Merge pull request #5753 from calvinballing/master

Fix various typos

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