feat: Stable Diffusion 1.x MCPサーバを実装

仕様書・設計書に基づき、ローカルCUDA GPU上のStable Diffusion 1.x互換モデルをMCP経由で利用するサーバを新規実装。

実装内容:
- generate_image、generate_guided_image、inpaint_imageの3ツールをMCP SDK v2で公開
- TOMLからcheckpoint、VAE、複数LoRAと重み、ControlNet、生成設定を読み込み
- モデル種別ごとの固定ディレクトリ解決と起動時の設定・ファイル存在検証を追加
- Depth/MiDaS、OpenPose body/full、ControlNet Inpaintの前処理とパイプライン切替を実装
- FP16/CUDA、1枚固定生成、ControlNetオンデマンドロード、単一GPU直列実行を実装
- Base64 PNG/JPEG入力の形式・サイズ検証と任意のdata URI入力に対応
- seed、サイズ、モード、時刻、処理時間をPNG metadataへ記録
- 任意のローカルPNG保存、生成エラー分類、CUDA OOM後のキャッシュ回復を追加
- --configと--versionを備えたstdio CLIエントリポイントを追加

開発・運用:
- Python 3.11およびuvによる依存・lock管理を構成
- config.example.tomlとセットアップ・運用手順をREADMEへ追加
- Ruff、Pyright、pytestの設定を追加
- 設定、パス解決、schema、画像検証、サービスルーティング、排他制御、MCP画像返却をテスト

検証結果:
- pytest: 30 passed
- Ruff lint/format: passed
- Pyright: 0 errors, 0 warnings
- CLI versionおよびMCPクライアント経由のPNG返却を確認
- 実GPU smoke testはCUDA GPUとモデルファイルがないため対象外

1 files changed, 133 insertions, 0 deletions

diff --git a/src/mcp_sd15/pipeline_factory.py b/src/mcp_sd15/pipeline_factory.py
new file mode 100644
index 0000000..cd92690
--- /dev/null
+++ b/src/mcp_sd15/pipeline_factory.py
@@ -0,0 +1,133 @@
+"""Lazy Diffusers construction kept outside the MCP layer."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from .config import AppConfig
+from .errors import ConfigError
+
+
+class PipelineFactory:
+    """Build SD1.x pipelines in FP16 without network access."""
+
+    def __init__(self, config: AppConfig) -> None:
+        self.config = config
+
+    @staticmethod
+    def _runtime() -> tuple[Any, Any]:
+        try:
+            import diffusers
+            import torch
+        except ImportError as exc:
+            raise ConfigError(f"inference dependency is unavailable: {exc}") from exc
+        if not torch.cuda.is_available():
+            raise ConfigError("CUDA GPU is required but torch.cuda.is_available() is false")
+        return torch, diffusers
+
+    def create_base(self) -> Any:
+        torch, diffusers = self._runtime()
+        try:
+            pipeline = diffusers.StableDiffusionPipeline.from_single_file(
+                str(self.config.model.checkpoint_path()),
+                torch_dtype=torch.float16,
+                local_files_only=True,
+            )
+            vae_path = self.config.model.vae_path()
+            if vae_path is not None:
+                pipeline.vae = diffusers.AutoencoderKL.from_single_file(
+                    str(vae_path),
+                    torch_dtype=torch.float16,
+                    local_files_only=True,
+                )
+            adapter_names: list[str] = []
+            adapter_weights: list[float] = []
+            for index, (item, path) in enumerate(
+                zip(self.config.model.loras, self.config.model.lora_paths(), strict=True)
+            ):
+                name = f"configured_lora_{index}"
+                pipeline.load_lora_weights(
+                    str(path.parent),
+                    weight_name=path.name,
+                    adapter_name=name,
+                    local_files_only=True,
+                )
+                adapter_names.append(name)
+                adapter_weights.append(item.weight)
+            if adapter_names:
+                pipeline.set_adapters(adapter_names, adapter_weights=adapter_weights)
+            pipeline.scheduler = self._scheduler(pipeline.scheduler, diffusers)
+            pipeline.set_progress_bar_config(disable=True)
+            return pipeline.to("cuda")
+        except ConfigError:
+            raise
+        except Exception as exc:
+            raise ConfigError(f"failed to load SD1.x model configuration: {exc}") from exc
+
+    def create_controlnet(self, path: Path) -> Any:
+        torch, diffusers = self._runtime()
+        try:
+            return diffusers.ControlNetModel.from_single_file(
+                str(path), torch_dtype=torch.float16, local_files_only=True
+            ).to("cuda")
+        except Exception as exc:
+            raise ConfigError(f"failed to load ControlNet model {path}: {exc}") from exc
+
+    @staticmethod
+    def create_guided_pipeline(base: Any, controlnet: Any) -> Any:
+        from diffusers.pipelines.controlnet.pipeline_controlnet import (
+            StableDiffusionControlNetPipeline,
+        )
+
+        components: dict[str, Any] = dict(
+            vae=base.vae,
+            text_encoder=base.text_encoder,
+            tokenizer=base.tokenizer,
+            unet=base.unet,
+            controlnet=controlnet,
+            scheduler=base.scheduler,
+            safety_checker=base.safety_checker,
+            feature_extractor=base.feature_extractor,
+            requires_safety_checker=getattr(base, "requires_safety_checker", False),
+        )
+        if getattr(base, "image_encoder", None) is not None:
+            components["image_encoder"] = base.image_encoder
+        pipeline = StableDiffusionControlNetPipeline(**components)
+        pipeline.set_progress_bar_config(disable=True)
+        return pipeline.to("cuda")
+
+    @staticmethod
+    def create_inpaint_pipeline(base: Any, controlnet: Any) -> Any:
+        from diffusers.pipelines.controlnet.pipeline_controlnet_inpaint import (
+            StableDiffusionControlNetInpaintPipeline,
+        )
+
+        components: dict[str, Any] = dict(
+            vae=base.vae,
+            text_encoder=base.text_encoder,
+            tokenizer=base.tokenizer,
+            unet=base.unet,
+            controlnet=controlnet,
+            scheduler=base.scheduler,
+            safety_checker=base.safety_checker,
+            feature_extractor=base.feature_extractor,
+            requires_safety_checker=getattr(base, "requires_safety_checker", False),
+        )
+        if getattr(base, "image_encoder", None) is not None:
+            components["image_encoder"] = base.image_encoder
+        pipeline = StableDiffusionControlNetInpaintPipeline(**components)
+        pipeline.set_progress_bar_config(disable=True)
+        return pipeline.to("cuda")
+
+    def _scheduler(self, scheduler: Any, diffusers: Any) -> Any:
+        classes = {
+            "dpmpp_2m": diffusers.DPMSolverMultistepScheduler,
+            "ddim": diffusers.DDIMScheduler,
+            "euler": diffusers.EulerDiscreteScheduler,
+            "euler_a": diffusers.EulerAncestralDiscreteScheduler,
+        }
+        options = dict(scheduler.config)
+        if self.config.generation.scheduler == "dpmpp_2m":
+            options.update(algorithm_type="dpmsolver++", solver_order=2)
+        return classes[self.config.generation.scheduler].from_config(options)