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"""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()
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:
pipeline.vae = diffusers.AutoencoderKL.from_single_file(
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] = []
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,
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
@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)
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