1034 lines
37 KiB
Python
1034 lines
37 KiB
Python
import gc
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import math
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import random
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import traceback
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from typing import Any
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import torch
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import numpy as np
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import collections
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from itertools import repeat
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from diffusers import (
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DDIMScheduler,
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PNDMScheduler,
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LMSDiscreteScheduler,
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EulerDiscreteScheduler,
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EulerAncestralDiscreteScheduler,
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DPMSolverMultistepScheduler,
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UniPCMultistepScheduler,
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LCMScheduler,
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DPMSolverSinglestepScheduler,
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KDPM2DiscreteScheduler,
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KDPM2AncestralDiscreteScheduler,
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HeunDiscreteScheduler,
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)
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from loguru import logger
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from iopaint.schema import SDSampler
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from torch import conv2d, conv_transpose2d
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def make_beta_schedule(
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device, schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3
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):
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if schedule == "linear":
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betas = (
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torch.linspace(
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linear_start**0.5, linear_end**0.5, n_timestep, dtype=torch.float64
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)
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** 2
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)
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elif schedule == "cosine":
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timesteps = (
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torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
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).to(device)
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alphas = timesteps / (1 + cosine_s) * np.pi / 2
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alphas = torch.cos(alphas).pow(2).to(device)
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alphas = alphas / alphas[0]
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betas = 1 - alphas[1:] / alphas[:-1]
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betas = np.clip(betas, a_min=0, a_max=0.999)
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elif schedule == "sqrt_linear":
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betas = torch.linspace(
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linear_start, linear_end, n_timestep, dtype=torch.float64
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)
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elif schedule == "sqrt":
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betas = (
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torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
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** 0.5
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)
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else:
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raise ValueError(f"schedule '{schedule}' unknown.")
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return betas.numpy()
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def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
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# select alphas for computing the variance schedule
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alphas = alphacums[ddim_timesteps]
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alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
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# according the the formula provided in https://arxiv.org/abs/2010.02502
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sigmas = eta * np.sqrt(
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(1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)
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)
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if verbose:
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print(
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f"Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}"
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)
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print(
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f"For the chosen value of eta, which is {eta}, "
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f"this results in the following sigma_t schedule for ddim sampler {sigmas}"
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)
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return sigmas, alphas, alphas_prev
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def make_ddim_timesteps(
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ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True
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):
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if ddim_discr_method == "uniform":
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c = num_ddpm_timesteps // num_ddim_timesteps
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ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
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elif ddim_discr_method == "quad":
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ddim_timesteps = (
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(np.linspace(0, np.sqrt(num_ddpm_timesteps * 0.8), num_ddim_timesteps)) ** 2
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).astype(int)
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else:
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raise NotImplementedError(
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f'There is no ddim discretization method called "{ddim_discr_method}"'
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)
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# assert ddim_timesteps.shape[0] == num_ddim_timesteps
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# add one to get the final alpha values right (the ones from first scale to data during sampling)
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steps_out = ddim_timesteps + 1
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if verbose:
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print(f"Selected timesteps for ddim sampler: {steps_out}")
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return steps_out
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def noise_like(shape, device, repeat=False):
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repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(
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shape[0], *((1,) * (len(shape) - 1))
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)
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noise = lambda: torch.randn(shape, device=device)
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return repeat_noise() if repeat else noise()
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def timestep_embedding(device, timesteps, dim, max_period=10000, repeat_only=False):
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"""
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Create sinusoidal timestep embeddings.
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:param timesteps: a 1-D Tensor of N indices, one per batch element.
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These may be fractional.
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:param dim: the dimension of the output.
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:param max_period: controls the minimum frequency of the embeddings.
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:return: an [N x dim] Tensor of positional embeddings.
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"""
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half = dim // 2
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freqs = torch.exp(
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-math.log(max_period)
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* torch.arange(start=0, end=half, dtype=torch.float32)
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/ half
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).to(device=device)
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args = timesteps[:, None].float() * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if dim % 2:
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embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
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return embedding
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###### MAT and FcF #######
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def normalize_2nd_moment(x, dim=1):
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return (
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x * (x.square().mean(dim=dim, keepdim=True) + torch.finfo(x.dtype).eps).rsqrt()
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)
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class EasyDict(dict):
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"""Convenience class that behaves like a dict but allows access with the attribute syntax."""
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def __getattr__(self, name: str) -> Any:
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try:
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return self[name]
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except KeyError:
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raise AttributeError(name)
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def __setattr__(self, name: str, value: Any) -> None:
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self[name] = value
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def __delattr__(self, name: str) -> None:
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del self[name]
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def _bias_act_ref(x, b=None, dim=1, act="linear", alpha=None, gain=None, clamp=None):
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"""Slow reference implementation of `bias_act()` using standard TensorFlow ops."""
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assert isinstance(x, torch.Tensor)
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assert clamp is None or clamp >= 0
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spec = activation_funcs[act]
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alpha = float(alpha if alpha is not None else spec.def_alpha)
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gain = float(gain if gain is not None else spec.def_gain)
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clamp = float(clamp if clamp is not None else -1)
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# Add bias.
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if b is not None:
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assert isinstance(b, torch.Tensor) and b.ndim == 1
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assert 0 <= dim < x.ndim
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assert b.shape[0] == x.shape[dim]
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x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
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# Evaluate activation function.
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alpha = float(alpha)
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x = spec.func(x, alpha=alpha)
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# Scale by gain.
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gain = float(gain)
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if gain != 1:
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x = x * gain
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# Clamp.
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if clamp >= 0:
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x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type
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return x
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def bias_act(
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x, b=None, dim=1, act="linear", alpha=None, gain=None, clamp=None, impl="ref"
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):
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r"""Fused bias and activation function.
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Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
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and scales the result by `gain`. Each of the steps is optional. In most cases,
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the fused op is considerably more efficient than performing the same calculation
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using standard PyTorch ops. It supports first and second order gradients,
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but not third order gradients.
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Args:
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x: Input activation tensor. Can be of any shape.
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b: Bias vector, or `None` to disable. Must be a 1D tensor of the same type
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as `x`. The shape must be known, and it must match the dimension of `x`
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corresponding to `dim`.
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dim: The dimension in `x` corresponding to the elements of `b`.
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The value of `dim` is ignored if `b` is not specified.
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act: Name of the activation function to evaluate, or `"linear"` to disable.
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Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
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See `activation_funcs` for a full list. `None` is not allowed.
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alpha: Shape parameter for the activation function, or `None` to use the default.
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gain: Scaling factor for the output tensor, or `None` to use default.
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See `activation_funcs` for the default scaling of each activation function.
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If unsure, consider specifying 1.
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clamp: Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
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the clamping (default).
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impl: Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
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Returns:
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Tensor of the same shape and datatype as `x`.
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"""
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assert isinstance(x, torch.Tensor)
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assert impl in ["ref", "cuda"]
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return _bias_act_ref(
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x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp
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)
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def _get_filter_size(f):
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if f is None:
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return 1, 1
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assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
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fw = f.shape[-1]
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fh = f.shape[0]
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fw = int(fw)
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fh = int(fh)
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assert fw >= 1 and fh >= 1
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return fw, fh
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def _get_weight_shape(w):
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shape = [int(sz) for sz in w.shape]
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return shape
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def _parse_scaling(scaling):
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if isinstance(scaling, int):
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scaling = [scaling, scaling]
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assert isinstance(scaling, (list, tuple))
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assert all(isinstance(x, int) for x in scaling)
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sx, sy = scaling
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assert sx >= 1 and sy >= 1
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return sx, sy
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def _parse_padding(padding):
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if isinstance(padding, int):
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padding = [padding, padding]
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assert isinstance(padding, (list, tuple))
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assert all(isinstance(x, int) for x in padding)
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if len(padding) == 2:
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padx, pady = padding
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padding = [padx, padx, pady, pady]
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padx0, padx1, pady0, pady1 = padding
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return padx0, padx1, pady0, pady1
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def setup_filter(
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f,
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device=torch.device("cpu"),
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normalize=True,
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flip_filter=False,
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gain=1,
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separable=None,
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):
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r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
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Args:
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f: Torch tensor, numpy array, or python list of the shape
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`[filter_height, filter_width]` (non-separable),
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`[filter_taps]` (separable),
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`[]` (impulse), or
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`None` (identity).
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device: Result device (default: cpu).
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normalize: Normalize the filter so that it retains the magnitude
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for constant input signal (DC)? (default: True).
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flip_filter: Flip the filter? (default: False).
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gain: Overall scaling factor for signal magnitude (default: 1).
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separable: Return a separable filter? (default: select automatically).
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Returns:
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Float32 tensor of the shape
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`[filter_height, filter_width]` (non-separable) or
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`[filter_taps]` (separable).
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"""
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# Validate.
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if f is None:
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f = 1
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f = torch.as_tensor(f, dtype=torch.float32)
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assert f.ndim in [0, 1, 2]
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assert f.numel() > 0
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if f.ndim == 0:
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f = f[np.newaxis]
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# Separable?
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if separable is None:
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separable = f.ndim == 1 and f.numel() >= 8
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if f.ndim == 1 and not separable:
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f = f.ger(f)
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assert f.ndim == (1 if separable else 2)
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# Apply normalize, flip, gain, and device.
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if normalize:
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f /= f.sum()
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if flip_filter:
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f = f.flip(list(range(f.ndim)))
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f = f * (gain ** (f.ndim / 2))
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f = f.to(device=device)
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return f
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def _ntuple(n):
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def parse(x):
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if isinstance(x, collections.abc.Iterable):
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return x
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return tuple(repeat(x, n))
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return parse
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to_2tuple = _ntuple(2)
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activation_funcs = {
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"linear": EasyDict(
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func=lambda x, **_: x,
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def_alpha=0,
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def_gain=1,
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cuda_idx=1,
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ref="",
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has_2nd_grad=False,
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),
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"relu": EasyDict(
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func=lambda x, **_: torch.nn.functional.relu(x),
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def_alpha=0,
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def_gain=np.sqrt(2),
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cuda_idx=2,
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ref="y",
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has_2nd_grad=False,
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),
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"lrelu": EasyDict(
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func=lambda x, alpha, **_: torch.nn.functional.leaky_relu(x, alpha),
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def_alpha=0.2,
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def_gain=np.sqrt(2),
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cuda_idx=3,
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ref="y",
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has_2nd_grad=False,
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),
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"tanh": EasyDict(
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func=lambda x, **_: torch.tanh(x),
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def_alpha=0,
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def_gain=1,
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cuda_idx=4,
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ref="y",
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has_2nd_grad=True,
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),
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"sigmoid": EasyDict(
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func=lambda x, **_: torch.sigmoid(x),
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def_alpha=0,
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def_gain=1,
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cuda_idx=5,
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ref="y",
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has_2nd_grad=True,
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),
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"elu": EasyDict(
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func=lambda x, **_: torch.nn.functional.elu(x),
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def_alpha=0,
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def_gain=1,
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cuda_idx=6,
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ref="y",
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has_2nd_grad=True,
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),
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"selu": EasyDict(
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func=lambda x, **_: torch.nn.functional.selu(x),
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def_alpha=0,
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def_gain=1,
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cuda_idx=7,
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ref="y",
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has_2nd_grad=True,
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),
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"softplus": EasyDict(
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func=lambda x, **_: torch.nn.functional.softplus(x),
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def_alpha=0,
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def_gain=1,
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cuda_idx=8,
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ref="y",
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has_2nd_grad=True,
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),
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"swish": EasyDict(
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func=lambda x, **_: torch.sigmoid(x) * x,
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def_alpha=0,
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def_gain=np.sqrt(2),
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cuda_idx=9,
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ref="x",
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has_2nd_grad=True,
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),
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}
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|
|
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def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl="cuda"):
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r"""Pad, upsample, filter, and downsample a batch of 2D images.
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Performs the following sequence of operations for each channel:
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1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
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2. Pad the image with the specified number of zeros on each side (`padding`).
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Negative padding corresponds to cropping the image.
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3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
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so that the footprint of all output pixels lies within the input image.
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4. Downsample the image by keeping every Nth pixel (`down`).
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|
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This sequence of operations bears close resemblance to scipy.signal.upfirdn().
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The fused op is considerably more efficient than performing the same calculation
|
|
using standard PyTorch ops. It supports gradients of arbitrary order.
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|
|
Args:
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x: Float32/float64/float16 input tensor of the shape
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`[batch_size, num_channels, in_height, in_width]`.
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f: Float32 FIR filter of the shape
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`[filter_height, filter_width]` (non-separable),
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`[filter_taps]` (separable), or
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`None` (identity).
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up: Integer upsampling factor. Can be a single int or a list/tuple
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`[x, y]` (default: 1).
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down: Integer downsampling factor. Can be a single int or a list/tuple
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`[x, y]` (default: 1).
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padding: Padding with respect to the upsampled image. Can be a single number
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or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
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(default: 0).
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flip_filter: False = convolution, True = correlation (default: False).
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gain: Overall scaling factor for signal magnitude (default: 1).
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impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
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Returns:
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Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
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"""
|
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# assert isinstance(x, torch.Tensor)
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# assert impl in ['ref', 'cuda']
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return _upfirdn2d_ref(
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x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain
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)
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|
|
|
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def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
|
|
"""Slow reference implementation of `upfirdn2d()` using standard PyTorch ops."""
|
|
# Validate arguments.
|
|
assert isinstance(x, torch.Tensor) and x.ndim == 4
|
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if f is None:
|
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f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
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assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
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assert not f.requires_grad
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batch_size, num_channels, in_height, in_width = x.shape
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# upx, upy = _parse_scaling(up)
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# downx, downy = _parse_scaling(down)
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upx, upy = up, up
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downx, downy = down, down
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# padx0, padx1, pady0, pady1 = _parse_padding(padding)
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padx0, padx1, pady0, pady1 = padding[0], padding[1], padding[2], padding[3]
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|
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# Upsample by inserting zeros.
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x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
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x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
|
|
x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
|
|
|
|
# Pad or crop.
|
|
x = torch.nn.functional.pad(
|
|
x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)]
|
|
)
|
|
x = x[
|
|
:,
|
|
:,
|
|
max(-pady0, 0) : x.shape[2] - max(-pady1, 0),
|
|
max(-padx0, 0) : x.shape[3] - max(-padx1, 0),
|
|
]
|
|
|
|
# Setup filter.
|
|
f = f * (gain ** (f.ndim / 2))
|
|
f = f.to(x.dtype)
|
|
if not flip_filter:
|
|
f = f.flip(list(range(f.ndim)))
|
|
|
|
# Convolve with the filter.
|
|
f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
|
|
if f.ndim == 4:
|
|
x = conv2d(input=x, weight=f, groups=num_channels)
|
|
else:
|
|
x = conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
|
|
x = conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)
|
|
|
|
# Downsample by throwing away pixels.
|
|
x = x[:, :, ::downy, ::downx]
|
|
return x
|
|
|
|
|
|
def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl="cuda"):
|
|
r"""Downsample a batch of 2D images using the given 2D FIR filter.
|
|
|
|
By default, the result is padded so that its shape is a fraction of the input.
|
|
User-specified padding is applied on top of that, with negative values
|
|
indicating cropping. Pixels outside the image are assumed to be zero.
|
|
|
|
Args:
|
|
x: Float32/float64/float16 input tensor of the shape
|
|
`[batch_size, num_channels, in_height, in_width]`.
|
|
f: Float32 FIR filter of the shape
|
|
`[filter_height, filter_width]` (non-separable),
|
|
`[filter_taps]` (separable), or
|
|
`None` (identity).
|
|
down: Integer downsampling factor. Can be a single int or a list/tuple
|
|
`[x, y]` (default: 1).
|
|
padding: Padding with respect to the input. Can be a single number or a
|
|
list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
|
|
(default: 0).
|
|
flip_filter: False = convolution, True = correlation (default: False).
|
|
gain: Overall scaling factor for signal magnitude (default: 1).
|
|
impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
|
|
|
|
Returns:
|
|
Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
|
|
"""
|
|
downx, downy = _parse_scaling(down)
|
|
# padx0, padx1, pady0, pady1 = _parse_padding(padding)
|
|
padx0, padx1, pady0, pady1 = padding, padding, padding, padding
|
|
|
|
fw, fh = _get_filter_size(f)
|
|
p = [
|
|
padx0 + (fw - downx + 1) // 2,
|
|
padx1 + (fw - downx) // 2,
|
|
pady0 + (fh - downy + 1) // 2,
|
|
pady1 + (fh - downy) // 2,
|
|
]
|
|
return upfirdn2d(
|
|
x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl
|
|
)
|
|
|
|
|
|
def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl="cuda"):
|
|
r"""Upsample a batch of 2D images using the given 2D FIR filter.
|
|
|
|
By default, the result is padded so that its shape is a multiple of the input.
|
|
User-specified padding is applied on top of that, with negative values
|
|
indicating cropping. Pixels outside the image are assumed to be zero.
|
|
|
|
Args:
|
|
x: Float32/float64/float16 input tensor of the shape
|
|
`[batch_size, num_channels, in_height, in_width]`.
|
|
f: Float32 FIR filter of the shape
|
|
`[filter_height, filter_width]` (non-separable),
|
|
`[filter_taps]` (separable), or
|
|
`None` (identity).
|
|
up: Integer upsampling factor. Can be a single int or a list/tuple
|
|
`[x, y]` (default: 1).
|
|
padding: Padding with respect to the output. Can be a single number or a
|
|
list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
|
|
(default: 0).
|
|
flip_filter: False = convolution, True = correlation (default: False).
|
|
gain: Overall scaling factor for signal magnitude (default: 1).
|
|
impl: Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
|
|
|
|
Returns:
|
|
Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
|
|
"""
|
|
upx, upy = _parse_scaling(up)
|
|
# upx, upy = up, up
|
|
padx0, padx1, pady0, pady1 = _parse_padding(padding)
|
|
# padx0, padx1, pady0, pady1 = padding, padding, padding, padding
|
|
fw, fh = _get_filter_size(f)
|
|
p = [
|
|
padx0 + (fw + upx - 1) // 2,
|
|
padx1 + (fw - upx) // 2,
|
|
pady0 + (fh + upy - 1) // 2,
|
|
pady1 + (fh - upy) // 2,
|
|
]
|
|
return upfirdn2d(
|
|
x,
|
|
f,
|
|
up=up,
|
|
padding=p,
|
|
flip_filter=flip_filter,
|
|
gain=gain * upx * upy,
|
|
impl=impl,
|
|
)
|
|
|
|
|
|
class MinibatchStdLayer(torch.nn.Module):
|
|
def __init__(self, group_size, num_channels=1):
|
|
super().__init__()
|
|
self.group_size = group_size
|
|
self.num_channels = num_channels
|
|
|
|
def forward(self, x):
|
|
N, C, H, W = x.shape
|
|
G = (
|
|
torch.min(torch.as_tensor(self.group_size), torch.as_tensor(N))
|
|
if self.group_size is not None
|
|
else N
|
|
)
|
|
F = self.num_channels
|
|
c = C // F
|
|
|
|
y = x.reshape(
|
|
G, -1, F, c, H, W
|
|
) # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c.
|
|
y = y - y.mean(dim=0) # [GnFcHW] Subtract mean over group.
|
|
y = y.square().mean(dim=0) # [nFcHW] Calc variance over group.
|
|
y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group.
|
|
y = y.mean(dim=[2, 3, 4]) # [nF] Take average over channels and pixels.
|
|
y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions.
|
|
y = y.repeat(G, 1, H, W) # [NFHW] Replicate over group and pixels.
|
|
x = torch.cat([x, y], dim=1) # [NCHW] Append to input as new channels.
|
|
return x
|
|
|
|
|
|
class FullyConnectedLayer(torch.nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_features, # Number of input features.
|
|
out_features, # Number of output features.
|
|
bias=True, # Apply additive bias before the activation function?
|
|
activation="linear", # Activation function: 'relu', 'lrelu', etc.
|
|
lr_multiplier=1, # Learning rate multiplier.
|
|
bias_init=0, # Initial value for the additive bias.
|
|
):
|
|
super().__init__()
|
|
self.weight = torch.nn.Parameter(
|
|
torch.randn([out_features, in_features]) / lr_multiplier
|
|
)
|
|
self.bias = (
|
|
torch.nn.Parameter(torch.full([out_features], np.float32(bias_init)))
|
|
if bias
|
|
else None
|
|
)
|
|
self.activation = activation
|
|
|
|
self.weight_gain = lr_multiplier / np.sqrt(in_features)
|
|
self.bias_gain = lr_multiplier
|
|
|
|
def forward(self, x):
|
|
w = self.weight * self.weight_gain
|
|
b = self.bias
|
|
if b is not None and self.bias_gain != 1:
|
|
b = b * self.bias_gain
|
|
|
|
if self.activation == "linear" and b is not None:
|
|
# out = torch.addmm(b.unsqueeze(0), x, w.t())
|
|
x = x.matmul(w.t())
|
|
out = x + b.reshape([-1 if i == x.ndim - 1 else 1 for i in range(x.ndim)])
|
|
else:
|
|
x = x.matmul(w.t())
|
|
out = bias_act(x, b, act=self.activation, dim=x.ndim - 1)
|
|
return out
|
|
|
|
|
|
def _conv2d_wrapper(
|
|
x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True
|
|
):
|
|
"""Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations."""
|
|
out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
|
|
|
|
# Flip weight if requested.
|
|
if (
|
|
not flip_weight
|
|
): # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
|
|
w = w.flip([2, 3])
|
|
|
|
# Workaround performance pitfall in cuDNN 8.0.5, triggered when using
|
|
# 1x1 kernel + memory_format=channels_last + less than 64 channels.
|
|
if (
|
|
kw == 1
|
|
and kh == 1
|
|
and stride == 1
|
|
and padding in [0, [0, 0], (0, 0)]
|
|
and not transpose
|
|
):
|
|
if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64:
|
|
if out_channels <= 4 and groups == 1:
|
|
in_shape = x.shape
|
|
x = w.squeeze(3).squeeze(2) @ x.reshape(
|
|
[in_shape[0], in_channels_per_group, -1]
|
|
)
|
|
x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]])
|
|
else:
|
|
x = x.to(memory_format=torch.contiguous_format)
|
|
w = w.to(memory_format=torch.contiguous_format)
|
|
x = conv2d(x, w, groups=groups)
|
|
return x.to(memory_format=torch.channels_last)
|
|
|
|
# Otherwise => execute using conv2d_gradfix.
|
|
op = conv_transpose2d if transpose else conv2d
|
|
return op(x, w, stride=stride, padding=padding, groups=groups)
|
|
|
|
|
|
def conv2d_resample(
|
|
x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False
|
|
):
|
|
r"""2D convolution with optional up/downsampling.
|
|
|
|
Padding is performed only once at the beginning, not between the operations.
|
|
|
|
Args:
|
|
x: Input tensor of shape
|
|
`[batch_size, in_channels, in_height, in_width]`.
|
|
w: Weight tensor of shape
|
|
`[out_channels, in_channels//groups, kernel_height, kernel_width]`.
|
|
f: Low-pass filter for up/downsampling. Must be prepared beforehand by
|
|
calling setup_filter(). None = identity (default).
|
|
up: Integer upsampling factor (default: 1).
|
|
down: Integer downsampling factor (default: 1).
|
|
padding: Padding with respect to the upsampled image. Can be a single number
|
|
or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
|
|
(default: 0).
|
|
groups: Split input channels into N groups (default: 1).
|
|
flip_weight: False = convolution, True = correlation (default: True).
|
|
flip_filter: False = convolution, True = correlation (default: False).
|
|
|
|
Returns:
|
|
Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
|
|
"""
|
|
# Validate arguments.
|
|
assert isinstance(x, torch.Tensor) and (x.ndim == 4)
|
|
assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype)
|
|
assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2])
|
|
assert isinstance(up, int) and (up >= 1)
|
|
assert isinstance(down, int) and (down >= 1)
|
|
# assert isinstance(groups, int) and (groups >= 1), f"!!!!!! groups: {groups} isinstance(groups, int) {isinstance(groups, int)} {type(groups)}"
|
|
out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
|
|
fw, fh = _get_filter_size(f)
|
|
# px0, px1, py0, py1 = _parse_padding(padding)
|
|
px0, px1, py0, py1 = padding, padding, padding, padding
|
|
|
|
# Adjust padding to account for up/downsampling.
|
|
if up > 1:
|
|
px0 += (fw + up - 1) // 2
|
|
px1 += (fw - up) // 2
|
|
py0 += (fh + up - 1) // 2
|
|
py1 += (fh - up) // 2
|
|
if down > 1:
|
|
px0 += (fw - down + 1) // 2
|
|
px1 += (fw - down) // 2
|
|
py0 += (fh - down + 1) // 2
|
|
py1 += (fh - down) // 2
|
|
|
|
# Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
|
|
if kw == 1 and kh == 1 and (down > 1 and up == 1):
|
|
x = upfirdn2d(
|
|
x=x, f=f, down=down, padding=[px0, px1, py0, py1], flip_filter=flip_filter
|
|
)
|
|
x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
|
|
return x
|
|
|
|
# Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
|
|
if kw == 1 and kh == 1 and (up > 1 and down == 1):
|
|
x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
|
|
x = upfirdn2d(
|
|
x=x,
|
|
f=f,
|
|
up=up,
|
|
padding=[px0, px1, py0, py1],
|
|
gain=up**2,
|
|
flip_filter=flip_filter,
|
|
)
|
|
return x
|
|
|
|
# Fast path: downsampling only => use strided convolution.
|
|
if down > 1 and up == 1:
|
|
x = upfirdn2d(x=x, f=f, padding=[px0, px1, py0, py1], flip_filter=flip_filter)
|
|
x = _conv2d_wrapper(
|
|
x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight
|
|
)
|
|
return x
|
|
|
|
# Fast path: upsampling with optional downsampling => use transpose strided convolution.
|
|
if up > 1:
|
|
if groups == 1:
|
|
w = w.transpose(0, 1)
|
|
else:
|
|
w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw)
|
|
w = w.transpose(1, 2)
|
|
w = w.reshape(
|
|
groups * in_channels_per_group, out_channels // groups, kh, kw
|
|
)
|
|
px0 -= kw - 1
|
|
px1 -= kw - up
|
|
py0 -= kh - 1
|
|
py1 -= kh - up
|
|
pxt = max(min(-px0, -px1), 0)
|
|
pyt = max(min(-py0, -py1), 0)
|
|
x = _conv2d_wrapper(
|
|
x=x,
|
|
w=w,
|
|
stride=up,
|
|
padding=[pyt, pxt],
|
|
groups=groups,
|
|
transpose=True,
|
|
flip_weight=(not flip_weight),
|
|
)
|
|
x = upfirdn2d(
|
|
x=x,
|
|
f=f,
|
|
padding=[px0 + pxt, px1 + pxt, py0 + pyt, py1 + pyt],
|
|
gain=up**2,
|
|
flip_filter=flip_filter,
|
|
)
|
|
if down > 1:
|
|
x = upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
|
|
return x
|
|
|
|
# Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
|
|
if up == 1 and down == 1:
|
|
if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
|
|
return _conv2d_wrapper(
|
|
x=x, w=w, padding=[py0, px0], groups=groups, flip_weight=flip_weight
|
|
)
|
|
|
|
# Fallback: Generic reference implementation.
|
|
x = upfirdn2d(
|
|
x=x,
|
|
f=(f if up > 1 else None),
|
|
up=up,
|
|
padding=[px0, px1, py0, py1],
|
|
gain=up**2,
|
|
flip_filter=flip_filter,
|
|
)
|
|
x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
|
|
if down > 1:
|
|
x = upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
|
|
return x
|
|
|
|
|
|
class Conv2dLayer(torch.nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels, # Number of input channels.
|
|
out_channels, # Number of output channels.
|
|
kernel_size, # Width and height of the convolution kernel.
|
|
bias=True, # Apply additive bias before the activation function?
|
|
activation="linear", # Activation function: 'relu', 'lrelu', etc.
|
|
up=1, # Integer upsampling factor.
|
|
down=1, # Integer downsampling factor.
|
|
resample_filter=[
|
|
1,
|
|
3,
|
|
3,
|
|
1,
|
|
], # Low-pass filter to apply when resampling activations.
|
|
conv_clamp=None, # Clamp the output to +-X, None = disable clamping.
|
|
channels_last=False, # Expect the input to have memory_format=channels_last?
|
|
trainable=True, # Update the weights of this layer during training?
|
|
):
|
|
super().__init__()
|
|
self.activation = activation
|
|
self.up = up
|
|
self.down = down
|
|
self.register_buffer("resample_filter", setup_filter(resample_filter))
|
|
self.conv_clamp = conv_clamp
|
|
self.padding = kernel_size // 2
|
|
self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size**2))
|
|
self.act_gain = activation_funcs[activation].def_gain
|
|
|
|
memory_format = (
|
|
torch.channels_last if channels_last else torch.contiguous_format
|
|
)
|
|
weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(
|
|
memory_format=memory_format
|
|
)
|
|
bias = torch.zeros([out_channels]) if bias else None
|
|
if trainable:
|
|
self.weight = torch.nn.Parameter(weight)
|
|
self.bias = torch.nn.Parameter(bias) if bias is not None else None
|
|
else:
|
|
self.register_buffer("weight", weight)
|
|
if bias is not None:
|
|
self.register_buffer("bias", bias)
|
|
else:
|
|
self.bias = None
|
|
|
|
def forward(self, x, gain=1):
|
|
w = self.weight * self.weight_gain
|
|
x = conv2d_resample(
|
|
x=x,
|
|
w=w,
|
|
f=self.resample_filter,
|
|
up=self.up,
|
|
down=self.down,
|
|
padding=self.padding,
|
|
)
|
|
|
|
act_gain = self.act_gain * gain
|
|
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
|
|
out = bias_act(
|
|
x, self.bias, act=self.activation, gain=act_gain, clamp=act_clamp
|
|
)
|
|
return out
|
|
|
|
|
|
def torch_gc():
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.ipc_collect()
|
|
gc.collect()
|
|
|
|
|
|
def set_seed(seed: int):
|
|
random.seed(seed)
|
|
np.random.seed(seed)
|
|
torch.manual_seed(seed)
|
|
torch.cuda.manual_seed_all(seed)
|
|
|
|
|
|
def get_scheduler(sd_sampler, scheduler_config):
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# https://github.com/huggingface/diffusers/issues/4167
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keys_to_pop = ["use_karras_sigmas", "algorithm_type"]
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scheduler_config = dict(scheduler_config)
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for it in keys_to_pop:
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scheduler_config.pop(it, None)
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|
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# fmt: off
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samplers = {
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SDSampler.dpm_plus_plus_2m: [DPMSolverMultistepScheduler],
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SDSampler.dpm_plus_plus_2m_karras: [DPMSolverMultistepScheduler, dict(use_karras_sigmas=True)],
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SDSampler.dpm_plus_plus_2m_sde: [DPMSolverMultistepScheduler, dict(algorithm_type="sde-dpmsolver++")],
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SDSampler.dpm_plus_plus_2m_sde_karras: [DPMSolverMultistepScheduler, dict(algorithm_type="sde-dpmsolver++", use_karras_sigmas=True)],
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SDSampler.dpm_plus_plus_sde: [DPMSolverSinglestepScheduler],
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SDSampler.dpm_plus_plus_sde_karras: [DPMSolverSinglestepScheduler, dict(use_karras_sigmas=True)],
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SDSampler.dpm2: [KDPM2DiscreteScheduler],
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SDSampler.dpm2_karras: [KDPM2DiscreteScheduler, dict(use_karras_sigmas=True)],
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SDSampler.dpm2_a: [KDPM2AncestralDiscreteScheduler],
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|
SDSampler.dpm2_a_karras: [KDPM2AncestralDiscreteScheduler, dict(use_karras_sigmas=True)],
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SDSampler.euler: [EulerDiscreteScheduler],
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|
SDSampler.euler_a: [EulerAncestralDiscreteScheduler],
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|
SDSampler.heun: [HeunDiscreteScheduler],
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|
SDSampler.lms: [LMSDiscreteScheduler],
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SDSampler.lms_karras: [LMSDiscreteScheduler, dict(use_karras_sigmas=True)],
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SDSampler.ddim: [DDIMScheduler],
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|
SDSampler.pndm: [PNDMScheduler],
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SDSampler.uni_pc: [UniPCMultistepScheduler],
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|
SDSampler.lcm: [LCMScheduler],
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}
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# fmt: on
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|
if sd_sampler in samplers:
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|
if len(samplers[sd_sampler]) == 2:
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|
scheduler_cls, kwargs = samplers[sd_sampler]
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|
else:
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|
scheduler_cls, kwargs = samplers[sd_sampler][0], {}
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return scheduler_cls.from_config(scheduler_config, **kwargs)
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|
else:
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|
raise ValueError(sd_sampler)
|
|
|
|
|
|
def is_local_files_only(**kwargs) -> bool:
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|
from huggingface_hub.constants import HF_HUB_OFFLINE
|
|
|
|
return HF_HUB_OFFLINE or kwargs.get("local_files_only", False)
|
|
|
|
|
|
def handle_from_pretrained_exceptions(func, **kwargs):
|
|
try:
|
|
return func(**kwargs)
|
|
except ValueError as e:
|
|
if "You are trying to load the model files of the `variant=fp16`" in str(e):
|
|
logger.info("variant=fp16 not found, try revision=fp16")
|
|
try:
|
|
return func(**{**kwargs, "variant": None, "revision": "fp16"})
|
|
except Exception as e:
|
|
logger.info("revision=fp16 not found, try revision=main")
|
|
return func(**{**kwargs, "variant": None, "revision": "main"})
|
|
raise e
|
|
except OSError as e:
|
|
previous_traceback = traceback.format_exc()
|
|
if "RevisionNotFoundError: 404 Client Error." in previous_traceback:
|
|
logger.info("revision=fp16 not found, try revision=main")
|
|
return func(**{**kwargs, "variant": None, "revision": "main"})
|
|
elif "Max retries exceeded" in previous_traceback:
|
|
logger.exception(
|
|
"Fetching model from HuggingFace failed. "
|
|
"If this is your first time downloading the model, you may need to set up proxy in terminal."
|
|
"If the model has already been downloaded, you can add --local-files-only when starting."
|
|
)
|
|
exit(-1)
|
|
raise e
|
|
except Exception as e:
|
|
raise e
|
|
|
|
|
|
def get_torch_dtype(device, no_half: bool):
|
|
device = str(device)
|
|
use_fp16 = not no_half
|
|
use_gpu = device == "cuda"
|
|
# https://github.com/huggingface/diffusers/issues/4480
|
|
# pipe.enable_attention_slicing and float16 will cause black output on mps
|
|
# if device in ["cuda", "mps"] and use_fp16:
|
|
if device in ["cuda"] and use_fp16:
|
|
return use_gpu, torch.float16
|
|
return use_gpu, torch.float32
|
|
|
|
|
|
def enable_low_mem(pipe, enable: bool):
|
|
if torch.backends.mps.is_available():
|
|
# https://huggingface.co/docs/diffusers/v0.25.0/en/api/pipelines/stable_diffusion/image_variation#diffusers.StableDiffusionImageVariationPipeline.enable_attention_slicing
|
|
# CUDA: Don't enable attention slicing if you're already using `scaled_dot_product_attention` (SDPA) from PyTorch 2.0 or xFormers.
|
|
if enable:
|
|
pipe.enable_attention_slicing("max")
|
|
else:
|
|
# https://huggingface.co/docs/diffusers/optimization/mps
|
|
# Devices with less than 64GB of memory are recommended to use enable_attention_slicing
|
|
pipe.enable_attention_slicing()
|
|
|
|
if enable:
|
|
pipe.vae.enable_tiling()
|