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-rw-r--r--modules/api/api.py18
-rw-r--r--modules/api/models.py2
-rw-r--r--modules/images.py4
-rw-r--r--modules/processing.py14
-rw-r--r--modules/safe.py4
-rw-r--r--modules/scripts.py4
-rw-r--r--modules/sd_hijack_inpainting.py6
-rw-r--r--modules/sd_hijack_unet.py2
-rw-r--r--modules/textual_inversion/dataset.py10
-rw-r--r--modules/textual_inversion/textual_inversion.py16
10 files changed, 40 insertions, 40 deletions
diff --git a/modules/api/api.py b/modules/api/api.py
index 89935a70..33845045 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -67,10 +67,10 @@ def encode_pil_to_base64(image):
class Api:
def __init__(self, app: FastAPI, queue_lock: Lock):
if shared.cmd_opts.api_auth:
- self.credenticals = dict()
+ self.credentials = dict()
for auth in shared.cmd_opts.api_auth.split(","):
user, password = auth.split(":")
- self.credenticals[user] = password
+ self.credentials[user] = password
self.router = APIRouter()
self.app = app
@@ -93,7 +93,7 @@ class Api:
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
- self.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
+ self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
@@ -102,9 +102,9 @@ class Api:
return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
return self.app.add_api_route(path, endpoint, **kwargs)
- def auth(self, credenticals: HTTPBasicCredentials = Depends(HTTPBasic())):
- if credenticals.username in self.credenticals:
- if compare_digest(credenticals.password, self.credenticals[credenticals.username]):
+ def auth(self, credentials: HTTPBasicCredentials = Depends(HTTPBasic())):
+ if credentials.username in self.credentials:
+ if compare_digest(credentials.password, self.credentials[credentials.username]):
return True
raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
@@ -239,7 +239,7 @@ class Api:
def interrogateapi(self, interrogatereq: InterrogateRequest):
image_b64 = interrogatereq.image
if image_b64 is None:
- raise HTTPException(status_code=404, detail="Image not found")
+ raise HTTPException(status_code=404, detail="Image not found")
img = decode_base64_to_image(image_b64)
img = img.convert('RGB')
@@ -252,7 +252,7 @@ class Api:
processed = deepbooru.model.tag(img)
else:
raise HTTPException(status_code=404, detail="Model not found")
-
+
return InterrogateResponse(caption=processed)
def interruptapi(self):
@@ -308,7 +308,7 @@ class Api:
def get_realesrgan_models(self):
return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)]
- def get_promp_styles(self):
+ def get_prompt_styles(self):
styleList = []
for k in shared.prompt_styles.styles:
style = shared.prompt_styles.styles[k]
diff --git a/modules/api/models.py b/modules/api/models.py
index f77951fc..a22bc6b3 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -128,7 +128,7 @@ class ExtrasBaseRequest(BaseModel):
upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.")
upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
- upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the choosen size?")
+ upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?")
upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
diff --git a/modules/images.py b/modules/images.py
index 8bcbc8d9..809ad9f7 100644
--- a/modules/images.py
+++ b/modules/images.py
@@ -438,7 +438,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
The directory to save the image. Note, the option `save_to_dirs` will make the image to be saved into a sub directory.
basename (`str`):
The base filename which will be applied to `filename pattern`.
- seed, prompt, short_filename,
+ seed, prompt, short_filename,
extension (`str`):
Image file extension, default is `png`.
pngsectionname (`str`):
@@ -599,7 +599,7 @@ def read_info_from_image(image):
Negative prompt: {json_info["uc"]}
Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
except Exception:
- print(f"Error parsing NovelAI iamge generation parameters:", file=sys.stderr)
+ print(f"Error parsing NovelAI image generation parameters:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
return geninfo, items
diff --git a/modules/processing.py b/modules/processing.py
index 7c4bcd74..df4c2bfc 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -150,11 +150,11 @@ class StableDiffusionProcessing():
# The "masked-image" in this case will just be all zeros since the entire image is masked.
image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
- image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
+ image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
# Add the fake full 1s mask to the first dimension.
image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
- image_conditioning = image_conditioning.to(x.dtype)
+ image_conditioning = image_conditioning.to(x.dtype)
return image_conditioning
@@ -202,7 +202,7 @@ class StableDiffusionProcessing():
source_image * (1.0 - conditioning_mask),
getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
)
-
+
# Encode the new masked image using first stage of network.
conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
@@ -540,7 +540,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
for n in range(p.n_iter):
if state.skipped:
state.skipped = False
-
+
if state.interrupted:
break
@@ -615,7 +615,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
image.info["parameters"] = text
output_images.append(image)
- del x_samples_ddim
+ del x_samples_ddim
devices.torch_gc()
@@ -707,7 +707,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
- """saves image before applying hires fix, if enabled in options; takes as an arguyment either an image or batch with latent space images"""
+ """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
def save_intermediate(image, index):
if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
return
@@ -723,7 +723,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
- # Avoid making the inpainting conditioning unless necessary as
+ # Avoid making the inpainting conditioning unless necessary as
# this does need some extra compute to decode / encode the image again.
if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
diff --git a/modules/safe.py b/modules/safe.py
index 7c89c4c2..479c8b86 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -80,7 +80,7 @@ def check_pt(filename, extra_handler):
# new pytorch format is a zip file
with zipfile.ZipFile(filename) as z:
check_zip_filenames(filename, z.namelist())
-
+
# find filename of data.pkl in zip file: '<directory name>/data.pkl'
data_pkl_filenames = [f for f in z.namelist() if data_pkl_re.match(f)]
if len(data_pkl_filenames) == 0:
@@ -108,7 +108,7 @@ def load(filename, *args, **kwargs):
def load_with_extra(filename, extra_handler=None, *args, **kwargs):
"""
- this functon is intended to be used by extensions that want to load models with
+ this function is intended to be used by extensions that want to load models with
some extra classes in them that the usual unpickler would find suspicious.
Use the extra_handler argument to specify a function that takes module and field name as text,
diff --git a/modules/scripts.py b/modules/scripts.py
index 23ca195d..722f8685 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -36,7 +36,7 @@ class Script:
def ui(self, is_img2img):
"""this function should create gradio UI elements. See https://gradio.app/docs/#components
The return value should be an array of all components that are used in processing.
- Values of those returned componenbts will be passed to run() and process() functions.
+ Values of those returned components will be passed to run() and process() functions.
"""
pass
@@ -47,7 +47,7 @@ class Script:
This function should return:
- False if the script should not be shown in UI at all
- - True if the script should be shown in UI if it's scelected in the scripts drowpdown
+ - True if the script should be shown in UI if it's selected in the scripts dropdown
- script.AlwaysVisible if the script should be shown in UI at all times
"""
diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py
index 5018b047..85e7281f 100644
--- a/modules/sd_hijack_inpainting.py
+++ b/modules/sd_hijack_inpainting.py
@@ -209,7 +209,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
else:
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2)
-
+
if isinstance(c, dict):
assert isinstance(unconditional_conditioning, dict)
c_in = dict()
@@ -278,7 +278,7 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
return x_prev, pred_x0, e_t
-
+
# =================================================================================================
# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
# Adapted from:
@@ -325,7 +325,7 @@ def should_hijack_inpainting(checkpoint_info):
def do_inpainting_hijack():
# most of this stuff seems to no longer be needed because it is already included into SD2.0
# p_sample_plms is needed because PLMS can't work with dicts as conditionings
- # this file should be cleaned up later if weverything tuens out to work fine
+ # this file should be cleaned up later if everything turns out to work fine
# ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
# ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
diff --git a/modules/sd_hijack_unet.py b/modules/sd_hijack_unet.py
index 1b9d7757..18daf8c1 100644
--- a/modules/sd_hijack_unet.py
+++ b/modules/sd_hijack_unet.py
@@ -4,7 +4,7 @@ import torch
class TorchHijackForUnet:
"""
This is torch, but with cat that resizes tensors to appropriate dimensions if they do not match;
- this makes it possible to create pictures with dimensions that are muliples of 8 rather than 64
+ this makes it possible to create pictures with dimensions that are multiples of 8 rather than 64
"""
def __getattr__(self, item):
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 2dc64c3c..88d68c76 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -28,9 +28,9 @@ class DatasetEntry:
class PersonalizedBase(Dataset):
- def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once'):
+ def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once'):
re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
-
+
self.placeholder_token = placeholder_token
self.width = width
@@ -50,14 +50,14 @@ class PersonalizedBase(Dataset):
self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
-
+
self.shuffle_tags = shuffle_tags
self.tag_drop_out = tag_drop_out
print("Preparing dataset...")
for path in tqdm.tqdm(self.image_paths):
if shared.state.interrupted:
- raise Exception("inturrupted")
+ raise Exception("interrupted")
try:
image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
except Exception:
@@ -144,7 +144,7 @@ class PersonalizedDataLoader(DataLoader):
self.collate_fn = collate_wrapper_random
else:
self.collate_fn = collate_wrapper
-
+
class BatchLoader:
def __init__(self, data):
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index e28c357a..daf3997b 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -133,7 +133,7 @@ class EmbeddingDatabase:
process_file(fullfn, fn)
except Exception:
- print(f"Error loading emedding {fn}:", file=sys.stderr)
+ print(f"Error loading embedding {fn}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
continue
@@ -194,7 +194,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
csv_writer.writeheader()
epoch = (step - 1) // epoch_len
- epoch_step = (step - 1) % epoch_len
+ epoch_step = (step - 1) % epoch_len
csv_writer.writerow({
"step": step,
@@ -270,9 +270,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
# dataset loading may take a while, so input validations and early returns should be done before this
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
old_parallel_processing_allowed = shared.parallel_processing_allowed
-
+
pin_memory = shared.opts.pin_memory
-
+
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)
latent_sampling_method = ds.latent_sampling_method
@@ -295,12 +295,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
loss_step = 0
_loss_step = 0 #internal
-
+
last_saved_file = "<none>"
last_saved_image = "<none>"
forced_filename = "<none>"
embedding_yet_to_be_embedded = False
-
+
pbar = tqdm.tqdm(total=steps - initial_step)
try:
for i in range((steps-initial_step) * gradient_step):
@@ -327,10 +327,10 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
c = shared.sd_model.cond_stage_model(batch.cond_text)
loss = shared.sd_model(x, c)[0] / gradient_step
del x
-
+
_loss_step += loss.item()
scaler.scale(loss).backward()
-
+
# go back until we reach gradient accumulation steps
if (j + 1) % gradient_step != 0:
continue
generated by cgit v1.2.1 (git 2.18.0) at 2024-09-20 19:32:36 +0000