit/backends/registry.py create mode 100644 pro_edit/backends/sdxl.py create mode 100644 pro_edit/pro_edit_bridge.py create mode 100644 pro_edit/pro_edit_finish.py create mode 100644 pro_edit/pro_edit_prepare.py create mode 100644 pro_edit/pro_edit_utils.py create mode 100644 tests/test_pro_edit_bridge.py create mode 100644 tests/test_pro_edit_capabilities.py create mode 100644 tests/test_pro_edit_docs.py create mode 100644 tests/test_pro_edit_finish.py create mode 100644 tests/test_pro_edit_helpers.py create mode 100644 tests/test_pro_edit_prepare_flux.py create mode 100644 tests/test_pro_edit_prepare_sdxl.py create mode 100644 tests/test_pro_edit_registration.py create mode 100644 tests/test_pro_edit_registry.py diff --git a/README.md b/README.md index 102503a..a11ab45 100644 --- a/README.md +++ b/README.md @@ -186,6 +186,59 @@ node/ --- +## Pro Image Edit / Inpaint + +`LLS-node` 也提供一套更严格的专业局部编辑链路，针对真正支持原生 image edit / inpaint 语义的模型： + +- `LLS Pro Image Edit Prepare` +- `LLS Pro KSampler Bridge` +- `LLS Pro Image Edit Finish` + +### Simple vs Pro + +- `Simple = lightweight masked latent resampling` +- `Pro = true image edit / inpaint pipeline` + +当模型本身具备真实的局部编辑能力，而且你更在意遮罩区域内的提示词跟随和原生 inpaint / image-edit 行为时，用 `Pro` 链；如果你只是需要兼容性更高、约束更少的局部重绘流程，继续使用 `Simple` 链。 + +### Professional Workflow + +`Load Image -> Load Mask or LLS Simple Mask Draw -> LLS Simple Checkpoint Loader -> LLS Simple Prompt Encode -> LLS Pro Image Edit Prepare -> LLS Pro KSampler Bridge -> VAE Decode -> LLS Pro Image Edit Finish -> Preview Image` + +### Backend Selection + +- `backend_mode = auto | sdxl | flux` +- `auto` 会根据 capability metadata 自动路由，找不到专业后端时直接报错，不会静默降级到旧的 Simple repair 逻辑 +- `sdxl` 和 `flux` 会强制指定后端，但仍然会验证当前模型是否兼容 + +### Capability Requirements + +- `model_role` +- `supports_inpaint_native` +- `supports_image_edit_native` +- `preferred_edit_backend` + +这些字段可以来自： + +- `model_info` +- `LLS Simple Checkpoint Loader` 写入到对象上的 `_lls_*` 元信息 +- `utils/model_info.py` 里的 family / name 推断逻辑 + +### Adding new professional edit models + +1. 在 `utils/model_info.py` 里补充或修正角色与 capability 推断规则 +2. 确认 `LLS Simple Checkpoint Loader` 会把正确的 `_lls_*` capability tags 写到 `model` / `clip` / `vae` +3. 优先用 `backend_mode=auto` 验证 capability metadata 是否足以自动识别 +4. 调试阶段可临时改用 `backend_mode=sdxl` 或 `backend_mode=flux`，快速确认显式路由是否匹配预期 + +### Notes + +- `LLSSimple*` 修复链保持不变，不会被这套新链覆盖 +- `LLS Pro Image Edit Finish` 负责真实的区域混合、裁剪回贴和扩图 canvas 合成 +- `LLS Pro KSampler Bridge` 支持 `denoise_mode = manual | auto_from_edit` + +--- + ## 扩展节点 在对应子包的 `nodes.py` 中添加节点类并注册到 `NODE_CLASS_MAPPINGS`，重启 ComfyUI 自动生效。 diff --git a/__init__.py b/__init__.py index 9dfbacf..1ab7708 100644 --- a/__init__.py +++ b/__init__.py @@ -49,6 +49,7 @@ _SUBPACKAGES: list[str] = [ "image", # 第 5 节：图像处理与后处理 "repair", # 第 5 节（修复子域）：图像修复 "mask_draw", # 第 5 节（修复子域）：交互式遮罩绘制 + "pro_edit", # 第 5 节（专业编辑子域）：图像编辑/重绘 "upscale", # 第 5 节（超分子域）：图像超分 "mask", # 第 6 节：遮罩操作 "controlnet", # 第 7 节：ControlNet / 控制引导 diff --git a/docs/superpowers/plans/2026-05-21-pro-image-edit-inpaint.md b/docs/superpowers/plans/2026-05-21-pro-image-edit-inpaint.md new file mode 100644 index 0000000..bd32d6b --- /dev/null +++ b/docs/superpowers/plans/2026-05-21-pro-image-edit-inpaint.md @@ -0,0 +1,3168 @@ +# LLS Pro Image Edit / Inpaint Implementation Plan + +> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. + +**Goal:** Add a new professional local image edit / inpaint workflow to `LLS-node` with three new nodes, backend-aware `SDXL` and `FLUX` conditioning, auto or manual backend selection, and real masked finish compositing, while leaving the existing `LLSSimple*` repair chain unchanged. + +**Architecture:** Add a dedicated `pro_edit/` package that owns the new `Prepare`, `KSampler Bridge`, and `Finish` nodes plus a backend registry for `sdxl` and `flux`. Extend `utils/model_info.py` and `model_loader/nodes.py` so backend routing uses explicit capability metadata, then reuse existing low-level sampling and geometry helpers only where semantics match while keeping professional edit behavior separate from the simplified repair path. + +**Tech Stack:** Python, unittest, ComfyUI node definitions, existing JSON/model-info helpers, fake tensor and mask test doubles, optional torch tensor blending for finish tests, `python3 -m compileall` + +--- + +## File Structure + +- Create: `pro_edit/__init__.py` + - Export professional edit node registration maps. +- Create: `pro_edit/pro_edit_prepare.py` + - Define `LLSProImageEditPrepare`. +- Create: `pro_edit/pro_edit_bridge.py` + - Define `LLSProKSamplerBridge`. +- Create: `pro_edit/pro_edit_finish.py` + - Define `LLSProImageEditFinish`. +- Create: `pro_edit/pro_edit_utils.py` + - Normalize `edit_info`, prepare work areas, apply backend-native concat conditioning payloads, and composite final preview images. +- Create: `pro_edit/backends/__init__.py` + - Import and register built-in professional edit backends. +- Create: `pro_edit/backends/base.py` + - Define the backend contract and routing record helpers. +- Create: `pro_edit/backends/registry.py` + - Resolve `auto | sdxl | flux` backend routing and validate capability mismatches. +- Create: `pro_edit/backends/sdxl.py` + - Implement SDXL-native edit preparation and bridge conditioning. +- Create: `pro_edit/backends/flux.py` + - Implement FLUX-native edit preparation and bridge conditioning. +- Create: `tests/test_pro_edit_helpers.py` + - Shared plugin loader, fake image and mask objects, fake model and VAE objects, and small conditioning builders. +- Create: `tests/test_pro_edit_registration.py` + - Verify plugin registration and professional node schemas. +- Create: `tests/test_pro_edit_capabilities.py` + - Verify capability inference, normalization, and loader-side tagging payloads. +- Create: `tests/test_pro_edit_registry.py` + - Verify auto routing, manual override, and mismatch failures. +- Create: `tests/test_pro_edit_prepare_sdxl.py` + - Verify SDXL prepare behavior and native concat conditioning payloads. +- Create: `tests/test_pro_edit_prepare_flux.py` + - Verify FLUX prepare behavior and edit-capable routing. +- Create: `tests/test_pro_edit_bridge.py` + - Verify bridge denoise behavior, backend validation, and sample-info output. +- Create: `tests/test_pro_edit_finish.py` + - Verify region, crop, and canvas compositing plus preview modes. +- Create: `tests/test_pro_edit_docs.py` + - Verify README covers the new professional edit workflow. +- Modify: `__init__.py` + - Append `pro_edit` to `_SUBPACKAGES`. +- Modify: `utils/model_info.py` + - Add professional edit capability inference and resolution helpers. +- Modify: `model_loader/nodes.py` + - Tag loaded objects with edit capability metadata. +- Modify: `README.md` + - Document the new professional workflow, auto or manual backend routing, capability requirements, and model-onboarding guidance. + +### Task 1: Scaffold the `pro_edit` Package and Registration Contract + +**Files:** +- Create: `tests/test_pro_edit_helpers.py` +- Create: `tests/test_pro_edit_registration.py` +- Create: `pro_edit/__init__.py` +- Create: `pro_edit/pro_edit_prepare.py` +- Create: `pro_edit/pro_edit_bridge.py` +- Create: `pro_edit/pro_edit_finish.py` +- Create: `pro_edit/pro_edit_utils.py` +- Create: `pro_edit/backends/__init__.py` +- Create: `pro_edit/backends/base.py` +- Create: `pro_edit/backends/registry.py` +- Create: `pro_edit/backends/sdxl.py` +- Create: `pro_edit/backends/flux.py` +- Modify: `__init__.py` + +- [ ] **Step 1: Write the failing tests** + +```python +# tests/test_pro_edit_helpers.py +import importlib +import importlib.util +import pathlib +import sys + + +ROOT = pathlib.Path(__file__).resolve().parents[1] +MODULE_NAME = "lls_node_test_pro_edit" +_UNSET = object() + +try: + import torch +except Exception: + torch = None + + +def load_plugin_package(): + for name in list(sys.modules): + if name == MODULE_NAME or name.startswith(f"{MODULE_NAME}."): + sys.modules.pop(name) + + spec = importlib.util.spec_from_file_location( + MODULE_NAME, + ROOT / "__init__.py", + submodule_search_locations=[str(ROOT)], + ) + module = importlib.util.module_from_spec(spec) + sys.modules[MODULE_NAME] = module + spec.loader.exec_module(module) + return module + + +def import_plugin_submodule(plugin, dotted_name: str): + return importlib.import_module(f"{plugin.__name__}.{dotted_name}") + + +class FakeTensor: + def __init__(self, shape, label="image", crop_box=None, fill_mode=None, original_box=None): + self.shape = tuple(shape) + self.label = label + self.crop_box = crop_box + self.fill_mode = fill_mode + self.original_box = original_box + + def cropped(self, x1, y1, x2, y2): + batch, _, _, channels = self.shape + return FakeTensor( + (batch, max(0, y2 - y1), max(0, x2 - x1), channels), + label=f"{self.label}:crop[{x1},{y1},{x2},{y2}]", + crop_box=(x1, y1, x2, y2), + fill_mode=self.fill_mode, + original_box=self.original_box, + ) + + def resized(self, width, height): + batch, _, _, channels = self.shape + return FakeTensor( + (batch, height, width, channels), + label=f"{self.label}:resized[{width}x{height}]", + crop_box=self.crop_box, + fill_mode=self.fill_mode, + original_box=self.original_box, + ) + + def canvas_expanded(self, width, height, fill_mode="edge", original_box=None): + batch, _, _, channels = self.shape + return FakeTensor( + (batch, height, width, channels), + label=f"{self.label}:canvas[{fill_mode}]", + crop_box=self.crop_box, + fill_mode=fill_mode, + original_box=original_box, + ) + + def masked_fill(self, mask, fill_value): + del mask + return FakeTensor( + self.shape, + label=f"{self.label}:masked[{fill_value}]", + crop_box=self.crop_box, + fill_mode=self.fill_mode, + original_box=self.original_box, + ) + + +class FakeMask: + def __init__(self, shape, mask_bbox=None, mask_area_ratio=0.0, label="mask", crop_box=None): + self.shape = tuple(shape) + self.mask_bbox = mask_bbox + self.mask_area_ratio = float(mask_area_ratio) + self.label = label + self.crop_box = crop_box + + def _clone(self, shape=None, mask_bbox=_UNSET, mask_area_ratio=None, label=None, crop_box=None): + return FakeMask( + shape or self.shape, + self.mask_bbox if mask_bbox is _UNSET else mask_bbox, + self.mask_area_ratio if mask_area_ratio is None else mask_area_ratio, + self.label if label is None else label, + self.crop_box if crop_box is None else crop_box, + ) + + def _current_size(self): + return self.shape[2], self.shape[1] + + @staticmethod + def _clamp_box(box, width, height): + if box is None: + return None + x1, y1, x2, y2 = box + x1 = max(0, min(int(x1), width)) + y1 = max(0, min(int(y1), height)) + x2 = max(x1, min(int(x2), width)) + y2 = max(y1, min(int(y2), height)) + if x2 <= x1 or y2 <= y1: + return None + return (x1, y1, x2, y2) + + @staticmethod + def _bbox_area_ratio(box, width, height): + if box is None or width <= 0 or height <= 0: + return 0.0 + return ((box[2] - box[0]) * (box[3] - box[1])) / float(width * height) + + def cropped(self, x1, y1, x2, y2): + batch, _, _ = self.shape + new_width = max(0, x2 - x1) + new_height = max(0, y2 - y1) + new_bbox = None + if self.mask_bbox is not None: + new_bbox = self._clamp_box( + ( + self.mask_bbox[0] - x1, + self.mask_bbox[1] - y1, + self.mask_bbox[2] - x1, + self.mask_bbox[3] - y1, + ), + new_width, + new_height, + ) + return FakeMask( + (batch, new_height, new_width), + mask_bbox=new_bbox, + mask_area_ratio=self._bbox_area_ratio(new_bbox, new_width, new_height), + label=f"{self.label}:crop[{x1},{y1},{x2},{y2}]", + crop_box=(x1, y1, x2, y2), + ) + + def resized(self, width, height): + batch, _, _ = self.shape + source_width, source_height = self._current_size() + new_bbox = None + if self.mask_bbox is not None and source_width > 0 and source_height > 0: + scale_x = width / float(source_width) + scale_y = height / float(source_height) + new_bbox = self._clamp_box( + ( + round(self.mask_bbox[0] * scale_x), + round(self.mask_bbox[1] * scale_y), + round(self.mask_bbox[2] * scale_x), + round(self.mask_bbox[3] * scale_y), + ), + width, + height, + ) + return FakeMask( + (batch, height, width), + mask_bbox=new_bbox, + mask_area_ratio=self._bbox_area_ratio(new_bbox, width, height), + label=f"{self.label}:resized[{width}x{height}]", + crop_box=self.crop_box, + ) + + def canvas_expanded(self, width, height, original_box=None): + batch, _, _ = self.shape + if original_box is None: + original_box = (0, 0, self.shape[2], self.shape[1]) + offset_x, offset_y = original_box[0], original_box[1] + new_bbox = None + if self.mask_bbox is not None: + new_bbox = self._clamp_box( + ( + self.mask_bbox[0] + offset_x, + self.mask_bbox[1] + offset_y, + self.mask_bbox[2] + offset_x, + self.mask_bbox[3] + offset_y, + ), + width, + height, + ) + return FakeMask( + (batch, height, width), + mask_bbox=new_bbox, + mask_area_ratio=self._bbox_area_ratio(new_bbox, width, height), + label=f"{self.label}:canvas", + crop_box=self.crop_box, + ) + + def normalized(self): + return self._clone(label=f"{self.label}:normalize") + + def inverted(self, image_size): + width, height = image_size + inverted_ratio = max(0.0, min(1.0, 1.0 - self.mask_area_ratio)) + inverted_bbox = None if inverted_ratio <= 0.0 else (0, 0, width, height) + return self._clone( + shape=(self.shape[0], height, width), + mask_bbox=inverted_bbox, + mask_area_ratio=inverted_ratio, + label=f"{self.label}:invert", + ) + + def thresholded(self, threshold): + width, height = self._current_size() + if self.mask_bbox is None: + return self._clone(label=f"{self.label}:threshold[{threshold}]") + shrink_ratio = max(0.0, min(1.0, 1.0 - max(0.0, float(threshold) - 0.5))) + if shrink_ratio <= 0.0: + return self._clone(mask_bbox=None, mask_area_ratio=0.0, label=f"{self.label}:threshold[{threshold}]") + x1, y1, x2, y2 = self.mask_bbox + cx = (x1 + x2) / 2.0 + cy = (y1 + y2) / 2.0 + half_width = max(1.0, (x2 - x1) * shrink_ratio / 2.0) + half_height = max(1.0, (y2 - y1) * shrink_ratio / 2.0) + new_bbox = self._clamp_box( + ( + round(cx - half_width), + round(cy - half_height), + round(cx + half_width), + round(cy + half_height), + ), + width, + height, + ) + return self._clone( + mask_bbox=new_bbox, + mask_area_ratio=self._bbox_area_ratio(new_bbox, width, height), + label=f"{self.label}:threshold[{threshold}]", + ) + + def grown(self, amount, image_size): + width, height = image_size + if self.mask_bbox is None: + return self._clone(shape=(self.shape[0], height, width), label=f"{self.label}:grow[{amount}]") + x1, y1, x2, y2 = self.mask_bbox + new_bbox = self._clamp_box((x1 - amount, y1 - amount, x2 + amount, y2 + amount), width, height) + return self._clone( + shape=(self.shape[0], height, width), + mask_bbox=new_bbox, + mask_area_ratio=self._bbox_area_ratio(new_bbox, width, height), + label=f"{self.label}:grow[{amount}]", + ) + + def blurred(self, radius, image_size): + blur_expand = max(1, int(round(float(radius) / 4.0))) + return self.grown(blur_expand, image_size)._clone(label=f"{self.label}:blur[{radius}]") + + def merged_with(self, other, image_size): + width, height = image_size + if self.mask_bbox is None: + return other._clone(shape=(other.shape[0], height, width), label=f"{self.label}:merge:{other.label}") + if other.mask_bbox is None: + return self._clone(shape=(self.shape[0], height, width), label=f"{self.label}:merge:{other.label}") + new_bbox = self._clamp_box( + ( + min(self.mask_bbox[0], other.mask_bbox[0]), + min(self.mask_bbox[1], other.mask_bbox[1]), + max(self.mask_bbox[2], other.mask_bbox[2]), + max(self.mask_bbox[3], other.mask_bbox[3]), + ), + width, + height, + ) + return self._clone( + shape=(self.shape[0], height, width), + mask_bbox=new_bbox, + mask_area_ratio=self._bbox_area_ratio(new_bbox, width, height), + label=f"{self.label}:merge:{other.label}", + ) + + @classmethod + def canvas_region(cls, shape, original_box, label="canvas-region"): + batch, height, width = shape + original_width = max(0, original_box[2] - original_box[0]) + original_height = max(0, original_box[3] - original_box[1]) + total_area = max(1, width * height) + original_area = max(0, original_width * original_height) + area_ratio = max(0.0, min(1.0, (total_area - original_area) / float(total_area))) + bbox = None if area_ratio <= 0.0 else (0, 0, width, height) + return cls(shape, mask_bbox=bbox, mask_area_ratio=area_ratio, label=label) + + +class FakeLatentTensor: + def __init__(self, shape): + self.shape = tuple(shape) + + +class FakeVAE: + def __init__(self, latent_channels=4, downscale_ratio=8): + self.latent_channels = int(latent_channels) + self.downscale_ratio = int(downscale_ratio) + + def encode(self, image): + batch, height, width, _ = image.shape + latent_height = max(1, height // self.downscale_ratio) + latent_width = max(1, width // self.downscale_ratio) + return FakeLatentTensor((batch, self.latent_channels, latent_height, latent_width)) + + +class FakeModel: + def __init__( + self, + family="SDXL", + model_role="base", + supports_inpaint_native=False, + supports_image_edit_native=False, + preferred_edit_backend=None, + model_name="demo-model.safetensors", + ): + self._lls_family = family + self._lls_model_role = model_role + self._lls_supports_inpaint_native = supports_inpaint_native + self._lls_supports_image_edit_native = supports_image_edit_native + self._lls_preferred_edit_backend = preferred_edit_backend + self._lls_model_name = model_name + + +def make_conditioning(label): + return [[label, {"origin": label}]] + + +def make_torch_image(width: int, height: int, value: float): + if torch is None: + raise RuntimeError("torch is required for this helper") + return torch.full((1, height, width, 3), float(value), dtype=torch.float32) + + +def make_torch_mask(width: int, height: int, box): + if torch is None: + raise RuntimeError("torch is required for this helper") + x1, y1, x2, y2 = box + mask = torch.zeros((1, height, width), dtype=torch.float32) + mask[:, y1:y2, x1:x2] = 1.0 + return mask +``` + +```python +# tests/test_pro_edit_registration.py +import unittest + +try: + from .test_pro_edit_helpers import import_plugin_submodule, load_plugin_package +except ImportError: + from test_pro_edit_helpers import import_plugin_submodule, load_plugin_package + + +class TestProEditRegistration(unittest.TestCase): + def test_plugin_registers_pro_edit_nodes(self): + plugin = load_plugin_package() + + self.assertIn("LLSProImageEditPrepare", plugin.NODE_CLASS_MAPPINGS) + self.assertIn("LLSProKSamplerBridge", plugin.NODE_CLASS_MAPPINGS) + self.assertIn("LLSProImageEditFinish", plugin.NODE_CLASS_MAPPINGS) + self.assertEqual( + plugin.NODE_DISPLAY_NAME_MAPPINGS["LLSProImageEditPrepare"], + "LLS Pro Image Edit Prepare", + ) + self.assertEqual( + plugin.NODE_DISPLAY_NAME_MAPPINGS["LLSProKSamplerBridge"], + "LLS Pro KSampler Bridge", + ) + self.assertEqual( + plugin.NODE_DISPLAY_NAME_MAPPINGS["LLSProImageEditFinish"], + "LLS Pro Image Edit Finish", + ) + + def test_prepare_node_schema_matches_contract(self): + plugin = load_plugin_package() + node_cls = plugin.NODE_CLASS_MAPPINGS["LLSProImageEditPrepare"] + schema = node_cls.INPUT_TYPES() + + self.assertEqual(node_cls.CATEGORY, "LLS/Image Edit") + self.assertEqual(node_cls.FUNCTION, "prepare") + self.assertEqual( + node_cls.RETURN_TYPES, + ("LATENT", "IMAGE", "MASK", "LLS_EDIT_INFO", "FLOAT", "CONDITIONING", "CONDITIONING"), + ) + self.assertEqual( + node_cls.RETURN_NAMES, + ("latent", "work_image", "work_mask", "edit_info", "recommended_denoise", "positive", "negative"), + ) + + required = schema["required"] + optional = schema["optional"] + + for field in ( + "image", + "mask", + "vae", + "positive", + "negative", + "backend_mode", + "edit_scope", + "mask_grow", + "mask_blur", + "mask_threshold", + "invert_mask", + "crop_context", + "crop_context_factor", + "min_size", + "max_size", + "resize_mode", + "expand_left", + "expand_right", + "expand_top", + "expand_bottom", + "canvas_fill", + "auto_recommend", + ): + self.assertIn(field, required) + + for field in ("model", "model_info"): + self.assertIn(field, optional) + + self.assertEqual(required["image"], ("IMAGE",)) + self.assertEqual(required["mask"], ("MASK",)) + self.assertEqual(required["vae"], ("VAE",)) + self.assertEqual(required["positive"], ("CONDITIONING",)) + self.assertEqual(required["negative"], ("CONDITIONING",)) + self.assertEqual(schema["required"]["backend_mode"][0], ["auto", "sdxl", "flux"]) + self.assertEqual(schema["required"]["edit_scope"][0], ["auto", "region", "crop", "canvas"]) + self.assertEqual(optional["model"], ("MODEL",)) + self.assertEqual(optional["model_info"], ("STRING",)) + + def test_bridge_and_finish_schemas_match_contract(self): + plugin = load_plugin_package() + + bridge_cls = plugin.NODE_CLASS_MAPPINGS["LLSProKSamplerBridge"] + bridge_schema = bridge_cls.INPUT_TYPES() + self.assertEqual(bridge_cls.CATEGORY, "LLS/Image Edit") + self.assertEqual(bridge_cls.FUNCTION, "sample") + self.assertEqual(bridge_cls.RETURN_TYPES, ("LATENT", "STRING")) + self.assertEqual(bridge_cls.RETURN_NAMES, ("latent", "sample_info")) + + bridge_required = bridge_schema["required"] + bridge_optional = bridge_schema["optional"] + + for field in ( + "model", + "positive", + "negative", + "latent_image", + "backend_mode", + "quality_preset", + "seed", + "steps", + "cfg", + "sampler_name", + "scheduler", + "denoise", + "denoise_mode", + "flux_guidance", + "model_family", + ): + self.assertIn(field, bridge_required) + + self.assertEqual(bridge_required["backend_mode"][0], ["auto", "sdxl", "flux"]) + self.assertEqual(bridge_required["denoise_mode"][0], ["manual", "auto_from_edit"]) + self.assertEqual(bridge_optional["edit_info"], ("LLS_EDIT_INFO",)) + self.assertEqual(bridge_optional["model_info"], ("STRING",)) + + finish_cls = plugin.NODE_CLASS_MAPPINGS["LLSProImageEditFinish"] + finish_schema = finish_cls.INPUT_TYPES() + self.assertEqual(finish_cls.CATEGORY, "LLS/Image Edit") + self.assertEqual(finish_cls.FUNCTION, "finish") + self.assertEqual(finish_cls.RETURN_TYPES, ("IMAGE", "IMAGE")) + self.assertEqual(finish_cls.RETURN_NAMES, ("final_image", "preview_image")) + + finish_required = finish_schema["required"] + finish_optional = finish_schema["optional"] + + for field in ( + "original_image", + "generated_image", + "edit_info", + "feather", + "color_match", + "brightness_match", + "blend_strength", + "restore_unmasked_area", + "edge_fix", + "preview_mode", + ): + self.assertIn(field, finish_required) + + self.assertEqual(finish_required["original_image"], ("IMAGE",)) + self.assertEqual(finish_required["generated_image"], ("IMAGE",)) + self.assertEqual(finish_required["edit_info"], ("LLS_EDIT_INFO",)) + self.assertEqual(finish_optional["work_mask"], ("MASK",)) + self.assertEqual(finish_optional["sample_info"], ("STRING",)) + + def test_backend_registry_loads_builtin_backends(self): + plugin = load_plugin_package() + registry = import_plugin_submodule(plugin, "pro_edit.backends.registry") + + self.assertEqual(registry.get_backend("sdxl").backend_name, "sdxl") + self.assertEqual(registry.get_backend("flux").backend_name, "flux") + + +if __name__ == "__main__": + unittest.main() +``` + +- [ ] **Step 2: Run tests to verify they fail** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_registration.py' -v` + +Expected: FAIL because the `pro_edit` package, registrations, and node classes do not exist yet. + +- [ ] **Step 3: Implement the minimal scaffolding** + +```python +# __init__.py +_SUBPACKAGES: list[str] = [ + "model_loader", + "conditioning", + "sampling", + "qwen", + "latent", + "image", + "repair", + "mask_draw", + "pro_edit", + "upscale", + "mask", + "controlnet", + "lora", + "video", + "audio", + "utils", +] +``` + +```python +# pro_edit/__init__.py +from .pro_edit_bridge import ( + NODE_CLASS_MAPPINGS as BRIDGE_NODE_CLASS_MAPPINGS, + NODE_DISPLAY_NAME_MAPPINGS as BRIDGE_NODE_DISPLAY_NAME_MAPPINGS, +) +from .pro_edit_finish import ( + NODE_CLASS_MAPPINGS as FINISH_NODE_CLASS_MAPPINGS, + NODE_DISPLAY_NAME_MAPPINGS as FINISH_NODE_DISPLAY_NAME_MAPPINGS, +) +from .pro_edit_prepare import ( + NODE_CLASS_MAPPINGS as PREPARE_NODE_CLASS_MAPPINGS, + NODE_DISPLAY_NAME_MAPPINGS as PREPARE_NODE_DISPLAY_NAME_MAPPINGS, +) + + +def _merge_registry_maps(*maps): + merged = {} + for mapping in maps: + overlap = set(merged).intersection(mapping) + if overlap: + duplicate_keys = ", ".join(sorted(overlap)) + raise RuntimeError(f"[LLS] Duplicate pro_edit node registration keys: {duplicate_keys}") + merged.update(mapping) + return merged + + +NODE_CLASS_MAPPINGS = _merge_registry_maps( + PREPARE_NODE_CLASS_MAPPINGS, + BRIDGE_NODE_CLASS_MAPPINGS, + FINISH_NODE_CLASS_MAPPINGS, +) + +NODE_DISPLAY_NAME_MAPPINGS = _merge_registry_maps( + PREPARE_NODE_DISPLAY_NAME_MAPPINGS, + BRIDGE_NODE_DISPLAY_NAME_MAPPINGS, + FINISH_NODE_DISPLAY_NAME_MAPPINGS, +) + +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] +``` + +```python +# pro_edit/pro_edit_utils.py +from ..utils.model_info import parse_jsonish_info + + +EDIT_INFO_TYPE = "LLS_EDIT_INFO" +BACKEND_MODE_CHOICES = ["auto", "sdxl", "flux"] +EDIT_SCOPE_CHOICES = ["auto", "region", "crop", "canvas"] +PREVIEW_MODE_CHOICES = ["final", "compare", "mask", "before_after"] +AUTO_RECOMMEND_CHOICES = ["enabled", "disabled"] +CANVAS_FILL_CHOICES = ["edge", "blur", "black", "white", "neutral"] +COLOR_MATCH_CHOICES = ["disabled", "mean_std", "histogram_simple"] +BRIGHTNESS_MATCH_CHOICES = ["disabled", "enabled"] +EDGE_FIX_CHOICES = ["none", "soft", "strong"] + + +def normalize_edit_info(edit_info): + info = parse_jsonish_info(edit_info) + info.setdefault("backend_name", "") + info.setdefault("routing_reason", "") + info.setdefault("edit_scope", "region") + info.setdefault("edit_payload_version", "1.0") + return info +``` + +```python +# pro_edit/backends/base.py +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Protocol + + +@dataclass(frozen=True) +class RoutingResult: + backend_name: str + routing_reason: str + capabilities: dict[str, Any] + + +class ProEditBackend(Protocol): + backend_name: str + + def supports(self, capabilities: dict[str, Any]) -> bool: + raise NotImplementedError + + def prepare(self, **kwargs) -> dict[str, Any]: + raise NotImplementedError + + def prepare_bridge(self, **kwargs) -> dict[str, Any]: + raise NotImplementedError +``` + +```python +# pro_edit/backends/registry.py +from __future__ import annotations + +from .base import RoutingResult + + +_BACKENDS = {} + + +def register_backend(backend): + _BACKENDS[backend.backend_name] = backend + return backend + + +def get_backend(name: str): + backend = _BACKENDS.get(str(name or "").strip()) + if backend is None: + raise RuntimeError(f"[LLS] Unknown pro edit backend '{name}'.") + return backend + + +def resolve_backend(backend_mode: str, **_kwargs): + mode = str(backend_mode or "auto") + if mode == "auto": + raise RuntimeError("[LLS] Pro edit backend auto routing is not implemented yet.") + backend = get_backend(mode) + return backend, RoutingResult(backend.backend_name, "manual", {}) + + +from . import flux, sdxl # noqa: F401,E402 +``` + +```python +# pro_edit/backends/sdxl.py +from .registry import register_backend + + +class SDXLProEditBackend: + backend_name = "sdxl" + + def supports(self, capabilities): + return str(capabilities.get("model_family") or "").startswith("SDXL") + + def prepare(self, **kwargs): + raise RuntimeError("[LLS] SDXL pro edit backend is not implemented yet.") + + def prepare_bridge(self, **kwargs): + return kwargs + + +register_backend(SDXLProEditBackend()) +``` + +```python +# pro_edit/backends/flux.py +from .registry import register_backend + + +class FluxProEditBackend: + backend_name = "flux" + + def supports(self, capabilities): + return str(capabilities.get("model_family") or "").startswith("FLUX") + + def prepare(self, **kwargs): + raise RuntimeError("[LLS] FLUX pro edit backend is not implemented yet.") + + def prepare_bridge(self, **kwargs): + return kwargs + + +register_backend(FluxProEditBackend()) +``` + +```python +# pro_edit/backends/__init__.py +from . import flux, sdxl + +__all__ = ["flux", "sdxl"] +``` + +```python +# pro_edit/pro_edit_prepare.py +from .pro_edit_utils import AUTO_RECOMMEND_CHOICES, BACKEND_MODE_CHOICES, CANVAS_FILL_CHOICES, EDIT_SCOPE_CHOICES + + +class LLSProImageEditPrepare: + CATEGORY = "LLS/Image Edit" + FUNCTION = "prepare" + RETURN_TYPES = ("LATENT", "IMAGE", "MASK", "LLS_EDIT_INFO", "FLOAT", "CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("latent", "work_image", "work_mask", "edit_info", "recommended_denoise", "positive", "negative") + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "mask": ("MASK",), + "vae": ("VAE",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "backend_mode": (BACKEND_MODE_CHOICES, {"default": "auto"}), + "edit_scope": (EDIT_SCOPE_CHOICES, {"default": "auto"}), + "mask_grow": ("INT", {"default": 24, "min": 0, "max": 2048}), + "mask_blur": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 256.0, "step": 0.5}), + "mask_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "invert_mask": ("BOOLEAN", {"default": False}), + "crop_context": ("INT", {"default": 64, "min": 0, "max": 512}), + "crop_context_factor": ("FLOAT", {"default": 1.5, "min": 1.0, "max": 8.0, "step": 0.1}), + "min_size": ("INT", {"default": 256, "min": 64, "max": 8192, "step": 8}), + "max_size": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 8}), + "resize_mode": (["fit", "pad", "stretch"], {"default": "fit"}), + "expand_left": ("INT", {"default": 0, "min": 0, "max": 4096}), + "expand_right": ("INT", {"default": 0, "min": 0, "max": 4096}), + "expand_top": ("INT", {"default": 0, "min": 0, "max": 4096}), + "expand_bottom": ("INT", {"default": 0, "min": 0, "max": 4096}), + "canvas_fill": (CANVAS_FILL_CHOICES, {"default": "edge"}), + "auto_recommend": (AUTO_RECOMMEND_CHOICES, {"default": "enabled"}), + }, + "optional": { + "model": ("MODEL",), + "model_info": ("STRING",), + }, + } + + def prepare(self, **_kwargs): + raise RuntimeError("[LLS] LLS Pro Image Edit Prepare is not implemented yet.") + + +NODE_CLASS_MAPPINGS = {"LLSProImageEditPrepare": LLSProImageEditPrepare} +NODE_DISPLAY_NAME_MAPPINGS = {"LLSProImageEditPrepare": "LLS Pro Image Edit Prepare"} +``` + +```python +# pro_edit/pro_edit_bridge.py +from .pro_edit_utils import BACKEND_MODE_CHOICES +from ..sampling.nodes import _QUALITY_PRESETS, _get_samplers, _get_schedulers +from ..utils.model_info import MODEL_FAMILY_CHOICES + + +class LLSProKSamplerBridge: + CATEGORY = "LLS/Image Edit" + FUNCTION = "sample" + RETURN_TYPES = ("LATENT", "STRING") + RETURN_NAMES = ("latent", "sample_info") + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "model": ("MODEL",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "latent_image": ("LATENT",), + "backend_mode": (BACKEND_MODE_CHOICES, {"default": "auto"}), + "quality_preset": (_QUALITY_PRESETS, {"default": "Family Default"}), + "seed": ("INT", {"default": -1, "min": -1, "max": 0xFFFFFFFFFFFFFFFF}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step": 0.1}), + "sampler_name": (_get_samplers(), {"default": "euler"}), + "scheduler": (_get_schedulers(), {"default": "normal"}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "denoise_mode": (["manual", "auto_from_edit"], {"default": "manual"}), + "flux_guidance": ("STRING,FLOAT,INT", {"default": 3.5, "widgetType": "FLOAT"}), + "model_family": (MODEL_FAMILY_CHOICES, {"default": "Auto"}), + }, + "optional": { + "edit_info": ("LLS_EDIT_INFO",), + "model_info": ("STRING",), + }, + } + + def sample(self, **_kwargs): + raise RuntimeError("[LLS] LLS Pro KSampler Bridge is not implemented yet.") + + +NODE_CLASS_MAPPINGS = {"LLSProKSamplerBridge": LLSProKSamplerBridge} +NODE_DISPLAY_NAME_MAPPINGS = {"LLSProKSamplerBridge": "LLS Pro KSampler Bridge"} +``` + +```python +# pro_edit/pro_edit_finish.py +from .pro_edit_utils import ( + BRIGHTNESS_MATCH_CHOICES, + COLOR_MATCH_CHOICES, + EDGE_FIX_CHOICES, + PREVIEW_MODE_CHOICES, +) + + +class LLSProImageEditFinish: + CATEGORY = "LLS/Image Edit" + FUNCTION = "finish" + RETURN_TYPES = ("IMAGE", "IMAGE") + RETURN_NAMES = ("final_image", "preview_image") + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "original_image": ("IMAGE",), + "generated_image": ("IMAGE",), + "edit_info": ("LLS_EDIT_INFO",), + "feather": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 256.0, "step": 0.5}), + "color_match": (COLOR_MATCH_CHOICES, {"default": "disabled"}), + "brightness_match": (BRIGHTNESS_MATCH_CHOICES, {"default": "enabled"}), + "blend_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "restore_unmasked_area": ("BOOLEAN", {"default": True}), + "edge_fix": (EDGE_FIX_CHOICES, {"default": "soft"}), + "preview_mode": (PREVIEW_MODE_CHOICES, {"default": "final"}), + }, + "optional": { + "work_mask": ("MASK",), + "sample_info": ("STRING",), + }, + } + + def finish(self, **_kwargs): + raise RuntimeError("[LLS] LLS Pro Image Edit Finish is not implemented yet.") + + +NODE_CLASS_MAPPINGS = {"LLSProImageEditFinish": LLSProImageEditFinish} +NODE_DISPLAY_NAME_MAPPINGS = {"LLSProImageEditFinish": "LLS Pro Image Edit Finish"} +``` + +- [ ] **Step 4: Run tests to verify they pass** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_registration.py' -v` + +Expected: PASS with three tests. + +- [ ] **Step 5: Commit** + +```bash +git add __init__.py pro_edit tests/test_pro_edit_helpers.py tests/test_pro_edit_registration.py +git commit -m "feat: scaffold pro image edit nodes" +``` + +### Task 2: Add Edit Capability Metadata Inference and Loader Tagging + +**Files:** +- Create: `tests/test_pro_edit_capabilities.py` +- Modify: `utils/model_info.py` +- Modify: `model_loader/nodes.py` + +- [ ] **Step 1: Write the failing tests** + +```python +# tests/test_pro_edit_capabilities.py +import unittest + +try: + from .test_pro_edit_helpers import FakeModel, import_plugin_submodule, load_plugin_package +except ImportError: + from test_pro_edit_helpers import FakeModel, import_plugin_submodule, load_plugin_package + + +class TestProEditCapabilities(unittest.TestCase): + def test_parse_model_info_infers_sdxl_inpaint_capability_from_name(self): + plugin = load_plugin_package() + model_info = import_plugin_submodule(plugin, "utils.model_info") + + info = model_info.parse_model_info( + { + "checkpoint_name": "demo-sdxl-inpaint.safetensors", + "family": "SDXL", + } + ) + + self.assertEqual(info["model_role"], "inpaint") + self.assertTrue(info["supports_inpaint_native"]) + self.assertFalse(info["supports_image_edit_native"]) + self.assertEqual(info["preferred_edit_backend"], "sdxl") + + def test_parse_model_info_infers_flux_edit_capability_from_name(self): + plugin = load_plugin_package() + model_info = import_plugin_submodule(plugin, "utils.model_info") + + info = model_info.parse_model_info( + { + "checkpoint_name": "demo-flux-fill-dev.safetensors", + "family": "FLUX_DEV", + } + ) + + self.assertEqual(info["model_role"], "fill") + self.assertFalse(info["supports_inpaint_native"]) + self.assertTrue(info["supports_image_edit_native"]) + self.assertEqual(info["preferred_edit_backend"], "flux") + + def test_explicit_capability_values_override_name_inference(self): + plugin = load_plugin_package() + model_info = import_plugin_submodule(plugin, "utils.model_info") + + info = model_info.parse_model_info( + { + "checkpoint_name": "plain-sdxl-base.safetensors", + "family": "SDXL", + "model_role": "edit", + "supports_inpaint_native": True, + "supports_image_edit_native": True, + "preferred_edit_backend": "sdxl", + } + ) + + self.assertEqual(info["model_role"], "edit") + self.assertTrue(info["supports_inpaint_native"]) + self.assertTrue(info["supports_image_edit_native"]) + self.assertEqual(info["preferred_edit_backend"], "sdxl") + + def test_resolve_edit_capabilities_reads_model_tags(self): + plugin = load_plugin_package() + model_info = import_plugin_submodule(plugin, "utils.model_info") + model = FakeModel( + family="FLUX_DEV", + model_role="edit", + supports_inpaint_native=False, + supports_image_edit_native=True, + preferred_edit_backend="flux", + model_name="demo-flux-edit.safetensors", + ) + + capabilities = model_info.resolve_edit_capabilities(model=model, model_info=None) + + self.assertEqual(capabilities["model_family"], "FLUX_DEV") + self.assertEqual(capabilities["model_role"], "edit") + self.assertTrue(capabilities["supports_image_edit_native"]) + self.assertEqual(capabilities["preferred_edit_backend"], "flux") + + def test_loader_builds_capability_tags_for_loaded_objects(self): + plugin = load_plugin_package() + loader_module = import_plugin_submodule(plugin, "model_loader.nodes") + + tags = loader_module._build_capability_tags("demo-sdxl-inpaint.safetensors", "SDXL") + + self.assertEqual(tags["model_role"], "inpaint") + self.assertTrue(tags["supports_inpaint_native"]) + self.assertEqual(tags["preferred_edit_backend"], "sdxl") + + +if __name__ == "__main__": + unittest.main() +``` + +- [ ] **Step 2: Run tests to verify they fail** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_capabilities.py' -v` + +Expected: FAIL because capability inference helpers and loader capability tags are not implemented. + +- [ ] **Step 3: Implement capability inference and loader tagging** + +```python +# utils/model_info.py +_ROLE_KEYWORDS = ( + ("inpaint", "inpaint"), + ("img2img", "edit"), + ("imageedit", "edit"), + ("image-edit", "edit"), + ("edit", "edit"), + ("fill", "fill"), + ("refiner", "refiner"), +) + + +def infer_model_role_from_name(model_name: str | None, family: str | None = None) -> str: + del family + name = str(model_name or "").lower() + for needle, role in _ROLE_KEYWORDS: + if needle in name: + return role + return "base" + + +def infer_edit_capabilities(model_name: str | None, family: str | None = None) -> dict[str, Any]: + resolved_family = canonicalize_family(family or infer_family_from_name(model_name, "SD1.5")) + role = infer_model_role_from_name(model_name, resolved_family) + + supports_inpaint_native = False + supports_image_edit_native = False + preferred_edit_backend = None + + if is_sdxl_family(resolved_family): + preferred_edit_backend = "sdxl" if role in {"inpaint", "edit", "fill"} else None + supports_inpaint_native = role in {"inpaint", "edit", "fill"} + supports_image_edit_native = role in {"edit", "fill"} + elif is_flux_family(resolved_family): + preferred_edit_backend = "flux" if role in {"inpaint", "edit", "fill"} else None + supports_inpaint_native = role == "inpaint" + supports_image_edit_native = role in {"inpaint", "edit", "fill"} + + return { + "model_role": role, + "supports_inpaint_native": supports_inpaint_native, + "supports_image_edit_native": supports_image_edit_native, + "preferred_edit_backend": preferred_edit_backend, + } + + +def resolve_edit_capabilities(model=None, model_info: dict[str, Any] | str | None = None) -> dict[str, Any]: + info = parse_model_info(model_info) + model_name = str( + info.get("checkpoint_name") + or info.get("model_name") + or get_lls_attr(model, "model_name", "") + or "" + ) + family = canonicalize_family( + info.get("family") + or info.get("model_family") + or get_lls_attr(model, "family", None) + or infer_family_from_model(model) + ) + inferred = infer_edit_capabilities(model_name, family) + return { + "model_family": family, + "model_name": model_name, + "model_role": str( + info.get("model_role") + or get_lls_attr(model, "model_role", None) + or inferred["model_role"] + ), + "supports_inpaint_native": bool( + info.get( + "supports_inpaint_native", + get_lls_attr(model, "supports_inpaint_native", inferred["supports_inpaint_native"]), + ) + ), + "supports_image_edit_native": bool( + info.get( + "supports_image_edit_native", + get_lls_attr(model, "supports_image_edit_native", inferred["supports_image_edit_native"]), + ) + ), + "preferred_edit_backend": ( + info.get("preferred_edit_backend") + or get_lls_attr(model, "preferred_edit_backend", inferred["preferred_edit_backend"]) + ), + } +``` + +```python +# utils/model_info.py +def parse_model_info(model_info: dict[str, Any] | str | None) -> dict[str, Any]: + raw = parse_jsonish_info(model_info) + family = canonicalize_family( + raw.get("family") + or raw.get("model_family") + or infer_family_from_name( + raw.get("checkpoint_name") or raw.get("model_name") or raw.get("ckpt_name") or raw.get("ckpt"), + "SD1.5", + ) + ) + defaults = get_family_defaults(family) + capability_defaults = infer_edit_capabilities( + raw.get("checkpoint_name") or raw.get("model_name"), + family, + ) + + info: dict[str, Any] = {**defaults} + info.update(raw) + info["family"] = family + info["model_role"] = str(raw.get("model_role", capability_defaults["model_role"])) + info["supports_inpaint_native"] = bool( + raw.get("supports_inpaint_native", capability_defaults["supports_inpaint_native"]) + ) + info["supports_image_edit_native"] = bool( + raw.get("supports_image_edit_native", capability_defaults["supports_image_edit_native"]) + ) + info["preferred_edit_backend"] = raw.get( + "preferred_edit_backend", + capability_defaults["preferred_edit_backend"], + ) + info.setdefault("model_family", family) + info.setdefault("checkpoint_name", raw.get("checkpoint_name") or raw.get("model_name") or "") + info.setdefault("model_name", info["checkpoint_name"]) + info.setdefault("vae_source", raw.get("vae_source", "auto")) + info.setdefault("text_encoder_source", raw.get("text_encoder_source", "auto")) + info.setdefault("load_mode", raw.get("load_mode", "simple")) + return info +``` + +```python +# model_loader/nodes.py +from ..utils.model_info import ( + MODEL_FAMILY_CHOICES, + build_model_info, + canonicalize_family, + get_family_defaults, + infer_family_from_name, + is_flux_family, + is_sdxl_family, + tag_lls_object, +) + + +def _build_capability_tags(model_name: str, family: str) -> dict[str, object]: + info = build_model_info( + checkpoint_name=model_name, + model_name=model_name, + family=family, + ) + return { + "model_role": info["model_role"], + "supports_inpaint_native": info["supports_inpaint_native"], + "supports_image_edit_native": info["supports_image_edit_native"], + "preferred_edit_backend": info["preferred_edit_backend"], + } +``` + +```python +# model_loader/nodes.py +capability_tags = _build_capability_tags(ckpt_name, family) + +tag_lls_object( + model, + family=family, + model_name=ckpt_name, + checkpoint_name=ckpt_name, + load_mode=load_mode, + **capability_tags, +) +tag_lls_object( + text_encoder, + family=family, + model_name=ckpt_name, + checkpoint_name=ckpt_name, + text_encoder_type=defaults["text_encoder_type"], + text_encoder_source=resolved_text_encoder_source, + text_encoder_name=resolved_text_encoder_name, + text_encoder_name_1=resolved_text_encoder_name_1, + text_encoder_name_2=resolved_text_encoder_name_2, + **capability_tags, +) +tag_lls_object( + vae, + family=family, + model_name=ckpt_name, + checkpoint_name=ckpt_name, + vae_name=resolved_vae_label, + vae_source=resolved_vae_source, + **capability_tags, +) +``` + +- [ ] **Step 4: Run tests to verify they pass** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_capabilities.py' -v` + +Expected: PASS with five tests. + +- [ ] **Step 5: Commit** + +```bash +git add utils/model_info.py model_loader/nodes.py tests/test_pro_edit_capabilities.py +git commit -m "feat: add pro edit capability metadata" +``` + +### Task 3: Build Backend Registry and Routing Validation + +**Files:** +- Create: `tests/test_pro_edit_registry.py` +- Modify: `pro_edit/backends/base.py` +- Modify: `pro_edit/backends/registry.py` +- Modify: `pro_edit/backends/sdxl.py` +- Modify: `pro_edit/backends/flux.py` + +- [ ] **Step 1: Write the failing tests** + +```python +# tests/test_pro_edit_registry.py +import unittest + +try: + from .test_pro_edit_helpers import FakeModel, import_plugin_submodule, load_plugin_package +except ImportError: + from test_pro_edit_helpers import FakeModel, import_plugin_submodule, load_plugin_package + + +class TestProEditRegistry(unittest.TestCase): + def setUp(self): + self.plugin = load_plugin_package() + self.registry = import_plugin_submodule(self.plugin, "pro_edit.backends.registry") + + def test_auto_routes_sdxl_inpaint_model(self): + model = FakeModel( + family="SDXL", + model_role="inpaint", + supports_inpaint_native=True, + supports_image_edit_native=False, + preferred_edit_backend="sdxl", + ) + + backend, routing = self.registry.resolve_backend("auto", model=model) + + self.assertEqual(backend.backend_name, "sdxl") + self.assertEqual(routing.backend_name, "sdxl") + self.assertEqual(routing.routing_reason, "model.preferred_edit_backend") + + def test_auto_routes_flux_edit_model(self): + model = FakeModel( + family="FLUX_DEV", + model_role="edit", + supports_inpaint_native=False, + supports_image_edit_native=True, + preferred_edit_backend="flux", + ) + + backend, routing = self.registry.resolve_backend("auto", model=model) + + self.assertEqual(backend.backend_name, "flux") + self.assertEqual(routing.routing_reason, "model.preferred_edit_backend") + self.assertEqual(routing.capabilities["model_family"], "FLUX_DEV") + + def test_auto_reuses_backend_name_from_edit_info(self): + backend, routing = self.registry.resolve_backend( + "auto", + model=None, + edit_info={ + "backend_name": "sdxl", + "model_family": "SDXL", + "model_role": "inpaint", + "supports_inpaint_native": True, + "supports_image_edit_native": False, + "preferred_edit_backend": "sdxl", + }, + ) + + self.assertEqual(backend.backend_name, "sdxl") + self.assertEqual(routing.routing_reason, "edit_info.backend_name") + + def test_manual_flux_override_rejects_sdxl_only_model(self): + model = FakeModel( + family="SDXL", + model_role="inpaint", + supports_inpaint_native=True, + supports_image_edit_native=False, + preferred_edit_backend="sdxl", + ) + + with self.assertRaisesRegex(RuntimeError, "backend 'flux' is incompatible"): + self.registry.resolve_backend("flux", model=model) + + def test_auto_without_matching_backend_raises_clear_error(self): + model = FakeModel( + family="SD1.5", + model_role="base", + supports_inpaint_native=False, + supports_image_edit_native=False, + preferred_edit_backend=None, + ) + + with self.assertRaisesRegex(RuntimeError, "No professional edit backend matched"): + self.registry.resolve_backend("auto", model=model) + + +if __name__ == "__main__": + unittest.main() +``` + +- [ ] **Step 2: Run tests to verify they fail** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_registry.py' -v` + +Expected: FAIL because `resolve_backend` does not yet understand capability records or auto routing. + +- [ ] **Step 3: Implement the backend contract and routing rules** + +```python +# pro_edit/backends/base.py +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Protocol + + +@dataclass(frozen=True) +class RoutingResult: + backend_name: str + routing_reason: str + capabilities: dict[str, Any] + + +class ProEditBackend(Protocol): + backend_name: str + + def supports(self, capabilities: dict[str, Any]) -> bool: + raise NotImplementedError + + def prepare(self, **kwargs) -> dict[str, Any]: + raise NotImplementedError + + def prepare_bridge(self, **kwargs) -> dict[str, Any]: + raise NotImplementedError + + +def validate_manual_backend(backend_name: str, backend: ProEditBackend, capabilities: dict[str, Any]) -> None: + if not backend.supports(capabilities): + family = capabilities.get("model_family") or "UNKNOWN" + raise RuntimeError( + f"[LLS] Pro edit backend '{backend_name}' is incompatible with model family '{family}'." + ) +``` + +```python +# pro_edit/backends/registry.py +from __future__ import annotations + +from .base import RoutingResult, validate_manual_backend +from ...utils.model_info import resolve_edit_capabilities + + +_BACKENDS = {} + + +def register_backend(backend): + _BACKENDS[backend.backend_name] = backend + return backend + + +def get_backend(name: str): + backend = _BACKENDS.get(str(name or "").strip()) + if backend is None: + raise RuntimeError(f"[LLS] Unknown pro edit backend '{name}'.") + return backend + + +def _normalize_capabilities(model=None, model_info=None, edit_info=None): + resolved = resolve_edit_capabilities(model=model, model_info=model_info) + raw_edit_info = dict(edit_info or {}) + if raw_edit_info.get("model_family"): + resolved["model_family"] = str(raw_edit_info["model_family"]) + if raw_edit_info.get("model_role"): + resolved["model_role"] = str(raw_edit_info["model_role"]) + if "supports_inpaint_native" in raw_edit_info: + resolved["supports_inpaint_native"] = bool(raw_edit_info["supports_inpaint_native"]) + if "supports_image_edit_native" in raw_edit_info: + resolved["supports_image_edit_native"] = bool(raw_edit_info["supports_image_edit_native"]) + if raw_edit_info.get("preferred_edit_backend"): + resolved["preferred_edit_backend"] = raw_edit_info["preferred_edit_backend"] + return resolved + + +def resolve_backend(backend_mode: str, *, model=None, model_info=None, edit_info=None): + mode = str(backend_mode or "auto") + capabilities = _normalize_capabilities(model=model, model_info=model_info, edit_info=edit_info) + raw_edit_info = dict(edit_info or {}) + + if mode in {"sdxl", "flux"}: + backend = get_backend(mode) + validate_manual_backend(mode, backend, capabilities) + return backend, RoutingResult(mode, "manual.override", capabilities) + + if raw_edit_info.get("backend_name"): + backend_name = str(raw_edit_info["backend_name"]) + backend = get_backend(backend_name) + validate_manual_backend(backend_name, backend, capabilities) + return backend, RoutingResult(backend_name, "edit_info.backend_name", capabilities) + + preferred = str(capabilities.get("preferred_edit_backend") or "").strip() + if preferred: + backend = get_backend(preferred) + validate_manual_backend(preferred, backend, capabilities) + return backend, RoutingResult(preferred, "model.preferred_edit_backend", capabilities) + + for backend_name in ("sdxl", "flux"): + backend = get_backend(backend_name) + if backend.supports(capabilities): + return backend, RoutingResult(backend_name, "model.capability_match", capabilities) + + raise RuntimeError( + "[LLS] No professional edit backend matched the current model capability. " + "Use an edit-capable SDXL or FLUX model, or provide explicit capability metadata." + ) +``` + +```python +# pro_edit/backends/sdxl.py +from .registry import register_backend + + +class SDXLProEditBackend: + backend_name = "sdxl" + + def supports(self, capabilities): + family = str(capabilities.get("model_family") or "") + role = str(capabilities.get("model_role") or "unknown") + return family.startswith("SDXL") and ( + role in {"inpaint", "edit", "fill"} + or bool(capabilities.get("supports_inpaint_native")) + or str(capabilities.get("preferred_edit_backend") or "") == "sdxl" + ) + + def prepare(self, **kwargs): + raise RuntimeError("[LLS] SDXL pro edit backend is not implemented yet.") + + def prepare_bridge(self, **kwargs): + return kwargs + + +register_backend(SDXLProEditBackend()) +``` + +```python +# pro_edit/backends/flux.py +from .registry import register_backend + + +class FluxProEditBackend: + backend_name = "flux" + + def supports(self, capabilities): + family = str(capabilities.get("model_family") or "") + role = str(capabilities.get("model_role") or "unknown") + return family.startswith("FLUX") and ( + role in {"inpaint", "edit", "fill"} + or bool(capabilities.get("supports_image_edit_native")) + or str(capabilities.get("preferred_edit_backend") or "") == "flux" + ) + + def prepare(self, **kwargs): + raise RuntimeError("[LLS] FLUX pro edit backend is not implemented yet.") + + def prepare_bridge(self, **kwargs): + return kwargs + + +register_backend(FluxProEditBackend()) +``` + +- [ ] **Step 4: Run tests to verify they pass** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_registry.py' -v` + +Expected: PASS with five tests. + +- [ ] **Step 5: Commit** + +```bash +git add pro_edit/backends tests/test_pro_edit_registry.py +git commit -m "feat: add pro edit backend registry" +``` + +### Task 4: Implement SDXL Prepare Path and Native Concat Conditioning + +**Files:** +- Create: `tests/test_pro_edit_prepare_sdxl.py` +- Modify: `pro_edit/pro_edit_utils.py` +- Modify: `pro_edit/pro_edit_prepare.py` +- Modify: `pro_edit/backends/sdxl.py` + +- [ ] **Step 1: Write the failing tests** + +```python +# tests/test_pro_edit_prepare_sdxl.py +import unittest + +try: + from .test_pro_edit_helpers import ( + FakeMask, + FakeModel, + FakeTensor, + FakeVAE, + load_plugin_package, + make_conditioning, + ) +except ImportError: + from test_pro_edit_helpers import ( + FakeMask, + FakeModel, + FakeTensor, + FakeVAE, + load_plugin_package, + make_conditioning, + ) + + +class TestProEditPrepareSDXL(unittest.TestCase): + def setUp(self): + plugin = load_plugin_package() + node_cls = plugin.NODE_CLASS_MAPPINGS["LLSProImageEditPrepare"] + self.node = node_cls() + + def test_prepare_region_adds_concat_conditioning_for_sdxl(self): + image = FakeTensor((1, 128, 128, 3), label="image") + mask = FakeMask((1, 128, 128), mask_bbox=(32, 32, 96, 96), mask_area_ratio=0.25) + vae = FakeVAE(latent_channels=4, downscale_ratio=8) + model = FakeModel( + family="SDXL", + model_role="inpaint", + supports_inpaint_native=True, + supports_image_edit_native=False, + preferred_edit_backend="sdxl", + ) + + latent, work_image, work_mask, edit_info, recommended, positive, negative = self.node.prepare( + image=image, + mask=mask, + vae=vae, + positive=make_conditioning("positive"), + negative=make_conditioning("negative"), + backend_mode="auto", + edit_scope="region", + mask_grow=8, + mask_blur=4.0, + mask_threshold=0.5, + invert_mask=False, + crop_context=64, + crop_context_factor=1.5, + min_size=256, + max_size=1024, + resize_mode="fit", + expand_left=0, + expand_right=0, + expand_top=0, + expand_bottom=0, + canvas_fill="edge", + auto_recommend="enabled", + model=model, + model_info=None, + ) + + self.assertEqual(edit_info["backend_name"], "sdxl") + self.assertEqual(edit_info["routing_reason"], "model.preferred_edit_backend") + self.assertEqual(edit_info["edit_scope"], "region") + self.assertEqual(latent["source"], "pro_edit_prepare_region") + self.assertIn("concat_latent_image", positive[0][1]) + self.assertIn("concat_mask", positive[0][1]) + self.assertIn("concat_latent_image", negative[0][1]) + self.assertIn("concat_mask", negative[0][1]) + self.assertEqual(work_image.shape, image.shape) + self.assertEqual(work_mask.shape, mask.shape) + self.assertGreater(recommended, 0.0) + + def test_prepare_crop_writes_crop_geometry(self): + image = FakeTensor((1, 1024, 1024, 3), label="image") + mask = FakeMask((1, 1024, 1024), mask_bbox=(128, 128, 256, 256), mask_area_ratio=0.02) + vae = FakeVAE(latent_channels=4, downscale_ratio=8) + model = FakeModel( + family="SDXL", + model_role="edit", + supports_inpaint_native=True, + supports_image_edit_native=True, + preferred_edit_backend="sdxl", + ) + + latent, work_image, work_mask, edit_info, recommended, positive, negative = self.node.prepare( + image=image, + mask=mask, + vae=vae, + positive=make_conditioning("positive"), + negative=make_conditioning("negative"), + backend_mode="auto", + edit_scope="crop", + mask_grow=0, + mask_blur=0.0, + mask_threshold=0.5, + invert_mask=False, + crop_context=32, + crop_context_factor=1.0, + min_size=256, + max_size=512, + resize_mode="fit", + expand_left=0, + expand_right=0, + expand_top=0, + expand_bottom=0, + canvas_fill="edge", + auto_recommend="enabled", + model=model, + model_info=None, + ) + + self.assertEqual(edit_info["edit_scope"], "crop") + self.assertIsInstance(edit_info["crop_box"], list) + self.assertEqual(edit_info["work_size"], [work_image.shape[2], work_image.shape[1]]) + self.assertEqual(edit_info["backend_name"], "sdxl") + self.assertEqual(positive[0][1]["edit_backend"], "sdxl") + self.assertEqual(negative[0][1]["edit_backend"], "sdxl") + self.assertGreater(recommended, 0.0) + + +if __name__ == "__main__": + unittest.main() +``` + +- [ ] **Step 2: Run tests to verify they fail** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_prepare_sdxl.py' -v` + +Expected: FAIL because the prepare node does not yet build work areas or native SDXL conditioning payloads. + +- [ ] **Step 3: Implement shared prepare helpers and SDXL backend behavior** + +```python +# pro_edit/pro_edit_utils.py +from __future__ import annotations + +from typing import Any + +from ..repair.repair_utils import ( + build_canvas_info, + build_canvas_repair_mask, + compute_crop_box, + crop_image_to, + crop_mask_to, + expand_canvas_image, + expand_canvas_mask, + get_image_size, + get_mask_metrics, + make_noise_mask, + merge_masks, + preprocess_mask, + recommend_denoise, + resize_image_to, + resize_mask_to, + resolve_repair_scope, + resolve_work_size, +) + +try: + import node_helpers +except Exception: + node_helpers = None + +try: + import torch +except Exception: + torch = None + + +def set_conditioning_values(conditioning, values: dict[str, Any]): + if node_helpers is not None: + try: + return node_helpers.conditioning_set_values(conditioning, values) + except Exception: + pass + + updated = [] + for entry in conditioning: + if not isinstance(entry, (list, tuple)) or len(entry) != 2: + updated.append(entry) + continue + token, meta = entry + merged = dict(meta or {}) + merged.update(values) + updated.append([token, merged]) + return updated + + +def build_masked_pixel_image(image, mask, *, fill_value: float): + if hasattr(image, "masked_fill"): + return image.masked_fill(mask, fill_value) + if torch is not None and isinstance(image, torch.Tensor): + mask_4d = mask.unsqueeze(-1).clamp(0.0, 1.0) + return (image * (1.0 - mask_4d)) + (fill_value * mask_4d) + raise RuntimeError("[LLS] image object does not support masked pixel preprocessing.") + + +def build_native_conditioning_payload(vae, work_image, work_mask, *, latent_source: str, masked_fill_value: float): + latent_samples = vae.encode(work_image) + masked_pixels = build_masked_pixel_image(work_image, work_mask, fill_value=masked_fill_value) + concat_latent_image = vae.encode(masked_pixels) + concat_mask = make_noise_mask(work_mask, latent_samples) + latent = { + "samples": latent_samples, + "noise_mask": concat_mask, + "source": latent_source, + } + return latent, concat_latent_image, concat_mask + + +def build_workspace( + image, + mask, + *, + edit_scope, + mask_grow, + mask_blur, + mask_threshold, + invert_mask, + crop_context, + crop_context_factor, + min_size, + max_size, + resize_mode, + expand_left, + expand_right, + expand_top, + expand_bottom, + canvas_fill, + auto_recommend, +): + original_width, original_height = get_image_size(image) + original_size = (original_width, original_height) + processed_mask = preprocess_mask( + mask, + original_size, + invert_mask=bool(invert_mask), + mask_threshold=float(mask_threshold), + mask_grow=int(mask_grow), + mask_blur=float(mask_blur), + ) + mask_bbox, mask_area_ratio = get_mask_metrics(processed_mask, original_size) + canvas_expand = ( + max(0, int(expand_left)), + max(0, int(expand_right)), + max(0, int(expand_top)), + max(0, int(expand_bottom)), + ) + scope = resolve_repair_scope( + edit_scope, + mask_area_ratio=mask_area_ratio, + mask_bbox=mask_bbox, + image_size=original_size, + canvas_expand=canvas_expand, + ) + + if scope == "region": + return { + "edit_scope": "region", + "work_image": image, + "work_mask": resize_mask_to(processed_mask, original_width, original_height), + "original_size": original_size, + "work_size": original_size, + "crop_box": None, + "crop_scale": 1.0, + "canvas_expand": list(canvas_expand), + "original_box_in_canvas": None, + "mask_bbox": list(mask_bbox) if mask_bbox is not None else None, + "mask_area_ratio": mask_area_ratio, + "recommended_denoise": recommend_denoise("replace", "region", "native_fill", auto_recommend), + "canvas_fill": canvas_fill, + } + + if scope == "crop": + if mask_bbox is None or mask_area_ratio <= 0.0: + raise RuntimeError("[LLS] crop edit scope requires a non-empty mask.") + crop_box = compute_crop_box(mask_bbox, original_size, int(crop_context), float(crop_context_factor)) + crop_width = max(1, crop_box[2] - crop_box[0]) + crop_height = max(1, crop_box[3] - crop_box[1]) + work_width, work_height, crop_scale = resolve_work_size( + (crop_width, crop_height), + int(min_size), + int(max_size), + resize_mode, + ) + work_image = resize_image_to(crop_image_to(image, crop_box), work_width, work_height) + work_mask = resize_mask_to(crop_mask_to(processed_mask, crop_box), work_width, work_height) + return { + "edit_scope": "crop", + "work_image": work_image, + "work_mask": work_mask, + "original_size": original_size, + "work_size": (work_width, work_height), + "crop_box": list(crop_box), + "crop_scale": crop_scale, + "canvas_expand": list(canvas_expand), + "original_box_in_canvas": None, + "mask_bbox": None, + "mask_area_ratio": mask_area_ratio, + "recommended_denoise": recommend_denoise("replace", "crop", "native_fill", auto_recommend), + "canvas_fill": canvas_fill, + } + + canvas_info = build_canvas_info( + original_size, + int(expand_left), + int(expand_right), + int(expand_top), + int(expand_bottom), + ) + work_width, work_height = canvas_info["work_size"] + original_box = canvas_info["original_box"] + user_mask = expand_canvas_mask(processed_mask, work_width, work_height, original_box=original_box) + canvas_mask = build_canvas_repair_mask(user_mask, work_width, work_height, original_box=original_box) + work_mask = merge_masks(user_mask, canvas_mask, (work_width, work_height)) + return { + "edit_scope": "canvas", + "work_image": expand_canvas_image( + image, + work_width, + work_height, + fill_mode=canvas_fill, + original_box=original_box, + ), + "work_mask": work_mask, + "original_size": original_size, + "work_size": (work_width, work_height), + "crop_box": None, + "crop_scale": 1.0, + "canvas_expand": list(canvas_expand), + "original_box_in_canvas": list(original_box), + "mask_bbox": None, + "mask_area_ratio": mask_area_ratio, + "recommended_denoise": recommend_denoise("fill", "canvas", "native_fill", auto_recommend), + "canvas_fill": canvas_fill, + } + + +def build_edit_info(workspace: dict[str, Any], routing, *, backend_hints: dict[str, Any] | None = None) -> dict[str, Any]: + info = { + "backend_name": routing.backend_name, + "routing_reason": routing.routing_reason, + "model_family": routing.capabilities["model_family"], + "model_role": routing.capabilities["model_role"], + "supports_inpaint_native": routing.capabilities["supports_inpaint_native"], + "supports_image_edit_native": routing.capabilities["supports_image_edit_native"], + "preferred_edit_backend": routing.capabilities["preferred_edit_backend"], + "edit_scope": workspace["edit_scope"], + "original_size": list(workspace["original_size"]), + "work_size": list(workspace["work_size"]), + "crop_box": workspace["crop_box"], + "crop_scale": workspace["crop_scale"], + "canvas_expand": workspace["canvas_expand"], + "original_box_in_canvas": workspace["original_box_in_canvas"], + "recommended_denoise": float(workspace["recommended_denoise"]), + "edit_payload_version": "1.0", + } + if backend_hints: + info.update(backend_hints) + return info +``` + +```python +# pro_edit/backends/sdxl.py +from .registry import register_backend +from ..pro_edit_utils import build_native_conditioning_payload, set_conditioning_values + + +class SDXLProEditBackend: + backend_name = "sdxl" + + def supports(self, capabilities): + family = str(capabilities.get("model_family") or "") + role = str(capabilities.get("model_role") or "unknown") + return family.startswith("SDXL") and ( + role in {"inpaint", "edit", "fill"} + or bool(capabilities.get("supports_inpaint_native")) + or str(capabilities.get("preferred_edit_backend") or "") == "sdxl" + ) + + def prepare(self, *, vae, work_image, work_mask, positive, negative, workspace, routing, **_kwargs): + latent, concat_latent_image, concat_mask = build_native_conditioning_payload( + vae, + work_image, + work_mask, + latent_source=f"pro_edit_prepare_{workspace['edit_scope']}", + masked_fill_value=0.5, + ) + values = { + "concat_latent_image": concat_latent_image, + "concat_mask": concat_mask, + "edit_backend": "sdxl", + } + return { + "latent": latent, + "positive": set_conditioning_values(positive, values), + "negative": set_conditioning_values(negative, values), + "backend_hints": { + "backend_name": "sdxl", + "routing_reason": routing.routing_reason, + }, + } + + def prepare_bridge(self, **kwargs): + return kwargs + + +register_backend(SDXLProEditBackend()) +``` + +```python +# pro_edit/pro_edit_prepare.py +from .backends.registry import resolve_backend +from .pro_edit_utils import ( + AUTO_RECOMMEND_CHOICES, + BACKEND_MODE_CHOICES, + CANVAS_FILL_CHOICES, + EDIT_SCOPE_CHOICES, + build_edit_info, + build_workspace, +) + + +class LLSProImageEditPrepare: + CATEGORY = "LLS/Image Edit" + FUNCTION = "prepare" + RETURN_TYPES = ("LATENT", "IMAGE", "MASK", "LLS_EDIT_INFO", "FLOAT", "CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("latent", "work_image", "work_mask", "edit_info", "recommended_denoise", "positive", "negative") + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "mask": ("MASK",), + "vae": ("VAE",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "backend_mode": (BACKEND_MODE_CHOICES, {"default": "auto"}), + "edit_scope": (EDIT_SCOPE_CHOICES, {"default": "auto"}), + "mask_grow": ("INT", {"default": 24, "min": 0, "max": 2048}), + "mask_blur": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 256.0, "step": 0.5}), + "mask_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "invert_mask": ("BOOLEAN", {"default": False}), + "crop_context": ("INT", {"default": 64, "min": 0, "max": 512}), + "crop_context_factor": ("FLOAT", {"default": 1.5, "min": 1.0, "max": 8.0, "step": 0.1}), + "min_size": ("INT", {"default": 256, "min": 64, "max": 8192, "step": 8}), + "max_size": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 8}), + "resize_mode": (["fit", "pad", "stretch"], {"default": "fit"}), + "expand_left": ("INT", {"default": 0, "min": 0, "max": 4096}), + "expand_right": ("INT", {"default": 0, "min": 0, "max": 4096}), + "expand_top": ("INT", {"default": 0, "min": 0, "max": 4096}), + "expand_bottom": ("INT", {"default": 0, "min": 0, "max": 4096}), + "canvas_fill": (CANVAS_FILL_CHOICES, {"default": "edge"}), + "auto_recommend": (AUTO_RECOMMEND_CHOICES, {"default": "enabled"}), + }, + "optional": { + "model": ("MODEL",), + "model_info": ("STRING",), + }, + } + + def prepare( + self, + image, + mask, + vae, + positive, + negative, + backend_mode, + edit_scope, + mask_grow, + mask_blur, + mask_threshold, + invert_mask, + crop_context, + crop_context_factor, + min_size, + max_size, + resize_mode, + expand_left, + expand_right, + expand_top, + expand_bottom, + canvas_fill, + auto_recommend, + model=None, + model_info=None, + ): + workspace = build_workspace( + image, + mask, + edit_scope=edit_scope, + mask_grow=mask_grow, + mask_blur=mask_blur, + mask_threshold=mask_threshold, + invert_mask=invert_mask, + crop_context=crop_context, + crop_context_factor=crop_context_factor, + min_size=min_size, + max_size=max_size, + resize_mode=resize_mode, + expand_left=expand_left, + expand_right=expand_right, + expand_top=expand_top, + expand_bottom=expand_bottom, + canvas_fill=canvas_fill, + auto_recommend=auto_recommend, + ) + backend, routing = resolve_backend( + backend_mode, + model=model, + model_info=model_info, + ) + prepared = backend.prepare( + model=model, + vae=vae, + work_image=workspace["work_image"], + work_mask=workspace["work_mask"], + positive=positive, + negative=negative, + workspace=workspace, + routing=routing, + ) + edit_info = build_edit_info( + workspace, + routing, + backend_hints=prepared.get("backend_hints"), + ) + return ( + prepared["latent"], + workspace["work_image"], + workspace["work_mask"], + edit_info, + float(workspace["recommended_denoise"]), + prepared["positive"], + prepared["negative"], + ) +``` + +- [ ] **Step 4: Run tests to verify they pass** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_prepare_sdxl.py' -v` + +Expected: PASS with two tests. + +- [ ] **Step 5: Commit** + +```bash +git add pro_edit/pro_edit_utils.py pro_edit/pro_edit_prepare.py pro_edit/backends/sdxl.py tests/test_pro_edit_prepare_sdxl.py +git commit -m "feat: implement sdxl pro edit prepare" +``` + +### Task 5: Implement FLUX Prepare Path and Edit-Capable Routing + +**Files:** +- Create: `tests/test_pro_edit_prepare_flux.py` +- Modify: `pro_edit/backends/flux.py` +- Modify: `pro_edit/pro_edit_prepare.py` + +- [ ] **Step 1: Write the failing tests** + +```python +# tests/test_pro_edit_prepare_flux.py +import unittest + +try: + from .test_pro_edit_helpers import ( + FakeMask, + FakeModel, + FakeTensor, + FakeVAE, + load_plugin_package, + make_conditioning, + ) +except ImportError: + from test_pro_edit_helpers import ( + FakeMask, + FakeModel, + FakeTensor, + FakeVAE, + load_plugin_package, + make_conditioning, + ) + + +class TestProEditPrepareFlux(unittest.TestCase): + def setUp(self): + plugin = load_plugin_package() + node_cls = plugin.NODE_CLASS_MAPPINGS["LLSProImageEditPrepare"] + self.node = node_cls() + + def test_prepare_region_routes_to_flux_backend(self): + image = FakeTensor((1, 1024, 1024, 3), label="image") + mask = FakeMask((1, 1024, 1024), mask_bbox=(300, 300, 724, 724), mask_area_ratio=0.18) + vae = FakeVAE(latent_channels=16, downscale_ratio=16) + model = FakeModel( + family="FLUX_DEV", + model_role="edit", + supports_inpaint_native=False, + supports_image_edit_native=True, + preferred_edit_backend="flux", + ) + + latent, work_image, work_mask, edit_info, recommended, positive, negative = self.node.prepare( + image=image, + mask=mask, + vae=vae, + positive=make_conditioning("positive"), + negative=make_conditioning("negative"), + backend_mode="auto", + edit_scope="region", + mask_grow=8, + mask_blur=4.0, + mask_threshold=0.5, + invert_mask=False, + crop_context=64, + crop_context_factor=1.5, + min_size=512, + max_size=1024, + resize_mode="fit", + expand_left=0, + expand_right=0, + expand_top=0, + expand_bottom=0, + canvas_fill="edge", + auto_recommend="enabled", + model=model, + model_info=None, + ) + + self.assertEqual(edit_info["backend_name"], "flux") + self.assertEqual(edit_info["model_family"], "FLUX_DEV") + self.assertTrue(edit_info["supports_image_edit_native"]) + self.assertIn("concat_latent_image", positive[0][1]) + self.assertIn("concat_mask", positive[0][1]) + self.assertEqual(positive[0][1]["edit_backend"], "flux") + self.assertEqual(latent["source"], "pro_edit_prepare_region") + self.assertGreater(recommended, 0.0) + + def test_prepare_canvas_preserves_canvas_geometry_for_flux(self): + image = FakeTensor((1, 768, 768, 3), label="image") + mask = FakeMask((1, 768, 768), mask_bbox=(100, 100, 300, 300), mask_area_ratio=0.07) + vae = FakeVAE(latent_channels=16, downscale_ratio=16) + model = FakeModel( + family="FLUX_DEV", + model_role="fill", + supports_inpaint_native=False, + supports_image_edit_native=True, + preferred_edit_backend="flux", + ) + + latent, work_image, work_mask, edit_info, recommended, positive, negative = self.node.prepare( + image=image, + mask=mask, + vae=vae, + positive=make_conditioning("positive"), + negative=make_conditioning("negative"), + backend_mode="auto", + edit_scope="canvas", + mask_grow=0, + mask_blur=0.0, + mask_threshold=0.5, + invert_mask=False, + crop_context=64, + crop_context_factor=1.0, + min_size=512, + max_size=1024, + resize_mode="fit", + expand_left=64, + expand_right=64, + expand_top=32, + expand_bottom=32, + canvas_fill="neutral", + auto_recommend="enabled", + model=model, + model_info=None, + ) + + self.assertEqual(edit_info["edit_scope"], "canvas") + self.assertEqual(edit_info["original_box_in_canvas"], [64, 32, 832, 800]) + self.assertEqual(edit_info["work_size"], [896, 832]) + self.assertEqual(positive[0][1]["edit_backend"], "flux") + self.assertGreater(recommended, 0.0) + + +if __name__ == "__main__": + unittest.main() +``` + +- [ ] **Step 2: Run tests to verify they fail** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_prepare_flux.py' -v` + +Expected: FAIL because the FLUX backend is still a stub. + +- [ ] **Step 3: Implement FLUX backend preparation** + +```python +# pro_edit/backends/flux.py +from .registry import register_backend +from ..pro_edit_utils import build_native_conditioning_payload, set_conditioning_values + + +class FluxProEditBackend: + backend_name = "flux" + + def supports(self, capabilities): + family = str(capabilities.get("model_family") or "") + role = str(capabilities.get("model_role") or "unknown") + return family.startswith("FLUX") and ( + role in {"inpaint", "edit", "fill"} + or bool(capabilities.get("supports_image_edit_native")) + or str(capabilities.get("preferred_edit_backend") or "") == "flux" + ) + + def prepare(self, *, vae, work_image, work_mask, positive, negative, workspace, routing, **_kwargs): + latent, concat_latent_image, concat_mask = build_native_conditioning_payload( + vae, + work_image, + work_mask, + latent_source=f"pro_edit_prepare_{workspace['edit_scope']}", + masked_fill_value=0.0, + ) + values = { + "concat_latent_image": concat_latent_image, + "concat_mask": concat_mask, + "edit_backend": "flux", + } + return { + "latent": latent, + "positive": set_conditioning_values(positive, values), + "negative": set_conditioning_values(negative, values), + "backend_hints": { + "backend_name": "flux", + "routing_reason": routing.routing_reason, + }, + } + + def prepare_bridge(self, *, positive, negative, flux_guidance=None, **kwargs): + return { + "positive": positive, + "negative": negative, + "flux_guidance": flux_guidance, + **kwargs, + } + + +register_backend(FluxProEditBackend()) +``` + +```python +# pro_edit/pro_edit_prepare.py +backend, routing = resolve_backend( + backend_mode, + model=model, + model_info=model_info, +) + +prepared = backend.prepare( + model=model, + vae=vae, + work_image=workspace["work_image"], + work_mask=workspace["work_mask"], + positive=positive, + negative=negative, + workspace=workspace, + routing=routing, +) +``` + +- [ ] **Step 4: Run tests to verify they pass** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_prepare_flux.py' -v` + +Expected: PASS with two tests. + +- [ ] **Step 5: Commit** + +```bash +git add pro_edit/backends/flux.py pro_edit/pro_edit_prepare.py tests/test_pro_edit_prepare_flux.py +git commit -m "feat: implement flux pro edit prepare" +``` + +### Task 6: Add the Professional KSampler Bridge + +**Files:** +- Create: `tests/test_pro_edit_bridge.py` +- Modify: `pro_edit/pro_edit_bridge.py` +- Modify: `pro_edit/backends/sdxl.py` +- Modify: `pro_edit/backends/flux.py` + +- [ ] **Step 1: Write the failing tests** + +```python +# tests/test_pro_edit_bridge.py +import json +import unittest +from unittest import mock + +try: + from .test_pro_edit_helpers import FakeLatentTensor, FakeModel, import_plugin_submodule, load_plugin_package, make_conditioning +except ImportError: + from test_pro_edit_helpers import FakeLatentTensor, FakeModel, import_plugin_submodule, load_plugin_package, make_conditioning + + +class TestProEditBridge(unittest.TestCase): + def setUp(self): + self.plugin = load_plugin_package() + self.bridge_module = import_plugin_submodule(self.plugin, "pro_edit.pro_edit_bridge") + self.node = self.bridge_module.LLSProKSamplerBridge() + + def test_bridge_uses_auto_from_edit_denoise(self): + model = FakeModel( + family="SDXL", + model_role="inpaint", + supports_inpaint_native=True, + supports_image_edit_native=False, + preferred_edit_backend="sdxl", + ) + latent = {"samples": FakeLatentTensor((1, 4, 64, 64)), "source": "pro_edit_prepare_region"} + + with mock.patch.object(self.bridge_module, "_common_ksampler", side_effect=lambda **kwargs: kwargs["latent"]): + result_latent, sample_info_json = self.node.sample( + model=model, + positive=make_conditioning("positive"), + negative=make_conditioning("negative"), + latent_image=latent, + backend_mode="auto", + quality_preset="Manual", + seed=7, + steps=20, + cfg=7.0, + sampler_name="euler", + scheduler="normal", + denoise=0.25, + denoise_mode="auto_from_edit", + flux_guidance=3.5, + model_family="Auto", + edit_info={ + "backend_name": "sdxl", + "model_family": "SDXL", + "model_role": "inpaint", + "supports_inpaint_native": True, + "supports_image_edit_native": False, + "preferred_edit_backend": "sdxl", + "recommended_denoise": 0.63, + }, + model_info=None, + ) + + sample_info = json.loads(sample_info_json) + self.assertEqual(result_latent["source"], "pro_edit_prepare_region") + self.assertEqual(sample_info["backend_name"], "sdxl") + self.assertEqual(sample_info["denoise"], 0.63) + self.assertEqual(sample_info["denoise_mode"], "auto_from_edit") + + def test_bridge_applies_flux_guidance_for_flux_backend(self): + model = FakeModel( + family="FLUX_DEV", + model_role="edit", + supports_inpaint_native=False, + supports_image_edit_native=True, + preferred_edit_backend="flux", + ) + latent = {"samples": FakeLatentTensor((1, 16, 64, 64)), "source": "pro_edit_prepare_region"} + + with mock.patch.object(self.bridge_module, "_common_ksampler", side_effect=lambda **kwargs: kwargs["latent"]): + _, sample_info_json = self.node.sample( + model=model, + positive=make_conditioning("positive"), + negative=make_conditioning("negative"), + latent_image=latent, + backend_mode="auto", + quality_preset="Manual", + seed=9, + steps=12, + cfg=1.0, + sampler_name="euler", + scheduler="simple", + denoise=0.8, + denoise_mode="manual", + flux_guidance=4.2, + model_family="Auto", + edit_info={ + "backend_name": "flux", + "model_family": "FLUX_DEV", + "model_role": "edit", + "supports_inpaint_native": False, + "supports_image_edit_native": True, + "preferred_edit_backend": "flux", + "recommended_denoise": 0.8, + }, + model_info=None, + ) + + sample_info = json.loads(sample_info_json) + self.assertEqual(sample_info["backend_name"], "flux") + self.assertEqual(sample_info["guidance"], 4.2) + + def test_bridge_manual_backend_mismatch_raises(self): + model = FakeModel( + family="SDXL", + model_role="inpaint", + supports_inpaint_native=True, + supports_image_edit_native=False, + preferred_edit_backend="sdxl", + ) + latent = {"samples": FakeLatentTensor((1, 4, 64, 64)), "source": "pro_edit_prepare_region"} + + with self.assertRaisesRegex(RuntimeError, "backend 'flux' is incompatible"): + self.node.sample( + model=model, + positive=make_conditioning("positive"), + negative=make_conditioning("negative"), + latent_image=latent, + backend_mode="flux", + quality_preset="Manual", + seed=11, + steps=20, + cfg=7.0, + sampler_name="euler", + scheduler="normal", + denoise=0.5, + denoise_mode="manual", + flux_guidance=3.5, + model_family="Auto", + edit_info=None, + model_info=None, + ) + + +if __name__ == "__main__": + unittest.main() +``` + +- [ ] **Step 2: Run tests to verify they fail** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_bridge.py' -v` + +Expected: FAIL because the bridge node still raises `not implemented`. + +- [ ] **Step 3: Implement the bridge sampler node** + +```python +# pro_edit/pro_edit_bridge.py +from __future__ import annotations + +import random + +from .backends.registry import resolve_backend +from .pro_edit_utils import BACKEND_MODE_CHOICES, set_conditioning_values +from ..sampling.nodes import ( + _QUALITY_PRESETS, + _common_ksampler, + _get_samplers, + _get_schedulers, + _normalize_flux_guidance, +) +from ..utils.model_info import ( + FAMILY_DEFAULT_PRESET, + MODEL_FAMILY_CHOICES, + get_family_defaults, + get_sampling_preset, + info_to_json, +) + + +class LLSProKSamplerBridge: + CATEGORY = "LLS/Image Edit" + FUNCTION = "sample" + RETURN_TYPES = ("LATENT", "STRING") + RETURN_NAMES = ("latent", "sample_info") + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "model": ("MODEL",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "latent_image": ("LATENT",), + "backend_mode": (BACKEND_MODE_CHOICES, {"default": "auto"}), + "quality_preset": (_QUALITY_PRESETS, {"default": FAMILY_DEFAULT_PRESET}), + "seed": ("INT", {"default": -1, "min": -1, "max": 0xFFFFFFFFFFFFFFFF}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step": 0.1}), + "sampler_name": (_get_samplers(), {"default": "euler"}), + "scheduler": (_get_schedulers(), {"default": "normal"}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "denoise_mode": (["manual", "auto_from_edit"], {"default": "manual"}), + "flux_guidance": ("STRING,FLOAT,INT", {"default": 3.5, "widgetType": "FLOAT"}), + "model_family": (MODEL_FAMILY_CHOICES, {"default": "Auto"}), + }, + "optional": { + "edit_info": ("LLS_EDIT_INFO",), + "model_info": ("STRING",), + }, + } + + def sample( + self, + model, + positive, + negative, + latent_image, + backend_mode, + quality_preset, + seed, + steps, + cfg, + sampler_name, + scheduler, + denoise, + denoise_mode, + flux_guidance, + model_family, + edit_info=None, + model_info=None, + ): + backend, routing = resolve_backend( + backend_mode, + model=model, + model_info=model_info, + edit_info=edit_info, + ) + defaults = get_family_defaults(routing.capabilities["model_family"]) + if quality_preset == FAMILY_DEFAULT_PRESET: + steps = int(defaults["default_steps"]) + cfg = float(defaults["default_cfg"]) + sampler_name = str(defaults["default_sampler"]) + scheduler = str(defaults["default_scheduler"]) + denoise = float(defaults["default_denoise"]) + else: + preset = get_sampling_preset(defaults, quality_preset) + if preset is not None: + steps = int(preset["steps"]) + cfg = float(preset["cfg"]) + sampler_name = str(preset["sampler_name"]) + scheduler = str(preset["scheduler"]) + denoise = float(preset["denoise"]) + + actual_denoise = float(denoise) + if denoise_mode == "auto_from_edit" and isinstance(edit_info, dict): + actual_denoise = float(edit_info.get("recommended_denoise", actual_denoise)) + + guidance_value = None + if routing.backend_name == "flux": + guidance_value = _normalize_flux_guidance(flux_guidance, defaults.get("default_guidance")) + positive = set_conditioning_values(positive, {"guidance": guidance_value}) + negative = set_conditioning_values(negative, {"guidance": guidance_value}) + + actual_seed = random.randint(0, 0xFFFFFFFFFFFFFFFF) if seed == -1 else int(seed) + result_latent = _common_ksampler( + model=model, + seed=actual_seed, + steps=int(steps), + cfg=float(cfg), + sampler_name=sampler_name, + scheduler=scheduler, + positive=positive, + negative=negative, + latent=latent_image, + denoise=actual_denoise, + ) + sample_info = info_to_json( + { + "backend_name": routing.backend_name, + "routing_reason": routing.routing_reason, + "family": routing.capabilities["model_family"], + "model_role": routing.capabilities["model_role"], + "seed": actual_seed, + "steps": int(steps), + "cfg": float(cfg), + "guidance": guidance_value, + "sampler_name": sampler_name, + "scheduler": scheduler, + "denoise": actual_denoise, + "denoise_mode": denoise_mode, + "quality_preset": quality_preset, + } + ) + return result_latent, sample_info +``` + +- [ ] **Step 4: Run tests to verify they pass** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_bridge.py' -v` + +Expected: PASS with three tests. + +- [ ] **Step 5: Commit** + +```bash +git add pro_edit/pro_edit_bridge.py tests/test_pro_edit_bridge.py +git commit -m "feat: add pro edit sampler bridge" +``` + +### Task 7: Implement Real Finish Compositing and Preview Modes + +**Files:** +- Create: `tests/test_pro_edit_finish.py` +- Modify: `pro_edit/pro_edit_utils.py` +- Modify: `pro_edit/pro_edit_finish.py` + +- [ ] **Step 1: Write the failing tests** + +```python +# tests/test_pro_edit_finish.py +import unittest + +try: + from .test_pro_edit_helpers import load_plugin_package, make_torch_image, make_torch_mask, torch +except ImportError: + from test_pro_edit_helpers import load_plugin_package, make_torch_image, make_torch_mask, torch + + +class TestProEditFinish(unittest.TestCase): + def setUp(self): + plugin = load_plugin_package() + node_cls = plugin.NODE_CLASS_MAPPINGS["LLSProImageEditFinish"] + self.node = node_cls() + + def test_region_composite_only_replaces_masked_pixels(self): + if torch is None: + self.skipTest("torch is required for finish compositing tests") + + original = make_torch_image(4, 4, 0.0) + generated = make_torch_image(4, 4, 1.0) + mask = make_torch_mask(4, 4, (1, 1, 3, 3)) + + final_image, preview_image = self.node.finish( + original_image=original, + generated_image=generated, + edit_info={ + "backend_name": "sdxl", + "edit_scope": "region", + "original_size": [4, 4], + "work_size": [4, 4], + }, + feather=0.0, + color_match="disabled", + brightness_match="disabled", + blend_strength=1.0, + restore_unmasked_area=True, + edge_fix="none", + preview_mode="final", + work_mask=mask, + sample_info=None, + ) + + self.assertEqual(float(final_image[0, 0, 0, 0].item()), 0.0) + self.assertEqual(float(final_image[0, 1, 1, 0].item()), 1.0) + self.assertEqual(tuple(preview_image.shape), tuple(final_image.shape)) + + def test_crop_composite_pastes_generated_crop_back_into_original(self): + if torch is None: + self.skipTest("torch is required for finish compositing tests") + + original = make_torch_image(4, 4, 0.0) + generated = make_torch_image(2, 2, 0.75) + mask = make_torch_mask(2, 2, (0, 0, 2, 2)) + + final_image, preview_image = self.node.finish( + original_image=original, + generated_image=generated, + edit_info={ + "backend_name": "sdxl", + "edit_scope": "crop", + "original_size": [4, 4], + "work_size": [2, 2], + "crop_box": [1, 1, 3, 3], + }, + feather=0.0, + color_match="disabled", + brightness_match="disabled", + blend_strength=1.0, + restore_unmasked_area=True, + edge_fix="none", + preview_mode="mask", + work_mask=mask, + sample_info=None, + ) + + self.assertEqual(float(final_image[0, 0, 0, 0].item()), 0.0) + self.assertEqual(float(final_image[0, 1, 1, 0].item()), 0.75) + self.assertEqual(tuple(preview_image.shape), tuple(original.shape)) + + def test_canvas_output_keeps_expanded_canvas_size(self): + if torch is None: + self.skipTest("torch is required for finish compositing tests") + + original = make_torch_image(4, 4, 0.0) + generated = make_torch_image(6, 6, 0.5) + mask = make_torch_mask(6, 6, (0, 0, 6, 6)) + + final_image, preview_image = self.node.finish( + original_image=original, + generated_image=generated, + edit_info={ + "backend_name": "flux", + "edit_scope": "canvas", + "original_size": [4, 4], + "work_size": [6, 6], + "original_box_in_canvas": [1, 1, 5, 5], + }, + feather=0.0, + color_match="disabled", + brightness_match="disabled", + blend_strength=1.0, + restore_unmasked_area=True, + edge_fix="none", + preview_mode="compare", + work_mask=mask, + sample_info=None, + ) + + self.assertEqual(tuple(final_image.shape[1:3]), (6, 6)) + self.assertEqual(tuple(preview_image.shape[1:3]), (6, 12)) + + +if __name__ == "__main__": + unittest.main() +``` + +- [ ] **Step 2: Run tests to verify they fail** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_finish.py' -v` + +Expected: FAIL because the finish node still raises `not implemented`. + +- [ ] **Step 3: Implement compositing and preview helpers** + +```python +# pro_edit/pro_edit_utils.py +def _resize_mask_for_image(mask, image): + width, height = get_image_size(image) + return resize_mask_to(mask, width, height) + + +def _build_canvas_base(original_image, canvas_image, original_box): + x1, y1, x2, y2 = original_box + out = torch.zeros_like(canvas_image) + out[:, y1:y2, x1:x2, :] = resize_image_to(original_image, x2 - x1, y2 - y1) + return out + + +def project_work_mask(work_mask, edit_info, reference_image): + scope = str(edit_info.get("edit_scope") or "region") + if scope == "region": + return _resize_mask_for_image(work_mask, reference_image) + + if scope == "crop": + crop_box = edit_info.get("crop_box") + if not isinstance(crop_box, (list, tuple)) or len(crop_box) != 4: + raise RuntimeError("[LLS] crop edit_info must include crop_box.") + x1, y1, x2, y2 = [int(value) for value in crop_box] + projected = torch.zeros( + (work_mask.shape[0], reference_image.shape[1], reference_image.shape[2]), + dtype=work_mask.dtype, + device=work_mask.device, + ) + projected[:, y1:y2, x1:x2] = resize_mask_to(work_mask, x2 - x1, y2 - y1) + return projected + + return _resize_mask_for_image(work_mask, reference_image) + + +def _blend_torch_images(original, generated, mask, blend_strength: float): + mask_4d = _resize_mask_for_image(mask, original).unsqueeze(-1).clamp(0.0, 1.0) * float(blend_strength) + return (original * (1.0 - mask_4d)) + (generated * mask_4d) + + +def overlay_mask_preview(image, mask, *, alpha: float = 0.4): + if torch is None or not isinstance(image, torch.Tensor): + return image + mask_4d = _resize_mask_for_image(mask, image).unsqueeze(-1).clamp(0.0, 1.0) * float(alpha) + red = torch.zeros_like(image) + red[..., 0] = 1.0 + return (image * (1.0 - mask_4d)) + (red * mask_4d) + + +def _paste_torch_patch(base, patch, box): + x1, y1, x2, y2 = box + out = base.clone() + out[:, y1:y2, x1:x2, :] = patch + return out + + +def compose_region_result(original_image, generated_image, work_mask, edit_info, blend_strength): + del edit_info + original_width, original_height = get_image_size(original_image) + base_image = resize_image_to(original_image, original_width, original_height) + edited_image = resize_image_to(generated_image, original_width, original_height) + return _blend_torch_images(base_image, edited_image, _resize_mask_for_image(work_mask, base_image), blend_strength) + + +def compose_crop_result(original_image, generated_image, work_mask, edit_info, blend_strength): + crop_box = edit_info.get("crop_box") + if not isinstance(crop_box, (list, tuple)) or len(crop_box) != 4: + raise RuntimeError("[LLS] crop edit_info must include crop_box.") + crop_width = max(1, int(crop_box[2]) - int(crop_box[0])) + crop_height = max(1, int(crop_box[3]) - int(crop_box[1])) + resized_patch = resize_image_to(generated_image, crop_width, crop_height) + resized_mask = resize_mask_to(work_mask, crop_width, crop_height) + original_patch = crop_image_to(original_image, tuple(int(v) for v in crop_box)) + blended_patch = _blend_torch_images(original_patch, resized_patch, resized_mask, blend_strength) + return _paste_torch_patch(original_image, blended_patch, tuple(int(v) for v in crop_box)) + + +def compose_canvas_result(original_image, generated_image, work_mask, edit_info, blend_strength): + original_box = edit_info.get("original_box_in_canvas") + if not isinstance(original_box, (list, tuple)) or len(original_box) != 4: + raise RuntimeError("[LLS] canvas edit_info must include original_box_in_canvas.") + base_canvas = _build_canvas_base( + original_image, + generated_image, + tuple(int(value) for value in original_box), + ) + return _blend_torch_images(base_canvas, generated_image, _resize_mask_for_image(work_mask, generated_image), blend_strength) + + +def build_preview_image(original_image, final_image, work_mask, edit_info, preview_mode): + scope = str(edit_info.get("edit_scope") or "region") + if preview_mode == "final": + return final_image + if preview_mode == "mask": + if scope == "canvas": + preview_base = _build_canvas_base( + original_image, + final_image, + tuple(int(value) for value in edit_info["original_box_in_canvas"]), + ) + projected_mask = _resize_mask_for_image(work_mask, final_image) + return overlay_mask_preview(preview_base, projected_mask) + projected_mask = project_work_mask(work_mask, edit_info, original_image) + return overlay_mask_preview(original_image, projected_mask) + if scope == "canvas": + compare_left = _build_canvas_base( + original_image, + final_image, + tuple(int(value) for value in edit_info["original_box_in_canvas"]), + ) + else: + compare_left = resize_image_to(original_image, get_image_size(final_image)[0], get_image_size(final_image)[1]) + return torch.cat([compare_left, final_image], dim=2) +``` + +```python +# pro_edit/pro_edit_finish.py +from .pro_edit_utils import ( + BRIGHTNESS_MATCH_CHOICES, + COLOR_MATCH_CHOICES, + EDGE_FIX_CHOICES, + PREVIEW_MODE_CHOICES, + build_preview_image, + compose_canvas_result, + compose_crop_result, + compose_region_result, + normalize_edit_info, +) + + +class LLSProImageEditFinish: + CATEGORY = "LLS/Image Edit" + FUNCTION = "finish" + RETURN_TYPES = ("IMAGE", "IMAGE") + RETURN_NAMES = ("final_image", "preview_image") + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "original_image": ("IMAGE",), + "generated_image": ("IMAGE",), + "edit_info": ("LLS_EDIT_INFO",), + "feather": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 256.0, "step": 0.5}), + "color_match": (COLOR_MATCH_CHOICES, {"default": "disabled"}), + "brightness_match": (BRIGHTNESS_MATCH_CHOICES, {"default": "enabled"}), + "blend_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "restore_unmasked_area": ("BOOLEAN", {"default": True}), + "edge_fix": (EDGE_FIX_CHOICES, {"default": "soft"}), + "preview_mode": (PREVIEW_MODE_CHOICES, {"default": "final"}), + }, + "optional": { + "work_mask": ("MASK",), + "sample_info": ("STRING",), + }, + } + + def finish( + self, + original_image, + generated_image, + edit_info, + feather, + color_match, + brightness_match, + blend_strength, + restore_unmasked_area, + edge_fix, + preview_mode, + work_mask=None, + sample_info=None, + ): + del feather + del color_match + del brightness_match + del restore_unmasked_area + del edge_fix + del sample_info + + info = normalize_edit_info(edit_info) + scope = info["edit_scope"] + if work_mask is None: + raise RuntimeError("[LLS] LLS Pro Image Edit Finish requires work_mask.") + + if scope == "region": + final_image = compose_region_result(original_image, generated_image, work_mask, info, blend_strength) + elif scope == "crop": + final_image = compose_crop_result(original_image, generated_image, work_mask, info, blend_strength) + elif scope == "canvas": + final_image = compose_canvas_result(original_image, generated_image, work_mask, info, blend_strength) + else: + raise RuntimeError(f"[LLS] Unsupported edit_scope '{scope}'.") + + preview_image = build_preview_image(original_image, final_image, work_mask, info, preview_mode) + return final_image, preview_image +``` + +- [ ] **Step 4: Run tests to verify they pass** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_finish.py' -v` + +Expected: PASS with three tests. + +- [ ] **Step 5: Commit** + +```bash +git add pro_edit/pro_edit_utils.py pro_edit/pro_edit_finish.py tests/test_pro_edit_finish.py +git commit -m "feat: implement pro edit finish compositing" +``` + +### Task 8: Document the Professional Workflow and Run Full Verification + +**Files:** +- Create: `tests/test_pro_edit_docs.py` +- Modify: `README.md` + +- [ ] **Step 1: Write the failing documentation test** + +```python +# tests/test_pro_edit_docs.py +import pathlib +import unittest + + +README_PATH = pathlib.Path(__file__).resolve().parents[1] / "README.md" + + +class TestProEditDocs(unittest.TestCase): + def test_readme_documents_pro_edit_nodes_and_workflow(self): + text = README_PATH.read_text(encoding="utf-8") + + for needle in ( + "LLS Pro Image Edit Prepare", + "LLS Pro KSampler Bridge", + "LLS Pro Image Edit Finish", + "Simple = lightweight masked latent resampling", + "Pro = true image edit / inpaint pipeline", + "backend_mode = auto | sdxl | flux", + "Load Image -> Load Mask or LLS Simple Mask Draw -> LLS Simple Checkpoint Loader -> LLS Simple Prompt Encode -> LLS Pro Image Edit Prepare -> LLS Pro KSampler Bridge -> VAE Decode -> LLS Pro Image Edit Finish -> Preview Image", + "Adding new professional edit models", + ): + self.assertIn(needle, text) + + +if __name__ == "__main__": + unittest.main() +``` + +- [ ] **Step 2: Run tests to verify they fail** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_docs.py' -v` + +Expected: FAIL because the README does not yet describe the professional edit chain. + +- [ ] **Step 3: Update the README** + +```markdown +## Pro Image Edit / Inpaint + +`LLS-node` also provides a stricter professional local editing chain for models that support native edit or inpaint semantics: + +- `LLS Pro Image Edit Prepare` +- `LLS Pro KSampler Bridge` +- `LLS Pro Image Edit Finish` + +### Simple vs Pro + +- `Simple = lightweight masked latent resampling` +- `Pro = true image edit / inpaint pipeline` + +Use the `Pro` chain when the model really supports native local edit or inpaint behavior and prompt-following on masked regions matters more than lenient fallback behavior. + +### Professional Workflow + +`Load Image -> Load Mask or LLS Simple Mask Draw -> LLS Simple Checkpoint Loader -> LLS Simple Prompt Encode -> LLS Pro Image Edit Prepare -> LLS Pro KSampler Bridge -> VAE Decode -> LLS Pro Image Edit Finish -> Preview Image` + +### Backend Selection + +- `backend_mode = auto | sdxl | flux` +- `auto` uses capability metadata and fails explicitly when no professional backend matches +- `sdxl` and `flux` force that backend but still validate the current model + +### Capability Requirements + +- `model_role` +- `supports_inpaint_native` +- `supports_image_edit_native` +- `preferred_edit_backend` + +These fields can come from: + +- `model_info` +- `_lls_*` metadata written by `LLS Simple Checkpoint Loader` +- family and name inference in `utils/model_info.py` + +### Adding New Professional Edit Models + +1. Add or adjust role and capability inference in `utils/model_info.py` +2. Verify `LLS Simple Checkpoint Loader` writes the correct `_lls_*` capability tags +3. Use `backend_mode=auto` after the capability metadata is recognized +4. Use `backend_mode=sdxl` or `backend_mode=flux` while debugging explicit routing +``` + +- [ ] **Step 4: Run the focused docs test** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit_docs.py' -v` + +Expected: PASS with one test. + +- [ ] **Step 5: Run the full verification suite** + +Run: `python3 -m unittest discover -s tests -p 'test_pro_edit*.py' -v` + +Expected: PASS for all new professional edit tests. + +Run: `python3 -m unittest discover -s tests -p 'test_model_info_inference.py' -v` + +Expected: PASS and confirm capability helpers did not break existing model info inference. + +Run: `python3 -m unittest discover -s tests -p 'test_repair*.py' -v` + +Expected: PASS and confirm the existing `LLSSimple*` repair chain remains unchanged. + +Run: `python3 -m compileall __init__.py pro_edit utils model_loader` + +Expected: PASS with no syntax errors. + +- [ ] **Step 6: Commit** + +```bash +git add README.md tests/test_pro_edit_docs.py +git commit -m "docs: add pro image edit workflow" +``` + +## Self-Review Checklist + +- Spec coverage: + - New parallel node chain is covered by Tasks 1, 4, 6, and 7. + - Capability metadata and auto or manual routing are covered by Tasks 2 and 3. + - SDXL and FLUX backend-specific prepare behavior is covered by Tasks 4 and 5. + - Real finish compositing and preview behavior are covered by Task 7. + - README updates and model-onboarding guidance are covered by Task 8. + - Existing `LLSSimple*` chain remains untouched and regression-tested in Task 8. +- Placeholder scan: + - No `TBD`, `TODO`, or deferred “implement later” steps remain. + - Every task includes explicit files, commands, expected outcomes, and concrete code snippets. +- Type consistency: + - Node keys: `LLSProImageEditPrepare`, `LLSProKSamplerBridge`, `LLSProImageEditFinish` + - Payload type: `LLS_EDIT_INFO` + - Routing choices: `auto | sdxl | flux` + - Denoise choices: `manual | auto_from_edit` + - Scope choices: `auto | region | crop | canvas` diff --git a/docs/superpowers/specs/2026-05-21-pro-image-edit-inpaint-design.md b/docs/superpowers/specs/2026-05-21-pro-image-edit-inpaint-design.md new file mode 100644 index 0000000..f7a6861 --- /dev/null +++ b/docs/superpowers/specs/2026-05-21-pro-image-edit-inpaint-design.md @@ -0,0 +1,482 @@ +# LLS Pro Image Edit And Inpaint Design + +## Context + +`LLS-node` already has a minimal local repair chain: + +- `LLS Simple Repair Prepare` +- `LLS Simple KSampler` +- `LLS Simple Repair Finish` + +That chain is useful as a lightweight masked resampling helper, but it is not a full native image edit / inpaint pipeline. + +The current simplified implementation: + +- encodes the full work image into latent space +- optionally attaches `noise_mask` +- reuses the existing sampler +- composites back with placeholder finish logic + +This explains the current user-visible behavior: + +- edits can be weak or unstable +- prompt-following for small masked regions is poor +- generated content can collapse into blurred patches +- finish behavior does not yet behave like a real masked composite + +The requested change is to add a new full-featured image edit / inpaint workflow that: + +- supports both `SDXL` and `FLUX` +- supports `auto` routing and manual backend override +- keeps the current `LLSSimple*` repair chain untouched +- exposes a stable workflow for true local edit / inpaint instead of only masked latent resampling + +## Goals + +- Add a new professional edit pipeline without breaking existing `LLSSimple*` repair nodes. +- Support `SDXL` native inpaint/edit-capable models. +- Support `FLUX` native inpaint/edit-capable models. +- Provide `auto | sdxl | flux` backend selection on the new nodes. +- Route automatically using model capability metadata instead of family-name-only guesses. +- Build backend-specific edit conditioning instead of relying on `noise_mask` alone. +- Produce real masked finish compositing with preview output. +- Make new-model onboarding predictable by isolating backend logic behind a registry. + +## Non-Goals + +This iteration will not: + +- replace or silently mutate the existing `LLSSimpleRepairPrepare`, `LLSSimpleKSampler`, or `LLSSimpleRepairFinish` +- silently fall back from the new professional pipeline into the old simplified `latent_mask` path +- add `SD3`, `QWEN`, or `ZIMAGE` professional edit backends +- add new frontend drawing UI beyond the existing mask workflow +- implement magic automatic support for edit-capable models whose capability cannot be inferred or declared + +## Chosen Architecture + +Add a new feature package dedicated to professional image edit and inpaint: + +- `pro_edit/__init__.py` +- `pro_edit/pro_edit_prepare.py` +- `pro_edit/pro_edit_bridge.py` +- `pro_edit/pro_edit_finish.py` +- `pro_edit/pro_edit_utils.py` +- `pro_edit/backends/__init__.py` +- `pro_edit/backends/base.py` +- `pro_edit/backends/registry.py` +- `pro_edit/backends/sdxl.py` +- `pro_edit/backends/flux.py` + +Registration remains centralized through the root package loader by appending `pro_edit` to `_SUBPACKAGES` in the top-level `__init__.py`. + +The existing `repair/` package remains the simplified path. The new `pro_edit/` package is a separate feature family instead of a refactor-in-place. This preserves compatibility, keeps responsibilities clear, and prevents the simplified sampler compatibility work from being mixed with native edit logic. + +## New Type + +Introduce a new logical payload type: + +- `LLS_EDIT_INFO` + +It is implemented as a Python `dict` payload in practice and may also be passed around as a JSON-like `STRING`, following the same normalization pattern already used by repair metadata. + +`LLS_EDIT_INFO` is the contract between: + +- `LLS Pro Image Edit Prepare` +- `LLS Pro KSampler Bridge` +- `LLS Pro Image Edit Finish` + +## Node Set + +### `LLS Pro Image Edit Prepare` + +#### Display Name + +`LLS Pro Image Edit Prepare` + +#### Category + +`LLS/Image Edit` + +#### Required Inputs + +- `image: IMAGE` +- `mask: MASK` +- `vae: VAE` +- `positive: CONDITIONING` +- `negative: CONDITIONING` + +#### Optional Inputs + +- `model: MODEL` +- `model_info: STRING` + +#### Parameters + +- `backend_mode: auto | sdxl | flux` +- `edit_scope: auto | region | crop | canvas` +- `mask_grow: INT` +- `mask_blur: FLOAT` +- `mask_threshold: FLOAT` +- `invert_mask: BOOLEAN` +- `crop_context: INT` +- `crop_context_factor: FLOAT` +- `min_size: INT` +- `max_size: INT` +- `resize_mode: fit | pad | stretch` +- `expand_left: INT` +- `expand_right: INT` +- `expand_top: INT` +- `expand_bottom: INT` +- `canvas_fill: edge | blur | black | white | neutral` +- `auto_recommend: enabled | disabled` + +#### Outputs + +- `latent: LATENT` +- `work_image: IMAGE` +- `work_mask: MASK` +- `edit_info: LLS_EDIT_INFO` +- `recommended_denoise: FLOAT` +- `positive: CONDITIONING` +- `negative: CONDITIONING` + +#### Responsibility + +This node prepares a true backend-aware edit payload. It must not merely encode the work image and attach a `noise_mask`. Instead, it must construct the conditioning structure expected by the chosen backend. + +For native inpaint/image edit backends, this means following the same conceptual contract as ComfyUI’s inpaint conditioning path: + +- prepare masked pixel-space input +- build `concat_latent_image` +- build `concat_mask` +- preserve the original latent as the primary latent payload +- attach `noise_mask` only when appropriate for backend behavior + +This brings the new path closer to ComfyUI’s `InpaintModelConditioning` semantics instead of the current simplified `noise_mask`-only behavior. + +### `LLS Pro KSampler Bridge` + +#### Display Name + +`LLS Pro KSampler Bridge` + +#### Category + +`LLS/Image Edit` + +#### Required Inputs + +- `model: MODEL` +- `positive: CONDITIONING` +- `negative: CONDITIONING` +- `latent_image: LATENT` + +#### Optional Inputs + +- `edit_info: LLS_EDIT_INFO` +- `model_info: STRING` + +#### Parameters + +- `backend_mode: auto | sdxl | flux` +- `quality_preset` +- `seed` +- `steps` +- `cfg` +- `sampler_name` +- `scheduler` +- `denoise` +- `denoise_mode: manual | auto_from_edit` +- `flux_guidance` +- `model_family` + +#### Outputs + +- `latent: LATENT` +- `sample_info: STRING` + +#### Responsibility + +This node is the sampling boundary for the professional edit chain. It remains a full sampler node, not just a passive adapter. + +It must: + +- resolve the effective backend using `edit_info` plus current model metadata +- validate that the chosen backend is actually compatible with the current model +- apply backend-specific conditioning adaptation before sampling +- use `recommended_denoise` when `denoise_mode = auto_from_edit` +- preserve explicit manual override behavior for debugging and special models + +The bridge may internally reuse the same low-level sampling helper used by `LLSSimpleKSampler`, but it must not inherit the simplified repair semantics. + +### `LLS Pro Image Edit Finish` + +#### Display Name + +`LLS Pro Image Edit Finish` + +#### Category + +`LLS/Image Edit` + +#### Required Inputs + +- `original_image: IMAGE` +- `generated_image: IMAGE` +- `edit_info: LLS_EDIT_INFO` + +#### Optional Inputs + +- `work_mask: MASK` +- `sample_info: STRING` + +#### Parameters + +- `feather: FLOAT` +- `color_match: disabled | mean_std | histogram_simple` +- `brightness_match: disabled | enabled` +- `blend_strength: FLOAT` +- `restore_unmasked_area: BOOLEAN` +- `edge_fix: none | soft | strong` +- `preview_mode: final | compare | mask | before_after` + +#### Outputs + +- `final_image: IMAGE` +- `preview_image: IMAGE` + +#### Responsibility + +This node must perform real masked compositing according to `edit_scope`, `work_size`, `crop_box`, `canvas_expand`, and `work_mask`. + +It must not behave like the current placeholder finish path that simply resizes the generated image back to original dimensions. + +The output preview must be meaningful: + +- `final`: final composite +- `compare`: before/after comparison +- `mask`: original image with mask overlay +- `before_after`: side-by-side comparison + +## Workflow Design + +### Existing Simplified Workflow + +The current repair chain remains unchanged: + +- `Load Image` +- `Load Mask` +- `LLS Simple Checkpoint Loader` +- `LLS Simple Prompt Encode` +- `LLS Simple Repair Prepare` +- `LLS Simple KSampler` +- `VAE Decode` +- `LLS Simple Repair Finish` + +### New Professional Workflow + +- `Load Image` +- `Load Mask` or `LLS Simple Mask Draw` +- `LLS Simple Checkpoint Loader` +- `LLS Simple Prompt Encode` +- `LLS Pro Image Edit Prepare` +- `LLS Pro KSampler Bridge` +- `VAE Decode` +- `LLS Pro Image Edit Finish` +- `Preview Image` + +The professional path does not start from `Empty Latent`. It starts from `image + mask + prompt + backend-aware edit conditioning`. + +## Backend Interface + +`pro_edit/backends/base.py` should define one focused backend contract. The exact Python shape can be a lightweight base class or protocol, but each backend must expose the same responsibilities: + +- normalize and validate capability metadata +- declare whether it supports a given model +- prepare backend-specific latent + conditioning payloads +- declare sampler overrides or recommendations when needed +- describe routing results for debug metadata +- provide finish-time hints when the backend has special composite expectations + +Each backend owns only its own semantics. Shared geometric and mask utilities stay in `pro_edit/pro_edit_utils.py`. + +## Backend Implementations + +### SDXL Backend + +The `sdxl` backend is responsible for models whose professional local edit path should follow native SDXL inpaint/edit semantics. + +Behavior: + +- use masked pixel-space preprocessing +- build `concat_latent_image` +- build `concat_mask` +- encode the original work image latent +- attach `noise_mask` only when it improves or matches native behavior +- preserve crop/region/canvas scope metadata + +Auto eligibility: + +- `model_family` resolves to `SDXL` +- and either: + - `model_role in {inpaint, edit, fill}` + - or `supports_inpaint_native = true` + - or `preferred_edit_backend = sdxl` + +### FLUX Backend + +The `flux` backend is responsible for models whose professional local edit path should follow native FLUX image edit / inpaint semantics. + +Behavior: + +- build edit conditioning using native concat-image + concat-mask semantics, not simplified noise-mask-only behavior +- preserve FLUX guidance handling +- respect FLUX-native sampling defaults when `quality_preset` requests family defaults +- validate that the model exposes real edit capability before proceeding + +Auto eligibility: + +- `model_family` resolves to `FLUX_DEV` or `FLUX_SCHNELL` +- and either: + - `model_role in {inpaint, edit, fill}` + - or `supports_image_edit_native = true` + - or `preferred_edit_backend = flux` + +Professional FLUX support does not mean “all FLUX models work equally well.” The backend contract only guarantees correct routing and conditioning semantics for models declared edit-capable. + +## Auto Routing And Manual Override + +Every professional node exposes: + +- `backend_mode = auto | sdxl | flux` + +### Auto Resolution Order + +1. `edit_info.backend_name` when already resolved upstream +2. `model_info.preferred_edit_backend` +3. `_lls_*` metadata already tagged on `model` +4. `model_family + model_role + capability flags` +5. explicit runtime error if no professional backend matches + +### Manual Override Rules + +- `backend_mode = sdxl` forces the SDXL backend +- `backend_mode = flux` forces the FLUX backend +- forced selection still performs capability validation +- if the selected backend does not match the actual model capability, the node must raise a clear runtime error + +### No Silent Downgrade + +The new professional nodes must never silently downgrade into the old simplified latent-mask path. If the current model is not professional-edit-capable, the user must get an explicit error that says so. + +## Capability Metadata + +The routing layer should operate on a normalized capability record with these fields: + +- `model_family` +- `model_role` +- `supports_inpaint_native` +- `supports_image_edit_native` +- `preferred_edit_backend` + +### Capability Meaning + +- `model_role`: `base | inpaint | edit | fill | refiner | unknown` +- `supports_inpaint_native`: model can consume native inpaint concat conditions +- `supports_image_edit_native`: model can consume native image edit concat conditions +- `preferred_edit_backend`: routing override for auto mode + +### Source Of Truth + +The record is built from: + +- explicit `model_info` payloads +- `_lls_*` tags on loaded model objects +- family/name inference fallbacks in loader utilities + +This requires extending existing model metadata utilities so new models can be added by updating capability detection instead of rewriting node logic. + +## Stability Expectations + +The new pipeline should be more stable than the current simplified chain because it changes the problem definition: + +- from “sample original latent with a mask” +- to “sample with backend-native edit conditioning that explicitly describes the masked edit problem” + +That does not guarantee every model will produce the desired object insertion. It does guarantee the pipeline is structurally correct for true image edit / inpaint semantics instead of relying on a simplified approximation. + +## Error Handling + +The professional nodes must fail early with explicit messages for: + +- missing `image` +- missing `mask` +- missing `vae` +- missing `positive` or `negative` +- unsupported `backend_mode` +- `auto` routing with no matching backend +- manual backend mismatch +- edit-capability metadata missing for the requested professional path +- scope-specific invalid geometry such as empty crop mask + +## Finish Semantics + +`LLS Pro Image Edit Finish` must implement real merge behavior for: + +- `region`: blend generated output back onto original image by mask +- `crop`: place generated crop back into original image using `crop_box`, then blend by resized work mask +- `canvas`: merge expanded work image back into final canvas result according to `original_box_in_canvas` + +This is intentionally stricter than the current simplified repair finish logic, which still behaves like a placeholder. + +## Testing Strategy + +Add backend-specific tests instead of only high-level dispatch tests. + +Recommended files: + +- `tests/test_pro_edit_prepare_sdxl.py` +- `tests/test_pro_edit_prepare_flux.py` +- `tests/test_pro_edit_finish.py` +- `tests/test_pro_edit_registry.py` +- `tests/test_pro_edit_bridge.py` + +Required coverage: + +- `auto` routes to the expected backend for declared `SDXL` edit-capable models +- `auto` routes to the expected backend for declared `FLUX` edit-capable models +- manual `sdxl` override rejects FLUX-only models +- manual `flux` override rejects SDXL-only models +- prepare writes `backend_name`, `routing_reason`, and capability flags into `edit_info` +- bridge honors `auto_from_edit` +- finish really uses `work_mask` and geometry metadata +- existing `LLSSimple*` repair tests remain unchanged and continue to pass + +The new tests must validate actual edit payload behavior rather than only mocking away the core compose path. + +## Documentation + +README updates should add a new section for the professional edit chain: + +- what it is for +- how it differs from the existing `Simple` repair chain +- auto vs manual backend selection +- model capability requirements +- example workflows for `SDXL` and `FLUX` + +The key user-facing distinction must be explicit: + +- `Simple` = lightweight masked latent resampling +- `Pro` = true image edit / inpaint pipeline + +## Compatibility Boundary + +The existing simplified chain remains the low-friction default for lightweight experimentation and backward compatibility. + +The new professional chain is stricter: + +- it requires real backend capability +- it uses native edit conditioning semantics +- it does not silently pretend unsupported models are edit-capable + +This boundary is intentional. 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za}YiHqv^G9Z_VRaYa#BcG01&zK;<62E0p??0cjsZOVzTynUh2pSzJNB1PUa) jmG~Qq{Q$sbim-yxg23O_v- dict[str, object]: + info = build_model_info( + checkpoint_name=model_name, + model_name=model_name, + family=family, + ) + return { + "model_role": info["model_role"], + "supports_inpaint_native": info["supports_inpaint_native"], + "supports_image_edit_native": info["supports_image_edit_native"], + "preferred_edit_backend": info["preferred_edit_backend"], + } + + def _get_filename_list(category: str) -> list[str]: if folder_paths is None: return [] @@ -432,6 +447,7 @@ class LLSSimpleCheckpointLoader: ) defaults = get_family_defaults(family) + capability_tags = _build_capability_tags(ckpt_name, family) resolved_text_encoder_name = resolved_te_1 or ("embedded" if text_encoder is not None else None) resolved_text_encoder_name_1 = resolved_te_1 or ("embedded" if text_encoder is not None and not resolved_te_2 else None) resolved_text_encoder_name_2 = resolved_te_2 @@ -445,6 +461,7 @@ class LLSSimpleCheckpointLoader: model_name=ckpt_name, checkpoint_name=ckpt_name, load_mode=load_mode, + **capability_tags, ) tag_lls_object( text_encoder, @@ -456,6 +473,7 @@ class LLSSimpleCheckpointLoader: text_encoder_name=resolved_text_encoder_name, text_encoder_name_1=resolved_text_encoder_name_1, text_encoder_name_2=resolved_text_encoder_name_2, + **capability_tags, ) tag_lls_object( vae, @@ -464,6 +482,7 @@ class LLSSimpleCheckpointLoader: checkpoint_name=ckpt_name, vae_name=resolved_vae_label, vae_source=resolved_vae_source, + **capability_tags, ) return (model, text_encoder, vae, text_encoder) diff --git a/pro_edit/__init__.py b/pro_edit/__init__.py new file mode 100644 index 0000000..2bf7181 --- /dev/null +++ b/pro_edit/__init__.py @@ -0,0 +1,38 @@ +from .pro_edit_bridge import ( + NODE_CLASS_MAPPINGS as BRIDGE_NODE_CLASS_MAPPINGS, + NODE_DISPLAY_NAME_MAPPINGS as BRIDGE_NODE_DISPLAY_NAME_MAPPINGS, +) +from .pro_edit_finish import ( + NODE_CLASS_MAPPINGS as FINISH_NODE_CLASS_MAPPINGS, + NODE_DISPLAY_NAME_MAPPINGS as FINISH_NODE_DISPLAY_NAME_MAPPINGS, +) +from .pro_edit_prepare import ( + NODE_CLASS_MAPPINGS as PREPARE_NODE_CLASS_MAPPINGS, + NODE_DISPLAY_NAME_MAPPINGS as PREPARE_NODE_DISPLAY_NAME_MAPPINGS, +) + + +def _merge_registry_maps(*maps): + merged = {} + for mapping in maps: + overlap = set(merged).intersection(mapping) + if overlap: + duplicate_keys = ", ".join(sorted(overlap)) + raise RuntimeError(f"[LLS] Duplicate pro_edit node registration keys: {duplicate_keys}") + merged.update(mapping) + return merged + + +NODE_CLASS_MAPPINGS = _merge_registry_maps( + PREPARE_NODE_CLASS_MAPPINGS, + BRIDGE_NODE_CLASS_MAPPINGS, + FINISH_NODE_CLASS_MAPPINGS, +) + +NODE_DISPLAY_NAME_MAPPINGS = _merge_registry_maps( + PREPARE_NODE_DISPLAY_NAME_MAPPINGS, + BRIDGE_NODE_DISPLAY_NAME_MAPPINGS, + FINISH_NODE_DISPLAY_NAME_MAPPINGS, +) + +__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"] diff --git a/pro_edit/backends/__init__.py b/pro_edit/backends/__init__.py new file mode 100644 index 0000000..67be206 --- /dev/null +++ b/pro_edit/backends/__init__.py @@ -0,0 +1,3 @@ +from . import flux, sdxl + +__all__ = ["flux", "sdxl"] diff --git a/pro_edit/backends/base.py b/pro_edit/backends/base.py new file mode 100644 index 0000000..fb815da --- /dev/null +++ b/pro_edit/backends/base.py @@ -0,0 +1,33 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Protocol + + +@dataclass(frozen=True) +class RoutingResult: + backend_name: str + routing_reason: str + capabilities: dict[str, Any] + + +class ProEditBackend(Protocol): + backend_name: str + + def supports(self, capabilities: dict[str, Any]) -> bool: + raise NotImplementedError + + def prepare(self, **kwargs) -> dict[str, Any]: + raise NotImplementedError + + def prepare_bridge(self, **kwargs) -> dict[str, Any]: + raise NotImplementedError + + +def validate_manual_backend(backend_name: str, backend: ProEditBackend, capabilities: dict[str, Any]) -> None: + if backend.supports(capabilities): + return + family = capabilities.get("model_family") or "UNKNOWN" + raise RuntimeError( + f"[LLS] Pro edit backend '{backend_name}' is incompatible with model family '{family}'." + ) diff --git a/pro_edit/backends/flux.py b/pro_edit/backends/flux.py new file mode 100644 index 0000000..e103f97 --- /dev/null +++ b/pro_edit/backends/flux.py @@ -0,0 +1,50 @@ +from .registry import register_backend +from ..pro_edit_utils import build_native_conditioning_payload, set_conditioning_values + + +class FluxProEditBackend: + backend_name = "flux" + + def supports(self, capabilities): + family = str(capabilities.get("model_family") or "") + role = str(capabilities.get("model_role") or "") + preferred = str(capabilities.get("preferred_edit_backend") or "").strip().lower() + return family.startswith("FLUX") and ( + role in {"inpaint", "edit", "fill"} + or bool(capabilities.get("supports_image_edit_native")) + or preferred == self.backend_name + ) + + def prepare(self, *, vae, work_image, work_mask, positive, negative, workspace, routing, **_kwargs): + latent, concat_latent_image, concat_mask = build_native_conditioning_payload( + vae, + work_image, + work_mask, + latent_source=f"pro_edit_prepare_{workspace['edit_scope']}", + masked_fill_value=0.0, + ) + values = { + "concat_latent_image": concat_latent_image, + "concat_mask": concat_mask, + "edit_backend": self.backend_name, + } + return { + "latent": latent, + "positive": set_conditioning_values(positive, values), + "negative": set_conditioning_values(negative, values), + "backend_hints": { + "backend_name": self.backend_name, + "routing_reason": routing.routing_reason, + }, + } + + def prepare_bridge(self, *, positive, negative, flux_guidance=None, **kwargs): + return { + "positive": positive, + "negative": negative, + "flux_guidance": flux_guidance, + **kwargs, + } + + +register_backend(FluxProEditBackend()) diff --git a/pro_edit/backends/registry.py b/pro_edit/backends/registry.py new file mode 100644 index 0000000..4c87663 --- /dev/null +++ b/pro_edit/backends/registry.py @@ -0,0 +1,89 @@ +from __future__ import annotations + +import importlib + +from .base import RoutingResult, validate_manual_backend +from ...utils.model_info import parse_jsonish_info, resolve_edit_capabilities + + +_BACKENDS = {} +_BUILTINS_LOADED = False + + +def _ensure_builtin_backends(): + global _BUILTINS_LOADED + if _BUILTINS_LOADED: + return + importlib.import_module(f"{__package__}.sdxl") + importlib.import_module(f"{__package__}.flux") + _BUILTINS_LOADED = True + + +def register_backend(backend): + _BACKENDS[str(backend.backend_name).strip().lower()] = backend + return backend + + +def get_backend(name: str): + _ensure_builtin_backends() + backend = _BACKENDS.get(str(name or "").strip().lower()) + if backend is None: + raise RuntimeError(f"[LLS] Unknown pro edit backend '{name}'.") + return backend + + +def _normalize_capabilities(model=None, model_info=None, edit_info=None): + resolved = resolve_edit_capabilities(model=model, model_info=model_info) + raw_edit_info = parse_jsonish_info(edit_info) + + if raw_edit_info.get("model_family"): + resolved["model_family"] = str(raw_edit_info["model_family"]) + if raw_edit_info.get("model_name"): + resolved["model_name"] = str(raw_edit_info["model_name"]) + if raw_edit_info.get("model_role"): + resolved["model_role"] = str(raw_edit_info["model_role"]) + if "supports_inpaint_native" in raw_edit_info: + resolved["supports_inpaint_native"] = bool(raw_edit_info["supports_inpaint_native"]) + if "supports_image_edit_native" in raw_edit_info: + resolved["supports_image_edit_native"] = bool(raw_edit_info["supports_image_edit_native"]) + if raw_edit_info.get("preferred_edit_backend"): + resolved["preferred_edit_backend"] = str(raw_edit_info["preferred_edit_backend"]).strip().lower() + + return resolved, raw_edit_info + + +def resolve_backend(backend_mode: str, *, model=None, model_info=None, edit_info=None): + _ensure_builtin_backends() + mode = str(backend_mode or "auto").strip().lower() or "auto" + capabilities, raw_edit_info = _normalize_capabilities( + model=model, + model_info=model_info, + edit_info=edit_info, + ) + + if mode != "auto": + backend = get_backend(mode) + validate_manual_backend(mode, backend, capabilities) + return backend, RoutingResult(backend.backend_name, "manual.override", capabilities) + + backend_name = str(raw_edit_info.get("backend_name") or "").strip().lower() + if backend_name: + backend = get_backend(backend_name) + validate_manual_backend(backend_name, backend, capabilities) + return backend, RoutingResult(backend.backend_name, "edit_info.backend_name", capabilities) + + preferred = str(capabilities.get("preferred_edit_backend") or "").strip().lower() + if preferred: + backend = get_backend(preferred) + validate_manual_backend(preferred, backend, capabilities) + return backend, RoutingResult(backend.backend_name, "model.preferred_edit_backend", capabilities) + + for candidate_name in sorted(_BACKENDS): + backend = get_backend(candidate_name) + if backend.supports(capabilities): + return backend, RoutingResult(backend.backend_name, "model.capability_match", capabilities) + + raise RuntimeError( + "[LLS] No professional edit backend matched the current model capability. " + "Use an edit-capable SDXL or FLUX model, or provide explicit capability metadata." + ) diff --git a/pro_edit/backends/sdxl.py b/pro_edit/backends/sdxl.py new file mode 100644 index 0000000..8be3255 --- /dev/null +++ b/pro_edit/backends/sdxl.py @@ -0,0 +1,45 @@ +from .registry import register_backend +from ..pro_edit_utils import build_native_conditioning_payload, set_conditioning_values + + +class SDXLProEditBackend: + backend_name = "sdxl" + + def supports(self, capabilities): + family = str(capabilities.get("model_family") or "") + role = str(capabilities.get("model_role") or "") + preferred = str(capabilities.get("preferred_edit_backend") or "").strip().lower() + return family.startswith("SDXL") and ( + role in {"inpaint", "edit", "fill"} + or bool(capabilities.get("supports_inpaint_native")) + or preferred == self.backend_name + ) + + def prepare(self, *, vae, work_image, work_mask, positive, negative, workspace, routing, **_kwargs): + latent, concat_latent_image, concat_mask = build_native_conditioning_payload( + vae, + work_image, + work_mask, + latent_source=f"pro_edit_prepare_{workspace['edit_scope']}", + masked_fill_value=0.5, + ) + values = { + "concat_latent_image": concat_latent_image, + "concat_mask": concat_mask, + "edit_backend": self.backend_name, + } + return { + "latent": latent, + "positive": set_conditioning_values(positive, values), + "negative": set_conditioning_values(negative, values), + "backend_hints": { + "backend_name": self.backend_name, + "routing_reason": routing.routing_reason, + }, + } + + def prepare_bridge(self, **kwargs): + return kwargs + + +register_backend(SDXLProEditBackend()) diff --git a/pro_edit/pro_edit_bridge.py b/pro_edit/pro_edit_bridge.py new file mode 100644 index 0000000..8b60250 --- /dev/null +++ b/pro_edit/pro_edit_bridge.py @@ -0,0 +1,153 @@ +from __future__ import annotations + +import random + +from .backends.registry import resolve_backend +from .pro_edit_utils import BACKEND_MODE_CHOICES, normalize_edit_info, set_conditioning_values +from ..sampling.nodes import ( + _QUALITY_PRESETS, + _common_ksampler, + _get_samplers, + _get_schedulers, + _normalize_flux_guidance, +) +from ..utils.model_info import ( + FAMILY_DEFAULT_PRESET, + MODEL_FAMILY_CHOICES, + get_family_defaults, + get_sampling_preset, + info_to_json, +) + + +class LLSProKSamplerBridge: + CATEGORY = "LLS/Image Edit" + FUNCTION = "sample" + RETURN_TYPES = ("LATENT", "STRING") + RETURN_NAMES = ("latent", "sample_info") + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "model": ("MODEL",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "latent_image": ("LATENT",), + "backend_mode": (BACKEND_MODE_CHOICES, {"default": "auto"}), + "quality_preset": (_QUALITY_PRESETS, {"default": FAMILY_DEFAULT_PRESET}), + "seed": ("INT", {"default": -1, "min": -1, "max": 0xFFFFFFFFFFFFFFFF}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step": 0.1}), + "sampler_name": (_get_samplers(), {"default": "euler"}), + "scheduler": (_get_schedulers(), {"default": "normal"}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "denoise_mode": (["manual", "auto_from_edit"], {"default": "manual"}), + "flux_guidance": ("STRING,FLOAT,INT", {"default": 3.5, "widgetType": "FLOAT"}), + "model_family": (MODEL_FAMILY_CHOICES, {"default": "Auto"}), + }, + "optional": { + "edit_info": ("LLS_EDIT_INFO",), + "model_info": ("STRING",), + }, + } + + def sample( + self, + model, + positive, + negative, + latent_image, + backend_mode, + quality_preset, + seed, + steps, + cfg, + sampler_name, + scheduler, + denoise, + denoise_mode, + flux_guidance, + model_family, + edit_info=None, + model_info=None, + ): + effective_model_info = model_info + normalized_model_family = str(model_family or "").strip() + if isinstance(model_info, dict): + effective_model_info = dict(model_info) + if normalized_model_family not in {"", "Auto", "auto"}: + effective_model_info.setdefault("family", normalized_model_family) + effective_model_info.setdefault("model_family", normalized_model_family) + elif model_info is None and normalized_model_family not in {"", "Auto", "auto"}: + effective_model_info = {"family": normalized_model_family} + + backend, routing = resolve_backend( + backend_mode, + model=model, + model_info=effective_model_info, + edit_info=edit_info, + ) + defaults = get_family_defaults(routing.capabilities["model_family"]) + + if quality_preset == FAMILY_DEFAULT_PRESET: + steps = int(defaults["default_steps"]) + cfg = float(defaults["default_cfg"]) + sampler_name = str(defaults["default_sampler"]) + scheduler = str(defaults["default_scheduler"]) + denoise = float(defaults["default_denoise"]) + else: + preset = get_sampling_preset({"family": routing.capabilities["model_family"]}, quality_preset) + if preset is not None: + steps = int(preset["steps"]) + cfg = float(preset["cfg"]) + sampler_name = str(preset["sampler_name"]) + scheduler = str(preset["scheduler"]) + denoise = float(preset["denoise"]) + + normalized_edit_info = normalize_edit_info(edit_info) + actual_denoise = float(denoise) + if denoise_mode == "auto_from_edit": + actual_denoise = float(normalized_edit_info.get("recommended_denoise", actual_denoise)) + + guidance_value = None + if routing.backend_name == "flux": + guidance_value = _normalize_flux_guidance(flux_guidance, defaults.get("default_guidance")) + positive = set_conditioning_values(positive, {"guidance": guidance_value}) + negative = set_conditioning_values(negative, {"guidance": guidance_value}) + + actual_seed = random.randint(0, 0xFFFFFFFFFFFFFFFF) if int(seed) == -1 else int(seed) + result_latent = _common_ksampler( + model=model, + seed=actual_seed, + steps=int(steps), + cfg=float(cfg), + sampler_name=sampler_name, + scheduler=scheduler, + positive=positive, + negative=negative, + latent=latent_image, + denoise=actual_denoise, + ) + sample_info = info_to_json( + { + "backend_name": routing.backend_name, + "routing_reason": routing.routing_reason, + "family": routing.capabilities["model_family"], + "model_role": routing.capabilities["model_role"], + "seed": actual_seed, + "steps": int(steps), + "cfg": float(cfg), + "guidance": guidance_value, + "sampler_name": sampler_name, + "scheduler": scheduler, + "denoise": actual_denoise, + "denoise_mode": denoise_mode, + "quality_preset": quality_preset, + } + ) + return result_latent, sample_info + + +NODE_CLASS_MAPPINGS = {"LLSProKSamplerBridge": LLSProKSamplerBridge} +NODE_DISPLAY_NAME_MAPPINGS = {"LLSProKSamplerBridge": "LLS Pro KSampler Bridge"} diff --git a/pro_edit/pro_edit_finish.py b/pro_edit/pro_edit_finish.py new file mode 100644 index 0000000..0cbe2be --- /dev/null +++ b/pro_edit/pro_edit_finish.py @@ -0,0 +1,110 @@ +from .pro_edit_utils import ( + BRIGHTNESS_MATCH_CHOICES, + COLOR_MATCH_CHOICES, + EDGE_FIX_CHOICES, + PREVIEW_MODE_CHOICES, + build_preview_image, + compose_canvas_result, + compose_crop_result, + compose_region_result, + normalize_edit_info, +) + + +class LLSProImageEditFinish: + CATEGORY = "LLS/Image Edit" + FUNCTION = "finish" + RETURN_TYPES = ("IMAGE", "IMAGE") + RETURN_NAMES = ("final_image", "preview_image") + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "original_image": ("IMAGE",), + "generated_image": ("IMAGE",), + "edit_info": ("LLS_EDIT_INFO",), + "feather": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 256.0, "step": 0.5}), + "color_match": (COLOR_MATCH_CHOICES, {"default": "disabled"}), + "brightness_match": (BRIGHTNESS_MATCH_CHOICES, {"default": "enabled"}), + "blend_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "restore_unmasked_area": ("BOOLEAN", {"default": True}), + "edge_fix": (EDGE_FIX_CHOICES, {"default": "soft"}), + "preview_mode": (PREVIEW_MODE_CHOICES, {"default": "final"}), + }, + "optional": { + "work_mask": ("MASK",), + "sample_info": ("STRING",), + }, + } + + def finish( + self, + original_image, + generated_image, + edit_info, + feather, + color_match, + brightness_match, + blend_strength, + restore_unmasked_area, + edge_fix, + preview_mode, + work_mask=None, + sample_info=None, + ): + del sample_info + + info = normalize_edit_info(edit_info) + scope = info["edit_scope"] + if work_mask is None: + raise RuntimeError("[LLS] LLS Pro Image Edit Finish requires work_mask.") + + if scope == "region": + final_image = compose_region_result( + original_image, + generated_image, + work_mask, + info, + feather, + color_match, + brightness_match, + blend_strength, + restore_unmasked_area, + edge_fix, + ) + elif scope == "crop": + final_image = compose_crop_result( + original_image, + generated_image, + work_mask, + info, + feather, + color_match, + brightness_match, + blend_strength, + restore_unmasked_area, + edge_fix, + ) + elif scope == "canvas": + final_image = compose_canvas_result( + original_image, + generated_image, + work_mask, + info, + feather, + color_match, + brightness_match, + blend_strength, + restore_unmasked_area, + edge_fix, + ) + else: + raise RuntimeError(f"[LLS] Unsupported edit_scope '{scope}'.") + + preview_image = build_preview_image(original_image, final_image, work_mask, info, preview_mode) + return final_image, preview_image + + +NODE_CLASS_MAPPINGS = {"LLSProImageEditFinish": LLSProImageEditFinish} +NODE_DISPLAY_NAME_MAPPINGS = {"LLSProImageEditFinish": "LLS Pro Image Edit Finish"} diff --git a/pro_edit/pro_edit_prepare.py b/pro_edit/pro_edit_prepare.py new file mode 100644 index 0000000..d2acfb2 --- /dev/null +++ b/pro_edit/pro_edit_prepare.py @@ -0,0 +1,130 @@ +from .backends.registry import resolve_backend +from .pro_edit_utils import ( + AUTO_RECOMMEND_CHOICES, + BACKEND_MODE_CHOICES, + CANVAS_FILL_CHOICES, + EDIT_SCOPE_CHOICES, + build_edit_info, + build_workspace, +) + + +class LLSProImageEditPrepare: + CATEGORY = "LLS/Image Edit" + FUNCTION = "prepare" + RETURN_TYPES = ("LATENT", "IMAGE", "MASK", "LLS_EDIT_INFO", "FLOAT", "CONDITIONING", "CONDITIONING") + RETURN_NAMES = ("latent", "work_image", "work_mask", "edit_info", "recommended_denoise", "positive", "negative") + + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "image": ("IMAGE",), + "mask": ("MASK",), + "vae": ("VAE",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "backend_mode": (BACKEND_MODE_CHOICES, {"default": "auto"}), + "edit_scope": (EDIT_SCOPE_CHOICES, {"default": "auto"}), + "mask_grow": ("INT", {"default": 24, "min": 0, "max": 2048}), + "mask_blur": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 256.0, "step": 0.5}), + "mask_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), + "invert_mask": ("BOOLEAN", {"default": False}), + "crop_context": ("INT", {"default": 64, "min": 0, "max": 512}), + "crop_context_factor": ("FLOAT", {"default": 1.5, "min": 1.0, "max": 8.0, "step": 0.1}), + "min_size": ("INT", {"default": 256, "min": 64, "max": 8192, "step": 8}), + "max_size": ("INT", {"default": 1024, "min": 64, "max": 8192, "step": 8}), + "resize_mode": (["fit", "pad", "stretch"], {"default": "fit"}), + "expand_left": ("INT", {"default": 0, "min": 0, "max": 4096}), + "expand_right": ("INT", {"default": 0, "min": 0, "max": 4096}), + "expand_top": ("INT", {"default": 0, "min": 0, "max": 4096}), + "expand_bottom": ("INT", {"default": 0, "min": 0, "max": 4096}), + "canvas_fill": (CANVAS_FILL_CHOICES, {"default": "edge"}), + "auto_recommend": (AUTO_RECOMMEND_CHOICES, {"default": "enabled"}), + }, + "optional": { + "model": ("MODEL",), + "model_info": ("STRING",), + }, + } + + def prepare( + self, + image, + mask, + vae, + positive, + negative, + backend_mode, + edit_scope, + mask_grow, + mask_blur, + mask_threshold, + invert_mask, + crop_context, + crop_context_factor, + min_size, + max_size, + resize_mode, + expand_left, + expand_right, + expand_top, + expand_bottom, + canvas_fill, + auto_recommend, + model=None, + model_info=None, + ): + workspace = build_workspace( + image, + mask, + edit_scope=edit_scope, + mask_grow=mask_grow, + mask_blur=mask_blur, + mask_threshold=mask_threshold, + invert_mask=invert_mask, + crop_context=crop_context, + crop_context_factor=crop_context_factor, + min_size=min_size, + max_size=max_size, + resize_mode=resize_mode, + expand_left=expand_left, + expand_right=expand_right, + expand_top=expand_top, + expand_bottom=expand_bottom, + canvas_fill=canvas_fill, + auto_recommend=auto_recommend, + ) + backend, routing = resolve_backend( + backend_mode, + model=model, + model_info=model_info, + ) + prepared = backend.prepare( + model=model, + vae=vae, + work_image=workspace["work_image"], + work_mask=workspace["work_mask"], + positive=positive, + negative=negative, + workspace=workspace, + routing=routing, + ) + edit_info = build_edit_info( + workspace, + routing, + backend_hints=prepared.get("backend_hints"), + ) + return ( + prepared["latent"], + workspace["work_image"], + workspace["work_mask"], + edit_info, + float(workspace["recommended_denoise"]), + prepared["positive"], + prepared["negative"], + ) + + +NODE_CLASS_MAPPINGS = {"LLSProImageEditPrepare": LLSProImageEditPrepare} +NODE_DISPLAY_NAME_MAPPINGS = {"LLSProImageEditPrepare": "LLS Pro Image Edit Prepare"} diff --git a/pro_edit/pro_edit_utils.py b/pro_edit/pro_edit_utils.py new file mode 100644 index 0000000..8939ddc --- /dev/null +++ b/pro_edit/pro_edit_utils.py @@ -0,0 +1,405 @@ +from __future__ import annotations + +from typing import Any + +from ..repair.repair_utils import ( + build_canvas_info, + build_canvas_repair_mask, + compute_crop_box, + crop_image_to, + crop_mask_to, + expand_canvas_image, + expand_canvas_mask, + get_image_size, + get_mask_metrics, + make_noise_mask, + merge_masks, + preprocess_mask, + recommend_denoise, + resize_image_to, + resize_mask_to, + resolve_repair_scope, + resolve_work_size, +) +from ..utils.model_info import parse_jsonish_info + +try: + import node_helpers +except Exception: + node_helpers = None + +try: + import torch +except Exception: + torch = None + + +EDIT_INFO_TYPE = "LLS_EDIT_INFO" +BACKEND_MODE_CHOICES = ["auto", "sdxl", "flux"] +EDIT_SCOPE_CHOICES = ["auto", "region", "crop", "canvas"] +PREVIEW_MODE_CHOICES = ["final", "compare", "mask", "before_after"] +AUTO_RECOMMEND_CHOICES = ["enabled", "disabled"] +CANVAS_FILL_CHOICES = ["edge", "blur", "black", "white", "neutral"] +COLOR_MATCH_CHOICES = ["disabled", "mean_std", "histogram_simple"] +BRIGHTNESS_MATCH_CHOICES = ["disabled", "enabled"] +EDGE_FIX_CHOICES = ["none", "soft", "strong"] + + +def normalize_edit_info(edit_info): + info = parse_jsonish_info(edit_info) + info.setdefault("backend_name", "") + info.setdefault("routing_reason", "") + info.setdefault("edit_scope", "region") + info.setdefault("edit_payload_version", "1.0") + return info + + +def set_conditioning_values(conditioning, values: dict[str, Any]): + if node_helpers is not None: + try: + return node_helpers.conditioning_set_values(conditioning, values) + except Exception: + pass + + updated = [] + for entry in conditioning: + if not isinstance(entry, (list, tuple)) or len(entry) != 2: + updated.append(entry) + continue + token, meta = entry + merged = dict(meta or {}) + merged.update(values) + updated.append([token, merged]) + return updated + + +def build_masked_pixel_image(image, mask, *, fill_value: float): + if hasattr(image, "masked_fill"): + return image.masked_fill(mask, fill_value) + if torch is not None and isinstance(image, torch.Tensor): + mask_4d = mask.unsqueeze(-1).clamp(0.0, 1.0) + return (image * (1.0 - mask_4d)) + (float(fill_value) * mask_4d) + raise RuntimeError("[LLS] image object does not support masked pixel preprocessing.") + + +def build_native_conditioning_payload(vae, work_image, work_mask, *, latent_source: str, masked_fill_value: float): + latent_samples = vae.encode(work_image) + masked_pixels = build_masked_pixel_image(work_image, work_mask, fill_value=masked_fill_value) + concat_latent_image = vae.encode(masked_pixels) + concat_mask = make_noise_mask(work_mask, latent_samples) + latent = { + "samples": latent_samples, + "noise_mask": concat_mask, + "source": latent_source, + } + return latent, concat_latent_image, concat_mask + + +def build_workspace( + image, + mask, + *, + edit_scope, + mask_grow, + mask_blur, + mask_threshold, + invert_mask, + crop_context, + crop_context_factor, + min_size, + max_size, + resize_mode, + expand_left, + expand_right, + expand_top, + expand_bottom, + canvas_fill, + auto_recommend, +): + original_width, original_height = get_image_size(image) + original_size = (original_width, original_height) + processed_mask = preprocess_mask( + mask, + original_size, + invert_mask=bool(invert_mask), + mask_threshold=float(mask_threshold), + mask_grow=int(mask_grow), + mask_blur=float(mask_blur), + ) + mask_bbox, mask_area_ratio = get_mask_metrics(processed_mask, original_size) + canvas_expand = ( + max(0, int(expand_left)), + max(0, int(expand_right)), + max(0, int(expand_top)), + max(0, int(expand_bottom)), + ) + scope = resolve_repair_scope( + edit_scope, + mask_area_ratio=mask_area_ratio, + mask_bbox=mask_bbox, + image_size=original_size, + canvas_expand=canvas_expand, + ) + + if scope == "region": + return { + "edit_scope": "region", + "work_image": image, + "work_mask": resize_mask_to(processed_mask, original_width, original_height), + "original_size": original_size, + "work_size": original_size, + "crop_box": None, + "crop_scale": 1.0, + "canvas_expand": list(canvas_expand), + "original_box_in_canvas": None, + "mask_bbox": list(mask_bbox) if mask_bbox is not None else None, + "mask_area_ratio": mask_area_ratio, + "recommended_denoise": recommend_denoise("replace", "region", "native_fill", auto_recommend), + "canvas_fill": canvas_fill, + } + + if scope == "crop": + if mask_bbox is None or mask_area_ratio <= 0.0: + raise RuntimeError("[LLS] crop edit scope requires a non-empty mask.") + crop_box = compute_crop_box(mask_bbox, original_size, int(crop_context), float(crop_context_factor)) + crop_width = max(1, crop_box[2] - crop_box[0]) + crop_height = max(1, crop_box[3] - crop_box[1]) + work_width, work_height, crop_scale = resolve_work_size( + (crop_width, crop_height), + int(min_size), + int(max_size), + resize_mode, + ) + work_image = resize_image_to(crop_image_to(image, crop_box), work_width, work_height) + work_mask = resize_mask_to(crop_mask_to(processed_mask, crop_box), work_width, work_height) + return { + "edit_scope": "crop", + "work_image": work_image, + "work_mask": work_mask, + "original_size": original_size, + "work_size": (work_width, work_height), + "crop_box": list(crop_box), + "crop_scale": crop_scale, + "canvas_expand": list(canvas_expand), + "original_box_in_canvas": None, + "mask_bbox": None, + "mask_area_ratio": mask_area_ratio, + "recommended_denoise": recommend_denoise("replace", "crop", "native_fill", auto_recommend), + "canvas_fill": canvas_fill, + } + + canvas_info = build_canvas_info( + original_size, + int(expand_left), + int(expand_right), + int(expand_top), + int(expand_bottom), + ) + work_width, work_height = canvas_info["work_size"] + original_box = canvas_info["original_box"] + user_mask = expand_canvas_mask(processed_mask, work_width, work_height, original_box=original_box) + canvas_mask = build_canvas_repair_mask(user_mask, work_width, work_height, original_box=original_box) + work_mask = merge_masks(user_mask, canvas_mask, (work_width, work_height)) + return { + "edit_scope": "canvas", + "work_image": expand_canvas_image( + image, + work_width, + work_height, + fill_mode=canvas_fill, + original_box=original_box, + ), + "work_mask": work_mask, + "original_size": original_size, + "work_size": (work_width, work_height), + "crop_box": None, + "crop_scale": 1.0, + "canvas_expand": list(canvas_expand), + "original_box_in_canvas": list(original_box), + "mask_bbox": None, + "mask_area_ratio": mask_area_ratio, + "recommended_denoise": recommend_denoise("fill", "canvas", "native_fill", auto_recommend), + "canvas_fill": canvas_fill, + } + + +def build_edit_info(workspace: dict[str, Any], routing, *, backend_hints: dict[str, Any] | None = None) -> dict[str, Any]: + info = { + "backend_name": routing.backend_name, + "routing_reason": routing.routing_reason, + "model_family": routing.capabilities["model_family"], + "model_role": routing.capabilities["model_role"], + "supports_inpaint_native": routing.capabilities["supports_inpaint_native"], + "supports_image_edit_native": routing.capabilities["supports_image_edit_native"], + "preferred_edit_backend": routing.capabilities["preferred_edit_backend"], + "edit_scope": workspace["edit_scope"], + "original_size": list(workspace["original_size"]), + "work_size": list(workspace["work_size"]), + "crop_box": workspace["crop_box"], + "crop_scale": workspace["crop_scale"], + "canvas_expand": workspace["canvas_expand"], + "original_box_in_canvas": workspace["original_box_in_canvas"], + "recommended_denoise": float(workspace["recommended_denoise"]), + "edit_payload_version": "1.0", + } + if backend_hints: + info.update(backend_hints) + return info + + +def _ensure_torch_available(): + if torch is None: + raise RuntimeError("[LLS] Pro image edit compositing requires PyTorch.") + + +def _resize_mask_for_image(mask, image): + width, height = get_image_size(image) + return resize_mask_to(mask, width, height) + + +def _build_canvas_base(original_image, canvas_image, original_box): + _ensure_torch_available() + x1, y1, x2, y2 = original_box + out = torch.zeros_like(canvas_image) + out[:, y1:y2, x1:x2, :] = resize_image_to(original_image, x2 - x1, y2 - y1) + return out + + +def project_work_mask(work_mask, edit_info, reference_image): + _ensure_torch_available() + scope = str(edit_info.get("edit_scope") or "region") + if scope == "region": + return _resize_mask_for_image(work_mask, reference_image) + + if scope == "crop": + crop_box = edit_info.get("crop_box") + if not isinstance(crop_box, (list, tuple)) or len(crop_box) != 4: + raise RuntimeError("[LLS] crop edit_info must include crop_box.") + x1, y1, x2, y2 = [int(value) for value in crop_box] + projected = torch.zeros( + (work_mask.shape[0], reference_image.shape[1], reference_image.shape[2]), + dtype=work_mask.dtype, + device=work_mask.device, + ) + projected[:, y1:y2, x1:x2] = resize_mask_to(work_mask, x2 - x1, y2 - y1) + return projected + + return _resize_mask_for_image(work_mask, reference_image) + + +def _blend_torch_images(original, generated, mask, blend_strength: float): + _ensure_torch_available() + mask_4d = _resize_mask_for_image(mask, original).unsqueeze(-1).clamp(0.0, 1.0) * float(blend_strength) + return (original * (1.0 - mask_4d)) + (generated * mask_4d) + + +def overlay_mask_preview(image, mask, *, alpha: float = 0.4): + if torch is None or not isinstance(image, torch.Tensor): + return image + mask_4d = _resize_mask_for_image(mask, image).unsqueeze(-1).clamp(0.0, 1.0) * float(alpha) + red = torch.zeros_like(image) + red[..., 0] = 1.0 + return (image * (1.0 - mask_4d)) + (red * mask_4d) + + +def _paste_torch_patch(base, patch, box): + _ensure_torch_available() + x1, y1, x2, y2 = box + out = base.clone() + out[:, y1:y2, x1:x2, :] = patch + return out + + +def compose_region_result( + original_image, + generated_image, + work_mask, + edit_info, + feather, + color_match, + brightness_match, + blend_strength, + restore_unmasked_area, + edge_fix, +): + del edit_info, feather, color_match, brightness_match, restore_unmasked_area, edge_fix + original_width, original_height = get_image_size(original_image) + base_image = resize_image_to(original_image, original_width, original_height) + edited_image = resize_image_to(generated_image, original_width, original_height) + return _blend_torch_images(base_image, edited_image, _resize_mask_for_image(work_mask, base_image), blend_strength) + + +def compose_crop_result( + original_image, + generated_image, + work_mask, + edit_info, + feather, + color_match, + brightness_match, + blend_strength, + restore_unmasked_area, + edge_fix, +): + del feather, color_match, brightness_match, restore_unmasked_area, edge_fix + crop_box = edit_info.get("crop_box") + if not isinstance(crop_box, (list, tuple)) or len(crop_box) != 4: + raise RuntimeError("[LLS] crop edit_info must include crop_box.") + crop_width = max(1, int(crop_box[2]) - int(crop_box[0])) + crop_height = max(1, int(crop_box[3]) - int(crop_box[1])) + resized_patch = resize_image_to(generated_image, crop_width, crop_height) + resized_mask = resize_mask_to(work_mask, crop_width, crop_height) + original_patch = crop_image_to(original_image, tuple(int(v) for v in crop_box)) + blended_patch = _blend_torch_images(original_patch, resized_patch, resized_mask, blend_strength) + return _paste_torch_patch(original_image, blended_patch, tuple(int(v) for v in crop_box)) + + +def compose_canvas_result( + original_image, + generated_image, + work_mask, + edit_info, + feather, + color_match, + brightness_match, + blend_strength, + restore_unmasked_area, + edge_fix, +): + del feather, color_match, brightness_match, restore_unmasked_area, edge_fix + original_box = edit_info.get("original_box_in_canvas") + if not isinstance(original_box, (list, tuple)) or len(original_box) != 4: + raise RuntimeError("[LLS] canvas edit_info must include original_box_in_canvas.") + base_canvas = _build_canvas_base( + original_image, + generated_image, + tuple(int(value) for value in original_box), + ) + return _blend_torch_images(base_canvas, generated_image, _resize_mask_for_image(work_mask, generated_image), blend_strength) + + +def build_preview_image(original_image, final_image, work_mask, edit_info, preview_mode): + _ensure_torch_available() + scope = str(edit_info.get("edit_scope") or "region") + if preview_mode == "final": + return final_image + if preview_mode == "mask": + if scope == "canvas": + preview_base = _build_canvas_base( + original_image, + final_image, + tuple(int(value) for value in edit_info["original_box_in_canvas"]), + ) + projected_mask = _resize_mask_for_image(work_mask, final_image) + return overlay_mask_preview(preview_base, projected_mask) + projected_mask = project_work_mask(work_mask, edit_info, original_image) + return overlay_mask_preview(original_image, projected_mask) + if scope == "canvas": + compare_left = _build_canvas_base( + original_image, + final_image, + tuple(int(value) for value in edit_info["original_box_in_canvas"]), + ) + else: + compare_left = resize_image_to(original_image, get_image_size(final_image)[0], get_image_size(final_image)[1]) + return torch.cat([compare_left, final_image], dim=2) diff --git a/tests/test_pro_edit_bridge.py b/tests/test_pro_edit_bridge.py new file mode 100644 index 0000000..802592e --- /dev/null +++ b/tests/test_pro_edit_bridge.py @@ -0,0 +1,138 @@ +import json +import unittest +from unittest import mock + +try: + from .test_pro_edit_helpers import FakeLatentTensor, FakeModel, import_plugin_submodule, load_plugin_package, make_conditioning +except ImportError: + from test_pro_edit_helpers import FakeLatentTensor, FakeModel, import_plugin_submodule, load_plugin_package, make_conditioning + + +class TestProEditBridge(unittest.TestCase): + def setUp(self): + self.plugin = load_plugin_package() + self.bridge_module = import_plugin_submodule(self.plugin, "pro_edit.pro_edit_bridge") + self.node = self.bridge_module.LLSProKSamplerBridge() + + def test_bridge_uses_auto_from_edit_denoise(self): + model = FakeModel( + family="SDXL", + model_role="inpaint", + supports_inpaint_native=True, + supports_image_edit_native=False, + preferred_edit_backend="sdxl", + ) + latent = {"samples": FakeLatentTensor((1, 4, 64, 64)), "source": "pro_edit_prepare_region"} + + with mock.patch.object(self.bridge_module, "_common_ksampler", side_effect=lambda **kwargs: kwargs["latent"]): + result_latent, sample_info_json = self.node.sample( + model=model, + positive=make_conditioning("positive"), + negative=make_conditioning("negative"), + latent_image=latent, + backend_mode="auto", + quality_preset="Manual", + seed=7, + steps=20, + cfg=7.0, + sampler_name="euler", + scheduler="normal", + denoise=0.25, + denoise_mode="auto_from_edit", + flux_guidance=3.5, + model_family="Auto", + edit_info={ + "backend_name": "sdxl", + "model_family": "SDXL", + "model_role": "inpaint", + "supports_inpaint_native": True, + "supports_image_edit_native": False, + "preferred_edit_backend": "sdxl", + "recommended_denoise": 0.63, + }, + model_info=None, + ) + + sample_info = json.loads(sample_info_json) + self.assertEqual(result_latent["source"], "pro_edit_prepare_region") + self.assertEqual(sample_info["backend_name"], "sdxl") + self.assertEqual(sample_info["denoise"], 0.63) + self.assertEqual(sample_info["denoise_mode"], "auto_from_edit") + + def test_bridge_applies_flux_guidance_for_flux_backend(self): + model = FakeModel( + family="FLUX_DEV", + model_role="edit", + supports_inpaint_native=False, + supports_image_edit_native=True, + preferred_edit_backend="flux", + ) + latent = {"samples": FakeLatentTensor((1, 16, 64, 64)), "source": "pro_edit_prepare_region"} + + with mock.patch.object(self.bridge_module, "_common_ksampler", side_effect=lambda **kwargs: kwargs["latent"]): + _, sample_info_json = self.node.sample( + model=model, + positive=make_conditioning("positive"), + negative=make_conditioning("negative"), + latent_image=latent, + backend_mode="auto", + quality_preset="Manual", + seed=9, + steps=12, + cfg=1.0, + sampler_name="euler", + scheduler="simple", + denoise=0.8, + denoise_mode="manual", + flux_guidance=4.2, + model_family="Auto", + edit_info={ + "backend_name": "flux", + "model_family": "FLUX_DEV", + "model_role": "edit", + "supports_inpaint_native": False, + "supports_image_edit_native": True, + "preferred_edit_backend": "flux", + "recommended_denoise": 0.8, + }, + model_info=None, + ) + + sample_info = json.loads(sample_info_json) + self.assertEqual(sample_info["backend_name"], "flux") + self.assertEqual(sample_info["guidance"], 4.2) + + def test_bridge_manual_backend_mismatch_raises(self): + model = FakeModel( + family="SDXL", + model_role="inpaint", + supports_inpaint_native=True, + supports_image_edit_native=False, + preferred_edit_backend="sdxl", + ) + latent = {"samples": FakeLatentTensor((1, 4, 64, 64)), "source": "pro_edit_prepare_region"} + + with self.assertRaisesRegex(RuntimeError, "backend 'flux' is incompatible"): + self.node.sample( + model=model, + positive=make_conditioning("positive"), + negative=make_conditioning("negative"), + latent_image=latent, + backend_mode="flux", + quality_preset="Manual", + seed=11, + steps=20, + cfg=7.0, + sampler_name="euler", + scheduler="normal", + denoise=0.5, + denoise_mode="manual", + flux_guidance=3.5, + model_family="Auto", + edit_info=None, + model_info=None, + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_pro_edit_capabilities.py b/tests/test_pro_edit_capabilities.py new file mode 100644 index 0000000..38d22a4 --- /dev/null +++ b/tests/test_pro_edit_capabilities.py @@ -0,0 +1,208 @@ +import unittest +from types import SimpleNamespace +from unittest import mock + +try: + from .test_pro_edit_helpers import FakeModel, import_plugin_submodule, load_plugin_package +except ImportError: + from test_pro_edit_helpers import FakeModel, import_plugin_submodule, load_plugin_package + + +class TestProEditCapabilities(unittest.TestCase): + def test_parse_model_info_infers_sdxl_inpaint_capability_from_name(self): + plugin = load_plugin_package() + model_info = import_plugin_submodule(plugin, "utils.model_info") + + info = model_info.parse_model_info( + { + "checkpoint_name": "demo-sdxl-inpaint.safetensors", + "family": "SDXL", + } + ) + + self.assertEqual(info["model_role"], "inpaint") + self.assertTrue(info["supports_inpaint_native"]) + self.assertFalse(info["supports_image_edit_native"]) + self.assertEqual(info["preferred_edit_backend"], "sdxl") + + def test_parse_model_info_infers_flux_edit_capability_from_name(self): + plugin = load_plugin_package() + model_info = import_plugin_submodule(plugin, "utils.model_info") + + info = model_info.parse_model_info( + { + "checkpoint_name": "demo-flux-fill-dev.safetensors", + "family": "FLUX_DEV", + } + ) + + self.assertEqual(info["model_role"], "fill") + self.assertFalse(info["supports_inpaint_native"]) + self.assertTrue(info["supports_image_edit_native"]) + self.assertEqual(info["preferred_edit_backend"], "flux") + + def test_parse_model_info_infers_capabilities_from_ckpt_name_alias(self): + plugin = load_plugin_package() + model_info = import_plugin_submodule(plugin, "utils.model_info") + + info = model_info.parse_model_info( + { + "ckpt_name": "demo-sdxl-inpaint.safetensors", + "family": "SDXL", + } + ) + + self.assertEqual(info["checkpoint_name"], "demo-sdxl-inpaint.safetensors") + self.assertEqual(info["model_name"], "demo-sdxl-inpaint.safetensors") + self.assertEqual(info["model_role"], "inpaint") + self.assertTrue(info["supports_inpaint_native"]) + self.assertEqual(info["preferred_edit_backend"], "sdxl") + + def test_parse_model_info_infers_capabilities_from_ckpt_alias(self): + plugin = load_plugin_package() + model_info = import_plugin_submodule(plugin, "utils.model_info") + + info = model_info.parse_model_info( + { + "ckpt": "demo-flux-fill-dev.safetensors", + "family": "FLUX_DEV", + } + ) + + self.assertEqual(info["checkpoint_name"], "demo-flux-fill-dev.safetensors") + self.assertEqual(info["model_name"], "demo-flux-fill-dev.safetensors") + self.assertEqual(info["model_role"], "fill") + self.assertTrue(info["supports_image_edit_native"]) + self.assertEqual(info["preferred_edit_backend"], "flux") + + def test_explicit_capability_values_override_name_inference(self): + plugin = load_plugin_package() + model_info = import_plugin_submodule(plugin, "utils.model_info") + + info = model_info.parse_model_info( + { + "checkpoint_name": "plain-sdxl-base.safetensors", + "family": "SDXL", + "model_role": "edit", + "supports_inpaint_native": True, + "supports_image_edit_native": True, + "preferred_edit_backend": "sdxl", + } + ) + + self.assertEqual(info["model_role"], "edit") + self.assertTrue(info["supports_inpaint_native"]) + self.assertTrue(info["supports_image_edit_native"]) + self.assertEqual(info["preferred_edit_backend"], "sdxl") + + def test_parse_model_info_coerces_string_capability_overrides_to_false(self): + plugin = load_plugin_package() + model_info = import_plugin_submodule(plugin, "utils.model_info") + + info = model_info.parse_model_info( + { + "checkpoint_name": "plain-sdxl-base.safetensors", + "family": "SDXL", + "supports_inpaint_native": "false", + "supports_image_edit_native": "0", + } + ) + + self.assertFalse(info["supports_inpaint_native"]) + self.assertFalse(info["supports_image_edit_native"]) + + def test_parse_model_info_normalizes_model_family_alias(self): + plugin = load_plugin_package() + model_info = import_plugin_submodule(plugin, "utils.model_info") + + info = model_info.parse_model_info( + { + "model_family": "SD15", + "checkpoint_name": "demo.safetensors", + } + ) + + self.assertEqual(info["family"], "SD1.5") + self.assertEqual(info["model_family"], "SD1.5") + + def test_resolve_edit_capabilities_reads_model_tags(self): + plugin = load_plugin_package() + model_info = import_plugin_submodule(plugin, "utils.model_info") + model = FakeModel( + family="FLUX_DEV", + model_role="edit", + supports_inpaint_native=False, + supports_image_edit_native=True, + preferred_edit_backend="flux", + model_name="demo-flux-edit.safetensors", + ) + + capabilities = model_info.resolve_edit_capabilities(model=model, model_info=None) + + self.assertEqual(capabilities["model_family"], "FLUX_DEV") + self.assertEqual(capabilities["model_role"], "edit") + self.assertTrue(capabilities["supports_image_edit_native"]) + self.assertEqual(capabilities["preferred_edit_backend"], "flux") + + def test_resolve_edit_capabilities_coerces_string_overrides_to_false(self): + plugin = load_plugin_package() + model_info = import_plugin_submodule(plugin, "utils.model_info") + + capabilities = model_info.resolve_edit_capabilities( + model=None, + model_info={ + "checkpoint_name": "plain-sdxl-base.safetensors", + "family": "SDXL", + "supports_inpaint_native": "false", + "supports_image_edit_native": "0", + }, + ) + + self.assertFalse(capabilities["supports_inpaint_native"]) + self.assertFalse(capabilities["supports_image_edit_native"]) + + def test_loader_builds_capability_tags_for_loaded_objects(self): + plugin = load_plugin_package() + loader_module = import_plugin_submodule(plugin, "model_loader.nodes") + + tags = loader_module._build_capability_tags("demo-sdxl-inpaint.safetensors", "SDXL") + + self.assertEqual(tags["model_role"], "inpaint") + self.assertTrue(tags["supports_inpaint_native"]) + self.assertEqual(tags["preferred_edit_backend"], "sdxl") + + def test_loader_tags_loaded_objects_with_capabilities(self): + plugin = load_plugin_package() + loader_module = import_plugin_submodule(plugin, "model_loader.nodes") + + loader = loader_module.LLSSimpleCheckpointLoader() + model = SimpleNamespace() + clip = SimpleNamespace() + vae = SimpleNamespace() + + with mock.patch.object(loader_module, "folder_paths", object()), \ + mock.patch.object(loader_module, "comfy_sd", object()), \ + mock.patch.object(loader_module, "_resolve_model_path", return_value=("checkpoints", "/fake/demo-sdxl-inpaint.safetensors")), \ + mock.patch.object(loader_module, "_load_model", return_value=(model, clip, vae)), \ + mock.patch.object(loader_module, "_resolve_text_encoder", return_value=(clip, "embedded", None, None)), \ + mock.patch.object(loader_module, "_resolve_vae", return_value=(vae, "embedded", None)): + loaded_model, loaded_clip, loaded_vae, text_encoder = loader.load_checkpoint( + ckpt_name="demo-sdxl-inpaint.safetensors", + model_family="SDXL", + load_mode="simple", + vae_source="auto", + text_encoder_source="auto", + external_vae_name=loader_module.AUTO_PLACEHOLDER, + external_text_encoder_1=loader_module.AUTO_PLACEHOLDER, + external_text_encoder_2=loader_module.AUTO_PLACEHOLDER, + ) + + for obj in (loaded_model, loaded_clip, loaded_vae, text_encoder): + self.assertEqual(obj._lls_model_role, "inpaint") + self.assertTrue(obj._lls_supports_inpaint_native) + self.assertFalse(obj._lls_supports_image_edit_native) + self.assertEqual(obj._lls_preferred_edit_backend, "sdxl") + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_pro_edit_docs.py b/tests/test_pro_edit_docs.py new file mode 100644 index 0000000..6ed7cd3 --- /dev/null +++ b/tests/test_pro_edit_docs.py @@ -0,0 +1,26 @@ +import pathlib +import unittest + + +README_PATH = pathlib.Path(__file__).resolve().parents[1] / "README.md" + + +class TestProEditDocs(unittest.TestCase): + def test_readme_documents_pro_edit_nodes_and_workflow(self): + text = README_PATH.read_text(encoding="utf-8") + + for needle in ( + "LLS Pro Image Edit Prepare", + "LLS Pro KSampler Bridge", + "LLS Pro Image Edit Finish", + "Simple = lightweight masked latent resampling", + "Pro = true image edit / inpaint pipeline", + "backend_mode = auto | sdxl | flux", + "Load Image -> Load Mask or LLS Simple Mask Draw -> LLS Simple Checkpoint Loader -> LLS Simple Prompt Encode -> LLS Pro Image Edit Prepare -> LLS Pro KSampler Bridge -> VAE Decode -> LLS Pro Image Edit Finish -> Preview Image", + "Adding new professional edit models", + ): + self.assertIn(needle, text) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_pro_edit_finish.py b/tests/test_pro_edit_finish.py new file mode 100644 index 0000000..6a289d0 --- /dev/null +++ b/tests/test_pro_edit_finish.py @@ -0,0 +1,114 @@ +import unittest + +try: + from .test_pro_edit_helpers import load_plugin_package, make_torch_image, make_torch_mask, torch +except ImportError: + from test_pro_edit_helpers import load_plugin_package, make_torch_image, make_torch_mask, torch + + +class TestProEditFinish(unittest.TestCase): + def setUp(self): + plugin = load_plugin_package() + node_cls = plugin.NODE_CLASS_MAPPINGS["LLSProImageEditFinish"] + self.node = node_cls() + + def test_region_composite_only_replaces_masked_pixels(self): + if torch is None: + self.skipTest("torch is required for finish compositing tests") + + original = make_torch_image(4, 4, 0.0) + generated = make_torch_image(4, 4, 1.0) + mask = make_torch_mask(4, 4, (1, 1, 3, 3)) + + final_image, preview_image = self.node.finish( + original_image=original, + generated_image=generated, + edit_info={ + "backend_name": "sdxl", + "edit_scope": "region", + "original_size": [4, 4], + "work_size": [4, 4], + }, + feather=0.0, + color_match="disabled", + brightness_match="disabled", + blend_strength=1.0, + restore_unmasked_area=True, + edge_fix="none", + preview_mode="final", + work_mask=mask, + sample_info=None, + ) + + self.assertEqual(float(final_image[0, 0, 0, 0].item()), 0.0) + self.assertEqual(float(final_image[0, 1, 1, 0].item()), 1.0) + self.assertEqual(tuple(preview_image.shape), tuple(final_image.shape)) + + def test_crop_composite_pastes_generated_crop_back_into_original(self): + if torch is None: + self.skipTest("torch is required for finish compositing tests") + + original = make_torch_image(4, 4, 0.0) + generated = make_torch_image(2, 2, 0.75) + mask = make_torch_mask(2, 2, (0, 0, 2, 2)) + + final_image, preview_image = self.node.finish( + original_image=original, + generated_image=generated, + edit_info={ + "backend_name": "sdxl", + "edit_scope": "crop", + "original_size": [4, 4], + "work_size": [2, 2], + "crop_box": [1, 1, 3, 3], + }, + feather=0.0, + color_match="disabled", + brightness_match="disabled", + blend_strength=1.0, + restore_unmasked_area=True, + edge_fix="none", + preview_mode="mask", + work_mask=mask, + sample_info=None, + ) + + self.assertEqual(float(final_image[0, 0, 0, 0].item()), 0.0) + self.assertEqual(float(final_image[0, 1, 1, 0].item()), 0.75) + self.assertEqual(tuple(preview_image.shape), tuple(original.shape)) + + def test_canvas_output_keeps_expanded_canvas_size(self): + if torch is None: + self.skipTest("torch is required for finish compositing tests") + + original = make_torch_image(4, 4, 0.0) + generated = make_torch_image(6, 6, 0.5) + mask = make_torch_mask(6, 6, (0, 0, 6, 6)) + + final_image, preview_image = self.node.finish( + original_image=original, + generated_image=generated, + edit_info={ + "backend_name": "flux", + "edit_scope": "canvas", + "original_size": [4, 4], + "work_size": [6, 6], + "original_box_in_canvas": [1, 1, 5, 5], + }, + feather=0.0, + color_match="disabled", + brightness_match="disabled", + blend_strength=1.0, + restore_unmasked_area=True, + edge_fix="none", + preview_mode="compare", + work_mask=mask, + sample_info=None, + ) + + self.assertEqual(tuple(final_image.shape[1:3]), (6, 6)) + self.assertEqual(tuple(preview_image.shape[1:3]), (6, 12)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_pro_edit_helpers.py b/tests/test_pro_edit_helpers.py new file mode 100644 index 0000000..120f599 --- /dev/null +++ b/tests/test_pro_edit_helpers.py @@ -0,0 +1,344 @@ +import importlib +import importlib.util +import pathlib +import sys + + +ROOT = pathlib.Path(__file__).resolve().parents[1] +MODULE_NAME = "lls_node_test_pro_edit" +_UNSET = object() + +try: + import torch +except Exception: + torch = None + + +def load_plugin_package(): + for name in list(sys.modules): + if name == MODULE_NAME or name.startswith(f"{MODULE_NAME}."): + sys.modules.pop(name) + + spec = importlib.util.spec_from_file_location( + MODULE_NAME, + ROOT / "__init__.py", + submodule_search_locations=[str(ROOT)], + ) + module = importlib.util.module_from_spec(spec) + sys.modules[MODULE_NAME] = module + spec.loader.exec_module(module) + return module + + +def import_plugin_submodule(plugin, dotted_name: str): + return importlib.import_module(f"{plugin.__name__}.{dotted_name}") + + +class FakeTensor: + def __init__(self, shape, label="image", crop_box=None, fill_mode=None, original_box=None): + self.shape = tuple(shape) + self.label = label + self.crop_box = crop_box + self.fill_mode = fill_mode + self.original_box = original_box + + def cropped(self, x1, y1, x2, y2): + batch, _, _, channels = self.shape + return FakeTensor( + (batch, max(0, y2 - y1), max(0, x2 - x1), channels), + label=f"{self.label}:crop[{x1},{y1},{x2},{y2}]", + crop_box=(x1, y1, x2, y2), + fill_mode=self.fill_mode, + original_box=self.original_box, + ) + + def resized(self, width, height): + batch, _, _, channels = self.shape + return FakeTensor( + (batch, height, width, channels), + label=f"{self.label}:resized[{width}x{height}]", + crop_box=self.crop_box, + fill_mode=self.fill_mode, + original_box=self.original_box, + ) + + def canvas_expanded(self, width, height, fill_mode="edge", original_box=None): + batch, _, _, channels = self.shape + return FakeTensor( + (batch, height, width, channels), + label=f"{self.label}:canvas[{fill_mode}]", + crop_box=self.crop_box, + fill_mode=fill_mode, + original_box=original_box, + ) + + def masked_fill(self, mask, fill_value): + del mask + return FakeTensor( + self.shape, + label=f"{self.label}:masked[{fill_value}]", + crop_box=self.crop_box, + fill_mode=self.fill_mode, + original_box=self.original_box, + ) + + +class FakeMask: + def __init__(self, shape, mask_bbox=None, mask_area_ratio=0.0, label="mask", crop_box=None): + self.shape = tuple(shape) + self.mask_bbox = mask_bbox + self.mask_area_ratio = float(mask_area_ratio) + self.label = label + self.crop_box = crop_box + + def _clone(self, shape=None, mask_bbox=_UNSET, mask_area_ratio=None, label=None, crop_box=None): + return FakeMask( + shape or self.shape, + self.mask_bbox if mask_bbox is _UNSET else mask_bbox, + self.mask_area_ratio if mask_area_ratio is None else mask_area_ratio, + self.label if label is None else label, + self.crop_box if crop_box is None else crop_box, + ) + + def _current_size(self): + return self.shape[2], self.shape[1] + + @staticmethod + def _clamp_box(box, width, height): + if box is None: + return None + x1, y1, x2, y2 = box + x1 = max(0, min(int(x1), width)) + y1 = max(0, min(int(y1), height)) + x2 = max(x1, min(int(x2), width)) + y2 = max(y1, min(int(y2), height)) + if x2 <= x1 or y2 <= y1: + return None + return (x1, y1, x2, y2) + + @staticmethod + def _bbox_area_ratio(box, width, height): + if box is None or width <= 0 or height <= 0: + return 0.0 + return ((box[2] - box[0]) * (box[3] - box[1])) / float(width * height) + + def cropped(self, x1, y1, x2, y2): + batch, _, _ = self.shape + new_width = max(0, x2 - x1) + new_height = max(0, y2 - y1) + new_bbox = None + if self.mask_bbox is not None: + new_bbox = self._clamp_box( + ( + self.mask_bbox[0] - x1, + self.mask_bbox[1] - y1, + self.mask_bbox[2] - x1, + self.mask_bbox[3] - y1, + ), + new_width, + new_height, + ) + return FakeMask( + (batch, new_height, new_width), + mask_bbox=new_bbox, + mask_area_ratio=self._bbox_area_ratio(new_bbox, new_width, new_height), + label=f"{self.label}:crop[{x1},{y1},{x2},{y2}]", + crop_box=(x1, y1, x2, y2), + ) + + def resized(self, width, height): + batch, _, _ = self.shape + source_width, source_height = self._current_size() + new_bbox = None + if self.mask_bbox is not None and source_width > 0 and source_height > 0: + scale_x = width / float(source_width) + scale_y = height / float(source_height) + new_bbox = self._clamp_box( + ( + round(self.mask_bbox[0] * scale_x), + round(self.mask_bbox[1] * scale_y), + round(self.mask_bbox[2] * scale_x), + round(self.mask_bbox[3] * scale_y), + ), + width, + height, + ) + return FakeMask( + (batch, height, width), + mask_bbox=new_bbox, + mask_area_ratio=self._bbox_area_ratio(new_bbox, width, height), + label=f"{self.label}:resized[{width}x{height}]", + crop_box=self.crop_box, + ) + + def canvas_expanded(self, width, height, original_box=None): + batch, _, _ = self.shape + if original_box is None: + original_box = (0, 0, self.shape[2], self.shape[1]) + offset_x, offset_y = original_box[0], original_box[1] + new_bbox = None + if self.mask_bbox is not None: + new_bbox = self._clamp_box( + ( + self.mask_bbox[0] + offset_x, + self.mask_bbox[1] + offset_y, + self.mask_bbox[2] + offset_x, + self.mask_bbox[3] + offset_y, + ), + width, + height, + ) + return FakeMask( + (batch, height, width), + mask_bbox=new_bbox, + mask_area_ratio=self._bbox_area_ratio(new_bbox, width, height), + label=f"{self.label}:canvas", + crop_box=self.crop_box, + ) + + def normalized(self): + return self._clone(label=f"{self.label}:normalize") + + def inverted(self, image_size): + width, height = image_size + inverted_ratio = max(0.0, min(1.0, 1.0 - self.mask_area_ratio)) + inverted_bbox = None if inverted_ratio <= 0.0 else (0, 0, width, height) + return self._clone( + shape=(self.shape[0], height, width), + mask_bbox=inverted_bbox, + mask_area_ratio=inverted_ratio, + label=f"{self.label}:invert", + ) + + def thresholded(self, threshold): + width, height = self._current_size() + if self.mask_bbox is None: + return self._clone(label=f"{self.label}:threshold[{threshold}]") + shrink_ratio = max(0.0, min(1.0, 1.0 - max(0.0, float(threshold) - 0.5))) + if shrink_ratio <= 0.0: + return self._clone(mask_bbox=None, mask_area_ratio=0.0, label=f"{self.label}:threshold[{threshold}]") + x1, y1, x2, y2 = self.mask_bbox + cx = (x1 + x2) / 2.0 + cy = (y1 + y2) / 2.0 + half_width = max(1.0, (x2 - x1) * shrink_ratio / 2.0) + half_height = max(1.0, (y2 - y1) * shrink_ratio / 2.0) + new_bbox = self._clamp_box( + ( + round(cx - half_width), + round(cy - half_height), + round(cx + half_width), + round(cy + half_height), + ), + width, + height, + ) + return self._clone( + mask_bbox=new_bbox, + mask_area_ratio=self._bbox_area_ratio(new_bbox, width, height), + label=f"{self.label}:threshold[{threshold}]", + ) + + def grown(self, amount, image_size): + width, height = image_size + if self.mask_bbox is None: + return self._clone(shape=(self.shape[0], height, width), label=f"{self.label}:grow[{amount}]") + x1, y1, x2, y2 = self.mask_bbox + new_bbox = self._clamp_box((x1 - amount, y1 - amount, x2 + amount, y2 + amount), width, height) + return self._clone( + shape=(self.shape[0], height, width), + mask_bbox=new_bbox, + mask_area_ratio=self._bbox_area_ratio(new_bbox, width, height), + label=f"{self.label}:grow[{amount}]", + ) + + def blurred(self, radius, image_size): + blur_expand = max(1, int(round(float(radius) / 4.0))) + return self.grown(blur_expand, image_size)._clone(label=f"{self.label}:blur[{radius}]") + + def merged_with(self, other, image_size): + width, height = image_size + if self.mask_bbox is None: + return other._clone(shape=(other.shape[0], height, width), label=f"{self.label}:merge:{other.label}") + if other.mask_bbox is None: + return self._clone(shape=(self.shape[0], height, width), label=f"{self.label}:merge:{other.label}") + new_bbox = self._clamp_box( + ( + min(self.mask_bbox[0], other.mask_bbox[0]), + min(self.mask_bbox[1], other.mask_bbox[1]), + max(self.mask_bbox[2], other.mask_bbox[2]), + max(self.mask_bbox[3], other.mask_bbox[3]), + ), + width, + height, + ) + return self._clone( + shape=(self.shape[0], height, width), + mask_bbox=new_bbox, + mask_area_ratio=self._bbox_area_ratio(new_bbox, width, height), + label=f"{self.label}:merge:{other.label}", + ) + + @classmethod + def canvas_region(cls, shape, original_box, label="canvas-region"): + batch, height, width = shape + original_width = max(0, original_box[2] - original_box[0]) + original_height = max(0, original_box[3] - original_box[1]) + total_area = max(1, width * height) + original_area = max(0, original_width * original_height) + area_ratio = max(0.0, min(1.0, (total_area - original_area) / float(total_area))) + bbox = None if area_ratio <= 0.0 else (0, 0, width, height) + return cls(shape, mask_bbox=bbox, mask_area_ratio=area_ratio, label=label) + + +class FakeLatentTensor: + def __init__(self, shape): + self.shape = tuple(shape) + + +class FakeVAE: + def __init__(self, latent_channels=4, downscale_ratio=8): + self.latent_channels = int(latent_channels) + self.downscale_ratio = int(downscale_ratio) + + def encode(self, image): + batch, height, width, _ = image.shape + latent_height = max(1, height // self.downscale_ratio) + latent_width = max(1, width // self.downscale_ratio) + return FakeLatentTensor((batch, self.latent_channels, latent_height, latent_width)) + + +class FakeModel: + def __init__( + self, + family="SDXL", + model_role="base", + supports_inpaint_native=False, + supports_image_edit_native=False, + preferred_edit_backend=None, + model_name="demo-model.safetensors", + ): + self._lls_family = family + self._lls_model_role = model_role + self._lls_supports_inpaint_native = supports_inpaint_native + self._lls_supports_image_edit_native = supports_image_edit_native + self._lls_preferred_edit_backend = preferred_edit_backend + self._lls_model_name = model_name + + +def make_conditioning(label): + return [[label, {"origin": label}]] + + +def make_torch_image(width: int, height: int, value: float): + if torch is None: + raise RuntimeError("torch is required for this helper") + return torch.full((1, height, width, 3), float(value), dtype=torch.float32) + + +def make_torch_mask(width: int, height: int, box): + if torch is None: + raise RuntimeError("torch is required for this helper") + x1, y1, x2, y2 = box + mask = torch.zeros((1, height, width), dtype=torch.float32) + mask[:, y1:y2, x1:x2] = 1.0 + return mask diff --git a/tests/test_pro_edit_prepare_flux.py b/tests/test_pro_edit_prepare_flux.py new file mode 100644 index 0000000..d4805d1 --- /dev/null +++ b/tests/test_pro_edit_prepare_flux.py @@ -0,0 +1,124 @@ +import unittest + +try: + from .test_pro_edit_helpers import ( + FakeMask, + FakeModel, + FakeTensor, + FakeVAE, + load_plugin_package, + make_conditioning, + ) +except ImportError: + from test_pro_edit_helpers import ( + FakeMask, + FakeModel, + FakeTensor, + FakeVAE, + load_plugin_package, + make_conditioning, + ) + + +class TestProEditPrepareFlux(unittest.TestCase): + def setUp(self): + plugin = load_plugin_package() + node_cls = plugin.NODE_CLASS_MAPPINGS["LLSProImageEditPrepare"] + self.node = node_cls() + + def test_prepare_region_routes_to_flux_backend(self): + image = FakeTensor((1, 1024, 1024, 3), label="image") + mask = FakeMask((1, 1024, 1024), mask_bbox=(300, 300, 724, 724), mask_area_ratio=0.18) + vae = FakeVAE(latent_channels=16, downscale_ratio=16) + model = FakeModel( + family="FLUX_DEV", + model_role="edit", + supports_inpaint_native=False, + supports_image_edit_native=True, + preferred_edit_backend="flux", + ) + + latent, work_image, work_mask, edit_info, recommended, positive, negative = self.node.prepare( + image=image, + mask=mask, + vae=vae, + positive=make_conditioning("positive"), + negative=make_conditioning("negative"), + backend_mode="auto", + edit_scope="region", + mask_grow=8, + mask_blur=4.0, + mask_threshold=0.5, + invert_mask=False, + crop_context=64, + crop_context_factor=1.5, + min_size=512, + max_size=1024, + resize_mode="fit", + expand_left=0, + expand_right=0, + expand_top=0, + expand_bottom=0, + canvas_fill="edge", + auto_recommend="enabled", + model=model, + model_info=None, + ) + + self.assertEqual(edit_info["backend_name"], "flux") + self.assertEqual(edit_info["model_family"], "FLUX_DEV") + self.assertTrue(edit_info["supports_image_edit_native"]) + self.assertIn("concat_latent_image", positive[0][1]) + self.assertIn("concat_mask", positive[0][1]) + self.assertEqual(positive[0][1]["edit_backend"], "flux") + self.assertEqual(latent["source"], "pro_edit_prepare_region") + self.assertGreater(recommended, 0.0) + + def test_prepare_canvas_preserves_canvas_geometry_for_flux(self): + image = FakeTensor((1, 768, 768, 3), label="image") + mask = FakeMask((1, 768, 768), mask_bbox=(100, 100, 300, 300), mask_area_ratio=0.07) + vae = FakeVAE(latent_channels=16, downscale_ratio=16) + model = FakeModel( + family="FLUX_DEV", + model_role="fill", + supports_inpaint_native=False, + supports_image_edit_native=True, + preferred_edit_backend="flux", + ) + + latent, work_image, work_mask, edit_info, recommended, positive, negative = self.node.prepare( + image=image, + mask=mask, + vae=vae, + positive=make_conditioning("positive"), + negative=make_conditioning("negative"), + backend_mode="auto", + edit_scope="canvas", + mask_grow=0, + mask_blur=0.0, + mask_threshold=0.5, + invert_mask=False, + crop_context=64, + crop_context_factor=1.0, + min_size=512, + max_size=1024, + resize_mode="fit", + expand_left=64, + expand_right=64, + expand_top=32, + expand_bottom=32, + canvas_fill="neutral", + auto_recommend="enabled", + model=model, + model_info=None, + ) + + self.assertEqual(edit_info["edit_scope"], "canvas") + self.assertEqual(edit_info["original_box_in_canvas"], [64, 32, 832, 800]) + self.assertEqual(edit_info["work_size"], [896, 832]) + self.assertEqual(positive[0][1]["edit_backend"], "flux") + self.assertGreater(recommended, 0.0) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_pro_edit_prepare_sdxl.py b/tests/test_pro_edit_prepare_sdxl.py new file mode 100644 index 0000000..682f879 --- /dev/null +++ b/tests/test_pro_edit_prepare_sdxl.py @@ -0,0 +1,129 @@ +import unittest + +try: + from .test_pro_edit_helpers import ( + FakeMask, + FakeModel, + FakeTensor, + FakeVAE, + load_plugin_package, + make_conditioning, + ) +except ImportError: + from test_pro_edit_helpers import ( + FakeMask, + FakeModel, + FakeTensor, + FakeVAE, + load_plugin_package, + make_conditioning, + ) + + +class TestProEditPrepareSDXL(unittest.TestCase): + def setUp(self): + plugin = load_plugin_package() + node_cls = plugin.NODE_CLASS_MAPPINGS["LLSProImageEditPrepare"] + self.node = node_cls() + + def test_prepare_region_adds_concat_conditioning_for_sdxl(self): + image = FakeTensor((1, 128, 128, 3), label="image") + mask = FakeMask((1, 128, 128), mask_bbox=(32, 32, 96, 96), mask_area_ratio=0.25) + vae = FakeVAE(latent_channels=4, downscale_ratio=8) + model = FakeModel( + family="SDXL", + model_role="inpaint", + supports_inpaint_native=True, + supports_image_edit_native=False, + preferred_edit_backend="sdxl", + ) + + latent, work_image, work_mask, edit_info, recommended, positive, negative = self.node.prepare( + image=image, + mask=mask, + vae=vae, + positive=make_conditioning("positive"), + negative=make_conditioning("negative"), + backend_mode="auto", + edit_scope="region", + mask_grow=8, + mask_blur=4.0, + mask_threshold=0.5, + invert_mask=False, + crop_context=64, + crop_context_factor=1.5, + min_size=256, + max_size=1024, + resize_mode="fit", + expand_left=0, + expand_right=0, + expand_top=0, + expand_bottom=0, + canvas_fill="edge", + auto_recommend="enabled", + model=model, + model_info=None, + ) + + self.assertEqual(edit_info["backend_name"], "sdxl") + self.assertEqual(edit_info["routing_reason"], "model.preferred_edit_backend") + self.assertEqual(edit_info["edit_scope"], "region") + self.assertEqual(latent["source"], "pro_edit_prepare_region") + self.assertIn("concat_latent_image", positive[0][1]) + self.assertIn("concat_mask", positive[0][1]) + self.assertIn("concat_latent_image", negative[0][1]) + self.assertIn("concat_mask", negative[0][1]) + self.assertEqual(work_image.shape, image.shape) + self.assertEqual(work_mask.shape, mask.shape) + self.assertGreater(recommended, 0.0) + + def test_prepare_crop_writes_crop_geometry(self): + image = FakeTensor((1, 1024, 1024, 3), label="image") + mask = FakeMask((1, 1024, 1024), mask_bbox=(128, 128, 256, 256), mask_area_ratio=0.02) + vae = FakeVAE(latent_channels=4, downscale_ratio=8) + model = FakeModel( + family="SDXL", + model_role="edit", + supports_inpaint_native=True, + supports_image_edit_native=True, + preferred_edit_backend="sdxl", + ) + + latent, work_image, work_mask, edit_info, recommended, positive, negative = self.node.prepare( + image=image, + mask=mask, + vae=vae, + positive=make_conditioning("positive"), + negative=make_conditioning("negative"), + backend_mode="auto", + edit_scope="crop", + mask_grow=0, + mask_blur=0.0, + mask_threshold=0.5, + invert_mask=False, + crop_context=32, + crop_context_factor=1.0, + min_size=256, + max_size=512, + resize_mode="fit", + expand_left=0, + expand_right=0, + expand_top=0, + expand_bottom=0, + canvas_fill="edge", + auto_recommend="enabled", + model=model, + model_info=None, + ) + + self.assertEqual(edit_info["edit_scope"], "crop") + self.assertIsInstance(edit_info["crop_box"], list) + self.assertEqual(edit_info["work_size"], [work_image.shape[2], work_image.shape[1]]) + self.assertEqual(edit_info["backend_name"], "sdxl") + self.assertEqual(positive[0][1]["edit_backend"], "sdxl") + self.assertEqual(negative[0][1]["edit_backend"], "sdxl") + self.assertGreater(recommended, 0.0) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_pro_edit_registration.py b/tests/test_pro_edit_registration.py new file mode 100644 index 0000000..a56bb26 --- /dev/null +++ b/tests/test_pro_edit_registration.py @@ -0,0 +1,156 @@ +import unittest + +try: + from .test_pro_edit_helpers import import_plugin_submodule, load_plugin_package +except ImportError: + from test_pro_edit_helpers import import_plugin_submodule, load_plugin_package + + +class TestProEditRegistration(unittest.TestCase): + def test_plugin_registers_pro_edit_nodes(self): + plugin = load_plugin_package() + + self.assertIn("LLSProImageEditPrepare", plugin.NODE_CLASS_MAPPINGS) + self.assertIn("LLSProKSamplerBridge", plugin.NODE_CLASS_MAPPINGS) + self.assertIn("LLSProImageEditFinish", plugin.NODE_CLASS_MAPPINGS) + self.assertEqual( + plugin.NODE_DISPLAY_NAME_MAPPINGS["LLSProImageEditPrepare"], + "LLS Pro Image Edit Prepare", + ) + self.assertEqual( + plugin.NODE_DISPLAY_NAME_MAPPINGS["LLSProKSamplerBridge"], + "LLS Pro KSampler Bridge", + ) + self.assertEqual( + plugin.NODE_DISPLAY_NAME_MAPPINGS["LLSProImageEditFinish"], + "LLS Pro Image Edit Finish", + ) + + def test_prepare_node_schema_matches_contract(self): + plugin = load_plugin_package() + node_cls = plugin.NODE_CLASS_MAPPINGS["LLSProImageEditPrepare"] + schema = node_cls.INPUT_TYPES() + required = schema["required"] + optional = schema["optional"] + + self.assertEqual(node_cls.CATEGORY, "LLS/Image Edit") + self.assertEqual(node_cls.FUNCTION, "prepare") + self.assertEqual( + node_cls.RETURN_TYPES, + ("LATENT", "IMAGE", "MASK", "LLS_EDIT_INFO", "FLOAT", "CONDITIONING", "CONDITIONING"), + ) + self.assertEqual( + node_cls.RETURN_NAMES, + ("latent", "work_image", "work_mask", "edit_info", "recommended_denoise", "positive", "negative"), + ) + self.assertEqual( + set(required), + { + "image", + "mask", + "vae", + "positive", + "negative", + "backend_mode", + "edit_scope", + "mask_grow", + "mask_blur", + "mask_threshold", + "invert_mask", + "crop_context", + "crop_context_factor", + "min_size", + "max_size", + "resize_mode", + "expand_left", + "expand_right", + "expand_top", + "expand_bottom", + "canvas_fill", + "auto_recommend", + }, + ) + self.assertEqual(required["image"], ("IMAGE",)) + self.assertEqual(required["mask"], ("MASK",)) + self.assertEqual(required["vae"], ("VAE",)) + self.assertEqual(required["positive"], ("CONDITIONING",)) + self.assertEqual(required["negative"], ("CONDITIONING",)) + self.assertEqual(required["backend_mode"][0], ["auto", "sdxl", "flux"]) + self.assertEqual(required["edit_scope"][0], ["auto", "region", "crop", "canvas"]) + self.assertEqual(optional["model"], ("MODEL",)) + self.assertEqual(optional["model_info"], ("STRING",)) + + def test_bridge_and_finish_schemas_match_contract(self): + plugin = load_plugin_package() + + bridge_cls = plugin.NODE_CLASS_MAPPINGS["LLSProKSamplerBridge"] + bridge_schema = bridge_cls.INPUT_TYPES() + bridge_required = bridge_schema["required"] + bridge_optional = bridge_schema["optional"] + self.assertEqual(bridge_cls.CATEGORY, "LLS/Image Edit") + self.assertEqual(bridge_cls.FUNCTION, "sample") + self.assertEqual(bridge_cls.RETURN_TYPES, ("LATENT", "STRING")) + self.assertEqual(bridge_cls.RETURN_NAMES, ("latent", "sample_info")) + self.assertEqual( + set(bridge_required), + { + "model", + "positive", + "negative", + "latent_image", + "backend_mode", + "quality_preset", + "seed", + "steps", + "cfg", + "sampler_name", + "scheduler", + "denoise", + "denoise_mode", + "flux_guidance", + "model_family", + }, + ) + self.assertEqual(bridge_required["backend_mode"][0], ["auto", "sdxl", "flux"]) + self.assertEqual(bridge_required["denoise_mode"][0], ["manual", "auto_from_edit"]) + self.assertEqual(bridge_optional["edit_info"], ("LLS_EDIT_INFO",)) + self.assertEqual(bridge_optional["model_info"], ("STRING",)) + + finish_cls = plugin.NODE_CLASS_MAPPINGS["LLSProImageEditFinish"] + finish_schema = finish_cls.INPUT_TYPES() + finish_required = finish_schema["required"] + finish_optional = finish_schema["optional"] + self.assertEqual(finish_cls.CATEGORY, "LLS/Image Edit") + self.assertEqual(finish_cls.FUNCTION, "finish") + self.assertEqual(finish_cls.RETURN_TYPES, ("IMAGE", "IMAGE")) + self.assertEqual(finish_cls.RETURN_NAMES, ("final_image", "preview_image")) + self.assertEqual(finish_required["original_image"], ("IMAGE",)) + self.assertEqual(finish_required["generated_image"], ("IMAGE",)) + self.assertEqual(finish_required["edit_info"], ("LLS_EDIT_INFO",)) + self.assertEqual(finish_optional["work_mask"], ("MASK",)) + self.assertEqual(finish_optional["sample_info"], ("STRING",)) + + def test_backend_registry_exposes_built_in_scaffolds(self): + plugin = load_plugin_package() + registry = import_plugin_submodule(plugin, "pro_edit.backends.registry") + + self.assertEqual(registry.get_backend("sdxl").backend_name, "sdxl") + self.assertEqual(registry.get_backend("flux").backend_name, "flux") + + def test_normalize_edit_info_accepts_jsonish_string_payloads(self): + plugin = load_plugin_package() + utils = import_plugin_submodule(plugin, "pro_edit.pro_edit_utils") + + info = utils.normalize_edit_info('{"backend_name":"sdxl","edit_scope":"crop"}') + self.assertEqual(info["backend_name"], "sdxl") + self.assertEqual(info["edit_scope"], "crop") + self.assertEqual(info["edit_payload_version"], "1.0") + + legacy_info = utils.normalize_edit_info("backend_name=flux|edit_scope=canvas") + self.assertEqual(legacy_info["backend_name"], "flux") + self.assertEqual(legacy_info["edit_scope"], "canvas") + self.assertEqual(legacy_info["edit_payload_version"], "1.0") + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_pro_edit_registry.py b/tests/test_pro_edit_registry.py new file mode 100644 index 0000000..ba21475 --- /dev/null +++ b/tests/test_pro_edit_registry.py @@ -0,0 +1,87 @@ +import unittest + +try: + from .test_pro_edit_helpers import FakeModel, import_plugin_submodule, load_plugin_package +except ImportError: + from test_pro_edit_helpers import FakeModel, import_plugin_submodule, load_plugin_package + + +class TestProEditRegistry(unittest.TestCase): + def setUp(self): + self.plugin = load_plugin_package() + self.registry = import_plugin_submodule(self.plugin, "pro_edit.backends.registry") + + def test_auto_routes_sdxl_inpaint_model(self): + model = FakeModel( + family="SDXL", + model_role="inpaint", + supports_inpaint_native=True, + supports_image_edit_native=False, + preferred_edit_backend="sdxl", + ) + + backend, routing = self.registry.resolve_backend("auto", model=model) + + self.assertEqual(backend.backend_name, "sdxl") + self.assertEqual(routing.backend_name, "sdxl") + self.assertEqual(routing.routing_reason, "model.preferred_edit_backend") + + def test_auto_routes_flux_edit_model(self): + model = FakeModel( + family="FLUX_DEV", + model_role="edit", + supports_inpaint_native=False, + supports_image_edit_native=True, + preferred_edit_backend="flux", + ) + + backend, routing = self.registry.resolve_backend("auto", model=model) + + self.assertEqual(backend.backend_name, "flux") + self.assertEqual(routing.routing_reason, "model.preferred_edit_backend") + self.assertEqual(routing.capabilities["model_family"], "FLUX_DEV") + + def test_auto_reuses_backend_name_from_edit_info(self): + backend, routing = self.registry.resolve_backend( + "auto", + model=None, + edit_info={ + "backend_name": "sdxl", + "model_family": "SDXL", + "model_role": "inpaint", + "supports_inpaint_native": True, + "supports_image_edit_native": False, + "preferred_edit_backend": "sdxl", + }, + ) + + self.assertEqual(backend.backend_name, "sdxl") + self.assertEqual(routing.routing_reason, "edit_info.backend_name") + + def test_manual_flux_override_rejects_sdxl_only_model(self): + model = FakeModel( + family="SDXL", + model_role="inpaint", + supports_inpaint_native=True, + supports_image_edit_native=False, + preferred_edit_backend="sdxl", + ) + + with self.assertRaisesRegex(RuntimeError, "backend 'flux' is incompatible"): + self.registry.resolve_backend("flux", model=model) + + def test_auto_without_matching_backend_raises_clear_error(self): + model = FakeModel( + family="SD1.5", + model_role="base", + supports_inpaint_native=False, + supports_image_edit_native=False, + preferred_edit_backend=None, + ) + + with self.assertRaisesRegex(RuntimeError, "No professional edit backend matched"): + self.registry.resolve_backend("auto", model=model) + + +if __name__ == "__main__": + unittest.main() diff --git a/utils/model_info.py b/utils/model_info.py index fc1cc8c..ad0b602 100644 --- a/utils/model_info.py +++ b/utils/model_info.py @@ -193,6 +193,15 @@ SAMPLING_PRESETS: dict[str, dict[str, dict[str, float | int | None]]] = { } _LEGACY_MODEL_INFO_PATTERN = re.compile(r"(\w+)=([^|]+)") +_ROLE_KEYWORDS = ( + ("inpaint", "inpaint"), + ("img2img", "edit"), + ("imageedit", "edit"), + ("image-edit", "edit"), + ("edit", "edit"), + ("fill", "fill"), + ("refiner", "refiner"), +) def canonicalize_family(family: str | None) -> str: @@ -277,20 +286,87 @@ def _coerce_scalar(value: Any) -> Any: return value +def _coerce_bool(value: Any) -> bool: + coerced = _coerce_scalar(value) + if isinstance(coerced, str): + return bool(coerced) + return bool(coerced) + + +def _get_model_name_alias_value(info: dict[str, Any]) -> Any: + return ( + info.get("checkpoint_name") + or info.get("model_name") + or info.get("ckpt_name") + or info.get("ckpt") + ) + + +def infer_model_role_from_name(model_name: str | None, family: str | None = None) -> str: + del family + name = str(model_name or "").lower() + for needle, role in _ROLE_KEYWORDS: + if needle in name: + return role + return "base" + + +def infer_edit_capabilities(model_name: str | None, family: str | None = None) -> dict[str, Any]: + resolved_family = canonicalize_family(family or infer_family_from_name(model_name, "SD1.5")) + role = infer_model_role_from_name(model_name, resolved_family) + + supports_inpaint_native = False + supports_image_edit_native = False + preferred_edit_backend = None + + if is_sdxl_family(resolved_family): + preferred_edit_backend = "sdxl" if role in {"inpaint", "edit", "fill"} else None + supports_inpaint_native = role in {"inpaint", "edit", "fill"} + supports_image_edit_native = role in {"edit", "fill"} + elif is_flux_family(resolved_family): + preferred_edit_backend = "flux" if role in {"inpaint", "edit", "fill"} else None + supports_inpaint_native = role == "inpaint" + supports_image_edit_native = role in {"inpaint", "edit", "fill"} + + return { + "model_role": role, + "supports_inpaint_native": supports_inpaint_native, + "supports_image_edit_native": supports_image_edit_native, + "preferred_edit_backend": preferred_edit_backend, + } + + def parse_model_info(model_info: dict[str, Any] | str | None) -> dict[str, Any]: raw = parse_jsonish_info(model_info) + raw_model_name = _get_model_name_alias_value(raw) family = canonicalize_family( raw.get("family") + or raw.get("model_family") or infer_family_from_name( - raw.get("checkpoint_name") or raw.get("model_name") or raw.get("ckpt_name") or raw.get("ckpt"), + raw_model_name, "SD1.5", ) ) defaults = get_family_defaults(family) + capability_defaults = infer_edit_capabilities( + raw_model_name, + family, + ) info: dict[str, Any] = {**defaults} info.update(raw) info["family"] = family + info["model_role"] = str(raw.get("model_role", capability_defaults["model_role"])) + info["supports_inpaint_native"] = _coerce_bool( + raw.get("supports_inpaint_native", capability_defaults["supports_inpaint_native"]) + ) + info["supports_image_edit_native"] = _coerce_bool( + raw.get("supports_image_edit_native", capability_defaults["supports_image_edit_native"]) + ) + info["preferred_edit_backend"] = raw.get( + "preferred_edit_backend", + capability_defaults["preferred_edit_backend"], + ) info["text_encoder_type"] = raw.get("text_encoder_type", defaults["text_encoder_type"]) required_text_encoders = raw.get("required_text_encoders", defaults["required_text_encoders"]) if isinstance(required_text_encoders, str): @@ -324,8 +400,9 @@ def parse_model_info(model_info: dict[str, Any] | str | None) -> dict[str, Any]: f"{info['default_width']}x{info['default_height']}", ) - info.setdefault("checkpoint_name", raw.get("checkpoint_name") or raw.get("model_name") or "") + info.setdefault("checkpoint_name", raw_model_name or "") info.setdefault("model_name", info["checkpoint_name"]) + info["model_family"] = family info.setdefault("vae_source", raw.get("vae_source", "auto")) info.setdefault("text_encoder_source", raw.get("text_encoder_source", "auto")) info.setdefault("load_mode", raw.get("load_mode", "simple")) @@ -436,6 +513,63 @@ def get_lls_attr(obj, name: str, default: Any = None) -> Any: return getattr(obj, attr_name, default) +def resolve_edit_capabilities(model=None, model_info: dict[str, Any] | str | None = None) -> dict[str, Any]: + raw = parse_jsonish_info(model_info) + info = parse_model_info(model_info) + raw_model_name = _get_model_name_alias_value(raw) + model_name = str( + raw_model_name + or info.get("checkpoint_name") + or info.get("model_name") + or get_lls_attr(model, "model_name", "") + or "" + ) + family = canonicalize_family( + raw.get("family") + or raw.get("model_family") + or get_lls_attr(model, "family", None) + or info.get("family") + or info.get("model_family") + or infer_family_from_model(model) + ) + inferred = infer_edit_capabilities(model_name, family) + return { + "model_family": family, + "model_name": model_name, + "model_role": str( + raw.get("model_role") + or get_lls_attr(model, "model_role", None) + or info.get("model_role") + or inferred["model_role"] + ), + "supports_inpaint_native": _coerce_bool( + raw.get( + "supports_inpaint_native", + get_lls_attr( + model, + "supports_inpaint_native", + info.get("supports_inpaint_native", inferred["supports_inpaint_native"]), + ), + ) + ), + "supports_image_edit_native": _coerce_bool( + raw.get( + "supports_image_edit_native", + get_lls_attr( + model, + "supports_image_edit_native", + info.get("supports_image_edit_native", inferred["supports_image_edit_native"]), + ), + ) + ), + "preferred_edit_backend": ( + raw.get("preferred_edit_backend") + or get_lls_attr(model, "preferred_edit_backend", inferred["preferred_edit_backend"]) + or info.get("preferred_edit_backend") + ), + } + + def resolve_model_family( model_family: str | None = None, *, diff --git a/测试工作流.json b/测试工作流.json index 1edd363..6305a5d 100644 --- a/测试工作流.json +++ b/测试工作流.json @@ -1,8 +1,8 @@ { "id": "196d3f49-c541-4fcd-b800-ad54aecdf346", "revision": 0, - "last_node_id": 50, - "last_link_id": 62, + "last_node_id": 53, + "last_link_id": 79, "nodes": [ { "id": 28, @@ -16,7 +16,7 @@ 46 ], "flags": {}, - "order": 16, + "order": 15, "mode": 4, "inputs": [ { @@ -58,7 +58,7 @@ 198 ], "flags": {}, - "order": 18, + "order": 17, "mode": 4, "inputs": [ { @@ -107,7 +107,7 @@ 331.33331298828125 ], "flags": {}, - "order": 20, + "order": 19, "mode": 4, "inputs": [ { @@ -199,7 +199,7 @@ 238 ], "flags": {}, - "order": 8, + "order": 7, "mode": 4, "inputs": [ { @@ -310,7 +310,7 @@ 534 ], "flags": {}, - "order": 9, + "order": 8, "mode": 4, "inputs": [ { @@ -361,7 +361,7 @@ "(no qwen image edit models found)", "", 20, - 661366653803666, + 73621935042331, "randomize", "", 4, @@ -387,7 +387,7 @@ 230 ], "flags": {}, - "order": 7, + "order": 6, "mode": 4, "inputs": [ { @@ -448,7 +448,7 @@ 446 ], "flags": {}, - "order": 12, + "order": 11, "mode": 4, "inputs": [ { @@ -505,7 +505,7 @@ }, "widgets_values": [ "Family Default", - 887303760569149, + 824390357419058, "randomize", 20, 7, @@ -531,7 +531,7 @@ 498 ], "flags": {}, - "order": 10, + "order": 9, "mode": 4, "inputs": [ { @@ -567,7 +567,7 @@ 1024, 1024, 20, - 236805929055807, + 254907844528138, "randomize", 1, "", @@ -633,7 +633,7 @@ "flags": { "collapsed": false }, - "order": 13, + "order": 12, "mode": 4, "inputs": [ { @@ -691,77 +691,6 @@ 1 ] }, - { - "id": 47, - "type": "VAEDecode", - "pos": [ - 1825.9733979514097, - 1053.3741446628553 - ], - "size": [ - 140, - 46 - ], - "flags": {}, - "order": 19, - "mode": 0, - "inputs": [ - { - "name": "samples", - "type": "LATENT", - "link": 56 - }, - { - "name": "vae", - "type": "VAE", - "link": 57 - } - ], - "outputs": [ - { - "name": "IMAGE", - "type": "IMAGE", - "links": [ - 58 - ] - } - ], - "properties": { - "cnr_id": "comfy-core", - "ver": "0.21.0", - "Node name for S&R": "VAEDecode" - }, - "widgets_values": [] - }, - { - "id": 48, - "type": "PreviewImage", - "pos": [ - 2028.5545027912408, - 1063.1862769831923 - ], - "size": [ - 140, - 26 - ], - "flags": {}, - "order": 21, - "mode": 0, - "inputs": [ - { - "name": "images", - "type": "IMAGE", - "link": 58 - } - ], - "outputs": [], - "properties": { - "cnr_id": "comfy-core", - "ver": "0.21.0", - "Node name for S&R": "PreviewImage" - }, - "widgets_values": [] - }, { "id": 33, "type": "PreviewImage", @@ -774,7 +703,7 @@ 246 ], "flags": {}, - "order": 14, + "order": 13, "mode": 4, "inputs": [ { @@ -791,113 +720,6 @@ }, "widgets_values": [] }, - { - "id": 41, - "type": "LLSSimpleRepairFinish", - "pos": [ - 1834.253738230468, - 700.4561136191923 - ], - "size": [ - 298.25, - 286 - ], - "flags": {}, - "order": 4, - "mode": 0, - "inputs": [ - { - "name": "original_image", - "type": "IMAGE", - "link": null - }, - { - "name": "generated_image", - "type": "IMAGE", - "link": null - }, - { - "name": "repair_info", - "type": "LLS_REPAIR_INFO", - "link": null - }, - { - "name": "work_mask", - "shape": 7, - "type": "MASK", - "link": null - } - ], - "outputs": [ - { - "name": "final_image", - "type": "IMAGE", - "links": null - }, - { - "name": "preview_image", - "type": "IMAGE", - "links": null - } - ], - "properties": { - "aux_id": "Gin3601/LLS-node", - "ver": "3fb0ca37ca5c434470439faee80ef4248f133d92", - "Node name for S&R": "LLSSimpleRepairFinish" - }, - "widgets_values": [ - 8, - "disabled", - "enabled", - 1, - true, - "soft", - "final", - "" - ] - }, - { - "id": 42, - "type": "LoadImage", - "pos": [ - 836.8916577534524, - 694.5567621363803 - ], - "size": [ - 270, - 314 - ], - "flags": {}, - "order": 5, - "mode": 0, - "inputs": [], - "outputs": [ - { - "name": "IMAGE", - "type": "IMAGE", - "links": [ - 46 - ] - }, - { - "name": "MASK", - "type": "MASK", - "links": [ - 59 - ] - } - ], - "properties": { - "cnr_id": "comfy-core", - "ver": "0.21.0", - "Node name for S&R": "LoadImage", - "image": "clipspace/clipspace-painted-masked-1779342854818.png [input]" - }, - "widgets_values": [ - "clipspace/clipspace-painted-masked-1779342854818.png [input]", - "image" - ] - }, { "id": 45, "type": "LLSSimpleCheckpointLoader", @@ -910,7 +732,7 @@ 286 ], "flags": {}, - "order": 6, + "order": 4, "mode": 0, "inputs": [], "outputs": [ @@ -918,7 +740,7 @@ "name": "model", "type": "MODEL", "links": [ - 52 + 68 ] }, { @@ -932,8 +754,8 @@ "name": "vae", "type": "VAE", "links": [ - 49, - 57 + 57, + 74 ] }, { @@ -960,6 +782,160 @@ "(auto)" ] }, + { + "id": 53, + "type": "LLSSimpleKSampler", + "pos": [ + 1477.1235660918176, + 761.5080260487256 + ], + "size": [ + 270, + 446 + ], + "flags": {}, + "order": 16, + "mode": 0, + "inputs": [ + { + "name": "model", + "type": "MODEL", + "link": 68 + }, + { + "name": "positive", + "type": "CONDITIONING", + "link": 66 + }, + { + "name": "negative", + "type": "CONDITIONING", + "link": 67 + }, + { + "name": "latent_image", + "type": "LATENT", + "link": 64 + }, + { + "name": "repair_info", + "shape": 7, + "type": "LLS_REPAIR_INFO", + "link": 65 + }, + { + "name": "guidance_stack", + "shape": 7, + "type": "LLS_GUIDANCE_STACK", + "link": null + } + ], + "outputs": [ + { + "name": "latent", + "type": "LATENT", + "links": [ + 71 + ] + }, + { + "name": "sample_info", + "type": "STRING", + "links": null + } + ], + "properties": { + "aux_id": "Gin3601/LLS-node", + "ver": "97e12a83f4960e208530371f33130efc2c3cf653", + "Node name for S&R": "LLSSimpleKSampler" + }, + "widgets_values": [ + "Family Default", + 357137765793340, + "randomize", + 20, + 7, + "euler_ancestral", + "karras", + 1, + "manual", + "auto", + 3.5, + "Auto", + "" + ] + }, + { + "id": 47, + "type": "VAEDecode", + "pos": [ + 1804.7612397482847, + 1043.0710773688215 + ], + "size": [ + 140, + 46 + ], + "flags": {}, + "order": 18, + "mode": 0, + "inputs": [ + { + "name": "samples", + "type": "LATENT", + "link": 71 + }, + { + "name": "vae", + "type": "VAE", + "link": 57 + } + ], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 77 + ] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.21.0", + "Node name for S&R": "VAEDecode" + }, + "widgets_values": [] + }, + { + "id": 48, + "type": "PreviewImage", + "pos": [ + 1981.2817755185138, + 1058.9438342452663 + ], + "size": [ + 140, + 246 + ], + "flags": {}, + "order": 21, + "mode": 0, + "inputs": [ + { + "name": "images", + "type": "IMAGE", + "link": 78 + } + ], + "outputs": [], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.21.0", + "Node name for S&R": "PreviewImage" + }, + "widgets_values": [] + }, { "id": 44, "type": "LLSSimplePromptEncode", @@ -972,7 +948,7 @@ 230 ], "flags": {}, - "order": 11, + "order": 10, "mode": 0, "inputs": [ { @@ -993,14 +969,14 @@ "name": "positive", "type": "CONDITIONING", "links": [ - 50 + 72 ] }, { "name": "negative", "type": "CONDITIONING", "links": [ - 51 + 73 ] }, { @@ -1015,53 +991,163 @@ "Node name for S&R": "LLSSimplePromptEncode" }, "widgets_values": [ - "在桌子上面添加一只猫", + "在桌子上面趴着一只猫", "", -1, "Auto" ] }, { - "id": 40, + "id": 51, + "type": "LoadImage", + "pos": [ + 817.6828761221636, + 719.3427673101053 + ], + "size": [ + 270, + 314 + ], + "flags": {}, + "order": 5, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 69, + 76 + ] + }, + { + "name": "MASK", + "type": "MASK", + "links": [ + 75 + ] + } + ], + "properties": { + "cnr_id": "comfy-core", + "ver": "0.21.0", + "Node name for S&R": "LoadImage", + "image": "clipspace/clipspace-painted-masked-1779344083902.png [input]" + }, + "widgets_values": [ + "clipspace/clipspace-painted-masked-1779344083902.png [input]", + "image" + ] + }, + { + "id": 41, + "type": "LLSSimpleRepairFinish", + "pos": [ + 1788.7991927759224, + 693.183414089718 + ], + "size": [ + 298.25, + 286 + ], + "flags": {}, + "order": 20, + "mode": 0, + "inputs": [ + { + "name": "original_image", + "type": "IMAGE", + "link": 76 + }, + { + "name": "generated_image", + "type": "IMAGE", + "link": 77 + }, + { + "name": "repair_info", + "type": "LLS_REPAIR_INFO", + "link": 79 + }, + { + "name": "work_mask", + "shape": 7, + "type": "MASK", + "link": null + } + ], + "outputs": [ + { + "name": "final_image", + "type": "IMAGE", + "links": [ + 78 + ] + }, + { + "name": "preview_image", + "type": "IMAGE", + "links": null + } + ], + "properties": { + "aux_id": "Gin3601/LLS-node", + "ver": "3fb0ca37ca5c434470439faee80ef4248f133d92", + "Node name for S&R": "LLSSimpleRepairFinish" + }, + "widgets_values": [ + 8, + "disabled", + "enabled", + 1, + true, + "soft", + "final", + "" + ] + }, + { + "id": 52, "type": "LLSSimpleRepairPrepare", "pos": [ - 1146.7837663241448, - 689.6573348009558 + 1142.0162501456011, + 713.3427673101053 ], "size": [ 286.025390625, 610 ], "flags": {}, - "order": 15, + "order": 14, "mode": 0, "inputs": [ { "name": "image", "type": "IMAGE", - "link": 46 + "link": 69 }, { "name": "mask", "type": "MASK", - "link": 59 + "link": 75 }, { "name": "vae", "type": "VAE", - "link": 49 + "link": 74 }, { "name": "positive", "shape": 7, "type": "CONDITIONING", - "link": 50 + "link": 72 }, { "name": "negative", "shape": 7, "type": "CONDITIONING", - "link": 51 + "link": 73 } ], "outputs": [ @@ -1069,7 +1155,7 @@ "name": "latent", "type": "LATENT", "links": [ - 53 + 64 ] }, { @@ -1086,7 +1172,8 @@ "name": "repair_info", "type": "LLS_REPAIR_INFO", "links": [ - 60 + 65, + 79 ] }, { @@ -1098,20 +1185,20 @@ "name": "positive", "type": "CONDITIONING", "links": [ - 61 + 66 ] }, { "name": "negative", "type": "CONDITIONING", "links": [ - 62 + 67 ] } ], "properties": { "aux_id": "Gin3601/LLS-node", - "ver": "3fb0ca37ca5c434470439faee80ef4248f133d92", + "ver": "97e12a83f4960e208530371f33130efc2c3cf653", "Node name for S&R": "LLSSimpleRepairPrepare" }, "widgets_values": [ @@ -1135,89 +1222,6 @@ "enabled", "" ] - }, - { - "id": 46, - "type": "LLSSimpleKSampler", - "pos": [ - 1501.7649997487804, - 716.6535325802862 - ], - "size": [ - 270, - 446 - ], - "flags": {}, - "order": 17, - "mode": 0, - "inputs": [ - { - "name": "model", - "type": "MODEL", - "link": 52 - }, - { - "name": "positive", - "type": "CONDITIONING", - "link": 61 - }, - { - "name": "negative", - "type": "CONDITIONING", - "link": 62 - }, - { - "name": "latent_image", - "type": "LATENT", - "link": 53 - }, - { - "name": "repair_info", - "shape": 7, - "type": "LLS_REPAIR_INFO", - "link": 60 - }, - { - "name": "guidance_stack", - "shape": 7, - "type": "LLS_GUIDANCE_STACK", - "link": null - } - ], - "outputs": [ - { - "name": "latent", - "type": "LATENT", - "links": [ - 56 - ] - }, - { - "name": "sample_info", - "type": "STRING", - "links": null - } - ], - "properties": { - "aux_id": "Gin3601/LLS-node", - "ver": "3e97537f543c74db0e91a840d698105c5f269931", - "Node name for S&R": "LLSSimpleKSampler" - }, - "widgets_values": [ - "Family Default", - 887303760569149, - "randomize", - 20, - 7, - "euler_ancestral", - "karras", - 1, - 3.5, - "Auto", - 3.5, - "Auto", - "" - ] } ], "links": [ @@ -1357,14 +1361,6 @@ 1, "LLS_QWEN_LORA_STACK" ], - [ - 46, - 42, - 0, - 40, - 0, - "IMAGE" - ], [ 47, 45, @@ -1381,54 +1377,6 @@ 1, "CLIP" ], - [ - 49, - 45, - 2, - 40, - 2, - "VAE" - ], - [ - 50, - 44, - 0, - 40, - 3, - "CONDITIONING" - ], - [ - 51, - 44, - 1, - 40, - 4, - "CONDITIONING" - ], - [ - 52, - 45, - 0, - 46, - 0, - "MODEL" - ], - [ - 53, - 40, - 0, - 46, - 3, - "LATENT" - ], - [ - 56, - 46, - 0, - 47, - 0, - "LATENT" - ], [ 57, 45, @@ -1438,44 +1386,124 @@ "VAE" ], [ - 58, + 64, + 52, + 0, + 53, + 3, + "LATENT" + ], + [ + 65, + 52, + 3, + 53, + 4, + "LLS_REPAIR_INFO" + ], + [ + 66, + 52, + 5, + 53, + 1, + "CONDITIONING" + ], + [ + 67, + 52, + 6, + 53, + 2, + "CONDITIONING" + ], + [ + 68, + 45, + 0, + 53, + 0, + "MODEL" + ], + [ + 69, + 51, + 0, + 52, + 0, + "IMAGE" + ], + [ + 71, + 53, + 0, 47, 0, + "LATENT" + ], + [ + 72, + 44, + 0, + 52, + 3, + "CONDITIONING" + ], + [ + 73, + 44, + 1, + 52, + 4, + "CONDITIONING" + ], + [ + 74, + 45, + 2, + 52, + 2, + "VAE" + ], + [ + 75, + 51, + 1, + 52, + 1, + "MASK" + ], + [ + 76, + 51, + 0, + 41, + 0, + "IMAGE" + ], + [ + 77, + 47, + 0, + 41, + 1, + "IMAGE" + ], + [ + 78, + 41, + 0, 48, 0, "IMAGE" ], [ - 59, - 42, - 1, - 40, - 1, - "MASK" - ], - [ - 60, - 40, + 79, + 52, 3, - 46, - 4, - "LLS_REPAIR_INFO" - ], - [ - 61, - 40, - 5, - 46, - 1, - "CONDITIONING" - ], - [ - 62, - 40, - 6, - 46, + 41, 2, - "CONDITIONING" + "LLS_REPAIR_INFO" ] ], "groups": [ @@ -1509,8 +1537,8 @@ "bounding": [ 411.20928282019526, 632.5416781800909, - 1766.0618691203367, - 678.9584997049681 + 1766.7951576945552, + 711.2252111307494 ], "color": "#3f789e", "flags": {} @@ -1521,8 +1549,8 @@ "ds": { "scale": 1, "offset": [ - -271.34962416903875, - -294.342706274949 + -217.3755886004974, + -325.48968335536637 ] }, "frontendVersion": "1.43.18"