Merge remote-tracking branch 'auto1111/dev' into shared-hires-prompt-test

extensions-builtin/Lora/extra_networks_lora.py

@@ -6,9 +6,14 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
     def __init__(self):
         super().__init__('lora')
 
+        self.errors = {}
+        """mapping of network names to the number of errors the network had during operation"""
+
     def activate(self, p, params_list):
         additional = shared.opts.sd_lora
 
+        self.errors.clear()
+
         if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional):
             p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
             params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
@@ -56,4 +61,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
                 p.extra_generation_params["Lora hashes"] = ", ".join(network_hashes)
 
     def deactivate(self, p):
-        pass
+        if self.errors:
+            p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items()))
+
+            self.errors.clear()

extensions-builtin/Lora/network.py

@@ -133,7 +133,7 @@ class NetworkModule:
 
         return 1.0
 
-    def finalize_updown(self, updown, orig_weight, output_shape):
+    def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
         if self.bias is not None:
             updown = updown.reshape(self.bias.shape)
             updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
@@ -145,7 +145,10 @@ class NetworkModule:
         if orig_weight.size().numel() == updown.size().numel():
             updown = updown.reshape(orig_weight.shape)
 
-        return updown * self.calc_scale() * self.multiplier()
+        if ex_bias is not None:
+            ex_bias = ex_bias * self.multiplier()
+
+        return updown * self.calc_scale() * self.multiplier(), ex_bias
 
     def calc_updown(self, target):
         raise NotImplementedError()

extensions-builtin/Lora/network_norm.py

@@ -0,0 +1,28 @@
+import network
+
+
+class ModuleTypeNorm(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["w_norm", "b_norm"]):
+            return NetworkModuleNorm(net, weights)
+
+        return None
+
+
+class NetworkModuleNorm(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+        super().__init__(net, weights)
+
+        self.w_norm = weights.w.get("w_norm")
+        self.b_norm = weights.w.get("b_norm")
+
+    def calc_updown(self, orig_weight):
+        output_shape = self.w_norm.shape
+        updown = self.w_norm.to(orig_weight.device, dtype=orig_weight.dtype)
+
+        if self.b_norm is not None:
+            ex_bias = self.b_norm.to(orig_weight.device, dtype=orig_weight.dtype)
+        else:
+            ex_bias = None
+
+        return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)

extensions-builtin/Lora/networks.py

@@ -1,3 +1,4 @@
+import logging
 import os
 import re
 
@@ -7,6 +8,7 @@ import network_hada
 import network_ia3
 import network_lokr
 import network_full
+import network_norm
 
 import torch
 from typing import Union
@@ -19,6 +21,7 @@ module_types = [
     network_ia3.ModuleTypeIa3(),
     network_lokr.ModuleTypeLokr(),
     network_full.ModuleTypeFull(),
+    network_norm.ModuleTypeNorm(),
 ]
 
 
@@ -31,6 +34,8 @@ suffix_conversion = {
     "resnets": {
         "conv1": "in_layers_2",
         "conv2": "out_layers_3",
+        "norm1": "in_layers_0",
+        "norm2": "out_layers_0",
         "time_emb_proj": "emb_layers_1",
         "conv_shortcut": "skip_connection",
     }
@@ -190,7 +195,7 @@ def load_network(name, network_on_disk):
         net.modules[key] = net_module
 
     if keys_failed_to_match:
-        print(f"Failed to match keys when loading network {network_on_disk.filename}: {keys_failed_to_match}")
+        logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")
 
     return net
 
@@ -203,7 +208,6 @@ def purge_networks_from_memory():
     devices.torch_gc()
 
 
-
 def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
     already_loaded = {}
 
@@ -244,7 +248,7 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
 
         if net is None:
             failed_to_load_networks.append(name)
-            print(f"Couldn't find network with name {name}")
+            logging.info(f"Couldn't find network with name {name}")
             continue
 
         net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
@@ -253,25 +257,38 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
         loaded_networks.append(net)
 
     if failed_to_load_networks:
-        sd_hijack.model_hijack.comments.append("Failed to find networks: " + ", ".join(failed_to_load_networks))
+        sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
 
     purge_networks_from_memory()
 
 
-def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
     weights_backup = getattr(self, "network_weights_backup", None)
+    bias_backup = getattr(self, "network_bias_backup", None)
 
-    if weights_backup is None:
+    if weights_backup is None and bias_backup is None:
         return
 
-    if isinstance(self, torch.nn.MultiheadAttention):
-        self.in_proj_weight.copy_(weights_backup[0])
-        self.out_proj.weight.copy_(weights_backup[1])
+    if weights_backup is not None:
+        if isinstance(self, torch.nn.MultiheadAttention):
+            self.in_proj_weight.copy_(weights_backup[0])
+            self.out_proj.weight.copy_(weights_backup[1])
+        else:
+            self.weight.copy_(weights_backup)
+
+    if bias_backup is not None:
+        if isinstance(self, torch.nn.MultiheadAttention):
+            self.out_proj.bias.copy_(bias_backup)
+        else:
+            self.bias.copy_(bias_backup)
     else:
-        self.weight.copy_(weights_backup)
+        if isinstance(self, torch.nn.MultiheadAttention):
+            self.out_proj.bias = None
+        else:
+            self.bias = None
 
 
-def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
     """
     Applies the currently selected set of networks to the weights of torch layer self.
     If weights already have this particular set of networks applied, does nothing.
@@ -294,21 +311,41 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
 
         self.network_weights_backup = weights_backup
 
+    bias_backup = getattr(self, "network_bias_backup", None)
+    if bias_backup is None:
+        if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
+            bias_backup = self.out_proj.bias.to(devices.cpu, copy=True)
+        elif getattr(self, 'bias', None) is not None:
+            bias_backup = self.bias.to(devices.cpu, copy=True)
+        else:
+            bias_backup = None
+        self.network_bias_backup = bias_backup
+
     if current_names != wanted_names:
         network_restore_weights_from_backup(self)
 
         for net in loaded_networks:
             module = net.modules.get(network_layer_name, None)
             if module is not None and hasattr(self, 'weight'):
-                with torch.no_grad():
-                    updown = module.calc_updown(self.weight)
+                try:
+                    with torch.no_grad():
+                        updown, ex_bias = module.calc_updown(self.weight)
 
-                    if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
-                        # inpainting model. zero pad updown to make channel[1]  4 to 9
-                        updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
+                        if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
+                            # inpainting model. zero pad updown to make channel[1]  4 to 9
+                            updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
 
-                    self.weight += updown
-                    continue
+                        self.weight += updown
+                        if ex_bias is not None and hasattr(self, 'bias'):
+                            if self.bias is None:
+                                self.bias = torch.nn.Parameter(ex_bias)
+                            else:
+                                self.bias += ex_bias
+                except RuntimeError as e:
+                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+                continue
 
             module_q = net.modules.get(network_layer_name + "_q_proj", None)
             module_k = net.modules.get(network_layer_name + "_k_proj", None)
@@ -316,21 +353,33 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
             module_out = net.modules.get(network_layer_name + "_out_proj", None)
 
             if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
-                with torch.no_grad():
-                    updown_q = module_q.calc_updown(self.in_proj_weight)
-                    updown_k = module_k.calc_updown(self.in_proj_weight)
-                    updown_v = module_v.calc_updown(self.in_proj_weight)
-                    updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
-                    updown_out = module_out.calc_updown(self.out_proj.weight)
+                try:
+                    with torch.no_grad():
+                        updown_q, _ = module_q.calc_updown(self.in_proj_weight)
+                        updown_k, _ = module_k.calc_updown(self.in_proj_weight)
+                        updown_v, _ = module_v.calc_updown(self.in_proj_weight)
+                        updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+                        updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight)
 
-                    self.in_proj_weight += updown_qkv
-                    self.out_proj.weight += updown_out
-                    continue
+                        self.in_proj_weight += updown_qkv
+                        self.out_proj.weight += updown_out
+                    if ex_bias is not None:
+                        if self.out_proj.bias is None:
+                            self.out_proj.bias = torch.nn.Parameter(ex_bias)
+                        else:
+                            self.out_proj.bias += ex_bias
+
+                except RuntimeError as e:
+                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+                continue
 
             if module is None:
                 continue
 
-            print(f'failed to calculate network weights for layer {network_layer_name}')
+            logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
+            extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
 
         self.network_current_names = wanted_names
 
@@ -357,7 +406,7 @@ def network_forward(module, input, original_forward):
         if module is None:
             continue
 
-        y = module.forward(y, input)
+        y = module.forward(input, y)
 
     return y
 
@@ -397,6 +446,36 @@ def network_Conv2d_load_state_dict(self, *args, **kwargs):
     return torch.nn.Conv2d_load_state_dict_before_network(self, *args, **kwargs)
 
 
+def network_GroupNorm_forward(self, input):
+    if shared.opts.lora_functional:
+        return network_forward(self, input, torch.nn.GroupNorm_forward_before_network)
+
+    network_apply_weights(self)
+
+    return torch.nn.GroupNorm_forward_before_network(self, input)
+
+
+def network_GroupNorm_load_state_dict(self, *args, **kwargs):
+    network_reset_cached_weight(self)
+
+    return torch.nn.GroupNorm_load_state_dict_before_network(self, *args, **kwargs)
+
+
+def network_LayerNorm_forward(self, input):
+    if shared.opts.lora_functional:
+        return network_forward(self, input, torch.nn.LayerNorm_forward_before_network)
+
+    network_apply_weights(self)
+
+    return torch.nn.LayerNorm_forward_before_network(self, input)
+
+
+def network_LayerNorm_load_state_dict(self, *args, **kwargs):
+    network_reset_cached_weight(self)
+
+    return torch.nn.LayerNorm_load_state_dict_before_network(self, *args, **kwargs)
+
+
 def network_MultiheadAttention_forward(self, *args, **kwargs):
     network_apply_weights(self)
 
@@ -473,6 +552,7 @@ def infotext_pasted(infotext, params):
     if added:
         params["Prompt"] += "\n" + "".join(added)
 
+extra_network_lora = None
 
 available_networks = {}
 available_network_aliases = {}

extensions-builtin/Lora/scripts/lora_script.py

@@ -23,9 +23,9 @@ def unload():
 def before_ui():
     ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
 
-    extra_network = extra_networks_lora.ExtraNetworkLora()
-    extra_networks.register_extra_network(extra_network)
-    extra_networks.register_extra_network_alias(extra_network, "lyco")
+    networks.extra_network_lora = extra_networks_lora.ExtraNetworkLora()
+    extra_networks.register_extra_network(networks.extra_network_lora)
+    extra_networks.register_extra_network_alias(networks.extra_network_lora, "lyco")
 
 
 if not hasattr(torch.nn, 'Linear_forward_before_network'):
@@ -40,6 +40,18 @@ if not hasattr(torch.nn, 'Conv2d_forward_before_network'):
 if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_network'):
     torch.nn.Conv2d_load_state_dict_before_network = torch.nn.Conv2d._load_from_state_dict
 
+if not hasattr(torch.nn, 'GroupNorm_forward_before_network'):
+    torch.nn.GroupNorm_forward_before_network = torch.nn.GroupNorm.forward
+
+if not hasattr(torch.nn, 'GroupNorm_load_state_dict_before_network'):
+    torch.nn.GroupNorm_load_state_dict_before_network = torch.nn.GroupNorm._load_from_state_dict
+
+if not hasattr(torch.nn, 'LayerNorm_forward_before_network'):
+    torch.nn.LayerNorm_forward_before_network = torch.nn.LayerNorm.forward
+
+if not hasattr(torch.nn, 'LayerNorm_load_state_dict_before_network'):
+    torch.nn.LayerNorm_load_state_dict_before_network = torch.nn.LayerNorm._load_from_state_dict
+
 if not hasattr(torch.nn, 'MultiheadAttention_forward_before_network'):
     torch.nn.MultiheadAttention_forward_before_network = torch.nn.MultiheadAttention.forward
 
@@ -50,6 +62,10 @@ torch.nn.Linear.forward = networks.network_Linear_forward
 torch.nn.Linear._load_from_state_dict = networks.network_Linear_load_state_dict
 torch.nn.Conv2d.forward = networks.network_Conv2d_forward
 torch.nn.Conv2d._load_from_state_dict = networks.network_Conv2d_load_state_dict
+torch.nn.GroupNorm.forward = networks.network_GroupNorm_forward
+torch.nn.GroupNorm._load_from_state_dict = networks.network_GroupNorm_load_state_dict
+torch.nn.LayerNorm.forward = networks.network_LayerNorm_forward
+torch.nn.LayerNorm._load_from_state_dict = networks.network_LayerNorm_load_state_dict
 torch.nn.MultiheadAttention.forward = networks.network_MultiheadAttention_forward
 torch.nn.MultiheadAttention._load_from_state_dict = networks.network_MultiheadAttention_load_state_dict
 

extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -25,9 +25,10 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
         item = {
             "name": name,
             "filename": lora_on_disk.filename,
+            "shorthash": lora_on_disk.shorthash,
             "preview": self.find_preview(path),
             "description": self.find_description(path),
-            "search_term": self.search_terms_from_path(lora_on_disk.filename),
+            "search_term": self.search_terms_from_path(lora_on_disk.filename) + " " + (lora_on_disk.hash or ""),
             "local_preview": f"{path}.{shared.opts.samples_format}",
             "metadata": lora_on_disk.metadata,
             "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)},

extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js

@@ -12,6 +12,7 @@ onUiLoaded(async() => {
         "Sketch": elementIDs.sketch
     };
 
+
     // Helper functions
     // Get active tab
     function getActiveTab(elements, all = false) {
@@ -377,6 +378,11 @@ onUiLoaded(async() => {
             toggleOverlap("off");
             fullScreenMode = false;
 
+            const closeBtn = targetElement.querySelector("button[aria-label='Remove Image']");
+            if (closeBtn) {
+                closeBtn.addEventListener("click", resetZoom);
+            }
+
             if (
                 canvas &&
                 parseFloat(canvas.style.width) > 865 &&
@@ -657,17 +663,20 @@ onUiLoaded(async() => {
         // Simulation of the function to put a long image into the screen.
         // We detect if an image has a scroll bar or not, make a fullscreen to reveal the image, then reduce it to fit into the element.
         // We hide the image and show it to the user when it is ready.
-        function autoExpand(e) {
+
+        targetElement.isExpanded = false;
+        function autoExpand() {
             const canvas = document.querySelector(`${elemId} canvas[key="interface"]`);
             const isMainTab = activeElement === elementIDs.inpaint || activeElement === elementIDs.inpaintSketch || activeElement === elementIDs.sketch;
 
             if (canvas && isMainTab) {
-                if (hasHorizontalScrollbar(targetElement)) {
+                if (hasHorizontalScrollbar(targetElement) && targetElement.isExpanded === false) {
                     targetElement.style.visibility = "hidden";
                     setTimeout(() => {
                         fitToScreen();
                         resetZoom();
                         targetElement.style.visibility = "visible";
+                        targetElement.isExpanded = true;
                     }, 10);
                 }
             }
@@ -675,9 +684,24 @@ onUiLoaded(async() => {
 
         targetElement.addEventListener("mousemove", getMousePosition);
 
+        //observers
+        // Creating an observer with a callback function to handle DOM changes
+        const observer = new MutationObserver((mutationsList, observer) => {
+            for (let mutation of mutationsList) {
+                // If the style attribute of the canvas has changed, by observation it happens only when the picture changes
+                if (mutation.type === 'attributes' && mutation.attributeName === 'style' &&
+                    mutation.target.tagName.toLowerCase() === 'canvas') {
+                    targetElement.isExpanded = false;
+                    setTimeout(resetZoom, 10);
+                }
+            }
+        });
+
         // Apply auto expand if enabled
         if (hotkeysConfig.canvas_auto_expand) {
             targetElement.addEventListener("mousemove", autoExpand);
+            // Set up an observer to track attribute changes
+            observer.observe(targetElement, {attributes: true, childList: true, subtree: true});
         }
 
         // Handle events only inside the targetElement

modules/api/models.py

@@ -50,10 +50,12 @@ class PydanticModelGenerator:
         additional_fields = None,
     ):
         def field_type_generator(k, v):
-            # field_type = str if not overrides.get(k) else overrides[k]["type"]
-            # print(k, v.annotation, v.default)
             field_type = v.annotation
 
+            if field_type == 'Image':
+                # images are sent as base64 strings via API
+                field_type = 'str'
+
             return Optional[field_type]
 
         def merge_class_params(class_):
@@ -63,7 +65,6 @@ class PydanticModelGenerator:
                 parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
             return parameters
 
-
         self._model_name = model_name
         self._class_data = merge_class_params(class_instance)
 
@@ -72,7 +73,7 @@ class PydanticModelGenerator:
                 field=underscore(k),
                 field_alias=k,
                 field_type=field_type_generator(k, v),
-                field_value=v.default
+                field_value=None if isinstance(v.default, property) else v.default
             )
             for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
         ]

modules/img2img.py

@@ -116,7 +116,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
             process_images(p)
 
 
-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
     override_settings = create_override_settings_dict(override_settings_texts)
 
     is_batch = mode == 5
@@ -166,12 +166,6 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
         prompt=prompt,
         negative_prompt=negative_prompt,
         styles=prompt_styles,
-        seed=seed,
-        subseed=subseed,
-        subseed_strength=subseed_strength,
-        seed_resize_from_h=seed_resize_from_h,
-        seed_resize_from_w=seed_resize_from_w,
-        seed_enable_extras=seed_enable_extras,
         sampler_name=sampler_name,
         batch_size=batch_size,
         n_iter=n_iter,

modules/launch_utils.py

@@ -173,9 +173,12 @@ def git_clone(url, dir, name, commithash=None):
         if current_hash == commithash:
             return
 
-        run_git('fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}", autofix=False)
+        if run_git(dir, name, 'config --get remote.origin.url', None, f"Couldn't determine {name}'s origin URL", live=False).strip() != url:
+            run_git(dir, name, f'remote set-url origin "{url}"', None, f"Failed to set {name}'s origin URL", live=False)
 
-        run_git('checkout', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
+        run_git(dir, name, 'fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}", autofix=False)
+
+        run_git(dir, name, f'checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
 
         return
 
@@ -243,7 +246,7 @@ def list_extensions(settings_file):
     disabled_extensions = set(settings.get('disabled_extensions', []))
     disable_all_extensions = settings.get('disable_all_extensions', 'none')
 
-    if disable_all_extensions != 'none':
+    if disable_all_extensions != 'none' or args.disable_extra_extensions or args.disable_all_extensions:
         return []
 
     return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
@@ -319,12 +322,12 @@ def prepare_environment():
 
     stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
     stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "5c10deee76adad0032b412294130090932317a87")
-    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "c9fe758757e022f05ca5a53fa8fac28889e4f1cf")
+    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "ab527a9a6d347f364e3d185ba6d714e22d80cb3c")
     codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
     try:
-        # the existance of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
+        # the existence of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
         os.remove(os.path.join(script_path, "tmp", "restart"))
         os.environ.setdefault('SD_WEBUI_RESTARTING', '1')
     except OSError:

modules/mac_specific.py

@@ -52,9 +52,6 @@ def cumsum_fix(input, cumsum_func, *args, **kwargs):
 
 
 if has_mps:
-    # MPS fix for randn in torchsde
-    CondFunc('torchsde._brownian.brownian_interval._randn', lambda _, size, dtype, device, seed: torch.randn(size, dtype=dtype, device=torch.device("cpu"), generator=torch.Generator(torch.device("cpu")).manual_seed(int(seed))).to(device), lambda _, size, dtype, device, seed: device.type == 'mps')
-
     if platform.mac_ver()[0].startswith("13.2."):
         # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124)
         CondFunc('torch.nn.functional.linear', lambda _, input, weight, bias: (torch.matmul(input, weight.t()) + bias) if bias is not None else torch.matmul(input, weight.t()), lambda _, input, weight, bias: input.numel() > 10485760)

modules/postprocessing.py

@@ -11,37 +11,32 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
 
     shared.state.begin(job="extras")
 
-    image_data = []
-    image_names = []
     outputs = []
 
-    if extras_mode == 1:
-        for img in image_folder:
-            if isinstance(img, Image.Image):
-                image = img
-                fn = ''
-            else:
-                image = Image.open(os.path.abspath(img.name))
-                fn = os.path.splitext(img.orig_name)[0]
-            image_data.append(image)
-            image_names.append(fn)
-    elif extras_mode == 2:
-        assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
-        assert input_dir, 'input directory not selected'
+    def get_images(extras_mode, image, image_folder, input_dir):
+        if extras_mode == 1:
+            for img in image_folder:
+                if isinstance(img, Image.Image):
+                    image = img
+                    fn = ''
+                else:
+                    image = Image.open(os.path.abspath(img.name))
+                    fn = os.path.splitext(img.orig_name)[0]
+                yield image, fn
+        elif extras_mode == 2:
+            assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
+            assert input_dir, 'input directory not selected'
 
-        image_list = shared.listfiles(input_dir)
-        for filename in image_list:
-            try:
-                image = Image.open(filename)
-            except Exception:
-                continue
-            image_data.append(image)
-            image_names.append(filename)
-    else:
-        assert image, 'image not selected'
-
-        image_data.append(image)
-        image_names.append(None)
+            image_list = shared.listfiles(input_dir)
+            for filename in image_list:
+                try:
+                    image = Image.open(filename)
+                except Exception:
+                    continue
+                yield image, filename
+        else:
+            assert image, 'image not selected'
+            yield image, None
 
     if extras_mode == 2 and output_dir != '':
         outpath = output_dir
@@ -50,14 +45,16 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
 
     infotext = ''
 
-    for image, name in zip(image_data, image_names):
+    for image_data, name in get_images(extras_mode, image, image_folder, input_dir):
+        image_data: Image.Image
+
         shared.state.textinfo = name
 
-        parameters, existing_pnginfo = images.read_info_from_image(image)
+        parameters, existing_pnginfo = images.read_info_from_image(image_data)
         if parameters:
             existing_pnginfo["parameters"] = parameters
 
-        pp = scripts_postprocessing.PostprocessedImage(image.convert("RGB"))
+        pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))
 
         scripts.scripts_postproc.run(pp, args)
 
@@ -78,6 +75,8 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
         if extras_mode != 2 or show_extras_results:
             outputs.append(pp.image)
 
+        image_data.close()
+
     devices.torch_gc()
 
     return outputs, ui_common.plaintext_to_html(infotext), ''

modules/processing.py

@@ -1,9 +1,11 @@
+from __future__ import annotations
 import json
 import logging
 import math
 import os
 import sys
 import hashlib
+from dataclasses import dataclass, field
 
 import torch
 import numpy as np
@@ -11,7 +13,7 @@ from PIL import Image, ImageOps
 import random
 import cv2
 from skimage import exposure
-from typing import Any, Dict, List
+from typing import Any
 
 import modules.sd_hijack
 from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
@@ -57,7 +59,7 @@ def apply_color_correction(correction, original_image):
 
     image = blendLayers(image, original_image, BlendType.LUMINOSITY)
 
-    return image
+    return image.convert('RGB')
 
 
 def apply_overlay(image, paste_loc, index, overlays):
@@ -104,97 +106,163 @@ def txt2img_image_conditioning(sd_model, x, width, height):
         return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
 
 
+@dataclass(repr=False)
 class StableDiffusionProcessing:
-    """
-    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
-    """
+    sd_model: object = None
+    outpath_samples: str = None
+    outpath_grids: str = None
+    prompt: str = ""
+    prompt_for_display: str = None
+    negative_prompt: str = ""
+    styles: list[str] = None
+    seed: int = -1
+    subseed: int = -1
+    subseed_strength: float = 0
+    seed_resize_from_h: int = -1
+    seed_resize_from_w: int = -1
+    seed_enable_extras: bool = True
+    sampler_name: str = None
+    batch_size: int = 1
+    n_iter: int = 1
+    steps: int = 50
+    cfg_scale: float = 7.0
+    width: int = 512
+    height: int = 512
+    restore_faces: bool = None
+    tiling: bool = None
+    do_not_save_samples: bool = False
+    do_not_save_grid: bool = False
+    extra_generation_params: dict[str, Any] = None
+    overlay_images: list = None
+    eta: float = None
+    do_not_reload_embeddings: bool = False
+    denoising_strength: float = 0
+    ddim_discretize: str = None
+    s_min_uncond: float = None
+    s_churn: float = None
+    s_tmax: float = None
+    s_tmin: float = None
+    s_noise: float = None
+    override_settings: dict[str, Any] = None
+    override_settings_restore_afterwards: bool = True
+    sampler_index: int = None
+    refiner_checkpoint: str = None
+    refiner_switch_at: float = None
+    token_merging_ratio = 0
+    token_merging_ratio_hr = 0
+    disable_extra_networks: bool = False
+
+    scripts_value: scripts.ScriptRunner = field(default=None, init=False)
+    script_args_value: list = field(default=None, init=False)
+    scripts_setup_complete: bool = field(default=False, init=False)
+
     cached_uc = [None, None]
     cached_c = [None, None]
 
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = None, tiling: bool = None, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = None, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
-        if sampler_index is not None:
+    comments: dict = None
+    sampler: sd_samplers_common.Sampler | None = field(default=None, init=False)
+    is_using_inpainting_conditioning: bool = field(default=False, init=False)
+    paste_to: tuple | None = field(default=None, init=False)
+
+    is_hr_pass: bool = field(default=False, init=False)
+
+    c: tuple = field(default=None, init=False)
+    uc: tuple = field(default=None, init=False)
+
+    rng: rng.ImageRNG | None = field(default=None, init=False)
+    step_multiplier: int = field(default=1, init=False)
+    color_corrections: list = field(default=None, init=False)
+
+    all_prompts: list = field(default=None, init=False)
+    all_negative_prompts: list = field(default=None, init=False)
+    all_seeds: list = field(default=None, init=False)
+    all_subseeds: list = field(default=None, init=False)
+    iteration: int = field(default=0, init=False)
+    main_prompt: str = field(default=None, init=False)
+    main_negative_prompt: str = field(default=None, init=False)
+
+    prompts: list = field(default=None, init=False)
+    negative_prompts: list = field(default=None, init=False)
+    seeds: list = field(default=None, init=False)
+    subseeds: list = field(default=None, init=False)
+    extra_network_data: dict = field(default=None, init=False)
+
+    user: str = field(default=None, init=False)
+
+    sd_model_name: str = field(default=None, init=False)
+    sd_model_hash: str = field(default=None, init=False)
+    sd_vae_name: str = field(default=None, init=False)
+    sd_vae_hash: str = field(default=None, init=False)
+
+    def __post_init__(self):
+        if self.sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
-        self.outpath_samples: str = outpath_samples
-        self.outpath_grids: str = outpath_grids
-        self.prompt: str = prompt
-        self.prompt_for_display: str = None
-        self.negative_prompt: str = (negative_prompt or "")
-        self.styles: list = styles or []
-        self.seed: int = seed
-        self.subseed: int = subseed
-        self.subseed_strength: float = subseed_strength
-        self.seed_resize_from_h: int = seed_resize_from_h
-        self.seed_resize_from_w: int = seed_resize_from_w
-        self.sampler_name: str = sampler_name
-        self.batch_size: int = batch_size
-        self.n_iter: int = n_iter
-        self.steps: int = steps
-        self.cfg_scale: float = cfg_scale
-        self.width: int = width
-        self.height: int = height
-        self.restore_faces: bool = restore_faces
-        self.tiling: bool = tiling
-        self.do_not_save_samples: bool = do_not_save_samples
-        self.do_not_save_grid: bool = do_not_save_grid
-        self.extra_generation_params: dict = extra_generation_params or {}
-        self.overlay_images = overlay_images
-        self.eta = eta
-        self.do_not_reload_embeddings = do_not_reload_embeddings
-        self.paste_to = None
-        self.color_corrections = None
-        self.denoising_strength: float = denoising_strength
-        self.sampler_noise_scheduler_override = None
-        self.ddim_discretize = ddim_discretize or opts.ddim_discretize
-        self.s_min_uncond = s_min_uncond or opts.s_min_uncond
-        self.s_churn = s_churn or opts.s_churn
-        self.s_tmin = s_tmin or opts.s_tmin
-        self.s_tmax = (s_tmax if s_tmax is not None else opts.s_tmax) or float('inf')
-        self.s_noise = s_noise if s_noise is not None else opts.s_noise
-        self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
-        self.override_settings_restore_afterwards = override_settings_restore_afterwards
-        self.is_using_inpainting_conditioning = False
-        self.disable_extra_networks = False
-        self.token_merging_ratio = 0
-        self.token_merging_ratio_hr = 0
+        self.comments = {}
 
-        if not seed_enable_extras:
+        if self.styles is None:
+            self.styles = []
+
+        self.sampler_noise_scheduler_override = None
+        self.s_min_uncond = self.s_min_uncond if self.s_min_uncond is not None else opts.s_min_uncond
+        self.s_churn = self.s_churn if self.s_churn is not None else opts.s_churn
+        self.s_tmin = self.s_tmin if self.s_tmin is not None else opts.s_tmin
+        self.s_tmax = (self.s_tmax if self.s_tmax is not None else opts.s_tmax) or float('inf')
+        self.s_noise = self.s_noise if self.s_noise is not None else opts.s_noise
+
+        self.extra_generation_params = self.extra_generation_params or {}
+        self.override_settings = self.override_settings or {}
+        self.script_args = self.script_args or {}
+
+        self.refiner_checkpoint_info = None
+
+        if not self.seed_enable_extras:
             self.subseed = -1
             self.subseed_strength = 0
             self.seed_resize_from_h = 0
             self.seed_resize_from_w = 0
 
-        self.scripts = None
-        self.script_args = script_args
-        self.all_prompts = None
-        self.all_negative_prompts = None
-        self.all_seeds = None
-        self.all_subseeds = None
-        self.iteration = 0
-        self.is_hr_pass = False
-        self.sampler = None
-        self.main_prompt = None
-        self.main_negative_prompt = None
-
-        self.prompts = None
-        self.negative_prompts = None
-        self.extra_network_data = None
-        self.seeds = None
-        self.subseeds = None
-
-        self.step_multiplier = 1
         self.cached_uc = StableDiffusionProcessing.cached_uc
         self.cached_c = StableDiffusionProcessing.cached_c
-        self.uc = None
-        self.c = None
-        self.rng: rng.ImageRNG = None
-
-        self.user = None
 
     @property
     def sd_model(self):
         return shared.sd_model
 
+    @sd_model.setter
+    def sd_model(self, value):
+        pass
+
+    @property
+    def scripts(self):
+        return self.scripts_value
+
+    @scripts.setter
+    def scripts(self, value):
+        self.scripts_value = value
+
+        if self.scripts_value and self.script_args_value and not self.scripts_setup_complete:
+            self.setup_scripts()
+
+    @property
+    def script_args(self):
+        return self.script_args_value
+
+    @script_args.setter
+    def script_args(self, value):
+        self.script_args_value = value
+
+        if self.scripts_value and self.script_args_value and not self.scripts_setup_complete:
+            self.setup_scripts()
+
+    def setup_scripts(self):
+        self.scripts_setup_complete = True
+
+        self.scripts.setup_scrips(self)
+
+    def comment(self, text):
+        self.comments[text] = 1
+
     def txt2img_image_conditioning(self, x, width=None, height=None):
         self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
 
@@ -343,7 +411,7 @@ class StableDiffusionProcessing:
             self.height,
         )
 
-    def get_conds_with_caching(self, function, required_prompts, steps, hires_steps, caches, extra_network_data):
+    def get_conds_with_caching(self, function, required_prompts, steps, caches, extra_network_data, hires_steps=None):
         """
         Returns the result of calling function(shared.sd_model, required_prompts, steps)
         using a cache to store the result if the same arguments have been used before.
@@ -375,10 +443,12 @@ class StableDiffusionProcessing:
         negative_prompts = prompt_parser.SdConditioning(self.negative_prompts, width=self.width, height=self.height, is_negative_prompt=True)
 
         sampler_config = sd_samplers.find_sampler_config(self.sampler_name)
-        self.step_multiplier = 2 if sampler_config and sampler_config.options.get("second_order", False) else 1
-        self.firstpass_steps = self.steps * self.step_multiplier
-        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, self.firstpass_steps, None, [self.cached_uc], self.extra_network_data)
-        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, self.firstpass_steps, None, [self.cached_c], self.extra_network_data)
+        total_steps = sampler_config.total_steps(self.steps) if sampler_config else self.steps
+        self.step_multiplier = total_steps // self.steps
+        self.firstpass_steps = total_steps
+
+        self.uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, total_steps, [self.cached_uc], self.extra_network_data, None)
+        self.c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, total_steps, [self.cached_c], self.extra_network_data, None )
 
     def get_conds(self):
         return self.c, self.uc
@@ -400,7 +470,7 @@ class Processed:
         self.subseed = subseed
         self.subseed_strength = p.subseed_strength
         self.info = info
-        self.comments = comments
+        self.comments = "".join(f"{comment}\n" for comment in p.comments)
         self.width = p.width
         self.height = p.height
         self.sampler_name = p.sampler_name
@@ -410,7 +480,10 @@ class Processed:
         self.batch_size = p.batch_size
         self.restore_faces = p.restore_faces
         self.face_restoration_model = opts.face_restoration_model if p.restore_faces else None
-        self.sd_model_hash = shared.sd_model.sd_model_hash
+        self.sd_model_name = p.sd_model_name
+        self.sd_model_hash = p.sd_model_hash
+        self.sd_vae_name = p.sd_vae_name
+        self.sd_vae_hash = p.sd_vae_hash
         self.seed_resize_from_w = p.seed_resize_from_w
         self.seed_resize_from_h = p.seed_resize_from_h
         self.denoising_strength = getattr(p, 'denoising_strength', None)
@@ -461,7 +534,10 @@ class Processed:
             "batch_size": self.batch_size,
             "restore_faces": self.restore_faces,
             "face_restoration_model": self.face_restoration_model,
+            "sd_model_name": self.sd_model_name,
             "sd_model_hash": self.sd_model_hash,
+            "sd_vae_name": self.sd_vae_name,
+            "sd_vae_hash": self.sd_vae_hash,
             "seed_resize_from_w": self.seed_resize_from_w,
             "seed_resize_from_h": self.seed_resize_from_h,
             "denoising_strength": self.denoising_strength,
@@ -580,10 +656,10 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Seed": p.all_seeds[0] if use_main_prompt else all_seeds[index],
         "Face restoration": opts.face_restoration_model if p.restore_faces else None,
         "Size": f"{p.width}x{p.height}",
-        "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
-        "Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
-        "VAE hash": sd_vae.get_loaded_vae_hash() if opts.add_model_hash_to_info else None,
-        "VAE": sd_vae.get_loaded_vae_name() if opts.add_model_name_to_info else None,
+        "Model hash": p.sd_model_hash if opts.add_model_hash_to_info else None,
+        "Model": p.sd_model_name if opts.add_model_name_to_info else None,
+        "VAE hash": p.sd_vae_hash if opts.add_model_hash_to_info else None,
+        "VAE": p.sd_vae_name if opts.add_model_name_to_info else None,
         "Variation seed": (None if p.subseed_strength == 0 else (p.all_subseeds[0] if use_main_prompt else all_subseeds[index])),
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
@@ -672,11 +748,19 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     if p.tiling is None:
         p.tiling = opts.tiling
 
+    if p.refiner_checkpoint not in (None, "", "None"):
+        p.refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(p.refiner_checkpoint)
+        if p.refiner_checkpoint_info is None:
+            raise Exception(f'Could not find checkpoint with name {p.refiner_checkpoint}')
+
+    p.sd_model_name = shared.sd_model.sd_checkpoint_info.name_for_extra
+    p.sd_model_hash = shared.sd_model.sd_model_hash
+    p.sd_vae_name = sd_vae.get_loaded_vae_name()
+    p.sd_vae_hash = sd_vae.get_loaded_vae_hash()
+
     modules.sd_hijack.model_hijack.apply_circular(p.tiling)
     modules.sd_hijack.model_hijack.clear_comments()
 
-    comments = {}
-
     p.setup_prompts()
 
     if type(seed) == list:
@@ -756,7 +840,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             p.setup_conds()
 
             for comment in model_hijack.comments:
-                comments[comment] = 1
+                p.comment(comment)
 
             p.extra_generation_params.update(model_hijack.extra_generation_params)
 
@@ -885,7 +969,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         images_list=output_images,
         seed=p.all_seeds[0],
         info=infotexts[0],
-        comments="".join(f"{comment}\n" for comment in comments),
         subseed=p.all_subseeds[0],
         index_of_first_image=index_of_first_image,
         infotexts=infotexts,
@@ -909,49 +992,51 @@ def old_hires_fix_first_pass_dimensions(width, height):
     return width, height
 
 
+@dataclass(repr=False)
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
-    sampler = None
+    enable_hr: bool = False
+    denoising_strength: float = 0.75
+    firstphase_width: int = 0
+    firstphase_height: int = 0
+    hr_scale: float = 2.0
+    hr_upscaler: str = None
+    hr_second_pass_steps: int = 0
+    hr_resize_x: int = 0
+    hr_resize_y: int = 0
+    hr_checkpoint_name: str = None
+    hr_sampler_name: str = None
+    hr_prompt: str = ''
+    hr_negative_prompt: str = ''
+
     cached_hr_uc = [None, None]
     cached_hr_c = [None, None]
 
-    def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, hr_checkpoint_name: str = None, hr_sampler_name: str = None, hr_prompt: str = '', hr_negative_prompt: str = '', **kwargs):
-        super().__init__(**kwargs)
-        self.enable_hr = enable_hr
-        self.denoising_strength = denoising_strength
-        self.hr_scale = hr_scale
-        self.hr_upscaler = hr_upscaler
-        self.hr_second_pass_steps = hr_second_pass_steps
-        self.hr_resize_x = hr_resize_x
-        self.hr_resize_y = hr_resize_y
-        self.hr_upscale_to_x = hr_resize_x
-        self.hr_upscale_to_y = hr_resize_y
-        self.hr_checkpoint_name = hr_checkpoint_name
-        self.hr_checkpoint_info = None
-        self.hr_sampler_name = hr_sampler_name
-        self.hr_prompt = hr_prompt
-        self.hr_negative_prompt = hr_negative_prompt
-        self.all_hr_prompts = None
-        self.all_hr_negative_prompts = None
-        self.latent_scale_mode = None
+    hr_checkpoint_info: dict = field(default=None, init=False)
+    hr_upscale_to_x: int = field(default=0, init=False)
+    hr_upscale_to_y: int = field(default=0, init=False)
+    truncate_x: int = field(default=0, init=False)
+    truncate_y: int = field(default=0, init=False)
+    applied_old_hires_behavior_to: tuple = field(default=None, init=False)
+    latent_scale_mode: dict = field(default=None, init=False)
+    hr_c: tuple | None = field(default=None, init=False)
+    hr_uc: tuple | None = field(default=None, init=False)
+    all_hr_prompts: list = field(default=None, init=False)
+    all_hr_negative_prompts: list = field(default=None, init=False)
+    hr_prompts: list = field(default=None, init=False)
+    hr_negative_prompts: list = field(default=None, init=False)
+    hr_extra_network_data: list = field(default=None, init=False)
 
-        if firstphase_width != 0 or firstphase_height != 0:
+    def __post_init__(self):
+        super().__post_init__()
+
+        if self.firstphase_width != 0 or self.firstphase_height != 0:
             self.hr_upscale_to_x = self.width
             self.hr_upscale_to_y = self.height
-            self.width = firstphase_width
-            self.height = firstphase_height
-
-        self.truncate_x = 0
-        self.truncate_y = 0
-        self.applied_old_hires_behavior_to = None
-
-        self.hr_prompts = None
-        self.hr_negative_prompts = None
-        self.hr_extra_network_data = None
+            self.width = self.firstphase_width
+            self.height = self.firstphase_height
 
         self.cached_hr_uc = StableDiffusionProcessingTxt2Img.cached_hr_uc
         self.cached_hr_c = StableDiffusionProcessingTxt2Img.cached_hr_c
-        self.hr_c = None
-        self.hr_uc = None
 
     def calculate_target_resolution(self):
         if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
@@ -1145,6 +1230,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         sd_models.apply_token_merging(self.sd_model, self.get_token_merging_ratio())
 
+        self.sampler = None
+        devices.torch_gc()
+
         decoded_samples = decode_latent_batch(self.sd_model, samples, target_device=devices.cpu, check_for_nans=True)
 
         self.is_hr_pass = False
@@ -1191,11 +1279,20 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         hr_prompts = prompt_parser.SdConditioning(self.hr_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y)
         hr_negative_prompts = prompt_parser.SdConditioning(self.hr_negative_prompts, width=self.hr_upscale_to_x, height=self.hr_upscale_to_y, is_negative_prompt=True)
 
-        hires_steps = (self.hr_second_pass_steps or self.steps) * self.step_multiplier
-        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.firstpass_steps, hires_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data)
-        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.firstpass_steps, hires_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data)
+        sampler_config = sd_samplers.find_sampler_config(self.hr_sampler_name or self.sampler_name)
+        steps = self.hr_second_pass_steps or self.steps
+        total_steps = sampler_config.total_steps(steps) if sampler_config else steps
+
+        self.hr_uc = self.get_conds_with_caching(prompt_parser.get_learned_conditioning, hr_negative_prompts, self.firstpass_steps, [self.cached_hr_uc, self.cached_uc], self.hr_extra_network_data, total_steps)
+        self.hr_c = self.get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, hr_prompts, self.firstpass_steps, [self.cached_hr_c, self.cached_c], self.hr_extra_network_data, total_steps)
 
     def setup_conds(self):
+        if self.is_hr_pass:
+            # if we are in hr pass right now, the call is being made from the refiner, and we don't need to setup firstpass cons or switch model
+            self.hr_c = None
+            self.calculate_hr_conds()
+            return
+
         super().setup_conds()
 
         self.hr_uc = None
@@ -1220,7 +1317,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         return super().get_conds()
 
-
     def parse_extra_network_prompts(self):
         res = super().parse_extra_network_prompts()
 
@@ -1233,35 +1329,53 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         return res
 
 
+@dataclass(repr=False)
 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
-    sampler = None
+    init_images: list = None
+    resize_mode: int = 0
+    denoising_strength: float = 0.75
+    image_cfg_scale: float = None
+    mask: Any = None
+    mask_blur_x: int = 4
+    mask_blur_y: int = 4
+    mask_blur: int = None
+    inpainting_fill: int = 0
+    inpaint_full_res: bool = True
+    inpaint_full_res_padding: int = 0
+    inpainting_mask_invert: int = 0
+    initial_noise_multiplier: float = None
+    latent_mask: Image = None
 
-    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = None, mask_blur_x: int = 4, mask_blur_y: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
-        super().__init__(**kwargs)
+    image_mask: Any = field(default=None, init=False)
 
-        self.init_images = init_images
-        self.resize_mode: int = resize_mode
-        self.denoising_strength: float = denoising_strength
-        self.image_cfg_scale: float = image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
-        self.init_latent = None
-        self.image_mask = mask
-        self.latent_mask = None
-        self.mask_for_overlay = None
-        if mask_blur is not None:
-            mask_blur_x = mask_blur
-            mask_blur_y = mask_blur
-        self.mask_blur_x = mask_blur_x
-        self.mask_blur_y = mask_blur_y
-        self.inpainting_fill = inpainting_fill
-        self.inpaint_full_res = inpaint_full_res
-        self.inpaint_full_res_padding = inpaint_full_res_padding
-        self.inpainting_mask_invert = inpainting_mask_invert
-        self.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
+    nmask: torch.Tensor = field(default=None, init=False)
+    image_conditioning: torch.Tensor = field(default=None, init=False)
+    init_img_hash: str = field(default=None, init=False)
+    mask_for_overlay: Image = field(default=None, init=False)
+    init_latent: torch.Tensor = field(default=None, init=False)
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        self.image_mask = self.mask
         self.mask = None
-        self.nmask = None
-        self.image_conditioning = None
+        self.initial_noise_multiplier = opts.initial_noise_multiplier if self.initial_noise_multiplier is None else self.initial_noise_multiplier
+
+    @property
+    def mask_blur(self):
+        if self.mask_blur_x == self.mask_blur_y:
+            return self.mask_blur_x
+        return None
+
+    @mask_blur.setter
+    def mask_blur(self, value):
+        if isinstance(value, int):
+            self.mask_blur_x = value
+            self.mask_blur_y = value
 
     def init(self, all_prompts, all_seeds, all_subseeds):
+        self.image_cfg_scale: float = self.image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
+
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
         crop_region = None
 
@@ -1275,13 +1389,13 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 
             if self.mask_blur_x > 0:
                 np_mask = np.array(image_mask)
-                kernel_size = 2 * int(4 * self.mask_blur_x + 0.5) + 1
+                kernel_size = 2 * int(2.5 * self.mask_blur_x + 0.5) + 1
                 np_mask = cv2.GaussianBlur(np_mask, (kernel_size, 1), self.mask_blur_x)
                 image_mask = Image.fromarray(np_mask)
 
             if self.mask_blur_y > 0:
                 np_mask = np.array(image_mask)
-                kernel_size = 2 * int(4 * self.mask_blur_y + 0.5) + 1
+                kernel_size = 2 * int(2.5 * self.mask_blur_y + 0.5) + 1
                 np_mask = cv2.GaussianBlur(np_mask, (1, kernel_size), self.mask_blur_y)
                 image_mask = Image.fromarray(np_mask)
 

modules/processing_scripts/refiner.py

@@ -0,0 +1,49 @@
+import gradio as gr
+
+from modules import scripts, sd_models
+from modules.ui_common import create_refresh_button
+from modules.ui_components import InputAccordion
+
+
+class ScriptRefiner(scripts.Script):
+    section = "accordions"
+    create_group = False
+
+    def __init__(self):
+        pass
+
+    def title(self):
+        return "Refiner"
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def ui(self, is_img2img):
+        with InputAccordion(False, label="Refiner", elem_id=self.elem_id("enable")) as enable_refiner:
+            with gr.Row():
+                refiner_checkpoint = gr.Dropdown(label='Checkpoint', elem_id=self.elem_id("checkpoint"), choices=sd_models.checkpoint_tiles(), value='', tooltip="switch to another model in the middle of generation")
+                create_refresh_button(refiner_checkpoint, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, self.elem_id("checkpoint_refresh"))
+
+                refiner_switch_at = gr.Slider(value=0.8, label="Switch at", minimum=0.01, maximum=1.0, step=0.01, elem_id=self.elem_id("switch_at"), tooltip="fraction of sampling steps when the switch to refiner model should happen; 1=never, 0.5=switch in the middle of generation")
+
+        def lookup_checkpoint(title):
+            info = sd_models.get_closet_checkpoint_match(title)
+            return None if info is None else info.title
+
+        self.infotext_fields = [
+            (enable_refiner, lambda d: 'Refiner' in d),
+            (refiner_checkpoint, lambda d: lookup_checkpoint(d.get('Refiner'))),
+            (refiner_switch_at, 'Refiner switch at'),
+        ]
+
+        return enable_refiner, refiner_checkpoint, refiner_switch_at
+
+    def setup(self, p, enable_refiner, refiner_checkpoint, refiner_switch_at):
+        # the actual implementation is in sd_samplers_common.py, apply_refiner
+
+        if not enable_refiner or refiner_checkpoint in (None, "", "None"):
+            p.refiner_checkpoint_info = None
+            p.refiner_switch_at = None
+        else:
+            p.refiner_checkpoint = refiner_checkpoint
+            p.refiner_switch_at = refiner_switch_at

modules/processing_scripts/seed.py

@@ -0,0 +1,111 @@
+import json
+
+import gradio as gr
+
+from modules import scripts, ui, errors
+from modules.shared import cmd_opts
+from modules.ui_components import ToolButton
+
+
+class ScriptSeed(scripts.ScriptBuiltin):
+    section = "seed"
+    create_group = False
+
+    def __init__(self):
+        self.seed = None
+        self.reuse_seed = None
+        self.reuse_subseed = None
+
+    def title(self):
+        return "Seed"
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def ui(self, is_img2img):
+        with gr.Row(elem_id=self.elem_id("seed_row")):
+            if cmd_opts.use_textbox_seed:
+                self.seed = gr.Textbox(label='Seed', value="", elem_id=self.elem_id("seed"), min_width=100)
+            else:
+                self.seed = gr.Number(label='Seed', value=-1, elem_id=self.elem_id("seed"), min_width=100, precision=0)
+
+            random_seed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_seed"), label='Random seed')
+            reuse_seed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_seed"), label='Reuse seed')
+
+            seed_checkbox = gr.Checkbox(label='Extra', elem_id=self.elem_id("subseed_show"), value=False)
+
+        with gr.Group(visible=False, elem_id=self.elem_id("seed_extras")) as seed_extras:
+            with gr.Row(elem_id=self.elem_id("subseed_row")):
+                subseed = gr.Number(label='Variation seed', value=-1, elem_id=self.elem_id("subseed"), precision=0)
+                random_subseed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_subseed"))
+                reuse_subseed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_subseed"))
+                subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=self.elem_id("subseed_strength"))
+
+            with gr.Row(elem_id=self.elem_id("seed_resize_from_row")):
+                seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=self.elem_id("seed_resize_from_w"))
+                seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=self.elem_id("seed_resize_from_h"))
+
+        random_seed.click(fn=None, _js="function(){setRandomSeed('" + self.elem_id("seed") + "')}", show_progress=False, inputs=[], outputs=[])
+        random_subseed.click(fn=None, _js="function(){setRandomSeed('" + self.elem_id("subseed") + "')}", show_progress=False, inputs=[], outputs=[])
+
+        seed_checkbox.change(lambda x: gr.update(visible=x), show_progress=False, inputs=[seed_checkbox], outputs=[seed_extras])
+
+        self.infotext_fields = [
+            (self.seed, "Seed"),
+            (seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
+            (subseed, "Variation seed"),
+            (subseed_strength, "Variation seed strength"),
+            (seed_resize_from_w, "Seed resize from-1"),
+            (seed_resize_from_h, "Seed resize from-2"),
+        ]
+
+        self.on_after_component(lambda x: connect_reuse_seed(self.seed, reuse_seed, x.component, False), elem_id=f'generation_info_{self.tabname}')
+        self.on_after_component(lambda x: connect_reuse_seed(subseed, reuse_subseed, x.component, True), elem_id=f'generation_info_{self.tabname}')
+
+        return self.seed, seed_checkbox, subseed, subseed_strength, seed_resize_from_w, seed_resize_from_h
+
+    def setup(self, p, seed, seed_checkbox, subseed, subseed_strength, seed_resize_from_w, seed_resize_from_h):
+        p.seed = seed
+
+        if seed_checkbox and subseed_strength > 0:
+            p.subseed = subseed
+            p.subseed_strength = subseed_strength
+
+        if seed_checkbox and seed_resize_from_w > 0 and seed_resize_from_h > 0:
+            p.seed_resize_from_w = seed_resize_from_w
+            p.seed_resize_from_h = seed_resize_from_h
+
+
+
+def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, is_subseed):
+    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
+        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
+        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
+
+    def copy_seed(gen_info_string: str, index):
+        res = -1
+
+        try:
+            gen_info = json.loads(gen_info_string)
+            index -= gen_info.get('index_of_first_image', 0)
+
+            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
+                all_subseeds = gen_info.get('all_subseeds', [-1])
+                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
+            else:
+                all_seeds = gen_info.get('all_seeds', [-1])
+                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
+
+        except json.decoder.JSONDecodeError:
+            if gen_info_string:
+                errors.report(f"Error parsing JSON generation info: {gen_info_string}")
+
+        return [res, gr.update()]
+
+    reuse_seed.click(
+        fn=copy_seed,
+        _js="(x, y) => [x, selected_gallery_index()]",
+        show_progress=False,
+        inputs=[generation_info, seed],
+        outputs=[seed, seed]
+    )

modules/scripts.py

@@ -3,6 +3,7 @@ import re
 import sys
 import inspect
 from collections import namedtuple
+from dataclasses import dataclass
 
 import gradio as gr
 
@@ -21,6 +22,11 @@ class PostprocessBatchListArgs:
         self.images = images
 
 
+@dataclass
+class OnComponent:
+    component: gr.blocks.Block
+
+
 class Script:
     name = None
     """script's internal name derived from title"""
@@ -35,9 +41,13 @@ class Script:
 
     is_txt2img = False
     is_img2img = False
+    tabname = None
 
     group = None
-    """A gr.Group component that has all script's UI inside it"""
+    """A gr.Group component that has all script's UI inside it."""
+
+    create_group = True
+    """If False, for alwayson scripts, a group component will not be created."""
 
     infotext_fields = None
     """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
@@ -52,6 +62,12 @@ class Script:
     api_info = None
     """Generated value of type modules.api.models.ScriptInfo with information about the script for API"""
 
+    on_before_component_elem_id = None
+    """list of callbacks to be called before a component with an elem_id is created"""
+
+    on_after_component_elem_id = None
+    """list of callbacks to be called after a component with an elem_id is created"""
+
     def title(self):
         """this function should return the title of the script. This is what will be displayed in the dropdown menu."""
 
@@ -90,9 +106,16 @@ class Script:
 
         pass
 
+    def setup(self, p, *args):
+        """For AlwaysVisible scripts, this function is called when the processing object is set up, before any processing starts.
+        args contains all values returned by components from ui().
+        """
+        pass
+
+
     def before_process(self, p, *args):
         """
-        This function is called very early before processing begins for AlwaysVisible scripts.
+        This function is called very early during processing begins for AlwaysVisible scripts.
         You can modify the processing object (p) here, inject hooks, etc.
         args contains all values returned by components from ui()
         """
@@ -212,6 +235,30 @@ class Script:
 
         pass
 
+    def on_before_component(self, callback, *, elem_id):
+        """
+        Calls callback before a component is created. The callback function is called with a single argument of type OnComponent.
+
+        May be called in show() or ui() - but it may be too late in latter as some components may already be created.
+
+        This function is an alternative to before_component in that it also cllows to run before a component is created, but
+        it doesn't require to be called for every created component - just for the one you need.
+        """
+        if self.on_before_component_elem_id is None:
+            self.on_before_component_elem_id = []
+
+        self.on_before_component_elem_id.append((elem_id, callback))
+
+    def on_after_component(self, callback, *, elem_id):
+        """
+        Calls callback after a component is created. The callback function is called with a single argument of type OnComponent.
+        """
+        if self.on_after_component_elem_id is None:
+            self.on_after_component_elem_id = []
+
+        self.on_after_component_elem_id.append((elem_id, callback))
+
+
     def describe(self):
         """unused"""
         return ""
@@ -232,6 +279,18 @@ class Script:
         """
         pass
 
+
+class ScriptBuiltin(Script):
+
+    def elem_id(self, item_id):
+        """helper function to generate id for a HTML element, constructs final id out of tab and user-supplied item_id"""
+
+        need_tabname = self.show(True) == self.show(False)
+        tabname = ('img2img' if self.is_img2img else 'txt2txt') + "_" if need_tabname else ""
+
+        return f'{tabname}{item_id}'
+
+
 current_basedir = paths.script_path
 
 
@@ -250,7 +309,7 @@ postprocessing_scripts_data = []
 ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
 
 
-def list_scripts(scriptdirname, extension):
+def list_scripts(scriptdirname, extension, *, include_extensions=True):
     scripts_list = []
 
     basedir = os.path.join(paths.script_path, scriptdirname)
@@ -258,8 +317,9 @@ def list_scripts(scriptdirname, extension):
         for filename in sorted(os.listdir(basedir)):
             scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
 
-    for ext in extensions.active():
-        scripts_list += ext.list_files(scriptdirname, extension)
+    if include_extensions:
+        for ext in extensions.active():
+            scripts_list += ext.list_files(scriptdirname, extension)
 
     scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
 
@@ -288,7 +348,7 @@ def load_scripts():
     postprocessing_scripts_data.clear()
     script_callbacks.clear_callbacks()
 
-    scripts_list = list_scripts("scripts", ".py")
+    scripts_list = list_scripts("scripts", ".py") + list_scripts("modules/processing_scripts", ".py", include_extensions=False)
 
     syspath = sys.path
 
@@ -349,10 +409,17 @@ class ScriptRunner:
         self.selectable_scripts = []
         self.alwayson_scripts = []
         self.titles = []
+        self.title_map = {}
         self.infotext_fields = []
         self.paste_field_names = []
         self.inputs = [None]
 
+        self.on_before_component_elem_id = {}
+        """dict of callbacks to be called before an element is created; key=elem_id, value=list of callbacks"""
+
+        self.on_after_component_elem_id = {}
+        """dict of callbacks to be called after an element is created; key=elem_id, value=list of callbacks"""
+
     def initialize_scripts(self, is_img2img):
         from modules import scripts_auto_postprocessing
 
@@ -367,6 +434,7 @@ class ScriptRunner:
             script.filename = script_data.path
             script.is_txt2img = not is_img2img
             script.is_img2img = is_img2img
+            script.tabname = "img2img" if is_img2img else "txt2img"
 
             visibility = script.show(script.is_img2img)
 
@@ -379,6 +447,28 @@ class ScriptRunner:
                 self.scripts.append(script)
                 self.selectable_scripts.append(script)
 
+        self.apply_on_before_component_callbacks()
+
+    def apply_on_before_component_callbacks(self):
+        for script in self.scripts:
+            on_before = script.on_before_component_elem_id or []
+            on_after = script.on_after_component_elem_id or []
+
+            for elem_id, callback in on_before:
+                if elem_id not in self.on_before_component_elem_id:
+                    self.on_before_component_elem_id[elem_id] = []
+
+                self.on_before_component_elem_id[elem_id].append((callback, script))
+
+            for elem_id, callback in on_after:
+                if elem_id not in self.on_after_component_elem_id:
+                    self.on_after_component_elem_id[elem_id] = []
+
+                self.on_after_component_elem_id[elem_id].append((callback, script))
+
+            on_before.clear()
+            on_after.clear()
+
     def create_script_ui(self, script):
         import modules.api.models as api_models
 
@@ -429,15 +519,20 @@ class ScriptRunner:
             if script.alwayson and script.section != section:
                 continue
 
-            with gr.Group(visible=script.alwayson) as group:
-                self.create_script_ui(script)
+            if script.create_group:
+                with gr.Group(visible=script.alwayson) as group:
+                    self.create_script_ui(script)
 
-            script.group = group
+                script.group = group
+            else:
+                self.create_script_ui(script)
 
     def prepare_ui(self):
         self.inputs = [None]
 
     def setup_ui(self):
+        all_titles = [wrap_call(script.title, script.filename, "title") or script.filename for script in self.scripts]
+        self.title_map = {title.lower(): script for title, script in zip(all_titles, self.scripts)}
         self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
 
         self.setup_ui_for_section(None)
@@ -484,6 +579,8 @@ class ScriptRunner:
         self.infotext_fields.append((dropdown, lambda x: gr.update(value=x.get('Script', 'None'))))
         self.infotext_fields.extend([(script.group, onload_script_visibility) for script in self.selectable_scripts])
 
+        self.apply_on_before_component_callbacks()
+
         return self.inputs
 
     def run(self, p, *args):
@@ -577,6 +674,12 @@ class ScriptRunner:
                 errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
 
     def before_component(self, component, **kwargs):
+        for callback, script in self.on_before_component_elem_id.get(kwargs.get("elem_id"), []):
+            try:
+                callback(OnComponent(component=component))
+            except Exception:
+                errors.report(f"Error running on_before_component: {script.filename}", exc_info=True)
+
         for script in self.scripts:
             try:
                 script.before_component(component, **kwargs)
@@ -584,12 +687,21 @@ class ScriptRunner:
                 errors.report(f"Error running before_component: {script.filename}", exc_info=True)
 
     def after_component(self, component, **kwargs):
+        for callback, script in self.on_after_component_elem_id.get(component.elem_id, []):
+            try:
+                callback(OnComponent(component=component))
+            except Exception:
+                errors.report(f"Error running on_after_component: {script.filename}", exc_info=True)
+
         for script in self.scripts:
             try:
                 script.after_component(component, **kwargs)
             except Exception:
                 errors.report(f"Error running after_component: {script.filename}", exc_info=True)
 
+    def script(self, title):
+        return self.title_map.get(title.lower())
+
     def reload_sources(self, cache):
         for si, script in list(enumerate(self.scripts)):
             args_from = script.args_from
@@ -608,7 +720,6 @@ class ScriptRunner:
                     self.scripts[si].args_from = args_from
                     self.scripts[si].args_to = args_to
 
-
     def before_hr(self, p):
         for script in self.alwayson_scripts:
             try:
@@ -617,6 +728,14 @@ class ScriptRunner:
             except Exception:
                 errors.report(f"Error running before_hr: {script.filename}", exc_info=True)
 
+    def setup_scrips(self, p):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.setup(p, *script_args)
+            except Exception:
+                errors.report(f"Error running setup: {script.filename}", exc_info=True)
+
 
 scripts_txt2img: ScriptRunner = None
 scripts_img2img: ScriptRunner = None

modules/sd_hijack_optimizations.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 import math
 import psutil
+import platform
 
 import torch
 from torch import einsum
@@ -94,7 +95,10 @@ class SdOptimizationSdp(SdOptimizationSdpNoMem):
 class SdOptimizationSubQuad(SdOptimization):
     name = "sub-quadratic"
     cmd_opt = "opt_sub_quad_attention"
-    priority = 10
+
+    @property
+    def priority(self):
+        return 1000 if shared.device.type == 'mps' else 10
 
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
@@ -120,7 +124,7 @@ class SdOptimizationInvokeAI(SdOptimization):
 
     @property
     def priority(self):
-        return 1000 if not torch.cuda.is_available() else 10
+        return 1000 if shared.device.type != 'mps' and not torch.cuda.is_available() else 10
 
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI
@@ -427,7 +431,10 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
     qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
 
     if chunk_threshold is None:
-        chunk_threshold_bytes = int(get_available_vram() * 0.9) if q.device.type == 'mps' else int(get_available_vram() * 0.7)
+        if q.device.type == 'mps':
+            chunk_threshold_bytes = 268435456 * (2 if platform.processor() == 'i386' else bytes_per_token)
+        else:
+            chunk_threshold_bytes = int(get_available_vram() * 0.7)
     elif chunk_threshold == 0:
         chunk_threshold_bytes = None
     else:

modules/sd_models.py

@@ -147,6 +147,9 @@ re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$")
 
 
 def get_closet_checkpoint_match(search_string):
+    if not search_string:
+        return None
+
     checkpoint_info = checkpoint_aliases.get(search_string, None)
     if checkpoint_info is not None:
         return checkpoint_info

modules/sd_samplers_cfg_denoiser.py

@@ -45,18 +45,23 @@ class CFGDenoiser(torch.nn.Module):
         self.nmask = None
         self.init_latent = None
         self.steps = None
+        """number of steps as specified by user in UI"""
+
+        self.total_steps = None
+        """expected number of calls to denoiser calculated from self.steps and specifics of the selected sampler"""
+
         self.step = 0
         self.image_cfg_scale = None
         self.padded_cond_uncond = False
         self.sampler = sampler
         self.model_wrap = None
         self.p = None
+        self.mask_before_denoising = False
 
     @property
     def inner_model(self):
         raise NotImplementedError()
 
-
     def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
         denoised_uncond = x_out[-uncond.shape[0]:]
         denoised = torch.clone(denoised_uncond)
@@ -100,7 +105,7 @@ class CFGDenoiser(torch.nn.Module):
 
         assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
 
-        if self.mask is not None:
+        if self.mask_before_denoising and self.mask is not None:
             x = self.init_latent * self.mask + self.nmask * x
 
         batch_size = len(conds_list)
@@ -202,6 +207,9 @@ class CFGDenoiser(torch.nn.Module):
         else:
             denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
 
+        if not self.mask_before_denoising and self.mask is not None:
+            denoised = self.init_latent * self.mask + self.nmask * denoised
+
         self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
 
         if opts.live_preview_content == "Prompt":

modules/sd_samplers_common.py

@@ -7,7 +7,16 @@ from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, s
 from modules.shared import opts, state
 import k_diffusion.sampling
 
-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
+
+SamplerDataTuple = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
+
+
+class SamplerData(SamplerDataTuple):
+    def total_steps(self, steps):
+        if self.options.get("second_order", False):
+            steps = steps * 2
+
+        return steps
 
 
 def setup_img2img_steps(p, steps=None):
@@ -83,7 +92,15 @@ def images_tensor_to_samples(image, approximation=None, model=None):
             model = shared.sd_model
         image = image.to(shared.device, dtype=devices.dtype_vae)
         image = image * 2 - 1
-        x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
+        if len(image) > 1:
+            x_latent = torch.stack([
+                model.get_first_stage_encoding(
+                    model.encode_first_stage(torch.unsqueeze(img, 0))
+                )[0]
+                for img in image
+            ])
+        else:
+            x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
 
     return x_latent
 
@@ -131,31 +148,29 @@ def replace_torchsde_browinan():
 replace_torchsde_browinan()
 
 
-def apply_refiner(sampler):
-    completed_ratio = sampler.step / sampler.steps
+def apply_refiner(cfg_denoiser):
+    completed_ratio = cfg_denoiser.step / cfg_denoiser.total_steps
+    refiner_switch_at = cfg_denoiser.p.refiner_switch_at
+    refiner_checkpoint_info = cfg_denoiser.p.refiner_checkpoint_info
 
-    if completed_ratio <= shared.opts.sd_refiner_switch_at:
+    if refiner_switch_at is not None and completed_ratio < refiner_switch_at:
         return False
 
-    if shared.opts.sd_refiner_checkpoint == "None":
+    if refiner_checkpoint_info is None or shared.sd_model.sd_checkpoint_info == refiner_checkpoint_info:
         return False
 
-    if shared.sd_model.sd_checkpoint_info.title == shared.opts.sd_refiner_checkpoint:
+    if getattr(cfg_denoiser.p, "enable_hr", False) and not cfg_denoiser.p.is_hr_pass:
         return False
 
-    refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(shared.opts.sd_refiner_checkpoint)
-    if refiner_checkpoint_info is None:
-        raise Exception(f'Could not find checkpoint with name {shared.opts.sd_refiner_checkpoint}')
-
-    sampler.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title
-    sampler.p.extra_generation_params['Refiner switch at'] = shared.opts.sd_refiner_switch_at
+    cfg_denoiser.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title
+    cfg_denoiser.p.extra_generation_params['Refiner switch at'] = refiner_switch_at
 
     with sd_models.SkipWritingToConfig():
         sd_models.reload_model_weights(info=refiner_checkpoint_info)
 
     devices.torch_gc()
-    sampler.p.setup_conds()
-    sampler.update_inner_model()
+    cfg_denoiser.p.setup_conds()
+    cfg_denoiser.update_inner_model()
 
     return True
 
@@ -192,7 +207,7 @@ class Sampler:
         self.sampler_noises = None
         self.stop_at = None
         self.eta = None
-        self.config = None  # set by the function calling the constructor
+        self.config: SamplerData = None  # set by the function calling the constructor
         self.last_latent = None
         self.s_min_uncond = None
         self.s_churn = 0.0
@@ -208,6 +223,7 @@ class Sampler:
         self.p = None
         self.model_wrap_cfg = None
         self.sampler_extra_args = None
+        self.options = {}
 
     def callback_state(self, d):
         step = d['i']
@@ -220,6 +236,7 @@ class Sampler:
 
     def launch_sampling(self, steps, func):
         self.model_wrap_cfg.steps = steps
+        self.model_wrap_cfg.total_steps = self.config.total_steps(steps)
         state.sampling_steps = steps
         state.sampling_step = 0
 
@@ -267,19 +284,19 @@ class Sampler:
             s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
             s_noise = getattr(opts, 's_noise', p.s_noise)
 
-            if s_churn != self.s_churn:
+            if 's_churn' in extra_params_kwargs and s_churn != self.s_churn:
                 extra_params_kwargs['s_churn'] = s_churn
                 p.s_churn = s_churn
                 p.extra_generation_params['Sigma churn'] = s_churn
-            if s_tmin != self.s_tmin:
+            if 's_tmin' in extra_params_kwargs and s_tmin != self.s_tmin:
                 extra_params_kwargs['s_tmin'] = s_tmin
                 p.s_tmin = s_tmin
                 p.extra_generation_params['Sigma tmin'] = s_tmin
-            if s_tmax != self.s_tmax:
+            if 's_tmax' in extra_params_kwargs and s_tmax != self.s_tmax:
                 extra_params_kwargs['s_tmax'] = s_tmax
                 p.s_tmax = s_tmax
                 p.extra_generation_params['Sigma tmax'] = s_tmax
-            if s_noise != self.s_noise:
+            if 's_noise' in extra_params_kwargs and s_noise != self.s_noise:
                 extra_params_kwargs['s_noise'] = s_noise
                 p.s_noise = s_noise
                 p.extra_generation_params['Sigma noise'] = s_noise
@@ -296,5 +313,8 @@ class Sampler:
         current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
         return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
 
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        raise NotImplementedError()
 
-
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        raise NotImplementedError()

modules/sd_samplers_kdiffusion.py

@@ -22,6 +22,12 @@ samplers_k_diffusion = [
     ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
     ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
+    ('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {"brownian_noise": True, "solver_type": "heun"}),
+    ('DPM++ 2M SDE Heun Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_ka'], {'scheduler': 'karras', "brownian_noise": True, "solver_type": "heun"}),
+    ('DPM++ 2M SDE Heun Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_exp'], {'scheduler': 'exponential', "brownian_noise": True, "solver_type": "heun"}),
+    ('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'discard_next_to_last_sigma': True, "brownian_noise": True}),
+    ('DPM++ 3M SDE Karras', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
+    ('DPM++ 3M SDE Exponential', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_exp'], {'scheduler': 'exponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
@@ -42,6 +48,12 @@ sampler_extra_params = {
     'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
     'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
     'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_dpm_fast': ['s_noise'],
+    'sample_dpm_2_ancestral': ['s_noise'],
+    'sample_dpmpp_2s_ancestral': ['s_noise'],
+    'sample_dpmpp_sde': ['s_noise'],
+    'sample_dpmpp_2m_sde': ['s_noise'],
+    'sample_dpmpp_3m_sde': ['s_noise'],
 }
 
 k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
@@ -64,9 +76,12 @@ class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
 
 
 class KDiffusionSampler(sd_samplers_common.Sampler):
-    def __init__(self, funcname, sd_model):
+    def __init__(self, funcname, sd_model, options=None):
         super().__init__(funcname)
 
+        self.extra_params = sampler_extra_params.get(funcname, [])
+
+        self.options = options or {}
         self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
 
         self.model_wrap_cfg = CFGDenoiserKDiffusion(self)
@@ -149,6 +164,9 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
             noise_sampler = self.create_noise_sampler(x, sigmas, p)
             extra_params_kwargs['noise_sampler'] = noise_sampler
 
+        if self.config.options.get('solver_type', None) == 'heun':
+            extra_params_kwargs['solver_type'] = 'heun'
+
         self.model_wrap_cfg.init_latent = x
         self.last_latent = x
         self.sampler_extra_args = {
@@ -190,6 +208,9 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
             noise_sampler = self.create_noise_sampler(x, sigmas, p)
             extra_params_kwargs['noise_sampler'] = noise_sampler
 
+        if self.config.options.get('solver_type', None) == 'heun':
+            extra_params_kwargs['solver_type'] = 'heun'
+
         self.last_latent = x
         self.sampler_extra_args = {
             'cond': conditioning,
@@ -198,6 +219,7 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
             'cond_scale': p.cfg_scale,
             's_min_uncond': self.s_min_uncond
         }
+
         samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         if self.model_wrap_cfg.padded_cond_uncond:

modules/sd_samplers_timesteps.py

@@ -49,12 +49,12 @@ class CFGDenoiserTimesteps(CFGDenoiser):
         super().__init__(sampler)
 
         self.alphas = shared.sd_model.alphas_cumprod
+        self.mask_before_denoising = True
 
     def get_pred_x0(self, x_in, x_out, sigma):
-        ts = int(sigma.item())
+        ts = sigma.to(dtype=int)
 
-        s_in = x_in.new_ones([x_in.shape[0]])
-        a_t = self.alphas[ts].item() * s_in
+        a_t = self.alphas[ts][:, None, None, None]
         sqrt_one_minus_at = (1 - a_t).sqrt()
 
         pred_x0 = (x_in - sqrt_one_minus_at * x_out) / a_t.sqrt()

modules/sd_samplers_timesteps_impl.py

@@ -11,21 +11,22 @@ from modules.models.diffusion.uni_pc import uni_pc
 def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
     alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
     alphas = alphas_cumprod[timesteps]
-    alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64)
+    alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
     sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
     sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
 
     extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
+    s_in = x.new_ones((x.shape[0]))
+    s_x = x.new_ones((x.shape[0], 1, 1, 1))
     for i in tqdm.trange(len(timesteps) - 1, disable=disable):
         index = len(timesteps) - 1 - i
 
         e_t = model(x, timesteps[index].item() * s_in, **extra_args)
 
-        a_t = alphas[index].item() * s_in
-        a_prev = alphas_prev[index].item() * s_in
-        sigma_t = sigmas[index].item() * s_in
-        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_in
+        a_t = alphas[index].item() * s_x
+        a_prev = alphas_prev[index].item() * s_x
+        sigma_t = sigmas[index].item() * s_x
+        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
 
         pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
         dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
@@ -42,18 +43,19 @@ def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=
 def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
     alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
     alphas = alphas_cumprod[timesteps]
-    alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64)
+    alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
     sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
 
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
+    s_x = x.new_ones((x.shape[0], 1, 1, 1))
     old_eps = []
 
     def get_x_prev_and_pred_x0(e_t, index):
         # select parameters corresponding to the currently considered timestep
-        a_t = alphas[index].item() * s_in
-        a_prev = alphas_prev[index].item() * s_in
-        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_in
+        a_t = alphas[index].item() * s_x
+        a_prev = alphas_prev[index].item() * s_x
+        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
 
         # current prediction for x_0
         pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()

modules/sd_vae.py

@@ -31,7 +31,9 @@ def get_loaded_vae_hash():
     if loaded_vae_file is None:
         return None
 
-    return hashes.sha256(loaded_vae_file, 'vae')[0:10]
+    sha256 = hashes.sha256(loaded_vae_file, 'vae')
+
+    return sha256[0:10] if sha256 else None
 
 
 def get_base_vae(model):

modules/shared_items.py

@@ -69,10 +69,11 @@ def reload_hypernetworks():
 ui_reorder_categories_builtin_items = [
     "inpaint",
     "sampler",
+    "accordions",
     "checkboxes",
-    "hires_fix",
     "dimensions",
     "cfg",
+    "denoising",
     "seed",
     "batch",
     "override_settings",
@@ -86,7 +87,7 @@ def ui_reorder_categories():
 
     sections = {}
     for script in scripts.scripts_txt2img.scripts + scripts.scripts_img2img.scripts:
-        if isinstance(script.section, str):
+        if isinstance(script.section, str) and script.section not in ui_reorder_categories_builtin_items:
             sections[script.section] = 1
 
     yield from sections

modules/shared_options.py

@@ -140,8 +140,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
     "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU", "NV"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors; use NV to produce same picture as on NVidia videocards"),
     "tiling": OptionInfo(False, "Tiling", infotext='Tiling').info("produce a tileable picture"),
-    "sd_refiner_checkpoint": OptionInfo("None", "Refiner checkpoint", gr.Dropdown, lambda: {"choices": ["None"] + shared_items.list_checkpoint_tiles()}, refresh=shared_items.refresh_checkpoints, infotext="Refiner").info("switch to another model in the middle of generation"),
-    "sd_refiner_switch_at": OptionInfo(1.0, "Refiner switch at", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}, infotext='Refiner switch at').info("fraction of sampling steps when the swtch to refiner model should happen; 1=never, 0.5=switch in the middle of generation"),
 }))
 
 options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
@@ -288,12 +286,12 @@ options_templates.update(options_section(('ui', "Live previews"), {
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
     "hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in shared_items.list_samplers()]}).needs_reload_ui(),
     "eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta DDIM').info("noise multiplier; higher = more unperdictable results"),
-    "eta_ancestral": OptionInfo(1.0, "Eta for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta').info("noise multiplier; applies to Euler a and other samplers that have a in them"),
+    "eta_ancestral": OptionInfo(1.0, "Eta for k-diffusion samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}, infotext='Eta').info("noise multiplier; currently only applies to ancestral samplers (i.e. Euler a) and SDE samplers"),
     "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
     's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 0.01}, infotext='Sigma churn').info('amount of stochasticity; only applies to Euler, Heun, and DPM2'),
     's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 10.0, "step": 0.01}, infotext='Sigma tmin').info('enable stochasticity; start value of the sigma range; only applies to Euler, Heun, and DPM2'),
     's_tmax':  OptionInfo(0.0, "sigma tmax",  gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}, infotext='Sigma tmax').info("0 = inf; end value of the sigma range; only applies to Euler, Heun, and DPM2"),
-    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.1, "step": 0.001}, infotext='Sigma noise').info('amount of additional noise to counteract loss of detail during sampling; only applies to Euler, Heun, and DPM2'),
+    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.1, "step": 0.001}, infotext='Sigma noise').info('amount of additional noise to counteract loss of detail during sampling'),
     'k_sched_type':  OptionInfo("Automatic", "Scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}, infotext='Schedule type').info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
     'sigma_min': OptionInfo(0.0, "sigma min", gr.Number, infotext='Schedule max sigma').info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
     'sigma_max': OptionInfo(0.0, "sigma max", gr.Number, infotext='Schedule min sigma').info("0 = default (~14.6); maximum noise strength for k-diffusion noise scheduler"),

modules/sub_quadratic_attention.py

@@ -58,7 +58,7 @@ def _summarize_chunk(
     scale: float,
 ) -> AttnChunk:
     attn_weights = torch.baddbmm(
-        torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+        torch.zeros(1, 1, 1, device=query.device, dtype=query.dtype),
         query,
         key.transpose(1,2),
         alpha=scale,
@@ -121,7 +121,7 @@ def _get_attention_scores_no_kv_chunking(
     scale: float,
 ) -> Tensor:
     attn_scores = torch.baddbmm(
-        torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+        torch.zeros(1, 1, 1, device=query.device, dtype=query.dtype),
         query,
         key.transpose(1,2),
         alpha=scale,

modules/txt2img.py

@@ -9,7 +9,7 @@ from modules.ui import plaintext_to_html
 import gradio as gr
 
 
-def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_name: str, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_name: str, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
+def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_name: str, n_iter: int, batch_size: int, cfg_scale: float, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, hr_checkpoint_name: str, hr_sampler_name: str, hr_prompt: str, hr_negative_prompt, override_settings_texts, request: gr.Request, *args):
     override_settings = create_override_settings_dict(override_settings_texts)
 
     p = processing.StableDiffusionProcessingTxt2Img(
@@ -19,12 +19,6 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
         prompt=prompt,
         styles=prompt_styles,
         negative_prompt=negative_prompt,
-        seed=seed,
-        subseed=subseed,
-        subseed_strength=subseed_strength,
-        seed_resize_from_h=seed_resize_from_h,
-        seed_resize_from_w=seed_resize_from_w,
-        seed_enable_extras=seed_enable_extras,
         sampler_name=sampler_name,
         batch_size=batch_size,
         n_iter=n_iter,

modules/ui.py

@@ -1,5 +1,4 @@
 import datetime
-import json
 import mimetypes
 import os
 import sys
@@ -13,7 +12,7 @@ from PIL import Image, PngImagePlugin  # noqa: F401
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
 
 from modules import gradio_extensons  # noqa: F401
-from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts, sd_samplers, processing, ui_extra_networks
+from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts, sd_samplers, processing, ui_extra_networks
 from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion
 from modules.paths import script_path
 from modules.ui_common import create_refresh_button
@@ -142,45 +141,6 @@ def interrogate_deepbooru(image):
     return gr.update() if prompt is None else prompt
 
 
-def create_seed_inputs(target_interface):
-    with FormRow(elem_id=f"{target_interface}_seed_row", variant="compact"):
-        if cmd_opts.use_textbox_seed:
-            seed = gr.Textbox(label='Seed', value="", elem_id=f"{target_interface}_seed")
-        else:
-            seed = gr.Number(label='Seed', value=-1, elem_id=f"{target_interface}_seed", precision=0)
-
-        random_seed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_seed", label='Random seed')
-        reuse_seed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_seed", label='Reuse seed')
-
-        seed_checkbox = gr.Checkbox(label='Extra', elem_id=f"{target_interface}_subseed_show", value=False)
-
-    # Components to show/hide based on the 'Extra' checkbox
-    seed_extras = []
-
-    with FormRow(visible=False, elem_id=f"{target_interface}_subseed_row") as seed_extra_row_1:
-        seed_extras.append(seed_extra_row_1)
-        subseed = gr.Number(label='Variation seed', value=-1, elem_id=f"{target_interface}_subseed", precision=0)
-        random_subseed = ToolButton(random_symbol, elem_id=f"{target_interface}_random_subseed")
-        reuse_subseed = ToolButton(reuse_symbol, elem_id=f"{target_interface}_reuse_subseed")
-        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=f"{target_interface}_subseed_strength")
-
-    with FormRow(visible=False) as seed_extra_row_2:
-        seed_extras.append(seed_extra_row_2)
-        seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=f"{target_interface}_seed_resize_from_w")
-        seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=f"{target_interface}_seed_resize_from_h")
-
-    random_seed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_seed')}", show_progress=False, inputs=[], outputs=[])
-    random_subseed.click(fn=None, _js="function(){setRandomSeed('" + target_interface + "_subseed')}", show_progress=False, inputs=[], outputs=[])
-
-    def change_visibility(show):
-        return {comp: gr_show(show) for comp in seed_extras}
-
-    seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
-
-    return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
-
-
-
 def connect_clear_prompt(button):
     """Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
     button.click(
@@ -191,39 +151,6 @@ def connect_clear_prompt(button):
     )
 
 
-def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
-    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
-        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
-        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
-    def copy_seed(gen_info_string: str, index):
-        res = -1
-
-        try:
-            gen_info = json.loads(gen_info_string)
-            index -= gen_info.get('index_of_first_image', 0)
-
-            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
-                all_subseeds = gen_info.get('all_subseeds', [-1])
-                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
-            else:
-                all_seeds = gen_info.get('all_seeds', [-1])
-                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
-
-        except json.decoder.JSONDecodeError:
-            if gen_info_string:
-                errors.report(f"Error parsing JSON generation info: {gen_info_string}")
-
-        return [res, gr_show(False)]
-
-    reuse_seed.click(
-        fn=copy_seed,
-        _js="(x, y) => [x, selected_gallery_index()]",
-        show_progress=False,
-        inputs=[generation_info, dummy_component],
-        outputs=[seed, dummy_component]
-    )
-
-
 def update_token_counter(text, steps):
     try:
         text, _ = extra_networks.parse_prompt(text)
@@ -429,44 +356,45 @@ def create_ui():
                                     batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
 
                     elif category == "cfg":
-                        cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
-
-                    elif category == "seed":
-                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
+                        with gr.Row():
+                            cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
 
                     elif category == "checkboxes":
                         with FormRow(elem_classes="checkboxes-row", variant="compact"):
                             pass
 
-                    elif category == "hires_fix":
-                        with InputAccordion(False, label="Hires. fix") as enable_hr:
-                            with enable_hr.extra():
-                                hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False, min_width=0)
+                    elif category == "accordions":
+                        with gr.Row(elem_id="txt2img_accordions", elem_classes="accordions"):
+                            with InputAccordion(False, label="Hires. fix", elem_id="txt2img_hr") as enable_hr:
+                                with enable_hr.extra():
+                                    hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False, min_width=0)
 
-                            with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
-                                hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
-                                hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
-                                denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
+                                with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
+                                    hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
+                                    hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
+                                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
 
-                            with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
-                                hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
-                                hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
-                                hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
+                                with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
+                                    hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
+                                    hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
+                                    hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
 
-                            with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
+                                with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=opts.hires_fix_show_sampler) as hr_sampler_container:
 
-                                hr_checkpoint_name = gr.Dropdown(label='Hires checkpoint', elem_id="hr_checkpoint", choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint")
-                                create_refresh_button(hr_checkpoint_name, modules.sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True)}, "hr_checkpoint_refresh")
+                                    hr_checkpoint_name = gr.Dropdown(label='Hires checkpoint', elem_id="hr_checkpoint", choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint")
+                                    create_refresh_button(hr_checkpoint_name, modules.sd_models.list_models, lambda: {"choices": ["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True)}, "hr_checkpoint_refresh")
 
-                                hr_sampler_name = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + sd_samplers.visible_sampler_names(), value="Use same sampler")
+                                    hr_sampler_name = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + sd_samplers.visible_sampler_names(), value="Use same sampler")
 
-                            with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
-                                with gr.Column(scale=80):
-                                    with gr.Row():
-                                        hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
-                                with gr.Column(scale=80):
-                                    with gr.Row():
-                                        hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
+                                with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
+                                    with gr.Column(scale=80):
+                                        with gr.Row():
+                                            hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
+                                    with gr.Column(scale=80):
+                                        with gr.Row():
+                                            hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
+
+                            scripts.scripts_txt2img.setup_ui_for_section(category)
 
                     elif category == "batch":
                         if not opts.dimensions_and_batch_together:
@@ -482,7 +410,7 @@ def create_ui():
                         with FormGroup(elem_id="txt2img_script_container"):
                             custom_inputs = scripts.scripts_txt2img.setup_ui()
 
-                    else:
+                    if category not in {"accordions"}:
                         scripts.scripts_txt2img.setup_ui_for_section(category)
 
             hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
@@ -506,9 +434,6 @@ def create_ui():
 
             txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
 
-            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
-            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
             txt2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
                 _js="submit",
@@ -522,8 +447,6 @@ def create_ui():
                     batch_count,
                     batch_size,
                     cfg_scale,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
                     height,
                     width,
                     enable_hr,
@@ -574,15 +497,9 @@ def create_ui():
                 (steps, "Steps"),
                 (sampler_name, "Sampler"),
                 (cfg_scale, "CFG scale"),
-                (seed, "Seed"),
                 (width, "Size-1"),
                 (height, "Size-2"),
                 (batch_size, "Batch size"),
-                (seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
-                (subseed, "Variation seed"),
-                (subseed_strength, "Variation seed strength"),
-                (seed_resize_from_w, "Seed resize from-1"),
-                (seed_resize_from_h, "Seed resize from-2"),
                 (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
                 (denoising_strength, "Denoising strength"),
                 (enable_hr, lambda d: "Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d)),
@@ -610,7 +527,7 @@ def create_ui():
                 steps,
                 sampler_name,
                 cfg_scale,
-                seed,
+                scripts.scripts_txt2img.script('Seed').seed,
                 width,
                 height,
             ]
@@ -780,20 +697,22 @@ def create_ui():
                                     batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
                                     batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
 
-                    elif category == "cfg":
-                        with FormGroup():
-                            with FormRow():
-                                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
-                                image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
-                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
+                    elif category == "denoising":
+                        denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
 
-                    elif category == "seed":
-                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
+                    elif category == "cfg":
+                        with gr.Row():
+                            cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+                            image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
 
                     elif category == "checkboxes":
                         with FormRow(elem_classes="checkboxes-row", variant="compact"):
                             pass
 
+                    elif category == "accordions":
+                        with gr.Row(elem_id="img2img_accordions", elem_classes="accordions"):
+                            scripts.scripts_img2img.setup_ui_for_section(category)
+
                     elif category == "batch":
                         if not opts.dimensions_and_batch_together:
                             with FormRow(elem_id="img2img_column_batch"):
@@ -836,14 +755,12 @@ def create_ui():
                                     inputs=[],
                                     outputs=[inpaint_controls, mask_alpha],
                                 )
-                    else:
+
+                    if category not in {"accordions"}:
                         scripts.scripts_img2img.setup_ui_for_section(category)
 
             img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
 
-            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
-            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
-
             img2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
                 _js="submit_img2img",
@@ -870,8 +787,6 @@ def create_ui():
                     cfg_scale,
                     image_cfg_scale,
                     denoising_strength,
-                    seed,
-                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
                     selected_scale_tab,
                     height,
                     width,
@@ -958,15 +873,9 @@ def create_ui():
                 (sampler_name, "Sampler"),
                 (cfg_scale, "CFG scale"),
                 (image_cfg_scale, "Image CFG scale"),
-                (seed, "Seed"),
                 (width, "Size-1"),
                 (height, "Size-2"),
                 (batch_size, "Batch size"),
-                (seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
-                (subseed, "Variation seed"),
-                (subseed_strength, "Variation seed strength"),
-                (seed_resize_from_w, "Seed resize from-1"),
-                (seed_resize_from_h, "Seed resize from-2"),
                 (toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
                 (denoising_strength, "Denoising strength"),
                 (mask_blur, "Mask blur"),

modules/ui_common.py

@@ -137,13 +137,17 @@ Requested path was: {f}
         generation_info = None
         with gr.Column():
             with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
-                open_folder_button = gr.Button(folder_symbol, visible=not shared.cmd_opts.hide_ui_dir_config)
+                open_folder_button = ToolButton(folder_symbol, elem_id=f'{tabname}_open_folder', visible=not shared.cmd_opts.hide_ui_dir_config, tooltip="Open images output directory.")
 
                 if tabname != "extras":
-                    save = gr.Button('Save', elem_id=f'save_{tabname}')
-                    save_zip = gr.Button('Zip', elem_id=f'save_zip_{tabname}')
+                    save = ToolButton('💾', elem_id=f'save_{tabname}', tooltip=f"Save the image to a dedicated directory ({shared.opts.outdir_save}).")
+                    save_zip = ToolButton('🗃️', elem_id=f'save_zip_{tabname}', tooltip=f"Save zip archive with images to a dedicated directory ({shared.opts.outdir_save})")
 
-                buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
+                buttons = {
+                    'img2img': ToolButton('🖼️', elem_id=f'{tabname}_send_to_img2img', tooltip="Send image and generation parameters to img2img tab."),
+                    'inpaint': ToolButton('🎨️', elem_id=f'{tabname}_send_to_inpaint', tooltip="Send image and generation parameters to img2img inpaint tab."),
+                    'extras': ToolButton('📐', elem_id=f'{tabname}_send_to_extras', tooltip="Send image and generation parameters to extras tab.")
+                }
 
             open_folder_button.click(
                 fn=lambda: open_folder(shared.opts.outdir_samples or outdir),

modules/ui_components.py

@@ -87,13 +87,23 @@ class InputAccordion(gr.Checkbox):
             self.accordion_id = f"input-accordion-{InputAccordion.global_index}"
             InputAccordion.global_index += 1
 
-        kwargs['elem_id'] = self.accordion_id + "-checkbox"
-        kwargs['visible'] = False
-        super().__init__(value, **kwargs)
+        kwargs_checkbox = {
+            **kwargs,
+            "elem_id": f"{self.accordion_id}-checkbox",
+            "visible": False,
+        }
+        super().__init__(value, **kwargs_checkbox)
 
         self.change(fn=None, _js='function(checked){ inputAccordionChecked("' + self.accordion_id + '", checked); }', inputs=[self])
 
-        self.accordion = gr.Accordion(kwargs.get('label', 'Accordion'), open=value, elem_id=self.accordion_id, elem_classes=['input-accordion'])
+        kwargs_accordion = {
+            **kwargs,
+            "elem_id": self.accordion_id,
+            "label": kwargs.get('label', 'Accordion'),
+            "elem_classes": ['input-accordion'],
+            "open": value,
+        }
+        self.accordion = gr.Accordion(**kwargs_accordion)
 
     def extra(self):
         """Allows you to put something into the label of the accordion.

modules/ui_extra_networks_checkpoints.py

@@ -19,6 +19,7 @@ class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage):
         return {
             "name": checkpoint.name_for_extra,
             "filename": checkpoint.filename,
+            "shorthash": checkpoint.shorthash,
             "preview": self.find_preview(path),
             "description": self.find_description(path),
             "search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""),

modules/ui_extra_networks_hypernets.py

@@ -2,6 +2,7 @@ import os
 
 from modules import shared, ui_extra_networks
 from modules.ui_extra_networks import quote_js
+from modules.hashes import sha256_from_cache
 
 
 class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
@@ -14,13 +15,16 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
     def create_item(self, name, index=None, enable_filter=True):
         full_path = shared.hypernetworks[name]
         path, ext = os.path.splitext(full_path)
+        sha256 = sha256_from_cache(full_path, f'hypernet/{name}')
+        shorthash = sha256[0:10] if sha256 else None
 
         return {
             "name": name,
             "filename": full_path,
+            "shorthash": shorthash,
             "preview": self.find_preview(path),
             "description": self.find_description(path),
-            "search_term": self.search_terms_from_path(path),
+            "search_term": self.search_terms_from_path(path) + " " + (sha256 or ""),
             "prompt": quote_js(f"<hypernet:{name}:") + " + opts.extra_networks_default_multiplier + " + quote_js(">"),
             "local_preview": f"{path}.preview.{shared.opts.samples_format}",
             "sort_keys": {'default': index, **self.get_sort_keys(path + ext)},

modules/ui_extra_networks_textual_inversion.py

@@ -19,9 +19,10 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
         return {
             "name": name,
             "filename": embedding.filename,
+            "shorthash": embedding.shorthash,
             "preview": self.find_preview(path),
             "description": self.find_description(path),
-            "search_term": self.search_terms_from_path(embedding.filename),
+            "search_term": self.search_terms_from_path(embedding.filename) + " " + (embedding.hash or ""),
             "prompt": quote_js(embedding.name),
             "local_preview": f"{path}.preview.{shared.opts.samples_format}",
             "sort_keys": {'default': index, **self.get_sort_keys(embedding.filename)},

modules/ui_extra_networks_user_metadata.py

@@ -93,11 +93,13 @@ class UserMetadataEditor:
         item = self.page.items.get(name, {})
         try:
             filename = item["filename"]
+            shorthash = item.get("shorthash", None)
 
             stats = os.stat(filename)
             params = [
                 ('Filename: ', os.path.basename(filename)),
                 ('File size: ', sysinfo.pretty_bytes(stats.st_size)),
+                ('Hash: ', shorthash),
                 ('Modified: ', datetime.datetime.fromtimestamp(stats.st_mtime).strftime('%Y-%m-%d %H:%M')),
             ]
 
@@ -115,7 +117,7 @@ class UserMetadataEditor:
             errors.display(e, f"reading metadata info for {name}")
             params = []
 
-        table = '<table class="file-metadata">' + "".join(f"<tr><th>{name}</th><td>{value}</td></tr>" for name, value in params) + '</table>'
+        table = '<table class="file-metadata">' + "".join(f"<tr><th>{name}</th><td>{value}</td></tr>" for name, value in params if value is not None) + '</table>'
 
         return html.escape(name), user_metadata.get('description', ''), table, self.get_card_html(name), user_metadata.get('notes', '')
 

modules/ui_loadsave.py

@@ -48,13 +48,13 @@ class UiLoadsave:
             elif condition and not condition(saved_value):
                 pass
             else:
-                if isinstance(x, gr.Textbox) and field == 'value':  # due to an undersirable behavior of gr.Textbox, if you give it an int value instead of str, everything dies
+                if isinstance(x, gr.Textbox) and field == 'value':  # due to an undesirable behavior of gr.Textbox, if you give it an int value instead of str, everything dies
                     saved_value = str(saved_value)
                 elif isinstance(x, gr.Number) and field == 'value':
                     try:
                         saved_value = float(saved_value)
                     except ValueError:
-                        saved_value = -1
+                        return
 
                 setattr(obj, field, saved_value)
                 if init_field is not None:

scripts/xyz_grid.py

@@ -175,14 +175,22 @@ def do_nothing(p, x, xs):
 def format_nothing(p, opt, x):
     return ""
 
+
 def format_remove_path(p, opt, x):
     return os.path.basename(x)
 
+
 def str_permutations(x):
     """dummy function for specifying it in AxisOption's type when you want to get a list of permutations"""
     return x
 
 
+def list_to_csv_string(data_list):
+    with StringIO() as o:
+        csv.writer(o).writerow(data_list)
+        return o.getvalue().strip()
+
+
 class AxisOption:
     def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None):
         self.label = label
@@ -199,6 +207,7 @@ class AxisOptionImg2Img(AxisOption):
         super().__init__(*args, **kwargs)
         self.is_img2img = True
 
+
 class AxisOptionTxt2Img(AxisOption):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -286,11 +295,10 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
             cell_size = (processed_result.width, processed_result.height)
             if processed_result.images[0] is not None:
                 cell_mode = processed_result.images[0].mode
-                #This corrects size in case of batches:
+                # This corrects size in case of batches:
                 cell_size = processed_result.images[0].size
             processed_result.images[idx] = Image.new(cell_mode, cell_size)
 
-
     if first_axes_processed == 'x':
         for ix, x in enumerate(xs):
             if second_axes_processed == 'y':
@@ -348,9 +356,9 @@ def draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend
     if draw_legend:
         z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]])
     processed_result.images.insert(0, z_grid)
-    #TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
-    #processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
-    #processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
+    # TODO: Deeper aspects of the program rely on grid info being misaligned between metadata arrays, which is not ideal.
+    # processed_result.all_prompts.insert(0, processed_result.all_prompts[0])
+    # processed_result.all_seeds.insert(0, processed_result.all_seeds[0])
     processed_result.infotexts.insert(0, processed_result.infotexts[0])
 
     return processed_result
@@ -374,8 +382,8 @@ class SharedSettingsStackHelper(object):
 re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
 re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\(([+-]\d+(?:.\d*)?)\s*\))?\s*")
 
-re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
-re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")
+re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*])?\s*")
+re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*])?\s*")
 
 
 class Script(scripts.Script):
@@ -390,19 +398,19 @@ class Script(scripts.Script):
                 with gr.Row():
                     x_type = gr.Dropdown(label="X type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type="index", elem_id=self.elem_id("x_type"))
                     x_values = gr.Textbox(label="X values", lines=1, elem_id=self.elem_id("x_values"))
-                    x_values_dropdown = gr.Dropdown(label="X values",visible=False,multiselect=True,interactive=True)
+                    x_values_dropdown = gr.Dropdown(label="X values", visible=False, multiselect=True, interactive=True)
                     fill_x_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_x_tool_button", visible=False)
 
                 with gr.Row():
                     y_type = gr.Dropdown(label="Y type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("y_type"))
                     y_values = gr.Textbox(label="Y values", lines=1, elem_id=self.elem_id("y_values"))
-                    y_values_dropdown = gr.Dropdown(label="Y values",visible=False,multiselect=True,interactive=True)
+                    y_values_dropdown = gr.Dropdown(label="Y values", visible=False, multiselect=True, interactive=True)
                     fill_y_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_y_tool_button", visible=False)
 
                 with gr.Row():
                     z_type = gr.Dropdown(label="Z type", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type="index", elem_id=self.elem_id("z_type"))
                     z_values = gr.Textbox(label="Z values", lines=1, elem_id=self.elem_id("z_values"))
-                    z_values_dropdown = gr.Dropdown(label="Z values",visible=False,multiselect=True,interactive=True)
+                    z_values_dropdown = gr.Dropdown(label="Z values", visible=False, multiselect=True, interactive=True)
                     fill_z_button = ToolButton(value=fill_values_symbol, elem_id="xyz_grid_fill_z_tool_button", visible=False)
 
         with gr.Row(variant="compact", elem_id="axis_options"):
@@ -414,6 +422,9 @@ class Script(scripts.Script):
                 include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id("include_sub_grids"))
             with gr.Column():
                 margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id("margin_size"))
+            with gr.Column():
+                csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False, elem_id=self.elem_id("csv_mode"))
+
 
         with gr.Row(variant="compact", elem_id="swap_axes"):
             swap_xy_axes_button = gr.Button(value="Swap X/Y axes", elem_id="xy_grid_swap_axes_button")
@@ -430,50 +441,71 @@ class Script(scripts.Script):
         xz_swap_args = [x_type, x_values, x_values_dropdown, z_type, z_values, z_values_dropdown]
         swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)
 
-        def fill(x_type):
-            axis = self.current_axis_options[x_type]
-            return axis.choices() if axis.choices else gr.update()
+        def fill(axis_type, csv_mode):
+            axis = self.current_axis_options[axis_type]
+            if axis.choices:
+                if csv_mode:
+                    return list_to_csv_string(axis.choices()), gr.update()
+                else:
+                    return gr.update(), axis.choices()
+            else:
+                return gr.update(), gr.update()
 
-        fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values_dropdown])
-        fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values_dropdown])
-        fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values_dropdown])
+        fill_x_button.click(fn=fill, inputs=[x_type, csv_mode], outputs=[x_values, x_values_dropdown])
+        fill_y_button.click(fn=fill, inputs=[y_type, csv_mode], outputs=[y_values, y_values_dropdown])
+        fill_z_button.click(fn=fill, inputs=[z_type, csv_mode], outputs=[z_values, z_values_dropdown])
 
-        def select_axis(axis_type,axis_values_dropdown):
+        def select_axis(axis_type, axis_values, axis_values_dropdown, csv_mode):
             choices = self.current_axis_options[axis_type].choices
             has_choices = choices is not None
-            current_values = axis_values_dropdown
+
+            current_values = axis_values
+            current_dropdown_values = axis_values_dropdown
             if has_choices:
                 choices = choices()
-                if isinstance(current_values,str):
-                    current_values = current_values.split(",")
-                current_values = list(filter(lambda x: x in choices, current_values))
-            return gr.Button.update(visible=has_choices),gr.Textbox.update(visible=not has_choices),gr.update(choices=choices if has_choices else None,visible=has_choices,value=current_values)
+                if csv_mode:
+                    current_dropdown_values = list(filter(lambda x: x in choices, current_dropdown_values))
+                    current_values = list_to_csv_string(current_dropdown_values)
+                else:
+                    current_dropdown_values = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(axis_values)))]
+                    current_dropdown_values = list(filter(lambda x: x in choices, current_dropdown_values))
 
-        x_type.change(fn=select_axis, inputs=[x_type,x_values_dropdown], outputs=[fill_x_button,x_values,x_values_dropdown])
-        y_type.change(fn=select_axis, inputs=[y_type,y_values_dropdown], outputs=[fill_y_button,y_values,y_values_dropdown])
-        z_type.change(fn=select_axis, inputs=[z_type,z_values_dropdown], outputs=[fill_z_button,z_values,z_values_dropdown])
+            return (gr.Button.update(visible=has_choices), gr.Textbox.update(visible=not has_choices or csv_mode, value=current_values),
+                    gr.update(choices=choices if has_choices else None, visible=has_choices and not csv_mode, value=current_dropdown_values))
 
-        def get_dropdown_update_from_params(axis,params):
+        x_type.change(fn=select_axis, inputs=[x_type, x_values, x_values_dropdown, csv_mode], outputs=[fill_x_button, x_values, x_values_dropdown])
+        y_type.change(fn=select_axis, inputs=[y_type, y_values, y_values_dropdown, csv_mode], outputs=[fill_y_button, y_values, y_values_dropdown])
+        z_type.change(fn=select_axis, inputs=[z_type, z_values, z_values_dropdown, csv_mode], outputs=[fill_z_button, z_values, z_values_dropdown])
+
+        def change_choice_mode(csv_mode, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown):
+            _fill_x_button, _x_values, _x_values_dropdown = select_axis(x_type, x_values, x_values_dropdown, csv_mode)
+            _fill_y_button, _y_values, _y_values_dropdown = select_axis(y_type, y_values, y_values_dropdown, csv_mode)
+            _fill_z_button, _z_values, _z_values_dropdown = select_axis(z_type, z_values, z_values_dropdown, csv_mode)
+            return _fill_x_button, _x_values, _x_values_dropdown, _fill_y_button, _y_values, _y_values_dropdown, _fill_z_button, _z_values, _z_values_dropdown
+
+        csv_mode.change(fn=change_choice_mode, inputs=[csv_mode, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown], outputs=[fill_x_button, x_values, x_values_dropdown, fill_y_button, y_values, y_values_dropdown, fill_z_button, z_values, z_values_dropdown])
+
+        def get_dropdown_update_from_params(axis, params):
             val_key = f"{axis} Values"
-            vals = params.get(val_key,"")
+            vals = params.get(val_key, "")
             valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
-            return gr.update(value = valslist)
+            return gr.update(value=valslist)
 
         self.infotext_fields = (
             (x_type, "X Type"),
             (x_values, "X Values"),
-            (x_values_dropdown, lambda params:get_dropdown_update_from_params("X",params)),
+            (x_values_dropdown, lambda params: get_dropdown_update_from_params("X", params)),
             (y_type, "Y Type"),
             (y_values, "Y Values"),
-            (y_values_dropdown, lambda params:get_dropdown_update_from_params("Y",params)),
+            (y_values_dropdown, lambda params: get_dropdown_update_from_params("Y", params)),
             (z_type, "Z Type"),
             (z_values, "Z Values"),
-            (z_values_dropdown, lambda params:get_dropdown_update_from_params("Z",params)),
+            (z_values_dropdown, lambda params: get_dropdown_update_from_params("Z", params)),
         )
 
-        return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]
+        return [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size, csv_mode]
 
-    def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):
+    def run(self, p, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size, csv_mode):
         if not no_fixed_seeds:
             modules.processing.fix_seed(p)
 
@@ -484,7 +516,7 @@ class Script(scripts.Script):
             if opt.label == 'Nothing':
                 return [0]
 
-            if opt.choices is not None:
+            if opt.choices is not None and not csv_mode:
                 valslist = vals_dropdown
             else:
                 valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals))) if x]
@@ -503,8 +535,8 @@ class Script(scripts.Script):
                         valslist_ext += list(range(start, end, step))
                     elif mc is not None:
                         start = int(mc.group(1))
-                        end   = int(mc.group(2))
-                        num   = int(mc.group(3)) if mc.group(3) is not None else 1
+                        end = int(mc.group(2))
+                        num = int(mc.group(3)) if mc.group(3) is not None else 1
 
                         valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]
                     else:
@@ -525,8 +557,8 @@ class Script(scripts.Script):
                         valslist_ext += np.arange(start, end + step, step).tolist()
                     elif mc is not None:
                         start = float(mc.group(1))
-                        end   = float(mc.group(2))
-                        num   = int(mc.group(3)) if mc.group(3) is not None else 1
+                        end = float(mc.group(2))
+                        num = int(mc.group(3)) if mc.group(3) is not None else 1
 
                         valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()
                     else:
@@ -545,22 +577,22 @@ class Script(scripts.Script):
             return valslist
 
         x_opt = self.current_axis_options[x_type]
-        if x_opt.choices is not None:
-            x_values = ",".join(x_values_dropdown)
+        if x_opt.choices is not None and not csv_mode:
+            x_values = list_to_csv_string(x_values_dropdown)
         xs = process_axis(x_opt, x_values, x_values_dropdown)
 
         y_opt = self.current_axis_options[y_type]
-        if y_opt.choices is not None:
-            y_values = ",".join(y_values_dropdown)
+        if y_opt.choices is not None and not csv_mode:
+            y_values = list_to_csv_string(y_values_dropdown)
         ys = process_axis(y_opt, y_values, y_values_dropdown)
 
         z_opt = self.current_axis_options[z_type]
-        if z_opt.choices is not None:
-            z_values = ",".join(z_values_dropdown)
+        if z_opt.choices is not None and not csv_mode:
+            z_values = list_to_csv_string(z_values_dropdown)
         zs = process_axis(z_opt, z_values, z_values_dropdown)
 
         # this could be moved to common code, but unlikely to be ever triggered anywhere else
-        Image.MAX_IMAGE_PIXELS = None # disable check in Pillow and rely on check below to allow large custom image sizes
+        Image.MAX_IMAGE_PIXELS = None  # disable check in Pillow and rely on check below to allow large custom image sizes
         grid_mp = round(len(xs) * len(ys) * len(zs) * p.width * p.height / 1000000)
         assert grid_mp < opts.img_max_size_mp, f'Error: Resulting grid would be too large ({grid_mp} MPixels) (max configured size is {opts.img_max_size_mp} MPixels)'
 
@@ -720,7 +752,7 @@ class Script(scripts.Script):
             # Auto-save main and sub-grids:
             grid_count = z_count + 1 if z_count > 1 else 1
             for g in range(grid_count):
-                #TODO: See previous comment about intentional data misalignment.
+                # TODO: See previous comment about intentional data misalignment.
                 adj_g = g-1 if g > 0 else g
                 images.save_image(processed.images[g], p.outpath_grids, "xyz_grid", info=processed.infotexts[g], extension=opts.grid_format, prompt=processed.all_prompts[adj_g], seed=processed.all_seeds[adj_g], grid=True, p=processed)
 

style.css

@@ -166,16 +166,6 @@ a{
     color: var(--button-secondary-text-color-hover);
 }
 
-.checkboxes-row{
-    margin-bottom: 0.5em;
-    margin-left: 0em;
-}
-.checkboxes-row > div{
-    flex: 0;
-    white-space: nowrap;
-    min-width: auto !important;
-}
-
 button.custom-button{
     border-radius: var(--button-large-radius);
     padding: var(--button-large-padding);
@@ -192,7 +182,7 @@ button.custom-button{
     text-align: center;
 }
 
-div.gradio-accordion {
+div.block.gradio-accordion {
     border: 1px solid var(--block-border-color) !important;
     border-radius: 8px !important;
     margin: 2px 0;
@@ -239,10 +229,14 @@ div.gradio-accordion {
 }
 
 [id$=_subseed_show] label{
-    margin-bottom: 0.5em;
+    margin-bottom: 0.65em;
     align-self: end;
 }
 
+[id$=_seed_extras] > div{
+    gap: 0.5em;
+}
+
 .html-log .comments{
     padding-top: 0.5em;
 }
@@ -352,7 +346,7 @@ div.gradio-accordion {
 }
 
 div.dimensions-tools{
-    min-width: 0 !important;
+    min-width: 1.6em !important;
     max-width: fit-content;
     flex-direction: column;
     place-content: center;
@@ -369,8 +363,8 @@ div#extras_scale_to_tab div.form{
     z-index: 5;
 }
 
-.image-buttons button{
-    min-width: auto;
+.image-buttons > .form{
+    justify-content: center;
 }
 
 .infotext {
@@ -391,19 +385,21 @@ div#extras_scale_to_tab div.form{
 
 /* settings */
 #quicksettings {
-    width: fit-content;
     align-items: end;
 }
 
 #quicksettings > div, #quicksettings > fieldset{
-    max-width: 24em;
-    min-width: 24em;
-    width: 24em;
+    max-width: 36em;
+    width: fit-content;
+    flex: 0 1 fit-content;
     padding: 0;
     border: none;
     box-shadow: none;
     background: none;
 }
+#quicksettings > div.gradio-dropdown{
+    min-width: 24em !important;
+}
 
 #settings{
     display: block;
@@ -1012,10 +1008,29 @@ div.block.gradio-box.popup-dialog > div:last-child, .popup-dialog > div:last-chi
 }
 
 div.block.input-accordion{
-    margin-bottom: 0.4em;
+
 }
 
 .input-accordion-extra{
     flex: 0 0 auto !important;
     margin: 0 0.5em 0 auto;
 }
+
+div.accordions > div.input-accordion{
+    min-width: fit-content !important;
+}
+
+div.accordions > div.gradio-accordion .label-wrap span{
+    white-space: nowrap;
+    margin-right: 0.25em;
+}
+
+div.accordions{
+    gap: 0.5em;
+}
+
+div.accordions > div.input-accordion.input-accordion-open{
+    flex: 1 auto;
+    flex-flow: column;
+}
+

webui-macos-env.sh

@@ -12,8 +12,6 @@ fi
 export install_dir="$HOME"
 export COMMANDLINE_ARGS="--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate"
 export TORCH_COMMAND="pip install torch==2.0.1 torchvision==0.15.2"
-export K_DIFFUSION_REPO="https://github.com/brkirch/k-diffusion.git"
-export K_DIFFUSION_COMMIT_HASH="51c9778f269cedb55a4d88c79c0246d35bdadb71"
 export PYTORCH_ENABLE_MPS_FALLBACK=1
 
 ####################################################################