From 69af07ab6703670c9080f49ec8808ce2bd96dd2f Mon Sep 17 00:00:00 2001 From: Bryce Date: Sat, 24 Sep 2022 00:29:45 -0700 Subject: [PATCH] style: fix lint issues --- imaginairy/api.py | 2 +- .../enhancers/face_restoration_codeformer.py | 2 +- imaginairy/img_utils.py | 2 +- imaginairy/modules/attention.py | 14 ++++---- imaginairy/modules/autoencoder.py | 12 ++++--- imaginairy/modules/clip_embedders.py | 6 ++-- imaginairy/modules/diffusion/ddpm.py | 15 +++++---- imaginairy/modules/diffusion/model.py | 32 ++++++++++--------- imaginairy/modules/diffusion/openaimodel.py | 4 +-- imaginairy/modules/diffusion/util.py | 12 +++---- imaginairy/modules/find_noise.py | 5 +-- imaginairy/samplers/base.py | 7 ++-- imaginairy/samplers/kdiff.py | 1 + imaginairy/samplers/plms.py | 1 + 14 files changed, 62 insertions(+), 53 deletions(-) diff --git a/imaginairy/api.py b/imaginairy/api.py index ad03122..7f5873c 100755 --- a/imaginairy/api.py +++ b/imaginairy/api.py @@ -213,7 +213,7 @@ def imagine( ddim_steps = int(prompt.steps / generation_strength) sampler.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=ddim_eta) try: - init_image, _, h = pillow_fit_image_within( + init_image = pillow_fit_image_within( prompt.init_image, max_height=prompt.height, max_width=prompt.width, diff --git a/imaginairy/enhancers/face_restoration_codeformer.py b/imaginairy/enhancers/face_restoration_codeformer.py index 97eb49f..b1c7e8c 100644 --- a/imaginairy/enhancers/face_restoration_codeformer.py +++ b/imaginairy/enhancers/face_restoration_codeformer.py @@ -68,7 +68,7 @@ def enhance_faces(img, fidelity=0): try: with torch.no_grad(): - output = net(cropped_face_t, w=fidelity, adain=True)[0] + output = net(cropped_face_t, w=fidelity, adain=True)[0] # noqa restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) del output torch.cuda.empty_cache() diff --git a/imaginairy/img_utils.py b/imaginairy/img_utils.py index b2cc817..e0f79cd 100644 --- a/imaginairy/img_utils.py +++ b/imaginairy/img_utils.py @@ -16,7 +16,7 @@ def pillow_fit_image_within(image: PIL.Image.Image, max_height=512, max_width=51 w, h = int(w * resize_ratio), int(h * resize_ratio) w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64 image = image.resize((w, h), resample=Image.Resampling.NEAREST) - return image, w, h + return image def pillow_img_to_torch_image(img: PIL.Image.Image): diff --git a/imaginairy/modules/attention.py b/imaginairy/modules/attention.py index 4d9f45a..1144d8e 100644 --- a/imaginairy/modules/attention.py +++ b/imaginairy/modules/attention.py @@ -85,7 +85,7 @@ class LinearAttention(nn.Module): self.to_out = nn.Conv2d(hidden_dim, dim, 1) def forward(self, x): - b, c, h, w = x.shape + b, c, h, w = x.shape # noqa qkv = self.to_qkv(x) q, k, v = rearrange( qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3 @@ -126,7 +126,7 @@ class SpatialSelfAttention(nn.Module): v = self.v(h_) # compute attention - b, c, h, w = q.shape + b, c, h, w = q.shape # noqa q = rearrange(q, "b c h w -> b (h w) c") k = rearrange(k, "b c h w -> b c (h w)") w_ = torch.einsum("bij,bjk->bik", q, k) @@ -178,9 +178,9 @@ class CrossAttention(nn.Module): if mask is not None: mask = rearrange(mask, "b ... -> b (...)") - max_neg_value = -torch.finfo(sim.dtype).max + _max_neg_value = -torch.finfo(sim.dtype).max mask = repeat(mask, "b j -> (b h) () j", h=h) - sim.masked_fill_(~mask, max_neg_value) + sim.masked_fill_(~mask, _max_neg_value) # attention, what we cannot get enough of attn = sim.softmax(dim=-1) @@ -189,7 +189,7 @@ class CrossAttention(nn.Module): out = rearrange(out, "(b h) n d -> b n (h d)", h=h) return self.to_out(out) - def forward_cuda(self, x, context=None, mask=None): + def forward_cuda(self, x, context=None, mask=None): # noqa h = self.heads q_in = self.to_q(x) @@ -258,7 +258,7 @@ class BasicTransformerBlock(nn.Module): dropout=0.0, context_dim=None, gated_ff=True, - checkpoint=True, + checkpoint=True, # noqa ): super().__init__() self.attn1 = CrossAttention( @@ -326,7 +326,7 @@ class SpatialTransformer(nn.Module): def forward(self, x, context=None): # note: if no context is given, cross-attention defaults to self-attention - b, c, h, w = x.shape + b, c, h, w = x.shape # noqa x_in = x x = self.norm(x) x = self.proj_in(x) diff --git a/imaginairy/modules/autoencoder.py b/imaginairy/modules/autoencoder.py index a0e123e..6d03e82 100644 --- a/imaginairy/modules/autoencoder.py +++ b/imaginairy/modules/autoencoder.py @@ -17,12 +17,13 @@ class AutoencoderKL(pl.LightningModule): lossconfig, embed_dim, ckpt_path=None, - ignore_keys=[], + ignore_keys=None, image_key="image", colorize_nlabels=None, monitor=None, ): super().__init__() + ignore_keys = [] if ignore_keys is None else ignore_keys self.image_key = image_key self.encoder = Encoder(**ddconfig) self.decoder = Decoder(**ddconfig) @@ -32,20 +33,21 @@ class AutoencoderKL(pl.LightningModule): self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) self.embed_dim = embed_dim if colorize_nlabels is not None: - assert type(colorize_nlabels) == int + assert isinstance(colorize_nlabels, int) self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) if monitor is not None: self.monitor = monitor if ckpt_path is not None: self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys) - def init_from_ckpt(self, path, ignore_keys=list()): + def init_from_ckpt(self, path, ignore_keys=None): + ignore_keys = [] if ignore_keys is None else ignore_keys sd = torch.load(path, map_location="cpu")["state_dict"] keys = list(sd.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): - logger.info("Deleting key {} from state_dict.".format(k)) + logger.info(f"Deleting key {k} from state_dict.") del sd[k] self.load_state_dict(sd, strict=False) logger.info(f"Restored from {path}") @@ -61,7 +63,7 @@ class AutoencoderKL(pl.LightningModule): dec = self.decoder(z) return dec - def forward(self, input, sample_posterior=True): + def forward(self, input, sample_posterior=True): # noqa posterior = self.encode(input) if sample_posterior: z = posterior.sample() diff --git a/imaginairy/modules/clip_embedders.py b/imaginairy/modules/clip_embedders.py index 402f5a6..88e5ec0 100644 --- a/imaginairy/modules/clip_embedders.py +++ b/imaginairy/modules/clip_embedders.py @@ -1,7 +1,7 @@ import kornia import torch -import torch.nn as nn from einops import repeat +from torch import nn from transformers import CLIPTextModel, CLIPTokenizer from imaginairy.utils import get_device @@ -102,7 +102,9 @@ class FrozenClipImageEmbedder(nn.Module): antialias=False, ): super().__init__() - self.model, preprocess = clip.load(name=model_name, device=device, jit=jit) + self.model, preprocess = clip.load( # noqa + name=model_name, device=device, jit=jit + ) self.antialias = antialias diff --git a/imaginairy/modules/diffusion/ddpm.py b/imaginairy/modules/diffusion/ddpm.py index 7736632..1638108 100644 --- a/imaginairy/modules/diffusion/ddpm.py +++ b/imaginairy/modules/diffusion/ddpm.py @@ -54,7 +54,7 @@ class DDPM(pl.LightningModule): beta_schedule="linear", loss_type="l2", ckpt_path=None, - ignore_keys=[], + ignore_keys=None, load_only_unet=False, monitor="val/loss", first_stage_key="image", @@ -77,6 +77,8 @@ class DDPM(pl.LightningModule): logvar_init=0.0, ): super().__init__() + ignore_keys = [] if ignore_keys is None else ignore_keys + assert parameterization in [ "eps", "x0", @@ -236,7 +238,6 @@ class LatentDiffusion(DDPM): conditioning_key=None, scale_factor=1.0, scale_by_std=False, - *args, **kwargs, ): self.num_timesteps_cond = ( @@ -251,7 +252,7 @@ class LatentDiffusion(DDPM): conditioning_key = None ckpt_path = kwargs.pop("ckpt_path", None) ignore_keys = kwargs.pop("ignore_keys", []) - super().__init__(conditioning_key=conditioning_key, *args, **kwargs) + super().__init__(conditioning_key=conditioning_key, **kwargs) self.concat_mode = concat_mode self.cond_stage_trainable = cond_stage_trainable self.cond_stage_key = cond_stage_key @@ -286,7 +287,9 @@ class LatentDiffusion(DDPM): """For creating seamless tiles""" for m in self.modules(): if isinstance(m, nn.Conv2d): - m.padding_mode = "circular" if enabled else m._initial_padding_mode + m.padding_mode = ( + "circular" if enabled else m._initial_padding_mode # noqa + ) def make_cond_schedule( self, @@ -436,7 +439,7 @@ class LatentDiffusion(DDPM): :param x: img of size (bs, c, h, w) :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1]) """ - bs, nc, h, w = x.shape + bs, nc, h, w = x.shape # noqa # number of crops in image Ly = (h - kernel_size[0]) // stride[0] + 1 @@ -595,7 +598,7 @@ class LatentDiffusion(DDPM): stride = self.split_input_params["stride"] # eg. (64, 64) df = self.split_input_params["vqf"] self.split_input_params["original_image_size"] = x.shape[-2:] - bs, nc, h, w = x.shape + bs, nc, h, w = x.shape # noqa if ks[0] > h or ks[1] > w: ks = (min(ks[0], h), min(ks[1], w)) logger.info("reducing Kernel") diff --git a/imaginairy/modules/diffusion/model.py b/imaginairy/modules/diffusion/model.py index abc688d..7cfd5c0 100644 --- a/imaginairy/modules/diffusion/model.py +++ b/imaginairy/modules/diffusion/model.py @@ -1,3 +1,4 @@ +# pylama:ignore=W0613,W0612 # pytorch_diffusion + derived encoder decoder import gc import logging @@ -5,8 +6,8 @@ import math import numpy as np import torch -import torch.nn as nn from einops import rearrange +from torch import nn from imaginairy.modules.attention import LinearAttention from imaginairy.modules.distributions import DiagonalGaussianDistribution @@ -17,7 +18,7 @@ logger = logging.getLogger(__name__) def get_timestep_embedding(timesteps, embedding_dim): """ - This matches the implementation in Denoising Diffusion Probabilistic Models: + Matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly @@ -286,10 +287,10 @@ def make_attn(in_channels, attn_type="vanilla"): ) if attn_type == "vanilla": return AttnBlock(in_channels) - elif attn_type == "none": + if attn_type == "none": return nn.Identity(in_channels) - else: - return LinAttnBlock(in_channels) + + return LinAttnBlock(in_channels) class Encoder(nn.Module): @@ -502,6 +503,7 @@ class Decoder(nn.Module): self.conv_out = torch.nn.Conv2d( block_in, out_ch, kernel_size=3, stride=1, padding=1 ) + self.last_z_shape = None def forward(self, z): # assert z.shape[1:] == self.z_shape[1:] @@ -656,22 +658,22 @@ class Resize(nn.Module): self.mode = mode if self.with_conv: logger.info( - f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode" + f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode" # noqa ) raise NotImplementedError() - assert in_channels is not None - # no asymmetric padding in torch conv, must do it ourselves - self.conv = torch.nn.Conv2d( - in_channels, in_channels, kernel_size=4, stride=2, padding=1 - ) + # assert in_channels is not None + # # no asymmetric padding in torch conv, must do it ourselves + # self.conv = torch.nn.Conv2d( + # in_channels, in_channels, kernel_size=4, stride=2, padding=1 + # ) def forward(self, x, scale_factor=1.0): if scale_factor == 1.0: return x - else: - x = torch.nn.functional.interpolate( - x, mode=self.mode, align_corners=False, scale_factor=scale_factor - ) + + x = torch.nn.functional.interpolate( + x, mode=self.mode, align_corners=False, scale_factor=scale_factor + ) return x diff --git a/imaginairy/modules/diffusion/openaimodel.py b/imaginairy/modules/diffusion/openaimodel.py index f867780..bc41840 100644 --- a/imaginairy/modules/diffusion/openaimodel.py +++ b/imaginairy/modules/diffusion/openaimodel.py @@ -4,6 +4,7 @@ from abc import abstractmethod import numpy as np import torch as th import torch.nn.functional as F +from omegaconf.listconfig import ListConfig from torch import nn from imaginairy.modules.attention import SpatialTransformer @@ -488,7 +489,6 @@ class UNetModel(nn.Module): assert ( use_spatial_transformer ), "Fool!! You forgot to use the spatial transformer for your cross-attention conditioning..." - from omegaconf.listconfig import ListConfig if isinstance(context_dim, ListConfig): context_dim = list(context_dim) @@ -753,7 +753,7 @@ class UNetModel(nn.Module): self.middle_block.apply(convert_module_to_f32) self.output_blocks.apply(convert_module_to_f32) - def forward(self, x, timesteps=None, context=None, y=None, **kwargs): + def forward(self, x, timesteps=None, context=None, y=None, **kwargs): # noqa """ Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. diff --git a/imaginairy/modules/diffusion/util.py b/imaginairy/modules/diffusion/util.py index 3963646..ef061f4 100644 --- a/imaginairy/modules/diffusion/util.py +++ b/imaginairy/modules/diffusion/util.py @@ -13,8 +13,8 @@ import math import numpy as np import torch -import torch.nn as nn from einops import repeat as e_repeat +from torch import nn from imaginairy.utils import instantiate_from_config @@ -57,7 +57,7 @@ def make_beta_schedule( def frange(start, stop, step): - """range but handles floats""" + """Range but handles floats""" x = start while True: if x >= stop: @@ -148,11 +148,11 @@ def checkpoint(func, inputs, params, flag): if flag: args = tuple(inputs) + tuple(params) return CheckpointFunction.apply(func, len(inputs), *args) - else: - return func(*inputs) + + return func(*inputs) -class CheckpointFunction(torch.autograd.Function): +class CheckpointFunction(torch.autograd.Function): # noqa @staticmethod def forward(ctx, run_function, length, *args): ctx.run_function = run_function @@ -252,7 +252,7 @@ class SiLU(nn.Module): class GroupNorm32(nn.GroupNorm): - def forward(self, x): + def forward(self, x): # noqa return super().forward(x.float()).type(x.dtype) diff --git a/imaginairy/modules/find_noise.py b/imaginairy/modules/find_noise.py index 93991dd..7d394d0 100644 --- a/imaginairy/modules/find_noise.py +++ b/imaginairy/modules/find_noise.py @@ -17,16 +17,13 @@ from imaginairy.vendored import k_diffusion as K def find_noise_for_image(model, pil_img, prompt, steps=50, cond_scale=1.0, half=True): - img_latent = pillow_img_to_model_latent( - model, pil_img, batch_size=1, device="cuda", half=half - ) + img_latent = pillow_img_to_model_latent(model, pil_img, batch_size=1, half=half) return find_noise_for_latent( model, img_latent, prompt, steps=steps, cond_scale=cond_scale, - half=half, ) diff --git a/imaginairy/samplers/base.py b/imaginairy/samplers/base.py index 2a73bae..45064da 100644 --- a/imaginairy/samplers/base.py +++ b/imaginairy/samplers/base.py @@ -1,3 +1,4 @@ +# pylama:ignore=W0613 import torch from torch import nn @@ -25,9 +26,9 @@ _k_sampler_type_lookup = { def get_sampler(sampler_type, model): - from imaginairy.samplers.ddim import DDIMSampler - from imaginairy.samplers.kdiff import KDiffusionSampler - from imaginairy.samplers.plms import PLMSSampler + from imaginairy.samplers.ddim import DDIMSampler # noqa + from imaginairy.samplers.kdiff import KDiffusionSampler # noqa + from imaginairy.samplers.plms import PLMSSampler # noqa sampler_type = sampler_type.lower() if sampler_type == "plms": diff --git a/imaginairy/samplers/kdiff.py b/imaginairy/samplers/kdiff.py index a1a1cb9..dae309a 100644 --- a/imaginairy/samplers/kdiff.py +++ b/imaginairy/samplers/kdiff.py @@ -1,3 +1,4 @@ +# pylama:ignore=W0613 import torch from imaginairy.img_log import log_latent diff --git a/imaginairy/samplers/plms.py b/imaginairy/samplers/plms.py index 978e9ff..eadad4d 100644 --- a/imaginairy/samplers/plms.py +++ b/imaginairy/samplers/plms.py @@ -1,3 +1,4 @@ +# pylama:ignore=W0613 """SAMPLING ONLY.""" import logging