stablediffusion-infinity/process.py

"""
https://github.com/Trinkle23897/Fast-Poisson-Image-Editing
MIT License

Copyright (c) 2022 Jiayi Weng

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import os
from abc import ABC, abstractmethod
from typing import Any, Optional, Tuple

import numpy as np

from fpie import np_solver

import scipy
import scipy.signal

CPU_COUNT = os.cpu_count() or 1
DEFAULT_BACKEND = "numpy"
ALL_BACKEND = ["numpy"]

try:
  from fpie import numba_solver
  ALL_BACKEND += ["numba"]
  DEFAULT_BACKEND = "numba"
except ImportError:
  numba_solver = None  # type: ignore

try:
  from fpie import taichi_solver
  ALL_BACKEND += ["taichi-cpu", "taichi-gpu"]
  DEFAULT_BACKEND = "taichi-cpu"
except ImportError:
  taichi_solver = None  # type: ignore

# try:
#   from fpie import core_gcc  # type: ignore
#   DEFAULT_BACKEND = "gcc"
#   ALL_BACKEND.append("gcc")
# except ImportError:
#   core_gcc = None

# try:
#   from fpie import core_openmp  # type: ignore
#   DEFAULT_BACKEND = "openmp"
#   ALL_BACKEND.append("openmp")
# except ImportError:
#   core_openmp = None

# try:
#   from mpi4py import MPI

#   from fpie import core_mpi  # type: ignore
#   ALL_BACKEND.append("mpi")
# except ImportError:
#   MPI = None  # type: ignore
#   core_mpi = None

try:
  from fpie import core_cuda  # type: ignore
  DEFAULT_BACKEND = "cuda"
  ALL_BACKEND.append("cuda")
except ImportError:
  core_cuda = None


class BaseProcessor(ABC):
  """API definition for processor class."""

  def __init__(
    self, gradient: str, rank: int, backend: str, core: Optional[Any]
  ):
    if core is None:
      error_msg = {
        "numpy":
          "Please run `pip install numpy`.",
        "numba":
          "Please run `pip install numba`.",
        "gcc":
          "Please install cmake and gcc in your operating system.",
        "openmp":
          "Please make sure your gcc is compatible with `-fopenmp` option.",
        "mpi":
          "Please install MPI and run `pip install mpi4py`.",
        "cuda":
          "Please make sure nvcc and cuda-related libraries are available.",
        "taichi":
          "Please run `pip install taichi`.",
      }
      print(error_msg[backend.split("-")[0]])

      raise AssertionError(f"Invalid backend {backend}.")

    self.gradient = gradient
    self.rank = rank
    self.backend = backend
    self.core = core
    self.root = rank == 0

  def mixgrad(self, a: np.ndarray, b: np.ndarray) -> np.ndarray:
    if self.gradient == "src":
      return a
    if self.gradient == "avg":
      return (a + b) / 2
    # mix gradient, see Equ. 12 in PIE paper
    mask = np.abs(a) < np.abs(b)
    a[mask] = b[mask]
    return a

  @abstractmethod
  def reset(
    self,
    src: np.ndarray,
    mask: np.ndarray,
    tgt: np.ndarray,
    mask_on_src: Tuple[int, int],
    mask_on_tgt: Tuple[int, int],
  ) -> int:
    pass

  def sync(self) -> None:
    self.core.sync()

  @abstractmethod
  def step(self, iteration: int) -> Optional[Tuple[np.ndarray, np.ndarray]]:
    pass


class EquProcessor(BaseProcessor):
  """PIE Jacobi equation processor."""

  def __init__(
    self,
    gradient: str = "max",
    backend: str = DEFAULT_BACKEND,
    n_cpu: int = CPU_COUNT,
    min_interval: int = 100,
    block_size: int = 1024,
  ):
    core: Optional[Any] = None
    rank = 0

    if backend == "numpy":
      core = np_solver.EquSolver()
    elif backend == "numba" and numba_solver is not None:
      core = numba_solver.EquSolver()
    elif backend == "gcc":
      core = core_gcc.EquSolver()
    elif backend == "openmp" and core_openmp is not None:
      core = core_openmp.EquSolver(n_cpu)
    elif backend == "mpi" and core_mpi is not None:
      core = core_mpi.EquSolver(min_interval)
      rank = MPI.COMM_WORLD.Get_rank()
    elif backend == "cuda" and core_cuda is not None:
      core = core_cuda.EquSolver(block_size)
    elif backend.startswith("taichi") and taichi_solver is not None:
      core = taichi_solver.EquSolver(backend, n_cpu, block_size)

    super().__init__(gradient, rank, backend, core)

  def mask2index(
    self, mask: np.ndarray
  ) -> Tuple[np.ndarray, int, np.ndarray, np.ndarray]:
    x, y = np.nonzero(mask)
    max_id = x.shape[0] + 1
    index = np.zeros((max_id, 3))
    ids = self.core.partition(mask)
    ids[mask == 0] = 0  # reserve id=0 for constant
    index = ids[x, y].argsort()
    return ids, max_id, x[index], y[index]

  def reset(
    self,
    src: np.ndarray,
    mask: np.ndarray,
    tgt: np.ndarray,
    mask_on_src: Tuple[int, int],
    mask_on_tgt: Tuple[int, int],
  ) -> int:
    assert self.root
    # check validity
    # assert 0 <= mask_on_src[0] and 0 <= mask_on_src[1]
    # assert mask_on_src[0] + mask.shape[0] <= src.shape[0]
    # assert mask_on_src[1] + mask.shape[1] <= src.shape[1]
    # assert mask_on_tgt[0] + mask.shape[0] <= tgt.shape[0]
    # assert mask_on_tgt[1] + mask.shape[1] <= tgt.shape[1]

    if len(mask.shape) == 3:
      mask = mask.mean(-1)
    mask = (mask >= 128).astype(np.int32)

    # zero-out edge
    mask[0] = 0
    mask[-1] = 0
    mask[:, 0] = 0
    mask[:, -1] = 0

    x, y = np.nonzero(mask)
    x0, x1 = x.min() - 1, x.max() + 2
    y0, y1 = y.min() - 1, y.max() + 2
    mask_on_src = (x0 + mask_on_src[0], y0 + mask_on_src[1])
    mask_on_tgt = (x0 + mask_on_tgt[0], y0 + mask_on_tgt[1])
    mask = mask[x0:x1, y0:y1]
    ids, max_id, index_x, index_y = self.mask2index(mask)

    src_x, src_y = index_x + mask_on_src[0], index_y + mask_on_src[1]
    tgt_x, tgt_y = index_x + mask_on_tgt[0], index_y + mask_on_tgt[1]

    src_C = src[src_x, src_y].astype(np.float32)
    src_U = src[src_x - 1, src_y].astype(np.float32)
    src_D = src[src_x + 1, src_y].astype(np.float32)
    src_L = src[src_x, src_y - 1].astype(np.float32)
    src_R = src[src_x, src_y + 1].astype(np.float32)
    tgt_C = tgt[tgt_x, tgt_y].astype(np.float32)
    tgt_U = tgt[tgt_x - 1, tgt_y].astype(np.float32)
    tgt_D = tgt[tgt_x + 1, tgt_y].astype(np.float32)
    tgt_L = tgt[tgt_x, tgt_y - 1].astype(np.float32)
    tgt_R = tgt[tgt_x, tgt_y + 1].astype(np.float32)

    grad = self.mixgrad(src_C - src_L, tgt_C - tgt_L) \
      + self.mixgrad(src_C - src_R, tgt_C - tgt_R) \
      + self.mixgrad(src_C - src_U, tgt_C - tgt_U) \
      + self.mixgrad(src_C - src_D, tgt_C - tgt_D)

    A = np.zeros((max_id, 4), np.int32)
    X = np.zeros((max_id, 3), np.float32)
    B = np.zeros((max_id, 3), np.float32)

    X[1:] = tgt[index_x + mask_on_tgt[0], index_y + mask_on_tgt[1]]
    # four-way
    A[1:, 0] = ids[index_x - 1, index_y]
    A[1:, 1] = ids[index_x + 1, index_y]
    A[1:, 2] = ids[index_x, index_y - 1]
    A[1:, 3] = ids[index_x, index_y + 1]
    B[1:] = grad
    m = (mask[index_x - 1, index_y] == 0).astype(float).reshape(-1, 1)
    B[1:] += m * tgt[index_x + mask_on_tgt[0] - 1, index_y + mask_on_tgt[1]]
    m = (mask[index_x, index_y - 1] == 0).astype(float).reshape(-1, 1)
    B[1:] += m * tgt[index_x + mask_on_tgt[0], index_y + mask_on_tgt[1] - 1]
    m = (mask[index_x, index_y + 1] == 0).astype(float).reshape(-1, 1)
    B[1:] += m * tgt[index_x + mask_on_tgt[0], index_y + mask_on_tgt[1] + 1]
    m = (mask[index_x + 1, index_y] == 0).astype(float).reshape(-1, 1)
    B[1:] += m * tgt[index_x + mask_on_tgt[0] + 1, index_y + mask_on_tgt[1]]

    self.tgt = tgt.copy()
    self.tgt_index = (index_x + mask_on_tgt[0], index_y + mask_on_tgt[1])
    self.core.reset(max_id, A, X, B)
    return max_id

  def step(self, iteration: int) -> Optional[Tuple[np.ndarray, np.ndarray]]:
    result = self.core.step(iteration)
    if self.root:
      x, err = result
      self.tgt[self.tgt_index] = x[1:]
      return self.tgt, err
    return None


class GridProcessor(BaseProcessor):
  """PIE grid processor."""

  def __init__(
    self,
    gradient: str = "max",
    backend: str = DEFAULT_BACKEND,
    n_cpu: int = CPU_COUNT,
    min_interval: int = 100,
    block_size: int = 1024,
    grid_x: int = 8,
    grid_y: int = 8,
  ):
    core: Optional[Any] = None
    rank = 0

    if backend == "numpy":
      core = np_solver.GridSolver()
    elif backend == "numba" and numba_solver is not None:
      core = numba_solver.GridSolver()
    elif backend == "gcc":
      core = core_gcc.GridSolver(grid_x, grid_y)
    elif backend == "openmp" and core_openmp is not None:
      core = core_openmp.GridSolver(grid_x, grid_y, n_cpu)
    elif backend == "mpi" and core_mpi is not None:
      core = core_mpi.GridSolver(min_interval)
      rank = MPI.COMM_WORLD.Get_rank()
    elif backend == "cuda" and core_cuda is not None:
      core = core_cuda.GridSolver(grid_x, grid_y)
    elif backend.startswith("taichi") and taichi_solver is not None:
      core = taichi_solver.GridSolver(
        grid_x, grid_y, backend, n_cpu, block_size
      )

    super().__init__(gradient, rank, backend, core)

  def reset(
    self,
    src: np.ndarray,
    mask: np.ndarray,
    tgt: np.ndarray,
    mask_on_src: Tuple[int, int],
    mask_on_tgt: Tuple[int, int],
  ) -> int:
    assert self.root
    # check validity
    # assert 0 <= mask_on_src[0] and 0 <= mask_on_src[1]
    # assert mask_on_src[0] + mask.shape[0] <= src.shape[0]
    # assert mask_on_src[1] + mask.shape[1] <= src.shape[1]
    # assert mask_on_tgt[0] + mask.shape[0] <= tgt.shape[0]
    # assert mask_on_tgt[1] + mask.shape[1] <= tgt.shape[1]

    if len(mask.shape) == 3:
      mask = mask.mean(-1)
    mask = (mask >= 128).astype(np.int32)

    # zero-out edge
    mask[0] = 0
    mask[-1] = 0
    mask[:, 0] = 0
    mask[:, -1] = 0

    x, y = np.nonzero(mask)
    x0, x1 = x.min() - 1, x.max() + 2
    y0, y1 = y.min() - 1, y.max() + 2
    mask = mask[x0:x1, y0:y1]
    max_id = np.prod(mask.shape)

    src_crop = src[mask_on_src[0] + x0:mask_on_src[0] + x1,
                   mask_on_src[1] + y0:mask_on_src[1] + y1].astype(np.float32)
    tgt_crop = tgt[mask_on_tgt[0] + x0:mask_on_tgt[0] + x1,
                   mask_on_tgt[1] + y0:mask_on_tgt[1] + y1].astype(np.float32)
    grad = np.zeros([*mask.shape, 3], np.float32)
    grad[1:] += self.mixgrad(
      src_crop[1:] - src_crop[:-1], tgt_crop[1:] - tgt_crop[:-1]
    )
    grad[:-1] += self.mixgrad(
      src_crop[:-1] - src_crop[1:], tgt_crop[:-1] - tgt_crop[1:]
    )
    grad[:, 1:] += self.mixgrad(
      src_crop[:, 1:] - src_crop[:, :-1], tgt_crop[:, 1:] - tgt_crop[:, :-1]
    )
    grad[:, :-1] += self.mixgrad(
      src_crop[:, :-1] - src_crop[:, 1:], tgt_crop[:, :-1] - tgt_crop[:, 1:]
    )

    grad[mask == 0] = 0
    if True:
        kernel = [[1] * 3 for _ in range(3)]
        nmask = mask.copy()
        nmask[nmask > 0] = 1
        res = scipy.signal.convolve2d(
            nmask, kernel, mode="same", boundary="fill", fillvalue=1
        )
        res[nmask < 1] = 0
        res[res == 9] = 0
        res[res > 0] = 1
        grad[res>0]=0
        # ylst, xlst = res.nonzero()
        # for y, x in zip(ylst, xlst):
        #     grad[y,x]=0
            # for yi in range(-1,2):
                # for xi in range(-1,2):
                    # grad[y+yi,x+xi]=0
    self.x0 = mask_on_tgt[0] + x0
    self.x1 = mask_on_tgt[0] + x1
    self.y0 = mask_on_tgt[1] + y0
    self.y1 = mask_on_tgt[1] + y1
    self.tgt = tgt.copy()
    self.core.reset(max_id, mask, tgt_crop, grad)
    return max_id

  def step(self, iteration: int) -> Optional[Tuple[np.ndarray, np.ndarray]]:
    result = self.core.step(iteration)
    if self.root:
      tgt, err = result
      self.tgt[self.x0:self.x1, self.y0:self.y1] = tgt
      return self.tgt, err
    return None