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manual fixes for some C408

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AUTOMATIC 2023-05-10 11:55:09 +03:00
parent a5121e7a06
commit d25219b7e8
7 changed files with 15 additions and 15 deletions
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4
extensions-builtin/LDSR/ldsr_model_arch.py
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@ -157,7 +157,7 @@ class LDSR:
def get_cond(selected_path): def get_cond(selected_path):
example = dict() example = {}
up_f = 4 up_f = 4
c = selected_path.convert('RGB') c = selected_path.convert('RGB')
c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0) 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() @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, 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): 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, z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
return_first_stage_outputs=True, return_first_stage_outputs=True,

2
extensions-builtin/LDSR/sd_hijack_autoencoder.py
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@ -237,7 +237,7 @@ class VQModel(pl.LightningModule):
return self.decoder.conv_out.weight return self.decoder.conv_out.weight
def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs): def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
log = dict() log = {}
x = self.get_input(batch, self.image_key) x = self.get_input(batch, self.image_key)
x = x.to(self.device) x = x.to(self.device)
if only_inputs: if only_inputs:

8
extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
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@ -375,7 +375,7 @@ class DDPMV1(pl.LightningModule):
@torch.no_grad() @torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): 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) x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N) N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row) n_row = min(x.shape[0], n_row)
@ -383,7 +383,7 @@ class DDPMV1(pl.LightningModule):
log["inputs"] = x log["inputs"] = x
# get diffusion row # get diffusion row
diffusion_row = list() diffusion_row = []
x_start = x[:n_row] x_start = x[:n_row]
for t in range(self.num_timesteps): for t in range(self.num_timesteps):
@ -1247,7 +1247,7 @@ class LatentDiffusionV1(DDPMV1):
use_ddim = ddim_steps is not None use_ddim = ddim_steps is not None
log = dict() log = {}
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
return_first_stage_outputs=True, return_first_stage_outputs=True,
force_c_encode=True, force_c_encode=True,
@ -1274,7 +1274,7 @@ class LatentDiffusionV1(DDPMV1):
if plot_diffusion_rows: if plot_diffusion_rows:
# get diffusion row # get diffusion row
diffusion_row = list() diffusion_row = []
z_start = z[:n_row] z_start = z[:n_row]
for t in range(self.num_timesteps): for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1: if t % self.log_every_t == 0 or t == self.num_timesteps - 1:

2
modules/api/api.py
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@ -165,7 +165,7 @@ def api_middleware(app: FastAPI):
class Api: class Api:
def __init__(self, app: FastAPI, queue_lock: Lock): def __init__(self, app: FastAPI, queue_lock: Lock):
if shared.cmd_opts.api_auth: if shared.cmd_opts.api_auth:
self.credentials = dict() self.credentials = {}
for auth in shared.cmd_opts.api_auth.split(","): for auth in shared.cmd_opts.api_auth.split(","):
user, password = auth.split(":") user, password = auth.split(":")
self.credentials[user] = password self.credentials[user] = password

8
modules/models/diffusion/ddpm_edit.py
View File

@ -405,7 +405,7 @@ class DDPM(pl.LightningModule):
@torch.no_grad() @torch.no_grad()
def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs): 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) x = self.get_input(batch, self.first_stage_key)
N = min(x.shape[0], N) N = min(x.shape[0], N)
n_row = min(x.shape[0], n_row) n_row = min(x.shape[0], n_row)
@ -413,7 +413,7 @@ class DDPM(pl.LightningModule):
log["inputs"] = x log["inputs"] = x
# get diffusion row # get diffusion row
diffusion_row = list() diffusion_row = []
x_start = x[:n_row] x_start = x[:n_row]
for t in range(self.num_timesteps): for t in range(self.num_timesteps):
@ -1263,7 +1263,7 @@ class LatentDiffusion(DDPM):
use_ddim = False use_ddim = False
log = dict() log = {}
z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
return_first_stage_outputs=True, return_first_stage_outputs=True,
force_c_encode=True, force_c_encode=True,
@ -1291,7 +1291,7 @@ class LatentDiffusion(DDPM):
if plot_diffusion_rows: if plot_diffusion_rows:
# get diffusion row # get diffusion row
diffusion_row = list() diffusion_row = []
z_start = z[:n_row] z_start = z[:n_row]
for t in range(self.num_timesteps): for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1: if t % self.log_every_t == 0 or t == self.num_timesteps - 1:

4
modules/models/diffusion/uni_pc/uni_pc.py
View File

@ -344,7 +344,7 @@ def model_wrapper(
t_in = torch.cat([t_continuous] * 2) t_in = torch.cat([t_continuous] * 2)
if isinstance(condition, dict): if isinstance(condition, dict):
assert isinstance(unconditional_condition, dict) assert isinstance(unconditional_condition, dict)
c_in = dict() c_in = {}
for k in condition: for k in condition:
if isinstance(condition[k], list): if isinstance(condition[k], list):
c_in[k] = [torch.cat([ c_in[k] = [torch.cat([
@ -355,7 +355,7 @@ def model_wrapper(
unconditional_condition[k], unconditional_condition[k],
condition[k]]) condition[k]])
elif isinstance(condition, list): elif isinstance(condition, list):
c_in = list() c_in = []
assert isinstance(unconditional_condition, list) assert isinstance(unconditional_condition, list)
for i in range(len(condition)): for i in range(len(condition)):
c_in.append(torch.cat([unconditional_condition[i], condition[i]])) c_in.append(torch.cat([unconditional_condition[i], condition[i]]))

2
modules/sd_hijack_inpainting.py
View File

@ -23,7 +23,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
if isinstance(c, dict): if isinstance(c, dict):
assert isinstance(unconditional_conditioning, dict) assert isinstance(unconditional_conditioning, dict)
c_in = dict() c_in = {}
for k in c: for k in c:
if isinstance(c[k], list): if isinstance(c[k], list):
c_in[k] = [ c_in[k] = [