Merge pull request #5753 from calvinballing/master

Fix various typos

README.md

@@ -82,8 +82,8 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
 - Use VAEs
 - Estimated completion time in progress bar
 - API
-- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. 
+- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
-- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
+- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
 - [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
 
 ## Installation and Running

javascript/contextMenus.js

@@ -9,7 +9,7 @@ contextMenuInit = function(){
 
   function showContextMenu(event,element,menuEntries){
     let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
-    let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop; 
+    let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
 
     let oldMenu = gradioApp().querySelector('#context-menu')
     if(oldMenu){
@@ -61,15 +61,15 @@ contextMenuInit = function(){
 
   }
 
-  function appendContextMenuOption(targetEmementSelector,entryName,entryFunction){
+  function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
-    
+
-    currentItems = menuSpecs.get(targetEmementSelector)
+    currentItems = menuSpecs.get(targetElementSelector)
-    
+
     if(!currentItems){
       currentItems = []
-      menuSpecs.set(targetEmementSelector,currentItems);
+      menuSpecs.set(targetElementSelector,currentItems);
     }
-    let newItem = {'id':targetEmementSelector+'_'+uid(), 
+    let newItem = {'id':targetElementSelector+'_'+uid(),
                    'name':entryName,
                    'func':entryFunction,
                    'isNew':true}
@@ -97,7 +97,7 @@ contextMenuInit = function(){
       if(source.id && source.id.indexOf('check_progress')>-1){
         return
       }
-      
+
       let oldMenu = gradioApp().querySelector('#context-menu')
       if(oldMenu){
         oldMenu.remove()
@@ -117,7 +117,7 @@ contextMenuInit = function(){
       })
     });
     eventListenerApplied=true
-  
+
   }
 
   return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
@@ -152,8 +152,8 @@ addContextMenuEventListener = initResponse[2];
     generateOnRepeat('#img2img_generate','#img2img_interrupt');
   })
 
-  let cancelGenerateForever = function(){ 
+  let cancelGenerateForever = function(){
-    clearInterval(window.generateOnRepeatInterval) 
+    clearInterval(window.generateOnRepeatInterval)
   }
 
   appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
@@ -162,7 +162,7 @@ addContextMenuEventListener = initResponse[2];
   appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
 
   appendContextMenuOption('#roll','Roll three',
-    function(){ 
+    function(){
       let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
       setTimeout(function(){rollbutton.click()},100)
       setTimeout(function(){rollbutton.click()},200)

javascript/progressbar.js

@@ -3,7 +3,7 @@ global_progressbars = {}
 galleries = {}
 galleryObservers = {}
 
-// this tracks laumnches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running
+// this tracks launches of window.setTimeout for progressbar to prevent starting a new timeout when the previous is still running
 timeoutIds = {}
 
 function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
@@ -20,21 +20,21 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
 
     var skip = id_skip ? gradioApp().getElementById(id_skip) : null
     var interrupt = gradioApp().getElementById(id_interrupt)
-    
+
     if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){
         if(progressbar.innerText){
             let newtitle = '[' + progressbar.innerText.trim() + '] Stable Diffusion';
             if(document.title != newtitle){
-                document.title =  newtitle;          
+                document.title =  newtitle;
             }
         }else{
             let newtitle = 'Stable Diffusion'
             if(document.title != newtitle){
-                document.title =  newtitle;          
+                document.title =  newtitle;
             }
         }
     }
-    
+
 	if(progressbar!= null && progressbar != global_progressbars[id_progressbar]){
 	    global_progressbars[id_progressbar] = progressbar
 
@@ -63,7 +63,7 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
                         skip.style.display = "none"
                     }
                     interrupt.style.display = "none"
-			
+
                     //disconnect observer once generation finished, so user can close selected image if they want
                     if (galleryObservers[id_gallery]) {
                         galleryObservers[id_gallery].disconnect();

javascript/ui.js

@@ -100,7 +100,7 @@ function create_submit_args(args){
 
     // As it is currently, txt2img and img2img send back the previous output args (txt2img_gallery, generation_info, html_info) whenever you generate a new image.
     // This can lead to uploading a huge gallery of previously generated images, which leads to an unnecessary delay between submitting and beginning to generate.
-    // I don't know why gradio is seding outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
+    // I don't know why gradio is sending outputs along with inputs, but we can prevent sending the image gallery here, which seems to be an issue for some.
     // If gradio at some point stops sending outputs, this may break something
     if(Array.isArray(res[res.length - 3])){
         res[res.length - 3] = null

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())):
+    def auth(self, credentials: HTTPBasicCredentials = Depends(HTTPBasic())):
-        if credenticals.username in self.credenticals:
+        if credentials.username in self.credentials:
-            if compare_digest(credenticals.password, self.credenticals[credenticals.username]):
+            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]

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.")

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

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)

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,

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
          """
 

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

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):

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):

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

scripts/prompt_matrix.py

@@ -18,7 +18,7 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
     ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]
     hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]
 
-    first_pocessed = None
+    first_processed = None
 
     state.job_count = len(xs) * len(ys)
 
@@ -27,17 +27,17 @@ def draw_xy_grid(xs, ys, x_label, y_label, cell):
             state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
 
             processed = cell(x, y)
-            if first_pocessed is None:
+            if first_processed is None:
-                first_pocessed = processed
+                first_processed = processed
 
             res.append(processed.images[0])
 
     grid = images.image_grid(res, rows=len(ys))
     grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
 
-    first_pocessed.images = [grid]
+    first_processed.images = [grid]
 
-    return first_pocessed
+    return first_processed
 
 
 class Script(scripts.Script):

webui.py

@@ -154,8 +154,8 @@ def webui():
 
         # gradio uses a very open CORS policy via app.user_middleware, which makes it possible for
         # an attacker to trick the user into opening a malicious HTML page, which makes a request to the
-        # running web ui and do whatever the attcker wants, including installing an extension and
+        # running web ui and do whatever the attacker wants, including installing an extension and
-        # runnnig its code. We disable this here. Suggested by RyotaK.
+        # running its code. We disable this here. Suggested by RyotaK.
         app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']
 
         setup_cors(app)