453 lines
14 KiB
Python
453 lines
14 KiB
Python
from enum import IntEnum
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from logging import getLogger
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from math import ceil
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from typing import Any, Dict, List, Literal, Optional, Tuple, Union
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from .models.meta import NetworkModel
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from .torch_before_ort import GraphOptimizationLevel, SessionOptions
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logger = getLogger(__name__)
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Param = Union[str, int, float]
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Point = Tuple[int, int]
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class SizeChart(IntEnum):
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unlimited = 0
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mini = 128 # small tile for very expensive models
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half = 256 # half tile for outpainting
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auto = 512 # auto tile size
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hd1k = 2**10
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hd2k = 2**11
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hd4k = 2**12
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hd8k = 2**13
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hd16k = 2**14
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hd32k = 2**15
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hd64k = 2**16
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class TileOrder:
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grid = "grid"
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kernel = "kernel"
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spiral = "spiral"
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class Border:
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def __init__(self, left: int, right: int, top: int, bottom: int) -> None:
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self.left = left
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self.right = right
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self.top = top
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self.bottom = bottom
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def __str__(self) -> str:
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return "(%s, %s, %s, %s)" % (self.left, self.right, self.top, self.bottom)
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def tojson(self):
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return {
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"left": self.left,
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"right": self.right,
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"top": self.top,
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"bottom": self.bottom,
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}
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def with_args(self, **kwargs):
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return Border(
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kwargs.get("left", self.left),
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kwargs.get("right", self.right),
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kwargs.get("top", self.top),
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kwargs.get("bottom", self.bottom),
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)
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@classmethod
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def even(cls, all: int):
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return Border(all, all, all, all)
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class Size:
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def __init__(self, width: int, height: int) -> None:
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self.width = width
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self.height = height
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def __iter__(self):
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return iter([self.width, self.height])
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def __str__(self) -> str:
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return "%sx%s" % (self.width, self.height)
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def add_border(self, border: Border):
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return Size(
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border.left + self.width + border.right,
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border.top + self.height + border.bottom,
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)
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def round_to_tile(self, tile=512):
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return Size(
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ceil(self.width / tile) * tile,
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ceil(self.height / tile) * tile,
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)
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def tojson(self) -> Dict[str, int]:
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return {
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"width": self.width,
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"height": self.height,
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}
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def with_args(self, **kwargs):
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return Size(
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kwargs.get("width", self.width),
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kwargs.get("height", self.height),
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)
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class DeviceParams:
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def __init__(
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self,
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device: str,
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provider: str,
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options: Optional[dict] = None,
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optimizations: Optional[List[str]] = None,
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) -> None:
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self.device = device
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self.provider = provider
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self.options = options
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self.optimizations = optimizations or []
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self.sess_options_cache = None
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def __str__(self) -> str:
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return "%s - %s (%s)" % (self.device, self.provider, self.options)
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def ort_provider(
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self, model_type: Optional[str] = None
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) -> Union[str, Tuple[str, Any]]:
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if model_type is not None:
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# check if model has been pinned to CPU
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# TODO: check whether the CPU device is allowed
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if f"onnx-cpu-{model_type}" in self.optimizations:
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return "CPUExecutionProvider"
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if self.options is None:
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return self.provider
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else:
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return (self.provider, self.options)
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def sess_options(self, cache=True) -> SessionOptions:
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if cache and self.sess_options_cache is not None:
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return self.sess_options_cache
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sess = SessionOptions()
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if "onnx-low-memory" in self.optimizations:
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logger.debug("enabling ONNX low-memory optimizations")
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sess.enable_cpu_mem_arena = False
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sess.enable_mem_pattern = False
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sess.enable_mem_reuse = False
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if "onnx-graph-disable" in self.optimizations:
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logger.debug("disabling all ONNX graph optimizations")
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sess.graph_optimization_level = GraphOptimizationLevel.ORT_DISABLE_ALL
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elif "onnx-graph-basic" in self.optimizations:
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logger.debug("enabling basic ONNX graph optimizations")
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sess.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_BASIC
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elif "onnx-graph-all" in self.optimizations:
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logger.debug("enabling all ONNX graph optimizations")
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sess.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
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if "onnx-deterministic-compute" in self.optimizations:
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logger.debug("enabling ONNX deterministic compute")
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sess.use_deterministic_compute = True
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if cache:
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self.sess_options_cache = sess
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return sess
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def torch_str(self) -> str:
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if self.device.startswith("cuda"):
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if self.options is not None and "device_id" in self.options:
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return f"{self.device}:{self.options['device_id']}"
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return self.device
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elif self.device.startswith("rocm"):
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if self.options is not None and "device_id" in self.options:
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return f"cuda:{self.options['device_id']}"
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return "cuda"
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else:
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return "cpu"
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class ImageParams:
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model: str
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pipeline: str
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scheduler: str
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prompt: str
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cfg: float
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steps: int
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seed: int
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negative_prompt: Optional[str]
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lpw: bool
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eta: float
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batch: int
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control: Optional[NetworkModel]
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input_prompt: str
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input_negative_prompt: str
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loopback: int
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tiled_vae: bool
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tiles: int
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overlap: float
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def __init__(
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self,
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model: str,
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pipeline: str,
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scheduler: str,
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prompt: str,
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cfg: float,
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steps: int,
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seed: int,
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negative_prompt: Optional[str] = None,
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eta: float = 0.0,
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batch: int = 1,
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control: Optional[NetworkModel] = None,
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input_prompt: Optional[str] = None,
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input_negative_prompt: Optional[str] = None,
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loopback: int = 0,
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tiled_vae: bool = False,
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tiles: int = 512,
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overlap: float = 0.25,
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stride: int = 64,
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) -> None:
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self.model = model
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self.pipeline = pipeline
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self.scheduler = scheduler
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self.prompt = prompt
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self.cfg = cfg
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self.steps = steps
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self.seed = seed
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self.negative_prompt = negative_prompt
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self.eta = eta
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self.batch = batch
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self.control = control
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self.input_prompt = input_prompt or prompt
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self.input_negative_prompt = input_negative_prompt or negative_prompt
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self.loopback = loopback
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self.tiled_vae = tiled_vae
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self.tiles = tiles
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self.overlap = overlap
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self.stride = stride
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def do_cfg(self):
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return self.cfg > 1.0
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def get_valid_pipeline(self, group: str, pipeline: str = None) -> str:
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pipeline = pipeline or self.pipeline
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# if the correct pipeline was already requested, simply use that
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if group == pipeline:
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return pipeline
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# otherwise, check for additional allowed pipelines
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if group == "img2img":
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if pipeline in ["controlnet", "lpw", "panorama", "pix2pix"]:
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return pipeline
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elif group == "inpaint":
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if pipeline in ["controlnet", "lpw", "panorama"]:
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return pipeline
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elif group == "txt2img":
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if pipeline in ["lpw", "panorama"]:
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return pipeline
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logger.debug("pipeline %s is not valid for %s", pipeline, group)
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return group
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def lpw(self):
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return self.pipeline == "lpw"
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def tojson(self) -> Dict[str, Optional[Param]]:
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return {
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"model": self.model,
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"pipeline": self.pipeline,
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"scheduler": self.scheduler,
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"prompt": self.prompt,
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"negative_prompt": self.negative_prompt,
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"cfg": self.cfg,
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"seed": self.seed,
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"steps": self.steps,
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"eta": self.eta,
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"batch": self.batch,
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"control": self.control.name if self.control is not None else "",
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"input_prompt": self.input_prompt,
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"input_negative_prompt": self.input_negative_prompt,
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"loopback": self.loopback,
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"tiled_vae": self.tiled_vae,
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"tiles": self.tiles,
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"overlap": self.overlap,
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"stride": self.stride,
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}
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def with_args(self, **kwargs):
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return ImageParams(
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kwargs.get("model", self.model),
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kwargs.get("pipeline", self.pipeline),
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kwargs.get("scheduler", self.scheduler),
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kwargs.get("prompt", self.prompt),
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kwargs.get("cfg", self.cfg),
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kwargs.get("steps", self.steps),
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kwargs.get("seed", self.seed),
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kwargs.get("negative_prompt", self.negative_prompt),
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kwargs.get("eta", self.eta),
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kwargs.get("batch", self.batch),
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kwargs.get("control", self.control),
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kwargs.get("input_prompt", self.input_prompt),
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kwargs.get("input_negative_prompt", self.input_negative_prompt),
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kwargs.get("loopback", self.loopback),
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kwargs.get("tiled_vae", self.tiled_vae),
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kwargs.get("tiles", self.tiles),
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kwargs.get("overlap", self.overlap),
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kwargs.get("stride", self.stride),
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)
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class StageParams:
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"""
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Parameters for a chained pipeline stage
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"""
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def __init__(
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self,
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name: Optional[str] = None,
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outscale: int = 1,
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tile_order: str = TileOrder.grid,
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tile_size: int = SizeChart.auto,
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# batch_size: int = 1,
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) -> None:
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self.name = name
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self.outscale = outscale
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self.tile_order = tile_order
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self.tile_size = tile_size
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class UpscaleParams:
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def __init__(
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self,
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upscale_model: str,
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correction_model: Optional[str] = None,
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denoise: float = 0.5,
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faces=True,
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face_outscale: int = 1,
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face_strength: float = 0.5,
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format: Literal["onnx", "pth"] = "onnx", # TODO: deprecated, remove
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outscale: int = 1,
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scale: int = 4,
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pre_pad: int = 0,
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tile_pad: int = 10,
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upscale_order: Literal[
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"correction-first", "correction-last", "correction-both"
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] = "correction-first",
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) -> None:
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self.upscale_model = upscale_model
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self.correction_model = correction_model
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self.denoise = denoise
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self.faces = faces
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self.face_outscale = face_outscale
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self.face_strength = face_strength
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self.format = format
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self.outscale = outscale
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self.pre_pad = pre_pad
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self.scale = scale
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self.tile_pad = tile_pad
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self.upscale_order = upscale_order
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def rescale(self, scale: int):
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return UpscaleParams(
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self.upscale_model,
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correction_model=self.correction_model,
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denoise=self.denoise,
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faces=self.faces,
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face_outscale=self.face_outscale,
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face_strength=self.face_strength,
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format=self.format,
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outscale=scale,
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scale=scale,
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pre_pad=self.pre_pad,
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tile_pad=self.tile_pad,
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upscale_order=self.upscale_order,
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)
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def resize(self, size: Size) -> Size:
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face_outscale = self.face_outscale
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if self.upscale_order == "correction-both":
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face_outscale *= self.face_outscale
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return Size(
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size.width * self.outscale * face_outscale,
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size.height * self.outscale * face_outscale,
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)
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def tojson(self):
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return {
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"upscale_model": self.upscale_model,
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"correction_model": self.correction_model,
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"denoise": self.denoise,
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"faces": self.faces,
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"face_outscale": self.face_outscale,
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"face_strength": self.face_strength,
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"format": self.format,
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"outscale": self.outscale,
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"pre_pad": self.pre_pad,
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"scale": self.scale,
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"tile_pad": self.tile_pad,
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"upscale_order": self.upscale_order,
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}
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def with_args(self, **kwargs):
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return UpscaleParams(
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kwargs.get("upscale_model", self.upscale_model),
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kwargs.get("correction_model", self.correction_model),
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kwargs.get("denoise", self.denoise),
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kwargs.get("faces", self.faces),
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kwargs.get("face_outscale", self.face_outscale),
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kwargs.get("face_strength", self.face_strength),
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kwargs.get("format", self.format),
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kwargs.get("outscale", self.outscale),
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kwargs.get("scale", self.scale),
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kwargs.get("pre_pad", self.pre_pad),
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kwargs.get("tile_pad", self.tile_pad),
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kwargs.get("upscale_order", self.upscale_order),
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)
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class HighresParams:
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def __init__(
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self,
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enabled: bool,
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scale: int,
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steps: int,
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strength: float,
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method: Literal["bilinear", "lanczos", "upscale"] = "lanczos",
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iterations: int = 1,
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):
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self.enabled = enabled
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self.scale = scale
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self.steps = steps
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self.strength = strength
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self.method = method
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self.iterations = iterations
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def resize(self, size: Size) -> Size:
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return Size(
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size.width * (self.scale**self.iterations),
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size.height * (self.scale**self.iterations),
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)
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def tojson(self):
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return {
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"enabled": self.enabled,
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"iterations": self.iterations,
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"method": self.method,
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"scale": self.scale,
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"steps": self.steps,
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"strength": self.strength,
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}
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