update

client/gui/dialogs/BlackholeResolveDialog.cpp

@@ -25,14 +25,6 @@ QPushButton* makeAlgoButton(const QString& title, QWidget* parent) {
 
 }  // namespace
 
-QString BlackholeResolveDialog::defaultModelInpaintPrompt() {
-    return QStringLiteral(
-        "中国画局部，需去除人物并只补背景：请严格依据掩膜外已见的笔墨皴擦、设色层次、留白与纸绢肌理，"
-        "在掩膜内自然延展，补全为山石、坡岸、云水、苔点或空灵留白等，与外缘笔法、墨色与气韵连贯衔接；"
-        "禁止出现人物轮廓、肢体、衣褶、五官、手足、发丝、肤色、投影及去人后的模糊晕带、鬼影边缘与色块台阶，"
-        "整体须像原画从未画入人物一样自然，不留修补感。");
-}
-
 BlackholeResolveDialog::BlackholeResolveDialog(const QString& blackholeName, QWidget* parent)
     : QDialog(parent),
       m_blackholeName(blackholeName) {
@@ -142,8 +134,7 @@ void BlackholeResolveDialog::buildDetailPage() {
         pLay->setSpacing(8);
 
         m_promptEdit = new QPlainTextEdit(panel);
-        m_promptEdit->setPlainText(BlackholeResolveDialog::defaultModelInpaintPrompt());
+        m_promptEdit->setPlaceholderText(QStringLiteral("可选"));
-        m_promptEdit->setPlaceholderText(QStringLiteral("可按画面增删；若全部删空，请求时仍使用内置默认描述"));
         m_promptEdit->setMinimumHeight(72);
         pLay->addWidget(m_promptEdit);
 

client/gui/dialogs/BlackholeResolveDialog.h

@@ -19,9 +19,6 @@ public:
     Algorithm selectedAlgorithm() const { return m_selectedAlgorithm; }
     QString promptText() const;
 
-    /// 模型补全（LaMa 等）内置正向提示：中国画去人物、补背景纹理，无人物残留。
-    [[nodiscard]] static QString defaultModelInpaintPrompt();
-
 private:
     void buildSelectPage();
     void buildDetailPage();

client/gui/main_window/MainWindow.cpp

@@ -5050,21 +5050,13 @@ void MainWindow::showBlackholeContextMenu(const QPoint& globalPos, const QString
         client->setBaseUrl(QUrl(base));
 
         QString immediateErr;
-        QString prompt = dlg.promptText();
+        const QString prompt = dlg.promptText();
-        if (prompt.isEmpty()) {
-            prompt = BlackholeResolveDialog::defaultModelInpaintPrompt();
-        }
         QNetworkReply* reply = client->inpaintAsync(
             cropPng,
             maskPng,
-            // LaMa（物体移除 / 擦除）
+            QStringLiteral("flux_fill"),
-            QStringLiteral("lama"),
             prompt,
-            // 抑制人物与去人常见伪影（中英混合便于通用后端）
+            QString(),
-            QStringLiteral(
-                "person, human, face, body, hands, feet, portrait, silhouette, skin, clothing folds, hair, "
-                "shadow, ghosting, blur halo, seam, banding, watermark, text, logo, signature, jpeg artifacts"),
-            // 去人物场景：strength 太低容易“保留人物残影”；适当提高更干净
             0.72,
             1024,
             &immediateErr);
@@ -5077,7 +5069,7 @@ void MainWindow::showBlackholeContextMenu(const QPoint& globalPos, const QString
         }
 
         auto* task = new CancelableTaskDialog(QStringLiteral("黑洞修复"),
-                                              QStringLiteral("正在请求后端模型补全，请稍候……"),
+                                              QStringLiteral("补全中…"),
                                               this);
         task->setAttribute(Qt::WA_DeleteOnClose, true);
         connect(task, &CancelableTaskDialog::canceled, this, [reply, task]() {

python_server/.env

@@ -0,0 +1 @@
+HF_TOKEN=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

python_server/app/routes/inpaint.py

@@ -15,7 +15,7 @@ router = APIRouter(tags=["inpaint"])
 @router.post("/inpaint")
 def inpaint(req: InpaintRequest) -> Dict[str, Any]:
     try:
-        model_name = req.model_name or "sdxl_inpaint"
+        model_name = req.model_name or "flux_fill"
         pil = b64_to_pil_image(req.image_b64).convert("RGB")
 
         if req.mask_b64:
@@ -24,13 +24,17 @@ def inpaint(req: InpaintRequest) -> Dict[str, Any]:
             mask = default_half_mask(pil)
 
         predictor = get_inpaint_predictor(model_name)
+        call_kw: Dict[str, Any] = {"strength": req.strength, "max_side": req.max_side}
+        if req.guidance_scale is not None:
+            call_kw["guidance_scale"] = req.guidance_scale
+        if req.num_inference_steps is not None:
+            call_kw["num_inference_steps"] = req.num_inference_steps
         out = predictor(
             pil,
             mask,
             req.prompt or "",
             req.negative_prompt or "",
-            strength=req.strength,
+            **call_kw,
-            max_side=req.max_side,
         )
 
         out_dir = OUTPUT_DIR / "inpaint"

python_server/app/routes/meta.py

@@ -43,19 +43,19 @@ def get_models() -> Dict[str, Any]:
                 "mask2former": {"name": "Mask2Former (not implemented)"},
             },
             "inpaint": {
-                "copy": {"name": "Copy (no-op)", "params": []},
+                "flux_fill": {
-                "sdxl_inpaint": {
+                    "name": "FLUX.1 Fill (default)",
-                    "name": "SDXL Inpaint",
+                    "params": [{"id": "prompt", "label": "prompt", "optional": True}],
-                    "params": [
-                        {"id": "prompt", "label": "提示词", "optional": True},
-                    ],
                 },
-                "lama": {"name": "LaMa (Erase / Remove Object)", "params": []},
+                "sdxl_inpaint": {
+                    "name": "sdxl_inpaint",
+                    "params": [{"id": "prompt", "label": "prompt", "optional": True}],
+                },
+                "lama": {"name": "lama", "params": []},
+                "copy": {"name": "copy (debug)", "params": []},
                 "controlnet": {
-                    "name": "ControlNet Inpaint (canny)",
+                    "name": "controlnet",
-                    "params": [
+                    "params": [{"id": "prompt", "label": "prompt", "optional": True}],
-                        {"id": "prompt", "label": "提示词", "optional": True},
-                    ],
                 },
             },
             "animation": {

python_server/app/schemas.py

@@ -52,6 +52,8 @@ class InpaintRequest(ImageInput):
     negative_prompt: Optional[str] = Field("", description="负向 prompt")
     mask_b64: Optional[str] = Field(None, description="mask PNG base64（可选）")
     max_side: int = Field(1024, ge=128, le=2048)
+    guidance_scale: Optional[float] = Field(None, ge=0.0, le=200.0, description="扩散 CFG；不传则各后端默认")
+    num_inference_steps: Optional[int] = Field(None, ge=1, le=150, description="采样步数；不传则各后端默认")
 
 
 class AnimateRequest(BaseModel):

python_server/app/services/predictors.py

@@ -76,6 +76,11 @@ def get_inpaint_predictor(model_name: str):
         _inpaint_cache[model_name] = _copy
         return _copy
 
+    if model_name == "flux_fill":
+        pred, _ = build_inpaint_predictor(UnifiedInpaintConfig(backend=InpaintBackend.FLUX_FILL))
+        _inpaint_cache[model_name] = pred
+        return pred
+
     if model_name == "sdxl_inpaint":
         pred, _ = build_inpaint_predictor(UnifiedInpaintConfig(backend=InpaintBackend.SDXL_INPAINT))
         _inpaint_cache[model_name] = pred

python_server/config.py

@@ -86,10 +86,17 @@ class InpaintModelInfo(TypedDict):
     family: str
     name: str
     description: str
-    backend: str  # "sdxl_inpaint" | "controlnet"
+    backend: str  # "flux_fill" | "sdxl_inpaint" | "controlnet"
 
 
 INPAINT_MODELS: List[InpaintModelInfo] = [
+    {
+        "id": "flux_fill",
+        "family": "FLUX",
+        "name": "FLUX.1 Fill",
+        "description": "FLUX Fill 专用补全",
+        "backend": "flux_fill",
+    },
     {
         "id": "sdxl_inpaint",
         "family": "SDXL",
@@ -100,8 +107,8 @@ INPAINT_MODELS: List[InpaintModelInfo] = [
     {
         "id": "controlnet",
         "family": "ControlNet",
-        "name": "ControlNet (placeholder)",
+        "name": "SD1.5 + ControlNet Inpaint",
-        "description": "ControlNet 补全/控制生成（当前统一封装暂未实现）。",
+        "description": "runway inpaint + v11p inpaint ControlNet。",
         "backend": "controlnet",
     },
 ]
@@ -159,10 +166,12 @@ class DepthConfig:
 
 @dataclass
 class InpaintConfig:
-    # 统一补全默认后端
+    # 统一补全默认后端（与 model key 对齐；实际路由以请求的 model_name 为准）
-    backend: Literal["sdxl_inpaint", "controlnet"] = "sdxl_inpaint"
+    backend: Literal["flux_fill", "sdxl_inpaint", "controlnet"] = "flux_fill"
-    # SDXL Inpaint 的基础模型（可写 HuggingFace model id 或本地目录）
+    # FLUX Fill（gated：须在 Hugging Face 同意条款，并建议设置环境变量 HF_TOKEN）
-    sdxl_base_model: str = "stabilityai/stable-diffusion-xl-base-1.0"
+    flux_fill_model: str = "black-forest-labs/FLUX.1-Fill-dev"
+    # SDXL：须为专用 inpaint 权重（勿用 xl-base 当补全底模）
+    sdxl_base_model: str = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
     # ControlNet Inpaint 基础模型与 controlnet 权重
     controlnet_base_model: str = "runwayml/stable-diffusion-inpainting"
     controlnet_model: str = "lllyasviel/control_v11p_sd15_inpaint"

python_server/config_loader.py

@@ -85,6 +85,12 @@ def get_sdxl_base_model_from_app(app_cfg: AppConfig | None = None) -> str:
     return app_cfg.inpaint.sdxl_base_model
 
 
+def get_flux_fill_model_from_app(app_cfg: AppConfig | None = None) -> str:
+    if app_cfg is None:
+        app_cfg = load_app_config()
+    return app_cfg.inpaint.flux_fill_model
+
+
 def get_controlnet_base_model_from_app(app_cfg: AppConfig | None = None) -> str:
     if app_cfg is None:
         app_cfg = load_app_config()

python_server/model/Inpaint/__init__.py

@@ -1 +1,3 @@
+from .flux_fill.loader import make_flux_fill_predictor
 
+__all__ = ["make_flux_fill_predictor"]

python_server/model/Inpaint/flux_fill/__init__.py

@@ -0,0 +1,3 @@
+from .loader import make_flux_fill_predictor
+
+__all__ = ["make_flux_fill_predictor"]

python_server/model/Inpaint/flux_fill/loader.py

@@ -0,0 +1,109 @@
+from __future__ import annotations
+
+import os
+from typing import Any, Callable
+
+import numpy as np
+from PIL import Image
+
+
+def make_flux_fill_predictor(
+    model_id: str,
+    device: str | None = None,
+) -> Callable[[Image.Image, Image.Image, str, str], Image.Image]:
+    import torch
+    from diffusers import FluxFillPipeline
+
+    if device is None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    if device == "cuda" and torch.cuda.is_bf16_supported():
+        torch_dtype = torch.bfloat16
+    elif device == "cuda":
+        torch_dtype = torch.float16
+    else:
+        torch_dtype = torch.float32
+
+    hf_token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN")
+
+    pipe = FluxFillPipeline.from_pretrained(
+        model_id,
+        torch_dtype=torch_dtype,
+        token=hf_token,
+    )
+
+    if device == "cuda":
+        try:
+            pipe.enable_model_cpu_offload()
+        except Exception:
+            try:
+                pipe.enable_vae_tiling()
+            except Exception:
+                pass
+            pipe.to("cuda")
+    else:
+        pipe.to(device)
+
+    def _predict(
+        image: Image.Image,
+        mask: Image.Image,
+        prompt: str,
+        negative_prompt: str = "",
+        strength: float = 1.0,
+        guidance_scale: float = 30.0,
+        num_inference_steps: int = 28,
+        max_side: int = 1024,
+        max_sequence_length: int = 512,
+        **_: Any,
+    ) -> Image.Image:
+        _ = negative_prompt
+        image = image.convert("RGB")
+        mask = mask.convert("L")
+
+        orig_w, orig_h = image.size
+        run_w, run_h = orig_w, orig_h
+        if max(orig_w, orig_h) > max_side:
+            scale = max_side / float(max(orig_w, orig_h))
+            run_w = int(round(orig_w * scale))
+            run_h = int(round(orig_h * scale))
+        run_w = max(8, run_w - (run_w % 8))
+        run_h = max(8, run_h - (run_h % 8))
+
+        if (run_w, run_h) != (orig_w, orig_h):
+            image_run = image.resize((run_w, run_h), resample=Image.BICUBIC)
+            mask_run = mask.resize((run_w, run_h), resample=Image.NEAREST)
+        else:
+            image_run = image
+            mask_run = mask
+
+        p = (prompt or "").strip()
+        if not p:
+            p = "Chinese ink painting background, natural texture, seamless fill, no figures"
+
+        st = float(strength)
+        st = min(1.0, max(0.05, st))
+
+        if device == "cuda":
+            try:
+                torch.cuda.empty_cache()
+            except Exception:
+                pass
+
+        out = pipe(
+            prompt=p,
+            image=image_run,
+            mask_image=mask_run,
+            height=run_h,
+            width=run_w,
+            strength=st,
+            guidance_scale=float(guidance_scale),
+            num_inference_steps=int(num_inference_steps),
+            max_sequence_length=int(max_sequence_length),
+        ).images[0]
+
+        out = out.convert("RGB")
+        if out.size != (orig_w, orig_h):
+            out = out.resize((orig_w, orig_h), resample=Image.BICUBIC)
+        return out
+
+    return _predict

python_server/model/Inpaint/inpaint_loader.py

@@ -4,6 +4,7 @@ from __future__ import annotations
 统一的补全（Inpaint）模型加载入口。
 
 当前支持：
+- FLUX Fill（diffusers FluxFillPipeline，如 FLUX.1-Fill-dev）
 - SDXL Inpaint（diffusers AutoPipelineForInpainting）
 - ControlNet（Stable Diffusion + ControlNet Inpaint）
 - LaMa（torchscript big-lama，本地封装）
@@ -11,7 +12,7 @@ from __future__ import annotations
 
 from dataclasses import dataclass
 from enum import Enum
-from typing import Callable
+from typing import Any, Callable
 
 import numpy as np
 from PIL import Image
@@ -21,10 +22,12 @@ from config_loader import (
     get_sdxl_base_model_from_app,
     get_controlnet_base_model_from_app,
     get_controlnet_model_from_app,
+    get_flux_fill_model_from_app,
 )
 
 
 class InpaintBackend(str, Enum):
+    FLUX_FILL = "flux_fill"
     SDXL_INPAINT = "sdxl_inpaint"
     CONTROLNET = "controlnet"
     LAMA = "lama"
@@ -36,6 +39,7 @@ class UnifiedInpaintConfig:
     device: str | None = None
     # SDXL base model (HF id 或本地目录)，不填则用 config.py 的默认值
     sdxl_base_model: str | None = None
+    flux_fill_model: str | None = None
 
 
 @dataclass
@@ -100,7 +104,7 @@ def _make_sdxl_inpaint_predictor(
     - 输出：PIL RGB 结果图
     """
     import torch
-    from diffusers import AutoPipelineForText2Image, AutoPipelineForInpainting
+    from diffusers import AutoPipelineForInpainting
 
     app_cfg = load_app_config()
     base_model = cfg.sdxl_base_model or get_sdxl_base_model_from_app(app_cfg)
@@ -112,13 +116,12 @@ def _make_sdxl_inpaint_predictor(
 
     torch_dtype = torch.float16 if device == "cuda" else torch.float32
 
-    pipe_t2i = AutoPipelineForText2Image.from_pretrained(
+    pipe = AutoPipelineForInpainting.from_pretrained(
         base_model,
         torch_dtype=torch_dtype,
         variant="fp16" if device == "cuda" else None,
         use_safetensors=True,
     ).to(device)
-    pipe = AutoPipelineForInpainting.from_pipe(pipe_t2i).to(device)
 
     # 省显存设置（尽量不改变输出语义）
     # 注意：CPU offload 会明显变慢，但能显著降低显存占用。
@@ -149,6 +152,7 @@ def _make_sdxl_inpaint_predictor(
         guidance_scale: float = 7.5,
         num_inference_steps: int = 30,
         max_side: int = 1024,
+        **_: Any,
     ) -> Image.Image:
         image = image.convert("RGB")
         # diffusers 要求 mask 为单通道，白色区域为需要重绘
@@ -257,6 +261,7 @@ def _make_controlnet_predictor(_: UnifiedInpaintConfig):
         num_inference_steps: int = 30,
         controlnet_conditioning_scale: float = 1.0,
         max_side: int = 768,
+        **_: Any,
     ) -> Image.Image:
         import cv2
         import numpy as np
@@ -316,6 +321,22 @@ def _make_controlnet_predictor(_: UnifiedInpaintConfig):
     return _predict
 
 
+def _make_flux_fill_predictor(cfg: UnifiedInpaintConfig):
+    import torch
+
+    from .flux_fill.loader import make_flux_fill_predictor as _flux_fill_factory
+
+    app_cfg = load_app_config()
+    model_id = cfg.flux_fill_model or get_flux_fill_model_from_app(app_cfg)
+
+    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"
+
+    return _flux_fill_factory(model_id=model_id, device=device)
+
+
 def _make_lama_predictor(cfg: UnifiedInpaintConfig):
     import torch
 
@@ -340,7 +361,7 @@ def _make_lama_predictor(cfg: UnifiedInpaintConfig):
         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
+        # big-lama：仅 (RGB, mask)；无文本条件。
         _ = (strength, guidance_scale, num_inference_steps, max_side)
         return lama(image, mask)
 
@@ -355,6 +376,9 @@ def build_inpaint_predictor(
     """
     cfg = cfg or UnifiedInpaintConfig()
 
+    if cfg.backend == InpaintBackend.FLUX_FILL:
+        return _make_flux_fill_predictor(cfg), InpaintBackend.FLUX_FILL
+
     if cfg.backend == InpaintBackend.SDXL_INPAINT:
         return _make_sdxl_inpaint_predictor(cfg), InpaintBackend.SDXL_INPAINT
 

python_server/server.py

@@ -1,12 +1,11 @@
-"""
-  source .venv/bin/activate   # 仓库根 hfut-bishe/.venv
-  cd python_server && uvicorn app.main:app --host 0.0.0.0 --port 8000
-"""
-
 from __future__ import annotations
 
 import os
 
+from dotenv import load_dotenv
+
+load_dotenv()
+
 from app.main import app
 
 __all__ = ["app"]