manual fixes for ruff (96d6ca41) · Commits · github_fork / Stable Diffusion Webui

extensions-builtin/LDSR/ldsr_model_arch.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -243,7 +243,7 @@ def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize
		x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
		log["sample_noquant"] = x_sample_noquant
		log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
		except:
		except Exception:
		pass

		log["sample"] = x_sample

extensions-builtin/LDSR/scripts/ldsr_model.py

+2 −1

Original line number	Diff line number	Diff line
		@@ -7,7 +7,8 @@ from basicsr.utils.download_util import load_file_from_url
		from modules.upscaler import Upscaler, UpscalerData
		from ldsr_model_arch import LDSR
		from modules import shared, script_callbacks
		import sd_hijack_autoencoder, sd_hijack_ddpm_v1
		import sd_hijack_autoencoder
		import sd_hijack_ddpm_v1


		class UpscalerLDSR(Upscaler):

extensions-builtin/LDSR/sd_hijack_autoencoder.py

+8 −2

Original line number	Diff line number	Diff line
		# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
		# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
		# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder

		import numpy as np
		import torch
		import pytorch_lightning as pl
		import torch.nn.functional as F
		from contextlib import contextmanager

		from torch.optim.lr_scheduler import LambdaLR

		from ldm.modules.ema import LitEma
		from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
		from ldm.modules.diffusionmodules.model import Encoder, Decoder
		from ldm.util import instantiate_from_config

		import ldm.models.autoencoder
		from packaging import version

		class VQModel(pl.LightningModule):
		def __init__(self,
		@@ -249,7 +254,8 @@ class VQModel(pl.LightningModule):
		if plot_ema:
		with self.ema_scope():
		xrec_ema, _ = self(x)
		if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
		if x.shape[1] > 3:
		xrec_ema = self.to_rgb(xrec_ema)
		log["reconstructions_ema"] = xrec_ema
		return log

extensions-builtin/LDSR/sd_hijack_ddpm_v1.py

+10 −16

Original line number	Diff line number	Diff line
		@@ -450,7 +450,7 @@ class LatentDiffusionV1(DDPMV1):
		self.cond_stage_key = cond_stage_key
		try:
		self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
		except:
		except Exception:
		self.num_downs = 0
		if not scale_by_std:
		self.scale_factor = scale_factor
		@@ -877,16 +877,6 @@ class LatentDiffusionV1(DDPMV1):
		c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
		return self.p_losses(x, c, t, args, *kwargs)

		def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
		def rescale_bbox(bbox):
		x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
		y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
		w = min(bbox[2] / crop_coordinates[2], 1 - x0)
		h = min(bbox[3] / crop_coordinates[3], 1 - y0)
		return x0, y0, w, h

		return [rescale_bbox(b) for b in bboxes]

		def apply_model(self, x_noisy, t, cond, return_ids=False):

		if isinstance(cond, dict):
		@@ -1157,8 +1147,10 @@ class LatentDiffusionV1(DDPMV1):

		if i % log_every_t == 0 or i == timesteps - 1:
		intermediates.append(x0_partial)
		if callback: callback(i)
		if img_callback: img_callback(img, i)
		if callback:
		callback(i)
		if img_callback:
		img_callback(img, i)
		return img, intermediates

		@torch.no_grad()
		@@ -1205,8 +1197,10 @@ class LatentDiffusionV1(DDPMV1):

		if i % log_every_t == 0 or i == timesteps - 1:
		intermediates.append(img)
		if callback: callback(i)
		if img_callback: img_callback(img, i)
		if callback:
		callback(i)
		if img_callback:
		img_callback(img, i)

		if return_intermediates:
		return img, intermediates
		@@ -1322,7 +1316,7 @@ class LatentDiffusionV1(DDPMV1):

		if inpaint:
		# make a simple center square
		b, h, w = z.shape[0], z.shape[2], z.shape[3]
		h, w = z.shape[2], z.shape[3]
		mask = torch.ones(N, h, w).to(self.device)
		# zeros will be filled in
		mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.

extensions-builtin/ScuNET/scunet_model_arch.py

+6 −3

Original line number	Diff line number	Diff line
		@@ -61,7 +61,9 @@ class WMSA(nn.Module):
		Returns:
		output: tensor shape [b h w c]
		"""
		if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
		if self.type != 'W':
		x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))

		x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
		h_windows = x.size(1)
		w_windows = x.size(2)
		@@ -85,8 +87,9 @@ class WMSA(nn.Module):
		output = self.linear(output)
		output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)

		if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
		dims=(1, 2))
		if self.type != 'W':
		output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2))

		return output

		def relative_embedding(self):

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