manual fixes for some C408 (d25219b7) · Commits · github_fork / Stable Diffusion Webui

extensions-builtin/LDSR/ldsr_model_arch.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -157,7 +157,7 @@ class LDSR:


		def get_cond(selected_path):
		example = dict()
		example = {}
		up_f = 4
		c = selected_path.convert('RGB')
		c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
		@@ -195,7 +195,7 @@ def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_s
		@torch.no_grad()
		def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
		corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
		log = dict()
		log = {}

		z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
		return_first_stage_outputs=True,

+1 −1

Original line number	Diff line number	Diff line
		@@ -237,7 +237,7 @@ class VQModel(pl.LightningModule):
		return self.decoder.conv_out.weight

		def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
		log = dict()
		log = {}
		x = self.get_input(batch, self.image_key)
		x = x.to(self.device)
		if only_inputs:

+4 −4

Original line number	Diff line number	Diff line
		@@ -375,7 +375,7 @@ class DDPMV1(pl.LightningModule):

		@torch.no_grad()
		def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
		log = dict()
		log = {}
		x = self.get_input(batch, self.first_stage_key)
		N = min(x.shape[0], N)
		n_row = min(x.shape[0], n_row)
		@@ -383,7 +383,7 @@ class DDPMV1(pl.LightningModule):
		log["inputs"] = x

		# get diffusion row
		diffusion_row = list()
		diffusion_row = []
		x_start = x[:n_row]

		for t in range(self.num_timesteps):
		@@ -1247,7 +1247,7 @@ class LatentDiffusionV1(DDPMV1):

		use_ddim = ddim_steps is not None

		log = dict()
		log = {}
		z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
		return_first_stage_outputs=True,
		force_c_encode=True,
		@@ -1274,7 +1274,7 @@ class LatentDiffusionV1(DDPMV1):

		if plot_diffusion_rows:
		# get diffusion row
		diffusion_row = list()
		diffusion_row = []
		z_start = z[:n_row]
		for t in range(self.num_timesteps):
		if t % self.log_every_t == 0 or t == self.num_timesteps - 1:

+1 −1

Original line number	Diff line number	Diff line
		@@ -165,7 +165,7 @@ def api_middleware(app: FastAPI):
		class Api:
		def __init__(self, app: FastAPI, queue_lock: Lock):
		if shared.cmd_opts.api_auth:
		self.credentials = dict()
		self.credentials = {}
		for auth in shared.cmd_opts.api_auth.split(","):
		user, password = auth.split(":")
		self.credentials[user] = password

+4 −4

Original line number	Diff line number	Diff line
		@@ -405,7 +405,7 @@ class DDPM(pl.LightningModule):

		@torch.no_grad()
		def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
		log = dict()
		log = {}
		x = self.get_input(batch, self.first_stage_key)
		N = min(x.shape[0], N)
		n_row = min(x.shape[0], n_row)
		@@ -413,7 +413,7 @@ class DDPM(pl.LightningModule):
		log["inputs"] = x

		# get diffusion row
		diffusion_row = list()
		diffusion_row = []
		x_start = x[:n_row]

		for t in range(self.num_timesteps):
		@@ -1263,7 +1263,7 @@ class LatentDiffusion(DDPM):

		use_ddim = False

		log = dict()
		log = {}
		z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
		return_first_stage_outputs=True,
		force_c_encode=True,
		@@ -1291,7 +1291,7 @@ class LatentDiffusion(DDPM):

		if plot_diffusion_rows:
		# get diffusion row
		diffusion_row = list()
		diffusion_row = []
		z_start = z[:n_row]
		for t in range(self.num_timesteps):
		if t % self.log_every_t == 0 or t == self.num_timesteps - 1: