Merge branch 'master' into gradient-clipping

15 files changed, 326 insertions, 114 deletions

diff --git a/modules/api/api.py b/modules/api/api.py
index a49f3755..688469ad 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -10,6 +10,7 @@ from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.sd_samplers import all_samplers
 from modules.extras import run_extras, run_pnginfo
+from PIL import PngImagePlugin
 from modules.sd_models import checkpoints_list
 from modules.realesrgan_model import get_realesrgan_models
 from typing import List
@@ -34,9 +35,21 @@ def setUpscalers(req: dict):
 
 
 def encode_pil_to_base64(image):
-    buffer = io.BytesIO()
-    image.save(buffer, format="png")
-    return base64.b64encode(buffer.getvalue())
+    with io.BytesIO() as output_bytes:
+
+        # Copy any text-only metadata
+        use_metadata = False
+        metadata = PngImagePlugin.PngInfo()
+        for key, value in image.info.items():
+            if isinstance(key, str) and isinstance(value, str):
+                metadata.add_text(key, value)
+                use_metadata = True
+
+        image.save(
+            output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
+        )
+        bytes_data = output_bytes.getvalue()
+    return base64.b64encode(bytes_data)
 
 
 class Api:
@@ -50,6 +63,7 @@ class Api:
         self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
         self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
         self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
+        self.app.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
         self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
         self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
         self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
@@ -201,11 +215,24 @@ class Api:
 
         return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
 
+    def interrogateapi(self, interrogatereq: InterrogateRequest):
+        image_b64 = interrogatereq.image
+        if image_b64 is None:
+            raise HTTPException(status_code=404, detail="Image not found") 
+
+        img = self.__base64_to_image(image_b64)
+
+        # Override object param
+        with self.queue_lock:
+            processed = shared.interrogator.interrogate(img)
+        
+        return InterrogateResponse(caption=processed)
+
     def interruptapi(self):
         shared.state.interrupt()
 
         return {}
-        
+
     def get_config(self):
         options = {}
         for key in shared.opts.data.keys():
@@ -214,10 +241,14 @@ class Api:
                 options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)})
             else:
                 options.update({key: shared.opts.data.get(key, None)})
-        
+
         return options
-        
+
     def set_config(self, req: OptionsModel):
+        # currently req has all options fields even if you send a dict like { "send_seed": false }, which means it will
+        # overwrite all options with default values.
+        raise RuntimeError('Setting options via API is not supported')
+
         reqDict = vars(req)
         for o in reqDict:
             setattr(shared.opts, o, reqDict[o])
@@ -233,13 +264,13 @@ class Api:
 
     def get_upscalers(self):
         upscalers = []
-        
+
         for upscaler in shared.sd_upscalers:
             u = upscaler.scaler
             upscalers.append({"name":u.name, "model_name":u.model_name, "model_path":u.model_path, "model_url":u.model_url})
-        
+
         return upscalers
-        
+
     def get_sd_models(self):
         return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": x.config} for x in checkpoints_list.values()]
 
@@ -251,11 +282,11 @@ 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):
         styleList = []
         for k in shared.prompt_styles.styles:
-            style = shared.prompt_styles.styles[k] 
+            style = shared.prompt_styles.styles[k]
             styleList.append({"name":style[0], "prompt": style[1], "negative_prompr": style[2]})
 
         return styleList
diff --git a/modules/api/models.py b/modules/api/models.py
index 2ae75f43..34dbfa16 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,11 +1,11 @@
 import inspect
 from pydantic import BaseModel, Field, create_model
-from typing import Any, Optional, Union
+from typing import Any, Optional
 from typing_extensions import Literal
 from inflection import underscore
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 from modules.shared import sd_upscalers, opts, parser
-from typing import List
+from typing import Dict, List
 
 API_NOT_ALLOWED = [
     "self",
@@ -65,6 +65,7 @@ class PydanticModelGenerator:
 
         self._model_name = model_name
         self._class_data = merge_class_params(class_instance)
+
         self._model_def = [
             ModelDef(
                 field=underscore(k),
@@ -167,6 +168,12 @@ class ProgressResponse(BaseModel):
     state: dict = Field(title="State", description="The current state snapshot")
     current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
 
+class InterrogateRequest(BaseModel):
+    image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
+
+class InterrogateResponse(BaseModel):
+    caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
+
 fields = {}
 for key, value in opts.data.items():
     metadata = opts.data_labels.get(key)
@@ -185,22 +192,22 @@ _options = vars(parser)['_option_string_actions']
 for key in _options:
     if(_options[key].dest != 'help'):
         flag = _options[key]
-        _type = str 
-        if(_options[key].default != None): _type = type(_options[key].default) 
+        _type = str
+        if _options[key].default is not None: _type = type(_options[key].default)
         flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
 
 FlagsModel = create_model("Flags", **flags)
 
 class SamplerItem(BaseModel):
     name: str = Field(title="Name")
-    aliases: list[str]  = Field(title="Aliases")
-    options: dict[str, str] = Field(title="Options")
+    aliases: List[str] = Field(title="Aliases")
+    options: Dict[str, str] = Field(title="Options")
 
 class UpscalerItem(BaseModel):
     name: str = Field(title="Name")
-    model_name: str | None = Field(title="Model Name")
-    model_path: str | None = Field(title="Path")
-    model_url: str | None = Field(title="URL")
+    model_name: Optional[str] = Field(title="Model Name")
+    model_path: Optional[str] = Field(title="Path")
+    model_url: Optional[str] = Field(title="URL")
 
 class SDModelItem(BaseModel):
     title: str = Field(title="Title")
@@ -211,23 +218,24 @@ class SDModelItem(BaseModel):
 
 class HypernetworkItem(BaseModel):
     name: str = Field(title="Name")
-    path: str | None = Field(title="Path")
+    path: Optional[str] = Field(title="Path")
 
 class FaceRestorerItem(BaseModel):
     name: str = Field(title="Name")
-    cmd_dir: str | None = Field(title="Path")
+    cmd_dir: Optional[str] = Field(title="Path")
 
 class RealesrganItem(BaseModel):
     name: str = Field(title="Name")
-    path: str | None = Field(title="Path")
-    scale: int | None = Field(title="Scale")
+    path: Optional[str] = Field(title="Path")
+    scale: Optional[int] = Field(title="Scale")
 
 class PromptStyleItem(BaseModel):
     name: str = Field(title="Name")
-    prompt: str | None = Field(title="Prompt")
-    negative_prompt: str | None = Field(title="Negative Prompt")
+    prompt: Optional[str] = Field(title="Prompt")
+    negative_prompt: Optional[str] = Field(title="Negative Prompt")
 
 class ArtistItem(BaseModel):
     name: str = Field(title="Name")
     score: float = Field(title="Score")
-    category: str = Field(title="Category")
\ No newline at end of file
+    category: str = Field(title="Category")
+
diff --git a/modules/extensions.py b/modules/extensions.py
index 897af96e..8e0977fd 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -34,8 +34,11 @@ class Extension:
         if repo is None or repo.bare:

             self.remote = None

         else:

-            self.remote = next(repo.remote().urls, None)

-            self.status = 'unknown'

+            try:

+                self.remote = next(repo.remote().urls, None)

+                self.status = 'unknown'

+            except Exception:

+                self.remote = None

 

     def list_files(self, subdir, extension):

         from modules import scripts

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 02b624e1..3371b18e 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -22,6 +22,8 @@ from collections import defaultdict, deque
 from statistics import stdev, mean

 

 

+optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}

+

 class HypernetworkModule(torch.nn.Module):

     multiplier = 1.0

     activation_dict = {

@@ -142,6 +144,8 @@ class Hypernetwork:
         self.use_dropout = use_dropout

         self.activate_output = activate_output

         self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True

+        self.optimizer_name = None

+        self.optimizer_state_dict = None

 

         for size in enable_sizes or []:

             self.layers[size] = (

@@ -163,6 +167,7 @@ class Hypernetwork:
 

     def save(self, filename):

         state_dict = {}

+        optimizer_saved_dict = {}

 

         for k, v in self.layers.items():

             state_dict[k] = (v[0].state_dict(), v[1].state_dict())

@@ -178,8 +183,15 @@ class Hypernetwork:
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name

         state_dict['activate_output'] = self.activate_output

         state_dict['last_layer_dropout'] = self.last_layer_dropout

-        

+

+        if self.optimizer_name is not None:

+            optimizer_saved_dict['optimizer_name'] = self.optimizer_name

+

         torch.save(state_dict, filename)

+        if shared.opts.save_optimizer_state and self.optimizer_state_dict:

+            optimizer_saved_dict['hash'] = sd_models.model_hash(filename)

+            optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict

+            torch.save(optimizer_saved_dict, filename + '.optim')

 

     def load(self, filename):

         self.filename = filename

@@ -202,6 +214,18 @@ class Hypernetwork:
         print(f"Activate last layer is set to {self.activate_output}")

         self.last_layer_dropout = state_dict.get('last_layer_dropout', False)

 

+        optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}

+        self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')

+        print(f"Optimizer name is {self.optimizer_name}")

+        if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):

+            self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)

+        else:

+            self.optimizer_state_dict = None

+        if self.optimizer_state_dict:

+            print("Loaded existing optimizer from checkpoint")

+        else:

+            print("No saved optimizer exists in checkpoint")

+

         for size, sd in state_dict.items():

             if type(size) == int:

                 self.layers[size] = (

@@ -219,11 +243,11 @@ class Hypernetwork:
 

 def list_hypernetworks(path):

     res = {}

-    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):

+    for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):

         name = os.path.splitext(os.path.basename(filename))[0]

         # Prevent a hypothetical "None.pt" from being listed.

         if name != "None":

-            res[name] = filename

+            res[name + f"({sd_models.model_hash(filename)})"] = filename

     return res

 

 

@@ -358,6 +382,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     shared.state.textinfo = "Initializing hypernetwork training..."

     shared.state.job_count = steps

 

+    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]

     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')

 

     log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)

@@ -410,8 +435,22 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     weights = hypernetwork.weights()

     for weight in weights:

         weight.requires_grad = True

-    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...

-    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)

+

+    # Here we use optimizer from saved HN, or we can specify as UI option.

+    if hypernetwork.optimizer_name in optimizer_dict:

+        optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)

+        optimizer_name = hypernetwork.optimizer_name

+    else:

+        print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")

+        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)

+        optimizer_name = 'AdamW'

+

+    if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.

+        try:

+            optimizer.load_state_dict(hypernetwork.optimizer_state_dict)

+        except RuntimeError as e:

+            print("Cannot resume from saved optimizer!")

+            print(e)

 

     steps_without_grad = 0

 

@@ -479,7 +518,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             # Before saving, change name to match current checkpoint.

             hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'

             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')

+            hypernetwork.optimizer_name = optimizer_name

+            if shared.opts.save_optimizer_state:

+                hypernetwork.optimizer_state_dict = optimizer.state_dict()

             save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)

+            hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.

 

         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {

             "loss": f"{previous_mean_loss:.7f}",

@@ -542,8 +585,12 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     report_statistics(loss_dict)

 

     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')

+    hypernetwork.optimizer_name = optimizer_name

+    if shared.opts.save_optimizer_state:

+        hypernetwork.optimizer_state_dict = optimizer.state_dict()

     save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)

-

+    del optimizer

+    hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.

     return hypernetwork, filename

 

 def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index aad09ffc..c2d4b51c 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork

 

 not_available = ["hardswish", "multiheadattention"]

-keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)

+keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)

 

 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):

     # Remove illegal characters from name.

diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
index 14db5076..90e0a2f0 100644
--- a/modules/ldsr_model_arch.py
+++ b/modules/ldsr_model_arch.py
@@ -101,8 +101,8 @@ class LDSR:
         down_sample_rate = target_scale / 4
         wd = width_og * down_sample_rate
         hd = height_og * down_sample_rate
-        width_downsampled_pre = int(wd)
-        height_downsampled_pre = int(hd)
+        width_downsampled_pre = int(np.ceil(wd))
+        height_downsampled_pre = int(np.ceil(hd))
 
         if down_sample_rate != 1:
             print(
@@ -110,7 +110,12 @@ class LDSR:
             im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
         else:
             print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
-        logs = self.run(model["model"], im_og, diffusion_steps, eta)
+        
+        # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
+        pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
+        im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
+        
+        logs = self.run(model["model"], im_padded, diffusion_steps, eta)
 
         sample = logs["sample"]
         sample = sample.detach().cpu()
@@ -120,6 +125,9 @@ class LDSR:
         sample = np.transpose(sample, (0, 2, 3, 1))
         a = Image.fromarray(sample[0])
 
+        # remove padding
+        a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
+
         del model
         gc.collect()
         torch.cuda.empty_cache()
diff --git a/modules/localization.py b/modules/localization.py
index b1810cda..f6a6f2fb 100644
--- a/modules/localization.py
+++ b/modules/localization.py
@@ -3,6 +3,7 @@ import os
 import sys

 import traceback

 

+

 localizations = {}

 

 

@@ -16,6 +17,11 @@ def list_localizations(dirname):
 

         localizations[fn] = os.path.join(dirname, file)

 

+    from modules import scripts

+    for file in scripts.list_scripts("localizations", ".json"):

+        fn, ext = os.path.splitext(file.filename)

+        localizations[fn] = file.path

+

 

 def localization_js(current_localization_name):

     fn = localizations.get(current_localization_name, None)

diff --git a/modules/safe.py b/modules/safe.py
index 348a24fc..a9209e38 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -23,11 +23,18 @@ def encode(*args):
 

 

 class RestrictedUnpickler(pickle.Unpickler):

+    extra_handler = None

+

     def persistent_load(self, saved_id):

         assert saved_id[0] == 'storage'

         return TypedStorage()

 

     def find_class(self, module, name):

+        if self.extra_handler is not None:

+            res = self.extra_handler(module, name)

+            if res is not None:

+                return res

+

         if module == 'collections' and name == 'OrderedDict':

             return getattr(collections, name)

         if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:

@@ -52,7 +59,7 @@ class RestrictedUnpickler(pickle.Unpickler):
             return set

 

         # Forbid everything else.

-        raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")

+        raise Exception(f"global '{module}/{name}' is forbidden")

 

 

 allowed_zip_names = ["archive/data.pkl", "archive/version"]

@@ -69,7 +76,7 @@ def check_zip_filenames(filename, names):
         raise Exception(f"bad file inside {filename}: {name}")

 

 

-def check_pt(filename):

+def check_pt(filename, extra_handler):

     try:

 

         # new pytorch format is a zip file

@@ -78,6 +85,7 @@ def check_pt(filename):
 

             with z.open('archive/data.pkl') as file:

                 unpickler = RestrictedUnpickler(file)

+                unpickler.extra_handler = extra_handler

                 unpickler.load()

 

     except zipfile.BadZipfile:

@@ -85,16 +93,42 @@ def check_pt(filename):
         # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle

         with open(filename, "rb") as file:

             unpickler = RestrictedUnpickler(file)

+            unpickler.extra_handler = extra_handler

             for i in range(5):

                 unpickler.load()

 

 

 def load(filename, *args, **kwargs):

+    return load_with_extra(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

+    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,

+    and returns that field's value:

+

+    ```python

+    def extra(module, name):

+        if module == 'collections' and name == 'OrderedDict':

+            return collections.OrderedDict

+

+        return None

+

+    safe.load_with_extra('model.pt', extra_handler=extra)

+    ```

+

+    The alternative to this is just to use safe.unsafe_torch_load('model.pt'), which as the name implies is

+    definitely unsafe.

+    """

+

     from modules import shared

 

     try:

         if not shared.cmd_opts.disable_safe_unpickle:

-            check_pt(filename)

+            check_pt(filename, extra_handler)

 

     except pickle.UnpicklingError:

         print(f"Error verifying pickled file from {filename}:", file=sys.stderr)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index c28e220e..74dfb880 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -46,25 +46,23 @@ class CFGDenoiserParams:
 

 

 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])

-callbacks_app_started = []

-callbacks_model_loaded = []

-callbacks_ui_tabs = []

-callbacks_ui_settings = []

-callbacks_before_image_saved = []

-callbacks_image_saved = []

-callbacks_cfg_denoiser = []

+callback_map = dict(

+    callbacks_app_started=[],

+    callbacks_model_loaded=[],

+    callbacks_ui_tabs=[],

+    callbacks_ui_settings=[],

+    callbacks_before_image_saved=[],

+    callbacks_image_saved=[],

+    callbacks_cfg_denoiser=[]

+)

 

 

 def clear_callbacks():

-    callbacks_model_loaded.clear()

-    callbacks_ui_tabs.clear()

-    callbacks_ui_settings.clear()

-    callbacks_before_image_saved.clear()

-    callbacks_image_saved.clear()

-    callbacks_cfg_denoiser.clear()

+    for callback_list in callback_map.values():

+        callback_list.clear()

 

 def app_started_callback(demo: Optional[Blocks], app: FastAPI):

-    for c in callbacks_app_started:

+    for c in callback_map['callbacks_app_started']:

         try:

             c.callback(demo, app)

         except Exception:

@@ -72,7 +70,7 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI):
 

 

 def model_loaded_callback(sd_model):

-    for c in callbacks_model_loaded:

+    for c in callback_map['callbacks_model_loaded']:

         try:

             c.callback(sd_model)

         except Exception:

@@ -82,7 +80,7 @@ def model_loaded_callback(sd_model):
 def ui_tabs_callback():

     res = []

     

-    for c in callbacks_ui_tabs:

+    for c in callback_map['callbacks_ui_tabs']:

         try:

             res += c.callback() or []

         except Exception:

@@ -92,7 +90,7 @@ def ui_tabs_callback():
 

 

 def ui_settings_callback():

-    for c in callbacks_ui_settings:

+    for c in callback_map['callbacks_ui_settings']:

         try:

             c.callback()

         except Exception:

@@ -100,7 +98,7 @@ def ui_settings_callback():
 

 

 def before_image_saved_callback(params: ImageSaveParams):

-    for c in callbacks_before_image_saved:

+    for c in callback_map['callbacks_before_image_saved']:

         try:

             c.callback(params)

         except Exception:

@@ -108,7 +106,7 @@ def before_image_saved_callback(params: ImageSaveParams):
 

 

 def image_saved_callback(params: ImageSaveParams):

-    for c in callbacks_image_saved:

+    for c in callback_map['callbacks_image_saved']:

         try:

             c.callback(params)

         except Exception:

@@ -116,7 +114,7 @@ def image_saved_callback(params: ImageSaveParams):
 

 

 def cfg_denoiser_callback(params: CFGDenoiserParams):

-    for c in callbacks_cfg_denoiser:

+    for c in callback_map['callbacks_cfg_denoiser']:

         try:

             c.callback(params)

         except Exception:

@@ -129,17 +127,33 @@ def add_callback(callbacks, fun):
 

     callbacks.append(ScriptCallback(filename, fun))

 

+    

+def remove_current_script_callbacks():

+    stack = [x for x in inspect.stack() if x.filename != __file__]

+    filename = stack[0].filename if len(stack) > 0 else 'unknown file'

+    if filename == 'unknown file':

+        return

+    for callback_list in callback_map.values():

+        for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:

+            callback_list.remove(callback_to_remove)

+

+

+def remove_callbacks_for_function(callback_func):

+    for callback_list in callback_map.values():

+        for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:

+            callback_list.remove(callback_to_remove)

+

 

 def on_app_started(callback):

     """register a function to be called when the webui started, the gradio `Block` component and

     fastapi `FastAPI` object are passed as the arguments"""

-    add_callback(callbacks_app_started, callback)

+    add_callback(callback_map['callbacks_app_started'], callback)

 

 

 def on_model_loaded(callback):

     """register a function to be called when the stable diffusion model is created; the model is

     passed as an argument"""

-    add_callback(callbacks_model_loaded, callback)

+    add_callback(callback_map['callbacks_model_loaded'], callback)

 

 

 def on_ui_tabs(callback):

@@ -152,13 +166,13 @@ def on_ui_tabs(callback):
     title is tab text displayed to user in the UI

     elem_id is HTML id for the tab

     """

-    add_callback(callbacks_ui_tabs, callback)

+    add_callback(callback_map['callbacks_ui_tabs'], callback)

 

 

 def on_ui_settings(callback):

     """register a function to be called before UI settings are populated; add your settings

     by using shared.opts.add_option(shared.OptionInfo(...)) """

-    add_callback(callbacks_ui_settings, callback)

+    add_callback(callback_map['callbacks_ui_settings'], callback)

 

 

 def on_before_image_saved(callback):

@@ -166,7 +180,7 @@ def on_before_image_saved(callback):
     The callback is called with one argument:

         - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.

     """

-    add_callback(callbacks_before_image_saved, callback)

+    add_callback(callback_map['callbacks_before_image_saved'], callback)

 

 

 def on_image_saved(callback):

@@ -174,7 +188,7 @@ def on_image_saved(callback):
     The callback is called with one argument:

         - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.

     """

-    add_callback(callbacks_image_saved, callback)

+    add_callback(callback_map['callbacks_image_saved'], callback)

 

 

 def on_cfg_denoiser(callback):

@@ -182,5 +196,4 @@ def on_cfg_denoiser(callback):
     The callback is called with one argument:

         - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.

     """

-    add_callback(callbacks_cfg_denoiser, callback)

-

+    add_callback(callback_map['callbacks_cfg_denoiser'], callback)

diff --git a/modules/scripts.py b/modules/scripts.py
index 366c90d7..637b2329 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -3,7 +3,6 @@ import sys
 import traceback

 from collections import namedtuple

 

-import modules.ui as ui

 import gradio as gr

 

 from modules.processing import StableDiffusionProcessing

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index c7c414ef..783992d2 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -24,11 +24,15 @@ samplers_k_diffusion = [
     ('Heun', 'sample_heun', ['k_heun'], {}),

     ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),

     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),

+    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),

+    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),

     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),

     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),

     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),

     ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),

     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),

+    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),

+    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),

 ]

 

 samplers_data_k_diffusion = [

diff --git a/modules/shared.py b/modules/shared.py
index a9e28b9c..e8bacd3c 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -86,6 +86,10 @@ parser.add_argument("--nowebui", action='store_true', help="use api=True to laun
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")

 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)

 parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)

+parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origins", default=None)

+parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)

+parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)

+parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)

 

 cmd_opts = parser.parse_args()

 restricted_opts = {

@@ -147,9 +151,9 @@ class State:
         self.interrupted = True

 

     def nextjob(self):

-        if opts.show_progress_every_n_steps == -1: 

+        if opts.show_progress_every_n_steps == -1:

             self.do_set_current_image()

-            

+

         self.job_no += 1

         self.sampling_step = 0

         self.current_image_sampling_step = 0

@@ -198,7 +202,7 @@ class State:
             return

         if self.current_latent is None:

             return

-            

+

         if opts.show_progress_grid:

             self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)

         else:

@@ -217,8 +221,6 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
 

 face_restorers = []

 

-localization.list_localizations(cmd_opts.localizations_dir)

-

 

 def realesrgan_models_names():

     import modules.realesrgan_model

@@ -317,6 +319,7 @@ options_templates.update(options_section(('system', "System"), {
 

 options_templates.update(options_section(('training', "Training"), {

     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),

+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),

     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),

     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),

     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),

@@ -406,7 +409,8 @@ class Options:
             if key in self.data or key in self.data_labels:

                 assert not cmd_opts.freeze_settings, "changing settings is disabled"

 

-                comp_args = opts.data_labels[key].component_args

+                info = opts.data_labels.get(key, None)

+                comp_args = info.component_args if info else None

                 if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:

                     raise RuntimeError(f"not possible to set {key} because it is restricted")

 

diff --git a/modules/ui.py b/modules/ui.py
index db6d59d5..030d4144 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -174,9 +174,9 @@ def save_pil_to_file(pil_image, dir=None):
 gr.processing_utils.save_pil_to_file = save_pil_to_file

 

 

-def wrap_gradio_call(func, extra_outputs=None):

+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):

     def f(*args, extra_outputs_array=extra_outputs, **kwargs):

-        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled

+        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats

         if run_memmon:

             shared.mem_mon.monitor()

         t = time.perf_counter()

@@ -203,11 +203,18 @@ def wrap_gradio_call(func, extra_outputs=None):
 

             res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]

 

+        shared.state.skipped = False

+        shared.state.interrupted = False

+        shared.state.job_count = 0

+

+        if not add_stats:

+            return tuple(res)

+

         elapsed = time.perf_counter() - t

         elapsed_m = int(elapsed // 60)

         elapsed_s = elapsed % 60

         elapsed_text = f"{elapsed_s:.2f}s"

-        if (elapsed_m > 0):

+        if elapsed_m > 0:

             elapsed_text = f"{elapsed_m}m "+elapsed_text

 

         if run_memmon:

@@ -225,10 +232,6 @@ def wrap_gradio_call(func, extra_outputs=None):
         # last item is always HTML

         res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"

 

-        shared.state.skipped = False

-        shared.state.interrupted = False

-        shared.state.job_count = 0

-

         return tuple(res)

 

     return f

@@ -1138,7 +1141,7 @@ def create_ui(wrap_gradio_gpu_call):
             outputs=[html, generation_info, html2],

         )

 

-    with gr.Blocks() as modelmerger_interface:

+    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:

         with gr.Row().style(equal_height=False):

             with gr.Column(variant='panel'):

                 gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")

@@ -1158,7 +1161,7 @@ def create_ui(wrap_gradio_gpu_call):
 

     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()

 

-    with gr.Blocks() as train_interface:

+    with gr.Blocks(analytics_enabled=False) as train_interface:

         with gr.Row().style(equal_height=False):

             gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")

 

@@ -1423,15 +1426,14 @@ def create_ui(wrap_gradio_gpu_call):
 

         if info.refresh is not None:

             if is_quicksettings:

-                res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))

+                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))

                 create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)

             else:

                 with gr.Row(variant="compact"):

-                    res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))

+                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))

                     create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)

         else:

-            res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))

-

+            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))

 

         return res

 

@@ -1442,7 +1444,7 @@ def create_ui(wrap_gradio_gpu_call):
     opts.reorder()

 

     def run_settings(*args):

-        changed = 0

+        changed = []

 

         for key, value, comp in zip(opts.data_labels.keys(), args, components):

             assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"

@@ -1452,18 +1454,20 @@ def create_ui(wrap_gradio_gpu_call):
                 continue

 

             oldval = opts.data.get(key, None)

-

-            setattr(opts, key, value)

-

+            try:

+                setattr(opts, key, value)

+            except RuntimeError:

+                continue

             if oldval != value:

                 if opts.data_labels[key].onchange is not None:

                     opts.data_labels[key].onchange()

 

-                changed += 1

-

-        opts.save(shared.config_filename)

-

-        return opts.dumpjson(), f'{changed} settings changed.'

+                changed.append(key)

+        try:

+            opts.save(shared.config_filename)

+        except RuntimeError:

+            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'

+        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'

 

     def run_settings_single(value, key):

         if not opts.same_type(value, opts.data_labels[key].default):

@@ -1567,11 +1571,10 @@ def create_ui(wrap_gradio_gpu_call):
             shared.state.need_restart = True

 

         restart_gradio.click(

-

             fn=request_restart,

+            _js='restart_reload',

             inputs=[],

             outputs=[],

-            _js='restart_reload'

         )

 

         if column is not None:

@@ -1641,6 +1644,17 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[component, text_settings],

             )

 

+        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]

+

+        def get_settings_values():

+            return [getattr(opts, key) for key in component_keys]

+

+        demo.load(

+            fn=get_settings_values,

+            inputs=[],

+            outputs=[component_dict[k] for k in component_keys],

+        )

+

         def modelmerger(*args):

             try:

                 results = modules.extras.run_modelmerger(*args)

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index a81de9a7..02ab9643 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -140,13 +140,15 @@ def install_extension_from_url(dirname, url):
         shutil.rmtree(tmpdir, True)

 

 

-def install_extension_from_index(url):

+def install_extension_from_index(url, hide_tags):

     ext_table, message = install_extension_from_url(None, url)

 

-    return refresh_available_extensions_from_data(), ext_table, message

+    code, _ = refresh_available_extensions_from_data(hide_tags)

 

+    return code, ext_table, message

 

-def refresh_available_extensions(url):

+

+def refresh_available_extensions(url, hide_tags):

     global available_extensions

 

     import urllib.request

@@ -155,13 +157,25 @@ def refresh_available_extensions(url):
 

     available_extensions = json.loads(text)

 

-    return url, refresh_available_extensions_from_data(), ''

+    code, tags = refresh_available_extensions_from_data(hide_tags)

+

+    return url, code, gr.CheckboxGroup.update(choices=tags), ''

+

+

+def refresh_available_extensions_for_tags(hide_tags):

+    code, _ = refresh_available_extensions_from_data(hide_tags)

 

+    return code, ''

 

-def refresh_available_extensions_from_data():

+

+def refresh_available_extensions_from_data(hide_tags):

     extlist = available_extensions["extensions"]

     installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}

 

+    tags = available_extensions.get("tags", {})

+    tags_to_hide = set(hide_tags)

+    hidden = 0

+

     code = f"""<!-- {time.time()} -->

     <table id="available_extensions">

         <thead>

@@ -178,17 +192,24 @@ def refresh_available_extensions_from_data():
         name = ext.get("name", "noname")

         url = ext.get("url", None)

         description = ext.get("description", "")

+        extension_tags = ext.get("tags", [])

 

         if url is None:

             continue

 

+        if len([x for x in extension_tags if x in tags_to_hide]) > 0:

+            hidden += 1

+            continue

+

         existing = installed_extension_urls.get(normalize_git_url(url), None)

 

         install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""

 

+        tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])

+

         code += f"""

             <tr>

-                <td><a href="{html.escape(url)}">{html.escape(name)}</a></td>

+                <td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a><br />{tags_text}</td>

                 <td>{html.escape(description)}</td>

                 <td>{install_code}</td>

             </tr>

@@ -199,7 +220,10 @@ def refresh_available_extensions_from_data():
     </table>

     """

 

-    return code

+    if hidden > 0:

+        code += f"<p>Extension hidden: {hidden}</p>"

+

+    return code, list(tags)

 

 

 def create_ui():

@@ -238,21 +262,30 @@ def create_ui():
                     extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)

                     install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)

 

+                with gr.Row():

+                    hide_tags = gr.CheckboxGroup(value=["ads", "localization"], label="Hide extensions with tags", choices=["script", "ads", "localization"])

+

                 install_result = gr.HTML()

                 available_extensions_table = gr.HTML()

 

                 refresh_available_extensions_button.click(

-                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update()]),

-                    inputs=[available_extensions_index],

-                    outputs=[available_extensions_index, available_extensions_table, install_result],

+                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),

+                    inputs=[available_extensions_index, hide_tags],

+                    outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result],

                 )

 

                 install_extension_button.click(

                     fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),

-                    inputs=[extension_to_install],

+                    inputs=[extension_to_install, hide_tags],

                     outputs=[available_extensions_table, extensions_table, install_result],

                 )

 

+                hide_tags.change(

+                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),

+                    inputs=[hide_tags],

+                    outputs=[available_extensions_table, install_result]

+                )

+

             with gr.TabItem("Install from URL"):

                 install_url = gr.Text(label="URL for extension's git repository")

                 install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")

diff --git a/modules/upscaler.py b/modules/upscaler.py
index 83fde7ca..c4e6e6bd 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -57,10 +57,18 @@ class Upscaler:
         self.scale = scale
         dest_w = img.width * scale
         dest_h = img.height * scale
+
         for i in range(3):
-            if img.width > dest_w and img.height > dest_h:
-                break
+            shape = (img.width, img.height)
+
             img = self.do_upscale(img, selected_model)
+
+            if shape == (img.width, img.height):
+                break
+
+            if img.width >= dest_w and img.height >= dest_h:
+                break
+
         if img.width != dest_w or img.height != dest_h:
             img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS)