Merge branch 'master' into 7flash/fix-api-compatibility

modules/api/api.py

@@ -2,14 +2,17 @@ import base64
 import io
 import time
 import uvicorn
-from gradio.processing_utils import decode_base64_to_file, decode_base64_to_image
-from fastapi import APIRouter, Depends, HTTPException
+from threading import Lock
+from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
+from fastapi import APIRouter, Depends, FastAPI, HTTPException
 import modules.shared as shared
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.sd_samplers import all_samplers, sample_to_image, samples_to_image_grid
+from modules.sd_samplers import all_samplers
 from modules.extras import run_extras, run_pnginfo
-
+from modules.sd_models import checkpoints_list
+from modules.realesrgan_model import get_realesrgan_models
+from typing import List
 
 def upscaler_to_index(name: str):
     try:
@@ -37,7 +40,7 @@ def encode_pil_to_base64(image):
 
 
 class Api:
-    def __init__(self, app, queue_lock):
+    def __init__(self, app: FastAPI, queue_lock: Lock):
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
@@ -48,6 +51,18 @@ class Api:
         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/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"])
+        self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
+        self.app.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
+        self.app.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
+        self.app.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
+        self.app.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
+        self.app.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
+        self.app.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
+        self.app.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
+        self.app.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
+        self.app.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -190,6 +205,66 @@ class Api:
         shared.state.interrupt()
 
         return {}
+        
+    def get_config(self):
+        options = {}
+        for key in shared.opts.data.keys():
+            metadata = shared.opts.data_labels.get(key)
+            if(metadata is not None):
+                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):
+        reqDict = vars(req)
+        for o in reqDict:
+            setattr(shared.opts, o, reqDict[o])
+
+        shared.opts.save(shared.config_filename)
+        return
+
+    def get_cmd_flags(self):
+        return vars(shared.cmd_opts)
+
+    def get_samplers(self):
+        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers]
+
+    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()]
+
+    def get_hypernetworks(self):
+        return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
+
+    def get_face_restorers(self):
+        return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers]
+
+    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] 
+            styleList.append({"name":style[0], "prompt": style[1], "negative_prompr": style[2]})
+
+        return styleList
+
+    def get_artists_categories(self):
+        return shared.artist_db.cats
+
+    def get_artists(self):
+        return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
 
     def launch(self, server_name, port):
         self.app.include_router(self.router)

modules/api/models.py

@@ -1,11 +1,10 @@
 import inspect
-from click import prompt
 from pydantic import BaseModel, Field, create_model
-from typing import Any, Optional
+from typing import Any, Optional, Union
 from typing_extensions import Literal
 from inflection import underscore
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
-from modules.shared import sd_upscalers
+from modules.shared import sd_upscalers, opts, parser
 from typing import List
 
 API_NOT_ALLOWED = [
@@ -132,6 +131,7 @@ class ExtrasBaseRequest(BaseModel):
     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.")
+    upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?")
 
 class ExtraBaseResponse(BaseModel):
     html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
@@ -166,3 +166,68 @@ class ProgressResponse(BaseModel):
     eta_relative: float = Field(title="ETA in secs")
     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.")
+
+fields = {}
+for key, value in opts.data.items():
+    metadata = opts.data_labels.get(key)
+    optType = opts.typemap.get(type(value), type(value))
+
+    if (metadata is not None):
+        fields.update({key: (Optional[optType], Field(
+            default=metadata.default ,description=metadata.label))})
+    else:
+        fields.update({key: (Optional[optType], Field())})
+
+OptionsModel = create_model("Options", **fields)
+
+flags = {}
+_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) 
+        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")
+
+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")
+
+class SDModelItem(BaseModel):
+    title: str = Field(title="Title")
+    model_name: str = Field(title="Model Name")
+    hash: str = Field(title="Hash")
+    filename: str = Field(title="Filename")
+    config: str = Field(title="Config file")
+
+class HypernetworkItem(BaseModel):
+    name: str = Field(title="Name")
+    path: str | None = Field(title="Path")
+
+class FaceRestorerItem(BaseModel):
+    name: str = Field(title="Name")
+    cmd_dir: str | None = Field(title="Path")
+
+class RealesrganItem(BaseModel):
+    name: str = Field(title="Name")
+    path: str | None = Field(title="Path")
+    scale: int | None = 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")
+
+class ArtistItem(BaseModel):
+    name: str = Field(title="Name")
+    score: float = Field(title="Score")
+    category: str = Field(title="Category")

modules/hypernetworks/hypernetwork.py

@@ -35,7 +35,8 @@ class HypernetworkModule(torch.nn.Module):
     }
     activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
+                 add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=True):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
@@ -48,8 +49,8 @@ class HypernetworkModule(torch.nn.Module):
             # Add a fully-connected layer
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
 
-            # Add an activation func
-            if activation_func == "linear" or activation_func is None:
+            # Add an activation func except last layer
+            if activation_func == "linear" or activation_func is None or (i >= len(layer_structure) - 2 and not activate_output):
                 pass
             elif activation_func in self.activation_dict:
                 linears.append(self.activation_dict[activation_func]())
@@ -60,8 +61,8 @@ class HypernetworkModule(torch.nn.Module):
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
-            # Add dropout expect last layer
-            if use_dropout and i < len(layer_structure) - 3:
+            # Add dropout except last layer
+            if use_dropout and (i < len(layer_structure) - 3 or last_layer_dropout and i < len(layer_structure) - 2):
                 linears.append(torch.nn.Dropout(p=0.3))
 
         self.linear = torch.nn.Sequential(*linears)
@@ -75,7 +76,7 @@ class HypernetworkModule(torch.nn.Module):
                     w, b = layer.weight.data, layer.bias.data
                     if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm:
                         normal_(w, mean=0.0, std=0.01)
-                        normal_(b, mean=0.0, std=0.005)
+                        normal_(b, mean=0.0, std=0)
                     elif weight_init == 'XavierUniform':
                         xavier_uniform_(w)
                         zeros_(b)
@@ -127,7 +128,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -139,11 +140,15 @@ class Hypernetwork:
         self.weight_init = weight_init
         self.add_layer_norm = add_layer_norm
         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
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+                                   self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+                                   self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
             )
 
     def weights(self):
@@ -171,7 +176,9 @@ class Hypernetwork:
         state_dict['use_dropout'] = self.use_dropout
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         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
+        
         torch.save(state_dict, filename)
 
     def load(self, filename):
@@ -191,12 +198,17 @@ class Hypernetwork:
         print(f"Layer norm is set to {self.add_layer_norm}")
         self.use_dropout = state_dict.get('use_dropout', False)
         print(f"Dropout usage is set to {self.use_dropout}" )
+        self.activate_output = state_dict.get('activate_output', True)
+        print(f"Activate last layer is set to {self.activate_output}")
+        self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
 
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
+                                       self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
+                                       self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
                 )
 
         self.name = state_dict.get('name', self.name)

modules/masking.py

@@ -49,7 +49,7 @@ def expand_crop_region(crop_region, processing_width, processing_height, image_w
     ratio_processing = processing_width / processing_height
 
     if ratio_crop_region > ratio_processing:
-        desired_height = (x2 - x1) * ratio_processing
+        desired_height = (x2 - x1) / ratio_processing
         desired_height_diff = int(desired_height - (y2-y1))
         y1 -= desired_height_diff//2
         y2 += desired_height_diff - desired_height_diff//2

modules/processing.py

@@ -134,11 +134,7 @@ class StableDiffusionProcessing():
             # Dummy zero conditioning if we're not using inpainting model.
             # Still takes up a bit of memory, but no encoder call.
             # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
-            return torch.zeros(
-                x.shape[0], 5, 1, 1, 
-                dtype=x.dtype, 
-                device=x.device
-            )
+            return x.new_zeros(x.shape[0], 5, 1, 1)
 
         height = height or self.height
         width = width or self.width
@@ -156,11 +152,7 @@ class StableDiffusionProcessing():
     def img2img_image_conditioning(self, source_image, latent_image, image_mask = None):
         if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
             # Dummy zero conditioning if we're not using inpainting model.
-            return torch.zeros(
-                latent_image.shape[0], 5, 1, 1,
-                dtype=latent_image.dtype,
-                device=latent_image.device
-            )
+            return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
 
         # Handle the different mask inputs
         if image_mask is not None:
@@ -174,11 +166,11 @@ class StableDiffusionProcessing():
                 # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
                 conditioning_mask = torch.round(conditioning_mask)
         else:
-            conditioning_mask = torch.ones(1, 1, *source_image.shape[-2:])
+            conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
 
         # Create another latent image, this time with a masked version of the original input.
         # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
-        conditioning_mask = conditioning_mask.to(source_image.device)
+        conditioning_mask = conditioning_mask.to(source_image.device).to(source_image.dtype)
         conditioning_image = torch.lerp(
             source_image,
             source_image * (1.0 - conditioning_mask),
@@ -426,13 +418,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     try:
         for k, v in p.override_settings.items():
-            opts.data[k] = v  # we don't call onchange for simplicity which makes changing model, hypernet impossible
+            setattr(opts, k, v) # we don't call onchange for simplicity which makes changing model, hypernet impossible
 
         res = process_images_inner(p)
 
     finally:
         for k, v in stored_opts.items():
-            opts.data[k] = v
+            setattr(opts, k, v)
 
     return res
 
@@ -673,10 +665,17 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
 
         if opts.use_scale_latent_for_hires_fix:
-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
-
             for i in range(samples.shape[0]):
                 save_intermediate(samples, i)
+
+            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 
+            # 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)
+            else:
+                image_conditioning = self.txt2img_image_conditioning(samples)
         else:
             decoded_samples = decode_first_stage(self.sd_model, samples)
             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
@@ -700,14 +699,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
             samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
+            image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
+
         shared.state.nextjob()
 
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
 
-        image_conditioning = self.txt2img_image_conditioning(x)
-
         # GC now before running the next img2img to prevent running out of memory
         x = None
         devices.torch_gc()

modules/scripts.py

@@ -18,6 +18,9 @@ class Script:
     args_to = None
     alwayson = False
 
+    """A gr.Group component that has all script's UI inside it"""
+    group = None
+
     infotext_fields = None
     """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
     parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example
@@ -218,8 +221,6 @@ class ScriptRunner:
 
             for control in controls:
                 control.custom_script_source = os.path.basename(script.filename)
-                if not script.alwayson:
-                    control.visible = False
 
             if script.infotext_fields is not None:
                 self.infotext_fields += script.infotext_fields
@@ -229,40 +230,41 @@ class ScriptRunner:
             script.args_to = len(inputs)
 
         for script in self.alwayson_scripts:
-            with gr.Group():
+            with gr.Group() as group:
                 create_script_ui(script, inputs, inputs_alwayson)
 
+            script.group = group
+
         dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
         dropdown.save_to_config = True
         inputs[0] = dropdown
 
         for script in self.selectable_scripts:
-            create_script_ui(script, inputs, inputs_alwayson)
+            with gr.Group(visible=False) as group:
+                create_script_ui(script, inputs, inputs_alwayson)
+
+            script.group = group
 
         def select_script(script_index):
-            if 0 < script_index <= len(self.selectable_scripts):
-                script = self.selectable_scripts[script_index-1]
-                args_from = script.args_from
-                args_to = script.args_to
-            else:
-                args_from = 0
-                args_to = 0
+            selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None
 
-            return [ui.gr_show(True if i == 0 else args_from <= i < args_to or is_alwayson) for i, is_alwayson in enumerate(inputs_alwayson)]
+            return [gr.update(visible=selected_script == s) for s in self.selectable_scripts]
 
         def init_field(title):
+            """called when an initial value is set from ui-config.json to show script's UI components"""
+
             if title == 'None':
                 return
+
             script_index = self.titles.index(title)
-            script = self.selectable_scripts[script_index]
-            for i in range(script.args_from, script.args_to):
-                inputs[i].visible = True
+            self.selectable_scripts[script_index].group.visible = True
 
         dropdown.init_field = init_field
+
         dropdown.change(
             fn=select_script,
             inputs=[dropdown],
-            outputs=inputs
+            outputs=[script.group for script in self.selectable_scripts]
         )
 
         return inputs

modules/sd_models.py

@@ -163,11 +163,11 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
+    if shared.opts.sd_checkpoint_cache > 0 and hasattr(model, "sd_checkpoint_info"):
+        sd_vae.restore_base_vae(model)
+        checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy()
 
-    checkpoint_key = checkpoint_info
-
-    if checkpoint_key not in checkpoints_loaded:
+    if checkpoint_info not in checkpoints_loaded:
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
         pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
@@ -197,17 +197,15 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
 
         model.first_stage_model.to(devices.dtype_vae)
 
-        if shared.opts.sd_checkpoint_cache > 0:
-            # if PR #4035 were to get merged, restore base VAE first before caching
-            checkpoints_loaded[checkpoint_key] = model.state_dict().copy()
-            while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
-                checkpoints_loaded.popitem(last=False)  # LRU
-
     else:
-        vae_name = sd_vae.get_filename(vae_file)
-        print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache")
-        checkpoints_loaded.move_to_end(checkpoint_key)
-        model.load_state_dict(checkpoints_loaded[checkpoint_key])
+        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
+        vae_message = f" with {vae_name} VAE" if vae_name else ""
+        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
+        model.load_state_dict(checkpoints_loaded[checkpoint_info])
+
+    if shared.opts.sd_checkpoint_cache > 0:
+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+            checkpoints_loaded.popitem(last=False)  # LRU
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file

modules/shared.py

@@ -44,6 +44,7 @@ parser.add_argument("--precision", type=str, help="evaluate at this precision",
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
 parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
 parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
+parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
 parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
@@ -99,7 +100,7 @@ restricted_opts = {
     "outdir_save",
 }
 
-cmd_opts.disable_extension_access = cmd_opts.share or cmd_opts.listen
+cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen) and not cmd_opts.enable_insecure_extension_access
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_swinir, devices.device_esrgan, devices.device_scunet, devices.device_codeformer = \
 (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'swinir', 'esrgan', 'scunet', 'codeformer'])
@@ -146,6 +147,9 @@ class State:
         self.interrupted = True
 
     def nextjob(self):
+        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
@@ -186,17 +190,21 @@ class State:
 
     """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
     def set_current_image(self):
+        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0:
+            self.do_set_current_image()
+
+    def do_set_current_image(self):
         if not parallel_processing_allowed:
             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:
+            self.current_image = sd_samplers.sample_to_image(self.current_latent)
 
-        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and self.current_latent is not None:
-            if opts.show_progress_grid:
-                self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)
-            else:
-                self.current_image = sd_samplers.sample_to_image(self.current_latent)
-
-            self.current_image_sampling_step = self.sampling_step
-
+        self.current_image_sampling_step = self.sampling_step
 
 state = State()
 
@@ -351,7 +359,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 
 options_templates.update(options_section(('ui', "User interface"), {
     "show_progressbar": OptionInfo(True, "Show progressbar"),
-    "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),
+    "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
     "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
     "return_grid": OptionInfo(True, "Show grid in results for web"),
     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
@@ -396,6 +404,15 @@ class Options:
     def __setattr__(self, key, value):
         if self.data is not None:
             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
+                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")
+
+                if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+                    raise RuntimeError(f"not possible to set {key} because it is restricted")
+
                 self.data[key] = value
                 return
 
@@ -412,6 +429,8 @@ class Options:
         return super(Options, self).__getattribute__(item)
 
     def save(self, filename):
+        assert not cmd_opts.freeze_settings, "saving settings is disabled"
+
         with open(filename, "w", encoding="utf8") as file:
             json.dump(self.data, file, indent=4)
 

modules/ui.py

@@ -276,7 +276,7 @@ def check_progress_call(id_part):
     image = gr_show(False)
     preview_visibility = gr_show(False)
 
-    if opts.show_progress_every_n_steps > 0:
+    if opts.show_progress_every_n_steps != 0:
         shared.state.set_current_image()
         image = shared.state.current_image
 
@@ -1052,6 +1052,8 @@ def create_ui(wrap_gradio_gpu_call):
                         extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")
                         show_extras_results = gr.Checkbox(label='Show result images', value=True)
 
+                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
+
                 with gr.Tabs(elem_id="extras_resize_mode"):
                     with gr.TabItem('Scale by'):
                         upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)
@@ -1079,8 +1081,6 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)
 
-                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
-
             result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
 
         submit.click(
@@ -1182,8 +1182,8 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
                     overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
@@ -1438,25 +1438,16 @@ def create_ui(wrap_gradio_gpu_call):
     def run_settings(*args):
         changed = 0
 
-        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"
-
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
-            if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
-                return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
+            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__}"
 
         for key, value, comp in zip(opts.data_labels.keys(), args, components):
             if comp == dummy_component:
                 continue
 
-            comp_args = opts.data_labels[key].component_args
-            if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
-                continue
-
-            if cmd_opts.hide_ui_dir_config and key in restricted_opts:
-                continue
-
             oldval = opts.data.get(key, None)
-            opts.data[key] = value
+
+            setattr(opts, key, value)
 
             if oldval != value:
                 if opts.data_labels[key].onchange is not None:
@@ -1466,20 +1457,18 @@ def create_ui(wrap_gradio_gpu_call):
 
         opts.save(shared.config_filename)
 
-        return f'{changed} settings changed.', opts.dumpjson()
+        return opts.dumpjson(), f'{changed} settings changed.'
 
     def run_settings_single(value, key):
-        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"
-
         if not opts.same_type(value, opts.data_labels[key].default):
             return gr.update(visible=True), opts.dumpjson()
 
         oldval = opts.data.get(key, None)
-        if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+        try:
+            setattr(opts, key, value)
+        except Exception:
             return gr.update(value=oldval), opts.dumpjson()
 
-        opts.data[key] = value
-
         if oldval != value:
             if opts.data_labels[key].onchange is not None:
                 opts.data_labels[key].onchange()
@@ -1632,9 +1621,9 @@ def create_ui(wrap_gradio_gpu_call):
 
         text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
         settings_submit.click(
-            fn=run_settings,
+            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),
             inputs=components,
-            outputs=[result, text_settings],
+            outputs=[text_settings, result],
         )
 
         for i, k, item in quicksettings_list: