diff options
| -rw-r--r-- | .gitignore | 2 | ||||
| -rw-r--r-- | README.md | 18 | ||||
| -rw-r--r-- | data/MODELS.md | 66 | ||||
| -rw-r--r-- | data/config.generate-image.toml | 13 | ||||
| -rw-r--r-- | doc/Design.md | 7 | ||||
| -rw-r--r-- | doc/Specifications.md | 7 | ||||
| -rw-r--r-- | doc/config.md | 8 | ||||
| -rw-r--r-- | pyproject.toml | 12 | ||||
| -rw-r--r-- | script/smoke_generate_image.py | 60 | ||||
| -rw-r--r-- | src/mcp_sd15/pipeline_factory.py | 12 | ||||
| -rw-r--r-- | tests/test_pipeline_factory.py | 99 | ||||
| -rw-r--r-- | uv.lock | 90 |
12 files changed, 337 insertions, 57 deletions
@@ -5,4 +5,4 @@ __pycache__/ .pyright/ *.py[cod] outputs/ -data/
\ No newline at end of file +/data/ @@ -5,11 +5,15 @@ ## セットアップ -1. `config.example.toml` をコピーし、モデルの root とファイル名を設定します。 -2. `uv sync` を実行します。 -3. `uv run mcp-sd15 --config path/to/config.toml` で起動します。 +1. NVIDIA driverがCUDA 13.2対応PyTorchを実行できることを`nvidia-smi`で確認します。 +2. `config.example.toml` をコピーし、モデルの root とファイル名を設定します。 +3. `uv sync` を実行します。PyTorchは専用indexからCUDA 13.2版が導入されます。 +4. `uv run mcp-sd15 --config path/to/config.toml` で起動します。 モデルは `<root>/checkpoints`、`vae`、`lora`、`controlnet` からだけ読み込みます。 +VAE componentは生成に常に必要です。`model.vae`を省略した場合はcheckpoint内蔵VAEを使い、 +指定した場合だけ外部VAEへ置き換えます。設定されたLoRAは起動時にすべてロードされ、3つの +生成ツールに共通して適用されます。MCP呼び出しごとのLoRA切替には対応しません。 Depth/Openpose の annotator 重みは `controlnet_aux` のローカルキャッシュに事前配置して ください。推論時のダウンロードは行いません。 @@ -20,3 +24,11 @@ mask は白い領域が再生成対象です。出力は PNG の画像 content GPU を使わないテストは `uv run pytest`、lint は `uv run ruff check .`、型検査は `uv run pyright` で実行できます。実モデルの smoke test は環境依存のため自動テストには 含めていません。 + +実GPUとMCP経路を使う`generate_image` smoke testは次のように実行できます。 + +```powershell +uv run python script\smoke_generate_image.py ` + --config data\config.generate-image.toml ` + --output data\outputs\generate-image-mcp-512.png +``` diff --git a/data/MODELS.md b/data/MODELS.md new file mode 100644 index 0000000..443737b --- /dev/null +++ b/data/MODELS.md @@ -0,0 +1,66 @@ +# generate_image test assets + +## Root + +```text +C:\Users\isaza\src\python\ollama-web\data\mcp\sd15-mcp\data +``` + +## Model + +| Item | Value | +|---|---| +| Repository | `stable-diffusion-v1-5/stable-diffusion-v1-5` | +| File | `checkpoints/v1-5-pruned-emaonly.safetensors` | +| Size | 4,265,146,304 bytes | +| SHA-256 | `6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa` | +| License | CreativeML OpenRAIL-M | +| Source | <https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5> | + +`generate_image`の動作確認に必要なbase checkpointだけを配置している。VAEはcheckpoint内蔵の +ものを使用し、LoRAとControlNetは使用しない。 + +## Supporting files + +`huggingface/`には、単一checkpointをDiffusersでオフラインロードするために必要な公式の +pipeline構成、scheduler構成、component構成、CLIP tokenizerがcacheされている。 + +サーバは`model.root/huggingface`を明示的なcache directoryとして使用し、 +`local_files_only=true`でモデルをロードする。 + +## Configuration + +動作確認用設定は`config.generate-image.toml`である。 + +```powershell +uv run mcp-sd15 --config data\config.generate-image.toml +``` + +生成PNGは`outputs/`へ保存される。 + +## Verification + +- configの読込とcheckpointパス検証: 成功 +- Safetensors header/tensor index読込: 成功、1,145 tensors +- SHA-256照合: Hugging Face掲載値と一致 +- ネットワーク遮断状態でのDiffusers pipeline構築: 成功 +- CPU smoke inference: 成功 + - prompt: `a small red cube on a white table` + - seed: `42` + - size: `64 x 64` + - steps: `1` + - output: `outputs/model-smoke-64.png` + +- CUDA runtime: PyTorch `2.14.0+cu132` / CUDA `13.2` +- GPU: NVIDIA GeForce RTX 5050、8,151 MiB +- MCP `generate_image` 512px GPU inference: 成功 + - size: `512 x 512` + - steps: `20` + - output: `outputs/generate-image-mcp-512.png` + - generation elapsed: 約19秒 + +## Safety note + +Diffusersのsingle-file pipelineではsafety checkerが自動ロードされず、現在のサーバ出力は +フィルタされない。公開サービスとして提供する場合は、ライセンス条件を確認したうえで +safety checkerまたは同等の出力制御を別途実装すること。 diff --git a/data/config.generate-image.toml b/data/config.generate-image.toml new file mode 100644 index 0000000..f9de573 --- /dev/null +++ b/data/config.generate-image.toml @@ -0,0 +1,13 @@ +[model] +root = "." +checkpoint = "v1-5-pruned-emaonly.safetensors" + +[generation] +steps = 20 +cfg_scale = 7.0 +scheduler = "dpmpp_2m" +default_size = "square_512" + +[output] +enabled = true +directory = "./outputs" diff --git a/doc/Design.md b/doc/Design.md index f22c24d..fd4ac63 100644 --- a/doc/Design.md +++ b/doc/Design.md @@ -113,7 +113,7 @@ mcp-sd15/ | 5 | checkpoint存在確認 | | 6 | VAE / LoRA / ControlNet存在確認 | | 7 | Base SD1.x pipelineを構築 | -| 8 | VAEを適用 | +| 8 | checkpoint内蔵VAEを確認し、外部VAE指定時は差し替え | | 9 | LoRAを順次ロード | | 10 | GPUへ配置 | | 11 | MCP Toolを公開 | @@ -170,7 +170,7 @@ Pydanticは必須とせず、`tomllib + dataclass + 明示的validation`を基 | 機能 | 実装方針 | |---|---| | Base model | SD1.x互換checkpointをロード | -| VAE | 設定された場合のみ置換 | +| VAE | pipeline componentとして必須。設定された外部VAEがある場合のみ置換 | | LoRA | 複数adapterをロード | | LoRA weight | `set_adapters`相当で適用 | | LoRA fuse | 将来オプション。初期実装では必須としない | @@ -349,7 +349,8 @@ GPU | Package | 用途 | |---|---| | `mcp` | MCP server | -| `torch` | CUDA / Tensor | +| `torch` | CUDA 13.2 / Tensor | +| `torchvision` | PyTorch画像model依存 | | `diffusers` | SD / ControlNet | | `transformers` | CLIP | | `peft` | LoRA | diff --git a/doc/Specifications.md b/doc/Specifications.md index 3d2cd28..0fb6451 100644 --- a/doc/Specifications.md +++ b/doc/Specifications.md @@ -14,7 +14,7 @@ LLM側には生成意図に必要な最小限の入力のみを公開し、モ |---|---| | 対応モデル | Stable Diffusion 1.x互換モデル | | モデル差し替え | 対応 | -| VAE差し替え | 対応 | +| VAE | 常時使用。checkpoint内蔵VAEまたは外部VAEへの差し替え | | LoRA | 複数指定・重み指定に対応 | | ControlNet | ControlNet 1.1 Depth / Openpose / Inpaint | | MCP経由のモデル変更 | 非対応 | @@ -127,7 +127,8 @@ LLM側には生成意図に必要な最小限の入力のみを公開し、モ | 項目 | 仕様 | |---|---| | Base checkpoint | 1ファイル | -| VAE | 0または1ファイル | +| VAE component | 必須。通常はcheckpoint内蔵VAEを使用 | +| 外部VAE | 0または1ファイル。指定時は内蔵VAEを差し替え | | LoRA | 0個以上 | | LoRA weight | LoRAごとに指定 | | ControlNet | Depth / Openpose / Inpaintを個別設定 | @@ -246,7 +247,7 @@ directory = "./outputs" | ID | 完了条件 | |---|---| -| G1 | TOMLからcheckpoint / VAE / 複数LoRAをロードできる | +| G1 | TOMLからcheckpoint / 任意の外部VAE / 複数LoRAをロードでき、VAE componentが常に存在する | | G2 | `generate_image`でPNG画像を1枚生成できる | | G3 | Depth / Openpose / Openpose Full生成が動作する | | G4 | Inpaintが元画像+maskから動作する | diff --git a/doc/config.md b/doc/config.md index f200cdc..c619d2d 100644 --- a/doc/config.md +++ b/doc/config.md @@ -19,6 +19,8 @@ uv run mcp-sd15 --config C:\path\to\config.toml <model.root>/ ├─ checkpoints/ │ └─ <model.checkpoint> +├─ huggingface/ +│ └─ Diffusersが使用するローカル構成cache ├─ vae/ │ └─ <model.vae> ├─ lora/ @@ -79,14 +81,20 @@ vae = "vae.safetensors" - `<root>/checkpoints/<checkpoint>`として解決 - 起動時にファイルの存在を確認 - サーバ初期化時にSD1.x pipelineとしてロードできなければ起動失敗 +- 単一checkpointの読込に必要なDiffusers構成は`<root>/huggingface` cacheから取得 ### `vae` +- Stable Diffusion pipelineのVAE component自体は必須 - 任意のファイル名 - 指定した場合は`<root>/vae/<vae>`として解決し、base modelのVAEを置換 - 指定時は起動時にファイルの存在を確認 - 省略時はcheckpointに含まれるVAEを使用 +`model.vae`が任意なのはVAE処理を省略できるという意味ではなく、外部VAEファイルによる +差し替えが任意という意味である。checkpoint内にもVAEが存在しない場合はpipelineを構築 +できないため起動に失敗する。 + ## 5. `[[model.lora]]` LoRAごとにarray of tablesを追加する。0個以上指定でき、記述順にadapter名が割り当てられる。 diff --git a/pyproject.toml b/pyproject.toml index 5b9cd71..ce5350f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,7 +18,8 @@ dependencies = [ "peft>=0.13", "pillow>=10.4", "safetensors>=0.4", - "torch>=2.4", + "torch==2.14.0+cu132", + "torchvision==0.29.0+cu132", "transformers>=4.45", ] @@ -33,6 +34,15 @@ dev = [ "ruff>=0.8", ] +[tool.uv.sources] +torch = { index = "pytorch-cu132" } +torchvision = { index = "pytorch-cu132" } + +[[tool.uv.index]] +name = "pytorch-cu132" +url = "https://download.pytorch.org/whl/cu132" +explicit = true + [tool.hatch.build.targets.wheel] packages = ["src/mcp_sd15"] diff --git a/script/smoke_generate_image.py b/script/smoke_generate_image.py new file mode 100644 index 0000000..9b52253 --- /dev/null +++ b/script/smoke_generate_image.py @@ -0,0 +1,60 @@ +"""Run generate_image through an in-process MCP client with the real GPU backend.""" + +from __future__ import annotations + +import argparse +import asyncio +import base64 +from pathlib import Path + +from mcp import Client +from mcp.types import ImageContent + +from mcp_sd15.config import load_config +from mcp_sd15.model_manager import ModelManager +from mcp_sd15.server import create_server +from mcp_sd15.service import ImageGenerationService + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--config", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument( + "--prompt", + default="a small red cube on a white table, studio photograph, detailed", + ) + parser.add_argument("--negative-prompt", default="blurry, low quality") + parser.add_argument("--size", default="square_512") + return parser.parse_args() + + +async def run(args: argparse.Namespace) -> None: + config = load_config(args.config) + backend = ModelManager(config) + server = create_server(ImageGenerationService(config, backend)) + async with Client(server, raise_exceptions=True) as client: + result = await client.call_tool( + "generate_image", + { + "positive_prompt": args.prompt, + "negative_prompt": args.negative_prompt, + "size": args.size, + }, + ) + if result.is_error or len(result.content) != 1: + raise RuntimeError(f"generate_image failed: {result}") + content = result.content[0] + if not isinstance(content, ImageContent) or content.mime_type != "image/png": + raise RuntimeError(f"generate_image returned unexpected content: {content}") + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_bytes(base64.b64decode(content.data, validate=True)) + print(f"generated={args.output.resolve()}") + + +def main() -> None: + asyncio.run(run(parse_args())) + + +if __name__ == "__main__": + main() diff --git a/src/mcp_sd15/pipeline_factory.py b/src/mcp_sd15/pipeline_factory.py index cd92690..12d95ba 100644 --- a/src/mcp_sd15/pipeline_factory.py +++ b/src/mcp_sd15/pipeline_factory.py @@ -28,11 +28,13 @@ class PipelineFactory: def create_base(self) -> Any: torch, diffusers = self._runtime() + cache_dir = str(self.config.model.root / "huggingface") try: pipeline = diffusers.StableDiffusionPipeline.from_single_file( str(self.config.model.checkpoint_path()), torch_dtype=torch.float16, local_files_only=True, + cache_dir=cache_dir, ) vae_path = self.config.model.vae_path() if vae_path is not None: @@ -40,6 +42,11 @@ class PipelineFactory: str(vae_path), torch_dtype=torch.float16, local_files_only=True, + cache_dir=cache_dir, + ) + if getattr(pipeline, "vae", None) is None: + raise ConfigError( + "the checkpoint does not provide a VAE and no external VAE was loaded" ) adapter_names: list[str] = [] adapter_weights: list[float] = [] @@ -69,7 +76,10 @@ class PipelineFactory: torch, diffusers = self._runtime() try: return diffusers.ControlNetModel.from_single_file( - str(path), torch_dtype=torch.float16, local_files_only=True + str(path), + torch_dtype=torch.float16, + local_files_only=True, + cache_dir=str(self.config.model.root / "huggingface"), ).to("cuda") except Exception as exc: raise ConfigError(f"failed to load ControlNet model {path}: {exc}") from exc diff --git a/tests/test_pipeline_factory.py b/tests/test_pipeline_factory.py new file mode 100644 index 0000000..454a5b1 --- /dev/null +++ b/tests/test_pipeline_factory.py @@ -0,0 +1,99 @@ +from __future__ import annotations + +from dataclasses import replace +from types import SimpleNamespace + +import pytest + +from mcp_sd15.config import LoRAConfig +from mcp_sd15.errors import ConfigError +from mcp_sd15.pipeline_factory import PipelineFactory + + +class FakePipeline: + def __init__(self, *, with_vae: bool = True) -> None: + self.vae = object() if with_vae else None + self.scheduler = SimpleNamespace(config={}) + self.lora_loads: list[dict[str, object]] = [] + self.adapters: tuple[list[str], list[float]] | None = None + self.device: str | None = None + + def load_lora_weights(self, directory: str, **kwargs: object) -> None: + self.lora_loads.append({"directory": directory, **kwargs}) + + def set_adapters(self, names: list[str], *, adapter_weights: list[float]) -> None: + self.adapters = (names, adapter_weights) + + def set_progress_bar_config(self, *, disable: bool) -> None: + assert disable is True + + def to(self, device: str) -> FakePipeline: + self.device = device + return self + + +class FakeScheduler: + @classmethod + def from_config(cls, config: dict[str, object]) -> object: + return {"scheduler": cls.__name__, **config} + + +def fake_diffusers(pipeline: FakePipeline, external_vae: object) -> SimpleNamespace: + class StableDiffusionPipeline: + @classmethod + def from_single_file(cls, path: str, **kwargs: object) -> FakePipeline: + pipeline.base_load = {"path": path, **kwargs} # type: ignore[attr-defined] + return pipeline + + class AutoencoderKL: + @classmethod + def from_single_file(cls, path: str, **kwargs: object) -> object: + pipeline.vae_load = {"path": path, **kwargs} # type: ignore[attr-defined] + return external_vae + + return SimpleNamespace( + StableDiffusionPipeline=StableDiffusionPipeline, + AutoencoderKL=AutoencoderKL, + DPMSolverMultistepScheduler=FakeScheduler, + DDIMScheduler=FakeScheduler, + EulerDiscreteScheduler=FakeScheduler, + EulerAncestralDiscreteScheduler=FakeScheduler, + ) + + +def test_base_pipeline_requires_vae_and_applies_all_loras(app_config, monkeypatch) -> None: + model = replace( + app_config.model, + vae="external.safetensors", + loras=( + LoRAConfig("detail.safetensors", 0.8), + LoRAConfig("style.safetensors", 0.5), + ), + ) + factory = PipelineFactory(replace(app_config, model=model)) + pipeline = FakePipeline() + external_vae = object() + runtime = (SimpleNamespace(float16="float16"), fake_diffusers(pipeline, external_vae)) + monkeypatch.setattr(factory, "_runtime", lambda: runtime) + + result = factory.create_base() + + assert result is pipeline + assert pipeline.vae is external_vae + assert pipeline.adapters == (["configured_lora_0", "configured_lora_1"], [0.8, 0.5]) + assert [item["weight_name"] for item in pipeline.lora_loads] == [ + "detail.safetensors", + "style.safetensors", + ] + assert all(item["local_files_only"] is True for item in pipeline.lora_loads) + assert pipeline.device == "cuda" + + +def test_checkpoint_without_vae_fails_pipeline_initialization(app_config, monkeypatch) -> None: + factory = PipelineFactory(app_config) + pipeline = FakePipeline(with_vae=False) + runtime = (SimpleNamespace(float16="float16"), fake_diffusers(pipeline, object())) + monkeypatch.setattr(factory, "_runtime", lambda: runtime) + + with pytest.raises(ConfigError, match="does not provide a VAE"): + factory.create_base() @@ -240,10 +240,10 @@ wheels = [ [[package]] name = "cuda-toolkit" -version = "13.0.3.0" +version = "13.2.1" source = { registry = "https://pypi.org/simple" } wheels = [ - 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