update

python_server/model/Inpaint/inpaint_loader.py

@@ -5,7 +5,8 @@ from __future__ import annotations
 
 当前支持：
 - SDXL Inpaint（diffusers AutoPipelineForInpainting）
-- ControlNet（占位，暂未统一封装）
+- ControlNet（Stable Diffusion + ControlNet Inpaint）
+- LaMa（torchscript big-lama，本地封装）
 """
 
 from dataclasses import dataclass
@@ -26,6 +27,7 @@ from config_loader import (
 class InpaintBackend(str, Enum):
     SDXL_INPAINT = "sdxl_inpaint"
     CONTROLNET = "controlnet"
+    LAMA = "lama"
 
 
 @dataclass
@@ -278,11 +280,13 @@ def _make_controlnet_predictor(_: UnifiedInpaintConfig):
             image_run = image
             mask_run = mask
 
-        # control image：使用 canny 边缘作为约束（最通用）
+        # control image：使用 Canny 边缘作为条件（更通用、更稳定）。
+        # 备注：当前项目的 ControlNet 权重未必是 inpaint 专用模型，
+        # 若直接把洞内置零作为条件，容易把输出“拉黑”。先回退到稳定方案。
         rgb = np.array(image_run, dtype=np.uint8)
         edges = cv2.Canny(rgb, 100, 200)
         edges3 = np.stack([edges, edges, edges], axis=-1)
-        control_image = Image.fromarray(edges3)
+        control_image = Image.fromarray(edges3, mode="RGB")
 
         if device == "cuda":
             try:
@@ -312,6 +316,37 @@ def _make_controlnet_predictor(_: UnifiedInpaintConfig):
     return _predict
 
 
+def _make_lama_predictor(cfg: UnifiedInpaintConfig):
+    import torch
+
+    app_cfg = load_app_config()
+    device = cfg.device
+    if device is None:
+        device = app_cfg.inpaint.device
+    device = "cuda" if device.startswith("cuda") and torch.cuda.is_available() else "cpu"
+
+    from .lama_torchscript import LaMaTorchscript
+
+    lama = LaMaTorchscript(device=device)
+
+    def _predict(
+        image: Image.Image,
+        mask: Image.Image,
+        _prompt: str,
+        _negative_prompt: str = "",
+        strength: float = 0.8,
+        guidance_scale: float = 7.5,
+        num_inference_steps: int = 30,
+        max_side: int = 1024,
+        **_kwargs,
+    ) -> Image.Image:
+        # LaMa 不使用 prompt/negative_prompt，也不使用 strength/steps 等扩散参数
+        _ = (strength, guidance_scale, num_inference_steps, max_side)
+        return lama(image, mask)
+
+    return _predict
+
+
 def build_inpaint_predictor(
     cfg: UnifiedInpaintConfig | None = None,
 ) -> tuple[Callable[..., Image.Image], InpaintBackend]:
@@ -326,6 +361,9 @@ def build_inpaint_predictor(
     if cfg.backend == InpaintBackend.CONTROLNET:
         return _make_controlnet_predictor(cfg), InpaintBackend.CONTROLNET
 
+    if cfg.backend == InpaintBackend.LAMA:
+        return _make_lama_predictor(cfg), InpaintBackend.LAMA
+
     raise ValueError(f"不支持的补全后端: {cfg.backend}")